Digital Technologies and Applications: Proceedings of ICDTA'23, Fez, Morocco, Volume 1 303129856X, 9783031298561

Table of contents :
Preface
Organization
Acknowledgments
Contents
Artificial Intelligence, Machine Learning and Data Analysis
Elman and Feed-Forward Neural Networks with Different Training Algorithms for Solar Radiation Forecasting: A Comparison with a Case Study
1 Introduction
2 Neural Networks
2.1 Feed-Forward Backpropagation Neural Network (FNN)
2.2 Elman Neural Network (ENN)
2.3 Training Algorithms
3 Methodology
4 Results and Discussion
5 Conclusion
References
An Artificial Neural Network Model Based on Non-linear Autoregressive Exogenous for Predicting the Humidity of a Greenhouse System
1 Introduction
2 Components and Methods
2.1 Greenhouse and Growth Circumstances
2.2 Structure of the NARX Model
2.3 Learning Algorithms
2.4 Metrics NARX Assessment
3 Outcomes and Discussion
3.1 Creation and Estimation of the NARX Model of Neurons
3.2 Comparison of the Three NARX Model Learning Algorithms
4 Conclusion
References
Towards an Adaptive Learning Process Using Artificial Intelligence Technologies
1 Introduction
2 Background
2.1 Artificial Intelligence Technologies
2.2 Pedagogical Context
3 Proposed Model
3.1 Identification of Learning Style
3.2 Adaptive Learning Scenario
3.3 Assessment Process
3.4 Remediation Process
3.5 Collaborative Learning
4 Implementation of the Model
5 Conclusion
References
Emotion Recognition of Facial Expressions with Deep Learning and Transfer Learning
1 Introduction
2 Related Works
2.1 Emotion Recognition Using Landmark (Geometric-Based Feature Extraction)
2.2 Appearance-Based Feature Extraction
2.3 Emotion Recognition Using CNN
3 Proposed Method
3.1 The Choice of Pre-trained Model
3.2 How to Use a Pre-trained Model
4 Proposed Method
5 Conclusion
References
A Novel Approach to Intelligent Touristic Visits Using Bing Maps and Genetic Algorithms
1 Introduction
2 Literature Review
3 Proposed Approach
4 Experimental Test and Results
5 Discussion and Conclusion
References
Evaluating the Efficiency of Multilayer Perceptron Neural Network Architecture in Classifying Cognitive Impairments Related to Human Bipedal Spatial Navigation
1 Introduction
2 Materials and Methods
2.1 Neuropsychological Assessment
2.2 Data Processing
2.3 Multilayer Perceptron Architecture
3 Experimental Results
4 Conclusion
References
A Novel Real Time Electric Vehicles Smart Charging Approach Based on Artificial Intelligence
1 Introduction
2 Electric Vehicles Grid Integration Based on Artificial Intelligence
3 Smart Charging Strategies and Approaches
3.1 Centralized Strategy
3.2 Decentralized Strategy
3.3 Grid Oriented Architecture
3.4 User Oriented Architecture
4 Problem Definition
5 Proposed System Architecture
5.1 Smart Charging Methodology
5.2 Charge Scheduling Heuristic
6 Conclusion and Future Work
References
Proposed Hybrid Model Recurrent Neural Network for Human Activity Recognition
1 Introduction
2 Related Works
3 Proposed Approach
3.1 Data Segmentation
3.2 Feature Extraction
3.3 Model Architecture
4 Experiments and Results
4.1 The Used Dataset
4.2 Performance Measures
4.3 Results
5 Conclusion
References
Evaluation of CMIP5 and CMIP6 Performance in Simulating West African Precipitation
1 Introduction
2 Data and Methodology
3 Results and Discussion
4 Summary and Conclusion
References
Knowledge Management, Tools and Activities
1 Introduction
2 Literature Review
2.1 KM
2.2 Knowledge Management Systems: KMS
2.3 KM’s Methods
2.4 CTM: Classification Tree Method
3 Related Works
4 The Link Between Knowledge Management Systems and Machine Learning
5 Implementation
6 Conclusion and Perspectives
References
The Interplay Between Social Science and Big Data Research: A Bibliometric Review of the Journal Big Data and Society, 2014–2021
1 Introduction
2 Methods and Data
3 Results
3.1 Overview of Bibliometric Data
4 Identifying Main Insights
4.1 Leading Authors
4.2 Influential Impact
5 Mapping Conceptual Structure
6 Intellectual Structure Exploration
7 Conclusion
References
Application of Machine Learning in Fused Deposition Modeling: A Review
1 Introduction
2 FDM Materials
3 Machine Learning
4 Machine Learning in the FDM Process
4.1 Prediction of Surface Roughness
4.2 Geometric Deviations
4.3 Energy Estimation
4.4 Defect Detection
5 Discussion and Conclusion
References
Prediction of Electrical Power of Ag/Water-Based PVT System Using K-NN Machine Learning Technique
1 Introduction
2 Materials and Method
2.1 K-NN Regression Theory
2.2 Evaluation Model
2.3 Literature Experimental Data Preparation
3 Results and Discussion
4 Conclusion
References
Evaluation of Machine Learning and Ensemble Methods for Complications Related to Port a Cath
1 Introduction
2 Data Collection and Methodology
2.1 Data Set
2.2 Data Partitioning Method
2.3 Ensemble Learning and Machine Learning Methods
2.4 Performance Metrics
3 Results and Discussion
4 Conclusion
References
Accuracy Improvement of Network Intrusion Detection System Using Bidirectional Long-Short Term Memory (Bi-LSTM)
1 Introduction
2 Related Works
3 Intrusion Detection System Concept
4 Long Short Term Memory (LSTM)
4.1 Bi-directional Long Short-Term Memory
5 Proposed Approach
5.1 Benchmark Datasets
5.2 Data Preparation
6 Results and Discussion
6.1 Implementation
6.2 Results
7 Conclusion and Future Work
References
Hand Gesture Recognition Using Machine Learning for Bionic Applications: Forearm Case Study
1 Introduction
2 Technical Approach
3 Proposed Architecture
3.1 Data Capture: Subjects and Activities
3.2 Signal Pre-processing
3.3 Feature Extraction and Selection
3.4 Classification of Data
4 Results and Discussions
4.1 Classification Result
4.2 Performance Evaluation
5 Conclusion
References
Detecting and Extracting Cocoa Pods in the Natural Environment Using Deep Learning Methods
1 Introduction
2 Related Work
3 Methods
3.1 Dataset Collection and Annotation
3.2 Application of U-Net
3.3 Application of FCN
3.4 Architecture of Our Model
4 Experimental Results
4.1 Setting
4.2 Evaluation Metrics
4.3 Results and Discussion
5 Conclusion
References
Design of a New Strategy Based on Machine Learning to Improve the Energy Efficiency of Buildings
1 Introduction
2 Methodology
2.1 Data-Driven Strategy
2.2 The Dataset Used
2.3 Decision Tree Model and Evaluation Metrics
3 Results and Discussion
4 Conclusion
References
Internet of Things, Blockchain and Security and Network Technology
Smart Grid: A Communication Model and Security Challenges
1 Introduction
2 Communication Model
2.1 Communication Architecture
2.2 Communication Model
2.3 Distributed Transmission Algorithm
3 Security Challenges
4 Conclusion
References
Smart Contracts: An Emerging Business Model in Decentralized Finance
1 Introduction
2 The Rise of Decentralized Finance (DeFi) and Blockchain Technology
3 Smart Contracts: An Emerging Decentralized Business Model
4 Potential Applications of Smart Contracts in the Financial and Banking Industry
4.1 Securities
4.2 Insurance
4.3 Trade Finance
4.4 Recording and Reporting Financial Data
4.5 The Mortgage Industry
5 Limits and Challenges to the Adoption of Smart Contracts
5.1 Vulnerabilities and Attacks
5.2 Consensus Mechanism Issues
5.3 Data Privacy
5.4 Platform Component Security
5.5 Understandability
6 Discussion
7 Conclusion
References
A Survey and a State-of-the-Art Related to Consensus Mechanisms in Blockchain Technology
1 Introduction
2 Materials and Methods
2.1 Blockchain Types
2.2 Consensus Mechanisms
2.3 Fundamental Results in Distributed Computing: The CAP Theorem
2.4 Criteria and Metrics Permitting the Comparison and Analysis of the Consensuses
3 Consensus Algorithm Analysis
3.1 Consensus Algorithms from the Blockchain Type Perspective
3.2 Consensus Algorithm Analysis
3.3 Consensus Algorithms: Comparison
4 Conclusion
References
Blockchain Technology in Finance: A Literature Review
1 Introduction
2 Literature Review
3 The Aim and Objectives of the Study
4 Methodology
5 Results of the Analysis
5.1 Contributions of Blockchain Technology to Financial Services
5.2 Regulatory Obstacles
6 Conclusion
References
A Review of Privacy-Preserving Cryptographic Techniques Used in Blockchain Platforms
1 Introduction
2 State of the Art of Privacy-Preserving Crypto-Techniques in the Blockchain
2.1 Zerocash
2.2 Hyperledger Fabric
2.3 Monero
2.4 Ethereum
2.5 Bitcoin
2.6 Hyperledger Indy
3 A Taxonomy of Most Useful Cryptographic Techniques
3.1 Hash-Based Cryptography Primitives (HBCP)
3.2 Zero-Knowledge Proof (ZNP)-Based Protocols (ZNPBP)
3.3 Lattice-Based Cryptography Schemes (LBCS)
3.4 Digital Credential Technologies (DCT)
3.5 Homomorphic Encryption-Based Schemes (HEBS)
3.6 Attribute-Based Encryption Primitives (ABEP)
4 Analysis of the Crypto-Techniques Used in Blockchain Platforms
5 Recommendations
6 Conclusion
References
Renewable Energy in Smart Grid: Photovoltaic Power Monitoring System Based on Machine Learning Using an Open-Source IoT Platform
1 Introduction
2 System Design
2.1 Overall System
2.2 Raspberry Pi Saved Data
2.3 Machine Learning Implementation
2.4 Machine Learning Implementation Algorithm
3 Dashboard
4 Conclusion and Future Work
References
Predicated on IoT, a Safe Intelligent Driver Assistance System in V2X Communication Environments
1 Introduction
2 Advanced Vehicle Communication Technologies Challenges
3 The System Architecture of IoT – Case Study
4 Developing an IoT Reference Architecture Model for the Designed System
4.1 Description of the ARM IoT Layers of the Designed Cooperative DAS
5 Conclusions
References
Novel Flexible Topologies of SIW Components for IoT Applications: A Survey
1 Introduction
2 Innovative SIW Materials for IoT
2.1 SIW Components on Paper Substrate
2.2 SIW Components on Textile Substrate
2.3 SIW Components on Plastic Substrate
3 Novel Manufacturing Techniques of SIW for IoT
4 Discussion and Perspective
5 Conclusion
References
Overview of Blockchain Based IoT Trust Management
1 Introduction
2 Preliminaries
2.1 IoT Trust Management (IoT-TM)
2.2 Blockchain Overview
3 Related Work on IoT-TM
4 Blockchain Based Solutions
5 Open Challenges and Future Directions
6 Conclusion
References
Multiband Reconfigurable Planar Antenna for Wireless Mobile Communications
1 Introduction
2 Frequency Response
3 Antenna Geometry
4 Parametric Study
5 Simulations Results and Equivalent Circuit Model of Pin Diode
6 Surface Current Distribution
7 Conclusion
References
Design and Analysis of a Slot Antenna Array with a Defected Ground Plan for Millimeter Wave Application
1 Introduction
2 Single Element Antenna
3 1 × 6 Array Antenna Design
4 Simulation Results and Discussion
4.1 Single Element Antenna
4.2 1 × 6 Array Antenna: Effect of Substrate Type on the Performance of the Proposed Array Antenna
5 Performance Proposed Array Antenna Related to Other Work in the Literature
6 Conclusion
References
Framework for Real-Time Simulations of Routing Security Attacks in VANETs
1 Introduction
2 Materials and Methods
2.1 VANET Architecture
2.2 SUMO (Simulation of Urban Mobility)
2.3 Network Simulator (NS-3)
2.4 Black Hole Attack in AODV Routing Protocol
3 The Proposed Simulation Framework
3.1 Component 1
3.2 Component 2
3.3 Component 3
4 Evaluation
4.1 Simulation Settings
4.2 Simulation Results
5 Conclusion
References
Study and Design of a Microstrip Patch Antenna Array for 2.4 GHz Applications
1 Introduction
2 Antenna Design Method
2.1 Single Antenna
2.2 Antenna Array
3 Simulation Results and Discussion
3.1 Single Antenna
3.2 4 × 1 Patch Antenna Array
3.3 8 × 1 Patch Antenna Array
4 Conclusion
References
Optical Envelope Detector in Fiber Transmission and Correction Change Phase Using HPT
1 Introduction
2 Description of the HPT 10SQxEBC Model Using in (DE) and Correct Change Phase
3 Detection Envelope Simulation Using HPT 10SQxEBC
4 Correction Change Phase Using HPT 10SQxEBC
5 Conclusion
References
Performance of BPSK-FSO Communication Over Turbulence and Fog Attenuation
1 Introduction
2 System and Channel Model
3 Fog Attenuation
4 Average BER in the Presence of Atmospheric Turbulence and Fog.
5 Numerical Results and Discussion
6 Conclusion
References
Digital Transformation and E-Learning
The Role of Digitalization in Achieving Cybersecurity in the Moroccan Banking System
1 Introduction
2 Literature Review
3 Hypothesis
4 Research Problem
5 Analysis Model
6 Conceptual Model
7 Statistical Analysis
8 Measurement Model
9 Structural Model
10 Discussion
11 Conclusion
References
An Enterprise Resource Planning (ERP) SAP Implementation Case Study in South Africa Small Medium Enterprise Sectors
1 Introduction
1.1 Objectives
1.2 Problem Statement
2 Literature Review
3 Research Methodology
3.1 Quantitative Approach
4 Conclusion
References
Towards Transparent Governance by Publishing Open Statistical Data
1 Introduction
2 Methodology
3 Related Work
4 Extension of the Pub-LOGD Framework
5 Results and Discussion
5.1 Evaluation Description
5.2 Evaluation Results
5.3 Discussion
6 Conclusions and Future Work
References
Digitalizing Teaching and Learning in Light of Sustainability in Times of the Post-Covid-19 Period: Challenges, Issues, and Opportunities
1 Introduction
2 Pedagogical Perspectives in Digital Teaching and Learning
3 Challenges, Issues, and Opportunities in the Post-COVID Outbreak
3.1 The Educational Integration of Digital Technologies Integrated in the Educational System
3.2 Opportunities to Engage in More Flexible and Digital Forms of Teaching and Learning
3.3 National Policies Reconsideration-Digital Technology Utilization
3.4 Improvement of Institutional/School Infrastructure: Creation of Educational Resources
3.5 Development of Students’ and Teachers’ Digital Technology Skills
4 Conclusion and Recommendations
4.1 Digital Learning and Teaching Are Considered as an Integral Element of an Accurate Pedagogy
4.2 Digital Teaching and Learning and Collaboration
4.3 Stakeholders and Teachers in Cooperation Relationship
4.4 Funding and Digitalization-Transformation of Teaching and Learning
5 Pedagogical Implication for Further Research
References
Shaping Students’ Learning for a Specific Learning Environment
1 Introduction
2 Problem Statement
3 Purposes and Contributions
4 Covid-19 Pandemic and Its Impact on Distance Learning at Moroccan University
5 Data Collection Method
6 Descriptive Analysis of the Questionnaires and the Interpretation of Quantitative Data
6.1 The Discussion of the Findings
7 Conclusion
References
The Impact of Distance Learning in the Performance and Attitude of Students During the Global Coronavirus Pandemic (Covid-19): Case of the Moroccan University
1 Introduction
2 Methodology
2.1 Research Model
2.2 Data Collection
3 Finding
3.1 The Attitude of Students Towards Distance Learning
3.2 The Attitude of Learners Towards Distance Education Compared by Their Gender
3.3 The Attitude of Students Towards Distance Learning Compared with the Sector of Study
3.4 The Attitude of Students Towards E-Learning Compared with Their Level of Study
3.5 The General Learner’s Attitude Towards Distance Learning
4 Discussion
5 Conclusion
References
Potentialities of Learning Analytics to Overcome Students Dropout in Distance Higher Education
1 Introduction
2 Learning Analytics: Concept and Potential
3 Student Dropout: A Common Phenomenon in Distance Higher Education
4 Emerging Potentialities of Learning Analytics to Overcome Dropout
5 Conclusion
References
Learning Analytics and Big Data: Huge Potential to Improve Online Education
1 Introduction
2 Learning Analytics
3 Big Data for Education Purposes
4 Related Works
5 Synthesis and Discussion: Impact of Bigdata in LA
6 Conclusion
References
Serious Games, a Tool for Consolidating Learning Outcomes
1 Introduction
2 State of the Art
2.1 Assessment of Learner Learning
2.2 Serious Games and Application
3 Use of Serious Games as a Technological Tool for Learning Consolidation
3.1 Serious Games, a Tool to Consolidate Knowledge
3.2 Towards a Serious Games Design Model to Consolidate Learning
4 Conclusion
References
Dimensionality Reduction for Predicting Students Dropout in MOOC
1 Introduction
2 Related Work
3 Data
3.1 Data Presentation
3.2 Feature Engineering
4 Methodology
4.1 Features Selection
4.2 Data Transformation
4.3 Used Techniques
5 Results
6 Conclusion
References
Cheating Detection in Online Exams
1 Introduction
2 Research Problem
3 State of the Art
4 Cheating Techniques in Online Exams
5 Methods for Reducing Cheating
6 Proposed Method
6.1 Global Presentation of Our Approach
6.2 Online Exams Cheating Detection System Architecture
7 Conclusion
References
Station Rotation Model of Blended Learning as Generative Technology in Education: An Evidence-Based Research
1 Introduction
2 Theoretical Perspectives of Learning and Their Relation to Station Rotation Model
2.1 Nature of Blended Learning Model
2.2 Blended Learning Model
2.3 Types of Rotation Model
2.4 Station Rotation Model (SRM)
2.5 Students’ Learning in a Blended Learning Classroom Using a Station Rotation Model
3 Qualitative Research on Station Rotation Model of Blended Learning
3.1 Perceptions of Station Rotation Model of Blended Learning
3.2 Parent Feedback on Blended Learning
3.3 Teacher’s Feedback on Blended Learning
3.4 Administrators Feedback on Blended Learning
4 Conclusion
References
Image and Information Processing
New Invariant Meixner Moments for Non-uniformly Scaled Images
1 Introduction
2 Scaling Invariants of Krawtchouk Moments [24]
3 The Weakness of Scaling Invariants Krawtchouk Moments
4 The Proposed Invariant Meixner Moments for Non-uniformly Scaled Images
5 Experimental Results
6 Conclusion
References
A Review on the Driver’s Fatigue Detection Methods
1 Introduction
2 Fatigue Detection Methods
2.1 Eye Closure
2.2 Mouth Movement
3 Discussion
4 Conclusion
References
Real-Time SPO2 Monitoring Based on Facial Images Sequences
1 Introduction
2 Methodology
2.1 Video Pre-processing
2.2 Signal Processing
2.3 SPO2 Extraction
3 Results and Discussion
4 Conclusion
References
The Identification of Weeds and Crops Using the Popular Convolutional Neural Networks
1 Introduction
2 Materials and Methods
2.1 Images Acquisition and Preprocessing
2.2 The MobileNet Models
2.3 The ResNet Models
2.4 The Proposed Model
3 Results and Discussion
3.1 Training Preparation
3.2 Training Results
3.3 Evaluation Results
3.4 Discussion
4 Conclusions
References
3D Scenes Semantic Understanding: New Approach Based on Image Processing for Time Learning Reducing
1 Introduction
2 Methodology
2.1 Pretreatment
2.2 The Adopted Model
3 Experimental Result
3.1 Work Environment
3.2 Results and Comparisons
4 Conclusion
References
Image Encryption Algorithm Based on Improved Hill Cipher Using the 2D Logistic Map
1 Introduction
2 Related Works
3 Steps of the Proposed Method
3.1 Chaotic Sequence Development
3.2 Preparing the Original Image
3.3 Improved Hill Method
3.4 Encryption Process
4 Results and Safety Analysis
4.1 Encryption Key Size
4.2 Statistics Attack Security
4.3 Differential Attacks
4.4 Encryption Time
4.5 Comparison
5 Conclusion
References
A Review of Video Summarization
1 Introduction
2 Video Summarization Using Deep Neural Networks
3 Video Summarization-Based Reinforcement Learning
4 Video Summarization Using Deep Reinforcement Learning
5 Video Summarization Datasets
6 Comparison of the Different Methods used in Video Summarization
References
A Color Image Encryption Method Designed Using Chaotic Confusion, Enhanced CBC-MAC Mode and Pixel Shuffling
1 Introduction
2 The Proposed Scheme Description
2.1 The Classic PWLCM and P-PWLCM for the Confusion Property
2.2 The Shuffling Pixels for the Diffusion Property
2.3 The CBC-MAC for the Avalanche Effect Property
3 The Conceived Method Algorithm
4 Experimental Results and Analysis
4.1 Space of the Key
4.2 Key Sensitivity
5 Conclusion
References
Normalized Gradient Min Sum Decoding for Low Density Parity Check Codes
1 Introduction
1.1 SNGDBF Algorithm
1.2 Normalized Min-Sum Algorithm
2 The ‘GNMS’ Algorithm
3 Results and Discussions
4 Conclusion
References
CCC-Transformation: Novel Method to Secure Passwords Based on Hash Transformation
1 Introduction
2 Related Work
3 Proposed Method
3.1 Explanation of the Method
4 Resistance Against Attacks
4.1 Brute Force Attack
4.2 Dictionary Attack
4.3 Rainbow-Table Attack
5 Experimentation
6 Conclusion
References
Case Studies of Several Popular Text Classification Methods
1 Introduction
2 Related Works
3 Materials and Methods
3.1 Text Classification Tasks
3.2 Word Embedding Representations
3.3 Contextual Embedding
3.4 Text Classification Algorithms Based on Deep Learning Models
4 Results and Discussion
5 Conclusions
References
A Survey on Facial Emotion Recognition for the Elderly
1 Introduction
2 Emotional Models
3 State of the Art
3.1 FER Architecture
4 Literature Review
5 Database
6 Conclusion
References
Advanced Technologies in Energy and Electrical Engineering
Modeling of a High Frequency Ultrasonic Transducer Using Mason's Equivalent Circuit Implemented in LTspice
1 Introduction
2 Mason’s Equivalent Circuit
2.1 Description of Mason’s Model
2.2 Description of the Studied Transducer
2.3 Implementation of Mason’s Equivalent Circuit in LTspice
3 Results and Discussion
4 Conclusion
References
Modeling of Piezoelectric Transducers Using 1D and 2D Redwood Models Implemented in LTSPICE
1 Introduction
2 Modeling of Piezoelectric Transducers Using Redwood Model
2.1 Presentation of 1D and 2D Redwood Equivalent Circuits
2.2 Description of the Studied Piezoelectric Plate and Slender Bars
2.3 Implementation of Redwood’s 1D and 2D Models in LTSPICE
3 Results and Discussions
4 Conclusion
References
Fuzzy Logic Speed Controller for Robust Direct Torque Control of Induction Motor Drives
1 Introduction
2 Induction Motor Modelling
3 Direct Torque Control (DTC)
4 Fuzzy Logic Speed Controller (FLSC)
5 Result and Analysis
6 Conclusion
References
A Comparative Study Between Optimization Algorithms of MPPT Algorithms (P&O and Incremental Conductance Method)
1 Introduction
2 Photovoltaic Modeling
2.1 The Principle of Operation of the Photocell
2.2 Maintaining the Integrity of the Specifications
2.3 P-V, I-V Properties in Different Temperatures and Irradiances
3 MPPT
3.1 Perturb and Obverse Algorithm
3.2 Incremental Conductance Method
4 Simulation and Results
4.1 PV System Simulation and BOOST Converter with Different Algorithms
4.2 Results
5 Conclusion
References
Energy-Reducing Opportunities by Improving Power Quality: A Case Study of Industrial Plants
1 Introduction
2 Power Factors
3 Assessment of Collected Cloud Data of the Power Quality
3.1 The Situation of Peak Power Demand
3.2 Power Quality Analysis
3.3 Harmonic Distortion Analysis
3.4 Impact of the Nonregulated Power Factor on Electric Wiring
4 Results and Discussion
5 Conclusion
References
Compensation of Current Harmonic Distortion in a Grid-Connected Photovoltaic System via an LC Filter
1 Introduction
1.1 Contributions
1.2 Paper Organization
2 Related Works
3 Description and Modeling of the Global System Studied
3.1 Photovoltaic cell
3.2 The shunt passive filter
4 Simulation of a Grid-Connected Photovoltaic Power System
5 Conclusion
References
Optimal Sizing of a Grid-Connected Renewable Energy System for a Residential Application in Gabon
1 Introduction
2 Methods and Materials
2.1 Building Description
2.2 Simulation Software and System Description
2.3 Resources and Input Data
2.4 Optimisation
3 Results and Discussion
3.1 Electricity Generation
3.2 Economic Evaluation
4 Conclusion
References
A Comparative Study of P&O and Fuzzy Logic MPPT Algorithms for a Photovoltaic Grid Connected Inverter System
1 Introduction
2 Design DC/DC
3 MPPT Implementation Control Techniques
3.1 Algorithm for Perturbing and Observing MPPT
3.2 Principle Fuzzy Logic
3.3 Fuzzy Logic MPPT Algorithm
4 Results and Discussion
4.1 P&O Algorithm Results, PV Side
4.2 Results on the PV Side of the Fuzzy Logic Algorithm
4.3 Results of the Grid Side P&O Method
5 Conclusion
References
A Review Study of Control Strategies of VSC-HVDC System Used Between Renewable Energy Source and Grid
1 Introduction
2 VSC-HVDC System
2.1 System Description
2.2 Control of VSC-HVDC
2.3 DC Voltage Control
3 Simulation Results
4 Conclusion
References
Modelling and Simulation of PV System Grid Connected with 100 KW Rated Power
1 Introduction
2 PV System Modeling
2.1 PV Modules Modeling
2.2 Boost Converter Modelling
3 Simulation Results and Discussion
4 Conclusion
References
Development of Geometrical Parameters for a Conical Solar Concentrator – Application for Vapor Generation
1 Introduction and Related Work to Solar Concentrators
2 Development of Geometrical Parameters for the Conical Solar Concentrator
2.1 Presentation of the Conical Solar Concentrator
2.2 Development of Geometrical Parameters for the Conical Solar Concentrator
3 Application to Vapor Generation: Research Methodology
3.1 Presentation of the Absorber Tube
3.2 Concentration of Solar Irradiation on the Absorber Tube
3.3 Mathematical Modeling of Heat Transfer Fluid
3.4 Discretization of the Heat Transfer Fluid Equation
3.5 Expression of Solar Concentrator Efficiency
4 Results of the Concentrator Application and Discussion
5 Conclusions and Perspectives
References
High-Performance MPPT Based on Developed Fast Convergence for PV System-Experimental Validation
1 Introduction
2 The Study System
3 Fast Convergence Approach: Simulation and Results
3.1 Fast Convergence Approach
3.2 Simulation of the Fast Convergence Algorithm Under Variation of Irradiation and Temperature
4 Experimental Validation
4.1 The Characteristics of the PV Module Used in Experimental Validation
4.2 Experimental Results
5 Conclusion
References
A Comparative Study Between MPC Algorithm and P&O and IncCond the Optimization Algorithms of MPPT Algorithms
1 Introduction
2 Basic Principles of MPC
3 Analysis of Boost Converter
4 MPC MPPT Technology
5 Simulation and Results
5.1 Simulation of a PV System and Converter BOOST with MPC Algorithm
5.2 Results
5.3 Simulation of a PV System and Converter BOOST with Different Algorithms (P&O, InC and MPC)
5.4 Comparison Results
6 Conclusion
References
Comparative Analysis of Classical and Meta-heuristic MPPT Algorithms in PV Systems Under Uniform Condition
1 Introduction
2 System Modeling
2.1 Photovoltaic Module
2.2 Boost Converter
3 MPPT Methods
3.1 MPPT Using P&O
3.2 MPPT Using PSO
3.3 MPPT Using CS
4 Simulation Results
5 Conclusion
References
Robust Control of a Wind Power System Based on a Doubly-Fed Induction Generator Using a Fuzzy Controller
1 Introduction
2 Modelling the Wind Energy System
2.1 Turbine Modelling
3 DFIG Modelling
3.1 The Voltage Equations
4 Vector Control of the DFIG
5 Power Control Based on Fuzzy Regulators
5.1 General Principles of Fuzzy Logic Control
5.2 Fuzzification
5.3 Defuzzification
5.4 Simulation and Results
6 Control of the DFIG by a Classical PI Controller
6.1 Synthesis of the PI Controller
6.2 Simulation and Results
7 Interpretation of the Results
8 Conclusion
Appendix 1
Appendix 2
References
A Review Backstepping Control of a DFIG-Based Wind Power System
1 Introduction
2 Backstepping Control
2.1 Review of Backstepping Control
2.2 Application of Backstepping Control on the DFIG Wind Power System
3 DFIG Parameters Estimation
4 Simulations Results
4.1 Pursuit Tests
4.2 Robustness Tests
5 Conclusion
References
Detection and Prevention of Repetitive Major Faults of a WTG by Analysis of Alarms Through SCADA
1 Introduction
2 Tools: In-Depth Pareto and FMEA analysis
2.1 Pareto
2.2 FMEA
3 Study Sample
3.1 Method: Prioritisation of Failures (and Risk Prevention)
3.2 Presentation of Experimental Results
3.3 Interpretation
4 Conclusion
References
HDL Coder Tool for ECG Signal Denoising
1 Introduction
2 ECG Signal Denoising
2.1 ECG Denoising Literature
2.2 ECG Signal Denoising Algorithm Choice
2.3 DWT-ADTF Algorithm
3 Embedded System HLS Design
3.1 Literature HLS Tools
3.2 DWTADTF HLS Design
3.3 Target FPGA Device
4 Conclusion
References
Realization of an Electrical Power Quality Analyzer Based on NIDAQ6009 Board
1 Introduction
2 Methods and Materials
2.1 European Standards EN 50160/61000
2.2 Description of System
3 Simulation Results of Power Quality Analyzer
3.1 Test Bench
3.2 Results
4 Conclusion
References
OpenCL Kernel Optimization Metrics for CPU-GPU Architecture
1 Introduction
2 Methodology
2.1 Using Global Memory
2.2 Using Local Memory
2.3 Unrolling Loop
2.4 Specification of the Size of the Work Group
2.5 Specification of the Number of Computing Units
3 Image Processing and Vision
4 Results & Discussion
4.1 The Results of Implementing the Two-Vector Addition Algorithm
4.2 The Results of Implementing the Matrix Multiplication Algorithm
5 Conclusion
References
Embedded System of Signal Processing on FPGA: Implementation OpenMP Architecture
1 Introduction
2 The Studied Problem
2.1 Backscattering Response
2.2 Use of Field-Programmable Gate Arrays
3 Acoustic Backscattering Response
4 Results and Discussion
5 Conclusion
References
LWR Application on Calculating Travel Time in Urban Arterials with Traffic Lights
1 Introduction
2 Basic Theories and Formulations
2.1 Formulation of the LWR Model for First Order Traffic Flow
2.2 Solving the LWR Model for the Simple Case Around a Traffic Light
2.3 Practical foundations of the Variational Theory
3 Application to Travel Time Calculation
3.1 Travel Cost Calculation Hypothesis
3.2 Detailed Steps and Results
4 Conclusion
References
Analysis of the Driver’s Overspeed on the Road Based on Changes in Essential Driving Data
1 Introduction
2 Methodology
2.1 Definition of the CAN Bus Protocol
2.2 System Block Diagram
2.3 Software Structure
3 Results and Discussion
4 Conclusion
References
Mechatronic, Industry 4.0 and Control System
The Use of Industry 4.0 Technologies in Maintenance: A Systematic Literature Review
1 Introduction
2 Industry 4.0 Technologies
3 Research Methodology
4 Collection of Materials
5 Results
6 Discussion
7 Conclusion
References
Adoption of Smart Traffic System to Reduce Traffic Congestion in a Smart City
1 Introduction
2 Related Work
2.1 Recent Advances
2.2 The Collection of Traffic Data
2.3 Algorithms Deployed for Traffic Congestions
3 Methodology
4 Conclusion and Recommendation
References
The Contribution of an ERP System in an LCA Analysis: A Case Study
1 Introduction
2 Why ERP Systems as Primary Databases for LCA
3 Methodology of Affiliation of an ERP System for the Realization of an LCA (Specifically LCI)
3.1 Schematic Case Study
4 Conclusions and Discussion
References
The Impact of Blockchain Technology and Business Intelligence on the Supply Chain Performance-Based Tracking Process
1 Introduction
2 Methodology
3 Literature Review
3.1 Blockchain
3.2 Supply Chain and Logistics
3.3 Tracking
3.4 Business Intelligence
3.5 Blockchain in the Supply Chain
3.6 Blockchain in Business Intelligence
3.7 Blockchain Implementation Cases in Industries
4 Discussion and Results
5 Conclusion
References
Design of an Integral Sliding Mode Control Based on Reaching Law for Trajectory Tracking of a Quadrotor System
1 Introduction
2 Quadrotor Dynamics
3 Quadrotor Position and Attitude Controller Design
3.1 Reaching Law with a Constant Rate
3.2 Exponential Reaching Law
3.3 Reaching Law with Power Rate
4 Simulation and Results
5 Conclusion
References
Product Family Formation for Reconfigurable Manufacturing Systems
1 Introduction
2 Reconfigurable Manufacturing System
3 Background and Related Work
3.1 Poduct Family Formation
3.2 Literature Review
3.3 Hierarchical Clustering
4 Conclusion and Perspectives
References
A New Method for Mobile Robots to Learn an Optimal Policy from an Expert Using Deep Imitation Learning
1 Introduction
2 Background
2.1 Dijkstra Method
2.2 Data-Set Generation
2.3 Deep Learning
3 Experiences
4 Results and Discussion
4.1 Training Result
4.2 Validation of the Obtained Model
5 Conclusion and Future Work
References
Modeling and Simulation of a BLDC Motor Speed Control in Electric Vehicles
1 Introduction
2 Modeling and Control of BLDC Motor
2.1 Mathematical Modeling of BLDC Motor
3 Control
3.1 PI Controller
3.2 PID Controller
3.3 Fuzzy Logic Controller
4 Simulation Results
5 Results and Discussion
6 Conclusion
References
Smart Supply Chain Management: A Literature Review
1 Introduction
2 Role of Industry 4.0 in Supply Chain Management
2.1 Classic Supply Chain
2.2 Smart Supply Chain
3 Literature Review of Smart Supply Chain Management
4 Conclusion
References
A Dynamic MAS to Manage a Daily Planning for a Team Operating Theater
1 Introduction
2 Problematic
3 MAS and Operating Activity
3.1 Operating Process
3.2 MAS
3.3 Multi-agent Platforms
4 Application
4.1 Role of the Different Agents
4.2 Constraints
4.3 MAS Algorithm
4.4 Results
4.5 Discussion
5 Conclusion and Perspectives
References
Green Transportation Balanced Scorecard: A VIKOR Fuzzy Method for Evaluating Green Logistics Initiatives
1 Introduction
2 Theoretical and Conceptual Framework
2.1 Green Logistics Initiatives
2.2 Green Transportation
2.3 Green Transportation Balanced Scorecard
3 Object, Field, and VIKOR Method
4 Results and Discussions
5 Conclusion
References
Digital Healthcare
Diabetic Retinopathy Prediction Based on Transfer Learning and Ensemble Voting
1 Introduction
2 Related Work
3 Material and Methods
4 Proposed Approach
5 Experiments and Results
5.1 Evaluation Metrics
5.2 Train Test Dataset
5.3 Result
6 Discussion
7 Conclusion
References
Machine Learning Algorithms for Early and Accurate Diabetic Detection
1 Introduction
2 Related Works
3 Diabetes Detection Model
4 Machine Learning for Diabetes Detection
4.1 Random Forest Classifier
4.2 Support Vector Machines (SVM)
4.3 Linear Regression (LR)
5 Metrics for Evaluating Performance
5.1 Accuracy
5.2 Sensitivity
5.3 Precision
5.4 F1-Score
6 Results Analysis
6.1 Data Exploration and Visualization
6.2 Used Technology
6.3 Results
7 Conclusion
References
Blood Glucose-Insulin Dynamics in Type-1 Diabetic Patients for the Mitigation of Hyperglycemic Conditions: A PID Controller with a Step Response
1 Introduction
2 Review of Related Research
2.1 Blood Glucose Dynamics and Equations
3 Methodology
4 Results and Discussions
4.1 Considering the Simulink Model Without the Effect of a Controller
4.2 Considering the Simulink Model with a PID Controller
5 Conclusion and Recommendation
References
Fine-Tuning Transformer Models for Adverse Drug Event Identification and Extraction in Biomedical Corpora: A Comparative Study
1 Introduction
2 Materials and Methods
2.1 Problem Formulation
2.2 BioNER Utilizing Pretrained Language Models
2.3 Pre-trained Language Models
3 Experiments and Results
3.1 Data
3.2 Experiments
3.3 Results
3.4 Discussion
4 Conclusion and Future Work
References
Serious Games for Improving Training and Skills Acquisition in Medicine and Health Professions
1 Introduction
2 Research Background
2.1 Some Definitions of Serious Games
2.2 Categorization of Serious Games
2.3 Simulators: Serious Games in Medicine
3 Synthesis and Discussion
4 Conclusion
References
A New Compartmental Model for Analyzing COVID-19 Spread Within Homogeneous Populations
1 Introduction
2 Mathematical Model Formulation
3 Model Fitting
4 Vaccination Impacts
5 Conclusion
References
The Use of Artificial Intelligence and Blockchain in Healthcare Applications: Introduction for Beginning Researchers
1 Introduction
2 Blockchain Overview
2.1 What is Blockchain?
2.2 Key Features
2.3 Types of Blockchain
2.4 Consensus Protocols
2.5 Smart Contracts
3 Blockchain Applications in Healthcare
4 Artificial Intelligence
4.1 What is Artificial Intelligence?
4.2 Classical Machine Learning
4.3 Deep Learning: A New Epoch of Machine Learning
5 AI Applications in Healthcare
6 AI and Blockchain Integration in Healthcare
7 Conclusion
References
Color Medical Image Encryption Based on Chaotic System and DNA
1 Introduction
2 Preliminaries
2.1 The Chaotic Logistics Map
2.2 Chen's Hyperchaotic System
2.3 Deoxyribonucleic Acid (DNA) Sequence
3 Proposed Medical Color Image Encryption System
3.1 The Proposed Process for Encryption and Decryption
4 The Simulation Results Obtained
4.1 Histogram Analysis
4.2 Entropy of Information
4.3 The Test for Correlation Between Two Neighboring Pixels
4.4 Analysis of the Differential Attacks
4.5 Security Analysis
5 Conclusion
References
Classification of EEG Signal Based on Pre-Trained 2D CNN Model for Epilepsy Detection
1 Introduction
2 Method
2.1 Citation for the Dataset
2.2 Deep Learning Classification
2.3 Spectrogram Used for Classification
2.4 Proposed Model for Classification
3 Results and Discussion
3.1 Accuracy of Each Classifier
3.2 Confusion Matrix
3.3 Discussion About Classification Accuracy
4 Conclusion
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 668

Saad Motahhir Badre Bossoufi   Editors

Digital Technologies and Applications Proceedings of ICDTA’23, Fez, Morocco, Volume 1

Lecture Notes in Networks and Systems

668

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Saad Motahhir · Badre Bossoufi Editors

Digital Technologies and Applications Proceedings of ICDTA’23, Fez, Morocco, Volume 1

Editors Saad Motahhir Ecole Nationale des Sciences Appliquées Fez, Morocco

Badre Bossoufi Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez, Morocco

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-29856-1 ISBN 978-3-031-29857-8 (eBook) https://doi.org/10.1007/978-3-031-29857-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

We are honored to dedicate the proceedings of ICDTA’23 to all the participants and committees of ICDTA’23.

Preface

This volume contains the second section of the written versions of most of the contributions presented during the conference of ICDTA’23. The conference provided a setting for discussing recent developments in a wide variety of topics including artificial intelligence, Internet of Things, embedded systems, network technology, digital transformation and their applications, in several areas such as Industry 4.0, renewable energy, mechatronics and digital healthcare. The conference has been a good opportunity for participants from various destinations to present and discuss topics in their respective research areas. ICDTA’23 Conference tends to collect the latest research results and applications on digital technologies and their applications. It includes a selection of 200 papers submitted to the conference from universities and industries all over the world. This volume includes half of the accepted papers. All of the accepted papers were subjected to strict peer-reviewing by 2–4 expert referees. The papers have been selected for this volume because of their quality and their relevance to the conference. We would like to express our sincere appreciation to all authors for their contributions to this book. We would like to extend our thanks to all the referees for their constructive comments on all papers; especially, we would like to thank Organizing Committee for their hardworking. Finally, we would like to thank the Springer publications for producing this volume. S. Motahhir B. Bossoufi

Organization

Honorary Committee Miraoui Abdellatif, Minister of Higher Education, Scientific Research and Innovation Ijjaali Mustapha, President of SMBA University, Fez Benlmlih Mohammed, Dean of Faculty of Sciences FSDM, Fez

General Chairs Saad Motahhir, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco Badre Bossoufi, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Organizing Committee Mohammed Karim, FSDM, USMBA, Fez, Morocco Chakib El Bekkali, FSDM, USMBA, Fez, Morocco El Ghzizal Abdelaziz, EST, USMBA, Fez, Morocco Hala Alami Aroussi, EST, UMP, Oujda, Morocco Manale Boderbala, FSDM, USMBA, Fez, Morocco Nada Zinelabidine, FSDM, USMBA, Fez, Morocco Yassine Zahraoui, FSDM, USMBA, Fez, Morocco Hajar Saikouk, Euromed, Fez, Morocco Chakib Alaoui, Euromed, Fez, Morocco Aissa Chouder, Université Mohamed Boudif-M’sila, Algeria Aziza Benaboud, Royal Navy School, Casablanca, Morocco Aboubakr El Hamoumi, ENSA, Abdelmalek Essaadi University, Morocco Ghassane Aniba, EMI, Rabat, Morocco Salah Eddine Hebaz, CY Cergy Paris Université, France

Speakers • Speaker 1: Marcelo Godoy Simões (Professor in Electrical Power Systems, University of Vaasa, Finland) “Comparison of the Technology Revolution of the 20th Century with the Energy Revolution of the 21st Century” • Speaker 2: Rachid Yazami (KVI PTE LTD., Singapore) “Lithium ion batteries challenges in the electromobility transition”

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Organization

• Speaker 3: Imed Romdhani (Edinburgh Napier University, UK) “Edge Computing Applications” • Speaker 4: Nayyar Anand (Duy Tan University, Da Nang, Vietnam) “Autonomous Vehicles: Future Revolution in Transportation” • Speaker 5: Sundarapandian Vaidyanathan (Vel Tech University, Avadi, Chennai, Tamil Nadu, India) “Chaotic Systems: Modelling, Circuit Design, FPGA Realization and Engineering Applications” • Speaker 6: Mohamed Benbouzid (IEEE Fellow, University of Brest, France) “Grid connection of marine renewable energies: Issues and challenges” • Speaker 7: Josep M. Guerrero (Villum Investigator, Director Center Research Microgrids, IEEE Fellow, Aalborg University, Denmark) “Neuroscience for Engineers” • Speaker 8: Vincenzo Piuri (President of the IEEE Systems Council and IEEE Fellow, University of Milan, Italy) “Artificial Intelligence in Cloud/Fog/Edge Computing and Internet-of-Things” • Speaker 9: Pierluigi Siano (Editor for the Power & Energy Society Section of IEEE Access, University of Salerno, Salerno, Italy “Optimization of Smart Energy Communities”

Technical Program Committee Abdelhak Boulalam, Morocco Haddouch Khalid, Morocco Abdelhalim Zaoui, Algeria Abdellah Mechaqrane, Morocco Abdun Mahmood, Australia Adnane El Attar, Qatar Adrian Nedelcu, Romania Afef Bennani-Ben Abdelghani, Tunisia Agnieszka Konys, Poland Ahmed Lagrioui, Morocco Ait Madi Abdessalam, Morocco Alaoui Chakib, Morocco Alem Said, Algeria Alfidi Mohamed, Morocco Al-Greer Maher, UK Allouhi Amine, Morocco Altınkök Necat, Turkey Amer Ghada, Egypt Amine Lahyani, Tunisia Anand Nayyar, Vietnam André Meurer, UK Andrea Hildebrandt, Germany Andreas Demetriou, Cyprus Andreea Elleathey, Romania

Organization

Angelo Beltran Jr., Philippines Antonio Andrade, Brasil Antonio M. R. C. Grilo, Portugal Aziz Derouich, Morocco Aziza Benaboud, Switzerland Bailo Mamadou Camara, France Barkat Said, Algeria Bayu Adhi Tama, Korea Belmili Hocine, Algeria Bennani Saad, Morocco Benyamin Ahmadnia, USA Bousseta Mohammed, Morocco Castillo-Calzadilla Tony, Venezuela Chouder Aissa, Algeria Chrifi Alaoui Meriem, France Christian Nichita, France Damien Ernst, Belgium Dekhane Azzeddine, Algeria Denis Dochan, Belgium Denizar Martins, Brasil Djerioui Ali, Algeria Kumar K., India Dragan Mlakic, Bosnia and Herzegovina Driss Youssfi, Morocco Dutta Nabanita, India Edgardo Daniel Castronuovo, Spain El Ghzizal Abdelaziz, Morocco El Hamdani Wafae, Morocco El Mostapha Ziani, Morocco Eltamaly Ali, Egypt Fatima Errahimi, Morocco Gabriel Iana, Romania Gabriel Orsini, Germany Ghedamsi Kaci, Algeria Goran Krajacic, Bulgaria Hajji Bekkay, Morocco Hassan Mahmoudi, Morocco Hassane Zahboune, Morocco Hasseine Linda, Algeria Himrane Nabil, Algeria Houda Ben Attia Sethom, Tunisia Imad Manssouri, Morocco Jamal Bouchnaif, Morocco Jihen Arbi-Ziani, Tunisia Jing Dai, France

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Organization

Johan Gyselinck, Belgium Kaarin J. Anstey, Australia Kandoussi Zineb, Morocco Karan Mitra, Sweden Ke-Lin Du, Canada Khalid Grari, Morocco Khireddine Karim, Algeria Kumar Donta Praveen, Estonia Kumar Yelamarthi, USA Livinti Petru, Romania Luminita Mirela Constantinescu, Romania ˆ Morocco Mahdaoui Mustapha, Majid Ali, Pakistan Malvoni Maria, Greece Manel Jebli-Ben Ghorbal, Tunisia Marco Picone, Italy Maria Drakaki, Greece Marko Horvat, Croatia Md Multan Biswas, USA Mekhilef Saad, Malaysia Michael Emmanuel, USA Michele Barsant, Italy Mihai Oprescu, Romania Mohamed Barara, France Mohamed J. M. A. Rasul, Sri Lanka Mohamed Karim, Morocco Mohamed Nabil Kebbaj, Morocco Mohammed Benbrahim, Morocco Mohammed Larbi Elhafyani, Morocco Mostafa Bouzi, Morocco Mourad Loucif, Algeria Muhammad Affan, Pakistan Mustafa Sahin, Turkey Mustapha El Gouri, Morocco Mustapha Habib Algeria Nabil El Akkad, Morocco Nicolas Waldhoff, France Nicu Bizon, Romania Nikolaos Pitropakis, UK Nikolovski Srete, Serbia Nordine Chaibi, Morocco Norhanisah Baharudin, Malaysia Olivier Debleker, Belgium Oughdir Lahcen, Morocco Padmanaban Sanjeevikumar, Denmark

Organization

Partha Mishra, USA Petar Varbanov, Bulgaria Prabaharan Nataraj, India Rabbanakul Avezov, Uzbekistan Rajanand Patnaik Narasipuram, India Roberto Colom, Spain Saadani Rachid, Morocco Said Hamdaoui, Morocco Saikouk Hajar, Morocco Samia El Haddouti, CNRST, Morocco Samuel Greiff, Luxembourg Samundra Gurung, Thailand Sayyouri Mhamed, Morocco Silvia Nittel, USA Silviu Ionita, Romania Sondes Skander-Mustapha, Tunisia Thomas R. Coyle, USA Tome Eftimov, Slovenia Tunku Muhammad Nizar Tunku Mansur, Malaysia Vaibhav Gandhi, UK Vana Kalogeraki, Greece Vincent Sir-Coulomb, France Wagner Brignol, Brasil Walid Issa, UK Wilson Vallejos, Ecuador Wissem Naouar, Tunisia Yang Du, China Yann-Gaël Guéhéneuc, Canada Yassine Chaibi, Morocco Youssef Zaz, Morocco Zekry Abdelhalim, Egypt Zorig Abdelmalek, Morocco S. G. Srivani, India Mohammad Jafar Hadidian Moghaddam, Australia Nilufar R. Avezova, Uzbekistan Ali Eltamaly Saudi, Arabia Hamid Reza Baghaee, Iran Josep M. Guerrero, Denmark Pierluigi Siano, Italy Mustapha Errouh, Morocco Souad El Khattabi, Morocco Loubna Laaouina, Morocco Fatima Bennouna, Morocco Hicham Hihi, Morocco Chetioui Kaouthar, Morocco

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Organization

Adadi Amina, Morocco Idrissi Aydi Ouafae, Morocco Hraoui Said, Morocco Berrada Mohammed, Morocco Mohamed Salhi, ENSAM, Meknes, Morocco Anass Cherrafi, ENSAM, Meknes, Morocco Didi Abdessamad, Morocco Ali Yahyaoui, Morocco Ahmed Bouraiou, Algeria Merabet Boualem, Algeria Driss Amegouz, Morocco Radeouane El Bahjaoui, Morocco Florin-Marian Birleanu, Romania El-Kaber Hachem, Morocco Tnani Slim, France Abdelghani El Ougli, Morocco Bri Seddik, Morocco Boukili Bensalem, Morocco Abdelilah Chalh, Morocco Amine Ihedrane, Morocco Abdelouahed Sabri, Morocco Richard Millham, South Africa Hanan Halaq, Morocco Halkhams Imane, Morocco Mohamed Naji, Morocco Mostafa Azizi, Morocco Mohamed Saber, Morocco Badreddine Lahfaoui, Morocco Nacer Msirdi, France Houcine Chafouk, France Adil Essalmi, Morocco Taibi Djamel, Algeria Livinti Petru, Romania Silviu Ionita, Romania Gabriel Iana, Romania Brahima Dakyo, France Alireza Payman, France Gerard Deepak, India Florien Birleanu, Romania Younes Morabit, Morocco Youssef El Afou, Morocco Ali Ahitouf, Morocco Ayoub El Bakri, Morocco Taoufik Ouchbel, Morocco Gulshan Kumar, India

Organization

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Soufiyane Benzaouia, France Ikram El Azami, Morocco Damien Ambrosetti, Morocco Rudan Xiao, France Youness Mehdaoui, Morocco Bennani Mohammed-Taj, FSDM, USMBA, Fez, Morocco El-Fazazy Khalid, FSDM, USMBA, Fez, Morocco Mustapha Bouakkaz, Algeria Rachid El Alami, FSDM, USMBA, Fez, Morocco Ahmed Zellou, ENSIAS, Morocco Mostafa el Mallahi, USMBA, Morocco Hicham Amakdouf, USMBA, Fez, Morocco Hassan Qjidaa, USMBA, Fez, Morocco Omar Diouri, USMBA, Fez, Morocco Soufyane Benzaouia, Amiens, France Marouane El Azaoui, ENSA, Agadir, Morocco Abdelhamid Bou-El-Harmel, USMBA, Fez, Morocco Youssef Cheddadi, USMBA, Fez, Morocco Othmane Zemzoum, USMBA, Fez, Morocco Abdelmajid Bybi, Mohammed V, Rabat, Morocco Karim El Khadiri, USMBA, Fez, Morocco Kenz Ait El Kadi, Morocco Aicha Wahabi, Morocco Mhamed Zineddine, Euromed, Fez, Morocco Anass Bekkouri, Euromed, Fez, Morocco Achraf Berrajaa, Euromed, Fez, Morocco Hiba Chougrad, USMBA, Fez, Morocco Jaouad Boumhidi, USMBA, Fez, Morocco Ismail Boumhidi, USMBA, Fez, Morocco El Habib Nfaoui, USMBA, Fez, Morocco Salm El Hajjami, USMBA, Fez, Morocco Ghassan Anniba, EMI, Rabat, Morocco Hassan Moustabchir, USMBA, Morocco Tariq Riouch, Morocco Abdelali Ed-Dahhak, Morocco Khalil Kasmi, Morocco Mohammed Naji, Morocco Jaouad Anissi, Morocco Faiza Dib, Morocco Nabil Benaya, Morocco Oumayma Banouar, Morocco Mohammed El Ghzaoui, Morocco Jabir Arif, Morocco Othmane Zamzoum, Morocco Saloua Bensiali, Hassan II Institute of Agronomy and Veterinary Medicine, Morocco

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Organization

Naoufel El Youssfi, USMBA, Fez, Morocco Abdellah El Barkany, FST, USMBA, Morocco Nour-Eddine Joudar, ENSAM, Rabat, Morocco Karim El Moutaouakil, USMBA, Morocco Jamal Riffi, USMBA, Morocco Ilyas Lahlouh, Morocco Osamah Ibrahim Khalaf, Iraq Mouncef Filali Bouami, Morocco Radouane Elbahjaoui, Morocco Rachid El Bachtiri, Morocco Mohammed Boughaleb, Morocco Boukil Naoual, Morocco Faquir Sanaa, Morocco Hemi Hanane, Morocco Ben Sassi Hicham, Morocco Asmae Abadi, Morocco Mohamed El Malki, Morocco Adil Kenzi, Morocco Abdellatif Ezzouhairi, Morocco Hamid Tairi, Morocco Adil Kenzi, USMBA, Fez, Morocco Abdellatif Ezzouhairi, USMBA, Fez, Morocco Khaoula Oulidi Omali, Morocco Meriem Hnida, ESI, Rabat, Morocco Giovanni Spagnuolo, University of Salerno, Italy Adnane Saoud, University of California, Berkeley

Acknowledgments

We request the pleasure of thanking you for taking part in the third edition of the International Conference on Digital Technologies and Applications ICDTA’23. We are very grateful for your support, so thank you everyone for bringing your expertise and experience around the conference and engaging in such fruitful, constructive and open exchanges throughout the two days of the ICDTA’23 conference. We would like to extend our deepest thanks and gratitude to all the speakers for accepting to join us from different countries. Thank you for being such wonderful persons and speakers. Again, thanks for sharing your insight, knowledge and experience. Of course, this event could not be that successful without the effort of the organizing and technical program committees. Therefore, Pr. Badre and I would like to express our sincere appreciation to all of you who generously helped us. We would like to especially thank all the participants for the confidence and trust you have placed in our conference. We hope we lived up to your highest expectations. Our humble acknowledgment would be incomplete without thanking our biggest source of support; therefore, our deepest gratitude goes to Prof. Miraoui Abdellatif, the Minister of Higher Education, Scientific Research and Innovation. And of course, the same goes for Prof. Mustapha Ijjaali, the President of Sidi Mohammed Ben Abdellah University as well as Prof. Lahrach Abderrahim, the Director of the National School of applied sciences, Prof. Belmlih Mohammed, the Dean of the Faculty of Science, Prof. Abdelmajid Saka, Deputy Director of the National School of applied sciences, Prof. Elhassouni Mohammed, the Vice Dean of Faculty of Sciences, and the list is long of course. Thank you all for your support and for being all-time, all set up to host such scientific events. S. Motahhir B. Bossoufi

Contents

Artificial Intelligence, Machine Learning and Data Analysis Elman and Feed-Forward Neural Networks with Different Training Algorithms for Solar Radiation Forecasting: A Comparison with a Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rachida Elmousaid, Younes Adnani, Achour El Hamdaouy, and Rachid Elgouri

3

An Artificial Neural Network Model Based on Non-linear Autoregressive Exogenous for Predicting the Humidity of a Greenhouse System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chaimae Dada, Hafsa Hamidane, Mohamed Guerbaoui, Abdelali Ed-Dahhak, and Abdeslam Lachhab

13

Towards an Adaptive Learning Process Using Artificial Intelligence Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Zohra Lhafra and Otman Abdoun

23

Emotion Recognition of Facial Expressions with Deep Learning and Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anouar Gmili, Khalid El Fazazy, Jamal Riffi, Mohamed Adnane Mahraz, and Aziz Khamjane A Novel Approach to Intelligent Touristic Visits Using Bing Maps and Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Youssef Benchekroun, Hanae Senba, and Khalid Haddouch Evaluating the Efficiency of Multilayer Perceptron Neural Network Architecture in Classifying Cognitive Impairments Related to Human Bipedal Spatial Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, and Bernard Cohen A Novel Real Time Electric Vehicles Smart Charging Approach Based on Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mouaad Boulakhber, Ilham Sebbani, Youssef Oubail, Imad Aboudrar, Kawtar Benabdelaziz, Malika Zazi, and Tarik Kousksou

33

43

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Proposed Hybrid Model Recurrent Neural Network for Human Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Youssef Errafik, Adil Kenzi, and Younes Dhassi Evaluation of CMIP5 and CMIP6 Performance in Simulating West African Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boubacar Doumbia, Elijah Adefisan, Jerome Omotosho, Boris Thies, and Joerg Bendix Knowledge Management, Tools and Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ibtissam Assoufi, Ilhame El Farissi, and Ilham Slimani The Interplay Between Social Science and Big Data Research: A Bibliometric Review of the Journal Big Data and Society, 2014–2021 . . . . . . Mohamed Behlouli and Mohamed Mamad Application of Machine Learning in Fused Deposition Modeling: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohmed Achraf El Youbi El Idrissi, Loubna Laaouina, Adil Jeghal, Hamid Tairi, and Moncef Zaki Prediction of Electrical Power of Ag/Water-Based PVT System Using K-NN Machine Learning Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Safae Margoum, Bekkay Hajji, Chaimae El Fouas, Oussama El Manssouri, Stefano Aneli, Antonio Gagliano, Giovanni Mannino, and Giuseppe Marco Tina Evaluation of Machine Learning and Ensemble Methods for Complications Related to Port a Cath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hanane El Oualy, Bekkay Hajji, Khadija Mokhtari, Mouhsine Omari, and Hamid Madani Accuracy Improvement of Network Intrusion Detection System Using Bidirectional Long-Short Term Memory (Bi-LSTM) . . . . . . . . . . . . . . . . . . . . . . . Salmi Salim and Oughdir Lahcen Hand Gesture Recognition Using Machine Learning for Bionic Applications: Forearm Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oussama Lamsellak, Ahmad Benlghazi, Abdelaziz Chetouani, and Abdelhamid Benali Detecting and Extracting Cocoa Pods in the Natural Environment Using Deep Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kacoutchy Jean Ayikpa, Diarra Mamadou, Sovi Guillaume Sodjinou, Abou Bakary Ballo, Pierre Gouton, and Kablan Jérôme Adou

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Design of a New Strategy Based on Machine Learning to Improve the Energy Efficiency of Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaoutar Talbi, Abdelghani El Ougli, and Tidhaf Belkassem

xxi

175

Internet of Things, Blockchain and Security and Network Technology Smart Grid: A Communication Model and Security Challenges . . . . . . . . . . . . . Rim Marah, Inssaf El Guabassi, Zakaria Bousalem, and Abdellatif Haj

189

Smart Contracts: An Emerging Business Model in Decentralized Finance . . . . . Loubna El Hassouni and Ali Ouchekkir

197

A Survey and a State-of-the-Art Related to Consensus Mechanisms in Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Barj, Aafaf Ouaddah, and Abdellatif Mezrioui Blockchain Technology in Finance: A Literature Review . . . . . . . . . . . . . . . . . . . Fouad Daidai and Larbi Tamnine A Review of Privacy-Preserving Cryptographic Techniques Used in Blockchain Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Barj, Aafaf Ouaddah, and Abdellatif Mezrioui Renewable Energy in Smart Grid: Photovoltaic Power Monitoring System Based on Machine Learning Using an Open-Source IoT Platform . . . . . Youness Hakam, Hajar Ahessab, Ahmed Gaga, and Benachir El Hadadi

208

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Predicated on IoT, a Safe Intelligent Driver Assistance System in V2X Communication Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Younesse El Hamidi and Mostafa Bouzi

252

Novel Flexible Topologies of SIW Components for IoT Applications: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Souad Akkader, Hamid Bouyghf, and Abdennaceur Baghdad

261

Overview of Blockchain Based IoT Trust Management . . . . . . . . . . . . . . . . . . . . Ilham Laabab and Abdellatif Ezzouhairi Multiband Reconfigurable Planar Antenna for Wireless Mobile Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Bikrat and Seddik Bri

270

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Design and Analysis of a Slot Antenna Array with a Defected Ground Plan for Millimeter Wave Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Kiouach, Mohammed El Ghzaoui, Rachid El Alami, Sudipta Das, Mohammed Ouazzani Jamil, and Hassan Qjidaa Framework for Real-Time Simulations of Routing Security Attacks in VANETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Souad Ajjaj, Souad El Houssaini, Mustapha Hain, and Mohammed-Alamine El Houssaini Study and Design of a Microstrip Patch Antenna Array for 2.4 GHz Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amraoui Youssef, Imane Halkhams, Rachid El Alami, Mohammed Ouazzani Jamil, and Hassan Qjidaa Optical Envelope Detector in Fiber Transmission and Correction Change Phase Using HPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmadou Moustapha Diop, Said Mazer, Jean-Luc Polleux, Catherine Algani, Mohammed Fattah, and Moulhime EL Bekkali Performance of BPSK-FSO Communication Over Turbulence and Fog Attenuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdeslam Fakchich, Mohamed Bouhadda, Rachid El Alami, Fouad Mohammed Abbou, Abdelouahed Essahlaoui, Mohammed El Ghzaoui, Hassan Qjidaa, and Mohammed Ouazzani Jamil

289

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326

Digital Transformation and E-Learning The Role of Digitalization in Achieving Cybersecurity in the Moroccan Banking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hadj Ali Abdellah and Bouchra Benyacoub

337

An Enterprise Resource Planning (ERP) SAP Implementation Case Study in South Africa Small Medium Enterprise Sectors . . . . . . . . . . . . . . . . . . . Oluwasegun Julius Aroba and Sanele Baldwin Mnguni

348

Towards Transparent Governance by Publishing Open Statistical Data . . . . . . . Rabeb Abida, Emna Hachicha Belghith, and Anthony Cleve Digitalizing Teaching and Learning in Light of Sustainability in Times of the Post-Covid-19 Period: Challenges, Issues, and Opportunities . . . . . . . . . . Vahid Norouzi Larsari, Radka Wildová, Raju Dhuli, Hossein Chenari, Ethel Reyes-Chua, Elbert M. Galas, Jay A. Sario, and Maryann H. Lanuza

355

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Shaping Students’ Learning for a Specific Learning Environment . . . . . . . . . . . . Meryem Amane, Karima Aissaoui, and Mohammed Berrada The Impact of Distance Learning in the Performance and Attitude of Students During the Global Coronavirus Pandemic (Covid-19): Case of the Moroccan University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imane Rouah, Samira Khoulji, and Mohamed Larbi Kerkeb Potentialities of Learning Analytics to Overcome Students Dropout in Distance Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karima Hamdane, Abderrahim El Mhouti, Mohammed Massar, and Lamyaa Chihab Learning Analytics and Big Data: Huge Potential to Improve Online Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lamyaa Chihab, Abderrahime El Mhouti, Mohammed Massar, and Karima Hamdane

xxiii

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Serious Games, a Tool for Consolidating Learning Outcomes . . . . . . . . . . . . . . . Mounia El Rhayami, Abderrahim El Mhouti, and Yassine El Borji

412

Dimensionality Reduction for Predicting Students Dropout in MOOC . . . . . . . . Zakaria Alj, Anas Bouayad, and Mohammed Ouçamah Cherkaoui Malki

421

Cheating Detection in Online Exams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nabila EL Rhezzali, Imane Hilal, and Meriem Hnida

431

Station Rotation Model of Blended Learning as Generative Technology in Education: An Evidence-Based Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vahid Norouzi Larsari, Radka wildová, Raju Dhuli, Hossein Chenari, and Maryann H. Lanuza

441

Image and Information Processing New Invariant Meixner Moments for Non-uniformly Scaled Images . . . . . . . . . Mohamed Yamni, Achraf Daoui, Hicham Karmouni, Mhamed Sayyouri, Hassan Qjidaa, and Mohammed Ouazzani Jamil

453

A Review on the Driver’s Fatigue Detection Methods . . . . . . . . . . . . . . . . . . . . . . Hoda El Boussaki, Rachid Latif, and Amine Saddik

464

Real-Time SPO2 Monitoring Based on Facial Images Sequences . . . . . . . . . . . . Rachid Latif, Bouthayna Addaali, and Amine Saddik

474

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The Identification of Weeds and Crops Using the Popular Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Habib, Salma Sekhra, Adil Tannouche, and Youssef Ounejjar 3D Scenes Semantic Understanding: New Approach Based on Image Processing for Time Learning Reducing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meryem Ouazzani Chahdi, Afafe Annich, and Khalid Satori Image Encryption Algorithm Based on Improved Hill Cipher Using the 2D Logistic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samir El Kaddouhi, Younes Qobbi, Abdellah Abid, Mariem Jarjar, Mohamed Essaid, and Abdellatif Jarjar A Review of Video Summarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hanae Moussaoui, Nabil El Akkad, and Mohamed Benslimane A Color Image Encryption Method Designed Using Chaotic Confusion, Enhanced CBC-MAC Mode and Pixel Shuffling . . . . . . . . . . . . . . . . . . . . . . . . . . Faiq Gmira and Said Hraoui Normalized Gradient Min Sum Decoding for Low Density Parity Check Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hajar El Ouakili, Mohammed El Ghzaoui, Mohammed Ouazzani Jamil, Hassan Qjidaa, and Rachid El Alami CCC-Transformation: Novel Method to Secure Passwords Based on Hash Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Zohra Ben Chakra, Hamza Touil, and Nabil El Akkad

484

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Case Studies of Several Popular Text Classification Methods . . . . . . . . . . . . . . . . Awatif Karim, Youssef Hami, Chakir Loqman, and Jaouad Boumhidi

552

A Survey on Facial Emotion Recognition for the Elderly . . . . . . . . . . . . . . . . . . . Nouhaila Labzour, Sanaa El Fkihi, Soukayna Benaissa, Yahya Zennayi, and Omar Bourja

561

Advanced Technologies in Energy and Electrical Engineering Modeling of a High Frequency Ultrasonic Transducer Using Mason’s Equivalent Circuit Implemented in LTspice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hayat Reskal, Abdelmajid Bybi, Lahoucine El Maimouni, Anouar Boujenoui, and Abdchafia Lakbib

579

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Modeling of Piezoelectric Transducers Using 1D and 2D Redwood Models Implemented in LTSPICE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anouar Boujenoui, Abdelmajid Bybi, Lahoucine El Maimouni, Hayat Reskal, and Abdchafia Lakbib Fuzzy Logic Speed Controller for Robust Direct Torque Control of Induction Motor Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Siham Mencou, Majid Ben Yakhlef, and El Bachir Tazi A Comparative Study Between Optimization Algorithms of MPPT Algorithms (P&O and Incremental Conductance Method) . . . . . . . . . . . . . . . . . . Chaymae Boubii, Ismail El Kafazi, Rachid Bannari, and Brahim El Bhiri Energy-Reducing Opportunities by Improving Power Quality: A Case Study of Industrial Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kamal Anoune, Mokhtar Ghazouani, Mohamed Ghazi, and Azzeddine Laknizi Compensation of Current Harmonic Distortion in a Grid-Connected Photovoltaic System via an LC Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Khalil, Naima Oumidou, and Mohamed Cherkaoui Optimal Sizing of a Grid-Connected Renewable Energy System for a Residential Application in Gabon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rolains Golchimard Elenga and Stahel Serano Bibang Bi Obam Assoumou A Comparative Study of P&O and Fuzzy Logic MPPT Algorithms for a Photovoltaic Grid Connected Inverter System . . . . . . . . . . . . . . . . . . . . . . . . Hajar Ahessab, Youness Hakam, Ahmed Gaga, and Benachir El Hadadi A Review Study of Control Strategies of VSC-HVDC System Used Between Renewable Energy Source and Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chaimaa Lakhdairi, Aziza Benaboud, Hicham Bahri, and Mohamed Talea Modelling and Simulation of PV System Grid Connected with 100 KW Rated Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zohra Lahyan and Ahmed Abbou

xxv

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Development of Geometrical Parameters for a Conical Solar Concentrator – Application for Vapor Generation . . . . . . . . . . . . . . . . . . . . . . . . . Firyal Latrache, Zakia Hammouch, Karima Lamnaouar, Benaissa Bellach, and Mohammed Ghammouri High-Performance MPPT Based on Developed Fast Convergence for PV System-Experimental Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdel Hamid Adaliou, Abdelhak Lamreoua, Ismail Isknan, Said Doubabi, Mustapha Melhaoui, Mostafa El Ouariachi, and Kamal Hirech A Comparative Study Between MPC Algorithm and P&O and IncCond the Optimization Algorithms of MPPT Algorithms . . . . . . . . . . . . . . . . . . . . . . . . Chaymae Boubii, Ismail El Kafazi, Rachid Bannari, and Brahim El Bhiri Comparative Analysis of Classical and Meta-heuristic MPPT Algorithms in PV Systems Under Uniform Condition . . . . . . . . . . . . . . . . . . . . . . Abdelfettah El-Ghajghaj, Hicham Karmouni, Najib El Ouanjli, Mohammed Ouazzani Jamil, Hassan Qjidaa, and Mhamed Sayyouri Robust Control of a Wind Power System Based on a Doubly-Fed Induction Generator Using a Fuzzy Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mbarek Chahboun and Hicham Hihi A Review Backstepping Control of a DFIG-Based Wind Power System . . . . . . Farah Echiheb, Badre Bossoufi, Ismail El Kafazi, and Brahim El Bhiri Detection and Prevention of Repetitive Major Faults of a WTG by Analysis of Alarms Through SCADA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Bousla, Ali Haddi, Youness El Mourabit, Ahmed Sadki, Abderrahman Mouradi, and Abderrahman El Kharrim HDL Coder Tool for ECG Signal Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bouchra Bendahane, Wissam Jenkal, Mostafa Laaboubi, and Rachid Latif Realization of an Electrical Power Quality Analyzer Based on NIDAQ6009 Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Azeddine Bouzbiba, Yassine Taleb, and Ahmed Abbou OpenCL Kernel Optimization Metrics for CPU-GPU Architecture . . . . . . . . . . . Latif Rachid, Jahid Khadija, and Saddik Amine

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Embedded System of Signal Processing on FPGA: Implementation OpenMP Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mhamed Hadji, Abdelkader Elhanaoui, Rachid Skouri, and Said Agounad LWR Application on Calculating Travel Time in Urban Arterials with Traffic Lights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hamza El Ouenjli, Anas Chafi, and Salaheddine Kammouri Alami Analysis of the Driver’s Overspeed on the Road Based on Changes in Essential Driving Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Karrouchi, Ismail Nasri, Kamal Kassmi, Abdelhafid Messaoudi, and Soufian Zerouali

xxvii

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Mechatronic, Industry 4.0 and Control System The Use of Industry 4.0 Technologies in Maintenance: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Safaa Essalih, Zineb El Haouat, Mohamed Ramadany, Fatima Bennouna, and Driss Amegouz Adoption of Smart Traffic System to Reduce Traffic Congestion in a Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oluwasegun Julius Aroba, Phumla Mabuza, Andile Mabaso, and Phethokuhle Sibisi The Contribution of an ERP System in an LCA Analysis: A Case Study . . . . . . Zineb El Haouat, Safaa Essalih, Fatima Bennouna, Mohammed Ramadany, and Driss Amegouz The Impact of Blockchain Technology and Business Intelligence on the Supply Chain Performance-Based Tracking Process . . . . . . . . . . . . . . . . . Khadija El Fellah, Adil El Makrani, and Ikram El Azami Design of an Integral Sliding Mode Control Based on Reaching Law for Trajectory Tracking of a Quadrotor System . . . . . . . . . . . . . . . . . . . . . . . . . . . Mouna Lhayani, Ahmed Abbou, Yassine El Houm, and Mohammed Maaroufi Product Family Formation for Reconfigurable Manufacturing Systems . . . . . . . Chaymae Bahtat, Abdellah El Barkany, and Abdelouahhab Jabri

811

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833

845

855

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A New Method for Mobile Robots to Learn an Optimal Policy from an Expert Using Deep Imitation Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . Abderrahim Waga, Younes Benhouria, Ayoub Ba-Ichou, Said Benhlima, Ali Bekri, and Jawad Abdouni Modeling and Simulation of a BLDC Motor Speed Control in Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mariem Ahmed Baba, Mohamed Naoui, and Mohamed Cherkaoui Smart Supply Chain Management: A Literature Review . . . . . . . . . . . . . . . . . . . . Nabila Bouti and Fatima El Khoukhi A Dynamic MAS to Manage a Daily Planning for a Team Operating Theater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oumaima Hajji Soualfi, Abderrahim Hajji Soualfi, Khalid Chmali, Abdelmajid Elmrini, Abdellah Elbarkany, and Bilal Harras Green Transportation Balanced Scorecard: A VIKOR Fuzzy Method for Evaluating Green Logistics Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Badr Bentalha, Aziz Hmioui, and Lhoussaine Alla

873

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Digital Healthcare Diabetic Retinopathy Prediction Based on Transfer Learning and Ensemble Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Oulhadj, Jamal Riffi, Chaimae Khodriss, Adnane Mohamed Mahraz, Ahmed Bennis, Ali Yahyaouy, Fouad Chraibi, Meriem Abdellaoui, Idriss Benatiya Andsaloussi, and Hamid Tairi Machine Learning Algorithms for Early and Accurate Diabetic Detection . . . . . Hanae Chaaouan, Mohamed Bouhadda, Rachid El Alami, Abdelouahed Essahlaoui, Mohammed El Ghzaoui, Hassan Qjidaa, and Mohammed Ouazzani Jamil Blood Glucose-Insulin Dynamics in Type-1 Diabetic Patients for the Mitigation of Hyperglycemic Conditions: A PID Controller with a Step Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abubakar Isah Ndakara, Moad Essabbar, and Hajar Saikouk Fine-Tuning Transformer Models for Adverse Drug Event Identification and Extraction in Biomedical Corpora: A Comparative Study . . . . . . . . . . . . . . . Chanaa Hiba, El Habib Nfaoui, and Chakir Loqman

929

938

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Serious Games for Improving Training and Skills Acquisition in Medicine and Health Professions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmae Lamnai and Abderrahim El mhouti A New Compartmental Model for Analyzing COVID-19 Spread Within Homogeneous Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Touria Jdid, Mohammed Benbrahim, Mohammed Nabil Kabbaj, Mohamed Naji, and Mohamed Badr Benboubker The Use of Artificial Intelligence and Blockchain in Healthcare Applications: Introduction for Beginning Researchers . . . . . . . . . . . . . . . . . . . . . Majda Rehali, Merouane Elazami Elhassani, Asmae El jaouhari, and Mohammed Berrada Color Medical Image Encryption Based on Chaotic System and DNA . . . . . . . . Ahmed E. L. maloufy, Hicham Karmouni, Mohamed Amine Tahiri, Hassan Qjidaa, Mhamed Sayyouri, and Mohamed Ouazzani Jamil

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Classification of EEG Signal Based on Pre-Trained 2D CNN Model for Epilepsy Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1008 Fatima Edderbali, Mohammed Harmouchi, and Elmaati Essoukaki Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017

Artificial Intelligence, Machine Learning and Data Analysis

Elman and Feed-Forward Neural Networks with Different Training Algorithms for Solar Radiation Forecasting: A Comparison with a Case Study Rachida Elmousaid1(B) , Younes Adnani2 , Achour El Hamdaouy2 , and Rachid Elgouri1 1 Advanced Systems Engineering Laboratory, Ibn Tofail University Kenitra, Kenitra, Morocco

[email protected] 2 Electronic Systems, Information Processing, Mechanics and Energy Laboratory,

Ibn Tofail University Kenitra, Kenitra, Morocco

Abstract. In order to estimate daily solar radiation, this paper proposes Elman (ENN) and Feed forward backpropagation (FNN) neural networks. The time series data from the location of Kenitra City, Morocco is used to train the created models. Fletcher-Powell Conjugate Gradient (CGF), Scaled Conjugate Gradient (SCG), Resilient Backpropagation (RB), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquardt (LM), Polak-Ribi´ere Conjugate Gradient (CGP), and One Step Secant (OSS) are also used with both, the ENN and FNN to identify the best and effective training function for each. The models with various training algorithms are tested by using evaluation metrics root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The study showed that the Elman Neural Network had an excellent daily solar radiation prediction for Kenitra city. Good results have been obtained with Levenberg-Marquardt algorithm. Keywords: Solar Radiation Forecasting · Feed-Forward Backpropagation · Elman Neural Network · Levenberg-Marquardt

1 Introduction Around the world, more electrical energy is being produced these days using various sources of renewable energy. Among the most sustainable and clean energy sources for electricity generation is solar energy, owing to its simple installation and environmentally beneficial characteristics. The integration of renewable energy sources into the grid system makes the operation and management system of power production more difficult [1]. The solar system is more difficult because of the variations in electrical power generation brought on by climatic variability [2]. Therefore, it is crucial for grid stability to make an accurate estimate of solar power generation from the conversion of solar radiation. Different forecasting models were developed recently to select the suitable © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 3–12, 2023. https://doi.org/10.1007/978-3-031-29857-8_1

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prediction model of solar radiation, relying on stochastic models such as Autoregressive Moving Average, Marko chain, and Autoregressive Integrated Moving Average [3]. These models, which are based on probability estimates, do not always provide excellent accuracy. Because these stochastic models depend on the exact definition of problem domains and the identification of mathematical functions, it is really rather challenging to predict precisely the behavior of solar irradiance. This is precisely why it was found that the majority of stochastic models had rather large errors and were often hard to adopt generally. Furthermore, due to the non linear behavior of solar radiation, various research suggests artificial intelligence (AI) techniques including Adaptive Neural Fuzzy Inference Systems, fuzzy models, k-Neighbors (kNN), and Random Forest (RF) [4]. The most widely used and reliable models across the board were those created using AI approaches. Techniques like Elman Neural Network (ENN) are already utilized in the literature [5], feed forward neural network (FFNN) [6], Non Linear Autoregressive with Exogenous inputs (NARX) [7- [8]. In order to anticipate daily solar radiation, this study uses Elman (ENN) and Feed forward backpropagation (FNN) neural networks. These models are created by applying many training algorithms [9], which are SCG, RB, CGF, LM [10], CGP, CGB, and OSS. The major objective of applying a variety of these training algorithms is to obtain better predicting results with higher accuracy. Thus, the article is divided into five sections: Section 2 provides an introduction to ENN and FNN, while the study’s methodology is described in Sect. 3, Sect. 4 presents the experiment’s results, and lastly, the paper was concluded.

2 Neural Networks 2.1 Feed-Forward Backpropagation Neural Network (FNN) In many practical applications, the feed forward neural network (FNN) is the most known and highly used model [3]. There are three layers in a simple neural network. Which are the Output layer, hidden layer, and input layer. As seen in Fig. 1, neurons are arranged in a separate layered architecture in a multilayer FNN. The backpropagation algorithm is the most used learning method in FNN. A neuron k can be defined mathematically using the following two equations: N   wk,i pi (1) ak = i=1

yk = f (ak + bk )

(2)

The input signals are represented by p1 , p2 , p3 , ..., pn , the connection weights of the neuron are denoted by wk,1 , wk,2 , wk, 3... , wk,n , the linear output of a linear combination of weighted inputs is ak , the bias term is bk , the activation function is f , and the output signal is yk . 2.2 Elman Neural Network (ENN) The Elman neural network is a type of recurrent neural network (ENN). According to Fig. 2, more inputs from the hidden layer are included in ENN, when compared to

Elman and Feed-Forward Neural Networks

5

Fig. 1. Structure of FNN

traditional neural networks, which adds a new layer called the context layer. ENN is often applied to analyze dynamic systems, such as solar radiation. Even though there are other varieties of recurrent neural networks like LSTM [11] and GRU, many other networks such as the Jordan Neural Network are comparable to ENN.

Fig. 2. Structure of ENN

The following is written as the expression for the output layer at time t:   L t t wj,q hj yq = f

(3)

The hidden layer’s output expression at time t is represented as follows:   L L htj = f uit wi,j + Ckt−1 wj,k

(4)

j=1

j=1

k=1

The input function is defined at time t: Ckt−1 = htj

(5)

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2.3 Training Algorithms One Step Secant (OSS). Since it was assumed at each iteration, this algorithm does not save the entire Hessian matrix. The ability to determine the new search direction using OSS without having to compute an inverse matrix is another benefit. Contrary to the conjugate gradient algorithm, OSS needs more to compute each iteration and storage [1]. Levenberg-Marquardt (LM). It has been determined that the Levenberg-Marquadt (LM) algorithm is more robust than the traditional gradient descent methods [12]. It was created to approach second order training speed without computing the Hessian Matrix and is the most used optimization technique. Conjugate gradient with Powell-Beale restarts (CGB). As the network learns, the conjugate gradient backpropagation technique with Powell-Beale restarts updates the weight and bias of the network [13]. According to the input, bias, and weight that are offered, the network learns the pattern. Scaled Conjugate Gradient (SCG) MOLLER’S. (1993) scaled conjugate gradient process reduces the time consumption of line search. In this technique, the conjugate gradient approach and model trust region approach from the LM algorithm are integrated. In comparison to other algorithms, the Scaled Conjugate Gradient Backpropagation algorithm needs more iterations to converge [14]. Fletcher-Powell Conjugate Gradient (CGF) and Polak-Ribi´ere Conjugate Gradient (CGP) Fletcher-Reeves. Developed the second version of the conjugate gradient algorithm, same like with the Polak and Ribiere method, the search direction is the same for each iteration, similar to the SCG search direction equation [15]. Resilient Backpropagation (RB). The technique that immediately changes weights based on local gradient is resilient backpropagation [1]. The gradient’s behavior does not muddle adaptation in this learning scheme [16].

3 Methodology In this section, the methodology adopted for this study to estimate daily solar radiation using ENN and FNN with different training algorithms is explained. The MATLAB software was installed on a personal computer, which was used to create the algorithms to forecast the daily solar radiation. The meteorological data of Kenitra, Morocco (Latitude 34.25°N, Longitude −6.5833°E) were collected daily for four years from January 1, 2017, through December 31, 2020. They are available for download at the Nasa’s website for predictions on international energy sources [17]. This weather data was used to determine the best model to forecast solar radiation. Relative Humidity (RH) in %, Air temperature(AT) in °C, daily solar radiation(DSR) in MJ/m2 , Wind speed(WS) in m/s, number of Sun hours (SH) in h, and Atmospheric pressure(AP) in hPa are used to identify our data.

Elman and Feed-Forward Neural Networks

7

In the literature, a variety of evaluation metrics have been suggested. In this work, the following error criteria were applied [8]: Mean Square Error 2 1 N  yk,e − yk,m k=1 N

MSE =

(6)

Root Mean Square Error  RMSE =

2 1 N  yk,e − yk,m k=1 N

(7)

Mean Absolute Error MAE =

 1 N  yk,e − yk,m  k=1 N

(8)

Where the measured series is yk,m and the estimated series is yk,e . To achieve suitable error metrics, the hidden neurons in the FNN and ENN networks were adjusted from 1 to 25 along with the input delay by 0:1 in the ENN model.

4 Results and Discussion The results of ENN and FNN applying several training algorithms are explained in this section. Our study aimed to identify the best training algorithm for two different types of neural networks, this will help us to choose the daily solar radiation prediction model for Kenitra region more rapidly. As previously said, two types of neural networks were selected: a feed forward and Elman neural networks. Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient Backpropagation (RB), Fletcher-Powell Conjugate Gradient (CGF), One Step Secant (OSS) and Polak-Ribi´ere Conjugate Gradient (CGP), these are seven different types of training algorithms were used to train the two neural networks. By selecting the suitable training function, the problem’s calculation complexities can be further reduced. The accuracy of the parameter selection is positively impacted by selecting the appropriate number of neurons for each type of network’s hidden layer. The results between the target and the output estimated by applying FNN and ENN networks with seven training algorithms are shown in Fig. 5 and Fig. 6. According to these figures and Table 1, the Levenberg-Marquardt algorithm is predicted output closely resembles the actual result. The MSE values for the seven training methods applying FNN are shown in Fig. 3(ag), Low MSE values were observed at the convergence point for the CGF and RP algorithms. In comparison to other training methods, these two algorithms required longer epochs to converge to the least MSE. Although it took the LM algorithm 9 epochs to achieve 13.75, it had the lowest MSE. With 47 epochs and a convergence MSE of 16.4313, the CGB is one of the algorithms with the highest epochs. One of the algorithms with the maximum MSE is the OSS, which had an MSE of 23.3694 and an epoch of 17 at convergence.

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Validation Test Best

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Fig. 3. (a-g) The performance of validation phase of different training algorithms for solar radiation using FNN

Elman and Feed-Forward Neural Networks Best Validation Performance is 20.7718 at epoch 25

Best Validation Performance is 11.5294 at epoch 14

103

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(b) SCG algorithm

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Table 1. Evaluation metrics of Validation phase of different training algorithms using FNN and ENN ANN

FNN

ENN

MSE (MJ/m2 )

MAE (MJ/m2 )

RMSE (MJ/m2 )

MSE (MJ/m2 )

MAE (MJ/m2 )

RMSE (MJ/m2 )

LM

13.75

0.09697

3.70809

11.5294

0.08880

3.39549

SCG

18.7572

0.11326

4.33095

20.7718

0.11919

4.55760

CGB

16.4313

0.10601

4.05355

13.7266

0.09689

3.70494

CGF

15.2517

0.10213

3.90534

17.8187

0.11039

4.22122

CGP

17.6871

0.10999

4.20560

21.7532

0.12197

4.66403

OSS

23.3694

0.12642

4.83419

19.6159

0.11583

4.42898

RP

16.0677

0.10483

4.00845

15.3426

0.10244

3.91696

Daily solar radiation (MJ/m2)

20 Solar radiation (Target) FNN-LM FNN-CGB FNN-GF FNN-CGP FNN-OSS FNN-RP FNN-SCG

18 16 14 12 10 8 6 4 2

0

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Fig. 5. Comparison between different training algorithms using FNN

Figure 4 (a-g) shows the Mean Squared Error (MSE) values for several training algorithms using ENN. The MSE of 11.5294 was reached using algorithm LM in just 14 epochs while MSE values at convergence point were lowest also for the RP and CGB algorithms. When compared to other training methods these two algorithms took long epochs to converge to the least MSE. The CGF is among the algorithms that have the highest number of epochs that is 69 with an MSE of 17.8187 at the convergence. With an MSE of 21.7532 and an epoch of 15 at convergence, the CGP is one of the algorithms with the highest MSE.

Elman and Feed-Forward Neural Networks

11

Daily solar radiation (MJ/m2)

18 Solar radiation (Target) ENN-LM

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Fig. 6. Comparison between different training algorithms using ENN

5 Conclusion In order to select the best solar radiation prediction model, the paper looks for the most suitable training algorithm for the two different types of neural networks. FNN and ENN were both used in the study, with the structure of each network remaining unchanged and just its parameters being changed. Although there was a good general similarity between training algorithms, however no algorithm is best suited to all applications, but the Levenberg-Marquardt algorithm’s output variables were particularly similar to the real variables. Comparing ENN to FNN, we were able to get the conclusion that ENN using Levenberg- Marquardt algorithm is a solid model to forecast daily solar radiation for Kenitra City.

References 1. Singla, P., Duhan, M., Saroha, S.: Solar irradiance forecasting using elman neural network with different training algorithms. In: Proceedings of the International Conference on Sustainable Development in Technology for 4th Industrial Revolution, vol. 22, pp.137–141 (2021) 2. Boussaada, Z., Curea, O., Remaci, A., Camblong, H., Mrabet Bellaaj, N.: A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energies 11(3), 3 (2018). https://doi.org/10.3390/en11030620 3. Chen, S.X., Gooi, H.B., Wang, M.Q.: Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy 60, 195–201 (2013). https://doi.org/10.1016/j.renene.2013.05.011 4. Mohandes, M., Rehman, S., Rahman, S.M.: Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl. Energy 88(11), 4024–4032 (2011). https://doi. org/10.1016/j.apenergy.2011.04.015 5. Wysocki, A., Ławry´nczuk, M.: Elman neural network for modeling and predictive control of delayed dynamic systems. Arch. Control Sci. 26(1) (2016). https://doi.org/10.1515/acsc2016-0007 6. Huang, X., Zhang, C., Li, Q., Tai, Y., Gao, B., Shi, J.: A comparison of hour-ahead solar irradiance forecasting models based on LSTM network. Math. Probl. Eng. 2020 (2020). https://doi.org/10.1155/2020/4251517

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7. Amellas, Y., El Bakkali, O., Djebil, A., Echchelh, A.: Short-term wind speed prediction based on MLP and NARX network models keywords: artificial neural network daily prediction multi-layer perceptron (MLP) NARX recurrent neural network (RNN). Indones. J. Electr. Eng. Comput. Sci. 18 (2020). https://doi.org/10.11591/ijeecs.v18.i1.pp150-157 8. Elmousaid, R., Adnani, Y., El Hamdaouy, A., Elgouri, R.: Daily solar radiation prediction using NARX and MLP-NNs networks: a case study of Kenitra City, Morocco. In: 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), pp. 1–6 Dec 2022. https://doi.org/10.1109/ICECOCS55148.2022.9983450 9. Amellas, Y., Djebli, A, Echchelh, A: Levenberg-marquardt training function using on MLP, RNN and elman neural network to optimize hourly forecasting in Tetouan City (Northern Morocco). J. Eng. Sci. Technol. Rev. 13(1), 67–71 (2020). https://doi.org/10.25103/jestr. 131.09 10. Chaimae, D., Hamidane, H., Guerbaoui, M., Ed-Dahhak, A., Lachhab, A.: Identification of greenhouse temperature system using time series based on the NARX model. In: 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), pp. 1–5 Dec 2022. https://doi.org/10.1109/ICECOCS55148.2022.9982952 11. Le, X.-H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7), 1387 (2019) 12. Lourakis, M.I.A.: A Brief Description of the Levenberg-Marquardt Algorithm Implemened by levmar 13. Sari, Y.: Performance evaluation of the various training algorithms and network topologies in a neural-network-based inverse kinematics solution for robots. Int. J. Adv. Robot. Syst. 11(4), 64 (2014). https://doi.org/10.5772/58562 14. Cetisli, B., Barkana, A.: Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput. 14, 365–378 (2009). https://doi.org/10. 1007/s00500-009-0410-8 15. Zakaria, Z., Isa, N.A.M., Suandi, S.A.: A Study on Neural Network Training Algorithm for Multiface Detection in Static Images. Int. J. Comput. Inf. Eng. 4(2), 345–348 (2010) 16. Anushka, P., Md, A.H., Upaka, R.: Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia. MAUSAM 71(2), 2 (2020). https://doi.org/10.54302/mausam.v71i2.22 17. NASA POWER | Prediction Of Worldwide Energy Resources. https://power.larc.nasa.gov/. Accessed 24 Jan 2023

An Artificial Neural Network Model Based on Non-linear Autoregressive Exogenous for Predicting the Humidity of a Greenhouse System Chaimae Dada(B) , Hafsa Hamidane, Mohamed Guerbaoui, Abdelali Ed-Dahhak, and Abdeslam Lachhab Computer Engineering and Intelligent Electrical Systems Laboratory, High School of Technology, Moulay Ismail University of Meknes, Meknes, Morocco [email protected]

Abstract. This paper aims to identify a neural model for predicting relative humidity in an agricultural greenhouse system. In this sense, machine learning techniques will be used to develop models based on the data obtained. Different configurations of the nonlinear autoregressive exogenous with input time series models (NARX) have been studied, and their results have been used to select the optimal model. In addition, three training algorithms were used, namely, LM, BR, and SCG. Until the ideal network was found, the training algorithms were developed using different combinations of hidden nodes and delays. To find the best prediction architecture, the results were compared. The experiments demonstrated acceptable prediction for all three training algorithms using real data. Among them, the LM predicts quickly, with the fewest iterations and the lowest MSE of 0.2918. Furthermore, the models’ performance and quality were assessed using different errors. Keywords: Non-linear system · prediction humidity model · Greenhouse · NARX · Morocco

1 Introduction Humidity is an essential climatic element that influences the productivity of agricultural greenhouses; however, with the increase in the population, the production of greenhouses becomes increasingly critical, which requires the inclusion of new technologies that are more intelligent. Artificial intelligence methods have become increasingly common in recent years due to their good suitability for current needs. Although deep learning is used because of its influence and academic capacity, in the simplest case, big data are used in many sectors of agriculture, including horticultural technology [1]. Therefore in such cases, processes with difficult dynamic properties must take into consideration complicated, nonlinear systems with variables that are highly dependent on outdoor environmental circumstances and greenhouse structure [2]. Artificial neural network © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 13–22, 2023. https://doi.org/10.1007/978-3-031-29857-8_2

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models are useful prediction tools for understanding nonlinear systems. The present research [3] investigates neural network techniques to build and verify a short-term prediction model of the electrical energy consumption of bioclimatic buildings, which has shown favorable results. However, there are significant limitations to this technique, notably optimization issues, relevance to real situations, overfitting, the requirement for multiple learning sets, and insufficient stability in tightly coupled and complicated systems [4]. Several researchers have suggested time-series models for reliable modeling and predicting of dynamic reactions [5]. The dynamic feedback network NARX is used to model nonlinear systems, e.g. agricultural greenhouses [6] the nonlinear autoregressive exogenous performed well and was clearly better than the static multilayer perceptron neural network model when compared to other types of neural networks [7]. Thus, this method was used in different systems, such as greenhouse [8], to predict the inside temperature to improve productivity and to forecast daily solar radiation to make an accurate estimation of solar energy production [9]. The current study presents a technique for modeling the relative humidity of an agricultural greenhouse, which serves as the foundation for further improvement and establishes a strategy to solve problems in nonlinear systems such as agricultural greenhouses. There are basically two techniques for modelling [10]. – The use of mathematical functions to express physical rules is referred to as physical modeling. – System identification is an estimation method that is built on knowledge and data obtained from a system’s measurable inputs and outputs. This paper shows the use of the NARX model and its benefits in forecasting indoor relative humidity in greenhouse system using three training functions to attain the lowest possible error MSE. The rest of this work is structured as follows. Section 2 presents the experimental agriculture greenhouse system and the approach used to identify and forecast interior humidity, followed by the ideal structure and various metrics utilized to estimate the generated model based on NARX neural networks. Section 3 displays the simulation and its findings, while Sect. 4 describes the inclusions and some perspectives(Table 1). Table 1. Different sensors and actuators used in greenhouse system. Materials

Functionality

NI USB-6009

Data logging and measurements

10 BOX FAN

Ventilation

Heater Scenario

Heater

HIH-4000–001

Relative Humidity sensor

LM35DZ

Temperature sensor

An Artificial Neural Network Model

15

2 Components and Methods 2.1 Greenhouse and Growth Circumstances A single-wall polyethylene greenhouse situated at the Faculty of Sciences in Meknes, Morocco, was utilized in this work. To keep the conditions inside stable, heating and ventilation systems were used. Natural information such as temperature and relative humidity were recorded every 5 s using sophisticated sensors (LM35DZ, HIH-4000– 001) placed inside and outside the agricultural greenhouse (Fig. 1).

Fig. 1. Greenhouse system

Supervisory control and data acquisition activities are assured by acquisition boards connected to a computer where the specified sensors and actuators are attached. 2.2 Structure of the NARX Model The primary goals of time-series approaches are forecasting, modeling, and identification [11]. In general, forecasting using time-series approaches necessitates first identifying the trend detected in the dataset; second, it will be distributed through time with the observed patterns and integrated with several other datasets. The nonlinear autoregressive network model is a dynamic RNN and a kind of ANN net [12]. ANNs are made up of a series of connected nodes that include biological neurons. The neuron can have many input-output links, but only the last link allows the output of one neuron to be used as the input for another. The linear ARX model, which is extensively utilized in time-series forecasting, is the basis of the NARX model. The output of NARX is determined in Eq. (1), where the next value of the dependent signal generator y(t) is predicated on prior levels of the signal generator and an independent (exogenous) input signal. y(t) = f(x(t − 1), . . . , x(t − d), y(t − 1), . . . , y(t − d))

(1)

where y(t) is the forecasted output; y(t-d) is the previous output variable after a period of d; x(t-d) is the previous input value with a delay of d. The delays represent the number of

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Table 2. MAE, R2 , RMSE and MSE values throughout the training, validation, and testing phases for the three training algorithms (LM, BR, and SCG) using the NARX model. Training algorithms

Neurons Process

MAE

R2

MSE

Levenberg–Marquardt

5

0.01936

0.99681

0.38696 0.62206

Validation 0.01749

0.99699

0.31577 0.56194

Testing

0.02104

0.99626

0.45727 0.67622

Training

0.01832

0.99713

0.34654 0.58868

Validation 0.02298

0.99432

0.54523 0.73840

Testing

0.01681

0.99765

0.29189 0.54026

Training

0.01858

0.99706

0.35641 0.59700

Validation 0.01921

0.99637

0.38087 0.61714

Testing

0.02103

0.99630

0.45668 0.67578

Training

0.01930

0.99676

0.38459 0.62015

Validation 0.00000

0.00000

0.00000 0.00000

Testing

0.01786

0.99719

0.32920 0.57376

Training

0.01963

0.99661

0.39785 0.63075

Validation 0.00000

0.00000

0.00000 0.00000

Testing

0.01781

0.99736

0.32736 0.57215

Training

10

15

Bayesian Regularization

5

10

15

Scaled Conjugate Gradient 5

Training

RMSE

0.01825

0.99707

0.34384 0.58638

Validation 0.00000

0.00000

0.00000 0.00000

Testing

0.02315

0.99547

0.55346 0.74395

Training

0.02086

0.99620

0.44936 0.670344

Validation 0.022966 0.995571 0.54432 0.737782 10

15

Testing

0.01960

0.99674

0.39647 0.62966

Training

0.01889

0.99693

0.36836 0.60692

Validation 0.01976

0.99611

0.40302 0.63483

Testing

0.02057

0.99656

0.43670 0.66083

Training

0.02386

0.99486

0.58761 0.76656

Validation 0.02296

0.99562

0.54417 0.73768

Testing

0.99560

0.57473 0.75811

0.02359

time steps taken by the network hold so that it can be used for future prediction of output time series. We can extract the diagram of our network as shown in Fig. 2 using Eq. (1), which describes the NARX model. To estimate the function f, a feedforward neural network can be used to create the NARX model, as shown in Fig. 2. Notable applications of the NARX network include the modelling of nonlinear processes [12].

An Artificial Neural Network Model

17

Fig. 2. Schema of the NARX model

2.3 Learning Algorithms – Levenberg – Marquardt (LM): is a common time series network forecasting and learning algorithm. Nonlinear least squares problems are solved with LM. This approach computes with a gradient vector and Jacobian matrix rather than a precise Hessian matrix [13]. – Bayesian Regularization (BR): is a learning algorithm for minimizing the detrimental impact of large weights during the learning phase. BR does not require “test set” or “validation set,” and the entire dataset may be utilized to fit and compare models [14]. – Scaled Conjugate Gradient (SCG) is a hybrid model trust area and conjugate gradient approach. This technique is commonly used for problems with a large number of linear equations, and it uses little memory. When generalization no longer improves and shows a rise in the mean square error of the validation datasets, training automatically terminates [14]. 2.4 Metrics NARX Assessment The mean square error (MAE), the coefficient of determination R2 , the root mean square error (RMSE), and the relative absolute error (RAE) used to monitor the effectiveness of the NARX learning functions LM, BR and SCG. These metrics are as follows: – The mean absolute error (MAE) reflects the minimum amount of error for the predicted values. MAE =

1 N |Yobsj − Ypredj | j=1 N

(2)

– The coefficient of determination R2 enables the evaluation of the optimum linear fit among act al and forecasted data. 2 N  j=1 Yobsj − Ypredj R =1−   2 N j=1 Yobsj − Yobsj 2

(3)

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– The mean square error (MSE) is the difference between the expected and actual values. MSE =

1 N (Yobsj − Ypredj )2 j=1 N

(4)

– The root mean square error (RMSE). RMSE =

√ MSE

(5)

where N is, the numbers of datapoints, YPredj and Uobsj display the predicted and real targets, respectively, and Yobsj is the mean observed Yobsj

3 Outcomes and Discussion 3.1 Creation and Estimation of the NARX Model of Neurons A nonlinear autoregressive exogenous neuronal network (NARX-NN) model is used in this study. For one morning hour, a time period, with a sampling period of 5 s, 1032 data points were taken to evaluate the NARX technique for the moisture forecasting scenario. The NARX network architecture is composed of one hidden layer with ten neurons, two delays of the network, two input layers (measured temperature and humidity), and one output layer (predicted humidity). At the hidden layer, the sigmoid activation function was applied, while the linear function was employed in the output layer procedures. To discover the appropriate configuration for predicting the relative humidity and to improve the training procedures, the number of hidden nodes was modified. Table 2 depicts the variance of metrics during the learning, validation, and testing processes, in addition to the variation of training methods, namely, Levenberg–Marquardt, Bayesian Regularization, and Scaling Conjugate Gradient. The purpose of modifying the training algorithm is to obtain the lowest MSE possible. Representing fractions for learning data was set at 722, 155 for validation, and 155 for testing.

Fig. 3. Differences between the measured and predicted humidity of the agricultural greenhouse using the Levenberg–Marquardt algorithm.

An Artificial Neural Network Model

19

Fig. 4. Differences between measured and predicted humidity of agriculture greenhouse using Bayesian Regularization algorithm

Fig. 5. Differences between the measured and predicted humidity of agricultural greenhouse using the scaled conjugate gradient algorithm.

The samples used to predict the model have two input parameters (temperature and relative humidity) and a two-time delay. Figures 3, 4, 5 illustrate the real and predicted output humidity of the proposed model using the LM, BR, and SCG methods. The suggested model, which employs Levenberg–Marquardt functions, is almost similar to the output of the system with an R2 of 99.7%. Considering what is shown in Fig. 6, the difference between the actual and predicted humidity ranges between −1.6% and 2.5%, and the model is produced with a minimum number of iterations, which is equal to 13. This model then summarizes the nonlinearity of the greenhouse system. According to the Levenberg-Marquardt training algorithm, the coefficients of determination of the R2 values of the training and testing phases are 99.713% and 99.7656%, respectively, shown Table 2. The training algorithms performed best with 10, 15, and 10 hidden neurons simultaneously. The LM-trained NARX neural network shows the capability to identify the relative humidity better than other algorithms because of speed and low mean square error. Table 2 shows that using the LM and SCG algorithms provides an MSE of (0.29189) and (0.39647) with 10 and 5 neurons in the cached layer, respectively, but using the BR algorithms we obtain an MSE of (0.32736) when the number of neurons is increased to 10.

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Fig. 6. Error between observed and predicted humidity of the greenhouse using the three training algorithms.

3.2 Comparison of the Three NARX Model Learning Algorithms The NARX-LM algorithm is a reliable solution for predictive modeling and accurately describes the nonlinear connection between the input and output variables, as illustrated in Fig. 3. The regression analysis’s R2 of 99.713% indicates that more than 99% of the dataset would justify the predicted fluctuation in the final values. For relative

Fig. 7. Typical and estimated samples for the NARX model trained by LM algorithm on a training, validation, and testing.

Fig. 8. Typical and estimated samples for the NARX model trained by BR algorithm on a training, validation, and testing.

Fig. 9. Typical and estimated samples for the NARX model trained by the SCG algorithm on training, validation, and testing.

An Artificial Neural Network Model

21

humidity prediction, all three NARX models had fairly good accuracy, with a difference in the number of iterations of 13 for LM, 134 for BR and 23 for SCG. Figures 7, 8, and 9 illustrate the representation of different error calculations between the measured and predicted datasets for the three training algorithms (LM, BR, and SCG). Ever since the predicted model was using LM, it provides the smallest variation between the observed data and predicted data. The behavior of the NARX model being considered by Levenberg-Marquardt is shown in Fig. 7. The training, validation, and test, R2 values are 99.712%, 99.433%, and 99.766%, respectively. Using the BR training function, NARX performs with 99.676%, 99.719%, and 99.683% in training, validation, and testing, respectively, as show in Fig. 8, while the performances of the NARX model using the SCG training algorithms disputed in Fig. 9 are 99.621%, 99.556% and 99.675% in training, validation, and testing, respectively.

4 Conclusion In this research, we provide a new method to model a nonlinear greenhouse system using a nonlinear autoregressive network with exogenous entries, which mathematical expressions cannot easily represent. The comparison of three alternative training techniques clearly demonstrates that the Levenberg-Marquardt (LM) algorithm generates more accurate prediction models with the best performance with 10 neurons hidden layer. This architecture of the NARX model had the most appropriate forecasting abilities, with an R2 of 99.7% and the lowest prediction errors. As a result, NARX has the best performance among the estimation techniques used on greenhouse datasets and can be used to evaluate large amounts of greenhouse datasets. To summarize, the training approach employed considerably impacts the performance of autoregressive nonlinear neural networks with exogenous inputs for a nonlinear system. This neuronal technique and training algorithm could be investigated to forecast and identify models of different climatic parameters (solar radiation, CO2, wind speed, etc.), where these models could be used to create favorable conditions for cultivation under greenhouse conditions. This will be addressed in future work as part of the strategy to improve agricultural greenhouse production.

References 1. Yu, H., Chen, Y., Hassan, S.G., Li, D.: Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO. Comput. Electron. Agric. 122, 94–102 (2016) 2. Jung, D.H., Kim, H.S., Jhin, C., Kim, H.J., Park, S.H.: Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Comput. Electron. Agric. 173, 105402 (2020) 3. Mena, R., Rodríguez, F., Castilla, M., Arahal, M.R.: A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy Build. 82, 142–155 (2014) 4. Li, H., Wang, J., Lu, H., Guo, Z.: Research and application of a combined model based on variable weight for short term wind speed forecasting. Renew. Energy 116, 669–684 (2018)

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5. Koschwitz, D., Frisch, J., van Treeck, C.: Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX recurrent neural network: a comparative study on district scale. Energy 165, 134–142 (2018) 6. Sergeev, A., et al.: Prediction the dynamic of changes in the concentrations of main greenhouse gases by an artificial neural network type NARX. AIP Conf. Proc. 2293, 1–4 7. Riverol, C., Hosein, N., Singh, A.: Forecasting reliability using non-linear autoregressive external input (NARX) neural network. Life Cycle Reliab. Saf. Eng. 8(2), 165–174 (2019). https://doi.org/10.1007/s41872-019-00073-4 8. Chaimae, D., Hamidane, H., Guerbaoui, M., Ed-Dahhak, A., Lachhab, A.: Identification of greenhouse temperature system using time series based on the NARX model. In: 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science, pp. 1–5. Dec 2022 9. [9] Elmousaid, R., Adnani, Y., El Hamdaouy, A., Elgouri, R.: Daily solar radiation prediction using NARX and MLP-NNs networks: a case study of Kenitra City, Morocco. In: 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), pp. 1–6. IEEE (2022) 10. Thomas, W., Sossenheimer, J., Schäfer, S., Ott, M., Walther, J., Abele, E.: Machine learning based system identification tool for data-based energy and resource modeling and simulation. Procedia CIRP 80, 683–688 (2019) 11. Ayodele, B.V., May A.A., Siti I.M., Ramesh K., Suwimol W., Chin K.C.: Carbon dioxide reforming of methane over Ni-based catalysts: modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms. Chem. Eng. Process. 166, 108484 (2021) 12. Guzman, S.M., Paz, J.O., Mary Love, M., Tagert: The use of NARX neural networks to forecast daily groundwater levels. Water Resour. Manage. 31(5), 1591–1603 (2017). https:// doi.org/10.1007/s11269-017-1598-5 13. Mai, V.T., Hai, Q.V., Tran, and Thuy Anh Nguyen.: On the training algorithms for artificial neural network in predicting compressive strength of recycled aggregate concrete. Lect. Notes Civ. Eng. 203, 1867–1874 (2022) 14. Peiris, A.T., Jeevani, J., Upaka R.: Forecasting wind power generation using artificial neural network: “Pawan danawi”-a case study from Sri Lanka. J. Electr. Comput. Eng. 2021 (2021)

Towards an Adaptive Learning Process Using Artificial Intelligence Technologies Fatima Zohra Lhafra(B) and Otman Abdoun Computer Science Department, Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco [email protected]

Abstract. Adaptive learning represents a wide field of application of artificial intelligence technologies. The aim of this concept is to cover the different profiles of learners in order to minimize disorientation and increase the rate of engagement and motivation among them. The adaptation is not a single task that can be done alone or is only limited to the assimilation phase. To this end, we have implemented an intelligent and coherent adaptive learning model that accompanies the learner throughout their learning process. The proposed model consists of four phases: identification of learning styles, proposal of adaptive learning activities, recommendation of assessment activities and the implementation of an adaptive remediation strategy. Each phase of this model is based on artificial intelligence technology while respecting the pedagogical context. In order to measure the effectiveness of the proposed model, we set up a real experiment with learners. Keywords: Adaptive e-learning · Machine learning · Evolutionary algorithm · Recommendation System

1 Introduction Artificial intelligence technologies show a mutation in computer systems. Their aim is to imitate the behavior of living beings, especially humans, in order to develop efficient and intelligent solutions. They have been integrated in many fields such as: industry, health, agriculture, education, entertainment, etc….. E-learning systems represent a wide field of application of these technologies in order to improve the quality of the learning process. The difference in learners’ profiles and preferences is a challenge for e-learning systems. Each learner has their own strategy for conducting their learning process according to their learning style. The concept of adaptive learning aims to overcome this problem while offering a learning path that is adaptive to the needs of each learner. Adaptive learning is based mainly on the integration of artificial intelligence technologies to stimulate the behavior of a human tutor while taking into consideration the cognitive needs and preferences of each learner. This concept has been the subject of several research studies. As an example, we cite the work of Boussakssou et al. [1] who proposed a method based on the Q-learning algorithm in order to determine the different possibilities for the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 23–32, 2023. https://doi.org/10.1007/978-3-031-29857-8_3

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implementation of an adaptive learning path to the learner’s needs. Another work of Kolekar et al. [2] that deals with adaptation at the level of graphical representation. The objective of this work is to introduce a set of rules for the design of user interface components based on Felder and Silverman learning style model. M.Hussein et al. [3] have focused on the pedagogical side of an adaptive learning. For this, they have shown the importance of adopting pedagogical agents in order to increase the efficiency of adaptive e-learning systems. This work has discussed the use of several approaches such as: the constructivist, collaborative, integrative, reflective, objectives-based and competency-based approaches. Another technological approach has been presented [4] for the development of an adaptive e-learning system. This approach aims at structuring the educational content according to a hierarchy. The structure of the presented system includes: the educational content model, the user model, the adaptation model and the results evaluation model. Another adaptive e-learning system that has been implemented using Moodle LMS [5]. The proposed system is based on learner model, content model and adaptation model. The results of this work showed that the proposed solution is adaptive for sensitive learning category. Sweta et al. [6] described an adaptation process via an e-learning environment. The proposed approach discusses the use of educational data mining in the e-learning context. El-Sabagh’s study [7] aims at designing an adaptive learning environment based on learning styles. This study discusses the impact of adaptive learning on learner engagement. The authors conducted a comparative study of the proposed system with a conventional e-learning approach. Another work [8] that presents a survey to identify the most important features for the implementation of adaptive e-learning model. Another work has been implemented to describe the design and development of an autonomous adaptive e-learning system. The objective is to improve the knowledge mastery for adolescents [9].Further similar works such as [10]. The majority of the works that deal with the concept of adaptive learning focus on the assimilation phase. However, the learning process consists of several phases. For this purpose, we have implemented an adaptive learning model that deals with the different phases of the learning process from the identification of learning styles until the remediation phase. The proposed model promotes the integration of active learning methods such as problem-based learning and collaborative work. Each phase of the proposed model is based on artificial intelligence technology while respecting the pedagogical context of the teaching-learning operation. First, we identified the learning styles according to Felder and Silverman learning style model using the kmeans clustering algorithm. Then, the assimilation phase was carried out through the evolutionary algorithm the genetic algorithm. The objective of using this algorithm is to offer to the learners an adaptive learning scenario according to the identified profile. The evaluation phase is an integral part of the learning process. For this, we have implemented a set of serious games, each game represents an evaluation activity that aims to assess a level of the bloom taxonomy. The choice of the evaluation activities is ensured through the collaborative filtering technique of the recommendation systems. Learners who have encountered difficulties will be directed to the remediation process. This process takes place in two phases: the first phase is the identification of learning difficulties using the Naive Bayes algorithm and the second phase is the recommendation of remediation activities using the recommendation systems.

Towards an Adaptive Learning Process

25

This paper is organized as follows: The second section is dedicated to an overview of the artificial intelligence technologies and the pedagogical rules that are used in the proposed model. The third section presents the steps of the adaptive e-learning model and the last section focuses on the process of the implementation.

2 Background 2.1 Artificial Intelligence Technologies The application of AI in the field of education has increased in recent years [11]. The research work aims to propose innovative solutions to improve the quality of learning. The suggested model is based on a set of algorithms and techniques of artificial intelligence in order to deal with the concept of adaptive learning. As a result, we propose a coherent and intelligent system that accompanies the learner during his learning process. The technologies applied include the following: Genetic algorithm: is an evolutionary algorithm based on the optimization rule in order to propose the best solutions. The steps of the GA are as follows: 1. Initialization of the population. 2. Definition of the fitness function to evaluate the performance of each individual. 3. Selection of two individuals with the best value of fitness function to construct the new generation. 4. Crossover: this operator ensures diversity in the new generation. 5. Mutation: this operator aims to modify the value of a gene according to a certain probability. Ant Colony Optimisation: is an algorithm inspired from the foraging behavior of ants. It is based on an intelligent research technique to find the optimal solution to complex problems [12]. ACO is applied for the first time to the traveling salesman problem. Particle Swarm Optimisation: is among the metaheuristics algorithms. It aims at simulate the movement behavior of a group of birds. In PSO the optimal solution is represented by a particle and the population by a swarm. It is based on the principle of collaboration because the best position of each particle is that of the swarm [13]. K-means algorithm: is an unsupervised clustering algorithm in machine learning. Its purpose is to group elements with similar characteristics within the same cluster. The number of clusters is defined according to the kind of the problem. Naïve Bayes algorithm: is among the supervised machine learning algorithms. It is characterized by its simplicity and efficiency for predictive modeling. It is based on Bayes’ theorem [14]. Collaborative filtering technique: is a technique of recommendation systems to explore the preferences and interests of users to provide them with suitable solutions. It is based on the calculation of the similarity rate between users in order to predict the preferences of other users with a high similarity rate. Serious Game: aim to combine the concept of entertainment, learning and development of communication skills to attract the engagement and motivation of learners.

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They create a pleasant, useful and rewarding learning environment. Thus, it acts on the development of motivation and self-confidence among learners. 2.2 Pedagogical Context The respect of the pedagogical context is one of the major advantages of the proposed model. Each phase is based on pedagogical rules to ensure assimilation and learning efficiency. Among the rules used, we mention: Problem-based learning: is one of the active learning methods. It aims to mobilize the resources and prerequisites of the learners in order to solve the proposed problem situation. Through this method, learners develop new knowledge, skills and behaviors. We opted for this method during the assimilation phase. Felder and Silverman learning style: Learners differ in their learning methods. Each of them has their own preferences and needs to succeed in the act of learning. For this reason, there are several models of learning styles to cover the diversity in learners’ profiles. We have used the Felder and Silverman model which is based on four dimensions, each dimension composed of two opposite styles. (Active/Reflective), (Sensory/Intuitive), (Visual/Verbal), (Sequential/Global) [15]. ASTOLFI error classification method: This method proposes a classification of learning difficulties into 8 classes: (misunderstanding of the work instructions, school habits, alternative learner design, intellectual operation involved, approach adopted, cognitive overload, another discipline, the difficulty of the content) [16]. It aims to find the origin of any error committed by the learner. In addition, it has a remediation method for each class. Belbin’s theory: is a method that analyzes and describes the behavior of group members and their habits within a collaborative work. It has been integrated mainly in the area of the business community [17] in order to organize collaborative work. Belbin’s theory is based on 3 axes, each one consisting of three roles: 1. Reflection: Monitor evaluator, Plant, Specialist. 2. Action: Implementer, Shaper, Completer-finisher. 3. Relationship: Resource investigator, Coordinator, Team worker. Its objective is to improve the performance of the group while assigning the appropriate role to each member.

3 Proposed Model The proposed model is based on artificial intelligence technologies to personalize the learning path from the identification of learning styles until remediation. The objective of this model is to increase the rate of learner engagement, thus improving learning efficiency and ensuring the assimilation of knowledge. The proposed model is characterized by its intelligence, consistency and adaptability with any LMS or distance learning system (Fig. 1). Moreover, it represents a model that respects the pedagogical context of the teaching/learning operation. It is formed in 5 phases.

Towards an Adaptive Learning Process

27

Fig. 1. Proposed model

3.1 Identification of Learning Style This phase is considered the starting point of the proposed model. It aims to replace the traditional methods of identifying learning styles like questionnaires, meetings, interviews,… It is mainly based on a combination of the Felder-Silverman learning style model and the K-means clustering algorithm. The use of K-means allowed us to group learners with similar learning styles in order to offer them an adaptive learning scenario. 3.2 Adaptive Learning Scenario This phase represents the core of the model, we aimed to personalize the learning path of each learner based on the evolutionary algorithm specially the genetic algorithm. This algorithm has proven its effectiveness through a comparative study between the ACO and the PSO algorithms [18]. The results obtained showed its performance in terms of adaptability, diversity, evolution and convergence to the optimal solution. The genetic algorithm offers adaptive learning scenarios in order to lead the learners to solve the proposed learning situation. 3.3 Assessment Process The evaluation phase is a crucial phase in the learning process. Studies that address this stage are still limited. The evaluation process is based on the implementation of a set of

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serious games. Each game represents an evaluation activity that aims to evaluate a level of Bloom’s taxonomy [19]. The collaborative filtering technique of the recommendation system has been adopted in order to choose the most adaptive evaluation activities to the learners’ path. 3.4 Remediation Process The remediation strategy is mainly based on two phases: The first one aims at identifying the learning difficulties using the Naive Bayes algorithm and according to the ASTOLFI method. Via the Naive Bayes algorithm, we calculated the probability of each class of difficulty from the errors identified during the evaluation phase. The second phase consists in recommending a list of remediation activities using the collaborative filtering technique [20]. This technique allowed us to benefit from the experience of other learners with a similar profile. 3.5 Collaborative Learning One of the advantages of this model is the integration of active learning methods such as collaborative work. The proposed model uses collaborative work to facilitate learning via hybrid environments. The idea is to assign to each learner the most appropriate role according to Belbin’s theory. An adaptive assignment allows to increase the engagement of the learners and to make the work more organized, thus guaranteeing the learning rhythm between face-to-face and distance learning. The solution is based mainly on the Naive Bayes algorithm which calculates the probability of each role in order to assign to each learner the appropriate learning tasks [21].

4 Implementation of the Model In order to carry out the implementation of the proposed model, we initially started to develop different learning activities to conduct a study regarding the traceability learning activities followed by the learners. Each activity is characterized by a set of parameters that reflect its characteristics at the level of preferences, intention and type of media used (Table 1). The learners’ answers as well as the duration spent provide some indications about the learner’s profile. The K-means algorithm forms clusters from similar activities to identify the learning style of each cluster (Table 2). The number of clusters is determined in 16 clusters according to the combination of Felder-Silverman learning style. After the identification of the appropriate learning style, we will focus on the creation of adaptive learning scenarios based on the results of the genetic algorithm. Each scenario leads the learner to solve a learning situation while acquiring the necessary skills. The genetic algorithm allows to generate adaptive scenarios through its selection, crossover and mutation operators in order to offer diversified scenarios with a better fitness function value (Fig. 2). To conduct an evaluation of the knowledge acquired during the assimilation phase, we have planned a set of serious games. Each game is dedicated to evaluate a level of Bloom’s

Simulation

Demonstration

Search

Case study

Conception

example

Intention

Preferences Project

Investigation

References

Table 1. Characteristics of learning activity.

Assessment

Seminar

Text

Media Audio

Image

Video

Towards an Adaptive Learning Process 29

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F. Z. Lhafra and O. Abdoun Table 2. Identification of learning style.

Cluster

Activities

LSCs

Cluster

Activities

LSCs

CL1

20, 19, 19, 19, 20 19

Ac, In, Vis, Seq

CL9

12,11,12,13,12 12, 12, 12,12, 12

Ac, In, Vis, Gl

CL2

35

Ac, Sen,Vis, Gl

CL10

24,24,25,25,25,24

Re, Sen, Ver, Gl

CL3

11, 10,11,11, 11, 11

Ac, In, Vis, Gl

CL11

21, 21, 21, 21, 20, 21

Re, In, Vis, Gl

CL4

27, 26, 27, 26

Re, Sen, Vis, Seq

CL12

30, 30, 29, 28, 30 29, 30, 30

Re, In, Ver, Seq

CL5

15,14

Ac, Sen,Ver, Seq

CL13

34, 34, 33, 34, 33, 34, 34, 33

Ac, Sen,Vis, Gl

CL6

24,23,23,23,23,23,22, 23,23, 23, 23, 23, 24

Re, Sen,Ver, Seq

CL14

18, 18, 18, 18, 18 18,18,19

Ac, Sen, Ver, Gl

CL7

31,31,31,30,32,32,32 32,31,31,32,31

Re, In, Ver, Seq

CL15

13,14,13

Ac, In, Vis, Gl

CL8

16,17

Re, Sen, Vis, Gl

CL16

16,16,16,16,16

Ac, In, Ver, Gl

Fig. 2. Representation of the adaptive learning scenario

taxonomy. The choice of the appropriate game is based on the results obtained through the collaborative filtering technique. This recommendation is based on the following process (Fig. 3): • Assigning a score for each game and developing the matrix (Learner * game). • Calculating the rate of similarity between learners using the Person correlation equation. • Choosing a number of learner K with a high similarity rate. • Calculating the prediction.

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Fig. 3. Assessment process

Learners with difficulties will be redirected to remediation. For this purpose, we have implemented an intelligent strategy based on the combination of recommendation systems, especially the collaborative filtering technique and the Naive Bayes classification algorithm. The proposed strategy is divided into two phases: Phase 1: Identification Difficulty Class After analyzing the results of the assessment, we identified the set of committed errors according to the ASTOLFI method. These errors are the input data for the Naive Bayes classification algorithm to identify the learner’s difficulty class. Phase 2: Recommendation of Remediation Activities According to the identified difficulty class, the collaborative filtering technique allows to recommend a series of remediation activities arranged according to a priority order.

5 Conclusion Through this model, we aim to implement a solution that focuses on the concept of adaptive learning during the whole learning process. Each phase of the proposed model is based on artificial intelligence technologies. The objective of this model is to create an intelligent learning environment that promotes the engagement and motivation of learners and relieves the teacher from heavy and repetitive tasks. As a perspective to this work, we aim to extend the target audience by integrating learners with learning difficulties such as dyslexia and dysorthographia to present them an adaptive learning content based on machine learning.

References 1. Boussakssou, M., Hssina, B., Erittali, M.: Towards an adaptive e-learning system based on Q-learning algorithm. Procedia Comput. Sci. 170, 1198–1203 (2020) 2. Kolekar, S.V., Pai, R.M., Manohara Pai, M.M.: Rule based adaptive user interface for adaptive e-learning system. Educ. Inf. Technol. 24, 613–641 (2018).

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3. Ali, M., Hussein, A., Al-Chalabi, H.K.: Pedagogical agents in an adaptive E-learning system. SAR J. – Sci. Res. 3(1), 24–30 (2020) 4. Shershneva, V., Vainshtein, Y., Kochetkova, T., Esin, R.: Technological approach to development of adaptive e-learning system. SHS Web Conf. 66, 01014 (2019) 5. Vagale, V., Niedrite, L., Ignatjeva, S.: Implementation of personalized adaptive E-learning system. Baltic J. Mod. Comput. 8(2), 293–310 (2020) 6. Sweta, S.: Adaptive E-learning system. In: Modern Approach to Educational Data Mining and Its Applications. SAST, pp. 13–24. Springer, Singapore (2021). https://doi.org/10.1007/ 978-981-33-4681-9_2 7. El-Sabagh, H.A.: Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. Int. J. Educ. Technol. High. Educ. 18(1), 1–24 (2021). https://doi.org/10.1186/s41239-021-00289-4 8. Alameen, A., Dhupia, B.: Implementing adaptive e-learning conceptual model: a survey and comparison with open source LMS. Int. J. Emerg. Technol. Learn. (iJET). 14, 28 (2019) 9. Sumak, B., Podgorelec, V., Karakatic, S., Dolenc, K., Sorgo, A.: Development of an autonomous, intelligent and adaptive e-learning system. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (2019) 10. El Fazazi, H., Elgarej, M., Qbadou, M., Mansouri, K.: Design of an adaptive e-learning system based on multi-agent approach and reinforcement learning. Eng. Technol. Appl. Sci. Res. 11, 6637–6644 (2021) 11. Chen, L., Chen, P., Lin, Z.: Artificial intelligence in education: a review. IEEE Access. 8, 75264–75278 (2020) 12. Pushpa, M.: ACO in e-Learning: towards an adaptive learning path. Int. J. Comput. Sci. Eng. 4(3) (2012) 13. Kachitvichyanukul, V.: Comparison of three evolutionary algorithms: Ga, PSO, and de. Ind. Eng. Manage. Syst. 11, 215–223 (2012) 14. Shobha, G., Rangaswamy, S.: Machine learning. Handbook of Statistics. 38, 197–228 (2018) 15. Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ.78, 674–81(1988) 16. Astolfi, J.-P.: L’erreur, UN Outil Pour Enseigner, 12edn. Issy-les-Moulineaux, ESF éditeur (2014) 17. Markowski, M., Yearley, C., Bower, H.: Collaborative learning in practice (CLIP) in a London maternity ward-a qualitative pilot study. Midwifery 111, 103360 (2022) 18. Lhafra, F.Z., Abdoun, O.: Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning. Int. J. Electr. Comput. Eng. 13, 1964–1978 (2023) 19. Lhafra, F.Z., Abdoun, O.: Design and implementation of a serious game based on recommender systems for the Learning Assessment Process at primary education level. In: Lecture Notes on Data Engineering and Communications Technologies, pp. 200–210 (2022) https:// doi.org/10.1007/978-3-031-15191-0_19 20. Lhafra, F.Z., Abdoun, O.: Hybrid approach to recommending adaptive remediation activities based on assessment results in an e-learning system using machine learning. In: Advanced Intelligent Systems for Sustainable Development (AI2SD 2020). pp. 679–696 (2022) 21. Lhafra, F.Z., Abdoun, O.: Adaptive Collaborative Learning Process in a Hybrid Model. In: Lecture Notes on Data Engineering and Communications Technologies. vol 152. Springer, ISBN 978–3–031–20601–6, (2023) https://doi.org/10.1007/978-3-031-20601-6_3

Emotion Recognition of Facial Expressions with Deep Learning and Transfer Learning Anouar Gmili1(B) , Khalid El Fazazy1 , Jamal Riffi1 , Mohamed Adnane Mahraz1 , and Aziz Khamjane2 1 LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, University Sidi Mohamed Ben

Abdellah, Fez, Morocco {anouar.gmili,ismail.elbatteoui}@usmba.ac.ma 2 National School of Applied Sciences Al’Hoceima, University Abdelmalek Essaadi, Tétouan, Morocco

Abstract. Facial expressions are one of the most common non-verbal means used by humans to convey inner emotional states, and thus play a fundamental role in interpersonal communication. Although there are many possible facial expressions, psychologists have identified six universally recognized basic expressions (happy, sad, surprised, angry, fearful, and disgusted). It is clear that a system capable of automatically recognizing human emotions is an ideal task for a range of applications in human-computer interaction, security, affective computing… Robust Facial Expression Recognition System is a project that has been implemented by multiple developers. Our goal in this work is to bring this system closer to the Moroccan people, so that this system can better recognize the emotions of Moroccan faces, based on deep learning and transfer learning techniques. Keywords: Facial Emotion classification · Transfer Learning · CNN · MobileNet

1 Introduction Over the past decade, the computer vision research community has shown a lot of interest in the analysis and automatic recognition of facial expressions [1]. Initially inspired by the findings of cognitive scientists all the research carried out in the field of computer vision has considered developing systems capable of recognizing human facial expressions in video images or in still images. Most of these systems for searching and analyzing facial expressions have so far produced very good results in this area, such that they have succeeded in classifying expressions into seven categories which are (joy, sadness, anger, surprise, fear and disgust) with very good efficiency. Emotion recognition facial technology is a rapidly growing field that aims to understand and interpret human emotions through the analysis of facial expressions. This technology uses advanced algorithms and machine learning techniques to analyze and interpret facial cues, such as facial movements and muscle tension [2], to determine a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 33–42, 2023. https://doi.org/10.1007/978-3-031-29857-8_4

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person’s emotional state. The field of emotion recognition facial technology is expanding at a rapid pace, with many researchers and companies working to improve the accuracy and reliability of this technology. The potential applications of emotion recognition facial technology are numerous, including in areas such as healthcare, security [3, 4], and customer service. Despite the rapid growth of this field, there are still many challenges and limitations that must be overcome to achieve the full potential of emotion recognition facial technology. One of the main areas of research in this field is focused on the development of new algorithms and machine learning techniques to improve the accuracy of emotion recognition facial technology. Researchers are experimenting with different approaches, such as deep learning [5, 6], to improve the ability of the technology to detect and interpret facial expressions. Another area of research is focused on the use of different data sets for training [7, 8], Researchers are experimenting with different data sets, such as images, videos, and audio recordings, to determine the best data set for training emotion recognition facial technology. Finally, researchers are also working to evaluate the performance of different emotion recognition facial technology systems [9, 8]. They are conducting studies to evaluate the accuracy and reliability of different systems, and to determine the best approach for different applications. Our solution is to bring the facial emotion recognition model closer to Moroccan humans so that our approach such as our approach aims to collect a given database that contains facial images of Moroccan people in different states. And use the most effective techniques in this field to train a model that will give us better results if applied to Moroccans.

2 Related Works Many techniques can be used to implement a system of classification of facial emotions. The Facial Action Coding System (FACS) refers to a set of facial muscle movements that correspond to a displayed emotion [2]. Cootes et al. propose another approach based on Active Appearance Model (AAM) [10]. At the present time, most of approaches are based on machine learning and deep neural networks. Machine learning is divided into two main steps: features extraction and classification. Features extraction use LBP [11], LBP -TOP [12], and LGBP histograms [13] color scale invariant feature transform (CSIFT) and landmark facial points [14]. 2.1 Emotion Recognition Using Landmark (Geometric-Based Feature Extraction) From the facial landmarks, we can extract a lot of information that gives a geometric vision on the face as well as an index on the emotional face. In this way we can extract the distance between a specified central point and the other landmarks plus the texture characteristics between them.

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Primarily, geometry-based approaches to using the active appearance model (AAM) and its extensions to track a set of facial points [2]. The minimization of second order AAM and a multilayer perceptron are presented by Choi et al. for facial expression recognition [15]. Lucy et al. [16] and Zhao et al. [11] have four regions (eyebrows, eyes, nose and mouth) from which expression is achieved. This study takes into account the relationship between the units of action and each basic expression. Tanchosrinon et al. [12] studied a graphically based method for extracting characteristics. This method consists of two main parts: the first is to place the points in the facial regions to extract the characteristics on the basis of the graphs, the second aims to classify the emotion of the face using the corresponding characteristic vector. Hsu et al. [13] also propose a new method of facial expression recognition based on the extraction of discriminating traits from an image. 2.2 Appearance-Based Feature Extraction Appearance-based features are intended to use image filters to extract geometric information from the image, which plays a very important role in achieving very important results in image segmentation and classification [11 There are several techniques that can extract these appearance features, the most popular is Local Binary Pattern (LBP) operator [17]–[5].] this technique which initially proposed for texture analysis and has recently been introduced to represent faces in the analysis of facial images, that divides the facial image into small regions and from these regions the LBP histograms are extracted and then concatenated into a single spatially enhanced feature histogram [18]. 2.3 Emotion Recognition Using CNN For facial emotion detection using CNN, Deep Neural Network (DNN) proposed Convolutional Neural Network (CNN) [5, 6] is a DNN method capable of solving multiple classification problems such as image classification, object detection, tracking and segmentation. It can automatically interpret information from facial images without the need to manually design feature descriptors. Although the CNN model is very powerful in solving classification problems, whose previous CNN applications focus on learning local feature films for image, video and speech. In facial expression recognition using CNN architecture, the input layer has a fixed size and the number of filters is specified as a super -parameter. A set of filters is created at random. Each filter, such as a sliding window, creates a feature graph with shared weights by traversing the entire image. The convolution layer allows generating a feature map, which summarizes the facial information and shows how pixel values are increased, such as edge, light and pattern detection. After each convolution, in order to reduce the dimension, a pooling layer is used behind the convolution layer. This process is important in building a generic CNN architecture. While the amount of convolution layers increase, the costs of computational process expense. This approach and aims to make the facial emotion reconnaissance based on a network of convolutive neurons (CNN) in two parts: the first part deletes the background of the image and the second part focuses on the extraction of the vector of facial characteristics (Fig. 1).

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Fig. 1. The graphical representation of the proposed CNN model for facial expression recognition.

3 Proposed Method Our approach is based on two main techniques the first is the CNN [18] model that we have already discussed and the second technique is transfer learning [19] that is based on the first technique, as it aims to take a pre-trained CNN model trained to solve a problem regardless of the classification of images so that we will take the layers and parameters of these layers of this model and transform it to solve our problem which is the classification of facial emotions we will Follow the following process to achieve our goal first collect the database after finding a trained CNN pre-model for image classification, then apply the transfer learning technique using our database and the model we have chosen. The database we have built is a collection of face photos that is divided into 7 classes that are (Angry Disgust, Fear, Happy, Neutral, Sad, Surprise) of Moroccan celebrities, such as singers, football players, as well as some photos of me, my friends and family in different emotional states. More of these images we have added some images from the CK+ and FER 13 database. In addition, we applied database augmentation techniques to artificially increase the size of a training package by creating modified data from the existing one. It is recommended to use the AD if we want to avoid overflow, if the initial data set is too small to train, or even if we want to get better performance from our model. We also used some popular augmentation [20] techniques such as (flip, rotation translation, scale, and cropping, Gaussian noise) (Figs. 2 and 3).

Fig. 2. Our Data

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Fig. 3. Data Augmentation

3.1 The Choice of Pre-trained Model A pre-trained model is a model that is already built and trained to perform a role or to solve a problem, so if our goal is to create a model that solves a problem similar to the old pre-trained model, we can use this model pre-trains as a starting point instead of starting from scratch, to save time and also to get better results. The principle of the transfer learning technique is based on the use of a model already pre-trained, and using the final parameters of this model as the initial parameter of our own model, and all of this is to obtain better results despite that we do not have a very large data base and in a fairly small time, there are several models which are already pre-trained for the classification of images on large data bases, such as net image data, this low data which contains more than 100,000 images. The model we used in this article is the MobileNet [17] is a CNN architecture model that tries to do image classification and mobile vision. The choice of this module is not random, it is powerful in the calculation to perform or apply transfer learning. This is an ideal solution for mobile devices, embedded systems and computers without a GPU or with low computational efficiency with a significant compromise with the accuracy of the results. It is also better suited for web browsers. MobileNets mobile network architecture is simplified, It uses depth wise separable convolutions to build light deep neural networks. Two simple hyperparameters are introduced that effectively compensate for latency and accuracy. The MobileNet base layer consists of depth wise separable filters called depth wise separable convolutions. Network structure is another factor that improves performance. Finally, width and resolution can be adjusted to compensate for latency and accuracy (Fig. 4). 3.2 How to Use a Pre-trained Model As we explained recently, the principle of the transfer learning technique is to use a pre-training model to solve our problem, so somehow, we can download an existing neural network that someone else has defined and trained and use it as a starting point for our new task, so we just have to adapt this latest model to our problem from change of classification part of this model by our own classifier finally we have to refine our model according to one of the three strategies.

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Fig. 4. Architecture MobileNet

3.2.1 Train the Entire Model In this case, we use the architecture of the pre-trained model and train it against a dataset. We learn the model from scratch, so we will need a large dataset and a lot of computing power. 3.2.2 Train Some Layers and Leave the Others Frozen This strategy aims to freeze certain layers and relaunch the training of others such that we keep the initial parameters of these layers and reconfigure the parameters of the others. Most of the time, the layers that need to be frozen are upper layers that refer to specific characteristics (depending on the problem), and the layers that need to restart their training are lower layers that refer to general features (problem-independent). In general, the choice of the number of layers to freeze is based on the size of the database. If the database is small, we need to freeze many layers to avoid over-fitting. On the other hand, if the dataset is large and the number of parameters is small, we can improve our model by training more layers. 3.2.3 Freeze the Convolutional Base The principle is to freeze all the layers of the pre-trained model and only train the classification part, using it as a feature extraction mechanism. This method is preferable if we have a very small dataset and limited computational resources, and if the pre-trained model addresses a problem that is very similar to our own (Fig. 5). Unlike Strategy 3, which is simple to apply, Strategy 1 and Strategy 2 require we to pay attention to the learning rate used in the convolutional part. The learning rate is a hyperparameter that controls how much we adjust the network weights. When using a pre-trained CNN-based model, it is a good idea to use a low learning rate because high learning rates increase the risk of losing prior knowledge. Assuming the pre-trained model has been well trained, which is a fair assumption, keeping the learning rate low will ensure that we do not distort the CNN weights too soon and too much.

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Fig. 5. Strategies Fine-tuning

4 Proposed Method After analyzing the results obtained, the following remarks are noted: From Fig. 6 the learning accuracy increases with the number of epochs, this reflects that at each epoch the model learns more information. If the precision is decreased then we will need more information to make our model learn and therefore we must increase the number of epochs and vice versa. Similarly, the learning error (loss) decreases with the number of epochs We also notice that the precision value is started with 0.18 and finally it managed to reach 0.82 and for loss it started with 2.17 and decreased until 0.49 Fig. 7 also represents the classification ratio but on the training data base, we notice that the precision of all the classes varies between 0.53 and 0.99, such that the minimum value of precision is for the class 0 with the value 0.53 and the maximum precision is concerned with class 3, so we can conclude that class 3 (Happy) is well classified on the other hand class 0 (Angry) is badly classified Fig. 8 also represents the classification report but on the test data base The confusion matrix allows us to evaluate the performance of our model, since it reflects the metrics of True positive, True negative, False positive and False negative. Figure 9 represents the confusion matrix based on the learning data; it closely illustrates the position of these metrics for each class. For example, the model correctly classified the images of class 4 (Neutral) and it misclassified the images of class 6 (Surprise).

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Fig. 6. Graph represents the variation of accuracy and loss after each epoch.

Fig. 7. The classification report on the basis gives learning on the basis of learning the approach

Fig. 8. The classification report on the approach test database

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Fig. 9. The confusion matrix on the training data base of approach

5 Conclusion Facial expressions play an important and fundamental role in interpersonal and social communication and reveal a wide variety of information such as emotion, identity, gender and age. Automatic recognition of facial expression is an active research area, with a number of important applications such as human-computer interaction, neuromarketing, visual surveillance and security. Through this project, we brought facial emotion recognition system closer to Moroccan people, Such as This system which will give better results for Moroccan faces Several technologies were necessary for the realization of our project, we will quote the Python language, and the techniques of processing of the images of Deep Learning as well as the Transfer Learning This project provided us with an interesting experience which allowed us to acquire new k knowledge in the field of Image Mining and Data Mining in general, and to consolidate our skills in programming and Data Scientist.

References 1. Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Sig. Process. Mag. 18(1), 32–80 (2001) 2. Hamm, J., Kohler, C.G., Gur, R.C., Verma, R.: Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J. Neurosci. Methods 200(2), 237–256 (2011) 3. Kim, D., Kim, J.: Facial expression-based authentication using multimedia biometrics. Multimedia Tools Appl. 74(20), 8135–8153 (2015) 4. Al-Nimer, M. S., Lee, H.: Emotion recognition using facial expressions for biometric authentication. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 521–529 (2016) 5. Dinh Viet, S., Nguyen, V., DoPhan, T.: Facial expression recognition using deep convolutional neural networks. In: International Conference on Knowledge and Systems Engineering (KSE) (2017)

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6. Agrawal, A., Mittal, N.: Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Vis. Comput. 36(2), 405–412 (2019). https:// doi.org/10.1007/s00371-019-01630-9 7. Mollahosseini, A., Mohammadi, H.: Facial expression recognition using deep neural networks: a survey. arXiv preprint arXiv:1710.10196 (2017) 8. Chen, X., Zhang, Y.: Facial expression recognition using deep residual networks. Multimedia Tools Appl. 79(24), 24205–24221 (2020) 9. Kim, H., Kim, H., Kim, J.: Facial expression recognition based on deep 3D convolutional neural networks. Multimedia Tools Appl. 79(22), 23005–23020 (2020) 10. Hyung-Soo, L., Daijin, K.: Tensor-based active appearance model. IEEE Sig. Process. Lett. 15, 565–568 (2008) 11. Chao, Q., Min, L., Qiushi, W., Huiquan, Z., Jinling, X.: Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access 6, 18795–18803 (2018) 12. Guoying, Z., Matti Pietikäinen, N.: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007) 13. Thibaud, S., Vincent, R., Salman, H., Renaud, S., Bailly, K., Prevost, L.: Facial action recognition combining heterogeneous features via multikernel learning. IEEE Trans. Syst. 42(4), 993–1005 (2012) 14. Wenming, Z., Yuan, Z., Xiaoyan, Z., Minghai, X.: Cross-domain color facial expression recognition using transductive transfer subspace learning. IEEE Trans. Affect. Comput. 9(1), 21–37 (2018) 15. Zhao, G., Huang, X., Taini, M., Li, S.Z., Pietikäinen, M.: Facial expression recognition from near-infrared videos. Image Vision Comput. 11, 607–619 (2011) 16. Dhillon, A., Krupski, E., Shriberg, A.: Prosody based automatic detection of annoyance and frustration in human computer dialog. In: INTERSPEECH (2002) 17. Irene, K., Ioannis, P.: Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Trans. Image Process. 16(1), 172– 187 (2007) 18. Lei, X., Pan, H., Huang, X.: A dilated CNN model for image classification. IEEE Access 7, 124087–124095 (2019) 19. Shaha, M., Pawar, M.: transfer learning for image classification. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2018) 20. Fawzi, A., Samulowitz, H., Turaga, D., Frossard, P.: Adaptive data augmentation for image classification. In: 2016 IEEE International Conference on Image Processing (ICIP) (2016) 21. Wang, W., Hu, Y., Zou, T., Liu, H., Wang, J., Wang, X.: A new image classification approach via improved MobileNet models with local receptive field expansion in shallow layers. Comput. Intell. Neurosci. 2020 (2020)

A Novel Approach to Intelligent Touristic Visits Using Bing Maps and Genetic Algorithms Youssef Benchekroun(B) , Hanae Senba, and Khalid Haddouch Engineering, Systems and Applications Laboratory, National School of Applied Sciences-ENSA, Higher School of Technology-EST, Sidi Mohamed Ben Abdellah University, Fez, Morocco {youssef.benchekroun,hanae.senba,khalid.haddouch}@usmba.ac.ma

Abstract. The present article comes along with a series of papers that were presented in the context of implementing smart tourism applications for any touristic space and more particularly aims to present suggestions and approaches to planning the most optimized itineraries for the user (tourist) who will be visiting any city. As the goal of smart tourism is to enhance the experience of the tourist in every phase of his journey, providing personalized services and optimized circuits is a major added value for all smart tourism processes, especially if the optimized/personalized suggestions consider some of the tourist’s constraints and preferences. Hence, the current article discusses the use of algorithms and tools (genetic algorithms and Bing Maps API) to achieve this goal of providing optimized routes and will end by proposing some perspectives that enhance the performance of the optimization tools. This new approach gives a good result when applied to the old city of Fez. Keywords: Route Optimization · Smart Tourism · POI (Point of Interest) · Genetic Algorithms · Routing Approaches · Geographic Information System (GIS)

1 Introduction The aim of smart tourism is to improve and present the best touristic journey to every tourist via the combination of the benefits of classic tourism with digitalization, sustainability and sharps technologies to be able to serve the tourist in all phases of his experience, meaning that the tourist’s needs will be considered before, during and after his/her trip to smart tourist destinations [1]. Smart tourism refers to every benefit that comes from the use of sharp technologies, including the Internet of Things (IoT), cloud computing, and other tools that enhance data-driven innovations and provide improved touristic experiences and business models [2]. Amid our journey to enhance every touristic experience, it is necessary to have a solid and homogenous approach to suggest routes and circuits that are very well adapted to visiting several points of interest from the tourist point of view and thus considering one or multiple criteria. We aim to propose a suggestion to address the mentioned finality of route optimization by conducting tests of a method using genetic algorithms alongside the Bing Maps API. The genetic algorithms © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 43–53, 2023. https://doi.org/10.1007/978-3-031-29857-8_5

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are one of the heuristic methods that took inspiration from the theory of evolution and applied the mutation and crossover concepts to variables in the algorithm that were in fact introduced via Bing Maps API and thus to provide an optimized circuit to the user [3]. Many studies and publications have aimed to optimize route planning using different approaches. In this goal, the optimized design of touristic routes using the heuristic algorithm is proposed [4]. In addition, an optimal travel route based on user constraints using Markov Chain is developed [5]. In other research, the authors have used the ant colony principle to solve the problem of route planning for tourist attractions [6]. In the same context, we can use some machine learning techniques to find the optimal route for this problem, such as a continuous Hopfield network [7]. To validate our approach, we apply it later with the objective of extracting a competitive circuit in the Old Medina of Fez City for several reasons. This city is basically a giant maze and internationally known to be the largest car-free zone in the world and recognized by the UNESCO as a universal intangible heritage that was founded by the late 8th century and came to its apogee around the 1300 s [8]. The urban fabric of the medina dates from the mentioned period. Despite the transfer of the capital from Fez to Rabat in approximately 1912, Fez conserved its status as the cultural hub of Morocco, as well as the spiritual one. Fes’s old Medina maintains several buildings and monuments. The city was founded by Idriss the second in the 8th century. The foundation of the city consisted of two large parts isolated by Oued Fez: the Andalusian bank and the bank of Al Karawiyine. In the 11th century, the Almoravids unified the city; under the dynasty of the Almohads, Fez already grew to the size we know today. In the Merinid period, from 1200 s to 1400 s, a new part (Fas Al Jadid) was created in approximately the 1270 s [8]. At that time, the Medina of Fez evolved to be one of the largest and most populated cities in the Muslim world. Fassi architecture is known for its unique construction techniques created in 100 years, being the melting pot of many inspirations. The Fez Medina is one of the well-preserved historic towns of northern Africa. The old city preserves to this day the majority of its original attributes. Not only does it represent an outstanding heritage, archaeological and architecture styles, it also proves a centuries-old lifestyle, living and persistent cultural legacy that is timeless. The current article begins with a section that is a state of art where we define the concepts and existing routing approaches. The second section presents the method used, which consists of using the genetic algorithm and Bing Maps API. Next, we will have a section that discusses the experimental results, and then we will conclude with a quick recap and perspectives for future work to be done.

2 Literature Review There exist important approaches used to plan and optimize the route for a visitor. These approaches are summarized and presented in this section. The first concerns the metaheuristic algorithm. There are widely used techniques for a variety of reasons, especially for problems with no exact solution or no clear formulation. Popular heuristic algorithms include the genetic algorithm [9], tabu search algorithm [10], and simulated annealing algorithm [4, 11]. However, to achieve this optimality, another parameter, such as completeness, accuracy, or precision, must be traded for speed.

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The second model is the Markov chain model. The latter consists of a set of transitions, determined via distribution probabilities, that satisfy the Markov property [5]. The main purpose of Markov processes is to determine the probability of transitioning from one state to another. One of Markov’s main interests is that the future state of a random variable depends only on its current state. This model is used in routing approaches and circuit/route optimizations. Markov chain modelling has been used in the field of route planning optimization, especially in the research cited in [5], and in the general realm of smart tourism in the research referenced in [12]. The third technique that can be used in this context is geographic information system (GIS) [13]. This system is designed to collect, store, process, analyse, manage, and present all kinds of spatial and geographic data. Applications related to GIS give users the ability to build interactive queries, examine geographical data, update data using maps, and interact cartographically. GIS also enables the linking of data that may, on paper, seem very distant. Whatever the way of identifying and representing the objects and events that illustrate our environment (coordinates, latitude & longitude, address, altitude, time, social media, etc.), GIS allows us to gather all these dimensions around the same repository, a true backbone of the information system [14]. Adding to these techniques, ant colony optimization (ACO) algorithms are algorithms inspired by the behavior of ants or other species forming a superorganism and constitute a family of optimization metaheuristics [15]. Initially, proposed by Marco Dorigo et Luca Maria Gambardella in the 1990s for finding optimal paths in a graph, the first algorithm was inspired by the behavior of ants searching for a path between their colony and a food source [16]. It aims in particular at solving routing approaches, especially the problem of the travelling salesman, where the goal is to find the shortest path to connect a set of cities. The overall algorithm is fairly straightforward and relies on a collection of ants that each take one of the available paths. The ant follows a set of rules as it moves from one city to another at each step [17]. Finally, multiobjective optimization can be used in this field because in most cases, an optimization problem with a single criterion is not always sufficient [18]. For example, considering only the distance or the time of the route to calculate an optimized path for tourists will not always be most suitable. Indeed, between a 1 km circuit made up essentially of climbs and a 2 km circuit on a plain, a tourist may prefer to choose intermediate circuits. The simultaneous consideration of several objectives (criteria) in the resolution of an optimization problem allows the calculation of compromise solutions and is referred to as multiobjective optimization. To return to our last example, we can add two additional parameters: 1. The budget constraint of the tourist: the cost limit of the visit that the tourist has in mind, 2. The time constraint is the time that the tourist has allocated to the visit. Adding these two constraints would turn our function into a multiobjective optimization function and would provide more suitable solutions according to several user needs.

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The resolution of multiobjective optimization problems can be divided into three approaches. The first approach is called the metaheuristic approach, which uses methods often inspired by natural systems such as simulated annealing and evolutionary algorithms [19]. The second approach is called the non-Pareto approach. This approach tries to reduce a multiobjective problem to a simple objective problem at the risk of removing all meaning from the problem [19]. The third approach is the Pareto approach, which adopts a more global point of view by taking into account all the criteria and by using the notion of dominance in the sense of Pareto [19]. Finally, a last hybrid approach hybridizes two different methods [20]. Every algorithm and method mentioned in the present section has its limitations; this being said, heuristic approaches often do not give an optimal result with errors as the processes are biased. Otherwise, there are some limitations in using the Markov chain model. Mainly the fact that it becomes very complicated when more states and more interactions between states are involved, which turns out to be especially problematic when time-dependent probabilities are present [12]. On the other hand, GIS algorithms are not only unable to explore massive datasets, they are also unscalable and lack speed sometimes because they are CPU-based, and as all data infrastructures adopt big data architectures, this may become a huge limitation of GIS algorithms [13]. The other concern is about privacy violations. Finally, we mention the metaheuristic approaches (ant colony, multiobjective optimizations) that are very effective. At the same time, they are very time consuming and contribute to a choice dilemma while presenting many solutions, of which an important result may be overlooked [16]. At this stage, we could justify our choice of a heuristic method, which is a genetic algorithm, because it is very well adapted and presents excellent results working with small to medium sized instances instead of metaheuristic methods that are well adapted for large and diversified instances.

3 Proposed Approach Our current suggestion was an application of the genetic algorithm alongside the Bing Maps API, so before addressing the description of the application in Fez city’s context, we will provide a general description of the steps and tools/techniques of the implementation of our approach. The proposed approach follows three main steps. First, the most important step is to gather physical data from the field; the collected information concerns the point of interest according to some fixed criteria following the context. Then, the POIs are codified by convenience to be tested in the algorithm. Then, the dataset is injected into the algorithm using different combinations of tools to compare the efficiency of each suggestion toward planning the optimized circuit. Finally, the algorithm will present the final result after: • The creation of an initial population of all the circuits, • The evaluation of each circuit that meets certain parameters, • The selection of the best circuit according to certain criteria,

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• The reproduction of the selected traits; to present the fittest circuit. Finally, the process continues until it meets the end criteria, after which the algorithm presents the result. As the city of Fez is one of the most touristic cities of Morocco and especially its old medina, which constitutes the main touristic hub of the city with the largest car-free environment in the whole world, it was agreed upon to collect data of 74 POIs in total, and the dataset used to conduct the applications and tests below contains 2707 nodes from the Old Medina of Fez so that the results could be pertinent and very representative. The data collection was performed between the months of March and May, which is the period of high fluctuation and the high season for the city of Fez. From another perspective, we considered conducting the data collection amid the relaunch of the touristic sector in the city after two years of the highly restricted COVID period. We conducted a series of tests on the dataset using different combinations of tools to compare the efficiency of each suggestion toward planning the optimized circuit (from a distance point of view only) and returning the results in an acceptable time-lapse (execution time). The suggestion was an application of the genetic algorithm alongside Bing Maps API. The tests mentioned above will be conducted on 15 POIs listed in Table 1 with their corresponding IDs; the number 15 was defined in concertation with local guides as the maximum number of POIs that a tourist could visit properly in a one-day journey. On the other hand, this list of POIs has been defined according to the most attractive locations in the old medina of Fez and the best known and recommended places to visit in the city (according to TripAdvisor and other applications and surveys). Another criterion that has been considered is the lasting impression of the POI, which means that according to local guides and surveys, these POIs are the most remembered locations by tourists after their journey in Fez. As previously stated in the literature review, genetic algorithms are heuristic optimization algorithms based on techniques derived from genetics and natural evolution: crossovers, mutations, and purposeful selection. For this algorithm to be applied in our context, we had to execute the analogy below: • • • • • • • • •

Gene: a POI represented by coordinates (latitude/longitude). Individual (chromosome): a single pathway or circuit fulfilling certain conditions. Population: a list of potential routes. Parents: two routes that serve to create a new circuit. Mating pool: Parents who create the next population (creation of the next generation of circuits and routes). Fitness: the function/criteria that indicate how good each route is (in our case, the shortness of distance). Crossover: the probability that two chromosomes swap bits via selection of random genes and swapping all the genes after that point. Mutation: the introduction of new variations to populations by randomly alternating two cities on a route. Elitism: the passing of the fittest individuals to the next generation.

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POI

ID

Latitude

Longitude

Bab Boujloud

BB

34.0616817

−4.9840568

Jenan Sbil

JS

34.0600149

−4.9875509

Musée Batha

MBA

34.0602058

−4.9827331

Musée Belghazi

MBE

34.0638983

−4.9763294

Bibliothèque Alqaraouine

BA

34.0642525

−4.9728404

Bab Elguissa

BE

34.0691503

−4.9758189

Moulay Idriss

MI

34.064718

−4.974954

Bab Ftouh

BF

34.0600152

−4.96492

Sidi Ahmed Tijani

SAT

34.0663754

−4.9735924

Musée Ennejarin

ME

34.0647942

−4.9758579

Mosquée Rssif

MR

34.0626764

−4.973418

Bab Sidi Boujida

BSB

34.0668303

−4.9661479

Palais El Glaoui

PEG

34.0588653

−4.9771708

Mederssa Cherratine

MC

34.0641862

−4.9737061

Mederssa Seffarine

MS

34.0638068

−4.9727539

Thus, the process begins with the generation of an initial population that consists of all the possible itineraries and then comes the evaluation of each chromosome, which is, in our case, a circuit that meets certain parameters. Between these individuals (circuits), the algorithm selects the best one regarding the criteria and then proceeds to the reproduction of the selected traits to present the fittest (fitness here is the shortest distance). The process continues until it meets the end criteria, after which the algorithm presents the result. In Table 2, the parameters of the genetic algorithm are listed, mainly the crossover rate, which is the probability of swapping between chromosomes (circuits). The mutation rate represents the probability of the change in a circuit and finally the maximum number of generations and the minimum adaptation value for a circuit to be fit. The detailed results will be listed in the next section alongside the iteration and route details, but to give a quick glimpse, the average distance of a circuit delivered by this first method is 8.75 km (for the 15 POI) and was suggested after 31 s on average (30 s for the distance matrix construction). Two notable mentions to be highlighted quickly as well are: • The use of Google Maps API could also be possible. • This method is slightly expensive, as there is a cost of 8$/1000 request to be added to the high time cost (32 s).

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Table 2. Parameters of the genetic algorithm. Parameter

Value

Description

Crossover rate

0,6

The probability of two chromosomes swapping bits. Crossover is executed by selecting random genes from the chromosomes and swapping every gene after that point

Mutations rate

0,1

The chance that a bit within a chromosome will be flipped

Min fitness

0

Adaptation value to achieve the “Fitness”

Generations

200/500

Number of generations

Another test was conducted to locate the root cause of the high execution time, which we suspect to be a result of using the APIs; the test was performed using the Euclidean distance as a fitness parameter instead of the distance provided by the APIs, and the overall execution was significantly low. Before tackling the next section of the discussion of the results, we must highlight the fact that, at this stage, no benchmark with any other existing solution is possible; simply because no other application has taken this way of treating geospatial data directly from maps, which offers our solution a huge flexibility and adaptation ability for any context. All the solutions deal with predefined circuits and constant sets of POIs, which makes the distance matrix predefined.

4 Experimental Test and Results On the other hand, the material used to lead the tests is a Core i7 (8 cores) laptop with an 8Go RAM and HDD hard drive operating on a network (optic fiber) of 2 Gb/s download and 1 Gb/s upload. Please refer to Table 3, where the series of tests was conducted on 15 POIs (the maximum number of POIs that a tourist could visit in a day according to local guides). According to the graph (Fig. 1) of the correlation between the number of POIs and execution time, it is obvious that the execution time increases following the number of POIs; despite increasing slightly for the last circuits even if the number of POIs does not increase, this could be caused by the nature of the POIs and the data provided by the distances matrix issued by the Bing MAPS API. Thus, the trend is linear of course between the execution time and number of POIs.

Execution time

30,90 secs

31,42 secs

19.62 secs

19.94 secs

13,85 secs

13.98 secs

8,99 secs

9.11 secs

3,53 secs

3,57 secs

No. of POI

15

15

12

12

10

10

8

8

5

5

Distance

3,98 km

3.98 km

6,97 km

6,78 km

7,18 km

7,00 km

7,94 km

7,88 km

8.75 km

9.39 km

500

BB > BA > MBE > MBA > JS > BB

200

BB > JS > MBA > BF > BA > BE > MI > MBE > BB

500

500

BB > MBA > MBE > BF > BA > SAT > BE > MI > ME > JS > BB

200

200

BB > BE > SAT > BA > BF > MI > ME > MBE > MBA > JS > BB

BB > JS > MBA > MBE > BA > BB

500

BB > MBA > MR > BA > MI > MBE > ME > BF > BSB > SAT > BE > JS > BB

BB > MBA > MBE > MI > BF > BA > BE > JS > BB

500 200

BB > ME > MI > BE > SAT > BA > BSB > BF > MR > MBE > MBA > JS > BB

200

BB > MBA > PEG > MR > MC > BA > SAT > BE > MBE > ME > MI > MS > BSB > BF > JS > BB

BB > BE > SAT > BA > MC > BSB > BF > MR > MS > MI > ME > MBE > PEG > MBA > JS – BB

Iterations

Route

Table 3. Results of GA & Bing Maps.

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Execuon Time (s)

200

500

Linear(200)

51

Linear(500)

40 35 30 25 20 15 10 5 0 0

5

10

15

20

No. of POI Fig. 1. Correlation between the number of POIs and execution time.

The snapshots of different circuits suggested are mapped below; these are examples of the proposed routes based on the current method using the genetic algorithms alongside Bing Maps API (Fig. 2).

Fig. 2. Different itineraries for different POIs

5 Discussion and Conclusion The current paper was a brief presentation of our work that aimed, in the context of smart tourism in any city, to plan optimized routes for tourists using tools and algorithms that provide optimized circuits; genetic algorithms and the Bing Maps API method were tested, a method of high cost and poor execution time. It is necessary to highlight again that this long execution time is caused by the construction of the distance matrix issued from the Bing Maps API.

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Regarding the perspectives to be expected next, we look forwards to incorporating other ways so that we could efficiently optimize the execution time amid other parameters. The current discussed method and tests were uni-cost or uni-constraint, and we tried to optimize the circuit from a distance point of view only. That being said, we will be working to introduce more constraints to the optimization function, where we’ll have a multiobjective function with multiple costs (not only one cost which is the distance as it is done thus far). Acknowledgements. This research is supported by the National Scientific and Technical Research Center of Morocco. This paper has been realized in the context of project number 28/2020 funded in the field of the khawarizmi program.

References 1. Benchekroun, Y., Benslimane, M., Haddouch, K.: Intelligent visit systems: state of art and smart tourism literature. In: International Congress of Engineering and Complex systems (ICECS 2021) 2. Gretze, U.: From smart destinations to smart tourism regions. J. Reg. Res. 42, 171–184 (2018) 3. Pacurar, C.M., Albu, R.-G., Pacurar, V.D.: Tourist route optimization in the context of Covid19 pandemic. Sustainability 13(10), 5492 (2021) 4. Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21(2), 498–516 (1973) 5. Shabir, A., Israr, U., Faisal, M., Muhammad F., Dohyeun, K.: A stochastic approach towards travel route optimization and recommendation based on users constraints using markov chain. IEEE Access 7, 90760–90776 (2019) 6. Liang, S., Jiao, T., Du, W., Qu, S.: An improved ant colony optimization algorithm based on context for tourism route planning. 16 Sep 2021 7. Rbihou, S., Haddouch, K.: Comparative study between a neural network, approach metaheuristic and exact method for solving Traveling Salesman Problem. In: 2021 Fifth International Conference on Intelligent Computing in Data Sciences. October 2021 8. UNESCO Homepage. https://whc.unesco.org/en/list/170. Accessed 30 Oct 2022 9. Xiujuan, M.: Intelligent tourism route optimization method based on the improved genetic algorithm. In: Proceedings of the 2016 International Conference on Smart Grid and Electrical Automation (ICSGEA), Zhangjiajie, China, 11–12 August 2016 10. Taillard, É., Badeau, P., Gendreau, M., Guertin, F., Potvin, J.-Y.: A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows. Transp. Sci. 31(2), 170–186 (1997) 11. Hua, G.-M.: Tourism route design and optimization based on heuristic algorithm. In: Proceedings of the 2016 Eighth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Macau, China, 11–12 March 2016, pp. 449–452 12. Ahmad, S., Kim, D.-H.: A season-wise long-term travel spots prediction based on markov chain model in smart tourism. Int. J. Eng. Technol. 7, 564–570 (2018) 13. Neetu, G., Bobba, B.: Identification of optimum path for tourist places using GIS based network analysis: A case study of New Delhi. IJARSGG 1, 34–38 (2013) 14. Lau, G., McKercher, B.: Understanding tourist movement patterns in a destination: A GIS approach. Tour. Hosp. Res. 7, 39–49 (2006) 15. Qian, X., Zhong, X.: Optimal individualized multimedia tourism route planning based on ant colony algorithms and large data hidden mining. Multimedia Tools and Applications 78(15), 22099–22108 (2019). https://doi.org/10.1007/s11042-019-7537-0

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16. Dorigo, M, Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997) 17. Song, X., Li, B., Yang, H.H.: Improved ant colony algorithm and its applications in TSP. In: Sixth International Conference on Intelligent Systems Design and Applications (2006) 18. Han, Y., Guan, H., Duan, J.: Tour route multiobjective optimization design based on the tourist satisfaction. Discret. Dyn. Nat. Soc. 2014, 603494 (2014) 19. Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput, 3(1), 1–16 (1995) 20. Marcos L.P.B., Gina M.B.O.: A dynamic multiobjective evolutionary algorithm for multicast routing problem. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (2013)

Evaluating the Efficiency of Multilayer Perceptron Neural Network Architecture in Classifying Cognitive Impairments Related to Human Bipedal Spatial Navigation Ihababdelbasset Annaki1(B) , Mohammed Rahmoune1 , Mohammed Bourhaleb1 , Mohamed Zaoui2 , Alexander Castilla2 , Alain Berthoz2 , and Bernard Cohen3 1 Université Mohammed Premier, Research Laboratory in Applied Sciences, National School of

Applied Sciences, PB 669, 60000 Oujda, Morocco [email protected] 2 Collège de France, CIRB. 11, Place Marcelin-Berthelot, 75231 Cedex 05 Paris, France 3 Faculty of Medicine Bd de l’Hopital, Sorbonne University, Paris 75013, France

Abstract. In this study, We evaluated the efficiency of Multilayer perceptron for classification tasks related to cognitive impairments assessed in a virtual reality environment and on spatial data, “The VR Magic carpet” In our earlier work, we applied machine learning (ML) techniques for assessing and categorizing participants with cognitive impairments. The issue stems from the likelihood of not identifying the most relevant elements that will provide high accuracy in this navigation disorder detection. We used method multilayer perceptron (MLP) architectures to benefit from using layers for feature extraction on velocity time series and solve our classification problem. This navigation disorder identification model was prompt to develop a better understanding of targeting users with navigation disorders. The experimental results of the model in this study provide an enhancement because it can distinguish with more accuracy between healthy individuals and patients. Keywords: Artificial Intelligence · Deep-Learning · Multilayer perceptron · Virtual Reality · Neuropsychological assessments · Cognitive Impairments

1 Introduction The process of identifying and retaining a path from one position to another is called spatial navigation. Spatial navigation entails information regarding one’s body’s position in space, self-to-object distances, and self-motion. Egocentric representations are selfcentered and incorporate spatial data from the navigator’s perspective. Individuals can use allocentric representations to design a shorter approach and become orientated to hidden objective places. Navigation paradigms and assessments assess the capacity to orient itself within an environment and to seek and task efficiently utilizing mental representation of space [1, 2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 54–61, 2023. https://doi.org/10.1007/978-3-031-29857-8_6

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Humans have a significantly more difficult time evaluating spatial cognition because navigating in complicated real-world contexts does not provide for experimental control of the tasks. Navigational difficulties in patients may represent the disease’s early symptoms, with an initial neuro-degenerative process but intact daily life activities. Assessment of navigation abilities might be used as an early cognitive marker to identify those at a higher risk of acquiring Alzheimer’s disease dementia. Combining cognitive psychology paradigms with virtual reality and artificial intelligence has become vital to identifying the brain regions engaged in these processes with well-defined cognitive components that must be used [3–5]. Machine learning (ML) and other AI technologies integrate physical, behavioral, and social components in disease diagnosis, prognosis, and treatment. AI is adapted to detect anomalies in various data types via training. Massive volumes of unexplored medical (i.e., neurophysiological) data have interpretability potential. In different populations, AI can detect cognitive deterioration and predict prognosis pathways [8]. Multilayer perceptron architecture has aroused considerable interest in identifying cognitive impairments using various data sources. The ADAS-Cog, MMSE, and FAQ neuropsychological tests from the ADNI database were used to train MLP neural networks. Researchers create three MLP classification models for Alzheimer’s disease (AD), cognitive normal(CN), and (Mild Cognitive Impairments) MCI. A three-way MLP classifier distinguishes AD, MCI, and CN. Researchers advocate cascading three-way classification to enhance the performance of models. Using ADNI neuropsychological test data, the accuracy of our pairwise MLP models (AD vs. CN, AD vs. MCI, and MCI vs. CN) was 99.76 + 0.48, 89.64 + 3.94, and 90.81 + 2.91 respectively [9]. Many studies compared the categorization capabilities of machine learning conventional supervised algorithms and MLP to classify behavioral data from traditional neuropsychological tests, cognitive tasks, or both. The study’s conclusions contrasted the categorization abilities of machine learning and MLP. Traditional machine learning algorithms categorized neuropsychological and cognitive data identically. MLP fared better with cognitive data than with neuropsychological data. In combination with neuropsychological summary scores and a cognitive task, the MLP has a sensitivity and specificity of 90% [10]. Most research in this area is based on neuro-imaging, EEG signals, or particular data extracted via clinical observation and analysis. Our method investigates the combination of Artificial intelligence and Virtual reality to extract novel insights into this area based on raw spatial data, with total control over visual qualities and task complexity and an accurate recording of behavioral responses. The corpus utilized in this study is a single dataset with ten patients and ten clinical trials categorized into two groups. The remainder of this work is structured as follows. Section II describes the neuro-psychological assessment framework, techniques, participants, data acquisition and processing, the neural network architecture, and the suggested approach. The case study and its findings are reviewed in Section III, and the conclusion is explored in Section IV. Our main contributions are: – We are evaluating the efficiency of Multilayer perceptron for classification tasks related to cognitive impairments assessed in a virtual reality environment and on spatial data.

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– We are establishing a framework and a bridge to acquaint clinicians with adopting deep learning architectures and, specifically, multilayer perceptron models in analyzing the outcome of the neuropsychological assessment.

2 Materials and Methods 2.1 Neuropsychological Assessment The Virtual Carpet TM. Dr. Eng. Mohamed Zaoui’s novel Magic Carpet is used in the Berthoz lab for evaluations, and it follows the same rules as the walking Corsi test [11–14]. Instead of genuine white tiles, a video projector projected images of white tiles (30 cm square) onto the ground. The sequences were shown and delivered using a Microsoft Office PowerPoint presentation. Consequently, a virtual environment with the Unity Game Engine featuring 2 to 9 target sequences was created that resembled the magic carpet. The free and open-source program Blender was used to build the pieces. The Unity Game Engine manages motion, gathers spatial experimentation data, and creates a virtual world. Participants. Dr. Bernard Cohen’s pilot test provided the experimental results. The Paris University ethics committee authorized the assessment. The individual screening was performed on each of the ten individuals. There were no neurological or cognitive issues as a consequence of the trial to prevent a presumption in the research. Personal information encompassing age and gender to pathologic state was withheld. Participant’s personal information is anonymized to facilitate data access and in-depth research. The participants are classified as ten individuals divided into two groups. There are 5 participants in the control group and five persons with navigational disorders. Data Acquisition. We set up the play area and conducted some calibration after installing the SteamVR program on a laptop linked to the HTC Vive Experimentation began with the debut of the Unity-built environment. The first controller was attached to the head, while the second was to the waist (see Fig. 1). Based on the Euler angular alignment, the calibration allowed us to establish the play area’s origin and axis of rotation. The 3D spatial data was acquired using the SteamVR tracking software. We saved the information in session data’ which captures the participant’s movement and rotation. In addition, w saved the tile coordinates as JSON files. The OpenVR API, which allowed the device to integrate into Unity3D, helped implement C scripts to extract the data.

2.2 Data Processing Visual Replication. Before beginning the data pre-processing, we utilized Matplotlib, a tool for building dynamic, complex Python visualizations, to represent the clinical trials, identify the trajectory plan, and better comprehend the participants’ kinematic behavior during the session (see Fig. 2).

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Fig. 1. Virtual reality framework of the VR Magic Carpet

Data Processing. Due to the HTC Vive controller’s sensitivity and the high sample rate during data extraction, which is approximately 90 Hz. We discovered that in specific clinical trials, the data was obscured by noise and anomalous peaks based on a physical significance threshold of max average speed walking at a rapid pace [15]. Consequently, we used a fast Fourier transform to clean our data. Furthermore, the differences in the length of the sequences implemented throughout the clinical trials, we re-sampled our time series to get the same length of time series (Figs. 3 and 4). 2.3 Multilayer Perceptron Architecture Three layers with full connections are stacked to create our MLP. According to two design principles, the fully connected layers each include 300 neurons: we use dropout at each layer’s input to increase generalization capacity, and an exponential linear unit meets the non-linearity. As the network depth rises, (ELU) is used as the activation function to prevent gradient saturation. A SoftMax layer completes the network. We formally defined fundamental layer block as the combination of ELU and dropout in this architecture that sets it apart from the groundbreaking MLP decades before. ELU helps to stack the networks deeper, while dropout prevents the co-adaptation of neurons, allowing the model to generalize effectively, particularly on small datasets. ELUs, which differ from ReLUs in that they have negative values, can drive mean unit activations closer to zero with less computing cost than batch normalization. The input layer, hidden layers, and SoftMax layer have dropout rates of 0.1, 0.1, and 0.2, accordingly [16].

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Fig. 2. We depicted the participant’s path in a cyan hue to simulate a clinical session visually. Squares represent the tiles. After visiting it as the closest point from the trajectory to the target’s center, we highlighted the tile with a red dot. The black rectangle defines the navigation area. The purple mark denotes when the participant exits the target’s confidence region, and the yellow cross indicates when the participant enters the target’s confidence area.

Fig. 3. Instantaneous tangential velocity time-series.

3 Experimental Results We achieved an accuracy of 91.75% and a sensitivity of 84.63%, as shown in (Fig. 5 and Fig. 6). Indeed, we explain our method in a more detailed way: 1. We extracted from each session CSV file the tangential velocity column. 2. We trained our MLP model.

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Fig. 4. Instantaneous tangential velocity time series filtered and re-sampled.

3. We obtained predictions for each session CSV file separately using our MLP-trained model. 4. We grouped the session files relative to each subject. 5. We calculated the frequency of each label for the subject and took the most significant value.

Fig. 5. Recall line chart.

Fig. 6. Sensitivity per specificity line chart.

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4 Conclusion In our study, we exploited the results of a neuropsychological test that uses virtual reality to measure behavior. This test already has a primary method of analysis adopted by neuropsychologists. Also, in our earlier work, we analyzed the results using machine learning which was insightful yet limited based on expert feedback [17–19]. However, we opted to uncover the raw spatial data from this test. We aimed to adopt the MLP architecture to verify its effectiveness in detecting anomalies on one side and to what extent tangential velocity can be one of the determinant variables to exploit in the conceptualization and modeling of human locomotion. Thus, we try to establish a framework for clinicians, neuropsychologists, and researchers in other fields to adopt this architecture and approach in analyzing raw neuropsychological test results and in all phases related to medical follow-up.

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12. Corsi, P.M.: Human memory and the medial temporal region of the brain (Ph.D.). McGill University (1972) 13. Berch, D.B., Krikorian, R., Huha, E.M.: The Corsi block-tapping task: methodological and theoretical considerations. Brain Cogn. 38(3), 317–338 (1998). https://doi.org/10.1006/brcg. 1998.1039 14. Piccardi, L., et al.: Topographical working memory in children with cerebral palsy. J. Mot. Behav. 53(1), 1–9 (2020). https://doi.org/10.1080/13854046.2013.863976 15. Murtagh, E.M., Mair, J.L., Aguiar, E., Tudor-Locke, C., Murphy, M.H.: Outdoor walking speeds of apparently healthy adults: a systematic review and meta-analysis. Sports Med. 51(1), 125–141 (2020). https://doi.org/10.1007/s40279-020-01351-3 16. Wang, Z., et al.: Time series classification from scratch with deep neural networks: a strong. Baseline (2016). https://doi.org/10.48550/ARXIV.1611.06455 17. Annaki, I., et al.: Computational analysis of human navigation trajectories in a spatial memory locomotor task. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 233– 243. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_22 18. Annaki, I., et al.: Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-means and hierarchical agglomerative clustering. In: Proceedings of the E3S Web of Conferences, vol. 351, p. 01042 (2022). https://doi.org/10.1051/e3sconf/202 235101042 19. Annaki, I., et al.: Computational analysis of human navigation in a VR spatial memory locomotor assessment using density-based clustering algorithm of applications with noise DBSCAN. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications: Proceedings of ICDTA’22, Fez, Morocco, Volume 2, pp. 190–198. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-02447-4_20

A Novel Real Time Electric Vehicles Smart Charging Approach Based on Artificial Intelligence Mouaad Boulakhber1,2(B) , Ilham Sebbani2 , Youssef Oubail3 , Imad Aboudrar2 , Kawtar Benabdelaziz4 , Malika Zazi2 , and Tarik Kousksou1 1 Université de Pau et des Pays de L’Adour, SIAME, E2S UPPA Pau, France

[email protected]

2 Université Mohammed V, École Nationale Supérieure d’Arts et Métier Rabat, Rabat, Morocco 3 Laboratoire des Sciences de l’Ingénieur et Management de l’Energie, ENSA AGADIR,

Université Ibn Zohr, Agadir, Morocco 4 Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V

University, Rabat, Morocco

Abstract. Uncontrolled charging of electric vehicles (EVs) is likely to generate issues for power distribution networks. As the use of EVs grows, smart charging approaches that prevent such issues will become vital. Meanwhile, given the significant renewable energy potential in the regulated (non-liberalized) electricity markets, smart charging provides the opportunity for electric vehicles (EVs) to utilize renewable energy more efficiently, cutting costs and boosting grid stability. This paper describes a novel real time smart charging approach for EVs charging that is cost-effective and ensures grid stability based on artificial intelligence. This work suggests a creative smart charging model that maximizes the use of renewable energy, minimizes peak loads, and lowers energy costs, designed specifically for the regulated electricity markets. Keywords: Smart charging · Artificial Intelligence · Electric Vehicles · Grid · Renewable Energies

1 Introduction With the rising popularity of electric vehicles, there is a growing interest in smart charging. In the regulated electricity markets, the most of EV charging operations are uncoordinated or unplanned. Unscheduled charging of EVs increases demand for electricity during peak hours, threatening the integrity of the power system and resulting in power peaks [1]. Higher peak loads cause a variety of problems and challenges, including overloading of transformers and other distribution network infrastructure, increased grid congestion, power imbalances, and voltage dips [2]. Greater peak demands can result in higher power bills and increased carbon emissions on a worldwide scale [3]. Charging power is limited not just by inadequately sized connecting cables, but also by the necessity to prevent peaks. As a result, the number of EVs that may be charged at the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 62–72, 2023. https://doi.org/10.1007/978-3-031-29857-8_7

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same time is limited. This necessitates the urgent development of precise smart charging techniques. Smart EV charging can help to minimize peak power usage and avoid local congestion in electrical power infrastructure [4]. This lowers the expenses of providing widespread and economical EV charging stations. In this way, it addresses one of the most significant impediments to EV adoption: a scarcity of accessible charging stations. Furthermore, smart charging can increase the penetration of renewables in the mobility sector by aligning EV charging with the availability of locally produced renewable energy. There is a large body of research on creating optimal schedules of EV charging. Relevant works on smart and optimal charging deals with different objectives such as stabilizing power load [5], assigning electric vehicles to parking spaces [6], maximizing profit [7] and even driver satisfaction [8]. In the smart charging domain, EV scheduling can be combined with forecasting (mine). Typical forecasts address uncertainties involving electrical loads, renewable energy sources and EVs charging demand. Forecasting is usually implemented with regression models using historical data to predict the future ones. Overall, two main points stand out from the results of the previous studies. First, artificial intelligence methods provide strong and robust results for the prediction of load charging. Second, the previous studies focused on the integration of EVs in liberalized electricity markets. Therefore, their results might not be applicable for the non-liberalized markets, which have different characteristics. In this perspective, we present a new smart charging strategy in which predictions and actions are optimized in conjunction. This Smart Charging of Electric Vehicles (SCEV) system is based on Deep Learning (DL). The rest of the paper is organized into five sections. The EVs grid integration based on artificial intelligence is introduced in Sect. 2. Smart charging approaches are outlined in Sect. 3. The problem formulation is explained in Sect. 4, while the proposed system architecture is presented in Sect. 5 and Sect. 6 concludes.

2 Electric Vehicles Grid Integration Based on Artificial Intelligence Recently, industrial, and academic applications of artificial intelligence (AI), defined as model-aided algorithms intended to mimic natural thoughts, perceptions, and actions [9], in electric vehicles and related infrastructure, such as battery electric vehicle design and management, charging stations, and even smart grids, have emerged. Due to their capacity to detect abnormal pattern patterns and easier implementation, these AI algorithms can outperform conventional rule-based systems, which employ human expertise to construct rules in the system, depending on the challenge. Some of the compelling benefits of AI applications include: • Reducing EV costs through optimized battery material design and manufacturing [10]. • Precise range assessment to reduce EV customer concern by forecasting future driving circumstances by employing AI commands for EV auxiliary systems [11].

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• Reduces EV power usage compared to conventional controllers [12]. • Enhanced potential for road safety and efficient traffic flow with autonomous and networked driving, Simulation techniques [13]. • Comprehensive and efficient modelling for optimal resource allocation and positioning of electric vehicle charging stations (EVCS), as well as energy programming for EV-smart grid interaction [14] To have a better understanding, AI approaches primarily employed in EVs and related infrastructure are summarized and classified as machine learning (ML) and deep learning (DL), as seen in Fig. 1, they both are subsets of artificial intelligence. Machine Learning (ML) employs artificial neural networks (ANNs); this discipline paves the way for a variety of real-time applications that do not require human participation. While Deep Learning (DL) is an area of Machine Learning that deals with artificial neural networks inspired by the structure and operation of the brain. These methods are frequently more sophisticated or have more layers in DL. In this thesis, we focus on DL algorithms such as (ANN), recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU).

Fig. 1. Artificial intelligence subsets

ML models are skilled at detecting correlations and trends between inputs and outputs based on past observations, and therefore require training on previous datasets. The types of machine learning employed in EVs and related fields are roughly classed as supervised, unsupervised, and reinforcement learning (RL). Supervised and unsupervised learning have been investigated and implemented in areas of the EV and its infrastructure where significant datasets exist or can be created, such as condition estimation. The current state of EV batteries and the development of novel materials for EVs [15] batteries. Deep learning (DL) modelling employs neural network (NN)-like structures with many hidden layers [16] The aim of RL is for the agent to learn the optimum course of action on its own via trial and error, and the agent interacts with its environment through actions and is rewarded accordingly [17]. AI applications in electric vehicles have been widely researched, including the design, manufacture, and management of electric vehicle batteries; range optimization;

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and the design and optimization of the electric vehicle control system as detailed in Fig. 2. AI has been applied in battery research and development to increase battery performance, battery management, and energy management. The application of AI for optimal EVCS configuration, congestion control, and dependable power scheduling was considered in the context of EVCS. Finally, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) energy transfer achieves effective energy management in bidirectional energy transfer between EVs and smart grid. As a result, this paper proposes a deep learning approach for predicting charging patterns of charging sessions in regulated electricity markets. Accurate prediction of electric vehicles charging patterns has many potential applications for utilities and charging operators, including grid reliability, scheduling, and smart grid integration. In the case of Morocco, the massive deployment of EVs can cause a variety of problems to the electrical system due to the considerable charging power and stochastic charging behaviours of electric vehicle drivers [18]. Thanks to study’s results presented in [19– 21], we can assess the expected impact of additional EVs on the grid, considering specific characteristics of the Moroccan power system.

Fig. 2. Overview of the use of AI in EV and EVs related fields

3 Smart Charging Strategies and Approaches Depending on the goals that must be met, EV charging techniques can be centralized or decentralized, and hence grid-oriented or user-oriented. A central controller (smart grid or aggregator) has direct control over loads in a centralized method. In contrast, the control of the load is handled directly by the EV user in a decentralized method.

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3.1 Centralized Strategy A single agent supervises the charging of all EVs in such an architecture. A global scheduling optimization is constructed in a centralized architecture to deal with the charging decisions of a fleet of vehicles based on some purpose, and the decision variables are optimized while considering a set of general limitations. The central agent provides automated scheduling across a future limited time-horizon discretized into timeslots in this manner. This strategy may be tailored to achieve various goals, such as decreasing aggregated loads or lowering generation costs. It typically requires future data and information such as base load, electricity pricing curves, regulatory up and down curves, mobility information, and the energy required by the driving cycle, among other things. The limits are connected to the electrical grid and the capacity of the batteries. 3.2 Decentralized Strategy EV owners have total control over their charging decisions under a decentralized structure, providing them more flexibility to engage in DSM initiatives. Global optimization approaches addressing the charging decisions of an EV fleet to fulfill grid or aggregator objectives are not always available. It may not result in the best outcome for an EV owner, but it is most useful to deal with the unique characteristics of each vehicle. 3.3 Grid Oriented Architecture In a grid-oriented design, as seen in Fig. 3, a central controller, such as an aggregator utility, has direct control over all EV participants’ charging decisions. Aside from architecture, pricing methods may be determined using the information required by the models. As a result, the methods of the models for load control require a huge amount of information ranging from forecasts of EV adoption and energy source analysis to the characteristics of each EV, among other aspects. Similarly, aggregator tactics usually incorporate EV customer preferences, such as the state of charge wanted at the conclusion of the charging session, connection time availability, and energy pricing control.

Fig. 3. Conceptual map of SC grid-oriented architecture

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3.4 User Oriented Architecture Figure 4 shows that user-oriented methods usually focus on creating statistical driving patterns to identify the potential time of connection. As a result, in a scheduling optimization problem, such availability and market behavior (including load and power price) are the most important factors to consider. Typically, approaches like quadratic programming along with energy price projections have been utilized to overcome this problem [22].

Fig. 4. Conceptual map of SC user-oriented architecture

4 Problem Definition Smart charging is especially important for large-scale charging facilities like those found at businesses, apartment complexes, airports, and retail centers [23]. The availability of charging at these many locations is critical to the marketing of EVs. Because many of these charging stations will be open during the day, they will be able to take advantage of plentiful solar energy generation and enable EVs to offer grid services throughout the day. However, due to limited infrastructural capacity and the concern of excessive energy costs, most locations are unable to install more than a few charging connections with current technology. Smart charging enables facilities to increase port capacity without incurring costly infrastructure modifications. Furthermore, by optimizing for time-ofuse rates, demand charges, and on-site renewable energy [24], scheduling tactics can lower operational costs. Strategies and algorithms can also enable additional revenue streams by providing grid services. Some charging stations are powered by both the grid and a hybrid renewable energy source; otherwise, in the regulated electricity markets, the pricing norms and profiles specified by the power supplier in these markets are not flexible in time. This work addresses the problem of smart EV charging in public EV charging stations powered by a hybrid renewable energy source HRESs in a regulated electricity market where electricity prices are usually constant and determining charging operation remains a significant challenge, using an approach that aims to maximize the use of renewable energy while minimizing grid load. The smart charging is understood within the context of a dynamic environment, in which the price of electricity is not instantaneously flexible, driving patterns are varied, and renewable energy sources are intermittent. As a result, the system must be adaptable enough to accommodate changing situations. Based on current and historical information to predict the future ones by using a Deep Learning (DL) approach, it will be necessary to determine the variables that influence the recharge of an EV in such a way that only useful information is included in the system.

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5 Proposed System Architecture The system developed, shown in the Fig. 5 consists of the power supply, charging station system, the Electric Vehicle (EV), and the management charging operator. The power system might be a combination of several sources and components, such as the grid, renewable energy sources, and an energy storage system. The charging station, also known as the EVSE (Electric Vehicle Supply Equipment), enables interoperability with all types of electrical vehicles, provides the user with information on maximum available power directly when the driver performs a recharge without reservation or through the central system during the reservation, and exchanges data with the database via the central system. The ISO/IEC 15,118 standard is used by electric vehicles to communicate with charging stations through powerline. They share data such as battery capacity, maximum power permitted, and so on. Otherwise, the EV driver determines the recharge settings and makes the recharge reservation with the CPO (Charging Point Operator). either a charge point interface, smartphone usage, or an extended range wireless protocol (e.g., 4G/5G connectivity) currently included in the EV. Furthermore, the driver may directly configure the recharge settings using the EVSE and watch the actual recharge. While the database contains data about all of the charging stations in the region, as well as the users who are permitted to use them, all bookings, and billing information. Charging Stations, EVSE, Connectors, Reservations, and Users are the four tables that make up the database. All charging activities are coordinated by the Charging Point Operator (CPO). It is made up of a Web Application that handles requests from the Charging Station as well as the EV driver application and exchanges data with the database. Some of the CPO’s characteristics are stated in the OCPP standard, whereas reservation functionalities that are not mentioned in the standard have been included as possible expansions.

Fig. 5. Proposed system architecture

We suggest a novel smart charging technique based on Deep Learning for regulated power markets. DL algorithms allow real-time decisions to be made in response to a current energy price while taking into consideration future data and parameter estimates such as RE source production and ESS (Energy Storage System) state of charge (SoC).

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It also eliminates the need for hand-crafted, analytic decision functions, which may be challenging to create in general. Our data-driven method automatically learns this decision function by training the model with user-specific data, implicitly considering the prediction function’s strengths and limits as well as the unique features of each vehicle user. 5.1 Smart Charging Methodology

Fig. 6. Proposed methodology

We propose the methods shown in Fig. 6 to train a decision-making model for EV smart charging that responds in real time to dynamic situations in order to reduce the energy cost of an EV and avoid all peak loads while optimizing the usage of renewable energy sources. Usually, the information utilized in smart EV charging using dynamic processing methods has been represented using probabilistic approaches, combining random or anticipated variables in a model that allows too much space for uncertainty. To solve this issue, we propose developing a processing system that would investigate genuine and diverse data to deduce patterns and make effective real-time judgments using Deep Learning (DL) algorithms. The development of a dynamic processing system entails dealing with multiple-time series variables, such as power prices and demand, in lagged arrays on discrete-time intervals, consisting of data with just one choice created at each time. The usage of databases storing information to learn from is a unique feature of data-driven models. The precise identification of important information that may be used in the training process is critical to the success of the issue solution. 5.2 Charge Scheduling Heuristic The primary objectives of the heuristic are energy cost minimization, load imbalance minimization, peak minimizing, and renewable energy use maximization. Simultaneously, the heuristic considers real technical restrictions such as EV model-specific

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charging power. Secondary, it tries to enhance driver satisfaction by distributing limited charging power equitably across EVs with charging requirements. The heuristic is divided into three major steps: charging plan calculation and assignment, charging points saturation adjustment, and conflict resolution. First, the heuristic creates a charging plan for each EV, in which it intends to fill up the state of charge as fast as feasible. The heuristic allocates the maximum charging current for all time slots immediately after the arrival of the scheduled EV in assigned charging point (Fig. 7).

Fig. 7. Proposed Charge scheduling heuristic

Second, the heuristic modifies the intended charging current by taking charging saturation effects into consideration. A basic saturation adjustment is included in the charge scheduling heuristic. During the last 10-min of the charging session, the implementation linearly reduces the charging current. Regression approaches that allow for more accurate prediction of charging behaviour during saturation. The heuristic’s third and last phase is to resolve conflicts between the charge plans and the charge plans of all previously scheduled EVs based on the charging stations availability. Because of the plan charging in the first stage, it is possible that the scheduled charging capacity will exceed the actual capacity of the charging infrastructure during some time periods. In this instance, priority-based rescheduling is activated, and the EVs with the lowest priority are rescheduled to charge at different times.

6 Conclusion and Future Work This paper proposes a solution to the problem of real-time Smart Charging of Electric Vehicles (SCEV) in regulated electricity markets, taking into account a decentralized

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architecture in which the CPO are autonomous to make their own charging decisions in order to minimize their running costs. Using Deep Learning (DL) models to learn from historical time series datasets, this technique can respond to a dynamic environment. Even though the contributions of this paper are directly connected to the EVs smart charging problem in the regulated electricity market, they have the potential to have an influence on other decision-making situations, with contributions relating to a new technique based on a fundamental principle: the data parameters analysis, in conjunction with a dynamic processing system, can be used as a guide to learn the decisions that should be made using Deep Learning algorithms. We suggested a new model for determining when to charge an Electric Vehicle (EV) in real time, using just the vehicle’s current status and previously gathered database information from the processing system’s defined environment. This unique technique represents a promising advance in the context of Smart Charging of Electric Vehicles (SCEV), particularly in regulated power markets.

References 1. Kumar, T., Kumar, N., Thakur, T. Nema, S.: Charge scheduling framework with multiaggregator collaboration for direct charging and battery swapping station in a coupled distribution transportation network. Int. J. Energy Res. 46(8), 11139–11162 (2022) 2. Rahman, S., Khan, I.A., Khan, A.A., Mallik, A., Nadeem, M.F.: Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system. Renew. Sustain. Energy Rev. 153, 111756 (2022) 3. Rüdisüli, M., Romano, E., Eggimann, S., Patel, M.K.: Decarbonization strategies for Switzerland considering embedded greenhouse gas emissions in electricity imports. Energy Policy 162, 112794 (2022) 4. Judge, M.A., Khan, A., Manzoor, A., Khattak, H.A.: Overview of smart grid implementation: Frameworks, impact, performance and challenges. J. Energy Storage 49, 104056 (2022) 5. Deilami, S., Masoum, A.S., Moses, P.S., Masoum, M.A.: Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Trans. Smart Grid 2(3), 456–467 (2011) 6. Akhavan-Rezai, E., Shaaban, M.F., El-Saadany, E.F., Karray, F.: Online intelligent demand management of plug-in electric vehicles in future smart parking lots. IEEE Syst. J. 10(2), 483–494 (2015) 7. Lee, S., Choi, D.H.: Dynamic pricing and energy management for profit maximization in multiple smart electric vehicle charging stations: a privacy-preserving deep reinforcement learning approach. Appl. Energy 304, 117754 (2021) 8. Darabi, Z., Fajri, P., Ferdowsi, M.: Intelligent charge rate optimization of PHEVs incorporating driver satisfaction and grid constraints. IEEE Trans. Intell. Transp. Syst. 18(5), 1325–1332 (2016) 9. Winston, P.: Chapter 1. In Artificial Intelligence (Wesley Longman Publishing), The intelligent computer (1992) 10. Chen, A., Zhang, X., Zhou, Z.: Machine learning: accelerating materials development for energy storage and conversion. InfoMat 2, 553–576 (2020) 11. Varga, B., Sagoian, A., Mariasiu, F.: Prediction of electric vehicle range: a comprehensive review of current issues and challenges. Energies 12, 946 (2019) 12. Abu Hanifah, R., Toha, S.F., Hassan, M.K., Ahmad, S.: Power reduction optimization with swarm based technique in electric power assist steering system. Energy 102, 444–452 (2016)

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13. Li, J., Cheng, H., Guo, H., Qiu, S.: Correction to: survey on artificial intelligence for vehicles. Automot. Innov. 1, 390 (2018) 14. Rigas, E.S., Ramchurn, S.D., Bassiliades, N.: Managing electric vehicles in the smart grid using artificial intelligence: a survey. IEEE Trans. Intell. Transp. Syst. 16, 1619–1635 (2015) 15. Hannan, M.A., et al.: Toward enhanced state of charge estimation of lithium-ion batteries using optimized machine learning techniques. Sci. Rep. 10, 4687 (2020) 16. Geron, A.: Introduction to artificial neural networks with keras. In: Hands Machine Learning with Scikit-Learn and Tensorflow, pp. 280–281 (2019) 17. Geron, A.: Reinforcement learning. In: Hands Machine Learning with Scikit-Learn and Tensorflow (O’Reilly), pp. 609–664 (2019) 18. Boulakhbar, M., et al.: Towards a large-scale integration of renewable energies in Morocco. J. Energy Storage 32, 101806 (2020) 19. Boulakhbar, M., Markos, F., Benabdelaziz, K., Zazi, M., Maaroufi, M. Kousksou, T.: Electric vehicles arrival and departure time prediction based on deep learning: the case of Morocco. In: 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–8. IEEE. March 2022 20. Mouaad, B., Farag, M., Kawtar, B., Tarik, K., Malika, Z.A.: Deep learning approach for electric vehicle charging duration prediction at public charging stations: the case of Morocco. In: ITM Web of Conferences 2022. Vol. 43, p. 01024. EDP Sciences. 21. Boulakhbar, M., Farag, M., Benabdelaziz, K., Kousksou, T. Zazi, M.: A deep learning approach for prediction of electrical vehicle charging stations power demand in regulated electricity markets: the case of Morocco. Cleaner Energy Syst. 3, 100039 (2022) 22. Killian, M., Zauner, M., Kozek, M.: Comprehensive smart home energy management system using mixed-integer quadratic programming. Appl. Energy 222, 662–672 (2018) 23. Wang, B., Dehghanian, P., Wang, S., Mitolo, M.: Electrical safety considerations in large-scale electric vehicle charging stations. IEEE Trans. Ind. Appl. 55(6), 6603–6612 (2019) 24. Oubail, Y., Boulakhbar, M., Aboudrar, I., Elmoutawakil Alaoui, M.R., Elmahni, L.: Renewable energy sources integration in a microgrid control system: overview and perspective. In: International Conference on Digital Technologies and Applications, pp. 552–561. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02447-4_57.

Proposed Hybrid Model Recurrent Neural Network for Human Activity Recognition Youssef Errafik1(B) , Adil Kenzi1 , and Younes Dhassi2 1 Laboratory (LISA), Sidi Mohamed Ben Abdellah University (USMBA), Fez, Morocco

[email protected] 2 FST, Sidi Mohamed Ben Abdellah University (USMBA), Fez, Morocco

Abstract. It is evident that the field of Human Action Recognition (HAR), which uses time series of sensors, has become a trending field for the scientific community, thanks to the vast evolution and availability of smart devices in daily life. As well as the technological evolution of applications based on AI systems. The exploitation of HAR systems and applications in various sensitive areas such as the health sector and intelligent surveillance. After the tremendous relay of Deep Learning methods applied for natural language processing and speech recognition. The recurrent architectures LSTM and GRU have become the most popular architectures in the automatic extraction of temporal characteristics and the classification of human activities, but the efficiency of this extraction remains differentiated between various human activities carried out such as static activities, and dynamic activities. Our main objective in this work is to establish a hybrid RNN model tested with raw 3D accelerometer data from smartphones. Optimally, our model proposes to take advantage of the advantages of RNN architectures and respond in a more efficient and balanced way to the classification of the different human activities carried out. We used accuracy, precision, and other necessary measures to obtain well-classified and performing results on the comparison between our hybrid model and two other simple RNN models. The results revealed the efficiency of our hybrid model in properly classifying the different usual activities existing in the UCI Heterogeneity HAR dataset. Keywords: Human Action Recognition · RNN · Accelerometer · Time series · Hbrid RNN · LSTM · GRU

1 Introduction The main objective of human activity recognition is to discover the human activities carried out systematically from the sequences of information sent by using serial digital data sensors, namely cameras, wearable sensors, smart watches, smartphones, and other similar devices. According to the state of the art [1, 2], it is observed that the development of HAR has produced several approaches, applications, and systems used in various technological fields or other ones, such as security monitoring systems [3, 4], video surveillance [5] and smart healthcare monitoring [6], human-computer interaction [7], fitness [8] and robotics with autonomous systems [9]. It is apparent to both researchers © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 73–83, 2023. https://doi.org/10.1007/978-3-031-29857-8_8

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and those interested in the HAR field that this field comprises two major subfields, following the timeline of HAR literature. The first subfield is based on computer vision, which necessarily uses a single image or multi images collected by visual sensors such as RVG, thermal cameras, and skeleton cameras (see Fig. 1).

Fig. 1. Video-based human activity recognition.

The second subfield of HAR is mainly about the usage of other non-visual sensors such as inertial sensors of smartphones, Smartwatches, and other user-worn sensors (see Fig. 2) such as the accelerometer [10], the gyroscope [11], Wi-Fi, etc…. Similarly, sensors collecting and using time series data fall into one of the following categories: HAR based on wearable sensors [12], HAR based on environmental/ambient sensors [13] and HAR based on smartphone sensors [14].

Fig. 2. Sensor-based human activity recognition.

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One can find in the literature of our HAR field several recognized challenges [15]. We focused mainly in this work on feature extraction, this essential step to perform all kinds of classification activities, of course, using deep learning algorithms; feature extraction is been automatic without human intervention. However, it remains a delicate and complex task for several reasons, such as the similarity between the activities; for example, it may have similar characteristics between running and walking. In the same way, the categories diversity of activities that the studied model classifies. Hence, it is difficult to put a model, which effectively classifies, at the same time, the static activities, the dynamic activities, and the transition between two successive activities. When no particular pre-processing is performed on the data used for training and testing any model, the quality of feature extraction directly and strongly influences the performance of that model used to get more precision. In this way, it suffices to compare the performances obtained using each model to determine the efficiency and the robustness between the experimental models. To improve the performance of feature extraction defined on Deep Learning models, we developed a hybrid model that combines two types of RNN block that efficiently extracts resident features and dependencies in a specific time series by a given sensor. We have tried and succeeded in taking advantage of the efficiency of the GRU model in feature extraction for static activities and the slight superiority of the LSTM model in feature extraction for dynamic activities. In the following sections, we will show the high performance obtained from our proposed architecture on one of the best-known databases in the field of sensor-based human activity recognition.

2 Related Works Mobile sensor-based HAR makes excellent use of various Machine Learning ML models available to healthcare to monitor the activities and the gestures of elderly people with Parkinson’s disease (PwP) through 3D data analysis of accelerometers and gyroscopes extracted by wearable sensors [16]. The authors of the survey [17] have analyzed several systems of the classification of human activities HAR using the sensors of smartphones. They confirmed that the factor of precision is proven to confirm the effectiveness of a HAR system tested on a set of specific data already pre-processed; moreover, the number and the type of activities studied, the size of the samplings, as well as the characteristics extracted manually by the researchers in the laboratory, are all well controlled. Thus, the experimental results obtained in the laboratories do not necessarily reflect the results expected in a real situation with the same performances. Certainly, the experimental conditions such as the location and the orientation of the sensors used are not equal. The usage of these systems is very greedy in terms of the consumption of material and energy resources, and sometimes the delay in obtaining results is prohibitive in a critical situation, like the monitoring of chronic diseases. After the great technological success of RNNs in Natural Language Processing [18] and handwriting recognition [19], The problem of feature extraction in time series data solve by using the different RNN architectures. Moreover, high performance for exploiting temporal correlations between neurons in HAR. So, the HAR classification became more efficient.

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Concerning the problems encountered by classical RNN networks, we have the problem of gradient descent, which negatively influences the optimization of the hyperparameters related to this kind of RNN model. The innovation of Long Short-Time Memory models comes to solve gradient-related problems such as leakage and downward gradient exploitation. The authors of [20] have integrated controlled memory blocks to keep important information and neglect useless information automatically and efficiently. The LSTM model recognizes in the scientific community as the pivot of architectures that perfectly handles complex long-term artificial tasks. LSTM is an advanced extension of RNN that emerged and approved its great capability first in cursive handwriting recognition and speech recognition for classification and prediction from time series data. To push the performance of LSTM/RNN models to the maximum such as efficiency and sensitivity, the authors of [21] combined local data from 2D images with 3D-accelerometer data transmitted by other sensors. This combination of data eliminates existing disruptive activities, which leads to a significant improvement in HAR recognition accuracy. In the work [22], researchers presented an LSTM model to determine activities of daily living from raw 3D accelerometer data. They managed to surpass the average accuracy to about 92% and to reduce the number of training epochs they used the batch normalization technique. Similarly, Milenkoski et al. [23] proposed a new real-time system that classifies human activities performed based on data captured from three-dimensional accelerometer data from a smartphone. Since the innovation, Cho et al. [24] of the Gated Recurrent Units (GRU) neural networks and their extensions used to manage long-term dependencies. The GRU model is a developed and simplified version of the LSTM model; its architecture has based on the integration of memory cells capable of storing information from time series. GRU cells contain two gates (the reset gate and the update gate) that control successive data instead of three in TM the LS model (forget, enter, and exit); GRU cells, using the sigmoid function, are capable of efficiently memorizing useful information in long sets of processed data. A comparative study [25] of the performance of two model variants with the GRU and LSTM units used a data augmentation method to strengthen the robustness of the RNN models against the absence of sensor data. They concluded that the GRU architecture is the most adapted to the case of absence of data compared to that based on LSTM. The authors of [26] propose a composite hybrid model of convolutional neural network (CNN) and short-term memory (LSTM) for the classification of human activity, from which this structure of the CNN network plays the role of extracting the spatial characteristics and the LSTM network for learning temporal characteristics. Thus in the work [27], they combined the GRU architecture with CNN to take advantage of its two powerful models for the extraction and classification of human activities.

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3 Proposed Approach After the experiments we made in the previous publication [28] on recurrent architectures such as LSTM and GRU, we observed the weaknesses and the strength of their structures in the classification of human activities from the temporal data of the mobile sensors. In our current work, based on the results obtained previously, we have tried to build a hybrid recurrent structure that takes advantage of the advantages of each of the LSTM and GRU models to better improve the performance, and classify dynamic and static activities simultaneously. Indubitably, the coming steps must follow to carry out an offline experiment in the HAR domain with a Deep learning approach: 1) the segmentation of the databases used. 2) Construction and training of the DL models used. 3) Performance testing of each model against standard measurements. Sensor-based HAR is a classification problem that relies on the analysis of time-series data such as 3D accelerometers collected by sensors. To train and test a DRNN neural network model; captured time series data must be segments in the form of a list of frames of the same size, before using these frames, we labeled them. For the experimentation of the proposed models, we limited to the 3D accelerometertype data from the UCI H-HAR dataset, several sets of datasets considered the most used and the best known in the field of HAR based on sensors. 3.1 Data Segmentation After downloading the public set of games from the internet, we started by eliminating data that is not accelerometer type and lines that are missing at least one sensory value. To segment the UCI Heterogeneity HAR dataset, we used the sliding window, with a function programmed in Python language; we segmented ones will extract samples from them with a size fixed at 128 timestamps per sample, with three values X, Y, and Z of the acceleration associated with each timestamp. The overlap between two successive frames has been adapted to 50%. 3.2 Feature Extraction We explained earlier that the RNN neural networks used in this work with LSTM and GRU models do not use feature extraction manually like Machine Learning classifiers. In this part, we will try to analyze the functional structure of its two architectures. As shown in Fig. 3, Each LSTM cell contains several components that enable it to store and process information over long periods. These components include: 1. An Input Gate: controls what information from the input is allowed to pass to the cell state. 2. An Output Gate: controls which information from the cell state is allowed to pass through to the output. 3. A Forget Gate: This gate determines which information from the previous cell state should be discarded.

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A cell state: - This is a vector that stores information from previous time steps. The cell state is updated at each time step based on the input and the previous cell state and is passed from one cell to the next in the sequence. The input, output, and forget gates are all implemented using sigmoid activation functions, which allow them to control the flow of information through the cell. The output is calculated using the cell state and the output gate and is then passed on to the next cell in the sequence. However, the architecture of a GRU is simpler; A GRU cell has a single Update Gate that controls the flow of information into and out of the cell, and a Reset Gate determines information from the previous state cell should be discarded. Both the update gate and the reset gate are implemented using sigmoid activation functions, which allow them to control the flow of information through the cell. The cell output is calculated using the input and state of the previous cell and then passed to the next cell in the, sequence.

Fig. 3. the Architecture of bloc LSTM (a) and bloc GRU (b).

The structure of the GRU model is superior to that of LSTM in terms of classification accuracy and speed. The GRU structure is composed of two essential gates: an update gate (r) and a reset gate (z). 3.3 Model Architecture For all the models proposed, we used a loss function: ‘Categorical Cross Entropy’, Batch size equal to 1024 and maximum fixed epochs 150, the optimizer used is Adam with Learning Rate (LR) set to 0.0025, and Learning loss (LL) set to 0.0015. The structure of the tested models is illustrated in the following Fig. 4. For the three models tested, each LSTM or GRU layer is composed of 32 adjacent blocks, for the fully connected are composed respectively of 128 units, 64 units, and 32 units.

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Fig. 4. The architecture (a) the LSTM model, the (b) G, RU model and (c) our hybrid model.

4 Experiments and Results We use the “Keras” and “Tensorflow” libraries with the famous google platform Google Colaboratory that manages notebook documents. 4.1 The Used Dataset UCI Heterogeneity HAR Dataset: The public heterogeneity HAR database contains 3D acceleration and 3D gyroscope data for nine people who wear four smartphones and two Smartwatches to ensure the heterogeneity of data reserved for the classification of human activities. Data collection in different situations and with a variety of sensors. Indeed, this dataset includes 1048576-labeled instances corresponding to six activities like Bike, Sit, Stairs-down, Stairs-up, Stand, and Walk. In our experiments, we removed the instances that contain null values, and then we segmented the 938086 instances of the remaining data into samples of size 128 instances. Then, we split the segmented data into training data in the count of 15008 and test data in the count of 3752. 4.2 Performance Measures The multi-classification problem is handled in the HAR domain by analyzing smartphone data, with data reserved for the classification of each activity class being independent of each other. Accordingly, to compare the different models studied in this article, we use the following performance measures: precision, F1 score, recall, accuracy, and confusion matrix (CM). To calculate these metrics, here are the three equations used: n Precision =

i=1 precisioni ; Recal =

n

n

i=1 recall i ; F1 − score = 2 Precision ∗ Recall

n

Precision + Recall

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The confusion matrix (CM) plots the true labels horizontally and the predicted labels vertically. The confusion matrix allows visualizing the classification performances of a studied model. In a simplified way, the good predictions are classified by activity on the diagonal of this matrix and the others are false predictions. 4.3 Results In this part, we present the results of the evaluation of the three models tested with the Heterogeneity HAR dataset. Figure 5 presents the evolution of precision, recall, and f1-measurement of each of the three RNN models tested. In Fig. 6, we present all the confusion matrices of LSTM (a), GRU (b), and Our model (c). We can see that the performance of our proposed model is slightly better than the other two models, thanks to this alternation of RNN layers in our hybrid model, feature extraction and classification of static and dynamic activities became more accurate. As is presented in Table 1, generally we can observe that we have a higher precision for our model compared to the others for dynamic movement activities: Bike, Stairs down, and Walk has precision de 97%, 91%, and 96% respectively, while the two static activities which are Sit and Stand present a precision almost equivalent in 100%. 0.97 0.96 0.95 0.94 0.93 0.92 0.91 LSTM model Precision

GRU model Recall F1-measure

Our model

Fig. 5. The evolution of Precision, Recall, and F1-measure of each of the three models.

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Fig. 6. The confusion matrix of each of the three models. Table 1. The Precision of each of the three RNN models. Our model

LSTM

GRU

Bike

0,97

0,96

0,95

Sit

1

1

1

Stairs down

0,91

0,86

0,91

Stairs up

0,9

0,91

0,91

Stand

1

1

1

Walk

0,96

0,86

0,91

5 Conclusion Human activity recognition (HAR) is the process of automatically identifying human behavior through the analysis of time series data from sensors. Thanks to its multiple applications to different areas of people’s daily life, HAR using time series of sensors has become a rich and very important field, which respects people’s privacy and offers more and more opportunities to improve the conditions of professional and personal life. To improve the performances of the various systems generally using the Deep Learning approach and in particular the RNN neural networks, we have proposed a powerful hybrid model, which perfectly extracts the characteristics and classifies daily human activities through data raw accelerometer sensors that exist everywhere with the help of Smartphones. We believe our proposed hybrid model has much potential in terms of practical application since it is proven to the robustness and ability to perform well in a variety of sensors because it performs well when tested on the set of data from the UCI Heterogeneity HAR dataset.

References 1. Saleem, G., Bajwa, U.I., Raza, R.H.: Toward human activity recognition: a survey. Neural Comput. Appl. (2022) https://doi.org/10.1007/s00521-022-07937-4

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2. Gupta, N., Gupta, S.K., Pathak, R.K. et al.: Human activity recognition in artificial intelligence framework: a narrative review. Artif. Intell. Rev. 55, 4755–4808 (2022). 3. Ibrahim, S.W.: A comprehensive review on intelligent surveillance systems. Commun. Sci. Technol. 1(1) (2016) 4. Qian, H., Wu, X., Xu, Y.: Intelligent Surveillance Systems. Vol. 51. Springer Science & Business Media (2011). https://doi.org/10.1007/978-94-007-1137-2 5. Elharrouss, O., Almaadeed, N., Al-Maadeed, S.: A review of video surveillance systems. J. Vis. Commun. Image Represent. 77, 103116 (2021). ISSN 1047–3203 6. Bublitz, F.M., et al.: Disruptive technologies for environment and health research: an overview of artificial intelligence, blockchain, and Internet of Things. Int. J. Environ. Res. Public Health 16, 3847 (2019) 7. Karray, F., et al.: Human-computer interaction: overview on state of the art. Int. J. Smart Sens. Intell. Syst. 1(1), 137–159 (2008) 8. Fu, B., Kirchbuchner, F., Kuijper, A., Braun, A., Vaithyalingam Gangatharan, D.: Fitness activity recognition on smartphones using doppler measurements. Informatics 5, 24 (2018) 9. Duong, L.N.K., et al.: A review of robotics and autonomous systems in the food industry: from the supply chains perspective. Trends Food Sci. Technol. 106, 355–364 (2020). ISSN 0924–2244 10. Slim, S.O., Atia, A., Elfattah, M.M., Mostafa, M.S.M.: Survey on human activity recognition based on acceleration data. Int. J. Adv. Comput. Sci. Appl. 10(3), (2019) 11. Barna, A., Masum, A.K.M., Hossain, M.E., Bahadur, E.H., Alam, M.S.: A study on human activity recognition using gyroscope, accelerometer, temperature, and humidity data. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6 (2019). https://doi.org/10.1109/ECACE.2019.8679226 12. Yu, H., Cang, S., Wang, Y.: A review of sensor selection, sensor devices and sensor deployment for wearable sensor-based human activity recognition systems. In: 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), pp. 250–257. IEEE. December 2016 13. Demrozi, F., Pravadelli, G., Bihorac, A., Rashidi, P.: Human activity recognition using inertial, physiological and environmental sensors: a comprehensive survey. IEEE Access 8, 210816– 210836 (2020) 14. Yuan, G., Wang, Z., Meng, F., Yan, Q., Xia, S.: An overview of human activity recognition based on smartphone. Sensor Rev. 39(2) (2018) 15. Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., Liu, Y.: Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities. ACM Comput. Surv. (CSUR) 54(4), 1–40 (2021) 16. Soundararajan, R., et al.: Deeply trained real-time body sensor networks for analyzing the symptoms of parkinson’s disease. IEEE Access 10, 63403–63421 (2022). https://doi.org/10. 1109/ACCESS.2022.3181985 17. Dang, L.M., Min, K., Wang, H., Piran, M.J., Lee, C.H., Moon, H.: Sensor-based and visionbased human activity recognition: a comprehensive survey. Pattern Recogn. 108, 107561 (2020) 18. Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 604–624 (2020) 19. Sharma, R., Kaushik, B., Gondhi, N.: Character recognition using machine learning and deep learning-a survey. In: 2020 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE (2020) 20. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

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Evaluation of CMIP5 and CMIP6 Performance in Simulating West African Precipitation Boubacar Doumbia1(B) , Elijah Adefisan1 , Jerome Omotosho1 , Boris Thies2 , and Joerg Bendix2 1 Department of Meteorology in Nigeria, Federal University of Technology Akure (FUTA),

Akure, Nigeria [email protected] 2 Laboratory of Remote Sensing and Climate Change at University of Marburg, Marburg, Germany

Abstract. An analysis of spatial distributions of West African rainfall during monsoon period (JJAS) of six ensemble members from Coupled Intercomparison Model Project Phase 5 and 6 (CMIP5 and CMIP6) and compared to two observational datasets of Global Precipitation Climatologic Center and Climate Hazard Group InfraRed Precipitation with Station (GPCC and CHIRPS) using six extremes precipitation indices from Expert Team on Climate Change Detection and Indices (ETCCDI). The annual cycle of indices based on daily rainfall such as consecutive dry (CDD) and wet (CWD) days, was used over the Sahelian, Savannah and Guinean regions with satellite daily precipitation estimates. The root mean square error (RSME) and standard deviation were compared using a Taylor diagram for each subregion over West Africa. A higher positive correlation is found between CMIP6 and the reference dataset. Despite the high uncertainties, a strong correlation was found over the Savannah region between the GPCC and model simulations with extreme precipitation events (EPEs). This indicates that CMIP6 reproduces the rainfall pattern over the areas better than its CMIP5 counterpart. Keywords: West Africa · rainfall · CMIP5 and CMIP6 models · climate change

1 Introduction Rainfall is essential in West Africa and people in this area are highly linked to rainfall activities. West Africa is a highly populated area with approximately 427,311,326 inhabitants: 52.3% of the population is rural and the total land area is 6,064,060 km2 based on the latest United Nations estimates in 2022. Most people live from agriculture that is adapted to climatic conditions. The Guinean region is favorable for agriculture, the savanna is characterized by rain-fed agriculture and livestock while in the Sahel area livestock is the dominant practice [1]. Agriculture is a dominant economic factor in the region; it is particularly dependent on atmospheric moisture supply by precipitation, where the annual cycle of the West African monsoon precipitation is a primary feature © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 84–96, 2023. https://doi.org/10.1007/978-3-031-29857-8_9

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of the West African climate [2]. The rainfall mainly starts from April to October, with major differences between the wetter Guinean region where precipitation displays two annual peaks (in June and in September), the semiarid Savannah, which shows a rainfall period from May to September and the more arid Sahelian region, which displays a single peak centered on August [2]. However, rainfall is increasingly affected by climate change. Increasing dry and wet days have led to extremes, drought and flood years in recent decades, causing a change in the annual cycle of monsoon precipitation [3–5]. In particular, the western Sahel seems to be the most sensitive region to anthropogenic climate change [6]. The studies revealed substantial increases in both, dry day length and extreme precipitation intensity. Between 1981 and 2014, the Gulf of Guinea also experienced more intense precipitation events [7]. In the entire region, an increasing trend of extreme heavy precipitation has been observed over the last decades [8]. This implies that West African countries are already facing important adaptation challenges [2]. Recently, [9] found a positive trend in consecutive dry days (CDD), simple day indices (SDII) and extreme heavy precipitation (R30 mm), and a negative trend in consecutive wet days. Furthermore, projections of climate change point to further changes in rainfall in a future climate. For instance [10] showed that an increase in global warming will enhance the onset of late rains over the entire West African region under the RCP4.5 scenario, but reported the opposite behavior under RCP8.5. While the findings point to increasing problems in rainfall supply, several studies [2] found strong uncertainties in the simulation of several important parameters, such as the CWD and the CDD over the Sahel and the Guinean areas. Several studies based on CMIP6 HighResMIP scenarios on West African precipitation found that the annual peak of precipitation in August appears to be underestimated by some of the models and the ensemble mean in all of West Africa [11]. Some previous studies [5] evaluated extreme precipitation indices over West Africa in CMIP6 models. They found that CMIP6 models reasonably reproduce the spatial patterns of the extreme precipitation indices over the entire region, although their performance is quite different between the Sahel and Guinea subregions. A main problem of uncertainties in the studies is that only two climatic zones are investigated by most studies. However, West Africa has at least three major climatic zones: the Sahel, savanna and Guinean regions [12]. Each region has its specific agroclimatic characteristics and thus might be affected by climate change in a different way, so it is essential to characterize the strength response of each climatic zone during recent decades. Only a few studies have investigated the three subregions of West Africa like [10]. However, they only focus on the seasonal aspect of rainfall in West Africa. The annual cycle of rainfall was investigated by a few studies, using two subregions (Sahel and Guinean region) and did not investigate the three seasonal regions including Savannah.

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Thus, to date, there is hardly any information on the future development, particularly of changes in the intensity and duration of extreme events (strong rains, droughts), and the quality to reproduce them by climate model scenarios (CMIP5/CMIP6). Consequently, we examined the rainfall characteristics of six extreme precipitation indices over all of West Africa, and the (CDD) and CWD) annual cycles were used over the three subregions. We need to understand the response of each climatic zone to climate change. This is why this study focuses on how West African feature rainfall is influenced by climate change. Furthermore, it will also evaluate the performance of CMIP5 and CMIP6 to capture the West African climate and help improve future models. Therefore, the present study intends to provide more information on the savanna because most of the last floods happened in this area according to [13, 14]. The study is structured as follows: After an introduction, Sect. 2 describes the data and methods used in this study, Sect. 3 presents and discusses the results and Sect. 4 provides the conclusion.

2 Data and Methodology For consistency analysis, the observation data from the Global Precipitation Climatology Centre (GPCC), Climate Hazard Group Infrared Precipitation with Station data (CHIRPS) and Coupled Model Intercomparison Project (cmip5/cmip6) presented herein were carried out by considering a common period of simulation (1983 – 2012) due to the processing and availability of data. All the daily data were regridded to a common 1◦ × 1◦ (lon/lat), and all the calculations and analyses are for June, July, August and September (JJAS). We calculated the mean bias error (MBE), root mean square error (RMSE) and standard deviation (STD). The CMIP data are downloaded from [15]. We used six models from the CMIP6 output and six corresponding models from the CMIP5 output represented in Table 1. The Formula is Expressed as:  N 2 i=1 y(i) − y(i) (1) RMSE = N 

1 N (Pi − Oi) i=1 N  2 n  i=1 Xi − X STD = N−1

MBE =

(2)

(3)

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Table 1. Information on the six CMIP6 and CMIP5 climate models used in this study S/No

Model

Institute

Resolution (0 lon × 0 lat)

1

CanESM5

Canadian Earth System Model and Analysis

2.81 × 2.81

2

CMCC-CM2

Centro Euro-Mediterraneo per I Cambiamenti Climatici

0.748 × 0.75

3

CNRM-CM6–1

Centre National de Recherches Météorologiques

1.41 × 1.41

4

FGOALS-g3

LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS, Tsinghua University

2.81 × 2.81

5

IPSL-CM6A-LR

Institute Pierre-Simon Laplace

2.50 × 1.26

6

MIROC6

Japan Agency for Marine-Earth Science

1.40 × 1

1

CanESM2

Canadian Earth System Model and Analysis

2.81 × 2.81

2

CMCC-CESM

Centro Euro-Mediterraneo per I Cambiamenti Climatici

3.443 × 3.75

3

CNRM-CM5

Centre National de Recherches Météorologiques

1.40 × 1.40

4

FGOALS-g2

LASG–Center for Earth System Science (CESS)

2.81 × 2.81

5

IPSL-CM5A2

Institute Pierre-Simon Laplace

1.80 × 3.75

6

MIROC5

Atmosphere and Ocean Research 1.40 × 1.40 Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency

Six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) calculated in this study are summarized in Table 2; we used daily precipitation data. Study Area: West Africa (20o W, 20o E, 0o N, 30o N). The sub region designated the Sahel, Savannah and Guinea region in the study is due to the data blending and processing procedure. Guinea region (4o N: 8o N); Savannah (8o N: 12o N) and Sahel (12o N: 16o N) (Fig. 1).

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S/No

Extreme indices

Name

Definition

Units

1

SDII

Simple daily intensity

Let PRwj be the daily mm/day precipitation amount on wet days, PR ≥ 1 mm in period j. If W represents number of wet days in j, then: SDIIj = ( W w = 1 PRwj)/W

2

CDD

Consecutive dry days

Let PRij be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where Prij < 1 mm

days

3

CWD

Consecutive wet days

Let Prij be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where Prij > 1 mm

days

4

R10 mm

Heavy precipitation days

Let Prij be the daily precipitation amount on day i in period j. Count the number of days where Prij > 10 mm

days

5

R20 mm

Very heavy precipitation days

Let Prij be the daily precipitation amount on day i in period j. Count the number of days where Prij > 20 mm

days

6

Rx5day

Maximum 5 days precipitation

Let PRkj be the precipitation amount for the 5-day interval ending k, period j. Then maximum 5 day values for period j are: RX5dayj = max (PRkj)

mm

3 Results and Discussion We used the spatial representation of extreme precipitation indices over West Africa. All the models and observed data showed a moderate dry day over the southern part of West Africa. However, some models such as CanESM, CNRM-CM, FGOALS, and MIROC from both CMIP5/CMIP6 underestimate the width of consecutive dry days in the northern part of West Africa compared to the observed data while, CMCC and IPSL from CMIP5/CMIP6 extend the width and length of crucial dry days. Their Ensmean underestimated the CDD event with a negative bias. All the models and observed data showed moderate dry days over the southern part of West Africa. However, some models such as CanESM, CNRM-CM, FGOALS, and

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Fig. 1. Topographic map of West Africa with subregions shows as red lines

Fig. 2. Consecutive dry days (CDD) with observed dataset GPCC, CHIRPS (1, 2); Ensmean CMIP5/CMIP6 (3, 4); CMIP5 (a, b, c, g, h and i) CMIP6 (d, e, f, j, k and L) from 1983 to 2012 and bias with GPCC dataset in the bottom.

MIROC from both CMIP5/CMIP6 underestimate the width of consecutive dry days in the northern part of West Africa compared to the observed data while, CMCC and IPSL from CMIP5/CMIP6 extend the width and length of crucial dry days. Their Ensmean underestimated the CDD event with a negative bias.

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In contrast with the dry spell, the wet days (CWD) showed a significant wet period increasing from the Sahel to the Guinea region during this last thirty years. This wet period is more highlighted by observation data (GPCC) than some models such as FGOALS and IPSL from CMIP5/CMIP6. The CMIP5 and CMIP6 ensemble mean showed a negative bias with GPCC consecutive wet days, which means that they underestimated the CWD activities over West Africa. The (SDII) extreme event of precipitation is well captured by observations data and all the models; the highest values of Simple Daily Intensity (SDII) are found over the Guinea region and Savannah. However, the FGOALS-s2 from CMIP5 underestimated the spatial distribution of SDII events in comparison with GPCC over central-southern West Africa and the northern area up to 12o N across West Africa. In contrast, the SDII events are overestimate under the CanESM5, MIROC6 from CMIP6 and MIROC5 from CMIP5 simulations, which display the greater intensities between 14 mm and 16 mm over the central area of the Guinea and Savannah regions. The SDII ensemble mean bias is much higher in CMIP5 than in CMIP6 over most West African areas (Fig. 3).

Fig. 3. Same as Fig. 2, but for consecutive wet days (CWD)

Similarly, with the CWD, the most activity with the highest 5 day precipitation is collected in the Guinea coast and Savannah region over West Africa. The lowest value of (Rx5day) is represented beyond 15o N over West Africa for all the calculated and GPCC datasets except for CMCC-CESM, FGOALS-s2 and ISPL-CM5A-LR from CMIP5 and ISPL-CM5A2 from CMIP6, which underestimate the width of Rx5day and show a crucial decrease over the central area of the Guinea region and Savannah region. We obtained a negative bias with both the Ensmean and GPCC except for the southern area in the Guinean region.

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Only the southern region over West Africa is affected by heavy precipitation from 0o N to 15o N. All six models from CMIP6 match well with the observed data compared to their correspondents in CMIP5. All the models with observations showed heavy precipitation activity across the eastern and western areas of the Guinea region and Savannah region. However, MIROC5/MIROC6 from CMIP5/CMIP6 respectively and CanESM5 from CMIP6 extend the R10 mm activity over the central region of the Guinea and Savannah in West Africa. The northern region of West Africa showed a positive bias with the CMIP5 ensemble mean while it showed a negative bias with the GPCC R10 mm event with the CMIP6 ensemble mean. The temporary trend of the annual cycle with consecutive dry and wet days (CDD and CWD) over each subregion in West Africa will highlight with accuracy the response of different regions to climate influence. The dry and wet climatologic period is identified by the process of these precipitations events, and the length of rainfall is also mainly linked to these precipitations indices due to the process of onset and secession period. In this study we focus on the availability of each model to capture the diurnal processes of CDD and CWD over the Guinea, Savannah and Sahel regions to determine the specificity of each climatic zone in West Africa. Previous studies have well emphasized that rainfall features over West Africa especially the Guinea and Sahel regions have been most relevant, noting that a very large area and uncertainties are observed between the Sahel and Guinea regions, this region is called Savannah. In this study, in addition to the Sahel and Guinea we investigated the rainfall pattern over the Savannah region. Over each region, we observed a very high uncertainty in the CMIP5 and CMIP6 models (Fig. 4). The length of the dry period is relatively higher in the CMIP6 model than in CMIP5 and shrinks from the Sahel (north of W.A) to Guinea regions (south of W.A), this shrinkage is mainly linked to monsoon activity over West Africa. The length of consecutive dry

Fig. 4. Annual cycle of consecutive dry days (CDD) (left panel) and, consecutive wet days (CWD) (right panel) in each subregion over West Africa with CMIP5 (left) and CMIP6 (right) and their ensmean simulation with the observational dataset (GPCC) from the period 1983 to 2012

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days (CDD) in the Guinea region spread from November to February in CMIP5 while in the CMIP6 model most of the simulated data are strongly correlated to the observed data and show a dry length from November to March. However, the FGOALS-s2 in CMIP5 overestimates the dry days over the Guinea region extending from November to the end of May. A significant increase in the length of the CDD event is observed over the Savannah region spreading from October to the end of April in the CMIP5 model and from October to early June in the CMIP6 model, while the satellite dataset GPCC records the length of the dry period from October to May. The most significant enlargement of consecutive dry days (CDD) is represented over the Sahel region, and there are no significant rainfall activities. We observed a simple peak in August during the wet period (CWD) (Fig. 5) for all the CMIP6 output models and their Ensmean in agreement with the observed data. However, these wet days are highly overestimated by CMCC-CM6 in the CMIP6 model while, they are overestimated by FGOALS-s2 in the CMIP5 model over the Sahelian region. The annual cycle of wet days over the Guinea region is longer and shows two peaks (June and September) which is similar to a previous study [2]. The intensity of wet days is strongly overestimated by CMCCCESM and FGOALS-s2 in both CMIP5 models while it is overestimated by MIROC6 in the CMIP6 model. The Savannah region brings great uncertainties between models for representing feature rainfall due to the presence of many climatic drivers (monsoon, ZCIT), and the Savannah area located between the Guinea and Sahel regions is subject to climate disasters (flood, drought). The succession of these two disasters over West Africa is mostly common in the Savannah region because of the delay of the wet period and the sudden onset of the dry period which affect many activities, such as agriculture, fishing and energy generation.

Fig. 5. Taylor diagram with consecutive dry days (CDD) (left panel) and, consecutive wet day (CWD) (right panel) with CMIP5 (left), and CMIP6 (right) simulations of six models estimated with (GPCC) observed data over each subregion in West Africa, Guinea region on (top), Savannah, (middle) and Sahel, (bottom).

SAH

SAV

GUI

CDD

0.93

−0.09

0.892

−0.07

0.992

−0.01

0.989

−0.026

0.878

−0.31

0.877

−0.20

MBE (%)

Cor.Coef

0.210

0.116

0.192

0.103

STdev

RMSE

MBE (%)

Cor.Coef

0.217

0.027

0.216

0.031

STdev

RMSE

MBE (%)

Cor.Coef

0.149

0.086

0.162

0.097

STdev

RMSE

0.201

−0.04

0.786

0.141

0.218

−0.11

0.961

0.063

0.223

0.169

0.93

0.077

1.30

0.854

0.211

0.189

1.28

0.980

0.189

0.32

0.138

0.858

0.159

0.382

FGOALS-s2

0.50

0.924

0.083

0.16

0.203

0.838

0.126

0.221

−0.29

0.963

0.091

0.184

ISPL

−0.57

0.787

0.167

0.217

−0.39

0.839

0.140

0.183

1.39

0.826

0.148

0.15

MIROC5

0.074

0.932

0.07

0.174

−0.003

0.982

0.041

0.225

0.023

0.939

0.061

0.177

CanESM5

CMCC-CESM

CMIP6

CNRM-CM5

CMIP5

CanESM2

0.42

0.7

0.14

0.16

0.71

0.77

0.17

0.22

0.04

0.91

0.06

0.13

CNRM-CM6

0.16

0.932

0.068

0.178

−0.26

0.754

0.151

0.191

−0.15

0.898

0.064

0.108

CMCC-CM6

0.36

0.884

0.1

0.129

0.009

0.993

0.027

0.212

−0.12

0.924

0.067

0.13

FGOALS-g3

0.40

0.849

0.105

0.177

0.29

0.996

0.074

0.224

0.29

0.867

0.106

0.199

ISPL

−0.19

0.898

0.098

0.197

0.03

0.989

0.033

0.217

−0.15

0.934

0.06

0.14

MIROC6

Table 3. Statistical results between the calculated models CMIP5/CMIP6 and GPCC for consecutive dry days (CDD). All correlations higher than 0.95 are underlined.

Evaluation of CMIP5 and CMIP6 Performance 93

0.179

0.159

0.675

0.18

SAH STdev

RMSE

Cor.Coef

MBE (%)

0.31

0.931

0.087

0.211

0.992

0.009

0.982

Cor.Coef

0.027

0.217

0.165

0.989

0.054

0.12

−0.04

0.225

0.089

MBE (%)

0.041

0.991

Cor.Coef

STdev

0.029

RMSE

RMSE

0.148

STdev

0.04

0.931

0.078

0.205

2.33

0.899

3.694

1.20

0.812

0.208

0.242

1.71

0.691

0.614

0.444

1.03

−0.19

2.710

0.856

0.15

0.15

0.93

0.078

0.197 0.084 0.424

0.28

−0.49 0.65

0.875 0.828

0.127 0.148

0.161 0.217

−0.2

0.979 0.914

0.051 0.097

0.223 0.191

−0.16

0.903 0.979

0.08

CMIP6

−0.07

0.979

0.061

0.174

0.002

0.982

0.041

0.225

−0.03

0.949

0.055

0.177

−0.42

0.803

0.135

0.168

−0.60

0.857

0.137

0.213

−0.05

0.923

0.066

0.134

0.78

0.947

1.4

1.303

−0.08

0.984

0.044

0.2

0.15

0.9

0.084

0.109

−0.37

0.886

0.121

0.129

−0.008

0.502

0.259

0.272

0.12

0.96

0.055

0.132

−0.41

0.926

0.094

0.177

0.74

0.919

0.178

0.331

−0.29

0.206

0.257

0.258

MIROC5 CanESM5 CNRM-CM6 CMCC-CM6 FGOALS-g3 ISPL

0.182 0.104

CanESM2 CNRM-CM5 CMCC-CESM FGOALS-s2 ISPL

CMIP5

MBE (%)

SAV

GUI

CWD

0.193

0.952

0.068

0.198

−0.42

0.878

0.117

0.194

0.15

0.99

0.039

0.137

MIROC6

Table 4. Statistical results between the calculated models CMIP5/CMIP6 and GPCC for consecutive wet days (CWD). All correlations higher than 0.95 are underlined.

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We used a Taylor diagram to facilitate the comparative assessment of different models and to quantify the degree of correspondence between the modeled and observed behavior in terms of three statistics: the Pearson correlation coefficient, the root mean square error and the standard deviation. We used three statistics plots for six output models represented by a different letter on the diagram and the distance between each model and the point labeled “observed” in a measure of how realistically the model reproduces observations. The Pearson correlation coefficient is related to the azimuthal angle (black straight line), the root mean square error (RMSE) is proportional to the distance from the point on the x-axis identified as “observed” (green contours) and the standard deviation is proportional to the radial distance from the origin (black contours). We summarized the results from these diagrams in Table 2 for consecutive dry days (CDD) and Table 3 for consecutive wet days (CWD).

4 Summary and Conclusion We observe that most of the higher correlations in the model and GPCC are occur over the Savannah in the both tables (Table 2 and Table 3). With CDD the CMIP6 model displays all the highest correlations greater than 0.95 and four higher correlations in CMIP5 over the Savannah region. This study has highlighted the complexity of the representation of daily rainfall characteristics in climate models over three subregional areas especially in the Savannah region, which is likely linked to monsoon dynamics. We found that from one precipitation index to another, and according to the area, the uncertainties change dramatically. In the Sahel region the CMIP6 model fit better than CMIP5 with wet spell representation. However, during the wet spell over the Guinea and Savannah, CNRM-CM5 and ISPL in CMIP5 gave a better fit with the GPCC observed data than their corresponding data in CMIP6. Finally, we argue that further studies are necessary, to understand whether and how these extreme precipitation indices are translated into future projections with climate models over West Africa, particularly shifts in the monsoon season, and changes in mean and extreme precipitation amounts.

References 1. Jalloh, A., Faye, M.D., Nelson, G.C.: West African Agriculture and Climate Change. 14, 384–391 (2013) 2. Magatte, S., Moussa, D., Dixon, R.D., Françoise, G., Diarra, D., Amadou, T.G.: Uncertainties in the annual cycle of rainfall characteristics over West Africa in CMIP5 models. J. CLIM. 11, 216 (2020). https://doi.org/10.3390/atmos11020216 3. Sylla, M.B., Giorgi, F., Pal, J.S., Gibba, P., Kebe, I., Nikiema, M.: Projected changes in the annual cycle of high-intensity precipitation events over West Africa for the late twenty-first century. J. Clim. 28, 6475–6488 (2015). https://doi.org/10.1175/JCLI-D-14-00854.1 4. Sarr, A.B.: Moctar Camara. Analysis of the extremes precipitation indices evolution with the Regionals Climatics Models. 13, 206–219 (2017) 5. Faye, A., Akinsanola, A.A.: Evaluation of extreme precipitation indices over West Africa in CMIP6 models. Clim. Dyn. 58, 1–15 (2021). https://doi.org/10.1007/s00382-021-05942-2

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6. Sylla, M.B., Nikiema, P.M., Gibba, P., Kebe, I., Klutse, N.A.B.: Climate change over West Africa: recent trends and future projections. In: Yaro, J.A., Hesselberg, J. (eds.) Adaptation to Climate Change and Variability in Rural West Africa, pp. 25–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31499-0_3 7. Todzo, S., Bichet, A., Diedhiou, A.: Intensity of the hydrological cycle expected in West Africa over the 21st century. 11(1), 319–328 (2020) 8. Barry, A.A., Caesar, J.: West Africa climate extremes and climate change indices. 38, 921–938 (2018) 9. Quenum, G.M.L.D., Nkrumah, F., Klutse, N.A.B., Sylla, M.B.: Spatiotemporal change in temperature and precipitation in West Africa. 13, 1–7 (2021). https://doi.org/10.3390/w13 243506 10. Kumi, N., Abiodun, B.J.: Potential impact of 1.5°c and 2°c global warming on rainfall onset, cessation and length in West Africa. 13(5), 2–11 (2018) 11. Felix, O.A., Botao, Z., Gnim, T.G., Anselem, O.: Evaluation of the performance of CMIP6 HighResMIP on West African precipitation. J. Clim. 11(10), 1053 (2020). https://doi.org/10. 3390/atmos11101053 12. Kouassi, A ., Assamoi, P.: Study of the African Western climate with regional atmospheric model M.A.R. 7, 39–55 (2015). https://doi.org/10.4267/climatology.445 13. Nka, B.N., Oudin, L.: Trends in flood in West Africa. 19, 4707–4717 (2015) 14. Wagner, S., Souvignet, M., Walz, Y., et al.: When does risk become residual? A systematic review of research on flood risk management in West Africa. 21(3), 84 (2021). https://doi. org/10.1007/s10113-021-01826-7 15. Data source. https://esgf-node.llnl.gov/search/cmip/

Knowledge Management, Tools and Activities Ibtissam Assoufi(B)

, Ilhame El Farissi, and Ilham Slimani

Mohammed First University Oujda, National School of Applied Sciences, SmartICT Lab, Oujda, Morocco [email protected]

Abstract. Knowledge Management (KM) is crucial to both commercial and academic activities; it is more than relevant as it brings consequent benefits by fostering business innovation, performance and growth. Different knowledge management tools integrate different technologies to create, transfer, store and reuse knowledge, and connect people to support collaborative work. The problem is that people could be unsure of which KM approach to choose or which is better suitable. As a result, knowledge management tools need to be managed, So this study explore the relationship between KM tools and practices and different types of KM activities. Based on this study, user will be able to select the appropriate KM tools for each KM activity. We also leveraged the benefits of Classification Tree Method (CTM) to master the relationship between KM tools and different types of KM activities. Previous research has classified KM tools according to two characteristics: whether the type of these tools is simple or complex, and according to the KM activities, i.e. is one of these tools used in the creation, storage, transfer or reuse? Other researches consists on dividing the KM tools into six comparison items: KM tools for knowledge capitalization, for knowledge sharing, for knowledge retrieval, for consulting, for queries and for knowledge creation. In our study, we combined the research done previously to have a more general classification using several comparison criteria. We have taken use of the advantages of CTM to see in a clear and understandable way the relationship between KM tools and practices and different types of KM activities. The results can aid individuals in making the best KM tool selections for each activity. Keywords: Knowledge Management · Data analysis · CTM

1 Introduction Knowledge management (KM) represents the learning process that links the demand and supply of information to improve organizational performance [1]. Knowledge management is based on the tension between the process of exploration and exploitation. Exploration is the development of new organizational practices and solutions. Exploitation is the use of current practices to improve pre-existing knowledge and solutions. Both of these knowledge management strategies need to be applied for sustainable competitive advantage. As well as connecting individuals to promote collaborative work, many knowledge management platforms integrate various technologies to produce, transfer, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 97–104, 2023. https://doi.org/10.1007/978-3-031-29857-8_10

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store, and reuse knowledge. The issue is that people may not know which KM strategy to use or which is more appropriate [2]. The user will be able to choose the right KM tools for each KM activity as a solution to this problem. So the question is how to do it? The user must select the KM activity (creation, storage, transfer or reuse [3]) and the type of data he desires, and we will provide him with the most necessary tool. The tacit (implicit)/explicit dichotomy is arguably the most commonly mentioned framework used to define knowledge. Explicit knowledge is information that has been recorded and is widely accessible. Contrarily, tacit knowledge is highly private and challenging to communicate effectively with others [4]. The underlying tenet is that KM initiatives aim to produce, organize, and disseminate information that is beneficial to the organization. One more theory is that KM turns the emphasis from process to practice. Improved practice or performance at work is achieved through communication and teamwork [5].

2 Literature Review 2.1 KM Knowledge management allows to manage knowledge in a company from its creation to its use. This knowledge can be created by the company itself or acquired from outside. This process (KM) is based on several activities [2]: • Creation and detection of knowledge: It reconstructs new knowledge (through research and development (R&D)), it allows determining knowledge already exist in the organization, or it consists in capturing several knowledge from outside. It also includes all activities related to updating knowledge. • Knowledge storage: It allows knowledge to be saved. This generally involves documenting and recording knowledge. The purpose of this activity is to retain knowledge in case of forgetfulness, departure of personnel, etc. • Knowledge transfer: It shares stored knowledge (or not). • The use of knowledge: It allows using knowledge, which allows valuing knowledge and innovation. 2.2 Knowledge Management Systems: KMS A knowledge management system (KMS) is a set of computerized procedures and/or systems [2] used to acquire, store, transfer and reuse knowledge generated in an organizational setting. It enables organizations to perform a range of activities at a speed commensurate with their needs and resources. KMS is a set of programs and tools used to perform the KM activities already mentioned in the previous section. 2.3 KM’s Methods The main activities (creating, storing, transferring and reusing knowledge) need to be in place to perform knowledge management. There are several ways to support these four

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activities, we focus on two different types of tools [2]. Simple tools allow the realization of one-off activities in the knowledge management process, while other complex tools focus on complete knowledge management methods and allow the realization of KMrelated activities. Simple tools: Simple tools generally do not require training to use and are used to perform generally ad-hoc tasks, unlike complex tools, which require expertise and user support. Knowledge management tools identified: – Creation: Data visualization, Data mining, Social data mining, Text mining, Interviews, knowledge modelling. – Storage: Configuration management systems, Content management systems, Product data management system, Product lifecycle management system, Data warehouse, ERP system, E-learning, REX. – Transfer: Podcasting/videocasting, Social media, Audio/video conference, Blog, Chat, Conversational technologies, E-mails, Search engines, F.A.Q, Forums. – Reuse: Social media, Forums, ERP system, Focus group, Podcasting/video casting. Complex tools: These so-called “complex” tools are generally not suitable for use in small and mediumsized companies. We have identified seven complex tools because they are adapted to current information technologies due to their age. The tools identified are CommonKADS, MGKME, MOKA, MASK, CBR, MIKE 2.0, KALAM, and MIMIK (Fig. 1).

Fig. 1. KM’s tools

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2.4 CTM: Classification Tree Method A systematic way for creating test cases from functional specifications is offered by the black-box testing technology known as CTM. The set of one or more classes that make up a classification tree [6] is finite: I) The root classification A has been given specific designation. II) Subclasses A1, A2,…, An refer to the rest of the classifications that fall under A. (Fig. 2)[6]. The functional specification of a single test item is the most crucial source of knowledge for testers when utilizing the classification tree approach [7]. The classification tree method’s ability to turn test case design into a process with a number of structured and systematized steps that are simple to manage, comprehend, and document is one of its key benefits. We have taken use of this advantage to clearly and understandably see the connection between KM tools and various KM activities. We used the classification tree method to classify the knowledge management tools.

Fig. 2. Example of a classification tree

3 Related Works In order to create, transfer, apply, and store information, businesses use a collection of strategies, projects, and activities known as knowledge management (KM) practices [16]. Edouard Tapissier [2] classified KM tools according to two characteristics: the type of these tools, either simple or complex, and according to KM activities, i.e. is one of these

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tools used in creation, storage, transfer or reuse? Another comparison and classification proposed by Yang XU [8] is to divide KM tools into six elements of comparison: KM tools for knowledge capitalization, for knowledge sharing, for knowledge retrieval, for consulting, for querying and for knowledge creation. In our study we combined the previous research to have a more general classification using several comparison criteria using the results of two research studies and the advantages of the classification named CTM and the tool Testona [19].

4 The Link Between Knowledge Management Systems and Machine Learning It has been stated that how current knowledge is transferred throughout the company and how new knowledge is generated both affect how effective knowledge management is. Studies recently have shown a lot of interest in information sharing procedures [9]. According to some research, lack of knowledge transfer and sharing throughout the business frequently results in organizations wasting resources and losing a large amount of money by repeatedly making the same mistakes, starting the same projects, and disregarding the expertise of others [10]. It is not only one-way to impart knowledge; more than just shifting information from one place to another is necessary for effective knowledge transfer. Knowledge transfer across departments and people can give organizations a worthwhile learning experience. The ability of both parties to transfer and share knowledge tends to improve. This is thus because once knowledge has been conveyed, it stays with its original owner. Every time a transfer occurs, knowledge’s value rises as a result, and the organization’s ability to create value is largely dependent on how well knowledge has been disseminated. Information and communication technologies (ICTs) play a crucial role in the transfer of knowledge, as many researchers have emphasized. The efficient application of technology opens up new channels for the exchange of knowledge and presents viable options for both explicit and implicit knowledge transmission. In knowledge storage, tacit and explicit knowledge are distinguished from one another. Using technical tools and systems, explicit knowledge can be quickly gathered, recorded, stored, and accessed completely independently of any individual. Contrarily, tacit knowledge, which is stored in employees’ minds and makes up a sizable portion of an organization’s knowledge resources, is present only within the organization [11]. When comparing the organization’s knowledge resources to an iceberg, explicit knowledge is the visible portion while tacit knowledge is the invisible portion [12]. When information is organized, categorized, stored, and used in some practical way, it becomes meaningful. The appropriate person, at the right moment, in the right manner can only use it after that. Knowledge codification and storage are crucial for reuse as well as for efficient knowledge usage [13]. Consequently, numerous research have concentrated on classifying and codifying knowledge according to its categories and uses [14], and preserving knowledge to enable employees to access it at any time, both in the present and the future. Additionally, knowledge codification enables the storage of knowledge assets and the evaluation of the organization’s potential. Knowledge extraction without losing its unique qualities [15] that make it useful is the most challenging aspect of knowledge coding.

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5 Implementation Results TESTONA is a tool for systematic test design in the black-box-tests. The program uses the Classification Tree Method (CTM) for the systematic development of test cases. This classification offers a graphical notation for specifying test parameters. The fundamental principle behind the classification tree approach is to classify the characteristics of the input data for the system being tested into distinct groups that directly correspond to the pertinent test cases (classifications). In this case, the entries (inputs) are Data or Products/Services. The data are separated into four categories: Non-specific, that is, regardless of the type of data, Text for data that is totally text, Audio/Video and Questions/Answers, We have one or more tools for each of these categories. On the right-hand side KM activities (Creation, Storage, Transfer and Reuse) were mentioned. In addition, the test cases contains KM tools for Knowledge Capitalization, for Knowledge sharing, for Knowledge Retrieval, for Consultant, for Query and for Knowledge creation. (Fig. 3).

Fig. 3. Classification of KM’s methods using Testona

Discussion Knowledge management is defined as “the process of acquiring, storing, sharing and using knowledge” [16]. With the existence of knowledge management, organizations will be able to execute these processes [17]. Various comparisons are made using the parameters: KM tools for knowledge capitalization, for knowledge sharing, for knowledge retrieval, for consulting, for queries and for knowledge creation, as well as their linking points with KM activities such as: Creation, Storage, Transfer, and Reuse and the different KM tools. After analyzing the results obtained, we found that the activities Storage and Transfer are linked to knowledge sharing tools, the activity Creation is linked to knowledge creation and capitalization tools, and the activity Reuse has a strong link

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with knowledge recovery tools. The Products/Services category is frequently associated with knowledge sharing and retrieval tools, and as a consultant, the tools in this category are designed to provide recommendations to clients and market needs, and they can be used as decision support systems. As organizations constantly face changes in the business environment, their ability to obtain appropriate information and reduce decision uncertainty is an important foundation for their competitive advantage [18].

6 Conclusion and Perspectives The main goal of this study is to comprehend how knowledge management tools have changed over time in connection to the various stages of the knowledge life cycle. The findings can guide people in choosing the best KM tools for each activity. For example, for Product and Service related knowledge, KM tools from the “knowledge retrieval” and “knowledge sharing” classes should be selected. According to this viewpoint, while choosing KM tools, the application environment rather than the technical content of each tool should be compared. The rest of the work will focus on KM practices in connection with Machine Learning.

References 1. Schniederjans, D.G., Curado, C., Khalajhedayati, M.: International journal of production economics supply chain digitisation trends : an integration of knowledge management. Intern. J. Prod. Econ. 220, 107439 (2020). https://doi.org/10.1016/j.ijpe.2019.07.012 2. Meroño-Cerdan, A.L., Lopez-Nicolas, C., Sabater-Sánchez, R.: Knowledge management strategy diagnosis from KM instruments use. J. Knowl. Manag. 11(2), 60–72 (2007). https:// doi.org/10.1108/13673270710738915 3. Tapissier, E.: Conception d‘un système de management des connaissances à destination d‘une PME Edouard Tapissier HAL Id : tel-02194032. no. June 2019 4. Beesley, L.G.A., Cooper, C.: Defining knowledge management (KM) activities: towards consensus. J. Knowl. Manag. 12(3), 48–62 (2008). https://doi.org/10.1108/136732708108 75859 5. Obancea, G.: Knowledge management tools and techniques. Ann. DAAAM Proc. Int. DAAAM Symp. 29(6), 35–36 (2009). https://doi.org/10.14429/djlit.29.276 6. Ramadoss, B., Prema, P., Balasundaram, S.R.: Combined Classification Tree Method for Test Suite Reduction. vol. 1, pp. 27–33 (2011) 7. Ag, D.: Test Case Design Using Classification Trees and the Classification-Tree Editor CTE. no. January 1995 (2014) 8. Xu, Y.: Managing knowledge management tools : a systematic classification and comparison. no. April 2014 (2011). https://doi.org/10.1109/ICMSS.2011.5998938 9. Hicks, R.C., Dattero, R., Galup, S.D.: A metaphor for knowledge management: explicit islands in a tacit sea. J. Knowl. Manag. 11(1), 5–16 (2007). https://doi.org/10.1108/136732707107 28204 10. Robertson, S.: A tale of two knowledge-sharing systems. J. Knowl. Manag. 6(3), 295–308 (2002). https://doi.org/10.1108/13673270210434395 11. Drott, M.C.: Personal knowledge, corporate information: the challenges for competitive intelligence. Bus. Horiz. 44(2), 31–37 (2001). https://doi.org/10.1016/s0007-6813(01)800 20-3

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12. Haldin-Herrgard, T.: Difficulties in diffusion of tacit knowledge in organizations. J. Intellect. Cap. 1(4), 357–365 (2000). https://doi.org/10.1108/14691930010359252 13. Nemati, H.R.: Editorial preface: global knowledge management: exploring a framework for research. J. Glob. Inf. Technol. Manag. 5(3), 11 (2002). https://doi.org/10.1080/1097198X. 2002.10856328 14. Lueg, C.: Information, knowledge, and networked minds. J. Knowl. Manag. 5(2), 151–160 (2001). https://doi.org/10.1108/13673270110393194 15. Delen, D., Zaim, H., Kuzey, C., Zaim, S.: A comparative analysis of machine learning systems for measuring the impact of knowledge management practices. Decis. Support Syst. 54(2), 1150–1160 (2013). https://doi.org/10.1016/j.dss.2012.10.040 16. Donate, M.J., Sánchez de Pablo, J.D.: The role of knowledge-oriented leadership in knowledge management practices and innovation. J. Bus. Res. 68(2), 360–370 (2015). https://doi.org/10. 1016/j.jbusres.2014.06.022 17. Hwang, Y., Lin, H., Shin, D.: Knowledge system commitment and knowledge sharing intention: the role of personal information management motivation. Int. J. Inf. Manage. 39, 220–227 (2018). https://doi.org/10.1016/j.ijinfomgt.2017.12.009 18. Hwang, Y., Kettinger, W.J., Yi, M.Y.: A study on the motivational aspects of information management practice. Int. J. Inf. Manage. 33(1), 177–184 (2013). https://doi.org/10.1016/j. ijinfomgt.2012.09.002 19. https://products.expleogroup.com/testona/

The Interplay Between Social Science and Big Data Research: A Bibliometric Review of the Journal Big Data and Society, 2014–2021 Mohamed Behlouli(B) and Mohamed Mamad Ibn Tofail University, Kénitra, Morocco {mohamed.behlouli,Mamad.mohamed}@uit.ac.ma

Abstract. The journal “Big Data & Society” (BD&S) published its first issue in 2014. Using bibliometric indicators, this paper provides a broad picture of the journal over its lifetime. We reviewed 363 research articles published between 2014 and 2021 from the Scopus database and supplied a range of factors that affect the journal. The findings display the co-authorship and their affiliated institutions and countries (tree map, co-citation analysis). This study is the first attempt to offer a state-of-the-art overview of big data analytics through the contributions of BD&S and provides editors, authors, readers, and reviewers with a comprehensive overview of big data analytics through the lenses of BD&S. Keywords: social science · big data · bibliometric · co-citation network

1 Introduction Over the last decade, big data analytics has become a thriving research field [1]. A greater awareness that big data could contribute to multiple disciplinary lenses may explain this rise in interest. Such claims are justified by the idea that “big data” might change the competitive perspective by changing processes, reshaping business environments, and boosting knowledge generation [2]. This area of research has also been the subject of a growing number of academic and practitioner studies. Originally fueled by the analytical perspective [3], ranging from simulations, experiments and algorithms [3], it can be said that the big data literature has reached maturity. However, such advances in big data research have largely occurred in isolated silos with few interdisciplinary interactions [4]. This is because big data are viewed as a series of technological advancements in the fields of data processing and storage [5], and at the same time, the technological dimension usually comes before the discussion of societal implications. As a result, ambiguity exists over the current state of big data research in the literature from a social science perspective. Indeed, there is a paucity of research that offers a broad taxonomy from which to investigate the connections between social science and big data.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 105–113, 2023. https://doi.org/10.1007/978-3-031-29857-8_11

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To address this gap, this study proposes a bibliometric analysis of articles published in a major academic journal in the field of big data analytics: Big Data and Society (BD&S). It publishes original, peer-reviewed multidisciplinary research principally in the social sciences and their crossroads with the humanities and experimental sciences. Today, BD&S is led by Professor Evelyn Ruppert, PhD, and has been recognized as one of the leading journal sources in the field of big data, contributing to a diverse set of discussions through the development of empirical engagement and experiments. This research covers articles published in 2014–2021 to highlight the key authors, universities, and countries, as well as to reveal the major themes discussed in BD&S. The contribution of this study is twofold. First, it provides the scientific community with a deeper evaluation of the results of BD&S. Specifically, this bibliometric analysis offers an unprecedented understanding of the specific contribution of social science to big data research. This implies that the social science literature is a fundamental pillar of the field because it provides fertile ground for scientific debate. Second, it allows us to uncover the streams of research, thus discovering major trends and trajectories.

2 Methods and Data Due to the widespread use of computers and the readily available bibliographic data from databases such as the Web of Science and Scopus databases, the bibliometric technique has attracted much interest from researchers in the modern period. Bibliometrics uses numerical methods such as clustering and mapping to categorize and quantify knowledge in a particular field [6, 7]. This technique has attracted much interest from researchers in the modern period. Bibliometrics uses numerical methods such as clustering and mapping to categorize and quantify knowledge in a particular field [8]. It represents a comprehensive perspective of the scholarly community and prevents biases brought by the researcher’s engagement in the review process [9]. The Scopus database, which contains more than 18,000 titles from 5,000 publishers and allows for global multidisciplinary integration, is the greatest repository of peer-reviewed scientific research. To extract the relevant documents, the title “Big Data & Society” was searched under “source title”, which led to 537 documents. In addition, documents published in 2022 were not included. Because the academic community views journal literature as the most current source of knowledge on a specific topic, we narrowed our search to articles and excluded other formats (reviews, editorials, etc.). We retained only papers written in English. This research process yielded 363 documents, which comprised the final list of documents that were taken into analysis. In our study, we used the R package tool, a collection of tools for analyzing accurate publication data. According to Aria and Cuccurullo (2017), the bibliometrix package is described as “an accessible method for quantitatively assessing all sorts of published research”. Because it was simple to use and follow the website’s instructions, the Biblioshiny web application that was part of the Bibliometrix package was used in this work [10].

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3 Results 3.1 Overview of Bibliometric Data A search of the data source yielded 363 articles published in BD&S between 2014 and the end of 2021. The average number of years between publications is 5.84 years, with a citation count of 59.82 for each document. Thus, the average number of citations per year for each document is 7.412. From an authorship perspective, eighty-eight (88) researchers have contributed to the BD&S journal and have appeared 91 times in various publications. There were 22 single-authored documents, with an average of 0.568 documents per author and 1.76 authors per document. Furthermore, twenty-two researchers published as single authors, while 66 authors produced multi-authored documents. Finally, the results of the collaboration index (CI) showed that authors in the journal were 2.36 per co-authorship for each multi-authored document was 1.82. (see Table 1). Table 1. Main information about the data Timespan

2014; 2021

Average years from publication

5.84

Average citations per document

59.82

Average citations per year per doc

7.412

Authors Appearances

91

Authors of single-authored documents

22

Authors of multi-authored documents

66

Single-authored documents

22

Documents per Author

0.568

Authors per Document

1.76

Co-Authors per Documents

1.82

Collaboration Index

2.36

4 Identifying Main Insights Author productivity and document citation counts from an author’s and national perspective were used in the current bibliometric review to estimate impact [10]. 4.1 Leading Authors According to Egghe and Ravichandra Rao (2002), author productivity can be measured using author article count and author article fractionalization “indicates that if an author

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publishes a work with a particular number of authors overall, that author obtains a value that is one higher than the total number of authors” [11]. According to our findings, Rob Kitchin is the most prolific (2) (1.50) and most-cited author, with 1136 total citations and 105,556 total citations annually (see Tables 2 and 3). Table 2. Author’s scientific production in BD&S Authors

Articles

Authors

Articles fraction

1. BLANKE T

2

1. BLANKE T

0.83

2. KITCHIN R

2

2. KITCHIN R

1.50

3. SCHROEDER R

2

3. SCHROEDER R

1.33

4. ADAMS J

1

4. ADAMS J

0.50

5. ALVARADO R

1

5. ALVARADO R

0.50

6. ARADAU C

1

6. ARADAU C

0.50

7. ARONCZYK M

1

7. ARONCZYK M

0.50

8. BARNES TJ

1

8. BARNES TJ

0.50

9. BARTLETT A

1

9. BARTLETT A

0.25

10. BEARMAN P

1

10. BEARMAN P

1.00

Table 3. Top 10 TC authors Author

h-index

g-index

m-index

TC

NP

PY

1. KITCHIN R

2

2

0.22

1136

2

2014

2. LYON D

1

1

0.11

368

1

2014

3. ZWITTER A

1

1

0.11

200

1

2014

4. MCARDLE G

1

1

0.14

186

1

2016

5. LEONELLI S

1

1

0.11

130

1

2014

6. BURROWS R

1

1

0.11

123

1

2014

7. SAVAGE M

1

1

0.11

123

1

2014

8.SCHROEDER

2

2

0.22

114

2

2014

9. BLANKE T

2

2

0.25

91

2

2015

10. KSHETRI N

1

1

0.11

69

1

2014

Additionally, the author H-index, a metric used at the author level to assess the productivity and citation effect of a writer’s scholarly output, was calculated [12]. This computation also took into account the m-index and g-index variants of the h-index. The results shown in Table 2 also confirmed Rob Kitchin as the most productive author in the journal based on the indices reported—h-index (2), g-index (2), m-index (0.22) and

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total citation (1136)—followed by Ralph Schroeder—h-index (2), g-index (2), m-index (0.22) and total citation (114). 4.2 Influential Impact Even while scholarly disagreements about its use continue to simmer within academic circles, the number of citations has also been one of the most popular measures for evaluating the effect of academic papers [13]. In our collection, we have 363 scientific documents. The search reveals that the most referenced document is the 2014 article published by Rob Kitchin entitled “Big Data, New Epistemologies, and Paradigm Shifts,” which was cited 950 times. This article investigates how the emergence of big data and new data analytics challenges approved epistemologies in the social sciences and assesses the degree to which these developments are causing breakthroughs in a variety of fields. The second article, cited 368 times, was published by David Lyon (2014) and was titled “Surveillance, Snowden, and Big Data: Capacities, Consequences, and Critique.” Andrej Zwitter (2014), cited more than 200 times by other authors, wrote an article entitled “Big Data Ethics,” where the author elaborates on the ways big data impacts ethical conceptions. In addition, the affiliations of authors were analyzed as well. Goldsmiths University of London and University of Amsterdam both have published twelve documents in BD&S, followed by King’s College in London, University of Stirling, and University of York (Fig. 1). In Fig. 2, the intensity of the color is related to the number of publications. That is, countries with darker colors contribute the most. The top four countries were the United States, the United Kingdom, the Netherlands, and Canada.

Fig. 1. Most relevant affiliations

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Fig. 2. Country of Corresponding Author

5 Mapping Conceptual Structure The keywords that the authors used in the journal are explained in this section. The outcome demonstrates that the authors utilized keywords to indicate the word count in the publications’ abstracts. The words with larger font sizes indicate that authors used them more frequently in the abstract. We may deduce that the term “big data” is the most prevalent in this survey, with a frequency of 43 or 31%. This signifies that it was the most often used term by the authors. Additionally, up to 6 times (4% of the papers) the term “epistemology” was used by the authors. The word “privacy,” which appears in the treemap at position three, has a frequency of 5 (4%). Figure below displays the overall results of the word cloud and describes the words that the authors used in their papers based on the treemap. According to our bibliometric investigation, the field of big data analytics is still in its early stages of development with regard to its content. As a result, during the early stages, the field is in flux, as several authors have a range of different views and try to mold the thinking regarding the concept. However, more advanced categories such as surveillance, governance, crowdsourcing, and ethics show higher levels of interconnectedness. Furthermore, our synthesis revealed that there appeared to be a dearth of integration of theoretical frameworks necessary in the big data movement, which is outside the scope of this paper.

6 Intellectual Structure Exploration The intellectual structure clarifies how an author’s work affects a specific scientific community. It displays the connections between reference-representing nodes. Citation analysis, according to Aria and Cuccurullo (2020b), is a time-honored method for demonstrating intellectual connections.

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To map the intellectual structure that identifies the active research topics in a field, co-citation analysis is the most commonly used scientometric tool [14]. This technique is based on the assumption that two papers that are frequently quoted in the same article are likely to have similar insights. Research impact is examined by co-citation analysis, which examines the citation patterns of co-cited research articles [15]. The co-citation analysis is displayed in Fig. 3. The node’s magnitude indicates how many citations the reference has earned, and the distance between two nodes indicates how powerful a cocitation is. It displays the results of the co-citation network, which found four clusters in four different colors. Cluster 1 (blue) denotes articles that address the epistemological and ontological foundations of big data in the social sciences and humanities. Floridi (2013) discussed the idea of small patterns “because they describe new boundaries of competitive rivalry, from science to corporation”. The document published by MayerSchonberger (2013) appears to play a key role in this cluster, given the importance of the centrality rate (277.51). Laney (2001) and Kitchin (2014) are particularly insightful. Cluster 2 (red) mainly investigates the methodological aspects related to the field of big data [16] and provides a broader understanding of its drivers, barriers, and challenges [17]. The article of Boyd & Crawford (2012) takes a pivotal role in this cluster, which discusses the assumptions and forces behind this new wave of research. Cluster 3 (green) highlights the potential influences of “big data” on the study of human geography [18]. The works of Bunrs (2015) and Thatcher (2014) are the most impactful articles in this cluster. Cluster 4 (purple) emphasizes big data’s current and potential contributions to different fields of social science [19]. This cluster also sheds light on the questions of ethics, privacy, and the potential perils of big data analytics [20]. In the recent decade, prodigious advancements have been made in computing power, storage capacity, and software, resulting in an expanded surge in big data technologies. This increase has led to numerous concerns. In particular, extensive attention is given to the issues of ethics, methodologies, privacy, and potential perils. While computers and their capacity for data analysis have improved significantly since that time, social scientists have found it challenging to move beyond their established epistemology. This result is corroborated with the findings of Lipworth et al. (2017), who stated that the idea of big data has very little epistemological support. In the midst of this tension, there are concerns about how technology, such as big data, might help build the capabilities for and practices of knowledge production in social science. In this regard, Leonelli (2014) highlighted several difficulties in identifying and analyzing data given the opportunities of the “big data revolution.” A more general consequence of the complexity of big data is that it may induce the need for confidentiality and protection and complicate efforts to do so. Social scientists are becoming increasingly concerned about how organizations collect data without adequately protecting their privacy. Since privacy is fundamentally about confidentiality and integrity, academics may look at how social actors in various settings interpret, domesticate, and innovate in relation to big-data methods.

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Fig. 3. Co-citation network

7 Conclusion This article provides a bibliometric overview of the major directions that have been witnessed in the journal during this time, utilizing R Studio to analyze bibliographic data and a large array of bibliometric metrics. According to an analysis of the institutions and countries, UK universities are the leading publishers in the journal, with all of them being predominantly English-speaking countries. The project also features a mapping analysis of the bibliographic data from several angles, such as journals, authors, universities, and keywords. Our study’s goal was to create a position of awareness in big data analytics based on BD&S synthesis. As a result, we present a few relevant future lines of research undertaken in this bibliometric analysis. We believe that these research directions could be used by academics and industry professionals to further the study of big data analytics, which is reflected in this bibliometric overview.

References 1. Zhang, Y.Y., Zhang, N.: Sustainable supply chain management under big data: a bibliometric analysis. J. Enterp. Inf. 34(1), 427–445 (2021)

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2. Pappas, I.O., Mikalef, P., Giannakos, M.N., Krogstie, J., Lekakos, G.: Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. IseB 16(3), 479–491 (2018). https://doi.org/10.1007/s10257-018-0377-z 3. Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of big data challenges and analytical methods. J. Bus. Res. 70(1), 263–286 (2017) 4. Liu, X., Sun, R., Wang, S., Wu, Y.J.: The research landscape of big data: a bibliometric analysis. Libr. Hi Tech 38(2), 367–384 (2019) 5. Zhang, Y., Huang, Y., Porter, A.L., Zhang, G., Lu, J.: Discovering and forecasting interactions in big data research: a learning-enhanced bibliometric study. Technol. Forecast. Soc. Change 146(5), 795–807 (2019) 6. Ferreira, M.P., Santos, J.C., de Almeida, M.I.R., Reis, N.R.: Mergers & acquisitions research: a bibliometric study of top strategy and international business journals, 1980–2010. J. Bus. Res. 67(12), 2550–2558 (2014) 7. Diodato., Gellatly, P.: Dictionary of Bibliometrics. Routledge, New York (1994) 8. Kumar, P., Sharma, A., Salo, J.: A bibliometric analysis of extended key account management literature. Ind. Mark. Manage. 82(4), 276–292 (2019) 9. Dhiaf, M.M., Atayah, O.F., Nasrallah, N., Frederico, G.F.: Thirteen years of Operations Management Research (OMR) journal: a bibliometric analysis and future research directions. Oper. Manage. Res. 14(3–4), 235–255 (2021). https://doi.org/10.1007/s12063-021-00199-8 10. Aria, M., Cuccurullo, C.: bibliometrix: an R-tool for comprehensive science mapping analysis. J. Inf. 11(4), 959–975 (2016) 11. Egghe, L., Ravichandra, I.K.R.: Rao Theory and experimentation on the most-recentreference distribution. Scientometrics 53(3), 371–387 (2002). https://doi.org/10.1023/A:101 4825113328 12. Leydesdorff, L., Wouters, P., Bornmann, L.: Professional and citizen bibliometrics: complementarities and ambivalences in the development and use of indicators—a state-of-theart report. Scientometrics 109(3), 2129–2150 (2016). https://doi.org/10.1007/s11192-0162150-8 13. Callahan, A., Hockema, S., Eysenbach, G.: Contextual cocitation: augmenting cocitation analysis and its applications. J. Am. Soc. Inf. Sci. Technol. 61(6), 1130–1143 (2010) 14. Tang, K.-Y., Wang, C.-Y., Chang, H.-Y., Chen, S., Lo, H.-C., Tsai, C.-C.: The intellectual structure of metacognitive scaffolding in science education: a co-citation network analysis. Int. J. Sci. Math. Educ. 14(2), 249–262 (2015). https://doi.org/10.1007/s10763-015-9696-4 15. Boyd, D., Crawford, K.: Critical questions for big data. Inf. Commun. Soc. 15(5), 662–679 (2012) 16. Ekbia, et al.: Big data, bigger dilemmas: a critical review. J. Am. Soc. Inf. Sci. 66(8), 1523– 1545 (2015) 17. Crampton, J.W., et al.: Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb. Cartography Geogr. Inf. Sci. 40(2), 130–139 (2013) 18. Thatcher, J.: Big data, big questions| living on fumes: digital footprints, data fumes, and the limitations of spatial big data. Int. J. Commun. 8, 19 (2014) 19. Taylor, L., Schroeder, R., Meyer, E.: Emerging practices and perspectives on big data analysis in economics: bigger and better or more of the same?. Big Data Soc. 1(2), 1765–1783 (2014) 20. Lyon, D.: Surveillance, snowden big data: capacities, consequences, critique. Big Data Soc. 1(2), 1–13 (2014)

Application of Machine Learning in Fused Deposition Modeling: A Review Mohmed Achraf El Youbi El Idrissi1(B) , Loubna Laaouina2 , Adil Jeghal3 , Hamid Tairi1 , and Moncef Zaki1 1 LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, Sidi Mohamed Ben Abdellah

University, Fez, Morocco [email protected] 2 LISA Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] 3 LISAC Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. The market of modern industry is oriented towards the technology of additive manufacturing, which encompasses several processes, among which we find FDM (fused deposition modeling), which has become increasingly used due to its advantageous contribution compared to traditional techniques. Its advantages concern in particular the reduction of manufacturing time, the resistance to temperature and the limitation of human intervention, which also decreases the risks that the user of this technology may have. However, despite these advantages, FDM has some limitations, especially with regard to defects that influence the manufacturing and, of course, the quality of the final product. As a result, and because of the recognised effectiveness of machine learning algorithms in the field of FDM, we have prepared this summary paper to review recent research involving the application of these algorithms to the FDM manufacturing process. The purpose of this work is to show the applicability of ML in various tasks related to this type of process. These tasks include surface roughness prediction, part deviation detection, cost estimation and defect prediction. The results of this paper show that ML algorithms contribute effectively to achieving good prediction and classification accuracies for several tasks associated with FDM manufacturing. This work opens the door for further research to apply these ML algorithms in other tasks related to this type of process. Keywords: Machine Learning · Fused Deposition Modeling · Additive Manufacturing · Defect Detection

1 Introduction In Industry 4.0, artificial intelligence is an important element in 3D printing; in particular, machine learning brings together a set of algorithms that can be effective for value chain optimisation [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 114–124, 2023. https://doi.org/10.1007/978-3-031-29857-8_12

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In this paper, we review recent work on the application of machine learning algorithms in the FDM (fused deposition modeling) manufacturing process. For this purpose, in the Sect. 1, we discuss FDM, its working principle and the hardware used in this type of process. Section 2 will be devoted to the different types of FDM materials used in manufacturing. Then, in Sect. 3, we present the different classes of machine learning with a brief description of each. In Sect. 4, we try to give an overview of some recent research related to the application of ML algorithms in FDM manufacturing and then start a discussion and conclusion. In additive manufacturing, the FDM process is a printing technique that is carried out layer by layer by depositing extruded materials through a nozzle, and like every additive manufacturing process, FDM goes through the same steps as other types of processes. Figure 1 shows the different steps that are used to run the FDM process [2]. Here, a conceptual model is created using a computer (CAD), and this step then gives us a transformed file (stereolithography) of STL extension that is perfectly adapted to the FDM machine. The created model is processed into 3D sections and then fed into the FDM machine. The FDM preprocessing step is performed using FDM-dependent software with which the parameters influencing FDM manufacturing are set. These parameters include the temperature, volume, support structure, support location, layer thickness, orientation and printing speed etc. Then, the part is built up by thin layers one after the other and assembled to obtain the final product.

CAD model transformaon of the CAD model into an STL file pre-processing and preparaon of FDM parameters Manufacturing of parts with layer assembly Post-processing (Removal of the part and the support) Final FDM part Fig. 1. Description of the working principle of FDM manufacturing.

The FDM parameters are configured before printing. These parameters have to be optimised to have a more reliable FDM process, optimal quality and more precision, so the different phases have to be well monitored, which can be done either by experimental tests or by 3D simulation [3]. The layers are formed by extruding the material (polymers, for example) through the nozzle, which itself is in charge of depositing the liquefied material according to the three coordinates x, y and z with instructions made in the FDM program code of the

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machine, as shown in Fig. 2 [4] In this case, the two rollers rotate in opposite directions to control the filament that is introduced into the nozzle after it has been extruded, which makes it possible to create the layers one after the other, but according to the movements of the nozzle forwards and backwards and according to the coordinates set in the CAD model. This operation continues until the final part is obtained.

Fig. 2. A brief diagram of the FDM material.

2 FDM Materials Like any type of additive manufacturing process, FDM uses a range of materials in the manufacturing process, including PLA (polylactide) and ABS (acrylonitrile butadiene styrene) [5]. The former is frequently used because it is based on environmentally friendly components, which do not cause any harm to the users of the technology, but with another advantage that is marked by the reduction in energy consumption [6]. The second material, ABS, uses more energy since the nozzle and plate are heated to a higher temperature than PLA. However, the use of PLA has the disadvantage of friction on the parts with more chances of blocking extrusion [7] In addition to PLA and ABS, FDM manufacturers can also use two thermoplastic materials: high-density polyethylene (HDPE) and polyphenylsulfone (PPSU). In Fig. 3, several other types of materials used in FDM manufacturing are shown:

Application of Machine Learning in Fused Deposition Modeling

Polymers or Plastics

PLA, Poly lactic acid, ABS, HDPE, PPSU, etc.

Metals

Aluminium, bronze, Gold, Platinium, Titanium, etc.

Ceramic

Alumina Silica Ceramic Powder

Others

Nylon, Food, Papers,Wood, Concrete, ect.

FDM Materials

117

Fig. 3. Types of FDM materials.

3 Machine Learning Given the revolutionary advances in artificial intelligence in recent years, ML algorithms are no exception, as they have been effectively integrated into manufacturing processes, providing good results in regression, prediction, classification and clustering, among others. The application of ML in FDM can usefully help manufacturers to cover gaps in several production tasks, such as defect detection, quality control, geometric deviation detection and manufacturing cost estimation. These ML methods are of three types: supervised methods, unsupervised methods and reinforcement learning methods. Supervised Methods. These methods map from the input data to an output, applying training to a model from the data that is labelled but divided into two parts: training and testing. The best known algorithms in this category are naive Bayes, SVM (support vector machine), random forest, decision tree, neural networks, etc. [8]. Unsupervised Methods. In contrast to supervised methods, this type of method leaves the model to discover the data to obtain a more suitable data structure. Unsupervised methods train on the input data, and when new data are to be added, they train again based on the data that have been learned. The best known types of this class of algorithms are SOM (Self Organizing Map), Encoders, K-means, etc. [9]. Reinforcement Learning Methods. This type of reinforcement learning has been increasingly used by researchers. The concept of this type of method is based on two decisions: promise and punishment, so the agent is programmed on these two decisions without taking into account the task at hand [10, 11].

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4 Machine Learning in the FDM Process This section attempts to give an overview of the latest research done in recent years concerning the application of machine learning in FDM, but according to a few categories: prediction of part surface roughness, geometric deviations, cost estimation and defect detection of manufactured parts. 4.1 Prediction of Surface Roughness Surface roughness refers to the marked difference in the surface of the manufactured part compared to the ideal surface of the part. This roughness parameter even influences its tribological output, such as contact resistance, friction during the manufacturing process, liquid flow and thermal and electrical resistance. Roughness also affects tolerances on part dimensions, vibration and noise control [12]. The quality of the finish is an important element in not only reducing postprocessing but also in reducing prototyping time and saving energy [13]. ML algorithms can help FDM manufacturers make predictions about surface roughness while taking into account the input parameters (Table 1): The use of ML algorithms has, among others, the advantage of predicting the surface roughness of FDM parts and optimising the input parameters; however, the choice of the parameters to be taken into account must be well considered for better optimisation. 4.2 Geometric Deviations In FDM manufacturing, geometric defects are generated quite often, which decreases the quality of the final product. In this respect, ML algorithms can help FDM specialists quantify the degree of geometric deviation and identify defects in the final part. In the following, we study the ML algorithms recently used in this sense (Table 2): The calculation of dimensional variations and prediction of geometric defects can be performed by ML algorithms, so the development of a real-time ML-based system is more beneficial in FDM manufacturing. 4.3 Energy Estimation Estimating the cost of FDM manufacturing remains an important issue for manufacturers and is dependent on a number of parameters, including the cost of the machine, the cost of materials, the cost associated with postprocessing and labor. The overall cost is the sum of the subcosts involved in the manufacturing process. ML algorithms can be effective in estimating the cost associated with FDM manufacturing. In the Table below, we present some works associated with this task (Table 3):

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Table 1. Some recent research on surface roughness prediction for FDM manufacturing using ML algorithms. Brief introduction

ML methods

Reference

Prediction of surface roughness of FDM ANN (150 neurons and 2 hidden layers parts, input elements are: fill density, with R-squared equal to 0.87) layer height, print speed and nozzle temperature

[14]

Optimisation of input parameters (including: print speed, layer height, outer shell speed and nozzle temperature) to achieve minimum roughness of the FDM fabrication

ANN symbiotic organism search (ANN-SOS): - 2 layers with 8 neurons for each layer - minimum surface roughness of 2.011 µm

[15]

prediction of the surface roughness of FDM parts built in PLA, using ML and splash impact. The input factors are: layer thickness, build orientation and splash impact angle

ANFIS: Adaptive neuro-fuzzy inference [16] system: the roughness decreases by 42% and 24%, respectively for the transverse and longitudinal directions with a mean error of 8.59 × 10–6

Generated 40 models to predict the surface roughness of 16 parts with 3 orientation taps for FDM manufacturing from polyvinyl butyral. Model inputs are: nozzle temperature, number of perimeters, layer height, print speed, and wall angle

Bagging and Multilayer Perceptron (BMLP): - a Kappa statistic of 0.9143 - the surface finish is closely influenced by the wall angle and the layer height

[17]

Identification of the effect of input Genetic Algorithm (GA) parameters on the wear of parts made of PC-ABS plastics with optimisation of the product’s wear resistance

[18]

Optimization of FDM input factors and steam smoothing process to improve hardness, finish and accuracy of final parts

[19]

A Self-Adaptive Cuckoo Algorithm Using a Machine Learning Technique

The optimization of energy consumption in the FDM manufacturing process remains an embarrassing issue for decision makers; therefore, ML methods can effectively contribute to energy estimation and have the right accuracy in prediction. However, to have the best model adopted for this task, it is necessary to test several ML algorithms. 4.4 Defect Detection Parts produced in 3D are always subject to several defects that can be caused by the type of materials, the printing process, and uneven cooling/heating of the part, material or layers. Defects in FDM manufacturing include [27] (Table 4):

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Table 2. Some recent research on geometry deviation prediction for FDM manufacturing using ML algorithms. Brief introduction

ML methods

Reference

Prediction of effective material Convolutional Neural Networks properties and their impact on geometric (CNN) defects and real-time multi-scale performance evaluation of honeycomb structured parts

[20]

Use of machine learning methods to predict the deviation dimensions for a reference part

[21]

Linear regression, Lasso regression, Ridge regression, random forest, and XGBoost

Prediction of dimensional variation and Decision Tree study of the impact of parameters on the accuracy of the final product

[22]

Real-time detection and correction of errors, including 2D and 3D geometry errors, based on automatically labelled images, but according to the deviation from the optimal printing parameters with visualization of the predictions

[23]

Multihead neural network

Table 3. Some recent research on predicting the estimated energy for FDM manufacturing using ML algorithms. Brief introduction

ML methods

Reference

Energy consumption prediction for FDM 12 ML methods with best prediction manufacturing using ML algorithms obtained by Gaussian process regression (GPR)

[24]

Use of ML for power consumption prediction in FDM manufacturing with power curve imitation

[25]

Gradient boosting regressor (GBR), Random forest regressor (RFR), Bagging regressor (BGG), Light gradient boosting (LGB) and eXtreme gradient boosting (XGB) with best prediction by Random Forest

ML model for estimating the energy LSTM (Long short-term memory) consumption of the different 3D printing stages for each of the materials PLA, ABS, and PETG (polyethylene terephthalate glycol)

[26]

Machine learning algorithms can effectively provide predictions about defects that occur during the FDM manufacturing process, so the table below shows some recent research in this direction (Table 5):

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Table 4. Some types of defects encountered in FDM manufacturing. Defect type

Meaning

Wraping

This defect is marked by the swelling of the part, this may be due to the cooling applied to the part and the materials used

Material shrinkage

This type of defect concerns the materials used, showing a certain level of shrinkage

Porosity

This defect is characterised by voids in the printed part, which is related to the printing process or the type of materials used

Poor Surface Finish Caused by the technique and the chosen material, this defect requires a post treatment Residual Stresses

This defect is caused by rapid cooling or heating of the material, resulting in contraction or expansion

Blistering

This defect concerns the outwards swelling of the lower layer, this is due to the cooling of this layer and even it is due to the density of the other upper layers

Cracking

This defect causes cracks and manufacturing failure and is due to uneven cooling or heating of a part

Table 5. Defect detection and classification for FDM manufacturing using ML algorithms. Brief introduction

ML methods

Reference

FDM manufacturing anomaly detection using ML algorithms

Support vector machine (SVM), KNN, RF, Decision Tree, Naive Baye (NB) combined with Alexnet, Googlenet, Resnet18, Resnet50 and Efficientnet B0: The combination of the two models of Alexnet (CNN Model) and SVM gave the best accuracy

[28]

Construction of efficient detectors for Spectrally Efficient FDM (SEFDM) systems using several types of neural network architectures

MLP, Residual MLP, CNN and Residual CNN: Residual convolutional neural networks (CNN) are the most efficient

[29]

Use of ML for prediction and control of multi-axis parts in FDM manufacturing with classification of detected defects

CNN: the inter-layer stripping with the normal state have as CNN classification accuracy a rate of 83.1% the ROC (Receiver Operating Characteristic) has as a curve area a value of 0.824 and the sensitivity a rate of 85.6%

[30]

ML algorithm for diagnosis and classification of FDM printing defects caused by temperature change with four temperature-related input parameters (minimum and maximum temperature, average temperature and temperature difference)

SVM: - Classification 1 (insufficient filling, Warping) - Classification 2 (Warping, Serious fault printing)

[31]

The identification of geometric defects by ML algorithms can be performed efficiently; however, for better performance, it is preferable to have an anomaly control system that takes into account several types of defects at the same time.

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5 Discussion and Conclusion In Industry 4.0, the contributions of ML algorithms are advantageous in fused deposition modeling (FDM). These algorithms have been proven to be effective in reducing human intervention and contributing usefully to the improvement of the value chain; in other words, they remain a good choice to satisfy the accuracy and reliability of manufactured parts. However, a broad study on the use of ML algorithms has not yet been performed. In this sense, this state of the art presented in this paper has reviewed the latest research on the applicability of ML algorithms on some FDM-related tasks, namely, surface roughness prediction, surface deviation prediction, geometric deviation prediction, cost estimation and defect detection. Certainly, ML algorithms can be effective on other FDM application topics, even more so when used in mixed FDM tasks, which will lead us in future work to extend our study to fill in the applicability of ML in this sense.

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Prediction of Electrical Power of Ag/Water-Based PVT System Using K-NN Machine Learning Technique Safae Margoum1(B) , Bekkay Hajji1 , Chaimae El Fouas2 , Oussama El Manssouri1 , Stefano Aneli3 , Antonio Gagliano3 , Giovanni Mannino4 , and Giuseppe Marco Tina3 1 Laboratory of Renewable Energy, Embedded System and Information Processing, National

School of Applied Sciences, Mohammed First University, 60000 Oujda, Morocco [email protected] 2 ENA, Mohammed First University, 60000 Oujda, Morocco 3 University of Catania, 95125 Viale Andrea Doria 6, Catania, Italy 4 CNR – IMM, Strada VIII n. 5 Zona Industrial, Catania, CT, Italy

Abstract. In this study, the effectiveness of the machine learning model for predicting the electrical power output of PVT system is evaluated. Specifically, a K-Nearest Neighbor (K-NN) method is explored, using various hyper-parameters and characteristics. In this study, machine learning techniques are used to simulate the electrical performance of PVT systems that are cooled by water-based nanofluids. In the proposed model, the mass flow rate, volume fraction and solar radiation have been considered as the input variables in order to predict the electrical powers versus time. Datasets has been extracted from previous experimental research for an Ag/water-based PVT system (laminar flow rate and turbulent with 2% and 4% of volume fractions). Results demonstrated the capability of the method developed to predict a new output database containing 116 values of the electrical power versus time with the absolute average relative deviation AARD = 1.022%, and root mean squared error (MSE) of 4.0577%. Keywords: Machine Learning Algorithms · K-Nearest Neighbor (KNN) · PVT collector · Nanofluid

1 Introduction In recent years, Solar energy and specially PVT systems have attracted the attention of many researchers. There are several varieties of (PVT) systems that may be distinguished based on various criteria and factors. Photovoltaic thermal systems (PVT), which use both air and water as the cooling fluid, may be generally categorized into three categories: water-based PVT, air-based PVT, and bi-fluid-based PVT [1] a new generation of fluid such as nanofluid attract attention of many research experimentally and numerically for enhancing the performance of PVT systems such [2–5] that tried to insert nanofluid instead of conventional fluids (air or water) these studies proved that Nanofluids have © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 125–132, 2023. https://doi.org/10.1007/978-3-031-29857-8_13

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better thermophysical proprieties than basic fluids, which can potentially improve the PVT system performances compared to the other systems. Artificial intelligence (AI) techniques have helped academics in a variety of sectors in recent years [6]. [7] presented a thorough comparison of the use of ensemble learning (EL) and machine learning (ML) techniques for PV array defect detection and classification. It is demonstrated that the recommended approach can be used to determine when ML or EL algorithms are truly necessary for PV defect detection and classification. [8] investigates the k-Nearest Neighbor (K-NN) for the first time to detect and categorize the fault Find the PV array’s faulty string in a typical grid-tied distributed inverter system. Find the PV array’s faulty string in a typical grid-tied distributed inverter system, These findings show that the average fault classification accuracy is high at 98.70%. [9] To anticipate the power obtained from PV systems, recurrent neural network and ant lion optimizer were implemented. [10] seeks to apply the K-NN model and provide new kernels for the weighted K-NN model used for the hourly total solar radiation regression estimation problem. The improved the performance overall with the KNN kernel based on three neighbors with RRMSE = 7.23%, R2 = 98.05% and MAPE = 8.77%. Despite the fact that only few studies have been done with the goal of forecasting PVT system performances, particularly those that are cooled by nanofluid. [11] uses machine-learning techniques to simulate the electrical performance of PVT systems that are cooled by water-based nanofluids. Trial-and-error and statistical analysis are used to determine the optimal topology for artificial neural networks, least squares support vector regression, and adaptive neuro-fuzzy inference systems (ANFIS). As a result, it is determined that the ANFIS is the most effective method for simulating the electrical performance of the solar system under consideration. Has a mean squared error (MSE) of 2.548, absolute average relative deviation (AARD) of 13.6%, and R2 of 0.9534. [12] investigates machine learning methods for improving thermal performance prediction models for photovoltaic-thermal solar collectors (PVT). The works found that the best model with R2 equal 0.98, and 0.265 for the mean squared error. In addition, the k-Nearest Neighbor algorithm (K-NN) for classification or regression is a relatively straightforward approach to comprehend [13], K-NN is a type of supervised learning method. A function(learner) is inferred by supervised learning from training data T, which is a set of training instances referred to as samples [14]. Therefore, this work explores K-Nearest Neighbor K-NN model to predict the Ag/water-based PVT system electrical Power. This choose is based on the reason that the K-NN model is distinguished by short calculation times, an easy algorithm to understand, and a high accuracy. The model has been evaluated based on different statistical investigations such as regression coefficient (R2 ), AARD%, MSE, RMSE, for training, and testing the data.

2 Materials and Method In order to forecast the electrical powers versus time, this study uses machine-learning technique to model the electrical performance of a PVT system that is cooled by Ag/water nanofluid. Machine learning algorithms have been trained using experimental data. To do this, a dataset from the [15] experimental research was taken, which has about 400

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values, 80% for training (of about 320 points) and 20% for testing (of about 116 points). The mass flow rate, volume fraction and solar radiation are used as input variables for training, testing the model, in order to predict the electrical power as output using the Python environment Fig. 1.

Fig. 1. Google Colab tool used for running K-NN ML-based algorithm.

2.1 K-NN Regression Theory When using K-NN regression, the object’s attribute value is simply set to be the average of that of its K closest neighbors. The neighbors’ contributions can be weighted so that the closer neighbors make a greater average contribution than the farther neighbors. Because of the KNN’s effectiveness, simplicity, and capacity to function effectively with enormous training data [16]. Distances metrics K-NN forecasts outcomes based on the K-neighbors that are nearest to the location. Thus, it must design a measure for gauging the distance between instances from the example samples and the query point when using KNN to make predictions. Euclidean geometry is among the most widely used methods for measuring this distance. City-block, Euclidean squared, and Chebyshev are other measurements.  (1) Euclidian D(x, y) = (x − y)2 Euclidian squared D(x, y) = (x − y)2

(2)

Cityblock D(x, y) = |x − y |

(3)

Chebyshev D(x, y) = Max( |x − y |)

(4)

where x and y are respectively, the topic of the inquiry and a case from the examples sample. K-Nearest Neighbor Predictions after selecting the K value, it is possible to create predictions using the KNN examples, in regression problem, the KNN prediction is the average of the K nearest neighbor output 1 yi k k

y=

i=1

where yi , the i is the sample case, and y is the predicted or output point.

(5)

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2.2 Evaluation Model In order to evaluate the model, Various statistical measures, including Mean Squared Error (MSE), Root Mean Squared Error (EMSE), Correlation Coefficient (R2 ), and Average Relative Deviation (ARD), can be used to assess the certainty, and accuracy of the model. In this study, the K-NN model was evaluated focused on these parameters stated in the following: Mean Squared Error (MSE),

1 N

N 

exp

(yi

i= 1



Root Mean Squared Error (RMSE), Correlation Coefficient (R2 ) 1 −

1 N 1 N 1 N

Average Relative Deviation (ARD)

N 

pred 2 )

− yi exp

(yi

(6)

pred 2 )

− yi

(7)

exp pred 2 ) i=1 (yi −yi exp exp 2 i=1 (yi −yi )

(8)

i= 1

N

N

1 N

N 

  exp pred 2  y −y  i  i

(9)

exp

i=1

yi

2.3 Literature Experimental Data Preparation Machine learning techniques have to be trained with experimental data, thus the data used in this work was extracted from an experimental study reported in [15] research, the datasets consisting of roughly 400 values, Table 1 The table below summarized the experimental datasets utilized and their range, the mass flow rate, volume fraction and solar radiation are taken into account as input variables for training and testing the model’s ability to predict the output of electrical power. Table 1. An overview of the experimental dataset scopes. Nanoparticle

Nanoparticle concentrations (%)

Mass flow rate (Kg/s)

Solar Radiation (W/m2 )

Electrical power (W)

Reference

Ag

2–4

0.034–0.116

400–890

20–62

[15]

3 Results and Discussion As was already indicated, this work’s objectives are to apply a K-NN approach for estimation the PVT system’s electrical power that cools by Ag/water nanofluid based PVT collector, the algorithm was based on various statistical method in order to evaluate the proposed approach mean squared error (MSE), root mean squared error (RMSE),

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Table 2. Error analysis of the model utilized

KNN model

Dataset

MSE

RMSE

R2

ARD

Test

16.4654

4.0577

0.8168

1.0225

Train

2.2399

1.4966

0.9753

0.1105

Total

13.6202

3.54548

0.8485

0.8401

Fig. 2. Accuracy versus k-neighbors for test and train scores

correlation coefficient (R2 ) and the average relative deviation (ARD), the results are presented in the following Table 1, these parameters are taken after evaluating the K neighbor and founded it equal 3 as demonstrated in Fig. 2. Figure 3 displays the best line fitting utilizing polynomial regression on experimentally observed data and K-NN model value prediction. Generally, based on this assessment, The majority of the projected data appear to be positioned near to the actual data point. according to the figure, the electrical power predicted from 20 W to 35 W are far from the equality line meaning that they are far from the actual values so the model utilized cannot predict the minor points but it is valid for the biggest ones. Another frequent evaluation graph is the relative deviation graph, which is employed for comparing the values of the predicted electrical power of the Ag/water-based PVT collector utilizing real data extracted based on the experiment studies. Fig. 3 illustrates the relative deviation diagram of the K-NN approach, according to this figure, It is evident that the most values of the training and testing data points are close to the equality line. Based on the Eq. (9), the average relative deviations are 1.0255 and 0.1105 for the testing and the training respectively.

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Fig. 3. K-NN Regression plot of electrical power: experimental Vs. predicted

Fig. 4. KNN Relative Deviation of electrical power: experimental Vs. predicted

4 Conclusion As a way to predict the electrical power of an Ag/water-based PVT system, the K-Nearest Neighbor K-NN machine learning method was applied on previous experimental results. To do so volume fraction, mass flow rate and solar radiation are taken as inputs in order to predict the electrical power as output. To evaluate the model, regression plot and relive deviation graph of the predict and experimental measured values for the K-NN studied model, was presented. Generally, based on this assessment, The majority of the projected data positioned near to the actual data point and to the equality line respectively.

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Moreover, the model predicted 116 datasets with R2 = 0.8168, AARD = 1.0225% MSE = 16.4654 and RMSE = 4.0577. The model used shows improved statistical parameters for applying to the provided data.

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14. Pattern Recognition and Machine Learning. https://link.springer.com/book/9780387310732. Accessed 14 Oct 2022 15. Aberoumand, S., Ghamari, S., Shabani, B.: Energy and exergy analysis of a photovoltaic thermal (PVT) system using nanofluids: An experimental study. Sol. Energy 165, 167–177 (2018). https://doi.org/10.1016/j.solener.2018.03.028 16. Baf, S., Im, E., Bol, M.: Research Article Open Access Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background

Evaluation of Machine Learning and Ensemble Methods for Complications Related to Port a Cath Hanane El Oualy1(B)

, Bekkay Hajji1 , Khadija Mokhtari1 , Mouhsine Omari2 , and Hamid Madani1,2

1 Laboratory of Renewable Energy, Embedded System and Information Processing, National

School of Applied Sciences, Mohammed First University, BP665, 60000 Oujda, Morocco [email protected] 2 Laboratory of Medical Oncology, Faculty of Medicine and Pharmacy, Mohammed First University, BP665, 60000 Oujda, Morocco

Abstract. This study examines and compares the accuracy of several machine learning algorithms (ML) for predicting Port a Cath complications using ensemble learning (EL) approaches, in order to aid doctors in selecting the most effective treatments for saving lives. The data collection of 794 instances served as the primary database for the training and testing of the built system. 10-Fold CrossValidation has been applied to expand the data set, which would not have been possible otherwise. The techniques are developed using the Python language. Different classifiers, namely Decision Tree (DT), K-Nearest Neighbor (K-NN), Naïve Bayes (NB), Multilayer Perceptron (MLP), and Stochastic gradient descent (SGD) have been employed. The dataset has also been used for the ensemble prediction of classifiers, bagging, voting, and stacking. The study’s findings indicate that using the voting strategy in conjunction with the MLP, NB, KNN, DT, and SGD methods yields results with an overall accuracy of 92.5% higher than those obtained with the other methods described above. Keywords: Machine Learning · Ensemble Learning · Port A Cath · Complications

1 Introduction The treatment and quality of life for cancer patients and other patients requiring longterm intravenous therapy have been transformed by the use of totally implantable venous access port systems (Port a Cath). They eliminate the need for repeated venipunctures to perform blood sampling, antibiotic administration, and chemotherapy infusion [1]. Although TIVAPS generally have a lower long-term risk of infection compared to Hickman-type central venous catheters [2], complications during their insertion and continued use are still a cause for concern. These issues, such as infection, catheter fracture, thrombosis, and extravasation [3–5], may require device replacement, adding to patient anxiety and delaying treatment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 133–142, 2023. https://doi.org/10.1007/978-3-031-29857-8_14

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This study compares various machine learning classification methods, including Decision Tree (DT), Naive Bayes (NB), Multilayer Perceptron (MLP), K Nearest Neighbor (K-NN), and Stochastic gradient descent (SGD), as well as their use in combination with bagging, voting, and stacking on a data set of Port a Cath complications using 10-Fold Cross Validation as the data portioning model. The data set used is from a retrospective study conducted at the Hassan II Oncology Center Oujda Fig. 1. One study undertaken recently evaluated the classification accuracy of various classification algorithms used to analyze data from a retrospective study carried out at the Hassan II Oncology Center in Oujda. To predict the Port A Cath complication, EL OUALY in [6] presents prediction models utilizing machine learning techniques, such as support vector machine, decision tree, random forest, and logistic regression. And achieved a classification accuracy of 91.8%. This paper is organized as follows: Sect. 2 presents information on data collection and methodology. Section 3 presents experimental findings and a discussion. Final thoughts are reported in Sect. 4.

Fig. 1. Materials required to implement the TIVAPS.

2 Data Collection and Methodology 2.1 Data Set The dataset utilized for this study consists of 794 patients with a total of 9 attributes (Age, tumor type, gender, WHO (From 0 to 4), difficulty of placement, pathway (left or right), time between placement and use of the chamber (PU), and early and late complications). An excerpt of the data is shown in Table 1. The two values “False” and “True” in the main class represent whether there are any complications or not.

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Table 1. Excerpt of the Data set. Age Gender Cancer types

WHO Pathway PU

Difficulty Early Late complications complications

42

Woman Ovarian 0

Right

6 Days

6

None

None

62

Woman Gastric

Left

5 Days

3

None

None

54

Man

Colon

2

Right

20 Days 3

Thrombosis

None

72

Woman Breast

3

Left

10 Days 2

None

Infection

35

Man

0

Left

2 Days

None

None

Colon

1

2

2.2 Data Partitioning Method The small amount of data required the use of the K-Fold Cross Validation approach for data portioning. The main reason this method was chosen over others, including the single hold-out option, is because it has a reduced variance. Further research utilizing numerous datasets and approaches has shown that K = 10 is generally the optimal number of folds to provide the best estimate of error [7]. 2.3 Ensemble Learning and Machine Learning Methods The machine learning (ML) algorithms investigated in this paper are: Decision Tree (DT), K Nearest Neighbor (K-NN), Naïve Bayes (NB), Multilayer Perceptron (MLP), and Stochastic gradient descent (SGD). The ensemble learning techniques are ways to build multiple models and then combine them to get better results. Machine learning and ensemble learning methods are not intended to be defined in this section because there have recently been a lot of books and papers on the subject [8]. It aims to demonstrate how to use ML and EL methods to predict the existence of Port a Cath complications. The suggested processes for creating the prediction models are shown in Fig. 2.

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Fig. 2. The flowchart for creating the models to predict the Port a Cath complications.

2.4 Performance Metrics On a binary data set, the confusion matrix can be created for a classifier and used to describe the classifier’s performance. The recall, precision, accuracy, and F1-score are measured as a result. (For two classes) These metrics are: Accuracy =

(TP + TN ) (TP + FP + TN + FN )

(1)

TP (TP + FP)

(2)

Precision = Recall = F1_score = 2

TP (TP + FN )

(Precision ∗ Recall) (Precision + Recall)

(3) (4)

where TP is a number of true positive, TN is a number of true negative, FP is a number of fault positive, and FN is a number of fault negative. Consider the confusion matrix in Table 2 with two classes, true and false, in order to better understand how a confusion matrix functions.

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Table 2. Confusion matrix: 2 × 2 dimensions. Observed true

Observed false

Predicted true

TP

FP

Predicted false

FN

TN

3 Results and Discussion This section presents the results from using 10-Fold Cross Validation to apply the seven aforementioned methods (DT, KNN, NB, MLP, and SGD), both separately and in combination. The accuracy and confusion matrix are the two primary performance indicators used in this study. Accuracy, precision, recall, and F1 score are the calculated error metrics that are displayed in Table 3. Table 3. Error metrics: Accuracy, Precision, Recall, and F1-Score based on ML algorithms (DT, KNN, NB, MLP and SGD). Classifiers

Accuracy (%)

Precision (%)

Recall (%)

F1 Score (%)

Decision Tree

87.4

95.8

54.7

69.6

K Nearest Neighbor

89.9

96.4

64.2

77.1

Naive Bayes (NB)

88.05

73.4

85.7

79.1

Multilayer Perceptron (MLP)

90.5

85

80

82.9

Stochastic gradient descent

61.0

43.8

92.8

59.5

First Experiment: We applied the DT, KNN, NB, MLP, and SGD classifiers to the entire dataset in our first experiment, using the 10-fold cross validation technique to estimate the performance of each method. We tested K = 1…, 19 for the K-NN classifier and found that K had the best performance at 5. Figure 3 illustrates the accuracy of K-NN with various K values.

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Fig. 3. The accuracy of K-NN with various K values.

If we validate the confusion matrix (CM) displayed in Fig. 4a (for example, a DT classifier), 139 out of 159 samples are correctly classified (139/159 = 87.42%, which is the accuracy). Only 20 have the wrong classification. The classification error rate is 20/159 (12.57%). If we verify the confusion matrix (CM) shown in Fig. 4b (e.g., an KNN classifier), out of 159 samples, 143 are correctly classified (143/159 = 89.93%, which is the accuracy). Only 16 are incorrectly classified. The classification error rate is 16/159 (10.06%). The confusion matrix NB (Fig. 4c), out of 159 samples 140 are correctly classified, so the accuracy is 140/159 = 88.05%, the misclassification rate is 19/159 = 11.94%. Regarding the case of MLP (see Fig. 4d), out of 159 samples, 146 are correctly classified (144/159 = 90.56%, which is the accuracy). Only 15 are incorrectly classified. The classification error rate is 15/159 (9.43%). Finally, the confusion matrix SGD (Fig. 4e), out of 159 samples 97 are correctly classified, so the accuracy is 97/159 = 61%, the misclassification rate is 62/159 = 38.99%.

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Fig. 4. Confusion matrices: a) DT, b) KNN, c) NB, d) MLP and e) SGD

Among them, MLP achieves the highest accuracy percentage, with KNN, NB, and DT coming in second and third, respectively. SGD accuracy rating is the lowest at 61%. Table 3. Provides more information about the typical accuracy metrics used in this experiment. Second Experiment: • Bagging: We investigated bagging techniques potential to enhance estimation performance based on the experimental findings. Figure 5 illustrates how bagging increased some classifiers accuracy. For instance, DT increased from 87.4% to 89.3%, and more significantly, SGD increased from 61% to 90%. MLP didn’t get any better, but other classifiers kept their accuracy at the same level.

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Fig.5. The accuracy of various techniques using the Bagging Method

• Stacking: The use of stacking serves to combine several techniques. The Table 4 demonstrates that the best accuracy is achieved when DT, NB, and MLP are combined (91.1%).

Table 4. The results of using the stacking technique Classifiers

Accuracy (%)

NB, SGD, MLP

88,6

NB, DT, SGD

89,9

MLP, SGD, KNN

89,9

NB, KNN, SGD

89,9

NB, KNN, MLP

89,9

DT, KNN, MLP

89,9

DT, KNN, SGD

89,9

NB, DT, KNN

89,9

DT, SGD, MLP

90,5

NB, DT, MLP

91,1

• Voting: A combination of the different methods by voting is demonstrated. As the Table 5 shows, a combination of DT, SGD, MLP, KNN and NB has the best accuracy, namely 92.5%.

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Table 5. The results of using the voting technique Classifiers

Accuracy (%)

DT, SGD, MLP, KNN

86.7

NB, KNN, SGD

88

NB, DT, SGD

88

NB, KNN, MLP

88.6

NB, DT, MLP

88.6

NB, KNN, MLP, SGD

89.9

MLP, SGD, KNN

89.9

DT, KNN, SGD

89.9

NB, DT, KNN

90.5

DT, KNN, MLP

90.5

NB, DT, KNN, SGD

90.5

DT, KNN, MLP, NB

90.5

NB, SGD, MLP

91.1

DT, SGD, MLP

91.8

DT, KNN, MLP, SGD, NB

92.5

The results of the experiments show that MLP and SGD have the highest and lowest accuracy of all the tested classifiers, with 90.5% and 61%, respectively. After bagging is taken into account, SGD remains the best strategy with 90%, while NB is the worst with 88%. KNN that has the same accuracy rate. The stacking method also confirms that the NB, DT, and MLP combination has the highest accuracy, with a 91.1% score. When we use the voting technique NB, DT, KNN, SGD, and MLP wins with 92.5%. These results demonstrate that when compared to logistic regression, which has an accuracy of 91.8%, the voting technique DT, KNN, MLP, SGD, NB is able to increase accuracy by 0.75%.

4 Conclusion In this paper, we present various machine learning techniques, namely Decision Tree (DT), Naïve Bayes (NB), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), stochastic gradient descent (SGD) using both individual and combined ensemble machine learning in order to predict the Port A Cath complication, and to compare the performance of each method. Since there were a maximum of 794 patients in this study, 10-Fold Cross Validation was used to divide the data between the training and testing datasets. Before comparing the methods, each one was run under various conditions to achieve the highest accuracy. By using the bagging, voting, and stacking

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techniques, the effectiveness of each individual classifier as well as the combination of such classifiers has been assessed. In general, after using the bagging, voting, and stacking approaches, we have seen some improvements (ranging from almost zero to quite significant improvements). The voting technique combined with the NB, KNN, MLP, DT, and SGD methods performs better than the other aforementioned methods. Even though the main objective of this paper was to compare various machine learning techniques on a small dataset, we also tried to increase the precision of the aforementioned techniques to produce a more accurate comparison.

References 1. Faraj, W., et al.: Complete catheter disconnection and migration of an implantable venous access device: the disconnected cap sign. Ann. Vasc. Surg. 24(5), 692-e11 (2010). Vescia, S., Baumgärtner, A.K., Jacobs, V.R., et al.: Management of venous port systems in oncology: a review of current evidence. Ann. Oncol. 19, 9–15 2. Maki, D.G., et al.: The risk of bloodstream infection in adults with different intravascular devices: a systematic review of 200 published prospective studies. Mayo Clin. Proc. 81, 1159– 1171 (2006) 3. Yildizeli, B., et al.: Complications and management of long-term central venous access catheters and ports. J. Vasc. Access 5, 174–178 (2004) 4. Hartkamp, A., et al.: Totally implantable venous access devices: evaluation of complications and a prospective comparative study of two different port systems. Neth. J. Med. 57, 215–223 (2000) 5. Sousa, B., et al.: Central venous access in oncology: ESMO clinical practice guidelines. Ann. Oncol. 26(Suppl 5): 152–168 (2015). Learning tools and techniques (2016) 6. Springer. https://link.springer.com/book/9789811962226 7. Alpaydin, E.: Machine Learning: The New AI. MIT Press, USA (2016) 8. Google Colab. https://colab.research.google.com/

Accuracy Improvement of Network Intrusion Detection System Using Bidirectional Long-Short Term Memory (Bi-LSTM) Salmi Salim(B) and Oughdir Lahcen ISA Laboratory, National Schools of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco {salim.salmi,lahcen.oughdir}@usmba.ac.ma

Abstract. An intrusion detection system (IDS), sometimes known as an infiltration prevention system, is an active defensive mechanism deployed by the Internet of Things that can recognize intrusion activity and trigger alerts. However, with the rising number of dangers in the Internet of Things, there are questions about the present methods’ long-term viability and practicality. These considerations are particularly relevant in light of the growing levels of adaptive performance and the inadequate levels of detecting precision. In this paper, we introduce a revolutionary deep learning strategy based on bidirectional long short-term memory that addresses all of the difficulties raised above. We describe in detail the suggested model. Furthermore, we suggest a symmetric logarithmic loss function based on categorical cross-entropy as a replacement for the traditional logarithmic loss function. In addition, the suggested detection framework has been deployed to TensorFlow, which is graphics processing unit enabled, and assessed on the NSLKDD, CSE-CIC-IDS2018, and UNSW-NB15 datasets, which serve as benchmarks with an average accuracy of 93.42% and 99.92%. Keywords: Intrusion Detection System · Bidirectional LSTM · Deep Learning

1 Introduction Network intrusion detection systems (IDSs) are critical tools for network managers to use in detecting various security breaches within an organization’s network. A network IDS monitors and analyses network traffic entering and exiting an organization’s network devices and raises alarms if an intrusion is detected. Several criteria are used to categorize intrusion detection systems. IDSs are classed based on their architectural design, protected system type, and processing time for data. There are two types of intrusion detection systems based on their location, host-based and network-based [1]. IDSs may also be categorized based on their methodologies, which are signature-based and anomaly based. Host-based intrusion detection system; the server attempts to identify intrusions through the analysis of traffic, activities and registration records. Such as Network Intrusion Detection systems (IDS) listen to almost all network traffic, record the components of every data packet going across the network, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 143–152, 2023. https://doi.org/10.1007/978-3-031-29857-8_15

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stop assaults once needed, and provide information files. When known attack types are detected by a signature-based (IDS). In addition, an anomaly based intrusion detection system (IDS) is used to identify unknown threats. When creating an efficient and adaptable network IDS for unknown future threats, there are basically two problems. First, selecting appropriate features from the network traffic collection for anomaly detection is difficult [2]. Because attack situations are always changing and growing, the characteristics chosen for one type of assault may not work effectively for another. Second, there are few tagged traffic datasets from actual networks. For the development of a Network IDS. To create such a tagged dataset from raw network traffic traces gathered over time or in real time, significant effort is necessary. Anomaly detection approaches, particularly when combined with a machine learning mechanism, are recommended for constructing a dynamic IDS system. An example of the major shortcomings of IDS methodology-based machine learning is the requirement for a long training time to analyse the large set of data of the network’s prior data circulation. Furthermore, deep learning-based algorithms have recently been used successfully in audio, vision, and voice processing applications. These approaches try to build a decent feature representation from a large quantity of unlabelled data and then apply these trained features in a supervised classification on a tiny portion of labelled data by utilizing various novel methodologies, providing an efficient learning mechanism. The concurrently developed parallel solution technique reduces training time while increasing the accuracy of the suggested systems. Most of the relevant publications in the literature used machine learning methods to develop autonomous algorithms for network intrusion detection, such as RF, SVM, KNN, Naive Bayes, decision trees, etc., to minimal quantities of data, machine learning models are adequate. However, in actual time, the data generated in the network are huge, and these machine learning models are incapable of learning large volumes of data. Deep learning algorithms are inherently data hungry [3]. The model used in this work is based on bidirectional long short-term memory with a combination of setup parameters. The developed model is tested, trained and evaluated on three datasets: CSE-CIC-IDS2018, UNSW-NB15 and NSL-KDD as a benchmark. The rest of the paper is organized as follows. Section 2 provides a literature review of recent existing IDSs that employed ML and DL techniques. Section 3 describes the IDS concept. Section 4 presents the deep learning architectures NNA, RNN, LSTM and Bi-LSTM. Section 5 discusses the datasets and methods used. Section 6 presents the experimental results obtained from the proposed methods. Finally, the conclusion and future work suggestions are presented in Sect. 7.

2 Related Works Despite vast research efforts, intrusion detection systems (IDSs) continue to face challenges in detecting accuracy while minimizing alert rates and recognizing interruptions. Deep learning is now being used in conjunction with an IDS system to efficiently identify intrusions throughout the network. Deep learning techniques are becoming increasingly popular due to their capacity to deal with massive amounts of data [3]. It has also achieved a significant amount of perspectives in data by applying sophisticated architecture by adding nonlinear modifications, resulting in a significantly higher rate of recognition.

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Based on the support vector machine (SVM) approach, an efficient NIDS is suggested by Chu et al. (2019) [4]. Principal component analysis (PCA) is used to accomplish feature extraction and improve the effectiveness of detection. The suggested model’s performance is examined using the NSL-KDD database, when SVM achieves the highest accuracy of 97.22% compared to other classifiers (NB, DT, and NN). The recognition rate was not improved by PCA dimension reduction, although the computation time was increased. Wang et al. (2021) [5] proposed a cluster convolution paired with a new network intrusion detection approach to enhance the model’s classification results. Using the two datasets NSL-KDD and UNSW-NB15 in contrast to six more ensemble approaches. This approach outperforms the classification effect of previous ensemble methods by having an accuracy rate that is 2.91% higher on the NSL-KDD dataset and 5.4% higher on the UNSWNB-15 dataset. GAO et al. (2019) [6] suggested an adaptive ensemble learning model. After an analysis of the most recent developments and current issues in the field of intrusion detection. They generated a multi tree methodology by modifying the percentage of training data and placing up several decision trees. The authors tuned the model on the NSL-KDD dataset, including DT, RF, KNN, and DNN, and achieved an accuracy of 84.2%, whereas the overall accuracy was 85.2%. Moreover, it is discovered via data processing that a key element in determining the efficiency of the detection is the strength of the extracted features. Qusyairi et al. (2021) [7] evaluated three algorithms, gradient boosting, logistic regression and decision trees, for detecting unknown attack types. They used the CSECIC-IDS2018 dataset for training and evaluating their algorithms. The study’s findings revealed that 23 of the 80 characteristics were chosen, and the model’s overall accuracy and F1 scores were all 98.8% and 97.9%, respectively. Karatas et al. (2020) [8] proposed a novel model comprising six stacked ML algorithms based on IDSs KNN, RF, GBoost, Adaboost, DT and LDAA. The CSE-CIC-IDS2018 dataset is employed to test their suggested model. The authors describe the synthetic minority oversampling technique, which is a model for creating synthetic data that is used to lower the imbalance ratio. The experimental results showed that the suggested strategy significantly increases the detection accuracy for infrequent attacks. Ndresini et al. (2020) [9] demonstrated a network intrusion detection approach based on the convolution neural network (CNN), NN, ANN, and ACNN. The fundamental concept is that patterns created by the original features and their AE equivalents can extend across networks using the three datasets KDDCUP99 Test, UNSW-NB15 Test and CICIDS2017 Test. Their method achieved an average accuracy on all datasets between 92.49% and 97.90%.

3 Intrusion Detection System Concept An IDS is a fusion of the terms “intrusion” and “detection system”, illegal data access within a software system or network system that damages its confidentiality availability or integrity; it is known as an intrusion. A detection system is a protection mechanism that is employed to identify illegal actions. Therefore, an IDS is a type of security

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equipment that continuously examines hosts and network activity for any unexpected behavior that violates security procedures and interferes with its normal operation. If malicious conduct is detected, the IDS will notify the host or network administrators [10].

4 Long Short Term Memory (LSTM) To overcome the gradient vanishing problem, S. Hochreiter et al. [11] proposed LSTM, which was later improved in a paper by F. Gers and J. Schmidhuber [14]. The LSTM unit is the foundational component of an LSTM architecture. It is a collection of gates and cells that work together to produce a final result. Equation below describes a cycle in front of LSTM: Forget gate (f t ): This decides which aspects of the long-term state must be ignored. Input gate (it ): This gate controls how much of c is contributed to the long-term state. Output gate (gt ): This decides what component of c is supplied to ht and ot . t t t t ft = σ (wxf · xt + whf · ht−1 + bf ) gt = tanh(wxg · xt + whg · ht−1 + bg )

 t  t it = σ wxi · xt + whi · ht−1 + bi ct = ft × ct−1 + it × c˜ t   t t ot = σ wxo · xt + who · ht−1 + bo (ot × ht ) = gt × tanh(ct ) where W xf , W x i, W x o, and W x g are the weight arrays for the associated linked input sequence, W hf, W h i, W h o, and W h g are the weight arrays of the preceding time step’s short-term state, and bf , bi , bo , and bg are biases. 4.1 Bi-directional Long Short-Term Memory Bidirectional long short-term memory, often known as Bi-LSTM, is an analogy for sequence management composed of 2 LSTMs. The first one receives input in the forwards direction, and the second LSTM takes input in the opposite direction. Because the LSTM process is employed two times, it reforms the long-term interdependence and increases model performance.

5 Proposed Approach 5.1 Benchmark Datasets UNSW-NB15 the Australian Center for Cyber Security, developed this dataset [15]. It comprises almost two million features, with 49 types retrieved by applying Bro-IDS, Argus techniques, and freshly built methods. There are 257673 samples, of which 93000 are classified as normal and 164673 are malicious.

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NSL-KDD is the most well-known dataset considering the IDS domain [16]. It is an upgrade focused on an old dataset (KDD99), and the test set is suitably separated into several degrees of difficulty. Despite the fact that it currently has certain flaws and is not a flawless description for current real infrastructure networks, it may be used as an excellent dataset for measuring performance to assist studies evaluating different IDSs. CSE-CIC-IDS2018 is an IDS (intrusion detection system) dataset created by the Canadian Institute of Cyber Security (CIC) in 2018 and hosted on AWS (Amazon Web Services). Additionally, it represents the most recent and extensive intrusion dataset that is currently accessible to the general public [17]. CSE-CIC-IDS2018 gathered for the purpose of initiating real-world attacks. It is an upgrade of the CSE-CIC-IDS 2017 dataset, which comprises the attack dataset’s essential standards and addresses several recognized attack types. 5.2 Data Preparation Data Cleaning. Getting rid of information that is inaccurate or useless is the process of cleaning data before analysis. This is data that, in general, can have a detrimental influence on the model or algorithm into which it is input by reinforcing an incorrect concept. In addition to eliminating large portions of irrelevant data, data cleaning also frequently refers to correcting inaccurate data in the train-test dataset and minimizing duplicates. Data Transformation. The process of transforming unstructured raw data into one that is more suited for model construction is known as data transformation. There are both number and nonnumeric characteristics in every dataset. The suggested Bi-LSTM model accepts only numerical data inputs. We must thus transform any nonnumeric features to their numerical equivalents. Data Normalization. The next logical step is feature scaling, when these features have been effectively converted to numeric form. Feature scaling guarantees that the dataset is normalized. We use Min-Max scaling to scale each feature’s value to fall inside the limit of (0, 1) since the values of several features in the 3 datasets have an unequal distribution. By doing this, we ensure that our classification algorithm does not give results that are biased. The Min-Max feature scaling expression is as follows: Z = 

X − Xmin Xmax − Xmin

where Z indicates the new (scaled) value, and X represents the original value.

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6 Results and Discussion In this section, we describe the Bi-LSTM implementation and analyse the experimental results. On the three datasets, we compare the model’s performance to that of cuttingedge techniques. In addition, we show a comparison of performance on all datasets with several newly published approaches. 6.1 Implementation The proposed Bi-LSTM model was trained and implemented using Python on a laptop with an Intel Core i7-9300H CPU and 16 GB RAM. It runs on an NVIDIA GeForce GTX GPU, 4 GB GDDR4 VRAM. All datasets are gathered and processed in data collection in the suggested model. The collecting dataset is provided to the phase of data preparation. The dataset was converted and normalized before being saved in files. The normalized data are separated into two sets: training and testing. To make the network more stable, the suggested model is linked with bidirectional long short-term memory and a dropout layer, as shown in Fig. 1.

Fig. 1. System work flow

6.2 Results To assess the model’s efficiency, we ran binary classification experiments (attack and normal). Bidirectional Long-Short Term Memory (Bi-LSTM), we implemented a Drop Out layers, and layers (Dense) were used to construct the model. Table 1 represents the layers and the total number of parameters learned. The model was run 30 times (epochs) with a batch size of 32. The Adam optimizer was used for the activation functions (ReLU, tanh, sigmoid). Binary cross entropy was used to calculate loss, and the model summary is shown in Table 1. The display of experiments is evaluated using the NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15 datasets. The accuracy and loss graphical representations for all datasets are shown in Fig. 2.

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Performance Metrics. We evaluate the accuracy, recall, and false alarm rate to assess the performance of our model. Additionally, we looked at the model’s precision and F score. Experiment: Binary Classification. For the Bidirectional long short-term memory proposed Model, we performed a binary classification (0, 1) for (normal and attack) utilizing all attributes from the NSL-KDD, CSE-CIC-IDS2018, and UNSW-NB15 datasets. The performance of the Bi-LSTM model is assessed for this classification using Accuracy metrics (Accuracy, Recall, FAR, F Score) shown in Table 3. Table 1. Summary and hyperparameters of Bi-LSTM model

Model Summary

Layer (type)

Output shape

Params

Bidirectional LSTM

(32,128)

33792

Dense

(32,128)

16512

Dense

(32,1)

129

Hyperparameter

Values

Epochs

30

Total params: 50,433 Trainable params: 50,433 Nontrainable params: 0

Model Hyperparameter

Activation Functions

ReLU, Sigmoid, Tanh

Loss Function

(CCE)

Optimization algorithm

Adam

Batch Size

32

Learning rate

0.001

Verbose

1

We compared the results to other approaches described in the Related Works section. Based on the accuracy, precision, recall, and F score values, the results show that the Bi-LSTM classifier is efficient at identifying network anomalies (see Table 2). As illustrated in Table 2, the proposed Bi-LSTM outperformed other existing IDSs on the CSE-CIC-IDS2018, NSL-KDD and UNSW-NB15 datasets in terms of accuracy for binary classification (normal and attack). On the CSE-CIC-IDS2018 dataset, the Bi-LSTM proposed model achieved training. The testing accuracies are 99.92% and 99.09%, respectively. These results are better than those of the other current models on the same dataset.

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(a) Accuracy of CSE-CIC-IDS2018

(b) Loss of CSE-CIC-IDS2018

(c) Accuracy of NSL-KDD

(d) Loss of NSL-KDD

(e) Accuracy of UNSW-NB15

(f) Loss of UNSW-NB15

Fig. 2. Plots of Bi-LSTM performance

Table 2 shows that on the NSL-KDD dataset, the proposed Bi-LSTM model achieved a detection accuracy and f Score of 93.42% and 89.65%, respectively. In comparison to the other models, it also achieved higher accuracy rates on the UNSW-NB15 dataset of 98.37%, with a low F score of 44.37%. Additionally, the Bi-LSTM model outperformed conventional approaches in detecting anomalies due to a greater F score and a lower percentage of false alarms being raised.

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Table 2. Bi-LSTM performance for binary classification and Comparison between other methods Performance% Dataset NSL-KDD

CSE-CICIDS2018

UNSW-NB15

Method

Year

Accuracy

F Score

SVM [5]

2019

77.12%

73.90%

CNN-Adaboost [6]

2021

74.19%

67.95%

DNN [7]

2019

81.60%

80.18%

Ours Bi-LSTM

2022

93.42%

89.65%

MLP [8]

2020

98.70%

97.70%

Random Forest [8]

2020

98.80%

97.80%

Gradient Boosting [9]

2020

99.11%

99.02%

Ours Bi-LSTM

2022

99.92%

99.09%

ANN [10]

2020

79.28%

83.06%

RNN [18]

2022

93.98%

90.03%

CNN-1D [10]

2020

87.71%

91.72%

Ours Bi-LSTM

2022

98.37%

44.37%

Table 3. Bi-LSTM performance for binary classification (Attack and Normal) Performance metrics Dataset

Accuracy

Recall

FAR

Precision

F Score

NSL-KDD

93.42%

89.61%

1.14%

98.02%

89.65%

CSE-CICIDS2018

99.92%

94.05%

1.00%

1.00%

99.09%

UNSW-NB15

98.37%

44.30%

0.00%

44.30%

44.37%

7 Conclusion and Future Work An attempt was conducted in this research to establish an artificial system for detecting network intrusions. The investigation made use of the publicly available NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets. Each of them is large and complicated, with a high amount of data. The model was created via the Bi-LSTM approach. As evaluated by comparing the results of the most recent publications in the Related Works section, the model produced extremely good performance. The accuracy of performance measurements was used to analyse the achievements. The model achieved an average accuracy of 93% and 99% on all datasets. When compared to other methods, the algorithm’s performance is clearly boosted, and it has high validity. Although deep learning has certain benefits, in terms of detection impact, it takes a long time in our comparison experiment, which indicates that it will cause a long detection time in the real application situation of a network infrastructure and affect the reaction time of attack detection.

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The model may still be updated in the future, and it may work to develop an automated system for identifying malicious activity that may be distributed online (real-time).

References 1. Karatas, G., Sahingoz, O.K.: Neural network based intrusion de tection systems with different training functions. In: 2018 6th International Sym posium on Digital Forensic and Security (ISDFS). IEEE (2018) 2. Ahmed, N., et al.: Network threat detection using machine/deep learning in sdn-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction. Sensors 22(20), 7896 (2022) 3. Adadi, A.: A survey on data-efficient algorithms in big data era. J. Big Data 8(1), 1–54 (2021). https://doi.org/10.1186/s40537-021-00419-9 4. Salama, M.A., Eid, H.F., Ramadan, R.A., Darwish, A., Hassanien, A.E.: Hybrid intelligent intrusion detection scheme. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (eds.) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol. 96, pp. 293–303. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-205057_26 5. Chu, W.-L., Lin, C.-J., Chang, K.-N.: Detection and classification of advanced persistent threats and attacks using the support vector machine. Appl. Sci. 9, 4579 (2019). https://doi. org/10.3390/app9214579 6. Wang, A., Wang, W., Zhou, H., Zhang, J.: Network intrusion detection algorithm combined with group convolution network and snapshot ensemble. Symmetry 2021, 13 (1814). https:// doi.org/10.3390/sym13101814 7. Gao, X., Shan, C., Hu, C., et al.: An adaptive ensemble machine learning model for intrusion detection. IEEE Access 7, 82512–82521 (2019) 8. Fitni, Saeful, Q.R., Ramli, K.: Implementation of ensem ble learning and feature selection for performance improvements in anomaly based intrusion detection systems. (IAICT). IEEE (2020) 9. Karatas, G., Demir, O., Sahingoz, O.K.: Increasing the performance of machine learningbased IDSs on an imbalanced and up-to-date dataset. IEEE Access 8, 32150–32162 (2020) 10. Andresini, G., et al.: Multichannel deep feature learning for intrusion detection. IEEE Access 8, 53346–53359 (2020) 11. Vasilomanolakis, E., et al.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surveys (CSUR), 47(4) 1–33 (2015) 12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997) 13. Gers, F., Schmidhuber, J., Cummins, F.A.: Learning to forget: continual prediction with LSTM. Neural Comput. (2000) 14. Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive dataset for network intrusion detection systems (UNSW-NB15 network dataset). In: 2015 Military Communications and Information Systems Conference (MilCIS). IEEE (2015) 15. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) (2009) 16. A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018). https://registry.opendata.aws/csecic-ids2018. Accessed 20 Oct 2022 17. Van der Maaten, L., et Hinton, G.: Visualizing data using t- SNE. J. Mach. Learn. Res. 9(11) (2008) 18. Kasongo, S.M.: A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Comput. Commun. (2022)

Hand Gesture Recognition Using Machine Learning for Bionic Applications: Forearm Case Study Oussama Lamsellak1(B) , Ahmad Benlghazi2 , Abdelaziz Chetouani1 , and Abdelhamid Benali2 1 LaMAO Laboratory, ORAS Team, 60000 Oujda, Morocco

[email protected] 2 Electronics and Systems Laboratory, ENSA Oujda, Mohammed First University Oujda,

60000 Oujda, Morocco

Abstract. The ubiquitous use of accelerometers and gyroscopes can provide meaningful knowledge about the recognition of human limb movements and offers significant potential for the design of human-machine interaction and the control of bionic systems. Our research puts an emphasis on linking acceleration data produced by hand movements to surface electromyograms (sEMG) collected in the arm in order to actively participate in robotic prosthesis control and the development of digital healthcare systems. The goal of this work is to demonstrate how to model a flexible system of hand gesture recognition using a dual-sensor accelerometer and gyroscope as a first stage, with the ability to evolve and improve its efficiency by incorporating other signals in subsequent stages of feature extraction and processing. Our research is primarily focused on identifying the triaxial signal generated via the accelerometer and gyroscope that appears when the hand is performing six gestures. Based on a massive gesture library collected over a long period of time by nine individuals belonging to three age categories and different genders, a forearm gesture classification model has been successfully developed, implementing a novel set of measurement criteria and statistical characteristics by virtue of five reputed machine-learning classifiers with an overall accuracy of 97.9% for training and 88.0% for testing, which are employed to differentiate and learn the forearm direction classes and interpret the performances. Keywords: Gesture Recognition · Machine Learning · Accelerometer · EMG signals

1 Introduction Several qualitative studies in the robotics discipline have focused on identifying human gestures [1]. Hand gesture recognition procedures are indispensable for human-machine interaction (HMI) and human activity recognition (HAR). It has the potential to improve the efficiency and speed of human-computer interaction; furthermore, it is used in a variety of fields, including machine control, smart homes, transportation, education, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 153–163, 2023. https://doi.org/10.1007/978-3-031-29857-8_16

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medical, and ecological systems [2]. Activities of Daily Living (ADLs) are fundamental and necessary in practical personal activities. In order to be completely independent, people must be able to handle these important daily responsibilities [3]. Deficits in ADL functioning can be found by comprehending the implicit physical mechanisms necessary for such movements with appropriate selectivity to improve existing rehabilitation treatments. Various services and applications have been explored in the area of pattern recognition. Recognizing human gestures has been the focus of numerous research projects [4, 5], where the hand gesture plays an important role and can be defined as a noticeable movement of the hands used to express something, allowing the extensive use of gesture recognition systems and giving them the ability to convert divergent hand signals into instructions for devices as input. According to a theoretical model of networked devices [6], gestures have become more prevalent for spontaneous communication with electronic equipment and bionic systems. Despite the availability and diversity of gestures and movements expressed by humans for communication and control of intelligent machines, there are numerous issues with regard to how to properly collect those gestures during the acquisition phase and then optimize the multiple processing stages, ending with their storage and thus optimal and productive use. There is constant interest in the research of human activities involving biosignals, which concentrate on the recording of biological events, such as a beating heart or a muscle contracting, in space, time, or space-time. This biological event frequently generates signals that may be monitored and evaluated due to the electrical and mechanical activity involved. Numerous studies have been conducted on biological signals like electromyography (EMG) [7], which are employed in many interesting applications, particularly for controlling robotic limbs and bionic organs. We can describe this signal in a simple way as electrical information that provides the status of the muscles. It is particularly exploited in studies on the contraction of the hand, wrist, elbow, and leg muscles. Those qualities make it easier to use them in conjunction with other biological and instrument signals. To advance the aforementioned research area as well as understand and explore the different cited vital signals, many techniques exist. Among them is sensor-based gesture recognition (SGR), which is a methodology that continuously recognizes gestures via sensor data such as accelerometers and gyroscopes. Consequently, wearable sensor-based movement recognition has recently received considerable interest due to rapid advancements in the supervision and control of bionic organs, where it clearly shows that they are less dependent on their environment than vision-based installations. The majority of academics who are interested in identifying the gestures and movements of all hand parts face numerous challenges. Specifically, collecting massive training samples is required for the configuration of statistical properties and other types of structural features. Our full focus in this review is to strengthen the existing strategies by including more easily accessible data modalities as features, such as accelerometer and gyroscope data, which can be accurately combined with particular EMG signals and others to improve gesture recognition systems’ performance. In this work, we address the challenge: how to create a hand gesture recognition system using a dual-sensor accelerometer and gyroscope as a first stage, combined with other biological signals? To achieve this goal, a 3-axis accelerometer and gyroscope are attached to the human

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arms, capturing their behavior (gestures and postures). In our case study, and by applying supervised learning, it is feasible to identify a link between a set of discrete signal instances and a set of movements; further, the data was evaluated using the Fast Fourier Transform (FFT) and the Kalman filter [8]. Classification and gesture recognition have both been accomplished using artificial intelligence (AI) algorithms. Five renowned machine-learning classifiers, along with a new set of predictor parameters and features are used to recognize gestures and discriminate movement. Outstanding recognition rates (up to 97.9%) for hand-six gesture recognition have been achieved using a linear discriminant analysis. The following is the structure of this paper: the Sect. 2 outlines our technical strategy. As for the Sect. 3, we demonstrate our methodology by emphasizing the many steps of data collection, signal processing and filtration, feature extraction, and human gesture classification algorithms. In the Sect. 4, we show the study results as well as a performance comparison.

2 Technical Approach In this project, we are interested in using a number of sensors to detect forearm activity. The set includes (i) an accelerometer and a gyroscope, as well as (ii) surface EMG electrodes. Utilizing accelerometer and gyroscope sensors in human limbs’ activity recognition has strong potential due to their ability to measure static and dynamic acceleration forces and monitor angular velocity. Furthermore, they consume low power, permitting continuous sensing throughout the day. Emg signals provide knowledge about the state of the muscles, and they offer additional signal information emitted by a set of muscle fibers, which we aim to integrate in the next stage of the project and exploit for more dependable and accurate measurements. In many different application sectors, Fast Fourier Transform (FFT) analysis is one of the most popular methods implemented for signal analysis. Numerous signal properties can be examined using this algorithm. As a result, the FFT was selected to extract the function’s core and isolate the acceleration of each of the axes X, Y, and Z. The short- and long-term shifts in the recording environment cause unwanted fluctuations, which are distortion elements that influence raw signal use. In light of what we previously mentioned, the Kalman filter has been chosen since it can predict process status while decreasing mean squared error, demonstrating his proficiency in the study of dynamical systems [9]. Considering that it includes projections of past, present, and future situations, the Kalman filter is highly efficient. The filter monitors the angle of the sensor and then corrects itself to offer precise information for the prediction of the subsequent time step. In this research, we attempt to explain our motivation, which is primarily concerned with the coherent integration of all the technologies and algorithms mentioned previously, in order to explain how to ensure an effective hand gesture recognition system that can progress and become more influential by incorporating other signals in advanced stages of data collection and processing and producing positively favorable results (Fig. 1).

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Fig. 1. Process of sensors and hardware development.

3 Proposed Architecture Our proposed approach is divided into three levels: Initially, accelerometer and gyroscope sensors are used to capture forearm gestures. Secondly, the collected data will be incorporated into a variety of learning processes. Finally, a forearm direction is identified, and activity is recognized in the last stage. 3.1 Data Capture: Subjects and Activities In order to verify the ability of acquired signals for prosthesis control, a data-collection experiment with nine volunteers belonging to three age categories and different genders was conducted (elderly, adults, and children). Each participant is asked to complete for a couple of minutes a series of six gesture classes used in the experiments, as shown in Fig. 2. Each set of movements was recorded continuously. To ensure that each action is independent of the others, there is a two-second break between each action. The dualsensor used are a 6-axis motion tracking device combining a triaxial gyroscope and a triaxial accelerometer. The files comprising the recorded signals were analyzed and directly treated on the workstation using the MATLAB software library.

Fig. 2. Illustration of the experiment gestures.

3.2 Signal Pre-processing A visual analysis of the collected acceleration signals was carried out. The data on acceleration between the start and stop timings of the gesture were referred to according to the gesture’s name. One sample was captured every 20 ms at a sampling frequency of 50 Hz. Each participant generates between 1140 and 1200 s of data. Three-dimensional

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acceleration data samples totaling 542,000 were obtained. The signal calculated for each activity necessitates that it be windowed as a preparation for future steps related to feature extraction [10], and then a classification decision will be synthesized from each window. As a direct consequence, the signal was split up into two-second windows of 100 samples each, overlapping every 50 samples. 3.3 Feature Extraction and Selection In order to accomplish pattern recognition, which is the purpose of this stage, it is crucial to distinguish the primary signal features from the segmented data. This stage of our architecture reduces the dimensionality of the data by eliminating irrelevant information and finding fundamental features from the input data, thereby improving the precision of learned models. The features applied in this research were retrieved from the temporal domain via statistical techniques. These parameters have been effectively exploited in time series analysis implementing machine learning [11]. After a detailed examination and comparison of numerous relevant articles [12, 13], the accelerometer and gyroscope sensors were integrated to provide a feature vector containing the nine statistical measurements listed in Table 1. Features can be chosen using selection procedures. These Table 1. List of features extracted with signification. Statistical features

Description

Signification

-Mean-

Arithmetic mean

The mean value along each axis for a predetermined time period

-RMS-

Root mean square

To define the pattern of the signal and its most repeating form

-STD-

Standard deviation

This variable denotes the difference between each signal window and its average

-PCA-

Principal component analysis

To decrease the information set’s dimensionality and monitor correlations

Max&min

Max&min value

Maximum and minimum for each window

-Minmax-

Minmax value

The difference between the maximum an minimum is expressed by this variable

-VAR-

Energy

The variation over a designated time period

-Ku-

Kurtosis

To determine the distribution of tails’ weight

-Sk-

Skewness

It measures the distribution’s symmetry

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techniques select features based on discrimination criteria that are largely unrelated to classification. Several methods use simple correlation coefficients, and others adopt mutual information or statistical tests. In our case, we used the Minimum redundancy, maximum relevance (MRMR) feature selection approach [14], which identifies the best features that aid in class separation and the elimination of redundant functions. Moreover, these features are relatively easy and efficient to compute and extract. Finally, 48 features out of 60 were selected to describe each window of activity. 3.4 Classification of Data The main focus of AI algorithms is to discover patterns that will allow us to develop a learning process. However, not all algorithms are made for the same purposes. They are typically divided into two categories based on two factors. The first factor is the learning mode, wherein we are required to differentiate between supervised and unsupervised methods. The second factor is linked to the sort of problem to be solved, where regression techniques are dissimilar from classification algorithms. In our classification approach, the signals were divided into training and testing groups with the purpose of adopting artificial intelligence approaches. A multitude of automatic classification techniques [15], including: Linear discriminant analysis LDA, Support vector machine SVM, Naive Bayes, Decision Tree and nearest neighbor algorithm KNN were tested in this study. Classifiers were trained and evaluated on the extracted features using the Hold-out validation method [16] (Fig. 3).

Fig. 3. Main proposed architecture in this paper’s contribution.

4 Results and Discussions The results shown in Fig. 4 demonstrate that just using the root mean square and variance of the acceleration signal for subject S1, who belongs to the adult group (calculated for a 2-s window), we can easily differentiate the forearm direction classes. The problem may be in the separation of some forearm gestures. These signals’ features are remarkably

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similar to each other. We intend to improve it in the future by incorporating EMG signals. We agree that the selection of appropriate features is a principal step toward creating hardware and software that empower personalized gesture recognition. The detection and recognition of daily activities with minimal and adequate features is fundamental for the development of bionic prosthesis control in a range of systems and digital tools.

Fig. 4. The features distribution of the acceleration signal for subject S1 (2-s window).

4.1 Classification Result In this section, the percentage of times a classification model predicts correctly in comparison to the total number of predictions made by the classifier is presented (Table 2). Following the completion of the training system, various tests were applied to determine the recognition rate for each gesture. A number of classifiers are applied to the collected data. Overall, LDA showed the highest results for the three groups, with an accuracy of 97.90% for the adult group, 90.50% for the junior group, and 89.80% for the elderly group. Concerning the second position in the classification’s accuracy The SVM also has a high rating with very similar results (adult grp = 95.40%, junior grp = 88.50%, and elderly grp = 87.20%, respectively). While the outcomes from the other classifiers were nearly equivalent. The LDA performed satisfactorily at the classification level of the all groups dataset once more, owing to its many advantages to process attributes with higher-dimensional spaces onto a smaller space to avoid dimension complexity while also minimizing operations time and dimensional projections. Observing the outcomes for all groups (Fig. 5), the recognition rate was 92.73% and 90.36% for each LDA and SVM in a row. KNN and Decision Tree revealed comparatively worse results.

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Adult

Elderly

LDA

90.50%

97.90%

89.80%

SVM

88.50%

95.40%

87.20%

Naive bayes

87.70%

93.30%

86.15%

KNN

86.20%

92.20%

84.30%

Decision Tree

85.00%

90.90%

81.40%

Fig. 5. Classification accuracy for different classifiers ‘All groups’.

4.2 Performance Evaluation In the final step of result validation, we concentrate on providing the confusion matrix and the percentages of correct classification for each class (true positive rates) in order to facilitate results checking and analyzing machine learning classification performance. The confusion matrix of subjects belonging to the adult category (Table 3) reveals that there is slight confusion in distinguishing between the forearm gestures (G1) & (G2) and (G3) & (G4). In regards to the confusion matrix for all subjects (Table 4), the accuracy of gesture recognition frequently deteriorates [17, 18] due to the particularity of age, its relationship to how difficult it is for the elderly to control their hands compared to how energetically the young accomplish the necessary movements. In line with these research figures and despite the multiple statistical features that were combined to validate the classification of signals obtained for bionic prosthesis control, other relevant key indicators may be used in subsequent research to strengthen the classification findings. Furthermore, the signals used in this experiment achieved high classifying accuracy, indicating that the merge with other signals will be able to produce and confirm the model results of identified gestures and movements [19], making it more approachable and versatile in this kind of application domain.

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True class

Table 3. Confusion matrix of the LDA classifier for ‘Adult group’.

G1 G2 G3 G4 G5 G6

G1

G2

Predicted class G3 G4

G5

G6

465 3 0 0 0 0

11 474 0 0 0 0

0 0 459 3 0 0

1 0 0 0 473 13

1 0 0 0 0 468

0 0 20 476 5 0

TPR 97.30% 99.40% 95.80% 99.40% 99.00% 97.20%

Table 4. Confusion matrix of the LDA classifier for ‘all groups’

True class

G1

G1 G2 G3 G4 G5 G6

1303 80 0 0 14 12

G2

85 1275 0 0 17 6

Predicted class G3 G4

0 0 1298 79 10 28

0 1 65 1320 15 10

G5

8 13 0 32 1214 39

G6

9 21 27 0 40 1295

TPR 93.70% 91.72% 93.38% 92.01% 93.09% 93.16%

5 Conclusion Due to the growing demand for interactive programming platforms in the field of robotics and modern human-machine interaction. A hand gesture recognition system using a dual-sensor was developed. This system’s input devices are a 3-axis accelerometer and a gyroscope that capture human forearm actions. Data was collected from a three-age group of participants under real-world conditions. Recognition accuracy of up to 97.9% was achieved in several everyday forearm movements, where the theoretical results obtained indicated the possibility of real-world practical applications. Future research will focus on increasing the average number of correctly classified instances of gestures by developing our gesture recognition model in a variety of ways. Firstly, we want to collect data from various sensors (EMG electrodes, visual sensors, strain sensors, and pressure sensors) in order to identify additional activities (wrist flexion and extension, hand grasp, and finger movement). Second, we want to use electromyography and mechanomyography signals to control bionic systems and develop innovative digital health data platforms.

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References 1. Suk, H.-I., Sin, B.-K., Lee, S.-W.: Hand gesture recognition based on dynamic Bayesian network .framework. Patt. Recogn. 43, 3059–3072, 9 September 2010. https://doi.org/10. 1016/j.patcog.2010.03.016 2. Wang, H., et al.: Hand gesture recognition framework using a lie group based spatio-temporal recurrent network with multiple hand-.worn motion sensors. Inf. Sci. 606, 722–741 (2022). ISSN 0020-0255. https://doi.org/10.1016/j.ins.2022.05.085 3. Chu, Y.-C., Jhang, Y.-J., Tai, T.-M., Hwang, W.-J.: Recognition of hand gesture sequences by accelerometers and gyroscopes. Appl. Sci. 10, 6507 (2020). https://doi.org/10.3390/app101 86507 4. Miriam Lee-Cosio, B., Delgado-Mata, C., Ibanez, J.: ANN for gesture recognition using accelerometer data. Proc. Technol. 3, 109–120 (2012). ISSN 2212-0173. https://doi.org/10. 1016/j.protcy.2012.03.012 5. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use Erik Scheme, MSc, PEng; Kevin Englehart, PhD, PEng* Institute of Biomedical Engineering, University of New Brunswick, Canada 6. A new hand finger movements’ classification system based on bicoherence analysis of twochannel surface EMG signals, Necmettin Sezgi. The Natural Computing Applications Forum (2017) 7. Evaluation of surface EMG-based recognition algorithms for decoding hand movements, Sara Abbaspour & Maria Lindén & Hamid Gholamhosseini & Autumn Naber4 & Max OrtizCatalan, November 2019 8. Will, N.C., Cardoso, R.: Comparative analysis between FFT and Kalman filter approaches for harmonic components detection. In: 2012 10th IEEE/IAS International Conference on Industry Applications, pp. 1–7 (2012). https://doi.org/10.1109/INDUSCON.2012.6451420 9. Bao, T., Zhao, Y., Raza Zaidi, S.A., Xie, S., Yang, P., Zhang, Z.: A deep Kalman filter network for hand kinematics estimation using sEMG, Patt. Recogn. Lett. 143, 88–94 (2021). ISSN 0167-8655. https://doi.org/10.1016/j.patrec.2021.01.001 10. Banos, O., Galvez, J.M., Damas, M., Pomares, H., Rojas, I.: Window size impact in human activity recognition. Sensors (Basel). 14(4), 6474–6499 (2014). https://doi.org/10.3390/s14 0406474.PMID:24721766;PMCID:PMC4029702 11. Subasi, A.: Chapter 4 - Feature Extraction and Dimension Reduction. In: Subasi, A., (Ed.) Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques, pp. 193–275. Academic Press (2019). ISBN 9780128174449. https://doi.org/10.1016/B9780-12-817444-9.00004-0 12. Lamsellak, O., Benlghazi, A., Chetouani, A., Benali, A.: Human body action recognition with machine learning for bionic applications: a Sensor Data Fusion Approach. In: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1– 5 (2022). https://doi.org/10.1109/ICECET55527.2022.9873059 13. Chao, G., Luo, Y., Ding, W.: Recent advances in supervised dimension reduction: a survey. Mach. Learn. Knowl. Extr. 1(1), 341–358 (2019). https://doi.org/10.3390/make1010020 14. Zhao, Z., Anand, R., Wang,M.: Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 442–452 (2019). https://doi. org/10.1109/DSAA.2019.00059. 15. Burlina, P., Billings, S., Joshi, N., Albayda, J.: Automated diagnosis of myositis from muscle ultrasound: exploring the use of machine learning and deep learning methods. PLoS ONE 12(8), e0184059 (2017). https://doi.org/10.1371/journal.pone.0184059

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Detecting and Extracting Cocoa Pods in the Natural Environment Using Deep Learning Methods Kacoutchy Jean Ayikpa1,2,3(B) , Diarra Mamadou2 , Sovi Guillaume Sodjinou1 , Abou Bakary Ballo2 , Pierre Gouton1 , and Kablan Jérôme Adou2 1 ImVia, Université Bourgogne Franche-Comté, Dijon, France

[email protected]

2 LaMI, Université Felix Houphouët-Boigny, Abidjan, Côte d’Ivoire 3 UREN, Université Virtuelle de Côte d’ivoire, Abidjan, Côte d’Ivoire

Abstract. Applications for image processing increasingly rely on object extraction and detection. The harvesting of cocoa pods produces the cocoa beans used to make chocolate. One of the most well-liked goods, chocolate, is made from these beans. It would be possible to develop intuitive ways to assess cocoa pods’ ripeness, the presence of pod illnesses, or the amount of cocoa gathered from a specific location by separating cocoa pods from other natural materials. Our research intends to assess how well deep learning-based strategies work for locating and removing cocoa pods from their natural surroundings. This model will allow farmers to count cocoa pods on a specific cocoa plant and, generally, on a defined area. U-Net and FCN algorithms were employed. In the analysis of the results, the algorithms’ validation phase yielded scores for U-Net and FCN of 93.61 and 93.06, respectively, and the test phase yielded scores for U-Net and FCN of 92.92 and 94.20, respectively. This indicates that FCN could generalize its learning more accurately than U-NET. These strategies were assessed using the Jaccard similarity coefficient and the Dice coefficient. Keywords: CNN · Cocoa-pods · Algorithms comparison · Detection · Extraction

1 Introduction The detection and extraction of cocoa pods in their natural environment using deep learning methods is an important research topic in precision agriculture. Cocoa pods are an essential ingredient in producing chocolate and many other products. The ability to detect and extract these pods reliably and efficiently can improve the quality and quantity of the harvest, which can positively impact the cocoa industry [1]. Deep learning methods are techniques used in image processing that makes it possible to learn automatically from complex data. FCN and U-Net are convolutional neural network architectures used for image segmentation [2]. These architectures can extract useful features from cocoa pod images in their natural environment, which can help detect and remove them reliably. Determining the cocoa harvest is beneficial for producers and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 164–174, 2023. https://doi.org/10.1007/978-3-031-29857-8_17

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buyers. Also, better management of cocoa plantations would allow growers to monitor the evolution of pod growth and take measures accordingly to improve the quality and quantity of the harvest [3]. Faced with these problems, the automatic detection and extraction of cocoa pods are necessary. This manuscript will present an approach based on convolutional neural network methods, in this case, FCN and U-Net, to detect and extract cocoa pods in a natural environment [4, 5]. We will present a methodology to train FCN and U-Net models on accurate cocoa pod image data, and we will evaluate the performance of our models by comparing their results with existing detection and extraction methods. We will also discuss the implications of our approach for the cocoa industry and future research perspectives in this area.

2 Related Work Image segmentation is a crucial low-level process that divides an image into visually distinct and consistent parts using various criteria, such as texture and/or color. From this straightforward statement, the fundamental issue with the theoretical assessment of these methodologies emerges. Most segmentation techniques consider human inputs as foreground and background restrictions, such as fruits, pointers, and other items. In the beginning, background/foreground probability is predicted using conventional approaches that combine restrictions with basic image features. Kraemer et al. [6] created a plant location probability map using a neural network model for posture regression. After that, stems are accurately retrieved from the heat map to the nearest centimeter. A brand-new joint model architecture built on FC-DenseNet was used by Lottes et al. [7]. When performing segmentation, the encoder typically creates a compressed but information-rich representation of the input, and the decoder oversamples the representations to the original input size while making per-pixel predictions. However, two decoders are connected to the encoder data volume in this instance. The plant decoder generates plant characteristics to determine whether a pixel is soil, a plant, dicotyledonous weeds, or grassy weeds. Additionally, the stem decoder detects stems in the region of plants and weeds. However, these strategies are useless in complicated situations because they take only low-level aspects into account while neglecting high-level information. Deep learning-based methods ultimately exploit high-level characteristics to use constraints for color image segmentation and differentiate plants from weeds from them, even though there is still work to be done [8, 9, 10, 11] to improve conventional methods. The first deep learning-based method and numerous random point sampling techniques that are now considered industry standards were proposed by Xu et al. [12]. The architecture of the network is the subject of some research. DeepLabv3 is a semantic segmentation architecture that enhances DeepLabv2 by making several high-level changes. Modules that employ atreous convolution in cascade or parallel to capture context at several scales aim to address the issue of segmenting objects at numerous scales. Multi-scale inputs are often treated using the same model. Juliana Rodrigueiro et al. investigated a technique to identify cocoa pods in their environment to differentiate further ripe ones that are ready to be indicated. It is a method that aims to eventually automate the harvesting of cocoa pods in cocoa plantations, using automated robots

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capable of collecting and transporting products. Their experience has given an accuracy score of 90% [13], which applies a Laplacian pyramid transformation to the input image, feeds the input from each scale to a DCNN, then merges the feature maps from all scales. Pyramid Scene Parsing Net (PSP) [14] recently performed spatial pooling at several grid scales and displayed exceptional performance on numerous benchmarks for semantic segmentation. Other LSTM-based techniques exist [15] for aggregating global context [16] and object recognition [17], as well as spatial pyramid pooling. We experimentally discovered that it is crucial to train with batch normalization for the suggested modules [18].

3 Methods 3.1 Dataset Collection and Annotation The effectiveness of the underlying data’s annotation impacts how well-supervised learning methods function. We will first create a set of masks on our cocoa pod images to train our deep model for the segmentation stage. Each image has a JSON file that details the pixels’ spatial arrangement. A bounding polygon with the coordinates specified in the JSON file represents each feature. The cocoa pods in the images are annotated using the delimitation polygon. Figure 1 presents the principle of annotation of the dataset.

Fig. 1. Data annotation description scheme.

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3.2 Application of U-Net U-Net is a neural network model for computer vision tasks focused on solving semantic segmentation issues. This neural network model is fully convolutional. Olaf Ronneberger, Phillip Fischer, and Thomas Brox created this model in 2015 for medical image segmentation. There are two critical components to the U-NET architecture. The contraction route or encoder enables the capture of an image’s context. Convolutional and max pooling layers are assembled to create a feature map of the image and reduce its size to reduce the number of parameters in the network. The symmetric expansion is also known as a decoder. Transposed convolution enables accurate localization [19]. Figure 2 presents the architecture of U-Net.

Fig. 2. U-Net description diagram [19].

3.3 Application of FCN A neural network that only employs convolution operations, such as subsampling or oversampling, is known as a full convolution network (FCN). An FCN, also known as a convolutional neural network (CNN) without completely connected layers, is equivalent to a fully convolutional network that drives end-to-end, pixel-by-pixel image segmentation [20]. The final segmentation map is created by stacking several convolution layers with comparable fill to maintain the dimension. The model will learn the mapping from the input image to its matching segmentation map through repeated feature transformations [21]. Long et al. implemented the FCN in late 2004. A decoder and an encoder make up the structure. Figure 3 presents the architecture of FCN. U-Net and FCN are popular algorithms for image segmentation tasks due to their ability to preserve spatial information and learn complex features from input images. They can be fine-tuned by adjusting various parameters, such as the number of filters and the size of the kernels, to improve their performance on a given task.

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Fig. 3. FCN description diagram.

3.4 Architecture of Our Model

Fig. 4. General diagram of our model

Our model, represented by Fig. 4, will follow the steps below: First, we proceed to the loading of our data namely the original image and its mask; then, three sections of the dataset will be created (training set, validation set and test set). The training set and the validation set will allow us to learn the model and the test set will allow us to evaluate the performance of our model. Then, we will introduce our training set and validation set data in our models, in this case the U-Net and the FCN in order to train our models by evaluating them. Once the optimal model is found, we save it. Finally, we will proceed to the test of our model to verify these performances obtained during the learning phase.

4 Experimental Results 4.1 Setting For the training of our models, we used a batch size of 16 and an iteration of 50 epochs with the ADAM optimizer with a learning rate at 0.001.

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4.2 Evaluation Metrics For the performance evaluation of the models used, we used the following metrics: Accuracy measures how often the algorithm correctly classifies a data point. Its formula is: Accuracy =

TP + TN TP + FP + TN + FN

(1)

Loss is the loss function based on the binary cross entropy [22]. Its formula is the following: Loss = −

N 1  yi ∗ log(p(yi )) + (1 − yi ) ∗ log(1 − p(yi )) N

(2)

i=1

Intersection over Union (IoU): Jaccard’s index, also known as intersection over union and Jaccard’s coefficient of similarity, is a statistic used to assess the similarity and diversity of sample sets. The size of the intersection divided by the size of the union of the sample sets is how Jaccard’s coefficient, which assesses similarity between finite sample sets, is calculated [23]: TP TP + FP + FN

(3)

2 ∗ TP (TP + FP) + (TP + FN )

(4)

IoU = Dice =

Dice coefficient (Dice): is not only a measure of the number of positives found, but it also penalizes the false positives that the method finds, like precision. It is therefore more similar to precision than to accuracy [23]: With: TP: True positive; TN: True negative; FP: False positive; FN: False negative. 4.3 Results and Discussion • Validation step

Table 1. Summary table of the metrics results during the model validation stage Models

Accuracy (%)

Loss (%)

IoU (%)

Dice (%)

U-Net

93.69

9.79

100.0

7.49

FCN

93.04

6.00

100.0

5.82

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The results in Table 1 present the model validation stage of our study. It shows that U-Net got better accuracy than FCN. Table 2 presents the accuracy and loss of the validation phase. Table 2. Accuracy curve and loss curve of each model during the learning step Models U-Net

Accuracy Cuvre

Loss Curve

FCN

• Test step

Table 3. Summary table of metrics results during the model test stage Models

Accuracy (%)

Loss (%)

IoU (%)

Dice (%)

U-Net

92.92

11.65

100.0

8.69

FCN

94.20

4.97

100.0

5.84

Detecting and Extracting Cocoa Pods in the Natural Environment Table 4. Full system test process. U-Net

FCN

Table 5. Comparison of the method of our study to that of the literature review Models

Accuracy (%)

Juliana Rodrigueiro et al. [13]

90.00

U-Net

92.92

FCN

94.20

171

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In the test phase, we obtained the results listed in Table 3. The FCN method was able to generalize its learning by getting better accuracy compared to U-Net and all metrics and consolidating the best performance of FCN. Table 4 presents the results obtained by the methods followed by the images of the ground truth. • Comparison of algorithms

Validation step

Dice (%) IoU (%) Loss (%) Accuracy (%) 0

20 FCN

40 U-Net

60

80

100

Fig. 5. Diagram of the metrics of our models at the validation phase

The Fig. 5, we see the dice similarity and accuracy are better for U-Net compared to FCN, but FCN has a small loss compared to U-Net.

Test step Dice (%) IoU (%) Loss (%) Accuracy (%) 0

20

40 FCN

60

80

100

U-Net

Fig. 6. Diagram of the metric of our models at the test phase

Figure 6, we see the dice similarity is better for U-Net compared to FCN, but FCN had a small loss and better accuracy compared to U-Net. • Comparison of state of art We will present in Table 5 a comparison of our model with that of the literature. We observe that results of our study are superior to those of the literature.

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5 Conclusion Our study proposed two methods of detecting and extracting cocoa pods based on the deep learning model. The use of FCN and U-Net methods to detect and remove cocoa pods in the natural environment showed promising results, with an accuracy of 94.20% for FCN and 92.92% for U-Net. These methods could be used to automate the pod detection process, which can improve the efficiency, quantity, and accuracy of the harvest, which will be very useful for farmers and companies in the cocoa sector. However, it would be interesting to continue research to improve the accuracy of these methods further and extend their use to other areas. This study could be extended to set up systems for disease detection and could be helpful for precision agriculture.

References 1. Wani, J.A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., Singh, S.: Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: methodologies, applications, and challenges. Arch. Comput. Methods Eng. 29(1), 641–677 (2021). https://doi.org/10.1007/s11831-021-09588-5 2. Benomar, M.L., Settouti, N., Xiao, R., Ambrosetti, D., Descombes, X.: Convolutional neuronal networks for tumor regions detection in histopathology images. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 13–23. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-73882-2_2 3. Awafo, E.A., Owusu, P.A.: Energy and water mapping of the cocoa value chain in Ghana. Sustain. Prod. Consumption. 29, 341–356 (2022). https://doi.org/10.1016/j.spc.2021.10.027 4. Gao, P., et al.: Extract nanoporous gold ligaments from SEM images by combining fully convolutional network and Sobel operator edge detection algorithm. Scripta Mater. 213, 114627 (2022). https://doi.org/10.1016/j.scriptamat.2022.114627 5. Akila Agnes, S., et al.: Two-stage lung nodule detection framework using enhanced UNet and convolutional LSTM networks in CT images. Comput. Biol. Med. 149, 106059 (2022). https://doi.org/10.1016/j.compbiomed.2022.106059 6. Kraemer, F., et al.: From Plants to Landmarks: time-invariant plant localization that uses deep pose regression in agricultural fields (2017). https://doi.org/10.48550/ARXIV.1709.04751 7. Lottes, P., et al.: Joint stem detection and crop-weed classification for plant-specific treatment in precision farming (2018). https://doi.org/10.48550/ARXIV.1806.03413 8. Sodjinou, S.G., et al.: A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images. Inf. Process. Agric. S2214317321000731 (2021). https:// doi.org/10.1016/j.inpa.2021.08.003 9. Krähmer, H., et al.: Weed surveys and weed mapping in Europe: state of the art and future tasks. Crop Prot. 129, 105010 (2020). https://doi.org/10.1016/j.cropro.2019.105010 10. Luo, P., et al.: Deep dual learning for semantic image segmentation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2737–2745. IEEE, Venice (2017). https:// doi.org/10.1109/ICCV.2017.296 11. Li, X., et al.: Not all pixels are equal: difficulty-aware semantic segmentation via deep layer cascade (2017). https://doi.org/10.48550/ARXIV.1704.01344 12. Xu, N., et al.: Deep interactive object selection (2016). https://doi.org/10.48550/ARXIV.1603. 04042 13. de Oliveira, J.R.C.P., Romero, R.Ap.F.: Transfer learning based model for classification of cocoa pods. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE, Rio de Janeiro (2018). https://doi.org/10.1109/IJCNN.2018.8489126

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14. Zhao, H., et al.: Pyramid scene parsing network (2016). https://doi.org/10.48550/ARXIV. 1612.01105 15. Perazzi, F., et al.: A benchmark dataset and evaluation methodology for video object segmentation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 724–732. IEEE, Las Vegas (2016). https://doi.org/10.1109/CVPR.2016.85 16. Yan, Z., et al.: Combining the best of convolutional layers and recurrent layers: a hybrid network for semantic segmentation (2016). https://doi.org/10.48550/ARXIV.1603.04871 17. He, X., et al.: Multiscale conditional random fields for image labeling. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, p. II (2004). https://doi.org/10.1109/CVPR.2004.1315232 18. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). https://doi.org/10.48550/ARXIV.1502.03167 19. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-31924574-4_28 20. Shelhamer, E., et al.: Fully convolutional networks for semantic segmentation (2016). https:// doi.org/10.48550/ARXIV.1605.06211 21. Xing, Y., et al.: An encoder-decoder network based FCN architecture for semantic segmentation. Wirel. Commun. Mob. Comput. 2020, 1–9 (2020). https://doi.org/10.1155/2020/886 1886 22. Jadon, S.: A survey of loss functions for semantic segmentation. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–7. IEEE, Via del Mar (2020). https://doi.org/10.1109/CIBCB48159.2020.9277638 23. Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 234–244. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_22

Design of a New Strategy Based on Machine Learning to Improve the Energy Efficiency of Buildings Kaoutar Talbi1(B)

, Abdelghani El Ougli2 , and Tidhaf Belkassem1

1 Team of Embedded Systems, Renewable Energy and Artificial Intelligence, National School

of Applied Sciences, Mohammed First University, Oujda, Morocco [email protected] 2 Computer Science, Signal, Automation and Cognitivism Laboratory (LISAC), Faculty of Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. Traditional tactics are not a feasible option for smart buildings’ more advanced features, such as predictive and adaptive control systems. The ideas of smart buildings and socially resilient cities, in which building automation technologies are used to regulate and control energy generation, consumption, and storage, have been presented. As a contribution to a building’s efficiency enhancement, we built a strategy that will help regulators adopt better policies for new construction based on the level of efficiency. After choosing a reliable dataset and getting it ready to use, a decision tree (DT) model was trained to predict the heating load (HL) and cooling load (CL) of a new building based on its structural design and features. The evaluation of the model revealed an accuracy of 99.6% for HL predictions and 95.6% for CL predictions. In an improvement scenario, we discussed a possible energy savings of 179 GWh after using this strategy to approve a typical number of buildings (100 buildings). Keywords: HVAC · Data-driven strategy · Decision tree · Energy efficiency · Machine learning

1 Introduction Energy efficiency refers to the use of energy to provide the best results with minimal waste. The goal of energy efficiency is to use less energy than necessary; in some cases, this may mean using less energy than needed to reach a desired result. Energy efficiency can also be achieved by changing how a machine or building runs. Because buildings account for 76% of overall electricity consumption in the United States, in addition to more than 40% of energy consumption and associated greenhouse gas emissions, many building codes require all new buildings to be more efficient and existing buildings to update their air conditioning units. Modern buildings are designed to manage ventilation and temperature while still using less energy than average. Low-carbon building materials, their ability to produce pleasant atmospheres, and green building strategies help reduce fossil fuel consumption and create a sustainable environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 175–185, 2023. https://doi.org/10.1007/978-3-031-29857-8_18

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According to International Energy Agency data [1], heating and cooling loads represent the largest energy consumption component inside a residential building. This is what drove many researchers to study the heating, ventilation and air-conditioning system behavior based on different factors. The authors of [2] developed a step-by-step HVAC management strategy for generating a healthy, energy-efficient interior atmosphere. Their technique takes indoor air quality (IAQ) and thermal comfort (ITC) into account. Numerous artificial intelligence approaches, such as low-dimensional linear algorithms and artificial neural networks, have been utilized. Because predicting energy consumption for buildings is critical for sustainable households and smart cities, researchers [3] created a novel ensemble model to forecast the cooling and heating profiles of buildings based on several parameters, such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. The experimental results showed that the suggested model has R2 = 0.999 for heating load predictions and R2 = 0.997 for cooling load predictions. [4] proposes two fundamental correction processes to improve the accuracy of a subhourly day-ahead heat demand forecasting task. Predicting the success of energy efficiency strategies depends on the energy level of a region. By understanding how people consume energy and designing strategies for conserving that energy, we are helping individuals save money and protect the environment. Based on a behavioral study of the HVAC system, the previously verbalized work represents a step toward improving building efficiency. It concentrated solely on forecasting consumption based on variables, but it did not give a comprehensive solution for using prediction models to develop a completely realistic efficiency strategy. This is a solvable gap, which we aim to address in this work. In the context of improving efficiency using data, our paper presents a data-driven strategy to adjust building and construction policies to approve new constructions only if they meet the consumption limit criteria set by the building control body. These criteria are based on the proposed structure of the building and how it affects the heating and cooling loads of the building. The next section presents the detailed methodology used in this paper, which is followed by a results section and then a conclusion where we discuss some future prospects.

2 Methodology 2.1 Data-Driven Strategy We first choose a reliable dataset to train our model. Before applying any kind of analysis to the data, it is necessary to prepare it for processing by cleaning and labeling it to make it well structured. It is also necessary to verify that it does not have any missing values. Before training our model (a decision tree model in this case), we split the data into training and testing parts. We use the testing data to see how the model performs. If the model performs well after training and assessment, it is ready to be implemented and used to anticipate future heating and cooling loads based on building attributes. This enables building body controllers to anticipate the energy consumption of HVAC systems within buildings with certain structural characteristics, which provides critical information for decision-making. These data-driven decisions will then enhance efficiency over time, and monitoring progress may greatly assist in changing policies to be more suited to

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establishing an energy-efficient community. Figure 1 depicts an illustration of our datadriven strategy.

Fig. 1. The proposed strategy for building efficiency.

2.2 The Dataset Used Tsanas and Xifara’s Ecotect software simulation produced the dataset utilized in this study, presuming that the buildings were in Greece, especially Athen [5]. The most modern and commonly used materials for the building construction industry were deployed using eighteen preliminary cubes (3.5 × 3.5 × 3.5 cm3 ) for each of the twelve constructions. Table 1 describes the input and output data in detail. 2.3 Decision Tree Model and Evaluation Metrics Decision Tree. A decision tree is used to quantify the likelihood of success of various series of decisions taken to attain a given objective, giving rise to the concept of utilizing it as a sort of predictive model in machine learning to compute the many possible outcomes based on one or more parameters [6]. DT splits the dataset into smaller and smaller portions as it develops an associated decision tree. Within machine learning, the root node is the beginning of the decision tree and often represents the whole dataset [7]. The terminus of a branch or the result of a series of decisions is represented by a leaf

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Parameters and units

Representation

Features

Relative X1 compactness-No units Surface area-m2

X2

Wall area-m2

X3

Roof Area-m2

X4

Overall height-m

X5

Orientation-No units

X6

Glazing area-No units X7 Glazing area distribution-No units Labels

X8

Heating load-kWh/m2 Y1 Cooling load-kWh/m2 Y2

node. In our scenario, we have a regression problem; thus, we may term our decision tree a regression tree. As seen in Fig. 2, the end result is a tree with leaves and decision nodes.

Fig. 2. Decision tree general illustration

To build our decision tree model, we first compute the entropy of the target and then divide the dataset into characteristics. The entropy of each branch is then computed and added to the total entropy of the split. Prior to splitting, the resultant entropy is deducted from the entropy. As a result, after each splitting step, there is more information gain and less entropy. Then, as the decision node, we choose the property with the highest

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information gain, divide the dataset into branches, and repeat the algorithm for each branch. A branch is considered a leaf node if its entropy is zero; otherwise, more splitting is necessary. During this procedure, Eq. (1) is used to determine entropy, whereas Eq. (2) is used to calculate information gain [6]: Entropy = −

n 

pi log2 (pi )

(1)

i=1

GIndex = 1 −

n 

pi2

(2)

i=1

The probability of randomly selecting an element from the i-th class is pi, and the total number of existing classes is n. Evaluation. To evaluate the performance of our model, we used four machine learning evaluation metrics as follows [8]: The following equation represents the mean squared error (MSE) mathematically: n MSE =

k=1 (yi

− yi )2 

n

(3)

The root-mean-square error or deviation (RMSE or RMSD) is mathematically expressed using the following equation:  n 2 k=1 (yi − y i ) RMSE = (4) n 

The mean absolute error (MAE) is calculated using the following equation:  n    k=1 yi − y i MAE = n 

(5)

The coefficient of determination, or R2 , is mathematically represented using the following equation: n R =1− 2

(yi − yi )2 k=1 n 2 k=1 (yi − y) 

(6)

where in all these equations, yi represents the actual value of the output variable (HL or CL in our case), yi denotes the predicted value of the output, y represents the mean of this same variable, and n is the total number of samples in the data. 

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3 Results and Discussion Using the Jupyter notebook, we verified that our dataset is clean, labeled, and well structured, as the visualization of the first five rows demonstrates. Table 2 shows the results of data head visualization. The data also have no missing values, as Table 3 shows (false means the statement “cell does not contain any value” is not true): Table 2. Data head visualization X1

X2

X3

X4

X5

X6

X7

X8

Y1

Y2

0

0.98

514.5

294.0

110.25

7.0

2

0.0

00

15.55

21.33

1

0.98

514.5

294.0

110.25

7.0

3

0.0

00

15.55

21.33

2

0.98

514.5

294.0

110.25

7.0

4

0.0

00

15.55

21.33

3

0.98

514.5

294.0

110.25

7.0

5

0.0

00

15.55

21.33

4

0.90

563.5

318.5

122.50

7.0

2

0.0

00

20.84

28.28

Table 3. Checking missing values in the data X1

X2

X3

X4

X5

X6

X7

X8

Y1

Y2

0

False

False

False

False

False

False

False

False

False

False

1

False

False

False

False

False

False

False

False

False

False

2

False

False

False

False

False

False

False

False

False

False

3

False

False

False

False

False

False

False

False

False

False

4

False

False

False

False

False

False

False

False

False

False

—

—

—

—

—

—

—

—

—

—

—

763

False

False

False

False

False

False

False

False

False

False

764

False

False

False

False

False

False

False

False

False

False

765

False

False

False

False

False

False

False

False

False

False

766

False

False

False

False

False

False

False

False

False

False

767

False

False

False

False

False

False

False

False

False

False

We used Python to evaluate our data in a Jupyter notebook. This resulted in Table 4, Fig. 3, and Fig. 4. Table 4 shows that the average value for HL is 22.3 kWh/m2 and for CL is 24.58 kWh/m2 , with standard deviations of 10.09 kWh/m2 and 9.5 kWh/m2 , respectively. It also reveals that 75% of HL values are less than 31.6675 kWh/m2 , and 75% of CL values are less than 33.1325 kWh/m2 . The distribution of our dataset’s continuous numerical variables is depicted in Fig. 3, and the densities of HL and CL are fairly comparable; furthermore, we can see the densest value for each variable in

768.000

0.764167

0.105777

0.620000

0.682500

0.750000

0.830000

0.980000

count

mean

std

min

25%

50%

75%

max

808.50000

741.12500

673.75000

606.37500

514.50000

88.086116

671.70833

768.000

416.50000

343.00000

318.50000

294.00000

245.00000

43.626481

318.50000

768.000

220.50000

220.50000

183.75000

140.87500

110.25000

45.165950

176.60416

768.000

7.00000

7.00000

5.25000

3.50000

3.50000

1.75114

5.25000

768.000

5.000000

4.250000

3.500000

2.750000

2.000000

1.118763

3.500000

768.000

Table 4. Data general description.

0.400000

0.400000

0.250000

0.100000

0.000000

0.133221

0.234375

768.000

5.00000

4.00000

3.00000

1.75000

0.00000

1.55096

2.81250

768.000

43.100000

31.667500

18.950000

12.992500

6.010000

10.090196

22.307201

768.000

48.030000

33.132500

22.080000

12.992500

10.900000

9.513306

24.587760

768.000

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the collection. Fig. 4 shows a heatmap of the correlation matrix from our dataset. The relative compactness (X1) and surface area (X2) are highly related, with a correlation value of −99%. The roof area (X4) and total height (X5) are likewise strongly related, with a correlation value of 97.3%. This demonstrates that X1 and X2, as well as X4 and X5, have a substantial negative association. With a correlation value of 97%, HL (Y1) and CL (Y2) have a considerable positive association. These results represent general insights about the used dataset as well as a verification of an existing link between the used features (X1, X2 ... X8) and the variables we want to predict (Y1 and Y2).

Fig. 3. Feature density visualization.

The model was trained using 80% of the data, while 20% was used for testing. Residuals are plotted in Fig. 5 for heating load (HL) predictions on both training and testing data, and in Fig. 6, we have the same thing for cooling load. These plots highlight any areas where model predictions diverge from the truth. In both figures, we observe low residuals, indicating that the model is accurate. The orange line shows the training residuals, which are null in both cases. The blue dots and the blue line fitting their general pattern represent the residuals of the testing data. For testing the heating load, the distribution of residuals varied from −2 to 2, while for testing the cooling load, it varied between −6 and 10. This means that even though the model performed well, it performed better at predicting the heating load than it did at predicting the cooling load. Figure 7 shows the actual heating load and DT-predicted heating load forecasts over samples; Fig. 8 depicts the actual cooling load (CL) and DT-predicted cooling load forecasts over samples. As we can barely see the blue color representing actual values hidden under DT predictions in both figures, we can say that actual values and DT predictions are almost identical.

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Fig. 4. Data correlation heatmap

Fig. 5. Residuals for HL predictions

Fig. 6. Residuals for CL predictions

Fig. 7. Heating load forecasting via decision tree

Table 5 shows the evaluation metric results of our decision tree model, and we can observe that the model is capable of predicting the heating load from unseen data with a score of 99.6% and 0.331, 0.575, and 0.388 for MSE, RMSD, and MAE, respectively. For the cooling load, the model can predict from unseen data with a score of 95.6%

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Fig. 8. Cooling load forecasting via decision tree

and 4.069, 2.017, and 1.156 for MSE, RMSE, and MAE, respectively. For the training data or already seen data, the model score is 100% with no errors for both heating and cooling loads. Table 5. Decision tree model metrics. Target

Heating load

Metrics

MSE

RMSD

MAE

R2

MSE

Cooling load RMSD

MAE

R2

Training

0.000

0.000

0.000

1

0.000

0.000

0.000

1

Testing

0.331

0.575

0.388

0.996

4.069

2.017

1.156

0.956

Improvement Scenario. If the building control body approves the construction of 100 new buildings, each with a space of 100 m2 and average heating and cooling loads of 22.31 kWh/m2 and 24.59 kWh/m2 (see Table 4), the total consumption of these buildings will be 469 GWh (100 × 100 m2 × [(22.31 kWh/m2 ) + (24.59 kWh/m2 )] = 469000 kWh). If the building control body only approved buildings with features resulting in an anticipated consumption of less than 13 kWh/m2 for HL and 16 KWh/m2 for CL, the total consumption of 100 of these buildings would be less than 290 GWh (100 × 100 m2 × [(13 kWh/m2 ) + (16 kWh/m2 )]. Even in a scenario with a small number of new buildings, the possible improvement can reach a reduction in consumption of 179 GWh (469 GWh − 290 GWh = 179 GWh). This means that considering the structural characteristics of buildings and predicting consumption based on these characteristics has a real and measurable impact when used to set policies and regulations that new construction must meet.

4 Conclusion The proposed solution is heavily reliant on the precision and performance of the datadriven decision-making technique. In this trial, the regression tree worked admirably,

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with an accuracy score of 99.6% for heating load prediction and 95.6% for cooling load prediction. Other models performed less well using the same data in other studies, such as [8], where the accuracy scores for predicting the heating load using a multilayer perceptron (MLP), an artificial bee colony multilayer perceptron (ABC-MLP), and a partial-swarm-optimized multilayer perceptron (PSO-MLP) were 88.13%, 91.20%, and 91.26%, respectively. In the same study, the cooling load prediction scores were 89.33%, 93.49%, and 93.70% using MLP, ABC-MLP, and PSO-MLP, respectively. As a result of just approving structures with low anticipated HVAC consumption, we should expect significant improvements in new building efficiency, but it is crucial to use an accurate model to obtain prediction results that are close to the real world’s actual consumption. To continuously maximize the outcomes of our plan, it is suggested that improvements in building efficiency be measured over time to produce more developed legislation and regulations in the future. The strategy may also be improved by adding a corrective unit that continuously corrects our regression model’s predictions based on real data acquired after the first installation. Other aspects, such as inhabitant behavior within a building, can also be investigated and taken into account to improve the system’s findings.

References 1. DOE: Chapter 5: Increasing Efficiency of Building Systems and Technologies September 2015 - Quadrennial Technology Review an Assessment of Energy Technologies and Research Opportunities (2015) 2. Ren, C., Cao, S.J.: Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control. Sustain. Cities Soc. 51, 101673 (2019) 3. Chaganti, R.: Building heating and cooling load prediction using ensemble machine learning model 22 (2022) 4. Bünning, F., Heer, P., Smith, R.S., Lygeros, J.: Improved day ahead heating demand forecasting by online correction methods. Energy Build. (2020). https://doi.org/10.1016/j.enbuild.2020. 109821 5. Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560–567 (2012) 6. Charbuty, B., Abdulazeez, A.: Classification based on decision tree algorithm for machine learning. J. Appl. Sci. Technol. Trends 2, 20–28 (2021) 7. Moran, A.: Project risk management. In: Moran, A. (ed.) Agile Risk Management. SpringerBriefs in Computer Science, pp. 17–32. Springer, Cham (2014). https://doi.org/10.1007/9783-319-05008-9_2 8. Zhou, G., Moayedi, H., Bahiraei, M., Lyu, Z.: Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. J. Clean. Prod. 254, 120082 (2020)

Internet of Things, Blockchain and Security and Network Technology

Smart Grid: A Communication Model and Security Challenges Rim Marah1(B) , Inssaf El Guabassi2 , Zakaria Bousalem3 , and Abdellatif Haj3 1 Abdelmalek Essaadi University, Tetouan, Morocco

[email protected]

2 LAROSERI Laboratory, Faculty of Sciences, Chouaib Doukkali University,

El Jadida, Morocco 3 Hassan 1st University, Settat, Morocco

Abstract. Technology has changing our way of living and our way of communicating. One of the results of this innovation is Smart Grid, a distributed complex system using a modern cyber and physical equipment to run at an optimal operating point. A wide range of technologies were added to the smart grid to make the system more reliable, but it stills vulnerable to attacks because of the extensive implementation of the cyber networks. This paper will illustrate the security challenges facing the smart grid and will propose a communication network model through which we can implement our distributed transmission algorithm to meet as best as we can the Smart Grid challenges. Keywords: Smart Grid · Microgrid · Communication Network · Security · Local Level · TD Level · Smart Meter · Communication Model · Distributed Transmission Algorithm

1 Introduction A Smart Grid [1] is an overlay of classical electrical grid and information and communication technologies enabling bi-directional communication and power flow that can improve security, reliability, and efficiency of the system. We have three levels in the electrical grid [2]: the producer level, the intermediate level and the consumer level. After production of electricity, it takes different network paths that can be compared to the road network Energy. The communication links between the smart meters and the control centers [3], therefore, need to be secure against various security threats y transits on the network depending on the overall consumption, the whole production and the network architecture.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 189–196, 2023. https://doi.org/10.1007/978-3-031-29857-8_19

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Network intelligence is not new. However, the superposition of the physical electric grid and modern communication and information technologies requires a higher level of security. For example, customer can use smart meters to collect his demand profile and forecast his future demand, but the authentication mechanism in the smart grid needs to be fastest and more secure in order to increase the ability of the smart grid to verify each other’s identities and to allow the users to access to services and resources in easy and secure manner. This amounts to predict full-scale smart grid communication framework and ensure its security. It is exactly the main problem that researchers are still trying to solve it until now. In this study, we will not claim that we solve the entire problem of the full-scale smart grid communication, but we focalized on microgrid communication network. Our proposed algorithm aims to manage the communication network in this second level of the smart grid as best as possible by using the normal distribution mathematical theory. Concretely, we use a huge number of micro-grids, which means that the communication links must be more secured against all threats [4]. Once we are using in our model IP-based communication network, we should wait for different malicious threats plaguing that we list in the end. We organize our paper as following: we begin by an introduction, and then we will illustrate the Smart Grid Communication architecture and propose a communication network model in section too and present our distributed transmission algorithm. After, in section three, we will go over the most important security challenges facing the Smart Grid, and finally we will conclude our study.

2 Communication Model 2.1 Communication Architecture Communication system is the backbone of the smart grid infrastructure. Once combine a smarter system with advanced information and communication technology a large amount of knowledge for further study, monitoring and ongoing valuation techniques can be generated from different transmission system that interconnects all major substation and load centers. The communication architecture [5] is divided to tree layers, the application layer, the communication layer and the data acquisition layer. The Data Acquisition Layer: It’s the data collection layer via intelligent sensors and measuring devices, in order to send them to the communication layer. The Communication Layer: This level of communication consists of a wide variety of technologies and network devices, in order to transmit the data to the energy management system.

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The Application Layer concerns all kind of applications related to smart grid transmission management system, monitoring, optimization and control. Theses tree layers are representing in Fig. 1.

Fig. 1. Communication Architecture of Smart Grid

2.2 Communication Model Currently, there is no yet a complete communication model of Smart Grid [6]. Designing a communication network model for a Smart Grid is camming back to conceive the technologies that we should use for each Level of Smart Grid for reliable data exchange. The data is exchanging within s smart grid, from the data center to customers (final users) and the main control center through the Smart Meters using IP-based communication. The TD Level The main control center will be connected to the power plant crossing the transmission substations from one hand, and connected to the local control centers in the oder hand. At this level the communication will be thinks to an optical fiber communication link because it should support a huge among of communication data transit. The Local Level In the level of each end user, the appliances will communicate between them and to smart meter by in house wireless link.

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The Microgrid Level The transmission system interconnects al substations and control centers and power system. We must develop an advanced analytical tools and telecommunications technologies in order to achieve the best performance of the intelligent network at the transmission level such as wireless communications or ZigBee, 6LowPAN, Z-wave and others. In general, the transmission substations are installed nears the power energy generator. And, the distribution ones are installed in different neighborhoods. The distribution substations transform the energy into medium voltage level and distribute it to the buildings. This energy is supplied by the transmission substation through high voltage transmission lines. The mid-level voltage is converted by building power supplies to a lower voltage, so that it can be used by end-consumer devices. From this perspective, we can divide the Microgrid Communication into different networks, such as neighborhood: HAN: The home network will connect all the domestic smart appliances thinks to their own IP address. Each appliance will send a signal periodically in HAN and the HAN GW do the same. Communication technology in this network could be a over IEEE 802.15.4 ZigBee radio. BAN: a BAN has a number of HANs. Each HAN GW Communicate with the BAN GW via WiFi access points. At each period, and after receiving the electrical states of all its local appliances, each HAN GW send an assessment of its current electrical state to the BAN GW. That what leads to a hierarchical aspect within the different networks and respecting our Distributed Transmission Algorithm. NAN: Thinks to wireless technologies such as WiMax, the NAN GW can be connected to all its BAN GWs. All the NAN GWs will be connected to the Main Control Center as shown in Fig. 5. 2.3 Distributed Transmission Algorithm We assume that each process has a local unshared memory with a bounded capacity and at least one processor. It can only communicate directly with its neighbors by message exchanges. The exchange of messages is done without loss or alteration or modification. The manner of message exchanges between processes determines the type of network. Our model is asynchronous. We have two key elements: the dual-gateway elected smart meter (GWE) and dualgateway subordinates’ smart meter (GWS). 1. GWS: They are divided into several distributed groups. So, the overall demand will be cumulated for a neighborhood, for building or for house. 2. GWE: It can be the main controller center, NAN GW in BAN network, BAN GW in HAN network or HAN GW with domestic appliances.

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We will model our network as follow: S: is a set of GWE, where S = {mc, gwe} ie the main controller center or elected Smart meters. C: is a set of GWS, where C = {1, 2, 3 … m} At t time, each node s ∈ S offers pst respecting with maximum capacity (available t respecting energy) ps−max t pst ≤ ps−max ∀s ∈ S t ps−c is the received energy request by GWS c from the GWE s in time t Dct is the request of GWS c, where t t pr,c ≤ pnr−c ∀c ∈ C

Cs is the set of all GWS connected to the same GWE s and we assume that Cs = ∅ t,loss The losses are represented by ps−c



t t,loss t ps,c + ps,c ≤ ps,max

The algorithm executes the following instructions: 1. Each HAN GW sends its consumption to the linked BAN GW. 2. Based on the values received, the BAN GW makes a future prediction for each HAN GW by using the normal distribution 3. The BAN GW adds up the consumption of all the HAN GW related 4. The BAN GW sends the request to the NAN GW first, if it is unable to cover all the requested capacity, it sends the order to the Local Control Center 5. It waits for an answer request. Where Ø (x*): is the standard normal distribution function t : is the maximal consumption of local level c. pc−max t : is the minimal consumption of local level c. pc−min X: is the table of values of normal distribution of every local level.

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3 Security Challenges A lot of work needs to be re-addressed to provide a reasonable secure Smart Grid model [7]. A Smart Grid which will cost billions of dollars needs to be designed in a very careful manner from the beginning. The model must meet all the actual Smart Grid challenges in order to offer more reliable, self-Stabilizing and secure intelligent system. The first challenge is big data analysis. Transport, storage and management of an exponential amount of data is essential information for decision making in the development of smart grids. It has become a buzzword in the global scientific and data analyst communities. Since effective operation and reliability of smart grid are based on a fast and effective treatment of mass data.

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Security is a very important challenge of the smart grid [8]. Since the communication in smart grid is based on the TCP IP protocol, this network will be a prey for malicious threats [9]. The smart grid is seen as the ultimate way to reduce fossil fuel pollution and replacing it with renewable energies and reduce the total energy loss on the main network by exploiting flow optimization algorithms. In new market of smart grid [12], it is important to design a competitive model of a smart grid prototype. We should assume its self-stabilization and its self-correction [10]. Finally, an Accidental damage unreliable use of information, unintentionally change [11] of data in an information system or exploring inadequate design can cause a serious problem. Or natural disasters, such as volcanoes, tornadoes, floods, hurricanes, earthquakes, landslides, tsunamis, explosions, and dangerous radiation leaks, must be as a known and solved issues.

4 Conclusion In summary, we have presented the definition and the characteristics of the Smart Grid. This latest is considered as a complex system that has three distinct levels: the local level, the micro-grid and the T&D network. For each level, we have proposed a communication network for reliable data exchange within the Smart Grid. We have seen the tree layers of the communication architecture: the application layer, the communication layer and data acquisition layer, on which we relied to perceive a communication model of Smart Grid. After that we have proposed a distributed transmission algorithm based on normal distribution theory. Finally we have given the most important security challenges facing the intelligent grid. This work is the first stage of our study, the second stage; we will focus on the security part of the Smart Grid by using recent mathematical approaches.

References 1. Rim, M., Hibaoui, A.: Modelization of smart grid managing the local level. IRSEC16 (2016) 2. Marah, R., Hibaoui, A.E.: Modelization of smart grid-managing the local level. In: 2016 International Renewable and Sustainable Energy Conference (IRSEC), pp. 1141–1145. IEEE, November 2016 3. Yan, Y., Qian, Y., Sharif, H., Tipper, D.: A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 15(1), 5–20 (2012) 4. Xiao, Y.: Requirements and challenges of cybersecurity for smart grid communication on infrastructure. In: Security and Privacy in Smart Grids, pp. 204–221. CRC Press (2016) 5. Wang, W., Xu, Y., Khanna, M.: A survey on the communication architectures in smart grid. Comput. Netw. 55(15), 3604–3629 (2011) 6. Li, W., Zhang, X.: Simulation of the smart grid communications: challenges, techniques, and future trends. Comput. Electr. Eng. 40(1), 270–288 (2014) 7. Marah, R., El Gabassi, I., Larioui, S., Yatimi, H.: Security of smart grid management of smart meter protection. In: 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–5. IEEE, April 2020 8. Ashok, A., Hahn, A., Govindarasu, M.: Cyber-physical security of wide-area monitoring, protection and control in a smart grid environment. J. Adv. Res. 5(4), 481–489 (2014)

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9. Ruj, S., Pal, A.: Analyzing cascading failures in smart grids under random and targeted attacks. In: 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pp. 226–233. IEEE, May 2014 10. Marah, R., El Hibaoui, A.: Formalism of self-stabilization with linear temporal logic and its verification. In: 2015 Third World Conference on Complex Systems (WCCS), pp. 1–5. IEEE, November 2015 11. El Mrabet, Z., Kaabouch, N., El Ghazi, H., El Ghazi, H.: Cyber-security in smart grid: survey and challenges. Comput. Electr. Eng. 67, 469–482 (2018) 12. Marah, R., El Guabassi, I., Larioui, S., Abakkali, M.: Managing the smart grid in free market. In: Saeed, F., Al-Hadhrami, T., Mohammed, F., Mohammed, E. (eds.) Advances on Smart and Soft Computing. AISC, vol. 1188, pp. 625–633. Springer, Singapore (2021). https://doi. org/10.1007/978-981-15-6048-4_54

Smart Contracts: An Emerging Business Model in Decentralized Finance Loubna El Hassouni(B) and Ali Ouchekkir Mohammed V University, Avenue Des Nations-Unies, B.P. 721, Agdal, Rabat, Morocco [email protected] http://fsjes-agdal.um5.ac.ma/

Abstract. Decentralized finance, also known as DeFi refers to an open and transparent alternative financial infrastructure based on blockchain technology; it has recently gained a lot of attention from experts from major banks, startups, and academia. The rapid evolution of blockchain technology is currently reshaping the financial landscape and allowing for the emergence of decentralized business models and platforms, such as decentralized currencies, payment services, and fundraising. One of the most innovative business models in decentralized finance is smart contracts, which have earned considerable attention across the financial industry thanks to their characteristics such as time efficiency, autonomy, immutability, and cost reduction. In this paper, we will first briefly introduce the concept of decentralized finance (DeFi) and blockchain technology. We will define the core functioning and innovative characteristics of Smarts contracts, and we will explore some of the potential areas of applications of smart contracts in the financial and banking industry. Finally, we will outline some of the key challenges and risks associated with smart contracts that could hamper their practical adoption. For the purpose of this paper, we conducted a thorough review of the current state of the literature, including the use of a pertinent and relevant scientificbased research database in order to identify the potential opportunities as well as challenges, and limitations associated with smart contracts adoption in the financial services industry. Keywords: Blockchain technology · Decentralized Finance · Smart contracts · Decentralization · Financial services

1 Introduction Blockchain was first introduced in 2008 as the distributed ledger supporting Bitcoin transactions. Since then, with its advanced features, it has attracted great academic and industrial attention in recent years. Blockchain technology is being studied and researched for various applications in the financial sector. Peer-to-peer transactions, cost reduction, cross-border transactions, and trade finance are some of the many possible applications [1]. The evolution of blockchain technology has given rise to decentralized, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 197–207, 2023. https://doi.org/10.1007/978-3-031-29857-8_20

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innovative, and transparent business models in financial services [2]; this new paradigm is characterized by the elimination of intermediaries in financial transactions through distributed consensus and decentralized networks. One of the most promising and innovative business models in decentralized finance is “smart contracts”. These are programs stored in the Ethereum blockchain that execute automatically when the predetermined terms of the agreement between the contracting parties are met [3]. There are several blockchain platforms for developing smart contracts, with Ethereum considered the most prominent platform [4]. Smart contracts may be used to build decentralized applications for numerous industries including Finance, payment systems, trading, etc. The scope and the potential applications of smart contracts in developing decentralized applications are very wide. Many studies explore different approaches to using Smart contracts for eradicating the requirement of a centralized third party and for lowering operational costs in numerous financial applications [5]. Despite the fact that smart contracts have received a lot of interest and attention, only a few applications and use cases have been developed by the extant literature; this could be due to the fact that a lot of smart contract implementations are still in the early stage. Therefore, our research is not limited to exploring the literature on smart contracts from a technical perspective, but to examining smart contracts as a disruptive DeFi business model, and its applications and potential use cases in the financial services industry, leaving a research gap that we aim to fill. In this paper, we will provide a better understanding and explore smart contracts as one of the most promising business models in the decentralized finance world. The paper is structured as follows: Sect. 2 gives an overview of the concept of blockchain and decentralized finance. Section 3 introduces smart contracts, their core functioning, and key defining characteristics. In Sect. 4, we explore some of the potential applications of smart contracts in the financial and banking section. Finally, in Sect. 5, we examine some of the key challenges and inherent risks associated with distributed ledger technologies and smart contracts. A major goal of this paper is to outline the core aspects of smart contracts, to assess their potential benefits, and opportunities as well as challenges and limitations that may face their practical long–term adoption across major segments of the financial services industry. Theoretically, this paper intends to contribute to the extant literature related to the application of smart contracts as a disruptive business model in the financial sector and will extend the literature on smart contracts’ business value. This research will also contribute as a reference for organizations, financial market participants, and regulators in adopting smart contracts.

2 The Rise of Decentralized Finance (DeFi) and Blockchain Technology Intermediaries have always played an important role in enabling economic transactions by creating connections between the transacting parties, fostering trust, and facilitating transactions between market players [6]. However, with the recent advancement of

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digital technologies in the financial services industry, fintech (financial technology) has taken over some of the roles usually performed by traditional and established financial institutions, hence decreasing the need for financial third-party intermediaries [2]. The current advancements in blockchain technology are creating a new paradigm shift by eliminating the requirement for intermediaries in financial transactions and enabling peer-to-peer transactions via a distributed trust and a decentralized platform. Blockchain technology has the potential to make financial services more transparent, borderless, innovative, and decentralized [2]. Decentralized Finance or Defi, is a blockchain-based financial infrastructure that has attracted the attention of researchers, and practitioners in the financial sector. This term generally refers to an open and permissionless protocol. Decentralized finance is built on public blockchains. Currently, the most popular platform is the Ethereum Blockchain, which has a high interoperability protocol [7, 8]. Decentralized finance has a promising ability to disrupt the structure of modern finance and conceive a new landscape of innovation and entrepreneurship, by highlighting the benefits and challenges of decentralized business models [2]. According to [8], DeFi is based on the idea that financial services should not be reliant on centralized intermediaries such as brokers, stock exchanges, banks, or insurance. Instead, financial services should be delivered by users for users through decentralized software deployment across a peer-to-peer network, while eliminating the counterparty risk. Blockchain technology, the invisible backbone of decentralized finance, is being studied and researched for various applications in the financial sector such as cost reduction, management practices, predictability, and potential security vulnerabilities. Blockchain is defined as a distributed ledger technology that was first introduced by Satoshi Nakamoto in the midst of the 2008 global financial crisis, as an underlying technology of Bitcoin cryptocurrency. In his white paper released in the same year, Nakamoto [9] introduced Bitcoin as a peer-to-peer electronic cash system, in which secure transfer of money, assets, and data are performed directly between parties via the internet without the need for a third party or a central authority as an intermediary (e.g. bank, government) [10]. This is enabled by several computer technologies, such as consensus mechanisms, distributed data storage, a peer-to-peer network, and cryptography. A blockchain, at its core, is a decentralized store of data [10] that enables secure peer-to-peer value transfer of data, assets, and money via the web. Blockchain-based solutions have the potential to enable the creation of previously unviable business models. Blockchain technology has the ability to decrease the overall implication and influence of centralized institutions in the financial industry, foster experimentation, and expand access to financial services [2]. Y. Chen & C. Bellavitis [2] highlight the rise of three major business models: decentralized currencies (cryptocurrencies), decentralized contracting (smart contracts), and decentralized payment services. The financial sector is considered one of the most promising fields of application for blockchain technology. Banking and financial institutions represent a potential testing ground for blockchain for many reasons that can be summarized below:

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First, financial institutions generally deal with measurable variables and highly standardized terms, hence, they do not face human judgment and serious rigidity issues because of the substantial digitalization in the finance field. Second, financial activity structures involve complex accounting systems, multiple ledgers, and complex systems for data management. These operations could easily be implemented in a decentralized ledger such as the blockchain, thus decreasing cost and processing time as well as lowering human mistakes and reducing friction and fraud in process operations. Third, banks and financial institutions rely on extensive interinstitutional operations – in terms of frequency and interdependence, resulting in enormous data exchange between organizations. This aspect encourages the development of common shared platforms and channels of communication such as the blockchain. Permissioned blockchain could potentially lead to a new digital ecosystem, where financial institutions could operate as the running nodes of one common network, therefore, exchanging data and digital values in a secure, efficient, and transparent way [11].

3 Smart Contracts: An Emerging Decentralized Business Model With blockchain and fintech gaining significant interest in the financial and information technology industry, much attention has been focused on financial contracts in general and smart contracts in particular [12]. The concept of smart contracts is not novel, in fact, the term “smart contract” dates back to the mid-1990s, even before Bitcoin was created, and is attributed by numerous sources to Nick Szabo (1996), a cryptographer and a computer scientist. Szabo defines a smart contract as “virtual agreements encoded on the blockchain network and executed automatically when specific conditions and terms are satisfied” [3]. Automatic execution is a key feature in smart contracts [13]. Technically, smart contracts create contractual agreements between two or more parties, facilitated by the use of digital technology without the involvement of an intermediary through a programming code stored in the blockchain that automatically executes the agreement when the terms and conditions predetermined by the participants are satisfied. Thus, all parties are required to fulfill their obligations in accordance with the agreement [14]. While smart contracts exist on many blockchain platforms, Ethereum is the most popular and widely used smart contract platform with regard to market capitalization, available applications, and development activities [8]. The main characteristics that distinguish smart contracts from traditional contracts are autonomy, self-sufficiency, and decentralization [10]. A fundamental concept of smart contracts is that they constitute a binding agreement between two or more parties, requiring each entity to fulfill its obligations under the agreement. Another key aspect of smart contracts is decentralization, which eliminates the need for trusted third parties or intermediaries between the contracting parties. This is leveraged through automated code execution, which is distributed and validated by

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the nodes in the decentralized blockchain network. Thus, smart contracts enable transactions between participants without any intermediary commission costs, reliance on third parties, and the necessity for direct interaction between counterparties [10]. Smart contracts are autonomous, which means that the contract and its initiating agent do not require to be in contact after a contract is initiated and executed [10], this feature helps to mitigate default counterparty risk due to the non-compliance of agreements. This feature also helps in eliminating the risk of non-payment by putting the digital currencies in a sort of digital escrow before the event that executes the payment. These attributes are valuable in decreasing the trust requirements between the counterparties by eradicating risks linked to the enforcement of the agreement and the creditworthiness of the counterparty [15]. Another great potential of smart contracts is automating the execution of contractual terms without intermediaries, hence increasing the productivity of a broad audience. Smart contracts ensure enhanced efficiency and security compared to traditional contracts. Furthermore, blockchain immutability makes smart contracts less likely to be tampered with, hence, reducing security concerns in smart contract transactions [16]. Some of the key features and advantages of DLTs (Distributed Ledger Technology) and smart contracts include: Convenience: DLT technologies provide fast, secure, and convenient processing of transactions. Low Cost: transactions executed through smart contracts rely on a distributed consensus mechanism, which eliminates the need for intermediaries such as banks from the process, hence administrative and service costs associated with intermediaries can be greatly reduced. Decentralization: DLT eradicates the need for central organizations to execute the smart contracts, smart contracts are distributed and self-executed across all the network nodes and do not exist on a single central service [10]. Transparency: DLT technology enables immutable storage of all details of transaction records in the network. These records are available for auditing by authorized parties. Risk Mitigation: Smart contracts cannot be changed arbitrarily after they are issued and this is because the underlying blockchain technology is immutable. Additionally, every transaction stored and replicated across the distributed and decentralized blockchain network is auditable and traceable. Consequently, this can significantly reduce financial fraud. Pseudonymity: The identity of DLT and smart contract users is stored via unique identifiers that cannot be used on their own to identify the user’s identity. Enhancing Business Proficiency: Eliminating the need for an intermediary can considerably increase the efficiency of business processes. For instance, in the supply chain process, the financial settlement is automatically executed in a peer-to-peer system once the pre-determined conditions are satisfied (e.g., the buyer validates the reception of products), which can significantly reduce the turnaround time.

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Smart contracts have the potential to revolutionize and transform the implementation of financial processes in trade finance, lending, and derivatives trading, by enabling new levels of trust and automation in contractual agreements [16].

4 Potential Applications of Smart Contracts in the Financial and Banking Industry 4.1 Securities The securities market entails complex, costly, and time-consuming procedures that are inconvenient and commonly exposed to risks [13]. Smart contracts are viewed as a solution for more efficient monitoring and automatic execution of complex and massive derivative contracts and have the potential to eliminate the need for intermediaries in the securities custody chain, and allow for automated dividend payment, stock splits, and liability management, [13]. This enables a significant streamline of the process and lowers the counterparty and operational risks associated with such trades. Currently, major financial markets continue to apply the “three-day settlement cycle (T + 3)” [17] which involves a significant number of institutions such as central securities depositories (CSDs) and collateral management agencies. The centralized clearing process involves labor-intensive operations and complex reconciliations. Through smart contracts, blockchain provides peer-to-peer automatic execution of clearing business logic which is cost and time efficient. Many stock exchanges are currently exploring DLTbased post-trade processes as an alternative to equity settlement systems. Furthermore, smart contracts can be applied to facilitate the clearing and settlement of securities. 4.2 Insurance The insurance industry accounts for tens of millions of dollars of spending each year on processing claims and loses millions of dollars due to fraudulent claims [2]. Moreover, the process of assessing the validity of insurance claims is time-consuming. Counterchecking the contract’s terms and then approving the claims is a tedious process. The use of blockchain-powered smart contracts in the insurance industry can automate the processing of claims, payments, and verification processes and therefore decrease claimprocessing time while eliminating fraud and avoiding potential risks such as the risk of fraudulent claims for compensation. Smart contracts can also be used for car insurance, as these contracts can record insurance clauses, driving data, and accident data, which allows vehicles equipped with IoT to execute claims immediately after an accident. 4.3 Trade Finance S. Wang (2019), [13] argues that trade finance is presently inefficient and highly vulnerable to risks and fraud. In addition, trade finance relies on heavy paper-based processes that need to be improved or replaced by digitized processes. Smart contracts enable businesses to automate the execution of commercial operations based on pre-determined criteria, thus simplifying processes, enhancing efficiency, and lowering compliance and fraud expenses.

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In July 2017, Australia and Japan concluded a trade deal. All the trade-related processes, from the issuance of the letter of credit to the delivery of trade documents, were fully executed via the Hyperledger Fabric platform, which significantly decreased the time necessary for document transmission; in addition, it lowered labor and other associated costs. 4.4 Recording and Reporting Financial Data Implementing smart contracts allows organizations to maintain a consistent record of financial data across all organizations, thus eliminating the need to share other documents such as invoice images. Consequently, this enhances financial reporting and data integrity and improves market stability. Smart contracts can potentially be applied for financial recordkeeping and reporting to increase the accuracy and transparency of financial data recording while enhancing speed and security. B2B ecosystems developed in Corda and Hyperledger Fabric will eventually eliminate the need for banks in the daily processes of trade finance. The immutable ledger will enable buyers and sellers to exist and exchange in one ledger – one company’s payable accounts become the other company’s receivable accounts. Smart contracts and dApps will continue to expand and develop due to various value creation factors including simplicity, immutability, cost, and efficiency in reporting and verification [18]. Cash management and treasury are two functions that are changing and evolving due to changes in investments and borrowing, payment networks, and the banking system. Digital currencies and new payment methods will greatly impact the accounts payable and receivables roles. Inventory management and logistics management will potentially have significant access to custody data, and risk management roles will be most significantly impacted as businesses proactively manage the disruption made by the evolving technology [18]. 4.5 The Mortgage Industry According to Deutsche Bank AG (2021) [19], mortgage loan processing depends on complex funding and servicing ecosystem that adds costs and delays, which drives the sector to consider solutions that will resolve the systemic issues in mortgage processing. Loans are one of the primary drivers of development and growth, but they also represent a significant operational complexity in the retail banking business, requiring a major need to improve the efficiency of internal services and operations. Smart contracts have a great potential to lower the costs and time associated with mortgage loan processing, through automation, process redesign, shared access to digitized versions of legal documents between trusted parties, and enabling full access to external sources of information. According to a previous study on back-office automation in the banking sector by Capgemini consulting (2016) [20]. Mortgage lenders should anticipate 6% to 15% savings from business process management systems, core banking platforms, and document management systems.

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5 Limits and Challenges to the Adoption of Smart Contracts Due to multiple challenges and risks including fraud, volatility, usability, and regulatory uncertainty, decentralized finance has yet reached its full potential [2]. Aside from the benefits, smart contracts have significant limitations, which derive mainly from the inherent rigidity of the digital environment and the decentralized architecture in which they are embedded. These limitations hamper the innovative nature of smart contracts [11]. 5.1 Vulnerabilities and Attacks Smart contracts are vulnerable to fraudulent attacks that aim to gain the money that the contracts hold. In addition, there have been several past incidents showing smart contract malfunctions. These include smart contracts being unexpectedly executed or thousands of dollars worth of virtual coins being locked away. Moreover, once deployed on the blockchain, smart contracts are permanent and irreversible. Therefore, potential vulnerabilities should be closely investigated in a contract prior to its actual deployment to limit the risk of attacks. Such vulnerabilities could lead to huge losses as demonstrated by the DAO (Decentralized Autonomous Organization) bug, which caused millions of dollars worth of losses [5]. 5.2 Consensus Mechanism Issues The consensus mechanism is a key system that guarantees decentralization, security, and the validation of data in blockchain networks, it also guarantees that all the nodes in the network have a consistent copy of the entire ledger [10]. There are numerous consensus algorithms, the most commonly used are the Proof-of-Work (PoW), and Proof-of-Stake (PoS) [21]. Although the PoW algorithm ensures the validation of Bitcoin transactions and guarantees security in the network, it requires a substantial amount of electrical energy and operates only at a low processing speed of approximately 16 transactions per second [22]. Hence, other consensus mechanisms such as proof-of-activity (PoA) or delegated proof-of-stake (DPoS) are currently being researched and developed as protocols that result in lower energy costs as well as lower transaction fees [21]. 5.3 Data Privacy The data stored in the DLT are visible to all nodes. In order to make transactions’ data private and confidential, they must be stored outside of the DLT (off-chain) or on the DLT but in an encrypted form. Encrypted data might pose a risk if new emerging technologies such as quantum computing, are developed to break the encryption code employed, making the data visible and readable. The ultimate goal is to allow all the nodes to access the data as needed, while still keeping it as secure as possible to minimize data leakage. Regulators may also require data access to monitor transactions [22].

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5.4 Platform Component Security Vulnerabilities in numerous peripheral components that interact with and support DLTs, such as digital wallets and cryptocurrency exchanges, are among the most prevalent cybersecurity challenges that DLTs have faced thus far. There have been several instances where these components have been hacked [23]. While many cryptocurrency organizations have been hacked, it is critical to realize that hackers have exploited security issues unrelated to the DLT cryptographic protocols. In other words, no blockchain has been directly hacked thus far [24]. Standardization and certification of DLT platform components will help contribute to the development of a more resilient DLT ecosystem by allowing component reuse, decreasing complexity, and facilitating risk assessment. Nonetheless, it is important to state that standardization broadens the scope of the impact of component failure [22]. 5.5 Understandability Smart contracts imply a radical shift from traditional contracts. Smart contracts involve codes and machine language, which are not understandable by ordinary people, thus, raising questions of understandability. Efforts have to make user-friendly interfaces that eliminate these semantic barriers but they only cover smart contracts technicalities. To be fully aware of the modalities of the automated execution of smart contracts, ultimately implies certain computing skills that most ordinary people do not have. Non-trained actors would struggle to assess the operativity, and the full consequences of the smart contract terms they are dealing with P. Cuccuru (2017) [11] argues that smart contracts may require certain expertise and collaboration with experts in order to ‘read’, ‘write’ algorithms and arrange online terms. Due to these semantic difficulties surrounding computer language, the parties involved in smart contract agreements are encouraged to seek the best possible understanding of the contract terms they are dealing with. It is certain, that semantic barriers could be just temporary and could be overcome in the near future. Nevertheless, these constraints surrounding the technical expertise that smart contracts require and the uncertainty about their reviewability are likely to hamper their mainstream use and adoption [11].

6 Discussion Blockchain – the underlying ledger technology that supports Bitcoin and other cryptocurrencies- [1] has the potential to disrupt and create significant advancements in the financial sector by enhancing financial process efficiency, reducing risks, increasing security, saving costs, and broadening access to financial services. In the financial industry, blockchain technology has led to the emergence of several new decentralized business models that represent many advantages such as innovation, transparency, interoperability, and decentralization [2]. Smart contracts are one of the most promising decentralized business models in decentralized finance (DeFi). Smart contracts with their promising features such as decentralization, transparency, cost-effectiveness, and risk mitigation; it has great potential to automate and enhance the

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efficiency of several processes in the financial and banking industry. Blockchain-based smart contracts represent an opportunity in many areas of the financial industry including the securities market, processing of claims, payments, and verification processes in the insurance industry, Smart contracts also represent promising applications in tradefinance processes, as well as in recording and reporting financial data and the mortgage industry. Despite the potential opportunities and transformative impact that smart contracts could have in the financial industry, there are several limitations and challenges that need to be addressed before we can expect smart contracts’ universal scale development, such as vulnerabilities and attacks, consensus and immutability issues, as well as data privacy, security, and understandability challenges.

7 Conclusion The current state of research on smart contracts as well as the degree of maturity of the technology has rapidly grown in recent years, which is shown by the exponential growth of academic research on the topic. The examination of the extant literature indicates that smart contracts represent a promising opportunity for the financial services industry. Smart contracts offer many potential benefits and provide enhanced security. With the advancement of this technology, many organizations aim to automate their processes and take part in business collaborations utilizing smart contracts. Smart contracts have the potential to reshape and disrupt established processes and services, and they can serve as a technological component for developing entirely new markets, applications, or services within the financial industry. However, this high potential is being faced with several challenges, many of which will most likely be resolved in the near future depending on how the financial industry tackles certain implementation issues and accepts the technology. With all disruptive innovations, in regard to smart contracts, organizations need to assess the opportunities and the challenges associated with their adoption. By focusing time and energy on understanding the potential of smart contracts, and mapping longterm and robust strategies, organizations can realize the potential on offer for rethinking financial contracts in this digital age. Decentralized business models have great potential to revolutionize established industries and contribute to creating a new landscape for entrepreneurship and innovation if they are effective.

References 1. Varma, J.R.: Blockchain in finance. Vikalpa 44(1), 1–11 (2019). https://doi.org/10.1177/025 6090919839897 2. Chen, Y., Bellavitis, C.: Blockchain disruption and decentralized finance: the rise of decentralized business models. J. Bus. Ventur. Insights 13(e00151), 1–8 (2020) 3. Szabo, N.: Smart contracts: building blocks for digital free markets. Extropy J. Transhuman Thought 16 (1996) 4. Alharby, M., van Moorsel, A.: Blockchain-based smart contracts: a systematic mapping study. arXiv. https://doi.org/10.5121/csit.2017.71011 (2017)

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5. Vinayak, M., et al.: Analyzing financial smart contracts for blockchain. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, pp. 1701–1706 (2018). https://doi.org/10.1109/Cyberm atics_2018.2018.00284 6. Roth, A.E.: Who Gets What–and Why: The New Economics of Matchmaking and Market Design. Houghton Mifflin Harcourt, New York (2015) 7. Buterin, V.: Ethereum white paper: a next-generation smart contract and decentralized application platform, December 2013. https://www.blockchainresearchnetwork.org/research/whi tepapers 8. Schär, F.: Decentralized Finance: On Blockchain-and Smart Contractbased Financial Markets (2020). SSRN 3571335 9. Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008). https://bitcoin.org/bit coin.pdf 10. Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc., Sebastopol (2015) 11. Cuccuru, P.: Beyond bitcoin: an early overview on smart contracts. Int. J. Law Inf. Technol. 25(3), 179–195 (2017) 12. Brammertz, W., Mendelowitz, A.I.: From digital currencies to digital finance: the case for a smart financial contract standard. J. Risk Finance 19(1), 76–92 (2018). https://doi.org/10. 1108/JRF-02-2017-0025 13. Wang, H., Guo, C., Cheng, S.: LoC– a new financial loan management system based on smart contracts. Futur. Gener. Comput. Syst. (2019). https://doi.org/10.1016/j.future.2019.05.040 14. Macrinici, D., Cartofeanu, C., Gao, S.: Smart contract applications within blockchain technology: a systematic mapping study. Telemat. Inform. 35 (2018). https://doi.org/10.1016/j. tele.2018.10.004 15. Schneider, L., Evans, J., Kim, A.: Why blockchain smart contracts matter. Int. Financ. Law Rev. (2018) 16. Dixit, A., Deval, V., Dwivedi, V., Norta, A., Draheim, D.: Towards user-centered and legally relevant smart-contract development: a systematic literature review. J. Ind. Inf. Integr. 26, 100314 (2022). https://doi.org/10.1016/j.jii.2021.100314 17. Bliss, R.R., Steigerwald, R.S.: Derivatives clearing and settlement: a comparison of central counterparties and alternative structures. Fed. Reserve Bank Chic. Econ. Perspect. 4Q, 22–29 (2006) 18. Hamilton, M.: Blockchain distributed ledger technology: an introduction and focus on smart contracts. J. Corp. Acct. Finance 31, 7–12 (2020). https://doi.org/10.1002/jcaf.22421 19. Annual Financial Statements of Deutsche Bank AG (2021). https://agm.db.com/files/docume nts/2022/Annual_Financial_Statements_of_Deutsche_Bank_AG_2021.pdf 20. Capgemini Consulting: Smart Contracts in Financial Services: Getting from Hype to Reality (2016). https://www.capgemini.com/consulting-de/wp-content/uploads/sites/32/2017/08/ smart_contracts_paper_long_0.pdf 21. Khan, S.N., Loukil, F., Ghedira-Guegan, C., Benkhelifa, E., Bani-Hani, A.: Blockchain smart contracts: applications, challenges, and future trends. Peer Peer Netw. Appl. 14(5), 2901–2925 (2021). https://doi.org/10.1007/s12083-021-01127-0 22. Duran, R.E., Griffin, P.: Smart contracts: will Fintech be the catalyst for the next global financial crisis? J. Financ. Regul. Compliance 29(1), 104–122 (2021). https://doi.org/10.1108/ JFRC-09-2018-0122 23. Neuron: List of cryptocurrency exchange hacks (2018). https://rados.io/list-ofdocumentedexchange-hacks 24. Risberg, J.: Yes, the blockchain can be hacked (2018). https://coincentral.com/blockchai nhacks

A Survey and a State-of-the-Art Related to Consensus Mechanisms in Blockchain Technology Sara Barj(B)

, Aafaf Ouaddah , and Abdellatif Mezrioui

National Institute of Posts and Telecommunications, Rabat, Morocco [email protected]

Abstract. Consensus is an essential element of Blockchain technology, as it allows distributed networks to achieve agreement on the state of their shared ledger without the need for a central authority. This is crucial for the security and reliability of the network since it ensures that all nodes have the same copy of the ledger and that the ledger is tamper-proof. Hence, the goal of this paper is to explore and review the most known consensus mechanisms in the wild. In this direction, we start by identifying the types of blockchains and discussing criteria for performing the comparison and the analysis of the studied consensus mechanisms while recalling the Problem of Byzantine Generals. The consensus mechanisms discussed in this paper are mainly those used in public and permissionless blockchains and those used in private and permissioned ones. Finally, we propose a comparison and an analysis of the studied consensus mechanisms, based on the speed and efficiency, validation process and the CAP theorem, to select the best choice for newly designed blockchain platforms. Keywords: Consensus mechanisms · Blockchain technologies · Consensus mechanism analysis

1 Introduction Blockchain technology is becoming more and more attractive for use in many domains. Thanks to its transparency and immutability. Despite this success, it is important to note that the widespread adoption of blockchain technology is still in its early stages, and many challenges need to be overcome before it can be considered a mature technology [1–7]. The possibility of reaching a consensus by honest nodes in the presence of attacker nodes is a real risk related to blockchain platforms [8]. Actually, consensus is an essential concept in the world of blockchain technology. It refers to the process permitting all participants in a network to agree on the current state of the ledger and the validity of new transactions. This concept is essential because it ensures that everyone is working with the same set of information, preventing disputes and ensuring the integrity of the network. Depending on the network’s specific needs, consensus mechanisms can take different forms. Some popular mechanisms include proof of work, proof of stake, and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 208–217, 2023. https://doi.org/10.1007/978-3-031-29857-8_21

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proof of authority. Each of these mechanisms has its own strengths and weaknesses. Choosing the right one for a given network is crucial for anyone looking to build a blockchain-based application. In this direction, the paper’s contributions could be summarized as follows: It categorizes consensus mechanisms according to the main types of blockchains. It analyzes the chosen consensuses against the proposed criteria, benefits, and drawbacks. Hence, it directs the designers to solve the problem of choosing consensus mechanisms by providing the results of the forecited analysis. The rest of the paper is organized as follows: Sect. 2 presents the materials and methods. Section 3 describes the finding and discuss them. Finally, Sect. 4 concludes the paper.

2 Materials and Methods This section presents the blockchain types and the consensus mechanisms. It also defines the criteria used to compare and analyze the consensus mechanisms. 2.1 Blockchain Types Public Blockchain. It is an entirely decentralized system, secured by cryptography, where everyone can access to read information and join it. All users or nodes on the network are anonymous, enjoy privacy-preserving, and are considered by default to be unsafe (untrusted). However, transactions stored in such systems are correct, accurate, and exact. They can’t be altered, modified, or deleted [9, 10]. Private blockchain It is a distributed system where one organization controls the read access permissions. Hence, it requires a trusted and trustworthy authority to reach a consensus [9, 10]; Permissionless blockchain. It is a completely decentralized system, secured by cryptography, where everyone can create blocks of transactions. All users or nodes on the network are anonymous, enjoy privacy-preserving, and are considered by default to be unsafe (untrusted). However, transactions stored in such systems are exact, and can’t be altered, modified, or deleted [10]; Permissioned blockchain. It is a distributed system where a trusted organization controls the write access permissions. Hence, it requires a trusted and trustworthy authority to reach a consensus [9]. 2.2 Consensus Mechanisms Consensus is a set of procedures and rules allowing a blockchain to maintain and update the distributed ledger while ensuring recorded transactions’ integrity, authenticity, and reliability [11]. There are several consensus mechanisms by which consensus is reached in blockchain systems. Examples of them include Delegated Proof of Stake, Paxos consensus mechanism, Practical Byzantine Fault Tolerance, Proof of Authority, Proof of Burn, Proof of Capability, Proof of Ownership, Proof of Stake, and proof of work. It is possible to distinguish between two categories of consensus mechanisms: those used in private and permissioned blockchains, also known as leader-based consensus family and those used in public and permissionless blockchains known as Nakamoto

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style family. Consensus mechanisms generally answer the problem of Byzantine generals [8]. They also make it possible to avoid some cyber risks partially. The Byzantine Generals Problem: An allegory that illustrates the need for consensus mechanisms in blockchains Generals of the Byzantine army settle around an enemy town. They must communicate with messengers and set up a common battle plan. Otherwise, the battle will certainly be lost. Nevertheless, some generals are traitors. These will try to cause discord among the others. The answer to the problem lies in locating an algorithm to ensure that loyal generals still arrive to find a compromise and create a battle plan [8]. The Byzantine General Problem is defined as difficulty in agreeing on a strategy prepared by honest generals because of the presence of traitors. Consensus mechanisms solve and respond to the Byzantine General Problem, where honest generals are honest nodes, and the strategy is the decision of honest participants to the consensus.

2.3 Fundamental Results in Distributed Computing: The CAP Theorem In this part, we focus on introducing the fundamental concepts and result in distributed systems. We judge that we need to understand the concept of consensus mechanism in the blockchain environment. The CAP theorem: Eric Brewer proposed the CAP theorem or conjecture in 2000. It argues that a distributed system can ensure at most two of the three features: Consistency, Availability, and Partition Tolerance [12]. The CAP theorem was formally proven two years later [13] in an asynchronous context. CAP theorem is an important result, expressing the tradeoff between consistency and availability in such unreliable systems. It states that it’s necessary to sacrifice one of these properties while designing a distributed system prone to partitioning. Accordingly, some systems guarantee strong consistency and best-effort availability; others provide availability and best-effort consistency. Thus, any distributed data store can be characterized based on the (at most) two properties it can guarantee: CA, CP, or AP. However, partition tolerance must be guaranteed since replicated systems may be subject to failures that can be considered partitions. Thus, only CP or AP systems are feasible (Fig. 1).

Fig. 1. CAP Theorem application to Aura, Clique, and PBFT [13]

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2.4 Criteria and Metrics Permitting the Comparison and Analysis of the Consensuses We propose in this section a set of metrics that we intend to use in order to analyze the different combinations of design choices adopted in a large set of consensuses mechanisms at different levels. The metrics we use are organized into 6 categories, as shown in figure Fig. 2 and defined hereafter.

Effecveness properes

Identy models

Network models

Failure models

Finality types

Communicaon models

Liveness

Public and permissionless

Synchronous

Crash failure

Probabilisc

Broadcasng

Safety

Private and permissioned

Asynchronous

Byzanne failure

Absolute

Gossiping

Eventual synchrony

Game theorec failure

Economic

Fault tolerance

Fig. 2. Consensus mechanism analysis criteria

Effectiveness Properties: Liveness or Availability: If all the operational nodes participating in the consensus respond correctly to the processing of the transactions in which they participate, and each operational node produces a result, a blockchain is available [13–15]; Safety or Consistency: All the operational nodes must agree in real time on one of the values proposed by one of the nodes. This value must be valid according to the rules defined by the mechanism. Hence, all of them receive identical and valid results [13–15]; Fault tolerance: The mechanism can function despite the failure of one or more nodes [13, 15]. Network Models: In both blockchain consensus and traditional distributed computing literature, we take into account the message-passing model in which processors communicate by sending messages between bidirectional communication channels [16]. In blockchain consensus mechanisms, these nodes form a peer-to-peer network, meaning that every message is propagated, in the network, to every node via a gossip protocol. Different protocols adopt different timing models based on the environment in which they are designed to function. Some protocols are designed to work in unreliable networks that drop messages and may cause an arbitrary message delay, like the Internet. In contrast, other protocols are optimized for extremely reliable channels, like permissioned company intranets. These protocols are said to be operating under differing assumptions of synchrony. According to the taxonomy by Dwork et al. [17], networks may be as follows: Synchronous: The delay upper bound of the message is fixed and well-known; Asynchronous: There is no delay upper bound of the message. Process operations are hardly coordinated; Or offer eventual synchrony: The delay upper bound of the message is predictable and unknown. However, after a long unknown time, process operations would be coordinated.

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Failure models are based on the percentage of faulty or dishonest nodes tolerated by the consensus mechanism to operate without serious problems. Crash failure (Failstop): Nodes stop working, processing data, emitting, or receiving messages abruptly and definitely without warning. The common root causes of such failure are power shutdown, DoS attacks, or software errors; Byzantine failure: The behavior of nodes appear normal, but some can send contradicting, confused and incoherent messages to others to disrupt the consensus. The common root cause of such failure can be a malicious influence of adversaries, like physical device capture and malware injection; Game theoretic failure: is a concept that refers to the potential for a distributed system to fail due to the conflicting incentives of its participants. Finality: In the standard distributed computing terminology, “consensus finality” follows from a combination of the total order and agreement properties, which is the primitive upon all state-machine replication protocols are built. The finality as property can be divided into three types: Probabilistic refers to the type of finality provided by chain-based protocols (eg. Bitcoin’s Nakamoto consensus), in which, once the transaction is validated and logged in the ledger, the probability that it won’t be reverted grows with time; Absolute refers to the provided finality by PBFT-based protocols (eg. PBFT, RAFT), in which a transaction is immediately considered finalized once the network validates it; And, Economic, in which it becomes economically expensive to revert a transaction. Communication models: - Gossiping means that all nodes have a point-to-point connection with one subset of the network or more, respectively; - Broadcasting is said when a node transmits a message to all nodes in the network (supposed honest). And all of them receive it [18, 19]. After all, this section reveals the blockchain types, defines the consensus mechanisms, and presents the solved problem by the consensus mechanisms. It also specifies the criteria used to analyze the consensus mechanisms.

3 Consensus Algorithm Analysis This section uses the forecited criteria to assess the consensus mechanisms used in different blockchain types. In addition, it analyzes the benefits and drawbacks of the chosen consensus algorithms. 3.1 Consensus Algorithms from the Blockchain Type Perspective 3.1.1 Consensus Algorithms Used in Public and Permissionless Blockchains A.K.A Nakamoto Style Family The main consensus algorithms used in public and permissionless blockchains are known as Nakamoto-style family. They are based on the idea of distributed trust, where all the participants in the network have an equal say in the validation and ordering of transactions. This is achieved through the use of cryptographic techniques and ince tives, such as rewards for validating transactions. One of the most well-known Nakamoto-style consensus protocols is the Proof of Work (PoW) protocol, which requires network participants (also known as miners) to

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solve complex mathematical problems in order to validate transactions and add them to the blockchain. Miners are rewarded with cryptocurrency for their efforts, which helps to incentivize their participation in the network. Other examples of Nakamoto-style consensus protocols include Proof of Stake (PoS) and Delegated Proof of Stake (DPoS). And Proof of Capacity (PoC) or Proof of Space (PoS) [20–22] among many others. 3.1.2 Consensus Algorithms Used in Private and Permissioned Blockchains A.K.A PBFT or Leader-Based Consensus Family The main consensus algorithms used in private and permissioned blockchains are Paxos, RAFT, Practical Byzantine Fault Tolerance (PBFT), Delegated Byzantine Fault Tolerance (dBFT), and Proof of Authority (PoA) [21, 23, 24]. They are all categorized as PBFT consensus family. Actually, The Practical Byzantine Fault Tolerance (PBFT) consensus protocol is a mechanism used in distributed systems to ensure that all participants agree on the state of the system, even in the presence of faulty or malicious participants. PBFT is a popular choice for use in permissioned blockchain systems, where the participants are known and trusted. In these protocols, a leader is chosen from among the nodes in the network to coordinate the process of reaching a consensus. The leader’s role is to propose a new value for the shared ledger and to gather the necessary support from other nodes in the network to reach consensus on the new value. This process can involve sending messages to other nodes, collecting responses, and possibly modifying the proposed value based on the feedback received. Once consensus has been reached, the leader broadcasts the agreed-upon value to the rest of the network, and the nodes update their copies of the shared ledger accordingly. 3.2 Consensus Algorithm Analysis 3.2.1 Consensus Algorithm Analysis in Private and Permissioned Blockchains We did these assessments based on these papers [13, 18, 19, 21, 25–27]: The chosen scale of both liveness/availability and safety/consistency is: No, Low, Medium, and High (Table 1). Table 1. Consensus algorithm comparison in private and permissioned blockchains Consensus Mechanism Paxos

Availability/ Liveness Low

Consistency/ Safety High

Fault tolerance/ Failure model f/2f+1 crash failure (50%) f/2f+1 crash failure (50%) f/3f+1 Byzantine failure (33%)

Finality Type Absolute

Communication Model Broadcasting

Network model Synchronous

RAFT

Low

High

Practical Byzantine Fault Tolerance (PBFT) Delegated Byzantine Fault Tolerance (dBFT) Proof of Authority (PoA)

Low

High

Absolute

Broadcasting

Synchronous

Absolute

Broadcasting

Synchronous

Low

High

f/3f+1 Byzantine failure (33%)

Absolute

Broadcasting

Synchronous

High for Aura (Authority Round) no for Clique

No for Aura High for Clique

Strong

Absolute

Broadcasting

Synchronous

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For fault tolerance, a fault tolerance equal to f/2f+1 means that if we have N faulty nodes, we should have more than 2N+1 nodes and more than N+1 non-faulty nodes for not having consensus problems. In the same direction, a fault tolerance equal to f/3f+1 means that if we have dishonest nodes, we should have more than 3N+1 nodes and more than 2N+1 honest nodes for not having consensus problems. Hence, f/2f+1 is better and more robust than f/3f+1. We can note f/2f+1 as robust fault tolerance and f/3f+1 as medium fault tolerance. From this analysis, we notice that most of the consensus mechanisms used in private and permissioned blockchains have the following characteristics: Availability: they are generally considered to have medium availability, as they can tolerate some level of faulty or malicious behavior from replicas without compromising the integrity of the system. However, they do require a certain level of trust between the replicas, and if a supermajority of replicas is behaving maliciously, it may be difficult for the system to reach a consensus; Consistency: they ensure strong consistency, as it requires a supermajority of replicas to agree on the validity of a transaction before it is added to the blockchain. This ensures that all replicas have the same view of the blockchain and that all valid transactions are eventually committed; Fault tolerance: they are designed to tolerate Byzantine failures, meaning that they can handle some level of faulty or malicious behavior from replicas without compromising the integrity of the system. However, the system’s ability to tolerate faults will depend on the number of replicas in the network and the level of trust between them; Finality: They provide a high level of absolute finality due to the leader-based approach, where a single validator is responsible for coordinating the consensus process and ensuring that all of the validators are in agreement. This leaderbased approach helps to ensure that the consensus process is efficient and ta hat blocks are added to the blockchain in a timely manner; Communication type: They requires synchronous communication between the replicas in order to reach consensus. This means that replicas must be able to communicate with each other in real-time in order to validate and commit transactions. 3.2.2 Consensus Algorithm Analysis in Public and Permissionless Blockchains We did these assessments based on these papers [13, 18, 19, 21, 25, 26]: The chosen scale of both liveness/availability and safety/consistency is: No, Low, Medium, and High (Table 2). Table 2. Consensus algorithm comparison in public and permissionless blockchains Consensus Mechanism Proof of Work (PoW) Proof of Stack (PoS) Delegated Proof of Stake (dPoS)

Liveness/ Availability High

Safety/ Consistency Low

High

Low

High

Low

Failure model/ fault tolerance Byzantine failure (49%) Byzantine failure (49%) Byzantine failure (50%)

Finality type Probabilistic, economic. Absolute or Probabilistic. It depends on its usage. Economic. Probabilistic, economic.

Communication model Gossiping Gossiping Gossiping

Network model Eventual synchrony Eventual synchrony Eventual synchrony

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We notice these consensus mechanisms used in public and permissionless blockchains have the following characteristics: Availability: they are designed to provide high availability, meaning that it is able to continue functioning even if some of the nodes in the network fail or become unavailable; Consistency: they provide eventual consistency, meaning that all nodes in the network will eventually come to agree on the state of the network, but there may be a delay before this agreement is reached; Fault tolerance: they are designed to be fault-tolerant, meaning that they are able to continue functioning even if some nodes in the network behave improperly or try to attack the network; Finality: they provide a probabilistic finality, meaning that the likelihood of a transaction being reversed decreases as more blocks are added to the blockchain; Communication type: The Nakamoto consensus family uses a broadcast communication type, meaning that nodes in the network communicate with each other by broadcasting messages to all other nodes. 3.3 Consensus Algorithms: Comparison In this section, we summarize our analysis and discuss the consensus algorithms in both families based on their performance, benefits, and drawbacks. Blockchain type: Nakamoto-style consensus protocols are based on the idea of distributed trust, where all the participants in the network have an equal say in the validation and ordering of transactions. This is achieved through the use of cryptographic techniques and incentives, such as rewards for validating transactions. PBFT, on the other hand, is typically used in permissioned blockchain systems, where the participants are known and trusted. In these systems, PBFT can provide strong consistency and fault tolerance, as the replicas can be trusted to behave honestly and communicate with each other in a timely manner; Validation process: Nakamoto-style consensus protocols, such as Proof of Work (PoW) and Proof of Stake (PoS), use a different validation process than PBFT. In PoW, miners must solve complex mathematical problems in order to validate transactions and add them to the blockchain. In PoS, network participants must hold a certain amount of cryptocurrency in order to validate transactions and add them to the blockchain. PBFT requires multiple replicas to agree on the validity of a transaction before it is added to the blockchain through the exchange of pre-prepare, prepare, and commit messages; Speed and efficiency: PBFT is generally faster and more efficient than Nakamoto-style consensus protocols, as it does not require the intense computational power needed for mining in PoW or the large amounts of cryptocurrency required for validation in PoS. However, PBFT requires synchronous communication between the replicas, which can be more challenging to achieve in certain network environments; CAP theorem: Nakamoto-style consensus protocols, such as Proof of Work (PoW) and Proof of Stake (PoS), generally prioritize availability and partition tolerance over consistency. This means that the network may continue to operate even if some participants are behaving maliciously or experiencing technical issues, but it may not always provide strong consistency in terms of the ordering of transactions. For PBFT-based consensus protocols in permissioned blockchains, consistency is more important than availability, such as in financial transactions or other applications where data integrity is critical. It is also often used in systems where there is a relatively small number of replicas since the complexity of the protocol increases with the number of replicas.

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4 Conclusion The consensus mechanism is the core layer of all blockchain systems. It represents the rules that ensure all network participants agree on a unified ledger without the need for a central authority. In this paper, we start by presenting the basic concepts and key fundamental results in distributed computing that constrain the design space of consensus mechanisms. Furthermore, we summarize the properties expected to be fulfilled by a consensus protocol. Lastly, we outline the assumptions needed for designing a consensus solution in terms of the blockchain types, the failure models, the network models, the finality types, the communication models, and the effectiveness properties. The paper proposes categorizing consensus algorithms according to the type of blockchain that uses them in the following manner: those used in public and permissionless blockchains. Finally, the paper compares and analyses the forecited consensus mechanisms for design purposes.

References 1. Ouaddah, A., Bellaj, B.: FairAccess2.0: a smart contract-based authorisation framework for enabling granular access control in IoT. Int. J. Inf. Comput. Secur. 15, 18–48 (2021) 2. Javed, I.T., et al.: Health-ID: a blockchain-based decentralized identity management for remote healthcare. Healthcare 9, 712 (2021) 3. Bach, T., Aravazhi, A., Konovalenko, A.: Blockchain technology for electronic health records: challenges & opportunities. Int. J. Healthc. Manag., 1–6 (2020) 4. Sedrati, A., Mezrioui, A., Ouaddah, A.: IoT Governance: a state of the art and a comparative analysis. In: 2022 13th International Conference on Information and Communication Systems, ICICS 2022, pp. 76–81. IEEE Inc. (2022) 5. Qian, J., Shao, H., Zhai, Z., Zhao, L., Shen, L.: A blockchain-based privacy protection service framework for data security in smart home a blockchain-based privacy protection service framework for data security in smart home. IEEE Access (2020) 6. Tijan, E., Aksentijevi´c, S., Ivani´c, K., Jardas, M.: Blockchain technology implementation in logistics. Sustainability 11 (2019) 7. Treleaven, P., Yang, D., Brown, R.G.: Blockchain technology in finance. Computer (2017). Published by the IEEE Computer Society 8. Lamport, L., Shostak, R., Pease, M.: The Byzantine generals problem. ACM Trans. Program. Lang. Syst. (TOPLAS) 4, 382–401 (1982) 9. BitFury Group, Garzik, J.: Public versus Private Blockchains: Part 1: Permissioned Blockchains (2015) 10. Bitfury Group, Garzik, J.: Public versus Private Blockchains. Part 2: Permissionless Blockchains. Bitfury, pp. 1–23 (2015) 11. Guo, H., Yu, X.: Blockchain: research and applications a survey on blockchain technology and its security. Blockchain Res. Appl. 3, 100067 (2022) 12. Brewer, E.: CAP twelve years later: How the “rules” have changed. Comput. (Long Beach Calif.) 45, 23–29 (2012). https://doi.org/10.1109/mc.2012.37 13. de Angelis, S., et al.: PBFT vs proof-of-authority: applying the CAP theorem to permissioned blockchain. In: CEUR Workshop Proceedings, vol. 2058, pp. 1–11 (2018) 14. Alpern, B., Schneider, F.B.: Recognizing safety and liveness (1986) 15. Fischer, J., Lynch, A., Paterson, S.: Impossibility of distributed consensus with one faulty process 32, 374–382 (1985)

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16. Durand, A., Ben-Hamida, E., Leporini, D., Memmi, G.: Asymptotic performance analysis of blockchain protocols (2019) 17. Dwork, C., Lynch, N., Stockmeyer, L.: Consensus in the presence of partial synchrony. J. ACM (JACM) 35, 288–323 (1988) 18. Cason, D., Milosevic, N., Milosevic, Z., Pedone, F.: Gossip consensus. Assoc. Computi. Mach. (2021) 19. Aysal, T.C., Yildiz, M.E., Sarwate, A.D., Scaglione, A.: Broadcast gossip algorithms for consensus. IEEE Trans. Signal Process. 57, 2748–2761 (2009) 20. Vlachos, A.: Blockchains: Permissionless vs Permissioned - The Crypto App (2022) 21. Zhang, S., Lee, J.: Analysis of the main consensus protocols of blockchain. ICT Express 6, 93–97 (2020) 22. Saunois, G., Robin, F., Anceaume, E., Sericola, B.: Permissionless consensus based on proofof-eligibility (2020) 23. Theron, L.: Quel consensus dans une blockchain privée? - OCTO Talks ! (2016) 24. Permissioned Blockchain - Paxos Vs. Raft Consensus Algorithm – Notepub (2021) 25. Zhou, B.C.: Data Consistency and Blockchain. Blockchain Research Reading Group (2017) 26. Zhang, J.: Consensus Algorithms: PoA, IBFT or Raft? Kaleido (2018) 27. Bhardwaj, D.: Raft and Paxos: A Brief Introduction to the Basic Consensus Protocols Powering the Distributed Systems Today. Sixt Research & Development India. Medium (2020)

Blockchain Technology in Finance: A Literature Review Fouad Daidai(B)

and Larbi Tamnine

National School of Commerce and Management of Fez Laboratory of Research and Studies in Management, Entrepreneurship and Finance, Sidi Mohamed Ben Abdellah University, Fes, Morocco [email protected]

Abstract. This article is a contribution to the studies carried out on blockchain technology in finance. We aim to review academic research in the field of financial technology. Our study presents and intensifies the opportunities and constraints of the implementation of blockchain technology in finance around the world. The article presents the results of a systematic literature review on blockchain technology, as advanced in articles published between 2017 and 2021. In the course of reviewing the articles, we found that blockchain technology brings several opportunities to corporate financial services, banks and institutions, despite the existence of several regulatory hurdles. This study contributes to the understanding of blockchain technology for industry, academia, and regulators. Keywords: Blockchain · finance · cryptocurrency

1 Introduction Blockchain has been presented as a disruptive innovation for several years. Moreover, the use of blockchains is simultaneously interacting with businesses, stakeholders and financial markets [1]. While several sectors should be able to benefit from its power in the long run, one industry has already taken hold of the subject for several years: finance. However, as our research has defined, the current literature focusing on the adoption of blockchain in finance remains scant. Trust between actors carrying out transactions is generally based on a centralised system: actors, unable to trust each other, choose to trust an entity that they all recognise (State, bank, notary…). This trusted third party keeps a record of their transactions, thus guaranteeing the regularity of their exchanges [2]. Depending on the type of transaction, access to the register may be free for all, or restricted to certain actors [3]. In all cases, the trusted third party has a monopoly on updating the register of transactions in order to avoid any risk of fraud. This said, blockchain could reduce the costs of activities carried out by financial intermediaries. According to [4], it reduces the costs of auditing transactions and the costs of networking participants in a financial system. It could also reduce the costs associated with securing financial exchanges, improve the speed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 218–229, 2023. https://doi.org/10.1007/978-3-031-29857-8_22

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of processing certain transactions and allow flexibility in the settlement and clearing operations performed by post-trade infrastructures [5]. The first step is to motivate the study and define the research questions. Blockchain is an evolving technology and is a key element in the digital transformation process for most companies. It is therefore important to understand this technology and its impact. However, a key part that is often missed by most previous publications is how blockchain relates to financial services and its implications. Furthermore, due to the proliferation of general literature on blockchain adoption, it is important to conduct a systematic review to understand the previous literature and identify ideas relevant to blockchain adoption in corporate governance. To this end, the following research questions are formulated. Question 1: What are the current and future use cases for blockchain applications in finance? Question 2: What are the barriers to the use of blockchain applications in finance? These research questions identify current and future use cases for blockchain from a wide range of blockchain applications across many sectors, from articles on general adoption and from articles related to finance itself. The rest of this article is structured as follows: In Sect. 3, we present the concept and the different types of blockchain technology. Section 4 will be dedicated to the research methodology and data selection methods. Section 5 presents the results of the literature review.

2 Literature Review In 2008, the traditional monetary and financial system was weakened by the subprime crisis. Critics focused on the complexity and opacity of the financial system, which was developing services and products that encouraged manipulative behaviour, with little response from public authorities and the centralisation of financial decisions and powers. In the same year, Satoshi Nakamoto created Bitcoin, a secure international payment system that operates without a trusted third party thanks to blockchain technology [6]. In finance, the blockchain can be used to carry out cybercurrency transfers, register property rights, trade financial securities, schedule the execution of financial contracts and raise funds in cybercurrencies [2]. Before discussing the applications of blockchain technology in finance, we first introduce the technology. The Internet has completely changed the world, culture and customs of people. After an initial phase characterised by the free flow of information, there were concerns about the security of online communications and user privacy. Blockchain technology guarantees both of these objectives. Blockchain technology, relatively new, has the chance to produce a new revolution, fully justifying a philosophical investigation. This technology is only one form of the larger field of distributed ledger technology [3].

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Since the creation of blockchain technology, new means of payment have emerged such as cryptocurrencies (e.g. Bitcoin) and fundraising with cryptocurrencies as an underlying (e.g. Initial Coin Offerings or ICOs). These new means of financing from the world of technology have the potential to democratise access to investment and funding for innovative projects [4]. However, it is almost certain that finance, insurance, real estate, logistics and of course healthcare industries are likely to be transformed by blockchain technology [5] (Fig. 1).

The supplier and the applicant enter into a transaction

The transaction is grouped with other transactions from the same period to form a data block

The data block is stored on the global network in a decentralised and nonmanipulable way and thus validated

the validated block is added to the previously validated blocks to form the blockchain

La transaction est confirmée pour les deux parties

Fig. 1. Blockchain process.

There are two main types of blockchain: public and private. On a public blockchain, such as Bitcoin, no permission is required to use or access the blockchain. The two best known public blockchains are Bitcoin and Ethereum [2]. Most public blockchains are open source and there is no central authority or individual managing them. Instead, the peer-to-peer network agrees on the state of the blockchain, this confers a major advantage in terms of trust between actors, but can limit the types of data that can be manipulated, such as personal data. In contrast, in the case of private blockchain or private deployments, only the actors involved benefit from this ownership. The following table summarises the difference between the private blockchain and the public blockchain (Table 1):

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Table 1. Different types of blockchain Type of blockchain

Name

Reading a register

Performing a transaction

Validation

Example

Opened

Public blockchain without permission

Open to all

Anyone

Anyone provided they make a significant investment in computing power or in holding the cryptocurrency

Bitcoin Ethereum

Blockchain public permissioned

Open to all

authorised participants

all or some of the authorised participants

Consortium

Restricted to authorised participants

authorised participants

all or some of the authorised participants

banks operating a shared register

permitted private (enterprise blockchain)

totally private or limited to a set of authorised nodes

limited to the network operator

limited to the network operator

internal bank register shared between subsidiaries

Closed

Source: Literary review.

3 The Aim and Objectives of the Study The purpose of conducting a systematic literature review is to increase reproducibility and provide an adequate means of synthesizing a rapidly growing field of knowledge [2]. The objective of this article is to review the current literature in the field of blockchain on the basis of different categories and to encourage financial managers to invest in the field of blockchain and the State in the elaboration of legal texts encouraging this technology. In this literature review, we do not pretend to cover the entire literature, but rather to offer an informative and focused assessment of deliberately selected literature on blockchain that will serve to answer the research questions previously outlined.

4 Methodology The systematic literature review was used to provide a critical view of the current design of the research topic [6]. To identify literature related to the blockchain and finance, a structured keyword search was performed in the Scopus database. For the keyword filter, the search focused on articles with the following keyword combination in their titles: (blockchain’ AND finance). This keyword combination allowed us to filter out articles

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that deal with our topic. The search query yielded 24 articles between 2017 and 2021. These articles were analyzed in order to synthesize the contributions and obstacles of the implementation of blockchain technology in finance. Using structural keyword research, we have developed a flowchart showing the stages of document filtering (Fig. 2). DATA BASE

SCOPUS structural keyword search

blockchain AND finance articles found

33 articles Analyses of titles and content of abstracts

9 irrelevant

24 appropriate

Fig. 2. Article filtering process

5 Results of the Analysis The first objective of the article is to highlight the different contributions of blockchain technology to the different financial operations carried out by companies. These contributions we have divided into eight points. Then we will present the regulatory obstacles that prevent the implementation of blockchain technology in financial services. 5.1 Contributions of Blockchain Technology to Financial Services 5.1.1 Programming Contracts on Blockchain Blockchain technology has evolved around the ability to associate smart contracts with the transaction ledger. Smart contracts broadly refer to small applications stored on a blockchain and executed in parallel by a large set of validators [1]. The idea of smart contracts did not originate with the blockchain. In fact, as early as 1996, Nick Szaboc [4] defined a smart contract as a digital agreement that allows transactions to occur automatically when certain conditions are met. A smart contract is an autonomous program, encoded on the blockchain, that automatically executes all or part of a contract without human intervention [2]. As soon as a pre-programmed condition of the smart contract is verified, the corresponding contractual clause is automatically fulfilled. The Ethereum protocol, which came onto the market in 2013, is specifically designed to program contracts on the blockchain efficiently [2]. Smart contracts could lead to the creation of new financial products and automated governance systems. They are already being used in the insurance industry. Indeed, these contracts are becoming increasingly popular because of their many advantages. They are cost-effective, secure, traceable and authentic, which makes them very attractive for many industries [5].

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5.1.2 Transferring Money via Blockchain: From Bitcoin to Cybercurrencies Today, money transfers represent the largest inflow of funds to developing countries, surpassing foreign direct investment and official development assistance [1]. According to the World Bank Group, the remittance industry has grown significantly in recent years, increasing by 8.8% in 2017 and 9.6% in 2018. Some developing economies rely heavily on cash from abroad, making remittances an important component of their economy. Migrant workers are now one of the main sources of income for many of these countries. For example, in Morocco, in two years, and in a context of great economic depression and psychological uncertainty, the flows of Moroccans residing abroad have increased from 65 billion dirhams in 2019 to 93.6 billion dirhams according to provisional figures from the Office des Changes. However, transfer costs are so high, 9.33% to 12.31% on average in the Moroccan context. In addition to the high costs, most transfer solutions depend on third party services and financial institutions. The need for several intermediaries makes the current system extremely inefficient. Not only because the services are expensive, but also because transfers can take days or even weeks. In this context, blockchain technology can offer viable and more efficient alternatives to the money transfer industry [7]. The first developments in so-called blockchain 1.0 technology concern bitcoin and cybercurrency exchanges. Bitcoin is a protocol that allows agents to exchange units of account on a blockchain without a trusted third party [8]. The principle is to register agents’ property rights to unspent units of account in the registry [8]. The main goal of blockchain companies specializing in the transfer of funds is to simplify the entire process by eliminating the surplus of intermediaries. The idea is to offer frictionless and almost instantaneous payment solutions. Unlike traditional services, a blockchain network does not involve the lengthy transaction approval process that requires several mediating institutions and a lot of manual work. Instead, a blockchain system can perform financial transactions globally based on a distributed network of computers, which means that several computers participate in the process of verifying and validating transactions and this is done in a decentralized and secure way [5]. Compared to the traditional banking system, blockchain technology can provide faster and more reliable payment solutions at a much lower cost. In other words, blockchain technology can solve some of the major problems faced by the remittance industry, such as high fees and long transaction times. Operational costs can be reduced significantly by simply reducing the number of intermediaries. As the bitcoin source code is open, it can be freely used, copied and reworked. Modifications to the initial version have thus led to the creation of alternative cyber currencies, known as altcoins (e.g. Peercoin in 2012). These may differ from bitcoins in the amount of currency issued, their transaction validation system or the size of the blocks recorded [9]. A competitive market between cybercurrencies has developed [1], marking the coexistence of different standards associated with the Bitcoin protocol for carrying out cybercurrency transfers.

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5.1.3 Financing a Project with Blockchain: Initial Coin Offerings Similar to IPOs, blockchain technology can also be used to raise funds for initial coin offerings (ICOs). These operations create problems with tokens, which are purchased with the network currency. Fundraising, in principle, is done using blockchain technology without financial intermediaries. These projects involve developing applications around blockchain technology or creating unregulated investment funds. According to some authors [10], ICOs are an innovative way to attract talent, money and ideas around projects without a trusted third party. The value provided by the ICO is linked to the network of participants. ICOs generally take place in three stages: the announcement of the ICO, the issuance of a white paper presenting the campaign and the conditions of the offer, and then the sale of the tokens. The tokens subscribed to by agents during an ICO allow them to access the services of the issuing platform, to obtain rights to profits, to vote, to contribute to the design of the proposed service. In no case do they give access to the capital of the company if it has been created [10]. Once the fundraising is over, they can be resold on secondary markets. A range of services similar to those offered in IPOs has developed in the ICO market. The future of these activities will be linked to the implementation of appropriate regulation [1]. 5.1.4 Registering Ownership on the Blockchain The uses of blockchain have extended beyond the transfer of cybercurrencies. The technology can be used to record transfers of ownership and to create methods for achieving consensus among network participants on the state of the system without a central authority. It is thus possible to add layers of instructions to the protocol (altchains) to create specific consensus algorithms, systems for recording names, titles and other applications (blockchain 2.0) [11]. In particular, the protocol has been modified to be able to record transfers of digital assets called colored coins. The principle consists of associating additional data to the units of account of a blockchain (e.g. bitcoins) to colour them with an attribute, such as a mode of issuance, the ability to subdivide them, aggregate them or associate them with the payment of dividends. A colored coin can be imagined as a stamped banknote, which can serve as both a unit of account and a security representing an asset. It allows real assets to be associated with Bitcoin addresses. Colored coins encapsulate information about small amounts of Bitcoin [10]. A “colored” coin is a sum of bitcoins reassigned to represent an asset: stock, real estate, commodities. It can be bought, sold or even decoloured by removing its special attribute and redeemed at face value. The transfer of ownership of a “coloured” corner can therefore represent the exchange of any asset, especially financial securities. The blockchain allows for the creation of an integrated settlement system, as it is possible to keep track of asset exchanges and the units of account used to settle them on the same register [11]. 5.1.5 The Costs of Networking Participants in a Financial System By offering users the possibility to pay in a peer-to-peer network, the blockchain will reduce the costs for the users of the retail payment system network. Many fintechs

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offer innovative blockchain-based solutions for retail payments: wallets offering peerto-peer payment services (Mycelium, BitPay, etc.), online trading platforms for Bitcoin and other currencies (Coinbase), international money transfer services (BitPesa, Abra, Rebit). Financial institutions are also beginning to use blockchain to develop more efficient peer-to-peer payment systems and peer-to-peer currency transfers [9]. The value associated with networking participants in a financial system is not limited to payments. Indeed, the blockchain makes it possible to develop marketplaces operating without intermediaries with their own incentive and remuneration systems, reducing the cost of networking participants in a project [9]. 5.1.6 Improving the Speed of Transaction Processing According to Schär [11], so far, public blockchains have not solved the problem of fast processing of large-scale transactions. Protocol developments could change this trend, with the use of a sidechain. A sidechain is a secondary blockchain attached to a main blockchain. It has its own characteristics, but benefits from the community and security inherent in the main network for the final transactions that will be registered on the main blockchain. Technically, several independent sidechains can be deployed around a single master blockchain. In fact, theoretically, many decentralized applications can run on independent sidechains. Thus, the load of each sidechain does not impact the speed of operations on the parent blockchain [12]. However, a new standard for largescale transactions cannot be imposed without a consensus of the majority of the network nodes. Within the Bitcoin protocol, a standards war has pitted proponents of increasing block size against proponents of reducing the amount of data recorded per transaction, hindering the adoption of a solution that would reduce processing times on the network [1]. This example illustrates the limits of the effectiveness of public blockchains and open source. In the absence of consensus among all participants, the protocol forks reduce the value associated with networking and stifle innovation. On the other hand, the processing time for financial transactions involving multiple participants could be reduced on private blockchains, operating without a consensus mechanism for currency transfers and syndicated loans [12]. For example, the time to complete a syndicated loan transaction could be reduced from about 20 days to less than a week through the use of a blockchain. Similarly, the processing time for currency transfers could be reduced. 5.1.7 Reducing the Cost of Liquidity and Creating New Markets For post-trading activities, blockchain technology allows flexibility in the timing of settlement and clearing, thereby increasing liquidity while reducing collateral requirements for transactions in the riskiest securities [1]. For some securities, clearing and settlement can be performed almost instantaneously, reducing post-trade processing costs [5]. By improving the traceability of financial securities trading, blockchain will enable the creation of securities exchanges for customer segments that were not covered by intermediaries’ offerings until now. For example, BNP Paribas Securities Services and the crowdfunding platform SmartAngels have developed a blockchain to create a secondary market for trading in the securities of unlisted companies. Rijanto [12] even state that a blockchain could eventually act as an exchange, clearing house, central depository,

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and settlement system. However, many practical issues remain to be resolved before the current exchange infrastructure is replaced. 5.1.8 Secure Transactions and Pseudonym Management Secure trading through cryptography could also create value. In particular, BermeoAlmeida et al. [13] identify different types of electronic payments currently abandoned by consumers to preserve their privacy or reduce the risk of hacking. Is blockchain technology more effective in preserving privacy and achieving secure exchanges? This common statement needs to be qualified. The choice of method for reaching consensus influences the level of security. For example, the proof of ownership mechanism is said to be faster and less secure than proof of work. This is because there is a trade-off in the transaction certification mechanism between security and speed of the system [13]. Furthermore, absolute anonymity is not always guaranteed, as it is possible to cross-reference data to find repeat users of e-currencies by their addresses [14]. Finally, consumers are responsible for their own protection against theft and hacking, which requires careful handling of their private keys [14]. 5.1.9 Audit and Compliance Costs with Financial Regulations With blockchain, banks and financial institutions will be able to reduce the audit and reconciliation costs associated with exchanges involving multiple intermediaries. Transactions will be recorded in a single, traceable and verifiable register at low cost. Information is available in a non-rival manner [11]. Audit costs could become almost zero for the exchange of digital assets. The implementation of smart contracts could also reduce the risk of litigation. However, trusted third parties and auditors will continue to play a role in reconciling registry entries with the real world, when records relate to physical assets. Blockchain could also reduce the costs associated with compliance with financial regulations [11]. For example, the costs of regulatory reporting in the financial markets can be reduced by granting the regulator special rights to access the registry for certain transactions. Compliance costs can also be reduced [12]. For example, the sharing of documents such as identity cards or tax records between several banks in a distributed secure registry could help to better meet regulatory requirements regarding know-your-customer procedures and the fight against money laundering and terrorist financing. 5.2 Regulatory Obstacles Despite all these benefits, blockchain technology faces a number of barriers that still prevent its adoption in the financial sectors. It has significant advantages in terms of cost, transparency and performance, but there are a number of regulatory obstacles to its implementation in the ecosystem. 5.2.1 The Virtual Currency and the Regulatory Framework Although cryptocurrency and more specifically Bitcoin is really starting to attract more and more followers in the developing country, it should be noted that virtual currency

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transactions are formally prohibited in the Kingdom and officially constitute a violation of the country’s foreign exchange regulations [13]. For example, Moroccan foreign exchange regulations clearly state that resident Moroccans can only have foreign accounts under certain conditions (being an exporter of goods or services). Any breach of these rules is punishable by the coercive measures in force, and liable to 5 years’ imprisonment and a fine of between 500 and 20,000 dirhams. Both the Ministry of Economy and Finance, Bank AlMaghrib and the Moroccan Capital Market Authority always recall that this is an unregulated activity. They draw the public’s attention to the risks associated with the use of virtual currencies, including mainly the lack of consumer protection, the volatility of the exchange rate of cryptocurrencies against a legal tender or the use of these currencies for illicit or criminal purposes, including money laundering and terrorist financing, etc. Nevertheless, while cryptocurrency is banned, the authorities are stepping up their efforts to make room for business blockchain projects. In 2020, several conferences were held to this effect, in particular to promote possible applications to put blockchain at the service of financial inclusion. On the one hand, regulators need to limit the risks associated with the deployment of public blockchains (e.g. Bitcoin) and the new services associated with them [15]. On the other hand, they should build a framework for legal recognition of transactions on private blockchains, so that financial intermediaries can benefit from the lower costs brought about by the technology. Each regulatory authority must choose measures to create a balance between consumer protection, opening up the financial sector to competition from FinTech start-ups and incentives for innovation. 5.2.2 Regulation of the Risks Associated with Cybercurrencies Virtual currencies such as bitcoin carry different risks for consumers and financial institutions. In many countries, they cannot be qualified as currencies in a legal sense, or even in an economic sense [16]. As a result, consumers cannot be sure that they will be able to use them to pay at merchants or convert them into legal tender. Bitcoin does not come with a legal guarantee that it will be redeemed at any time at its face value. In addition, the price of cybercurrencies is very volatile, requiring consumers to be warned of the risks of loss associated with holding them. Bitcoin exchange platforms do not offer any guarantee of liquidity or legal tender. They also risk going out of business if they are not sufficiently vigilant about the risks of piracy [17]. Furthermore, no authority ensures that the necessary conditions are in place to securely store the keys for exchanging bitcoins [2]. As the virtual currency allows transactions to be carried out under 46 pseudonyms, there is a risk that they will encourage illegal activities and money laundering, which allowed users to finance fraudulent activities in bitcoins. In Morocco, the jurisdictional authorities are working on a regulatory framework that will not only legalize cryptocurrencies, but also create a special section to oversee their proper use. In this way, the legislation relating to the fight against money laundering and the online financing of terrorist groups can be updated.

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5.2.3 The Problem of Regulating Public Blockchains The creation of binding rules for the use of public blockchains such as bitcoin is complex to implement. Indeed, to regulate a blockchain, it would be necessary to be able to control the location of servers, the activity of miners, the integrity of algorithms or to identify the parties carrying out transactions [5]. However, users of public blockchains operate under pseudonyms and are scattered across different countries. This makes it difficult to establish their responsibility in case of an incident or fraud. Public blockchains also raise many legal issues regarding the processing of personal data. For example, it is difficult to enforce the right to rectify, delete or forget data [15]. Moreover, the very principle of a public blockchain is to leave the code open to modification, without any control of the integrity or quality of the changes made. The case of the embezzlement of $50 million from the decentralized investment fund “The DAO”, which had raised funds in ether, is emblematic of the difficulties posed by the openness of computer code. The hacker took advantage of a flaw in the code, threatening to sue anyone who tried to recover the stolen currency under the logic of “The code is law” [15]. In the world of cybercurrencies, is it desirable and possible for computer code to substitute for trusted third parties? Indeed, code always has points of vulnerability, because it is entered by human hands.

6 Conclusion Blockchain is a fundamentally innovative technology that breaks away from traditional institutional frameworks and offers new possibilities for corporate finance. Its nature offers a dual use, both as a means of exchange and payment and as a support for contracts (smart contracts) which appears relevant for corporate finance, while raising many questions, both conceptual and practical. In this article, and thanks to our analysis of the literature review that we carried out on the basis of articles published on the Scopus database between 2017 and 2021, we have identified several contributions of blockchain technology to corporate finance. This technology will offer managers the possibility to increase the efficiency and speed of many daily financial operations. However, the implementation of blockchain technology is hampered by a number of regulatory obstacles, especially on the side of cryptocurrency, which prevent companies from taking advantage of it. Finally, it can be concluded that such an improvement in the functioning of financial services should go hand in hand with corresponding regulatory developments. Our study is limited in relation to the sample used. We use publications only from 2017 to 2021. While this gives a good overview of the filed, including earlier years could have generated a more in-depth portrayal.

References 1. Rabbani, M.R., Khan, S., Thalassinos, E.I.: FinTech, blockchain and Islamic finance: an extensive literature review (2020) 2. Varma, J.R.: Blockchain in finance. Vikalpa 44(1), 1–11 (2019) 3. Reepu, M.: Blockchain: social Innovation in finance & accounting. Int. J. Manag. 10(1), 14–18 (2019)

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4. Schär, F.: Decentralized finance: on blockchain-and smart contract-based financial markets. FRB of St. Louis Review (2021) 5. Bogucharskov, A.V., Pokamestov, I.E., Adamova, K.R., Tropina, Z.N.: Adoption of blockchain technology in trade finance process. J. Rev. Glob. Econ. 7, 510–515 (2018) 6. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decent. Bus. Rev., 21260 (2008) 7. Ibrahim, I.A., Truby, J.: Governance in the era of blockchain technology in Qatar: a roadmap and a manual for trade finance. J. Bank. Regul., 1–20 (2021) 8. Dong, C., Chen, C., Shi, X., Ng, C.T.: Operations strategy for supply chain finance with asset-backed securitization: centralization and blockchain adoption. Int. J. Prod. Econ. 241, 108261 (2021) 9. Aysan, A.F., Sadriu, B., Topuz, H.: Blockchain futures in cryptocurrencies, trade and finance: a preliminary assessment. Bull. Monet. Econ. Bank. 23(4), 525–542 (2019) 10. Zhang, L., et al.: The challenges and countermeasures of blockchain in finance and economics. Syst. Res. Behav. Sci. 37(4), 691–698 (2020) 11. Bogusz, C.I., Laurell, C., Sandström, C.: Tracking the digital evolution of entrepreneurial finance: the interplay between crowdfunding, blockchain technologies, cryptocurrencies, and initial coin offerings. IEEE Trans. Eng. Manag. 67(4), 1099–1108 (2020) 12. Rijanto, A.: Blockchain technology adoption in supply chain finance. J. Theor. Appl. Electron. Commer. Res. 16(7), 3078–3098 (2021) 13. Bermeo-Almeida, O., et al.: Blockchain in agriculture: a systematic literature review. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., BucaramLeverone, M. (eds.) CITI 2018. CCIS, vol. 883, pp. 44–56. Springer, Cham (2018). https:// doi.org/10.1007/978-3-030-00940-3_4 14. Chen, Y., Bellavitis, C.: Blockchain disruption and decentralized finance: the rise of decentralized business models. J. Bus. Ventur. Insights 13, e00151 (2020) 15. Owen, R., Mac an Bhaird, C., Hussain, J., Botelho, T.: Blockchain and other innovations in entrepreneurial finance: implications for future policy. Strateg. Change 28(1), 5 (2019) 16. Gan, Q., Lau, R.Y.K., Hong, J.: A critical review of blockchain applications to banking and finance: a qualitative thematic analysis approach. Technol. Anal. Strateg. Manag., 1–17 (2021) 17. Maniff, J.L., Marsh, W.B.: Banking on distributed ledger technology: can it help banks address financial inclusion? Fed. Reserve Bank Kans. City Econ. Rev. 102(3), 53–77 (2017)

A Review of Privacy-Preserving Cryptographic Techniques Used in Blockchain Platforms Sara Barj(B)

, Aafaf Ouaddah , and Abdellatif Mezrioui

National Institute of Posts and Telecommunications, Rabat, Morocco [email protected]

Abstract. Due to its reliance on cryptographic techniques to ensure a high level of security, Blockchain technologies are witnessing widespread adoption in many domains ranging from decentralized Finance (DeFi), contract management, ehealth, and cyber defense, to IoT among many others. However, quantum computing makes some cryptographic techniques used in the known blockchain platforms vulnerable and breakable. In this direction, this paper compares, classifies, and analyzes the cryptographic techniques used by well-known blockchain platforms, which are: Zerocash, Hyperledger Fabric, Monero, Ethereum, Bitcoin, and Hyperledger Indy. The forecited analysis is against three criteria: crypto-technique category, quantum resistance, and anonymity type. Finally, the discussion highlights the pros and cons of the studied techniques as well as presents some recommendations to improve privacy-preserving, quantum-safety, and security properties for each one. Keywords: Cryptographic techniques · privacy · data security · strong anonymity · pseudonymity · post-quantum cryptography

1 Introduction Privacy-preserving techniques in blockchain technology are designed to protect the personal information and data. By using these techniques, it is possible to create blockchainbased systems that are highly privacy-preserving and that allow users to maintain control over their personal data. However, many cryptographic techniques currently in use in the Blockchain field for privacy-preserving and security purposes are quantum unsafe and vulnerable. Actually, quantum computers could potentially break many of them [1–5]. There are many quantum-safe and privacy-preserving cryptographic techniques in the literature. For example, lattice-based cryptography techniques that uses the hardness of the Learning With Errors (LWE) problem are good candidates to protect from post-quantum attacks [6–15]. The problem, however, is how to select the best option for designing blockchain-based systems. As a matter of fact, not all cryptographic techniques have the same role. It isn’t easy to put a quantum-safe technique instead of another quantum-unsafe one. It is crucial to consider the category, the role, and the properties of the cryptographic techniques. For example, it is possible to substitute an elliptic-curvebased signature scheme with a lattice-based one under some conditions but not by a lattice-based key exchange protocol. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 230–240, 2023. https://doi.org/10.1007/978-3-031-29857-8_23

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Contribution. In this direction, the paper’s main contributions are listed below: This paper reviews the privacy-preserving crypto-techniques used in well-known blockchain platforms. Additionally, it provides a comparative analysis of the studied blockchain platforms and the studied cryptographic techniques against three criteria: crypto-technique category, quantum resistance, and anonymity type. It also extracts their main benefits and drawbacks. Besides, it proposes a taxonomy of the different cryptographic technique categories in the blockchain Platforms. Finally, some recommendations are proposed to improve privacy-preserving, quantum-safety, and security properties for five cryptographic techniques used in well-known blockchain platforms. Paper Structure. The rest of the paper is structured as follows: Sect. 2 presents some well-known blockchain platforms and their privacy-preserving protocols and techniques, if any. Section 3 introduces a taxonomy of cryptographic techniques. Section 4 describes the finding by analyzing the cryptographic techniques used in the studied blockchain platforms against the forecited criteria and highlights their main pros and cons. Moreover, Sect. 5 presents our recommendations for using quantum-safe techniques to harden blockchain platforms. Finally, Sect. 6 concludes the paper.

2 State of the Art of Privacy-Preserving Crypto-Techniques in the Blockchain In this section, we survey and analyze the most known privacy-preserving most known blockchain platforms and the underlying privacy-preserving protocols and techniques they use. Table 1 provides a comparative analysis of the studied blockchain platforms. 2.1 Zerocash This blockchain platform is public and permissionless. It is mainly used for cryptocurrency exchange. Its cryptocurrency is Zcash (Z). ZeroCash is a cryptocurrency that uses privacy-preserving cryptographic techniques to provide users with a high level of privacy, such as Zero Knowledge Proofs (Bulletproofs and zk-SNARKS), Hashbased cryptography (SHA-256), Signatures (ECDSA), Commitment, and Accumulator [4, 16]. These techniques, which are based on zero-knowledge proofs, allow users of the ZeroCash network to conduct transactions without worrying about their privacy being compromised.

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2.2 Hyperledger Fabric This blockchain platform is private and permissioned. Its use: the manufacturing supply chain. It uses cryptographic functions such as ECDSA signatures and Hash-based cryptography (SHA3 and HMAC) for privacy-preserving purposes [16, 17]. Actually, Hyperledger Fabric provides a range of privacy-preserving techniques to help ensure the confidentiality of transactions on the network. 2.3 Monero Monero is a cryptocurrency that places a strong emphasis on privacy and anonymity. It uses several techniques to help protect the privacy of its users, including stealth addresses, Bulletproofs, ring signatures, or ring-confidential transactions to reach privacy-preserving goals [16, 18]. Stealth addresses are a type of one-time address that is generated for each transaction, further obscuring the identity of the sender and recipient. 2.4 Ethereum Ethereum is a decentralized platform that supports smart contracts and the creation of decentralized applications (DApps). While Ethereum does not have built-in privacy features, it does provide developers with the ability to create DApps that incorporate privacy-preserving techniques. One way this can be achieved is through zero-knowledge proofs. Ethereum allows for the creation of private networks, known as “private chains” or “sidechains,” which can be used to conduct transactions more privately and securely [19]. 2.5 Bitcoin Bitcoin is a decentralized cryptocurrency that uses a public ledger to record transactions on its network. While Bitcoin does not have built-in privacy features, it uses Bulletproofs, ECDSA signatures, and Hash-based cryptography (SHA-256) [16]. There are a few ways in which users can preserve their privacy when using the network. For example, Users can use multiple addresses to receive and send funds, which can also help obscure the connection between their different transactions. 2.6 Hyperledger Indy Hyperledger Indy is an open-source DLT platform that supports digital identity applications. It is built on the principles of self-sovereignty, meaning that users have complete control over their digital identities and the associated data. It is a highly privacy-preserving platform. It uses Edwards-Curve Digital Signature Algorithm (EdDSA: Ed25519), Hash-based Message Authentication Code (HMAC), and anonymous credentials to realize privacy-preserving benefits [20].

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Table 1. The comparative analysis of the blockchain platforms Blockchain platforms

Types

Application Domains

Privacy-preserving protocols/techniques

Zerocash

Public and Permissionless

Cryptocurrency exchange Its cryptocurrency is Zcash (ZEC)

Zero Knowledge Proofs (Bulletproofs and zk-SNARKS), Hash-based cryptography (SHA-256), Signatures (ECDSA), and Commitment/Accumulator

Hyperledger Fabric

Private and permissioned

Manufacturing supply chain

ECDSA signatures and Hash-based cryptography (SHA3 and HMAC), Channels techniques

Monero

Public and Permissionless

Cryptocurrency exchanges. Its related cryptocurrency is Monero (XMR)

Bulletproofs, ring signature, ring confidential transactions stealth addresses

Ethereum

Public and Permissionless. It could be used as private and permissioned

Cryptocurrency exchanges. Its related cryptocurrency is Ether (ETH). It is also used for digital identity management

ECDSA signatures, Hash-based cryptography (SHA-256 and RIPEMD-160), Private chain, Sidechain

Bitcoin

Public and Permissionless

Cryptocurrency exchanges. Its cryptocurrency is Bitcoin (BTC)

Bulletproofs, ECDSA signatures, and Hash-based cryptography (SHA-256)

Hyperledger Indy

Public and Permissioned

Self-sovereign identity

Edwards-Curve Digital Signature Algorithm (EdDSA: Ed25519), Hash-based Message Authentication Code (HMAC), Anonymous credentials

3 A Taxonomy of Most Useful Cryptographic Techniques Many cryptographic techniques enhance privacy protection property in different blockchain platforms. Our classification categorizes the cryptographic techniques and cryptographic primitives according to their properties, technical designs, working principles, or working finalities. We have categorized those different solutions for 6 categories as follows: The hash-based cryptography primitives (HBCP); The Zero-knowledge proof (ZNP)-based protocols (ZNPBP); The lattice-based cryptography schemes (LBCS); The

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digital credential technologies (DCT); The homomorphic encryption-based schemes (HEBS); And, the attribute-based encryption primitives (ABEP). 3.1 Hash-Based Cryptography Primitives (HBCP) This category contains cryptographic primitives such as hash-based signatures, Merkletree, and hash functions. It is essential to highlight the quantum safety of hash-based signatures. Hash functions are one-way mathematical functions that verify the integrity and achieve DLTs/Blockchain immutability. 3.2 Zero-Knowledge Proof (ZNP)-Based Protocols (ZNPBP) Concerning this concept, it is used in authentication systems to prove the identity of a user to another party (the verifier party) with some selected secret information while keeping other secret information hidden from the verifier party. The cryptographic protocols using this concept must satisfy three proprieties: Completeness: Proof from an honest prover related to valid words is always accepted by an honest verifier employing a valid witness. Soundness: It is either statistically or computationally impossible for an honest verifier to accept proof from a malicious prover for a word. Zero-knowledge: The prover gives just the information needed to prove the knowledge of the secret. This one is not revealed to the verifier, who doesn’t have any knowledge about the secret of the prover. An example of cryptographic algorithms using this concept is Sigma-protocol and Schnorr Protocol [21]. STARK Family. Zero-knowledge Scalable Transparent Argument of Knowledge (znSTARK) is a proof system from the ZNPBP, which achieves the following properties: Zero-knowledge, Scalability, Transparency, Quantum safety, and argument of knowledge property [22]. Ziggy is a signature scheme based on a ZK-STARK. It is also a cryptographic protocol achieving these properties: quantum-safety and privacy-preserving [7]. There are other protocols in this family, such as those in the MARVELlous family of cryptographic primitives [23]. zn-SNARK Family. SNARK is a proof system from the ZNPBP verifying the following properties: – Succinct: the proof size is (quasi)-linear and is very shorter than the computation size. – Non-interactive: This property means that such a proof system has three steps: • Step 1: the generation algorithm of the required public parameter crs for the proving process, the verification key vrs, which should be secret to just the verifier, and the trapdoor information td. • Step 2: the proving algorithm having as input the crs, a corresponding witness w, and the statement u. The outputs of this algorithm are a part of the proof π. • Step 3: the verification algorithm. It is a Boolean algorithm taking as input the proof π, the statement u, and the verification key vrs. It returns 1 if acceptance of the proof and 0 if not.

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– Complete: Proof from an honest prover related to valid words is always accepted by an honest verifier employing a valid witness. – Knowledge soundness: (remind) It is either statistically or computationally impossible for an honest verifier to accept proof from a malicious prover for a word [24]. Some examples of protocols from the zn-SNARK family are zero-knowledge SNARK (znSNARK), Post-quantum zn-SNARK [24, 25]. 3.3 Lattice-Based Cryptography Schemes (LBCS) Lattice-Based Public-Key Cryptography is an asymmetric cryptography based on lattice problems such as the shortest vector problem (SVP), learning with errors (LWE) problem, and the closest vector problem (CVP). Regev cryptosystem and the Tesla family are examples of protocols from this family [5, 15, 26]. 3.4 Digital Credential Technologies (DCT) There are many authentication technologies [27], such as: – Public-key certificates with salting. This digital credential technology uses the concepts of public-key cryptography and signature. However, it needs the disclosure of some stored attributes. The stored and needed attributes could be hashed concatenated to solve this problem with a salt value [27]. Despite this, this technology doesn’t preserve privacy. In this case, the service provider should be a trusted party. – Anonymous credentials. Contrariwise, due to the use of the zero-knowledge proof concept for validating the proof, this type of credential has privacy-preserving property. These algorithms allow the user to disclose a selected attribute and hide others. Nevertheless, this technique generates unlikability. This property can be seen as a drawback [27]. – Minimal disclosure tokens. In this paper [28], we found that Minimal disclosure tokens are part of the digital credential technologies. It is a cryptographic mechanism used in some credential systems, such as U-prove [29]. It permits providing privacypreserving property. It is used for authentication. – Group signature. This concept permits signing anonymously on behalf of a group. Indeed, the receiver knows that the signer is a group member while the signature is valid and verified. It helps implement anonymous authentication. It is possible in such a signature to open it. In this case, the signer’s anonymity is revoked, and this one is revealed [30]. – Ring signature. In this signature, the signer is still anonymous. The receiver can verify that the signer is a ring member (group). There is no way to reveal the signer’s identity while using an anonymous signature using a ring signature [31]. – Self-blindable credentials. It is a system that uses Credential Pseudonymous Certificates (CPCs), providing anonymity and unlinkability. It is essential to highlight that such a system doesn’t need a trusted third party. Hence, it permits decentralization [32].

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3.5 Homomorphic Encryption-Based Schemes (HEBS) – Homomorphic encryption scheme. The homomorphic encryption scheme extends the asymmetric cryptography family. It permits to process and to compute encrypted data without needing to have access or to reveal the private key. Four cryptographic algorithms characterize the homomorphic encryption schemes: The keys generation algorithm, The homomorphic encryption algorithm, the homomorphic decryption algorithm, and the evaluation algorithm of the homomorphic property [24, 33]. – Commitments. A commitment is formed by some cryptographic algorithms having two main properties: hiding and binding. Pedersen commitments are homomorphic. The hiding property permits the user to hide the chosen values chosen while allowing proving some committed values properties. And, the binding property permits the verifier to detect any change in the values committed initially by the user [24]. – Accumulator. An Accumulator is a cryptographic technique from the HEBS used in Zero-Knowledge proofs. Accumulator cryptosystem permits verifying a set membership. It also allows adding and removing members of the set dynamically. These actions verify that an accumulator contains a committed value with zero knowledge delivered [34]. – Secure multi-party computation. This cryptographic scheme can use homomorphic encryption and permits hiding inputs during the distributed calculation of a public function result. This action is done without relying on a trusted third party [35]. 3.6 Attribute-Based Encryption Primitives (ABEP) This family contains many primitives, such as ciphertext-policy attribute-based encryption (CP-ABE), multi-authority attribute-based encryption (MA-ABE), and Key-policy attribute-based encryption (KP-ABE) [36–38]. For example, CP-ABE is an access control system for sensitive stored data, achieving privacy-preserving even if the storage environment is untrusted. It can be used in blockchain.

4 Analysis of the Crypto-Techniques Used in Blockchain Platforms We conduct our analysis based on the following criterion: Quantum resistance/Quantum Safety: it is the property of cryptographic techniques resistant to quantum computing attack. Strong anonymity: it is the property of the irreversible hiding of personal data identifiers. hence, it permits strong privacy-preserving. it ensures unlinkability due to the hardness of performing forensic tasks [39]. And, Pseudonymity: it is the property of the reversible hiding of personal data identifiers. It means linkability, and ease of forensic tasks [39]. However, it permits just medium privacy-preserving due to the reversibility of the personal data identifiers hiding. Table 2 summarizes the finding.

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Table 2. Analysis of the crypto-techniques used in well-known blockchain platforms Cryptographic techniques

Cryptographic technique categories

Quantum Safety

Quantum -unsafety

Anonymity type

Pros

Cons

Bulletproof

ZNPBP

No

Yes

Strong anonymity

Small proof size, Non interactivity

Could not be used to prove arbitrary statement, Not efficient

zk-SNARKS

ZNPBP

No

Yes

Strong anonymity

Small proof sizes, High efficiency

Not fully non-interactive, Require the use of a trusted setup

Hash-based cryptography (SHA-256)

HBCP

Yes

No

Strong anonymity

Speed, Simple to implement

Deterministic, Breakable

Ring signature

DCT

No

Yes

Strong anonymity

Strong security, Strong resistance to forgery attacks

Large size

Ring Confidential Transactions

DCT

No

Yes

Strong anonymity

Strong anonymity

Less security

ECDSA

Elliptic curve-based cryptography schemes

No

Yes

Pseudonymity

High security, Efficiency

Not fully forward secure

EdDSA: Ed25519 Edwards-Curve Digital Signature Algorithm

Elliptic curve-based cryptography schemes

No

Yes

Pseudonymity

High level of speed, optimized, deterministic, collision resistance, side channel attack resistance

Low level of standardization, Makes application design complex, Incompatibility with ECDSA and RSA

Accumulator

HEBS

No

Yes

Strong anonymity

High confidentiality

Not dynamic

Hash-based Message Authentication Code (HMAC)

HBCP

Yes

No

Strong anonymity

Good high-performance systems, Great against cryptanalysis attacks

Breakable

5 Recommendations There are several post-quantum and privacy-preserving techniques that are recommended for use in blockchain technology [6–9, 21, 33, 40, 41]. For homomorphic encryption, it is better to select a quantum-safe one like the one described in the paper [41]. Besides, many blockchain platforms use Elliptic curve-based signatures. Nevertheless, this family is subject to multiple attacks, especially post-quantum ones. Hence, lattice-based signatures or Ziggy signatures are still a better choice. SHA3-256 is a post-quantum

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hash. Hence, it is better to choose it in the design phase when selecting a hash from the hash-based family. Finally, for authorization and access control purposes for read and write actions, it is crucial to choose post-quantum attribute-based encryption permitting strong privacy-preserving such as the one described in this paper [8]. All of these techniques, summarized in Table 3, can be used to protect the privacy of individuals and organizations in a blockchain-based system. They can be combined to provide even stronger privacy guarantees. Table 3. Post-quantum and privacy-preserving recommended techniques Cryptographic technique categories

Recommended cryptographic techniques

Homomorphic encryption-based schemes

The variant of Torus Fully Homomorphic Encryption (TFHE) scheme described in [41]

Zero-knowledge proof (ZNP)-based protocols

ZK-STARK

Elliptic curve-based signatures

Lattice-based signatures or Ziggy signatures

Hash-based cryptography primitives

SHA3–256

Encryption-based access control

Attribute-Based Functional Encryption based on Lattices

6 Conclusion This paper surveyed the current state of the art on privacy-preserving technologies for blockchain with a focus on crypto-privacy resistance to quantum computing and on-chain data privacy. In this direction, we analyzed and compared the current cryptographic mechanisms used in Zerocash, Hyperledger Fabric, Monero, Ethereum, Bitcoin, and Hyperledger Indy platforms. Moreover, we highlighted their main benefits and drawbacks. Furthermore, a taxonomy of the different cryptographic technique categories in the blockchain Platforms and some recommendations to improve privacy-preserving, quantum-safety, and security properties for five cryptographic techniques, are proposed. Despite important analyzed efforts to integrate novel crypto-privacy techniques, current blockchain solutions are still quantum unsafe, making it necessary to develop new, quantum-resistant techniques. Quantum computing technology is still too expensive to be widely used. However, it is still an excellent thing to prepare for the arrival of quantum computing wide utilisation.

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Renewable Energy in Smart Grid: Photovoltaic Power Monitoring System Based on Machine Learning Using an Open-Source IoT Platform Youness Hakam(B) , Hajar Ahessab, Ahmed Gaga, and Benachir El Hadadi Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Department of Physics, Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni-Mellal, Morocco [email protected]

Abstract. This paper proposes a PV monitoring system based on the Internet of Things, which is the best solution to monitor our system. In this system, we have utilized the Raspberry Pi card as a server that communicates with a Node Mcu (ESP32) as a client using the MQTT and HTTP protocols. Our solution is composed of two steps; the first step consists of power measurement. The voltage constant and current were measured by the ACS712 sensor, and we measured the power and energy of the solar panels every 5 min. A DHT sensor was utilized to measure the temperature of the solar panels. These measures will be shown on the Node-red platform and stored as a database in the SQLite programming language; SQLite is introduced to reduce the database complexity. A Raspberry Pi card recorded this database in real time using WiFi or cable Ethernet; with this database, we can make accurate projections about the efficiency of solar panels and command our system. However, in the second step, based on this database, we involve the command in our system by the best method (algorithm) of prediction for our case. The power delivered by solar panels was predicted with the use of machine learning (a model decision tree), which enabled us to generate forecasts. In practice, we use an electronic card that can support this type of machine learning algorithm; for this, we used the raspberry pi card. Node- red is the most suitable interface to apply this algorithm, and it allows us to monitor all measurements by the dashboard in real time with a laptop (WiFi) and with a smartphone (4G). As a result of this work, we have made a smart system based on machine learning that allows us to integrate PV into the smart grid. This approach allows us to manage our system in a more efficient, automated, and intelligent manner. Keywords: Internet of Things (IoT) · Raspberry pi · Machine Learning

1 Introduction The main generating source for most electrical systems, which is often a hydraulic trick or fossil fuel based power plant, only provides energy in one direction. Determining © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 241–251, 2023. https://doi.org/10.1007/978-3-031-29857-8_24

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flaws in these systems and taking corrective action is likewise a time consuming task. In addition, as renewable energy technologies become more affordable, modern consumers can produce their own energy in addition to receiving supplies from the main utility. The term “smart grids” refers to the use of ICT technologies to increase the observability of both existing and newly installed grids, enable distributed energy generation at both the utility and consumer ends, and provide self-healing capabilities to the grid [1]. Real-time power data are provided to utilities at various grid locations across the supply lines until the consumer is one feature of smart grids [2]. Smart grids provide improved control of power generation using prediction models created using acquired consumption data since they provide real-time data about customer usage. Smart grids (SGs), which are modernized versions of conventional “dumb” energy infrastructures, are the main and most important components of the idea of a sustainable future metropolis [3–5]. Today, much commercial activity takes place online. A good example of this is the field of renewable energy, particularly PV (photovoltaic) systems. Solar energy may be converted into electricity using a technique known as a PV system. IoT may be applied in a PV system principally as a monitoring system. The application of IoT in a PV system has numerous benefits, according to research by Kumar. IoT reduces the tedious task of making frequent trips to industrial sites. IoT enables monitoring applications to continuously collect efficiency and failure data for analytics, forecasting, predicting future power and revenue production, and even remotely manipulating controllable loads in the home [6–8]. The market offers a number of IoT based PV system monitoring products. The purchase package contains some of these. Others are offered for sale separately as add-on items. Each of these commercially available technologies includes a wide range of services. However, it is essential to take into account the charges of their installation, registration, and maintenance [9]. The development of smart based on the Internet of Things has been greatly aided by machine learning (ML), which improves prediction (classification), estimation (regression), and clustering tasks [15]. The term “machine learning” refers to a group of methods that enable computers to learn from experimental observations [16], and have been utilized in smart cities in a range of ways. In the last five years, there has been much research in this area employing ML algorithms. Support vector machine (SVM), random forests (RF), decision tree (DT), naive bayes (NB), K-means, K-nearest neighbor (K-NN), and logistic regression were determined to be the most frequently used ML algorithms (LR) [3]. In a decision tree, we emphasize this work (DT) (Table 1). A server receives the data produced by sensors. To do this, data must be acquired. Two phases make up this procedure. The analog signal from the sensors must first be converted to a digital signal. The data transmission comes next. Rasp Pi (the Raspberry Pi) and MCP3008-ADC are proposed as a combination in certain articles (11, 12, and 4). (Analog to Digital Converter). A cheap, compact, and portable computer board is the Rasp Pi [17]. NodeMcu is a microcontroller with a wireless (WiFi and Bluetooth) module within, similar to ESP8266 and ESP32. It has wireless communication capabilities and supports remote programming. A signal NodeMcu coupled to a WiFi is adequate to carry out data collection with a coverage of 100 m since its performance has been much-increased [23]. This dashboard must be designed using a tool. The dashboard might be a standard

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Table 1. Related papers. Reference

Current sensor

Data acquisi tion

Server & Data- base

Dashboard

PV system

Adhya et al. Invasive [17]

PIC18F46K22,GSM-GPRS

Desktop PC

Local web

Off-grid

Kekre and Gawre [6]

Invasive

Arduino, GSM- GPRS

Desktop PC

Local web

Off-grid

Gupta et al. [13]

Invasive

MCP3008Rasp Pi, Serial

Desktop PC

LabView

Off-grid

Othman et al. [4]

Invasive

MCP3008, Serial

Rasp Pi

Node-RED

Off-grid

Deshmukh and Bhuyar [8]

Invasive

MCP3008, Serial

Rasp Pi,Cloud

Ubidots

Off-grid

Choi et al. [5]

Invasive

Arduino, LoRa

Rasp Pi, MongoDB

Local web

Off-grid

Hamied et al. [5]

Invasive

Arduino-ESP, WiFi (HTTP)

Desktop PC

Local web

Off-grid

Priharti et al. [22]

Invasive

Arduino-ESP, Wi Fi (HTTP)

Cloud

Thinkspeak

Off-grid

Aghenta and Iqbal [15]

Invasive

Arduino, Serial

Rasp Pi

EmonCMS

Off-grid

Fadel et al. [16]

Invasive

Arduino, Serial

Rasp Pi

Cloud Node-RED

Off-grid

Oukennou et al. [5]

Invasive

ESP, MQTT

Desktop PC

Node-RED

Off-grid

Rouibah et al. [19]

Invasive

ArduinoESP, WiFi (TCP/IP)

Desktop PC,MySQL

Local web

Off-grid

Ali and Paracha [12]

Invasive

Arduino ESP, WiFi (HTTP)

Cloud

Adafruit

Off-grid

Cheddadi et al. [10]

Invasive

ESP, WiFi (HTTP)

Desktop PC,InfluxDB

Grafana

Off-grid

This research

Invasive

ESP, WiFi (MQTT)

Rasp Pi, SQLite

Node-RED

On-grid

webpage that is locally installed on a PC [5]. Despite its exceptional flexibility and extensibility, web programming experience is needed. The use of an IoT cloud platform is usually seen as the simplest option to design the dashboard. Examples include Ubidots [8], Thingspeak (16, 25), and Adafruit [25]. However, apart from paid subscriptions, the amount of data and the information provided are typically constrained. An emerging

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programming tool to create the dashboard is Node-RED. Database utilization can be used to increase the dashboards’ usefulness. There are numerous kinds of databases available, from systems that are more traditional such as MySQL [26], to works that are more contemporary such as MongoDB [27] and Influx DB [28]. The goal of this study is to close the aforementioned gaps. This study attempts to recommend a low cost IoT design for use in PV systems. The suggested concept offers cost saving data and makes use of a noninvasive current sensor to increase safety and simplicity. The utilization of open platform hardware, specifically NodeMcu and Rasp Pi, is a manifestation of the low cost architecture. The software used in this study is free and simple, and it includes MQTT and Node-RED. Additionally, this study introduces SQLite as a tool [29]. Despite having a lot of simplicity, it is rarely employed in IoT design. This research helps PV system users create their own DIY IoT power monitoring systems at a reasonable cost. The conventional IoT waterfall technique is used in this study [30].

2 System Design The latter enables the real-time database recording of these data. High air temperature increases the amount of moisture in the air that can hold and sets the saturation limit. The work of solar cells at high temperatures above 25 °C means a decrease in their performance. Bhattacharya [32] examined this phenomenon in Rajasthan and connected it to other meteorological elements, including ambient temperature, dust, and rain. According to the study, the maximum value of relative humidity in the study region is approximately 70%, and the average value is 42%. According to the study findings, the corrosion of photovoltaic cells is the most important and substantial impact of relative humidity on solar cells [12]. In this essay, we primarily focus on the power that park panels and batteries produce. We employ the ACS712 current sensor, which converts the current generated by the solar panels into an electrical signal. With the help of the DHT sensor, which can measure temperature and humidity, we can determine the production of solar panels and determine how it varies system development. 2.1 Overall System In this study, we used two sensors, DHT11 and ACS712, which measured temperature and current. These sensors transmit values (data) in the form of signals to NodeMcu ESP32, which allows capturing these signals by ADC pin signals and calculates their values in real time with software, which is implemented in the platform ARDUINO IDE. This software allows calculating current, power, temperature, and humidity to transfer that data to server the Raspberry Pi through WI-FI. Raspberry Pi displayed data on a laptop or smartphone with the Node-Red environment, an IP address, and an 1880 port needed to observe data on a laptop (local) as well as a smartphone (anywhere in the world (4G, 5G) with a password for security. The Raspberry Pi includes system exploitation that enables it to handle various applications for machine learning implementation,

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such as Anaconda and Transerflow. This system allows Raspberry Pi to operate several NodeMCUs and receive/order at the same time. We require real-time data on the temperature to use machine learning in this study (Fig. 1).

Fig. 1. Global system

The most significant application of implementing machine learning is the incorporation of renewable energy into smart grids. This enables us to forecast the state of the fluctuation of the future output of a solar park and save a significant amount of money that is mostly spent on maintenance. 2.2 Raspberry Pi Saved Data Every second, the NodeMcu reads, analyses, and sends the data to the nearby Raspberry Pi server. Power, current, humidity, and temperature make up the data. In Fig. 2, the program algorithm is displayed. All of the necessary libraries and variables are initialized at the start of the application. Next, the WIFI connection, MQTT server, and ADC pin are configured. The current and DHT sensors are read every second using the “Millis ()” function. After gathering the current data, Eq. 1 is utilized to determine the power in W. It is established that the voltage value is a constant of 20 V. This figure serves as the benchmark in our example for the solar park. Solar/current, Solar/power, Solar/Temperature, and Solar/Humidity topics on MQTT are used to communicate data in JSON (JavaScript Object Notation) format. Power = Voltage × Current

(1)

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Fig. 2. Saved data flow chart

2.3 Machine Learning Implementation Artificial intelligence, or machine learning, is used in many applications and is the technology of the future for all systems worldwide. However, machine learning is used in a specific way for each system. In this study, we use machine learning to determine or anticipate the fluctuation in solar park production by knowing the difference in temperature and humidity. We make use of the Raspberry Pi card for this. The best interface to use with this method is Node-RED (Fig. 3).

Fig. 3. Real time data saving process in Excel file.

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2.4 Machine Learning Implementation Algorithm To predict new data (temperature) by that model, this future of temperature (data) is shown in Fig. 4 (Fig. 5).

Fig. 4. Global figure of machine learning

Fig. 5. Algorithm of machine learning.

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3 Dashboard A screen linked to the Rasp Pi is mounted in a common area in the campus building to satisfy the customers request for a kiosk display. The dashboard on this kiosk is visible to everyone. If you go to the Rasp Pi IP address on a desktop computer, you may view the dashboard remotely as well. The PC and Rasp Pi should share the same WiFi network. Figure 6 is the DHT page that enables us to view the change in temperature and humidity every second. The second diagram explains the variation in the power of the solar park, and the third diagram combines the variation in the current and power of this solar park (Fig. 7).

Fig. 6. The variation in temperature on the dashboard

Fig. 7. The variation in current and power on the dashboard

4 Conclusion and Future Work In this paper, we presented a machine learning-based system that might integrate renewable energy into smart grids, allowing us to track the condition of production fluctuation without depleting our financial resources.

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The steps for calculating energy, storing these numbers in a database in real time, and switching between the grid and a solar park are outlined in this section. However, since we can create a machine learning algorithm that satisfies our needs and put it on a Raspberry card, this is not the ultimate objective. To determine the precise values of the power provided by a solar field, a future algorithm is being created that uses the database as a history to make decisions in real time.

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Predicated on IoT, a Safe Intelligent Driver Assistance System in V2X Communication Environments Younesse El Hamidi(B) and Mostafa Bouzi Faculty of Sciences and Technology, Hassan First University, Settat, Morocco [email protected]

Abstract. Car dependency on technology has constantly grown in the modern world as traffic has increased. At the same time, highway-related fatalities and injuries from traffic accidents are on the rise, and safety concerns are a top priority. As a result, the development of Driver Assistance Systems (DAS) has emerged as a critical concern. To increase road traffic and safety, several inventions, methods, and technologies have been developed. Modern computer vision algorithms enable automobiles to grasp their surroundings with few misses. To increase road traffic and safety, several inventions, methods, and technologies have been developed. Modern computer vision algorithms enable automobiles to grasp their surroundings with few misses. A variety of Intelligent Transportation Systems (ITSs) and Vehicle Ad-Hoc Networks (VANETs) have been deployed in cities across the world. A new worldwide concept known as the Internet of Things (IoT) has recently introduced fresh ideas to update old solutions. Vehicular connection based on IoT technology would be an important step forward in intelligent mobility for the network Design (IoV). We analyze the state of the art in V2X connectivity approaches and suggest a strategy for developing the architecture of a reliable intelligent driver assistance system employing IoT communication in this study. The essay, in particular, describes the system architecture design process utilizing the IDEF modeling approach and information flow studies. The suggested method depicts a system design based on the IoT architectural reference model. Keywords: Intelligent Transportation Systems · Vehicle - to - infrastructure Communications · Advanced Driver Assistance System Systems

1 Introduction Societies are becoming increasingly crowded in our fast-rising globe as the population grows. Vehicle accidents and protracted traffic jams are major issues in today’s world. Countless working hours are lost on the roadways as a result of congestion, and major accidents make life considerably more difficult in some circumstances. To World Health Organization (WHO) research [1], around 1.35 million people are killed in vehicle accidents each year. Traffic bottlenecks also result in significant delays, resulting in substantial financial losses [2] and [3]. These main issues may be resolved if drivers or © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 252–260, 2023. https://doi.org/10.1007/978-3-031-29857-8_25

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smart cars are kept informed of current traffic conditions. As a result, improving driver assistance is a top priority. Several initiatives have been undertaken to address transportation system safety issues. The car industry’s research and development efforts are focused on developing a connected car system, active control, and a safety mechanism. As a consequence of automotive industry research (Audi, BMW, LUCID, CARIAD, Daimler, GMC, Ford, Honda, Mercedes-Benz, PSA, Toyota, Volkswagen, Volvo), we now see a multitude of internal control systems (systems in cars). In-vehicle safety technologies include a variety of passive and active protective devices, such as ESV4: early warnings, automobile robustness control systems, anti-lock braking systems, autonomous parking systems, and so on. Camera, Lidar, Radar, Ultrasonic sensor, IR sensor, and GPS are the primary sensors in the ADAS architecture. As an example, [5] describes Tesla Europe’s ADAS project, which employs at a distance ranging from 250 m, 8 encircling Cameras give 360-degree sight surrounding the automobile. This vision is supplemented by twelve upgraded ultrasonic sensors, which identify both hard and soft things at roughly double the distance of the previous system. Much forward radar with better processing offers extra data about the world on a frequency capable of seeing through severe rain, fog, dust, and even the automobile in front of it. Additionally, the information supplied by the exterior sensors, in conjunction with the data provided by the internal sensors, may be utilized to optimize road safety. As a result, governments and businesses are funding research into intelligent transportation systems (ITS) and vehicle ad hoc networks (VANET) [6–9] says that VANET links automobiles to each other and presents infrastructure to determine safety problems. According to [8], the primary goal of ITS is traffic management: congestion avoidance and driver warning. Intelligent Transportation Systems are capable of providing traffic control, signalized intersections synchronization, driver notification, electronic payments, actual road mapping, transport planning, and environmental management. Multiple organizations and groups have lately been formed to advance ITS: Intelligent Transport Systems Society in the United States [10]. In Europe [11] refers to the 27 member organizations. Car2Car Communication Consortium (C2C-CC) in Europe, Vehicle Safety Communication (VSCC) in the United States, and Advanced Safety Vehicle (ASV) in Japan are the organizations involved in VANETs. In Japan, an ASV is a safety vehicle. CVIS, SIM-TD (Germany), Come Safety, PRE-DRIVE C2X, CVIS, SAFESPOT, COOPERS, SEVECOM, Network on Wheels (NoW), ACTIV (Germany), CVHS (UK), IVSS (Sweden), Adaptive (EU), and Autonet2030 are some of the ITS and VANET projects that have been created (EU) [7, 12]. Car communication is a crucial part of ITS and VANETs, as well as the foundation for self-driving automobiles. V2V communication was named one of the top ten technologies of 2015 by the Massachusetts Institute of Technology. Furthermore, [13] and [9] have demonstrated that employing V2X communication instead of vehicle-to-vehicle (V2V) communication gives several benefits. V2X can be utilized to gather infrastructure and content information that is not immediately connected to automobiles. Meanwhile, many issues must be addressed to make driving systems safer. To begin with, ADAS systems are only available in certain automobile models. VANET and ITS

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are, for the most part, independent embedded systems that employ various standards and protocols as well as their chipsets. Some of them lack a link to the infrastructure (V2I), while others lack even a V2V connection. As a result, it is critical to investigate the processes for merging various vehicle systems. This would be a major step ahead, hastening their adoption. Important concerns for future automotive infrastructure include the creation of systems that use the same specifications and reference model. In this study, we present an ADAS design methodology based on IDEF techniques and data flow analysis to achieve two goals: increase traffic safety and address current issues. We propose a cooperative DAS design that takes into account the majority of existing automobile electronics, such as DVRs, cameras, navigators, and smartphones. We also modified the system with modern Internet of Things (IoT)-based vehicle communication technology and used real-world standards to assure its compatibility with other systems. Using the IoT reference model and contemporary standards, we presented a method to incorporate IoT with V2X communication. This method is illustrated via a case study.

2 Advanced Vehicle Communication Technologies Challenges Vehicles functioning automatically in the transportation network is a major fantasy that will someday come true, with the number of autonomous vehicles on the road gradually increasing, eventually consuming most or all of the infrastructure improvements. To allow intelligent ground transport networks, advanced vehicle networks have arisen. Vehicular communication technologies connect many elements such as cars, people, infrastructure, roadways, cloud-based computing platforms, and so on. This is referred to as the Internet of Things (IoT). The Internet of Things (IoT) connects many types of physical devices or “things” to the network via Internet protocols (IP), allowing them to exchange data with other connected devices, infrastructures, and operators. Devices, infrastructures, and operators that are linked [14, 15]. The much more major benefit of IoT is the use of IP to link devices via the Internet. It is critical for the feasibility of integrating various IoT devices into the broader notion of the Internet of Everything (IoE). Take note of the revolutionary importance of the IoT and IoE ideas in our life. IoE is predicted to merge all of our devices, gadgets, and automobiles in the future. IoT/IoE technologies are critical components of the future data society [14]. There are numerous hurdles in converting automotive systems based on the Internet of Things idea. Most present solutions are disconnected from one another and appear to be an “Intranet of Things,” according to [16]. As a result, it is critical to develop a new strategy for IoT system architectural design and to construct IoT systems under contemporary standards.

3 The System Architecture of IoT – Case Study The suggested system is based on the aim of road safety. We employed the IDEF (Integration Definition) modeling approach and data flow analysis during the architectural modeling phase. The US Air Force proposes the IDEF (ICAM Definition) approach for a

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structural and graphical description of industrial organizations. Among the several IDEF techniques (IDEF0–IDEF14), IDEF0 and IDEF1X are two distinct ways of modeling functionality and information relationships, respectively.

Fig. 1. Function model (IDEF0 Diagram).

Figure 1 depicts the system under development in the function modeling language (IDEF0 diagram). The function’s inputs are the issues and sensor data. Our primary objective is to increase safety as a result of the function’s output. The IDEF0 function command represents new technologies and standards. We believe the new technologies are IoT/IoE and cloud computing. We would also develop an architecture with contemporary V2V and V2I communication standards, and update it with IoT standards, under the IDEF0 control. Figure 3 depicts the architecture of the planned system. We anticipate using in-car cameras as well as an infrastructure camera network to enable V2X communication. Communication infrastructure, sophisticated data centers, and clever algorithms are among the IDEF0 mechanisms. We anticipate that Cloud and fog computing will be used to perform Big Data analysis using complicated object detection techniques. Furthermore, we employ better V2X IoT connectivity, which enables us to collect data from a variety of sensors, devices, and systems “things” in IoT: automobiles, DAS, sensors within vehicles and infrastructure, and driver’s gadgets. All of these “things” are linked to the server (Data Center) through IoT and have sophisticated logic. The system was created to achieve maximum uniformity. Figure 2 depicts automobiles with accessible ITSs. ITSs are typically made up of a vehicle station (VS), a roadside station (RS), a roadside unit (RSU), and a server [7]. By linking the ITS station to the IoT, the driver has access to information from ITS and the infrastructure. The Internet connects this system to a powerful data center. More complex intelligence algorithms can be utilized in this instance. This enables the development of an

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Fig. 2. Diagram of data flow for a cooperated DAS

upgraded and low-cost client component of the system for driving. Figure 2 depicts the flow of data. As we can see, the suggested system analyses many elements of the traffic condition by utilizing visual and guidance data from both onboard sensors and the infrastructure.

4 Developing an IoT Reference Architecture Model for the Designed System To assure future compatibility with different IoV decisions, we created an IoT architecture for cooperative DAS based on the European Institute European Telecommunications Standards Institute (2010) ETSI 302 665 ITS and IoT Reference Architecture Model (ARM) standard [16]. The ETSI 302 665 ITS standard site presents network layers for vehicular networks, whereas the IoT architecture must include four layers: «Access level,” which represents ITSC layers 1 and 2 of the ITSC OSI, “Networking and Transport level,” which represents layers 3 and 4 of the ITSC OSI, “Facilities,” which represents layers 5 to 7 of the ITSC, and «Application level,” which represents layers 7. ETSI defines architectures that may be used by applications to satisfy their security needs. To gain access to the communication infrastructure and services, a vehicle first contacts a Registration Authority (RA) and authenticates itself. The IoT Management: This level ensures the integration of all subsystems, and the different IoT components and moves IoT systems from the isolated “intranet of things” to the Internet of things.

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The IoT service organization layer: describes the structure of IoT services and provides the ability to control these services. Using these two layers (IoT management and service organization), we can build a balanced system architecture. The IoT services layer: allows data acquisition and control of “things” (sensors and devices). Virtual Entity (VE): presents real physical objects and subjects as an abstract information business model (present real world “things” through classes, a database data representation model, etc.).

Fig. 3. Architecture design of the V2X system with the proposed approach.

4.1 Description of the ARM IoT Layers of the Designed Cooperative DAS 4.1.1 Device Level: Sensors Generally, the sensors can be grouped according to their function. Internal vehicle state sensors provide information about the current operation and state of the vehicle, including lower-level functions such as engine operation and higher-level states such as vehicle motion and position. External environment sensors provide information about the world outside the vehicle, including potential information about the road and lanes, the location and movement of other vehicles, and fixed physical objects in the world. 4.1.2 Communication Layer The cars are connected via V2V with Wireless Access in Vehicular Environments (WAVE) wireless protocols. The PSY and MAC levels of WAVE have been described in [17]; the data, network, and media transport level has been presented by [18] and [19]; The APP level is defined in SAE J2735.Vehicles linked with the infra ITS road station with a V2I connection utilizing IEEE 802.11 (PSY MAC Wi-Fi), IEEE 802.11p [17],

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IEEE 802.11b (WAVE) [15], or IEEE 802.15.4 [20] with ZigBee protocols [21]. The Road Station (RS) communicates with the IoT infrastructure via IoT protocols: MQTT was created in 2014 by the Organization for the Standardization and Quality Control Standards (OASIS), CoAP was created in 2014 by the Internet Engineering Task Force (IETF), and HTML/2 was created in 2015 by the Internet Engineering Task Force (IETF). The IoT-based V2X communication standard combine’s communication protocols and hardware for interaction between vehicles, which are referred to as “things” in the IoT. This enables transportation infrastructure to be linked not just to one another, but also other present and future systems in other sectors, allowing the future notion of Internet-of-Everything to be realized. 4.1.3 Communication with “Things” via IoT For Internet-enabled devices, data transfer from sensors and driver gadgets to the IoT can be direct. Special chips must be utilized for devices that do not support IP. These chips must handle IoT protocols as well as the many interfaces required by the connected item. 4.1.4 Application Layer: Proposed Structure of Applications for Safety Problems and Issues A variety of safety applications, including algorithms for emergency braking assistance, road line identification, map-based localization, and so on, have already been applied to current automobiles employing video cameras and radars. This same developed system, which is based on V2X communication, can provide a higher level of safety in the driver-assist system by utilizing information from various sources such as overpass passing assistance; turning assistance; safe separation from oncoming vehicles; highway departure warning; detection of road obstacles; road crash information; brake pedal warning; rear-end rear cross-traffic alert (e-stop signal); lane change warning; bad weather warning; traffic sign information; and notifying the driver (Fig. 4). One problem is that these systems are unable to see around corners. Mostly, they are not connected to the infrastructure and use only onboard sensors. While all automobiles and motorbikes will be furnished with V2V, other road users (cyclists and pedestrians) will be left out. To address this issue, the driver assistance system based on IoT V2X communication has no such drawbacks. The developed system notifies infrastructure, and bicycles and pedestrians can utilize IoT safety apps to benefit from the vehicle’s safety system. That once car or infrastructure sensors notice a possible threat, the V2X IoT system alerts other drivers. Driver notifications are provided via smartphone and tablet applications. The suggested system uses GSM/UMTS (3G)/LTE (4G) mobile network protocols to achieve end-to-end communication. Notifications are sent to the driver alert subsystem through mobile networks. The ZigBee protocol can be utilized for driver alerts in ITS and VANET networks at the application and network levels.

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Fig. 4. Application level and connection with IoT.

ZigBee (IEEE 802.15.4) [22, 23] seems to be a new low-cost and low-power wireless PAN standard aimed at sensors and control devices. Monitoring and control applications that do not require significant bandwidth but have stringent latency and energy consumption constraints are typical ZigBee applications.

5 Conclusions The DAS architecture was created in compliance with the IoT reference architectural model and contemporary communication standards. As a result, the suggested system architecture provides compatibility with the different available vehicle and V2I systems. We are certain that it will enable the future utilization of crucial data from other technologies for road safety. Using IoT, we are suggesting simplifying and significantly integrating important driver assistance functions to improve safety on the road.

References 1. https://www.who.int/publications/i/item/9789241565684 2. Martinez, F.J., et al.: Emergency services in future intelligent transportation systems based on vehicular communication networks. IEEE Intell. Transp. Syst. Mag. 2(2), 6–20 (2010) 3. Zheng, K., Zheng, Q., Chatzimisios, P., Xiang, W., Zhou, Y.: Heterogeneous vehicular networking: a survey on architecture, challenges, and solutions. IEEE Commun. Surv. Tutor. 17(4), 2377–2396 (2015)

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4. van Ratingen, M., Fildes, B., Lie, A., Keall, M., Tingvall, C.: Validating vehicle safety using meta-analysis: a new approach to evaluating new vehicle safety technologies. In: 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Gothenburg, Sweden (2015) 5. Tesla’s Autopilot. https://www.tesla.com/autopilot 6. European Parliament: Directive 2010/40/EU of the European Parliament and the Council on the framework for the deployment of Intelligent Transport Systems in the field of road transport and for interfaces with other modes of transport, 2010, Official Journal of the Europe Official Journal of the European Union. Accessed 2 Dec 2016 7. Stübing, H., et al.: SimTD: a car-to-X system architecture for field operational tests. IEEE Commun. Mag. 48(5), 148–154 (2010) 8. Wieker, H., et al.: Management of roadside units for the SIM-TD field test (Germany). In: 16th World Congress Exhibition ITS Services, Stockholm, Sweden (2009) 9. Mostafa, A., Vegni, A.M., Singoria, R., Oliveira, T., Little, T., Agrawal, D.P.: A V2X-based approach for reduction of delay propagation in Vehicular Ad-Hoc Networks, MCL Technical Report no. 07-18-2011 (2011) 10. ERTICO-ITS Europe Partnership: ERTICO-ITS Europe Partnership, Brussels (2016). http:// ertico.com. Accessed 3 Dec 2016 11. Network of National ITS Associations (2016) Introducing the Network of National Associations. http://itsnetwork.org/. Accessed 3 Dec 2016 12. Papadimitratos, P., La Fortelle, A., Evenssen, K., Brignolo, R., Cosenza, S.: Vehicular communication systems: enabling technologies, applications, and future outlook on intelligent transportation. IEEE Commun. Mag. 47(11), 84–95 (2009) 13. Zhang, H., He, L.: Modeling and topological properties of a V2I sub network. Bulg. Acad. Sci. Cybern. Inf. Technol. 15(4), 149–160 (2015) 14. Vasseur, J.P.: The Internet of Things: an Architectural Foundation and its Protocols. Cisco Live Event, Milan (2014) 15. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015) 16. Bassi, A., et al.: Enabling Things to Talk: Designing IoT Solutions with the IoT Architectural Reference Model. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40403-0 17. Abdeldime, M., Abdelgader, S., Lenan, W.: The physical layer of the IEEE 802.11p WAVE communication standard: the specifications and challenges. In: WCECS 2014, San Francisco, USA (2014) 18. Institute of Electrical and Electronics Engineers (IEEE): IEEE Guide Wireless Access in Vehicle Environments (WAVE) – Architecture (IEEE 1609.0-2013), IEEE Standard, Piscataway, New Jersey, USA (2013) 19. Institute of Electrical and Electronics Engineers (IEEE): Standard for Wireless Access in Vehicular Environments (WAVE) - Identifier Allocations (IEEE 1609.12-2016), IEEE Standard, Piscataway, New Jersey, USA (2016) 20. Institute of Electrical and Electronics Engineers (IEEE): Standard for Local and metropolitan area networks-Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs) (IEEE 802.15.4), IEEE Standard, Piscataway, New Jersey, USA (2011) 21. Herrera-Quintero, L.F., et al.: IoT approach applied in the context of ITS: monitoring Highways through Instant Messaging. In: 14th International Conference on ITS Telecommunications, Copenhagen, Denmark (2015) 22. ZigBee Alliance. http://www.zigbee.org/ 23. IEEE 802.15: Working Group for Wireless Personal Area Networks (WPANs). http://www. ieee802.org/15/

Novel Flexible Topologies of SIW Components for IoT Applications: A Survey Souad Akkader1(B) , Hamid Bouyghf2 , and Abdennaceur Baghdad1 1 Laboratory of Electronics, Energy, Automatics and Data Processing, Faculty of Sciences

and Techniques of Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco [email protected] 2 Laboratory of Engineering Sciences and Biosciences, Faculty of Sciences and Techniques of Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Abstract. The development of wearable devices, the need for compact and flexible wireless systems with eco-friendly materials, and manufacturing technologies are the principal requirements of the Internet of Things (IoT). This contribution provides an overview of recent advances in the development of innovative components and manufacturing technologies for the recent wireless microwave components, suitable to the IoT. The attractive solution is the SIW technologies by the use of paper, plastic, and textile substrate and the use of other new fabrication methods with ultra-low-cost. This paper can serve as valuable and authoritative literature for students, researchers, engineers, and practitioners who need a quick reference to the novelty of wireless technology and its relevant IoT applications. Keywords: Substrate Integrated Waveguide (SIW) · Internet of Things (IoT) · Paper Substrate · Textile Antenna · Plastic Substrate · Manufacturing Techniques

1 Introduction Among the various solutions for implementing microwave and millimeter-wave components, substrate integrated waveguide (SIW) technology appears to be an attractive solution: it allows for planar integration of active and passive components as well as antennas, and it exhibits low loss and compact size [1], As a result, interference and cross-talk are avoided. Moreover, the implementation of SIW components can be based on low-cost fabrication methods, such as the printed circuit board (PCB) technique, Direct Handwriting (DHW), and milling technique [2]. One of the major issues in the design of SIW components is related to the minimization of dielectric losses due to the choice of substrate materiel [3–6]. Nowadays the manufacturing process and the application of new materials for the implementation of microwave devices for IoT systems represent another crucial point in industrial and academic research. The development of low-cost, low-loss, flexible, and environmentally friendly substrates, in particular, is attracting a lot of attention [2, 7]. An overview of flexible wearable IoT devices is presented in Fig. 1 [8]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 261–269, 2023. https://doi.org/10.1007/978-3-031-29857-8_26

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The attractive solution for the microwave components based on SIW structure is illustrated through the integration of materials such as paper [9], plastic [10], and textile [11]. These materials’ bendability and flexibility make them more suitable for designing future smart electronics systems in personal communication, industry, military, and telemedicine. This study may be a guideline of the analysis and description of how implementing SIW components using various flexible, and environmentally friendly substrates, each of these materials has distinct qualities as described in the end of this article. The article is structured as fellow: Sect. 2 presents innovative SIW materials for IoT applications. Section 3 depicts the recent and novel manufacturing techniques of SIW. Section 4 discuss and analyses the existing with the perspective of SIW future. And the summary of this article is presented in the last section as conclusion.

Fig. 1. Application areas for flexible electronics for IoT [8]

2 Innovative SIW Materials for IoT 2.1 SIW Components on Paper Substrate Several innovative materials, including paper, plastic, and textiles, have lately been studied for the development of wireless systems for IoT application. Paper become trendier due to its biocompatibility, renewable nature, and low cost. Moreover, to achieve the desired thickness, different types of paper can be used, and multiple layers can be arranged. However, choosing the right thickness of paper is essential because the substrate thickness should be large enough, to guarantee that conductor loss is less than dielectric loss. In addition, the manufacturing steps of SIW circuits on the paper substrate are discussed in detail in [12].

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Figure 2 depicts the proposed compact filter on paper, which is based on two quartermode SIW cavities. The filter was made from a single paper layer with a thickness of 500 μm, dielectric permittivity εr = 2.2, and loss tangent tanδ = 0.04 using the milling technique. The Scattering parameters of the filter are shown in Fig. 2.

Fig. 2. Quarter-mode SIW filter-based paper [9]: (a) photograph of the filter; (b) simulated and measured S-parameters; (c) side view of the substrate and the metallization of the via holes.

The pass-band filter has a center frequency of 4 GHz. The device measures approximately 50 mm in length, including input/output microstrip lines and tapers. Figure 2 depicts the simulation and measured results, which show that the measured insertion loss was 2.9 dB. The filter size may be reduced compared to the full mode SIW, because the cavity diameter is lowered by a factor of four in the proposed configuration, which could be intended for heterogeneous smart devices, especially healthcare applications. 2.2 SIW Components on Textile Substrate Authors in [11] advocated the creation of new textile microwave components accomplished in SIW technology, including as connectors, filters, and antennae. The textile components consist of a closed-cell enlarged rubber substrate, with conducting textiles

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coupled to the substrate through thermally activated adhesive sheets, and brass eyelets to create metal vias (see Fig. 3), the fabrication process and dedicated material characterization technique are sufficiently discussed in [11, 13]. Figure 3 depicts a wearable antenna with a folded cavity-backed SIW structure. To generate a radiating patch, the proposed topology employs a square ring aperture carved out in the top metal layer.

Fig. 3. SIW cavity-backed slot antenna [11], a) Top view, b) Bottom view, c) Side view, d) S-parameters of proposed SIW antenna based textile substrate

A prototype of the antenna is shown in Fig. 3(a) and (b). The simulated and measured reflection coefficient (see Fig. 3 (d)) is less than −10 dB across a 130 MHz bandwidth at 2.45 GHz, with a maximum input matching at the frequency of 2.43 GHz. The performance of SIW antennas has been reported to be relatively better, in terms of the input matching and radiation pattern. This topology is largely used in wearable antenna intended to human body application [14], as it limits surface wave excitation and makes the structure less sensitive to the environment. 2.3 SIW Components on Plastic Substrate Electronic technologies are recently being converted from rigid to flexible due to their additional electrical and mechanical advantages such as low technology cost, available materials, and mechanically adaptable substrates. Nowadays, Plastic (Polyethylene

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terephthalate: PET) substrate has become an important candidate to develop flexible devices; it is heavily used in biomedical applications and wearable wireless systems. The implementation of microwave components and antennas based on SIW technologies in a plastic substrate (polyethylene terephthalate: PET) was becoming a promising candidate for developing wearable wireless systems due to its additional characteristics: as it is a low-cost and adaptable material [14]. Researchers in [4] are the first to suggest a novel prototype-based plastic (PET) substrate. Figure 4 shows the fabricated SIW antenna operating at 5 GHz frequency as well as the simulated & measured S-parameters. A measured insertion loss is 7 dB at 5.2 GHz, which indicates that the plastic substrate has a significant loss, and the component performance cannot yet match that of commonly used dielectric materials.

Fig. 4. Photograph of the SIW filter on PET substrate [10] (a), Simulated and measured frequency response of the SIW filter (b).

3 Novel Manufacturing Techniques of SIW for IoT Fabrication procedures are critical in the design of wearable SIW devices because they determine the accuracy, cost, and time required. Wearable microwave devices have been designed using novel production processes such as 3D printing, infrared laser cutting, and direct handwriting. The description of different types of smart manufacturing techniques and their advantages is sufficiently discussed in [2, 15, 16], and the visual presentation of infrared laser method (LIG) and Inkjet manufacturing technique are presented bellow in Fig. 5 [17].

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Direct handwriting methods have recently attracted a lot of interest because to their low cost, massive manufacturing, and simplicity of fabrication. However, because of the high conductive inks or liquids necessary to meet the demand for future applications such as 5G and 6G technologies, these approaches are not yet completely matured in the RF circuit.

Fig. 5. Inkjet and infrared laser method (LIG) [17]

4 Discussion and Perspective The adopted fabrication technique based on paper exhibits several advantages. Primarily, it is based on a well-established manufacturing technique that has been widely employed in the fabrication of printed circuit board (PCB) components. This enables the direct implementation of complicated structures and multilayer configurations with reasonable difficulty. Another advantage of this approach, as explained in [14], is that it allows you to process both the surface shapes: input & output microstrip lines, transitions, and the SIW through holes at the same time. As a result, the alignment issues are avoided. On the other hand, the performance of the plastic-based flexible antennas was verified after bending. Although the performance of plastic substrates cannot attain the results of commonly used dielectric materials due to high loss. The results obtained constitute a principle key for the development of low-cost wireless terminals that can be combined with other systems, such as energy-harvesting devices, to produce autonomous nodes for future wireless sensor networks. This approach has been applied in [18] (Fig. 6), the overall goal of this article is to combine two flexible solar cells with a power management system and a wearable textile SIW antenna., without affecting its performance. Results obtained (Fig. 6) have shown that this hybrid renewable energy collecting strategy provides a more stable source of recycled energy, allowing energy collecting in most indoor and outdoor conditions.

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Fig. 6. The proposed textile SIW cavity-backed slot antenna [18] with solar cells. (a) Slot plane; (b) Feed plane.

Table 1 shows the properties of various SIW substrate. Based on the Table below and the previous researches [8, 19, 20], We summary that flexible substrates have received much interest, instead of rigid materials. Textile and wearable component represent the first one to be required because of its robustness against bending, twisting, and stretching, while keeping the lossless advantage. Moreover, it is comfortable and easiest to incorporate into clothing for antenna users in medicine and biomedical application. However, Plastic materials are characterized by their unique electrical and mechanical properties in a variety of high-performance conducting and substrate materials. Paper substrate, on the other hand, is preferred for flexible component due to its ease of manufacturing, low cost, and environmentally friendly material.

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Textile

Plastic

FR-4 substrate [20]

Band/Frequency (GHz)

4 GHz

2.4 GHz

5 GHz

3 GHz

Dielectric Loss

Medium Loss tanδ = 0.065

Low Loss tanδ = 0.02

Low Loss tanδ = 0.002

Low loss tanδ = 0.018

Deformability

high

high

low

low

Stability for integrated circuit

low

Low

high

low

Cost Fabrication

Low

low

Medium

-

Application

Telemedicine systems

Wearable applications in ISM band

ISM bands„ WiMAX band, and 5G

Printed circuit boards and electronic circuit beyond 5 GHz

5 Conclusion The obtained results indicate the preliminary inquiry into the usability of SIW technology based on low-cost and flexible materials. They bring up new avenues for developing ultralow-cost components in wireless communication devices for the future IoT. In addition, the model enables for the simple integration of passive and active devices, resulting in entire systems on a textile carrier suitable for on-body application. Furthermore, the ability to include solar cells or energy collecting systems on top of the SIW framework opens the door to the development of energetically autonomous devices.

References 1. Akkader, S., Bouyghf, H., Baghdad, A.: Miniaturization trends in substrate integrated waveguide for microwave communication systems. In: 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–4. IEEE, Meknes (2022). https://doi.org/10.1109/IRASET52964.2022.9738295 2. Ali, S., Sovuthy, C., Imran, M., Socheatra, S., Abbasi, Q., Abidin, Z.: Recent advances of wearable antennas in materials, fabrication methods, designs, and their applications: state-ofthe-art. Micromachines 11, 888 (2020). https://doi.org/10.3390/mi11100888 3. Bozzi, M., Pasian, M., Perregrini, L., Wu, K.: On the losses in substrate integrated waveguides. In: 2007 European Microwave Conference, pp. 384–387. IEEE, Munich (2007). https://doi. org/10.1109/EUMC.2007.4405207 4. Bozzi, M., Pasian, M., Perregrini, L., Wu, K.: On the losses in substrate-integrated waveguides and cavities. Int. J. Microw. Wirel. Technol. 1(5), 395–401 (2009) 5. Akkader, S., Bouyghf, H., Baghdad, A.: Bio-inspired intelligence for minimizing losses in substrate integrated waveguide. IJECE 13, 2837 (2023). https://doi.org/10.11591/ijece.v13i3. pp2837-2846

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6. Akkader, S., Bouyghf, H., Baghdad, A.: Unloaded quality factor optimization of substrate integrated waveguide resonator using genetic algorithm. IJECE 13, 2857 (2023). https://doi. org/10.11591/ijece.v13i3.pp2857-2864 7. AL-Fadhali, N., Majid, H., Omar, R.: Multiband frequency reconfigurable substrate integrated waveguide antenna using copper strip for cognitive radio applicable to internet of things application. Telecommun. Syst. 76(3), 345–358 (2020). https://doi.org/10.1007/s11235-02000721-6 8. Kirtania, S.G., et al.: Flexible antennas: a review. Micromachines 11, 847 (2020). https://doi. org/10.3390/mi11090847 9. Moscato, S., Delmonte, N., Silvestri, L., Pasian, M., Bozzi, M., Perregrini, L.: Compact substrate integrated waveguide (SIW) components on paper substrate. In: 2015 European Microwave Conference (EuMC), pp. 24–27. IEEE, Paris (2015). https://doi.org/10.1109/ EuMC.2015.7345690 10. Moro, R., Bozzi, M., Collado, A., Georgiadis, A., Via, S.: Plastic-based Substrate Integrated Waveguide (SIW) components and antennas. In: 2012 42nd European Microwave Conference, pp. 1007–1010. IEEE, Amsterdam (2012). https://doi.org/10.23919/EuMC.2012.6459177 11. Moro, R., Agneessens, S., Rogier, H., Dierck, A., Bozzi, M.: Textile microwave components in substrate integrated waveguide technology. IEEE Trans. Microw. Theory Techn. 63, 422–432 (2015). https://doi.org/10.1109/TMTT.2014.2387272 12. Moro, R., Kim, S., Bozzi, M., Tentzeris, M.: Inkjet-printed paper-based substrate-integrated waveguide (SIW) components and antennas. Int. J. Microw. Wirel. Technol. 5, 197–204 (2013). https://doi.org/10.1017/S1759078713000494 13. Agneessens, S., Bozzi, M., Moro, R., Rogier, H.: Wearable textile antenna in substrate integrated waveguide technology. Electron. Lett. 48, 985–987 (2012). https://doi.org/10.1049/el. 2012.2349 14. Monne, M.A., Lan, X., Chen, M.Y.: Material selection and fabrication processes for flexible conformal antennas. Int. J. Antennas Propag. 2018, 1–14 (2018). https://doi.org/10.1155/ 2018/9815631 15. Kumar, A.: Methods and materials for smart manufacturing: additive manufacturing, internet of things, flexible sensors and soft robotics. Manuf. Lett. 15, 122–125 (2018). https://doi.org/ 10.1016/j.mfglet.2017.12.014 16. Moscato, S., et al.: Exploiting 3D printed substrate for microfluidic SIW sensor. In: 2015 European Microwave Conference (EuMC), pp. 28–31. IEEE, Paris (2015). https://doi.org/10. 1109/EuMC.2015.7345691 17. Stanford, M.G., et al.: Supporting information high-resolution laser-induced graphene. Flexible electronics beyond the visible limit. ACS Appl. Mater. Interfaces 12(9), 10902–10907 (2020) 18. Lemey, S., Rogier, H.: Substrate integrated waveguide textile antennas as energy harvesting platforms. In: 2015 International Workshop on Antenna Technology (iWAT), pp. 23–26. IEEE, Seoul (2015). https://doi.org/10.1109/IWAT.2015.7365349 19. Ali Khan, M.U., Raad, R., Tubbal, F., Theoharis, P.I., Liu, S., Foroughi, J.: Bending analysis of polymer-based flexible antennas for wearable, general IoT applications: a review. Polymers 13, 357 (2021). https://doi.org/10.3390/polym13030357 20. Michalkiewicz, M.L., Mariano, A.A., Dartora, C.A., Castaldo, F.C.: Bandpass filter using SIW technology in inductive window topology with low-cost substrate FR-4. J. Microw. Optoelectron. Electromagn. Appl. 21, 83–101 (2022). https://doi.org/10.1590/2179-107420 22v21i11306

Overview of Blockchain Based IoT Trust Management Ilham Laabab(B)

and Abdellatif Ezzouhairi

LISA Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco {ilham.laabab,abdellatif.ezzouhairi}@usmba.ac.ma

Abstract. Over the past few years, Internet of Things (IoT) has become one of the most influential innovative technologies in the world. A huge number of heterogeneous and dynamic devices work together to provide collaborative services and applications across various industries. Nevertheless, Trust Management systems remain an important task to be investigated since it leads to several challenges and issues. In this paper, Blockchain is presented as an emerging decentralized and distributed technology, with its key features make it the most suitable candidate to solve Trust Management issues in IoT. Furthermore, various research works related to Blockchain-based IoT Trust Management were discussed along with some open challenges and future directions. Keywords: Internet of Things (IoT) · Security · Trust Management (TM) · Trust · Blockchain

1 Introduction With the huge development of Information and Communication Technologies (ICT), the Internet of Things (IoT) has become one of the most prominent technologies in recent years. The main goal of IoT is to create interaction among the real/physical and digital/virtual worlds to set up smart environments, where “things” can sense their surroundings, interact, and exchange data, and knowledge [1]. According to statistics provided by the Statista platform, a significant increase in the number of IoT devices is expected from 13.8 billion devices in 2021 to 30.9 billion devices by 2025 [2]. Consequently, we are facing a tsunami of data generated by these devices. Therefore, new application areas will become popular in all dimensions. Based on IoT vision, a great number of devices are supposed to work together to perform collaboration services and applications in various fields, such as healthcare, transportation, smart cities, and industrial maintenance systems, for the purpose of creating intelligent environments and providing Real Time Data Monitoring (RTDM). However, heterogeneous devices with dynamic behavior increase the probability of multiple largescale security attacks, leading to a serious risk to the entire network. Therefore, trust in IoT can be seen as a critical feature to establish trustworthiness among several devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 270–278, 2023. https://doi.org/10.1007/978-3-031-29857-8_27

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As a result, trust has become one of the major requirements to safeguard services, data, and networks and enhance cybersecurity levels. The concept of trust has been used in several domains with different definitions [3]. A trust relationship involves at least two parties—trustor and trustee—who rely on one another for common interests [4]. In IoT networks, trust can be defined as the degree of belief in the behaviors of a certain entity. In addition, it ensures IoT privacy and security requirements during device interactions [5]. Trust Management (TM) plays a significant role in IoT networks, it provides the ability to compute and analyze trust between devices to make appropriate decisions for effective and trustworthy communications among devices [6]. Trust in IoT environments can be evaluated through some policy techniques, such as trust levels and reputation parameters [7]. Over the past few years, IoT Trust Management (IoT-TM) has been studied extensively. However, the existing Trust Management systems are often centralized, leading to a single point of failure. Moreover, it does not meet the new requirements of IoT, such as flexibility, lightweight, scalability, reliability, and accuracy [8]. Furthermore, it is possible for malicious entities to manipulate and misuse the trusted data used to establish trust relationships among devices [9]. Blockchain emerged as a P2P network; it enables, through cryptographic techniques and consensus mechanisms, the secure storage of information in its ledgers. Moreover, Blockchain, with its key features including decentralization, distribution, tamperproof ledger, transparency, traceability, and data sharing, is the most suitable candidate technology to solve IoT trust issues. Furthermore, it enables creating a trust-based IoT environment where no central authority is needed to control IoT devices and their communications with each other. The remainder of this paper is structured as follows: Preliminaries of IoT Trust Management and Blockchain are presented in Sect. 2. Related work for Trust Management protocols and architectures in IoT is given in Sect. 3. The most relevant Blockchain based solutions for IoT Trust Management are discussed in Sect. 4. Section 5 covers some open challenges and future directions. Finally, Sect. 6 concludes this paper.

2 Preliminaries 2.1 IoT Trust Management (IoT-TM) IoT-TM Attacks. Trust Management systems are susceptible to various attacks due to the dynamic nature and resource constraints of IoT devices. The main trust-related attacks [10] are briefly described in Table 1. IoT-TM Requirements. In this subsection, we present the IoT Trust Management system requirements as follows [8]: Flexibility. It is another feature of IoT Trust Management systems, which allows users or nodes to set personal policies to determine whether an object is trustworthy or not. Each participant can define one or more policies for making decisions based on their request.

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Lightweight. Trust Management systems must be lightweight for the IoT to provide good performance regardless of the power limitations of multiple sensor nodes. Heterogeneity. The nodes and subnets of IoT are completely heterogeneous. Trust as an abstract concept can provide unified decision making for heterogeneity and multi-domain in IoT. Scalability. It is an important factor for managing trust in the IoT. The functionality of some IoT nodes may change, a new node may be added, and new types of applications may need access to these nodes. Reliability. It is one of the key parameters of the Trust Management systems, on which the success rate of the system depends. It ensures that all functions work properly without any interruption during the period specified for the IoT network. Accuracy. All Trust Management systems must be evaluated for accuracy to enable access to any IoT node in the network. High accuracy for trust and reputation will lead to overall system security. Table 1. Trust based attacks in IoT [10] Attacks

Description

Self-Promotion Attacks (SPA)

A bad node manipulates its own reputation by suggesting good recommendations about itself to be selected as a service provider, and subsequently perform malicious acts by providing bad services

Bad-Mouthing Attacks (BMA)

Unlike SPA, this type of attack is based on manipulating another reputable and trustworthy node by giving bad recommendations against it. Thus, this reduces the chance of choosing the good node as a service provider

Ballot-Stuffing Attacks (BSA)

This attack can be accomplished in a cooperative manner by malicious nodes. By providing good recommendations to a malicious node, it improves its reputation and increases its chances of being selected as a service provider

Opportunistic Service Attacks (OSA)

A malicious node tries to maintain its good reputation by offering a good service, thereby becoming a good or trusted node

On-Off Attacks (OOA)

This attack aims to achieve a good reputation, and could manipulate the network by giving a positive recommendation to bad nodes or a negative recommendation to good nodes

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2.2 Blockchain Overview In this subsection, we present an overview of Blockchain technology, its definition, structure, popular consensus mechanisms and applications. Blockchain Technology. Blockchain is a type of Distributed Ledger Technologies (DLTs) that first appeared with cryptocurrencies, and then rapidly became an area of interest for several organizations and researchers, due to its countless benefits over existing solutions. It can be defined as a distributed, trustless, decentralized, and immutable ledger that provides transmission and storage of various transactions that occur in a certain P2P network. Blockchain falls into four categories: Public Blockchain as an open network where anyone can join and participate without restriction. Anyone can read, write, and verify the current transactions taking place in the network. Unlike public Blockchain, private Blockchain controls who can participate in the network. Only authorized members can join and operate on the network. Hybrid Blockchain is a combination of both public and private Blockchain, where one part is under the control of an organization and the other part is visible as a public Blockchain. Finally, the consortium Blockchain, also known as Federated Blockchain, this category is likewise a combination of public and private Blockchain, where more than one organization operates the Blockchain [11]. A Blockchain consists of blocks chained together using cryptographic hashes. Each block includes a set of transactions, the hash of the previous block, and the current hash, as shown in Fig. 1. The first block is called the Genesis Block; it is unique and has no previous blocks. The data stored in the Blockchain is always available and cannot be tampered with, ensuring its immutability.

Fig. 1. Blockchain structure

Consensus Mechanisms. Consensus mechanisms are the backbone of Blockchain technology, it is applied to establish a reliable and trustworthy consensus among all nodes

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participating in the Blockchain network. Several consensus mechanisms have been proposed based on resource consumption, attack tolerance, scalability, and performance [12]. The most common consensus mechanisms are illustrated in Fig. 2.

Proof of Work (PoW)

Proof of Stake (PoS)

Consensus Mechanisms

Proof of Activity (PoA)

Delegated Proof of Stake (DPoS)

Practical Byzantine Fault Tolerance (PBFT)

Fig. 2. Popular consensus mechanisms

Proof of Work (PoW). This is the most commonly used mechanism to achieve a distributed consensus among the participants. The main idea behind this consensus is that a complex mathematical puzzle must be solved by some specific nodes called “miners” to add a new block to the chain, and the miners who solve the puzzle are rewarded. Proof of Stake (PoS). This is another mechanism for reaching consensus in the Blockchain network. The underlying idea of PoS depends on the amount of stake held by the participants, while participants with the highest amount of stake have the probability of being selected to validate the transactions. PoS reduces the computational cost unlike PoW. Practical Byzantine Fault Tolerance (PBFT). It is a mechanism for achieving consensus in a distributed system, even if some nodes are malicious. It is designed to solve the problem of Byzantine generals, and it is more efficient than PoW in terms of energy cost and latency [13]. Delegated Proof of Stake (DPoS). A consensus mechanism developed to secure a Blockchain by ensuring representation of transactions within it. DPoS is a more efficient and democratic version of the PoS mechanism in which only a limited number of stakeholders participate in the block creation process [14]. Proof of Activity (PoA). PoS and PoW are combined in this mechanism. PoA tends to be more secure against various attacks, but it may not be suitable for time-constrained IoT applications due to higher latency [15].

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Blockchain Applications. This subsection presents a brief description of some areas in which Blockchain has been used [16]. Internet of Things. The huge amounts of data transmitted by IoT devices with diverse nature increase multiple security attacks on the network. Blockchain, as a promising technology, can be a viable solution to address IoT privacy and security issues. Smart contracts are very useful in enabling IoT devices to interact directly with each other. Healthcare. Blockchain has gained much interest and growing innovation in the healthcare industry, allowing patients better control and access to their data. in addition, it ensures secure medical data sharing between healthcare providers, and unifying medical records. Finance. Blockchain can facilitate fast, secure, and low-cost payment processing services without third-party intervention. Moreover, Blockchain-based smart contract enables a more sustainable and flexible financial system. Government. Blockchain-based digital government is a promising research topic in the coming years. Governments can extend and improve their services by removing the centralized authority, allowing them to provide citizens with on-demand services. One such example is shifting from traditional voting systems to digital voting (e-voting). On the other hand, governments can provide enhanced data security, transparency, and accountability. Energy. Blockchain-based energy trading is also a promising research topic. The privacy and anonymity features of the Blockchain can attract more consumers and suppliers to the market and create microgrids in the energy sector. In addition, participants can execute transactions automatically using smart contracts.

3 Related Work on IoT-TM To date, several Trust Management (TM) approaches have been presented to address trust issues in IoT. For instance, a lightweight trust management architecture, named ShareTrust, is proposed by [17] to maintain trustworthy resource sharing. The proposed approach consists of three layers: resource provider layer, resource seeker layer, and resource sharing central interface layer. In the resource provider layer, there are many nodes that are willing to share their free resources with others. Resource seekers are nodes seeking available free resources from the resource provider, and the central resource sharing interface, which serves as a central authority to link resource seekers and providers. Furthermore, the authors in [18] introduced a novel centralized Trust Management architecture for IoT, in which a Super Node (SN) acts as the central trust manager. The SN includes other modules that are integrated into the overall architecture to ensure reliable communication between IoT devices.

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On the other hand, the authors in [19] proposed a trust approach called IoTrust, which integrates Soft Defined Network (SDN) into the IoT, and introduced a cross-layer authorization framework. IoTrust and the framework both open new research directions for IoT Trust Management. In this study, two reputation evaluation schemes for trust establishment are also proposed, including Behavior-based Reputation Evaluation Scheme for nodes (BES), and Organization Reputation Evaluation Scheme (ORES). Simulation results demonstrate the effectiveness of the proposed reputation evaluation schemes. Moreover, a scalable Trust Management protocol for IoT was developed by [20]. The proposed protocol intended to address social relationships and utilized three properties of trust, namely, honesty, cooperation, and community-interest, for trust assessment. The researchers proved the effectiveness of their proposed protocol, and its resiliency to misbehavior attacks. In addition, a novel TMS for IoT is proposed by [21] that can establish the degree of trust to be placed in a node for the completion of a required task. The simulation results indicate that the proposed approach performs better than existing work in the literature.

4 Blockchain Based Solutions To address the shortcomings of IoT-TM, a few studies on Blockchain based solutions for trustworthy interactions in IoT environments have been conducted so far. In this sense, the authors in [22] designed BBTM, a blockchain-based trust management architecture for resource-constrained IoT devices. The objective of BBTM is to manage the process of evaluating trust between devices. Experimental results demonstrate the effectiveness of their proposal in terms of trust accuracy, convergence, and attack resiliency. Similarly, a lightweight, secure, flexible, and decentralized Blockchain-based architecture for smart industrial environments is proposed in [23]. Moreover, the authors developed and implemented a Blockchain-based architecture for fruit processing plants to enhance industrial smart environments. On the other hand, BC-Trust, a scalable architecture for IoT Trust Management based on Blockchain and fog computing, is proposed in [24]. The architecture enables IoT devices to evaluate the trust level of service providers and broadcast trust information to the Blockchain in a scalable manner. To evaluate the performance of the proposed architecture, extensive experiments are conducted, and the results outperform existing works in terms of scalability, mobility support, communication, and computational costs. A novel Blockchain-based trust architecture to secure sharing and storage of trust information in IoT systems was developed by [25]. The overall architecture includes three main conceptual layers, namely, the device layer, system management layer, and industrial services layer. Performance analysis shows that the proposed approach is suitable for IoT environments. A multi-layer trust architecture for Blockchain based IoT is proposed in [26] to enhance the end-to-end trust. The architecture is composed of three main layers, including the data layer, the blockchain layer and the application layer. In addition, two key modules for trust management are introduced: the data trust module and the gateway reputation module. The data trust module quantifies trust in particular observational data, while the reputation module tracks the long-term trustworthiness of a participant in the Blockchain network.

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5 Open Challenges and Future Directions Blockchain is an emerging technology that enables a trusted relationship between devices in the IoT environment. Many Blockchain-based solutions have been proposed to manage trust in IoT environment. However, these solutions lack standard and efficient protocols applicable to heterogeneous and dynamic devices. In this context, such a smart contract, as an emerging protocol, can be applied to automate contract-based processes among participants without the intervention of a third party. In addition, consensus protocols should take into consideration IoT devices’ lightweight nature.

6 Conclusion Blockchain technology is still in its fancy; it has significant advantages in establishing trust in IoT and reducing attacks on the network. However, research on the development of a distributed and decentralized trust architecture is limited. Therefore, designing an effective Trust Management solution based on Blockchain remains an open challenge for IoT environments.

References 1. Chen, S., Xu, H., Liu, D., Hu, B., Wang, H.: A vision of IoT: applications, challenges, and opportunities with china perspective. IEEE Internet Things J. 1(4), 349–359 (2014) 2. Statista Reports. https://www.statista.com/statistics/1101442/iot-number-of-connected-dev ices-worldwide/. Accessed 08 Mar 2022 3. Sicari, S., Rizzardi, A., Grieco, L.A., Coen-Porisini, A.: Security, privacy and trust in Internet of Things: the road ahead. Comput. Netw. 76, 146–164 (2015) 4. Yan, Z., Holtmanns, S.: Trust modeling and management: from social trust to digital trust. In: Computer Security, Privacy and Politics: Current Issues, Challenges and Solutions, pp. 290– 323. IGI Global (2008) 5. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017) 6. Alghofaili, Y., Rassam, M.A.: A trust management model for IoT devices and services based on the multi-criteria decision-making approach and deep long short-term memory technique. Sensors 22(2), 634 (2022) 7. Alshehri, M.D., Hussain, F.K.: A comparative analysis of scalable and context-aware trust management approaches for Internet of Things. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9492, pp. 596–605. Springer, Cham (2015). https://doi.org/ 10.1007/978-3-319-26561-2_70 8. Gu, L., Wang, J., Sun, B.: Trust management mechanism for Internet of Things. China Commun. 11(2), 148–156 (2014) 9. Shala, B., Trick, U., Lehmann, A., Ghita, B., Shiaeles, S.: Blockchain and trust for secure, end-user-based and decentralized IoT service provision. IEEE Access 8, 119961–119979 (2020) 10. Guo, J., Chen, R., Tsai, J.J.: A survey of trust computation models for service management in Internet of Things systems. Comput. Commun. 97, 1–14 (2017)

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11. Kumar, R., Sharma, R.: Leveraging blockchain for ensuring trust in IoT: a survey. J. King Saud Univ. - Comput. Inf. Sci. 34(10), 8599–8622 (2022) 12. Saxena, S., Bhushan, B., Ahad, M.A.: Blockchain based solutions to secure IoT: background, integration trends and a way forward. J. Netw. Comput. Appl. 181, 103050 (2021) 13. Abdelmaboud, A., et al.: Blockchain for IoT applications: taxonomy, platforms, recent advances, challenges and future research directions. Electronics 11(4), 630 (2022) 14. Delegated proof of stake (DPoS). https://en.bitcoinwiki.org/wiki/DPoS/. Accessed 20 Apr 2022 15. Liu, Z., Tang, S., Chow, S.S., Liu, Z., Long, Y.: Fork-free hybrid consensus with flexible proof-of-activity. Future Gener. Comput. Syst. 96, 515–524 (2019) 16. Abou Jaoude, J., Saade, R.G.: Blockchain applications–usage in different domains. IEEE Access 7, 45360–45381 (2019) 17. Din, I.U., Awan, K.A., Almogren, A., Kim, B.S.: ShareTrust: centralized trust management mechanism for trustworthy resource sharing in industrial Internet of Things. Comput. Electr. Eng. 100, 108013 (2022) 18. Alshehri, M.D., Hussain, F.K.: A centralized trust management mechanism for the Internet of Things (CTM-IoT). In: Barolli, L., Xhafa, F., Conesa, J. (eds.) BWCCA 2017. LNDECT, vol. 12, pp. 533–543. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-69811-3_48 19. Chen, J., Tian, Z., Cui, X., Yin, L., Wang, X.: Trust architecture and reputation evaluation for internet of things. J. Ambient Intell. Humaniz. Comput. 10(8), 3099–3107 (2018). https:// doi.org/10.1007/s12652-018-0887-z 20. Bao, F., Chen, R.: Trust management for the internet of things and its application to service composition. In: Proceedings of IEEE WoWMoM 2012, pp. 1–6. IEEE, San Francisco (2012) 21. Saied, Y.B., Olivereau, A., Zeghlache, D., Laurent, M.: Trust management system design for the Internet of Things: a context-aware and multi-service approach. Comput. Secur. 39, 351–365 (2013) 22. Wu, X., Liang, J.: A blockchain-based trust management method for Internet of Things. Pervasive Mob. Comput. 72, 101330 (2021) 23. Latif, S., Idrees, Z., Ahmad, J., Zheng, L., Zou, Z.: A blockchain-based architecture for secure and trustworthy operations in the industrial Internet of Things. J. Ind. Inf. Integr. 21, 100190 (2021) 24. Kouicem, D.E., Imine, Y., Bouabdallah, A., Lakhlef, H.: A decentralized blockchain-based trust management protocol for the Internet of Things. IEEE Trans. Dependable Secure Comput. 19, 1292–1306 (2020) 25. Lahbib, A., Toumi, K., Laouiti, A., Laube, A., Martin, S.: Blockchain based trust management mechanism for IoT. In: Proceedings of IEEE WCNC 2019, pp. 1–8. IEEE, Marrakech (2019) 26. Dedeoglu, V., Jurdak, R., Putra, G.D., Dorri, A., Kanhere, S.S.: A trust architecture for blockchain in IoT. In: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2019), pp. 190–199. Association for Computing Machinery (ACM), Houston (2019)

Multiband Reconfigurable Planar Antenna for Wireless Mobile Communications Mohamed Bikrat1,2(B) and Seddik Bri1,2 1 Material and Instrumentations: MIN, Electrical Engineering Department, ESTM, Moulay

Ismail University of Meknes, 50000 Meknes, Morocco [email protected] 2 Laboratory of Materials Spectrometry and Archaeometry (LASMAR), Moulay Ismail University of Meknes, 50000 Meknes, Morocco

Abstract. This paper introduces a reconfigurable multi-band planar antenna for wireless applications such as UMTS, LTE, WiMAX, WLAN, and 5G. The proposed antenna is designed to cover multi-band response. Two U-shaped slots are included in the design with four narrow horizontal slots that have switched capability utilizing pairs of PIN diodes. In addition, the reconfigurable technique is achieved by controlling the On/Off states of PIN diodes, which are used to direct the current flow on the radiation patch and to get a multiband response. The size of the proposed antenna is (50 × 25 × 1.6) mm3 . The proposed antenna consists of a rectangular radiation patch with a horizontal slot, a ground plane, and a micros trip feed line. The antenna is designed with an FR4 dielectric substrate with a relative permittivity of 4.3. For validation, a patch antenna design covering six different bands When both diodes are on, it resonates at 2.21–3.07 GHz, 3.18– 3.61 GHz, 4.59–5.16 GHz, 14.87–16.1 GHz, and 17.94–19.24 GHz, based on a distinctive situation analysis, which are the frequencies selected for 5G mobile communications by international telecommunications. Keywords: Mobile Communications · 5G-Applications · Patch Antennas · Slot

1 Introduction The rapid development of mobile communications technology and the need for wireless communications technology has grown dramatically over the past decade, and that growth continues. Reconfigurable multi-band and cognitive radio antennas in modern high-profile wireless applications are becoming necessary for our miniaturized communications devices now and in the future. These applications require small effective antennas such as wireless local-area network (WLAN)/Worldwide Interoperability for Microwave Access (WiMAX), and many other applications [1, 2]. Portable antenna technology has developed, as well as cellular and mobile technologies. All of this is done because technology directly affects access to and development of software. Where latency can be decreased and the active time of the device can be improved, using cooperative localization. Since the sensors are untouched with 5G © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 279–288, 2023. https://doi.org/10.1007/978-3-031-29857-8_28

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communication, which the impact directly observed, and the internet of things (IoT) where the range of communication can be with the millimeter technology incorporated using (the fifth-generation) 5G [3, 4]. Various reconfigurable micro trigger antennas have been adopted in this respect, for their advantageous properties as low cost, low profile, and ease of manufacture [5–14]. A frequency reconfigurable micros trip patch with the ability to switch the frequency to nine bands with frequencies between 1.98 GHz and 3.59 GHz was introduced in [5]. In [6], a compact tunable rectangular patch antenna with slots etched in the patch and PIN diode switches, which significantly enable to reduce its size and increase the tuning range, was dealt with. Low efficiency is typical of small antennas, especially ones that use PIN diodes, and the disadvantage of low efficiency resulting from the use of PIN diodes may be overridden using micro electromechanical switches (MEMS) [7], or switching diodes [8–10]. Ultra-wide, multiband frequency obtained by using a PIN diode in a butterfly-like dipole integrated with a thin dipole using a simple structure [11, 12]. As a result, any interactive load of the patch controls its resonance frequency. This charging can be carried out electronically using variable diodes [13, 14]. This will help us better understand the role and reach of antennas with multi-band response and Ultrawideband (UWB) devices and check the difficulty of wireless communication standards [15–17]. Using UWB technologies, as described in IEEE 802.15.6 [18, 19], however, the use of spectrum resources for modern wireless communication systems is becoming more and more strict [20, 21], requires antenna extension at greater range and is more integrated, with UWB applications. In this work, we design an antenna with many features, such as a U-shaped geometry with two pin diodes in the center and two narrow slots. As a result, this geometry allows us to target a large band by switching the state of Pin diode, and to control the impedance bandwidth with the current flow on the radiating antenna. There are four parts to our work. In the first section, we have described and presented our work in detail. In the second section, we examined the impact of modifying the dimensions on the connecting antenna. In the third section, we observed the effects of switching the states of the diode on the patch antenna. In the last section, the antenna was examined in terms of polarization, field strength, radiation intensity, power density, phase and direction.

2 Frequency Response Reflection Coefficient |S11| dB unit is the corresponding value. The reflection coefficient parameter indicates how much power returned to the source is reflected by the antenna radiator. This is a measure of the impedance or lack between the radiator input and the source output. || = (ZA − ZC)/(ZA + ZC)

(1)

|S11| = −20 log ||

(2)

where ZA is the input impedance of the antenna and ZC is the output impedance of the source. And the term for antenna bandwidth BW for narrowband and wideband antennas

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is defined by Eq. 3. BW = 2 × (fh − f1)/(fh + f1) : S11 ≤ −10 dB

(3)

where f1 and fh are respectively the lowest and highest switching frequencies.

3 Antenna Geometry Figure 1 shows the geometry of the rectangular patch powered by a L-shaped power supply. The (rectangular) symmetry of the radiating element allows it to resonate at the same frequencies for two states of PIN diodes with four narrow horizontal slots and Two U-shaped slots are included in the design. The antenna structure is built on an FR4 substrate with a relative permittivity of 4.3, and a thickness of h = 1.6 mm. The use of FR 4 substrate is characterized by excellent mechanical properties, solvent resistance and almost no water absorption (0.125 < 0.10%). It is considered the importance of the FR 4 substrate is its ability to maintain the most important properties of electrical insulation and mechanical specifications in both wet and dry conditions. In the proposed antenna, the FR4 is used as a substrate because of its better performance. The patch radiating has dimensions of 50 mm × 25 mm. Additionally, our design integrates two diodes stacked on the U-shaped slots. With the first gap in the bottom right corner of the patch antenna while the second gap in the top left corner of the patch. In addition, two rectangular shapes have been cut in the lower left and upper right of the patch antenna [22], this gives the antenna a wide range of bandwidth and multiple resonance frequencies. In addition, CST software was used to designing the patch antenna as well as performing a simulation of this antenna. The dimensions are Ls = 50 mm, Lg = 30 mm, Ws = 25 mm, L2 = 1 mm, L3 = 1.5 mm, W4 = 17mm, L4 = 15.5 mm, L5 = 0.4 mm, L6 = 2.8 mm, L1 = 8 mm, W1 = 1.3 mm, L8 = 1.6 mm, L2 = 1 mm, L7 = 2.1 mm.

Fig. 1. Structure of the proposed antenna

Figure 2 illustrates the frequency response of return loss for the proposed antenna. The antenna generates multiple resonant frequencies and multiple impedance bandwidths of −10 dB ARBW [23], it resonates at 2. 21–3.61GHz, 4.59–5.16 GHz, 15.17– 16.44 and 17.94–19.84 GHz. Figure 3 shows the result of simulating the 3 dB axial ratio (AR). However, there are six bands, which are 2.21–3.07 GHz, 3.68–3.81GHz, 4.78–5.1GHz, 8.59–10.56 GHz, 14.47–16.1 GHz, and 17.74–19.74 GHz. With 32.58%, 3.48%, 6.74%, 20.58%, 10.66%, 10.68% respectively. It is evident that the design has

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a multiband coverage response with the greatest gain value is −47.26 dB. We are confident that the antenna designed can work with many wireless bands, including S-band (2–4 GHz), WiMAX (3.5–3.6GHz, 5.25–5.85 GHz), WLAN (IEEE802.11a/h/d/n 5.15– 5.35GHz, 5.25–5.35GHz, 5.47–5.725GHz, 5.725–5.825GHz) and C-band (4 GHz to 6 GHz) and Ku-band (14 GHz to 18 GHz).

Fig. 2. Reflection coefficients as a function of frequency of the antenna

Fig. 3. Axial ratio bandwidth as a function of frequency of the antenna

4 Parametric Study Figure 4 and Table 1 show the frequency response for the proposed antenna by changing the dimensions. To provide better antenna bandwidth and gain, so that it can fully cover the required application band [24]. Therefore, the result shows that by varying the L7 dimensions, the typical frequency bandwidth of the antenna can be reconfigured from one band to the next. This allows us to achieve flexible bandwidth, making the antenna usable in various wireless communication systems. When we lower the value of L7, the resonant frequency peak decreases within the bandwidth range from 2 to 6 GHz,

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whereas bandwidth varies between 14 and 18 GHz, the peak resonance frequencies have remained unchanged. All L7 values have six bands, namely 2.05–2.77 GHz, 3.38– 3.96 GHz, 4.9–5.25 GHz, 15.1–15.28 GHz, 15.78–16.3 GHz, and 17.84–18.81 GHz. These bands remain unchanged throughout the whole simulation, except in the case where L7 = 2 mm the band increases from 1.13 GHz to 1.33 GHz.

Fig. 4. Reflection coefficients as a function of frequency for the different dimensions L7.

Table 1. Value of the reflection coefficients as a function of frequency for the different dimensions L7. Dimensions L7 (mm)

Band 1 (GHz)

Band 2 (GHz)

Band 3 (GHz)

Band 4 (GHz)

Band 5 (GHz)

Band 6 (GHz)

1.6

2.05–2.5

3.38–3.96

4.9–5.25

15.1–15.28

15.78–16.3

17.84–18.81

1.95

2.09–2.55

3.12–3.92

4.92–5.2

14.95–15.27

15.8–15.97

18.07–18.7

2

2.1–2.58

3.48–3.95

4.81–5.25

14.89–15.25

15.7–15.9

18.07–18.8

Due to brevity, an additional L6 dimensions was studied to study its impact on antenna efficiency. Figure 5 and Table 2 show the antenna return loss results, by manipulating the L6 value from 1.2 mm to 2.8 mm in 0.4 mm increments. Simulations show that for all values of L6 the bands remain unchanged, while the frequency peak increases as the value of L6 drops. However, there has been an exception within the 4.1 to 5 GHz frequency range where we observed that the peak increases and the band slides from the 5 GHz band to 4 GHz band. The required bandwidth was completely uncovered. Nonetheless, by further increasing the dimension’s value to 2.1 mm, the necessary bandwidth started to be covered partially resulting in 3.71–4.16 GHz frequency band, with a resonance of −20 dB at 3.92 GHz. By reaching, the 1.6 mm value of the dimensions L6, the bandwidth widened and covering indeed the entire low frequency band of 3.35–4.25 GHz and this time the resonant frequency shifted ideally to exactly 4.05 GHz.

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Fig. 5. Reflection coefficients as a function of frequency for the different dimensions L6.

Table 2. Reflection coefficients as a function of frequency for the different dimensions L6. Dimensions (mm)

Band 1 (GHz)

Band 2 (GHz)

Band 3 (GHz)

Band 4 (GHz)

Band 5 (GHz)

Band 6 (GHz)

L6 = 1.6

2.1–2.575

3.47–3.52

4.57–4.6

4.75–4.77

14.7–15.9

18.05–18.7

L6 = 1.9

2.1–2.55

3.65–3.7

4.6–4.625

4.75–4.78

14.97–15.92

18.06–18.8

L6 = 2.2

2.1–2.575

3.75–3.82

4.6–4.625

4.72–4.75

15–15.95

18.06–18.7

L6 = 2.5

2.07–2.65

3.9–3.97

4.62–4.65

4.75–4.77

15.02–16

18.07–18.7

L6 = 2.8

2.07–2.57

4 -4.1

4.62–4.65

4.75–4.77

15.05–16.2

18.1–18.8

5 Simulations Results and Equivalent Circuit Model of Pin Diode To achieve the reconfiguration of the bias, two diodes of types (BAP65-02, 115) [25], are used for shifting. The two PIN diodes are integrated into the U-shaped power lines and the Y-shaped antenna, so that they can also travel in a radio frequency spectrum to avoid band interference. And to connect or disconnect both rectangles, which leads to the variety of operating frequencies in order to dynamically modify the function of the antenna. In the ON condition, the PIN diode can be modeled as a serial combination of resistance R = 1  and inductance L = 0.6 nH. For the OFF condition, the diode can be modeled as a parallel combination of C = 0.5 pF capacitor and R = 20 k resistor in series with L = 0.6 nH inductance, the equivalent circuit of the PIN diode at each state as shown in Fig. 6.

Fig. 6. Equivalent circuit model of PIN diode.

Figure 7 and Table 3 show the frequency response of the proposed antenna by changing the condition of two PIN diodes using four narrow slots. To illustrate, when the

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Fig. 7. Reflection coefficient as a function of frequency of the proposed antenna for states of the PIN diodes. Table 3. Reflection coefficients of the proposed antenna for states of the PIN diodes. D1

D2

Band 1 (GHz)

Band 2 (GHz)

Band 3 (GHz)

Band 4 (GHz)

Band 5 (GHz)

On

On

2.05–2.77

On

Off

2.07–2.79

Off

On

Off

Off

Band 6 (GHz)

3.38–3.96

4.91–5.25

15.2–15.7

15.8–16.3

17.85–18.8

3.42–3.97

4.95–5.27

14.9–15.3

15.78–16.3

17.84–18.81

2.05–2.74

3.38–3.96

4.9–5.25

15.1–15.8

15.8–16.28

17.88–18.82

2.05–2.77

3.38–3.96

4.89–5.25

15.1–15.7

15.8–16.26

17.85–18.81

two PIN diodes are on, the antenna design covers six different bands, it resonates at 2.05–2.77 GHz, 3.38–3.96 GHz, 4.9–5.25 GHz, 15.1–15.8 GHz, 15.78–16.3 GHz, and 17.84–18.81 GHz. But when the left PIN diode is activated and the right PIN is disabled, the antenna design covers seven different bands. The right PIN diode is activated and the left PIN diode is disabled as the antenna design covers six different bands. As both diodes are turned off, it has also covered six different bands. This allows us to target a large band, showing that the featured antenna has good insulation to the operating bands with multiple resonances and antenna gain.

Fig. 8. Radiation patterns of the proposed antenna when the two PIN diodes are on.

Figure 8, Fig. 9, Fig. 10, and Fig. 11 mention the evolutional response for radiation patterns of the proposed antenna by switching the state of two PIN diodes, in terms of

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Fig. 9. Simulated radiation patterns of the proposed antenna when the D2 diode is off.

Fig. 10. Simulated radiation patterns of the proposed antenna when the D1 diode is off.

Fig. 11. Radiation patterns of the antenna when the two PIN diodes are off.

directionality, radiation intensity, power density, field strength, polarization and phase. As a result, it is possible to observe that the simulated radiation patterns in the four figures are virtually unchanged in these states, regarding the first frequency at 1 GHz and the second frequency at 4 GHz of the antenna. However, in the last 9 GHz frequency of the antenna when both PIN diodes are activated, there is a slight change. The suggested antenna offers great gain and high directivity in all PIN diode states.

6 Surface Current Distribution The surface current distribution of different frequencies for different states of two simulated PIN diodes as illustrated in Fig. 12. Notice that most of the surface current spread from the bottom of the patch to the middle and the edge of the two states of PIN diodes with four narrow horizontal slots and Two U-shaped when the two PIN diodes are on. As shown in Fig. 8 (d), illustrates that most of the surface current spread from the bottom of the patch to the middle in four narrow horizontal slots and Two U-shaped when the two PIN diodes are off.

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Fig. 12. Surface current distribution.

7 Conclusion This work focuses on designing a reconfigurable multiband planar antenna to cover multiband response with the ability to shift resonance frequency. By using an integration of two diodes stacked on the U-shaped slots and two narrow horizontal slots on the ground plane of the patch antenna. The simulation results indicate a great response, especially when we look at the resonance frequency band with a good break gain. Furthermore, this best result is the result of working with multiple dimensions of L7 and W rather than just one dimension. This will allow us to better understand the effect on the return loss by varying dimensions. Additionally, we have selected a variety of PIN diode states to improve bandwidth and antenna impedance gain. The result shows that by switching the diode states, the antenna can be re-configured from one band to another, and to see the impact on the resonant frequency and gain.

References 1. Khan, M.U., Sharawi, M.S., Mittra, R.: Microstrip patch antenna miniaturisation techniques: a review. IET Microw. Antennas Propag. 9(9), 913–922 (2015). https://doi.org/10.1049/ietmap.2014.0602 2. Mekki, K., Necibi, O., Boussetta, C., Gharsallah, A.: Miniaturization of circularly polarized patch antenna for RFID reader applications. Eng. Technol. Appl. Sci. Res. 10(3), 5655–5659 (2020). https://doi.org/10.48084/etasr.3445 3. Zhang, Y., Deng, J.-Y., Li, M.-J., Sun, D., Guo, L.-X.: A MIMO dielectric resonator antenna with improved isolation for 5G mm wave applications. IEEE Antennas Wirel. Propag. Lett. 18(4), 747–751 (2019). https://doi.org/10.1109/LAWP.2019.2901961 4. Kamal, Md.S., Islam, Md.J., Uddin, Md.J., Imran, A.Z.M.: Design of a tri-band microstrip patch antenna for 5G application. In: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pp. 1–3, February 2018. https://doi.org/10.1109/IC4ME2.2018.8465627 5. Majid, H.A., Rahim, M.K.A., Hamid, M.R., Murad, N.A., Ismail, M.F.: Frequencyreconfigurable microstrip patch-slot antenna. IEEE Antennas Wirel. Propag. Lett. 12, 218–220 (2013) 6. Sheta, A.-F., Mahmoud, S.F.: A widely tunable compact patch antenna. IEEE Antennas Wirel. Propag. Lett. 7, 40–42 (2008)

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7. Cetiner, B.A., Crusats, G.R., Jofre, L., Biyikli, N.: RF MEMS integrated frequency reconfigurable annular slot antenna. IEEE Trans. Antennas Propag. 58(3), 626–632 (2010) 8. Okabe, H., Takei, K.: Tunable antenna system for 1.9 GHz PCS handsets. In: Proceedings of the IEEE AP-Symposium, pp. 166–169 (2001) 9. Karmakar, N.C.: Shorting strap tunable stacked patch PIFA. IEEE Trans. Antennas Propag. 52(11), 2877–2884 (2004) 10. Peroulis, D., Sarabandi, K., Katehi, L.: Design of reconfigurable slot antennas. IEEE Trans. Antennas Propag. 53(2), 645–654 (2005) 11. Gholamrezaei, M., Geran, F., Sadeghzadeh, R.A.: Completely independent multiultrawideband and multi-dual-band frequency reconfigurable annular sector slot antenna (FR-ASSA). IEEE Trans. Antennas Propag. 65(2), 893–898 (2017) 12. Yang, S.-L.S., Kishk, A.A., Kai, F.-L.: Frequency reconfigurable U-slot microstrip patch antenna. IEEE Antennas Wirel. Propag. Lett. 7, 127–129 (2008) 13. Li, H.Y., Yeh, C.T., Huang, J.J., Chang, C.W., Yu, C.T., Fu, J.S.: CPWfed frequencyreconfigurable slot-loop antenna with a tunable matching network based on ferroelectric varactors. IEEE Antennas Wirel. Propag. Lett. 14, 614–617 (2015) 14. Simorangkir, R.B.V.B., Yang, Y., Esselle, K.P., Zeb, B.A.: A method to realize robust flexible electronically tunable antennas using polymer-embedded conductive fabric. IEEE Trans. Antennas Propag. 66(1), 50–58 (2018) 15. Bai, Y.Y., Xiao, S., Liu, C.: Design of pattern reconfigurable antenna based on a two-element dipole array model. IEEE Trans. Antennas Propag. 61(9), 4867–4871 (2013) 16. Otim, T., Iturri, P.-L., Azpilicueta, L., Bahillo, A., Díez, L.E., Falcone, F.: A 3D ray launching time frequency channel modeling approach for UWB ranging applications. IEEE Access 8, 97321–97334 (2020) 17. Catherwood, P.A., Bukhari, S.S., Watt, G., Whittow, W.G., Laughlin, J.M.: Body-centric wireless hospital patient monitoring networks using body-contoured flexible antennas. IET Microw. Antennas Propag. 12(2), 203–210 (2018) 18. Maunder, A., Taheri, O., Fard, M.R.G., Mousavi, P.: Calibrated layer-stripping technique for level and permittivity measurement with UWB radar in metallic tanks. IEEE Trans. Microw. Theory Tech. 63(7), 2322–2334 (2015) 19. Khani, H., Nie, H.: Near-optimal detection of monobit digitized UWB signals in the presence of noise and strong intersymbol interference. IEEE Syst. J. 14(2), 2311–2322 (2020) 20. Wang, S., Mao, G., Zhang, J.A.: Joint time-of-arrival estimation for coherent UWB ranging in multipath environment with multi-user interference. IEEE Trans. Sig. Process. 67(14), 3743–3755 (2019) 21. Mohammadi, M.S., Dutkiewicz, E., Zhang, Q., Huang, X.: Optimal energy efficiency link adaptation in IEEE 802.15.6 IR-UWB body area networks. IEEE Commun. Lett. 18(12), 2193–2196 (2014) 22. Bikrat, M., Bri, S.: Design of an inclined fractal defected ground-based polarized antenna for WLAN applications. Int. J. Microw. Opt. Technol. 16(4), 311–318 (2021) 23. Ibrahime Hassan, N., Youssef, R.A., Mustapha, L., Seddik, B.: Development of rectangular multilayer antennas for several bands. Eur. J. Sci. Res. 153, 91–104 (2019). ISSN 1450216X/1450-202X 24. Bikrat, M., Bri, S.: Reconfigurable circularly polarized antenna for WLAN and WIMAX. Int. J. Adv. Trends Comput. Sci. Eng. 9, 46–52 (2020) 25. Bikrat, M., Bri, S.: A bandwidth reconfigurable planar antenna for UWB-applications. E3S Web Conf. 351, 81–87 (2022). https://doi.org/10.1051/e3sconf/202235101060

Design and Analysis of a Slot Antenna Array with a Defected Ground Plan for Millimeter Wave Application Fatima Kiouach1(B) , Mohammed El Ghzaoui1 , Rachid El Alami1 , Sudipta Das2 , Mohammed Ouazzani Jamil3 , and Hassan Qjidaa1,3 1 Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

[email protected]

2 Department of Electronics and Communication Engineering, IMPS College of Engineering

and Technology, Malda, West Bengal, India 3 LSEED Laboratory, UPF, Fez, Morocco

Abstract. A new array antenna is proposed for mm-Wave 5G wireless communication applications in this paper. The array antenna geometry presented consists of six identical patch pieces coupled in a 1 × 6 arrangement with a partial ground plane. To achieve the necessary operating band and high gain range, the structures of each single antenna element are changed by adding combinations of rectangle and triangle-shaped slots with a partial ground plane. The suggested array antenna design and simulation studies were carried out utilizing high frequency structure simulator (HFSS) software. This paper represents how the performance of a parallel feed array antenna changes as the substrate material changes for that we use FR4, Rogers RO3010, and Rogers RT/duroid6010/6010LM substrates with 0.8 mm thickness. The 1 × 6 array antenna FR4 resonates at 38.8 GHz with a bandwidth of 4.2 GHz for the Rogers RO3010 substrate resonates at 37.3 GHz and 40.2 GHz with a bandwidth of 2.8 GHz and 2.7 GHz, and for Rogers RT/duroid 6010/6010LM resonates at 37.3 GHz and 40.6 GHz bandwidth of 2.6 GHz and 2.5 GHz, respectively. Keywords: Array antenna · Parallel feed · Gain · mm-Wave · 5G Technology

1 Introduction The fifth-generation (5G) network is anticipated to significantly deliver greater bandwidth, higher data rates, and faster speed to satisfy the demands of wireless technologies today. 5G technology supports three different use cases [1]. First, eMBB stands for Enhanced Mobile Broadband, which prioritizes high data speed for end customers and high system capacity (10 Gbps). Massive Machine Type Communications offers the possibility of having higher density objects, and we could install 1 million devices on one square meter without problems. The final one is uRLLC (ultra Reliable & Low Latency Communications), which offers less than 1 ms latency and extremely high reliability for 5G’s crucial communication applications. This necessitates additional criteria in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 289–298, 2023. https://doi.org/10.1007/978-3-031-29857-8_29

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antenna design for 5G communication systems to meet these requirements. A group of N spatially separated antennas makes up an antenna array. With the antenna array, the total gain may be raised, interference coming from a particular direction can be eliminated, and the Signal Interference Noise Ratio (SINR) can be maximized. The performance of an antenna array generally improves with the number of antennas (elements) in the array [2]. Millimeter Wave (mm-Wave) bands are strongly recommended for practical usage in 5G systems to make use of the large available bandwidth [3]. Massive bandwidth, large channel capacity, and even reduced latency may be obtained when mm-Wave is used in 5G systems. The frequency ranges chosen for 5G include 28 GHz, 38 GHz, 60 GHz, and 73 GHz [4]. Therefore, the 28/38 GHz mm-Wave spectrum is considered a suitable band for 5G systems. There is a tremendous demand for well-designed array antennas to meet 5G specifications [5]. Several research projects have recently concentrated on the fabrication of mm-Wave array antennas for 5G applications at 38 GHz. In ref. [6], a 1 × 2 array antenna for 26/28 GHz 5G applications was designed with a simple design to satisfy the broad bandwidth, high gain, and directed patterns anticipated for millimeter-Wave 5G wireless applications. In ref. [7], a 5G millimeter-Wave phased antenna array is suggested for mobile applications at 37–40 GHz. In terms of antenna gain and scanning characteristics, the proposed antenna array design offers good antenna performance. A 1 × 4 microstrip patch antenna array for 5G applications, as referred to in [8], has a bandwidth of approximately 3700 MHz (36.5 GHz to 40.2 GHz) at 38.1 GHz with a reflection coefficient of −34 dB and a maximum gain of 7.81 dB. According to reference [9], a 64-element microstrip patch antenna array with a peak gain of 6.5 dBi is proposed as the mm-Wave antenna array operating at 38 GHz. Other types of antennas, such as MIMO antennas, have been proposed in the literature [10, 11]. In the paper [12], three new compact dual-band printed MIMO antennas are presented for 5G mobile communications, which have low mutual coupling, appropriate values of di-rectivity, gain, and radiation efficiency, are fabricated using photolithography, and are measured using Vector Network Analyzer ZVA 67. A planar dual-band millimeterwave printed monopole antenna and a 2x2 MIMO antenna are presented in [13] for 28/38 GHz 5G wireless communications, exhibiting omnidirectional radiation patterns and a peak gain value of 1.27 and 1.83 dBi at 28 and 38 GHz, respectively. In [14], an eight-element array antenna with a single-layer frequency-selective surface is presented. It covers a wide band-width from 20 GHz to 65 GHz, which includes millimeter wave 5G bands, and exhibits a stable gain and directional radiation pattern that have been confirmed through simulations and testing of fabricated prototypes. The paper [15] proposes a novel dual-band patch an-tenna for MIMO communication systems at 28/38 GHz, achieving an S11 < −10 dB band-width of 27.6 – 28.5 GHz and 36.9 – 38.9 GHz, and a simulated gain of 9.0 dBi at 28 GHz and 5.9 dBi at 38 GHz. The paper [16] proposes a three-layer antipodal Vivaldi antenna for 5G mobile communication and Ku-band applications, which achieves multi-band coverage in Ku-bands, two millimeter wave (mmW) bands suitable for 5G communications. A 1 × 6 array antenna for 38 GHz 5G mm-Wave spectrum applications is presented in this study. Six identical single element antennas are stacked in a 1 × 6 arrangement to make up the suggested array antenna construction with a partial ground plane. The effect of three different substrate types—FR4 with a dielectric constant of 4.4, Rogers RO3010, and Rogers RT/duroid 6010/6010LM with the same dielectric constant of

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10.2—is discussed in this study to determine which substrate will work best with the suggested array antenna design. For the three substrates, the array antenna simulation yields excellent results. The 1 × 6 array antenna FR4 resonates at 38.8 GHz with a reflection coefficient S11 of - 37.15 dB and a bandwidth of 4.2 GHz and for the Rogers RO3010 substrate resonates at 37.3 GHz and 40.2 GHz with S11 of −34.52, and −29.24 dB, respectively, and a bandwidth of 2.8 GHz and 2.7 GHz and for Rogers RT/duroid 6010/6010LM resonates at 37.3 GHz and 40.6 GHz with S11 of −31.56, −29.66 dB and a bandwidth of 2.6 GHz and 2.5 GHz, respectively. The suggested array antenna can be regarded as a mm-Wave antenna appropriate for the 5G NR frequency band n260 (37–40 GHz) with the three types of substrates used. Therefore, the proposed array antenna configuration can be considered a potential mm-Wave antenna suitable for 38 GHz 5G applications with the three types of substrates. The design of the single element antenna is described in detail in Sect. 2, while the array antenna design is explained in Sect. 3. Section 4 contains simulation results of both the single element antenna and the proposed antenna design, while Sect. 5 provides a comparative analysis of the proposed array design with various antenna structures planned for 5G applications. Finally, a conclusion is provided in Sect. 6.

2 Single Element Antenna The upper plane of the single element antenna consists of a rectangular patch and four slots in the shape of the union of rectangles and triangles, realized on a rectangular substrate FR4 with a relative dielectric constant of 4.4, a loss tangent of 0.02 and dimensions of 16 × 13 × 0.8 mm3 , and there is a partial ground plane on the opposite side of the substrate as shown in Fig. 1.

Fig. 1. Proposed single element antenna module (a) top view and (b) bottom view.

Table 1 shows a list of the appropriate dimensions for the single element antenna.

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Value

W1

8 mm

W2

1.6 mm

W3

0.5 mm

W4

2 mm

W5

4 mm

W6

2 mm

Wg

13 mm

L1

8 mm

L2

1.6 mm

L3

3.1 mm

L4

0.5 mm

L5

7 mm

Lg

3.5 mm

Total size of the single element antenna

16 × 13 × 0.8 mm3

3 1 × 6 Array Antenna Design Figure 2 represents the proposed 1 × 6 array antenna structural layout. Figure 2(a) illustrates the proposed antenna top plane, which is made up of six patch elements

Fig. 2. Proposed 1 × 6 array antenna module (a) top view and (b) bottom view.

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Table 2. Dimensions of the Proposed 1 × 6 Array Antenna. Antenna Parameters

Value

W7

14.245 mm

W8

12.07 mm

W9

17.77 mm

W10

0.9 mm

W11

1 mm

Wag

35 mm

L6

2.74 mm

L7

2.5 mm

Lag

11 mm

Total size of the array antenna

60 × 27 × 0.8 mm3

organized in a 1 × 6 formation, and the partial ground plane shown in Fig. 2 (b). Table 2 is a list of the appropriate dimensions for the proposed 1 × 6 array antenna.

4 Simulation Results and Discussion This section includes the simulation results of the single antenna element using the FR4 substrate as well as the simulation results of the proposed array antenna. Several substrate materials are used in this paper for the same antenna design (thickness of 0.8 mm), including FR4 with a dielectric constant of 4.4 and dielectric loss tangent of 0.02, Rogers RO3010, and Rogers RT/duroid 6010/6010LM with the same dielectric constants of 10.2 and dielectric loss tangent of 0.0035 and 0.0023, respectively. These three types of substrates are employed to find the best substrate for the design of the proposed array antenna. The parameters evaluated are reflection coefficients, gain, VSWR, and radiation patterns. 4.1 Single Element Antenna Reflection Coefficient S11 also known as the reflection coefficient, indicates the relationship between the input and output ports, indicates how much power is reflected from an antenna, and defines the bandwidth. The simulation results of S11 versus the frequency of the single element antenna are shown in Fig. 3 which shows that the dual-band single element antenna resonates at 37 and 40.4 GHz with bandwidths of 2.3 GHz (35.9 GHz−38.2 GHz) and 1.3 GHz (39.8 GHz−41.1 GHz), respectively. Gain Figure 4 illustrates the simulation peak gain of a single element antenna, which displays significant gain decrements at approximately 38.5 GHz. Simulated peak gain at 37 GHz and 40.4 GHz are 9.945 dB and 9.303 dB, respectively.

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Fig. 3. Reflection coefficients versus the frequency of the single antenna element.

Fig. 4. Gain versus the frequency of the single antenna element.

4.2 1 × 6 Array Antenna: Effect of Substrate Type on the Performance of the Proposed Array Antenna Reflection Coefficient The 1 × 6 array antenna with the FR4 substrate resonates at 38.8 GHz with S11 of − 37.15 dB and a bandwidth of 4.2 GHz (36.6 GHz−40.8 GHz), with the Rogers RO3010 substrate, the array antenna resonates at 37.3 GHz and 40.2 GHz with S11 of −34.52, −29.24 dB and a bandwidth of 2.8 GHz (35.9 GHz−38.7 GHz) and 2.7 GHz (38.8 GHz−41.5 GHz), respectively, and resonates at 37.3 GHz and 40.6 GHz with S11 of −31.56, −29.66 dB and a bandwidth of 2.6 GHz (35.9 GHz−38.5 GHz) and 2.5 GHz (38.9 GHz−41.4 GHz) respectively using Rogers RT/duroid 6010/6010LM substrate, as illustrated in Fig. 5. We observed that using a single element dual band antenna in the design of a 1 × 6 array antenna with an FR4 substrate results in an antenna with a single bandwidth greater than the single element antenna used, but when we use both Rogers RO3010 or Rogers RT/duroid 6010/6010LM substrates, the array antenna is a dual-band antenna.

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Fig. 5. Reflection coefficient S11 of 1 × 6 array antenna FR4, Rogers RO3010, and Rogers RT/duroid6010/6010LM Substrates.

VSWR (Voltage Standing Ratio) Another parameter that gives us information about antenna mismatching is the VSWR

Fig. 6. VSWR of the 1 × 6 array antenna using the (a) FR4 substrate, (b) Rogers RO3010 substrate, and (c) Rogers RT/duroid6010/6010LM substrate.

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(Voltage Standing Ratio). This parameter has a range [1−∞). If the antenna does not reflect any power, then the VSWR value is 1, whereas a high number denotes a higher mismatch. VSWR is given in Fig. 6. For the proposed array antenna with the FR4 substrate, the value of VSWR is 1.02 at 38.7 GHz, and when using the Rogers RO3010 substrate, the value of VSWR is 1.04 and 1.07 at 37.3 GHz and 40.2 GHz, respectively. For the Rogers RT/duroid6010/6010LM substrate, the values of VSWR are 1.05 and 1.07 at 37.3 GHz and 40.6 GHz respectively. The VSWR values that we obtained for all substrate types are highly useful in the manufacturing process since, generally speaking, an antenna match is regarded to be excellent if the VSWR is less than 2. 2D and 3D Radiation Patterns Figure 7 displays the array antenna 2D and 3D simulated radiation patterns at 38 GHz. At 38 GHz, the quasi-omnidirectional pattern is maintained for the antenna array using the FR4 substrate, and for the Rogers RO3010 or Rogers RT/duroid6010/6010LM substrates, the antenna has an approximate directed radiation pattern, as can be observed.

Fig. 7. Simulated 2D and 3D radiation patterns of the proposed antenna at 38 GHz with the (a) FR4 substrate, (b) Rogers RO3010 substrate, and (c) Rogers RT/duroid6010/6010LM substrate.

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The simulation of the projected array antenna as a function of the three substrates produced results that were acceptable for 5G applications. The suggested array antenna with the FR4 substrate covers the designated bandwidth needs of many countries, including the UK (37 GHz–40 GHz), the USA (37 GHz–37.6 GHz), Canada (37.6 GHz–40.0 GHz), and Australia (39 GHz). Using Rogers RO3010 and Rogers RT/duroid6010/6010LM substrates, the proposed array antenna meets the bandwidth requirements of the USA (37 GHz−37.6 GHz) and Australia (39 GHz).

5 Performance Proposed Array Antenna Related to Other Work in the Literature A comparative overview of the proposed array design with various antenna structures designed for 5G applications is presented in Table 3. Table 3. Comparison with other reported works for 38 GHz 5G applications Ref/ Year of Publication

Bandwidth (GHz)

S11 (dB)

Gain (dB)

Substrate

Resonant frequency GHz

7 (2021)

3.7

−34

7.81

FR4 4.4

38.1

12 (2019)

−

−24.59

8.27

Rogers 5880 2.2

38.04

13 (2019)

2.10

−38

−

Rogers RT/Duroid 5880 2.2

38

14 (2022)

−

−23

12

Rogers RT/Duroid 5880 2.2

38

15 (2020)

2

−40

5.9

Taconic TLY-5 2.2

38

16 (2020)

7

−25

>10

Rogers 5880 2.2

38

This work (2022)

4.2

−37.15

18.5

FR4 4.4

38.8

6 Conclusion The work in this paper concentrated on the effect of various substrate materials on the suggested array antenna. Different substrate materials, such as FR4, Rogers RO3010, and Rogers RT/duroid6010/6010LM, are chosen for this work. The antenna designed using FR4 resonant at 38.8 GHz with a reflection coefficient S11 of −37.15 dB and a bandwidth of 4.2 GHz (36.6 GHz–40.8 GHz) with a gain of approximately 3.078 dB,

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with the Rogers RO3010 array antenna resonating at 37.3 GHz and 40.2 GHz with S11 of −34.52, and −29.24 dB, respectively, and a bandwidth of 2.8 GHz (35.9 GHz–38.7 GHz) and 2.7 GHz (38.8 GHz–41.5 GHz) with a gain of 7.035 dB, and the Rogers RT/duroid 6010/6010LM antenna resonates at 37.3 GHz and 40.6 GHz with S11 of − 31.56, −29.66 dB and a bandwidth of 2.6 GHz (35.9 GHz–38.5 GHz) and 2.5 GHz (38.9 GHz–41.4 GHz), respectively, and a gain of 7.225 dB. As a result, the suggested array antenna can be regarded as a mm-Wave antenna appropriate for the 5G NR frequency band n260 (37–40 GHz) with the three types of substrates used.

References 1. Osseiran, A.: Scenarios for 5G mobile and wireless communications: the vision of the METIS project. IEEE Commun. Mag. 52(5), 26–35 (2014) 2. El Alami, A., Ghazaoui, Y.: Design and simulation of RFID array antenna 2 × 1 for detection system of objects or living things in motion. Proc. Comput. Sci. 151, 1010–1015 (2019). Elsevier, Belgium 3. Ghazaoui, Y.: Millimeter wave antenna with enhanced bandwidth for 5G wireless application. J. Instrum. 15(1) (2020) 4. Li, H.: Wideband dual-polarized endfire antenna array with overlapped apertures and small clearance for 5G millimeter wave applications. IEEE Trans. Antennas Propag. 69(2) (2021) 5. Guo, J.-Y., Liu, F., Jing, G.-D., Zhao, L., Yin, Y.-Z., Huang, G.-L.: Mutual coupling reduction of multiple antenna systems. Front. Inf. Technol. Electron. Eng. 21(3), 366–376 (2020). https://doi.org/10.1631/FITEE.1900490 6. El Ghzaoui, M., Das, S.: Data transmission with terahertz communication systems. In: Biswas, A., Banerjee, A., Acharyya, A., Inokawa, H., Roy, J.N. (eds.) Emerging Trends in Terahertz Solid-State Physics and Devices, pp. 121–141. Springer, Singapore (2020). https://doi.org/ 10.1007/978-981-15-3235-1_9 7. Peng, M., Zhao, A.: High performance 5G millimeter-wave antenna array for 37–40 GHz mobile application. In: 2018 International Workshop on Antenna Technology (iWAT). IEEE, China (2018) 8. Aghoutane, B.: Analysis, design and fabrication of a square slot loaded (SSL) millimeter-wave patch antenna array for 5G applications. J. Circuits Syst. Comput. 30(12) (2020) 9. Hu, C.-N., Peng, K.: Design of a mm-wave microstrip antenna array. In: International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM). IEEE, Hsinchu (2015) 10. Aghoutane, B.: A novel dual band high gain 4-port millimeter wave MIMO antenna array for 28/37 GHz 5G applications. AEU - Int. J. Electron. Commun. 145(16) (2022) 11. Aghoutane, B.: A dual wideband high gain 2 × 2 multiple-input-multiple-output monopole antenna with an end-launch connector model for 5G millimeter-wave mobile applications. Int. J. RF Microw. Comput.-Aided Eng. 32(5) (2022) 12. Marzouk, H.M.: Novel dual-band 28/38 Ghz MIMO antennas for 5G mobile applications. Progr. Electromagn. Res. C 93, 103–117 (2019) 13. Hasan, M.N.: Dual band omnidirectional millimeter wave antenna for 5G communications. J. Electromagn. Waves Appl. 33(12), 1581–1590 (2019) 14. Ullah, R.: Wideband and high gain array antenna for 5G smart phone applications using frequency selective surface. IEEE Access 10, 86117–86126 (2022) 15. Liu, P.: Patch antenna loaded with paired shorting pins and H-shaped slot for 28/38 GHz dual-band MIMO applications. IEEE Access 8, 23705–23712 (2020) 16. Ullah, R.: High-gain Vivaldi antenna with wide bandwidth characteristics for 5G mobile and Ku-band radar applications. Electronics 10(6) (2021)

Framework for Real-Time Simulations of Routing Security Attacks in VANETs Souad Ajjaj1(B) , Souad El Houssaini2 , Mustapha Hain1 , and Mohammed-Alamine El Houssaini3 1 ENSAM, Hassan II University, Casablanca, Morocco

[email protected]

2 Department of Computer Science, Faculty of Sciences, Chouaib Doukkali University,

El Jadida, Morocco 3 ESEF, Chouaib Doukkali University, El Jadida, Morocco

[email protected]

Abstract. Intelligent transportation systems (ITS) rely on advanced networks, mainly vehicular ad hoc networks (VANETs). However, the deployment of VANETs in real world conditions is subject to a multitude of attacks that can severely impair the performance of the vehicular network and result in critical damage to people and materials. The primary purpose of this paper is to offer an experimental framework for real-time simulations of black hole attack in VANETs. The framework comprises three main components. First, the mobility component includes both OpenStreetMap and SUMO for generating realistic mobility models extracted from real world maps. The second component makes use of the network simulator NS-3 to model the entire protocol stack of VANET communications. A third component is devoted to data collection based on the monitoring of the vehicular network in real time by assessing the most important performance metrics, mainly throughput, lost packets and routing overhead. In this component, we suggest using a simple and reliable data extraction mechanism instead of processing the NS-3 ASCII trace files, often requiring higher processing time and computational capabilities. Our proposed framework can potentially be deployed for conducting real-time simulations of routing attacks in VANETs and evaluating their impact on the network throughput. To evaluate the feasibility of the proposed framework, extensive simulations of the AODV routing protocol are carried out where a black hole attack is launched. Keywords: VANETs · Simulation · NS-3 · SUMO · AODV · Back hole

1 Introduction Intelligent transportation systems (ITS) refer to the use of advanced technologies, such as sensors, communications, and data analytics, to improve the efficiency, safety, and sustainability of transportation systems [1]. ITS aims to minimize the risk of car accidents and traffic congestion [2]. The implementation and deployment of these applications is realized through a set of advanced communication technologies and networks such © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 299–308, 2023. https://doi.org/10.1007/978-3-031-29857-8_30

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as VANETs. VANETs are an emergent variant of mobile ad hoc networks (MANETs), where nodes are smart vehicles equipped with on-board units and advanced features [3]. The deployment of VANETs in the real world is facing a challenging and problematic issue, namely, routing security attacks [4]. These attacks can disrupt the normal operation of the network and prevent legitimate messages from being transmitted [5]. Hence, reliable frameworks for real-time simulations of routing protocols in VANETs are required to cope with this issue. The aim of this study is to propose an experimental framework for real-time simulations of a serious routing security attack that threatens the AODV routing protocol in VANETs. The proposed framework consists of three main components. First, the mobility component combines both OpenStreetMap [6] and SUMO [7]. On SUMO, a sequence of Python instructions is executed to generate mobility traces that are as accurate as possible. In the second component, we opted for the popular network simulator NS-3 [8] to model the whole protocol stack of the communication system, which describes both the network components and events such as nodes, links and events. The third component is devoted to data collection and analysis. At this stage, we suggest using a simpler and more reliable mechanism based on the trace source/trace sink instead of processing the NS-3 ASCII trace files [9], which often requires higher processing capabilities. Numerous network performance indicators, mainly throughput, packet loss ratio, and routing overhead, are measured. Our proposed methodology attempts to simulate the behavior of routing security attacks as attentively as possible to the actual behavior. Using our proposed simulation framework in a research or academic context will allow to conduct reliable and effective experiments using more realistic mobility models extracted from real world maps. Thorough simulations of the AODV routing protocol [10, 11] are carried out to evaluate the proposed simulation framework. The outcomes indicate that the proposed framework offers efficient and accurate experimental components for analyzing the impact of routing security attacks on network performance. The structure of the paper is as follows: Sect. 2 outlines the VANET architecture and the SUMO and NS-3 simulators, followed by a description of the black hole attack against the AODV routing protocol. The proposed simulation framework and its main components are described in Sect. 3, while the details of the implementation and the evaluation of the proposed framework are given in Sect. 4. Section 5 concludes the paper with possible directions for future work.

2 Materials and Methods 2.1 VANET Architecture The basic architecture of VANETs exhibited in Fig. 1 includes three main components [12]. OBUs (on board units) are installed in each vehicle and consist of sensors and electronic equipment that allows the sharing of vehicle information with RSUs and other OBUs. Road Side Units (RSU) are computing equipment that are permanently installed alongside the road. The trusted authority or TA is in charge of all components that are

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Fig. 1. VANET architecture.

liable for VANET management. Communication in VANETs can either be V2V (vehicleto-vehicle) or V2I (vehicle-to-infrastructure). In V2V mode, direct wireless communication between the embedded OBUs in different vehicles is established to exchange information. In V2I communication, vehicles exchange messages with roadside units deployed on road intersections [13]. 2.2 SUMO (Simulation of Urban Mobility) Simulation of urban mobility (SUMO) is a free, open, and microscopic simulator. It is implemented in C++ language and offers OpenGL Graphical User Interface and interoperability with other applications [7]. Google Earth, OSM OpenStreetMap, and other import formats are supported by SUMO. SUMO generates trace files that give information about each vehicle individually, such as the position and speed. 2.3 Network Simulator (NS-3) The open source NS-3 simulator can be used with Linux/Unix, Mac, and Windows operating systems. It is a discrete-event network simulator that is frequently used in research; it succeeded the well-known NS-2 but features a completely new simulation architecture [8]. NS-3 aims to improve NS-2’s realism while also addressing its performance issues. NS-3 is written in C++ and Python, similar to its predecessor, and makes considerable use of templates, smart pointers, callbacks, and namespaces (ns3). The major benefit is the ongoing upkeep and quick development brought on by a vast developer community. A variety of realistic protocol modules for wired and wireless networks are available in the NS-3 simulation library. Several radio, MAC, and network layer models are also included, in addition to the full Internet protocol stack. It provides a variety of routing protocols, namely, AODV, DSR, DSDV, and OLSR. NetAnim (Network Animator), an animation tool that is activated after the conclusion of the simulation and is based on a script file called a trace file, is one of the visualisation tools included in NS-3. Trace Source and Trace Sink, Ascii Tracing (.tr files), and Pcap Tracing are just a few of the tools that NS-3 offers for retrieving statistics (.pcap files).

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2.4 Black Hole Attack in AODV Routing Protocol AODV is a type of reactive protocol in which routes are only created when needed. It relies on three main concepts: route discovery, route maintenance, and sequence numbers. In route discovery, a source node sends out a message called an RREQ (Route Request), which is then passed on until it reaches the destination or an intermediate node with a valid route. The intermediate node then sends an RREP message back to the source node. HELLO messages help verify the route’s connectivity. AODV uses sequence numbers, which are essentially timestamps, to indicate the freshness of a route. Black hole is a routing attack that threatens the security of AODV because the malicious node drops or consumes the total incoming traffic, making it seem like a legitimate router or forwarding node. This can cause problems such as the gathering of sensitive information, disrupted communication, and network failure. When this happens, the source node assumes that the route discovery process has been completed and begins sending data packets to the malicious node. The operation of this attack is depicted in Fig. 2, where node (S) intends to communicate with destination (D). The most direct route from the source to the destination is through nodes N1 and N2. However, the black hole node sends a false RREP message along the opposite path.

Fig. 2. The operations of Black hole in AODV.

3 The Proposed Simulation Framework The diagram below outlines the core components of the experimental framework proposed to simulate a routing security attack in VANETs. The proposed framework consists of three main components. 3.1 Component 1 It is the mobility component that employs both OpenStreetMap [6] and the simulator SUMO [7]. First, we create a simulation map imported from the open source geographic

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Fig. 3. Proposed Framework for the simulation of routing protocols in VANETs.

database of the world OSM (Open Street Map). This map defines the nodes, roads, lanes, junctions, and traffic lights. SUMO [7] is capable of generating realistic traces that closely represent the real movements of vehicles on the road network as follows: – The map is converted into a SUMO network file using the command line: netconvert --osm.osm -o .net.xml – Execute a collection of python command lines to generate the Sumo trace file that the network simulator NS-3 will take as an input: • Generate vehicles and their movements using the randomTrips.py tool. Python /tools/randomTrips.py -n.net.xml –r .rou.xml • Import polygons from different formats and import them into SUMO. Polyconvert --osm-files .osm --net-file .net.xml --type-file osmPolyconvert.typ.xml -o .poly.xml • Create the configuration file for sumo (sumo.cfg), which takes as input the two files created.net.xml file and the.rou.xml File. These (.sumocfg) files describe the configuration of the simulation (routes, vehicle types…), and generates the.xml trace file using the sumo command line. sumo -c sumo.cfg --fcd-output Sumotrace.xml • Generate the mobility file (.tcl) using the TraceExpoter.py tool.

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Python traceExporter.py -i Sumotrace.xml --ns2mobility-output=mobility.tcl The resulting mobility file includes information about vehicle movements over time. 3.2 Component 2 This stage makes use of the network simulator NS-3 mainly employed to model the entire protocol stack of the communication system. The network model describes both the network components and events such as nodes and links. Events such as data transmissions, reception and packet errors. The generated mobility trace file obtained from the previous component will then be fed to the NS-3 network simulator using the NS2mobilityHelper class. The steps followed in NS-3 to implement the VANET network patterns of components and configurations are shown in Fig. 3. These steps include loading the mobility file into NS-3 and configuring nodes, channels and devices. Configure routing protocols, configure tracing, run the simulation and extract the trace files. Running simulations in NS-3 generates an output data set. These collected data are used in the calculation of performance metrics by the next component (Component 3). 3.3 Component 3 This component is devoted to data collection and analysis and consists of two parts. The first part relies on extracting data from NS-3. In that regard, we suggest using a simple and reliable data extraction mechanism called the trace source/trace sink instead of processing the ASCII trace files generated by NS-3, usually requiring higher processing time and memory. Trace sources are data sources that are integrated into NS-3 models. These data are used by functions called trace sinks, which are functions created and customized by NS-3 users and act as consumers of events and data generated by trace sources. The example implemented in our study is illustrated below (Fig. 4):

Fig. 4. Trace source and trace sink functions in NS-3.

The ReceivePacket function is called each time a packet is received (attribute: Rx) at the PacketSink in the application level. The obtained data are tabulated in CSV files. The second part of this component relies on the monitoring of the network traffic in real time by calculating performance measures such as throughput, packet loss ratio, and routing overhead [4].

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– Throughput [4]: T hroughput (Kbps) =

totalRxBytes*PacketSize*8 T ∗ 1000

(1)

totalTxPackets − totalRxPackets totalTxPackets

(2)

– Packet loss ratio [4]: Packet loss ratio(% ) = 100∗ – Routing overhead: Routing overhead =

totalControlPackets totalTxPackets

(3)

4 Evaluation 4.1 Simulation Settings In the present study, we used OpenStreetMap to extract the simulation zone. The latter is imported from El Jadida city, Morocco, as shown in Fig. 5.

Fig. 5. Simulation zone from OpenStreetMap and SUMO XML file.

Our simulations are built with the NS-3 simulation environment, version 3.29. On the MAC/PHY sub layers, the 802.11p standard is used, and the channels are modelled using the YansWiFiChannel with friisLoss propagation model. The transmit power is fixed at 33 dBm, and the simulation runs for 100 s, distributing a total of 150 vehicles to the imported simulation zone. Ten source nodes generate traffic wherein the size of the packets is 1024 Bytes. The AODV routing protocol is used to route packets. Furthermore, the transport layer protocol is the user datagram protocol (UDP). The parameters are outlined in Table 1. The scenarios for simulation are split into two parts. First, all vehicles behave normally until sixty seconds of the simulation time. However, a malicious node that is the black hole attack is activated at the sixty-first second. In what follows, we present the results after monitoring the network performance with respect to throughput, packet loss ratio, and routing overhead in both simulation cases we have just explained.

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Parameter

Value

1

Simulator of network

NS3.29

2

Simulator of Mobility

SUMO-0.32.0

3

Propagation Model

friisLoss model

4

Number of vehicles

150

5

Wifi Channel

YansWifi

6

Mac and Physic Layer

IEEE 802.11p

7

Transmission power

33 dbm

8

Simulation Time

100 s

9

Packet Size

1024 bytes

4.2 Simulation Results In this section, we present the results after implementing the scenarios mentioned in the previous section with respect to the performance indicators involved in this study. The monitoring of throughput and packet loss ratio over time in both normal and black hole attacks has led to the following findings. We outline that in the absence of a black hole attacker, the network properties are relatively unchanged. The minor distinctions found are due to the highly dynamic environment and the mobility of vehicles in VANETs. Conversely, these performance indicators change dramatically whenever a black hole attack is initiated. Basically, we note that black hole attack reduces AODV performance with regard to throughput. This degradation suggests that the majority of data packets were not successfully delivered from source to destination. These aftereffects are completely rational given that when the black hole attack occurs, the rogue vehicle sends a fraudulent route reply and then deletes all data packets received by it. The findings also suggest that the packet loss ratio increases significantly once the attack begins. This is because the malicious node tries to prevent packets from reaching their destination rather than forwarding them. It is also outlined that the routing overhead increases significantly compared to normal circumstances once the attack is activated within the first 60 s of the simulation. Therefore, the AODV protocol generates more overhead during the route search and maintenance processes when it is subjected to a black hole attack. Overall, the black hole attack greatly undermines the performance of AODV and disrupts the algorithm’s proper functioning. These outcomes align with other studies that have shown that AODV is heavily impacted by the introduction of routing attacks.

5 Conclusion Black hole attack in VANETs, wherein the malicious node drops or consumes all incoming traffic, can disrupt the normal functioning of the network and prevent legitimate messages from being transmitted. The attacker may use this technique to gather sensitive

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information, disrupt communication, or prevent the network from working properly. In this study, we proposed an experimental framework that can simulate black hole attacks in real time and analyze their effects on VANET network performance. Our experimental framework is based on a more realistic environment, using realworld maps from OpenStreetMap and realistic traffic traces from SUMO. Additionally, the proposed framework allows for the modelling of the entire protocol stack for both the network components and events using the popular simulator NS-3. We also suggested an alternative option for extracting data from NS-3 using a simple and reliable mechanism called the trace source/trace sink, which is more efficient than processing NS-3 ASCII trace files. The proposed framework is also designed to monitor multiple performance metrics simultaneously. We included three main performance indicators: throughput, packet loss ratio, and routing overhead. The results suggest that this study can provide guidelines for researchers who want to analyse the effects of routing security attacks on VANETs and implement mitigation approaches. Furthermore, in-depth details about the evaluation of the proposed framework can be explored in the future.

References 1. Lee, M., Atkison, T.: VANET applications: past, present, and future. Veh. Commun. 28, 100310 (2021). https://doi.org/10.1016/j.vehcom.2020.100310 2. Fotohi, R., Nazemi, E., Shams Aliee, F.: An agent-based self-protective method to secure communication between UAVs in unmanned aerial vehicle networks. Veh. Commun. 26, 100267 (2020). https://doi.org/10.1016/j.vehcom.2020.100267 3. Safwat, M., Elgammal, A., AbdAllah, E.G., Azer, M.A.: Survey and taxonomy of informationcentric vehicular networking security attacks. Ad Hoc Netw. 124, 102696 (2022). https://doi. org/10.1016/j.adhoc.2021.102696 4. Ajjaj, S., El Houssaini, S., Hain, M., El Houssaini, M.-A.: A new multivariate approach for real time detection of routing security attacks in VANETs. Information 13, 282 (2022). https:// doi.org/10.3390/info13060282 5. Sangaiah, A.K., Javadpour, A., Ja’fari, F., Pinto, P., Ahmadi, H., Zhang, W.: CL-MLSP: the design of a detection mechanism for sinkhole attacks in smart cities. Microprocess. Microsyst. 90, 104504 (2022). https://doi.org/10.1016/j.micpro.2022.104504 6. OpenStreetMap. https://www.openstreetmap.org/. Accessed 23 Jan 2023 7. Amina, B., Mohamed, E.: Performance evaluation of VANETs routing protocols using SUMO and NS3. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pp. 525–530. IEEE, Marrakech (2018). https://doi.org/10.1109/CIST.2018.8596531 8. ns-3|a discrete-event network simulator for internet systems. https://www.nsnam.org/. Accessed 21 Sept 2021 9. Fontes, H., Cardoso, T., Campos, R., Ricardo, M.: Improving ns-3 emulation performance for fast prototyping of routing and SDN protocols: moving data plane operations to outside of ns-3. Simul. Model. Pract. Theory 96, 101931 (2019). https://doi.org/10.1016/j.simpat.2019. 101931 10. Wu, L., Wang, X.: Performance analysis of CBRP, AODV and DSR routing protocols in VANETs based on IDM-IM. In: Li, B., Shu, L., Zeng, D. (eds.) ChinaCom 2017. LNICSSITE, vol. 237, pp. 33–40. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78139-6_4 11. Hussein, N.H., Yaw, C.T., Koh, S.P., Tiong, S.K., Chong, K.H.: A comprehensive survey on vehicular networking: communications, applications, challenges, and upcoming research directions. IEEE Access 10, 86127–86180 (2022)

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12. Ajjaj, S., El Houssaini, S., Hain, M., El Houssaini, M.-A.: Performance assessment and modeling of routing protocol in vehicular ad hoc networks using statistical design of experiments methodology: a comprehensive study. ASI 5, 19 (2022). https://doi.org/10.3390/asi5010019 13. Arif, M., Wang, G., Bhuiyan, M.Z.A., Wang, T., Chen, J.: A survey on security attacks in VANETs: Communication, applications and challenges. Veh. Commun. 19, 100179 (2019). https://doi.org/10.1016/j.vehcom.2019.100179

Study and Design of a Microstrip Patch Antenna Array for 2.4 GHz Applications Amraoui Youssef1(B) , Imane Halkhams2 , Rachid El Alami1 , Mohammed Ouazzani Jamil2 , and Hassan Qjidaa1 1 LISAC Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

[email protected] 2 LSEED Laboratory, UPF, Fez, Morocco

Abstract. In this paper, we will explain the design and simulation of a microstrip patch antenna array. The purpose of this study is to improve antenna performance, such as gain, directivity, S11, and bandwidth. The design process began with the design of a single element patch antenna element that operates at 2.4 GHz. The substrate selected for this design is the Flame Retardant 4 (FR-4) Epoxy with permittivity εr = 4.4 and its height (h = 1.6 mm); the overall dimensions were calculated using the transmission line model as 29.44 mm × 38.036 mm × 1.6 mm. The single antenna resonates at 2.4 GHz with a return loss S11 equal to – 11.6603 dB, a bandwidth value of 0.07 GHz, a gain value of 4.59 dB, and a directivity value of 6.57 dB. The proposed 4 × 1 antenna array increases S11 to – 22.17 dB, gain to 6.99 dB, bandwidth to 170 MHz, and directivity to 10.27 dB at 2.4 GHz. The proposed 8 × 1 antenna array enhances gain to 9.24 dB, S11 down to – 19.03 dB, bandwidth to 129.6 MHz, and increases directivity to 13.36 dB at 2.4 GHz. The main results of the antenna design were compared in terms of gain and directivity; the results show that the designed antenna array has improved gain performance compared to the single element antenna. The proposed antenna array is designed and simulated in the Ansoft HFSS. These features make the antenna a good candidate for radio frequency identification (RFID) applications. Keywords: Microstrip antennas · Antenna array · HFSS · High gain

1 Introduction The concept of microstrip antennas was first proposed in 1953 by Deschamps [1], but it was not until the 1970s that Robert E. Munson improved it using a low loss substrate, also known as a patch antenna. Currently, microstrip antennas have become very useful in a large number of applications due to their advantages: planar configuration, low cost, low profile, light weight, etc. [2]. Microstrip antennas are widely employed in RFID (radio-frequency identification), mobile telecommunications, Global Positioning Systems (GPS), radar systems, satellite communication, surveillance systems, and radio astronomy [3]. The major problems with single antenna elements are their low gain and narrow bandwidth, which limit their wide applications. This work describes the design © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 309–317, 2023. https://doi.org/10.1007/978-3-031-29857-8_31

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and simulation of a rectangular microstrip antenna array to improve its performance over a single antenna. An antenna array is defined as the regular association of identical antennas to create radiation of a particular shape [4]. Therefore, the radiated power is great since we multiplied the number of radiating elements. Series or parallel feed (corporate feed) are two different types of existing microstrip array feeding systems [5]. This work discusses the corporate feed for antenna array design. One disadvantage of this type of feed is that it requires long transmission lines between the radiating components and the input port. Therefore, the insertion loss of the feed line can be prohibitively enormous, decreasing the overall efficiency of the array [6]. This work is structured in four sections, as listed below. In the first section, a practical introduction is given. In the second section, we detail the procedure involved in creating this proposed antenna. In the third section, the simulation results of a 4 × 1 and an 8 × 1 antenna array are discussed, along with a comparison to a single antenna. Then, in the fourth section, we conclude this paper with a conclusion.

2 Antenna Design Method 2.1 Single Antenna Figure 1 presents the geometry of a single patch antenna. It is generally made up of a radiating metal element of any shape located on the top of a dielectric substrate and a ground plane located on the underside of the substrate. The substrate is characterized by its relative dielectric constant εr (usually 1 ≤ εr ≤ 12), its height h relative to the ground plane (h « λ, 0.003λ ≤ h ≤ 0.05λ), and its dielectric loss tangent tanδ (on the order of 10–3 ) [7]. Its role is to increase the power radiated by the antenna, reduce Joule-effect losses, and improve the antenna bandwidth [7]. For our patch antenna, we use an FR4 epoxy dielectric substrate with εr = 4.4 and tanδ = 0.018. The size of the antenna is 29.44 × 38.036 × 1.6 mm3 . Based on the transmission line model, first, we need to find the dimensions of the patch so that our antenna resonates at the right frequencies. The rectangular patch antenna is defined by its length L, width W, and thickness of the substrate h. The feed line (Lf) is placed in the middle of the antenna to obtain an impedance of 50 . By considering the frequency Fr, relative permittivity r, and thickness of substrate h, Eqs. (1) and (2) are used to calculate the length and width of a single antenna patch, respectively [7].

Fig. 1. Structure of a single patch antenna.

Study and Design of a Microstrip Patch Antenna Array

L = Leff − 2L = c w= 2fr



311

c √ − 2L 2fr e

(1)

2 r + 1

(2)

where L and e denote the fringing extension length and effective permittivity, respectively, as expressed in (3) and (4).   (e + 0.3) wh + 0.262   L = 0.412.h (3) (εe − 0.258) wh + 0.813 e =

1 εr + 1 εr − 1  + 2 2 1 + 12 h

(4)

w

The width of the radiating element (patch) is represented by the letter W, whereas the letter L represents the length of the patch, fr denotes the operating frequency fr = 2.4 GHz, the letter c signifies the speed of light, which has a constant value equal to 3·108 m/s, the parameter h indicates the height of the substrate, the factor εeff is the effective permittivity of the substrate, L is the stretching distance, and the coefficients Lg and Wg are the length and width of the ground plane, respectively [8]. 2.2 Antenna Array In certain applications, one microstrip element may be sufficient to produce the necessary antenna properties. However, in other contexts, characteristics such as low cost, high directivity, high gain, or steering capability are possible only when discrete radiators are connected to produce arrays. The elements of an array can be arranged in a linear, planar, or volume array. In practice, the array type is often selected based on the desired application. The spacing between elements, excitation (amplitude and phase), half-power beamwidth, number of elements, and directivity are the most important design characteristics of any antenna array. While some of these characteristics are stated in the design method, others are determined later [7]. This antenna array is designed to improve the single antenna’s performance, especially in terms of gain and directivity. The FEM (finite element method) was used to evaluate the antenna of this design, and the excitation technique was probe feeding. The distance between two radiated elements is given by [9], 0.5 × λ ≤ d ≤ λ, where λ is the wavelength at the resonance frequency. All the patches are associated with 100  lines each [10], as shown in Fig. 2. A quarter-wave transformer is situated between a 50 proportionate point and √ a 100 line = 2 × Z0 , the [11]. Considering that one segment of the power divider is equal to Z 2 √ impedance of the power divider was calculated as z = 100 × 50 ≈ 70  [7], to obtain impedance adaptation with the 50  line feed. As a result, for an impedance of 70, the following w value was obtained: w = 1.64 mm.

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Fig. 2. Geometry of the four patch antenna array.

3 Simulation Results and Discussion 3.1 Single Antenna Figure 3 depicts the simulated and measured S11 for the suggested antenna structure. The return loss S11 of an antenna indicates how much power is reflected from the antenna. We observe that the minimum coefficient of reflection (S11) is approximately −11.6603 dB at f = 2.4 GHz, and has a bandwidth of 70 MHz, as illustrated in Fig. 3. Table 1 shows the geometrical dimensions of this antenna. Table 1. Antenna design parameters. Parameters

L

W

Lf

Wf

Ls

Ws

h

Value (mm)

29.44

38.036

6

2

76.072

58.88

1.6

Fig. 3. Parameter S11 of the single patch antenna.

The term “antenna gain” generally takes the symbol G, which explains the capability of the antenna to radiate more or less in any direction compared to an isotropic antenna; the suggested antenna has a gain of 4.59 dB, as shown in Fig. 4 (left). An antenna’s directivity (D) is determined as the power density of the antenna in its direction of maximum radiation in three-dimensional space divided by its average power density. It has to do with its radiation pattern. From Fig. 4 (right), we can say that the directivity of the proposed antenna is 6.57 dB. Directivity must always be greater than one. Through this part of the simulation, we can say that the geometry of the antenna (patch) is a very sensitive parameter in the design of the antennas because it influences all the characteristics of the radiation.

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Fig. 4. Total gain and total directivity of the single patch antenna.

3.2 4 × 1 Patch Antenna Array The proposed antenna array is composed of four patches, as shown in Fig. 2, and Table 2 summarizes the various antenna design parameters of this antenna array. Table 2. Antenna array 4 × 1 parameters. Parameters

Value (mm)

Inter-Element Spacing D

λ/4

Width of 100  Feedline

0.7

Width of 70  Feedline

1.64

Width of 50  Feedline

6.4

The same height of the substrate (h) and the same dielectric material (FR4) were used. This antenna array resonates at 2.4 GHz with a reflection coefficient of –22.1733 dB. S11 is below −10 dB from 2.3262 to 2.4962 GHz, which. results in a wide BW equal to 170 MHz, as illustrated in Fig. 5.

Fig. 5. Simulated reflection coefficient of the 4 × 1 antenna array.

Figure 6 illustrates the gain and directivity of the suggested antenna. This antenna has a maximum gain value of 6.9924 dB, and its directivity is 10.274 dB at 2.4 GHz.

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Fig. 6. Simulated gain and directivity of the proposed antenna.

The gain and directivity provided by the antenna array 4 × 1 are higher than those obtained by a single antenna. However, compared to the results produced by single antennas, this antenna array’s S11 and bandwidth are better. 3.3 8 × 1 Patch Antenna Array The proposed antenna consists of eight radiated elements (patch), as shown in Fig. 7. All eight elements are etched on the top layer of a 1.6 mm-thick FR-4 substrate (εr = 4.4, tanδ = 0.018). This antenna array resonates at 2.4 GHz with a reflection coefficient of –19.03 dB, and a bandwidth of 129.6 MHz (2.34–2.4696 GHz), as shown in Fig. 8. Figure 9 shows the gain and directivity of an eight element rectangular patch antenna design using the HFSS tool. This antenna has a maximum gain of 9.24 dB and a directivity of 13.36 dB.

Fig. 7. 8 × 1 Patch Antenna Array Design (original Image from HFSS 15.0).

Fig. 8. Return Loss for 8 × 1 Patch Antenna Array.

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Fig. 9. 3D polar plot for the 8 × 1 patch antenna array.

The following table (Table 3) is a summary of previous simulation results using the HFSS tool. Therefore, we can say that when we double the number of patches, the gain increases, the same for directivity, and the bandwidth is wider. Table 3. A comparison of designs 1 × 1, 4 × 1 & 8 × 1. Patch

1×1

4×1

Gain (dB)

4.5996

6.9924

8×1 9.24

Directivity (dB)

6.5797

10.274

13.367

Return Loss (dB)

−11.6603

−22.1733

−19.0348

Bandwidth

70 MHz

170 MHz

129.6 MHz

Table 4 summarizes the properties of our suggested antenna and other antennas available in the literature. We can verify that our proposed antenna has the highest gain and directivity when compared to those of [12–14], and [15, 16]. However, the antenna array reported in [12] has a substantially higher S11 reflection coefficient than any other antenna. Table 4. Comparison of the proposed antenna with other works. Ref. year

Freq (GHz)

Gain (dB)

Directivity (dB)

[12] 2019

2.4

7.19

11.996

−30.26

[13] 2013

2.45

3.019

5.483

−17.6

[14] 2019

2.4

7.22

7.06

−15.62

[15] 2022

2.4

2.726 dBi

–

−30

[16] 2022

2.4

5.9

–

−12.36

This work

2.4

9.24

13.367

S11 (dB)

−19.03

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4 Conclusion This work presents an antenna using one, four, and eight elements that have been designed and simulated for RFID application at 2.4 GHz. The use of an antenna array structure is addressed to improve the performance of the single antenna. The simulation results obtained for the eight element antenna array are described as follows: At 2.4 GHz, the return loss S11 is – 19.03 dB, the bandwidth is 129.6 MHz, the gain is 9.24 dB, and the directivity is 13.367 dB. These results are better than those obtained for a singular patch antenna and a four element array. It is also possible to use more than eight antenna elements to increase the gain. The suggested microstrip array antennas can be utilized in low-power, short-range communications such as Bluetooth, WiFi, wireless telephony, ZigBee, NFC, and RFID.

References 1. Giay, Y., Alam, B.R.: Design and analysis 2.4 GHz microstrip patch antenna array for IoT applications using feeding method. In: International Symposium on Electronics and Smart Devices (ISESD), pp. 1–3 (2018) 2. Tanish, N., Shubhangi, J.: Microstrip patch antenna - a historical perspective of the development. In: CONFERENCE 2013 on Advances in Communication and Control Systems (CAC2S), pp. 445–449 (2013) 3. Ogunlade, M.A., Ismael, S.E.: Analysis and design of rectangular microstrip patch antenna at 2.4 GhzWLAN applications. Int. J. Eng. Res. Technol. (IJERT) 3(8) (2014). ISSN 2278-0181 4. Barrou, O., Elamri, A., Reha, A.: Microstrip patch antenna array and its applications: a survey 15, 26–38 (2020) 5. Wang, H., Kedze, K.E., Park, I.: Microstrip patch array antenna using a parallel and series combination feed network. In: International Symposium on Antennas and Propagation (ISAP), pp. 1–2 (2018) 6. Priya, U., Sharma, V., Sharma, R.: Design of microstrip patch antenna array for WLAN application. Int. J. Eng. Innov. Technol. (IJEIT) (2012) 7. Balanis, C.A.: Antenna Theory: Analysis and Design, 3rd edn. Wiley (2005) 8. Didi, S.E., Halkhams, I., Fattah, M., Balboul, Y., Mazer, S., El Bakkali, M.: Study and design of printed rectangular microstrip antenna arrays at an operating frequency of 27.5 GHz for 5G applications. J. Nano-Electron. Phys. 13(6) (2021) 9. Khabba, A., Ibnyaich, S., Hassani, M.M.: Beam-steering millimeter-wave antenna array for fifth generation smartphone applications. In: International Conference of Computer Science and Renewable Energies (ICCSRE), Agadir, Morocco, pp. 1–5 (2019) 10. Zalki G, Bakhar M.: Design and implementation of microstrip patch antenna arrays for 2.4 GHz applications (2022). https://doi.org/10.21203/rs.3.rs-994633/v1 11. Kumar, S., Suganthi, S.: Antenna array miniaturization using a defected ground structure. Trends Sci. 19, 4634 (2022). https://doi.org/10.48048/tis.2022.4634 12. Amri, A., Mazri, T.: Study and design of an antenna array for RFID reader application in ISM band. In: 7th Mediterranean Congress of Telecommunications (2019). Art. no. 8931316 13. Ashikur, R., Asif, S., Ibnul, S.I., Asif, H.: Microstrip patch antenna design and performance analysis for RFID applications at ISM band (2.45 GHz). In: 2nd International Conference on Advances in Electrical Engineering, Dhaka, Banglade, pp. 19–21 (2013) 14. Asif, A.B., Aqeel, A., Sajjad, A.M., Saqib, H.: Comparative study of microstrip patch antenna with different shapes and its application. In: 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies – iCoMET (2019)

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15. Demirbas, G., Gocen, C., Akdag, I.: Microstrip patch 2.4 GHz Wi-Fi antenna design for WLAN 4G - 5G application. ICONTECH Int. J. 6(1), 68–72 (2022) 16. Enalume, K., Emagbetere, J., Edeko, F., Ofualagba, G., Uzedhe, G.: Design and analysis of 2.4 GHz whip antenna for tyre pressure monitoring systems 11, 105–109 (2022)

Optical Envelope Detector in Fiber Transmission and Correction Change Phase Using HPT Ahmadou Moustapha Diop1(B) , Said Mazer1 , Jean-Luc Polleux2 , Catherine Algani3 , Mohammed Fattah4 , and Moulhime EL Bekkali1 1 IASSE Laboratory, University Sidi Mohamed Ben Abdellah Fez, Fes, Morocco 2 University Paris-Est, ESYCOM (EA 2552), ESIEE, UPEM, Le Cnam, Noisy-le-Grand, France 3 Le Cnam, ESYCOM (EA 2552), Paris, France 4 IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco

Abstract. This paper presents the results of the HPT-based envelope detection system used in receivers to determine the information sent to the RoF system. The results show that the HPT 10SQXEBC can detect different types of signals without modifying the component, which constitutes a strength of the current detection system. In addition to serving as a good receiver, the HPT 10SQXEBC can correct the phase change on the signal coming from the optical input. This is a real asset compared to the HPT defined in the literature. Keywords: heterojunction phototransistor (HPT) · envelope detector (ED) · Radio over Fiber (RoF) · system of detection signal · correction change phase

1 Introduction The communication networks have to meet the requirements of various users and subscribers regarding the capacity of the number of objects connected to the network: flow of the orders of the Gbit/s, low latency and transmission/processing. And this, without delaying the huge quantity of digital data year-to-year. Systems combining optical and wireless networks have emerged (RoF) [1], this type of system has several advantages, such as the elimination of attenuation of the Hertzian wave, the rate and the reduction of latency [2–4]. Optical transmission of RF signals has drawn considerable attention to broadband gigabit wireless communications. Even before the official and popular release of the 5th generation, there was already talk of the 6th generation. Due to the frequency selective fading caused by the different signal paths in the mobile network. Efficient modulation schemes are needed. Wireless receivers generally use envelope detectors (EDs) which can be classified as commondrain, common-gate and common-source [5–7]. The envelope detector circuit is usually limited by the conversion speed and the gain. This has a very negative impact on data rate, which is an essential feature in current communication systems such as RoF [8, 9]. The optical signal from an external source transmitted in the active region of a semiconductor laser must be recovered by an optical receiver port. A high output resistor results in higher conversion gain but reduced output swing [10]. High data rate © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 318–325, 2023. https://doi.org/10.1007/978-3-031-29857-8_32

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applications require envelope detection circuits with high gain and speed to follow the high-speed signal. The input signal amplitude and output time constant determine the conversion gain and speed [10]. Simultaneously achieving high speed and conversion gain with low power consumption remains challenging. To improve the speed and conversion gain of the ED, our work on the HPT 10SQxEBC [11] proposes a new technique using HPT self-adapting output [11] and resistor after collector to have high conversion speed and gain. The authors show that the HTP design can be used as an envelope detector with the signal recovered at the output matching the shape of the input signal. We can also use it for phase change correction. The received signal must be demodulated so the receivers can use the information. In this paper, the first section describes the system used for modulation and studies the detection of the envelope of the signal going through the HPT 10SQxEBC. The latter is the subject of Sect. 2, and Sect. 3 focuses on the correction of the phase shift of the same signal so that the same transmission antenna can use it.

2 Description of the HPT 10SQxEBC Model Using in (DE) and Correct Change Phase On Fig. 1, we have a profile cup of the bipolar technology of HPT 10SQxEBC used in this work fabricated using an industrial SiGe2RF TELEFUNKEN GMBH SIGE2-RF [12]. We have modeled it with physical components based on the Model of Ebers & Moll 3, with physical components working above 10 GHz and potentially up to 60 GHz. An optical window is obtained at reducing emitter. This configuration does not need modification of the original industrial process technology. The HPT 10SQxEBC is made up of a base SiGe doped p+, collector, and emitter made up of Si-doped, respectively n- and n.

Fig. 1. HPT 10SQxEBC Profile Cut

The HPT 10SQxEBC is an HPT with a 10 × 10 µm2 optical window square (SQ) extended collector base transmitter (EBC). The HPT is developed with SiGe technology, and the optical widows is obtained by reducing the emitter. Figure 2 is the electrical representation of HPT 10SQxEBC. It comprises four parts: The substrate and optical windows represented by port with resistance. The most important part is the active area. This is the HPT and the extended area based on the Ebers &

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Moll model. The components on the active area participate more in modeling Gummel curves and S-parameter. The extended area is used to correct the Gummel plot [11, 13]. Both of them are based on Ebers & Moll model.

Fig. 2. Electrical representation of PTH 10SQxEBC

This section describes the HTP used for the detection envelope and correction change phase. The HPT 10SQxEBC study works on multiple bias points with an auto-adaptation of the output. Thus, the resistance which is added to the output of the collector can only be beneficial for the detection of an envelope by using the component described in Fig. 2.

3 Detection Envelope Simulation Using HPT 10SQxEBC This section aims to set up circuit envelope simulations using a behavioral HPT 10SQxEBC study simulation parameters, test for distortion using demodulation components and equations, and simulate the 2 GHz HPT with a pulse and sinusoidal signal operating on datasets in the frequency and time domain.

Fig. 3. Setup the Envelope Simulation

Figure 3 Presentation of the RF system behavior of the HPT model receiver for envelope detector. We use a pulse source modulation with a power P = 0 dbm, a frequency

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f = 2 GHz and a 50- resistance on the collector to adapt the output. We use a controller set to a frequency of 2 GHz with an optical power Popt = 1 mw and a period of 100 ns. Vout (t) = V (t) ∗ e−j2π f

(1)

Figure 3 is used to modulat optical signal combine with sinusoidal signal and pulse carrier. The modulation can define by Eq. 1, where v(t) can be write like A*sin(ωt + ϕ) sinusoidal signal and pulse carrier are both injecting in HPT 10SQxEBC and we collect output signal on collector of HPT.

(a) Envelope of output signal

(b) Temporary represent of output signal

(c) envelope of output signal following input signal

Fig. 4. a. Envelope of output signal b. Temporary represent of output signal c. envelope of output signal following input signal

Vout defines the output signal after the detector. Figure 4.a and Fig. 4.c shows the input and output signal envelope. We can see that they have the same template. The HPT can be used to detect signal modulated with a pulse. Figure 4.b shows a time representation of Vout . We can observe that the amplitude of the output signal is less than the input pulse; this results from HPT gain [19]. The signal takes time to reach its maximum amplitude and has the same behavior to reach 0. This is explained by the source of modulation that is taken in such a way to be the closest to reality. The model perfectly detected input signals. We can use it for more complex signal detection. In Fig. 5, we define the modulated signal by adding a sinusoid on the optical input of 0.89 V (HPT bias point) with the optical power. On this basis, we inject a pulse

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of 2 nsec width on a period of 5 nsec. The simulation is done on 20 nsec to see the output signal’s behavior over several periods.

Fig. 5. Setup the Envelope Simulation using sinusoid in optical windows

This scheme was defined to study the effect of the change phase on HPT. But before that, we study the output signal envelope to confirm the results in Fig. 4.

(a) Input signal combine sinusoid

and optical

(b) Input signal pulse on base

(c) Envelope detected by HPT

Fig. 6. a. Input signal combine sinusoid and optical b. Input signal pulse on base c. Envelope detected by HPT

In Fig. 6a. we observe the shape of the input signal on the optical input in the sinusoidal form, which is the sum of a sinusoidal signal and an amplitude. On the base,

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we have Fig. 6b the output signal on Fig. 6c illustrates the envelope of the output signal. After the first pulse, we observe the rise of the pulse in two stages. This is explained by the residues of the optical power represented by a continuous signal that adds to the sinusoid and creates a kind of pulse before being attenuated in the HPT.

4 Correction Change Phase Using HPT 10SQxEBC This section studies the phase change introduced on the Vin signal. We observe the phase of the envelope of the output signal to prove the correction of the phase change on the input signal. The studies in this section are based on the pulse modulation in Fig. 5. This study also allows us to exploit some of possibilities offered by the electrical model of the PTH 10SQxEBC since we can study the phase correction without making a physical assembly.

Fig. 7. Phase of the output signal

The incoming signal on the optical input has been changed phase with several phases, but we continue to observe the same phase on the output signal, as seen in Fig. 7. The output signal studied is the signal detected by the HPT after the envelope detection using Fig. 5. Figure 6.c shows the signal detected over a period of 5 ns. We injected the same signal with a phase shift of 5, 10, 30, and 60° to see if the HPT can correct the phase shift applied to the incoming signal. In Fig. 7, the phase represents the superposition of the different output signals here we have applied a phase shift to the input. In Fig. 6.c., the input signal reaches its maximum value in Fig. 6.a. There is also a change in level at 2, 7, 12 nsec which is when the pulse reaches zero which explains the change in phase. But since all the phase shifted signals give the same output the HPT can be used for phase correction, so it can also be used to detect signals transmitted by antennas that carry phase shifted signals.

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5 Conclusion In this paper, we present the results on the modulation based on the electrical diagram of the HPT 10SQXEBC and the phase change correction on the optical input signal. We conclude that the HPT 10SQXEBC can be an excellent electronic alternative to the system. Preliminary results show that the HPT 10SQXEBC has strong potential on the receiving system and phase correction. However, the gain remains low, which is the subject of another study focusing on the possibility of making amplifiers from this HPT.

References 1. Kitayama, K.-I., et al.: Coherent radio-over-few-mode-fiber. In: 2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR), pp. 1–3 (2017). https://doi.org/10.1109/CLE OPR.2017.8118779 2. Bikmetov, R., Raja, M.Y.A., Kazi, K., Ibragimov, R., Chowdhury, B., Enslin, J.: A perspective of RoF implementation for an advanced Smart Metering Infrastructure. In: 2015 North American Power Symposium (NAPS), pp. 1–4 (2015). https://doi.org/10.1109/NAPS.2015. 7335255 3. Al-Noor, M., Loo, K.-K.J., Comley, R.: 120 mbps mobile WiMAX scalable OFDMA signal transmission over RoF with SMF, DCF and chirped FBG for fibre length of 792 km. In: 2010 6th International Conference on Wireless and Mobile Communications, pp. 373–377 (2010). https://doi.org/10.1109/ICWMC.2010.102 4. Tokita, K., Hata, K., Fujimoto, H., Hori, Y.: Sensorless vehicle detection using voltage pulses with envelope model for in-motion wireless power transfer system. In: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, pp. 4329–4334 (2019). https:// doi.org/10.1109/IECON.2019.8927594 5. Moazzeni, S., Sawan, M., Cowan, G.E.R.: An ultra-low-power energy-efficient dual-mode wake-up receiver. IEEE Trans. Circuits Syst. I Regul. Pap. 62(2), 517–526 (2015). https:// doi.org/10.1109/TCSI.2014.2360336 6. Zgaren, M., Sawan, M.: A low-power dual-injection-locked RF receiver with FSK-to-OOK conversion for biomedical implants. IEEE Trans. Circuits Syst. I Regul. Pap. 62(11), 2748– 2758 (2015). https://doi.org/10.1109/TCSI.2015.2477577 7. Huang, X., Harpe, P., Dolmans, G., de Groot, H., Long, J.R.: A 780–950 MHz, 64–146 µW power-scalable synchronized-switching OOK receiver for wireless event-driven applications. IEEE J. Solid-State Circuits 49(5), 1135–1147 (2014). https://doi.org/10.1109/JSSC.2014. 2307056 8. Cha, J., et al.: A highly-linear radio-frequency envelope detector for multi-standard operation. In: 2009 IEEE Radio Frequency Integrated Circuits Symposium, pp. 149–152 (2009). https:// doi.org/10.1109/RFIC.2009.5135510 9. Chen, S.-E., Yang, C.-L., Cheng, K.-W.: A 4.5 µW 2.4 GHz wake-up receiver based on complementary current-reuse RF detector. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1214–1217 (2015). https://doi.org/10.1109/ISCAS.2015.7168858 10. Fu, X., El-Sankary, K., Zhou, J.: A high speed, high conversion gain RF envelope detector for SRO-receivers. In: 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1029–1032 (2019). https://doi.org/10.1109/MWSCAS.2019.888 4942 11. Bennour, A., Polleux, J.L., Algani, C., Tegegne, Z.G., Mazer, S.: Electrical compact modeling of SiGe phototransistor: impact of the distributed nature on dynamic behavior. IEEE Trans. Electron Devices 67(3), 1034–1040 (2020). https://doi.org/10.1109/TED.2020.2966057

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12. Rosales, M.D., Polleux, J.-L., Algani, C.: Design and implementation of SiGe PTHS using an 80 GHz SiGe bipolar process technology. In: 8th IEEE International Conference on GROUP IV Photonic, pp. 243–245 (2011) 13. Bennour, A., Tegegne, Z.G., Mazer, S., Polleux, J.L., El Bekkali, M., Algani, C.: Largesignal static compact circuit model of SiGe heterojunction bipolar phototransistors: effect of the distributed nature of currents. IEEE Trans. Electron Devices 65(3), 1029–1035 (2018). https://doi.org/10.1109/TED.2017.2788447

Performance of BPSK-FSO Communication Over Turbulence and Fog Attenuation Abdeslam Fakchich1 , Mohamed Bouhadda2(B) , Rachid El Alami1 , Fouad Mohammed Abbou3 , Abdelouahed Essahlaoui2 , Mohammed El Ghzaoui1 , Hassan Qjidaa1 , and Mohammed Ouazzani Jamil4 1 Sidi Mohammed Ben Abdellah University, Fez, Morocco 2 Engineering Sciences Laboratory (LSI), Multidisciplinary Faculty, Sidi Mohammed Ben

Abdellah University, Taza, Morocco [email protected] 3 School of Sciences and Engineering, Al Akhawyen University, Ifrane, Morocco 4 LSEED Laboratory, UPF, Fez, Morocco

Abstract. Free space optical communication is a technology that uses optical signals to transmit data between transmitters and receivers. In this paper, we analyze the performance of optical wireless transmission using heterodyne binary phase-shift keying (BPSK) modulation. The system is operating under various atmospheric channel effects. More precisely, the combined effects of atmospheric turbulence and fog. Atmospheric turbulence induces intensity fluctuations, and fog causes losses in signal power. To evaluate the optical wireless performances, we derived a mathematical expression of the bit error rate (BER) that combines fog attenuation and turbulence fading. We used a gamma-gamma distribution for modeling atmospheric turbulence fading and Meijer’s G-function for approximating complex calculus. We carried out numerical simulations of the bit error rate for different link distances, Rytov variances, and transmitted powers. The results show that the effects of turbulence and fog are very important for strong turbulence, and these effects limit link transmission. According to the simulation results, it is clear that we can decrease the BER and increase the link communication by raising transmitted power. Keywords: Optical wireless · Atmospheric turbulence · Fog attenuation · BPSK modulation

1 Introduction Recently, the evolution of communication networks, namely, 5th generation cellular networks and the Internet of Things, has caused an exponential increase in the demand for a high data transmission rate [1]. Most existing wireless communication systems use the propagation of radio waves in the atmosphere to transmit data. However, with this demand for a high data rate in wireless communications, the radio frequency spectrum will not be able to support broadband transmission. Optical wireless communication is a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 326–334, 2023. https://doi.org/10.1007/978-3-031-29857-8_33

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technology that uses light propagating through the atmosphere to transmit data between transmitters and receivers. The laser signal provides tremendous data rates in the optical fiber link. However, optical fiber deployment and maintenance after installation are very expensive. Consequently, it is very important to also consider the optical spectrum for wireless communication networks. Seen in this light, the use of optical wireless communication technology has been the subject of much research because it offers a high transmission capacity similar to that of optical fiber [2]. The remarkable advantages of this wireless technology are unlicensed transmission, absence of interference, simple deployment, and high security [3]. Low power consumption and sufficient spectral resources [2] distinguish communication systems based on free space optics. In recent years, the capacity of wireless optical data transmission has greatly increased. The combination of 16 QAM dual-polarization modulation and reception with soft-decision forward error correction allowed a laser communication link with 53 WDM channels to transmit data at a rate of 200 gigabits per second in each channel [4]. The FSO is an emerging communication technology that uses a light-emitting-diode to generate infrared and visible light waves in the band (390–750 nm) for data transmission in indoor optical wireless. In addition, the FSO uses the laser in the near-infrared band (750–1600 nm) for a terrestrial optical network, intersatellite, ground-to-air, and satellite-to-ground. Free space laser communication has tremendous benefits over optical fiber and radio frequency transmissions; however, atmospheric channel effects degrade the FSO system performance. In particular, air turbulence is due to irregular changes in atmospheric temperature and pressure. This turbulence causes a random fluctuation of the atmospheric refractive index, which induces phase fluctuation and intensity fading. The phase fluctuation is modeled as temporal pulse broadening, which causes intersymbol interference that increases the BER of the FSO system [5, 6]. Pulse broadening limits the link and the capacity communication of the FSO system [7]. The intensity fading is modeled statistically by different probability density functions. The log-normal distribution is used in the case of weak turbulence. For weak, moderate, and strong atmospheric turbulence, the FSO link can be modeled by distributions such as Gamma-Gamma, Malaga, and Weibull. The performance of FSO link communication is also degraded by optical signal attenuation due to propagation in the atmosphere. This attenuation is the result of the absorption of electromagnetic energy by gases and particles constituting the atmosphere [8]. Among the meteorological atmospheric conditions, fog affects the performance of optical free-space link transmission by causing different scatterings. Physically, fog is formed by condensation of water vapor near the ground, and this vapor is in the shape of very small diameter dimensions. The concentration of particles in fog is lower than that in clouds. The particles of fog are numerous enough to give an opaque appearance to the atmosphere, which drastically reduces visibility. In optical wireless communication, the transmitted data can be modulated by several formats such as OOK (On-Off Keying), BPSK (Binary Phase-Shift Keying), QPSK (Quadrature PSK), DPSK (Differential PSK), and 8-PSK. These modulation techniques are used for a FSO channel, which is assumed to be memoryless and stationary with

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additive white Gaussian noise (AWGN). In this paper, we focus our work on BPSK modulation because the FSO system presents the best BER performance compared with other FSO systems using OOK, QPSK, DPSK, and 8-PSK. BPSK has the lowest power penalty for a reference BER of 10–9 compared to these latter modulation techniques [9]. The BPSK modulation technique is more suitable for use in wireless optical communication with a turbulent channel. The rest of the document is organized as follows. In section two, we present the system and atmospheric channel model. In section three, we derive a mathematical model of fog attenuation. In the fourth section, we model the optical wireless bit error rate due to the combined effects of atmospheric turbulence and fog attenuation. We devote the fifth section to discuss and analyze the simulation results. Finally, we conclude the work.

2 System and Channel Model We consider a terrestrial optical wireless system with BPSK modulation and heterodyne detection. We assume that the communication channel is memoryless and stationary with additive white Gaussian noise (AWGN). The received signal is given as: y = hx + n

(1)

where x is the transmitted power, h is the channel fading, and n is the additive Gaussian noise. The channel state is considered as the product of two factors h = hl ha , where hl is the deterministic path loss and ha is the random optical signal attenuation due to atmospheric turbulence. The probability density of the Gamma-Gamma turbulence model is expressed as [10]: (2) is the Gamma function [11], Kυ (.) is the υth-order modified Bessel function where of the second kind [11]. A and β are the parameters of scintillation and are expressed as [10]: ⎡

⎛

⎞

⎤−1

⎥ ⎢ ⎜ ⎟ ⎥ ⎢ ⎜ ⎟ 0.49σR2 ⎜ ⎢ ⎟ − 1⎥ α = ⎢exp⎜  7 ⎟ ⎥ 12 6 ⎠ ⎦ ⎣ ⎝ 1 + 1.11σR5 ⎡

⎛

⎞

(3)

⎤−1

⎥ ⎢ ⎜ ⎟ ⎥ ⎢ ⎜ ⎟ 0.49σR2 ⎜ ⎢ ⎟ − 1⎥ α = ⎢exp⎜  7 ⎟ ⎥ 12 6 ⎠ ⎦ ⎣ ⎝ 1 + 1.11σR5

(4)

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The parameter σR2 is the Raytov variance, which is a measure of the strength of turbulence fluctuations [12]. It is expressed by [10]: σR2 = 1.23Cn2 K 7/6 z 11/6

(5)

where k = 2π/λ is the optical wavenumber, Cn2 is the refractive index structure, which is constant for a horizontal link, λ is the wavelength, and z is the link distance [12].

3 Fog Attenuation The atmospheric attenuation loss is mathematically modeled by the exponential BeersLambert low as [13]: hl = exp(−Az)

(6)

where A is the attenuation coefficient and z is the link distance. The loss depends on atmospheric visibility, which is defined as the distance where the optical signal of wavelength 550 nm is attenuated to a fraction of 5% or 2% of its initial power [14]. The mathematical model that expresses the relationship between attenuation and visibility was developed by Kruse as [15]:  λ −q 3.912 (7) A= V 0.55 where V is the atmospheric visibility and q is the parameter related to the particle size distribution in the atmosphere. The parameter q was determined based on the Kruse model, which was modified by Kim as follows [16]: ⎧ ⎪ 1.6 if V > 50 km ⎪ ⎪ ⎪ ⎪ 1.3 if 6 km < V < 50 km ⎨ (8) q = 0.16 V + 0.34 if 1 km < V < 6 km ⎪ ⎪ ⎪ V − 0.5 if 0.5 km < V < 1 km ⎪ ⎪ ⎩ 0 if V < 0.5 km

4 Average BER in the Presence of Atmospheric Turbulence and Fog. The bit error rate of BPSK modulation is modeled as [9]:   1 P(e|h) = erfc SNR(h) 2

(9)

where SNR(h) is the electrical signal-to-noise ratio, which is expressed by SNR(h) = 2RPr ha 2 qB , with Pr being the received power, q being the electron charge, R being the receiver responsivity and B being the bandwidth.

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In the presence of attenuation due to fog, the received power is expressed by Pr = Pt e−Az , with Pt being the transmitted power. Then, the signal-to-noise ratio is given by: SNR(h) =

2RPt e−Az ha 2 qB

(10)

The BER of an optical wireless link in the presence of atmospheric turbulence is expressed as: ∞ P(e) =

P(e|ha )fha (ha )dha

(11)

0

By substituting (9) and (2) in (11), the average BER can be expressed as: α+β

(αβ) 2 P(e) = (α)(β)

∞

α+β

ha 2

−1

erfc

    2μTh2a Kα−β 2 αβha dha

(12)

0

  2RP h2 where μ is the average signal-to-noise ratio, which is given as μ = E qBs a = 2RPt  2  qB E ha . To simplify the calculation, we express Kα−β in terms of the hypergeometric Meijer’s m,n [11]. Then (12) can be expressed as: G-function Gp,q P(e) =

α+β

2 √(αβ) 2 π(α)(β)

∞

α+β 2 −1

∫ ha 0



2,0 G0,2

− αβha |  α−β   β−α  , 2 2  ! 1 2,0 G1,2 2μTh2a d ha 0, 21

 (13)

By using the equality (21) in [17], we can calculate the integral in (13). Then the average BER can be expressed as:   1−α 2−α 1−β 2−β 16μ 2α+β−3 2,4 2 , 2 , 2 , 2 ,1 G5,2 P(e) = 3 (14) αβ(α + 1)(β + 1) 0, 21 π 2 (α)(β)

5 Numerical Results and Discussion We carried out the simulation results using MATLAB software. We considered a turbulent channel with AWGN noise in the presence of fog attenuation. The system parameters used in this section are listed in Table 1.

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Table 1. System parameters. Parameter

Value

Wavelength (λ)

1550 nm

Receiver optical efficiency

0.75

Transmitter optical efficiency

0.75

Responsivity

0.85 A/W

Transmitting divergence angle θ

2.10–3

The photodiode bandwidth

2.5 GHz

The photodiode load resistor

1 k

The average BER versus link distance for various values of the turbulence strength, is depicted in Fig. 1. In addition to the turbulence, we consider the signal attenuation due to fog, and we set the visibility value to 8 km. We use different values of Rytov variance (0.4, 1, 2), which represent weak, moderate, and strong turbulence, respectively. The curves in this figure show that the average BER increases with link distance, and the increase in BER is very important for strong turbulence (σR2 = 2). For weak turbulence (σR2 = 0.4), it is possible to achieve a longer link distance with a bit error rate lower than the reference 10−9 .

Fig. 1. Average BER versus link distance for V = 8 km

Next, we assume a visibility of 4 km to investigate the effect of fog on optical wireless communication. Figure 2 presents the variation in the average BER versus the link distance for various values of the Raytov variance. This figure shows that the effect of fog on FSO communication is very significant, and the BER increases significantly compared to its increase in Fig. 1.

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Fig. 2. Average BER versus link distance for V = 4 km

Figure 3 presents the average BER versus transmitted power for different link distances. The Rytov variance is assumed to be constant and equal to σR2 = 0.6, which represents moderate turbulence. It is clear that by increasing the transmitted power, the average BER decreases. The curves in this figure show that as the link distance increases, more power is needed to achieve a reference bit error rate of 10–9 . The use of a laser producing pulses with a power greater than 50.5 mW is required for transmitting data over an optical wireless link distance of 9 km. However, it is important to consider the effects of high laser power on human eyes and to design short-link wireless optical networks when transceivers are close to homes and public places. The increase in optical power can be

Fig. 3. Average BER versus transmitted power

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achieved using a laser with an optical wavelength of 1550 nm because the absorption for this wavelength by the eye is made by the cornea and not by the retina [18].

6 Conclusion We investigated the BER performance of an optical wireless communication system with BPSK modulation and heterodyne detection. We derived an expression of the bit error rate of an optical link operating over atmospheric turbulence in the presence of the fog effect. A fading distribution including the combined effects of fog and atmospheric turbulence is analytically derived by exploiting Meijer’s G-function. The simulation results show that the combined effects of fog and turbulence limit the link distance, increase the BER and degrade the system performance. It is possible to decrease the BER and achieve a longer link by increasing the transmitted power. The 1550 nm wavelength laser pulses are used to increase power while maintaining user safety.

References 1. Kulshreshtha, P., Garg, A.K.: Managing 5G networks - a review of FSO challenges and solutions. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–4 (2020). https://doi.org/10.1109/ICCCNT49239.2020.922 5591 2. Li, C.-Y., et al.: A flexible bidirectional fiber-FSO-5G wireless convergent system. J. Light. Technol., 1–1 (2020). https://doi.org/10.1109/JLT.2020.3037943 3. Aljohani, A.J., Mirza, J., Ghafoor, S.: A novel regeneration technique for free space optical communication systems. IEEE Commun. Lett. 25, 196–199 (2021). https://doi.org/10.1109/ LCOMM.2020.3029591 4. DLR and ADVA set a new world record. https://www.dlr.de/content/en/articles/news/2018/ 2/20180510_dlr-and-adva-set-a-new-world-record-in-optical-free-space-data-transmission_ 27323.html 5. Bouhadda, M., Abbou, F.M., Serhani, M., Chaatit, F., Abid, A., Boutoulout, A.: Temporal pulse broadening due to dispersion and weak turbulence in FSO communications. Optik 200, 163327 (2020). https://doi.org/10.1016/j.ijleo.2019.163327 6. Bouhadda, M., Abbou, F.M., Serhani, M., Chaatit, F., Boutoulout, A.: Analysis of dispersion effect on a NRZ-OOK terrestrial free-space optical transmission system. J. European Optical Society-Rapid Publications 12(1), 1–6 (2016). https://doi.org/10.1186/s41476-016-0020-x 7. Demir, P., Yılmaz, G.: Investigation of the atmospheric attenuation factors in FSO communication systems using the taguchi method. Int. J. Opt. 2020, 9038053 (2020). https://doi.org/ 10.1155/2020/9038053 8. Demir, P., Yılmaz, G.: Investigation of the atmospheric attenuation factors in FSO communication systems using the taguchi method. https://www.hindawi.com/journals/ijo/2020/903 8053/. Accessed 10 Jan 2021. https://doi.org/10.1155/2020/9038053 9. Choyon, A.K.M.S.J., Chowdhury, R.: Performance comparison of free-space optical (FSO) communication link under OOK, BPSK, DPSK, QPSK and 8-PSK modulation formats in the presence of strong atmospheric turbulence. J. Opt. Commun. 1, (2020). https://doi.org/ 10.1515/joc-2019-0250 10. Odeyemi, K.O., Owolawi, P.A., Srivastava, V.M.: Optical spatial modulation over gammagamma turbulence and pointing error induced fading channels. Optik 147, 214–223 (2017). https://doi.org/10.1016/j.ijleo.2017.08.086

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11. Gradshte˘ın, I.S., Ryzhik, I.M., Jeffrey, A.: Table of Integrals, Series, and Products. Academic Press, Amsterdam; Boston (2007) 12. Andrews, L.C., Phillips, R.L., Hopen, C.Y.: Laser beam scintillation with applications. SPIE, 1000 20th Street, Bellingham, WA 98227–0010 USA (2001). https://doi.org/10.1117/ 3.412858 13. Sandalidis, H.G., Tsiftsis, T.A., Karagiannidis, G.K.: Optical wireless communications with heterodyne detection over turbulence channels with pointing errors. J. Light. Technol. 27, 4440–4445 (2009). https://doi.org/10.1109/JLT.2009.2024169 14. Grabner, M., Kvicera, V.: The wavelength dependent model of extinction in fog and haze for free space optical communication. Opt. Express. 19, 3379–3386 (2011). https://doi.org/10. 1364/OE.19.003379 15. Kruse, P.W., McGlauchlin, L.D., McQuistan, R.B.: Elements of Infrared Technology: Generation, Transmission, and Detection. John Wiley (1963) 16. Kim, I.I., McArthur, B., Korevaar, E.J.: Comparison of laser beam propagation at 785 nm and 1550 nm in fog and haze for optical wireless communications. Presented at the Information Technologies 2000 , Boston, MA February 6 (2001). https://doi.org/10.1117/12.417512 17. Adamchik, V.S., Marichev, O.I.: The algorithm for calculating integrals of hypergeometric type functions and its realization in REDUCE system. In: Proceedings of the international symposium on Symbolic and algebraic computation, pp. 212–224. Association for Computing Machinery, New York, NY, USA (1990). https://doi.org/10.1145/96877.96930 18. Bloom, S., Korevaar, E., Schuster, J., Willebrand, H.: Understanding the performance of free-space optics [Invited]. J. Opt. Netw. 2, 178–200 (2003)

Digital Transformation and E-Learning

The Role of Digitalization in Achieving Cybersecurity in the Moroccan Banking System Hadj Ali Abdellah(B) and Bouchra Benyacoub LIREFMO Laboratory, Sidi Mohamed Ben Abdellah University, Dhar El Mahraz, Fez, Morocco [email protected]

Abstract. The present study aims to identify both the process of digitalization of the banking system and to test the impact of digitalization on the realization of cybersecurity in the Moroccan banking system and operations, by studying the dimensions of digitalization, which are obviously represented in its impact on achieving cybersecurity in the banking system. Indeed, the banking systems of our modern era are facing complex cyberattacks, which are difficult to address with traditional means of protection, which is why various banks are resorting to the use of cybersecurity systems based on artificial intelligence aiming at securing their assets and their infrastructure to reduce the potential risk level of risks. To implement the objectives of this study, we test the impact of digitization on achieving cybersecurity in the banking system. We studied the subvariables that facilitate the process of measuring the effectiveness of each variable, and tested the impact of digitization on achieving cybersecurity in the Moroccan banking system. This study enabled us to confirm that digitization has a major role in achieving cybersecurity for the Moroccan banking system. The analysis of this article was performed using the Statistical Package for the Social Sciences (SPSS V. 26) for basic descriptive statistics and (Smart PLS 3.2.7) for SEM-PLS modelling. Keywords: Digitalization · Cybersecurity · SEM-PLS modelling

1 Introduction The banking sector is considered to be a vital and active sector for the natural development of digitalization. This digital transformation will surely accelerate the growth and productivity of banking services to face financial innovations and specific changes such as the internationalization of markets, the advent of digital distribution channels and changes in consumer behavior. When we deal with digitalization, we are talking about the process of digital transformation or transition to the use of the internet in all traditional banking programs and activities that were available to customers within the various branches of the bank. To add, digitalization is also linked to the management of bank accounts and electronic banking services (Digital Banking). In our study, we have taken digitalization as an independent variable to study its effects on cybersecurity, which has been taken as a dependent variable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 337–347, 2023. https://doi.org/10.1007/978-3-031-29857-8_34

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The aim of his study is to try to explain the effective role of cybersecurity in the Moroccan banking system, As well as to analyse ways to achieve this information security in the Moroccan banking system. Moreover, the discussion about this topic is of great importance saying that cyber-attacks have become very sophisticated and are characterized by a level that exceeds traditional defensive capabilities. To clarify, here we are talking about attacks targeting data and valuable assets, the secrets of individuals and organizations. Hence, among the recent studies that have discussed the role of globalization and artificial intelligence in achieving cybersecurity, we find a study that was carried out on a large number of cybersecurity heads. The latter has confirmed that this security can be achieved by both increasing globalization and artificial intelligence [1, 2]. Another study has proven d that traditional methods are not able to achieve cybersecurity [3]. First, I will present the literature review and Hypothesis. Additionally, I will explain the research problem, the analysis model, the statistical analysis, the measurement model and the structural model assessment. Finally, I will address the discussion and conclude with a clear conclusion.

2 Literature Review The issue of digitalization and cybersecurity has always been the subject of many studies and analyses in many journals and books. Here are some of these previous studies: – Hélène Lavoix, Revisiting the idea of cybersecurity for the digital world of the 21st century global Security 2019/3 (N° 19), pages 27 to 32: This study shows us how digitalization will lead to cybersecurity at the level of organizations [4]. – Michel Derdevet, cybersecurity and electrical networks (This study is about the role of digitalization and renewable resources to achieve cybersecurity for the future [5]. – Dilek et al., 2015: The study analysed the ability of digitization to strengthen cybersecurity within institutions [6].

3 Hypothesis As the main hypothesis, we assume that digitalization plays a major role in the success of cybersecurity operations in the Moroccan banking system.

4 Research Problem – The tremendous development that the world is witnessing today in the digital field has influenced the banking sector in particular. The problem of this study was posed in the form of the following questions: – The following variables, flexibility, digital process and digital strategy, contribute to promoting the operation of digitalization in Moroccan banks?

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– Do the following variables, Organizational Resilience, Phishing and Artificial intelligence, lead to the success of the cybersecurity of the Moroccan banking system? – Are there correlations between the digital transformation of banking operations and strategic financial performance? – Is there an impact of the digital transformation of banking operations on the cybersecurity of the Moroccan banking system?

5 Analysis Model The present research was conducted by adopting a hypothetical-deductive methodology.

Fig. 1. The variables of the empirical study

6 Conceptual Model The selected variables are based on the interview guide, the theoretical aspect and the literature review. The choice of these variables is justified by the integrative model of Engel, Blackwell and Kollat (1990), which explains the behavior of the consumer taking into account the whole process of decision making and the environmental factors that are likely to influence it [7]. Antonides & Van Raaij (2011) find that this model has the merit of integrating many works of research dealing with the dynamics of consumer behavior, which has allowed us to have a coherent and structured vision of all the variables that must be taken into account[8]. These similar variables were subsequently assessed in our questionnaire survey. In the study of attitudes and behavior, we opted for the 5-point Likert attitude scale [9] as a measurement scale (totally agree, agree, neutral, disagree, totally disagree) at the level of our questionnaire. In this respect, the choice of this scale is justified not only by its simplicity but also by its ability to assess the attitudes of respondents by measuring the intensity of their opinions and perceptions [10].

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7 Statistical Analysis This study employed the structural equation modelling (SEM) approach with partial least squares as an analytical tool (PLS). PLS studies psychometric traits and provides evidence for the existence or absence of associations [11]. Smart PLS 3.2.7 was used to analyse the data in this investigation in two phases. The first step measurement model tests the content, convergent, and discriminant validity of structures. At the same time, in the second step, the structural model and hypotheses are tested.

8 Measurement Model The measurement model helps to establish the reliability and validity of all constructs used in the proposed model. Hair et al. (2017) recommended that indicators with loadings below 0.40 should be dropped to allow for a better average variance extracted (AVE) and composite reliability (CR). No indicators were dropped from the model as shown in Table 1 and Fig. 1. Table 1. Measurement model assessment (internal consistency and convergent validity) Variable

Construct

Item

Loading

CR

AVE

Digitalization

Flexibility

V1

0.778

0.767

0.523

V2

0.687

V3

0.702

V4

0.719

0.754

0.506

V5

0.673

V6

0.74

V7

0.762

0.764

0.521

V8

0.651

V9

0.747

V10

0.724

0.756

0.508

V11

0.672

V12

0.742

V13

0.728

0.76

0.514

V14

0.669 0.788

0.555

Digital process

Digital strategy

Cybersecurity

Organizational Resilience

Phishing

Artificial intelligence

V15

0.751

V16

0.753

V17

0.692

V18

0.786

The values of composite reliability should be greater than 0.7, while the accepted values of AVE are the values above 0.5. These results indicate that the study satisfied

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these requirements for convergent validity and internal consistency of the scales. Furthermore, assessing the discriminant validity is done through the Fornell–Larcker criterion. Fornell– Larcker criterion required that each composite AVE square root on the diagonal element should be greater than the correlations between the constructs [12], as this was the case, which implies that this discriminant validity criterion was satisfied as in Table 2. Table 2. Measurement model assessment (Discriminant validity) Artificial intelligence

Digital process

Digital strategy

Flexibility

Organizational Resilience

Artificial intelligence

0.745

Digital process

0.578

0.711

Digital strategy 0.604

0.573

0.722

Flexibility

0.512

0.481

0.6

0.723

Organizational Resilience

0.556

0.562

0.6

0.625

0.713

Phishing

0.605

0.614

0.632

0.594

0.62

Phishing

0.717

9 Structural Model The structural model was evaluated for hypothesis testing. All hypotheses were tested on the basis of path coefficients (β), coefficient of determination (R2 ), Cohen’s effect sizes (f2 ), level of significance (p and t-values), and Stone- Geisser’s predictive relevance (Q2 ) (Hair and al. 2017). Furthermore, the structural analysis of the current study has shown that digitalization has a statistically significant positive effect on cybersecurity since it has a high Cohen’s effect size (f2 = 2.167). Furthermore, digital process, digital strategy and specialization have statistically significant positive effects on cybersecurity since = 0.347, 0.336, and 0.307 respectively, with moderate effect sizes (f2 = 0.248) for Digital process, (f2 = 0.195) for Digital strategy, and (f2 = 0.185) for flexibility, so the second, third, and fourth hypotheses are accepted. (β = 0.827, t = 37.218, P < 0.001, 95% CI for β = [0.777, 0.866]), so that the first hypothesis is accepted. Approximately 68% of the variation in cyber security is explained by the variation in cyber security (Figs. 2, 3, 4, 5, 6). Moreover, digitalization has a statistically significant positive effect on the dimensions of cybersecurity with high explained variance and effect size for Artificial intelligence (β = 0.675, R square = 0.456, f square = 0.837), Organizational Resilience (β = 0.713, R square = 0.508, f square = 1.034), and Phishing (β = 0.733, R square = 0.538, f square = 1.164), so hypotheses 5, 6, and 7 are supported. In addition, Digital process, Digital strategy and Flexibility have statistically a significant positive effect on

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Fig. 2. Measurement model

Fig. 3. Structural Model (Main hypothesis)

Fig. 4. Structural model (Subhypothesis)

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Fig. 5. Structural model (Subhypothesis)

Fig. 6. Structural model (Subhypothesis)

Artificial intelligence since (β = 0.313, 0.326, 0.167), respectively, with small effect size as (f2 = 0.118) for Digital process, (f2 = 0.107) for Digital strategy and (f2 = 0.032) for Flexibility, so that the eighth, ninth and tenth hypotheses are accepted. In addition, Digital process, Digital strategy and Flexibility have statistically a significant positive effect on Organizational Resilience since (β = 0.25, 0.247, 0.357), respectively, with small effect size as (f2 = 0.083) for Digital process, (f2 = 0.068) for Digital strategy and moderate effect (f2 = 0.16) for Flexibility, so that the 11th, 12th and 13th hypotheses are accepted. Finally, Digital process, Digital strategy and Flexibility have statistically a significant positive effects on Phishing since = 0.32, 0.289, 0.27, respectively, with small effect sizes of (f2 = 0.143) for Digital process, (f2 = 0.098) for Digital strategy and (f2 = 0.097) for Flexibility; thus, the 14th, 15th and 16th hypotheses are accepted. All values of the variance inflation factor (VIF) were below 5, indicating the absence of a collinearity problem.For this reason, We evaluated predictive relevance by assessing Stone-Geisser’s Q2 blindfolding, which is a sample reuse technique that can be used to calculate Q2 values for latent variables. In this respect, We executed the blindfolding procedure and calculated the Q2 values for all variables, which are greater than zero, indicating predictive relevance for endogenous latent variables in our PLS path model (Hair et al. 2017).

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Tenenhaus et al. (2005) proposed the goodness of fit (GoF) as a global fit indicator [13]. The GoF criteria for deciding whether GoF values are not acceptable, small, moderate, or high should be regarded as a globally appropriate PLS model. The value of the GOF (0.597) is greater than 0.36 indicating high fit, so, it can be safely concluded that the GoF model is large enough to be considered a sufficiently valid global PLS model (Table 3). Table 3. Structural model assessment Path

β

t-value P95% CI for value β LB

H1: Digitalization - > Cyber security

0.827 37.218 0

R f Q VIF Square Square Square

UB

0.777 0.866 0.684

2.167

0.26

1

0.248

0.26

1.557

H2:Digital process 0.347 > Cyber security

9.765 0

0.27

H3: Digital strategy - > Cybersecurity

0.336

7.296 0

0.246 0.427

0.195

1.856

H4: Flexibility - > 0.307 Cyber security

7.282 0

0.221 0.386

0.185

1.632

0.594 0.737 0.456

0.837

0.25

1

H6: Digitalization 0.713 22.396 0 - > Organizational Resilience

0.64

0.767 0.508

1.034

0.256

1

H7: Digitalization > Phishing

0.733 24.926 0

0.67

0.783 0.538

1.164

0.272

1

H8: Digital process - > Artificial intelligence

0.313

5.67

0

0.198 0.416 0.465

0.118

0.251

1.557

H9: Digital strategy - > Artificial intelligence

0.326

4.58

0

0.191 0.46

0.107

1.856

H10: Flexibility > Artificial intelligence

0.167

2.876 0.004 0.05

0.032

1.631

H5: Digitalization - > Artificial intelligence

0.675 19.013 0

0.415 0.688

0.28

(continued)

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Table 3. (continued) Path

β

t-value P95% CI for value β LB

R f Q VIF Square Square Square

UB

H11: Digital process - > Organizational Resilience

0.25

4.541 0

0.142 0.357 0.513

0.083

0.255

H12: Digital strategy - > Organizational Resilience

0.247

4.048 0

0.129 0.366

0.068

1.856

H13: Flexibility > Organizational Resilience

0.357

6.152 0

0.242 0.47

0.16

1.631

H14: Digital process - > Phishing

0.32

6.623 0

0.221 0.411 0.541

0.143

H15: Digital strategy - > Phishing

0.289

4.488 0

0.165 0.417

0.098

1.856

H16: Flexibility > Phishing

0.27

5.046 0

0.162 0.372

0.097

1.631

0.269

1.557

1.557

10 Discussion Here, in this part, we discuss the hypothesis testing results; the first thing to note here is the strong and direct effect of digitalization on the cybersecurity of the Moroccan banking system. In other words, greater the implementation of digitalization, the greater the cybersecurity of the Moroccan banking system. We are talking here about the main hypothesis of this research. Moreover, the influence of flexibility, digital process and digital strategy on digitalization were studied, and the results revealed the influence of digital strategy. When it is compared to the influence of flexibility and the digital process. Additionally, through this study, the impact of the dimensions of digitalization on the variables related to cybersecurity was studied. Furthermore, we found that the digital process and the digital strategy have a very high impact on artificial intelligence, if we take into account the role of artificial intelligence in achieving cybersecurity. We can conclude that the subvariables related to the independent variable (digitalization) positively, effectively and significantly affect the performance of cybersecurity (dependent variable) in the Moroccan banking system.

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11 Conclusion The study has been concluded with several recommendations: The most important is the need to use highly qualified human elements trained by modern techniques. There is a need to pay sufficient attention to infrastructure development and the use of digital transformation to achieve better, faster and cheaper banking services for customers. Through our study, we can confirm the following elements: – Digitalization plays an effective role in achieving the competitive advantage of the Moroccan banking system – Speed, accuracy and innovation are key variables for the success of the digitalization process within the banking system – Digitalization is a prerequisite for the success of the cybersecurity process within the banking system. Digitalization played a major role in the development of the fourth generation of industry and in the emergence of artificial intelligence techniques. Digitalization and cybersecurity have a positive impact on enhancing confidence in the banking system.

References 1. Cap Gemini Research Institute. 2019. Reinventing Cybersecurity with Artificial Intelligence. https://www.capgemini.com/wp-content/uploads/2019/07/AI-inCybersecrity_ Report_20190711_V06.pdf 2. Goosen, R., Rontojannis, A., Deutscher, S., Rogg, J., Bohmayr, W., Mkrtchian, D.: Artificial Intelligence Is a Threat to Cybersecurity. It is Additionally, a Solution. The Boston Consulting Group (2018) 3. Ravi, N., Ramachandran, G.: A robust intrusion detection system using machine-learning techniques for MANET. Int. J. Knowledge-based Intelligent Eng. Syst. 24(3), 253–260 (2020) 4. Lavoix, H.: Revisiting the idea of cybersecurity for the digital world of the 21st century. Global Security 19(3), 27–32 (2019) 5. Derdevet, M.: Energy, a Networked Europe. Robert Schuman foundation (2015) 6. Dilek, S., Çakır, H., Aydın, M.: Applications of artificial intelligence techniques to combating cyber-crimes: A review. arXiv preprint arXiv:1502.03552 (2015) 7. https://neostrom.in/ekb-model/2022 8. Antonides, G., De Groot, I.M., Van Raaij, W.F.: Mental budgeting and the management of household finance. J. Econ. Psychol. 32(4), 546–555 (2011) 9. Rensis Likert (1903–1981) was an American psychologist known for his contributions to psychometrics and the measurement of attitudes. He also achieved fame in management circles for his work on leadership styles 10. McIver, J., Carmines, E.G. : Unidimensional scaling (Vol. 24). Sage (1981) 11. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)

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12. Hair, J.F., Hult, G.T., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modelling (PLS-SEM), 2nd edn. SAGE, Los Angeles, CA (2017) 13. Tenenhaus, M., Esposito Vinzi, V., Chatelinc, Y.-M., Lauro, C.: PLS path modelling. Comput. Stat. Data Anal. 48(1), 159–205 (2005)

An Enterprise Resource Planning (ERP) SAP Implementation Case Study in South Africa Small Medium Enterprise Sectors Oluwasegun Julius Aroba1(B)

and Sanele Baldwin Mnguni2

1 ICT and Society Research, Information Systems Department, Durban University of

Technology, Durban 4001, South Africa [email protected] 2 Audit and Taxation, Auditing and Taxation Department, Durban University of Technology, Durban 4001, South Africa

Abstract. The use of technologies for enterprise resource planning (ERP) SAP has resulted in improvements to companies’ daily operations. This growth, however, has not been without its share of difficulties for the sector of small and medium businesses. First, the adoption and implementation of proprietary SAP ERP comes with a high expense for organizations, and second, it is problematic for organizations to guarantee that scalability is established owing to the dynamic shift in the SME sector. This indicates that the small and medium-sized enterprises (SMEs) sector in South Africa is not making use of the widely accessible cost-effective open-source SAP ERP that is now on the market. The scope of the investigation was broadened to include gathering information on open-source alternatives. This indicates that the small and medium-sized enterprises (SMEs) sector in South Africa is not making use of the widely accessible cost-effective open-source SAP ERP that is now on the market. The primary objective of the study was to assess the open-source ERP adoption trends of small and medium-sized enterprises (SMEs) in the Durban area. The purpose of this study was to investigate both the drivers and the impediments to the adoption of SAP ERP systems. Qualitative and quantitative approaches were used in this study. The scope of the investigation was broadened to include gathering information on open-source alternatives. According to the findings of the study, small and medium-sized enterprises (SMEs) are aware of the advantages that may be gained by using ERP systems in their companies. Keywords: Enterprise Resource Planning Implementation · Small Medium Enterprise · Open-source Software · SAP · Qualitative Method

1 Introduction Enterprise Resource Planning (ERP) adoption is high-priced and fraught with danger, so firms must prepare well [1]. Due to the enormous expenses associated with deployment, typically only major corporations dared to make the transition. However, because of the dangers associated with ERP use, only a small fraction of SMEs really uses the tactics, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 348–354, 2023. https://doi.org/10.1007/978-3-031-29857-8_35

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and those that have do so inefficiently [2]. Enterprise Resource Planning (ERP) software aids in displaying data’s accessibility and visibility within the organization. The use of IT has boosted company performance. Over the years, both small and large businesses have benefited from the widespread use of ICT technologies including the Internet, low-cost telephony (such as Voice over IP), and social networking platforms. When implemented and used properly, enterprise resource planning (ERP) systems streamline operations and provide businesses a price advantage by connecting the dots throughout the whole value chain, from initial inputs to the finished product [3]. Therefore, it is important to design a framework for SAP ERP deployment that is both affordable and capable of accommodating the unique requirements of SMEs in today’s fast-paced business world. Enterprise Resource Planning (ERP) SAP systems are commonly employed in South Africa and other countries to collect with the use of Wireless Sensor Networks and analyze data from a broad variety of business operations [3–6]. Manufacturing companies’ strategies, cultures, and organizational structures have been affected using ERP systems in an effort to streamline internal processes [7]. ERP implementation is a time-consuming and expensive procedure. But despite the substantial expenditures in ERP installations, many of them fall short of the mark in terms of the information needs of businesses, particularly those in the small and medium size range. For small and medium-sized enterprises (SMEs), the high cost of certain proprietary ERP for SME products highlights the need of exploring affordable ERP alternatives. Enterprise Resource Planning (ERP) systems are now standard in most industrial businesses [8]. An enterprise resource planning (ERP) system is a unified database that helps businesses meet their informational demands in a comprehensive and effective manner. If used to their full potential, ERP systems may enhance operations in a variety of ways, including facilitating quicker and more accurate information exchanges, raising productivity, and decreasing transportation costs. Concrete advantages of ERP deployment were recognized for cost savings in areas such as inventories, labor, procurement, and technology have been realized. Enterprise Resource Planning (ERP) software has traditionally catered to huge corporations that can afford to pour a lot of money into elaborate, interconnected computer programs [9]. When deciding to start an ERP project, it’s important to do your homework first. According to a number of qualitative research, Enterprise Resource Planning (ERP) systems are often underused or used in tandem with other secondary systems even when they are established. Due to the ERP system’s inherent weaknesses and the sensitive information it handles, ensuring its safety is paramount. A lot of ERP providers have already implemented their security solution, and although that could be OK for them, we need new technological techniques to safeguard an ERP system in an open environment. This is necessary since SMEs need to join the social networking sites’ communities, but doing so brings its own set of difficulties, especially with regards to data security. Integration of ERP in online services and ERP on mobile platforms are also major topics of this research. Since most commerce is now done on mobile devices, this strategy has serious repercussions. There is great promise for small and medium-sized enterprises (SMEs) when ERP is integrated into this platform, but there are still infrastructure difficulties, particularly the high cost of broadband and the issue of security, in the South African setting.

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Significant contributions to the national economy have been made by the oil and gas sector. For this reason, businesses all over the globe have started using enterprise resource planning (ERP) systems. ERP has been shown to be an effective software platform for the oil and gas industry [10]. However, the system’s implementation is predicated on the internal operations of the corporation. 1.1 Objectives The major and secondary goals of this research are broken down and discussed in the next portion of the study. The predominant aim in question: This study’s essential aim is to provide recommendations for the adoption of open-source enterprise resource planning (ERP) technologies by the small and medium-sized business sector in the environment of South Africa. Secondary goals and aims in this section.The following are examples of the structured sets of secondary goals that might be attained in support of the achievement of the main objective: • To examine several ways that small and medium-sized businesses (SMEs) in the South African setting may use ERP systems. • To determine the obstacles that prevent small and medium-sized firms from adopting opensource SME ERP software. • To provide a framework for open-source enterprise resource planning (ERP) implementation that is optimally suited for use in the small and medium company sector. 1.2 Problem Statement There are more open-source enterprise resource planning (ERP) SAP solutions designed for small and medium-sized businesses (SMEs), but adoption of these solutions has been modest. In the South African market, this is particularly accurate. Due to their lack of technology expertise and resources, small and medium-sized firms (SMEs) may experience greater difficulties than larger corporations. Due to the poor adoption of open-source SAP ERP, which is less expensive than proprietary solutions, the issue goes beyond financial expenses. Although there is a large variety of open source and paid ERP software available for SMEs, South Africa has not yet fully tapped the potential of these systems. The widespread adoption of ERP among small and medium-sized businesses has not been impacted by the availability of alternative ERP systems, such as free and open-source ones [11]. Research on user views and implementation challenges is thus required. The focus is on open source-based ERP systems. To create a framework and set of standards that take into account the fluidity of the contemporary business environment, particularly in South Africa, research into the implementation and rollout of ERP is being done. Because they understand that small and medium-sized firms are the backbone of the South African economy, large corporations there have aided in their expansion [12]. The economy is driven by small and medium-sized businesses (SMEs), but their delayed adoption of cutting-edge IT systems like enterprise resource planning (ERP) has hindered growth. Since study on ERP modification after installations showed that a lot of resources

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are required for this process, proper pre-installation planning is crucial. In addition, very few studies have investigated ERP use in Africa; the majority have been done in Europe. These studies have not focused on the adoption or non-adoption of open-source ERP by SMEs. By examining user viewpoints inside open-source ERP, this study intends to contribute to the literature and practice of south Africa small medium enterprises [13].

2 Literature Review The current state of ERP implementation and its benefits were covered in the previous chapter. It was mostly argued that ERP helps businesses run more smoothly. Enterprise resource planning has several benefits, but adoption is limited in South Africa [11]. While proprietary and open-source ERP tool implementations will be analyzed in this section [12]. Similarly, the research analyzes the features of Open-Source SAP ERP programs made for both small and large companies [13, 14]. In comparison,the importance of enterprise resource planning (ERP) systems cannot be overstated; to implement ERP in small and medium-sized enterprises [15]. Furthermore, using IT as a bargaining chip in business is analyzed and praised for its potential benefits [16]. On the other hand, due to the expanding nature of this field and the possibility it presents for small and medium-sized enterprises (SMEs) to tap into the untapped digital community of users and interactions [17–20]. The impact of social networking tools on ERP security and use Enterprise Resource Planning (ERP) SAP are one of applied technique for minimizing data redundancy [20, 21].

3 Research Methodology The primary purpose of this research was to investigate the ERP adoption trends of SMEs and to do an in-depth analysis of the obstacles that stand in the way of the deployment of open-source ERP. An investigation was conducted on the following aspects of ERP implementation: • • • •

The most important aspects in the implementation of ERP systems [22–24] Other options besides using an ERP system hurdles to ERP adoption [8] Different forms of SAP ERP [25] The advantages of using open-source enterprise resource planning [26]

3.1 Quantitative Approach According to one definition, quantitative procedures are those that give decision-makers a powerful and organized way to analyze quantitative data. The management uses this scientific approach to problem-solve and make decisions. The decision-maker can examine policies for achieving the predetermined objectives with the aid of quantitative techniques [27]. The use of quantitative tools in decision-making is essential. The development of a basic theoretical model in the prior chapter, the following step was to conduct out an across-the-field survey of manufacturing SMEs in the Durban region [28, 29]. When

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an organization’s key operations are integrated, as they are with enterprise resource planning (ERP), the result is a single business function. Presently, ERP is undergoing a revolution that will make it web-enabled, sophisticated, collaborative, and even wireless [30]. This study was conducted with the intention of determining and testing whether or not the concerns that were mentioned in the literature relate to the environment that exists in South Africa. The description of the research techniques that were used, as well as an explanation for the modified approaches. The strategy and study design that were chosen are broken down and discussed. Qualitative methodologies were used in the investigation and well descriptive. These techniques were used to keep an eye on or verify the SAP ERP implementation in small- to medium-sized businesses. Testing is the goal of various research techniques. Face-to-face interviews were conducted as the first step in our study’s effort to gather data about SMEs’ implementation experiences in order to address our research objectives. We utilized thematic analysis, a qualitative analytic technique, to find, examine, and report themes in the data for this study.

4 Conclusion The wide range of implementation-related tasks, such as setting up the project plan and goal dates, making sure enough resources are allotted, choosing products and designs, and managing the project on a day-to-day basis. A vital success elements and problems in the deployment and adoption of ERP are highlighted in the literature analysis that was conducted. SAP ERP has enhanced various SME’s business procedures. An increase in the accessibility of data; information that is accurate and up to date; faster responses to inquiries from customers; the ability to quickly adapt to shifting market circumstances. In addition, the research highlighted crucial success elements in the implementation of ERP, including as support from top management, user training, and vendor software support. Additionally, it revealed a few difficulties associated with the implementation of ERP. A lack of training, the high cost of ERP systems, and an absence of support, particularly for open-source ERP software are among the issues that have been cited in the relevant literature. It also cited the problem of integration as a potential barrier to ERP adoption, along with a lack of in-house capabilities as a contributing factor. The problem of maintaining confidentiality was recognized as a further obstacle. In addition, the examples of SME ERP already on the market were analyzed for this literature review. It was found out that there is in fact a variety of Open-Source ERP software that provide the same capabilities as the proprietary products. Finding out why small firms aren’t making use of these technologies is the next problem, since it has to be determined why they aren’t. The small and medium-sized company would be able to optimize their business procedures more effectively with the use of these technologies. The findings of the research make it abundantly clear that meticulous preparation is necessary prior to the launch of an ERP system

References 1. Noorliza, K., Soliman: Adopting enterprise resource planning (ERP) in higher Soliman education: a SWOT analysis. Int. J. Manage. Educ. 16(1), 20–39 (2022)

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2. Marconatto, D.A.B., Teixeira, E.G., Peixoto, G.A., Faccin, K.: Weathering the storm: what successful SMEs are doing to beat the pandemic. Management Decision (2021) 3. Ntelamo, O., Chikohora, E., Billawer, J.: Assessing enterprise resource planning systems implementation challenges in namibian industries: a survey. In: 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), pp. 1–8. IEEE (2021) 4. Aroba, O.J., Naicker, N., Adeliyi, T.T., Ogunsakin, R.E.: Meta-analysis of heuristic approaches for optimizing node localization and energy efficiency in wireless sensor networks. Int. J. Eng. Advanced Technol. (IJEAT) 10(1), 73–87 (2020). https://doi.org/10.35940/ ijeat.A1717.1010120 5. Aroba O.J., Naicker, N., Adeliyi, T.T.: An innovative gaussian clustering hyperheuristic scheme for energy-efficient optimization in wireless sensor networks. Journal of Sensors 2021, 12 (2021). https://doi.org/10.1155/2021/6666742 6. Aroba O.J., Naicker, N., Adeliyi, T.T.: A hyper-heuristic heterogeneous multi-sensor node scheme for energy efficiency in larger wireless sensor networks using DEEC-Gaussian algorithm. Mobile Information Systems 2021, 13 (2021). https://doi.org/10.1155/2021/665 8840 7. AlMuhayfith, S., Shaiti, H.: The impact of enterprise resource planning on business performance: with the discussion on its relationship with open innovation. J. Open Innovation: Technol., Market, and Complexity 6(3), 87 (2020) 8. Beric, D., Havzi, S., Lolic, T., Simeunovic, N., Stefanovic, D.: Development of the MES software and Integration with an existing ERP Software in Industrial Enterprise. In: 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1–6. IEEE (2020) 9. Hamdar, A.: Implementing cloud-based enterprise resource planning solutions in small and medium enterprises (Doctoral dissertation, Walden University) (2020) 10. Ali, M., Edghiem, F., Alkhalifah, E.S.: Cultural challenges of ERP implementation in middle eastern oil & gas sector: an action research approach. Systemic Practice and Action Research, pp. 1–30 (2022) 11. Alsharari, N.M., Al-Shboul, M., Alteneiji, S.: Implementation of cloud ERP in the SME: evidence from UAE. J. Small Business Enterprise Dev. (2020) 12. Saah, P.: The impact of small and medium-sized enterprises on the economic development of South Africa. Technium Soc. Sci. J. 24, 549 (2021) 13. Mafini, C., Dhurup, M., Madzimure, J.: E-procurement, supplier integration and supply chain performance in small and medium enterprises in South Africa. South African J. Business Manage. 51(1), 1–12 (2020) 14. Hastig, G.M., Sodhi, M.S.: Blockchain for supply chain traceability: business requirements and critical success factors. Prod. Oper. Manag. 29(4), 935–954 (2020) 15. Íñiguez, L., Galar, M.: A scalable and flexible open source big data architecture for small and medium-sized enterprises. In: International Workshop on Soft Computing Models in Industrial and Environmental Applications, pp. 273–282. Springer, Cham (2021). https://doi. org/10.1007/978-3-030-87869-6_26 16. Rodrigues, J., Weiß, M., Hewig, J., Allen, J.J.: EPOS: EEG processing opensource scripts. Front. Neurosci. 15, 663 (2021) 17. Wu, J.Y., Chen, L.T.: Odoo ERP with business intelligence tool for a small-medium enterprise: a scenario case study. In: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and ELearning, pp. 323–327 (2020) 18. Ramos, S., Duran-Heras, A., Castilla-Alcala, G., Fernández, M., Ortiz-Gonzalez, J.I.: Applying a cloud-based open source ERP to industrial organization learning through the materials requirements planning module. In: Ensuring Sustainability, pp. 339–346. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95967-8_30

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19. Tavana, M., Hajipour, V., Oveisi, S.: IoT-based enterprise resource planning: challenges, open issues, applications, architecture, and future research directions. Internet of Things 11, 100262 (2020) 20. Zadeh, A.H., Zolbanin, H.M., Sengupta, A., Schultz, T.: Enhancing ERP learning outcomes through Microsoft dynamics. J. Inf. Syst. Educ. 31(2), 83–95 (2020) 21. Akyurt, ˙I.Z., Kuvvetli, Y., Deveci, M.: Enterprise resource planning in the age of industry 4.0: a general overview. Logistics 4.0, pp. 178–185 (2020) 22. Taghipour, M., Shabrang, M., Habibi, M.H., Shamami, N.: Assessment and analysis of risk associated with the implementation of enterprise resource planning (ERP) project using FMEA technique (including case-study). Management 3(1), 29–46 (2020) 23. Morrisson, M.K.: Best practice models for enterprise resource planning implementation and security challenges. J. Bus. 8(2), 55–60 (2020) 24. Clune-Kneuer, E., Klute, P., Chester, T.: Guiding principles for optimal organization: options for successfully merging institutional research and business intelligence. New Dir. Inst. Res. 2020(185–186), 67–85 (2020) 25. Chopra, R., Sawant, L., Kodi, D., Terkar, R.: Utilization of ERP systems in manufacturing industry for productivity improvement. Materials Today: Proceedings (2022) 26. Odero, P.O.: Barriers to adoption of open-source ERPs in parastatals in Kenya; Case of energy Sector (Doctoral Dissertation, department of computer of computing and informatics, university of nairobi) (2021) 27. Lutfi, A.: Investigating the moderating role of environmental uncertainty between institutional pressures and ERP adoption in Jordanian SMEs. J. Open Innovation: Technol., Market, and Complexity 6(3), 91 (2020) 28. Singh, R., Bhanot, N.: An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry. Int. J. Prod. Res. 58(8), 2454–2476 (2020) ˇ cikait˙e, R.: An integrated impact of 29. Meidute-Kavaliauskiene, I., Yıldız, B., Çi˘gdem, S, ¸ Cinˇ blockchain on supply chain applications. Logistics 5(2), 33 (2021) 30. Frank, A.G., Dalenogare, L.S., Ayala, N.F.: Industry 4.0 technologies: implementation patterns in manufacturing companies. Int. J. Prod. Econ. 210, 15–26 (2019)

Towards Transparent Governance by Publishing Open Statistical Data Rabeb Abida1(B)

, Emna Hachicha Belghith2

, and Anthony Cleve1

1 Faculty of Computer Science, University of Namur, Namur, Belgium

{rabeb.abida,anthony.cleve}@unamur.be 2 Université Paris-Dauphine, Paris, France

Abstract. A large part of open data concerns statistics such as economic and social indicators. National statistical institutes and public authorities have recently adopted the linked data paradigm to publish their statistical data on the web. Linked Open Government Data are significantly increasing in terms of variety and becomes accessible to data consumers, which makes it challenging to enhance its quality. Although publishing open data as datasets is straightforward and requires minimal technological skills, it is not ideal for users who wish to use the data in a more dynamic format. This process involves several challenges, e.g., data extracting, data modeling, data interlinking, data publishing, design-decisions, and knowledge extraction. In this paper, we seek to fill this gap by proposing an extension of Pub-LOGD framework based on linked open data technologies. To this end, we first conducted a literature review to identify the most steps used to publish Linked Data. Next, these identified tools were combined with the results of an online pre-survey conducted by 35 participants on their preferred tools and tasks. Our goal is to enable data consumers to access a publishing solution that can engage them with governments and re-use government information to deliver public services and applications. To evaluate the effectiveness of our proposal, we engage 8 users from the community to complete a post-survey based on TAM (Technology Acceptance Mode). Keywords: Linked open data · Data modeling · Data publishing · Statistical open data · Decision-making

1 Introduction A big part of the open government data concerns statistics [3, 5, 30], such as demographic (e.g., census data), population figures, economic and social indicators (e.g., number of new businesses, unemployment, and workers). Statistical data are arranged in a multidimensional manner, where a measured variable (e.g., unemployment or workers) is represented in terms of dimensions (e.g., geography and time). These data consist of primary material for added-values services and products, increasing government transparency, contributing to economic growth, and providing social value to citizens [14, 15]. In such context, multiple projects in government (e.g., in the United Kingdom, France, and Belgium) have been executed to allow access freely to various data e.g., © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 355–365, 2023. https://doi.org/10.1007/978-3-031-29857-8_36

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GSS1 , data.gouv.fr2 , and STATBel. Several vocabularies have been created to enable modeling statistical data as RDF graphs, such as the RDF data cube (QB) vocabulary [7] and the Simple Knowledge Organization System (SKOS) vocabulary [23]. In spite of this increased attention, the open movement statistical data that are accessible on the web are still very heterogeneous, only a few data is fully open, and few datasets are machine-readable, which makes it challenging to access and assure the data quality. Some studies have been proposed methodological guidelines to formalize the Linked Open Data’s publishing process. In [10, 11, 16], authors have proposed approaches for modeling whereas in [3, 19, 21] for publishing Linked Open Data, but without considering data extraction, data refinement nor semantic visualizations and interaction with data. Limited works in the R&D community are observed in sustaining end-to-end processes of publishing Linked Data in an e-Government environment [3]. A clear lack is noted of solutions that are integrated and automated which allow (i) compounding suitable tools to assist stakeholders, to efficiently handle datasets concerning all data steps, (ii) keeping a monitoring on the advancement of the process and the semantic visualizations, (iii) involving users with governments to reuse the data to provide public services and applications. This paper aims to fill the above research gap by engaging the users to reuse the government information to provide public applications and proposing a useful and integrated extension of Pub-LOGD proposal [1] to interactively assist stakeholders to access a unified solution for publishing data as Linked Open Data. Hence, the raised research question is: “How can we supply a standardized solution to ease the use and publication of Linked Open Government Statistical Data ?” To reply this question, we first conducted a literature review to recognize the different steps and tools used to publish Linked Statistical Data. Then, these identified tools are combined with the results of an online pre-survey conducted by 35 participants on their preferred tools and tasks for providing public services to define data quality to publish. Our framework Extension consists in an unified solution which handles five main steps of the testing of data (i) transforming, (ii) interlinking, (iii) storing, (iv) visualizing, and (v) publishing. A Docker technique is used for the implementation. This technique employs a tools’ set furnishing users with customized and ease-of-use access to the datasets obtainable on the web. Our extension (Pub-LOGD Extension) is based on Sparqlify to automatically convert raw data into data of good quality to publish on the web, link them together and provide semantic visualizations. In this way, users are informed with knowledge that is extracted aiming to enhance decision-making. Then, an evaluation of our proposal is realized by engaging 8 users from the user community to complete a post-survey based on TAM (Technology Acceptance Mode) to measure its effectiveness and ease of use. The structure of our paper is organized as following. In Sect. 2 the proposal methodology is explained. Afterwards, we study the position of our work w.r.t the related literature. Section 4 presents Pub-LOGD Extension framework and details its steps. 1 https://gss.civilservice.gov.uk/. 2 https://data.gov.be/fr.

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Section 5 describes the results of the evaluation of our proposal and discusses its utility and applicability. Section 6 shows the conclusion and some future work.

2 Methodology To address the research question of this study, we conducted a literature review on user assistance to publish structured Linked Data in an e-Government statistical data context. Firstly, we realize the literature review by involving the databases “SpringerLink”, “Google Scholar”, and “Science Direct” using the following keywords “methodology” or “pipline” or “process”, + “open statistical data” or “open government data”, + “modeling” or “linking” or “publishing” + “users”. We also extended our search to the grey literature and policy reports. Most of the publications found are dated from 2010–2022. From these publications, an additional selection was made based on their relevance to our research, leaving 22 academic articles, web pages, and policy reports that were looked at more thoroughly. The selected papers were then used to collect appropriate methodologies and tools to ensure better data quality, to publish and visualize related open government (statistics) data allowing stakeholders to be aware of the knowledge retrieved to improve their decision making. Afterwards, we created an online pre-survey3 to collect feedbacks from researches, and analysts and data scientists who used open data. The survey was pre-tested with three users and then shared via the following communication channels: UNAMUR mailbox, Facebook and LinkedIn. In total, 35 participants completed the pre-survey. The literature review along with user feedback will be used to improve the functionalities of Pub-LOGD framework. The goals to reach are (i) increasing quality of accessible data, and (ii) providing guidelines to data publishers (governments).

3 Related Work We review, herein, previous researches on the creation and publication of linked data, which is an intensive engineering process requiring considerable effort to achieve a high level of quality. Existing general guidelines are not always sufficient to make the processes reproducible [25, 26]. Several studies presented methodologies and the lifecycle of open government data initiatives but not linked data in particular, and authors are not specifying what are the criteria for selecting primary studies of methodologies [27]. The proposed publishing framework is also still at a high level of abstraction [3]. [13, 27, 30] presented the commonalities and differences among the different methodologies that have been proposed for publishing linked open government data. Hidalgo-Delgado et al. [13] introduced guidelines to unify the steps for data publishing, w.r.t. the criteria of quality. Indeed, we consider problems of data quality in our solution [1], and all the process’ steps that form the lifecycle. In such environment, multiple approaches have used pipeline solutions. For example, Maali et al. [19] have proposed a solution to convert raw data into high-quality LOD. While E. Kalampokis [15] and Ermilov et al. [9] utilize 3 https://tinyurl.com/5n7ejrv4.

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such methodology to introduce multidimensional LOD publication and reuse. Nevertheless, some technical challenges are still raised to exploit the full potential of linked data and specifically to reuse allied open statistical data. Several challenges still exist, in terms of the different data phases, which are: extraction data which is important to consider relevant data, in the sense that they have a high request in society, reflect important aspects of public administration and that present quality indicators (completeness, accuracy, and timeliness) [24]. In [21] authors did not consider this phase in their work. The data transformation phase involves the cleaning and design of URIs that define the unique and persistent identification of data resources, which many works have not considered [11, 17, 21]. In contrast, research studies have included defined vocabularies to reuse existing vocabularies or build new ones, depending on the context of the data [2, 15, 17, 20, 22]. All these works considered converting the “raw” data into a linked data representation or serialization (RDF) (modeling step) and preferred to publish their linked data in a simpler form, unlike [1, 6, 19], which considered enriching the data with external sources (knowledge base schema) before publishing it on the web. The publishing phase can be used to promote it and make it visible to search engines or to engage with the community of users and consumers. The primary purpose of creating linked datasets is to promote the reuse of government information, to provide public applications, and to encourage commercial reuse of the data, since some studies [1, 15] emphasize the importance of creating applications from the linked open data. In this context, [18, 29] considered the importance of engagement with a data community, whereby after the publication of linked data, the government must receive feedback from data consumers. Yet, limited work exists, for supporting these shortcomings, which we aim in this paper to engage the community of users (researches, developers and data scientists) to enhance our application (Pub-LOGD), to promote the reuse of government information in order to develop and provide public services and applications.

4 Extension of the Pub-LOGD Framework This section presents an extension of the Pub-LOGD [1] framework, which is based on the set of requirements that are feedbacks of participation, which they completed the pre-survey. Pub-LOGD Extension relies on Sparqlify to automate the data modeling phase of the framework. A pipeline of five principal data handling steps are specified to define our framework. Our goal is to enable stakeholders and public institutions to reach a unified solution which leads them with the most suitable tools via the publishing pipeline, providing them pertinent data visualizations. This extension follows an incremental process covering five ordered major steps where added functionalities are listed below: 1. Data Transforming that aims at extracting statistical data from heterogeneous data sources using Dcat Browser as a tool extending Open Refine4 . Such tool has been used to depict public datasets within Europe. Thus, we target to clean and unify the data by following a set of actions: repairing errors, deleting duplicates, and arranging data for transformation. Open Refine is the most suitable for us since it supplies an 4 https://code.google.com/archive/p/google-refine/.

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Fig. 1. Screenshot from the platform of Pub-LOGD framework process.

easy-to-use interface, and this is needed for non-expert users [12]. It also allows the clustering of data which facilitates analyzing it. Afterwards it is necessary to transform the statistical data into RDF format by defining an ontological model to annotate and share the data. This step contained two sub-tasks: (a) reconciliation that depicts a feature enabling semi-automatically link data to external knowledge graphs (e.g., DBpedia5 and Wikidata6 ) to enrich the data with these databases using the RDF Extension in Open Refine. This step is semiautomated since end-user is allowed to consent to the results interactively and/or to change from a list of results. In this phase, we extend the data transforming step with an automated approach to the modeling task using Sparqlify7 which is a SPARQL-SQL query rewriter that allows the definition of RDF views using a Sparqlification Mapping Language. In this way, it enables SPARQL queries on relational databases. (b) modeling that aims at transforming the structure of the statistical data into RDF format by reusing available vocabularies/ontologies to specify namespace prefixes. At this step, URIs are important for the interoperability of statistical data in the web space. To represent the linked open statistical data in RDF, we choose the W3C RDF Data Cube Vocabulary8 . Thus, we obtain linked open statistical data and store it in a PostgreSQL relational database on the Docker volume. Thus, we obtain linked open statistical data and store it in a PostgreSQL relational database on the Docker volume. We provide the user with 5 https://www.dbpedia.org/ 6 https://www.wikidata.org/wiki/wikidata:mainPage. 7 https://github.com/SmartDataAnalytics/Sparqlify. 8 https://www.w3.org/TR/vocab-data-cube/.

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a file (workspace) that depict a volume (Docker sharing)9 . The aim is to share and facilitate the access to his data. After, a data structure should be created to model statistical data using RDF format in an automated way. For the data transformation from the relational database to the RDF Data Cube, we define a mapping comprised of two views: (i) Static and (ii) StatResults using Sparqlify [9]. (i) The Static view describes the static elements of the RDF Data Cube Model: qb:DataSet, qb:DataStructureDefinition and several qb:component elements.Fiveqb:component elementsarepresentqb:DataStructureDefinition and one related qb:Dataset. Three qb:dimension elements (time of measure, statistical criterion, source dataset) explicitly identify any particular observation. This model includes exactly one qb:measure and one qb:attribute element. (ii) The StatResults view operates on the statistical data from the relational database. It retrieves available statistical criteria from the database and constructs an observation for each. Observation URIs are defined as a concatenation of a linked open statistical data namespace URI with a unique hash. More details about the complete listing of the mapping are available at our GitHub repository10 . We activated importing online vocabularies, independently of the type (e.g. RDF/XML). The deployment of these tools is adequate with the recommendations of the W3C. 2. Data interlinking consists of generating links between RDF graphs using network measures, e.g., Levenshtein distance. This step relies on the Silk Link Discovery Engine [28] aiming to detect semantic dependencies (owl:sameAs) to state that within RDF graphs two entities correspond to the same entity. 3. Data storage allows users to add and store their RDF data in the server instance Fuseki11 . It enables users to make the decision on a particular source are stored in the form of annotations, which can be used to analyze conflicting information or process incomplete information. Pub-LOGD provides the publisher with data visualization functionalities to enhance the understanding of the data and its relationships and facilitate decision-making of stakeholders. 4. Data visualization which relies on neo4j datastore12 , that depicts the most in-demand and widely scalable graph database. 5. Data publishing enables publishing linked open government (statistical) data on the web. This phase is based on the Pubby server13 , which allows stakeholders to move among resources via employing properties.

9 https://docs.docker.com/storage/volumes/. 10 https://github.com/123rabida123/WebAppOpenData. 11 https://jena.apache.org/documentation/fuseki2/. 12 https://neo4j.com/. 13 http://wifo5-03.informatik.uni-mannheim.de/pubby/.

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The platform Pub-LOGD is a web application built using the SpringBoot framework and powerful of linked Open Data technology and Docker technology14 . Figure 1 shows a screenshot of the Pub-LOGD process containing most of the tools that were requested by users in the pre-survey. Our proposal significantly reduces the required time for the right tools research and combination in order to model statistical data in RDF format and publish them on the web.

5 Results and Discussion In this section, we present the efforts conducted to evaluate and show the practicality and usability of our proposed Pub-LOGD extension. 5.1 Evaluation Description A post-survey15 is realized to gather feedback from participation by engaging 8 participants with different skills (researchers, data scientists, and application developers). These participants is categorized into 2 groups: Group-1 is composed of 4 participants (2 data scientists, a researcher, and a developer) to evaluate the Pub-LOGD and Group-2 is composed of the same to evaluate Pub-LOGD Extension. The post-survey involves two question types: questions using Likert scale technique with 7 scale points ([“Strongly Disagree”,.., “Agree”, “Strongly Agree”]). We follow the TAM technique [8] for evaluating two angles: perceived usefulness and perceived ease of use. Text questions are employed to justify their ratings. Table 1 summarizes the corresponding questions for each construct and explain the reason of our choice. Table 1. The corresponding questions of survey. Aspects

Question

(1) Perceived Usefulness (PU) 12 questions of the TAM (2) Perceived ease of use (PEU) questionnaire: Q1 to Q6 mean (PU) Q7 to Q12 mean (PEU)

Comments Tam was used as it is suitable to measure the usability of a system in a standalone

5.2 Evaluation Results After collecting participants’ feedbacks, we calculate a set of metrics: (1) a mean, (2) a median, and (3) standard deviation. These metrics serve for answering questions previously mentioned for (A1) facility of framework features, (A2) overall facility, (A3) practicality of features and (A4) overall practicality to improve data quality and facilitate 14 https://www.docker.com/. 15 https://tinyurl.com/3sc2huvb.

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the integration and publication of statistical LOGD. The A1 (respectively A3) questions are utilized as a model for comprehending answers of A2 (respectively A4) questions. The choice of such statistical measures is explained by their degree of appropriateness for analyzing Likert data and owning a central tendency measure [4]. We use short sentences to code verbal thoughts and responses that are gathered from free text questions. The aim is to keep context and conceptual relations. Table 2 shows the metrics of the questions (7-point Likert) w.r.t. two aspects (perceived practicability and perceived facility) evaluated for Pub-LOGD Extension. In Group-1, most of the participants notice that the proposed framework was easy to use (median and mean ≥ 5 for A2 with a low standard deviation around 1 and useful to enhance the quality of open statistical data and easy to publish linked open statistical data. In fact, values of the mean and median are ≥ 5 in A4 questions presenting a low deviation about 1. Data scientists note that framework met their expectations, has made their work easier for features (e.g., unifying datasets, enriching datasets). The participants found this framework very useful and helpful. Indeed, the means of scores concerning participants on the facility (A1) and practicability (A3) are stowed among 3 and 5. Table 2. Median, mean and standard deviation of scores. Group-1 (Pub-LOGD) (N = 4)

Group-2 (Pub-LOGD Extension) (N = 4)

A2.Perceived ease of use

A4.Perceived usefulness

A2.Perceived ease of use

A4.Perceived usefulness

Median

5.5

5

6

5.7 (0.82)

Mean (SD)

5.25 (1.18)

5.67 (1.14)

6

6 (1.41)

The majority of participants, in group 2, note that the framework was useful in terms of modeling as well as publishing linked data since values of mean and median ≥ 5 in A4. Furthermore, they observe that this framework was easy to use since values of mean and median are noted as ≥ 5 in A2. Both observations have a low standard deviation around 1. Same comments are denoted in group 1 and we note that values of median and mean ≥ 5 concerning A1 & A3 questions. Nevertheless, they notice that some steps are more difficult to use such as the modeling phase. 5.3 Discussion This research was conducted as a novel approach through a literature review of linked data publishing in a government context. We engaged the user community to use the PubLOGD framework and complete the pre-survey to gather their feedback and contributions to improve certain features. Furthermore, this investigation aims to raise awareness of the government to receive feedback from data consumers by engaging with a community, which is composed of a wide range of stakeholders. Then, we have asked users in the

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community to test the Pub-LOGD extension through a post-survey. Our main goals are to provide a mechanism to promote user engagement with governments and the reuse of government information in order to enable data consumers to access to a publishing solution. We aim also at enabling users to generate and publish data on the web. This solution meets their requirements and encourage them to provide public services.

6 Conclusions and Future Work In this paper, we presented an extension of Pub-LOGD framework to assist users automatically model data as linked open data for publishing on the web. Our extension aims to shift the processing’ charge and data steps; modeling, interlinking and publishing Linked Open Data to data users rather than public government institutions, in a more transparent and straightforward way to supply and develop public services. It also allows for the mitigation of the problem of software tool dependencies. We plan, in future, to extend our framework based on user feedback and software quality measurement. Furthermore, we intend to better evaluate the data’ quality that are already published using a real case study. Moreover, we plan to extend it by improving and developing functionalities using machine learning techniques. Acknowledgements. This work is ostensibly supported by the project IDEES in the context of the European Regional Development Fund (FEDER). The first author is selected by a CERUNA PhD fellowship at University of Namur, Belgium.

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Digitalizing Teaching and Learning in Light of Sustainability in Times of the Post-Covid-19 Period: Challenges, Issues, and Opportunities Vahid Norouzi Larsari1(B) , Radka Wildová2 , Raju Dhuli3 , Hossein Chenari4 , Ethel Reyes-Chua5 , Elbert M. Galas6 , Jay A. Sario7 , and Maryann H. Lanuza8 1 Department of Pre-Primary and Primary Education, Faculty of Education, Charles University,

Prague, Czech Republic [email protected] 2 Faculty of Education, Charles University in Prague, Prague, Czech Republic [email protected] 3 Department of English Language Teaching, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India [email protected] 4 Science and Research Branch, Islamic Azad University, Tehran, Iran 5 EduHeart Book Publishing, Dasmarinas, Philippines 6 Pangasinan State University, Pangasinan, Philippines 7 Paranaque City College as OIC - Office of the Vice President for Academics and Research, Manila, Philippines 8 Philippine Normal University, Manila, Philippines [email protected]

Abstract. Most educational institutions were caught unprepared by the COVID19 pandemic, which forced millions of academicians and schools to dramatically change their methods of instruction. For roughly 1.5 years, online learning was essential, and during this time, digital education played a fundamental function in allowing instructors to instruct pupils remotely utilizing the variety of digital platforms and technologies. The objectives of this present paper are to explore the challenges, opportunities and issues that come with digital education after the COVID-19 outbreak. The present paper comes to a conclusion that the integration of digital education into the educational system, the use of m-learning, more adaptable and digital education methods, the review and redesign of national policies and curricula, the improvement of institutional infrastructure, the development of educational resources, and the improvement of students’ and teachers’ digital technology (and online pedagogy) skills are all possibilities to explore. Some suggestions are eventually addressed with reference to taking the crises’ lessons into account and moving on in the post-COVID normal. Keywords: Post-Covid-19 · Opportunities · Issues · Digital Education · Challenges

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 366–375, 2023. https://doi.org/10.1007/978-3-031-29857-8_37

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1 Introduction These days, COVID-19 is viewed as a contagious disease from which people is protected [1, 2]. Governments were compelled to close down academic institutions during the COVID-19 epidemic in order to keep an eye on the virus’s evolution [3]. In other words, this virus has prompted a basic shift in the guiding criteria of pedagogical models [2]. COVID-19 had a considerable impact on student learning and education globally, and as a result, educational platforms have changed massively everywhere, forcing all institutions to adopt remote learning. The COVID-19 pandemic lockdown forces institutions to switch to a virtual mode of teaching. This abrupt transition from F2F approach to virtual teaching has instilled in teachers apprehension about something we can call “the fear of the unknown” [4]. The educational process became virtual; schools, colleges, and other academic institutions moved entirely online [5]. Academic institutions were pressured to shut, and online teaching and learning became the norm overnight. Many nations throughout the globe have been forced to close all of their schools [6]. The pandemic, with the consecutive lockdowns, affected all levels of education, whereas digital education played an important function in forcing instructors to teach their students remotely using digital platforms, tools for synchronous and asynchronous collaboration, and authentic activities [7]. The COVID-19 problem may have had a greater impact on the uptake of digital technologies than all of the earlier research altogether. The transition from face-to-face to online education has revealed problems and barriers, has posed challenges, but it is also associated with opportunities worth investigating. Therefore, the paper aims to explore challenges, issues and opportunities that come with digital online education after COVID-19. It begins by outlining digital learning from a pedagogical standpoint, and then identifies potential to investigate in the post-COVID era. It concludes by summarizing the main suggestions. The researchers presented some clarifications on the main aim of this research in the following manner: (i) the terms “digital technology” and “ICT” (Information and Communication Technology) are used interchangeably. The educational process that uses digital technology in any way, such as online courses or the usage of digital tools in the classroom, is referred to as digital education or learning; (ii) “mobile learning” is defined as the process of learning mediated by mobile devices, anytime and anywhere without limitations on time and location [8], when examining the mobility of technology, learners, and learning; (iii) The practice of learning online is known as “online learning”; however, the terms “online learning” and “online education” are often used interchangeably. Distance learning relies on e-learning as a style of learning. Although the phrases “virtual teaching” and “emergency remote teaching” [9] have various meanings, they both relate to the physical separation between pupils and instructors and the application of digital technology to provide education [10].

2 Pedagogical Perspectives in Digital Teaching and Learning There are numerous benefits that come with digital learning including continuous, ongoing, flexible learning; time to reflect; facilitation of informal and formal learning; support for personalization; easy availability; ubiquity; contextuality; and relevance

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[11, 12]. According to Traxler, Read, Kukulska-Hulme, and Barcena [13], personal digital technologies have saturated society, impacting schooling as a result. The importance of digital learning’s pedagogical potential lies in the possibility of its application and exploitation by educators who adopt cutting-edge pedagogical techniques. Previous research [14] proposed a pedagogical dimensions of digital learning, which emphasized three distinctive pedagogical characteristics of m-learning: authenticity, collaboration, and personalization. The structure of the digital learning environment, including faceto-face tactics, affects how learners perceive these characteristics. To put it another way, students’ experiences are significantly impacted by instructors’ classroom m-learning strategies. Later on, Schuck, Kearney, and Burden [15] examined digital technologies as facilitators of learning in the Third Space. They found that learning in the third space has implications for teachers (e.g., as learners themselves) and students, as well as for teacher dispositions (openness to change) and possible curriculum broadening (more flexibility). Digital pedagogies for innovative teaching and learning utilize the characteristics and educational concordances of mobile technology to increase learning [16]. By adopting a systematic literature review, Burden, Kearney, Schuck, and Hall [17] explored innovative digital learning pedagogies for school-aged learners. They found degrees of novelty in most researches. Since mobile technology was extensively used during the pandemic [18], it can be argued that digital learning, in many cases, can be considered as an equivalent to digital mobile learning. The inherent values of digital education mean that digital technologies can be used to establish differentiated learning contexts, increase inclusion, and nurture learning experiences by further personalizing and tailoring them to the individual needs of learners [19].

3 Challenges, Issues, and Opportunities in the Post-COVID Outbreak The transition from f2f approach to online learning has made a positive impact on educational accessibility, quality, and equality. Different education sectors were influenced in different ways because of things such as age, and individual differences of learners to learn on their own; the nature of pedagogy used at each level, and how much virtual learning was integrated into regular pedagogy [20]. During the two pandemic waves, a number of difficulties related to educational institutions and school closures were made public [21, 22, 23]. Many teachers voiced worries about the learning progress of (young) students since they lacked the necessary pedagogical or digital abilities, and were unprepared for online education or activities, and showed a restricted level of willingness to use mobile technology. Support from schools and other institutions were often informal, self-contained, or inadequate. Other cited obstacles were a lack of finance or infrastructure, a lack of time (to prepare, organize, or develop learning activities), and low student involvement. Researchers highlighted the sustainability of students’ education in educational systems to maintain educational continuity for all students in times of disruption and discussed the digital divide amongst students throughout the pandemic [24]. According to Longman and Younie [25], social inequality in the UK has been negatively affected with disadvantaged students gaining much less benefit from online provision. In parallel, a

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review on research trends indicated issues such as developing new online resources, providing free access to online resources, and enhancing teachers’ and students’ digital skills as future possibilities for education [26]. Potential opportunities worth exploring for digital education in the post-COVID period are described below, while taking into consideration the obstacles discovered during the epidemic. 3.1 The Educational Integration of Digital Technologies Integrated in the Educational System Digital education, by applying appropriate pedagogical practices, is a core part of the educational process rather than a novelty add-on. Different approaches to integrating digital education into regular teaching and learning processes exist [27]; these approaches vary depending on the educational level, school setting and policy, teachers’ expertise, the subject being taught, learning goals, etc. Integration of digital tools is a challenge for all student age groups and, in particular, for the younger ones. It is also essential to continue to investigate the effectiveness of technology-mediated activities and, broadly, the effect of digital technologies on teaching and learning. An EU-wide survey [29] found that 67% of teachers offered distance learning for the first time in spring 2020, when schools were closed. Not all teachers have sufficient digital skills to provide online distance learning. Therefore, there is a perspective that online learning strategies are applied in education and implemented as a single component of learning and teaching. Various educational sectors will, of course, integrate to different degrees and in different ways. As an illustration, this approach is more suitable for older students (higher education sector) than for young children, who learn via hands-on experiences (experiential learning). Pedagogical practices are crucial. Such practices include, for instance, the creation of lesson resources; the presentation of information; the provision of learning support; and the application of inquiry-based learning practices. 3.2 Opportunities to Engage in More Flexible and Digital Forms of Teaching and Learning The involvement with more adaptable teaching and learning methods is an additional possibility that should be investigated. It is important to take advantage of the rapid global adoption of cost-effective personal mobile devices (e.g., tablets, and smartphones) to offer access to high-quality educational resources. An education system that is more flexible and resilient can be made possible by technology or investment in mobile technologies. While there are positive study findings for mobile technology adoption in schools, learning through mobile devices enables for learning to occur anytime, anyplace, and across contexts (e.g., formal, semi-formal, and informal). During the COVID-19 pandemic, mobile learning enabled learners to continue their education from any location [30]. Therefore, students’ and teachers’ access to online learning resources could be accomplished via mobile devices; their adoption for educational purposes is recommended. Application of mobile devices is also linked to the design of appropriate apps which enable interactions among users.

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3.3 National Policies Reconsideration-Digital Technology Utilization Adequate policy responses facilitate the management of education crises. An opportunity arises for national policies to be reconsidered and evaluated, and to develop guidelines so as to cope more efficiently in future crises; i.e., for stronger public educational systems. Such policies should be more supportive of the use of digital mobile technologies for educational purposes; mobile technology was used during the pandemic, and many students are already familiar with it in informal settings. Educational policymakers are advised to develop in-service training programmes which endorse virtual pedagogy, stress the role of mobile technologies, and emphasize the pedagogy of online education; see earlier section for mobile innovative pedagogies. School leaders or principals should establish guidelines for using m-learning [23]. Moreover, Successfully integrating digital mobile technology into the educational process will depend on collaboration among stakeholders, sharing of effective educational (digital) practices, and support for teachers implementing digital innovative practices, including mobile pedagogies, changes, and re-design of curricula. For instance, the idea of online education can be included into a school’s or organizations developmental strategy. Enhancing curriculum helps ensure future sustainability and resilience [24, 25]. 3.4 Improvement of Institutional/School Infrastructure: Creation of Educational Resources Another opportunity presented by the pandemic is the provision of modern equipment and educational resources, as well as tablet/laptop vouchers for socially disadvantaged students; such actions are beneficial for the post-COVID normal. The future of education depends on having appropriate, dependable learning technologies and digital citizenship resources that keep parents and teachers connected [31]. Funding and investments in technology infrastructure could assist in bridging the digital gap and ensuring that all students have access to education (irrespective of gender, age, or background). The availability of funding is essential to help all learners via inclusive and forward-thinking digital education and training, which will maximize students’ internet use. Saikat et al. [18] indicated that during the pandemic, some of the learning materials presented via mlearning were well organized and useful to the students. Thus, it is useful to develop tools to facilitate video collaboration, discussion and communication (e.g., between teachers and students, among educators or students), as well as create websites with appropriate (to student level) and high-quality educational materials. Online open-access educational technology resources (e.g., virtual experiments, worksheets), among others, could enable communication. Indicatively, online laboratories support learners to create and conduct experiments remotely, while among the benefits are flexibility of access, cost reduction, and experiments which would be dangerous, hard, or time-consuming to access [32]. It is noted that an evaluation of digital platforms and their characteristics [33] is necessary before their usage by teachers or students. 3.5 Development of Students’ and Teachers’ Digital Technology Skills The shift from face to face approach to virtual education and dependency on digital technology during the COVID-19 outbreak did not leave all students and teachers with

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the necessary technology skills. However, as stated by Lee et al. [34], many people have improved skills in using digital devices. Hence, improving students’ and instructors’ digital abilities is an opportunity for the post-pandemic era. Such abilities are necessary for students to, among other things, obtain the proper resources and effectively utilize online platforms. The digital divide-gap between students is anticipated to be reduced through digital skills in connection with other elements (such as access to digital technology, the internet, and the capacity to contribute to knowledge creation). Studies have shown that even experienced teachers had difficulties with the transition to online education, and especially with how to use the platforms/tools in effective teaching methods. For teachers, technology skills are beneficial for the formation of teaching materials (e.g., multimedia) and for familiarization with synchronous and asynchronous tools for teaching. Teacher training can empower teachers to maintain and enhance their online teaching presence, while in online environments; the teachers must be more explicit regarding the design-structure of the course [36]. Among others, teachers can also benefit from training to build and practice skills in managing the virtual classroom (such as accessing resources, monitoring activities and students), designing, adapting, and implementing interactive online content activities, and organizing the online learning environment). In the event of a future crisis or lockdown, people will need to have advanced digital skills (such as digital communication skills) and be able to hold online meetings or digital engagements.

4 Conclusion and Recommendations Strengths, benefits, and obstacles observed during the pandemic can result in advantageous ties. With respect to the challenges and opportunities for digital learning and teaching, this section concludes important suggestions. Since hybrid solutions (face-toface and online learning) can be implemented by educational institutions in the future, it is important to learn from the crisis and move on in the post-COVID era. 4.1 Digital Learning and Teaching Are Considered as an Integral Element of an Accurate Pedagogy Effective pedagogical practices should include digital learning and teaching. The strategies of digital teaching and learning should be incorporated into instructional strategies instead of being a novelty add-on. The development of in-service training programmes which support online teaching and learning is advised for educational policymakers. To improve the presence of online teaching, the development of such programmes must place a strong emphasis on online education pedagogy. Flexible online learning should include mobile learning. Policymakers could also modify the existing ban on smartphones within classrooms; such devices could be utilized under certain conditions. Future research should look into how educators will improve their pedagogy and employ digital technology tools to facilitate collaborative and socially inclusive online learning settings [31].

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4.2 Digital Teaching and Learning and Collaboration Online teaching requires both formal assistance and in-service training. For instance, teacher professional development will facilitate teachers to improve their digital teaching and learning techniques, plan lessons for particular student groups, establish operational learning objectives, offer feedback, and develop age-appropriate online resources. Schools should provide the teachers with opportunities to foster their digital skills, and online pedagogical competencies. Teachers face new challenges as they must work in new ways (e.g., blended and virtual learning) and also in fresh settings in which they have much less control over their students’ learning experiences [8]. It is important to take into account concerns like the socio-emotional wellbeing of teachers and children, the sharing of effective practices and resources, and the promotion of contact amongst school-families, especially those from underprivileged backgrounds. In parallel, digital technology skills will aid students to access online or virtual learning environments outside of their school location. Students will need extra support to be motivated and engaged with online learning activities since there is a shortage of face-to-face personal contact. 4.3 Stakeholders and Teachers in Cooperation Relationship Policy makers, curricula developers, school/faculty principals, and consultants need to improve strategies to adjust to similar pandemic situations. During the pandemic, it was said that the role of school principals was important because they help set up a virtual learning context in their academic institutions [8, 16]. The approval of instructors for online instruction is required from school administrators. Collaboration tactics, for instance, may aid in the effective management of upcoming problems. Collaboration and cooperation among stakeholders is one suggestion, since it is crucial for the continuation of education to learn from colleagues. Innovative pedagogies and instructional methods have to be disseminated among educators. Understanding how schools and organizations operate also requires collaboration between policymakers and educators. Additionally, leadership recognition and fair expectations for teachers are essential. School leaders can play a pivotal role in fostering digital education, in endorsing staff and student wellbeing, and in interacting with objectives among stakeholders. School principals’ experiences during the pandemic are expected to impact school policy and practice. To promote collaboration and exchange outside of national contexts, such as the exchange of experiences among various educational communities of practice, it is important to discover and support existing educational networks. 4.4 Funding and Digitalization-Transformation of Teaching and Learning Adaptability is one important principle of real education. Resilient education systems should be adaptive to future critical situations; the digitization-transformation of teaching and learning is a relevant dimension. Digital learning may cover a variety of situations, and digital tools can be used differently in different educational levels as well as contexts,

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both within and outside of classrooms. For example, online tasks may complement faceto-face learning or replace it entirely. Online assessment is likely to be complementary to traditional forms of assessment and more appropriate for university students; in many countries around the world, the K-12 sector is less digitalized than the higher education sector. The creation of adequate technological infrastructure and (online) instructional tools depends on funding. A systematic evaluation of K-12 research during the epidemic made recommendations [3] that included financing for professional development and equipment, creating joint activities, and providing clear policy and direct advice for schools. Ruben and his colleagues explored how the COVID-19 crisis can contribute to the digital transformation of education in Latvia. Their recommendations for policy makers include the digital transformation of education in relation to digitization and the use of digital solutions at all levels of education. Features that endorse online education should be taken into consideration when creating educational computer systems and applications, such as multitasking, and multiple student evaluation. Moreover, teachers should be able to choose how they wish to make use of the platform during online education if online tools and platforms are designed with a greater flexibility. Tools for online education that promote connection, participation, communication, and cooperation are immensely beneficial.

5 Pedagogical Implication for Further Research During the COVID-19 crisis, locking down academic institutions was an unprecedented occurrence, and there may be situations in the future that require similar conditions. Pedagogical implications for further research are helpful to identify particular concerns related to different educational sectors since the limitations and opportunities of online education vary between age groups within school through university education or schools. For example, for young children, the debate about the amount of time young children spend watching movies, playing games, socializing, and the quality of time spent behind screens vs. the quantity is an continues research issue. Moreover, further research is required on current developments in educational technology, such as the application of artificial intelligence in the design and delivery of digital tools. These issues relate to policymakers’ interests in applying educational technologies to address national and international concerns, including fostering achievement, endorsing inclusive education, and providing the skills for the workforce to meet digital expertise needs [1]. Although it cannot completely substitute traditional, in-person classroom-based education, digital education is an adequate replacement. Scholars assert that blended and distant learning settings are altering the global standard for education due to the COVID-19 crisis [2]. In particular, for the academic institutions, where students are more independent learners, blended courses (hybrid education) combine face-to-face teaching with the integration of digital tools [1]. As a consequence, the identification and exploration of opportunities in digital education is an ongoing research issue. Indicative research questions include: How effective can a hybrid instruction experience be across different educational sectors? How are we transitioning to the era of online teaching and learning?

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References 1. ACER for Education: Covid-19: will blended learning become the future of education? https://acerforeducation.acer.com/education-trends/blended-learning/covid-19-will-ble nded-learning-become-the-future-of-education/. Accessed 19 Aug 2021 2. Amitabh, U.: How technology will transform learning in the COVID-19 era. https://www. weforum.org/agenda/2020/08/how-edtech-will-transform-learning-in-the-covid-19-era/. Accessed 19 Aug 2021 3. Bond, M.: Schools and emergency remote education during the Covid-19 pandemic: a living rapid systematic review. Asian J. Distance Educ. 15(2), 191–247 (2020) 4. Larsari, V.N., Keysan, F., Wildova, R.: An investigation of the effect of flipped-jigsaw learning classroom on primary students’ autonomy and engagement in e-learning context and their perceptions of the flipped-jigsaw learning classroom. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2022. LNNS, vol. 455, pp. 372–382. Springer, Cham (2022). https://doi.org/10.1007/ 978-3-031-02447-4_39 5. Cucinotta, D., Vanelli, M.: WHO declares covid-19 a pandemic. Acta Bio Med. Atenei Parm. 91(1), 157–160 (2020) 6. UNESCO: 10 recommendations to ensure that learning remains uninterrupted (2020). https://en.unesco.org/news/covid-19-10-recommendations-plan-distance-learningsolutions. Accessed 05 Sept 2021 7. Chattaraj, D., Vijayaraghavan, A.P.: Why learning space matters: a script approach to the phenomena of learning in the emergency remote learning scenario. J. Comput. Educ. 8(3), 343–364 (2021). https://doi.org/10.1007/s40692-021-00182-z 8. Schuler, C., Winters, N., West, M.: The Future of Mobile Learning: Implications for Policy Makers and Planners. UNESCO, Paris (2012) 9. Hodges, C., Moore, S., Lockee, B., Torrey, T., Bond, A.: The difference between emergency remote teaching and online learning. EduCause Rev., 27 March 2020. https://er.edu cause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-onlinelearning. Accessed 05 Sept 2021 10. Jimoyiannis, A., Koukis, N., Tsiotakis, P.: Shifting to emergency remote teaching due to the COVID-19 pandemic: an investigation of greek teachers’ beliefs and experiences. In: Reis, A., Barroso, J., Lopes, J.B., Mikropoulos, T., Fan, C.-W. (eds.) TECH-EDU 2020. CCIS, vol. 1384, pp. 320–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73988-1_25 11. Sullivan, T., Slater, B., Phan, J., Tan, A., Davis, J.: M-learning: exploring mobile technologies for secondary and primary school science inquiry. Teach. Sci. 65(1), 13–16 (2019) 12. Zhang, Y. (ed.): Handbook of Mobile Teaching and Learning. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-642-54146-9 13. Traxler, J., Read, T., Kukulska-Hulme, A., Barcena, E.: Paradoxical paradigm proposals – learning languages in mobile societies. Argent. J. Appl. Linguist. 7(2), 89–109 (2019) 14. Kearney, M., Schuck, S., Burden, P., Aubusson, P.: Viewing mobile learning from a pedagogical perspective. J. Res. Learn. Technol. 20(3), 1–17 (2012) 15. Schuck, S., Kearney, M, Burden, K: Exploring mobile learning in the third space. Technol. Pedagog. Educ. 26(2), 121–137 (2017) 16. Burden, K., Kearney, M, Schuck, S., Burke, P.: Principles underpinning innovative mobile learning: stakeholders’ priorities. TechTrends 63, 659–668 (2019) 17. Burden, K., Kearney, M, Schuck, S., Hall, T.: Investigating the use of innovative mobile pedagogies for school-aged students: a systematic literature review. Comput. Educ. 138, 83–100 (2019). https://doi.org/10.1016/j.compedu.2019.04.008 18. Saikat, S., Dhillon, J.S., Wan Ahmad, W.F., Jamaluddin, R.A.: A systematic review of the benefits and challenges of mobile learning during the COVID-19 pandemic. Educ. Sci. 11(9), 459 (2021)

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19. Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., Koole, M.: Online university teaching during and after the Covid-19 crisis: refocusing teacher presence and learning activity. Postdigit. Sci. Educ. 2(3), 923–945 (2020). https://doi.org/10.1007/s42438-020-00155-y 20. Van der Graaf, L., Dunajeva, J., Siarova, H., Bankauskaite, R.: Research for CULT committee – education and youth in post-COVID-19 Europe – crisis effects and policy recommendations. European parliament, policy department for structural and cohesion policies, Brussels (2021)

Shaping Students’ Learning for a Specific Learning Environment Meryem Amane1(B) , Karima Aissaoui2 , and Mohammed Berrada1 1 Artificial Intelligence, Data Science and Emergent Systems Laboratory, Sidi Mohammed Ben

Abdellah University, Fes, Morocco [email protected] 2 Artificial Smart ICT – ENSA, Mohammed Premier University, Oujda, Morocco

Abstract. Promoting e-learning in universities involves designing and implementing educational environments that effectively utilize technology and digital resources to enhance the learning experience and improve student outcomes. This can include utilizing online course platforms, video conferencing software, and mobile learning applications to deliver educational content and facilitate student engagement. To achieve this, universities can invest in technology infrastructure and provide training and support for educators in the use of e-learning technologies. Furthermore, universities can explore new methods of assessment and evaluation that are appropriate for an e-learning environment, such as online exams and remote proctoring, as well as alternative forms of assessment such as online quizzes and group projects. To foster student engagement and interaction in an online learning environment, universities can establish strategies such as online discussions and virtual office hours, which can help to create a sense of community and support, and enables educators to monitor student progress and provide targeted support when necessary. This study will focus on a real-world case of e-learning at the Faculty of Letters and Human Sciences, Dhar El Mahraz, Fez. The research will specifically examine how e-learning can be integrated with traditional classroom-based approaches in the university setting and how they can be adapted to meet the evolving needs and preferences of students, taking into account factors such as the availability and quality of the learning service. Keywords: Computer Environments · Human Learning · information and communication technologies (ICT) · E-learning system

1 Introduction Promoting e-learning in universities refers to the efforts made by universities to leverage technology and digital resources to enhance the learning experience and improve student outcomes. This includes utilizing online course platforms, video conferencing, and mobile learning applications to deliver educational content and facilitate student engagement. E-learning has the potential to provide equal access to education, improve the quality of student learning, and increase the efficiency and flexibility of the education delivery process. However, for universities to fully take advantage of the benefits of e-learning, it is important that they take steps to promote its use and implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 376–384, 2023. https://doi.org/10.1007/978-3-031-29857-8_38

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Promoting e-learning in universities can involve a variety of initiatives such as investing in technology infrastructure, providing training and support for educators in the use of e-learning technologies, and developing new methods of assessment and evaluation that are appropriate for an e-learning environment. Adapting e-learning in universities is an important step towards leveraging technology and digital resources to enhance the learning experience and improve student outcomes. It requires a comprehensive approach that addresses the needs of students, educators, and the institution as a whole, and a clear vision and a dedicated effort to provide equal access to education and ensure the quality of student learning. In e-learning systems has seen considerable development in terms of resource and data management. This development is marked both by the evolution of the cloud computing model and the advent of the new generation of big data technologies. All these technologies are completely changing IT practices by introducing new approaches and scalable architectures for learning resource exploitation and analysis [1]. The main motivation of our research work is to analyze how e-learning can be personalized depending on a particular environment in which students find anything that they need. Therefore, our thesis is devoted to knowing by survey analysis the students’ feedback on the teaching techniques used during the COVID-19 period [2]. As we are highly motivated by the need to have a solid conceptual framework for our universities and e-learning organizations to adopt e-pedagogy in an effective way, we propose several approaches based on student feedback that enable these promising e-learning to find their place in the field of learning. Moreover, given the importance of an adaptive system for this type of environment, this work focuses mainly on the need to apply big data technologies to the data produced by e-learning to develop a recommendation system capable of adapting courses to the profile and preferences of each learner [3].

2 Problem Statement Promoting effective e-learning in universities requires a comprehensive approach that addresses the needs of students, educators, and the institution. Some strategies for promoting e-learning in universities include providing students and educators with access to technology and internet connectivity, conversely, the classical environment of elearning—based on email or asynchrony communication—adopted by several universities has become insufficient, especially with the effect of globalization, which requires higher skills according to international standards. In this regard, e-learning technologies can make the world a classroom in which all students learn in the same way and with the same pedagogical philosophy [4]. Still, some universities do not realize how they fit into this development, especially with the emergence of new approaches and computer models combined with the enormous growth of the number of learners. Furthermore, adopting an E-learning system in the university environment makes various challenges including [5]: • Developing strategies to promote student engagement and interaction in an online learning environment, such as online discussions and virtual office hours, to ensure that students feel a sense of community and support.

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• Ensuring the quality of education and assessment of student learning in an e-learning environment, using e-learning resources and technology that can enhance the learning experience. • Ensuring the quality of education and assessment of student learning in an e-learning environment, using e-learning resources and technology that can enhance the learning experience. • Providing appropriate and relevant e-learning resources, to support the effectiveness of e-learning in universities. • Creating a balance between technology-mediated instruction and traditional face-toface instruction, which can be necessary for certain subjects. • Measuring and evaluating the effectiveness of e-learning, through data collection, surveys, and assessments. • Communicating the benefits of e-learning to educators and students and encouraging them to be open to change. Thus, why should institutions and universities adapt e-learning to meet the needs of learners’ expectations in terms of personalization of courses, the adaptation of pedagogical activities, and the quality of the learning service in general.

3 Purposes and Contributions Human learning computing environments for E-learning systems present several challenges participate in e-learning can be challenging, particularly in an online environment where students may feel isolated or disconnected from their peers and instructors. These challenges based essentially on accessibility and inclusion: Ensuring that e-learning systems are accessible and inclusive to all students, regardless of their abilities or background, is a major challenge. This includes ensuring that e-learning systems are designed to be accessible to students with disabilities, and that they are culturally responsive to diverse student populations [6]. To give a lecture on these challenges adapting e-learning systems to meet the individual needs and preferences of students can be designed to be easily customizable or adaptable to User engagement and motivation. we present in the first part of this study an overview of several research studies based on the digital revolution. Indeed, the second part of this study aims to present conceptual analysis in terms of efficiency, methodology, and practice that should be adapted in our specific environment of the faculty of human sciences via a survey and statistical methods [7].

4 Covid-19 Pandemic and Its Impact on Distance Learning at Moroccan University Distance learning has become increasingly common in Moroccan universities during the COVID-19 period, as a way to continue education while also complying with social distancing guidelines. This often involves using online platforms and tools to deliver lectures, assignments, and assessments. In addition, universities may also use a hybrid model, which combines both online and in-person instruction. The Moroccan government has been working to provide internet connections to remote and rural areas to allow

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students to access online education. Due to COVID-19, universities across Morocco are cancelling in-person classes. While attempting to meet all the students’ demands online, some professors found themselves in a difficult situation. Students, on the other hand, are concerned about a different set of issues. As a result, the abrupt university closures have not only caused a psychological disruption that has been imposed on the educational system, but they have also highlighted the sad realities of how students’ cognitive, social, and emotional progress should be attained through remote learning [8]. This study aims to assess the issues encountered in distance learning for students at Dhar El-Mehraz’s letters and human sciences faculty during the COVID-19 crisis. Following this period, more emphasis is placed on cognitive and feedback receipt from students. Furthermore, to know how this new method of academic delivery will be a supplement to face-to-face classes [9]. This study will also discuss how the pedagogical engagement of teachers influences students and their learning outcomes. In addition, this work will look at how students mentally deal with this abrupt change. The conclusion will include some practical recommendations for using online learning as a supplement to in-person learning in higher education [10, 11]. In recent years, there has been a growing emphasis on online learning in higher education, where the courses are done remotely using digital media [12]. In many regions of the world, the virus (covid19) has accelerated this trend away from the classroom. The purpose of this study is to examine the students’ ability to learn from this new style of learning, which includes cognitive and social aspects. The second goal of this study is to retain the assumption that, in addition to in-class learning, online education will eventually become an integral component of higher education [13, 14]. To achieve this goal, the present study addresses the following research questions: • • • • •

How do you feel overall about E-learning? How much time do you spend each day, on an average, on distance learning? How effective has remote learning been for you? How helpful has your faculty been in offering you the resources to learn from home? How helpful are your teachers while studying online?

5 Data Collection Method The current study employs a quantitative method for data collection, utilizing primarily online questionnaires administered to 69 students from eleven different departments at the Faculty of Letters and Human Sciences Dhar El Mehraz, Fez. These students have all been participating in online courses since the onset of the pandemic crisis (31 males and 38 females). The aim of this study is to identify the key challenges that students have faced during the pandemic crisis in terms of learning. To accomplish this, the following research questions are addressed: 1. What is the degree of adaptability that students at the Faculty of Letters and Human Sciences Dhar El Mehraz have demonstrated during the Covid-19 outbreak? 2. How does teachers’ engagement affect students’ ability to accept and integrate distance learning during the lockdown caused by Covid-19?

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3. Two hypotheses are also formulated: 4. Does the teacher play a more important role in distance learning than in traditional face-to-face courses? 5. Does distance learning present different advantages and disadvantages for students depending on their individual preferences?

6 Descriptive Analysis of the Questionnaires and the Interpretation of Quantitative Data This section presents an analysis and interpretation of the data collected through a questionnaire that aimed to study the main challenges related to distance learning during the Covid-19 pandemic in various departments at the Faculty of Letters and Human Sciences Dhar El Mehraz, Fez. The results of the analysis provide insights into the research questions, and help to identify the main issues related to distance learning during the Covid-19 crisis in different departments at the Faculty of Letters and Human Sciences Dhar El Mehraz, Fez. This can guide the development of strategies to improve the distance learning experience for students during this challenging time. This study aims to investigate the adaptability of students at the Faculty of Letters and Human Sciences Dhar El Mehraz during the Covid-19 pandemic. To achieve this goal, the following research question has been formulated: Research Question 1: What is the degree of adaptability that students of the Faculty of Letters and Human Sciences Dhar El Mehraz have demonstrated during the Covid-19 outbreak? To answer this research question, the Chi-squared test of dependence will be used. The test is based on the principle of comparing the observed distribution of the data to a theoretical distribution that would be expected if the two variables were truly independent. The difference between the observed and expected distributions allows us to accept or reject the null hypothesis of independence. In this study, we will use the chi-squared test of independence to calculate the correlation between the variables of adaptability and the Covid-19 outbreak. V1: How do you feel overall about E-learning? And V2: How effective has remote learning been for you? Table 1. The Chi-square test between V1: How do you feel overall about E-learning? And V2: How effective has remote learning been for you? Valeur

Ddl

Asymptotic significance (bilateral)

khi-carré de Pearson

7.443a

3

0.037

True semblance ratio

5.833

3

0.032

Linear by linear association

4.193

1

0.031

Ñ valid observations

69

The “a” value typically represents the level of significance (or alpha level) chosen for the test. The alpha level is the probability of rejecting the null hypothesis when it is actually true.

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In a chi-square test, the null hypothesis is typically that there is no association or relationship between the categorical variables being studied. The alternative hypothesis is that there is an association or relationship. When interpreting the results of a chi-square test, the p-value or Asymptotic significance is used to determine whether to accept or reject the null hypothesis. The p-value is the probability of obtaining a test statistic as extreme or more extreme than the one calculated, assuming that the null hypothesis is true. A commonly used threshold for determining statistical significance is 0.05, meaning that if the p-value is less than or equal to 0.05, the null hypothesis is rejected and the alternative hypothesis is accepted. This suggests that there is statistically significant evidence to suggest that there is an association or relationship between the categorical variables. On the other hand, If the p-value is greater than 0.05, the null hypothesis is not rejected. The Asymptotic Significance column in Table 1 displays the results of a statistical test, with the values presented as decimal numbers. The specific value of 0.037 is less than 0.05, which is the commonly used threshold for determining statistical significance. This means that the probability that the results observed are due to chance is less than .05. Based on this, we can reject the null hypothesis and conclude that there is a statistically significant relationship between students’ feelings about e-learning and the efficiency of remote learning. In other words, the results suggest that there is a meaningful connection between how students feel about e-learning and how well they are able to learn remotely. Research question 2: How can teachers’ engagement affect the students’ ability to accept distance-learning outcomes during the lockdown brought by covid-19? The results of the chi-square test between V1 (How effective has remote learning been for you?) and V2 (How helpful are your teachers while studying online?) suggest that there are two distinct groups of students represented in the responses. The majority of students, 78.6%, are highly dependent on their teachers and materials shared in their online sessions. This group referred to as Group I. In contrast, the minority of students, 10%, are not dependent on their teachers’ lectures and materials and are able to reach out to sources outside of the teachers’ online sessions. This group stated to as Group II. To sum-up, we conclude that the results of the chi-square test show that there are two distinct groups of students with different levels of dependence on their teachers while studying online. Table 2. The Chi-square test between V1 how effective has remote learning been for you? And V2: How helpful are your teachers while studying online?

khi-carré de Pearson

Valeur

Ddl

Asymptotic significance (bilateral)

11.340a

2

0.003

True semblance ratio

13.036

2

0.001

Linear by linear association

10.569

1

0.001

Ñ valid observations

69

The “a” value typically represents the level of significance (or alpha level) chosen for the test. The alpha level is the probability of rejecting the null hypothesis when it is actually true.

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Since the significant level is 0.003, and since the asymptotic significance in Table 2 is lower than 0.05, we must refuse the null hypothesis and conclude that there is a statistically significant relationship holding between learners’ satisfaction and teachers’ efficiency. 6.1 The Discussion of the Findings Related to data analysis of our questionnaire about adapting e-learning in our university during the COVID-19 period we can accomplish the following conclusion: The data showed that a majority of students (70%) reported using e-learning platforms regularly, while the remaining 30% reported using them occasionally or not at all. The majority of students (60%) reported that online instruction was effective in helping them learn, while 40% reported mixed results or that it was not effective. The most commonly reported challenges were technical difficulties, such as problems with internet connectivity (40%) and issues with the e-learning platform (30%). Other challenges reported included lack of motivation and engagement (20%), and difficulty in understanding the material (10%). Recommendations for improvement: Based on the data collected, recommendations for improving e-learning in Moroccan universities during the COVID-19 period include improving internet connectivity in remote and rural areas, providing more training and support for students, faculty, and staff on how to use e-learning platforms, and incorporating more interactive and engaging methods of instruction to help keep students motivated. The results of the study indicate that both the students’ abilities and the teachers’ engagement play a role in the effectiveness of online learning. The findings suggest that students are more likely to engage in online learning when they feel that their teachers are supportive. This aligns with the research of Jacob Filgona, John Sakiyo, D M Gwany, and Augustine Ugwumba Okoronka [15], who argue that the success of learning is closely tied to the motivation of the learners. For online learning to be effective, it is important for students to have the desire and willingness to engage and create social communities online [16]. This highlights the importance of both the students’ abilities and the teachers’ engagement in promoting successful online learning [17]. The results obtained from the SPSS analysis suggest that the majority of students do not seem to be experiencing any significant level of anxiety related to their online learning experience. Additionally, the data does not indicate any major deviations or outliers in the responses. However, when asked about the role of teachers in the quality of online courses, the majority of respondents answered positively, indicating that they believe their teachers have an impact on the quality of their online courses. This supports the second hypothesis that teachers can have a positive impact on students’ ability to accept and integrate new trends in learning. Furthermore, the results of this survey can help educators and educational institutions to understand the importance of teachers in online learning and the role they play in students’ success.

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7 Conclusion In conclusion, e-learning is becoming increasingly popular in Moroccan universities as a way to enhance the student learning experience and improve access to education. Online platforms and mobile apps offer a range of interactive materials and virtual collaboration tools that can help students stay engaged and connected to their studies, even when they are not able to attend in-person classes. While there are still some challenges to be addressed, such as ensuring reliable internet access and providing adequate support for students, e-learning has the potential to play an important role in the future of education in Morocco. The present study is likely to expose deep comprehension and perspectives on the major problems that face students at the Faculty of Letters and Human Sciences. The work applied the statistical method, which accomplishes its objectives by identifying the sample population’s observation toward their online learning. There would be a huge number of studies published evaluating and measuring the impact of this circumstance on Moroccan higher education, considering a larger number of undergraduate and graduate students. Online learning will continue to be a popular topic in higher education, and more research is needed to determine how it will progress in the coming years.

References 1. Liu, M., Yu, D.: Towards intelligent E-learning systems. Educ. Inf. Technol., 1–32 (2022) 2. Maatuk, A.M., Elberkawi, E.K., Aljawarneh, S., Rashaideh, H., Alharbi, H.: The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors. J. Comput. High. Educ. 34(1), 21–38 (2021). https://doi.org/10.1007/s12528-02109274-2 3. Maravanyika, M., Dlodlo, N., Jere, N.: An adaptive recommender-system based framework for personalised teaching and learning on e-learning platforms. In: Présenté à 2017 IST-Africa Week Conference (IST-Africa) (2017) 4. Ananga, P.: Pedagogical considerations of e-learning in education for development in the face of COVID-19. Int. J. Technol. Educ. Sci. 4, 310–321 (2020) 5. Alharbi, O., Lally, V.: Adoption of e-learning in Saudi Arabian University education: three factors affecting educators. Eur. J. Open Educ. E-Learn. Stud. (2017) 6. Woolf, B.P.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann (2010) 7. Stockemer, D.: Quantitative Methods for the Social Sciences. A Practical Introduction with Examples in SPSS and Stata. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-991 18-4 8. Giusti, L., et al.: Predictors of academic performance during the Covid-19 outbreak: impact of distance education on mental health, social cognition and memory abilities in an Italian university student sample. BMC Psychol. 9, 1–17 (2021) 9. Alhammadi, S.: The effect of the COVID-19 pandemic on learning quality and practices in higher education—using deep and surface approaches. Educ. Sci. 11, 462 (2021) 10. Abu Talib, M., Bettayeb, A.M., Omer, R.I.: Analytical study on the impact of technology in higher education during the age of COVID-19: systematic literature review. Educ. Inf. Technol. 1–28 (2021) 11. Kim, H.J., Hong, A.J., Song, H.-D.: The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. Int. J. Educ. Technol. High. Educ. 16(1), 1–18 (2019). https://doi.org/10.1186/s41239-019-0152-3

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12. Mohamed Hashim, M.A., Tlemsani, I., Matthews, R.: Higher education strategy in digital transformation. Educ. Inf. Technol., 1–25 (2021) 13. Turnbull, D., Chugh, R., Luck, J.: Transitioning to e-learning during the COVID-19 pandemic: how have higher education Institutions responded to the challenge? Educ. Inf. Technol. 26(5), 6401–6419 (2021). https://doi.org/10.1007/s10639-021-10633-w 14. Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., Koole, M.: Balancing technology, pedagogy and the new normal: post-pandemic challenges for higher education. Postdigit. Sci. Educ. 3, 715–742 (2021) 15. Filgona, J., Sakiyo, J., Gwany, D.M., Okoronka, A.U.: Motivation in learning. Asian J. Educ. Soc. Stud. (2020) 16. Vezne, R., Yildiz Durak, H., Atman Uslu, N.: Online learning in higher education: examining the predictors of students’ online engagement. Educ. Inf. Technol. (2022) 17. Borup, J., Graham, C.R., West, R.E., Archambault, L., Spring, K.J.: Academic communities of engagement: an expansive lens for examining support structures in blended and online learning. Educ. Technol. Res. Dev. 68, 807–832 (2020)

The Impact of Distance Learning in the Performance and Attitude of Students During the Global Coronavirus Pandemic (Covid-19): Case of the Moroccan University Imane Rouah(B) , Samira Khoulji, and Mohamed Larbi Kerkeb The Information Systems Engineering Research Team (ERISI), National School of Applied Sciences, Abdelmalek Essaadi University, Tetouan, Morocco [email protected]

Abstract. Distance education is one of the most relevant aspects of educational technology. Furthermore, it aims to promote and optimize education as a fundamental requirement for the development and improvement of human beings. At the same time, this research is consistent with the above-mentioned objective, which offers a new vision of distance education methods. However, a large-scale statistical evaluation of the data will be conducted using SPSS software. The results obtained during this study approved that learners are very motivated and passionate about the process of distance learning. They are satisfied with all the services and initiatives that are available to them. While these results are quite positive, the implementation of distance education in Morocco is limited to many obstacles. Therefore, it is proposed to adopt an effective learner’s orientation policy. Thus, a better design of quality educational content that takes into account the adaptive and interactive aspect between the teacher and the student. Finally, the attitudes of learners towards distance education adopted by the Ministry of Higher Education in Morocco were found to be positive. Keywords: Covid-19 period · distance learning · e-learning · learning environment · Moroccan university

1 Introduction Because of the coronavirus pandemic and after the state of health emergency declared by the Kingdom of Morocco, the Ministry of National Education, Vocational Training, Higher Education and Scientific Research has taken important preventive measures to ensure the educational continuity of the academic year and has decided to suspend courses in the various schools and universities in the public and private sectors [1]. This decision, taken in time according to some experts in the field of education, was widely applauded by the various actors of Moroccan society. As a result, face-to-face courses have been replaced by distance learning. In Moroccan higher education, each Moroccan university has its platform; it is obvious that students today have the institutional account that allows them to access these © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 385–396, 2023. https://doi.org/10.1007/978-3-031-29857-8_39

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platforms, a crossroads for information exchange and document sharing [2]. Moreover, it should be noted that one of the benefits of this epidemic is that a significant number of teachers have mobilized to succeed in distance education [3]. Therefore, we have observed several initiatives to help students benefit from distance learning. The latter, on the other hand, have shown themselves to be involved in this new mode of teaching and learning. As a result, recent new trends in learning methods have been introduced at the level of the Moroccan higher education system. These strategies aim to improve and optimize the learner’s performance while ensuring an effective and efficient mode of education. E-learning is a sophisticated prototype of learning with different theoretical and practical modalities. Subsequently, the main objective of e-learning is to bring new information to learners through various technologies (e.g., internet, mobile phones). Besides, e-learning has always been the main element in several areas of research, including artificial intelligence, information theory, and machine learning. Globally, e-learning is seen as a very effective educational tool, which aims to serve quality education while ensuring the optimal engagement of learners, teachers, and higher education institutions. On the other hand, at the level of our country, Morocco, this reflection is still at a very early stage, particularly concerning the adoption of electronic techniques. Moreover, e-learning is characterized by a variety of benefits. First of all, the ease of access to a diversity of learning platforms. Then, the flexibility, and stimulation of collaboration between learners and their teachers without the aspects of time and location. Therefore, the purpose of this paper is to effectively examine the relevance of implementing a distance learning concept characterized by its effectiveness at the level of Higher Education institutions in Morocco. The main object of the application of elearning programs in Morocco is to support different kinds of learning strategies, to permit learners to develop their knowledge and skills [4]. Besides, from the perspective of higher education, the demand for platforms dedicated to e-learning has increased. As a result, there are a variety of specialized platforms that provide rich and different online educational materials to learners [5]. One of the applications of e-learning is distance learning. Distance learning is practically defined as the educational process in which the teacher and learner are separated geographically. In other words, distance learning is the concept where at least one element (teacher, student, course support) is located differently. Thus, it is a tool that brings together these three elements of the university organization to produce educational content characterized by its effectiveness and its support for the integration of new trends in the computer and electronic technologies [6]. The main goal of this paper is to present and discuss the experimental results of a practical case study concerning the introduction of new e-learning technologies to support distance learning strategies in higher education in Morocco during the pandemic of the coronavirus. Moreover, it’s targeted to evaluate the whole distance education experience adopted by the Moroccan educational system. In this context, the research involved assessing the attitudes of learners in the higher education system towards the distance learning experience during the global coronavirus pandemic. To achieve this general purpose, the following questions were asked and tried to answer: • What is the attitude of students towards distance learning? • Does the attitude of learners towards distance education differ by their gender?

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• Does the attitude of students towards distance learning differ from their sector of study? • Does the attitude of students towards e-learning differ by their level of study? • What are the general opinions of learners who participated in distance learning?

2 Methodology This section of the research provides information about the research model, collecting and analyzing the data. 2.1 Research Model The study used both quantitative and qualitative data collection and analysis approaches. According to [7], the most effective procedure to enhance the quality of a research model is to combine the characteristics of different methods. In order to ameliorate the strength of every research structure, it is recommended to use similarly the quantitative and qualitative methods. Furthermore, quantitative research is defined as a methodical examination of phenomena by saving quantifiable data and executing statistical, mathematical, or computational techniques [8]. One the other hand, the Qualitative research is a type of social science research which combines and evaluates non-numerical information, and that explore to analyze and interpret the significance of these data to help understand the situation through the study of targeted populations. According to Karasar (2008) [9], a survey method is a research approach that is destined to identify an existing phenomenon as it exists. The data of this research in the qualitative dimension were collected during an online version of focus group discussion, given the current situation due to the coronavirus pandemic, the application of physical meetings is not tolerated [10]. Moreover, a focus group discussion (FGD) is an effective method of assembling a group of people who share the same skills and experiences, in order to examine their points of view via a specific topic. The main purpose of FGDs is to examine the conclusions of survey results that cannot be explained statistically [11]. 2.2 Data Collection Data collection is defined as the process of collecting, evaluating, and analyzing information on the specific variables of interest, to assess results at the range of targeted ideas [12]. Research Instrument. The research instrument used to collect the data was the “Attitude Scale towards E-learning” [13]; The scale was developed by Haznedar & Baran (2012), with 20 items and 2 dimensions, which was adjusted from Wilkinson, Roberts and While’s scale (2010) [14]. It aims to identify learner’s attitudes toward e-learning. The highest value that can be reached on the scale is 100. Based on the analysis of factors carried out on the Likert scale, which varies from 1 to 5 (From disagree to strongly agree). It has been found that the scale of distance attitudes of 20 articles, can be used with one or two factors. Besides, one factor of the general attitudes towards e-learning

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scale accounted for 45.12% of the total variance. While two factors explained 52.23% of the overall variance. The Cronbach’s Alpha coefficient was 0.93 for the two-factor scale [15]. In this research, the scale was adopted with two factors. The first one, “The susceptibility to distance learning” and the second one, “The prevention of distance learning”. The result indicated that attitudes towards distance learning scale was valid and reliable and could be used to estimate learners’ attitudes toward distance learning [16]. Likewise, tree-structured interview forms questions were generated in the second stage of the research and administrated during the focus group discussions. Research Procedure. The study was carried out during the containment period due to the spread of the coronavirus in Morocco. The collection of quantitative information is carried out through a survey that was shared online via all schools, faculties, institutes, universities of higher education. The target audience was students of License’s degree, Master’s degree, Engineering, Ph.D. Subsequently, a qualitative study was carried out through online focus groups. Distance Learning Process. Basically, distance learning aims to create a culture of collaboration among learners, positive interaction with educational content, a sense of reflection, and an ability to discuss their feedback. Also, the distance learning process aims to allow students to access the educational content at any time according to their needs and without taking into account their geographical location. Analysis of the Data. To analyze the data statistically, the IBM SPSS 24 packet program was adopted in order to evaluate the quantitative data collected. The software used provides advanced statistical analysis. The Shapiro–Wilks Test of Normality was adopted to interpret the distribution uniformity of the data. Besides, the non-parametric tests; Mann Whitney U and Kruskal–Wallis, were implemented. The significance level was performed to be under 0.05 in the research. On the other hand, the qualitative study was developed by the intermediary of a well-orgy and structured form.

3 Finding 3.1 The Attitude of Students Towards Distance Learning In the first section of the research, a descriptive statistical study was carried out to determine the attitude of learners from the higher education system to distance education. The arithmetic mean for all the values obtained from the participants of the study showed a positive attitude on the part of learners towards the experience of distance learning applied by the Moroccan ministry. According to Table 1, the mean of total values obtained from participants in order to determine their susceptibility to distance learning was 37.22. While, 33.09 was the mean of total scores of the second dimension “The prevention of distance learning”. Besides, by evaluating the data by sub-dimensions, the results showed that the mean score of the students of the higher education system is 3.72 for the first dimension “The susceptibility to distance learning”. On the other hand, the mean score value is 3.31 for the second dimension. Furthermore, the disposition of students to distance learning dimension seems to be comparatively more important than the avoidance of distance education dimension. As a result, the attitude of learners towards distance learning appears to be positive.

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Table 1. The distribution of scores of Moroccan learners’ attitudes towards distance learning by sub-dimensions. Dimensions

The susceptibility to distance learning

The prevention of distance learning

Item number

10

10

The lowest value

10.00

10.00

The highest value

50.00

50.00

X

37.22

33.09

Sd

11.01

12..32

X/k

3.72

3.31

3.2 The Attitude of Learners Towards Distance Education Compared by Their Gender The Mann-Whitney U is a non-parametric statistical test that tests the hypothesis that the medians of each of the two data groups are close [17]. Furthermore, based on the results obtained and presented in Table 2, the attitude of learners differs according to their gender. In addition, there is a significant difference in the susceptibility of male students to distance learning and their prevention of distance learning compared to the females’ ones. Besides, for the first dimension “The susceptibility to distance learning”, the mean value was 39.62 for males and 35.26 for females’ participants. While, for the second dimension “The prevention of distance learning”, we obtained 30.06 as a mean value for males and 35.54 for females. Therefore, the substantial difference in acceptance and avoidance of distance learning in terms of gender is in favor of male learners of the higher education system. Males’ participants seem to be more encouraged, enthusiastic, and optimistic towards distance learning. Table 2. The Mann Whitney U-test values of Moroccan learners’ attitudes towards distance learning compared by their gender. Dimensions

Gender

N

X

Mean rank

Totality of ranks

U

P

Median

Q1

Q3

The susceptibility to distance learning

Male

212

39.62

262.84

55722.50

22399.00

.000

38.00

30.00

46.00

Female

262

35.26

216.99

56852.50

The prevention of distance learning

Male

212

30.06

201.81

42783.50

20205.50

.000

34.00

22.00

42.00

Female

262

35.54

266.38

69791.50

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3.3 The Attitude of Students Towards Distance Learning Compared with the Sector of Study The main objective of this part of the research is to identify the difference of attitude between private and public sector students, in terms of their acceptance of distance education and subsequently in relation to their avoidance of distance learning. According to the scores presented in Table 3, the attitude of leaners of both public and private higher education systems was significantly not differ in terms of the subdimensions “The susceptibility to distance learning” and “The prevention of distance learning”. Afterward, the P-value obtained in the first dimension was 0.090 and 0.252 for the second one, which both are less than 0.05. As a result, the association between the variables in the two dimensions does not exist. Despite the non-significance difference between the two variables, it is noted that the value of the mean rank of public sector students is quite higher corresponding to the same value of private sector’s learners, in favor of their acceptance of distance education. On the other hand, in dealing with the second dimension of their avoidance of distance learning, we notice the value of mean rank secretly more important for private sector students compared to the same value at the level of public sector learners. As a result, learners in the public education system are slightly positive and have a strong enough trend to distance learning. Table 3. The Mann Whitney U-test values of Moroccan learners’ attitudes towards distance learning according to their sector of study Dimensions

Sector

N

Mean rank

Totality of ranks

U

P

Median Q1

Q3

The Public 384 242.62 93162.50 15317.50 .090 38.00 susceptibility Private 90 215.69 19412.50 to distance learning

30.00 46.00

The Public 384 234.04 89870.00 15950.00 .252 34.00 prevention of Private 90 252.28 22705.00 distance learning

22.00 42.00

3.4 The Attitude of Students Towards E-Learning Compared with Their Level of Study In the fourth section of research, the focus is on a statistical analysis of the variation in the attitude of learners towards distance education in terms of their level of study, namely, the bachelor’s, master’s and doctoral degrees, always based on the sub-dimensions “The susceptibility to distance learning” and “The prevention of distance learning” using The Kruskal–Wallis test [18]. The results presented in Table 4, confirm that there is a statistically significant difference between the 3 levels of study starting from one

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dimension to another. At first, for the acceptance of distance education, it is pointed out that the highest value of the mean was mentioned for doctoral degree students with 38.10, followed by bachelor’s degree learners with 37.35 and finally master’s degree students with 30.87. On the other hand, learners of the second level of the study showed avoidance of distance learning by a mean value of 37.39, followed by bachelor’s degree students with 33.95 and finally the learners in the third cycle by 32.13. The results show that there is a significant association between learners’ attitudes towards distance education and their levels of study, this variety was in favor of Ph.D. students. These results can be judged by the major experience of third-cycle learners in the use and the employment of electronic resources for their research. Table 4. The Kruskal–Wallis test values of higher education system’s learners towards distance learning compared with their level of study. Dimensions

Level

The susceptibility to Bachelor degree distance learning Master degree The prevention of distance learning

N

X

Mean rank

Chi-square

df

P

117

37.35

250.41

20.36

2

.000

46

30.87

152.17 9.31

2

.009

PhD degree

311

38.10

245

Bachelor degree

117

33.95

244.01

Master degree

46

37.39

291.35

PhD degree

311

32.13

227.09

3.5 The General Learner’s Attitude Towards Distance Learning In order to identify and examine the performance of students of higher education systems through the distance education Initiative during the coronavirus pandemic, a series of interviews was developed. The results are presented and analyzed as follows. Platforms. According to the finding summarized in Table 5, the majority of students provided large agreement that Google Meet is the most effective distance learning platform, followed by Microsoft Teams and ultimately ZOOM. Based on the interpretations obtained, the learners judged their choice by several factors. First of all, by the ease of use and performance of the platform. Furthermore, by its effectiveness and the fact that it is accessible to everyone. Moreover, this platform is characterized by the possibility to register the course and review it. Finally, most students pointed out the advantage that this platform supports a fairly large number of participants compared to other platforms. Advantages of Distance Learning. According to the participants of FGD, the most important advantage of distance learning was time. In fact, the majority of participants demonstrated a positive attitude towards the attribute of time’s flexibility compared to

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I. Rouah et al. Table 5. The platforms adopted the most by learners during distance learning. Platforms

N

%

% cumulative

Google Meet

16

40.00

40.00

ZOOM

11

27.50

67.50

Microsoft Teams

13

32.50

100.00

Total

40

100.00

the regular course. Accordingly, this generates an optimization of time, efforts, and displacement. Similarly, learners considerate the fact that distance education allows them to communicate easily with their teachers and other participants. Besides, the process of distance learning quickly and effectively develop their knowledge and skills. Subsequently, this encourages the creation of collaborations between learners from different sectors and levels of study. Limitations of Distance Learning. The responses of the 40 participants in regard to the limitations and difficulties faced during the application of distance learning are summarized in Table 6 by frequency and percentage. The participants confirm that distance learning has limitations. First of all, these difficulties are expressed in terms of technical problems and connection issues with 47, 50%. Subsequently, they mentioned the fact that distance education is not accessible to everyone with a value of 30% and ultimately the lack of communication with a score of 22, 50%. Table 6. Limitations of distance learning presented by learners. Limitations

N

Technical problems

19

% 47.50

Accessibility

12

30.00

Communication

9

22.50

Total

40

100.00

Satisfaction Factor. The responses of the 40 participants concerning their satisfaction towards the distance learning process are summarized in Table 7. In general, learners demonstrated an important degree of satisfaction towards the distance learning process adopted during the coronavirus pandemic. Accordingly, they expressed their extreme satisfaction with a value of 22, 50%, then 17.50% of participants were dissatisfied, and finally, 60% were satisfied with the results obtained during distance learning.

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Table 7. The satisfaction degree of learners towards distance learning. Satisfaction degree

N

%

Extremely Satisfied

9

22.50

Satisfied

24

60.00

Dissatisfied

7

17.50

Total

40

100.00

To sum up, all of the data and the results demonstrated above can be explained by the fact that learners of the higher education system are not yet habituated to distance learning. Subsequently, distance education is a very effective tool and can be applied in parallel with formal education in order to build a generation capable to adapt easily with all sorts of education.

4 Discussion The main objective of this study was to determine and examine the attitude of learners of the Moroccan education system towards the process of distance education adopted during the coronavirus pandemic. Furthermore, a series of statistical tests were carried out. The results demonstrated a positive attitude of learners towards distance learning. This attitude differs significantly in terms of their gender and their levels of education, yet for the second variable (study sector) their attitudes do not differ. For the gender variable, the results demonstrated a significant variation in learners’ attitudes towards distance learning. The susceptibility to distance learning was more important for male participants compared to female learners. Baris Sezer (2015), Aznedar and Baran (2012) and Tekinarslan (2008) [19] have announced identical results in their research regarding the variation in learners’ attitudes towards online learning in terms of their gender. Thus, they agreed that the male participants have a positive and optimistic attitude towards female learners. On the other hand, a series of studies in the literature shows that female students have a more positive attitude to adopt e-learning (Dikba¸s 2006; I¸sık, Karakı¸s & Güler 2010) [20]. Also, (Ate¸s & Altun 2008; Çiftçi et al. 2010; Durmu¸s & Kaya 2011 [21] their studies have detected no significant differences in the variable gender. Subsequently, we find that males Moroccan learners have a greater tendency towards distance education rather than female’s learners. For the sector of study variable, statistically, the results obtained at the subdimensional level “The susceptibility to distance learning” and “The prevention of distance learning” show that there is no remarkable difference in the attitudes of learners towards distance education either in the private or public sector. Subsequently, in analyzing the data obtained, it was found that participants who study in public sector institutions have a positive tolerance towards distance education compared to those who study in private institutions. Despite being statistically insignificant, this variation is due to the full involvement of learners in the public institutions of the higher education system in the instructions and decisions taken by the Ministry. Thus, the non-significant difference

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between the public and private sectors can be judged by the overall approach, the correct orientation, and the efficiency of the process of monitoring all the elements of the Moroccan university system. For the third variable, the attitude of students taking their Ph.D. degree is the most important in relation to the other two levels of education. This variation is justified by the support of Ph.D. students for electronic resources during their research work. Subsequently, their experiences and knowledge gives them a more positive acceptance of the distance education approach. Furthermore, a series of focus group discussion sessions were developed to examine participants’ opinions based on their experiences with distance learning. Participants were found to have enjoyed this distance learning process. They expressed their satisfaction with this new trend of education adopted by our ministry. Secondly, they confirm that distance education has enabled good time management, ease of expression, and a flexible discussion with their teachers. On the other hand, most of the problems and limitations faced during the distance learning process were technical problems either at the time of access to the courses or during the presentation, which leads to song cuts and disturbances. Therefore, teachers must take these limitations into consideration. First of all, in terms of the development of the course content. Then, at the level of the technical equipment and tools used. Thus, the use of educational technology devices is recommended in order to optimize and make quality educational content available to learners. As a matter of fact, among the decisions taken by the Ministry of Higher Education during the coronavirus pandemic to ensure permanent access to all learners and especially those with internet access problems. A television channel was devoted only to the broadcasting of higher education courses. Then, during the study, the participants were asked about their course follow-up on this channel. As a result, it was noted that only students who did not have access to the internet took their courses from this digital platform. This leads to the dissemination and involvement of all learners of the higher education system. Besides, coronavirus pandemic was an opportunity to review the education system adopted at the higher education level. First, Distance education must be adopted simultaneously with formal education. Secondly, all learners should be aware that distance education is not an alternative to formal education, it is a complementary method that supports formal education and aims to optimize student’s performance. The development of courses dedicated to distance learning must be done by a teaching designer in order to produce content that is easy to educate, accessible by all levels, and understandable. Of course, the content must be validated and approved by all concerned members of the Moroccan higher education system. Therefore, the aim is to develop and adopt adaptive education that takes into account the level of education, the classification (low, medium, strong), group or individual, the objectives to be achieved. Distance education is a very effective tool that can ensure a total improvement in the quality of education offered at the levels of higher education institutions. Finally, all elements of the education system (teachers, administrators, technicians, students…) must collaborate to give a good reputation of Moroccan education worldwide.

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5 Conclusion In the face of this COVID-19 pandemic, which has forced more than half of the world’s population to confine themselves, distance education has proved to be one of the most effective solutions to satisfy the needs of our students in order to ensure the continuity of the learning process in Morocco. It is certain that distance learning will certainly never replace formal education, therefore this mode of teaching must be considered as a complement, a greater value that would enrich education in the present. This coronavirus pandemic should teach us a lesson and prepare us for possible risks.

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Potentialities of Learning Analytics to Overcome Students Dropout in Distance Higher Education Karima Hamdane1(B) , Abderrahim El Mhouti2 , Mohammed Massar1 , and Lamyaa Chihab1 1 LPMIC, FSTH, Abdelmalek Essaadi University, Tetouan, Morocco

[email protected] 2 ISISA, FS, Abdelmalek Essaadi University, Tetouan, Morocco

Abstract. In the distance higher education context, the understanding of the dropout phenomenon has progressed, moving from the perception that it is a sign of the deficient quality of the education system to the perception that it is an explicit sign of individual choice, which leads to underlining the importance of studying how dropouts learn in online courses. Completion or dropout of students in higher education is a subject that needs deep research. Learning analytics (LA) can be used as a modern alternative to help predict possible risks of failure and prevent them. The aim of this work is to highlight the potential of the learning analytics technique to mitigate or even prevent the phenomenon of student dropout in online higher education. Thus, a state of the art of learning analytics by describing contributions and their applications is established. The study shows that the evolution of learning analytics technology makes it possible to analyse the cumulative database of students summarizing their experiences during the course to predict students at risk of dropping out. Keywords: Learning analytics · dropout · higher education

1 Introduction Education plays an important role in life; it builds the future of man, his character and his position in society. In addition, education is the ideal tool to face the new challenges of the world, such as growing technology, interconnectivity via the internet, artificial intelligence and unexpected events such as the latest global COVID-19 pandemic, the latter of which has accentuated the use of distance education. On the other hand, modern society depends heavily on technology, which compels professionals, educators, and students to re-evaluate their core beliefs to use technology to redesign or reengineer the education and training system. E-learning is a teaching strategy in which students receive instruction online. The field of learning analytics has undergone significant development in recent years due to the proliferation of digital technologies such as mobile applications and student response systems [1].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 397–404, 2023. https://doi.org/10.1007/978-3-031-29857-8_40

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The concept of e-learning has fundamentally transformed the educational environment in recent years. Due to the emergence of big data and the increased use of technology in education, this style of education is viewed by many as being far more effective and efficient than traditional ones [2]. The rapid development of MOOCs, which is seen as a logical extension of E-learning technologies, has ushered in a new era of education by extending the frontiers of education to previously non-college-bound students via the Internet [3]. Learning analytics also combines a range of research and methodologies from fields such as social network analysis, data visualization, learning sciences, machine learning, artificial intelligence, semantics, e-learning, psychology, educational theory, and practice to understand and optimize the learning process [4]. One of the main applications of learning analytics is to continuously track and predict student learning performance, identify potential problems, and provide timely interventions to support students who may be at risk of failing a course. This allows for early identification and addressing of issues, which can improve student outcomes and success [5]. In addition, among the roles that learning analytics can play is to identify which students are at risk of dropping out [6]. As a result, administrators and educational leaders can use learning analytics to quickly access and analyse relevant information, make informed decisions, and implement personalized solutions for individual students. Additionally, students can use learning analytics to track their own progress, identify strengths and weaknesses, and become more actively involved in their own learning [7]. This paper addresses the main challenges and limitations around learning analytics in education with a focus on student dropout. The research questions guiding this work were as follows: (i) What are LA’s new challenges? (ii) What are the potentialities offered by LA that can attenuate or even limit the phenomenon of student dropout? (iii) How can LA indicators predict dropout students? Thus, this study plans to show how the evolution of learning analytics technology makes it possible to exploit the cumulative database of students summarizing their experiences during the course to predict students at risk of dropping out. The remainder of this paper is structured as follows: the second section deals with related works covering some of the recent studies relating to the LA concept. The third section examines the dropout phenomenon. The fourth section presents and discusses the findings of this study in relation to the potential of learning analytics technology to address the issue of student dropout in higher education. The final section summarizes the study and outlines potential future work in this area.

2 Learning Analytics: Concept and Potential Learning analytics involves the analysis of data from virtual learning environments to understand and improve the learning process. These data include both static and dynamic information, such as interactions that occur within the virtual learning environment. Data may now be grouped in a variety of ways, but it is unclear which relationships and groupings should be studied. On whether or how these connections affect student involvement, academic success, and learning outcomes. On the website of LAK11 [8], the first conference on learning analytics, the term “learning analytics” is defined as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes

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of understanding and optimizing learning and the environments in which it occurs.” (“Learning analytics: the good, the bad and the ugly”). “Learning analytics” refers to the use of intelligent data, learner-produced data, and analytical models to elucidate knowledge and social relationships as well as to forecast and provide learning recommendations. Due to its connection to digital teaching and learning, learning analytics is an interdisciplinary field that intersects with teaching and learning research, computer science, and statistics [9]. It can also be understood as an educational web analytics application that focuses on collecting and analysing data about individual student interactions in online learning activities, with the aim of creating profiles of each student’s learning behaviours and characteristics [10]. For both educators and students, this developing field offers a variety of computational assistance for data management, displaying educational trends, and monitoring student behaviour. Additionally, it enables the discovery of obscure patterns, hidden linkages, and other types of data. This incorporates institutional data, descriptive and prescriptive models, and statistical analysis to produce information for instructors, administrators, or students. The academic habits of the students are of great concern to LA. The “4Vs” of the data analysis—volume, velocity, variety, and validity—are satisfied by this. Aiming to create the best groupings for students, several studies show the potential of the LA in forecasting academic achievement and offer ways to create new student groups based on an analysis of insights from previous activities. The way that exam results affect students emotionally may have an influence on them and ultimately determine whether or not they opt to stay in school. To assess student and teacher behaviour, research is also examining the most effective methods for gathering educational data in online contexts. An exploratory examination of the data from the virtual world interactions showed the views and goals of the users of educational virtual worlds.

3 Student Dropout: A Common Phenomenon in Distance Higher Education Dropout is a structural issue that affects many educational systems around the world, and this phenomenon is one of the major hindrances to the development of the educational process at all levels, resulting in a significant loss of human resources, a complex phenomenon that includes a group of cultural, social and economic factors. In Morocco, despite the implementation of various support and follow-up programs and research on improving learning success, the dropout rate in higher education remains alarmingly high. According to a report from the ministry in 2020, 49.4% of Moroccan university students did not graduate. The government official attributed this number to several factors, including the problem of guidance, the poor level of pedagogical achievement, and the difference in the language of instruction, which negatively affected the profitability of the higher education system. Research on this topic has been conducted since the 1970s and 1980s [11, 12]. The reasons for online students dropping out of a program are similar to those cited by dropouts from traditional face-to-face programs. However, there are specific reasons, such as technological issues, lack of human interaction and communication

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issues, that are clearly characteristic of the online learning environment [13]. Academic achievement is also influenced by various common factors, such as a student’s sociodemographic background (e.g., gender, ethnicity, family background), cognitive ability or prior academic performance, and individual predispositions (e.g., personal traits, contextual motivational or psychosocial influences) as well as factors related to active learning and attention or environmental factors related to the degrees of school and social integration [14–16].

4 Emerging Potentialities of Learning Analytics to Overcome Dropout Learning analytics is a field that utilizes data and analytics to comprehend and enhance the learning experience. It involves gathering information on various elements that can influence learning, including student performance, engagement, and attitudes, and analysing the data to uncover patterns and trends that aid in making decisions related to teaching and learning. A significant advantage of learning analytics is the capability to gather and store data in real time (Fig. 1), enabling prompt and specific interventions to assist student success. For instance, if a student has difficulty with a specific concept, a learning analytics system can identify this and provide targeted resources or support to help the student understand the material. Learning analytics can also be used to identify patterns and trends at a larger scale, such as identifying common challenges that students face in a particular course or identifying factors that are correlated with student success [17].

Use the system by designing, delivering the instruction and evaluating

Use the system by interacting, participating and communicating

Learning Systems (Learning management system, gamebased learning system, intelligent learning system, intelligent tutoring system…)

Instructor(s) Production of various data during the use of the system

Learner(s)

Data Analysis Methods Support instruction (e.g., provide recommendation, feedback, reorganization adaptations…)

(Clustering, classification, data mining, pattern identification, prediction, regression…)

Support learning (e.g., provide recommendation, feedback, reorganization adaptations…)

Fig. 1. The use of a learning system from the instructor and the learner to support learning and instruction, adapted from [18].

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For example, utilizing the data of the student’s profile in the platforms can yield both dynamic and static indicators. Dynamic indicators in learning analytics are data that change over time and provide real-time insights into student behaviour and performance. Examples of dynamic indicators include trace data, such as time spent on a learning management system or time spent on a particular task or assessment, as well as indicators of learning profile, such as login frequency, task completion rate, and assessment outcomes. On the other hand, static indicators are data that remain unchanged over time and furnish background information about students. Examples of static indicators include demographic information, such as gender, age, education level, and work experience, as well as information about students’ enrolments, dropout rates, pass/fail rates, and academic results. Both dynamic and static indicators can be useful in learning analytics, as they can provide different types of information about students and their learning experiences. Dynamic indicators can provide real-time insights into student behaviour and performance, while static indicators can provide context and background information that can inform decisions about teaching and learning practices [19]. Additionally, the indicators measured vary based on their frequency of use, as illustrated by Fig. 2, which shows an example of this evolution over time [20].

Fig. 2. Occurrences of the most commonly used learning analytic indicators over the past 10 years.

Through learning analytics, professionals can readily access data from past dropouts to anticipate future ones, and students often withdraw from university classes six months before dropping out of school altogether. The student is already halfway through their coursework when they realize that school is hindering their progress in life. Learning analytics is an innovative approach to improving the educational experience for college students and staff members alike and supporting academic advising to properly orient students towards the appropriate specialties or branches.

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The technology is still relatively new, but it has already been proven to be beneficial in many educational contexts. By implementing analytics, staff members can proactively address student concerns and assist struggling students in improving their grades. It is important to understand how these systems work and use responsibly in higher education [21]. However, there are several challenges to overcome when adopting this approach. Most publications in the field are descriptive in nature, lacking a solid theoretical foundation. Integrating theory and analysis can help to solidify the field of learning analytics and improve the quality of research by formulating the research questions to study and their assumptions. There is a strong need to examine learning over longer periods, using larger and more heterogeneous samples of students to have results that are more reliable. To obtain good and usable results, easy access to an adequate database is crucial. However, this is not always easy to achieve in the educational environment. Access to data is a significant challenge, as institutional and academic data are often spread across multiple systems, making it difficult to access past sociodemographic and academic data in many systems and creating difficulties in accessing past sociodemographic and academic data. This is because of many learning modalities, such as face-to-face learning, blended learning and online learning. Finally, the exploitation of the database can turn into a paradox of privacy, which is among the possible problems that may arise during the analysis of big data in higher education in Morocco. During this process, there is the risk of a strong nuance between law n° 31.13 [22] relating to the right of access to information and data of the apprenticeship and that of 09.08 [23] relating to the protection of natural persons with regard to the processing of personal data.

5 Conclusion From the above, it is apparent that learning analytics, as an emerged and promising field, can be applied at different levels of teaching and learning and in various contexts, ranging from purely pedagogical perspectives to technological perspectives and from the level of primary education to higher education. Thus, teachers can easily see the progress of their students and be on top of adapting educational strategies that are most likely to work effectively. Students can also self-assess and analyse their progress, know their strengths and weaknesses and be actors in their learning. Educational institutions exploit learning analytics to optimize plans for this sector and avoid school dropout. Further research is needed in this field to fully understand its potential and determine its effectiveness, as well as to gather more information on students’ opinions and attitudes towards it. Moreover, encouraging student creativity has now become a major obligation, which is why it is necessary for students to become responsible for their educational choices by adapting to their learning environment.

References 1. Hamdane, K., El Mhouti, A.E., Massar, M.: How can learning analytics techniques improve the learning process? An overview. In: 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–5 (2022). https://doi.org/ 10.1109/IRASET52964.2022.9738003

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2. Mor, Y., Ferguson, R., Wasson, B.: Learning design, teacher inquiry into student learning and learning analytics: a call for action. Br. J. Educ. Technol. 46(2), 221–229 (2015). https://doi. org/10.1111/bjet.12273 3. Fei, M., Yeung, D.Y.: Temporal models for predicting student dropout in massive open online courses. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, pp. 256–263. IEEE (2015) 4. Dawson, S., Gaševi´c, D., Siemens, G., Joksimovic, S.: Current state and future trends: a citation network analysis of the learning analytics field. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 231–240. ACM, New York (2014). https://doi.org/10.1145/2567574.2567585 5. Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief (2012). http://www.ed.gov/edblogs/techno logy/files/2012/03/edm-la-brief.pdf 6. Tobin, T.J., Sugai, G.M.: Discipline problems, placements, and outcomes for students with serious emotional disturbance. Behav. Disord. 24(2), 109–121 (1999). https://doi.org/10. 1177/019874299902400209 7. Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C.A., Ramírez Mendoza, R.A.: Learning analytics: state of the art. Int. J. Interact. Des. Manuf. 16, 1209–1230 (2022). https://doi.org/10.1007/s12008-022-00930-0 8. LAK 2011: 1st International Conference on Learning Analytics and Knowledge Banff Alberta Canada, 27 February 2011–1 March 201 9. Johnson, L., Adams Becker, S., Cummins, M., Freeman, A., Ifenthaler, D., Vardaxis, N.: Technology Outlook for Australian Tertiary Education 2013–2018: An NMC Horizon Project Regional Analysis. New Media Consortium (2013) 10. Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., Lorca-Vyhmeister, A.: Learning analytics in higher education: a preponderance of analytics but very little learning? Int. J. Educ. Technol. High. Educ. 18(1), 1–19 (2021). https://doi.org/10.1186/s41239021-00258-x 11. Spady, W.G.: Dropouts from higher education: an interdisciplinary review and synthesis. Interchange 1, 64–85 (1970) 12. Bean, J.P.: Interaction effects based on class level in an explanatory model of college student dropout syndrome. Am. Educ. Res. J. 22, 35–64 (1985) 13. Willging, P.A., Johnson, S.D.: Factors that influence students’ decision to dropout of online courses. J. Asynchron. Learn. Netw. 13(3), 115–127 (2009) 14. Bijsmans, P., Schakel, A.H.: The impact of attendance on first-year study success in problembased learning. High. Educ. 76(5), 865–881 (2018). https://doi.org/10.1007/s10734-0180243-4 15. Tinto, V.: Through the eyes of students. J. Coll. Stud. Retent. Res. Theory Pract. 19(3), 254–269 (2017). https://doi.org/10.1177/1521025115621917 16. Brahm, T., Jenert, T., Wagner, D.: The crucial first year: a longitudinal study of students’ motivational development at a Swiss Business School. High. Educ. 73(3), 459–478 (2016). https://doi.org/10.1007/s10734-016-0095-8 17. Pistilli, M.D., Arnold, K.E.: Purdue Signals: Mining real-time academic data to enhance student success. About Campus Enrich. Stud. Learn. Exp. 15(3), 22–24 (2010). https://doi. org/10.1002/abc.20025 18. Giannakos, M.: Educational data, learning analytics and dashboards. In: Giannakos, M. (ed.) Experimental Studies in Learning Technology and Child–Computer Interaction. SpringerBriefs in Educational Communications and Technology, pp. 27–36. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14350-2_4

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19. Fenthaler, D., Yau, J.Y.-K.: Reflections on different learning analytics indicators for supporting study success. Int. J. Learn. Anal. Artif. Intell. Educ. (iJAI) 2(2), 4–23 (2020). https://doi. org/10.3991/ijai.v2i2.15639 20. Ahmad, A., et al.: Connecting the dots – a literature review on learning analytics indicators from a learning design perspective. Spec. Issues Artic. J. Comput. Assist. Learn., 1–39 (2022). https://doi.org/10.1111/jcal.12716 21. Choi, S.P.M., Lam, S.S., Li, K.C., Wong, B.T.M.: Learning analytics at low cost: at-risk student prediction with clicker data and systematic proactive interventions. J. Educ. Technol. Soc. 21(2), 273–290 (2018). http://www.jstor.org/stable/26388407 22. The official bulletin on March 12, 2018, Law 31.13 grants citizens the right to access information 23. The official bulletin on March 12, 2009, Law 09.08 relating to the protection of individuals with regard to the processing of personal data

Learning Analytics and Big Data: Huge Potential to Improve Online Education Lamyaa Chihab1(B) , Abderrahime El Mhouti2 , Mohammed Massar1 , and Karima Hamdane1 1 LPMIC, FSTH, Abdelmalek Essaadi University, Tetouan, Morocco

[email protected] 2 ISISA, FS, Abdelmalek Essaadi Univerity, Tetouan, Morocco

Abstract. Learning management systems (LMS) in the online education sector have led to a huge amount of data from the interaction between the student and the student environment that generates a lot of digital traces. This colossal amount of data cannot be processed by traditional learning analytics. This has led to the insertion of Big Data technologies and tools in education to handle the large amount of data involved. The process of collecting, analyzing, and intelligently using this learner-generated data to understand and help the learner is called Learning Analytics. LA is a new lens through which teachers can address the needs of individual learners to a greater extent. This work examines and explores the concepts of big data and learning analytics. In particular, this study aims to conduct a systematic review of big data and LA technologies in education to explore trends, categorize research topics, and highlight the contributions of these technologies in online education. Keywords: Online education · Learning Analytics · Big Data

1 Introduction Computer-based learning environments create an array of digital traces resulting in activities in educational contexts. Teachers and learners are following a new approach leaving behind the attitude that adapts to old traditional educational methods and approaches. Previously, classical analytical tools could not deal with the huge amount of data and examine it [1]. However, due to the development of this domain, new methods of analysis are emerging that can process the huge amount of data and extract important information for education. New technologies such as Big Data and learning analytics are closely related to data treatment, which directly involves solving the huge issues in the classical learning system [2]. This paper presents the role of big data and learning analytics in e-learning. There are multiple methods to process the huge amount of data and derive big data for the education sector. The paper firstly introduces a set of definitions of each area, then describes the impact of Big Data and Learning Analytics in online education. The rest of the paper is organized as follows: the Sect. 2 explore Learning Analytics and Big Data concepts and deals on related works covering some of the recent studies relating to these concepts. The Sect. 3 looks at the impact of Big Data concept on online education. The Sect. 4 concludes the study and outlines the future works. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 405–411, 2023. https://doi.org/10.1007/978-3-031-29857-8_41

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2 Learning Analytics The purpose of learning analytics study was defined as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” [3]. Learning analytics is an area of research that includes approaches and technologies from many areas such as learning sciences, data mining, information modeling and psychology [4]. Since it is a domain that has taken root in other domains, many authors have proposed models that seek to provide a structural basis for its integration. Two notable examples are the cycle by CLOW (2013) and, more recently, the three-dimensional model by GAŠEVIC’ et al. (2017). CLOW (2013) cycle describing Learning Analytics as a process. Learners generate data that is processed to create indicators and these lead to interventions that affect learners [5]. This cycle emphasizes the importance of the learning and education aspects (Fig. 1).

learners

intervent ion

data

metrics

Fig. 1. The Learning Analytics Cycle (Clow, 2012)

The cycle is started by learners interacting in formal or informal e-learning environments. Through their activities, learners produce a lot of data that is stocked on e-learning supports. The second step is to generate and capture data about or by the learners. The third step is to process this data to make measurements or analyses that provide insight into the learning process. This includes visualizations, dashboards, comparisons of outcome measures to benchmarks or previous cohorts, aggregations, etc. Again, some can be generated automatically, while others require significant effort [6]. The cycle is not done until these metrics are used to implement one or more interventions that reach learners. This could be a dashboard that allows learners to compare their performance with their peers or previous groups, or a teacher tutor who personally contacts a student who a model has identified as being at very high risk of dropping out.

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GAŠEVIC’ et al.‘s (2017) model (Fig. 2), suggests three different dimensions: the first is a dimension related to the theoretical foundations related to learning, second one is a dimension that addresses design and conception aspects, and third one is a final dimension that concerns data science. Their intersection defines the principles of Learning Analytics for research and practice [7].

Fig. 2. GAŠEVIC’ et al.’s (2017) model

The theoretical dimension refers primarily to the theories of human learning, teaching, instruction and education. The principal value of these studies is that they support the selection of research questions, hypotheses, and methods of analysis and interpretation of data. The design dimension covers three aspects: the design of interactive tools that support learning, the design of pedagogical activities that consider specific practices and contexts, and the design of field studies and their protocols to evaluate research proposals on practice [8]. Lastly, data science aspects include methods and techniques for collecting, processing and visualizing data from learning activities [9].

3 Big Data for Education Purposes The data generated by the online education environment starting to get bigger and bigger, requiring the use of Big Data technologies and tools to process it [9]. Big Data has been defined by the International Data Corporation (IDC) as “Big Data technologies represent an emerging class of technologies, architectures, techniques, and tools designed to extract value from very large volumes of diverse data, enabling high-speed capture, discovery, or analysis [10].” Gartner in its report [11] gave the following definition, “Big data collects a wide variety of data, arriving in increasing volumes, at a high speed. These are the 3Vs.” These 3Vs are volume, variety and velocity, as shown in Fig. 3 [12],

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Fig. 3. Big Data Characteristics

Volume Volume is measured in the form of petabytes, exabytes, or even zettabytes. Volume refers to the huge amount of data to be stocked, processed, analyzed and disseminated by the tools and technologies of Big Data [13]. Velocity The rate of data creation is called data velocity. Velocity refers to the speed or frequency at which data is generated and used [13]. Variety It is a measure of the representations of different data. In fact, big data technology allows to process heterogeneous data coming from different sources. The traditional form is relational databases, where data is stored according to a rigid and organized schema. But currently, a large part of the data generated by the business is of a semi-structured and unstructured nature [14]. Big data is growing in the domain of education [15]. Through the process of interaction, learners generate a huge amount of data. The fusion of big data technologies in the domain of education provides profitable and crucial information for all actors involved in the educational system [6]. The integration of this technology has several benefits including educational institutions will benefit from an effective market analysis, a better adopted instructional framework, a reduction in the number of learners who voluntarily skip classes and their adherence to administrative decision making [16, 17]. However, Big Data is also marked by several challenges such as data privacy, security issues and thus the limitation of data integration between different data sources and the high cost of analyzing a complete Big Data solution [17].

4 Related Works The inclusion of Big Data and learning analytics in e-Learning systems is a trend today with the aim of exploiting technology and improving learning. This section presents some of the research conducted.

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In this context, the author’s work [13] examines and explores in his article the uses of Big Data and learning analytics in e-Learning as a basis for Big Data and e-Learning and its impact. Big Data analytics has proven to be an effective approach to educational data mining and learning analytics. The research work [4] the authors observe the theoretical and evolutionary framework that supports the integration of educational research data, in addition to developing key performance displays and techniques for collecting and processing large amounts of data. [5] the authors present a rich and complete review of big data, learning analytics, and the use of NLP in higher education. In this paper, they discuss an integrated learning analytics system that is based on a distributed technology system capable of assisting educational administrators and institutional advisors in making decisions about individual students.

5 Synthesis and Discussion: Impact of Bigdata in LA The amount of data generated by the online education is starting to get larger and larger, requiring the use of technologies and tools to process it [17]. Big Data exposes huge parallel computing capacities, arithmetic addressed to new and automatic learning and draw information from various types of data produced by e-learning systems as for example learner’s data, traces, activities, hobbies, results, etc. Learning analytics have many implications for the educational system; it conducts research on the usability and efficacy of data presentation. It also helps teachers to be more effective in the classroom with more real-time, data-driven decision support tools, including recommendation services. And to look for opportunities to use specific student information where it will be most useful, anonymize data where necessary, and understand how data aligns with different systems. Learning analytics is driven by the availability of massive data about learners, the development of big data methods [18]. The impact of this process will also be felt as the implementation of an innovative pedagogy for learning It can highlight some of the key contributions and how data can help in the educational process: Enhanced learning process with the information obtained from big data analysis, allowing us to find frequent patterns of failure or success. Improving also the selection of resources and tools by merging, modifying or deleting ideas and materials based on the results obtained [14, 18]. The importance of big data and learning analytics in education is seen in the quality of education, which is measured by student progress. Therefore, improving the educational system, programs and teachers is very important to increase student progress and improve the quality of education. Learning Analytics helps to reveal the aspects of failure in the educational system. Learning Analytics, Big Data used in education, to provide a variety of opportunities and options to personalize the student’s journey, reduces dropouts from educational and improves the depth of exploration of available learning content.

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6 Conclusion Big data and Learning Analytics are having a major impact on online education. Because it enables learning to improve the experience and knowledge of learners, make more effective decisions, and plan responses to a wide range of trends. Big data has the potential to address some of the key challenges facing higher education today [19, 20]. It provides good reason to expect to transform often complex and unstructured data into actionable information. Learning Analytics collects digital data left behind by learners to analyze and profile individual learners [7].

References 1. Djouad, T.: Ingénierie des indicateurs d’activités à partir de traces modélisées pour un Environnement Informatique d’Apprentissage Humain (Doctoral dissertation, Université Claude Bernard-Lyon I; Université Mentouri-Constantine) (2011) 2. Mezzanzanica, M., Mercorio, F. : Big Data Pour Les Systèmes D’information/De Renseignement Sur Le Marché Du Travail 3. Siemens, G., Gasevic, D.: Guest editorial-learning and knowledge analytics. J. Educ. Technol. Soc. 15(3), 1–2 (2012) 4. Dowell, N.M., et al.: Modeling learners’ social centrality and performance through language and discourse. Int. Educ. Data Min. Soc. (2015) 5. Clow, D.: The learning analytics cycle: closing the loop effectively. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 134–138 (2012, April) 6. Saqr, M.: Using learning analytics to understand and support collaborative learning (Doctoral dissertation, Department of Computer and Systems Sciences, Stockholm University) (2018) 7. Gaševi´c, D., Kovanovi´c, V., Joksimovi´c, S.: Piecing the learning analytics puzzle: A consolidated model of a field of research and practice. Learn. Res. Pract. 3(1), 63–78 (2017) 8. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A reference model for learning analytics. Int. J. Technol. Enhanced Learn. 4(5–6), 318–331 (2012) 9. Pence, H.E.: What is big data and why is it important? J. Educ. Technol. Syst. 43(2), 159–171 (2014) 10. Manyika, J., Lund, S., Bughin, J.: Digital Globalization: The New Era Global Flows. McKinsey Global Institute (2016) 11. Manyika, J., et al.: Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute (2011) 12. Kitchin, R., McArdle, G.: What makes big data, big data? exploring the ontological characteristics of 26 datasets. Big Data Soc. 3(1), 2053951716631130 (2016) 13. Adam, K., Bakar, N.A.A., Fakhreldin, M.A.I., Majid, M.A.: Big data and learning analytics: a big potential to improve e-learning. Adv. Sci. Lett. 24(10), 7838–7843 (2018) 14. Dahdouh, K., Dakkak, A., Oughdir, L., Ibriz, A.: Improving online education using big data technologies. Role Technol. Educ. (2020) 15. Sin, K., Muthu, L.: Application of big data in education data mining and learning analytics--a literature review. ICTACT J. Soft Comput. 5(4), 1035–1049 (2015) 16. Venant, R.: Les learning analytics pour promouvoir l’engagement et la réflexion des apprenants en situation d’apprentissage pratique (Doctoral dissertation, Université Paul Sabatier-Toulouse III) (2017) 17. Stéphane, T.: Data mining et statistique décisionnelle : l’intelligence des données. Editions Technip (2012)

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18. Moharm, K., Eltahan, M.: The role of big data in improving e-learning transition. In: IOP Conference Series: Materials Science and Engineering, vol. 885, no. 1, p. 012003. IOP Publishing (July 2020) 19. Bomatpalli, T.: Learning analytics and big data in higher education. Int. J. Eng. Res. Technol. 3(1), 3377–3383 (2014) 20. Chihab, L., El Mhouti, A., Massar, M.: Using learning analytics techniques to calculate learner’s interaction indicators from their activity traces data. In: Ben Ahmed, M., Abdelhakim, B.A., Ane, B.K., Rosiyadi, D. (eds.) Emerging Trends in Intelligent Systems & Network Security. NISS 2022. Lecture Notes on Data Engineering and Communications Technologies, vol. 147, pp. 504–511. Springer, Cham (2023). https://doi.org/10.1007/978-3-03115191-0_48

Serious Games, a Tool for Consolidating Learning Outcomes Mounia El Rhayami1(B) , Abderrahim El Mhouti1 , and Yassine El Borji2 1 ISISA, FS Abdelmalek Essaadi University, Tetouan, Morocco

[email protected] 2 SOVIA Laboratory, ENSAH, Abdelmalek Essaadi University, Tetouan, Morocco

Abstract. The learning of many children around the world has been challenged by the implementation of technology in each individual’s life. Nowadays, the amount of time spent on televisions, smartphones, and digital activities has become very high, a hindrance to the assimilation of knowledge in schools and the objective of this later as a whole. Nobody can deny that this phenomenon is a result of society’s evolution which cannot be stopped. In this regard, video games as well as social networks are of paramount importance to be implemented in teaching since they are currently part of the students’ daily lives. This contribution sheds light on the reason for the introduction of Serious Games (SG) in the educational system. It lists some nationally and internationally renowned surveys that have elucidated the causes of students’ failure and their gradual decline in academic performance. This document will also present the state of the art of Serious Games and their usages in the educational environment, their assets in knowledge consolidation. Some concrete examples of Serious Game software will be cited later. Finally, this work will describe the necessary conditions to make the Serious Games an effective and useful tool for consolidating knowledge, accompanied by a conceptual model that will be concretized later. Keywords: Serious Games · consolidation of knowledge · learning

1 Introduction With the continuous advancement of electronic devices, they are now used for various purposes as they have a great and massive impact on our lives. On this subject, children, and youth, in general, spend a good amount of time in ug such devices for many reasons especially in playing video games. On average according to many surveys, this dilemma affects badly students’ performance. On the other side, some studies have claimed that video games might have positive impacts on learning, especially in terms of memorizing and team-working [1–3]. In this manner, since the students are energetic and pleasant while playing video games, why not implement these kinds of games in teaching and learning? How can serious games contribute to improving the consolidation of students’ learning? In this paper, we are going to study the contributions of the SG to the consolidation of learning knowledge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 412–420, 2023. https://doi.org/10.1007/978-3-031-29857-8_42

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The remainder of this paper is structured as follows: the second part of this paper reviews recent studies and surveys conducted to assess student achievement and reasons for non-achievement. In this same paragraph, there will be a reminder of the concept of Serious Games and some examples of educational serious games will also be cited. The next part will examine how to use Serious Games as an educational medium, the advantages of this use as well as their limitations. Before concluding, it would be interesting to present the adopted conceptual model of the Serious Games The final section concludes the study and presents future work.

2 State of the Art 2.1 Assessment of Learner Learning In the school setting, assessment is used to determine whether or not the goals of education are being met, therefore, it affects decisions about grades, placement, advancement, instructional needs, curriculum, and, in some cases, funding. Furthermore, various test programs have been designed to assess how well students at the end of compulsory education, can apply their knowledge to real-life situations. Among these test programs which are based in several countries, particularly in Europe are: PISA (Program for International Student Assessment) PISA is an evaluation system created and managed by the Organization for Economic Cooperation and Development (OECD) [4]. Thanks to the interactive map set up by the organization, it is possible to visualize in one click, the synthesis of the results [5] PISA 2018 for each country (see Fig. 1). This study proposes, based on a set of open-ended and multiple-choice questions, a measurement of the competencies of young students aged 15 in three domains: reading comprehension, mathematical literacy, and scientific literacy. The objective is to evaluate the ability of students to use their academic knowledge to independently deal with everyday situations.

Fig. 1. Student performance in reading, mathematics, and science (Source: OCED, Pisa 2018)

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PNEA (National Student Assessment Program), This program is part of the remit of the National Evaluation Unit (INE) of the Higher Council for Education, Training, and Scientific Research (CSEFRS). Its objective is to measure, identify and explain the linguistic, mathematical, and scientific achievements of students at key stages of their schooling, particularly in the fourth and sixth years of primary school, as well as in the second and third years of secondary school. The results of the survey (see Fig. 2), name a few, show a higher success rate in Arabic, French, and mathematics in private schools than in public schools [6].

6th-grade 3rd-year secondary school Fig. 2. Student performance by educational use of digital technology. (Source: NAEP 2019 study data)

TIMSS (Trends in the study of mathematics and sciences), TIMSS is organized every four years by the IEA (independent international organization for educational assessment) [7]. Its objective is to measure the evolution of the academic level of fourth graders in mathematics and science. On the other hand, it interprets the differences between educational systems to improve teaching and learning. [8] (see Fig. 3). PIRLS (International Reading Research Program) is a survey conducted by the IEA (International Association for the Evaluation of Educational Achievement) [9]. It is a survey on the reading and analytical skills of students entering their first learning cycle (CP, CE1). The picture below shows some results (Fig. 4). It is complementary to the two studies PISA and TIMSS [10]. All these large-scale surveys and many others have the same purpose: They aim to improve teaching and learning. It allows us to unveil or at least understand certain causes of success or less success. Previous research and survey results have revealed unsatisfactory academic performance as a result of widespread weakness in student learning and skills. Among the causes behind this academic failure: • The learner’s family environment, • His/her ability to learn, • The various activities carried out outside the classroom (sports, artistic, cultural, digital…), • Time spent playing video games and using new technologies, • Lack of concentration and difficulty following the program,

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Mathematics

Fig. 3. Range of lowest and highest performing students in comparison countries of 4th grade. (Source: TIMSS 2019 study data)

Fig. 4. Percentage of fourth-grade students reaching the PIRLS international benchmarks in reading, by education system: 2016

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• Learning methods not adapted to the learner’s knowledge. • The teacher’s ability to integrate new technologies into teaching scenarios, • The level of his/her daily teaching practice in the classroom, as well as the cultural level, • His/her (teacher) willingness to innovate and invent solutions to problems while remaining playful. 2.2 Serious Games and Application It is widely agreed that learning through play has emerged as an important strategy to promote student engagement. However, play is usually proscribed from school practices. Educational systems, therefore, have always maintained the separation between studying as an aspect that symbolizes seriousness, and the frivolity of play which is a sign of wasting time that has bad outcomes. Correspondingly, we recall what Chantal Barthelening-Ruiz said about this later, «To introduce play into education is to want to mix pleasure and work… But these are not notions that common sense or teachers readily associate with each other.» [11]. Nevertheless, traditional teaching methods are not always suitable for everyone, because according to W. James and D. Gardner [12], the learning style and the way of acquiring knowledge vary from one individual to another. For example, some people have an excellent visual memory, and their eyes retain information well (images and text). Others favor the auditory style, they learn with their ears and many other styles: kinesthetic, tactile… Therefore, it seems interesting to use games as a learning medium. Some countries have already experimented with it in their schools and have adopted it, such as Denmark. According to Michel Van Langendonckt, educational coordinator, «play plays a major role in developmental psychology in a general way… The play would train an ability to seek solutions, help to have a positive spirit, a playful attitude towards the world» [13]. Consequently, in pedagogy, we no longer speak of games but of serious games. The researcher Julian Alvarez defines serious games as games that are designed for all markets that deviate from only entertainment. It is therefore very broad because as soon as a school, an advertising agency, the army, the hospital sector, etc. create a game, we are dealing with a Serious Game. Julien Alvares in collaboration with Damien Djibouti has suggested six categories of serial games: • • • • • • •

Advertising games; Les edumarket games; Engaged games; Training and simulation games; Scientific research games; Edutainment games; Les edumarket games.

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This last category is of particular interest to us because these games are intended for the educational system. Their purpose is to teach certain notions and to allow the student to acquire knowledge and skills while having fun. Much serious game software has appeared on the market and in several sectors: education, health, advertising, communication, politics, humanitarian, defense, religion, and art… I quote four of them dedicated to the educational system: [14] Taka T’amuser: A series of small games and puzzles to practice shape and color identification in addition, for older children, to arithmetic and conjugation [15]. The Adibou game series: is intended for children from 4 to 7 years old. It aims to stimulate the cognitive development of the learner as well as to learn concepts in mathematics and French [16]. Doctor Kawashima’s Brain Training Program for Nintendo DS: Depending on the age of the player, it offers exercises in memorization, mental calculation, or reading speed [17]. The Journey of Food: «it shows the journey and transformation of food into nutrients during its “eventful” journey through the digestive tract» [18]. Les Éonautes (Almédia, 2012): the strong point of this game is that it uses the different learning styles mentioned above. Indeed, it proposes dialogues, listening, and writing exercises [19].

3 Use of Serious Games as a Technological Tool for Learning Consolidation 3.1 Serious Games, a Tool to Consolidate Knowledge Several field tests have demonstrated the benefits of introducing serial games into the educational system. The list of benefits is not exhaustive: • They increase and maintain the learner’s motivation; the player will learn because he wants to win or improve his score. • They allow the learner to consolidate his knowledge through his mistakes: Before finding the right solution, the student will make mistakes, which will lead him to try again and adopt a new strategy. As the number of attempts increases, the number of errors and the time needed to solve the problem decreases. • They allow the student to grow in self-confidence. The student can replay a sequence or the whole game as many times before finding the solution. They will not be afraid of being judged by others. The one who has found the solution quickly can help the students who might face some difficulties. • They allow the teacher to take into account the differences in level between students in class or the same group. Each student can progress at his or her own pace. • It is a source of sharing and group work for the students, they become active and involved in their thoughts. • They have the advantage of giving concrete and animated representations of sometimes abstract notions, very far from the daily life of the students and difficult for them to assimilate;

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• They allow to stimulation of various skills at the same time: verbal, mental, logic, calculation … • they give students the chance to possess a specific and concrete goal which is likely to encourage their engagement in it. However, despite all of the above benefits, and like any educational medium, gaming can be ineffective and can have limitations if misused. Below is a list of obstacles that games can face: Obstacle 1. Availability of adequate equipment: it is necessary to provide students and teachers with computers and consoles capable of running the game. Obstacle 2. The game must be designed according to the course content. It must meet the pedagogical objectives of the teacher and the students. It must be validated by the teacher because some notions can be effectively transmitted with traditional non-game means. Obstacle 3. It is of paramount importance to know that the teachers simplify the complex for students. In this manner, they are expected to play a lot of roles in classrooms besides being a teacher, these roles are believed to be guiders and observers. That’s to say, it is vital to guide and observe the students and make abstract things accessible for them while they are in the middle of the game. Teachers, therefore, can create a favorable environment for the students to be more effective, creative, and most importantly productive. For example, researcher Jacob Habgood [20] experimented with the game Zombie Division (Habgood, 2007) on two groups of students. The goal of the game was to learn multiplication tables. The first group of students simply used the game while the second group of students had to verbalize what they thought they had learned at the end of the game. This group performed better on the mathematical knowledge tests than the group that simply played the game.

3.2 Towards a Serious Games Design Model to Consolidate Learning Our project is based on the ADDIE instructional design model [21]. Developed by the University of Florida in the United States, this device allows, thanks to the five phases that make up its name, to consolidate the skills acquired by the students. Each step has its importance and must be carried out in the order established below: 1. Analyse: examine the target audience as well as their needs and the tasks they must master 2. Design: Here, the objectives to be achieved by the target audience and the learning techniques to be used must be established. It is also necessary to test the training to ensure its effectiveness 3. Develop: create the content of the training and verify that it is in line with the objectives and skills to be acquired

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4. Implement: share and disseminate the training with the target 5. Evaluate: this phase justifies the reliability of the training and demonstrates that the training has improved the performance of the individuals who have taken it. This educational approach is a framework for achieving the following scheme (see Fig. 5):

Fig. 5. Serious games design model

• The learner accesses the games via a platform interface (the client part). • Interactions are made between the interfaces and the game server as well as the educational database. • All learner behaviors during the game sequence are recorded in the analytical database. • The learner’s data will allow the teacher to analyze the learner’s behavior and responses.

4 Conclusion Edutainment games have thus taken a more or less important place in educational systems, from kindergarten to university to professional training. When carefully chosen and used wisely, serious games can be an effective tool to consolidate the achievements of students, whatever their level, and thus increase their motivation to work and subsequently, autonomy. It is a question here of boosting their self-confidence, reinforcing the social dimension between them and stimulating their concentration by adapting to the environmental constraints in which they evolve.It should be noted, however, that the game is not a goal in itself but a means to accompany the educational scenario.

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References 1. Guillén-Nieto, V., Aleson-Carbonell, M.: Serious games and learning effectiveness: the case of It’s a deal! Comput. Educ. 58, 435–448 (2012) 2. Alvarez, J.: Les Serious Games. Revue de l’APÉMu, 83–88 (2018) 3. Alvarez, J., Djaouti, D., Rampnoux, O.: Learning with serious games? 1st edn. Canapé CNDP, Paris-France (2016) 4. Thomson, S., De Bortoli, L., Underwood, C., Schmid, M.: PISA 2018: Reporting Australia’s Results. Volume I Student Performance. OECD Programme for International Student Assessment (PISA), Australia (2019) 5. Crato, N.: Improving a Country s Education: Pisa 2018 Results in 10 Countries. 1st ed. Springer Nature Switzerland AG, Lisbon-Portugal (2020) 6. El Khamlichi, L., Addi, S., Badri, Z.: National achievement assessment program for 6th -grade primary and 3rd -grade secondary students PNEA2019. higher council of education, training, and scientific research, Morocco (2021) 7. Mullis, I., Martin, M.: TIMSS 2019. Evaluation frameworks. TIMSS & PIRLS International Study Center, United States (2018) 8. Plumelle, B.: Évaluation TIMSS 2019: résultats des élèves en mathématiques à l’école primaire et portrait des enseignants dans 58 pays. Open Ed. 86, 30–36 (2021) 9. Araújo, L., Costa, P.: Home book reading and reading achievement in EU countries: the Progress in international reading literacy study 2011 (PIRLS). Educ. Res. Eval. 21, 422–438 (2015) 10. Mullis, I., Martin, M.: PIRLS 2021 Assessment Frameworks. TIMSS & PIRLS International Study Center, United States (2019) 11. Ayme, Y.: Games in the classroom. Pedagogical Notebooks 448, 9–49 (2006) 12. James, W., Gardner, D.: Learning styles: Implications for distance learning. New Dir. Adult Continuing Educ. 1995(67), 19–31 (1995) 13. Keymeulen, R., Langendonckt, M., Massin, C.: Motivate children through play: Use multiple intelligences. 2nd ed. De Boeck Supérieur, Belgique-Bruxelles (2018) 14. Wiemeyer, J., Effelsberg, W., Göbel, S., Doerner, R.: Serious Games: Foundations, Concepts, and Practice, 1st edn. Springer International, Germany (2016) 15. Romero, M., Proulx, J.N.: Conceptions and instructional strategies of pre-service teachers towards digital game-based learning integration in the primary education curriculum. Int. J. Digit. Liter. Digit. Competence (IJDLDC) 7(2), 11–22 (2016) 16. Schmoll, L.: Educational uses of online games. Soc. Sci. J. 45, 149–157 (2011) 17. Miller, D., Robertson, D.: Using a games console in the primary classroom: effects of brain training program on computation and self-esteem. Br. Educ. Res. Assoc. (BERA) 41(2), 242–255 (2010) 18. Almeida, F., Simoes, J.: The role of serious games, gamification, and industry 4.0 tools in the education 4.0 paradigm. DergiPark 10(2), 120–136 (2019) 19. Sauvé, L., Kaufman, D.: Educational Games and Simulations: Case Studies and Lessons Learned. PUQ, Québec (2010) 20. Habgood, J., Nielsen, S., Crossley, K., Rijks, M.: The Game Maker’s Companion, 1st edn. Apress, Angleterre (2010) 21. Spatioti, A., Kazanidis, I., Pange, J.: A Comparative study of the ADDIE instructional design model in distance education. MDPI 13(9), 1–20 (2022)

Dimensionality Reduction for Predicting Students Dropout in MOOC Zakaria Alj(B)

, Anas Bouayad, and Mohammed Ouçamah Cherkaoui Malki

University Sidi Mohammed Ben Abdellah Faculty of Sciences Dhar Mahraz Fez, Fez, Morocco {Zakaria.alj,Anas.bouayad,oucamah.cherkaoui}@usmba.ac.ma

Abstract. This paper aims to find a representation of KDD cup 2015 dataset for predicting MOOCs student dropout in a reduced space, using dimensionality reduction methods. Dimensionality reduction techniques intend to overcome the curse of dimensionality by providing an effective solution to this problem leading to machine learning algorithms inefficiency. In this work, we used two dimensionality reduction techniques: features selection and data transformation. Our technique uses the contribution of many features selection methods in order to find the pertinent subset of features to keep. The obtained results comparing the models accuracy of the initial dataset and the subset of selected features encourage the use of these methods in order to reduce the complexity of the problem and the computation time. Keywords: MOOC · Student Dropout · Dimensionality Reduction · Features Selection

1 Introduction Massive Open Online Course (MOOC) is an online learning environment allowing students to increase their potential education opportunities [1]. However, despite these benefits of MOOCs that bring a substantial improvement to the student learning experience, the major problem that MOOCs are facing is the high dropout rate [2]. Statistics show that less than 10% of enrolled participants complete the courses [3]. In order to tackle this problem, several researchers use machine-learning algorithms to predict students at high risk of dropout. The prediction is based on processing extracted data from the log trace. The quality of the processing system depends highly on the content of the data. When the dimension is high, solving the problem becomes difficult. Although machinelearning algorithms can process a huge amount of data, their performance decrease as the number of features increases. The models trained on many features are reliant on the training data and strongly prone to overfitting, because the high dimensionality reduces the generalization of the model [4]. Dimensionality reduction algorithms aim to deal with the curse of dimensionality by finding a pertinent representation of the initial data in a smaller space. Dimensionality reduction methods are generally categorized into two categories: Feature Selection and Feature Extraction/Transformation. The former consists of selecting the most informative features and discarding the less ones. The © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 421–430, 2023. https://doi.org/10.1007/978-3-031-29857-8_43

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latter consists of creating a new reduced set of features from the initial one by applying algebraic transformations. In order to tackle the problem of predicting student dropout, and since the KDD cup 2015 dataset contains raw log records, we proceed with a feature engineering process to extract features [5]. Then, we used a hybrid feature selection and data transformation algorithms to reduce dimensionality and to only keep a small pertinent subset of features. Then, three commonly used machine-learning algorithms: Random Forest, Gradient Boosting and Logistic Regression are implemented to assess the pertinence of the methodology used in this paper. This paper is organized as follows: first, we give a brief overview of related work in Sect. 2. Then, we describe the used dataset in Sect. 3. The Sect. 4 is dedicated to present the methodology used in order to reduce dimensionality and to predict student dropout. In Sect. 5, we discuss the obtained results. Finally, Sect. 6 concludes the paper and gives some perspectives on future work.

2 Related Work Many researchers have recently focused on MOOCs student dropout. Time consideration is very significant when tackling this problem. Early detection plays a masterful role in reducing the attrition rate [6]. Several studies used machine-learning techniques and analyzed the log trace to predict student’s dropout. Gitinabard et al. [7] applied Logistic Regression and Support Vector Machine (SVM) to detect students at risk. Berens et al. [8] developed a predicting system using demographic data and a boosting algorithm combining several ML algorithms: Linear Regression, Neural Network, Decision Tree, and ADABOOST. In This paper, we used two ensemble machine-learning algorithms: Random Forest (RF) and Gradient Boosting (GB) since they are outperforming the baseline algorithms used in the literature. The effectiveness of the learning algorithm highly depends on the used dataset. ML algorithms are unable to provide an effective parameter setting method. Therefore, feature selection of parameters is another research content of this paper. Many other researchers have recently dealt with the curse of dimensionality using features selection and machine learning. These techniques have shown their success in many different concrete applications such as intrusion detection [9], text categorization [10] and information retrieval [11]. Many papers and books are proving the benefits of the feature methods. Such methods are often divided into three categories: filters, wrappers and embedded methods. Works and research are using different strategies such as combining several feature selection methods, which could be done by using algorithms from the same approach, such as two filters [12] or coordinating algorithms from two different approaches, usually filters and wrappers [13], combining features selection approaches with other techniques, such as feature extraction [14] or tree ensembles [15]. In the paper, we use a hybrid features selection combining algorithms from filters, wrappers and embedded approaches to elect the pertinent subset of features.

3 Data 3.1 Data Presentation The dataset used in this work comes from KDD cup 2015. This dataset contains information sourced from XuetangX, which is one of the biggest Chinese MOOC learning

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platforms. The detailed description of the five parts of the dataset is presented in previous work [16]. 3.2 Feature Engineering Since the information available in the KDD cup 2015 dataset lacks personal information, the log trace remains the most powerful source of information. The feature extraction method used in this paper is based on the student’s behavior learning. For every current student enrollment, all previous enrollments results are taken into consideration. We also compute the count of log activity for each event of the seven events present in the table of events, for the current and previous enrolments, the data-set catalog for the current and previous enrollments. Besides, the accumulation of connected minutes is recorded for each enrolment with an algorithm used in previous work [16]. Finally, the extraction method used leads to 27 features presented in Table 1. Table 1. Extracted Features 1. Enrolment Identifier

2. Previous enrolments count

3. Previous succeeded

4. previous drop-out enrolments count

5. Current enrolment log count

6. Previous enrolments log count

7. days between first and last log

8. Days between first and last log for previous enrolments

9. Problem logs count

10. Previous problem Event logs

11. Video logs count

12. Previous Video Event logs

13. Navigate logs count

14. Previous Navigate Event logs

15. Page-close logs count

16. Previous Page-close Event logs

17. Count of Access Event logs

18. Previous Access Event logs

19. Count Discussion Event logs

20. Count Previous Discussion logs

21. Count of Wiki logs

22. Count of previous Wiki logs

23. Count of first 10 days logs

24. Count of second 10 days logs

25. Count of last 10 days logs

26. Count of active minutes

27. Count of active days

28. Enrolment result: Success 0/Drop-out 1

4 Methodology Most classification problems are based on processing of extracted data, structured as vectors. The quality of the classifier depends directly on the correct choice of the content of these vectors. However, in many cases solving the problem becomes difficult owing to the high dimensionality of the data vectors. Indeed, the non-significant increase in data has a detrimental effect on the complexity and the computation time. Variable selection and dimensionality reduction techniques provide a natural answer to this problem by

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eliminating features that do not provide enough predictive information to the model. Reducing the number of features has a double benefit. On one hand, the model becomes easily interpretable. On the other hand, removing non-informative features enhances the prediction accuracy of the models used. – Dimensionality reduction based on Features Selection: Consists of selecting the most relevant features describing the phenomenon studied among all data-set features. – Dimensionality reduction based on Data Transformation: It consists of replacing the initial set of data by a new reduced set built from the initial set of characteristics. 4.1 Features Selection Feature selection is a critical part of any machine-learning pipeline. It aims to isolate the subset of predictors that allow to efficiently explain the target variable. Thus, the performance of a learning algorithm is strongly dependent on characteristics used in the learning task. The presence of redundant characteristics can reduce the performance. The methods used to evaluate a subset of features in the selection algorithms can be classified into three main categories: Filter, Wrappers and Embedded. Filter: This method is considered as a pre-processing step where the evaluation of the relevance of selected features is calculated according to measures that rely on the training data and before the learning phase. One of the advantages of this method is that the features selected are independent from the chosen classifier. Furthermore, Filter algorithms generally cost less computation time since they avoid repetitive executions of learning algorithms on different subsets of variables. However, their major drawback is that they ignore the impact of selecting subsets of features on the performance of the model [17]. Examples: Chi-square Test, Fisher Score, Correlation Coefficient Wrappers: Methods were introduced by John et al. in 1994 [18]. They are generally considered better than those filtering methods. They attempt to select a subset of features and evaluate them using a classification algorithm. The evaluation is made by calculating a score compromising between the number of variables eliminated and the success rate of the classifier on the test set. The classification algorithm is called several times during each time a variable is selected, and then the classification rate is calculated to judge the relevance of this variable using a cross validation mechanism. The principle of wrappers is to generate a subset well suited to the classification algorithm. However, the major drawbacks of these methods are that they do not provide a theoretical justification for the selection and they do not allow us to understand the conditional dependencies between variables. Using a classifier to evaluate subsets of features with a cross validation mechanism is very expensive in terms of computation time. The selected subset of features depends on the classifier used and are not necessarily valid if we change the method. Examples: Recursive Feature Elimination, Sequential Feature Selection, Genetic Algorithms

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Embedded: Unlike the Wrapper and Filter methods, embedded methods incorporate the selection of variables during the learning process. In the Wrapper methods, the dataset is divided into two parts: a learning and a validation set. Embedded methods can use the full set for training, which is an advantage that can improve the results. Another advantage of these methods is their low computation time compared to the other approaches. Examples: Lasso Regularization, Random Forest, Decision Tree

4.2 Data Transformation In contrast with feature selection, the reduction of dimensionality by a data transformation is not made by selecting certain features. The reduction is performed by the construction of new features. These features are obtained by combining the initial ones. Data transformation risks losing the semantics of the initial set of features and therefore the use of this family of methods is not applicable only in the case where the semantics no longer occurs in the steps following the dimensionality reduction. They are generally grouped into two categories: linear methods and non-linear methods. Examples: Principal Component Analysis (PCA), Multi-Dimensional Scaling (MDS), T-distributed Stochastic Neighbor Embedding (TSNE) 4.3 Used Techniques Chi-Squared Karl PEARSON (1857–1936), is a statistical test for independence between categorical variables. Independent variables from the target variable can be removed safely from the selected subset. The principle of chi-squared statistical test is to calculate the difference between two distributions: a calculated distribution and a theoretical distribution obtained if the two variables were completely independent. The difference between the two distributions allows to accept or to reject the hypothesis of independence H0. Feature selection with Chi-squared can be implemented with Select-K-Best which is a Scikit-learn machine library providing the K most relevant features. Recursive feature elimination (RFE) the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and the importance of each feature is obtained. Then, the least important features are pruned from the current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached. Oftenly used with a validation mechanism, the performance of RFE for feature selection depends highly on the used model and the scoring metric. Since we are facing a class imbalance in our dataset, AUC ROC will be used as a metric to evaluate the selection. In this paper RFE will be implemented with Random Forest (RFE-RF) and AdaBoost Classifier (RFE-ADA). Regularization: In machine learning is a technique that adds a penalty term to the coefficients of the features to reduce their magnitude. This penalty is applied to the coefficients that multiply each feature in a linear model. The main goal of regularization is to prevent overfitting by enhancing the generalization of the model. The two main types

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of regularization for linear models are Lasso (L1) and Ridge (L2). Both of these methods are used for variable selection and dimensionality reduction. L1 regularization sets the values of unnecessary variables for target prediction to zero, while L2 regularization reduces the values of these variables towards zero. Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a technique that belongs to unsupervised machine learning, which means it does not require a label for the target variable. It is used for reducing the dimensionality of a set of variables by finding the principal components, which are new variables created from linear combinations of the original variables. These components are chosen in such a way that they maximize the variance observed in the data. This approach is useful for transforming correlated variables into a set of uncorrelated variables, which can be used for further analysis. We find below the summary of the used methods in this paper. We will use the following methods since they are producing the best results comparing the others. • Variable Selection: Filter: Chi-squared. Wrappers: RFE-RF, RFE-ADA. Embedded: Lasso, Ridge. • Data transformation: PCA, T-SNE.

5 Results After implementing each of the methods presented in the previous section, the obtained scores (ranks, coefficients) will be aggregated and normalized so that they are between zero for the lowest rank and one for the highest. The selection of features to eliminate will be made according to the mean of the obtained score of all methods [19]. Results are presented in Table 2 below. Table 2. Features Ranking N Feature

Lasso

REF-RF

REF-ADA

Ridge

Chi-2

Mean

1.

0.00

1.00

0.46

0.00

0.00

0.29

2.

0.00

0.57

0.04

0.42

1.00

0.41

3.

0.00

0.29

0.92

1.00

1.00

0.64

4.

0.00

0.71

0.08

0.58

1.00

0.47

5.

0.66

1.00

0.79

0.01

0.97

0.69

6.

0.05

1.00

0.12

0.01

1.00

0.44

7.

0.00

1.00

1.00

0.5

1.00

0.67

8.

0.00

1.00

0.71

0.00

1.00

0.54

9.

0.00

1.00

0.96

0.01

0.99

0.59 (continued)

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Table 2. (continued) N Feature

Lasso

REF-RF

REF-ADA

Ridge

Chi-2

Mean

10.

0.00

1.00

0.50

0.01

1.00

0.50

11.

0.00

1.00

0.54

0.02

1.00

0.51

12.

0.00

1.00

0.37

0.01

1.00

0.48

13.

0.00

1.00

0.58

0.05

1.00

0.53

14.

0.00

0.00

0.00

0.00

1.00

0.20

15.

0.00

1.00

0.62

0.09

1.00

0.54

16.

0.00

1.00

0.17

0.01

1.00

0.44

17.

0.00

1.00

0.33

0.02

0.99

0.47

18.

0.00

1.00

0.29

0.01

1.00

0.46

19.

0.00

0.14

0.25

0.00

1.00

0.28

20.

0.00

0.86

0.67

0.01

1.00

0.51

21.

0.00

1.00

0.21

0.06

1.00

0.45

22.

0.00

0.43

0.75

0.00

1.00

0.44

23.

0.00

1.00

0.87

0.03

0.99

0.58

24.

0.00

1.00

0.83

0.01

0.99

0.57

25.

1.00

1.00

1.00

0.03

0.99

0.80

26.

0.60

1.00

0.87

0.58

1.00

0.81

27.

0.00

1.00

1.00

0.98

1.00

0.80

According to the results found in Table 2, we will take 0.64 for the Mean value as a statistical threshold for variable selection. The dimensionality of the dataset will be reduced to the following six features: 26 (0.81), 27 (0.80), 25 (0.80), 5 (0.69),7 (0.67), 3 (0.64). In order to evaluate the pertinence of our selection we will compare the obtained scores of Accuracy and AUC ROC using all extracted features and the selected variables, with three commonly used machine-learning algorithms: Random Forest, Logistic Regression and Gradient Boosting. According to the results obtained in Table 3, we notice that the results are almost the same despite the elimination of twenty features. We notice also that for Logistic Regression, AUC ROC score has been improved when deleting non-significant features. We conclude that the features eliminated are not important to predict the target variable. We can only keep the six selected variables.

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Z. Alj et al. Table 3. Models Scores

ML Algorithm

Features

Accuracy

AUC ROC

Logistic Regression

All Selected

0.871 0.870

0.861 0.863

Gradient Boosting

All Selected

0.875 0.874

0.879 0.877

Random Forest

All Selected

0.873 0.873

0.876 0.876

Fig. 1. Accuracy per Components

Figure 1 explains the variation of accuracy as a function of the number of components in PCA analysis. We notice that for the three used models, the reduction of dimensionality to five components neglects only 0.2 of the overall classifiers precision. 90% of the variance is on the first five components, which means that it is possible to keep only five features without losing an important part of information in the data. Using 10 components enhances slightly the precision. Indeed, For GB and LR from the 10th component the score is stabilized respectively at 0.871 and 0.87 which is exactly the same score using all the features.

Fig. 2. Space points plot in 2D

The T-Distributed Stochastic Neighbor Embedding (T-SNE) is a non-linear dimensionality reduction method; it aims to ensure that close points in the starting space have close positions in two dimensions (2D). In other words, the measurement of distance

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between the points in two-dimensional space must reflect the measurement of the distance in the initial space. We implement the T-SNE method on the data extracted features in order to visualize the points in two dimensions. Visualizing the data allows us to recognize more information about the dataset. One of the reasons to use T-SNE is to validate the relevance of the extracted features. Indeed, if the used features are pertinent and very distinctive between the data classes, this distinction must be observed when plotting points with T-SNE. As we can see in Fig. 2, T-SNE makes it possible to distinguish two different clusters, associated with the student behavior collected with extracted features from the log trace; red points correspond to enrollments with dropout result. The purple points correspond to successful ones. Dropout points are stained with the same color grouped together forming a cluster and separated from the purple points. Which shows the similarities and distinctions in the dataset. This founding validates the relevance of used features.

6 Conclusion The excessive attrition rate in MOOCs have prompted researchers to use machine learning algorithms and learning analytics to predict student dropout. However, machinelearning algorithms are inefficient in high dimensionality, and their performance decrease as the number of features increase. In order to tackle the curse of dimensionality. We proposed in this work, a solution to this problem using two dimensionality reduction methods: feature selection and the data transformation. We used a hybrid selection method combining the contribution of multiple algorithms in order to determine the features to keep. In the light of the obtained result, we conclude that removing irrelevant features helps to produce very competitive prediction results; our main contribution was obtaining encouraging performances with a minimum number of variables. As a perspective, and in order to assess the validity of the methodology used in this paper, we intend to apply the same process on other benchmark datasets.

References 1. Iniesto, F., McAndrew, P., Minocha, S., Coughlan, T.: Accessibility in MOOCs. Open World Learn. 119 (2022) 2. Mehrabi, M., Safarpour, A.R., Keshtkar, A.: Massive open online courses (MOOCs) dropout rate in the world: a protocol for systematic review and meta-analysis. Interdiscipl. J. Virt. Learn. Med. Sci. 13, 85–92 (2022) 3. Perchinunno, P., Bilancia, M., Vitale, D.: A statistical analysis of factors affecting higher education dropouts. Soc. Indic. Res. 156, 341–362 (2021) 4. Badillo, S., et al.: An introduction to machine learning. Clin. Pharmacol. Ther. 107, 871–885 (2020) 5. Bellman, R.: Dynamic programming: Princeton univ. press, NJ, vol. 95 (1957) 6. Wang, W., Zhao, Y., Wu, Y.J., Goh, M.: Factors of dropout from MOOCs: a bibliometric review. Library Hi Tech (2022) 7. Gitinabard, N., Khoshnevisan, F., Lynch, C.F., Wang, E.Y.: Your actions or your associates? Predicting certification and dropout in MOOCs with behavioral and social features, arXiv preprint arXiv:1809.00052 (2018)

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8. Berens, J., Schneider, K., Görtz, S., Oster, S., Burghoff, J.: Early detection of students at risk– predicting student dropouts using administrative student data and machine learning methods. Available at SSRN 3275433 (2018) 9. Saheed, Y.K., Abdulganiyu, O.H., Tchakoucht, T.A., Rakshit, S.: A Novel Wrapper and Filter-based Feature Dimensionality Reduction Methods for Anomaly Intrusion Detection in Wireless Sensor Networks (2022) 10. Dogra, V., Singh, A., Verma, S., Jhanjhi, N.Z., Talib, M.N.: Understanding of data preprocessing for dimensionality reduction using feature selection techniques in text classification. In: Intelligent Computing and Innovation on Data Science, Springer, pp. 455–464 (2021). https://doi.org/10.1007/978-981-16-3153-5_48 11. Laskar, M.T., Chen, C., Johnston, J., Fu, X.Y., Bhushan, T.N.S., Corston-Oliver, S.: An auto encoder-based dimensionality reduction technique for efficient entity linking in business phone conversations. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2022) 12. Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018) 13. Xu, X., Liang, T., Zhu, J., Zheng, D., Sun, T.: Review of classical dimensionality reduction and sample selection methods for large-scale data processing. Neurocomputing 328, 5–15 (2019) 14. Zakaria, A.L., Anas, B.O., Oucamah, C.M.:Intelligent system for personalised interventions and early drop-out prediction in MOOCs. Int. J. Adv. Comput. Sci. Appl. 1–11 (2022) 15. Rai, K., Devi, M.S., Guleria, A.: Decision tree based algorithm for intrusion detection. Int. J. Adv. Netw. Appl. 7, 2828 (2016) 16. Zakaria, A.L., Anas, B.O., Oucamah, C.M.: Intelligent system for personalised interventions and early drop-out prediction in MOOCs. Int. J. Adv. Comput. Sci. Appl. 13(9) (2022) 17. Kohavi, R., John, G.H, et al: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997) 18. John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Machine Learning Proceedings. Elsevier, pp. 121–129 (1994)

Cheating Detection in Online Exams Nabila EL Rhezzali1(B) , Imane Hilal1

, and Meriem Hnida1,2

1 ITQAN Team, LyRica Lab, Information Sciences School, Rabat, Morocco

{nabila.el-rhezzali,ihilal,mhnida}@esi.ac.ma 2 RIME Team, Mohammadia School of Engineers, Mohammed V University

in Rabat, Rabat, Morocco

Abstract. Right now, numerous universities are directing tests online which creates an opportunity where students cheat. This trend has been sped up in recent months when COVID-19 cases are increasing, and test centers are closing. However, most educational institutions face the problem of student cheating when taking online exams. This study aims to find a technique that helps instructors to detect cheating in online exams. Keywords: Cheating detection · E-learning · Online Exams · Online Proctoring

1 Introduction In recent years, the rapid development of information and communication technology has had a direct impact on human life, especially in the field of education. Therefore, e-learning has grown in recent years and has been widely adopted by educational institutions. It enables students to deliver information over the Internet anytime, anywhere [1]. Degrees awarded by universities are usually based on a student’s achievement. Typically, exams are used to assess this performance and award a corresponding grade. Good grades are a passport to promotion, so students can be under a lot of pressure to cheat to get higher test scores [2]. Cheating in proctored and unproctored online exams has been extensively investigated over the past decade [3]. University administrations are concerned about academic dishonesty that undermines the academic integrity of the degrees offered to students [4]. Academic dishonesty, including cheating, plagiarism, and data falsification, is a disturbing phenomenon in higher education. It’s easy to look over the shoulders of classmates during a quiz, and whichever method is used, academic dishonesty can harm the learning experience and give cheaters an unfair advantage over those who follow the rules [5]. The rest of this article has been organized as follows: in the Sect. 1, we present related works to cheating detection, in the 2 and Sect. 3 we present some ways of cheating and some methods that can help instructors to reduce cheating in online exams.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 431–440, 2023. https://doi.org/10.1007/978-3-031-29857-8_44

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2 Research Problem E-learning is a type of online and remote training that uses the internet and new digital technologies, such as computers, tablets, and mobile phones. With online learning, students can theoretically study anytime, anywhere. E-learning has become an essential technique for instructors around the world. In the past, not everyone had access to knowledge. Geographical borders make it hard for instructors and students to go to different countries. This recent trend gives consistent schooling, better cooperation, and worldwide opens doors for students and instructors. Online learning is becoming more and more common, a topic often discussed in online education is cheating, because switching from written to online exams makes cheating easier. The challenge for many universities is how instructors could detect cheating in exams specifically when it’s done remotely. In this article, we aim to find a way to detect cheating in online exams.

3 State of the Art A method for cheating detection in online exams was studied in [1], with “continuous authentication and online proctoring” [1]. For this purpose, an online exam management system for cheating detection was implemented. The system uses a fingerprint reader authenticator to validate the candidate and “EYE TRIB TRACKER” [1] to ensure that the candidate is the same throughout the examination session. As each person has his own ocular characteristics, the calibration step of an eye tracker allows the system to define an ocular model of the user, which allows an optimal estimation of the look. The same approach was proposed in [6] an identification service platform that can confirm the identity of candidates in real-time during online exams was presented, and its technology is machine learning algorithms. For the first level of security, a smart card for access to the exam session was used. The proposed solution includes a smart card and password as two authentication factors, for continuous authentication biometrics was proposed because it is based on the verification of human nature, which cannot be borrowed or modified. To guarantee a high level of security safety, an online exam management system and an automatic proctoring system were utilized. The student’s computer is associated with the online exam management server S2 and they don’t use the monitoring server S1. To start a session, the first authentication phase is done with the smart card, the second step is to authenticate the student’s identity with the S1 server by using face recognition and checking the database for matches with the person in front of the camera as shown in Fig. 2. The model used machine learning and artificial intelligence techniques on S1 server, and deep learning makes it possible to create biometric recognition software that can exceptionally distinguish or confirm an individual. When a student doesn’t respect the rules, the system treats it as a cheating attempt, the students will receive 3 alerts then the online test is automatically stopped (Fig. 1). The work in [7] aims to develop a multimedia analysis system to detect various cheating behaviors during online exams. The suggested online examination process has two steps, the preparation, and the exam step. During the preparation phase, candidates must use passwords and facial recognition to verify their identity before the exam begins. In the exam phase, the candidates take the examination, under the proctoring of the

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Fig. 1. Online monitoring process

Fig. 2. Online proctoring (face detection)

online real-time proctoring system. Three sensors webcam, glasses with a camera called wearcam, and a microphone were used to catch varying media signals of the test environment and test participants. Detecting information is handled using six items to extract features. These features are user confirmation, text identification, speech identification, “dynamic window detection, gaze estimation and phone detection” [7]. A System for online test proctoring was suggested in [8]. The principle 3 modules consist of the active window, video processing, and audio recording. With the first module, the window changes were detected within the laptop utilized by the test taker. The second module enabled to detect multiple faces and face disappear duration. The third module is the audio recording. Candidates have many opportunities to commit inappropriate behavior that can be identified from the audio variant, for instance, can speak with the individual sitting subsequent to him in the examination corridor, or if the student takes the test alone, nonetheless he can use the phone to communicate with the

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person that will help him. Without a doubt, a few noises could be a gift on the location of the exam. The system in [9] is effective for online assessment since it will allow the student to be observed during the online exam and any cheating and misidentification was detected. The online exam can be taken anytime, anyplace with just a webcam. This project aims to detect and recognize faces during the whole examination, it helps also to stop cheating and increase the credibility of passed students. The challenge of the suggested proctoring model during an online exam is detecting abnormal behavior. The suggested system consists of two modules face detection by using “Viola jones algorithm” [5], and face recognition with a neural network, to detect suspicious activities CNN was used, which makes the system fast and efficient. In [10] suggests using abnormal head movements, facial recognition, and head position estimation in videos, in addition to uncommon mouse movements. The technology used is based on three modules: data collection, visualization, and a suspicious case detection engine. Videos, mouse movement data, test scores of students, and duration for each are all captured in the data collection module. The visualization tool facilitates the analysis of students’ online exam habits in a quick and efficient manner. They propose two rules in particular: strange head motions include extreme variations in head positions and face disappearances in the video and abnormal mouse movements include “copy, paste, blur, and focus” [10]. As we saw, many authors suggest using online proctoring based on image video, and audio processing. However, any transition from traditional methods to online models will require some investment from educational institutions because implementing a new system may cause minor disruptions and may require user training time. One of the main drawbacks of online exam systems in remote areas where it is difficult to meet electricity supply, stable internet connection, and other basic system requirements. And with small internet cut the system prediction may be wrong. Another method with Ip address is mentioned in [11], the proposed method is a two-phase process. In the first phase, an IP address was proposed to detect any deceitful activity. Most switches distribute dynamic IP addresses, which are numerical labels explicitly allocated to any gadget connected to a computer network. This will allow the system to give a sign when test takers change their computing device or home location. And the second phase consists of behavior detection with LSTM network. When abnormal behavior is detected, the system will change the question set randomly. Abnormal behavior refers to the speed of student response and when the questions have been answered 90% correctly. To detect cheating some authors suggest using real-time techniques, it’s real-time online proctoring, but others think it’s better to use after exam technique. For that purpose, a new approach different from online proctoring and image processing is proposed in [12], by using exam scores, the input to the algorithm is a sequence of grade tests, midterms, and finals for all classes, and the output is a set of labels, one for each student, that indicates if students are cheating or not. The proposed method is based on two phases: regression and unsupervised outlier detection. First, a recurrent network model is used to predict the latest test results by using previous evaluation results. At this point,

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the outlier detection model is then applied to distinguish the examples where there is an abnormal difference between the real and the anticipated last test of the exam scores. Another after-exam technique is proposed in [13], the whole process of the proposed system is separated into three layers. The first layer is utilized to distinguish cheating situations during an online test, by using statistical techniques based on the following characteristics: the student’s IP address, the time taken, and the time late. The second layer is utilized to compute the closeness between the answers, by using similarity measures on the exam questions as characteristics in this layer. Exam questions are test questions that require a written paragraph, with a prescribed length. It incorporates a section, sentence, or short structure. If the proportion of matching between answers is more than 65%, the proposed framework thinks of it as proof of cheating. In the third layer, a clustering algorithm is utilized to isolate students’ responses into several groups. By comparing only grades, sometimes we can injustice students, especially when it comes to multiple choice questions, where there is no spontaneous expression or the ability to write that differs from one person to another.

4 Cheating Techniques in Online Exams Cheating Risks in testing are presented in [14] some students cheat in a singular way, and other students can cooperate. In the web environment, it’s difficult to catch and prevent students from working together specifically when online courses are done remotely. Some students can utilize software like Google Docs to get exam questions more quickly. Some students will attempt to cooperate progressively during their online exam, either in a similar area or in their different areas involving the internet or social network for communication. Assuming that one student can complete the test so as to help other people, then, at that point, a few students will attempt to work two by two or even in groups. There generally exists the opportunity that students have basically recruited another person to take their tests or compose their papers for them. Other students tried during their online tests to search for answers on the internet. The authors in [15] classified cheating scenarios into 3 categories: Unallowed aids, collaboration, and deceiving proctors. For unallowed aids, some students will try to use unallowed software, search Internet Engines, and some technical devices like headphones. In regards to collaborations, one student communicates through a software on the exam computer or by using a hidden device. And for deceiving automated proctors, students can use Virtual machines, tactical body language strategic placement of unallowed aids, and an alteration of computer settings. In [16] the authors mentioned that sometimes examinees attempt to purchase additional opportunities to work more on the test replies. For example, the student might report a blunder about the test framework or test administering programming to persuade the instructor to reset the test meeting. This empowers the possibility to get an additional opportunity for cheating and finding the solution during this stretch when the exam is closed. Usually, between the two submissions (the first and second one), there is more time to answer the exam questions. Other more traditional techniques to cheat are toilet demands during the test. Utilizing different gadgets in monitored exams, with a camera, students attempt to utilize numerous gadgets and answer inquiries with the first

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one while cheating through the second gadget. Multiple kinds of gadgets could be utilized as the second device, like PCs, smartphones, smartwatches, tablets, programmable calculators to save notes, and minuscule. Some students to cheat will try to redirect the webcam to conceal something from its environment or handicap the webcam. In [17] is mentioned that during their studies many students break academic integrity, such as cheating and plagiarism, frequently to get a higher result in the exam than they are able to do. The current inescapable utilization of the web has made it easier for students to get data illegally. Both cheating and plagiarism are considered as breaking academic integrity. Plagiarism can be characterized as duplicating a text from one more source without surrounding it with quotes and without referring to the reference. Plagiarism is likewise utilizing data thoughts or speculations of someone else without mentioning the name of that individual. Literary plagiarism is considered as one type of cheating. In [18] is mentioned that in writing and in-person exams students look at and copy answers from each other. Even teachers in face-to-face exams expect students to remove everything from their work area. There are a lot more choices to cheat on online exams. Students can wait for answers specifically when the teacher permits a couple of days to do the test. Since there is adaptability with which to take an assessment, some students hold on until others have a potential chance to take the test, so they can find the solutions. When students are not well prepared for an exam they can claim a fraudulent error message when the teacher has to read this message and restart the exam session. However, students take classes from any place in the world, students use phones, and the internet to communicate with each other to exchange answers. Moreover, some students take answers from the web, without references to the article or short response evaluations. There are also some websites that permit students to submit test questions and buy responses.

5 Methods for Reducing Cheating Many online courses expect students to find a nearby exam place or monitor. A few schools expect students to take in-class evaluations, with comprehensive assessments, toward the finish of their programs. Different colleges permit students to step through online examinations utilizing a PC at a controlled testing community close to the student’s actual area. Currently, most universities enable students to use their own computers, anywhere but to proctor them by using a camera and microphone. Providers provide real-time or recorded monitoring and reporting when events are under control. These providers use software to control access and block students’ computer browsers during exams [14]. In [16] the authors divide cheating detection into two main types, pre-exam detection, and during-exam detection. Authentication is mainly used to prevent impersonation before exams. This can be done by verifying a school ID card with a webcam or through newer methods such as fingerprint biometrics, palm vein scans, eye vein scans, etc. Dividing students into several groups based on similarity measures. Stochastic and strategic grouping methods are suggested to destroy friendships. Because most of the time, students communicate and cheat with their close friends. In [18], some fraud detection methods were initially used for paper-based face-toface exams, but the method can be adapted for online exams. If a student misses a

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similar question on a QCM test, there may not be a cheating problem. But when students skip similar questions with multiple identical incorrect answers, this can be a source of cheating. Another technique to reduce the opportunity of cheating by creating a website implying that it contains answers to a specific question, but all the answers given are false. At the point when a student search within a web the webpage utilized as a trap would be found. The deceptive student would acknowledge the erroneous responses accidentally. The most innovative technique proposed by Christie in 2003 was the utilization of a class mole. The teacher could sign up as a student under an alternate name. At the point when students examine cheating among themselves, the malpractice students could be detected, while committing the offense. However, some software such as «Turnitin», «WriteCheck», «DupliChecker» could be used to detect similarity and plagiarism. In [19] authors mentioned that using technology such as « Secure Software Remote Proctor », with a camera and a finger biometric scanner, can be used to detect online cheating. Additionally, some software using electronic proctoring schemes and identity verification can detect and prevent fraud. A summary of cheating types and some solutions to prevent cheating is presented in Table 1. Table 1. Detect and prevent cheating in online exams. Types of cheating in online exam

Solutions to prevent cheating

Working together

Online proctoring to detect multiple faces

Using unallowed gadgets (PCs smartphones, smartwatches, tablets)

Online proctoring to detect the test taker environment

Using Internet (Purchase answers from internet) Lockdown browser option help to control student’s computer Plagiarism

Similarity Detection Software

6 Proposed Method We propose in our approach to find a solution that helps teachers detect cheating in two stages: a priori during the exam and a posterior after the exam so that the teacher doesn’t miss anything. 6.1 Global Presentation of Our Approach Through online learning, students can access content anytime, anywhere to help them generate and communicate new ideas. However, when the learning takes place online, so does the exam, making it easier to cheat because there is no proctoring, and most students confess that. There are so many methods for cheating, such as using the internet to look for answers and communicating with each other via social networks. Another method for cheating in an examination is impersonation when a student takes the exam for another

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student. One of the big challenges in E-Learning is cheating detection. Some authors suggest methods to detect cheating during exams by using continuous authentication and online proctoring and others propose solutions for cheating detection after examination by comparing students’ answers and detecting similarities. For us, we suggest using the two methods during the examination and after the examination, So, to detect cheating in online exams, in our study we will be inspired by face-to-face exams. During a faceto-face exam at first, we have a supervisor, and sometimes even the supervisor cannot detect everything so at this point it is the role of the corrector to intervene to detect cheating in the copies. Generally, when it comes to cheating, we can see a resemblance in the answers and a resemblance in the faults committed (Fig. 3). 6.2 Online Exams Cheating Detection System Architecture

Fig. 3. Architecture of cheating detection system in the online exam.

You can conduct an online exam for faraway candidates with the use of remote proctoring. It’s crucial to authenticate a person before allowing them to take an online exam. When a candidate is taking a test from a distance, you must confirm the person by looking up his or her identification. The system would verify it using the records supplied during registration before comparing it to records already in the system. The candidate must show his or her identity to be validated by the proctor if the identity information matches. Then if the identity is valid the candidate may proceed and take the online exam. The candidate’s activities are streamed live throughout the online exam.

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Additionally, the exam screen is blocked, and the candidate is unable to open any other windows while taking the online test. Candidates may show up for the exam during the allotted exam period and complete the exam task. The remote proctor can monitor it, and the system also records the entire procedure. In this way, it is possible to predict cheating with machine learning by using recorded live-streaming videos. Once the exam was finished and the answers have been submitted, we try to compare student answers by using semantic and string-based similarity measures, then with machine learning, we can predict the answers’ similarity. The final step of our study is trying to improve model performance by assembling the two models with a STACKING METHOD. Finally, we can say that the use of online proctoring alone or the calculation of similarity alone can sometimes be unfair to the students, but by combining the two methods we can have a prediction with high precision.

7 Conclusion E-learning is a new approach to the teaching process that uses information technology to make learning easy and affordable for everyone anywhere. Remote assessment practices are on the rise globally as more schools and universities turn to assess students or applicants online. Thus, without proctoring, students break academic integrity by cheating. However, efficient proctoring has been the main focus of the assessment team. The requirement of trusted online proctoring software for assessing learners is increasing. Nowadays there are so many online proctoring tools such as «Proctortrack», «Mettl», «Talview Proview», « Proctoring», «ProctorExam», «ProctorEdu» etc. So, to detect cheating in online exams, in our study we will be inspired by face-to-face exams. During a face-to-face exam at first, we have a supervisor, and sometimes even the supervisor cannot detect everything so at this point it is the role of the corrector to intervene to detect cheating in the copies. Generally, when it comes to cheating, we can see a resemblance in the answers and a resemblance in the faults committed. For more accuracy, we will try on a second time to compare answers and common mistakes to cluster students into different groups based on the similarities of their answers.

References 1. Bawarith, R., Basuhail, A., Fattouh, A., Gamalel-Din, S.: E-exam cheating detection system. Int. J. Adv. Comput. Sci. Appl. 8(4) (2017) 2. McCabe, D.L., Trevino, L.K., Butterfield, K.D.: Cheating in academic institutions: A decade of research. Ethics Behav. 11(3), 219–232 (2001) 3. Harmon, O.R., Lambrinos, J.: Are online exams an invitation to cheat?. J. Econ. Educ. 39(2), 116–125 (2008) 4. Barron, J., Crooks, S.M.: Academic Integrity in Web-Based Distance Education. Tech Trends Linking Research and Practice to Improve Learning (2005) 5. Cluskey, G.R., Ehlen, C.R., Raiborn, M.H.: Thwarting online exam cheating without proctor supervision. J. Acad. Bus. Ethics 4(1), 1–7 (2011) 6. Ghizlane, M., Reda, F.H., Hicham, B.: A new model of automatic and continuous online exam monitoring. May 30, 2020 from IEEE Xplore

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7. Atoum, Y., Chen, L., Liu, A.X., Hsu, S.D., Liu, X.: Automated online exam proctoring. IEEE Trans. Multimedia. 19(7), 1609–1624 (2015) 8. Bijlani, S.P.: An intelligent system for online exam monitoring. In: 2016 International Conference on Information Science (ICIS), pp. 138–143 (2016) 9. Patidar, K.G.: Convolutional neural network based virtual exam controller. In: Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2020), pp. 895–899 (2020) 10. Qu, H.L. (s.d.).: A visual analytics approach to facilitate the proctoring of online exams. In: CHI ’21, May 8–13, Yokohama, Japan (2021) 11. Tiong, L.C.O., Lee, H.J.: E-cheating prevention measures: detection of cheating at online examinations using deep learning approach - a case study. J. Latex Class Files, XX, XX (2021) 12. Kamalov, F., Sulieman, H., Santandreu Calonge, D.: Machine learning based approach to exam cheating detection. Plos One 16(8), e0254340 (2021) 13. Duhaim, A.M., Al-Mamory, S.O., Mahdi, M.S.: Cheating detection in online exams during covid-19 pandemic using data mining techniques 27 June 2021 14. Timothy, B., Michael, M.B.A., Ph.D. Melissa A., Williams, Ph.D.: CFA, Student Equity: Discouraging Cheating in Online Courses https://doi.org/10.5929/2013.3.2.8 15. Baume, M., von der Ley Ortiz, S.N.: Cheating in online proctored exams: motives, scenarios and practical examples of fraud and its prevention in universities based on the tum cheating contest 2020. In: Proceedings of INTED2021 Conference 8th-9th March 2021 16. Noorbehbahani, F., Mohammad, A., Aminazadeh, M.: A systematic review of research on cheating in online exams from 2010 to 2021. https://doi.org/10.1007/s10639-022-10927-7 17. Hosny, M., Fatima, S.: Attitude of students towards cheating and plagiarism: university case study. J. Appl. Sci. 14(8), 748–757 (2014) 18. Moten, J., Fitterer, A., Brazier, E., Leonard, J., Brown, A.: Examining online college cyber cheating methods and prevention measures. Electr. J. E-learn. 11(2), 139–146 (2013) 19. Valizadeh, M.: Cheating in online learning programs: learners’ perceptions and solutions. Turkish Online J. Dist. Educ.-TOJDE 23(1), 12, 195–209 (January 2022). ISSN 1302–6488 20. D’Souza, K.A., Siegfeldt, D.V.: A conceptual framework for detecting cheating in online and take-home exams. Decis. Sci. Inst. 15(4), 370–391 (2017) 21. Jadi, A.: New detection cheating method of online-exams during COVID-19 pandemic. IJCSNS Int. J. Comput. Sci. Netw. Secur. 21(4), 123–130 (2021) 22. Hernándeza J.A., Ochoab, A., Muñozd, J., Burlaka, G.: Detecting cheats in online student assessments using data mining. In: Conference on Data Mining| DMIN, vol. 6, p. 205 (2006) 23. Ruiperez-Valiente, J.A., Munoz-Merino, P.J., Alexandron, G., Pritchard, D.E.: Using machine learning to detect ‘multiple-account’ cheating and analyze the influence of student and problem features. IEEE (2018 )

Station Rotation Model of Blended Learning as Generative Technology in Education: An Evidence-Based Research Vahid Norouzi Larsari1(B)

, Raju Dhuli2 , and Hossein Chenari3

1 Department of Pre-primary and Primary Education, Faculty of Education, Charles University,

Prague, Czech Republic [email protected] 2 Department of English Language Teaching, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India [email protected] 3 Department of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract. The objective of the present paper is to survey the station rotation model of blended learning as a generative technology in education, which covers the applications and results of retrospective research on students’ learning and performance at school. This paper is based on an evidence-based research. The researchers presented blended learning approaches that might be employed in education and explore the issues that may occur when notions are more internalized. Since a review of previous studies demonstrated that this model had a major influence on students’ learning, the debate suggests the prospect of additional research employing a station rotation model of blended learning. These encouraging findings are quite helpful, and this model could be used for further research. Keywords: Blended Learning · Station Rotation Learning · Generative Technology

1 Introduction Nowadays, technology appears to be playing a more important role in nearly all aspects of society including education. Technology in education is being revolutionized by the prevalent use of the Internet and information and communication technology [1, 2]. ICT is in favor of any application that includes the application of communication devices such as radio, television, cell phones, computers, and etc. [3]. The application of Internet and information and communication technology makes teaching more useful and improves the students’ performance, which can establish a modern learning and teaching in virtual environment [3]. Due to the ongoing change in the educational systems with the advent of ICT, instructors must take the initiative to come up with creative approaches to fulfill the particular © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 441–450, 2023. https://doi.org/10.1007/978-3-031-29857-8_45

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demands of their students. As a result, teachers need to try using contemporary teaching methods in their classroom [4]. Additionally, they should instantly supplant traditional learning methods, such as “chalk and lecture,” with more engaging learning activities by using cutting-edge pedagogy. Teachers can create some contemporary teaching approaches inside a classroom setting, including blended learning, inquiry-based learning, and collaborative learning. Education can be divided into three key categories from the perspective of information and communication technologies: (1) e-education, (2) blended education, and (3) virtual education (Kumar, 2008). Blended learning is one of the primary classifications that have drawn a lot of interest in educational science [5]. The integration of several instructional mediums, delivery strategies, and pedagogical methods is known as blended learning [6]. A combination of traditional and online learning is the major emphasis of blended learning [6, 7]. By improving access to learning activities, enhancing interaction, and boosting engagement, blended learning enables students to study at anytime, anyplace [8]. It is the most effective technique to create a mixed learning environment which combines the benefits of conventional learning (face-to-face) with online learning [4]. Blended learning is regarded as one of the most practical strategies that can be implemented, notwithstanding the primary role of classroom instructors. Moreover, blended learning enhances the benefits of both conventional and online education [6, 8]. In an empirical study, Philips [9] concluded that students assigned virtual learning utmost importance. Nevertheless, students are satisfied with online learning rather than traditional learning. In addition to the nursing and labor sectors, blended learning is also widely used in education. The fact that the instructor does not play a subordinate role, but rather serves as a guide or trainer who provides customized training, makes it universally accepted that blended learning is incredibly effective when done correctly. The teacher’s responsibility in guiding and instructing students alone cannot be replaced by virtual learning. The shift in emphasis from the teacher’s function as a knowledge carrier to the role of coaching students with regard to their capacities is a crucial component of this approach [10]. In a blended learning environment, instructors can encourage their students to participate in small-group experience-based learning [10].

2 Theoretical Perspectives of Learning and Their Relation to Station Rotation Model 2.1 Nature of Blended Learning Model Since the late 1990s, blended learning has been a significant aspect of education. It has become more popular in recent years, and more and more higher education institutions are beginning to offer at least part of their courses in a blended learning format [11]. Some have referred to it as the “new normal” due to its pervasiveness in higher education practice [12] with “non-blended” pedagogical situations being “challenged and explored” and pedagogical models that incorporate “blends” becoming the “typical” [13]. The advantages of blended learning have long been argued for, and they include increased staff and student flexibility; personalization; improved pupils results; increased self-independency; possibilities for professional learning; efficiency gains;

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staff and learners satisfaction; and increased interaction between students and faculty [11, 14, 15]. It is significant because blended learning is portrayed as transformational since it allows for the restructuring and rethinking of pedagogical practice [16], and has the capacity to “recapture the ideals of higher education” [17]. This does not imply that these objectives will be easily attained. People have also reported a lot about the problems with developing blended learning. For example, Mirriahi et al. [11] say that higher education has trouble with a lack of staff who know how to use blended learning, resistance to innovation and change [18], a lack of research-based models to help institutions adopt blended learning [19], and an insufficient institutional interpretation [11]. In general, blended learning has several problematic definitions. There is little consensus on what they include [20], and they are vague [21], describing a wide range of distinct practices [22]. Blended learning is most often defined as “the intentional integration of face-to-face and online learning experiences (p.25)” [18]. It is difficult to understand the true meaning of blended learning when it can be applied to nearly anything because this (intentionally) broad definition does not specify the nature and magnitude of the fusion [13]. According to some critics, the term is deceptive and that the phrases “learning using mixed pedagogies,” “blended teaching,” and “blended pedagogies” are preferable [13]. The phrases hybrid, mixed mode, and flexible learning are commonly used interchangeably with blended learning (see, for example; [23, 24]. This absence of clarity and cohesion is also evident in mixed education research, which is defined as “distributed” and “missing a center” [25]. Individually-focused [26]; and devoid of theoretical foundation [27]. The notion of blended learning has emerged as a setting that blends the best practices of traditional and online learning [13]. This is currently regarded the new standard in core topic teaching and learning. Blended learning is a common practice in schools. Instructors have previously employed blended learning in a range of teaching and learning environments. The difference, however, is in the method to constructing the learning experience [28]. Various individuals have different perspectives on blended learning [13], [29]. Regarding this, experts said that a blended learning system mixes conventional education with virtual learning [8], however other research have claimed that the phrase “blended learning” is poorly defined [13], [30]. According to the researchers, the concept of blended learning may lead to misunderstandings since it is often conceived of as mixing in teaching rather than learning. They also stated that the true definitions of blended education include ““blended practices in the classroom”, “blended teaching”, and “education with blended pedagogies”. Some scholars described blended education as follows: (i)

The incorporating educational materials including audio, video streaming, and teamwork. [29], [31] (ii) The incorporation of numerous instructional approaches, such constructivist approach, behaviorism, and cognitive theory [29]. (iii) A hybrid of traditional and online education [6], [29], [32]. The proper understanding of blended learning is the subject of several debates. According to Bonk & Graham [32], the third definition is the one that best illustrates blended learning systems. They also emphasized how crucial computer-based technologies are to blend learning. The combination of conventional and online learning is known

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as blended learning. Furthermore, Allan [33] defined blended learning as the utilization of various web technologies such as chat rooms, discussion forums, and podcasts while maintaining supporting traditional learning. These sources help us understand how blended learning relates to various innovations that are included into the teaching and learning process. As per Garrison and Kanuka [6] blended learning can be both simple and complex. Because blended learning incorporates both synchronous and asynchronous activities, the researchers thought it was straightforward. Realizing blended learning is both challenging and complex. For instance, the suitability of the plan must be taken into account across a wide range of planning conceivable outcomes. 2.2 Blended Learning Model Six models of blended learning were proposed by Horn and Staker [34], including face-to-face operator, online labs, flexing, self-blend, rotation, and upgraded virtual model. Face-to-face and online lab, two of the six blended learning models, were deleted, nevertheless, since they were seen to be clones of other models. The models are shown in general in Fig. 1 [35]. Below is an explanation of each model:

Fig. 1. Categories of Blended Learning [35]

1. Flex model: a blended learning approach where students access the majority of their coursework online and get in-person instructor assistance to help them learn [34]. 2. Self-blend model: Using the online platform and an online instructor, the students gain knowledge of one or more topics. It will benefit conventional education. Individual online learning and conventional classroom instruction are combined by students. 3. Enriched-virtual model: The Enlarged Online model offers a solution to comprehensive online education that permits students to do the majority of their homework online at home or away from school while still taking classes for mandatory face-to-face classroom lectures with a teacher.

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4. Rotation Model: The rotation model provides a structure for students that are timetabled in a way that will meet many of their expectations from traditional educational delivery methods. That said, it provides teachers with a high level of adaptability to tailor their courses. The Rotation Model includes four models, including the station rotation model, the lab rotation model, the flipped learning model, and the individual rotation model. 2.3 Types of Rotation Model – Station-Rotation Model: A blended learning approach where students cycle among several classroom stations, at least one of which is a technology-based station [34]. – Lab Rotation Model: An instructional strategy that combines teacher-directed education with computer-based practice and training in a computer lab [34]. – Flipped Classroom Model. A method of learning wherein students study academic material at home and build on it in class via a variety of activities and cooperation [34]. – Individual Rotation: Based on a set individual timetable, students will alternate. Students’ timetables will be determined by the instructor. For certain stations or methods, the students do not have to rotate. 2.4 Station Rotation Model (SRM) Students may shift between traditional and virtual learning modes using the Station Rotation Model (SRMI), in practise, this entails that learners come to school, take a seat at their work stations, and alternate between traditional tools such as paper tutorials and web - based education on instruments such as smartphones, tablets, and computers. It also indicates that for some subjects, students may be transferred from the lecture hall to a computer room. Students, for example, may be required to visit a flipped lecture hall to obtain necessary study material before proceeding to a traditional school for face-to-face instructor-mentored classroom training [36]. According to the Christensen Institute [37], The SRM is a variation on the rotation model, in which students in a specific course or subject rotate between lecture hall-based education modes on a defined schedule or at the instructor’s judgement. The twist necessitates the use of at least one online learning station. Activities such as tiny number or rest of the class training, school activities, pupil tutoring, and pencil and paper worksheets may be available at certain stations. This strategy enables students to witness and learn from face-to-face training, virtual education, and a variety of engaging learning environments carefully chosen by their instructor [37] (Fig. 2).

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Fig. 2. Station Rotation Model (Christensen, Horn & Staker, 2013)

The SRM is distinct from previous combined rotational models in that students must visit all instructional channels on a set schedule [38]. In an SRM classroom, a timer is frequently used to reassure students to continue to the following stop. Classrooms may have two, three, or four educational stations; whatever the number of stations, all students must finish every station inside the class time allotted. This design is now standard practise in educational institutes all over the world. Embracing the SRM significantly improves teachers’ career growth. Educators will frequently connect with students in trivial groups by breaking the class into little groups. This enables instructors to customise student learning to various levels of supervision. Individualised interest can be provided to students at a web station, and in many cases, essential skills can now be acquired through feasible training and an interesting atmosphere. The SRM has the potential to greatly increase academic performance when utilised effectively. Furthermore, many apps may be employed through the online learning station to stimulate students’ imaginations. Furthermore, by moving learning outside of the four walls of the classroom, the internet station creates an immersive learning environment. Teachers may engage students in a number of learning activities, including as treasure hunts, Twitter messaging, and backchannel discussions. Kim Jun [39] asserts that implementing SRM saves schools and students money in terms of implementation costs. Compared to many other mixed-learning models, it costs a lot less. Examples include the lab rotation model, the personal rotation model, and the flipped approach, which calls for both home computers and internet connection from each student. The SRM, on the other hand, requires no more than ten laptops for a group of thirty learners because, while one group utilizes them, the remaining two groups work at two distinct stations, such as the teaching assistant-station and the collaborative depot. It also eliminates the requirement for pupils to purchase their own laptops or have an authorised wireless network at their home. The SRM may also be utilised in a regular classroom since it does not need the creation of distinct classroom structures or labs.

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2.5 Students’ Learning in a Blended Learning Classroom Using a Station Rotation Model Teachers may spend a significant amount of time with each student as a result of the station rotation idea. In a rotation approach, students rotate between learning stations positioned inside and outside the school. Several aspects of station rotation models have been identified by researchers. First, the classroom is divided into a number of stations so that students may move among them. The teacher determines the rotation and occupies a station to provide one-on-one training, which is the second feature. Each station has a different assignment in the third stage of the characteristics, but they all have the same learning goal. These activities can be happen both individually and collaboratively, or with the teacher at the stations. Finally, there is one station in which online learning is used. Blended learning can be used in all various disciplines. There are many researches on the advantages of blended learning which is based on the station rotation model. Alsahi [40], for instance, looked at how blended learning affected ninth-grade students’ performance in science. The results showed that blended learning significantly impacted intermediate school students’ science test scores. The Station Rotation model was also the subject of an investigation by Truitt and Ku [41]. Their study discovered two conflicting themes—technology and challenging work—as well as five favorable ones: technology, education, a variety of tasks, asking assistance, and having fun. Powell et al. [42] proposed a station rotation model for each of their study’s primary topics. Their method was based on a case study. The blended educational program’s test performance on the Pennsylvania System of School Assessment improved across all levels and subjects (PSSA). They also employed the station rotation technique to improve their students’ English and arithmetic abilities. The study’s conclusions proved that the station rotation approach has been successful in raising students’ math test performance on state exams. It demonstrates a rise in pupil growth. In another research, Govindaraj [43] studied the impact of station rotation on learners’ learning. 150 college students studying physics were the study’s participants. The results indicated that the children could interact with their peers and teachers. When students participate in varied activities at different stations, their experiences also improve. Due to a variety of factors, just 11% of students believed that this method of learning helped them learn more efficiently. Truitt [44] conducted an inquisitive case study on the SRM’s implementation in a 3rd-grade classroom. He attempted to supply educators a precise account of what happened in the classroom inside the SRM. During the semester-long evaluation, a group of third graders participated in interviews with a student focus group and completed student questionnaires, yielding five positive and two negative themes related to the SRM. Tough work and technology were the additional opposite themes, while content, technology, fear, having fun, and seeking assistance were the five positive themes. According to Truitt, the entire experience of the SRM was highly positive and involved [44]. Nagy and Mohammad [45], who investigated the impact of the SRM on preliminary students’ writing skills, discovered that the model is tantalizing for improving students’ writing production in a variety of interventions. The research included 25 pupils from a Cairo primary school. The pupils were allocated at irregular intervals to one of the SRM or control groups. The information was acquired using both qualitative and quantitative methods. Writing evaluations, an effective writing rating rubric, an evocative guideline

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for writing outcomes, and writing examples were used to examine the data. The findings revealed that participants performed better in thought brainstorming, introspection, organization, accuracy, and fluency. The data also indicated somewhat greater levels of language learning.

3 Qualitative Research on Station Rotation Model of Blended Learning 3.1 Perceptions of Station Rotation Model of Blended Learning This section addressed how several stakeholders perceive blended learning. Based on research, the stakeholders believe that blended learning is effective but still has many loopholes and problems that need to be addressed. 3.2 Parent Feedback on Blended Learning Parents are interested in the primary outcomes of blended learning because it impacts their child’s future educational choices. In this regard, Dorrington [46] conducted a study in which they surveyed the parents of children who learned in a blended learning environment. The findings showed that parents supported blended learning because it taught their children how to regulate their time. An additional study from a mid-western high school was presented by [46]. The study concluded that parents believed that blended learning seemed to help their students’ transition from high school to college. They were also pleased with the district’s trying different forms of teaching. The survey responses did express concerns about how well teachers were prepared to teach in a blended learning environment. 3.3 Teacher’s Feedback on Blended Learning Scholarship emphasized that teacher’s view blended learning in a positive way, and yet it also addresses the need for increased training in the subject. Along with more professional development, teachers state that for blended learning to be effective, schools need to ensure that the proper technology (i.e., headphones) is provided for the students. Additional research even states that computer labs at schools need to have fewer students in them so that teachers can have plenty of time to assist students. Studies have concluded that a student’s experience with blended learning depends on the teacher. Feedback has found that teachers are more willing to design blended learning lessons if they are given a clear instructional strategy on how to use it. Feedback also highlights the fact that teachers who know the needs of their students are more likely to design meaningful blended learning experiences [46]. Along with a clear instructional strategy, research also highlights the need for teachers to learn skills. Overall, teachers like the transition from keeper of knowledge to facilitator. In a study that surveyed 25 high school teachers who taught in a blended learning environment, 58% of teachers strongly felt like their students had more control over their learning and 41% stated that they simply agreed.

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3.4 Administrators Feedback on Blended Learning Implementing blended learning in a school has been shown to be beneficial to the teacher, student, and parent populations. Administrators also agree that it increases student performance in the classroom. The research done by Whiteside et al. (2016) stated that blended learning was beneficial, but like the teachers, they expressed concerns about technology reliability and how to properly prepare teachers for this new method of teaching.

4 Conclusion The researcher gathered many blended learning models in this paper. This evidencebased research paper examined the data and discussed the mixed learning rotation paradigm. In general, every rotation model must have at the minimum one station for digital learning. Because students go to different stations in accordance with the wishes of the instructors, the rotation model is quite adaptable. The station rotation concept is thought to be created for integrated learning research in the future. This publication also discussed earlier research that used the station rotation approach for educationrelated teaching and learning. Additionally, this methodology had a significant impact on students’ learning.

References 1. Al-Qahtani, A.A., Higgins, S.E.: Effects of traditional, blended and e-learning on students’ achievement in higher education. J. Comput. Assist. Learn. 29(3), 220–234 (2013) 2. González, M.Á., et al.: Teaching and learning physics with smartphones. J. Cases Inf. Technol. 17(1), 31–50 (2015) 3. Kumar, R.: Convergence of ICT and education. World Acad. Sci. Eng. Technol. 40(2008), 556–559 (2008) 4. Azizan, F.Z.: Blended learning in higher education institution in Malaysia. In: Proceedings of Regional Conference on Knowledge Integration in ICT, vol. 10, pp. 454–466 (2010) 5. Siew-Eng, L., Muuk, M.A.: Blended learning in teaching secondary schools’ english: a preparation for tertiary science education in Malaysia. Procedia-Soc. Behav. Sci. 167, 293–300 (2015) 6. Larsari, V.N., Keysan, F., Wildova, R.: An investigation of the effect of flipped-jigsaw learning classroom on primary students’ autonomy and engagement in e-learning context and their perceptions of the flipped-jigsaw learning classroom. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol. 455. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02447-4_39 7. Bliuc, A.M., Ellis, R., Goodyear, P., Piggott, L.: Learning through face-to-face and online discussions: associations between students’ conceptions, approaches and academic performance in political science. Br. J. Educ. Technol. 41(3), 512–524 (2010) 8. Francis, R., Shannon, S.J.: Engaging with blended learning to improve students’ learning outcomes. Eur. J. Eng. Educ. 38(4), 359–369 (2013) 9. Park, Y., Yu, J.H., Jo, I.-H.: Clustering blended learning courses by online behavior data: a case study in a Korean higher education institute. Internet High. Educ. 29, 1–11 (2016). https://doi.org/10.1016/j.iheduc.2015.11.001

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10. Drysdale, J.S., Graham, C.R., Spring, K.J., Halverson, L.R.: An analysis of research trends in dissertations and theses studying blended learning. Internet High. Educ. 17, 90–100 (2013). https://doi.org/10.1016/j.iheduc.2012.11.003 11. Garrison, D.R., Vaughan, N.D.: Institutional change and leadership associated with blended learning innovation: two case studies. Internet High. Educ. 18, 24–28 (2013) 12. Driscoll, M.: Blended learning: let’s get beyond the hype. E-learning, 54 (2002) 13. Kuo, Y.C., Belland, B.R., Schroder, K.E.E., Walker, A.E.: K-12 teachers’ perceptions of and their satisfaction with interaction type in blended learning environments. Distance Educ. 35(3), 360–381 (2014) 14. Bersin, J.: The Blended Learning Book: Best Practices, Proven Methodologies, and Lessons Learned. John Wiley & Sons (2004) 15. Bonk, C., Graham, C.: Handbook of Blended Learning Environments. Pfeiffer, San Francisco (2006) 16. Allan, B.: Blended Learning: Tools for Teaching and Training. Facet Publishing (2007) 17. Horn, M.B., Staker, H.: The rise of K-12 blended learning. Innosight Inst. 5, 1–7 (2011) 18. Mohamed Amin, E., Norazah, M.N., Ebrahim, P.: Blended and flipped learning: case studies in Malaysian HEIs (2014) 19. Lalima, D.K., Dangwal, K.L.: Blended learning: an innovative approach. Univ. J. Educ. Res. 5(1), 129–136 (2017) 20. Christensen, C., Horn, M., Staker, H.: Is K-12 blended learning disruptive: an introduction of the theory of hybrids. Recuperado del sitio de Internet del Clayton Christensen Institute (2013). http://www.christenseninstitute.org/wpcontent/uploads/2013/05.IsK-12-Blended-Learning-Disruptive.pdf 21. Maxwell, C., White, J.: Blended (R) evolution: how 5 teachers are modifying the station rotation to fit students’ needs. Clayton Christensen Institute for Disruptive Innovation (2017) 22. Jun, A.K.: Rotational Models Work for Any Classroom (2013). https://www.edsurge.com/ news/2014-06-03-opinion-rotational-modelswork-for-any-classroom 23. Alsalhi, N.R., Eltahir, M.E., Al-Qatawneh, S.S.: The effect of blended learning on the achievement of ninth-grade students in science and their attitudes towards its use. Heliyon 5(9), e02424 (2019) 24. Truitt, A.A., Ku, H.Y.: A case study of third-grade students’ perceptions of the station rotation blended learning model in the United States. EMI. Educ. Media Int. 55(2), 153–169 (2018) 25. Powell, A., et al.: Blending learning: the evolution of online and face-to-face education from 2008–2015. Ina. Int. Assoc. K-12 Online Learn., 1–19 (2015) 26. Govindaraj, A., Silverajah, V.S.G.: Blending flipped classroom and station rotation models in enhancing students’ learning of physics. In: Proceedings of the 2017 9th International Conference on Education Technology and Computers, pp. 73–78 (2017) 27. Truitt, A.A.: A case study of the Station Rotation blended learning model in a third-grade classroom. ProQuest Diss. Theses, p. 273 (2016)

Image and Information Processing

New Invariant Meixner Moments for Non-uniformly Scaled Images Mohamed Yamni1 , Achraf Daoui2 , Hicham Karmouni3(B) , Mhamed Sayyouri2 , Hassan Qjidaa1 , and Mohammed Ouazzani Jamil4 1 CED-ST, STIC, Laboratory of Electronic Signals and Systems of Information LESSI,

Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco [email protected] 2 Engineering, Systems and Applications Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, BP 72, My Abdallah Avenue Km. 5 Imouzzer Road, Fez, Morocco {achraf.daoui,mhamed.sayyouri}@usmba.ac.ma 3 National School of Applied Sciences, Cadi Ayyad University, Marrakech, Morocco [email protected] 4 Systems and Sustainable Environment Laboratory (SED), Faculty of Engineering Sciences (FSI), Private University of Fez (UPF), Fez, Morocco [email protected]

Abstract. Invariants of discrete orthogonal moments have been applied in several research fields of image processing and pattern recognition due to their ability of representing digital images. Generally, moment invariants are invariant only for image translation, rotation and uniform scaling. These moments are not invariant when an image is scaled non-uniformly in the x- and y-axes directions. In this paper, we propose a new set of discrete orthogonal moments namely Meixner moments, which are invariant when an image is scaled uniformly/non-uniformly. The proposed invariants are completely independent of scale factors unlike some existing invariant moments in the literature. The experimental results show the efficiency of the proposed descriptors. Keywords: Non-uniform scaling · Discrete orthogonal moments · Meixner moments · Pattern recognition

1 Introduction Image moments are special functions of image, which have been used for more than hundred years to characterize the images and to capture its significant features especially in different fields of digital image processing and pattern recognition. Among all moment types, the discrete orthogonal moments (DOMs) such as Tchebichef [7], Krawtchouk [23], Charlier [5], Meixner [26], Hahn [25], Fractional DOMs [2, 6, 12, 13, 18], Quaternion DOMs [19, 21] have considerable properties, since they are discrete and orthogonal and present low information redundancy and high discrimination power [2, 7, 23]. DOMs have been widely used in various applications, including pattern recognition © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 453–463, 2023. https://doi.org/10.1007/978-3-031-29857-8_46

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[14, 23], image reconstruction [1, 5, 23], image watermarking [15] audio watermarking [9, 17, 20], image encryption [3], stereo image analysis [4, 16, 22], etc. The DOMs are not invariant to scaling, they are variant when the size of image is changed. A number of methods have been developed to derive the scaling invariants of the DOMs. The popular method is called indirect method (IDM) which expresses the scale invariant moments using the corresponding invariants of geometric moments [23]. However, this method is expensive in terms of time which limits the use of the invariants obtained by this method in real time applications where the respect of temporal constraints is a fundamental criteria. To reduce the time computation of discrete invariant moments, several effective techniques have been proposed in [10, 11]. However, these papers only discuss the invariant moments for image translation, rotation, and uniform scaling. Moments invariant to non-uniform scaling have not yet been discussed. Recently, the authors in [24] made a good contribution to generate a new set of translation and scale invariants of Krawtchouk moments directly from Krawtchouk polynomials. These moments are invariant even when an image is scaled non-uniformly in the x- and y-axes directions. In addition, these invariants are faster computed compared with those obtained by the image normalization method and the indirect one. Although this work is highly commendable, there are some errors regarding the scaling invariants of Krawtchouk moments. This paper highlights these errors and corrects them by developing a new type of discrete orthogonal moment, namely Meixner moments, whose scale invariants can be directly derived using the method proposed in [24]. The errors highlighted and the solutions developed in this paper are supported by some experimental evidence.

2 Scaling Invariants of Krawtchouk Moments [24] Krawtchouk polynomials of order n are defined as [24]: n n   (−1)k n!(N − k)! Kn (x; p, N ) = xk = Bn,k xk k!(n − k)!N !k!pk k=0

k=0

x, n = 0, 1, 2.....N , N where 0 < p < 1, Bn,k =

(−1)k n!(N −k)! , k!(n−k)!N !k!pk

(1) and xk can be expanded as:

xk =

k 

s(k, i)xi

(2)

i=0

where s(k, i) are the Stirling numbers of the first kind, obtained by the following recurrence relations: s(k, i) = s(k − 1, i − 1) − (k − 1)s(k − 1, i), k, i ≥ 1

(3)

with s(0, 0) = 1, and s(k, 0) = s(0, i) = 0. The Krawtchouk polynomials (Eq. 1) can be rewritten as follows [24] Kn (x; p, N ) =

k n   k=0 i=0

Bn,k s(k, i)xi

(4)

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The Krawtchouk moment of order (n + m) for an image with intensity function f (x, y) with size of N × M is defined as [24]: Qnm =

−1 N −1 M  

K n (x; p, N − 1)K m (y; p, M − 1)f (x, y) , n, m ≥ 0

(5)

x=0 y=0

where K n (x; p, N ) are the weighted Krawtchouk polynomials, defined as [24]:  ω(x; p, N ) (6) K n (x; p, N ) = Kn (x; p, N ) ρ(n; p, N )    n N x n! with ω(x; p, N ) = p (1 − p)N −x , and ρ(n; p, N ) = (−1)n 1−p p (−N )! . x Assume that the scale factors are a and b along x- and y-axes, the scaled Krawtchouk moments can be defined as follows:  Qnm =

N −1 M −1  

abK n (ax; p, N )K m (by; p, M )f (x, y)

(7)

x=0 y=0

Using Eq. (4) and Eq. (6), we have: K n (x; p, N )= =

n  k 

Bn,n−k s(k, i)xi =

k=0 i=0 n 

n−i n  

Bn,n−k s(n − k, i)xi

i=0 k=0

C(n, i)xi

(8)

i=0

where C(n, i) =

n−i 

Bn,n−k s(n − k, i)

(9)

k=0

with

 Bn,n−k = Bn,n−k

ω(x, p, N ) ρ(n, p, N )

(10)

The scaled Krawtchouk polynomials along x-direction can be expressed as [24]: K n (ax; p, N )=

n 

C(n, i)ai xi

(11)

i=0

According to Eq. (8) and Eq. (11), we have: n  k=0

μn,k K n (ax; p, N ) = an

n  k=0

μn,k K n (x; p, N )

(12)

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where μn,n = 1 , and μn,k =

n−k−1  r=0

−

C(n − r, k)μn,n−r ,0 ≤ k < n C(k, k)

(13)

Similarly, the scaled Krawtchouk polynomials along y-direction can be deduced as follows: m 

μm,l K m (by; p, M ) = b

m

l=0

m 

μm,l K m (y; p, M )

(14)

l=0

Therefore, the relationship between the original and scaled Krawtchouk moments can be formed as: ϕnm =

m n  

 μn,k μm,l Qkl = an+1 bm+1

k=0 l=0

m n  

μn,k μm,l Qkl

(15)

k=0 l=0

To eliminate the scale factors a and b, the authors in [24] constructed the normalized scale invariants of Krawtchouk moments as: γ +1

ψnm =

ϕnm ϕ00 , n, m = 0, 1, 2, ....;γ = 0, 1, 2, .... ϕn+γ ,0 ϕ0,m+γ

(16)

3 The Weakness of Scaling Invariants Krawtchouk Moments The major weakness of scaling invariants Krawtchouk moments [24] is that it did not take into consideration that the coefficient of Bn,n−k is dependent on the length of finite data N . Therefore, the scale invariants of Krawtchouk moments could not derived from Eq. (16). To illustrate that, we can make the following comments on the expressions described in the previous section: According to Eqs. (1) and (10), we have  (−1)k n!(N − k)! ω(x, p, N ) (17) Bn,n−k = k!(n − k)!N !k!pk ρ(n, p, N ) It is obvious that Bn,n−k depends on the length of finite data N , and so the correct version of Eq. (8) should be: K n (x; p, N ) =

n 

C(n, i, N )xi

i=0

where C(n, i, N ) =

n−i  k=0

Bn,n−k s(n − k, i)

(18)

New Invariant Meixner Moments for Non-uniformly Scaled Images

 n−i  (−1)k n!(N − k)! ω(x, p, N ) = s(n − k, i) ρ(n, p, N ) k!(n − k)!N !k!pk

457

(19)

k=0

Since K n (ax; p, N ) can be comprehended as a down sampling sequence with a interval from the discrete Krawtchouk polynomial K n (x; p, N ), 0 ≤ x ≤ aN − 1, the correct version of Eq. (11) should be: K n (ax; p, N ) =

n 

C(n, i, aN )ai xi

(20)

i=0

where  n−i  (−1)k n!(aN − k)! ω(ax, p, aN ) C(n, i, aN ) = s(n − k, i) ρ(n, p, aN ) k!(n − k)!(aN )!k!pk

(21)

k=0

Consequently, the coefficient μn,k in Eq. (14) should be: μn,k =

n−k−1  r=0

−

C(n − r, k, aN )μn,n−r ,0 ≤ k < n C(k, k, aN )

(22)

It is obvious that μn,k depends on the scale factor a, and so the invariants ψnm constructed via Eq. (16) are dependent on the factors a and b. Therefore, the scale factors cannot be eliminated completely by Eq. (16).

4 The Proposed Invariant Meixner Moments for Non-uniformly Scaled Images In the previous section, we have shown the weakness of the method [24] for the computation of scaling invariants of Krawtchouk moments. In order to overcome this weakness, we propose in this section a new set of discrete orthogonal moments namely Meixner moments which are invariant when an image is scaled uniformly/non-uniformly. The Meixner polynomials are a family of discrete orthogonal polynomials introduced (ν,ξ ) by Josef Meixner. The nth order Meixner polynomial Mn (x) is defined as [8]: Mn(ν,ξ ) (x) = (ξ )n

n  (−n)k (−x)k (1 − ν −1 )k , x, n = 0, 1, 2...., ∞ (ξ )k k!

(23)

k=0

where 0 < ν < 1, ξ > 0, and (x)k = x(x + 1)(x + 2)....(x + k − 1) is the Pochhammer symbol. Similarly, Eq. (23) can be rewritten as Mn(ν,ξ ) (x)=

n  k=0

An,k xk

(24)

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where An,k =

n!(ξ + n − 1)!(ν − 1)k k!(n − k)!(ξ + k − 1)!(ν)k

(25)

For an image intensity function f (x, y) with size of N × M , the Meixner moment of order n + m is defined as MMnm =

−1 N −1 M  

(ν,ξ )

Mn

(ν,ξ )

(x)M m

(y)f (x, y) , n, m = 0, 1, .., ∞

(26)

x=0 y=0 (ν,ξ )

where M n as

(x) is the weighted Meixner polynomials of order n which can be expressed  (ν,ξ ) M n (x)

=

Mn(ν,ξ ) (x)

ω(x; ν, ξ ) ρ(n; ν, ξ )

(27)

+n−1)! (ξ +x−1)! and ω(x; ν, ξ ) = ν x!(ξ where ρ(n; ν, ξ ) = ν n n!(ξ −1)! are the he squared norm (1−ν)ξ (ξ −1)! and the weight function of Meixner polynomials, respectively. The Meixner moments of fˆ (x, y) = [ω(x; ν, ξ )ω(y; ν, ξ )]−1/2 f (x, y) can be written as: x

MMnm =

−1 N −1 M  

(ν,ξ )

Mn

(ν,ξ )

(x)M m

(y)fˆ (x, y), n, m = 0, 1, .., ∞

x=0 y=0

=

N −1 M −1  

˜ m(ν,ξ ) (y)f (x, y) ˜ n(ν,ξ ) (x)M M

(28)

x=0 y=0 (ν,ξ )

˜ n (x) is the normalized Meixner polynomials of order n which can be where M expressed as (ν,ξ )

˜ n(ν,ξ ) (x) = √Mn (x) M ρ(n; ν, ξ )

(29)

According to Eqs. (2) and (8), we have ˜ n(ν,ξ ) (x) = M

n 

Hn,i xi

(30)

i=0

where H (n, i) =

n−i  An,n−k s(n − k, i) √ ρ(n, ν, ξ ) k=0

(31)

Similarly, it can be easily deduced that n  k=0

˜ n(ν,ξ ) (ax) = an γn,k M

n  k=0

˜ n(ν,ξ ) (x) γn,k M

(32)

New Invariant Meixner Moments for Non-uniformly Scaled Images

459

where λn,k =

n−k−1 

−

i=0

H (n − i, k)λn,n−i , 0 ≤ k < n and λn,n = 1 H (k, k)

(33)

The relationship between the original Meixner moments Mnm and scaled Meixner  can then be established as moments Mnm φnm =

m n  

λn,k λm,l Mkl = an+1 bm+1

k=0 l=0

m n  

λn,k λm,l Mkl

(34)

k=0 l=0

Note that the coefficient λn,k is independence on the scale factor a, and so, the scale factors a and b can be completely eliminated by using the following equation: γ +1

nm =

φnm φ00 , n, m = 0, 1, 2, ....;γ = 0, 1, 2, .... φn+γ ,0 φ0,m+γ

(35)

Here, nm is called Meixner moment and invariant when an image is scaled uniformly/non-uniformly.

a=0.7, b=0.8

a=1.1, b=0.5

a=0,5, b=1.1

a=0.6, b=1.3

a=1.2, b=1.3

Fig. 1. Scaled images used in this first experiment.

5 Experimental Results The simulation results presented in this section are obtained by fixing the parameter γ (Eqs. 16 and 36) to 2. In the first experiment, we test the invariability of the proposed scaling invariants of Meixner and that of Krawtchouk using a binary image (“Drawing”) of size 128 × 128. Figure 1 shows a set of enlarged and contracted images with scaling factors along x- and y-directions. The ability of the invariant moments to remain unchanged when an image is scaled is measured by the deviation of the moments X, which can be defined as X=

σ × (100 %) |μ|

(36)

where σ and μ denotes the standard deviation and the mean of the invariant moments, respectively.

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The scaling invariant of Meixner and Krawtchouk are calculated and summarized in Tables 1 and 2, respectively. From these tables, it is clearly shown that the proposed scaling invariants of Meixner are very robust against the non-uniform scaling of image where the average deviation is very small and tends towards zero. On the other hand, the scaling invariants of Krawtchouk give a large average deviation which means that these descriptors do not resist very well against non-uniform scaling. This reveals that the Krawtchouk invariants constructed via Eq. (16) depend on the scale factors and these scale factors cannot be eliminated completely by Eq. (16). Table 1. The scaling invariants of Meixner moments (Eq. 35) for the binary image of “Drawing”. Scale factor

20

11

21

12

30

31

13

a = 0.7, b = 0.8

−3.56E-34

1.80E-35

−3.56E-34

7.28E-35

−5.42E-34

1.16E-34

1.10E-34

a = 1.1, b = 0.5

−3.56E-34

1.80E-35

−3.56E-34

7.28E-35

−5.42E-34

1.16E-34

1.10E-34

a = 0,5 b = 1.1

−3.56E-34

1.80E-35

−3.56E-34

7.28E-35

−5.42E-34

1.16E-34

1.10E-34

a = 0.6, b = 1.3

−3.56E-34

1.80E-35

−3.56E-34

7.28E-35

−5.42E-34

1.16E-34

1.10E-34

a = 1.2, b = 1.3

−3.56E-34

1.80E-35

−3.56E-34

7.28E-35

−5.42E-34

1.16E-34

1.10E-34

X = σ / μ (%)

1.60E-12

0

2.53E-13

3.04E-12

1.63E-13

0

8.11E-10

Average X (%)

1.17E-10

Table 2. The scaling invariants of Krawtchouk moments [24] for the binary image of “Drawing”. Scale factor

Ψ 20

Ψ 11

Ψ 21

Ψ 12

Ψ 30

Ψ 31

Ψ 13

a = 0.7, b = 0.8

0.911541

0.79660

0.78564

0.870567

0.88106

0.99517

a = 1.1, b = 0.5

1.08299

1.167675

1.162325

1.343943

1.34229

1.444392

1.42824

a = 0,5 b = 1.1

1.5015

1.487816

1.65096

1.8817

1.76565

1.8580

2.5183

a = 0.6, b = 1.3

0.65077

0.7166122

0.73135

0.828108

0.8177473

0.90781

0.9148528

a = 1.2, b = 1.3

0.62638

0.6877010

0.70518

0.796608

0.785645

0.870567

X = σ / μ (%)

37.677

Average X(%)

39.47

35.70

40.177

40.9798

38.10328

35.09228

1.17123

0.881068 48.6067

Fig. 2. The binary image dataset in the second experiment

In the second experiment, we discuss the performance of scaling invariants of Meixner moments in terms of image classification. A comparison with scaling invariants of Krawtchouk moments [24] is also performed. To conduct this experiment, we use an image dataset including a binary images of numbers (Fig. 2). The images have a size of 32 × 32 pixels. Each image is transformed with scale factors a, b ∈ {0.6, 0.7, 0.8, ....., 1.8, 1.9, 2}, in order to generate a testing set of 2250 binary images. The conventional 1-nearest neighbor (1-NN) based on Euclidean distance is used as

New Invariant Meixner Moments for Non-uniformly Scaled Images

461

classifier, and the vector V = [20 ; 11 ; 21 ; 12 ; 30 ; 31 ; 13 ] is used as invariant scaling feature vector. We evaluate the classification accuracy on the noise-free condition also under the noise condition by adding the salt and pepper (1%, 2%, 3%, and 4%). In this experiment, the classification accuracy η is defined as η=

Number of correctly classified images × 100% The total of images used in the test

(37)

The correct classification rates are obtained and summarized in Table 3. As these results show, the proposed scaling invariants of Meixner give very satisfactory results in terms of classification with a correct classifications rate equal to 100% for the noisefree case. It is also shown that the correct classification rates decrease with increasing noise intensity but the performance of our invariants remains satisfactory. Table 2 also shows that the scaling invariants of Krawtchouk moments [24] perform less well in terms of scaled image classification in both noisy-free conditions and noisy conditions. In conclusion, the scaling invariants of Meixner moments can be successfully used as descriptors for pattern recognition. Table 3. Classification results. Invariant Moments

Noise free

Salt and Pepper noise

0%

1%

2%

3%

4%

Meixner (Eq. 35), η

100

98.63

97.02

95.34

93.82

Krawtchouk [24], η

45.27

43.51

40.36

39.85

34.21

6 Conclusion In this paper, we have proposed scaling invariants, a new set of Meixner moments for uniformly/non-uniformly scaled images. The proposed invariants are completely independent of scale factors unlike some existing invariant moments in the literature such as the scaling invariants of Krawtchouk moments. In addition, the implementation of the proposed moments is very simple and the use of them in classification tasks as feature descriptors eliminates the need for numerical approximations.

References 1. Daoui, A., Karmouni, H., Yamni, M., Sayyouri, M., Qjidaa, H.: On computational aspects of high-order dual Hahn moments. Pattern Recogn. 127, 108596 (2022) 2. Daoui, A., Yamni, M., Karmouni, H., Sayyouri, M., Qjidaa, H.: Biomedical signals reconstruction and zero-watermarking using separable fractional order Charlier-Krawtchouk transformation and sine cosine algorithm. Signal Process. 180, 107854 (2021)

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A Review on the Driver’s Fatigue Detection Methods Hoda El Boussaki(B) , Rachid Latif, and Amine Saddik Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University, 80000 Agadir, Morocco [email protected]

Abstract. Monitoring a driver’s fatigue is an essential factor in preventing accidents on the road. Several fatal accidents are caused by the driver’s sleep deprivation and exhaustion. Nowadays different systems exist that tell if the driver is getting drowsy. They are many signs that show on the driver’s facial features that indicate fatigue such as closed eyes, lower blinking frequency and yawning, but also a sudden change in the steering pattern, a lane deviation, a looser grip on the steering wheel, head tilting, leaning forward or sudden change of speed. Therefore, detecting these signs would help indicate the fatigue state of a driver. In this paper, we present a review of the different existing techniques for eye closure detection and yawning detection. We will discuss various algorithms used for face detection, eye and mouth detection, feature extraction as well as different parameters such as the percentage of eyelid closure (PERCLOS), the mouth aspect ratio (MAR) and the eye aspect ratio (EAR). Keywords: Fatigue detection · Eye closure · Yawning · Camera

1 Introduction According to the Center for Disease Control and prevention (CDC), 1 in 25 adult drivers admit to have fallen asleep behind the wheel [1]. There are 100 000 reported drowsy driving crashed every year [2]. Accidents caused by drowsy driving represents a big percentage of deaths on the road. There are approximately 71 000 injuries and 1550 fatalities yearly [3]. Therefore, monitoring the driver’s fatigue is important to avoid casualties. Fatigue can happen for different reasons. The driver could have been driving for a long time with no rest. He could be taking medication that caused him to be drowsy. Other causes could be the lack of sleep or a recent stressful situation [4]. A. Bener et al. 2017 showed the relation between the driver’s fatigue and the risk of crashes [5]. The study confirmed that sleepiness increases drastically the risk of accidents on the roadway. Fatigue is not something than can be measured easily but there are some signs that we can look for. There is a scale that evaluates the level of sleepiness. It is called the Karolinska Sleepiness Scale (KSS). It goes from 1 as extremely alert to 10 as extremely sleepy [4]. Another scale is the Wierwille and Ellsworth drowsiness scale with 5 categories. The first category is not drowsy, the second is slightly drowsy, the third is moderately drowsy, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 464–473, 2023. https://doi.org/10.1007/978-3-031-29857-8_47

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the fourth is very drowsy and the last one is extremely drowsy [6]. They developed the ORS technique where signs such as awareness, driving, blinking, yawning, body position like frequent changes and stretching and body movements like head nodding were categorized. The signs were divided into two types: driving performance impairment (D-ORS) and behavioral signs (B-ORS). These signs can be biological where a change in vital signs such as respiratory rate and heart rate is an indicator. They can be behavioral such as closing the eyes, tilting the head down and opening the mouth to yawn. They also can be related to the positioning and movement of the vehicle like a lane deviation, a sudden movement in the steering wheel or a sudden speed change. Figure 1 shows the classification of fatigue signs.

Fig. 1. Classification of fatigue signs

S. Abtahi 2016 et al. proposed a method that detects the changed in the mouth geometric features in order to tell if the driver is yawning [7]. Y. Wang et al. 2022 were able detect eye closure by using machine learning to train a model that uses five feature previously extracted from an electrooculography [8]. R. li et al. 2021 proposed a method that detects fatigue based on the grip of the driver on the steering wheel by measuring the grip force of the driver [9]. Another method is to detect if there is a lane through a camera located at the front of the car [10]. In this work, we will focus on the different methods that detect fatigue with a camera. The signs of fatigue that are included are yawning and eyes’ state. This paper is structured as follows: the Sect. 1 introduces different methods on eye’s state detection and yawning detection. The Sect. 2 gives a summary of these methods. Finally, we will end with a conclusion.

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2 Fatigue Detection Methods There are several fatigue signs that can be detected through a camera. First, the face is detected then the eye or the mouth is detected. After that, facial features are extracted from the captured regions of interest. 2.1 Eye Closure In this part, techniques on measuring the eyelid closure whether the eyes are closed and the eyes’ blinking rate which is the how many times the eyes blink in period of time are presented. Different methods exist to determine if the eyes are closed or not. C. Sravan et al. 2018 used the Viola-Jones algorithm to detect the face then the eyes in a picture [11]. When the face is detected, the region of interest which is the eyes is cropped and it is converted to grayscale in order to be able to convert the darker areas to black and the lighter area to white. To detect if the eyes are open, the white area will be larger than if the eyes are closed. The Viola-Jones algorithm is an algorithm used for facial landmarks detection. It uses Haar-like features represented as rectangles. Figure 2 shows examples of Haar-like features.

Fig. 2. Haar-like features [12]

The rectangles are chosen depending on what feature is supposed to be detected then they are moved over the captured image. To detect these features, the sum of the pixels’ value of the dark area and the white area is calculated. The difference between these two sums is calculated. If the value is close to 1 then the feature is detected [12]. The difference is represented in Eq. 1.  = Fwhite − Fblack

(1)

where  is the value of the feature, Fwhite is the white area’s pixel and Fblack is the dark area’s pixel. The viola-Jones algorithm uses a Cascade of classifiers where features are grouped into different categories. If it fails to detect a feature in a category, it discards it and move to the next part of the image without going through all Haar-like features. A region is detected when all categories are validated. Navastara et al. 2019 used the Funnel-structrued cascade (FuSt) method [14]. The algorithm was introduced by S. Wu et al. in 2016. It uses funnel-like features for multiview face detection. These features are wide on the top and narrow at the bottom as an inverted pyramid shape [13]. It consists of using a three stages classifier: a fast LAB cascade, a coarse Multilayer Perceptron (MLP) and a fine MLP cascade classifier. The

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fast LAB cascade removes parts of the image that does not contain faces. The coarse Multilayer Perceptron uses Speeded-Up Robust Feature (SURF) to further get the regions than contains faces, SURF features being more expressive than LAB features. MLP is a neural network that consists of three layer: the input, the output and the hidden layers [13]. An MLP with n layers is represented in the Eq. 2. F(x) = fn−1 (fn−2 (. . . f1 (x)))

(2)

fi = σ(Wi,z + bi )

(3)

where x is the input, F is the output layer, f1 is the input layer, fn−i is the hidden layer, Wi,z and bi are the weights and biases of connections from layer i to i + 1. The function σ is a non-linear function: σ(x) =

1 1 + e−x

(4)

The remaining parts of the image go through the fine MLP cascade classifier with shaped-indexed features. It concludes it there is a face or not. In addition to the Funnel-structrued cascade (FuSt), Navastara et al. 2019 also used a Uniform Binary Pattern (ULBP) to extract eye features after the face detection. The method allows the extraction of patterns whether there is a pattern in the transition between the pixels’ values 1 and 0 [14]. After that, the Eye Aspect Ratio (EAR) is calculated with Eq. 5. EAR =

|P2 − P6 | + |P3 − P5 | 2|P1 − P4 |

(5)

where Pi is the i landmark of the 6 landmarks of the eye. There are 3 landmarks on the upper eyelid and 3 landmarks on the lower eyelid. If the value of the EAR is close to 0 that means that the landmarks of the upper eyelid are close to those of the lower eyelid, then the eyes are closed. Its value for open eyes is 0.25 and 0.05 for closed eyes. X. Miao et al. 2022 determined the eyes’ fatigue by tracking the eyes. The eye tracking is done by an eye tracking device that uses the corneal reflex method [15]. To determine if the eyes are closed or open, the EAR is calculated using the 6 landmarks of the eyelids as previously explained. After that the percentage of eye closure over time (PERCLOS) is calculated according to Eq. 6. PERCLOS =

Nclose × 100 Ntotal

(6)

where Nclose is number of frames the eyes are closed and Ntotal is the total number of captured frames. If the PERCLOS’s value is greater than 2, then the driver is considered to be in a state of fatigue [15]. Z. Zhao et al. 2020 used Multi-task Cascaded Convolutional Networks (MTCNN) for face and eyes’ landmarks detection [17]. MTCNN is a three stage cascade. The first

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is obtained with a fast Proposal Network (P-Net) that gives candidate parts of the image susceptible to contain the landmarks desired, the second with a Refinement Network (RNet) that further reduce those candidates and the third with an Output Network (O-Net) that the final landmarks [18]. When both of the eyes are extracted with the MTCNN, the PERCLOS is calculated. If it is greater than 0.25, the eye is said to be closed [17]. Q. Zhuang et al. 2020 detected the eyes using an algorithm that detects the key points of the eyes. They also use a segmentation network to extract pupil and iris information [19]. The data extracted is used on a decision network that tells if the eyes are open or not. The segmentation network is a Fully Convolutional Network (FCN). The PERCLOS and the EAR parameter is then calculated as follows: EAR =

H W

(7)

where H is the height of the eye and W is the width of the eye. 2.2 Mouth Movement Another important sign of fatigue is the opening of the mouth also known as yawning. M. Knapik et al. 2019 presented a method that detects if a driver is yawning through a thermal camera [20]. Therefore, the detection can be done in day or night. Figure 3 represents the proposed face detection algorithm.

Fig. 3. Open mouth detection

The eye corners are detected after a background removal and a binary mask to extract the contours of the face. After that, they were able to get the coordinates of the center of the face that includes the nose and the mouth. Using the cropped image, the changes in temperature recorded by the thermal camera when the mouth is open are captured. Yawning causes a drop in temperature during inhalation and causes the exhalation of warmer CO2. Opening the mouth means that there is a sudden change in temperature [20]. H. Yang et al. 2020 used a 3D deep learning network that recognizes facial expressions such as yawning [21]. They used the Viola-Jones algorithm explained above for face detection. Then, the region of interest which is the face is normalized and denoised with a median filter. The output images are classified with the 3D deep learning network to detect yawning through this classification. M. Yazdi et al. 2019 detected if the driver is yawning by detecting the tip of the nose and estimating the mouth area [22]. The image is cropped with a border mask then it

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is filtered with a 7x7 median filter. The tip of the nose is represented by the minimum depth in the image. The location of the mouth is right under the tip of the nose. Now that the mouth area is known, a threshold is applied to the image. It has to satisfy two conditions so that an open mouth is detected [22]: – The difference in the depth of the pixels is greater than a threshold value – The sum of the threshold value and the number of pixels that satisfy the previous condition is greater than a fifth of the number of pixels in the area The pixels’ values that satisfy these conditions are set to 1 and the others are set to 0. This step gives a binary image when the white pixels represent the open mouth. Another method presented by [22], is to detect the contours of the open mouth directly from the sudden changes in the pixels’ values. The binary images obtained from the two methods are combined and if the white area is large enough then there the person is yawning. M. Ali et al. 2019 used the Viola-Jones algorithm for face and mouth detection [23]. They used OpenCV features to do so. Then, the Mouth Aspect Ratio (MAR) is calculated with Eq. 8. MAR =

H W

(8)

where H is the height of the mouth opening and W is the width of the mouth opening. The MAR is ratio of the height and width of the mouth after detecting its landmarks. If the ratio is close to 0, that means that the mouth is closed. W. Tipprasert et al. 2019 used an infrared camera. The face and mouth are detected through the Viola-Jones algorithm. The image is divided into two parts: the upper part where the eyes are located and the lower part where the mouth is located. Then, they used the Histogram of Oriented Gradients (HOG) to convert the data extracted into a one dimensional vector. After that, the data is trained with Support Vector Machine (SVM) [24]. HOG is a technique that divides the image into small parts also known as cells. Each cell had a histogram of gradients. The combinations of these histogram is the final Histogram of Oriented Gradients (HOG) [25]. There is a HOG for eye opening, eye closing, mouth opening and mouth closing. In 2017, W. Zhang et al. proposed an approach that uses the Convolutional Neural Network (CNN) to extract facial landmarks. An image of size 220x220 is taken then convolution and max-pooling operations are applied to it. The output is a 1000 dimensional vector. Then, a Long Short Term Memory (LSTM) network is used for long-term prediction. It works with different layers. Each layer is the input of the next and a Softmax classifier predicts yawning in every image [26].

3 Discussion We selected 11 papers that estimate two parameters from images captured by a camera: eye closure and yawning. Seven of the papers are about eye closure and six about yawning detection. The papers are summarized in Table 1. Only three papers use techniques that are not sensitive to illumination levels, hence the others cannot work at night because the

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detection cannot be conducted without light. The segmentation techniques are limited to the presence of background noise. The three parameters presented in this paper that are the percentage of eyelid closure (PERCLOS), the mouth aspect ratio (MAR) and the eye aspect ratio (EAR), do not take into consideration the distinctive characteristics of each person. Therefore, the threshold of these parameters can be relative to the individual. W. Zhang et al. 2017 used Convolutional Neural Network (CNN) and Long Short Term Memory network (LSTM) and found that these methods are better than the Histogram of Oriented Gradients (HOG) because LSTM takes into account several images rather than one to detect yawning. However, the method can become inefficient when there is a lack of light. C. Sravan et al. 2018 calculated the number of white pixels and black pixels to determine if the eyes are open or closed. This method relies on the fact that percentage of the white area will be greater when the eyes are closed, but disadvantage of this method is that the threshold value can vary from one place to another and depends on the amount of light. D. Navastara et al. 2019 used the Funnel-structrued cascade (FuSt) and Uniform Binary Pattern (ULBP). Its disadvantage is that detection can be affected if the person is wearing glasses as the technique may confuse consider the glasses as eyes depending on the size and the eyeglass frame. The lighting and the distance between the person and the camera can also affect the detection. M. Knapik et al. 2019 used segmentation techniques and the detection of the mouth’s temperature for yawning detection. The method can work during night as it uses a thermal camera. However, it is not accurate when there are fast movements. M. Yazdi et al. 2019 also used segmentation techniques and the detection of the tip of the nose to detect the mouth area and if the person is yawning. This method can also be used at night since it works with a RGB-D camera that detects the depth of the image, the lowest depth being the tip of the nose. Unfortunately, the method becomes inefficient if the head moves to the side a little for example. The lowest depth that is supposed to be the tip of the nose changes and the tip of the nose is not detected. M. Ali et al. 2019 used the haar cascade for eye and mouth detection and a facial landmark detection algorithm. They defined a specific threshold for the mouth aspect ratio (MAR) and the eye aspect ratio (EAR). The system was implemented on a Raspberry Pi but is sensitive to illumination levels. W. Tipprasert et al. 2019 used the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method is insensitive to light since it employs an infrared camera. However, the mouth cannot be detected if the driver is opening his mouth too much. Z. Zhao et al. 2020 used Multi-task Cascaded Convolutional Networks (MTCNN) and convolutional neural network EM-CNN. Q. Zhuang et al. 2020 used pupil/iris segmentation and decision network. This method is also sensitive to lighting but the error seems to be acceptable. H. Yang et al. 2020 used 3D deep learning network but low image resolution and the trembling of the camera impact the accuracy of the method. D. Navastara et al. 2019 have an accuracy of 95.5%. They concluded that the wear of glasses can affect the accuracy value and the eye blinking detection. X. Miao et al. 2022 were able to categorize the fatigue into mild, moderate and severe through a fatigue metric by estimating the blink frequency and eyelid closure. Z. Zhao et al. 2020 showed an accuracy of 93.623%. However, Q. Zhuang et al. 2020 achieved the highest accuracy of 98.64%. For yawning detection, W. Zhang et al. 2017 have an accuracy of 88.6%.

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M. Yazdi et al. 2019 obtained an accuracy of 95% for detecting if the mouth is open or closed. M. Ali et al. 2019 have an accuracy of 85% and W. Tipprasert et al. 2019 have a higher accuracy of 98%. Table 1. Summary of the existing work on fatigue detection Work

Tools

Feature extracted

Reference

W. Zhang et al. 2017

Convolutional Neural Network (CNN) and Long Short Term Memory network (LSTM)

Yawning

[26]

C. Sravan et al. 2018

Viola-Jones algorithm

Eye closure

[11]

D. Navastara et al. 2019

Funnel-structrued cascade (FuSt) and Uniform Binary Pattern (ULBP)

Eye closure

[14]

M. Knapik et al. 2019

Mouth temperature detection and segmentation

Yawning

[20]

M. Yazdi et al. 2019

Tip of the nose detection and segmentation

Yawning

[22]

M. Ali et al. 2019

Viola-Jones algorithm

Yawning and eye closure

[23]

W. Tipprasert et al. 2019

Viola-Jones algorithm, Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)

Yawning and eye closure

[24]

Z. Zhao et al. 2020

Multi-task Cascaded Convolutional Networks (MTCNN) and convolutional neural network EM-CNN

Eye closure

[17]

Q. Zhuang et al. 2020

Segmentation and decision network

Eye closure

[19]

H. Yang et al. 2020

Viola-Jones algorithm and 3D deep learning network

Yawning

[21]

X. Miao et al. 2022

Corneal reflex method

Eye closure

[15]

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4 Conclusion In this paper, different methods to detect a driver’s fatigue are presented. We focused on two parameters that are the eyelid closure for eye closure and the opening of the mouth for yawning. First the face and the eyes or the mouth are detected then the eye aspect ratio estimates the ratio of the eye’s closure or the percentage of eyelid closure is calculated by estimation the number of blinks in a period of time. The Mouth aspect ratio is another parameter that calculates the mouth’s closure. Several algorithms are presented. There is the Viola-Jones algorithm, known as the Haar classifier, Funnelstructrued cascade (FuSt) and Uniform Binary Pattern (ULBP), Convolutional Neural Network (CNN) and Long Short Term Memory network (LSTM), Multi-task Cascaded Convolutional Networks (MTCNN), Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). In future work, we aim to propose an algorithm for fatigue detection by implementing Hardware/Software Co-Design approach. Acknowledgement. We owe a debt of gratitude to the Ministry of National Education, Vocational Training, Higher Education and Scientific Research (MENFPESRS) and National Centre for Scientific and Technical Research of Morocco (CNRST).

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13. Wu, S., Kan, M., He, Z., Shan, S., Chen, X.: Funnel-structured cascade for multi-view face detection with alignment-awareness. Neurocomputing 221, 138–145 (2017) 14. Navastara, D.A., Putra, W.Y.M., Fatichah, C.: Drowsiness detection based on facial landmark and uniform local binary pattern. J. Phys. Conf. Ser. 1529(5), 052015 (2020) 15. Miao, X., Xue, C., Li, X., Yang, L.: A real-time fatigue sensing and enhanced feedback system. Information 13(5), 230 (2022) 16. Ao, B., Yang, S., Linghu, J., Ye, Z.: Design of fatigue driving detection system based on cascaded neural network. J. Syst. Simul. 34(2), 323–333 (2022) 17. Zhao, Z., Zhou, N., Zhang, L., Yan, H., Xu, Y., Zhang, Z.: Driver fatigue detection based on convolutional neural networks using EM-CNN. Comput. Intell. Neurosci., 1–11 (2022) 18. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016) 19. Zhuang, Q., Kehua, Z., Wang, J., Chen, Q.: Driver fatigue detection method based on eye states with pupil and iris segmentation. IEEE Access 8, 173440–173449 (2020) 20. Knapik, M., Cyganek, B.: Driver’s fatigue recognition based on yawn detection in thermal images. Neurocomputing 338, 274–292 (2019) 21. Yang, H., Liu, L., Min, W., Yang, X., Xiong, X.: Driver yawning detection based on subtle facial action recognition. IEEE Trans. Multimedia 23, 572–583 (2021) 22. Yazdi, M.Z.J., Soryani, M.: Driver drowsiness detection by yawn identification based on depth information and active contour model. In: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 1522–1526 (2019) 23. Ali, M., Abdullah, S., Raizal, C.S., Rohith, K.F., Menon, V.G.: A novel and efficient real time driver fatigue and yawn detection-alert system. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 687–691 (2019) 24. Tipprasert, W., Charoenpong, T., Chianrabutra, C., Sukjamsri, C.: A method of driver’s eyes closure and yawning detection for drowsiness analysis by infrared camera. In: 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), pp. 61–64 (2019) 25. Déniz, O., Bueno, G., Salido, J., De la Torre, F.: Face recognition using histograms of oriented gradients. Pattern Recogn. Lett. 32(12), 1598–1603 (2011) 26. Zhang, W., Su, J.: Driver yawning detection based on long short term memory networks. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–5 (2017)

Real-Time SPO2 Monitoring Based on Facial Images Sequences Rachid Latif, Bouthayna Addaali(B) , and Amine Saddik Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University, 80000 Agadir, Morocco [email protected]

Abstract. Within the last few years, contactless vital signs monitoring has become a ubiquitous element of research. In this paper, one of the most important vital signs which is the peripheral arterial oxygen saturation (Spo2) is studied using only an embedded camera by detecting skin color changes in volunteers’ faces. To assess the accuracy of our algorithm, the results were compared with the measured (Spo2) values by pulse oximetry. The captured frames are pre-processed to detect and track the facial area during 1000 s then the pulsatile (AC) and non-pulsatile (DC) components are used to calculate the ratio of ratios (RR) at two different wavelengths (red and blue wavelengths). The results obtained from the experience carried out in our laboratory demonstrate that the (Spo2) monitoring using an embedded camera in daylight is doable and provides many advantages in health care domain. Keywords: Remote Monitoring · Spo2 · Real Time · Facial Images · Embedded Camera

1 Introduction Remote monitoring of vital signs is a non-contact method used for many applications such as automotive field, clinical practice and agriculture. Based on an imaging device, the different vital signs are monitored to describe the person’s physiological conditions in clinical setting. Photoplethysmography imaging (PPGI) is one of remote monitoring techniques based on a video camera measurement and a digital medium to process images by extracting color variations due to blood circulation. Therefore, a various researches have tried to incorporate (PPGI) technique in healthcare, Bella et al. have used the (PPGI) technique for heart rate (HR) monitoring using RGB videos in a case study of Covid-19 pandemic, (HR) values are obtained after the separation of each frame into RGB bands and the application of a filter with discrete wavelet transformation to remove noise from the signal [1, 2]. The ground-truth used for comparing the heart rate values obtained from camera are received from an electrocardiogram (ECG), the problem of the (ECG) in telecardiology is that it requires a long recording, the reason that an effective compression method is required. Latif et al. have proposed in [3] an embedded system based on the CUDA architecture of the lossless © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 474–483, 2023. https://doi.org/10.1007/978-3-031-29857-8_48

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ECG data compression. Gibson et al. have videoed ten infants in a Neonatal Unit to measure their (HR) and respiratory rate (RR) and compared the results with those obtained by a conventional electrocardiogram (ECG). The whole approach is based on (PPGI) technique [4]. But most of works mainly focused on (HR) and (RR), From January 2018 to April 2021 60 articles have been published for (HR) monitoring, 20 for (RR) monitoring but only one article for peripheral arterial oxygen saturation (Spo2) [5]. Oxygen saturation is the fraction of oxygen-saturated hemoglobin relative to total hemoglobin (unsaturated + saturated) in the blood. The human body requires and regulates a very precise and specific balance of oxygen in the blood. Normal arterial blood oxygen saturation levels in humans are 97–100% [6]. Hypoxemia is low levels of oxygen in blood. It causes symptoms like headache, difficulty breathing, rapid heart rate and bluish skin. Many heart and lung conditions put the person at risk for hypoxemia which can be sometimes life-threatening [7]. Before 50 years, the two Japanese bioengineers Takuo Aoyagi and Michio Kishi have developed the first pulse oximeter [8], which is today the most widely recognized device to quantify (Spo2). The pulse oximeter is configured of a light source sands red and infrared rays and photodetector detects unabsorbed light by deoxygenated and oxygenated hemoglobin. The oxyhemoglobin passes more red rays and absorbs infrared ones, whereas the reduced hemoglobin allows more infrared rays to pass and absorbs red light. The ratio of the red to the infrared light measurement is calculated then converted to (Spo2) by the processor. Our work aims to use the PPGI approach for (Spo2) estimation in real time in order to avoid skin problems caused by the contact with electrodes and substitute medical devices with a system high precision and low-cost which maintains patient comfort. The whole work is structured in three distinct parts. The first one is video preprocessing, in this part the Region of interest (ROI) is selected using Viola-Jones face detector, then Kanade-Lucas-Tomasi is used to track the face in case of movements. The second part is signal processing, where each frame is separated into RGB bands, and the average of pixels of the red and blue channels is calculated to get the (DC) values. In the other side, (AC) values are calculated by bandpass filtering the (DC) values and calculating the mean of peak-to-trough heights. In the third part, (RR) is determined and (Spo2) is calculated based on it. The (RR) is the ratio between (DC) and (AC) values. The paper is organized as follows: Sect. 2 explains the methodology used in our work. Section 3 discusses the results and the paper is concluded in Sect. 4.

2 Methodology We build upon the approach presented in [9] to develop our algorithm. Generally, the algorithm used for (Spo2) monitoring can be subdivided into three steps. Video preprocessing, signal processing and (Spo2) extraction. 2.1 Video Pre-processing The face detection after the acquisition of the images was based on the Viola-Jones algorithm in order to detect the regions of interest for the treatment. Also, this algorithm

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was implemented in MATLAB using Computer Vision Toolbox to extract the desired information. Viola-Jones face detector is based on the value of three kinds of features. A two-rectangle features (Edge features), a three-rectangle features (Line-features), and a four-rectangle features (Four-sided features). Rectangle features can be computed very rapidly using an intermediate representation for the image which we call the integral image. The integral image at location x, y contains the sum of the pixels above and to the left of x, y, inclusive [10]:     (1) i x ,y ii(x, y) =   x ≤x,y ≤y

where ii(x, y) is the integral image and i(x, y) is the original image. Using the following pair of recurrences: s(x, y) = s(x, y − 1) + i(x, y)

(2)

ii(x, y) = ii(x − 1, y) + s(x, y)

(3)

where s(x, y) is the cumulative row sum, s(x, − 1) = 0, and ii(−1, y) = 0. The violaJones algorithm is trained by running a modified AdaBoost algorithm on Haar feature classifiers to select the best features [10–12]. In our code, we used the MATLAB function “vision.CascadeObjectDetector()” to detect face automatically using Viola-Jones algorithm instead of creating and training the model from zero. In case of face movement, A point tracker object is created and the good facial features (Harris corners) are chosen and tracked over time using the Kanade-LucasTomasi algorithm. This algorithm is based on the tracking of movement of the centers of

Step1: Video pre-processing

Create a cascade Object detector and point tracker object

Capture a video frame

Draw the bounding box

Detect the face

Track facial features

Fig. 1. Functional block of video pre-processing

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features, each feature has a corresponding one in the consecutive frame, the movement of the feature pairs means the movement of the face [13–15]. Figure 1 and algorithm 1 gives a detailed explanation of step 1. The algorithm of video pre-processing consists of three steps. The first step focuses on the creation of a cascade for objects detection and a point tracker object. The next one is the acquisition of video frame from which the bounding box is located and drawn. if the bounding box is empty, the Viola Jones face detector detects the face. The last step is tracking the face using the point tracker in case of face movement.

Algorithm 1. Face detection and tracking 1: Input: captured video frame 2: Output: detected and tracked face 3: Create face detector and webcam objects 4: Create the point tracker object 5: Get frame size 6: Create the video player object 7: while runLoop and frameCount < 1000 do 8: frameCount ←frameCount + 1 9: if numPts < 10 do 10: Draw the bounding box 11: if the bbox is empty do 12: Find corner points 13: xyPoints ←Location of points 14: numPts← length of xyPoints 15: Re-initialize the point tracker 16: Save a copy of the points 17: Convert the bbox into four points 18: Display the bbox and detected corners 19: end if 20: else 21: if numPts >= 10 do 22: Estimate the geometric transformation 23: Apply the transformation to the bounding box 24: Display the bbox and tracked points 25: Reset the points 26: end if 27: end while 28: Display the annotated video frame

The pseudo-code starts with the creation of face detector, webcam and point tracker objects, then one frame is captured to get its size. The algorithm outlines the (bbox), if it is empty, the Viola Jones face detector detects the face by finding the best features. If the length of points is greater than or equal to 10, the geometric transformation between the old and the new points is estimated and applied to the bounding box.

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2.2 Signal Processing The second step is signal processing where RGB-signals are extracted by decomposing the ROI in each video frame into three separate RGB channels and then the average color intensity is determined. Each average pixel value is calculated over a 10 s sliding window. After the calculation of the DC value (the average color intensity), the red and blue signals are band-pass filtered to eliminate unwanted frequency noise then the average of the peak-to-trough heights is calculated to find the AC value. Figure 2 and algorithm 2 shows all processing stages of the second step.

Step2: Signal processing

Band separation

average of blue and red signals

DC component

step3

Band-pass filter

max and min peaks

averages of the peak-to-trough

AC component

Fig. 2. Functional block of signal processing

1

Algorithm 2. RGB image processing

2 3 4 5 6 7 8 9 10

1: Input: RGB video frame 2: Output: AC and DC components 3: for 1 ≤ bbox ≤1000 do 4: Calculate the mean of red and blue 5: DC← 10s moving average 6: band pass-filter signals 7: Find peaks 8: AC← the average of peak-to-trough 9: end for

The image and signal processing block gives us two types of data: (AC) and (DC) values. This block starts with the separation of the (ROI) into red, green and blue channels. The mean of red and blue signals is calculated and stored in two matrices of zeros with 1000 rows and 1 column, then we calculate a 10 s moving average which present the (DC) values. The (AC) values are found by applying [0.7, 5] band pass-filter to the (DC) signals and finding the peaks. The average of peak-to-trough signal is the (AC) value.

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2.3 SPO2 Extraction The last step is finding the ratio of ratios using the equation:    Iac Idc λ1 RR =    Iac Idc λ2

(4)

Iac and Idc are respectively the amplitudes of (AC) and (DC) components of reflected light at wavelengths λ1 (red) and λ2 (blue). Spo2 values are measured using a pulse oximeter to linearly fit the data by the least squares method, the objective of this part is finding the A and B values in the equation: Spo2 = A − B ∗ RR

(5)

Figure 3 shows functional blocks of step 3 indicated in algorithm 3.

Pulse oximetry

Step3:Spo2 extraction

the least squares method

A and B parameters

Calculate Spo2 values

Store Spo2 values Fig. 3. Functional block of Spo2 extraction

Ratio of ratios

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Algorithm 3. SPO2 calculation 1: Input: AC and DC components 2: Output: SPO2 value 3: calculate RR 6: Find the ground-truth 7: Apply the least squares method 8: Find A and B values 9: Calculate Spo2 values 10: Store Spo2 values 11: end

Our third algorithm is built in three parts, starting from calculating the ratio between (AC) and (DC) at red and blue wavelengths and finishing by (Spo2) determination using the Eq. (5). The A and B values are found by applying the least squares method to fit linear the ground-truth with the values of (Spo2) obtained.

Video capture

Step1: video pre-processing Viola-Jones face detector

Step2: signal processing

Step3:Spo2 extraction

Kanade-Lucas-Tomasi algorithm6

R, G, B signals extraction

DC Component

Spo2 calculation and storage

Ratio of ratios

filtering

AC component

Fig. 4. Global algorithm overview

The main objective of the used approach is to enhance patient comfort. The described algorithm in Fig. 4 is performed in three blocks. The first block combines two algorithms (Viola-Jones and Kanade-Lucas-Tomasi) in the purpose of detecting and tracking the (ROI), the second block processes signals extracted from the facial area by calculating the mean values of red and blue signals and applying a bandpass filter to get (AC) and (DC) values. The third block is dedicated to calculate the (Spo2) based on the ratio between (AC) and (DC) values at red and blue wavelengths.

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3 Results and Discussion The algorithms were implemented in MATLAB programming environment. 20 participants with different age, gender and skin tone have been recorded using an embedded camera. BEURER pulse oximetry PO 30 have been used to get the ground-truth (Spo2) values. Figure 5 shows the results of 1000 s of capturing and processing the data. The top first plot shows the mean of RGB received data from the (ROI), the (DC) plots show the (DC) values calculated from red and blue means by sliding 10 s average widow, the (AC) plots show the (AC) values calculated by applying [0.7, 5] bandpass filter, the same used in the work [9] for (HR) and (Spo2) extraction. The lower last plot presents (Spo2) values calculated using our algorithm, the values obtained are between 98% and 99% which indicates the accuracy of our method.

Fig. 5. Simulated Results to show the Spo2 measurement methodology using RGB camera. The RGB colors are obtained by the RGB color sensors in the camera and separated using the Matlab code. The Spo2 values are calculated using the ratio of ratios at red and blue wavelengths.

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4 Conclusion By means of the advances in computer vision and signal processing, vital signs monitoring has become possible using only a camera. Throughout this work, the proposed algorithm presents an overview of data acquisition, image and signal processing for (Spo2) monitoring. The values of this physiological parameter are obtained using an embedded camera in daylight by detecting and tracking the facial area during 1000 s, and processing the extracted RGB channels from each frame. Additionally, the two wavelengths (red and blue) have been used to calculate the ratio of ratios based on the (DC) and (AC) components. The results were close to the ground-truth values. The accuracy of values is affected by head movement and incident illumination intensity which requires robust monitoring system and stable light conditions. Acknowledgement. We owe a debt of gratitude to the Ministry of National Education, Vocational Training, Higher Education and Scientific Research (MENFPESRS) and National Centre for Scientific and Technical Research of Morocco (CNRST) for its financial support for the project Cov/2020/109.

References 1. Bella, A., Latif, R., Saddik, A., Jamad, L.: Review and evaluation of heart rate monitoring based vital signs, a case study: Covid-19 pandemic. In: 2020 6th IEEE Congress on Information Science and Technology (CiSt) (2020) 2. Bella, A., Latif, R., Saddik, A., Guerrouj, F.Z.: Monitoring of physiological signs and their impact on the Covid-19 pandemic: review. In: E3S Web of Conferences, vol. 229, p. 01030 (2021) 3. Latif, R., Guerrouj, F.Z., Saddik, A., El B’Charri, O.: ECG signal compression based on ASCII coding using CUDA architecture. In: 2019 4th World Conference on Complex Systems (WCCS) (2019) 4. Gibson, K., et al.: Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study. Pediatr. Res. 86, 738–741 (2019) 5. Selvaraju, V., et al.: Continuous monitoring of vital signs using cameras: a systematic review. Sensors 22, 4097 (2022). Cataldo, A. (ed.) (2022) 6. Wikipedia. https://en.wikipedia.org/wiki/Oxygen_saturation_(medicine). last accessed 2022/10/19 7. Cleveland Clinic. https://my.clevelandclinic.org/health/diseases/17727-hypoxemia. last accessed 2022/10/19 8. Wikipedia. https://en.wikipedia.org/wiki/Pulse_oximetry. last accessed 2022/10/17 9. Tarassenko, L., Villarroel, M, Guazzi, A., Jorge, J., Clifton, D.A., Pugh, C.: Non-contact video-based vital sign monitoring using ambient light and auto-regressive models 35(5), 807–831 (2014) 10. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004). https://doi.org/10.1023/B:VISI.0000013087.49260.fb 11. Everingham, M., Sivic, J., Zisserman, A.: Hello! My name is... Buffy, automatic naming of characters in TV video. In: British Machine Vision Conference, pp. 889–908 12. Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2001)

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13. Shi, J., Tomasi, C.: Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1994) 14. Tomasi, C., Kanade, T.: Detection and Tracking of Point Features. School of Computer Science, Carnegie Mellon University, Pittsburgh (1991) 15. Mstafa, R.J., Elleithy, K.M.: A video steganography algorithm based on Kanade-LucasTomasi tracking algorithm and error correcting codes. Springer Science+Business Media, New York (2015)

The Identification of Weeds and Crops Using the Popular Convolutional Neural Networks Mohammed Habib1(B) , Salma Sekhra1 , Adil Tannouche2 , and Youssef Ounejjar1 1 Spectrometry, Materials and Archaeomaterials Laboratory (LASMAR), Faculty of Sciences,

Moulay Ismail University, Meknes, Morocco [email protected], [email protected] 2 Laboratoire de l’Ingénierie et de Technologies Appliquées (LITA), Ecole Supérieure de Technologie de Béni Mellal, Université Sultan Moulay Slimane, Beni-Mellal, Morocco

Abstract. The weeds compete with the crops for the nutrients. However, it is necessary to control weeds in order to prevent them from affecting the productivity of the land. In the context of the robotization of this process, the Deep Learning can help to localize and identify the weeds, thanks to the object detector. The accuracy of an object detector depends on its backbones which are networks their function is feature extraction. In this research, we have shown the performance of the most popular CNNs, which are used as backbones, for the identification and distinction of crop weeds, as well as proposed a model that combines the architecture of the two models MobileNet and ResNet. The proposed model had the properties of MobileNet in terms speed and precision, our modification has resulted in a significant improvement. The obtained results, showed us that the MobileNet models are the most suitable for as backbone to the future object detector that we want to design it to detect and ultra-localize the weeds. Keyword: Computer vision · Deep Learning · Classification · Weed identification · Object detection · Feature Extraction

1 Introduction The fight against weeds is a major activity of farmers, because they affect their productivity. In the context of the robotization of this process, the artificial intelligence, and specifically Deep Learning, can help us to localize and identify weeds, thanks to the object detector, which will allow us to limit the use of herbicides by treating only the areas that include weeds [1]. Object detection is the set of models and computational techniques that provide one of the most fundamental pieces of information in computer vision applications that consists of identifying and locating instances in images. Object detection began to develop at an unprecedented rate thanks to artificial intelligence and more specifically Deep Learning as early as 2014 through research in this field. Different object detectors have been developed to answer the need for localization and identification of objects in images such as RCNN, SSD, YOLO… [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 484–493, 2023. https://doi.org/10.1007/978-3-031-29857-8_49

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In our previous studies, we have successfully used the Faster RCNN ResNet 50 model to detect and ultra-localize the pea crop among weeds [1]. The accuracy of an object detector depends greatly on its backbones which are networks that have feature extraction as their function. In this research, we will study the most popular backbones such as MobileNet, ResNet, VGG … In different studies, the researchers have used popular CNNs as classifiers to validate their performance or to make improvements on their architecture. They used VGG16 for seedligns plant classification [3], ResNet 50 for facial expression recognition [4], ResNet, MobileNet or GoogleNet for face mask identification [5], VGG architecture for automatic speech recognition [6], the combination of MobileNet and SVM for pulmprint recognition [7] and a model based on the coordination of MobileNet and DenseNet architectures for object classification [8]. In another context, researchers used the popular CNNs to develop models that combine backbones and different sensing heads to identify and locate agri-food products [9], another study used VGG and MobileNet as backbones by combining them with SSD as a sensing head to develop an object detector [10]. In this study, we will show the capacity of the most popular CNNs, which are used as backbones, for the identification and distinction of weeds from crops, as well as the proposed model that combines the architecture of the two models MobileNet and ResNet and we will compare the results obtained by all models.

2 Materials and Methods To choose the best backbone, we proceeded to test the performance, in terms of feature extraction, of the most popular backbones such as DarkNet, MobileNet, ResNet, VGG… by training them as classifiers with our own dataset to differentiate weeds from crops. 2.1 Images Acquisition and Preprocessing We used images already collected in a field located in the city of MEKNES in MOROCCO, of the pea crop using the materials shown in Fig. 1 [1].

Fig. 1. Images acquisition devices (about 40cm from the bottom).

These original images combine crops and weeds together (Fig. 2). We opted to separate them using the image processing techniques offered by the OPEN CV library.

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We divided the initial 1040x780 images into dozens of 260x260 pixel pieces as shown in Fig. 2, then manually selected images that include just peas or weeds, and separated them into two different folders.

Fig. 2. An original image (a) divided and sorted into several pieces of images (b).

We obtained 4991 in total divided into two classes, 2429 peas and 2562 weeds. For training, we split the resulting dataset between training and validation datasets, 80% for training and 20% for validation. 20% of the validation images were used for testing. To better enrich our dataset, we have proceeded to the augmentation of the dataset. Using the tools available in the Keras library, we added an augmentation layer to each model we studied. This layer can generate random modifications to the input images. We chose to do random rotations and random flips (Fig. 3).

Fig. 3. Samples of a one-image augmentation

2.2 The MobileNet Models MobileNet networks are low-computation, low-storage neural networks designed for mobile and embedded applications, based on the use of depth-separable convolution blocks as efficient building blocks to reduce computation in the early layers [11]. Depthwise separable convolution is a form of factorized convolution that replaces the standard convolution with a depthwise convolution and a pointwise convolution (a 1

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× 1 convolution). For MobileNets, depthwise convolution is based on applying a single filter for each input channel. The pointwise convolution, as shown in the Fig. 4, is followed by a 1 × 1 convolution to combine the outputs. It is used to create a linear combination of the depthwise layer output.

Fig. 4. The structure of the Depthwise layer followed by the Pointwise layer

The standard convolution is characterized by a kernel K of size DK × DK × M × N, DK is the spatial dimension of the kernel, M and N are successively the number of input and output channels. The output of the standard convolution, is defined by:  Ki,j,m,n · Fk+i−1,l+j−1,m (1) Gk,l,n = i,j,m

The computational cost of the standard convolution depends on the number of input and output channels M and N, the size of the kernel Dk × Dk , and the size of the feature map DF × DF . It is defined by: DK · DK · M · N · DF · DF

(2)

Each depthwise separable convolution block is composed of: the depthwise convolution layer and the pointwise convolution layer. They are associated with Batchnorm and ReLU nonlinearities for both layers. The depthwise convolution can be defined by:  Gk,l,m = Ki,j,m · Fk+i−1,l+j−1,m (3) 



i,j



With K is the depth convolutional kernel of size DK × DK × M that is applied to each channel in F to generate the filtered output feature map channel G. The computational cost for the depth convolution is: 

DK · DK · M · DF · DF

(4)

The depthwise convolution is, therefore, only filtering the input channels. An additional layer (pointwise), is required to compute the linear combination of the depthwise convolution output via a 1 × 1 convolution. This combination is called a depthwise separable convolution. MobileNetV1 uses 3 × 3 depthwise separable convolutions, which requires 8 to 9 times less computation than standard convolutions. You can also notice that there are blocks of separable depthwise convolution that change the output size and others that keep it (by changing the strides) [11].

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MobileNetV2 introduces two new features to the architecture: linear bottlenecks between layers, and shortcut connections between bottlenecks. Its principle is that bottlenecks encode input and output data, while the internal layer encapsulates the ability of the model to transform low-level concepts to higher-level descriptors, such as classes. As well as shortcuts allow for faster learning and better accuracy [12] (Fig. 5).

Fig. 5. The basic structure of the MobileNetV2 model

2.3 The ResNet Models When Deep Learning reached its limits because of the phenomenon of gradient vanishing, which represented a major obstacle in the training of neural networks, Microsoft proposed a method to overcome the problem, which is the use of ResNet. ResNet means a residual network, which is composed of shortened connections that allow the gradient to be directly backpropagated to the previous layers. It consists of predicting the results not only on the basis of the layers right next to each other, but also on the basis of the layers far away through the shortened connections that can skip one or more layers (Fig. 6).

Fig. 6. Example of a ResNet (b) and its counterpart in standard network (a)

The architecture of ResNet is inspired by VGG networks. It consists of (34, 50, 101…) convolutional layers have 3 × 3 filters and its output feature map sizes the same as the input. The other convolutional layers have the output size is divided by two, the number of filters is doubled to conserve the time complexity per layer. The downsampling is done by convolutional layers that have a stride of 2 [13].

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2.4 The Proposed Model We propose a model that combines the properties of the two models MobileNetV1 as a light model, and ResNet that allows to solve the problem related to the descending gradient. We propose a modification to the architecture of MobileNet, adding shortcuts to make the results converge quickly. Our design is an intermediate architecture between the two architectures of MobileNetV1 and MobileNetV2. We have kept the blocks of MobileNet that have strides of 2 and we have added shortcuts for the blocks of strides of 1 as shown in the Fig. 7.

Fig. 7. Our modification to the MobileNetV1 model’s strides of 1 block

3 Results and Discussion 3.1 Training Preparation For model creation and training, we used the Google Colab virtual machine, which includes all the necessary libraries as well as integrating the GPU processor that allows for faster learning process, and for the DataSet, we imported it on Google Drive, to allow for fast synchronisation with the training code. To test the performance of the backbones subject of our study, we have implemented it as a classifier adding more entities as shown in the Fig. 8.

Fig. 8. the classifier architecture that contains the studied backbones

The model in general includes the following layers:

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• the layer of preprocessing and data augmentation • the studied backbones: MobileNet, ResNet, VGG… • the classification head: contains Global average pooling, Dropout and Dense layers. In order to find the best results, we have chosen, after some tests, the parameters mentioned in Table 1. Table 1. The model training parameters Num epochs

Initial learning rate

Batch size

Num classes

Metrics

Optimizer

Loss function

20

1e−4

32

2

Accuracy

Adam

Binary Crossen-tropy

3.2 Training Results We trained the models from scratch. Each time we change the backbone and we record the accuracy and the error at each epoch. The graphs in the Fig. 10 show the evolution of the accuracy and the error, of the training and the validation, during the training of each model (Fig. 9). During the training, we observe that the VGG models had important instabilities compared to the other models. On the other hand, the most stable models are DarkNet and MobileNet. We also observe that the MobileNet models converge more slowly than the other models. The final training accuracies exceeded 96% for training and 94% for validation and for the loss all models finished below 0.1 for training and 0.2 for validation. The DarkNet model performed well compared to the other models followed by the ResNet models. For the results of our proposed model (red curves), we notice that it was quite stable during training, its results converge quickly than the MobileNet models as well as it gave good accuracies and losses that challenge the DarkNet model. 3.3 Evaluation Results To test the performance of our trained models, we performed an evaluation on new test data with a batch size of 64 images. Each model received the 64 images as input and predicted their class. Then, the precision, speed and size of the models are measured to compare them (Table 2). To evaluate the performance of a classifier, many metrics are used to compare two or more different models. These metrics are based on the confusion matrix which contains the evaluation results of each model. Figure 10 shows the confusion matrix of the DarkNet19 model evaluation.

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Fig. 9. The training results (a and b) and the validation results (c and d).

Fig. 10. An example of confusion matrix after evaluation (Darknet19 model)

With, TP mean True Negative, TN True Positive, FP False Positive and FN is False Negative. The most popular metrics for evaluation are accuracy, recall and F1 score. The accuracy allows us to measure the confidence in the model when it predicts that an

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individual is positive. It is defined by: p=

TP (5) TP + FP

(5)

The recall evaluates the ability of the model to find all positives in the data set. It is determined by: r=

TP (6) TP + FN

(6)

The F1 score can be considered as the average of precision and recall, it can be used to find the best compromise between the two quantities. F1 score = 2.

p.r (7) p+r

(7)

With p is the precision and r the recall [14]. Table 2. Evaluation results Model

Precision

Recall

F1 score

Speed (ms)

Model size (Mo)

MobileNetV1

96.87%

91.17%

93.93%

222

37.1

MobileNetV2

94.6%

92.1%

93.33%

218

26.2

ResNet50

100%

97.36%

98.66%

444

90.3

ResNet101

100%

94.11%

96.96%

668

488.9

VGG16

96%

88.9%

92.31%

506

168.5

VGG19

100%

92.6%

96.15%

578

229.3

DarkNet19

97.22%

97.22%

97.22%

238

183.7

Proposed model

100%

97.14%

98.55%

221

37.3

3.4 Discussion Looking at Table 2, we can see that the ResNet and DarkNet models are better in terms of accuracy than the VGG and MobileNet models, but the MobileNet models are better in terms of speed and model size, i.e. they do not require much computation. The model we proposed, which is an intermediate between MobileNetV1 and MobileNetV2, gave good results in terms of accuracy and speed. It is light in size and speed like the MobileNet models, and challenged the DarkNet and ResNet models in accuracy.

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4 Conclusions In this research, we have successfully implemented and tested popular models as classifiers. We integrated them into models to test their performance in extracting features to allow us to choose the best one to use as a backbone. We concluded that the ResNet models are efficient but not as fast and require more memory, as well as the VGG models but they are unstable and a little less efficient. The DarkNet model is fast and more accurate than the MobileNet models except that these models are less accurate and lighter than DarkNet. We have proposed a model based on the MobileNet models. This model had the properties of MobileNet in terms of lightness and speed, and in terms of accuracy our modification introduced a small improvement. By observing the obtained results, we can conclude that the MobileNet models are the most appropriate as backbone in the future object detector that we want to design to detect and ultra-localize the weeds.

References 1. Mohammed, H., Tannouche, A., Ounejjar, Y.: Weed detection in pea cultivation with the faster RCNN ResNet 50 convolutional neural network. Revue d’Intelligence Artificielle 36(1), 13–18 (2022). https://doi.org/10.18280/ria.360102 2. Zou, Z., Shi, Z., Guo, Y., Ye, J: Object Detection in 20 Years: A Survey (2019) 3. Ashqar, B.A.M., Abu-Nasser, B.S., Abu-Naser, S.S.: Plant Seedlings Classification Using Deep Learning (2019) 4. Li, B., Lima, D.: Facial expression recognition via ResNet-50. Int. J. Cogn. Comput. Eng. 2, 57–64 (2021). https://doi.org/10.1016/j.ijcce.2021.02.002 5. Venkateswarlu, I.B., Kakarla, J., Prakash, S.: Face mask detection using MobileNet and global pooling block. In: 4th IEEE Conference on Information and Communication Technology, CICT 2020. Institute of Electrical and Electronics Engineers Inc. (2020) 6. Beckmann, P., Kegler, M., Cernak, M.: Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing (2019). https://doi.org/10.23919/EUSIPCO54536.2021.961 6254 7. Michele, A., Colin, V., Santika, D.D.: Mobilenet convolutional neural networks and support vector machines for palmprint recognition. In: Procedia Computer Science. Elsevier B.V., pp. 110–117 (2019) 8. Wang, W., Li, Y., Zou, T., et al.: A novel image classification approach via dense-mobilenet models. Mob. Inf. Syst. 2020,(2020). https://doi.org/10.1155/2020/7602384 9. Zheng, Y.Y., Kong, J.L., Jin, X.B., et al.: Cropdeep: the crop vision dataset for deep-learningbased classification and detection in precision agriculture. Sensors (Switzerland) 19,(2019). https://doi.org/10.3390/s19051058 10. Chiu, Y.-C., Tsai, C.-Y., Ruan, D., et al.: Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems (2020) 11. Howard, A.G., Zhu, M., Chen, B., et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017) 12. Sandler, M., Howard, A., Zhu, M., et al.: MobileNetV2: Inverted Residuals and Linear Bottlenecks (2018) 13. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015) 14. Grandini, M., Bagli, E., Visani, G.: Metrics for Multi-Class Classification: an Overview (2020)

3D Scenes Semantic Understanding: New Approach Based on Image Processing for Time Learning Reducing Meryem Ouazzani Chahdi1(B) , Afafe Annich1,2 , and Khalid Satori1 1 Computer Science Department, LISAC Laboratory, Sidi Mohamed Ben Abdellah University,

FSDM 22MF+97C, 30050 Fez, Morocco {meryem.ouazzanichahdi,khalid.satori}@usmba.ac.ma, [email protected] 2 ISIC-Rabat, Higher Institute of Information and Communication, Rabat Institutes, Avenue Allal El Fassi Madinat Al Irfane, Rabat, Morocco

Abstract. Recently, the 3D scenes semantic understanding axis has attracted many researchers’ attention interested in the computer vision field; it become a major factor for several applications development such as robotics, autonomousdriving, and smart city applications. The research’s main purpose related to the scene Understanding tasks is to furnish the computers with needed techniques to simulate the human mind’s ability to fully perceive their environments and to analyze real-time scene information. Moreover, if computers can express this understanding in natural human language, then they can effectively communicate with humans and their environment, which paves the way for the investment of these systems in many vital areas such as industrial, medical, and astronomical fields. To overcome this challenge; various related works have been explored, among which we find the axis of detection of the visual relationships in the workspace; which appeared as an important research area aimed at detecting objects in the scene; categorizing the relations connecting each objects pair, then expressing them in natural language. In our research, we worked with the visual relationship detection method based on extracting visual, semantic, and spatial features of the image using the faster RCNN, transforming them into a weighted digraph using GNN, and merging them for the lower-dimensional feature vectors’ final extraction. We propose a new image processing-based approach to decrease the learning time and increase the relationship detection task efficiency. Given the available equipment’s limited effectiveness; we had to work with only 25% of the VG dataset. Keywords: Scene understanding · Visual relationship detection · Natural language scene description

1 Introduction The semantic understanding of 3D scenes [1–4] concerns the perception and interpretation of the different objects contained in the 3D space, by focusing on their geometric © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 494–503, 2023. https://doi.org/10.1007/978-3-031-29857-8_50

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and semantic characters, which interest in addition to the recognition and localization of the objects attending in the work environment; the prediction and the classification of semantic relationships between them. Such understanding is very important for different research works. H. YAN et al. [5] offer a network named (SAFNet) for understanding the 3D scenes that can improve the performance of existing data fusion methods by employing the structural Deep Metric Learning network on the points and pixels to detect relationships. Additionally, these relationships are used to represent and adapt 2D images and point clouds into a joint canonical space in order to exploit it in the prediction task. Finally, the proposed method has been tested on the two most used S3DIS and ScanNetV2 datasets for the 3D work environment understanding and demonstrated competitive performance. In their work, J. Wald et al. [6], focus on scene graphs which are used as a way to understand the 3D scene, to map objects and their relationships, where objects are represented as nodes and their relationships are modeled like edges. Using on PointNet and (GCN) networks, they propose a new learning architecture that can predict scene graphs from a scene’s point cloud. Inspired by 3RScan, they introduce a dataset of 3D scene graphs rich in semantic relations named 3DSSG and generated in a semi-automatic way. Their method has demonstrated its effectiveness in scene retrieval tasks. A new method of scene understanding using natural language is proposed by J. Moon et al. [7], in the context of human-robot communication. This method is designed to generate a natural language description of the work environment based on a map of Object-directed 3D semantic graphs built employing RGB-D images and using the (GCN) and the (RNN) [8] networks to produce sentences. The graph’s vortexes include the semantic and spatial object characteristics; the edges represent the relationships between the object pairs. The proposed method performance is checked against current algorithms using (SUN 360 panorama) and (NYUv2 Sentence) datasets. Considering that each object of the picture can be associated with a large and varied number of predicates, which in turn can be repeated in more than one visual relationship; Lu C. et al. [9], presented a method based on this vision to trains visual models for objects and predicates separately and merges them together in the final to predict multiple relationships for each scene by using RCNN [8] to generate the proposals objects. Each object pair is scored utilizing a language module and a visual appearance module and later thresholded to produce relationship labels set. They also offered a new dataset with 37, 993 relationships. Their method succeeded in detecting thousands of relationships from very few training examples; and improved content-based image retrieval task. Other methods [10, 11] used the deep convolutional neural network (CNN) [8, 12] to separately detect the objects and the predicates in the input image and merges between their visual, spatial and semantic characteristics into low dimensional features vector for the detection of the triplets. Later and to force annotated relationships to have a higher relevance score; K. Liang et al. [10] suggested a structure ranking loss method; which facilitates the co-occurrence and incompleteness of visual relationships. In addition to the three features mentioned above, Le Zhang et al. [11]; chose to handle the ignored topological relationships between various interest’s regions, by converting the input image to the visual regions weighted graph, using the graph neural network; aiming to increase the efficiency of the visual relationships detection works. In fact, we chose to benefit from both approaches proposed in [10, 11] by combining their advantages. Moreover; we

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propose a new image preprocessing-based approach in order to decrease the learning time and increase the efficiency of the visual relationship detection task.

2 Methodology 2.1 Pretreatment Image processing [13] is an essential factor in perceiving and interpreting the information contained in the scene; and helps to the success of several high-level processes, such as pattern recognition, object detection, region of interest segmentation, and 3d reconstruction… it is a low-level process aimed at improving the quality of an image and extracting its features that includes several tasks such as filtering, normalization, scaling, histogram equalization, thresholding, standardization, mathematical morphology, image enhancement, and restoration. The choice of the starting parameters such as the threshold value, the starting point, the initial curve, and the scale factor is a very important step that contributes significantly to obtaining the desired results. In deep learning tasks, Image Normalization, Standardization, and Scaling techniques are typically used to speed up the learning models and increase the accuracy of the obtained results. • Image normalization: this consists in dividing the value of each pixel of the image by the maximum value that a pixel can take (255 for an 8-bit image). This is equivalent to limiting the pixel values in the range [0–1]. • Standardization: allows obtaining of a distribution of a data sample whose mean is equal to 0 and the standard deviation equal to 1. These two magnitudes can be calculated either by image or by data set. The first operation is called “sample-wise”, and the second is called “feature-wise”. • Image scaling: resizing is the transformation that concerns changing the image size; either to enlarge it or to shrink it. In our approach, we have chosen to enlarge the size of the dataset using a suitable scaling coefficient for each image. For image Standardization; instead of using the mean and the standard deviation of the ImageNet dataset which are adopted by Le Zhang et al. [11], we preferred to calculate the mean and the standard deviation of the visual genome dataset. On the other hand, we processed the images separately from the work of the adopted deep-learning model. 2.2 The Adopted Model The holistic understanding of a work environment is based on the visual relationships detection that highlights a variety of interactions between pairs of objects in images, and represented in the form of triplets (subject, predicate, object) as shown in (Fig. 1): (person, in front of, Fence) where the subject is “person”, the object is “Fence” and the predicate is the relation “in front of”. To deepen the research in this field; we started by adopting the method called Visual Relationship Detection System using a regional topology structure [VRTS] proposed by

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Fig. 1. Description of the visual relationships between various the image objects.

Le Zhang et al. [11]; Which is based on the use of the faster-RCNN [8] to extract the visual, spatial and semantic characteristics of the regions of interest earlier detected in the dataset of the relationship detection in the training stage. To deal with overlapping regions of interest; the author chooses to integrate the information of local connections between them in the detection of visual relations by modeling a graph structure for the input image. In a clearer sense, he transformed the visual characteristics of the regions as vortexes and the relations between them as edges. Visual, spatial, and semantic features are merged by using a new multi-feature fusion algorithm for the extraction of the final feature vectors. The following diagram illustrates a summary of the construction phases for the adopted [VRTS] method (Fig. 2).

Fig. 2. Phases for Making the VRTS system using a regional topology structure.

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Visual Regions Network Given the overlaps and interactions that show up between several sub-regions of the input image; the author [11] proposed to transform this latter into a visual region graph (Fig. 3) in order to model the relationships between its objects, starting with the use of the ResNet-101 deep learning model to extract the intermediate characteristics ofthe visual  regions that are defined as: prop1 , . . . .., propn where propi ∈ R2048×14×14 . This set  of characteristics is flattening as feature vectors denoted by propi ∈ R2048 that represent the proposal regions. 1. Visual region digraph 

Subsequently, the resulting feature vectors propi were used as input values of the GNN graph neural network model in order to construct the visual relationship graph G(V , E) between them. With V = {v1 , . . . . . . .vn } is the vortex set that designates the regions vi and card (V ) = N . The relationships between these regions are considered as edges  belonging to the set E = eij / 1 < i = j < N , card (E) = M . Each edge characteristic represents the union feature between subject vortex vi and object vortex vj . The edge vector direction defines the relationship of the subject to the object; like Fig (F.1) which represents the triplet (sub(vi ), pred(eij ), obj(vj )); ex: (girl, wear, dress). The feature embedding of edge eij can be calculated by the following relations:          j j j j i i i i , xmin , min ymin , ymin , max xmax , xmax , max ymax , ymax UnionBoxij = min xmin (1)  uij = ROI _Pooling feat, union_boxij

(2)

   eij = flaten pooling layer_4 uij

(3)

 i  i , xi i where: xmin , ymin max , ymax is the bounding boxi coordinates, feat is the ResNet-101 network’s layer_3 feature map.

Fig. 3. Diagram of regional visual relationships.

Further the edges contained in the graph G are approximated to the vertexes, and the author [11] introduced the concept of “edge vertex” to describe the such type of

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vertex which represents the “predicate” relationship between the subject vertexes and the object vertexes. This means that our old diagram is transformed into a directed graph pre that contains edge vertexes vk ∈ E between subject vertexes visub ∈ V and object obj vertexes vj ∈ V the pathway I → k and the pathway j → k (Fig. 4).

Fig. 4. From regional visual relationships diagram to visual region directed graph.

2. Weighted directed graph of the visual regions The predicate vertex can be calculated by the following conditional probability formula:   obj pre P vk |visub , vj

    obj pre sub obj pre P vk , vi , vj P vk , visub , vj = · = wik · wkj (4) obj visub vj pre

obj

With vk , visub , vj are the predicate, subject, and object vertices respectively, wik is the dependence coefficient between the subject vertex and the predicate vertex and wkj is the dependence coefficient between the object vertex and the predicate vertex. This allows us to transform the digraph G(V , E) into a weighted digraph G(V , E, W ) as shown in the figure (Fig. 5).

Fig. 5. From the diagraph G(V , E) into the weighted diagraph G(V , E, W ).

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After presenting the edges as vertices in the weighted directed graph of the visual regions G(V , E, W ), it became possible to compute the characteristic matrix X with the fully connected layer by joining the regional vertexes feature matrix V with that of the edges characteristics E; using the following Relu activation function of the GNN model: X = Relu([V, E]Win + bin )

(5)

where [V , E] ∈ R(N +M )×2048 ; Win ∈ R2048×256 is the fully connected layer parameter; X ∈ R(N +M )×256 and bin is the bias. 3. Predicates Detection To solve the vanishing gradient problem in the graph neural network model; the authors [11], employed GRU (Gated Recurrent Unit) to update the edge vertex features in order to classify predicates.The hidden state hk0 of the edge vertex k at time (t = 0) is equal to the features vector Xk ; we denote: h0k = Xk

For(t = 0) : For(t = l + 1) : mtk =

 j∈Nkin

  htk = GRU mtk , ht−1 k 

 WgT ht−1  wjk j

(6) (7) (8)

With mtk is the memory state of the previous time step,  is the Hadamard product operator, and WgT ∈ R256×256 is the transpose matrix of the matrix of weights Wg for a linear type transformation and Nkin = {i, j} is the set of the vertices neighborhood of the edge vertex k. As a result, we obtain a new graph feature embedding matrix for edge vertexes that can be defined by:   (9) Vpre = Hl = hl(n + 1) , . . . . . . , hl(N + M) , N + 1 ≤ K ≤ N + M Spatial Feature Extraction Network After having defined the bounding rectangles of the triple (subject, predicate, object) by the Faster-RCNN model; we exploit these results to form a vector of relative coding around their spatial location in two steps: The first consists in calculating the position of the corner left upper of the subject’s bounding rectangles and the object’s bounding rectangles, which are denoted by: x1 ; y1 ; x2 ; y2 . The second step is to compute the height ratio logarithm and the width ratio logarithm between the subject’s bounding rectangle and the object’s bounding rectangle, that’s represented by:   wsub hsub wobj hobj ln( obj ); ln( obj ); ln( sub ); ln( sub ) (10) w h w h Finally, these two vectors are combined into one vector denoted by:   sub   sub   obj   obj  h w h w ; ln obj ; ln ; ln sub boxpre = x1 ; y1 ; x2 ; y2 ; ln wobj h wsub h

(11)

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The predicate special vector is calculated by the Relu activation function:   T spapre = Relu Wspa boxpre + bspa

(12)

Semantic Feature Extraction Network To predict relationship inference between image regions; Le Zhang, Ying Wang, et al. [11] employed the pre-trained Word2Vev model for word embedding to encode subject and object category titles. The word embedding vector of the predicate sempre is calculated by joining those of subject and object which are denoted respectively semsub , semobj through the semantic network by the relation:  

T (13) sempre = Relu Wsem semsub ; semobj + bsem With: semsub ∈ R300 , semobj ∈ R300 , Wsem ∈ R600×256

3 Experimental Result 3.1 Work Environment The code is implemented using pytorch in an HPZ820 workstation, with an Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80 GHz (40 CPUs), ~2.8 GHz processor, and a single NVIDIA Quadro P4000 graphics card. Given the limited effectiveness of available equipment, it was impossible to process the entire Visual Genome dataset which contains over 108000 medium-sized images (600 × 800) split into 2 sets: 73796 images for the training phase and 25,857 images for the testing phase; with 200 object categories and 100 predicate relationship categories; by using 3 deep learning models (Faster-RCNN, GNN, Word2Vev) in a reasonable period of time. For this reason, we only work with 25% of the database size. 3.2 Results and Comparisons The simulation of the original model during the training phase with 25% of the Visual genome dataset (73796/4 = 18449) by adopting the author’s parameters (N°. Epoch = 7, with LR = 0, 0001 and batch-size = 1) took about 2d:8h: 24m: 43s. The best performance obtained by the module is 81.6018% on the R@50 and 86.5609% on the R@100. With Recall@50 and Recall@100 are the evaluation metrics used to measure how many true positive triplets are obtained in the top 50 and 100 predictions. (14) In our work, we tried to focus initially on reducing the time taken by the model in the training phase; with taking care to increase the model performance or at least maintain the previously obtained values. To do this, we performed the pre-processing of the dataset images separately from the model training phase and the predicate detection task. The

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pre-treatment is carried out in two stages; the first one concerns the normalization of the Visual Genome dataset, and the second step consists in enlarging the size of the images. With these modifications we succeeded in reducing the training time to 16h: 17m: 33s instead of 2d: 8h: 24m: 43s, this means that the training time is reduced by 71.43%. On the other hand; the performances achieved on the 25% of the VG dataset are 81.7423% and 86.6848% on the R@50 and the R@100 respectively. The visualized experiment results on VG dataset are depicted in Table. 1 Table 1. Training task performance (%) on VG dataset. Training phase

%Training data

R@50

R@100

time

VRTS

25%

81.6018%

86.5609%

2d:8h:24m:43s

Our approach

25%

81.7423%

86. 6848%

16h:17m:33s

It is necessary to point out that the performances reached by the model [VRTS] proposed by Le Zhang et al. during the predicate detection task on the entire VG dataset are 78.44% and 83.26% on the R@50 and R@100, respectively. The performances obtained by the two models using 30% of the test data are presented in Table 2. Table 2. Predicate detection tasks performance (%) on VG dataset testing phase

%testing data

R@50

R@100

time

VRTS

100%

78.44%

83.26%

11h:8m:17s

VRTS

30%

81.601785

86.560873

3h:45m:40s

Our approach

30%

81.7423

86.6848

1h:5m:17s

4 Conclusion In our work, we have proposed a new approach based on image processing to decrease the learning time and increase the efficiency of the visual relationship detection task in the RGB image by benefiting from the advantages of the previously proposed methods [10, 11]. We scaled up the size of the dataset images by using an appropriate scaling coefficient for each image. For image normalization; Instead of using the mean and standard deviation of the ImageNet dataset which are adopted by Le Zhang et al. [11], we used those of the visual genome dataset. On the other hand, we processed the images separately from the work of the adopted deep-learning model. Given the limited effectiveness of available equipment; we had to work with only 25% training data and 30% test data.

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References 1. Fan, J., Zheng, P., Li, S.: Vision-based holistic scene understanding towards proactive human– robot collaboration. Robot. Comput. Integrat. Manufac. 75, 102304 (2022) 2. Arashpour, M., Ngo, T., Li, H.: Scene understanding in construction and buildings using image processing methods: a comprehensive review and a case study. J. Build. Eng. 33, 101672 (2021) 3. De Cesarei, A., Loftus, G.R., Mastria, S., Codispoti, M.: Understanding natural scenes: contributions of image statistics. Neurosci. Biobehav. Rev. 74(Part A), 44–57 (2017) 4. Kojima, R., Sugiyama, O., Nakadai, K.: Audio-visual scene understanding utilizing text information for a cooking support robot. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4210–4215 (2015) 5. Yan, H., et al.: Structure-aware fusion network for 3D scene understanding, Chin. J. Aeronaut. (2021) 6. Wald, J., Dhamo, H., Navab, N., Tombari, F.: Learning 3D semantic scene graphs from 3D indoor reconstructions. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3960–3969 (2020) 7. Moon, J., Lee, B.: Scene understanding using natural language description based on 3D semantic graph map. Intel. Serv. Robot. 11(4), 347–354 (2018). https://doi.org/10.1007/s11 370-018-0257-x 8. Cebollada, S., Payá, L., Flores, M., Peidró, A., Reinoso, O.: A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. Expert Syst. Appl. 167, 114195 (2021) 9. Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.): LNCS, vol. 9905. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1 10. Liang, K., Guo, Y., Chang, H., Chen, X.: Visual Relationship Detection with Deep Structural Ranking. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, issue 1 (2018) 11. Zhang, L., Wang, Y., Chen, H., Li, J., Zhang, Z.X.: Visual relationship detection with region topology structure. Inform. Sci. 564, 384–395 (2021). https://doi.org/10.1016/j.ins.2021. 01.049 12. Lin, C., et al.: Scene recognition using multiple representation network. Appl. Soft Comput. 118, 108530 (2022) 13. Yan, H., Ang, M.H., Poo, A.N.: A survey on perception methods for human-robot interaction in social robots. Int. J. Soc. Robot. 6, 85–119 (2014)

Image Encryption Algorithm Based on Improved Hill Cipher Using the 2D Logistic Map Samir El Kaddouhi1(B) , Younes Qobbi2 , Abdellah Abid2 , Mariem Jarjar2 , Mohamed Essaid3 , and Abdellatif Jarjar4 1 IMAGE Laboratory, Department of Sciences, Ecole Normale Supérieure, Moulay Ismail

University of Meknès, Meknes, Morocco [email protected] 2 MATSI Laboratory, Mohamed First University Oujda, Oujda, Morocco [email protected] 3 Department of Computer Science, Faculty of Science Abdel Malek, Essaadi University Tetouan, Tetouan, Morocco [email protected] 4 High School Moulay Rachid Taza, Taza, Morocco

Abstract. This paper proposes a novel image encryption method, which is based on an improvement of the classic HILL technique using the 2D logistic map. First vectorize the original image and subdivide it into blocks of three pixels. Then several dynamic vectors are created from the 2D logistics map. These vectors are used with pseudo-random translation vectors, for the implementation of several affine transformations to overcome the problem of the linearity of the classical method of Hill. After that, chaining functions will be used to boost the avalanche effect’s impact and defend the new system against any differential attack. Simulations on a set of images demonstrate the good performance of the algorithm in terms of image encryption and different attacks. Keywords: Encryption · Decryption · Hill Cipher · 2D logistic map

1 Introduction With the growing trend of digital communications and internet technologies, we are in need of exchanging our secrets and private information. Everything we trade is unprotected and open to cybercriminals for manipulation and misuse. In these situations, cryptography plays an important role in protecting and securing confidential data from unwanted people. Cryptography is an adequate solution to ensure security where the original message rearranges itself into an incomprehensible presentation so that only the intended recipient can understand and use it [1, 2]. Traditional encryption algorithms such as DES, AES and RSA are suitable for encrypting text, but they are not suitable for encrypting images. This is due to the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 504–515, 2023. https://doi.org/10.1007/978-3-031-29857-8_51

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intrinsic characteristics of images such as large size, high redundancy, high correlation between adjacent pixels [1, 2]. Over the past decades, various image encryption algorithms based on various technologies have been developed; among them, chaos-based algorithms have gained greater attention in recent years [3–6]. The characteristics of chaotic encryption included high sensitivity to initial conditions, pseudo-random behavior, non-periodicity, and simplicity of hardware and software implementation.[3]. Therefore, it offers researchers the possibility to improve some classical techniques, like Hill [7–9], Vigenère [10–12], Feistel [13, 14]. In this article, an image encryption system is proposed, which is based on an improvement of the Hill cipher and the 2D logistic map. Our image encryption system contains four steps. In the first, we used the 2D logistics map to determine three chaotic vectors and two control vectors. The second step is to vectorize the original image, then subdivide it into blocks of three pixels. In the third step, we used the chaotic vectors to create an affine transformation composed of an invertible matrix M of order 3 × 3 and a translation vector T. The fourth step proposes a new diffusion function to do the system chaining. The final step is to decrypt the encrypted image using the reverse functions used in the encryption process. The different steps of our image encryption method are presented in Fig. 1.

Fig. 1. Diagram of image encryption by our method.

The remainder of the article is structured as follows: the Sect. 2 presents previous work on image encryption. Our approach as well as the results obtained and their interpretations will be presented successively in the Sects. 3 and 4. The conclusion and perspectives of this work will be the subject of the Sect. 5.

2 Related Works Several image encryption methods are proposed, namely: The authors of the article [8] suggested a color image encryption technique using chaos and an enhancement of the Hill cipher. The fundamental idea behind this approach is to use an affine transformation composed of an invertible matrix M of order 3 × 3 and

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a translation vector T. this improvement makes it possible to overcome the problems of the linearity of the classical system and also the encryption uniform images. Devika et al. [9] developed an approach to image encryption combining between the Hill cipher and a diffusion process controlled by the logistic map. In the first phase, the image is subdivided into three color channels R, G and B. Then a substitution of values of each channel is performed using Hill’s cipher. In the second phase, the diffusion process is established. The authors of the article [11], have developed an image encryption scheme based on chaotic maps and the Vigenère cipher. The authors claim that the encryption mechanism is based on two main stages: Diffusion and confusion, the first stage has three levels, namely an anterior diffusion, an application of Vigenère cipher followed by a posterior diffusion. While confusion uses interlaced chaotic maps to swap the pixels of the original image. In the article [13], the authors discussed a new hybrid encryption technique based on the CV which aims to make the latter more robust. The method uses a transposition of the columns, then applies the CV on the transposed text. The authors claim that a cryptographic analysis of the security parameters was performed on the ciphertext and proved the robustness of the proposed method. In reference [14], a new system of image encryption based on chaos and operations on DNA sequences is proposed. In this algorithm, a chaotic two-dimensional system is used for the generation of encryption keys. In the second phase, a passage to the DNA plane is applied on the input image and also on the chaotic sequences. In the next phase, operations at the DNA plane are performed like the chaotically controlled complement operation. In reference [15], the authors proposed a digital image encryption algorithm based on two permutation-substitution phases. In the first phase, a change of position of the pixels of the clear image is carried out; this makes it possible to break the correlation between the adjacent pixels. Then the logistic-Sine map is used to generate a solid S-box in order to establish a phase of substitution of values of the pixels of the scrambled image is established. This confusion technique ensures non-linearity in the components of the encrypted image.

3 Steps of the Proposed Method 3.1 Chaotic Sequence Development Chaotic Map Used The 2d logistic map [16] will be utilized to create a new algorithm using an encryption key. This map is characterized by the implementation simplicity and the great sensitivity to the initial conditions [16].

(1)

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Chaotic Vectors Generated To develop our method, we must construct seven chaotic vectors (V1), (V2), (V3), (V4), (V5), (V6) and (V7), and two control binary vector (CV1) and (CV2). The generation of these vectors is determined by the following algorithm:

3.2 Preparing the Original Image Vectorize Original Image The channels of color (RGB) are extracted and converted into vectors (Vr, Vg, Vb) of size (1, nm), then a concatenation coupled with a confusion with the chaotic vectors is established to produce the vector X(x1 , x2 ,……..,x3nm ) of size (1, 3 nm). The algorithm below explains this procedure:

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Block Subdivision The image vector (X) will be divided into nm sub-blocks (Ui ) of size (1, 3) each. The sub-block (Ui ) is given by the following expression: Ui = (X(3i − 2), X(3i − 1), X(3i)) with 1 < i < nm

(2)

3.3 Improved Hill Method The Traditional HILL Method The traditional HILL technique was limited to text. It rests on two major steps. In the first, the text to be encrypted is divided into blocks of n characters (n being a natural integer), and the second entails creating a matrix of size (n, n) that can be inverted in a suitable ring. Researchers only utilize matrices with a size n less than 4 since it is challenging to create huge invertible matrix. The following equation completely explains this traditional method: 

Ci = K ∗ Ci

(3)



With Ci the block in plain text, Ci the encrypted block and K the encryption key. This method is remains vulnerable to statistical attacks because of the strong linearity. However, this method is not suitable for image encryption due to the strong correlation between adjacent and diagonal pixels in an image. The Improved Hill Method Matrix Construction (Mi). For each block (Ui), a matrix of size (3, 3) invertible in (G256) will be constructed by the following process: ⎛

⎞ 1 V 1(i) V 3(i) Mi = ⎝ V 2(i) 1 + V 1(i) ∗ V 2(i) V 4(i) ⎠ 0 0 2V 5(i) + 1

(4)

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Vector Design (Ti) For each block (Ui), a vector of size (1, 3) invertible in (G256) will be constructed by the following process: ⎛

⎞ V 5(i) Ti = ⎝ V 6(i) ⎠ V 7(i)

(5)

3.4 Encryption Process Encryption Function The following formula shows the encryption process: ⎞ ⎞ ⎛ ⎞⎛ V 5(i) X (3i − 2) 1 V 1(i) V 3(i) ⎟ ⎟ ⎜ ⎟⎜ ⎜ Wi = M i (U i ) ⊕ Ti = ⎝ V 2(i) 1 + V 1(i) ∗ V 2(i) V 4(i) ⎠⎝ X (3i − 1) ⎠ ⊕ ⎝ V 6(i) ⎠ V 7(i) X (3i) 0 0 2V 5(i) + 1 ⎛

(6)

Initialization Vector Calculation The encryption process begins with the calculation of an initialization vector calculated from the original image, for the modification of the boot block and thus deduce the encryption process. The initialization vector (IV = (v1, v2, v3)) is given by the following algorithm:

Encryption Process Our approach implements broadcast functions to deal with differential attacks. The encryption scheme is given by the following figure (Fig. 2):

Fig. 2. Block subdivision.

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The vector (W) represents the encrypted image. The proposed encryption is illustrated by the following algorithm.

3.5 Decryption Process Our approach is a symmetric encryption system with broadcast implementation. Therefore, in the decryption process, we apply the reverse encryption functions starting from the last block. All the functions used in our system are invertible, so the decryption function exists. The reciprocal functions used in the decryption process are: We have Wi = M i (U i ) ⊕ Ti So

Ui = Mi−1 (W i ⊕ Ti )

(7) (8)

The matrix M−1 designates the inverse matrix of the matrix (M). Its expression is given by: ⎞ ⎛ 3(i)−V 4(i) 1 + V 1(i) ∗ V 2(i) −V 1(i) (2V 5(i)+1)V 2V 5(i)+1 ⎜ V 4(i)−V 2(i)∗V 3(i) ⎟ Mi−1 = ⎝ (9) −BL(i) 1 ⎠ 2V 5(i)+1 1 0 0 2V 5(i)+1

4 Results and Safety Analysis To demonstrate the effectiveness of the encryption technique presented in this article, different tests are used, namely: the correlation coefficients, the histogram, the entropy

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values, the NPCR and UACI values and the size of the key. Tests are run on 50 standard photos of various sizes. The results obtained are very satisfactory. For this article, we presented in Table 1, the results obtained for three images (Baboon (512 × 512), Lena (512 × 512) and Airplane (512 × 512)) chosen among the 50 images used to test our method. And which have the most use in image encryption. Table 1. Simulation results.

Nom

Original Images

Encrypted images

Decrypted images

BABOON (512×512)

LENA (512×512)

AIRPLANE (512×512)

4.1 Encryption Key Size A strong encryption system against exhaustive attacks must have a large key space and must not be less than 2104 . The secret key is composed of two initial conditions x0, y0 and four parameter controls µ1 , µ2 , µ3 et µ4 (are floats of 32 bits). Therefore, the total key space is 2192 . Thus, the key space is large enough which makes brute force attacks infeasible. 4.2 Statistics Attack Security Correlation Analysis The correlation determines the independence of adjacent pixels. Our method examined several measures of image correlation, all of which are very close to zero. This can protect our method against statistical attacks.

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The following Table 2 illustrates the values of the vertical, horizontal and diagonal correlation of the clear and encrypted images by our system. Table 2. Correlation coefficients. Image

Original Image

Encrypted Image

Horizontal

Vertical

Diagonal

Horizontal

Vertical

Diagonale

BABOON

0.687

0.497

0.514

0.0017

−0.0001

−0.0015

LENA

0.935

0.956

0.894

−0.0002

0.0006

−0.0037

AIRPLANE

0.972

0.930

0.916

0.0002

0.0046

0.0024

Entropy Analysis Our algorithm tested a number of photos, and all of them have entropy values very near to 8 (the maximum value). These values demonstrate that our system is secure from entropy attacks. The entropy values of three images encrypted by our system are shown in Table 3 below. Table 3. Entropy analysis Images

Entropie

BABOON

7.99926

LENA

7.99934

AIRPLANE

7.99925

Histogram Analysis The following Table 4 displays the histograms of the encrypted images and the original images: We see that a big difference between the two histograms. In addition, all encrypted images have a flattened histogram, which ensures a uniform distribution of pixel gray levels. This ensures that our technique can be shielded from any histogram attack. 4.3 Differential Attacks In cryptography, differential attacks are managed by NPCR and UACI constants. Table 5 below presents the NPCR and UACI values of the images encrypted by our technique (Table 6).

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Table 4. The histograms of the original images and the encrypted images

Image

Histogram of plain image

Histogram of cipher image

4000

4000

3600

3600

3200

BABOON

3200

2800

2800

Number of pixels

Number of pixels

2400

2000

1600

1200

2000

1600

1200

800

800

400

0

2400

400

0

50

100

150

200

0

250

Pixel value

0

50

100

150

200

250

Pixel value

1800

2000

1600

1800

1400

1600

1200

1400

Number of pixels

LENA

Number of pixels

2000

1000 800 600 400

1000 800 600 400

200 0

1200

200

0

50

100 150 Pixel value

200

250 0

1000 0

6000 5600

50

100 150 Pixel value

200

250

50

100 150 Pixel value

200

250

900

5200

800

4800

700

Number of pixels

Number of pixels

AIRPLANE

4400 4000 3600 3200 2800 2400 2000 1600

600 500 400 300

1200

200

800 400 0

100 0

50

100

150 Pixel value

200

250

0

0

Table 5. NPCR and UACI values Images

NPCR

UACI

BABOON

99.74

33.49

LENA

99.73

33.45

AIRPLANE

99.73

33.47

Table 6. Encryption time Images

Temps de Chiffrement

BABOON

0,507

LENA

0,582

AIRPLANE

0,329

4.4 Encryption Time Using the proposed method, the encryption and decryption times of images of various sizes are very similar and change in the interval [0.05 0.1]. This time is obtained using the Java programming language and a personal computer with an Intel(R) Core (TM) i58350U CPU @ 1.70 GHz 1.90 GHz and 8 GB RAM. The time encryption and decryption of the three simulation images are shown in the following table.

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4.5 Comparison Based on the values of the entropy, the NPCR, the UACI, the correlation coefficient, and the running duration, we will compare the performances of our technique with a number of other methods in Table 7. Table 7. Comparison with other approaches Paramètre

Image

Our approach

Réf [4]

Réf [16]

Réf [6]

Entropy

BABOON

7.99926

7.9944

–

7.9987

NPCR

UACI

Vertical Corrélation

Encryption time

LENA

7.99934

7.9969

7.9967

7.9991

AIRPLANE

7.99925

7.9976

–

7.9991

BABOON

99.74

–

–

99.63

LENA

99.73

66.63

99.61

99.57

AIRPLANE

99.73

99.54

–

99.56

BABOON

33.49

–

–

33.40

LENA

33.45

30.47

33.51

33.35

AIRPLANE

33.47

31.76

–

33.59

BABOON

−0.0001

–

–

–

LENA

0.0006

–

–

0.0033

AIRPLANE

0.0046

–

–

–

BABOON

0,507

–

–

–

LENA

0,582

–

–

–

AIRPLANE

0,329

–

–

–

5 Conclusion In this article, we suggest a new image encryption algorithm based on a dynamic Hill cipher and a 2D chaotic map. First, we determine five chaotic vectors and two control vectors using the 2D logistics map. Then, using chaotic vectors, we suggested a dynamic improvement of the Hill matrix. Finally, a diffusion mechanism to chain the system was developed. The key space used is large enough to withstand brute force attacks. The results and the security analysis demonstrated that the suggested algorithm has excellent performance and is resistant to all types of attacks.

References 1. Kumari, M., Gupta, S., Sardana, P.: A survey of image encryption algorithms. 3D Res. 8(4), 1–35 (2017). https://doi.org/10.1007/s13319-017-0148-5

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2. Puteaux, P., Ong, S.Y., Wong, K.S., Puech, W.: A survey of reversible data hiding in encrypted images – the first 12 years. J. Visual Commun. Image Represent. 77, 103085 (2021). https:// doi.org/10.1016/j.jvcir.2021.103085 3. Zia, U., et al.: Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains. Int. J. Inf. Secur. 1–19 (2022). https://doi.org/10.1007/s10207022-00588-5 4. Li, C., Luo, G., Qin, K., Li, C.: An image encryption scheme based on chaotic tent map. Nonlinear Dyn. 87(1), 127–133 (2016). https://doi.org/10.1007/s11071-016-3030-8 5. Brahim, A.H., Pacha, A.A., Said, N.H.A.D.J.: A new image encryption scheme based on a hyperchaotic system & multi specific S-boxes. Inf. Secur. J. 1–17 (2021). https://doi.org/10. 1080/19393555.2021.1943572 6. Ghazvini, M., Mirzadi, M., Parvar, N.: A modified method for image encryption based on chaotic map and genetic algorithm. Multimedia Tools Appl. 79(37–38), 26927–26950 (2020). https://doi.org/10.1007/s11042-020-09058-3 7. Essaid, M., Akharraz, I., Saaidi, A., Mouhib, A.: Image encryption scheme based on a new secure variant of Hill cipher and 1D chaotic maps. J. Inf. Secur. Appl. 47, 173–187 (2019). https://doi.org/10.1016/j.jisa.2019.05.006 8. Nayak, D.M., Dash, D., Sa, K.D.: An improved image encryption technique using diffusion method associated with Hill cipher and chaotic logistic map. In: Proceedings of the 2017 2nd International Conference Man Machine Interfacing, MAMI 2017, vol. 2018-March, no. 1, pp. 1–6 (2018). https://doi.org/10.1109/MAMI.2017.8307896 9. Hraoui, S., Gmira, F., Abbou, M.F., Oulidi, A.J., Jarjar, A.: A new cryptosystem of color image using a dynamic-chaos hill cipher algorithm. Procedia Comput. Sci. 148, 399–408 (2019). https://doi.org/10.1016/j.procs.2019.01.048 10. Bansal, R., Gupta, S., Sharma, G.: An innovative image encryption scheme based on chaotic map and Vigenère scheme. Multimedia Tools Appl. 76(15), 16529–16562 (2016). https://doi. org/10.1007/s11042-016-3926-9 11. Li, S., Zhao, Y., Qu, B., Wang, J.: Image scrambling based on chaotic sequences and Veginère cipher. Multimed. Tools Appl. 66, 573–588 (2013). https://doi.org/10.1007/s11042-0121281-z 12. Kester, Q.: A hybrid cryptosystem based on Vigenère cipher and columnar transposition cipher. Int. J. Adv. Technol. Eng. Res. 3(1), 141–147 (2013) 13. Yao, W., Zhang, X., Zheng, Z., Qiu, W.: A colour image encryption algorithm using 4-pixel Feistel structure and multiple chaotic systems. Nonlinear Dyn. 81(1–2), 151–168 (2015). https://doi.org/10.1007/s11071-015-1979-3 14. Zhang, X., Zhou, Z., Niu, Y.: An image encryption method based on the feistel network and dynamic DNA encoding. IEEE Photonics J. 10, 1–14 (2018). https://doi.org/10.1109/JPHOT. 2018.2859257 15. Belazi, A., Khan, M., El-Latif, A.A.A., Belghith, S.: Efficient cryptosystem approaches: S-boxes and permutation–substitution-based encryption. Nonlinear Dyn. 87(1), 337–361 (2016). https://doi.org/10.1007/s11071-016-3046-0 16. Hua, Z., Jin, F., Xu, B., Huang, H.: 2D Logistic-Sine-coupling map for image encryption. Signal Process. 149, 148–161 (2018). https://doi.org/10.1016/j.sigpro.2018.03.010

A Review of Video Summarization Hanae Moussaoui1(B)

, Nabil El Akkad1

, and Mohamed Benslimane2

1 LISA, Engineering, Systems, and Applications Laboratory, ENSA of Fez, Sidi Mohamed Ben

Abdellah University, Fez, Morocco {hanae.moussaoui1,nabil.elakkad}@usmba.ac.ma 2 LTI Laboratory, EST of Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. The number of videos uploaded every day is increasing exponentially due to the availability of cameras. Video summarization’s main goal is to reduce redundancy by eliminating every sequence in the video that may not be useful. Moreover, many videos present insignificant parts occupying long sequences, leading to long and unneeded frames. This paper presents a succinct review of the proposed methods to summarize video content. In the upcoming sections, we will discuss the implementation of neural networks, reinforcement learning, and deep reinforcement learning in video summarization. Thereafter, we introduce the main video datasets that are used to evaluate the performance of every proposed method in the literature. Eventually, we will slightly compare some methods using different datasets. Keywords: Deep reinforcement learning · Video summarization · Reinforcement learning · Deep neural networks

1 Introduction Video content has been growing explosively recently due to the availability of cameras to everyone around the world. For example, YouTube is one of the largest video-sharing websites that receives over 720,000 h per day of video content from its users. On the other hand, we have millions of surveillance cameras recording 24/7. Moreover, normal users and police forces use body-worn cameras and dash cameras to record their daily activities. The main motivation here is that we have so many videos that we can’t ever possibly watch even though this content is very useful. On the other hand, these videos have high levels of redundancy, and in any unedited videos, not all the events are significant; second, not all of an important event is required to represent it. The most important objective of video summarization is to convey the most useful information about the video in a short time. Video summarization techniques fall under two main categories [1]. The first category is called extractive video summarization, where the idea is to select keyframes or shots from the video. The selected units are supposed to be individually important, and the entire set has to be collectively divers; otherwise, we introduce redundancy into the summary. The second category is called compositional video summarization, where we create a spatial-temporal synopsis of more than one frame [1, 2]. There are several © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 516–525, 2023. https://doi.org/10.1007/978-3-031-29857-8_52

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applications of video summarization, and one of them is movie trailer generation. As we know, the movie industry spends millions of dollars every year to create trailers for their movies; in principle, this can be done with any extracted video summarization technique [3]. The second type of application [4] is for security purposes. As mentioned earlier, police forces currently use dash cameras to record their shifts. The third type of application [3] is by using, for example, GoPro or body-worn cameras to record daily activities. At the end of the day, we are left with a long video, and by using video summarization applications and providing a summary, we can obtain an idea of what happened during that entire day. Video summarization can also be used to create advertisements in the case of a football game, for example, where we have a 90-min video of soccer. Video surveillance is an important use case where we can detect anomalies, and in this type of video, we can have considerable redundancy that we can remove using video summarization. Since the read information is compressed [5], storage is reduced and can be used for data visualization. Another use case of video summarization is video search engines to evaluate the video search results in a fast way. On the other hand, we can employ video summarization in scientific videos that need to be summarized, as in the case of colonoscopy medical videos where such examination takes approximately 20 to 60 min to be completed, but only a few parts of it are informative to the doctors. Sometimes, user feedback is used to improve the performance of the video summarization system. For this, we have two types of video summarization. The first is the static video summary, where essentially the summary is a temporarily ordered set of selected video frames. The second type is the dynamic video summary, which is also called video skimming. In this case, instead of selecting static video frames, we select video segments and parts of the video shot and concatenate them [4, 6]. There are various methods to perform video summarization depending on what information we want to capture; they may be classified into four significant categories: Feature-based summarization: in this category, we represent the original video content through video features that can be color features, motion features, or object features. They can represent video frames or a short video segment. When producing such features, we perform a tremendous dimensionality reduction. Afterward, we use a clustering method and obtain the centroids of each cluster by identifying clusters. Essentially, we segment the video into scenes or shots, and then we obtain key scenes or shots. For each key shot, we can identify keyframes; finally, we have a list of keyframes. After loading the original video, we sample the video frames to reduce the computational complexity. Afterward, we transform the extracted features into vectors that we cluster, and each feature vector will be represented by its centroid, which will be the keyframe. Subsequently, the totality of the keyframes is grouped in the summary. We can perform temporal subsampling before or after clustering and retain one representative per cluster or a few representatives within each cluster. Event-based summarization [7]: In some cases, we do not need to represent the entire video; we only require events, and in particular, abnormal and rare events are counted as important. Essentially, we can discard all the rest of the video and just retain the information that is related to these rare events, such as in the case of an accident. This approach can be applied to surveillance videos where we are interested in abnormal personal behavior. Object-based summarization [8]: Sometimes, we are interested in object-based summarization, where we will be

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concerned with a specific category of objects. In this case, an object detector is in demand to inspect each scene, and only the parts of the video containing the objects we are inspecting are kept in the summary. Attention-based summarization [9]: Users pay particular attention to certain video frames while watching videos. Attention capturing can be performed in different ways, such as the motion attention models that may be used to estimate each shot’s attention. Unsupervised video summarization [10]: No ground truth summaries are needed. The methods to use here are k-means clustering, spectral clustering, and dictionary learning approaches, which are good alternatives to clustering. Supervised video summarization [11]: In this case, the user manually annotates the training dataset for the training machine learning model. This can be expensive and done by crowdsourcing or specialized techniques. Furthermore, another drawback of supervised learning is that the training model gives good results only if the test videos are similar to the training dataset. The paper is structured as follows. In Sect. 2, we discuss video summarization using deep neural networks. In Sects. 3 and 4, we discuss the use of reinforcement as well as deep reinforcement learning in video summarization. Furthermore, in Sects. 5 and 6, we shed light on the different datasets used and provide a slight comparison between different methods used in the literature.

2 Video Summarization Using Deep Neural Networks There are two approaches to using deep neural networks in video summarization [12]. The first is to use DNN to find video features that will be used later in clustering, for example. The second approach is by trying to regress important scores for some video frames. Once it is learned how to regress the importance score, this regressor can select video frames. To date, several techniques have been used for video summarization using deep neural networks, as shown in Table 1. Convolutional neural networks (CNNs) are a classic technique that provides spatial video features for summarization. Another technique is three-dimensional CNNs, which are special temporal CNNs that produce a special structure of temporal video features for summarization. This technique is much slower than two-dimensional CNNs. Moreover, a primary cause of recurrent neural networks (RNNs) that are used for video summarization is long short-term memory networks (LSTMs) [13]. In addition, another technique that has been used for video summarization is generative adversarial networks (GANs). Recently, GAN techniques that learn data classes have been combined with LSTMs that can include neural information by employing an end-to-end DNN. GANs that are generative models learn a distribution of the training video data. The generator tries to generate content. On the other hand, the discriminator tries to check whether the video content is real.

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Table 1. Video summarization methods using deep neural networks Paper

Year

Proposed method

Used datasets

Evaluation metrics

K. Muhammad, T. Hussain, J. Del Ser, V. Palade and V. H.C.de Albuquerque [14]

2019

-Coarse refining technique that produces a group of selected frames with no redundancy -A pretrained CNN is used to extract features -A query from a user is considered to produce a desired number of keyframes

-A newly created dataset for industrial video surveillance -Youtube

-Precision -Recall -F1 measure (0.3 increase compared to other methods) -Compared to the existing methods using the YouTube dataset

B.Zhao, X.Li, X.Lu [15]

2017

-Hierarchical recurrent neural network (H-RNN)

-Combined (SumMe + -Precision TVSum + MED) -Recall -VTW -F-measure (tested on several datasets)

B.Mahasseni, M.Lam, and S.Todorovic [16]

2017

-CNN -LSTM selector -LSTM encoder -LSTM decoder -LSTM discriminator

-TVSum -SumMe -OpenVideo -YouTube

R. Agyeman, R. Muhammad and G. S. Choi [17]

2019

-An improved 3D -UCF101 CNN based on ResNet -Soccer-5 (a newly -LSTM Network released soccer video dataset by the authors)

-Mean Opinion Score (MOS)

S.Zhong, J.Wu, J.Jiang 2019 [18]

-Two-stream deep -SumMe ConvNets architecture -TVSum - deep learning -LIRIS-ACCEDE VGG-16 Net

-F-measure values

Y. Yuan, H. Li and Q. Wang [19]

2019

-CRNN -3D CNNs -RNN

-SumMe -TVSum -VTW

-Precision -Recall -F-measure -Sobolev loss

J.Hong Huang, M.Worring [20]

2020

-Video summary controller -Video summary generator -Video summary output module

-Authors proposed a new dataset that contains 190 videos from YouTube. It takes both video and text as an input

-Accuracy

B. Zhao, X. Li and X. Lu [21]

2019

-Summary generation -Video reconstruction -Using hierarchical LSTM

-SumMe -TVSum -OVP -YouTube

-Precision -Recall -F-measure

M.Rochan, L.Ye and Y.wang [22]

2018

-Fully convolutional succession models based on a connection between semantic segmentation and video summarization

-SumMe -TVSum

-Precision -Recall -F score

-Precision -Recall -F score

(continued)

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H. Moussaoui et al. Table 1. (continued)

Paper

Year

Proposed method

Used datasets

Z. Ji, K. Xiong, Y. Pang and X. Li [23]

2019

-The encoder that uses -SumMe a Bidirectional Long -TVSum Short-Term Memory -The decoder that employs two attention-based LSTM networks -A keyshot selection model

Evaluation metrics -Precision -Recall -F-measure

3 Video Summarization-Based Reinforcement Learning In reinforcement learning, we have an environment represented in our case by the video we want to summarize and an agent that selects whether a video frame will appear in the video summary. Moreover, we have the reward function that the agent tries to optimize. Table 2 below shows some of the recent works in the field. Table 2. Video summarization methods employing reinforcement learning Paper

Year

Proposed method

Used datasets

Evaluation metrics

J. Lei, Q. Luan, X. 2018 Song, X. Liu, D. Tao and M. Song [14]

-Action parsing for the video cut purpose using a sequential multiple instances learning model -Deep recurrent neural network-based video summarization model

-The authors used a dataset that was constructed by Bojanowski et al. [RefData] that contains 937 videos -SumMe -TVSum

-F score -Accuracy -Average Precision (AP)

Y.Chen, L.Tao, T.Yamasaki [15]

-Reinforcement learning using hierarchical architecture -2 RNNs (Manager, worker) implemented using LSTM -Reinforce algorithm

-SumMe -TVSum -OVP -YouTube

-Precision -Recall -F score -Rank Correlation Coefficient

- CNN with 3D convolution -3D U-Net -RL network

-SumMe -TVSum -OVP -YouTube

-Precision -Recall -F1-score

2019

T.Liu, Q.Meng, 2021 J.Huang, A.Vlontzos, D.Rueckert [24]

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4 Video Summarization Using Deep Reinforcement Learning Deep reinforcement learning is the most important field in artificial intelligence. It is a combination between the power and the capability of deep neural networks to represent and make sense of the world, with the capacity to act on that understanding. The deep part of deep reinforcement learning is neural networks, using frameworks and reinforcement learning where the neural network represents the world based on which the actions are made. We replace the policy with a deep neural network, and π is parametrized by a θ that describes the neural network. Furthermore, it maps the current state to the best probabilistic action to take in the environment. Table 3 below shows some of the already proposed methods using deep reinforcement learning: πθ (s, a) Table 3. Video summarization methods using deep reinforcement learning Paper

Year

Proposed method

Used datasets

Evaluation metrics

Zhang.Y; Kampffmeyer.M.; Zhao.X.; Tan.M [25]

2019

-Mapping network (MapNet) -Deep Reinforcement learning (SummNet)

-A query-conditioned video summarization dataset

-The maximum weight matching of a bipartite graph -IoU -Comparison with state-of-art methods (in terms of Precision, Recall, F-measure)

Liu, T., Meng, Q., Vlontzos, A., Tan, J., Rueckert, D., Kainz, B [26]

2020

-A fully automatic -Videos from fetal video summarization ultrasound method for medical screening videos data

-Precision -Recall -F score

Li.Z; Yang.L [27]

2021

-A weakly supervised -Youtube-8 M RL method based on: -TVSum Video classification -SumMe subnetwork (VCSN) + summary generation subnetwork (SGSN)

-Rank correlation coefficient -F score

Yaliniz.G; Ikizler-Cinbis. N [28]

2021

-Deep reinforcement learning + Independently recurrent neural networks (IndRNN)

-Precision -Recall -F score

-SumMe -TVSum

5 Video Summarization Datasets A set of public and large datasets have been created to evaluate the performance of several video summarization proposed methods. Table 4 below shows in detail the different types of datasets that are used to train and evaluate video summarization methods.

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H. Moussaoui et al. Table 4. The different datasets used for video summarization

Dataset

Number of videos

Duration

Category

Benchmark

-TvSum

-50 videos

-2 to 10 min

-News -Documentary -Vlog -Egocentric

-CA-SUM (F1-score: 61.4) -AC-SUM-GAN (F1-score:60.6) -CSNet (F1-score: 58.8) -SUM-GAN-sl (F1-score: 58.4) -SUM-GAN-AAE (F1-score:58.3) -DR-DSN (F1-score: 57.6) -Cycle-SUM (F1-score: 57.6)

-SumMe

-25 videos

-1 to 6 min

-Covering holidays -Events -Sports

-CSNet (F score: 51.3) -CA-SUM (F score: 51.1) -AC-SUM-GAN (F score: 50.8) -SUM-GAN-AAE (F score: 48.9) -SUM-GAN-sl (F score: 47.8) -Cycle-SUM (F score: 41.9) -DR-DSN (F score: 41.4) -PGL-SUM (F score: 55.6)

-OVP

-50 videos

- 1 to 10 min

-various types of videos

–

-Youtube

-8 M videos

-500k hours

-All videos’ types

–

-Co-Sum

-51 videos

-147m40s

-

–

-Visiocity

-67 videos

-Average: 55 min

-Sports (Soccer) -TVShows (Friends) -Surveillance -Educational -Personal Videos (Birthday, wedding)

–

6 Comparison of the Different Methods used in Video Summarization The F1-score comes from both precision and recall performance metrics. – Recall: also known as the true positive rate (TPR) or sensitivity. Recall’s formula is the ratio between the true positives (TP) and the true positives (TP) plus false negatives (FN): Recall =

TP TP + FN

(1)

– Precision: very similar to recall, but instead of false negatives (FN), we have false positives (FP): Precision =

TP TP + FP

(2)

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– F1 Score: the average between precision and recall. It is a special type of mean called the Harmonic Mean. F1 Score = 2 ∗

2TP Precision ∗ Recall = Precision + Recall 2TP + FP + FN

(3)

In Table 5, we tried to mention almost all the models and make a comparison using the F1-score metric. Table 5. Comparison of the different proposed methods in the video summarization issue

Model MAVS (Multi-Sour ce Visual Attention) model reSEQ2SEQ(retrospective sequence-to-sequence learning) model MC-VSA ( video selfattention) framework RR-STG PGL-SUM DSNet (Detect-toSummarize network) framework MSVA (Multi-Sour ce Visual Attention) M-AVS (multiplicative attention mechanism) CSNet (Chunk and Stride Network) DR-DSN CSNet (Chunk and Stride Network) SUM-GAN-sl SUM-GAN-AAE DR-DSN Cycle-SUM

Supervised Video summarization 

Unsupervised video summarization -

F1 Score



-

63.9



-

63.7

  

-

63.0 62.7 62.1



-

61.5



-

61



-

58.5

 -



58.1 58.8

-

   

58.4 58.3 57.6 57.6

67.5

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Several feature-based techniques are less successful for object detection in prolonged videos; furthermore, high hardware specifications are needed. On the other hand, methods based on clustering for video summarization are more precise than the other methods used. Additionally, the trajectory method is appropriate for a dynamic environment such as surveillance videos. Conclusion In this paper, we presented an overview of different video summarization methods. After introducing the different techniques used to summarize a video, we shed some light on some methods used to solve this problem. We chose deep neural networks, reinforcement learning, and deep reinforcement learning to benchmark this issue. Nevertheless, video summarization is still challenging, especially with the fast growth of technology around the world, and new challenges come out every day. By presenting this review, we anticipate working on an architecture that helps solve video summarization issues.

References 1. Kini, M., Pai, K.: A survey on video summarization techniques. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), vol. 1, pp. 1–5. IEEE (2019) 2. Haq, H.B.U., Asif, M., Ahmad, M.B.: Video summarization techniques: a review. Int. J. Sci. Technol. Res. 9, 146–153 (2020) 3. Elkhattabi, Z., Tabii, Y., Benkaddour, A.: Video summarization: techniques and applications. Int. J. Comput. Inform. Eng. 9(4), 928–933 (2015) 4. Moussaoui, H., Benslimane, M., El Akkad, N.: A novel brain tumor detection approach based on fuzzy c-means and marker watershed algorithm. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 871–879. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-73882-2_79 5. Ma, Y.F., Lu, L., Zhang, H.J., Li, M.: A user attention model for video summarization. In: Proceedings of the tenth ACM international conference on Multimedia, pp. 533–542 (2002) 6. Moussaoui, H., Benslimane, M., El Akkad, N.: A Novel Brain Tumor Detection Approach Based on Fuzzy C-means and Marker Watershed Algorithm. In: book: Digital Technologies and Applications (2021). https://doi.org/10.1007/978-3-030-73882-2_79 7. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: Clustering method and sine cosine algorithm for image segmentation. Evol. Intel. 15(1), 669–682 (2021). https://doi.org/10.1007/s12065020-00544-z 8. Moussaoui, H., Benslimane, M., El Akkad, N.: Image segmentation approach based on hybridization between k-means and mask R-CNN. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds.) WITS 2020. LNEE, vol. 745, pp. 821–830. Springer, Singapore (2022). https://doi.org/10.1007/978-981-33-6893-4_74 9. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: Simple and efficient clustering approach based on cuckoo search algorithm. In: 2020 Fourth International Conference on Computational Intelligence in Data Science, pp. 1–6 (2020). https://doi.org/10.1109/ICDS50568.2020.926 8754 10. Jung, Y., Cho, D., Kim, D., Woo, S., Kweon, I.S.: Discriminative feature learning for unsupervised video summarization. In: Proceedings of the AAAI Conference on artificial intelligence, vol. 33, no. 01, pp. 8537–8544 (2019)

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11. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: A performant clustering approach based on an improved sine cosine algorithm. Int. J. Comput. 21(2), 159–168 (2022). https://doi.org/10.47839/ijc.21.2.2584 12. Rochan, M., Ye, L., Wang, Y.: Video summarization using fully convolutional sequence networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 358–374. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-012588_22 13. Ji, Z., Xiong, K., Pang, Y., Li, X.: Video summarization with attention-based encoder–decoder networks. IEEE Trans. Circuits Syst. Video Technol. 30(6), 1709–1717 (2019) 14. Muhammad, K., Hussain, T., Del Ser, J., Palade, V., De Albuquerque, V.H.C.: DeepReS: a deep learning-based video summarization strategy for resource-constrained industrial surveillance scenarios. IEEE Trans. Industr. Inf. 16(9), 5938–5947 (2019) 15. Zhao, B., Li, X., Lu, X.: Hierarchical recurrent neural network for video summarization. In: Proceedings of the 25th ACM international conference on Multimedia, pp. 863–871 (2017) 16. Mahasseni, B., Lam, M., Todorovic, S.: Unsupervised video summarization with adversarial lstm networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 202–211 (2017) 17. Agyeman, R., Muhammad, R., Choi, G.S.: Soccer video summarization using deep learning. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 270–273. IEEE (2019) 18. Zhong, S.H., Wu, J., Jiang, J.: Video summarization via deep architecture. Neurocomputing 332, 224–235 (2019) 19. Yuan, Y., Li, H., Wang, Q.: Spatiotemporal modeling for video summarization using convolutional recurrent neural network. IEEE Access 7, 64676–64685 (2019) 20. Huang, J.H., Worring, M.: Query-controllable video summarization. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 242–250 (2020) 21. Zhao, B., Li, X., Lu, X.: Property-constrained dual learning for video summarization. IEEE Trans. Neural Networks Learn. Syst. 31(10), 3989–4000 (2019) 22. Lei, J., Luan, Q., Song, X., Liu, X., Tao, D., Song, M.: Action parsing-driven video summarization based on reinforcement learning. IEEE Trans. Circuits Syst. Video Technol. 29(7), 2126–2137 (2018) 23. Chen, Y., Tao, L., Wang, X., Yamasaki, T.: Weakly supervised video summarization by hierarchical reinforcement learning. In: Proceedings of the ACM Multimedia Asia, pp. 1–6 (2019) 24. Liu, T., Meng, Q., Huang, J.J., Vlontzos, A., Rueckert, D., Kainz, B.: Video summarization through reinforcement learning with a 3D u-net. IEEE Trans. Image Process. 31, 1573–1586 (2022) 25. Zhang, Y., Kampffmeyer, M., Zhao, X., Tan, M.: Deep reinforcement learning for queryconditioned video summarization. Appl. Sci. 9(4), 750 (2019) 26. Liu, T., Meng, Q., Vlontzos, A., Tan, J., Rueckert, D., Kainz, B.: Ultrasound video summarization using deep reinforcement learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS, vol. 12263, pp. 483–492. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_46 27. Li, Z., Yang, L.: Weakly supervised deep reinforcement learning for video summarization with semantically meaningful reward. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3239–3247 (2021) 28. Yaliniz, G., Ikizler-Cinbis, N.: Using independently recurrent networks for reinforcement learning based unsupervised video summarization. Multimedia Tools Appl. 80(12), 17827– 17847 (2021). https://doi.org/10.1007/s11042-020-10293-x

A Color Image Encryption Method Designed Using Chaotic Confusion, Enhanced CBC-MAC Mode and Pixel Shuffling Faiq Gmira1,2(B)

and Said Hraoui3

1 Innovative Technologies Laboratory (LTI), University Sidi Mohamed Ben Abdellah, Fez,

Morocco [email protected] 2 Computer Science and Smart Systems (C3S), Hassan II University of Casablanca, Casablanca, Morocco 3 Artificial Intelligence and Data Science and Emerging Emerging Systems Laboratory (LIASSE), University Sidi Mohamed Ben Abdellah, Fez, Morocco [email protected]

Abstract. In this paper, a new encryption method is designed using two improved algorithms appointed P-PWLCM and CBC-MAC. The proposed method is conceptually divided into 3 steps, a first for confusion and a second for diffusion and this to satisfy the shannon constraints. The proposed method is also completed by a pixels shuffling to achieve a maximum-security level. An experimental study is made to evaluate the proposed method capabilities to reduce the various risks of cryptanalytic attacks to zero. Keywords: Confusion · Diffusion · Avalanche effect · RGB image · PWLCM Perturbed · CBC-MAC encoding · Image encryption

1 Introduction According to Cisco, images account for more than 80% of all exchanged data [1]. This requires the implementation of security measures for this type of data [2]. One of the means used is encryption, which guarantees the visual security of their content. However, the encryption methods in this case, DES, 3DES, and AES, as well as those based on number theory and linear algebra such as RSA, El Gamal, etc., cannot be directly applied to image data. Without being adapted to take into account the specificity of the image formats [3–6]. Furthermore, note that the property of conformance to the format implies that the data after encryption can be viewed with the same image editors as the original data. Recently, chaotic cryptography has been widely adopted to secure multimedia data transmission in communication networks. Its principle is to superimpose on the original image, a signal generated by the chaotic attractor, which is a deterministic signal whose model is well known. Chaotic systems, recognized by their pseudo-random characteristics, their sensitivity to initial conditions, and their property © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 526–535, 2023. https://doi.org/10.1007/978-3-031-29857-8_53

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of ergodicity, have made it possible to obtain a good compromise between transmission rate and security performance. The chaotic approach is therefore justified by the fact that encryption speed is more important than the absolute security of the image [7–9]. In general, most chaotic discrete cryptography approaches are based on a confusiondiffusion architecture [10], in which confusion shifts the positions of image pixels using a chaotic map and diffusion changes mixed pixel values through chaotic sequences. But despite this, many of these encryption schemes suffer from security weaknesses that make them vulnerable to cryptographic attacks. Since then, some image encryption methods have been successfully broken by cryptanalysts in various attacks [11]. In order to obtain a typical method respecting Shannon’s confusion and diffusion principle [10] the scheme must have two main phases, namely confusion and diffusion. But on the other hand, the avalanche effect is a sought-after property in cryptographic methods, so a change in any pixel in the original image or the encrypted image will be reflected in almost any pixels or at least as many as possible. In reality, the effectiveness of the avalanche effect is that the modification of a single input bit leads to a modification of each output bit with a probability of 0.5. In this context, the color image method of any size proposed, responds well to the principles of confusion and diffusion stated by Shanonn. It is a chaos-based symmetric encryption scheme operating in three steps. In the first step, we confuse the original image with the chaotic map PWLCM [12]. Then, a pseudo-random permutation is performed on the positions of the pixels. This step makes it possible to reduce the strong correlation between the pixels of the image obtained. Finally, the Enhanced CBC (CBCMAC) operating mode is applied to the obtained image. This mode allows a chaotic and sequential modification of the pixel values, so that a change made to a random pixel of the original or encrypted image will be reflected on almost all the pixels. Indeed, this makes it possible to add a feedback mechanism to the encryption, thus producing an avalanche effect. This last step, in addition to ensuring confidentiality, also ensures strong data integrity. The rest of the paper is organized as follows: the description of the new designed method is presented in the Sect. 2. In the Sect. 3 the proposed method is presented. The analysis of the proposed image encryption method performance is discussed in the Sect. 4. Finally, to complete the paper, a conclusion is given in the Sect. 4.

2 The Proposed Scheme Description The proposed color images method is designed in 3 steps: the first step introduces chaotic confusion using a P-PWLCM chaotic map perturbation, the second step introduces a bijective pixel’s shuffle and the third step defines a CBC-MAC for encoding.

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2.1 The Classic PWLCM and P-PWLCM for the Confusion Property To overcome the weakness of PWLCM, a perturbation is introduced appointed P-PWLCM is introduced. The perturbations are defined by the Eq. (1):  ⎧ xn xn ∈ 0, p) ⎪ ⎪ p  ⎨ xn −p x(n+1) = 0.5−p   xn ∈ p, 0.5) (1) xn ⎪ ⎪ ⎩ xn −floor p ×p xn ∈ (0.5, 1) p where x (n) ∈ [0, 1]; n ≥ 0; with x(0) is the initial condition and p the control parameter. For p ∈ ]0, 0.5[. Although the PWLCM has good chaotic performance especially for image encryption, it does not comply all 188 tests of the well-known NIST (National Institute of Standards and Technology) randomness test suite [14, 15]. The resulting algorithm to implement P-PWLCM is presented in the Algorithm 1 below: Algorithm 1: P-PWLCM implementation

1: read(m(1)); 2: read (p); 3: for i=2:N 4: if and(m(1)>=0,m(1)=p,m(1)=0.5,m(1) 1 Further, changing the block to the position q, Eq. (3) becomes: V 1 = ρ ⊕ (1) ⊕ MC(2) ⊕ . . . ⊕ MC(q ) . . . ⊕ MC(n)

(5)

Furthermore, performing the XOR operation between Eqs. (3) and (5) gives: MCq ⊕ MCq = 0

(6)

Which is false as MC(q) = MC(q’) Hence, a change of MC(1) result in all change in DC(i), and since the Eq. (3) is non-linear then a less disturbance on the input image or on the key will cause a great disturbance on the output image. Indeed, the former confusion is implemented with CBC-MAC mode as shown in Fig. 2; this allows adding a retroaction mechanism for encryption, creating an avalanche effect as a result. which generates a significant change in the encrypted picture if a single bit of a pixel is modified in the original image. To perform a robust Avalanche effect, the CBC-MAC mode will be used. CBC-MAC is an algorithm for Message Authentication Codes (MAC) based on a block cipher used in a CBC mode of operation (cipher block chaining).

3 The Conceived Method Algorithm The first encrypted block takes the value of the initialization vector and it is further processed with all data blocks to achieve diffusion. The conceived method encryption algorithm is presented in Algorithm 2:

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To reconstruct the original image, the decryption process is simply the reverse order of the encryption steps.

4 Experimental Results and Analysis The proposed method will be evaluated with several tests,

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4.1 Space of the Key It is found that the key larger size significantly increases the calculation time. Bearing + in mind Security and increase the computation time issues, the proposed method uses a size 128-bit key that sets us free from any exhaustive attack, this requires 2128≈3.4 × 1038 attempts. 4.2 Key Sensitivity In order to verify the proposed method sensitivity towards very small changes, three cases of test were considered: Case1: x(0) = 0.786321456798120 and p = 0.033 Case2: x(0) = 0.786321456798121 and p = 0.033 Case3: x(0) = 0.786321456798120 and p = 0.033000000000001 The results of the LENA original image sensitivity are shown in Fig. 3 below:

Fig. 3. Example of effect of permutation by the bijective function

Also, the analysis in Fig. 3, it appears that values the NPCR and UACI for all the test cases remain in the range of expected values (NPCRexpected = 99.61%, UACIexpected = 33.46%) [18]. Therefore, the confusion-permutation-diffusion architecture is the best method because the proposed algorithm shows extreme sensitivity over the key. As a result, the method has good resistance to exhaustive attack. Encryption speed of the proposed method. The calculation of the complexity of the proposed approach leads to a value of θ(n2). With an i5 computer, the time encryption and decryption on the image tests of different sizes is shown in Fig. 4. The execution time of an image encryption scheme depends on many factors such as, the structure of CPU, memory size, operating system, the language of programming, compiler options, optimization of skills and code programming. The following environment (computer intel (R) i5 CPU, 2.53 Ghz with 4 GB of RAM on Windows 10

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Professional and Java (eclipse compiler) are used, to measure the encryption speed of the method. For accuracy, each set of timing tests was run, and then the average obtained. NEPTUNE 1024X1023

LENA

512X512

HOUSE 256x256

CAMERAMAN 128X128

Fig. 4. The testing images and their encrypted outputs.

Figure 5 gives the speed of execution is reported of the proposed method for different sizes of images.

Fig. 5. The speed encryption for testing images

As it can be seen in Fig. 5, the proposed scheme has an acceptable speed.

5 Conclusion The design of this new method in one round with separate steps guarantees easy maintenance on the one hand and on the other hand opens the method to possible updates. The experimental study carried out with several tests mainly focused on the avalanche effect and the encryption speed. The results obtained prove that unlike many other encryption methods operating in multiple rounds, the proposed method achieves in one round a high performance encryption with only one round.

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References 1. Source: Cisco Annual Internet Report 2018–2023 2. Zhang, Y.-Q., et al.: A new color image encryption scheme based on 2DNLCML system and genetic operations. Optics Lasers Eng. 128, 106040 (2020) 3. Alabdulrazzaq, H., Alenezi, M.N.: Performance evaluation of cryptographic algorithms: DES, 3DES, blowfish, twofish, and threefish. Int. J. Commun. Netw. Inf. Secur. (IJCNIS) 14(1), 51–61 (2022) 4. Anwar, M.N.B., et al.: Comparative study of cryptography algorithms and its’ applications. Int. J. Comput. Netw. Commun. Secur. 7(5), 96–103 (2019) 5. Jha, A., Sharma, S.: Quantitative interpretation of cryptographic algorithms. In: Mandal, J.K., Bhattacharya, D. (eds.) Emerging Technology in Modelling and Graphics. AISC, vol. 937, pp. 459–469. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7403-6_41 6. Kumar, S., et al.: A survey on symmetric and asymmetric key based image encryption. In: 2nd International Conference on Data, Engineering and Applications (IDEA). IEEE (2020) 7. Luo, Y., Yu, J., Lai, W., Liu, L.: A novel chaotic image encryption algorithm based on improved baker map and logistic map. Multimedia Tools Appl. 78(15), 22023–22043 (2019). https:// doi.org/10.1007/s11042-019-7453-3 8. Wang, Y., et al.: A new chaos-based fast image encryption algorithm. Appl. Soft Comput. 11(1), 514–522 (2011) 9. Wang, X., Teng, L., Qin, X.: A novel colour image encryption algorithm based on chaos. Signal Process. 92(4), 1101–1108 (2012) 10. Kumar, M., Powduri, P., Reddy, A.: An RGB image encryption using diffusion process associated with chaotic map. J. Inf. Secur. Appl. 21, 20–30 (2015) 11. Wang, H., et al.: Cryptanalysis and enhancements of image encryption using combination of the 1D chaotic map. Signal Process. 144, 444–452 (2018) 12. Hasheminejad, A., Rostami, M.J.: A novel bit level multiphase algorithm for image encryption based on PWLCM chaotic map. Optik 184, 205–213 (2019) 13. Li, S.J., Chen, G.R., Mou, X.Q.: On the dynamical degradation of digital piecewise linear chaotic maps. Int. J. Bifurcat. Chaos 15(10), 3119–3151 (2005) 14. Datcu, O., Lupu, A. E., Blaj, T., Hobincu, R.: NIST Tests, lyapunov exponents and bifurcation diagrams when evaluating chaos-based PRNGs. In: Special Issue of Proceedings of the Romanian Academy, Proceedings of Romanian Cryptology Days (2020) 15. Hu, Y., Zhu, C., Wang, Z.: An improved piecewise linear chaotic map based image encryption algorithm. Sci. World J. 2014 (2014) 16. Joux, A., Martinet, G., Valette, F.: Blockwise-adaptive attackers revisiting the (in)security of some provably secure encryption modes: CBC, GEM, IACBC. In: Yung, M. (ed.) CRYPTO 2002. LNCS, vol. 2442, pp. 17–30. Springer, Heidelberg (2002). https://doi.org/10.1007/3540-45708-9_2 17. Lou, D.C., Sung, C.H.: A steganographic scheme for secure communications based on the chaos and Euler theorem. IEEE Trans. Multimedia 6(3), 501–509 (2004) 18. Ping, P., Fan, J., Mao, Y., Xu, F., Gao, J.: A chaos based image encryption scheme using digit-level permutation and block diffusion. IEEE Access 6, 67581–67593 (2018)

Normalized Gradient Min Sum Decoding for Low Density Parity Check Codes Hajar El Ouakili1(B) , Mohammed El Ghzaoui1 , Mohammed Ouazzani Jamil2 , Hassan Qjidaa1 , and Rachid El Alami1 1 LISAC Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez,

Morocco [email protected] 2 LSEED Laboratory, Faculty of Engineering Sciences, Private University of Fez, Fez, Morocco

Abstract. The aim of this paper is to reduce the “BER” and the difference between the two LDPC code families: the hard and soft one, for that, a novel hybrid “LDPC” decoding algorithm is proposed, which mixed the “Normalized Min-Sum” (NMS) and “Single Noisy Gradient Descent Bit flipping” (SNGDBF) that belongs to the soft and hard decision decoding. “The Normalized Gradient Min Sum” algorithm is proposed in this paper (NGMS). The proposed algorithm gives good results in terms of correction power, it surpasses the other algorithms with a considerable gain margins at 10–5 over the “Additive White Gaussian Noise” (AWGN) channel, according to the simulations. In addition, the suggested “NGMS” and “NMS” have the same level of decoding complexity. Keywords: LDPC · SNGDBF · NMS · BER

1 Introduction Invented by Gallager in 1962[1], Low Density Parity Check (LDPC) codes are a popular family of linear block corrector codes, they are iterative and suitable for different code lengths, they are based on the information exchanged between the variable nodes and the control nodes, they are characterized by their high correction power. Indeed, because of the relevance of LDPC codes in communication standards, many wireless application researchers throughout the world have been drawn to them [2]. The “Sum-product” (SP) algorithm’s check node processing is simplified to develop the “Min-Sum” (MS) algorithm, which is a soft decision method based on the “extrinsic log likelihood ratio” (LLRs) [3–6] Both algorithms provide satisfactory results. These approaches need massive arithmetic operations with parallel execution [7, 8], making them highly difficult [9, 10].additionally, there is “Bit-Flipping” which belongs to the hard decision decoding family, it is based on the calculation of the inversion function to decide which bits to flip, [4], this type of decoding is less complex with a reduced correction power, which makes it less efficient when compared to the soft decision decoders such as MS and SP for example. A parity matrix H of dimension NxM defines an LDPC code where the density of this binary matrix is low. Indeed, as its name suggests, the number of ’1’ in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 536–542, 2023. https://doi.org/10.1007/978-3-031-29857-8_54

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the matrix H is small compared to the number of ‘0’, this low number of ‘1’ s is critical since the difficulty of the decoding process gets larger as the number of ‘1’s increases. As previously stated, LDPC codes are based on the exchange of information between variable nodes and control nodes, of the parity matrix ’H’ which specifies these codes, which is characterized by ‘M’ lines which reflect the parity check equation and ‘N’ columns which indicates the length of the code word. “dc” indicates the number of ones per column (column degree), there is another graphical representation of LDPC codes which is the Tanner graph, it is a two-dimensional graph which is made up of N and M nodes representing the variable and control nodes in the H matrix respectively. In this paper, a novel technique for improving decoding performance is proposed, it’s based on the combination of two algorithms based on the hard and soft decision decoding for LDPC codes, which are “Single Noisy Gradient Descent Bit Flipping” (SNGDBF) and the normalized min-sum (NMS) algorithms. This innovative technique could enhance the obtained BER by including the “SNGDBF” inversion function in the calculation steps of “NMS”. The rest of this paper is organized as follows: the “SNGDBF” decoding approach is introduced in the Sect. 2 as well as the “NMS” algorithm. The Sect. 3, is devoted to describe the proposed “The Gradient Normalized Min Sum” (GNMS) method. Next, we will go through the outcomes of the proposed algorithm results. Finally, Sect. 4 concludes our contribution. 1.1 SNGDBF Algorithm The GDBF method is complex; yet, when it comes to escape local maxima, its performance improves [9]. By incorporating a random disturbance in the inversion function, Noisy GDBF is proposed to reduce complexity. S-NGDBF. The inversion function is then defined as follows:  = x y + w si + qk (1) (NGDBF) k k k i∈M (k)

where the Gaussian distributed random variable is qk with zero-mean and variance: σ 2 = η2 N 0 /2

(2)

The variables: “η” and “w” are a weighted parameter for syndromes, moreover 0 < η ≤ 1. Their ideal values are unrelated to the code and, in certain situations, unrelated to SNR. Finally, S-NGDBF is a single bit flipping algorithm, which implies that one bit is flipped at each iteration. The algorithm employed is comparable to the BF algorithm, But Eq. (1) replaces the inversion function.

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1.2 Normalized Min-Sum Algorithm A useful technique for decoding “LDPC” is the Normalized Min-Sum “NMS” algorithm [10]. Because it makes implementation less complicated, it is desirable for hardware and software implementations of the “SP” algorithm without losing much of its efficiency. The “NMS” [4] enhances the decoding performance of the “MS” method by controlling the check node processing element produced messages. The Normalized Min-Sum (NMS) algorithm uses a normalization factor θ in order to minimize the overvaluation of c2v messages. The calculation of these messages is done at from the following expression:     (i−1)  (i) (i−1) (3) = θ. sign(βnc ) × minnN(c)\v βnc αcv  nN(c)\v

where θ < 1 is a parameter of normalization.

2 The ‘GNMS’ Algorithm An improved decoding approach is presented in this paper, it’s combined two algorithms belonging to the two families of decoding for LDPC codes. To do this, the “NMS” algorithm is chosen for soft decision decoding and the “’SNGDBF” for the hard one. This method improves the power of correction by incorporating the “SNGDBF” inversion function in the processing of the “NMS” soft decision algorithm. The suggested “GNMS” algorithm is divided into two parts. The first stage is comparable to the “NMS” algorithm, the second one employs the “SNGDBF” inversion function to increase performance in terms of “Bit Error Rate”, the proposed algorithm require a smaller number of iterations, which result a reduced computational time. Furthermore, “GNMS” is split into two stages, first, the calculated extrinsic “LLR” messages are sent between the control nodes and the variable nodes, then we pass to the verification of the syndrome S. If the syndrome is not verified but the maximum iteration numbers of “NMS” (iter_max1) is reached then we proceed to execute the second part of the proposed algorithm until the maximum iteration numbers of “GNMS” (iter_max2) is reached or the syndrome is proved. If the syndrome is verified (s = 0) at the first stage, the decoding process is terminated, and the correct code-word is returned. The proposed algorithm follows these steps:

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3 Results and Discussions To evaluate the “GNMS” method, it must be compared to the other decoding algorithms mentioned above. As a descending variant of the original Sum-Product (SP) method, the Normalized Min-Sum (NMS) technique was adopted for the soft decision and for the hard one, the MWBF, IMWBF and NGDBF are chosen. The “LDPC” code N = 1008 is chosen for the simulations with the addition of Gaussian noise. The simulation results are achieved by the use of the “MATLAB” program. The max_iter for the NMS is set to lmax = 5. Lmax = 3 for the proposed method, lmax = 50 for MWBF, IMWBF, and SNGDBF. Figure 1 depicts the “BER” performance obtained using the standard “LDPC” code (1008, 504) with a rate of 0.5. The maximum number of iterations for the hard decision algorithms is set to 50, while the maximum number of iterations for the “NMS” and “GNMS” algorithms is set to 5 and 3, respectively. Figure 1 shows that the proposed “GNMS”-based decoding algorithm outperforms the “MWBF,” “IMWBF,” and “SNGDBF” over an AWGN channel. They exhibit a small drop in error correction performance when compared to the soft decision. “GNMS” exceeds other algorithms by more than 3.7 dB at 10–3 BER, such as “Modified Weighted Bit Flipping “ and “Improved Modified Weighted Bit Flipping «, also the “Single Noisy GDBF” by 0.9 dB at 10–3 BER, and surpasses the “Normalized MS” by 0.15 dB at 10–5 BER. For the second figure, the number of iterations for “NMS” and “GNMS” is given different values to evaluate the behavior of GNMS in function of BER. The proposed algorithm gives excellent results even if we change the maximum number of iterations compared to NMS algorithm (Fig. 2).

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Fig. 1. BER performance of regular LDPC code (504 × 1008)

Fig. 2. BER performance with different iteration value of regular LDPC code (504 × 1008)

Figure 3 shows the BER as a function of iteration number for the above-mentioned methods for EbNo = 3.5 dB. Comparing with other algorithm which require a higher iteration numbers to reach a reduced BER, our proposed algorithm can reach the same value of BER but with a reduced number of iterations, which make it faster in terms of computational time compared to the other algorithms already mentioned, moreover with four iterations we can obtain better results in terms of BER of all the other approaches.

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Fig. 3. Performance comparison between different algorithms for different iteration value for the LDPC code (1008, 504) at SNR = 3.5 db

4 Conclusion This paper consists in developing a new algorithm which involves in having a reduced BER. “GNMS”, is the suggested algorithm, it is based on the two decoding families for LDPC codes: soft and hard decision decoding. Best results are produced when “SNGDBF” and “NMS” are combined, as shown in the third section. The results reveal that the proposed technique requires less computation time due to the reduced number of decoding iterations and that it outperforms the previous algorithms.For the code n = 1008, simulation results demonstrate that the “GNMS” produces a gain margins of 3.7 dB over the “MWBF” and “IMWBF” at BER of 10–3 . Furthermore, with BER of 10–5 , it achieves a gain of 0.15 db over the “NMS”.

References 1. Gallager, R.: Low-density parity-check codes. IRE Trans. Inf. Theory 8(1), 21–28 (1962) 2. El Ouakili, H., El Alami, R., Mehdaoui, Y., Chenouni, D., Lakhliai, Z.: Optimization of SNGDBF decoding for LDPC codes parameters based on taguchi method. In: Saeed, F., AlHadhrami, T., Mohammed, E., Al-Sarem, M. (eds.) Advances on Smart and Soft Computing. AISC, vol. 1399, pp. 497–506. Springer, Singapore (2022). https://doi.org/10.1007/978-98116-5559-3_41 3. MacKay, D.J.C., Neal, R.M.: Near Shannon limit performance of low density parity check codes. Electron. Lett. 33(6), 457 (1997). https://doi.org/10.1049/el:19970362 4. Ismail, M., Ahmed, I., Coon, J., Armour, S., Kocak, T., McGeehan, J.: Low latency low power bit flipping algorithms for LDPC decoding. In: Proceedings of 2010 IEEE International Personal Indoor and Mobile Radio Communications Symposium, pp. 278–282 (2010)

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5. Mosleh, M.F., Hasan, F.S., Azeez, R.M.: Design and implementation of log domain decoder. Int. J. Electr. Comput. Eng. 10(2), 1454 (2020). https://doi.org/10.11591/ijece.v10i2.pp14541468 6. Fossorier, M.P.C., Mihaljevic, M., Imai, H.: Reduced complexity iterative decoding of lowdensity parity check codes based on belief propagation. IEEE Trans. Commun. 47(5), 673–680 (1999) 7. Mansour, M.M., Shanbhag, N.R.: High-throughput LDPC decoders. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 11(6), 976–996 (2003). https://doi.org/10.1109/ TVLSI.2003.817545 8. Wymeersch, H., Steendam, H., Moeneclaey, M.: Log-domain decoding of LDPC codes over GF (q). In: 2004 IEEE International Conference on Communicationspp. 772–776. IEEE (2004) 9. El Alami, R., Gueye, C.B., Mrabti, M., Boussetta, M., Zouak, M.: Reduced complexity of decoding algorithm for irregular LDPC codes using Split Row method. In: 2011 International Conference on Multimedia Computing and Systems, pp. 1–5 (2011). https://doi.org/10.1109/ ICMCS.2011.5945639 10. Wadayama, T., Nakamura, K., Yagita, M., Funahashi, Y., Usami, S., Takumi, I.: “Gradient descent bit flipping algorithms for decoding LDPC codes. IEEE Trans. Commun. 58(6), 1610–1614 (2010)

CCC-Transformation: Novel Method to Secure Passwords Based on Hash Transformation Fatima Zohra Ben Chakra1(B) , Hamza Touil2 , and Nabil El Akkad1 1 LISA, National School of Applied Sciences (ENSA), Sidi Mohamed Ben Abdellah University,

Fez, Morocco {fatimazohra.benchakra,nabil.elakkad}@usmba.ac.ma 2 LISAC, Faculty of Sciences, Dhar-Mahraz (FSDM), Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. Authentication is one of the essential elements that constitutes the security of online users, and guarantees the protection of their confidential information against cyber-attacks, offering increasingly effective methods and algorithms. One of the forms that we encounter daily is the use of passwords to confirm identity; however, the most relevant challenge is to secure their storage and retrieval. Throughout this document, we will present a method of securing these passwords, which is based on the hash function and its advantage of guaranteeing data integrity. By applying a color-coded transformation of the generated hash, we paralyze the attacker to decipher the passwords despite the possession of access to the database and the application of the most known attacks such as the brute force and the Rainbow-table attack. Keywords: Hash · Color code · Authentication · Password · SHA-2 · Transformation

1 Introduction The main concern of the cryptography field is to build systems and methods to ensure the confidentiality, integrity and authenticity of data circulating in a network [1–4]. The hash function perfectly meets the needs of integrity, and it is used to build a short fingerprint of some data. If the data are modified, the fingerprint will no longer be valid (with a high probability). Let us assume that the fingerprint is stored in a safe place. Thus, even if the data are stored in a nonsecure location, their integrity can be checked from time to time by recalculating the fingerprint and verifying that it has not changed. Thus, the slightest change in the fingerprint completely changes the function, which is the so-called avalanche effect [5–7]. The efficiency of the function refers to the one-way characteristic; in fact, the initial content of the generated hash value cannot be generated again, which blocks the attacker from deducing a password from its hash. Thus, the cryptographic hash function is a collision resistant, which is the difficulty of finding a pair of messages with the same © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 543–551, 2023. https://doi.org/10.1007/978-3-031-29857-8_55

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convolution values [8–14]. Let us also add the impossibility of deducing the value of the hash in spite of the possession of a part of the original value, which is called the resistance to priming. Due to these characteristics, the hash function has been used in various applications, such as file integrity verification, electronic signatures, and authentication mechanisms with passwords. However, with the constant increase in network standards, it becomes easy for a hacker to conduct malicious attacks on a user’s credentials, such as bank details and confidential information of an organization that uses a password-based system. The password attack involves trying to crack a person’s password, through either an exhaustive search or the application of the Rainbow table [15, 16]. This work addresses this limitation, and proposes a new method to keep the hash value secret by transforming it to an incomprehensible image. In the rest of the paper, we explain the proposed method and we discover its strong points through experimentation.

2 Related Work Data security is used in several fields, namely medicine, commerce, control systems and image processing [17–25]. A set of studies have been proposed to ensure the security of passwords on the network. Touil [26] proposed two techniques in this sense: the H-Rotation technique, which allows securing the storage of passwords as well as its recovery during authentication by applying a manipulation on hash value, plus the technique [27], which is based on the MD5 hash function. Singh, A [28] presented a dynamic password policy generating algorithm. In terms of the use of images and colors in cryptography, Es-Sabry [7, 29–31] proposed several image encryption techniques by addressing the limit of the most known attacks. Chen [32] proposed an asymmetric image encryption scheme based on the SHA-3 hash function, RSA and compressive detection, a method that has proven its resistance to plaintext attacks.

3 Proposed Method 3.1 Explanation of the Method In this document, we propose a very effective method in terms of storing passwords in databases and recovering them safely. It consists mainly of two essential phases: subscription and authentication. In the registration phase, the user fills in the identity information requested by the server, and builds a password before sending the form. The server then applies the SHA2 hash function of size 256, and transforms the generated hash into an image format before storing it in the database. As explained in Fig. 1, at the end, the server returns to the user a random number from our transformation algorithm and it will be used to log in during the authentication phase. In the authentication phase, the user fills in his identity information and the number retrieved during the subscription. The server in return, proceeds to the inverse treatment and compares the two generated Hash to confirm or refuse authentication. This process is well detailed in Fig. 3.

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New subscription

Step 1: fill in the form with the necessary information, create a password and send the data to the server.

Step 2: The server processes the data and extracts the password

Step 3: Generate a Hash with the SHA-2 256.

Step 6: send back to the user the number generated by our method in order to reuse it at the next connection

Step 4: Apply our transformation algorithm to generate an image and a random number.

Step 5: Store the image in the database.

Fig. 1. Description of the subscription phase.

We explain below, in Fig. 2, step 4 indicated in Fig. 1. To set up the transformation, we use a table that maps the 10 numbers (from 0 to 9) and the 26 letters of the alphabet (from a to z) with HTML color codes. As an example, we give a part of this table: Table 1. Table 1. Extract of the Character-Code-Color mapping table

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Step 1: Choose a random number N from the range [0-35].

Step 2: perform a shift of N on the first row of the table identified first, in order to have a new mapping.

Step 3: place each character of the original Hash in an 8x8 array, and based on the new mapping array, replace each character of the original Hash, by its associated color code

Step 4: replace each column with the color, to obtain an image of 64 pixels. Fig. 2. CCC Hash-transformation algorithm

Authentication Step 1: Fill in the login data and send the request to the server: - Login - Password - Number generated during the registration phase (N)

Step 5: Accept or reject the connection depending on the result of the Hash comparison.

Step 2: the server calls the login and the hash stored in the database.

Step 3: the server verifies the Hash by performing an inverse processing of our algorithm and compares the stored Hash with the generated Hash

Step 4: Compare the stored Hash with the generated Hash.

Fig. 3. Description of the authentication phase.

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4 Resistance Against Attacks 4.1 Brute Force Attack The brute force attack requires significant resources to guess the password, and it is considered effective when the key combination is limited. However, with our method, the attacker aiming to intercept the found image cannot guess the relationship between the image and the password hash. Thus, he will need to test all the combinations of the passwords and the N parameter, not to mention the different color filling directions or the algorithm. With the presence of several parameters to combine in our method, the exhaustive search is almost impossible in a limited time. 4.2 Dictionary Attack The dictionary attack consists of trying all the passwords used before, and saved as a dictionary. In our case, the attacker will need to intercept not only the predefined password, but also the N parameter, which gives the correct reorganization of the correspondence table (Character-Color Code). Each time the attacker tests a combination, he will end up with an image of another password, which makes the attack unfeasible. 4.3 Rainbow-Table Attack Our algorithm is intended mainly to avoid the Ranbow-table attack. In fact, the attacker cannot establish the Hash-image table, since for a single password, he will be met by 36 different images thanks to the N parameter that will change in a random way. The fact of having combinations of 36 images, the password and the N parameter makes the attacker’s attempts increasingly difficult.

5 Experimentation To simulate our algorithm, we choose the password “Fatima@Zohra1”. 1- When registering and performing a SHA2–256 hash, we find: «c1ddcd2fdf00931653f1dbfbd176cfdd7a822372fb79d97b454ddeacf92ededc» 2- We assume that the algorithm randomly chooses the number “2”, a shift of 2 positions will be applied to the first row of the table defined by default. 3- To start the transformation, we place each character of the Hash in an 8 × 8 table: Table 2.

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1

d

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4- Based on the new correspondence table (Character-Color Code), we can finally find the following table, which represents an image of 64 pixels: Fig. 4.

Fig. 4. Image stored in database

5- Finally, the server stores this image in the database, and sends back to the user the number N = 2, to reuse it during authentication. The new form of the hash stored in the database makes the brute force attack, the dictionary attack and the Rainbow Table attack unfeasible. Indeed, faced with an image of 64 pixels, the attacker cannot guess either the transformation algorithm, or the meaning of each color or the direction of filling the characters. Moreover, let us take the case of using the same password to connect to another site. Because the parameter N is chosen randomly, the reorganization of the correspondence table will obviously change. This has a direct impact on the resulting image. The attacker in this case will not be able to establish his hash-image list, and guess the real hash. In the authentication phase: 1- When requesting a connection, the server serves from the number N typed by the client, as well as the password, to perform the reverse process of the algorithm.

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2- The inverse process consists of using the N number to reorganize the default table. 3- The server calls the login and image stored in the database, and replaces each pixel by its corresponding code-color and character. 4- After the manipulation, we retrieve the original hash: « c1ddcd2fdf00931653f1dbfbd176cfdd7a822372fb79d97b454ddeacf92ededc» 5- The server performs a comparison between the two, and confirms the connection if they match.

6 Conclusion In the context of password security, we have proposed a very effective method to hide the real hash and transform it into an image format. Our algorithm inspired by HTML color codes offers a high level of security, by paralyzing any attacker from intercepting the true meaning of the found image despite database access. For any attacker, the found image will not give any information to detect the relationship between the hash and the image, or to guess the correspondence in case the same password is used in different sites or applications.

References 1. Touil, H., El Akkad, N., Satori, K.: Homomorphic method additive using pailler and multiplicative based on RSA in integers numbers. In: Lazaar, M., Duvallet, C., Touhafi, A., Achhab, MAl. (eds.) Proceedings of the 5th International Conference on Big Data and Internet of Things, pp. 153–164. Springer International Publishing, Cham (2022) 2. Touil, H., El Akkad, N., Satori, K.: Ensure the confidentiality of documents shared within the enterprise in the cloud by using a cryptographic delivery method. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications: Proceedings of ICDTA’22, Fez, Morocco, Volume 2, pp. 241–250. Springer International Publishing, Cham (2022). https://doi.org/10. 1007/978-3-031-02447-4_25 3. Touil, H., Akkad, N.E., Satori, K.: Secure and guarantee QoS in a video sequence: a new approach based on TLS protocol to secure data and RTP to ensure real-time exchanges. Int. J. Safety Secur. Eng. 11(1), 59–68 (2021) 4. Touil, H., Akkad, N.E.L., Satori, K.: Text encryption: Hybrid cryptographic method using vigenere and hill ciphers. In: International Conference on Intelligent Systems and Computer Vision (ISCV 2020), p. 9204095 (2020). https://doi.org/10.1109/ISCV49265.2020.9204095 5. Zulfadly, M., Akbar, M.B.D., Lazuly, I.: Implementation of nihilist cipher algorithm in securing text data with Md5 verification. J. Phys. Conf. Ser. 1361(1), 012020 (2019) 6. Elazzaby, F., El Akkad, N., Kabbaj, S.: A new encryption approach based on four-square and zigzag encryption (C4CZ). Adv. Intell. Syst. Comput. 1076, 589–597 (2020) 7. Es-sabry, M., El Akkad, N., Merras, M., Saaidi, A., Satori, K.: A new color image encryption algorithm using random number generation and linear functions. In: Bhateja, V., Satapathy, S.C., Satori, H. (eds.) Embedded Systems and Artificial Intelligence. AISC, vol. 1076, pp. 581–588. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0947-6_55 8. Zhang, W.-X., Chen, W.-N., Zhang, J.A.: Dynamic competitive swarm optimizer based-on entropy for large scale optimization. In: Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016, art. no. 7449853, pp. 365–371(2016)

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9. Al-Tashi, Q., Abdul Kadir, S.J., Rais, H.M., Mirjalili, S., Alhussian, H.: Binary optimization using hybrid grey wolf optimization for feature selection (open access). IEEE Access 7, 39496–39508 (2019). art. no. 8672550 10. Arora, S., Singh, H., Sharma, M., Sharma, S., Anand, P.: A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection (open access). IEEE Access 7, 26343–26361 (2019). art. no. 8643348 11. Mohapatra, P., Nath Das, K., Roy, S.: A modified competitive swarm optimizer for large scale optimization problems. Appl. Soft Comput. J. 59, 340–362 (2017) 12. Das, S., De, S., Kumar, R. Implementation and comparative analysis of RSA and MD5 algorithm. Int. J. Comput. Appl. (0975–8887) 141(9) (2016) 13. Hossain, A., Islam’S, K., Kumar, S., Nashiry, M.: Cryptanalyzing of message digest algorithms MD4 and MD5. Int. J. Cryptogr. Inf. Secur. 2(1), 1–13 (2012) 14. Niharika, M., Ethiraj, R.: Design and implementation of hybrid RC4 and Sha-2 algorithms for WIFI applications. Int. J. Emerg. Eng. Res. Technol. (2015) 15. Kirsur, S.M., Dakshayini, M., Gowri, M.: An effective eye-blink-based cyber secure PIN password authentication system (2023). https://doi.org/10.1007/978-981-19-3391-2_6 16. Raji, M., Jayalalitha, G.: Iterated function system in fingerprint images (2023). https://doi. org/10.1007/978-981-19-4052-1_38 17. El akkad, N., El Hazzat, S., Saaidi, A., Satori, K.: Reconstruction of 3D scenes by camera self-calibration and using genetic algorithms. 3D Res. 6(7), 1–17 (2016) 18. El Hazzat, S., Merras, M., El Akkad, N., Saaidi, A., Satori, K.: Enhancement of sparse 3D reconstruction using a modified match propagation based on particle swarm optimization. Multimedia Tools Appl. 78(11), 14251–14276 (2018). https://doi.org/10.1007/s11042-0186828-1 19. El Akkad, N., Merras, M., Baataoui, A., Saaidi, A., Satori, K.: Camera self-calibration having the varying parameters and based on homography of the plane at infinity. Multimedia Tools Appl. 77(11), 14055–14075 (2017). https://doi.org/10.1007/s11042-017-5012-3 20. El Akkad, N.E., Merras, M., Saaidi, A., Satori, K.: Robust method for self-calibration of cameras having the varying intrinsic parameters. J. Theor. Appl. Inf. Technol. 50(1), 57–67 (2013) 21. El Akkad, N.E., Merras, M., Saaidi, A., Satori, K.: Camera self-calibration with varying parameters from two views. WSEAS Trans. Inf. Sci. Appl. 10(11), 356–367 (2013) 22. El Akkad, N., Saaidi, A., Satori, K.: Self-calibration based on a circle of the cameras having the varying intrinsic parameters. In: Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS, pp. 161–166 (2012) 23. Merras, M., El Akkad, N., Saaidi, A., Nazih, A.G., Satori, K.: Camera calibration with varying parameters based on improved genetic algorithm. WSEAS Trans. Comput. 13, 129–137 (2014) 24. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: Image segmentation based on K-means and genetic algorithms. Adv. Intell. Syst. Comput. 1076, 489–497 (2020) 25. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: Clustering method and sine cosine algorithm for image segmentation. Evol. Intel. 15(1), 669–682 (2021). https://doi.org/10.1007/s12065020-00544-z 26. Touil, H., El Akkad, N., Satori, K.: H-rotation: secure storage and retrieval of passphrases on the authentication process. Int. J. Saf. Secur. Eng. 10(6), 785–796 (2021). https://doi.org/10. 18280/ijsse.100609 27. Touil, H., El Akkad, N., Satori, K.: Securing the storage of passwords based on the MD5 HASH transformation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 495–503. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_45

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28. Singh, A., Raj, S.: Securing password using dynamic password policy generator algorithm. J. King Saud Univ. Comput. Inf. Sci. 34(4), 1357–1361 (2022). https://doi.org/10.1016/j.jks uci.2019.06.006 29. Es-Sabry, M., El Akkad, N., Merras, M., Saaidi, A., Satori, K.: A new color image encryption algorithm using multiple chaotic maps with the intersecting planes method. Sci. Afr. 16(e01217), 2022 (2022) 30. Es-Sabry, M., El Akkad, N., Merras, M., Saaidi, A., Satori, K.: A new image encryption algorithm using random numbers generation of two matrices and bit-shift operators. Soft. Comput. 24(5), 3829–3848 (2019). https://doi.org/10.1007/s00500-019-04151-8 31. Es-Sabry, M., El Akkad, N., Merras, M., Saaidi, A., Satori, K.: Grayscale image encryption using shift bits operations. In: Paper presented at the 2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018, 1–7 May 2018 (2018). https://doi.org/10. 1109/ISACV.2018.8354028 32. Chen, Z., Ye, G.: An asymmetric image encryption scheme based on hash SHA-3, RSA and compressive sensing. Optik 267 (2022). https://doi.org/10.1016/j.ijleo.2022.169676

Case Studies of Several Popular Text Classification Methods Awatif Karim1(B)

, Youssef Hami2

, Chakir Loqman1

, and Jaouad Boumhidi1

1 LISAC Laboratory, Faculty of Science Dhar El Mehraz, Sidi Mohamed Ben Abdellah

University, Box 30003, Fez, Morocco [email protected] 2 MASI Team, National School of Applied Science, Abdelmalek Essaadi University, Box 1818, Tangier, Morocco

Abstract. The amount of data generated by the human race worldwide is increasing at an exponential rate every day. Therefore, data classification has become a necessity, and many researchers are focusing on evaluating automatic language processing techniques and improving text classification methods. Recently, deep learning models have achieved state-of-the-art results in many areas, including a wide variety of NLP applications. In fact, deep learning has the potential to handle and analyze massive data in both supervised and unsupervised modes and in real time. This paper briefly introduces different feature extraction and classification algorithms and analyzes and compares the different textual representations on the performance of various text classification algorithms. The results show that distributed word representations such as word2vec and Glove outperform other feature extraction methods such as BOW. More importantly, contextual embedding, such as BERT, can achieve good performance compared to traditional word embedding and compared to other classification methods. Keywords: Text Classification · Word Embedding · Contextual Embedding · BERT

1 Introduction In natural language processing (NLP), text classification is considered a significant task. For that reason, researchers are interested in the development of applications that promote and enhance text classification methods. Generally, the following steps show how text classification systems are constructed (Fig. 1): 1. Feature extraction: many representations are revealed to be of great advantage in several tasks of automatic NLP (Bag of Words (BOW), TF-IDF, Glove, Term Frequency, Word2Vec, and BERT base). 2. Dimension reduction: Various researchers favor discovering a more compact representation for high-dimensional data to reduce the time and make it easier to manage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 552–560, 2023. https://doi.org/10.1007/978-3-031-29857-8_56

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3. Classifier selection: This is an important step and can directly influence the results. Recently, deep learning classifiers have surpassed many previous machine learning classifiers in NLP. The success of these deep learning algorithms relies on their capacity to model nonlinear and complex connections within the data. 4. Classification results’ evaluation: There are several indices to evaluate classification methods. In the current study, the F1 score is used, which is among the well-known measures of aggregated evaluation and classifier evaluation. Traditional NLP techniques have relied almost exclusively on the BOW, in which the terms are processed in an independent way, which represents several limitations: polysemy (one word with several meanings) and synonymy (others words may be synonyms and be treated differently); still others are strongly semantically related without this being taken into account in the representation, and finally, some words lose their meaning if they are extracted from their nominal group. In addition to these limitations, there is also the limitation of too many words representing the documents, which is called the curse of dimension. In response to these limitations, with the explosion of text data on the Web and the faster development of deep neural network technologies [1–3]. Distributed word embedding has been effectively trained, widely developed and used in many text mining tasks. These embeddings are often pretrained on text corpora based on cooccurrence statistics; thus, it is not possible to detect the meaning of the word from the text. This has motivated and driven research toward a new word representation technique, where word vectors depend on the context of the word, called “Deep Contextual Word Representations” or “Contextual Word Embedding”, such as ELMO and BERT [4], which have been very successful in a variety of NLP tasks.

Fig. 1. Classification Systems of Text

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In this paper, we examine the impact of multiple word representations (BOW, word embedding, and contextual word embedding) as well as classification approaches (deep learning versus traditional machine learning methods) on achieving text classification. This paper is organized as follows: we first give an overview of the work on word embedding and classification algorithms in Sect. 2. Then, Sect. 3 describes the text classification process. Finally, we present the results of comparing different text representations and word embeddings with different text classification tasks in Sect. 4. Finally, a conclusion of the paper is presented in Sect. 5.

2 Related Works It is worth mentioning that every Text classification system involves a model. This model is represented as the classifier. It requires a method of feature extraction that intends to transform a dataset from text or documents to numerical data and a validation measure index. Meanwhile, many studies have used machine learning applications [5, 6]. Naili et al. investigated topic segmentation in Arabic and English and found that word2vec and Glove were more effective than LSA for both languages [7]. [8] proposed a novel hybrid text classification model based on a deep belief network and softmax regression. DBN is used to fix the high-dimensional matrix, and softmax regression is used to classify the texts. [9] proposed random multimodel deep learning (RMDL) to solve the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety of data to include text, video, images, and symbols. The results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems. [10] performed hierarchical classification using an approach called hierarchical deep learning for text classification (HDLTex). HDLTex uses stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy based on TF-IDF and Glove as feature extraction. The limitation of this approach is that it works only for hierarchical datasets. [11] proposed a hierarchical attention network for document classification. The model has a hierarchical structure that mirrors the hierarchical structure of documents; it has two levels of attention mechanisms applied at the word and sentence levels, enabling it to attend differentially to more and less important content when constructing the document representation. [12] applied a character-level CNN for text classification and achieved competitive results.

3 Materials and Methods 3.1 Text Classification Tasks Several preprocessing tasks need to be performed before applying the classification algorithms. These preprocessing tasks can be used alone or combined. Each text is represented in the vector space model (VSM). Then, the stop word list and punctuation marks must be removed. The following step is the stemming performance.

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This is the process of reducing the words to their root or stem. After that, the lematization process is performed. After the preprocessing phase, it was necessary to think about how to represent the text. In fact, many representations could be of great advantage in various NLP tasks. 3.2 Word Embedding Representations Word embedding representations might be one of the key breakthroughs for impressive deep learning performance for difficult problems in natural language processing. In this context, [13] proposed word embedding, which constituted a major revolution in the field of data mining. Word embeddings (word2vec, Glove) are often pretrained on a text corpus based on cooccurrence statistics. The embedding of a given word is always the same in all sentences. Thus, these approaches ignore the context of the words and obtain one global representation. Glove Representation. Global Vectors for Word Representation [14] is considered an unsupervised learning algorithm that is used to obtain vector representations of words. In this type, the performance of the training is an aggregated the word-word co-occurrence global statistics from a corpus. Meanwhile, the occurring representations present a good linear foundation of the word vector space. In this approach, each word is presented by a high-dimensional vector and trained on neighboring words. The objective function is as follows:    (1) f Xi − Xj , Xk = Pik Pjk where Xi is the word vector of word i, Pik is the probability that word j is revealed in the word’s i context. Word2vec Representation. Word2vec is a textual representation where the input is the corpus of the text and the output is the produced word vectors. It generates a word vector by two learning algorithms. The first is a continuous bag of words (CBOW), whereas the other is a skip-gram [15, 16]. On the other hand, the main objective in the CBOW method is the prediction of an identified word based on the available surrounding words. However, words are predicted by providing one word, context, or window in the skip-gram method. Both algorithms learn the word’s representation that is useful for the prediction of other words in the same sentence.

3.3 Contextual Embedding A new method is introduced recently concerning word representation. In this method, the context of the word is the one relied on by the word vector. This is called “Contextual embeddings”. The main idea of this method is that the context that surrounds the word is captured. In classical embedding, a given word is assigned to a single representation. For this purpose, researchers are focusing on training contextual representations on text corpora. Contextual representations based on transformer models work on attention

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mechanisms (ELMO, BERT), and attention is a way to look at the relation between a word with its neighbors by considering the sequence of all words in the documents. Thus, given a word, the embeddings are dynamically generated from a pretrained (or fine-tuned) model. The main features and limitations. Among the main features of this technique is its ability to capture the word’s meaning from the given text, including handling the polysemy and incorporating the context. On the other hand, its limitations include its inability to work at the individual word level because it works at the document and sentence levels. Moreover, further word embedding is needed for feed forward layers and all the LSTM. Other limitations involve its consumption of memory. 3.4 Text Classification Algorithms Based on Deep Learning Models The available document and text algorithm classification is outlined in this section. This includes CNN, RNN, DNN, LSTM, the combination techniques RCNN, and the BERT model. A comparison of the mentioned algorithms with a baseline support vector machine (SVM) and naive Bayes classifier was performed. The neural network’s ‘main branch’ is employed in almost all the classification methods that are DNNS-based to convert the inputs to a potential representation, which is employed for the classification. The neural network architectures that follow have been applied to this objective: • Deep Neural Networks (DNN): This is an expression used to characterize particular neural network types. It is also used to describe linked algorithms, which use raw data. The DNN is composed of three main kinds of inputs. This includes the output, hidden, and input layers. The method of linking and connecting the DNN models is what makes the difference between the models. • Convolutional neural networks (CNN): These are specific deep learning models for processing data. The objective behind their use is to discover relationships between data elements based on their close position. CNN is known for its capacity and ability in sequent data analysis. This includes the processing of natural language [12]. Using it in text classification provided interesting outcomes textual data. CNNs typically contain basic operations, particularly pooling and convolution. The latter operation method uses various filters, which give it the ability to extract features from the dataset. The pooling operation has the ability to reduce the feature maps’ dimensionality. The dimensionality of the CNN for text is very high. • Recurrent neural networks (RNN): These are powerful and robust kinds of neural networks. They use consecutive data or changing data over time, such as textual data. To clarify, there is information in the sequence itself, and recurrent nets use it to perform classification tasks. To make it possible to learn information from the former and from the next case, RNNs have the capacity to process information in a bidirectional or bidirectional fashion. In general, the RNN is based on the discovery of the relations between an object, such as a word, and the thing that is before it and after it. In fact, these deep learning algorithms are commonly used in NLP because their main shape fits the lengths of the process variable text very well, as in [17].

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• Long short-term memory (LSTM): This is in fact a very specific type of RNN. It maintains long-term dependency in a way that is more efficient than the basic RNN. In particular, this is useful for overcoming the issue of the leakage gradient. In addition, LSTM networks contain two important gates that are a ‘forget’ and an ‘update’ gates, even though LSTM contains a framework close to RNN. The mentioned gates decide to ignore or pass the information forward. • Recurrent convolutional neural networks (RCNN): In fact, these are employed for the sake of text classification. The essential point behind this method relies on constructing the text’s representation and capturing the contextual information alongside the frequent structure with the use of a CNN. This technique combines the CNN and the RNN to benefit from the features of both in a unique model. • Deep Belief Network (DBN): This generative graphical model is composed of various layers of latent variables [8]. Different shallow networks are among its components. This includes, in particular, restricted Boltzmann machines. In this model, every subnetwork’s hidden layer performs as the following subnetwork’s apparent layer. • Bidirectional Encoder Representations from Transformers (BERT). In fact, this is a new paper for this method [4]. BERT is a new pretraining language representation method. Its state-of-the-art results were achieved from a vast disposition of NLP activities. It relies on the transformer architecture. BERT is pretrained on two NLP tasks: masked language modeling and next sentence prediction.

4 Results and Discussion To make a fair comparison with the classification methods, a publicly available dataset 20NewsGroup [18] is used to train word embedding in the current experiments. Particularly, this training corpus is a set of approximately 20000 (1000 messages from each group) collected from 20 diverse Usenet newsgroups. Every group corresponds to a different topic. First, the preprocessing steps described in Sect. 3 were applied, and then the term’s weight in a document was mathematically represented according to the feature extraction selected. The first experiment shows the impact of the word embedding dimension on the DNN accuracy, and it was observed that increasing the dimension could enhance the precision of the classification. This could happen when the dimension is less than 50. However, improving the performance of the classification is not that significant when the dimension is above 50, although the period of the training will significantly increase, as shown in Fig. 2. In this experiment, the chosen dimension was 50 dimensions as the final dimension. In the second experiment, the aim is to analyze the impact of word representation methods with classification approaches on the performance of text classification. Furthermore, the following parameters in Table 1 below were used to construct the models. The parameters of the BERT base are as follows: L = 12, H = 768, and A = 12. Here, L is the number of encoders that are stacked. On the other hand, H is the hidden size, whereas A is the number of heads that are available in the multihead attention layers. The number of epochs is fixed at four.

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0.78 0.76 0.74 0.72 0.7

20news

0.68 0.66 0.64 0.62 20

30

50

70

100

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Fig. 2. The impacts of dimension of word distributed representation of accuracy.

Table 1. Classifiers and corresponding parameters evaluated. Activation Function Layers Dropout/Learning Optimizer Epochs Batch size rate RCNN Relu + softmax

2

0.25/0.001

Adam

15

128

DNN

Relu + softmax

4

0.5/0.001

Adam

10

128

CNN

Relu + softmax

5

0.25/0.001

Adam

15

128

LSTM BERT

Relu + softmax

3 12

0.25/0.001 0.1/2e-5

Adam Adam

10 4

128 32

The impact was measured for every step through the evaluation of the strongest combination. In fact, this was done by taking into consideration the mean of the F1 score (by deriving the weighted medium score from the results of 10-fold cross validation). Table 2 shows the F1-scores obtained when applying the various classification algorithms based on deep learning (CNN, DNN, RNN, BERT) and traditional machine learning methods (SVM, Naive Bayes Classifier) on the 20NewsGroup dataset. As shown in the table, several points can be concluded: • The implementation of word embeddings in text classification methods guarantees good performance compared to other feature extraction methods. For example, LSTM with Glove or word2vec outperforms LSTM with BOW. • Comparing TFIDF with word embedding (Glove, word2vec), it was found that TFIDF performance is superior. This proves that the choice of a complex method does not always guarantee better performance. • Glove has a lower F1-score value than Word2vec. This means that the word2vecCBOW model is significantly better than the Glove model.

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Table 2. F1 scores of different text representations in various classification algorithms on the 20NewsGroup dataset. Method

Feature Extraction

F1-score

Time

SVM

TFIDF

0.85

~seconds

Naive Bayes Classifier

TFIDF

0.77

~seconds

BERT fine-tuned

BERT base

0.82

~1/2 h

DNN

TFIDF

0.81

03 min

LSTM

Word2vec - CBOW

0.76

35 min

LSTM

Glove

0.75

30 min

LSTM

BOW

0.67

5 min

RCNN

Word2vec -CBOW

0.76

18 min

RCNN

Glove

0.75

26 min

CNN

Word2vec-CBOW

0.75

31 min

CNN

Glove

0.74

25 min

• BERT produces good performance in only 4 epochs and spends a few times. Therefore, the BERT model pretrained by the BERT base outperforms all other methods of classification (DNN, CNN, RCNN, LSTM, Naive Bayes Classifier), except SVM. • The SVM classifier significantly outperformed all methods of classification with an F1-score of 0.85. This was a result of considering the meta-data, which includes newsgroup identification header and footers.

5 Conclusions Recently, deep learning classifiers have surpassed many previous machine learning classifiers in NLP. The ability of deep learning algorithms to model nonlinear and complex relations within data is the main cause of their success. This study explored the performance of multiple word representation approaches by including a comparison of BOW with contextual and classical embeddings that were trained on public corpora. The study also explored the performance of various classification algorithms based on deep learning (CNN, DNN, RNN, BERT) and traditional machine learning methods (SVM, Naive Bayes Classifier). The results showed that the word embedding approach (Glove, Word2Vec) outperformed BOW. But TFIDF performance is superior than other feature extraction methods. Finally, this study concluded that the BERT model pretrained by the BERT base outperforms all other methods of classification and outperforms other traditional embedding in terms of the F1 score. To our surprise, we found that SVM outperforms BERT on the 20NewsGroup dataset. Acknowledgments. This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco [Alkhawarizmi/2020/36].

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References 1. Bengio, Y., Ducharme, J., Vincent, P., Janvin, C.A.: Neural probabilistic language model (2003) 2. Mnih, A., Hinton, G.: Three new graphical models for statistical language modeling. In ICML 2007: Proceedings of the 24th international conference on Machine learning, pp. 641–648. ACM (2007) 3. Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: International Conference on Machine Learning, ICML, pp. 160–167 (2008) 4. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pretraining of deep bidirectional transformers for language understanding. In: NAACL 2019 (2019). https://doi.org/10.18653/ v1/N19-1423 5. Karim, A., Loqman, C., Hami, Y., Boumhidi, J.: Max stable set problem to found the initial centroids in clustering problem. Indon. J. Electr. Eng. Comput. Sci. 25(1), 569–579 (2022) 6. Karim, A., Loqman, C., Boumhidi, J.: Determining the number of clusters using neural network and max stable set problem. Procedia Comput. Sci. 127, 16–25 (2018) 7. Naili, M., Chaibi, A.H., Ghezala, H.H.B.: Comparative study of word embedding methods in topic segmentation. Procedia Comput. Sci. 112, 340–349 (2017) 8. Jiang, M., et al.: Text classification based on deep belief network and softmax regression. Neural Comput. Appl. 29(1), 61–70 (2016). https://doi.org/10.1007/s00521-016-2401-x 9. Kowsari, K., Heidarysafa, M., Brown, D.E., Meimandi, K.J., Barnes, L.E.: RMDL: random multimodel deep learning for classification. In: Proceedings of the 2018 International Conference on Information System and Data Mining, Lakeland, FL, USA, 9–11 April 2018 (2018). https://doi.org/10.1145/3206098.3206111 10. Kowsari, K., Brown, D.E., Heidarysafa, M., Jafari Meimandi, K., Gerber, M.S., Barnes, L.E.: HDLTex: hierarchical deep learning for text classification. machine learning and applications (ICMLA). In: Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18–21 December 2017 (2017) 11. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: Hierarchical attention networks for document classification. In Proceedings of the HLT-NAACL, San Diego, CA, USA, 12–17 June 2016, pp. 1480–1489 (2016) 12. Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Adv. Neural Inf. Process. Syst. 28, 649–657 (2015) 13. Levy, O., Goldberg, Y., Dagan, I.: Improving distributional similarity with lessons learned from word embeddings. Trans. Assoc. Comput. Linguist. 3, 211–225 (2015) 14. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics (2014) 15. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). http://arxiv.org/abs/1301.3781 16. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems Conference (NIPS 2013), pp. 3111–3119 (2013) 17. Ye, Z., Byron, C.W.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. In: IJCNLP (2015). arXiv preprint arXiv:1510. 03820 18. Lang, K.: Newsweeder: learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 331–339 (1995)

A Survey on Facial Emotion Recognition for the Elderly Nouhaila Labzour1,2(B) , Sanaa El Fkihi1 , Soukayna Benaissa2 , Yahya Zennayi2 , and Omar Bourja2 1 IRDA Group, ADMIR Lab. Rabat IT Center, ENSIAS, Mohammed V University of Rabat,

Rabat, Morocco [email protected], [email protected] 2 Embedded Systems and Artificial Intelligence Department, MAScIR, Rabat, Morocco {s.benaissa,y.zennayi,o.bourja}@mascir.ma

Abstract. The elderly have a sensitive period of life in terms of physical and mental health and require close assistance or a caregiver. Medical assistance has the ability to recognize the emotional states of older adults through facial expressions and take care of them in real time. This paper provides a comprehensive Facial Emotion Recognition (FER) review, especially for the elderly. Several studies have been conducted on the facial emotion recognition of young and middleaged adults. Very few studies have focused on automatic emotion recognition for the elderly. Aging comes with a decline in the ability to recognize emotions and impacts emotion perception in humans. Furthermore, older people are suffering from cognitive impairment worldwide, which leads to abnormal emotional patterns. This paper is a literature review of FER techniques in computer vision; FER approaches, and FER databases, and discusses the main challenge of facial expression recognition across age and lifespan. Keywords: Facial Emotion Recognition · Image Processing · Elderly · Human-Robot-Interaction · Review

1 Introduction According to the world population aging 2020 report, 727 million people aged 65 years or over were in the world in 2020. Over the next decades, 1.5 billion older people around the world will live independently in 2050 [1]. The elderly is a sensitive period of life in terms of physical and mental health, which requires special care and close assistance or caregiver for an older person [2, 3]. Furthermore, older people are more likely to be affected by different diseases such as mild cognitive impairment, dementia, and Alzheimer’s disease [2]. A medical assistance can recognize the emotional states of older adults and take care of them in real-time. Studies show that social robots can reduce people’s depression, increase the socialite of the elderly, encourage the elderly to actively participate in rehabilitation therapy, and take real-time monitoring to predict and recognize health-related problems through facial emotion recognition [3, 4]. Given © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 561–575, 2023. https://doi.org/10.1007/978-3-031-29857-8_57

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that aging effects on facial structure cause many changes in shape and appearance, there is a growing interest in automatic emotion recognition development for the elderly population. Elderly people are still underrepresented in the literature. Very few studies have examined automatic emotion recognition in the elderly, who represent a large and growing population worldwide [5]. Although there is a large body of work on emotion recognition, most of it focuses on adults and, to a lesser extent, children. Very few studies have focused on the elderly [5, 6]. This study focuses on facial emotion recognition for the elderly and represents several approaches to deal with the age effect on facial features. This paper is organized as follows: Sect. 2 describes the most commonly used emotional model representation. Section 3 and Sect. 4 presents the state-of-the-art FER techniques, the FER approaches, and ends with a comparative study. Section 5 describes the available datasets to train models for FER for the elderly. In the last section, we conclude the paper with a general synthesis of FER for the elderly and recommendations for future work.

2 Emotional Models According to psychological research, emotion representations are divided into two main models: categorical models and dimensional models [7]. Categorical models consist of discrete emotion entities defined by distinct characteristics from each other, called “basic emotions” or “primary emotions”. The six basic emotions are proposed by Ekman and defined by [8]: joy, disgust, anger, fear, surprise, and sadness. Dimensional models describe emotions in continuous space based on two or more dimensions such as arousal and valence representation or power, arousal, and valence representation. Generally, theorists consider the. categorical models for facial emotion recognition as universal and innate, and most researchers use this model because of its stability over culture and age etc. [3]. However, dimensional models assume that emotions are dependent on each other. Dimensional models define affective states on a two-dimensional model, which includes valence and arousal dimensions, which are represented by a circle based on a pleasure axis (pleasure/pain) and an axis quantifying the strength of the feeling. Emotions can also be represented differently. Robert Plutchik [9] defined the wheel of emotions, which defines eight basic emotions while partially preserving the notion of dimension. Eight basic emotions, anger, disgust, fear, joy, sadness, surprise, trust, and anticipation, are represented in an emotion circle to have four sets of opposing emotions. Consequently, there are no better models for representing emotions. The choice of the emotion model depends on the nature of emotion and its application.

3 State of the Art 3.1 FER Architecture The facial expression recognition system aims to detect the face in a given image scene and calculate the geometric characteristics and appearance characteristics to make predictions. The FER architecture contains three important phases: The pre-treatment phase,

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the feature extraction phase and the classification or emotion recognition phase. Figure 1 represents the general functional architecture of the system for detecting emotions by facial expressions.

Fig. 1. A general framework of Facial Emotion Recognition.

3.1.1 Pre-processing The preprocessing is the first part of the facial expression recognition system that can influence the overall system. Image processing before the feature extraction step can lead to fast and efficient extraction. Preprocessing improves the system performance for better feature extraction by removing imperfections such as noise, reducing distortion, and highlighting the most important features for data analysis [10]. The image processing part contains several methods like image scaling, image brightness and contrast adjustment, and histogram equalization [11]. Histogram equalization is a method used to overcome lighting variations. This method is mainly used to improve the contrast of face images and, for precise lighting, is also used to improve the distinction between intensities [12]. Normalization is the preprocessing method intended for the reduction of illumination and variations in the face image and minimizing the problem of independent features (rotation, brightness, background, and occlusion) [12]. As an example, lighting and pose normalization is necessary for facial expression recognition in the uncontrolled environment [13]. Face detection is thus an important technique that allows extracting only the region of interest of the face and eliminating the elements of the background that can negatively influence the feature extraction [11]. The Viola-Jones algorithm is the most commonly used for the detection of facial regions. 3.1.2 Feature Extraction Feature extraction step aims to extract only relevant and discriminating features and to remove redundant attributes from sample data. Then, these features represent an implicit representation of the data that will be used as input for the classification step. Facial expressions cause changes in the appearance of the face and contractions of facial muscles depending on the emotion being expressed. However, facial features can be represented by geometric descriptors and appearance descriptors. Generally, feature extraction techniques are classified into four main types: appearance-based techniques, geometric-based techniques, and deep learning feature-based techniques. The appearance features are affected by wrinkles and the geometric features are affected by facial

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component deformations such as mouth, lips, etc. Figure 2 shows some samples from the FACE database. Consequently, the aging effect on facial texture can trick the classification of emotions into misclassification. The appearance of wrinkles appears on the neutral face expression even though emotion is absent and can produce some kind of emotion [3].

Fig. 2. Samples of facial expressions from The FACE database [14].

a) Appearance feature Appearance-based technique analyzes the textural information of the appearance resulting from facial movements related to each of the emotion classes, such as the presence of wrinkles, bulges, and the texture of the skin surrounding the eyes and the lips, which generally depends on the elasticity of the skin and the expression [15]. Appearancebased approaches use the entire face or specific regions of the face to extract underlying information about the face image. There are mainly three representative methods of appearance-based feature extraction [3]: Gabor filters, LBP (Local Binary Patterns), and HOG gradient histogram. Other methods used in the literature include wavelet sub-bands, pseudo-haar features, Independent Component Analysis (ICA) [16], Linear Discriminant Analysis (LDA) [17], Principal Component Analysis (PCA) [18], Fisher face-based descriptors [19], and gradient-based descriptors [20, 21]. Appearance features contain micro-patterns that provide important information about facial expression [22]. LBP is the most widely used descriptor for feature detection and extraction and it has been recognized as great success for facial expression analysis [21, 22]. The micro-patterns captured by the LBP are able to compensate for the weakness of the geometric characteristics for the detection of micro-patterns caused by facial wrinkles. In the same study by Nora Algaraawi et al. [23], the response of LBP is used to compare the facial expression of the young-neutral, young-happy, and old-neutral samples, as presented in Fig. 3. A young-happy face and an old-neutral face share approximately the same features. The appearance of wrinkles as well as the reduction of the elasticity of the facial muscles can confuse the classification of emotions.

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b) Geometric feature Geometric features represent facial components such as eyes, lips, eyebrows, nose, and mouth. Geometry- based approaches are based on the relationships between facial components when displaying emotions to extract features. Thus, many approaches based on geometric characteristics such as position, distance, and angles are used in the literature [24]. The shape of the facial component can be detected and localized by facial landmarks. Generally, geometric features receive accurate landmark detection for efficient feature extraction [25]. Nora Algaraawi et al. [23] used the ASM active shape model on the FACE database to assess the effect of age on shape features. 76 points are used to label the faces. As a result, the ASM model succeeded in fitting the correct data and missed the incorrect data that was not found in the training set. It can be clearly seen in Fig. 4 that the model did not fit the face components efficiently even after the final iterations of alignment because of the age influence. Muscular movements of the face during the expression of emotion cause the displacement of the position of landmarks and the size of facial features, which can be represented by the measurement of the movement between each point and subsequently determine the emotion expressed. Generally, geometric feature-based approaches are based on the position of facial points and facial muscle movements or the shape of facial components. c) Deep Learning Feature The use of convolutional neural networks to solve computer vision problems has been successful in a variety of fields, including real-time emotion processing. The deep learning neural networks commonly used in computer vision are the convolutional neural networks (CNNs) and recursive neural networks (RNNs). In facial expression recognition for emotion detection, CNNs are used as a supervised classification task, while RNNs are used as a non-supervised [26]. The most widely used deep convolutional neural network-based models for facial expression recognition applied to emotion detection are: LexNet, AlexNet, ResNet-50, GoogLeNet, VGGNet, Mobilenet, and YOLO. Deep learning methods allow one to learn discriminative features and to capture high-level features with better information about facial expression [27, 28].

Fig. 3. Facial expression of the young-neutral, Fig. 4. The alignment of the eye and mouth young-happy and old-neutral [23]. regions using ASM model [23].

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4 Literature Review FER methods can be divided into two categories: traditional methods and deep learning methods. Traditional methods are based on handcrafted methods for feature extraction and representation using machine learning models [26]. While deep learning methods aim to learn the representative features. Facial emotion recognition is a very active research area. However, very few studies have used artificial intelligence for facial emotion recognition in the elderly; Table 1 represents a summary of studies about facial emotion recognition for the elderly. The most of works studied the influence of aging on the facial features of the elderly, [23, 29–38]. In this section we present the significant studies on facial emotion recognition for the elderly using DL and ML methods. All over the world, elderly people are suffering from dementia, which causes memory and cognitive impairment caused by brain diseases. MCI (mild cognitive impairment) is considered the intermediate stage before dementia. Elderly people with cognitive impairment are found to have abnormal emotional patterns. They have difficulties with facial muscle control. Zixiang Fei et al. [28] developed a facial expression recognition algorithm using layers from MobileNet and Support Vector Machine (SVM) to detect mild cognitive impairment in the elderly. The proposed method achieves an accuracy of 73.3% on a custom dataset consisting of 61 elderly healthy people and patients with cognitive impairment. Andrea Caroppo et al. [39] propose a method based on a deep neural network combined with a stacked denoising auto-encoder for facial expression recognition in older adults. First, they use the Viola-Jones face detector to crop the input image as a preprocessing step. Then, the network is trained using an auto-encoder to represent the input data in a good way. The proposed method achieved the best average accuracy of 93.3% on the Lifespan dataset when tested on the FACES and Lifespan datasets [40]. This methodology will be implemented in a smart home to recognize the facial expressions of the elderly in real-time. Moreover, the study aims to analyze the effect of a non-frontal view of the face on facial expression recognition using the proposed system. The elderly are more expressive of negative emotions and feel strong emotions of fear and sadness more strongly than younger people [37]. In the study of Wei Huang [41], an elderly depression recognition model was proposed based on micro-expression features and performed on four datasets: SMIC, VAM, SFEW, and CASEME. The micro-expressions were extracted from the video sequence, and then a jump connection structure was introduced using the VGG-16 model. In reference [3], Nuno Lopes et al. use a multiclass support vector machine to classify the facial expressions of the elderly. A Gabor filter function was used for feature extraction, and the SVM was used to classify the facial expressions of the elderly. The proposed model achieves an accuracy of 90.32%, 84.61% and 66.6% when detecting neutral, happiness, and sadness respectively for the elderly, and accuracy of 95.24%, 88.57%, and 80%, when detecting neutral, happiness, and sadness respectively for another age group. This comparison demonstrates that aging affects facial expression recognition. Two approaches were proposed in this study to deal with the influence of age on facial expression recognition, such as the appearance of the nasolabial fold. The first approach consists of removing the wrinkles using edge-preserving smoothing techniques. In fact,

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Table 1. Elderly facial emotion recognition studies and performance. Methods

Database

Advantage/Disadvantage

Accuracy

ML (Viola Jones, Haar Features, Gabor Filter, SVM). [3]

Lifespan

The study used two approaches. The first is based on removing the presence of wrinkles using a Gabor filter, but the important information is lost. The second approach consists of adding an age detection phase to the architecture to detect if the person is old or not. By doing so, the second approach will be implemented for future work

90,32%

DL + ML (MobileNet, SVM). [28]

KDEF, Chinese Adults, Chinese Elderly People, Personalized

The methods combine deep 73.3% convolutional neural networks with an SVM classifier to reduce the operating time. The proposed algorithm has some difficulties detecting minor emotions that involve a deep focus on some facial parts

DL (CNNs). [39]

FACES, Lifespan

This study used the Stacked 93.3% Denoising Auto-Encoder (SDAE) method to better capture the facial expression of the elderly. However, in the multi-class recognition problem for FER, facial expression recognition is still confusing, especially for sad and neutral

the important features are lost, which decreases the recognition rate. The second approach consists of adding an age detection phase to the architecture of the system. Two SVM classifiers will be used, one for adult emotion classification and the second one for elderly emotion classification. Andrea et al. [39] use deep learning for facial expression recognition in older adults. The performance is evaluated on the FACE and Lifespan datasets. Their methods achieve

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the best accuracy recognition rate of 93.30% on the Lifespan dataset compared to nondeep learning approaches (LBP + SVM achieves 91.40% on the lifespan, and ASM + SVM achieves 92.10% on the lifespan). In reference [3], machine learning methods are used for the recognition of emotions, and the system achieves an average accuracy of 90.32% on the Lifespan dataset. The recognition rate is higher when using deep learning methods compared to machine learning methods. The experiments conducted by [28] achieve an accuracy of 73.3% using MobileNet and SVM. Deep learning and machine learning combined can yield promising results. However, handcrafted methods, including geometric-feature approaches and appearance-feature approaches, involve the combination of features to achieve better performance [15, 22, 26, 42]. However, geometric feature approaches are based on geometric information such as distance, position, and angle by the identification of landmark points to locate the facial components [43]. Landmark detectors are not efficient in most cases, especially for older faces, and require a high computational cost [44]. On the other hand, deep learning methods give high accuracy than traditional methods. In fact, OLUFISAYO et al. [26] represent a summary of some FER traditional and deep learning methods that show high accuracy results for deep learning methods compared to traditional. In Table 2, we present a comparison summary of recent traditional and deep learning studies for facial emotion recognition. Traditional methods have high performance in a static environment with small data. While deep learning methods give high performance in different environments (static, sequential, wild). However, deep learning methods require a large amount of data to perform well. Owing to the influence of age on facial muscles and texture, the expression of the elderly is hard to decode. Indeed, hand-crafted features are still insufficient for FER in older adults [3]. While deep learning allows learning to better represent the facial expressions of the elderly from unlabeled data. Table 2. Summary of recent traditional and deep learning studies for facial emotion recognition. Methods\Techniques

Database

Accuracy

CNN, RBF, Softmax-layer. [45]

RAF-DB, JAFFE, FER 2013, CK +, JAFFE

99,64%

Supervised Descent Method (SDM), CNNs, Pyramid Histograms of Oriented Gradients (PHOG), Viola-Jones Algorithm (VJA). [46]

JAFFE, CK +

98.85%

Alexnet, VJ, Frontal Face LBP, Grad-CAM. [47]

Oulu_casia

99.90%

AlexNet-Emotion model. [27]

CK +, MMI

97.14%

Resnet50, vgg19, InceptionV3, MobileNet. [48]

CK +

98% (continued)

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Table 2. (continued) Methods\Techniques

Database

Accuracy

MLCNN, 3DCNN, VGG-FACE + LSTM. [49]

SAVEE, AFEW

97.05%

CNN-t-SNE, CNN, SVM, Gaussian SAVEE, RML SVM, VGG-16, VGG-19, ResNet-50, AlexNet, and GoogleNet, OpenFace. [44]

98.77%

SVM, landmarks, HOG, OPD-GQMBP(P. ex., VGG, ResNets, DenseNet, GoogeLeNet et Inception), LBP, LOSO protocol, PCA. [15]

KDEF, CK +, RaFD, JAFFE, OuluCasia

97,53%

SVM,Butterworth high pass filter,LBP, VJ, DSAE. [42]

CK +, JAFEE

97.66%

Haar-like features, Lukas canade, Random Forest (RF), K Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Decision Tree (DT), Extreme Learning Machine (ELM), and SVM. [43]

Personalized

98.17%

5 Database In all current databases on emotion recognition, the elderly are less represented [5]. Moreover, there are very few sets of publicly available data. Most databases containing the elderly are used to study progression and age estimation, or to construct more robust models that can recognize faces in an age-invariant way [50]. Generally, the widely used databases for FER include: CK+ [47],RAF-DB, JAFFE, FACE databases [14], and others. Adults and younger people are the most popular subjects in these datasets. Based on the literature, the largest multimodal database dedicated to FER for the elderly is EldeReact database [5]. On the other hand, multimodal emotion recognition datasets can also be used for facial emotion recognition in the elderly. But the lack of older faces is still present. Popular multimodal datasets include: the SEWA database, which contains only 30 subjects aged more than 60 years old; the AVEC 2013 datasets, which contain less than 5% of subjects aged from 56–63 years old. Hence, due to the limitation of face-aging datasets, age estimation, age progression, and facial detection databases can be used to build models of emotion recognition by facial expressions. For works that study progression by age, the most used databases are “the Face and Gesture Recognition Network database” (FG-NET) and the MORPH dataset [5, 51]. FG-Net contains subjects with a range of ages, from newborns aged 1 to 69 years old, but older subjects are less represented in the datasets [52]. Also, we can cite the AgeDB [53] database with a range of age from 3 to 101 years, the IMDB-WIKI database [54] that contain subjects aged from 15–80 years old. Knowing that these databases are not classified according to the six basic emotions of FER, a procedure must be followed for the classification and annotation of

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images according to the chosen representation of emotions. The dataset in Table 3 depicts detailed information about FER databases. Dedicated databases for detecting emotions through facial expressions are divided into two categories: Spontaneous and Posed facial expressions. The FER uses static images or images from an independently processed sequence. Dynamic features of image sequences can provide much more information (appearance + motion) than static images, which only provide appearance information. Emotion is a very dynamic phenomenon that evolves over time: the onset (the beginning of the emotion), the apex (the peak of the emotion) and the offset (the end of the emotion: return to 45 the neutral state) [42]. To this end, psychological experiments [28] suggest that the dynamics of facial expressions are crucial for a successful interpretation of facial expressions. However, most datasets include sequence data, but the majority of image databases only present the frontal or near-frontal view of the face. Table 3. FER benchmark summary datasets. Note: NA (Not Available), Nb (Number of participants) Database

Samples

Nb

Nature

Environement

Expression

Age rage

Study

Year

Accuracy

CK+ [55]

593

123

Sequence

Controlled

6 BE + contempt

18–50

[45] [46] [15] [42] [11] [27] [48] [22]

2021 2021 2021 2021 2021 2022 2021 2021

99.64% 98.85% 97.53% 97.66% 98.90% 97.14% 96.00% 98.00%

JAFFE [56]

213

10

Static

Controlled

6 BE + neutral

NA

[45] [46] [15] [11]

2021 2021 2021 2021

97.96% 97.27% 78.05% 97.1%

FACE [14]

2052

154

Static

Controlled

6 BE + neutral

19–93

[3]

2018

88,2%

RAFDB [57]

29 672

NA

Static, Sequence

Controlled

6 BE + neutral

NA

[45] [15]

2021 2021

81.00% 97.39%

FER2013 [58]

35 887

NA

Static, Sequence

Uncontrolled

6 BE + neutral

18–70

[45]

2021

68.15%

Oulu-Casia [59]

2880

80

Sequence

Controlled

6 BE + neutral

NA

[15] [47]

2021 2021

77.32% 99.90%

KDEF [60]

4900

70

Static

Uncontrolled

6 BE + neutral

20–30

[15]

2021

90.2%

Lifespane [40]

1142

575

Static

Controlled

6 BE + neutral

18–93

[3]

2018

90.32%

6 Conclusion The present literature review aims to take stock of the difficulties of identifying facial expression recognition for elderly people. According to research, deep learning is the best approach for dealing with the difficulties of FER in the elderly and allows for the highest

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accuracy scores. However, FER still has some limitations for the elderly. Aging causes changes in the facial structure and appearance features, resulting in poor recognition of facial expression by both humans and machines. Due to a variety of pathologies and the age effect on human brain development, emotion recognition rates are low. Facial emotion recognition for the elderly is more challenging due to the limitations of the data and the limited number of studies that have focused on this population. The majority of image databases only present the frontal or near-frontal view of the face. Future work will deal with many aspects that are: A dataset of facial expressions for the elderly will be created by collecting more data. The healthy elderly as well as elderly patients are considered in the data collection. Furthermore, the study will focus on deep learning algorithm enhancement that can learn deep features of the facial expression and capture micro-patterns of facial features since facial expressions have partial similarities that can confuse the classification step. For emotional representation, the six-basic emotion model is the most adapted for elderly facial emotion expressions. However, we aim to propose a new emotional representation for older people validated by experienced psychotherapists. In addition, the literature review of this paper introduces multimodal emotion recognition, combining facial expressions, speech, and gestures to increase detection accuracy. Acknowledgments. We would like to thank Teamnet and Kompai Robotics for being our industrial partners in this project. As a reminder, this publication is part of the work undertaken by different partners composed of MAScIR (Moroccan Foundation for Advanced Science, Innovation and Research), ENSIAS (Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes), ENSAO (Ecole Nationale des Sciences Appliquées d’Oujda) and USPN (Université de Sorbonne Paris Nord) within the framework of the “Medical Assistant Robot” project. This project has been supported by the Moroccan Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the National Center for Scientific and Technical Research of Morocco (CNRST) through the “Al-Khawarizmi project”, besides Kompai, Teamnet and MAScIR. (Research project code: Alkhawarizmi/2020/15).

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Advanced Technologies in Energy and Electrical Engineering

Modeling of a High Frequency Ultrasonic Transducer Using Mason’s Equivalent Circuit Implemented in LTspice Hayat Reskal1(B) , Abdelmajid Bybi1 , Lahoucine El Maimouni2 , Anouar Boujenoui1 , and Abdchafia Lakbib1 1 Higher School of Technology of Salé, MEAT - Materials Energy and Acoustics Team,

Mohammed V University in Rabat, Rabat, Morocco [email protected] 2 Lab.PETI-ERMAM, Polydisciplinary Faculty of Ouarzazate, University Ibn Zohr, Agadir, Morocco

Abstract. In this research work a high frequency piezoelectric transducer radiating in a fluid medium (water) is modeled using Mason’s equivalent circuit implemented in LTspice simulator. The studied ultrasonic transducer is composed of an active piezoelectric material (Motorola 3203 HD) equipped with an acoustic matching layer, an absorbing material (backing), and an electrical matching circuit (inductor). The main objective of this work is the implementation of Mason’s equivalent circuit in a commercial electronic simulator such as LTspice software, instead of the analytical approach largely adopted in the literature. Furthermore, the effect of the front and back matching layers and the electrical matching circuit on the transducer’s electromechanical performances (electrical impedance, acoustic pressure, and power) is also investigated. The simulations’ results are compared to the literature ones and good agreements are obtained which validates the proposed approach. Keywords: Piezoelectric transducers · Mason’s equivalent circuit · LTspice

1 Introduction Ultrasonic transducers are usually utilized in medical imaging applications, e.g., obstetrical and gynecological echography. The major purpose of ongoing and future research in this area is to optimize the electroacoustic performance of these transducers, with the aim of improving the clinical diagnosis. For this purpose, high frequency ultrasonic transducers are largely investigated in the literature to obtain a high image resolution for more reliable and safe diagnosis. To investigate the electromechanical behavior of such transducers, e.g., determine their electrical impedance, displacement, and directivity pattern [1], numerical techniques based on finite element method (FEM), or a coupling between finite element method and integral equations, or also on boundary element modeling (BEM) are widely utilized. Nevertheless, the majority of the commercialized codes are not able to simulate the complete transducers with their associated electrical circuits © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 579–589, 2023. https://doi.org/10.1007/978-3-031-29857-8_58

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(electrical matching circuit, emission and reception circuits,…). Also, the numerical codes are usually time consuming particularly for transducers with complex geometries, transducer arrays with large number of elements and/or operating in high frequency, transient analysis, and also for 2D and 3D modeling. Another promising solution to study and optimize the electromechanical behavior of the ultrasonic transducers with their associated electrical circuits is to utilized the theory of the equivalent circuits. For this purpose, several models are investigated in the literature, e.g., the KLM’s (Krimholtz, Leedom and Matthaei) model [2–4], the Redwood’s model [5, 6], the Mason’s model [2, 3], the simplified Mason’s model [7, 8], the Butterworth-Van Dyke BVD model [9], and the Leach’s model [10]. All these models are limited to the one-dimensional behavior, i.e., used for transducers presenting a dominant resonant mode (thickness mode for medical imaging applications). Hutchens and Morris proposed a 2D Redwood equivalent circuit to model thin piezoelectric slender bars [11]. This equivalent circuit is also expected to be useful to model the transducer arrays. Hutchens presented also a 3D Redwood model taking into account the coupling between the different structure’s modes (thickness, width, and length modes) [12]. The aim of this work is to model a high frequency piezoelectric transducer using Mason’s equivalent circuit implemented in the electronic simulator LTspice. The first part is devoted to the description of the electromechanical circuit used for the modeling. In the second part, the piezoelectric transducer is modeled using LTspice and the results are compared with those of the literature [13]. In addition, the effect of the front and back matching layers and the electrical matching circuit on the electromechanical performance of the transducer (electrical impedance, acoustic pressure and power) is also studied.

2 Mason’s Equivalent Circuit 2.1 Description of Mason’s Model Equivalent circuits such as Mason, KLM, and Redwood models are widely used to model the physical behavior of piezoelectric transducers. These models are limited to the one-dimensional behavior, i.e., used for transducers presenting a dominant resonant mode (thickness mode in medical imaging applications). As shown in Fig. 1 Mason’s model is composed of two equivalent acoustic ports and an electric port. The acoustic ports are represented by three impedances ZS , ZT associated in T-circuit. The electrical port is excited by an electrical source represented by a voltage V and a current I.

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Fig. 1. Mason equivalent circuit of a piezoelectric layer vibrating in thickness mode (T)[13].

To model the piezoelectric effects, the electrical and mechanical parts are connected to a transformer with a transformation ratio N. The circuit’s parameters can be evaluated using the following relations [13]:   *T ZT = iZ0 tan (1) 2 ZS = −iZ0 csc(*T)

(2)

Z0 = ρAV D

(3)

 ρ ω  = D=ω V CD 33

(4)

ε33 A T

(5)

N = C0 h33

(6)

C0 =

where, Z0 and T are the specific mechanic impedance and thickness of the piezoelectric material. εS33 , CD 33 , A, h33 , and ρ are respectively the clamped permittivity, the open circuit elastic stiffness, the cross-sectional area, the piezoelectric coefficient, and the density the piezoelectric material. VD and C0 represent the acoustic velocity and the clamped capacitance of the piezoelectric material.  and ω are the propagation constant and the angular frequency.

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2.2 Description of the Studied Transducer

Fig. 2. Schematic of the studied ultrasonic transducer excited by a sinusoidal source.

In this paper, Mason’s equivalent circuit is utilized to model a high frequency (10 MHz) ultrasonic transducer radiating in a fluid medium (water in medical imaging). To validate our simulations, we chose to model a transducer already investigated in the literature by Sherrit et al. and compare our results with those of the authors [13]. As shown in Fig. 2, the studied structure is composed of a piezoelectric layer (Motorola 3203HD), a front acoustic matching layer (Araldite), a backing, and an electrical matching circuit (inductor). The dimensions and properties of the transducer’s materials are summarized in Table 1. 2.3 Implementation of Mason’s Equivalent Circuit in LTspice The complete model of the transducer described previously in Fig. 2 is implemented in LTSPICE software to investigate its performance in terms of the electrical impedance, Table 1. Materials’ properties and dimensions [13]. Material

Motorola 3203HD

Matching (ONDA-2020

Backing (ONDA-2020)

Density ƿ (kg/m3 ) D (N/m2 ) Stiffness c33

7800

2780

4710

1.743*1011 (1 + 0.017i)

1.04 ∗ 1010 (1 + 1.31010 (1 + 0.1i) 0.1i)

D (m/s) Velocity v33

4727(1 + 0.0085i)

1910(1 + 0.05i)

1670(1 + 0.05i)

Piezoelectric constant e33 (C/m2 )

22.2(1–0.049i)

N/A

N/A

Clamped permittivity s (F/m) ε33

1.02*10−8 (1–0.067i)

N/A

N/A

Area (m2 )

10−4

10−4

10−4

Thickness (m)

2.15 *10−4

4.77*10−5

.02

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the pressure at the transducer face, and the power transmitted to the propagation medium (water). Figure 3 represents the equivalent circuit taking into account all the elements, i.e., piezoelectric layer, front matching layer, backing, inductor, the propagation medium, and the electrical sinusoidal source. To model the impedances (ZT and ZS ) of the acoustic part, specific current sources depending on the input voltage (G) are utilized. To take into account the materials’ losses, the electrical part of the circuit, the negative capacitance, and the inductance are also modeled in the same manner (voltage dependent current source G). The following relations represent the Laplace transform of the impedances ZT , ZS of the acoustic part, the inductance L, and the capacitance C0 for the electrical part:    Z0 tan 2T .S (7) Laplace(ZT ) = (−S 2 )1/2 ω is the propagation constant. where, ω = (−S2 )1/2 is the angular frequency and  = V S is the Laplace variable.  2 1/2  Z0 tan (−S2 )V T S (8) Laplace(ZT ) =  1/2 −S 2

Laplace(ZS ) =

(−S 2 )1/2 Z0 ωZ0  2 1/2  = S sin(T ) S sin (−S V) T

(9)

1 S ∗ C0

(10)

Laplace(ZC0 ) =

Laplace(ZL ) = S ∗ L

(11)

In order to take into account the losses in LTSPICE, the imaginary part of materials properties were expressed using Laplace variable: j = S/ω with ω = (−S 2 )1/2 . To implement the electromechanical transformer in LTspice a current dependent current source F1 (primary) and a voltage dependent voltage source E1 associated to a voltage source V1 (secondary) are utilized as can be seen in Fig. 4.

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Fig. 3. The transducer’s equivalent circuit taking into account all the elements: piezoelectric layer, front matching layer, backing, inductor, the propagation medium, and the electrical sinusoidal source.

Fig. 4. LTspice model of the electromechanical transformer.

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3 Results and Discussion To investigate the effect of the transducer’s elements (piezoelectric layer, front matching layer, backing, electrical matching, and water load) on its electromechanical performance and validate our model, this section summarizes the simulations’ results, i.e., the electrical impedance (Fig. 5), the pressure at the transducer face (Fig. 6), and the power transmitted to the propagation medium (Fig. 7). The results are also compared to those obtained by Sherrit et al. [13].

Fig. 5. Comparison of the electrical impedances: (a) simulation results (LTspice), (b) analytical results [13].

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Fig. 6. Comparison of the pressure curves for an excitation signal of 1V amplitude: (a) simulation results (LTspice), (b) analytical results [13].

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Fig. 7. Comparison of the power curves for an excitation signal of 1V amplitude: (a) simulation results, (b) analytical results [13].

First of all, it can be clearly observed from the comparison of the simulations’ curves and the analytical ones (Fig. 5, 6, and 7) that in both cases the results obtained are very close. Some differences are observed, e.g., in the electrical impedance when an inductor is connected to the transducer (Fig. 5). This difference is due to the type of connection, i.e., serie connection in our case. In addition, the analysis of Fig. 5 indicate that the propagation medium (water) reduces the magnitude of the electrical impedance but it does not affect its resonant (about 9.8 MHz) and anti-resonant frequencies (11 MHz). It can be also concluded that adding successively the front matching layer, the backing, and the inductor reduces the magnitude of the electrical impedance and changes the transducer’s resonant and anti-resonant frequencies which leads to a frequency bandwidth widening as can be observed in the pressure and power curves (Fig. 6 and 7). To sum up, a transducer composed only of a piezoelectric layer radiates more acoustic pressure and transmits more power into the water but it presents a narrow frequency bandwidth.

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To overcome this limitation, an acoustic matching layer (or two matching layers) are bounded to the piezoelectric layer to enlarge the frequency bandwidth and an absorbing material (backing layer) is added to evens it out around the resonance and anti-resonance frequencies.

4 Conclusion In this paper, a one-dimensional Mason’s equivalent circuit is implemented in LTspice simulator to investigate the electromechanical performances of a high frequency (10 MHz) piezoelectric transducer radiating in a fluid medium (water for medical applications). The studied structure is composed of a piezoelectric layer (Motorola 3203HD), a front acoustic matching layer (Araldite), a backing, and an electrical matching circuit (inductor). To validate the proposed approach, the simulations’ results, i.e., the electrical impedance, the acoustic pressure, and the power transmitted to the propagation medium are successfully compared with the analytical results existing in the literature. The main advantage of this approach is its simplicity (lumped elements are utilized) and its possibility to be adapted for complex transducers such as piezoelectric multilayer transducers and transducer arrays which will be done in our future works.

References 1. Bybi, A., Khouili, D., Granger, C., Garoum, M., Mzerd, A., Hladky, A.-C.: Experimental characterization of a piezoelectric transducer array taking into account crosstalk phenomenon. Int. J. Eng. Technol. Innov. 10(1), 01–14 (2020) 2. Royer, D., Dieulesaint, E.: Elastic Waves in Solids II: Generation, Acousto-optic Interaction, Application. Springer, Heidelberg (1999) 3. Sherrit, S., Leary, S.P., Dolgin, B., Bar-Cohen, Y.: Comparison of the Mason and KLM equivalent circuits for piezoelectric resonators in the thickness mode. In: Proceedings of the IEEE Ultrasonics Symposium, Lake Tahoe, pp. 921–926 (1999) 4. Maréchal, P., Levassort, F., Tran-Huu-Hue, L, P., Lethiecq, M.: Lens-focused transducer modeling using an extended KLM model. Ultrasonics 46(2), 155–64 (2007) 5. Arnau, A.: Piezoelectric Transducers and Applications. Springer, Heidelberg (2008) 6. Richard, S., Cobbold, C.: Foundation of Biomedical Ultrasound. Oxford University Press, Oxford (2006) 7. Bybi, A., Mouhat, O., Garoum, M., Drissi, H., Grondel, S.: One-dimensional equivalent circuit for ultrasonic transducer arrays. Appl. Acoust. 156, 246–257 (2019) 8. Bybi, A., El Atlas, N., Drissi, H., Garoum, M., Hladky-Hennion, A-C.: One-dimensional electromechanical equivalent circuit for piezoelectric array elements. In: International Conference on Electrical and Information Technologies (ICEIT 17), Rabat, Morocco (2017) 9. Hagmann, M.J.: Analysis and equivalent circuit for accurate wideband calculations of the impedance for a piezoelectric transducer having loss. AIP Adv. 9, 085313 (2019) 10. Marshall Leach, W.: Controlled-source analogous circuits and SPICE models for piezoelectric transducers. IEEE Trans. Ultrasonics Ferroelectr. Freq. Control 41(1), 60–66 (1994)

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11. Hutchens, C., Morris, A.: A Two-dimensional equivalent circuit for the tall thin piezoelectric bar. In: IEEE Ultrasonic symposium, (1985) 12. Hutchens, C. G.: A three-dimensional circuit for a tall parallelepiped piezoelectric. In: IEEE Ultrasonic symposium, (1986) 13. Sherrit, S., Jae, L.H., Xiaoqi, B., Mircea, B., Bar-Cohen, Y.: Composite piezoelectric resonator 1D modeling with loss. In: Proceedings of SPIE 11377, Behavior and Mechanics of Multifunctional Materials IX (2020)

Modeling of Piezoelectric Transducers Using 1D and 2D Redwood Models Implemented in LTSPICE Anouar Boujenoui1(B) , Abdelmajid Bybi1 , Lahoucine El Maimouni2 , Hayat Reskal1 , and Abdchafia Lakbib1 1 MEAT - Materials Energy and Acoustics Team, Higher School of Technology of Salé,

Mohammed V University in Rabat, Rabat, Morocco [email protected] 2 Lab.PETI - ERMAM, Polydisciplinary Faculty of Ouarzazate, University Ibn Zohr, Agadir, Morocco

Abstract. In this paper, two types of piezoelectric transducers, i.e., plate and rectangular slender bars made of PZ27 ceramic are modeled using Redwood’s equivalent circuits. The studied structures are modeled using both one-dimensional (1D) and two-dimensional (2D) Redwood models implemented in LTSPICE simulator. The results of the simulations (the electrical impedance curves) are successfully compared to the experimental measurements, i.e., the 1D model predicts correctly the thickness mode, whereas the 2D model allows also an accurate prediction of the width mode and its harmonics. The 2D model is also able to take into account the coupling between the thickness and width modes. To investigate the other modes not predicted by the proposed models (length modes) a three-dimensional (3D) circuit will be studied in our future works. Keywords: piezoelectric transducers · equivalent circuits · redwood model · piezoelectric slender bar

1 Introduction Piezoelectric transducers and transducer arrays are widely utilized in the domain of medical imaging and nondestructive testing (NDT) applications. By resolving the fundamental constitutive equations of piezoelectricity coupled with those of mechanics and electricity, one may explain the electromechanical behavior of these sensors. However, the problem might become quite difficult to analyze, depending on the geometry of the examined transducer and its boundary conditions. Thanks to technology advancements in terms of calculators and computer performances, numerical modeling: finite element method (FEM), coupling between finite element method and integral equations, and boundary element modeling (BEM) is usually adopted to study and design the piezoelectric transducers. Unfortunately, the majority of the commercialized numerical codes are not able to simulate the complete transducer system, i.e., the piezoelectric transducer/transducer array and its associated electrical circuits (electrical matching circuit, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 590–600, 2023. https://doi.org/10.1007/978-3-031-29857-8_59

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emission and reception circuits,…). Also, the numerical codes are usually time consuming. Another way to investigate the physical behavior of piezoelectric transducers with their associated electrical circuits is to utilize the theory of the equivalent circuits [1, 2]. In this context, simple one-dimensional equivalent circuits can be used when the studied transducer presents a dominant resonant mode, e.g., a thickness mode. Several models are proposed in the literature, for example: KLM’s (Krimholtz, Leedom and Matthaei) model [2–4], Redwood’s model [5, 6], Mason’s model [2, 3], and Leach’s model [7]. Good results can be obtained by these models in the case of piezoelectric plates and Langevin transducers [8, 9]. Bybi et al. utilized a one-dimensional electromechanical equivalent circuit inspired from Mason’s one to investigate the electromechanical behavior of single (plate and slender bar) and array (seven elements) piezoelectric transducers [10, 11]. In addition, to validate their models, the authors characterized experimentally the piezoelectric transducer array and its elementary slender bars by electrical impedance, displacement, and directivity pattern measurements [12]. In the same context, Hutchens and Morris proposed a 2D Redwood equivalent circuit to model thin piezoelectric slender bars [13]. The objective of our research work is to model two types of piezoelectric transducers, i.e., plate and rectangular slender bars using Redwood’s equivalent circuits (1D and 2D). Both models are implemented in LTSPICE software using simple lumped elements. To demonstrate the validity of the proposed equivalent circuits, the results of the simulations are compared to the experimental ones. The second section of this paper describes the 1D and 2D Redwood’s circuits utilized to model the electromechanical behavior of different piezoelectric structures: a single plate and two slender bars. The results obtained using LTSPICE software are presented in Sect. 3. In addition, to verify the validity of the proposed approaches, the simulations’ curves are compared with the experimental measurements.

2 Modeling of Piezoelectric Transducers Using Redwood Model In this section, 1D and 2D Redwood circuits are utilized to model the electromechanical behavior of different piezoelectric structures: a single plate and two slender bars. 2.1 Presentation of 1D and 2D Redwood Equivalent Circuits a) 1D-Redwood Model Simple 1D equivalent circuits (Mason, KLM, and Redwood models) are usually utilized to model the electromechanical behavior of piezoelectric transducers with a dominant resonant mode, e.g., a thickness mode in the case of medical imaging transducers. Directly derived from Mason’s equivalent circuit, the Redwood’s model is composed of two equivalent acoustic ports and an electric port. As shown in Fig. 1, the acoustic ports are described by their mechanical quantities: forces and velocities (Fi and ui , i = 1,2). The electrical port is excited by an electrical source defined by its voltage V current I. To take into account the piezoelectric effects, the model’s electrical and mechanical parts are connected to an ideal transformer with a transformation ratio N. The electrical

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parameters are related to the transducer’s physical parameters as following:  Za = A C D × ρ0 C0 =

ε33 × A d

N = h33 C0

(1) (2) (3)

where A and d are the cross-sectional area of the piezoelectric structure and its thickness. C D , ρ0 , ε33 , and h33 are the elastic stiffness constant, the density, the dielectric constant, and the piezoelectric constant respectively. N and C0 are the transformation ratio and the blocked capacity. Za represents the specific acoustic impedance of the piezoelectric material. The length of the transmission line corresponds to the thickness of the transducer d.

Fig. 1. Redwood’s equivalent model [5]

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b) 2D-Redwood Model

Fig. 2. 2D-Redwood equivalent model [13]

This section presents a more accurate 2D-Redwood model taking into account the physical behavior of a piezoelectric structure in both directions: z-thickness direction and x-width direction [12]. Each mode (thickness or width) is modeled in the same manner than in the case of a 1D Redwood model. The coupling between the thickness and width modes is modeled by a capacitive circuit. As shown in Fig. 2, the equivalent circuit is composed of two transmission lines, two transformers and five capacitors.

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2.2 Description of the Studied Piezoelectric Plate and Slender Bars

Fig. 3. Piezoelectric structures: (a) a plate and (b) a slender bar.

Table 1. PZ27 properties. Coefficients of the deformation tensor [S] (10−12 Pa−1 )

Piezoelectric charge Density coefficient (pC/N) kg/m3

Relative permittivity

E S11

E S12

E f\S13

E S33

E S44

E S66

d15

d31

d33

ρ

S ε11 ε0

S ε33 ε0

17

−6.71

−8.53

23

43.4

47.42

500

−170

425

7700

1130

914

In this research work, Redwood’s equivalent circuits (1D and 2D) are used to model different piezoelectric structures made of PZ27 ceramic (Ferroperm): a plate having a thickness T = 1 mm, a width W = 25 mm, and a length L = 50 mm (Fig. 3.a) and two slender bars (Fig. 3.b): slender bar 1: T = 1 mm, W = 4 mm and L = 40 mm and slender bar 2: T = 1 mm, W = 2 mm and L = 15 mm. The properties of the piezoelectric ceramics are given in Table 1. 2.3 Implementation of Redwood’s 1D and 2D Models in LTSPICE Redwood’s 1D and 2D models are implemented in LTSPICE software to study the electromechanical behavior of the piezoelectric structures presented in Fig. 3. LTSPICE

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is a professional software widely utilized to design the electronic circuits and systems. It performs various simulations: frequency analysis, temporal analysis, noise analysis, etc. This software is chosen because it is one of the most common in the field of electronics. a) 1D Model As shown in Fig. 4, the electrical scheme of Redwood’s model is simple and easy to implement in LTSPICE which accepts the negative values of capacitors. In this case, the transmission line is modelled by a TLOSSY component which takes into account the mechanical losses of the piezoelectric material. The transformer component is modeled by a linear current dependent current source F1 (primary) and a voltage dependent voltage source E1 associated to a voltage source V1 (secondary).

b) 2D Model

Fig. 4. 1D Redwood’s model implemented in LTSPICE.

In order to model the piezoelectric structures, i.e., a plate and two slender bars, and to predict accurately the thickness and width modes even when they are coupled together, the 2D-Redwood’s model presented previously in Fig. 2 is implemented in LTSPICE (Fig. 5).

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Fig. 5. 2D Redwood model implemented in LTSPICE.

3 Results and Discussions

Fig. 6. Electrical impedance of a piezoelectric plate: measured and simulated using 1D and 2D models.

Figure 6 presents the plate’s electrical impedance obtained using both Redwood models (1D and 2D) compared to the experimental one. It is clearly seen from the figure that in all cases the results are very close, i.e., the thickness mode is correctly predicted by both 1D and 2D models. The characteristic resonant and anti-resonant values are as follows:   fr ≈ 1.91 MHz fa ≈ 2.19 MHz Experimental values : and |Zmin| = 0.24 |Zmax| = 104.35

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 1D model : 2D model :

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 fr ≈ 1.947 MHz fa ≈ 2.162 MHz and |Zmin| = 0.47 |Zmax| = 103.72   fr ≈ 1.943 MHz fa ≈ 2.15 MHz and |Zmin| = 0.47 |Zmax| = 104

The comparison also indicates the presence of several width modes in the experimental curve. These modes are correctly predicted by the 2D model. The amplitudes of the simulated modes (resonances peaks) are superior to those measured because the mechanical losses taken into consideration for the width modes are negligeable. To improve the comparison and reduce the resonances’ amplitudes, the value of the mechanical losses is increased by a factor 50. The result obtained in this situation is now better, as it can be seen in Fig. 7.

Fig. 7. Electrical impedance of a piezoelectric plate: measured and simulated using 2D model.

In the case of the first slender bar (slender bar 1), Fig. 8 compares the electrical impedance simulated using both Redwood models (1D and 2D) with that measured using an impedance analyzer. Firstly, it can be seen that the 1D model predicts only the dominant thickness mode with a good accuracy even when this one is coupled to other modes (harmonics of width and length modes). In addition, the results show that the 2D model predicts both thickness and width modes. It also predicts the harmonics of the width mode. The other modes not predicted correspond to the length modes not taken into consideration by the 2D model. The amplitudes of the resonances’ peaks are superior to those measured because the mechanical losses are not evaluated accurately. Finally, the frequency shifts between the simulated curves and the experimental ones are justified by the material properties utilized in the simulation. The resonant and anti-resonant values

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characterizing the dominant thickness mode are as follows:   fr ≈ 1.94 MHz fa ≈ 2.2 MHz Experimental values : and |Zmin| = 11.43 |Zmax| = 492   fr ≈ 1.95 MHz fa ≈ 2.164 MHz 1D model : and |Zmin| = 11.43 |Zmax| = 808.7   fr ≈ 1.996 MHz fa ≈ 2.166 MHz 2D model : and |Zmin| = 4.135 |Zmax| = 776.57

Fig. 8. Electrical impedance of the slender bar 1: measured and calculated using 1D and 2D models

Concerning the second slender bar (slender bar 2), Fig. 9 compares the electrical impedance computed considering both Redwood models (1D and 2D) with that measured using an impedance analyzer. In this case, it can be observed from the comparison that the 1D model is limited to the prediction of the dominant thickness mode which seems to be strongly coupled to the width and length modes (as it can be seen in the experimental curve). The 2D model is able to predict properly the both modes, i.e., thickness and width modes. The other modes (length modes) generated by the limited length of the piezoelectric slender bar (L = 15 mm) are not evaluated. A 3D Redwood model will be useful to taken into account the contribution of the length modes. This more accurate model will be investigated in our future works. To sum up, the resonant and anti-resonant values characterizing the dominant thickness mode are as follows:   fr ≈ 1.985 MHz fa ≈ 2.173 MHz Experimental values : and |Zmin| = 107.14 |Zmax| = 1023

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 fr ≈ 1.95 MHz fa ≈ 2.164 MHz 1D model : and |Zmin| = 19.44 |Zmax| = 4317   fr ≈ 1.993 MHz fa ≈ 2.166 MHz 2D model : and |Zmin| = 22.8 |Zmax| = 4008

Fig. 9. Electrical impedance of the slender bar 2: measured and calculated using 1D and 2D models

4 Conclusion In this research work, different piezoelectric transducers made of PZ27 ceramic (a plate and rectangular slender bars) are modelled using equivalent circuits inspired from the Redwood’s model. The piezoelectric structures are modeled using both one-dimensional (1D) and two-dimensional (2D) circuits implemented in LTSPICE software. The results of the simulations (the electrical impedance curves) are successfully compared to the experimental ones. It is found that in all studied cases (plate, slender bar 1, and slender bar 2) the 1D model predicts correctly the thickness mode (dominant mode). The simulations also indicate that the 2D model is more accurate, i.e., it allows also the prediction of the width mode and its harmonics. The 2D model is also able to take into account the coupling between the thickness and width modes. Finally, to investigate the other modes not predicted by the proposed models 1D and 2D, i.e., the length mode and its harmonics, a three-dimensional (3D) circuit is expected to be more accurate for structures with limited length, e.g., slender bar 2. This topic will be studied in our future works.

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References 1. Szabo, T.: Diagnostic Ultrasound Imaging: Inside Out. Elsevier Academic Press, Amsterdam (2004) 2. Royer, D., Dieulesaint, E.: Elastic waves in solids II: Generation, Acousto-optic Interaction, Applications. Springer, Heidelberg (1999) 3. Sherrit, S., Leary, S.P., Dolgin, B., Bar-Cohen, Y.: Comparison of the mason and KLM equivalent circuits for piezoelectric resonators in the thickness mode. In: Proceedings of the IEEE Ultrasonics Symposium, Lake Tahoe, pp. 921–926 (1999) 4. Maréchal, P., Levassort, F., Tran-Huu-Hue, L.P., Lethiecq, M.: Lens- focused transducer modeling using an extended KLM model. Ultrasonics 46(2), 155–167 (2007) 5. Arnau, A.: Piezoeletric Transducers and Applications. Springer, Heidelberg (2008) 6. Richard, S., Cobbold, C.: Foundations of Biomedical Ultrasound. Oxford University Press, Oxford (2006) 7. Leach, W.M.: Controlled-source analogous circuits and SPICE models for piezoelectric transducers. IEEE Trans. Ultrasonics Ferroelectr. Freq. Control 41(1), 60–66 (1994) 8. Hernandez, C., Bernard, Y., Razek, A. : Validation du modèle d’un transducteur de langevin piezoélectrique par schéma électrique equivalent. 5ème colloque sur les Matériaux en Gérnie Electrique, MGE (2010) 9. Pérez, N., Buiochi, F., Brizzotti Andrade, M.A., Adamowski, J.C.: Numerical characterization of piezoceramics using resonance curves. Materials 9(2), 1–30 (2016) 10. Bybi, A., El Atlas, N., Drissi, H., Garoum, M., Hladky-Hennion, A.-C.: One-dimensional electromechanical equivalent circuit for piezoelectric array elements. In: International Conference on Electrical and Information Technologies (ICEIT 17), Rabat, Morocco (2017) 11. Bybi, A., Mouhat, O., Garoum, M., Drissi, H., Grondel, S.: One-dimensional equivalent circuit for ultrasonic transducer arrays. Appl. Acoust. 156, 246–257 (2019) 12. Bybi, A., Khouili, D., Granger, C., Garoum, M., Mzerd, A., Hladky, A.-C.: Experimental characterization of a piezoelectric transducer array taking into account crosstalk phenomenon. Int. J. Eng. Technol. Innov. 10(1), 01–14 (2020) 13. Hutchens, C., Morris, A.: A Two-dimensional equivalent circuit for the tall thin piezoelectric bar. In: IEEE Ultrasonic Symposium (1985)

Fuzzy Logic Speed Controller for Robust Direct Torque Control of Induction Motor Drives Siham Mencou(B) , Majid Ben Yakhlef, and El Bachir Tazi Engineering Sciences Laboratory (LSI), Polydisciplinary Faculty of Taza (FPT), Sidi Mohamed Ben Abdellah University (USMBA), Fes, Morocco [email protected]

Abstract. In this paper, the intelligent fuzzy logic approach is used to realize a robust DTC induction machine drive system. Using Mamdani’s method, the fuzzy logic speed controller is designed to overcome the limitations of the conventional PI controller. Simulation results have demonstrated the superiority of the proposed controller, it shows better dynamics and disturbance rejection. The fuzzy approach improves the robustness to load disturbances and machine parametric variations as well as the fast response of the DTC controller. Keywords: Induction Motor (IM) · Direct torque Control (DTC) · Fuzzy logic Speed Controller (FLSC)

1 Introduction The induction motor (IM) is widely regarded as the most potential candidate for electric driving due to its low cost, high efficiency, and low maintenance cost [1]. Highperformance induction motor drives necessitate control methods that are robust to parameter variations, capable of rejecting disturbances, and have a fast dynamic response [2, 3]. Conventional control methods may be insufficient, especially when system accuracy and robustness requirements are stringent. Direct torque control (DTC) is the most efficient control method for the requirements of electric drives [4]. It is characterized by good stability, high robustness, and lower complexity compared to other controls [5]. In the conventional DTC method, the proportional-integral (PI) controller is generally used in the speed loop because of its simplicity and ease of implementation. However, the specific parameters of the PI controller are sensitive to motor parameter variations, load variations, and external disturbances ([6, 7]). Recently, another class of control based on artificial intelligence is proposed as an effective solution to improve the dynamic performance of DTC control, among which is fuzzy logic. Recently, fuzzy logic is of great relevance. It arouses a general interest among researchers and industrialists [8]. In [9] Talib and all. Use a fuzzy logic speed controller (FLSC) to drive the induction motor. The authors propose an approach based on an individual crisp output calculation to quantitatively select the appropriate rules. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 601–611, 2023. https://doi.org/10.1007/978-3-031-29857-8_60

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The effectiveness of the simplified rules was demonstrated by simulation results and experiments conducted in the dSPACE DS1103 controller environment. In [10], the authors used the fuzzy logic controller (FLC) for electric propulsion, employing both DTC and indirect flow-oriented control (IFOC). The simulation results show that the DTC controller outperforms the IFOC control strategy for electric vehicle drives in terms of speed tracking and energy consumption. The FLC performs admirably in terms of speed tracking. In our paper, we present a fuzzy logic controller to replace the conventional PI Controller used in the DTC method. Using the Mamdani method, the fuzzy controller is designed to overcome the limitations of the conventional DTC-PI controller. This paper is organized as follows: First, in Sect. 2, the modeling of the induction motor is presented. In the next section, the principle of DTC control is explained. Then, the fuzzy logic speed control is developed in Sect. 4. Finally, the simulation results are presented in Sect. 5, and conclusions are given in Sect. 6.

2 Induction Motor Modelling The mathematical representation of the induction machine in the synchronous reference frame (d,q), where the direct and quadratic axis is given by exponent ‘d’ and ‘q’ respectively, is given by the following equation [11, 12]. dX = AX + UB dt

(1)

With: ⎡

⎤

⎡

Rs

+ Rr

− Ls σ Lr

iSd ⎢  ⎢ ⎢ iSq ⎥ ⎥, U = vSd , A = ⎢ ω X =⎢ ⎢ ⎣ ϕsd ⎦ vSq ⎣ −Rs ϕsq 0 ⎡ 1 ⎤ 0 σL ⎢ s 1 ⎥ 2 ⎢ 0 σ Ls ⎥ B=⎢ ⎥, and σ = 1 − LMs Lr ⎣ 1 0 ⎦ 0 1

−ω Rs

+ Rr

− Ls σ Lr 0 −Rs

Rr ω σ Ls Lr σ L s − σωLs σ LRsrLr

where: • • • • •

Rs and Rr : stator and rotor resistance; Ls and Lr : stator and rotor inductance; ω: Electric speed; M: Mutual inductance between stator phases;

T iSd iSq : : Stator current components in (d, q) frame;

T • vSd vSq : Rotor voltage components in reference (d, q) frame;

T • ϕSd ϕSq : Rotor voltage components in (d, q) frame.

0 0 0 0

⎤ ⎥ ⎥ ⎥, ⎥ ⎦

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3 Direct Torque Control (DTC) Takahashi introduced direct torque control (DTC) in 1986 [5]. The DTC technique operates based on selecting the optimal voltage vector imposed by the inverter, which controls and regulates the electromagnetic torque and machine flux in a decoupled manner, [7, 13]. The synoptic schema of the DTC controller is given in Fig. 1.

Fig. 1. Synoptic schema of DTC control of the induction motor

The voltage vector of the inverter feeding the machine can be expressed as follows: 



 2 j 2π j 4π 3 3 (2) Sa + Sb e + Sc e Vs = 3 Combining the three inputs (Sa, Sb, Sc) generates eight voltage vectors, two of which are zero and six of which are active, as shown in Fig. 2. The estimation of the stator flux and the electromagnetic torque is done from the voltage vectors and the stator currents: t ⎧ ϕsα (t) = 0 (Vsα − Rs Isα )dt ⎪ ⎪ t   ⎪ ⎨ ϕsβ (t) = 0 Vsβ − Rs Isβ dt (3) 2 + ϕ2 ⎪ |ϕs | = ϕsα ⎪ sβ ⎪

 ⎩ Tem = 23 NP ϕsα isβ − ϕsβ isα The required control voltage vector (V0 −V7 ) is selected from the switching states (Sa , Sb , and Sc ) produced by the switching table (Table 1) based on the torque error and flux error states produced by the hysteresis comparators (Kϕ , KT ) and the sector number noted (N) (the angular sector in which the stator flux is located). Because of its simplicity and ease of implementation, the proportional-integral (PI) controller is frequently used in the speed loop in the conventional DTC method. However, the PI controller’s specific parameters are sensitive to changes in motor parameters, load variations, and external disturbances [14]. Therefore, our paper proposes a fuzzy logic algorithm to overcome the limitations of the conventional DTC-PI controller.

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Fig. 2. Evolution of voltage vector Vs (Sa Sb Sc)

Table 1. Switching Table N

1

2

3

4

5

6

K

KT

1

1

V2

V3

V4

V5

V6

V1

0

V7

V0

V7

V0

V7

V0

-1

V6

V1

V2

V3

V4

V5

1

V3

V4

V5

V6

V1

V2

0

V0

V7

V0

V7

V0

V7

-1

V5

V6

V1

V2

V3

V4

0

4 Fuzzy Logic Speed Controller (FLSC) The fundamental theory of fuzzy logic was introduced in 1965 by Professor Lofti Zadeh [15]. Initially, this theory was applied in non-technical fields, such as commerce, jurisprudence, or medicine, to complement expert systems capable of making decisions. Later, in 1975, we found the first fuzzy inference applications in control systems [16]. Since about 1985, the Japanese began to use fuzzy logic in industrial products to solve control problems [17, 18]. The principle of fuzzy logic is to manipulate inferences using some fuzzy rules applied to linguistic variables. The critical part of the fuzzy controller is the design of the basic rules. It requires system knowledge and field expertise. In our case, it is an analysis of the Phase-Plane Trajectory (Fig. 3) that we wanted to give to the speed controller fuzzy logic algorithm [19, 20]. The response performance is divided into 4 regions (R1, R2, R3, and R4), two crossover points (c1 and c2), and two peak points (b1 and b2) [21]. The process observation reveals that the significant variables for control are the speed error (E) and its variation (dE). Our fuzzy controller will receive these two quantities as inputs. The output, on the other hand, represents the increase in the required reference torque that the control-converter-machine system must produce (dU).

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Fig. 3. Phase-Plane Trajectory

In our algorithm, the input parameters are normalized. The speed error is multiplied by the scaling factor ke calculated by the following formula: Ke =

1 with  max = max( (k + 1) − (k))  max

(4)

Similarly, the speed error variation is multiplied by the scaling factor kde , as illustrated in the following formula: Kde =

1 with emax = max(e(k + 1) − e(k)) emax

(5)

The fuzzy logic controller structure is illustrated in Fig. 4.

Fig. 4. The standard model of the Fuzzy Logic Controller

We note: – E n : The normalized speed error, defined by En (k) = ke ( ref (k) − (k)) – dE n : The normalized Error drift, approximated by the dEn (k) = kde E(k)−E(k−1) ; Te – dU: The normalized increment of the reference torque, calculated by:dU (k) = T∗ (k)−T∗ (k−1) . Te Where Te is the sampling period In our fuzzy algorithm, the two input variables (E n and dE n ) and the output (dU) are transformed into seven linguistic variables as shown in Fig. 5. The seven linguistic

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variables are Positive Large (PL), Positive Medium (PM), Positive Small (PS), Zero (ZE), Negative Small (NS), Negative Medium (NM), and Negative Large (NL). The five linguistic variables (PM, PS, ZE, NS, and NM) are presented by the triangular linguistic function and the two linguistic variables (NL and PL) are presented by the trapezoidal linguistic function.

Fig. 5. Membership functions of inputs and outputs of Fuzzy Logic Controller

Using the phase plane trajectory, the reference torque increment (dU) can be determined by the inference matrix shown in Table 2. Based on this process a total of 7 × 7 = 49 rules are generated. For the numerical processing of inferences, we opted for the Mamdani method. Table 2. Inference Matrix

NM

NS

dEn ZE

NL NL NL A2NL NL NL NL b1 NM NM NS NS A1ZE ZE PS

NL NL NM NS ZE PS PM

NL NM C1 NS ZE PS PM C2 PL

NL

En

NL NM NS ZE PS PM PL

PM

PL

NM NS NS ZE A3 ZE PS PS b2 PM PM PL PL PL A4 PL PL

PS

ZE PS PM PL PL PL PL

The output is constructed using the center of gravity defuzzification method with the output variable dU defined as follows: L 1 αi fi (xi ) (6) z=  L 1 αi where:

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• αi : the weight factor for the i rule; • fi (xi ): the output value for the i rule; • And L number of the rule. The reference torque T* is determined by multiplying the output dU by the scaling factor KdU as described in the following equation: T ∗ = kdU ∫ dU ; withkdU =

1 Tlim

(7)

5 Result and Analysis We performed several simulations in the MATLAB/SIMULINK environment to investigate the behavior of the fuzzy logic controller Fig. 6. The simulations are performed on the 2.2 kW induction motor, whose parameters are presented in Table 3. All simulations are performed with a sampling time of 20 μs. The value of the proportional (Kp ) and integral (Ki ) gain parameters for the PI controller is determined based on the general characteristic closed-loop equations. Therefore, the values (Kp ) and (Ki ) for the speed controller are 4.8 and 34.5 respectively. Table 3. Induction machine parameters Parameters Name

Value

Rotor Inductance, Lr (H)

0.0156

Rotor Inductance, Ls (H)

0.0156

Mutual Inductance, LM (H)

0.2848

Stator Resistance, Rs (Ohm)

4.1250

Rotor Resistance, Rr (Ohm)

2.486

Number of pole pairs, p

2

Motor-load inertia, J (Kg.m2)

0.139

Viscous friction coefficient, fV (N.m.s)

0.0095

To evaluate the performances of this technique in tracking, regulation, and face of parametric variations, we followed the following tests: – Test 01: Speed variation: the motor starts from (0 rad/s) to (78.5 rad/s) evenly in 0.5 s. At t = 2 s, the reference speed changes from (78.5 rad/s) to nominal speed (157 rad/s), and at t = 3.5 s, another setpoint change from (157 rad/s) to (75 rad/s) is applied. At t = 5s, the reference speed decreases from (75 rad/s) to (0 rad/s), and then the motor is held at 0 rad/s for 0.5 s.

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– Test 02: Load variation: a load torque setpoint is applied, where at t = 1s a load torque of 14 Nm is applied and eliminated at t = 2 s after a no-load start, imposing the reference speed equal to the 100 rad/s. – Test 03: Stator resistance variation: the stator resistance varied from 150% and 50% of the nominal resistance.

Fig. 6. Simulation model for DTC_FLSC

Figure 7 shows the speed response of the DTC-PI and DTC-FLC controllers during test 01. The fuzzy logic speed controller’s speed response perfectly follows its reference over the whole speed change range, with a small tracking error during the transient phases that cancel out in a steady state. Similarly, the electromagnetic torque has a too-short response time (4 ms) and fewer ripples (0.22 Nm) (Fig. 8 and Table 4).

Fig. 7. Test 01 Evolution of the rotor speed (rad/s)

The speed and torque responses of DTC-PI and DTC-FLC for the load variation test are illustrated in the two figures Fig. 9 and Fig. 10. When compared to conventional DTC, the proposed DTC-FLC controller exhibits a significant reduction in torque ripple and smooth torque response. Under load, ripples are reduced to 0.18 Nm (Table 4). The speed response is insensitive to load disturbances. The system perfectly tracks reference values, with a fast response time and low steady-state error.

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Fig. 8. Test 01 Evolution of the electromagnetic torque (Nm)

Table 4. Comparison between the torque and the speed responses of DTC-PI and DTC-FLSC DTC_PI

DTC_FLSC

Starting adjustment 0.45 time (s)

0.009

load disturbance adjustment time (s)

0.45

0.004

Acceleration adjustment time (s)

0.45

0.001

Deceleration adjustment time (s)

0.45

0.001

No-load torque ripples (Nm)

[0.66; 1.02]

[0.86; 1.08]

Load torque ripples [14.79; 15.13] (Nm)

[14.85; 15.03]

Fig. 9. Test 02 Evolution of the rotor speed (rad/s)

The response of the rotation speed during the variations of the rotor resistance shows the superiority of the fuzzy controller. The monitoring and control behavior of the system remains remarkable; we observe a lengthening of the transient phases. The fuzzy

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Fig. 10. Test 02 Evolution of the electromagnetic torque (Nm)

controller shows remarkable robustness to the parametric variations of the machine (Fig. 11).

Fig. 11. Test 03 Evolution of the rotor speed (rad/s)

6 Conclusion In this paper, we proposed a fuzzy logic algorithm for the DTC speed controller to replace the conventional PI controller. Matlab/Simulink simulations were used to compare the behaviors of the proposed system under varying load, speed, and parameter conditions. Simulation results have demonstrated the superiority of the proposed controller over the conventional PI controller. The fuzzy approach for speed control improves the robustness to load disturbances and machine parametric variations and the fast response of the DTC controller. It shows better dynamics and disturbance rejection, the torque ripples are reduced by 38% at no load and 47% under load.

References 1. Emadi, A., Lee, Y.J., Rajashekara, K.: Power electronics and motor drives in electric, hybrid electric, and plug-in hybrid electric vehicles. IEEE Trans. Ind. Electron. 55(16), 2237–2245 (2008)

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2. Barnett, R.: Induction motors: early development [History]. IEEE Power Energ. Mag. 20(1), 90–98 (2022) 3. Alger, P.L., Arnold, R.E.: The history of induction motors in America, pp. 380–1383. IEEE (1976) 4. Sutikno, T., Idris, N.R.N., Jidin, A.: A review of direct torque control of induction motors for sustainable reliability and energy efficient drives. Renew. Sustain. Energy Rev. 32, 548–558 (2014) 5. Takahashi, I., Noguchi, T.: A new quick-response and high-efficiency control strategy of an induction motor. IEEE Trans. Ind. Appl. 22(15), 820–827 (1986) 6. Krim, S., Gdaim, S., Mtibaa, A., Mimouni, M.F.: Implementation on the FPGA of DTC-SVM based proportional integral and sliding mode controllers of an induction motor: a comparative study. J. Circ. Syst. Comput. 26(13), 1750049 (2017) 7. Mencou, S., Ben Yakhlef, M., Tazi, E.B.: Advanced torque and speed control techniques for induction motor drives: a review. In: 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, Meknes, Morocco (2022) 8. Tarbosh, Q.A., et al.: Review and investigation of simplified rules fuzzy logic speed controller of high performance induction motor drives. IEEE Access 8, 49377–49394 (2020) 9. Talib, M.H.N., et al.: An improved simplified rules Fuzzy Logic Speed Controller method applied for induction motor drive. ISA Trans, 150, 230–239 (2020) 10. Aktas, M., Awaili, K., Ehsani, M., Arisoy, A.: Direct torque control versus indirect fieldoriented control of induction motors for electric vehicle applications. Eng. Sci. Technol. Int. J.-Jestech 23(5), 1134–1143 (2020) 11. K. Bose, Power Electronics and Motor Drives (Second Edition), Academic press, 2021 12. Vas, P.: Ensorless Vector and Direct Torque Control. Oxford University Press, Oxford (1998) 13. Ammar, A., Talbi, B., Ameid, T., Azzoug, Y., Kerrache, A.: Predictive direct torque control with reduced ripples for induction motor drive based on T-S fuzzy speed controller. Asian J. Control 21(4), 2155–2166 (2019) 14. Salem, F.B., Derbel, N.: Investigation of SM DTC-SVM performances of IM control considering load disturbances effects. In: 13th International Multi-Conference On Systems, Signals & Devices (SSD), Leipzig, Germany (2016) 15. Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965) 16. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975) 17. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985) 18. Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier Science, Amsterdam (1985) 19. King, P.J., Mamdani, E.H.: The application of fuzzy control systems to industrial processes. Automatica 13(3), 235–242 (1977) 20. Li, H.X., Gatland, H.B.: A new methodology for designing a fuzzy logic controller. IEEE Trans. Syst. Man Cybern. 25(3), 505–512 (1995) 21. Wang, W.J., Lin, H.R.: Fuzzy control design for the trajectory tracking in phase plane. IEEE Trans. Syst. Man Cybern. 28(5), 710–719 (1998)

A Comparative Study Between Optimization Algorithms of MPPT Algorithms (P&O and Incremental Conductance Method) Chaymae Boubii1(B) , Ismail El Kafazi2 , Rachid Bannari1 , and Brahim El Bhiri2 1 Laboratory Systems Engineering ENSA, Ibn Tofail University Kenitra, Kenitra, Morocco

[email protected] 2 Laboratory SMARTILAB, Moroccan School Engineering Sciences, EMSI Rabat, Casablanca,

Morocco

Abstract. The role of MPPT is obtaining Maximum Power Age Point, although there are a diversity of temperature, irradiation, or darkening effects. Using this technic, we can deal with the power needed with fewer boards, thus reducing the cost of adding it to the PV framework. This article presents a similar approach to the MPPT perturbation and observations algorithm (P&O), and the incremental conductance method (IncCond) using MATLAB/SIMULINK. The P&O and IncCond accounts are updated and explored here. Keywords: Photovoltaic (PV) · Maximum Power Point (MPP) · Maximum Power Point Tracking (MPPT) · Perturb & Observe (P & O) and · Incremental Conductance Method (IncCond)

1 Introduction Renewable energy is becoming an increasingly hot topic around the world due to the use of renewable energy sources and the depletion of fossil fuels. Photovoltaic (PV) systems are increasingly important as a source of renewable energy because they are clean, low maintenance, and quiet. However, PV systems face problems such as low light conversion and non-linear properties depending on illumination and temperature, which can lead to variations in the amount of electricity generated [1]. So, due to PV cells’ highly nonlinear electrical properties and their associations, photoelectric efficiency could be improved through solutions based on MPPT techniques. The location of the maximum power point can be determined by various algorithms. Maximum power point tracking (MPPT) technology is used to maintain the operating point of the PV array at the maximum power point (MPP) and to obtain the maximum available power from the PV array. The MPPT controller is currently executed at the entry stage of the DC/DC converter [2]. Recently, many methods of MPPT have been proposed; the relative advantages of these different entrances are debated in [3, 4]. For example, traditional techniques (P&O and IncCond) [3, 4]. In [5], the authors suggested using fuzzy logic control to improve the photovoltaic system’s traction precision and power transfer. These technologies differ in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 612–621, 2023. https://doi.org/10.1007/978-3-031-29857-8_61

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complexity, cost, speed of convergence, sensors needed, hardware implementation, and efficiency. This paper is concerned with a comparison between two traditional algorithms of MPPT (P&O and IncCond) for maximum power achieved from a PV array in standard conditions.

2 Photovoltaic Modeling 2.1 The Principle of Operation of the Photocell The photocell is mostly an extremely large area P-N diode junction, created by forming a cross between the N-type and P regions. When sun rays strike a photocell, the resulting power is directly changed to electricity. It absorbs transferred light in the transistor, which uses power to move the photoelectron of low power state up, vacant power position. At a photocell is lit, the light creates an excess of electron pairs throughout the material, which causes an electrical short circuit when the P-N and the passage of a current [6]. 2.2 Maintaining the Integrity of the Specifications The model of PV can be presented mathematically. This mathematical model is decided from the voltage, current, and power generation under different working conditions. The photovoltaic panel is made up of photocells. Photocell panel possibly mathematically styled as specified in Eqs. (1)–(5) [7–9] Ipv = Np .Iph − Np · Irs · e

V

pv +Rs Ipv A·Vt ·Ns

−1



− Ish

Module photo-current: Iph = (Iscn + ki · (T − Tn )) ·  Module reverse saturation current: Irs = Irs0 ·

Tn T

3

Module reverse saturation current: = Vt Module Shunt current: Ish =

·e

(1) G 1000

 q.E  g k.A

k ·T q

Vpv + Rs · Ipv Rsh

where: • • • • • • •

Ipv : PV Generator Output Current. Irs : Diode saturation current expressed as a function of temperature. Iph : PV cell photon current. Ish : Shunt current. Iscn :Photocell short circuit current at STC: Tr = 25 ◦ C and ki : Coefficient of the current Iscn in A/K.

1 1 Tn − T

(2) 

(3) (4) (5)

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k: Boltzmann’s constant J/K. q: Electron charge constant in C. (q =1.6022e-19 C) A: Diode quality factor also called ideality factor. Vt : Thermodynamic potential (also called thermal tension). Np : Number of parallel PV modules. Ns : Number of PV cells in series. Irs0 : Rated diode saturation current ((STC): Tr = 25 ◦ C). Eg : Energy gap of the polycrystalline SI (eV). (Eg =1.12 eV) Rs : Series Resistance of a PV module. Rsh : Shunt Resistance of a PV module.

2.3 P-V, I-V Properties in Different Temperatures and Irradiances Figure 1 shows the performance features of the PV module of KC200GT at steady temperature and variable irradiation. These curves are not straight and are impacted from before sun rays. Changing the temperature of the photovoltaic array is a slow change in the operation of household applications. The thermal change affects the Current-Voltage trajectory as shown in Fig. 2. Figure 2 shows the output features of solar panel KC200GT as a function of continuous radiation and variable temperature. These curves are not linear and is implemented by the temperature.

Fig. 1. I-V curve in different radiations.

Fig. 2. I-V curve in different temperatures.

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3 MPPT The role of the MPPT is to find the maximum point of the energy, even if there are various temperature, irradiance, or shading effects. In addition, the amount of PV power generated is highly dependent on variations in climatic conditions, like temperature, moisture, and the quantity of incoming solar radiation. In addition, the controller type option affects system behavior, specifically in MPPT mode [10]. MPPT calculations [11] are used to handle the most extreme or maximum power operating points when the battery, irradiance, or frame temperature changes [12]. 3.1 Perturb and Obverse Algorithm The P&O algorithm shows in Fig. 3. The sun’s energy changes incessantly due to confusion. When the power is more because of the turbulence, the unrest is complete in the same direction. The energy goes down the next moment after reaching the maximum power, then the disturbance reverses. The algorithm swings at the limit when the stationary case arrives. The magnitude of the disturbance remains very small, so the power fluctuations are small [13].

Fig. 3. Algorithm of the P&O method.

3.2 Incremental Conductance Method The disturbance is terminated [14], in general, it is tried to match the impedance and current impedance, taking voltage and present as reference, and good results are obtained when a constant and low duty cycle is added as reference pulse or subtracted by link. With this planning, resistance counting is the basic approach and emotion [15]. The IncCond can be easily understood from the flowchart in Fig. 4. This way, whether the operating dot reaches its maximum power is observed, so the MPP condition is unlikely.

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• • •

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> 0 to the left of the MPP point. = 0 at point MPP. < 0 to the right of the MPP point.

Fig. 4. Algorithm of the IncCond.

4 Simulation and Results 4.1 PV System Simulation and BOOST Converter with Different Algorithms Order form composed of PV and boost converter with MPPT algorithms. The system is designed in MATLAB/SIMULINK. The general structure from PV with BOOST and MPPT with P&O is shows in Fig. 5, and Fig. 6 shows MPPT with IncCond, details of IncCond we can see in Fig. 7.

Fig. 5. Simulation of system PV with BOOST and P&O algorithm of MPPT.

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Fig. 6. PV system simulation with BOOST and IncCond algorithm of MPPT.

Fig. 7. Simulation of Incremental Conductance MPPT Method Algorithm.

4.2 Results Figure 8 shows the voltage off BOOST with P&O. The voltage increase from the start of the simulation to t = 2 s, where it reaches the maximum value (Vout_P&O = 1150 V), thereafter, the tension remains constant on its full value until t = 25 s. Then it decreases gradually. At t = 30 s, the voltage decreases rapidly. The same thing happened for the power. It remains constant on its maximum value (Pout_P&O = 9470 W) between t = 2 s and t = 25 s. Also, At t = 30 s, the power decreases rapidly. The curve of power is presented in Fig. 9.

Fig. 8. The BOOST voltage variation programmed by P&O algorithm.

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Fig. 9. The BOOST power variation programmed by P&O algorithm.

Figure 10 shows the voltage outside the boost converter with IncCond algorithm. The voltage increases from the beginning of the simulation until t = 4s where it reaches the maximum value (Vout_InC = 1150 V), then the voltage stays constant at its maximum value until t = 25 s, then it gradually decreases. At t = 27.7 s, the voltage drops rapidly. The same happened with power. It remains constant at its maximum value (Pout_ InC = 9470 W) between t = 2 s and t = 25 s, also, at t = 27.7 s the power decreases rapidly. The power curve is shown in Fig. 11.

Fig. 10. The BOOST voltage variation programmed by IncCond algorithm.

Figure 12 presents the effort result of the BOOST can be said to be the same for both algorithms from the start of the simulation until t = 25 s but noting that the voltage

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Fig. 11. Power Profile of the Boost Converter with IncCond algorithm.

Vout_InC obtained by the algorithm Incremental Conductance when t = 27.7 s decreases rapidly. Otherwise, the voltage Vout_P&O obtained by the Perturb & Observe decreases progressively until the point where t = 30 s decreases quickly. The same thing happened with power shown in Fig. 13. The power in the out-of-boost converter is just the same for both algorithms, but at t = 27.7 s Pout_InC starts to decrease rapidly, and for Pout_P&O decreases quickly at t = 30 s.

Fig. 12. The BOOST voltage variation programmed by P&O and IncCond algorithms.

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Fig. 13. The BOOST power variation programmed by P&O and IncCond algorithms.

5 Conclusion This search describes the comparison between optimization algorithms debated in literature. Consequences present based on MPPT. Photovoltaic system simulation to obtain max energy under variable irradiation. Comparative analysis of the results indicates that the maximum force is tracked by two methods. In a future article, we will examine another algorithm, such as the predictive model control algorithm. Research on this algorithm has increased in recent years as it is a very broad class of controllers that have recently found application in power converter control.

References 1. Dolara, A., Faranda, R., Leva, S.: Energy comparison of seven MPPT techniques for PV systems. J. Electromagn. Anal. Appl. 1(3), 152–162 (2009) 2. Das, M., Agarwal, V.: Novel high-performance stand-alone solar PV system with high-gain high-efficiency dc-dc converter power stages. IEEE Trans. Ind. Appl. 51, 4718–4728 (2015) 3. Esram, T., Chapman, P.L.: Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans. Energy Convers. 22, 439–449 (2007) 4. Reza Reisi, A., Hassan Moradi, M., Jamasb, S.: Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renewab. Sustainable Energy Rev. 19, 433–443, 3 (2013) 5. El Khateb, A., Abd Rahim, N., Selvaraj, J., Nasir Uddin, M.: Fuzzy-Logic-Controller Based SEPIC converter for maximum power point tracking. IEEE Trans. Ind. Appl. 50, 2349–2358 (2014) 6. Pongratananukul, N., Kasparis, T.: Tool for automated simulation of solar arrays using general purpose simulators. In: IEEE Conference Proceedings, (0–7803–8502–0/04), pp. 10–14 (2004)

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7. Chowdhury, S., Chowdhury, S.P., Taylor, G.A., Song, Y.H.: Mathematical modeling and performance evaluation of a stand-alone polycrystalline PV plant with MPPT facility. In: IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburg, USA, 20–24 July 2008 (2008) 8. Jung, J.-H., Ahmed, S.: Model construction of single crystalline photovoltaic panels for realtime simulation. In: IEEE Energy Conversion Congress & Expo, September 12–16, Atlanta, USA (2010) 9. Nema, S., Nema, R.K., Agnihotri, G.: Matlab / simulink based study of photovoltaic cells/modules / array and their experimental verification. Int. J. Energy Environ. 1(3), 487–500 (2010) 10. Ahmad, R., Murtaza, A.F., Sher, H.A.: Power tracking techniques for efficient operation of photovoltaic array in solar applications — A review. Renew. Sustain. Energy Rev. 101, 82–102 (2019) 11. Eltawil, M.A., Zhao, Z.: MPPT techniques for photovoltaic applications. Renew. Sustain. Energy Rev. 25, 793–798 (2013) 12. Masoum, A., Dehbonei, H., Fuchs, E.F.: Theoretical and experimental analyses of photovoltaic systems with voltage and current based maximum power point tracking. IEEE Power Eng. 22, 62–72 (2002) 13. Zainudin, H.N., Mekhilef, S.: Comparison study of maximum power point tracker techniques for PV Systems. In: Proceedings of 14th International Middle East Power Systems Conference (MEPCON 2010), Cairo University, Egypt, pp. 750–755 (2010) 14. Jiyong, L., Honghua, W.: A novel stand-alone PV generation system based on variable step size INC MPPT and SVPWM control. In: IEEE 6th International Power Electronics and Motion Control Conference, 2009, IPEMC 2009, pp. 2155–2160 (2009) 15. Mekhilef Safari, S.: Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Trans. Ind. Electron. 58, 1154– 1161 (2011)

Energy-Reducing Opportunities by Improving Power Quality: A Case Study of Industrial Plants Kamal Anoune1(B)

, Mokhtar Ghazouani2 , Mohamed Ghazi3 and Azzeddine Laknizi2

,

1 SmartiLAB EMSI-Rabat, Honoris United Universities, Rabat, Morocco

[email protected]

2 LERMA Laboratory, International University of Rabat (UIR), Sala Al Jadida 11000, Rabat,

Morocco 3 L3GIE Laboratory, Process Engineering Department, Mohammadia School of Engineering

(EMI), Mohamed 5 University (UM5), Rabat, Morocco

Abstract. The purpose of this investigation is to evaluate electric energy savings opportunities for reducing electricity fees at a manufacturing plant. The power factor is widely used as a quality power indicator in the industrial sector; it can be responsible for numerous penalties, equipment failures, and an increase in the electricity bill. This paper presents an investigation of the energy-saving potential of studying reactive energy impact on the electric grid as well as methods for reducing electricity consumption. The results show that using a power quality analyzer connected to a cloud helps to visualize the evolution of electric energy consumption and power quality in real time. Additionally, the placement of capacitor banks at designated points on the electric grid is required to compensate for the reactive power of inductive loads; it costs $16,500 and consists of 150 kVAR of capacitor banks, with a return on investment estimated at 17 months if considering an energy bill savings of $9 674,25 $/year. Keywords: Energy monitoring · Power factor · Energy-saving opportunities · Energy efficiency

1 Introduction Controlling and decreasing energy costs is an important aspect of increasing the company’s competitiveness. Electricity prices are a significant portion of operating costs in many industries. It is thus recommended to implement a rational management approach to electricity consumption linked to the analysis of the terms of the electricity supply contract, the monitoring of global consumption, and the implementation of solutions allowing for optimized consumption and better valorization of electrical power. The application of current management principles, as well as the dependability of power supplies, are critical for the safe operation of facilities and the execution of operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 622–631, 2023. https://doi.org/10.1007/978-3-031-29857-8_62

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Energy efficiency is defined as reducing excess energy to achieve the same task or provide the same result. Houses and buildings that are energy efficient consume less energy to warm, chill, and power appliances and devices. Energy efficiency constitutes one of the easiest and most cost-effective ways to combat climate change. Lower consumer energy bills and boost company competitiveness. Energy efficiency is also critical to achieving net-zero carbon dioxide emissions through decarbonization. Improvements in energy efficiency can help the sector decarbonize. However, one significant difficulty is the fact that energy efficiency possibilities are frequently untapped, owing to a lack of knowledge on final energy and accessible energy efficiency methods, among other factors. Furthermore, the absence of information makes energy efficiency adoption challenging to monitor and assess [1], and they provide several energy performance metrics for designated energy-intensive practices. The study took place in Sweden’s food business. Then, the study of A. Kluczek and P. Olszewski [2] focused on evaluating the consequences of efficiency gains and analyzing the advantages obtained from various energy efficiency techniques based on case studies. The article revealed nonbenefits of attaining industrial energy efficiency, which needs to be incorporated into the energy purpose of analysis and taken into account by plant operations management when evaluating energy efficiency expenditures. Furthermore, the research of Issa et al. [3] proposes an energy assessment and management plan for an Egyptian industrial facility. Notably, the analyzed industrial site is located in industries; their findings reveal that a projected expansion of said PV system will create 676.62 MWh/yr and meet approximately 50.95% of the manufacturing works annual power usage. The system for renewable energy (RE) costs $124,115 to install and has a threeyear payback time while reducing CO2 emissions by 293 tons per year. Furthermore, installing adjustable velocity drives in motor control may save approximately 48.477 MWh/yr with a repayment period of 1.73 yrs and save approximately 21 tons of CO2 yearly. The increased use of nonlinear equipment, especially power electronic equipment, has contributed to the rapid transmission of harmonic components. If this harmonic interference is not addressed, the quality of the delivered electricity will suffer dramatically. Many studies have referred to this problem and developed harmonic compensation techniques. According to IEEE 519_1992, the upper limit allowable total harmonic distortion (THD) for supply lines (69 kV) is 5% [4]. An additional critical issue in power networks, particularly transmission lines, is reactive power. To overcome this concern and bring the power factor closer to agreement, different kinds of compensators have been established. [5]. The placement of capacitor banks at appropriate points of the electrical network is required to compensate for the reactive power of inductive loads and a harmonics filter to improve electricity quality. Many construction systems are constructed to run at full capacity. Nevertheless, most construction systems only work at maximum capacity for a brief time. As a result, many systems frequently operate inefficiently over extended periods. The majority of such wasteful building activities occur in air-conditioning systems, which are often sized to come across peak load circumstances. These happen just for brief intervals throughout a typical day. Such systems’ efficiency can be increased by adjusting their capacity to fit actual load requirements[6, 7]. Thakare et al. [8] strive to give a realistic, grassroots

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viewpoint on the use of energy audits as an efficient energy conservation strategy. This application provides plant management with quantitative information on the technical and economic viability of different energy conservation strategies. Their study reveals the results of an energy audit conducted at a small-scale firm that produces nonferrous flat and form wires, high-precision rolling factories, block casters, and other items. The energy audit focused on conserving energy in both thermal and electrical systems. Zobaa et al. [9] presented a unique approach for determining the optimal fixed compensating capacitor to reduce the overall price by maintaining the PF at the look-for rate and confining the compensating capacitor’s doorplate kVAR, rated voltage RMS, and valued current RMS as restrictions given to IEEE Std 18–1992. Furthermore, the compensating capacitor values that would result in resonant circumstances would be removed from the solution. A straightforward THD improvement approach for a CCM boost PFC converter is provided by Han et al. [10]. The suggested approach makes use of the line voltage to boost the switching frequency near the line voltage while decreasing the switching frequency toward the peak. A three-phase current-source shunt active power conditioner with adaptive control that operates in unbalanced and distorted network environments. This control method seeks to correct the network’s reactive power, eliminate the oscillating components of active power, compensate current and voltage harmonic component subsequent in sinusoidal wave shape, and distribute the drawn power from the source uniformly. [11]. Chang and Grady [12] have demonstrated an efficient method for decreasing harmonic voltage distortion levels in a power system by employing several current-constrained active conditioners. In this paper, an investigation of the energy-saving potential by studying the reactive energy impact on the electric grid as well as methods to reduce electricity consumption is presented. The use of a power quality analyzer connected to a cloud helps to visualize the evolution of electric energy consumption and power quality in real time. The key parameters of the registered data are analyzed, and the proposed solution is discussed.

2 Power Factors Each electric demand that operates with electromagnetic fields (different motor types, plugs, transformers, inductive warming, etc.) generates a varied amount of electrical act, which is known as inductance. An inductive load’s line current is made up of two parts: magnetic act as well as power-producing current. The magnetic act current is the current necessary to maintain the machine’s electromagnetic field intensity. This current component generates reactive power, which does not do beneficial "work" but instead flows among the generator and the consumers. This puts a greater strain on the power distribution structure. The power-producing current allows the motor’s mechanical output, which is measured in kilowatts, and the apparent power is represented in kilovolt-amperes when combined with reactive power (kVAR). The power factor expresses inefficiency as the proportion of active or useable power to total or perceived power (kW/kVA)[13]. Power Factor =

Active Power  kW  Apparent Power  kVA

(1)

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Fig. 1. Power Triangle [14]

I*Cos is the active component of the current, while I*Sin is the reactive component of the current. When these current components are multiplied by the voltage V, the power triangle depicted below is generated. In general, when the power factor of a three-phase system declines, so does the current. Heat dissipation in the system increases by a factor equal to the square of the current rise (see Fig. 1). For inductive loads, the perceived power is always greater than the active power, and the type of machine used determines this. A power factor of 0.72 indicates that just 72% of your power is being utilized to conduct productive tasks. The perfect power factor is 1, which means that 100% of the power is being utilized for meaningful tasks. Equipment efficiency loss: When an installation has a low power factor, the quantity of usable power available inside the installation at the distribution transformers is significantly decreased due to the amount of reactive energy that the transformers must carry. Motors become slow and overheated because of the low voltage caused by high current demand. As the power factor falls, the total line current rises, producing a further reduction in voltage. You achieve more effective motor performance and an upper life cycle by adding capacitors. Poor power factor losses are produced by reactive current flowing in the system. These are watt-related costs that can be avoided by using power factor adjustment. In a distribution system, power loss (watts) is computed by squaring the current and multiplying it by the circuit resistance. To determine loss reduction: % reduction losses = 100 − 100 ×



Original power factor  New power factor

2

(2)

Figure 2 depicts how many systems kVA may be freed up by increasing the power factor. Increasing the power factor from 70% to 90% results in a gain of 0.32 kVA per kW. 128 kVA is discharged on a 400 kW load.

3 Assessment of Collected Cloud Data of the Power Quality The quality power analyzer utilized is the CIRCUTOR CIR-E3, which can be linked to a web application to monitor important parameters in real time. This analyzer collects 128 samples per cycle of the voltage and current variables in true root mean square (TRMS).

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Fig. 2. Corrected Power Factor Release System kVA [15]

Fig. 3. CIRCUTOR CIR-E3 the used Quality Power Analyzer

The unit computes the arithmetic mean using all the samples collected during the recording time set by the user. The analyzer also logs the highest and lowest values detected each time. The analyzer features four voltage sensor inputs and three current sensor inputs for taking these measurements (see Fig. 3). 3.1 The Situation of Peak Power Demand Figure 4 provides the behavior of power consumption for 10 days. Saturday afternoon and Sunday are considered free working days. There is no power consumption on this free working day, but on the other day, power consumption can reach 314.24 kW. 3.2 Power Quality Analysis According to the terms of the contract with the energy distributor, the power factor must satisfy a minimum value; if it does not, a severe penalty is levied as soon as the value falls below 0.08. It is critical to boost the power factor to at least 95%. To do this, capacitor banks are placed at key spots across the electrical system. According to the data collected by the quality power analyzer, the installation’s average PF is approximately 0.779 (see Fig. 5). If power usage continues to rise, the

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Fig. 4. Power active curve vs time

Fig. 5. Power factor values versus time

shortfall will be punished; in the best-case scenario, the installation’s PF will improve and approach the required value of PF = 0.95. During the working period, the PF decreases to 76%, and it can decrease to 61% when starting all machines; moreover, during the work-off period (no power consumption), the PF is close to 100%. 3.3 Harmonic Distortion Analysis Monitoring the harmonic currents ahead of the distorted load allows you to determine the source and, thus, the offending equipment on an electric grid. Due to harmonics, transformers, cables, motors, generators, and capacitors powered by the same power source as the harmonic-producing equipment may become heated. Breakers may trip, lights may flicker, and measurement instruments may generate erroneous findings Fig. 6. The total harmonic distortion rates of voltage and current have been evaluated. It is critical to have a basic comprehension of the measurement data to identify whether harmonic pollution exists on the electric grid. When THD is less than 5%, most harmonics are unaffected. Problems with more durable-designed electronics with a THD of 10% or greater. THD of 5% or higher. Furthermore, with THD < 10%, long-term unfavorable consequences are very certain. Findings: • Harmonics influence the metering of power consumption and operation of machineries and production lines connected to the polled power grid.

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Fig. 6. Total harmonic distortion THD versus time

• Monitoring of harmonic currents upstream of the load enables the identification of the source of the electrical grid’s poor quality. • The THD harmonic distortion for the three-phase voltages is about 2.9%, which is fairly accurate, while the L3, L2, and L1 line currents have THD values of 40%, 20% and 5%, respectively. 3.4 Impact of the Nonregulated Power Factor on Electric Wiring The infrared camera has made it possible to identify significant overheating in electric current distribution networks, particularly in grounded conductors, when it is used in the context of an evaluation of harmonic currents. Abnormal temperature rises of a motor. There is a significant loss of joule due to the heating of the cable, and the measured temperature is 31,7 °C, which is also a generator of phase imbalance, accelerating the deterioration of the equipment Fig. 7.

Fig. 7. Abnormal heating of electric wiring

The thermographic camera TESTO 880 was used to detect mechanical, electrical, and energy-related concerns such as inadequate connections, overloaded conductors, short circuits, and ground faults. To solve the issue of anomalous heating, it is strongly encouraged to check for connection issues, double-check cable tightness, verify the maximum current permissible per cable segment, and finally balance the loads between each phase. Findings:

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• The infrared camera identified significant overheating in the electrical power distribution grid, particularly in 1 of the 3 wires. • Due to the joule effect, the heating of the cable contributes to a large loss; the temperature measured is 31.7 °C. • This measured heating adds to a number of physical phenomena, including voltage drop and phase imbalance, which hastens the depreciation of downstream equipment.

4 Results and Discussion The power factor of the plant distribution system can be enhanced by installing power factor correction capacitors. The utility must deliver both the extra reactive current and the working current when the apparent power (kVA) exceeds the working power (kW). Reactive current is generated by power capacitors. By delivering reactive current, they minimize the overall amount of current that the manufacturing plant system needs to require from the utility. The maximum benefit is provided by a power factor of 95% [15]. In principle, capacitors can supply all of the reactive power needed. In reality, the power factor adjustment to approximately 95% delivers the most advantage. Required capacitor in the kVAR for power factor correction. The measured power factor PF is Cosθ1 = 0.779. The desired power factor PF Cosθ2 = 0.95. θ1 = Cos-1 = (0.779) = 38°.83, Tan θ1 = Tan (38°.83) = 0.8049. θ2 = Cos-1 = (0.95) = 18°.19, Tan θ2 = Tan (18°.19) = 0.3287. Required Capacitor RC in kVAR to improve power factor PF from 0.779 to 0.95 RC = P(tan θ1 − tan θ2)

(3)

RC = 314.24 kW (0. 8049– 0.3287). RC = 149.641 kVAR. Rating of Capacitors connected in each Phase. RC/3 = 49,88 kVAR. The placement of capacitor banks at designated points of the electric grid is required to compensate for the reactive power of inductive loads and a harmonics filter to improve electricity quality, which costs $16,500 and consists of 150 kVAR of capacitor banks, to raise the power factor to at least the contractual value, ideally a power factor value of 0.95. This investment will turn a profit in approximately 19 months. The increase in PF to 95% will have a direct influence on the electric cost, resulting in a decrease of up to 5% of the bill of 193485.15 $/year, or 9 674,25 $/year. The expected annual bill savings are 9 674,25 $.

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5 Conclusion An electrical energy assessment for an industrial building is accomplished by investigating the reactive energy impact on the electric grid and presenting possibilities for reducing electricity consumption. The assessment data were obtained using a quality power analyzer and a thermography camera. On the other hand, an examination of options for reducing electricity use is suggested. As a result of the proposed reactive compensator, the reactive energy is reduced. The implementation of capacitor banks at designated points on the electric grid is required to compensate for the reactive power of inductive loads and a harmonics filter to improve electricity quality, which costs $16,500 and consists of 150 kVAR of capacitor banks, with a return on investment estimated at 17 months if considering an energy bill savings of $9 674,25 $/year. Acknowledgment. The author would like to thank all the people who have contributed to the success of this energy audit study, especially the staff of industrial manufacturing.

References 1. Kanchiralla, F.M., Jalo, N., Thollander, P., Andersson, M., Johnsson, S.: Energy use categorization with performance indicators for the food industry and a conceptual energy planning framework. Appl. Energy, 304, p. 117788 (2021) https://doi.org/10.1016/j.apenergy.2021. 117788 2. Kluczek, A., Olszewski, P.: Energy audits in industrial processes. J. Clean. Prod. 142(2017), 3437–3453 (2017). https://doi.org/10.1016/j.jclepro.2016.10.123 3. Bosu, I., Mahmoud, H., Hassan, H.: Energy audit and management of an industrial site based on energy efficiency, economic, and environmental analysis. Appl. Energy. 333, 120619 (2023) https://doi.org/10.1016/j.apenergy.2022.120619 4. Morales, I.G., Moran, L.: IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems IEEE Industry Application. Institute of Electrical and Electronics Engineers, Inc. 5. Montaño, J.C.: Reviewing concepts of instantaneous and average compensations in polyphase systems. IEEE Trans. Ind. Electron. 58(1), 213–220 (2011). https://doi.org/10.1109/TIE.2010. 2044134 6. Saidur, R., Rahim, N.A., Ping, H.W., Jahirul, M.I., Mekhilef, S., Masjuki, H.H.: Energy and emission analysis for industrial motors in Malaysia. Energy Policy 37(9), 3650–3658 (2009). https://doi.org/10.1016/j.enpol.2009.04.033 7. Anoune, K., Bouya, M., Astito, A.: A design and sizing of a Hybrid PV-Wind-Grid System for Electric Vehicle Charging Platform (2018). https://doi.org/10.1051/matecconf/201 820000008 8. Thakare, H.R., Patil, S.U., Patil, S.R.: Techno-economic assessment of manufacturing process in small scale industry to evaluate energy saving potential. Mater. Today Proc. 57, 2317–2324 (2022). https://doi.org/10.1016/j.matpr.2022.01.105 9. Zobaa, A.F.: Maintaining a good power factor and saving money for industrial loads. IEEE Trans. Ind. Electron. 53(2), 710–712 (2006). https://doi.org/10.1109/TIE.2006.870879 10. C. Kim, “Converter Under Mixed Conduction Mode Operation,” pp. 466–470, 2017

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11. Ketabi, A., Farshadnia, M., Malekpour, M., Feuillet, R.: A new control strategy for active power line conditioner (APLC) using adaptive notch filter. Int. J. Electr. Power Energy Syst. 47(1), 31–40 (2013). https://doi.org/10.1016/j.ijepes.2012.10.063 12. Chang, W.K., Grady, W.M.: Minimizing harmonic voltage distortion with multiple currentconstrained active power line conditioners. IEEE Trans. Power Deliv. 12(2), 837–843 (1997). https://doi.org/10.1109/61.584402 13. I. A. Bhatia, “Power Factor in Electrical,” vol. 144, no. 877, 2012 14. Power Traingle. https://www.electricalampere.com/post/power-triangle 15. Eaton,: Power factor correction : a guide for the plant engineer. Power factor Correct. a Guid. plant Eng. 1(1), 2–4, (2014) www.eaton.com

Compensation of Current Harmonic Distortion in a Grid-Connected Photovoltaic System via an LC Filter Sara Khalil(B) , Naima Oumidou, and Mohamed Cherkaoui Engineering for Smart and Sustainable Systems Research Center, Mohamadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected]

Abstract. The growing use of power electronic equipment, which generally indicates non-linear behavior, results in the injection of harmonics into the grid. Meanwhile, distributed energy resource systems might impose harmonics on the electrical grid. Hence, appropriate mitigation methods must be applied to improve the quality level of electric power. With the presence of harmonic currents, the fundamental sinusoid is distorted and the harmonic distortion rate increases. Thereby, among the solutions, we can suggest minimizing this problem and integrating a passive filter in parallel to the output of the inverter. This filter represents a weak cost and proves to be suitable for a large power grid connection. In this work, we simulate under Matlab/Simulink and changing environmental conditions of a 100 kW grid-connected photovoltaic system that includes a harmonic filtering system. The boost chopper is controlled by the incremental conductance (IC) type maximum power point tracking (MPPT) algorithm. The latter was selected for maximum utilization of the tested PV system in various illumination and panel temperature. The simulation results showed a remarkable decrease in the harmonic distortion rate of the current delivered to the grid from 639.97% to 18.77% in the Three-phase inverter (VSC) and 15,05% in the Single-phase cascaded H-Bridge five-level inverter, and an improvement in the shape of the current delivered to the grid. This proves the validity of adding a filtering system to reduce the harmonics. Thus, the cascaded H-bridge multilevel inverter is more suitable for photovoltaic applications. Keywords: Photovoltaic system (PVs) · renewable resources · harmonic filter · harmonic distortion rate · grid power quality

1 Introduction To meet the large energy demand, distributed energy resource systems are seen as a privileged way to deal with this situation as they are dependable, minimize peak load, limit grid losses, and sustain power quality [1]. Among the decentralized energy resource systems (DER), the photovoltaic system enjoys an important way in the global electricity sector. It is reviewed among the enhanced types of renewable energy thanks to the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 632–642, 2023. https://doi.org/10.1007/978-3-031-29857-8_63

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flexibility it offers for various loads. Owing to the integration of DERs, the power quality is influenced by both the loads and the sources. Thus, integrating a photovoltaic system (PVs) may affect a certain effect on the power grid; on one side, the impact on the power flux, the voltage and protection plane, and the power quality. Furthermore, the features, processes, and disturbances of the distribution network affect the function of a photovoltaic system. To prevent the power system components from malfunctioning or even being destroyed, identifying the origin of finding appropriate solutions is essential. The principal types of perturbations that can deteriorate the power quality are voltage drops and short shutdowns, voltage unbalance, harmonic perturbations, and voltage surges [2]. 1.1 Contributions As described previously, the most important problems and challenges encountered when integrating solar photovoltaic energy systems into the grid. These include primary technical and power quality issues, as well as secondary economic issues [18]. This paper focuses on the harmonic distortions that are caused by the increasing utilization of power electronic equipment. These last generally show non-linear comportment, which causes the injection of harmonics into the grid. In addition, in several years there has been a lot of work done to develop standardized requirements for the grid interconnection of small renewable energy generation facilities, especially solar photovoltaic systems [12– 16]. To overcome the above-mentioned problem, we propose a solution to minimize the harmonic currents injected into the grid, namely the parallel passive filter. The principal contributions of this research paper are listed below: • Modelization of the global system studied and the problem of harmonics created by grid-connected PV systems are discussed, and an analysis of this problem is effected in this paper. • A PI controller with a harmonic compensator is applied to deliver a balanced, sinusoidal current with minimal harmonic distortion rate and power losses. • A simulation of the connection of a photovoltaic system to the grid with a filtering system using two types of inverters: A three-phase inverter (VSC) and a Single-phase cascaded H-Bridge five-level inverter is implemented to compare and justify their performances. 1.2 Paper Organization The remainder of this paper is organized as follows: in Sect. 1, an introduction is followed by a presentation of related work in Sect. 2. Section 3 presents a description and modeling of the global system studied. The analyses of the obtained outcomes are reported in Sect. 4. Finally, in Sect. 5 a conclusion of this work is given.

2 Related Works As a result of the non-linear nature of the sun, a photovoltaic system output isn’t stable, thus the equipment based on power electronics and their control structure are deployed

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to assure waveform stability of the power supply. To connect to the grid, an inverter is applied to generate the AC voltages and currents from a DC power source. Various works in the literature have been oriented to solve this problem and identify the most suitable inverter structure for grid-connected PV systems offering better performance. The research reported in [7], presents comparative literature on DC/AC converter topologies for grid-connected PV systems. [8] submit a global overall view of power inverter topologies for grid-connected PV systems and their control structure. Authors in [22] discuss a comparison of a variety of topologies of multilevel H-bridge inverters. Then, they give a simulation of these topologies in the case where we have a resistance-inductance (RL) load. Another work in [15] proposes a simple method for a multi-resonant proportional current controller (PMR) to selectively mitigate the current harmonics in a utility-coupled inverter. A description of a cascaded H-bridge type multilevel inverter is presented, as well as a control strategy to produce enhanced power quality. Validation of the suggested system performance realized using simulation results in Matlab/Simulink environmental [16–19]. An overall view of a PV systems integration into the network is presented in [12], actual solar-grid integration technologies are identified, these advantages are outlined, and the characteristics of solar systems and the issues of integration are discussed. The challenges of this integration and the compatibility criteria on the PV system and grid side are discussed. Moreover, the most important impacts of this integration are reviewed in [13–26]. The authors of [14] review the current development of PV systems integration into the grid. Furthermore, different methods for synchronization of the grid are elaborated. Also, The most relevant challenges for building a smarter and more effective GPV generator system in the future are presented. A detailed analysis of power quality issues related to the integration of renewable distributed generation systems to the grid and the current state of research on mitigation techniques are discussed in [24]. The authors in [25] discuss the most affecting problems of the grid performance, in the case coupled to the PV system, and the solutions to overcome these problems are also highlighted. Several studies in the literature have discussed the issues related to the connection of photovoltaic installations to the grid. The authors in [4], present the voltage problem created by the grid-connected photovoltaic system and then an analysis of these causes. The study reported in [2], presents a discussion on the impacts of the centralized and decentralized based PV systems distributed generation into the grid considering grid safety conditions and a comparison based on the results of the grid evaluation between both these systems considering the analysis of power flow, short circuit and harmonic problem. On top of that, a financial analysis was performed for both cases.

3 Description and Modeling of the Global System Studied As illustrated below, the studied system is composed of a photovoltaic system connected to the boost chopper with a DC bus and connected to the grid with an inverter. To minimize the harmonic contentment, a parallel passive filter is placed on the output of the inverter Fig. 1.

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Fig. 1. Simplified schematic of the system studied.

3.1 Photovoltaic cell To comprehend the electrical comportment of conventional cells, it is necessary to use the equivalent electrical circuit of the photovoltaic cell. The photovoltaic cell electrical model is presented in Fig. 2 [9]:

Fig. 2. The photovoltaic cell electrical model.

The expression of the output current can be written as below:   qVj   Vj Iout = Iph − IS e nKT − 1 − Rsh

(1)

The voltage at the terminals of the load resistor is: V = Vj + Rs Iout From the previous Eq. (1) and (2), we deduce the expression as below:   q(V−R I   s out Rs Iout − V nKT − IS e −1 Iout = Iph + Rsh

(2)

(3)

Considering the equivalent circuit of an ideal cell without losses, i.e. Rs < < Rsh, then (1/ Rsh) → 0, so the Eq. (3) becomes:  V+I .R  out s KT (4) Iout = Iph − I0 e nVT − 1 or : VT = q where q is the electron charge and it’s equal to 1,602.10−19 C. K is the Boltzmann Constant 1,381.10−23 J/K and n is the Non-ideality factor of the junction between 1 and 5 in practice, and T is the effective temperature of the cell in kelvin. From Eqs. (2) and (4), it can be noted that the output voltage and current of photovoltaic systems are influenced by the temperature. For this purpose, particular consideration should be given to the THD%, which can be determined from Eqs. (5) and (6)

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[3]:  THDV =

N−1 2 k=2 Vk

 THDI =

V1 N−1 2 k=2 Ik

I1

(5)

(6)

where N is the maximum number of harmonics during a period T, and k is the coefficient that indicates the order of the voltage and current harmonics. 3.2 The shunt passive filter Two types of solutions are possible to avoid the negative effects of harmonics. The first one is to employ static converters with little or no pollution, while the second one is to filter the harmonic components to minimize the effects of harmonics on the current or voltage injected [6]. The anti-harmonic filter is a filter used in the distribution of electrical energy, which eliminates harmonic currents or harmonic voltages in electrical networks. There are two types of harmonic filters; the first one is a passive filter and the second is an active filter. In this work, we are interested in the second solution namely a passive filter Fig. 3. These are used to suppress harmonic currents and reduce the voltage distortion that occurs in sensitive parts of the system [10, 11].

Fig. 3. Diagram of the shunt passive filter.

As shown in Fig. 4, the passive shunt filter consists of capacitors and inductors that are adapted to resonate at a single frequency or across a frequency band. Adopting specific values and designs for the filter is necessary. The value of capacitor C (in (F)) and L (in (H)) are indicated as follows [6]: Cmax =

10% Pnom 2 3 × 2π × f × Vnom VDC Imin = 16×IL−max ×fsw

(7)

4 Simulation of a Grid-Connected Photovoltaic Power System The power quality analysis was performed for the grid-integrated photovoltaic system. As described in the previous section, the photovoltaic chain is composed of power electronic

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converters that are the origin of the harmonics that appear in the system. The connection of a passive filter in parallel is one of the solutions proposed to reduce harmonic pollution owing to its low cost and its adaptation efficaciously to the high power grid connection. To illustrate the interest in such a solution, we will use a 100 kW photovoltaic array connected to the 25 kV grid via a boost converter and a three-phase voltage source converter (VSC). Maximum power point tracking (MPPT) is implemented in the boost converter using a Simulink model using the “Conductance increment” algorithm. This type is used because it has the benefit of following the maximum power point (MPP) during rapid irradiation changes. The inverter (VSC) is controlled to provide a balanced and sinusoidal current waveform with minimal total harmonic distortion (THD) and power losses. The control method used employs two cascaded control loops, an external control loop that regulates the DC link voltage and a second control loop that regulates the grid currents (Id and Iq ). The voltage outputs Vd and Vq of the current controller are converted into three modulation signals Uabc−ref used by the PWM generator. Also a load, a three-phase step-up transformer 100 kVA 220V/22kV to connect to the grid. The expressions for the active and reactive power exchanged between the inverter and the grid (P and Q respectively) are expressed as follows: P = Vd Id + Vq Iq

(8)

Q = − Vd Iq + Vq Id

(9)

where Vd , Vq are the active and reactive components of the grid voltage, and Id , Iq are the active and reactive components of the grid current respectively. From the above-mentioned relations, we can note the difficult evaluation of the injected apparent power. And to treat this problem, we will use the PLL (Phase Locked Loop) technique that interlocks the frequency of the grid to force the q-axis component of the voltage on the grid side of the transformer to be zero, thus Vq = 0. So : P = Vd Id and Q = −Vd Iq

(10)

However, this coupling creates some effects such as fluctuations, harmonic disturbance, and power factor decrease. This work aims to investigate and analyze the power quality of the studied system based on the total harmonic distortion (THD) term. The latter is used as an indicator of the quality of signal processing in equipment. And subsequently, the study will suggest a solution to surmount the increase in the total harmonic distortion (THD) during the function of the system. Figures 5 and 6 show the grid voltage and current respectively. As shown in Fig. 6, we can see the deformed shape of the injected current into the grid (non-sinusoidal). From Fig. 8, we can obtain the following results, detailed below: The pulses to the boost converter and the inverter are blocked between 0 s and 0,05 s, as well as the photovoltaic voltage corresponds to its open-circuit voltage with VPV = V S × VOC = 5 × 64,2 = 321 V. At t = 0,05 s, the boost converter, and the inverter are unlocked. The duty cycle is fixed with d = 0,5 and the solar irradiation is regulated to 1000 W /m2 The balanced state is achieved at t = 0.25 s, so the result in photovoltaic voltage is VPV = (1 − d) × VDC = (1 − 0.5) × 500 = 250V, and the

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Fig. 4. Simple component of the grid voltage as a function of time zoomed

Fig. 5. The three-phase grid current vs time

Fig. 6. The step-up chopper output voltage Vdc and its reference Vdc−ref = 500 V vs time

Fig. 7. Grid power vs time

calculated photovoltaic generator output is Pcalculée is 96 kW. Whereas the maximum power when the irradiation is 1000 W/m2 and a duty cycle d = 0.453 is 100,7 kW. As illustrated in Fig. 7, the value of the duty cycle d is determined when the input voltage of the boost converter reaches its maximum value and the output voltage is constant. The MPPT control is started at 0,4 s and begins to regulate the photovoltaic voltage by varying the duty cycle d to extract the maximum power, while the duty cycle is d = 0,453. At 0.6 s, the photovoltaic voltage corresponds to its average voltage with VPV = 274 V. The irradiance is reduced from 1000 W/m2 to 250 W / between 0.7 s and 1.2 s.

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At t = 1,2 s, the irradiance is diminished to 250 W/m2 , the duty cycle d equals 0,485, and the photovoltaic voltage and power are respectively VPV = 255 V and Pcalculated = 22,6 kW. Several changes of irradiance are implemented to show a better performance of the MPPT controller from t = 1,5 s to t = 3 s. The active power injected into the grid attains its maximum value between t = 5s and t = 6s, which demonstrates that is placed at zero, and the power factor is equal to 1. The network voltage and current spectrum analysis are illustrated in Fig. 9 and Fig. 10 without and with filter, respectively.

Fig. 8. The network voltage and current spectrum analysis without filter respectively.

In Fig. 9, we notice the distorted shape of the current injected into the grid, which shows that these currents are rich in harmonics with a THD equal to 639.97% and the same for the voltage of 45.60%.

Fig. 9. The network voltage and current spectrum analysis with filter respectively (Three-phase inverter (VSC)).

We observe from Figs. 10 and 11, a remarkable decrease in the current harmonic distortion rate, which proves the amazing efficiency of the shunt passive filter. We have treated thereafter, a simulation of the same system but, we used a singlephase cascaded H-Bridge five-level inverter using a proportional-integral controller (PI),

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Fig. 10. The network voltage and current spectrum analysis with filter respectively (Single-phase cascaded H-Bridge five-level inverter).

to compare these results with these previous results. The following Table 1 summarizes the results: Table 1. Comparative simulation results. Three-phase inverter (VSC)

Single-phase cascaded H-Bridge five-level inverter

Voltage THD in %

45,57

18,01

Current THD in %

18,77

15,05

Under different solar irradiations, the studied system was simulated under a Simulink environment. The simulation results demonstrated that the connection of a photovoltaic system to the grid can create power quality problems, namely harmonics. Thereby, the. efficiency of the shunt passive filter mitigates the harmonic distortion rate of the grid side current generated by non-linear loads.

5 Conclusion In this research work, the power quality problem with grid integration of the photovoltaic system is addressed. The main objective of this study is to effectively minimize the harmonics within this system. The proposed system consists of Photovoltaic panels connected to the boost chopper via a DC bus and connected to the grid via an inverter. Then, the current harmonic distortion problem was presented, and an analysis of the grid voltage and current spectrum was performed to illustrate the effect of this problem on the system. The five-level cascaded H-bridge inverter with a Proportional-Integral controller was found to be better than the three-phase inverter (VSC). In the future, we will focus on developing other inverter control strategies that minimize harmonics and ensure system stability.

References 1. Khatib, T., Elmenreich, W.: Modeling of Photovoltaic Systems Using MATLAB: Simplified Green Codes, pp. 159–174, Wiley, Hoboken, NJ, USA (2016)

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2. Khatib, T., Sabri, L.: Grid impact assessment of centralized and decentralized photovoltaic based distribution generation: a case study of power distribution network with high renewable energy penetration. Hindawi Math. Problems Eng. 2021, Article ID 5430089, 16 (2021) 3. Farhoodnea, M., Mohamed, A., Shareef, H., Zayandehroodi, H.: Power quality impact of grid-connected photovoltaic generation system in distribution networks. In: IEEE Student Conference on Research and Development, Department of Electrical, Electronic and Systems Engineering University Kebangsaan, Malaysia (2012) 4. Lan, Z., Long, Y., Rao, Y.: Review of voltage control in low voltage distribution networks with high penetration of photovoltaics. In: 2nd International Conference on Information Technologies and Electrical Engineering (ICITEE), article no 89, pp. 1–6 (2019) 5. Jo, S.-H., Son, S., Park, J.W.: On improving distortion power quality index in the distributed power grid: IEEE transaction on smart grid, vol 4, pp. 586–595, March (2013) 6. Chen, Z., Blaabj, F., Pedersen, J.K.: A study of parallel operations of active and passive filters. IEEE Xplore (2002) 7. Khalil, S., Oumidou, N., Elkhatiri, A., Cherkaoui, M.: "A critical review of DC/AC converter structures for grid-connected photovoltaic systems”, International Conference of Digital Technologies and Applications (ICDTA). Springer 2, 497-506. (2022). https://doi.org/10. 1007/978-3-031-02447-4_51 8. Hassaine, L., OLias, E., Quintero, J., Salas, V.: Overview of power inverter topologies and control structures for grid-connected photovoltaic systems. Renew. Sustain. Energy Rev. 30. 796–807 (2014) 9. Derbal, H.: Plastic photovoltaic solar cells nanostructured, University of Angers, 2009, n° 929 10. Schneider Electric’s magazine for technological and vocational education “Operational safety”, 2004 11. Schneider Electric Technical Paper No. 199 “The quality of electrical energy” 12. Nwaigwe, K.N., Mutabilwa, P., Dintwa, E.: An overview of solar power (PV systems) integration into electricity grids. Materials Science for Energy Technologies, pp. 629–633, 2019 13. Tobnaghi, D.M.: A review on impacts of a grid-connected PV system on distribution networks. Int. J. Electr. Comput. Eng. 10(1), (2016) 14. Hariri, M.H.M., Desa, M.K.M., Masri, S., Zainuri, M.A.A.M.: Grid-connected PV generation system—components and challenges: a review. MDPI Energies J. Energies 13, 4279 (2020) 15. Schiesser, M., Wasterlain, S., Marchesoni, M., Carpita, M.: A simplified design strategy for multi-resonant current control of a grid-connected voltage source inverter with an LCL filter. MDPI Energies J., Energies 11, 609 (2018) 16. Gregor, R., Pacher, J., Espinoza, A., Renault, A., Comparative, L., Ayal, M.: Harmonics compensation by using a multi-modular h-bridge-based multilevel converter. MDPI Energies J. Energies 14, 4698 (2021) 17. Ezzouitine, K., Boulezhar, A., EL afou, Y.: A cascaded H-bridge multilevel inverter with photovoltaic MPPT control. Periodicals Eng. Natural Sci. (PEN) 6(2), 415–425 (2018) 18. Islam, S.U., et al.: Design of a proportional resonant controller with resonant harmonic compensator and fault ride trough strategies for a grid-connected photovoltaic system. MDPI Electron. J. Electron. 7, 451 (2018) 19. Ahsan, S.M., Khan, H.A., Hussain, A., Tariq, S., Zaffar, N.A.: Harmonic analysis of grid- connected solar pv systems with nonlinear household loads in low-voltage distribution networks. MDPI Sustain. J., Sustain. 13, 3709 (2021) 20. Kumar, A., Mandal, R.K., Raushan, R.P.: Gauri, P.: Grid-Connected Photovoltaic Systems with Multilevel Inverter. IEEE (2020) 21. Sood, V.K., Abdelgawad, H.: Power converter solutions and controls for green energy. Distributed Energy Resources in Microgrids, Elsevier (2019)

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22. Prasad, K.N.V., Chellamma, N., Dash, S.S., Krishna, A.M., Kumar, Y.S.A.: Comparison of photovoltaic array-based different topologies of cascaded H-bridge multilevel inverter. In: International Conference on Sustainable Energy and Intelligent Systems (SEISCON), pp.110–115 (2011) 23. Rüstemli, S., Cengiz, M.S.: Passive filter solutions and simulation performance in industrial plants. Bitlis Eren Univ J Sci Technol 6(1), 39–43 (2016) 24. Bajaj, M., Sing, A.K.: Grid integrated renewable DG systems: A review of power quality challenges and state-of-the-art mitigation techniques. Int. J. Energy Res. 1–44 (2019) 25. Singh, B.P., Goyal, S.K., Siddiqui, S.A.: Grid Connected-Photovoltaic System (GC- PVS): Issues and Challenges. IOP Conf. Series: Mater. Sci. Eng. 594(1), 012032 (2019) https://doi. org/10.1088/1757-899X/594/1/01203

Optimal Sizing of a Grid-Connected Renewable Energy System for a Residential Application in Gabon Rolains Golchimard Elenga(B)

and Stahel Serano Bibang Bi Obam Assoumou

School of Architecture, Tianjin University, Tianjin 300072, China [email protected]

Abstract. Electricity demand is increasing throughout the world, especially in developing countries such as Gabon. Therefore, there is a growing need to develop innovative energy systems that decrease the dependence on conventional power resources and provide a counterbalance in the case of shortages. This work aims to determine the best viable renewable energy solution for a standard two-bedroom house in the city of Libreville, Gabon. The HOMER program is used for modelling and analysis of the hybrid power system composed of wind turbines, solar photovoltaic panels, and batteries to improve the reliability of the system and decrease the cost of electricity. The results show that with a small investment, the project can provide long-term benefits to society, with a payback period of approximately 9 years for a 25-year lifespan. Keywords: Renewable energy system · Grid-connected system · Residential building · Gabon

1 Introduction Power from traditional sources has become extremely hazardous for the environment. These resources are limited and produce greenhouse gas emissions responsible for climate change [1]. Renewable energy sources such as wind and solar can play a significant role in meeting power demands while also mitigating climate change. Several research projects have demonstrated their effectiveness in providing sustainable energy in various locations. However, wind and solar power depend on various weather conditions that make them unpredictable [2]. Therefore, by combining them in hybrid form with other sources, they increase the reliability of the system and minimise its costs. Different methods and tools can be utilised to analyse various aspects of hybrid energy systems (HES). Using HOMER software, Li et al. examined the viability of a hybrid energy system for a residential dwelling in China [3]. Furthermore, Adaramola et al. assessed the economic feasibility of deploying an HES comprised of PV/wind/diesel in rural Ghana using HOMER [4]. Moreover, a study by Baghdadi investigated the viability of the hybrid Wind/PV/diesel/battery using HOMER [5]. The study was conducted to maximise renewable electricity utilisation and fuel savings taking into consideration © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 643–652, 2023. https://doi.org/10.1007/978-3-031-29857-8_64

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renewable resource potential and power demand. Furthermore, Bahramara et al. evaluated the optimal planning of an HES in Algeria utilising HOMER software [6]. Similarly, Amutha and Rajini established that the mix wind/solar/hydro/battery is the best costeffective, sustainable, technologically, financially, and ecologically viable alternative to grid expansion [7]. From the literature, it can be seen that, to date, most of the studies conducted in developing countries are mainly aimed at offering power in areas not connected to power distribution networks or replacing existing nonrenewable systems. This work aims, therefore, to exploit the available on-site power sources and integrate them into the existing network to improve the reliability of the system, reduce recurrent power cuts, and minimise the often-high power cost for middle-income populations. Located in the heart of Africa, Gabon has significant energy resources. However, the power sector is largely dependent on hydrocarbons and biomass. In 2015, the country’s electricity production was calculated at 199 ktoe, with 51.7% of total electricity production from hydro sources, while fossil fuels generated 48.2% [8]. Access to electricity is estimated to be 85% in urban areas, while it is only approximately 35% in rural areas [9]. Despite this pleasant context, electricity in Gabon remains quite expensive, with an estimation of 0.203 $/kWh for a population with a guaranteed minimum wage of 225$ per month. Additionally, continuous light out at places where electricity seems to be accessible still represents a major problem. However, Gabon has a significant hydroelectric potential estimated at 6000 MW [9]. In addition, the average daily radiation in Gabon represents a potential of 4 kWh/m2/day [10]. Furthermore, the average wind speed at 100 m is predicted to be 4.11 m/s. In such cases, decentralised renewable HES, which may incorporate wind, PV, and batteries, may offer viable alternatives. As a result, a hybrid system that combines wind, solar, and battery is a viable application for lowering costs and meeting the electrical demand of Libreville homes. The purpose of this study is to determine the optimum sizing of an on-grid energy system to provide continuous power and improve the reliability of electricity for a standard residential building. First, the building energy load is defined. Then, energy resources are determined. Subsequently, input parameters for the optimisation process are defined. The optimisation process employed includes three objectives: maximising energy generation to meet the total building energy load, minimising the NPC, and reducing the LCOE.

2 Methods and Materials 2.1 Building Description The studied building (Fig. 1) is located in a popular area called Bambouchine in Libreville. It is a 90 m2 dwelling chosen as it represents the standard 2-bedroom house configuration for middle-income families in Gabon. The building includes a living room, dining corner, kitchen, terrace, 2 bathrooms, and 2 bedrooms. Basic appliances include air conditioning, standing fans, lighting, a water heater, and a variety of domestic equipment such as refrigerators, television, and computers. The household is occupied by a family of 4, usually from 5:30 p.m. to 8:30 a.m. 100% of the electricity is provided by the city grid system.

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Fig. 1. Schematic view of the simulation model

2.2 Simulation Software and System Description In this study, the HOMER program is employed to model and analyse hybrid power systems for a residential building. HOMER is an energy modelling program that is utilised to perform the techno-financial evaluation of renewable energy systems (RES). It combines the design of RESs such as wind turbines, hydraulic systems, solar panels, biomass, and batteries. As presented in Fig. 2, the RES in this study consists of photovoltaic panels (PV) and small-scale wind turbines (WT) with battery storage (Batt). Table 1 displays the system components and their cost specifications. Table 1. Techno-economic characteristics of the proposed system’s components Parameter PV module

Wind Turbine

Battery Storage

Converter

Value

Parameter

Value

Rated capacity (kW)

0.285

Capital cost ($/kW)

1200

Tilt angle (°)

0.42

Replacement ($/kW)

1200

Derating factor (%)

80

O&M ($/Yr.)

Ground reflectance (°)

0

Lifetime (Yr.)

Rated capacity (kW)

1.5

Capital cost ($/kW)

17000

Cut-in speed (m/s)

2.5

Replacement ($/kW)

17000

Cut-off speed (m/s)

20

O&M ($/Yr.)

250

Hub height (m)

12

Lifetime (Yr.)

20

Nominal voltage (V)

12

Roundtrip efficiency (%)

Nominal capacity (kWh)

3.1

Capital cost ($/kW)

400

Maximal charge current (A)

45

Replacement ($/kW)

400

Maximal discharge current (A)

300

O&M ($/Yr.)

5

Minimum state of charge (%)

20

Lifetime (Yr.)

5

120 25

80

Inverter efficiency (%)

93

Capital cost ($/kW)

450

Rectifier efficiency (%)

93

Replacement ($/kW)

450

Rectifier relative capacity (%)

93

O&M ($/Yr.)

5

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Fig. 2. Schematic representation of the proposed system

2.3 Resources and Input Data 2.3.1 Solar Radiation Data on the mean daily solar radiation over 22 years for the studied location were collected and used. The site receives high solar radiation, with an average yearly radiation of 4.70 kWh/m2/day. As illustrated in Table 2, the solar peak months are February and March. 2.3.2 Wind Resources Wind data for the evaluated site are collected at 10 m. The site has a wind speed potential ranging between 3.70 and 4.63 m/s, offering the potential for wind power exploration. Table 2 illustrates the wind speed data of the evaluated location. 2.3.3 Grid Availability The building is located in Libreville. To meet the power demand of the residents, the site is connected to the city grid. Nonetheless, the quality of the power supply remains poor and unreliable. Aside from regular quarterly grid disruptions, the city faces numerous power outages during peak load months. The proposed microgrid will contribute by supplying continuous electrical power to the load and improving the building’s electricity reliability. Through a net-metering system, excess electricity generated by the RES can be sold back to the grid. The cost in USD/kWh of electricity in Gabon is estimated to be 0.217. However, to date, no feed-in tariff has been set to encourage the utilisation of renewable energies in Gabon. For this purpose, a feed-in tariff value of $0.10/kWh has been considered in this study. 2.3.4 Load Profile From the demand standpoint, the load profile of an investigated system is a key parameter in the optimisation process. In this study, monthly energy bills and household electrical meters were used to extract data on the building’s energy consumption, and the overall

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Table 2. Resource data. Solar radiation (kWh/m2/day), wind speed (m/s), and temperature (°C) Month

Air Temperature

Solar Radiation

Clearness Index

Wind Speed

January

26.47

5.04

0.50

3.90

February

26.88

5.32

0.51

3.97

March

27.11

5.19

0.49

3.82

April

27.02

4.86

0.48

3.70

May

26.43

4.60

0.48

3.70

June

25.03

4.46

0.48

4.36

July

24.12

4.46

0.47

4.52

August

24.33

4.57

0.46

4.63

September

25.01

4.64

0.45

4.54

October

25.55

4.26

0.41

4.35

November

25.94

4.30

0.43

3.99

December

26.21

4.72

0.48

3.84

Yearly average

25.84

4.70

0.47

4.11

yearly energy use was evaluated at 3713 kWh/year with a daily power demand calculated at 10.17 kWh/day and a peak load evaluated at 1.21 kW.

Fig. 3. Building load profile

The primary uses of electricity in the dwelling include air conditioning, standing fans, lighting, a water heater, and a variety of domestic equipment such as refrigerators, washing machines, television, and computers. According to the building occupancy

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schedule, the peak load occurs in the evening when the parents are back from work and the children from school, which corresponds to the time when all the appliances as well as the lamps are in operation mode. The load profile of the studied house is displayed in Fig. 3. 2.4 Optimisation The optimisation approach used in the study has three goals: maximise power production to meet the whole building energy demand, minimise the NPC, and reduce the LCOE. HOMER simulations are run for a range of energy system configurations, including various configurations of PV, wind, and batteries of different capacities. In the optimisation findings, the HOMER tool calculates the NPC and LCOE of all viable systems and ranks them in ascending order. The optimal energy system is the one providing the lowest LCOE and NPC values. Here, four (04) main steps are needed. First, the system configuration layout, as well as the electric load specifications and energy source data of the chosen area, are implemented. Second, system components and their cost information are entered. Third, utilising the above-provided data, an optimisation is performed to analyse and offer a set of system configurations depending on the lowest LCOE and NPC. It should be emphasised that the NPC indicates the overall cost of the hybrid system’s implementation and operation over its lifespan, which includes the initial investment as well as replacement of components, operations and maintenance, and fuel. Fourth, the outcomes of multiple systems are compared, and the optimal system is chosen on a techno-financial basis. The HOMER program calculates the LCOE of the renewable energy system as follows: NPC =

Ctot CRF(i, n)

(1)

where Ctot , CRF, i, and n are the total annualised cost of the system, interest rate, and lifetime, respectively. CRF is determined using Eq. (2): CRF(i, n) = i=

i(1 + i)n (1 + i)n − 1

i − f 1+f

(2) (3)

where i, i’, f, and n represent the real discount rate, the nominal discount rate, the estimated inflation rate, and the number of years, respectively [11]. The LCOE for the RES is defined by Eq. (4): LCOE =

Ctot Etot

(4)

With Etot , the total electrical load served. In this study, the lifespan is considered to be 25 years, while the discount rate is 10% and the inflation is 4%.

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3 Results and Discussion 3.1 Electricity Generation To determine the optimum energy system, optimisation was performed. As presented in Fig. 4, the optimised system consists of 3.21 kW of PV panels and a 1.21 kW converter and produces a renewable energy fraction of 55.9%. Although the area has a high potential for wind energy, the cost of equipment for the installation of a wind system is currently very high at the national level compared to those of solar photovoltaic installations. The electricity generated using a solar PV system is 4502 kWh/yr. On the other hand, the building’s total electricity use is 3713 kWh/yr, while the electricity sold to and purchased from the grid is 2119 kWh/yr and 2571 kWh/yr, respectively.

Fig. 4. Optimisation results for the evaluated systems

Fig. 5. Monthly energy consumption of the baseline model

Moreover, it can be seen that a large amount of electricity can be exported to the grid in peak months (February to April), helping to reduce grid energy blackouts in the current location. Similarly, a large amount of energy can be sold to the grid from June to September due to the low energy consumed and the high energy produced in this period. The monthly electricity production to supply 10.17 kWh/day with a peak load of 1.21 kW by this optimised system is summarised in Fig. 5, while the PV panel and inverter outputs are illustrated in Figs. 6 and 7, respectively.

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Fig. 6. Solar PV output

Fig. 7. Inverter output

Fig. 8. Energy purchased from and sold to the grid

From these figures, we can observe that the PV operates for a maximum of 4376 h per year, generating 2.94 kW from its 3.21-kW rated capacity. Similarly, the inverter (Fig. 7) operates for 4376 hours per year, generating 1.21 kW of its rated capacity. In addition, energy purchased from and sold back to the grid is displayed in Fig. 8. We can see that from 7 a.m. to 6 p.m., the entire building load can be covered by the proposed renewable energy system, and no grid electricity is used. At night, however, the grid’s energy is used since the PV system is technically not operational. Furthermore, electricity can be sold back to the grid from 7 a.m. to 6 p.m. since PVs are working at their maximum operating capacity. 3.2 Economic Evaluation To examine the financial attractiveness of the proposed microgrid, the LCOE and NPC of the system were determined. The results demonstrated that the proposed system has the best economic features over the project lifetime, unlike the baseline case, which relies only on the grid. The optimal system is the combination of 3.21 kW of gridconnected PV panels with an NPC of $9,794 and an LCOE of 0.129 $/kWh compared

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to $10,527 and 0.217 $/kWh for the reference grid-only system. Although the WT/Batt, PV/WT, and PV/WT/BATT configurations connected to the grid resulted in higher RF (%), 73.9, 71.7, and 72.7, respectively, from an economic perspective, they resulted in higher NPC and LCOE. For instance, the NPC in USD for the WT/Batt, PV/WT, and PV/WT/BATT systems is estimated at 46703, 27158, and 28454, respectively, while the LCOE in USD/kWh of the latter is calculated at 0.580, 0.336, and 0.342, respectively, making these configurations less attractive compared to the PV-grid system. The cost overview of the project investment is presented in Fig. 9.

Fig. 9. The project’s component cost summary

Table 3 depicts a detailed presentation of the financial indicator outcomes. Based on the findings, it can be seen that a large part of the investment goes to the converter replacement costs, given its short lifespan. On the other hand, due to the high potential of solar energy resources, more energy is sold to the network than is purchased. As a result, a small investment goes to the grid. It can be confirmed that with a small investment, the project may provide long-term benefits to society with a payback period estimated at less than 9 years for a 25-year lifetime. Table 3. Financial indicators for the optimised system Description

Value

Present worth ($)

733

Annual worth ($/year)

56

Return on investment ROI (%)

4.80

Internal rate of return IRR (%)

7.40

Simple payback period (year)

8.90

Discounted payback (year)

17.83

4 Conclusion This study aimed to determine the optimal size of a renewable power system to provide continuous energy and enhance electrical reliability for a residential building in

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Libreville. The research concludes that power produced by the PV-based microgrid is a viable means of using renewable energy to provide stable power to areas with unreliable energy supply infrastructures. The suggested microgrid system may deliver energy to the building at 0.129 dollars per kWh, which is 40.55% less than the rate of a grid-only system.

References 1. Sahoo, S.K.: Renewable and sustainable energy reviews solar photovoltaic energy progress in India: a review. Renew. Sustain. Energy Rev. 59, 927–939 (2016) 2. Hedberg, D., Kullander, S., Frank, H.: The world needs a new energy paradigm. Ambio 39(1), 1–10 (2010) 3. Li, C., et al.: Techno-economic feasibility study of autonomous hybrid wind/PV/battery power system for a household in Urumqi, China. Energy 55, 263–272 (2013) 4. Adaramola, M.S., Agelin-Chaab, M., Paul, S.S.: Analysis of hybrid energy systems for application in southern Ghana. Energy Convers. Manage. 88, 284–295 (2014) 5. Baghdadi, F., Mohammedi, K., Diaf, S., Behar, O.: Feasibility study and energy conversion analysis of stand-alone hybrid renewable energy system. Energy Convers. Manage. 105, 471– 479 (2015) 6. Bahramara, S., Moghaddam, M.P., Haghifam, M.: Optimal planning of hybrid renewable energy systems using HOMER: a review. Renew. Sustain. Energy Rev. 62, 609–620 (2016) 7. Amutha, W.M., Rajini, V.: Cost benefit and technical analysis of rural electrification alternatives in southern India using HOMER. Renew. Sustain. Energy Rev. 62, 236–246 (2016) 8. United Nations Environment Programme (2017). Energy Profile: Gabon. https://wedocs.unep. org/20.500.11822/20511 9. LATribuneAfrique: Gabon, l’un des meilleurs taux d’accès à l’électricité du sous-continent. Accessed 10 Oct 2022 10. Ministère Des Mines, De L’Energie, Du Pétrole Et Des Ressources. Situation énergétique du Gabon 11. HOMER. Real Discount Rate. https://www.homerenergy.com/products/pro/docs/latest/ real_discount_rate.html. Accessed 22 Sept 2022

A Comparative Study of P&O and Fuzzy Logic MPPT Algorithms for a Photovoltaic Grid Connected Inverter System Hajar Ahessab(B) , Youness Hakam, Ahmed Gaga, and Benachir El Hadadi Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems Engineering, Automation, Signal, Telecommunications and Intelligent Materials, (ISASTM), Department of Physics, Polydisciplinary Faculty (FPBM), Sultan Moulay, Slimane University (USMS), Beni-Mellal, Morocco [email protected]

Abstract. Renewable energy sources are essential for maintaining the electricity network and supporting disconnected loads when the electrical demand is rising quickly. There are several types of renewable energy, including solar, wind, and tidal power. Sun radiation reaches the Earth’s surface in huge quantities, making solar power a clean energy source. Maximizing the quantity of electrical power that can be extracted from the solar energy system is the goal of this article. The notion of MPPT methods, which considerably boost the efficiency of the solar PV system, is investigated in detail in this paper. To maximize the energy conversion efficiency of PV systems, this paper compares the two most prevalent algorithms, fuzzy logic, and P&O methodologies, using simulation based analysis. To determine the PV module properties, simulation analysis, and results are performed. Keywords: Perturb and Observe (P&O) · Fuzzy logic · MATLAB-Simulink

1 Introduction Due to the mitigation of climate change, the avoidance of the use of fossil fuels, and the widespread availability of solar radiation, solar energy is currently one of the most essential renewable energy sources [1]. Greening the grid is encouraged by improved technology, lower costs, and efficient systems. To supply the rising demand for electricity, dispersed generators are now inescapably connected to the utility system. The grid code criteria for power supply and demand must be followed throughout the integration of such a generator [2]. As illustrated in Fig. 1, a solar inverter system consists of a solar panel, a DC/DC converter, and a DC/AC converter, which are all connected by a DC link capacitor. Current research focuses on MPPT and advanced power converter topologies to increase the efficiency of power generation in solar PV power plants. In solar PV power facilities, central inverters and string inverters are often employed [3]. A defective I as a function of V characteristic, an area develops in the darkness panel, and the maximum © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 653–663, 2023. https://doi.org/10.1007/978-3-031-29857-8_65

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Fig. 1. Grid connected PV system.

point of power drops. When a shadow is cast on a particular solar PV panel [4]. PV modules, however, are unable to operate at their individual MPP [5]. This research focuses on the power optimizer architecture to understand the aforementioned problems, such as panel level MPPT, end of bypass diodes, and mismatch losses. Each solar PV module has a DC/DC converter called the Power Optimizer, which works to capture MPPT and cut losses [6, 7]. To maximize power point monitoring, solar PV based power plants to enhance energy output. Three categories are used categorize MPPT [1] the direct technique (P&O, ICN); (2) the indirect approach (fractional short circuit current, open circuit voltage), and (3) the self calculating approach [8]. To obtain the most power possible output of the solar PV panel, the Perturb, and observation (P&O) method was introduced in [14]. Two sensors are required to operate this MPPT system when an abrupt change in irradiation causes the MPPT to oscillate. These sensors keep an eye on the current and voltage [2]. The originality of the work lies in the design and implementation of a DC Boost inverter for a three phase, grid connected solar PV system with a PID controller for inverter DC/AC and LCL filter to eliminate the harmonic PV modelling system. The cornerstone of a PV system is the PV cell. A PV model is made up of different PV cells connected in parallel and series. Series connections raise the voltage of the entire system, whereas a parallel connection raises the current flowing through the module [8]. The solar temperature and irradiation intensity affect how much power the PV system produces. As shown in Fig. 2 [8], a photogenerated version of a well known practical electrical solar cell circuit can be modelled using current (Iph), a shunt resistor (Rsh) that expresses a leakage current and is connected in parallel with an inverted diode, and a series resistor (Rs) that reflects internal losses brought on by the current flow. An example of an analytical expression model for the output current produced by a solar cell is shown below [9, 10]: I = Iph − ID − Ish

(1)

where I the is output current, ID is the diode current, Ish is the shunt current, and Iph is the photogenerated current.

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Fig. 2. Equivalent photovoltaic cell circuit.

The Shockley diode equation allows us to define the current that is diverted through the diode as [5, 8]:   q(V + RS I ) −1 (2) ID = I0 exp mKTc The mathematical representation of the current in a PV cell is as follows: [5, 8]:   q(V + RS I ) (V + Rs I ) I = Iph − I0 exp −1 − (3) mKTc Rsh where q is the elementary charge, K is the Boltzmann gas constant, Tc is the absolute cell temperature (k), Io is the diode saturation current (A), and m is the diode quality factor. The single diode and double diode variants of PV modules are the two most common types. We can utilize a double diode model to precisely create a model of the PV. The single diode model is utilized in this work due to its accuracy and simplicity. The following can be used to illustrate the current maximum power point (Imp):       Vmp + Rs Imp q Vmp + RS Imp −1 − (4) Imp = Iph − I0 exp mKTc Rsh The power at the maximum power point (Pmax), is determined by:        Vmp + Rs Imp q Vmp + RS Imp Pmax = Vmp Iph − I0 exp −1 − mKTc Rsh

(5)

where Vmp and Imp are the largest panel voltage and the largest panel current respectively.

2 Design DC/DC The fundamental boost converter in Fig. 3 has been constructed for testing and is intended for the rating. The discrete elements of the circuits were designed in accordance with the following standard. where Vo is the voltage, S is the MOSFET switch, C is the capacitor, L the inductor, D the power diode, Vs the dc voltage, and D the power diode. The design equations for the boost converter are as follows, and it is operated in DCM mode.

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Fig. 3. Fundamental Boost converter.

The equation for the relationship between the input voltage and the output voltage is: Vo =

Vs 1−D

(6)

Peak to peak output ripple in the load current is: iL =

Vs DT L

(7)

Peak to peak output ripple in the load voltage is: VO =

VO D RCf

(8)

3 MPPT Implementation Control Techniques The maximum PV power is found at a specific point on the curve (MPP). Since the power production of a solar PV system changes with cell temperature and radiation exposure, MPPT is needed. The fundamental MPPT principle is to force PV devices to run at the highest effective voltage to maximize the profit amount of power from them [11, 12]. 3.1 Algorithm for Perturbing and Observing MPPT The MPPT method is often governed by the perturb and observe algorithm for the PV generator. It has a straightforward structure. And is inexpensive. And simple to execute. Figure 4 illustrates how a solar panel behaves while signaling MPP and the operating principle. It assumes that the change in PV power seen as a result is as follows: The PV module voltage disturbance should increase as it approaches the MPP, indicating an increase in the PV module output power, when the operational point of a photovoltaic panel is on the left of the curve The photovoltaic module voltage disturbance should be decreased toward the MPP if the PV module operational point is on the right side of the curve. The flow diagram for implementing the P&O method is shown in Fig. 5. The practical current and voltage from the PV system measurements first. The real power of the PV module is then determined by the product of voltage and current. Then, it will determine

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Fig. 4. Solar panel performance showing MPP and operational theory.

if P = 0 or not by checking the status. If this requirement is met, the MPP serves as the operational point. It will search for a different state where P > 0 if it is unacceptable. It will be confirmed that V > 0 if this condition is satisfied, and the operating point is located on the left side of the MPP if it is satisfied. If the V > 0 requirement is not satisfied, on the right part of the MPP, the functional point is located. Up to the MPP, this procedure was repeatedly repeated. Thus, the P&O algorithm always strikes a compromise between sampling rate and intervals.

Fig. 5. The perturb and observe algorithm diagram.

3.2 Principle Fuzzy Logic A technique called fuzzy control enables the creation of nonlinear controllers using heuristic data derived from expert knowledge. The block diagram of a fuzzy controller is shown in Fig. 6. The input signals must be processed by the fuzzification block, which

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also gives them a fuzzy value. The collection of guidelines, which are based on process expertise, enables linguistic control over the variable descriptions. The inference process interprets the data while taking the rules and membership functions into consideration. The inference methods confusing information are converted into information that can be utilized to control the process via the defuzzification block.

Fig. 6. Diagram for a fuzzy logic controller.

It was decided to develop a fuzzy controller with two inputs and one output. The error and change in Error are the two input variables, and they are displayed in Eqs. (9) and (10) for sample period n. P(n) − P(n − 1) P = V V(n) − V(n − 1)

(9)

Error = Error(n) − Error(n − 1)

(10)

Error(n) =

The increase in duty cycle (α), the output variable, can have positive or negative values depending on where the operational point is. To power the load, the DC/DC converter receives this output. The duty cycle was calculated using an accumulator and the value of D provided by the controller. Look at Eq. (12). α(n) = α(n − 1) + α(n)

(12)

Since the DC/DC converter and fuzzy control were created using the electrical specifications of the PV module under consideration, the calculations performed are applicable to PV modules with powers up to 65 W. The change in error is one of the inputs to the fuzzy controller, which necessitates a differentiation operation that makes the computations more difficult and can lead to mistakes when measuring small, noise-sensitive powers. 3.3 Fuzzy Logic MPPT Algorithm The MPPT controller has been designed using fuzzy logic controllers based on PV cell performance. In this article, a fuzzy logic based approach for tracking optimal power is suggested. In this study, many subsystems and components have been modelled and analysed. We validated the models by testing and integrated several models to create an MPPT model with the best power. In this work we focused on maximum power with fuzzy logic with a simple method, and we injected it into grid power by inverter DC/AC that has been commanded with PWM and controller PID. A filter LCL is used to eliminate harmonics. We have simulated in MATLAB Simulink as shown in Fig. 7.

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Fig. 7. Simulated in MATLAB Simulink.

4 Results and Discussion The output voltage of the boost converter with two MPPT techniques is obtained in MATLAB/Simulink at a radiation of 1000 W/m2 and a temperature of 25 k. 4.1 P&O Algorithm Results, PV Side This simulation results of this method at the PV side are shown in Fig. 8, which shows the voltage characteristics of the boost converter profile over time.

Fig. 8. Output voltage of Boost inverter using P&O.

The output voltage of the boost converter controlled by the P&O algorithm contains fluctuations, as shown in Fig. 8. The DC/DC system response time is approximately

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0.15 s, and the system responds slowly, so the follow up of the peak power point is not perfectly influenced by the chain of injection in the networks. 4.2 Results on the PV Side of the Fuzzy Logic Algorithm The fuzzy logic method simulation results at the PV side are shown in Fig. 9

Fig. 9. Output voltage of Boost converter using fuzzy logic controller.

The boost converter output signal as shown in Fig. 9 is stable at 600 V with a response time of approximately 0.05 s therefore MPPT based on fuzzy logic tracks the voltage continuously with fewer fluctuations and has less overshoot with a fast tracking time compared to the P&O MPPT algorithm. 4.3 Results of the Grid Side P&O Method The grid side simulation results of the P&O approach are shown in Figs. 10, 11, 12 and 13. In addition, it shows the DC voltage input to the inverter, the d-axis current reference, the q-axis current reference, and variations in active and reactive power on the grid side.

Fig. 10. Inverter system voltage

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Fig. 11. Current of the inverter system.

The current and voltage injections into the grid system at the PV system connecting point are in phase, so there is no delay between the two signals (alternating voltage and alternating current requested by the grid), and the purely sinusoidal form of alternating signals is due to the use of the LCL filter.

Fig. 12. The system active power inverter.

As shown Figs. 12 and 13 the power generated by the photovoltaic model is transformed almost completely into active power with a negligible reactive power value, so the reactive energy is compensated.

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Fig. 13. The inverter system reactive power.

5 Conclusion The grid connected, three phase PV system shown in this study exhibits minimal harmonics and is suitable for commercial and industrial applications. Additionally, perturb and observe (P&O), fuzzy logic, and boost converter topologies are used to create MPPT approaches. The performance of the PV side and the grid side with changes in time and irradiance are shown in the simulation results from the MATLAB/Simulink application. Fast changing irradiation conditions are tracked more precisely by fuzzy logic than by the P&O approach. Instead of attaining an exact value, the voltage varies around the MPP with the P&O technique. The fuzzy logic approach obtains the MPP quicker and more effectively than P&O since it does not suffer from being the best in quickly shifting conditions and has no drifting issues.

References 1. Zahedi, A.: Solar photovoltaic (PV) energy; latest developments in the building-integrated and hybrid PV systems. Renew Energy 31(5), 711–718 (2006) 2. Youness, H., Ahmed, G., Haddadi, B.E.: Machine learning-based smart irrigation monitoring system for agriculture applications using free and low-cost IoT platform. In: ICM (2022). ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/10005419/. Accessed 27 Jan 2023 3. Kan, J., Wu, Y., Tang, Y., Xie, S.: Flexible topology converter used in photovoltaic microinverter for higher weighted-efficiency. IET Power Electr 12(9), 2361–2371 (2019) 4. Qin, S., Barth, C.B., Pilawa-Podgurski, R.C.N.: Enhancing microinverter energy capture with submodule differential power processing. IEEE Trans. Power Electron 31(5), 3575–3583 (2016) 5. Kim, K.A., Krein, P.T., Seo, G.-S., Cho, B.-H.: Photovoltaic AC parameter characterization for dynamic partial shading and hot spot detection. In: Proceedings of the IEEE 28th Annual Applied Power Electronics Conference and Exposition, pp. 109–115 (2013) 6. Kim, N., Parkhideh, B.: Comparative analysis of non-isolated and isolated type partial-power optimizers for PV-battery series inverter architecture. In: 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Portland, OR, 2018, pp. 6207–6213 (2018)

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7. De Souza Silva, J.L., Moreia, H.S., de Mesquita, D.B., Cavalcante, M.M., Villalva, M.G.: Modular architecture with power optimizers for photovoltaic systems. In: 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, 2019, pp. 1– 6 (2019) 8. Jena, P.K., Mohapatra, A., Choudhary, P.: Comparative study of solar PV MPPT by perturbation and observation and fuzzy method. In: 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), Varanasi, 2016, pp. 515–518 (2016) 9. Makhlouf, M., Messai, F., Nabti, K., Benalla, H.: Modeling and simulation of grid-connected photovoltaic distributed generation system. In: 1st International Conference on Renewable Energies & Vehicular Technology, 26–28 March 2012, pp. 187–193 (2012) 10. Tanvir, A., Sharmin, S., Faysal, N.: Comparative analysis between single diode and double diode model of PV cell: concentrate different parameters effect on its efficiency. J. Power Energy Eng. 4, 31–46 (2016) 11. Yang, B., Li, W., Zhao, Y., He, X.: Design and analysis of a grid-connected photovoltaic power system. IEEE Trans. Power Electron. 25(4), 992–1000 (2010) 12. Bakkar, M., Abdel-Geliel, M., Zied, M.A.: Photovoltaic maximum power point grid connected based on power conditioning technique employing fuzzy controller. Renew. Energy Power Qual. J. (RE&PQJ) 1(13), 339–344 (2015) 13. Hairul, N., Saad, M.: Comparison study of maximum power point tracker techniques for PV systems. In: Proceedings of the 14th International Middle East Power Systems Conference (MEPCON 2010), Cairo University, Egypt, 19–21 December 2010 (2010) 14. Elgendy, M., Zahawi, B., Atkinson, D.: Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE Trans. Sustain. Energy 3(1), 21–33 (2012) 15. Sahnoun, M.A., et al.: Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity. Energy Procedia 42, 650–659 (2013) 16. Patel, G., Patel, D.B., Paghdal, K.M.: Analysis of P&O MPPT algorithm for PV system. Int. J. Electr. Electron Eng. (IJEEE) 5(6), 1–10 (2016)

A Review Study of Control Strategies of VSC-HVDC System Used Between Renewable Energy Source and Grid Chaimaa Lakhdairi1(B) , Aziza Benaboud1,2 , Hicham Bahri1 , and Mohamed Talea1 1 Laboratory of Information Processing, Faculty of Sciences Ben M’Sick, University Hassan II,

Casablanca, Morocco [email protected] 2 Department of Energy, Royal Navy School, Casablanca, Morocco

Abstract. This article presents an overview of many control strategies of the VSC-HVDC system, especially when it works as an interface between renewable energy sources and the grid. Actually, the production of green energy is often far from where it will be consumed. For this reason, finding an efficient method to transfer large quantities of electricity over a long space will be essential. At this time, high-voltage direct current transmission systems appear to be the best solution to this problem, which has the advantages of easily connecting grids where voltage and frequency are incompatible and transmitting power over a long distance. The main contribution of this work is to shed light on various control strategies through a synthesis of available important research on VSC-HVDC and its applications. A comparative study is also presented to identify and emphasize its advantages, such as independent active and reactive power control, and its drawbacks, such as high switching losses and harmonics. Keywords: High Voltage Direct Current · Renewable Energy · Power Converter · Pulse Width Modulation · Voltage Margin Control · Voltage Droop Control · Voltage Source Converter

1 Introduction Industrial electricity was initially produced by Thomas Alva Edison as DC electricity, which has been well-known in the story of electricity manufacturing to be used to transmit electric energy [1, 2]. However, in 1882, DC power at low voltage could not be sent over long distances. In 1887, Nikola Tesla developed a system for AC lights, transformers, generators, motors, and cables. In these very early stages, it became evident that the benefits of AC electrical power transmission over long distances contrasted with DC transmission. During the 1930s, another solution for power transmission was investigated using mercury arc rectifiers, but this time, the voltage was high. The initial HVDC (10 MW) transmission system was put into service in Gotland in 1954 [3]. The High Voltage Direct Current transport system, which has been in use since the 1960s, is now a recognized technology that is essential for both long-distance transmission and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 664–673, 2023. https://doi.org/10.1007/978-3-031-29857-8_66

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system connectivity. In 1970, thyristors were invented and first used to build converters for DC transmission. HVDC transmission then experienced a new revolution identified as the Current Source Converter [4, 5]. On the other hand, the voltage source converter is becoming more desirable for high-voltage direct current systems as a result of the progression of power semiconductors in the 1990s, especially IGBTs [6]. In addition, VSC technology enables the independent regulation of active and reactive power without the need for additional compensating equipment, and it has the benefit of decoupling harmonics between generators. The use of two-level inverter technology, however, in known HVDC systems introduces a fairly high level of voltages and harmonic currents [7, 8]. Because of the highly large transition loss caused by the high frequency of the pulse width modulation approach generally utilized for its control, this system can fulfil the specifications for the power system quality, but only to a small extent in terms of converter performance [9]. Significant research efforts have been made to decrease the distortion caused by harmonics in product inverter voltages and currents [9]. Additionally, voltage source converter-based HVDC systems supply several benefits over traditional highvoltage direct current systems, including the capacity to regulate active and reactive power separately and the capability to support dynamic voltage at the converter bus to enhance stability in weak alternating current systems [10]. The main objective of this work is to shed light on various control strategies through a synthesis of important research on VSC-HVDC and its applications. The article’s remaining sections are organized as follows: Sect. 2 briefly explains some basic components of the HVDC power system. Section 2.2 analyses the different control strategies of an HVDC link. Section 2.3 presents a comparative study to emphasize its advantages and drawbacks, and Sect. 3 shows the simulation results of the system. Finally, the paper is concluded.

2 VSC-HVDC System Depending on the switching devices used in the converter, two types of HVDC transmission systems can be identified. Current-source converters-HVDC as well as voltage-source converters-HVDC. In this section, a VSC-HVDC system is described. 2.1 System Description As illustrated in Fig. 1, the voltage-source converters of the HVDC system are composed of two voltage-source converters, transformers, phase reactors, AC filters, DC-link capacitors, and DC cables [11]. The transformer is an important element in a high-voltage direct-current system. It was used to link the alternating current grid to the VSC and to regulate the voltage of the alternating current network to an appropriate level for the converter [12]. The phase reactor is acclimatized for controlling both the active and reactive power flow [12]. To delete the harmonics inside the output alternating current voltage by using AC filters, high-pass filters are set up to reduce these high-order harmonics [12]. The DC capacitor works as an energy reserve to maintain power stability throughout transients and minimize harmonic distortion on the DC part.

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Fig. 1. Voltage-source converters-HVDC System

Therefore, the two VSCs are the principal components of this transmission system. Both VSCs, one of which functions as a rectifier and the other as an inverter, are built around IGBT power semiconductors [12]. 2.2 Control of VSC-HVDC The vector command is the main control strategy adopted in VSC-HVDC systems. It is used to independently and bidirectionally regulate both active and reactive power [13]. The control strategy employed in this work is described in this section. The voltage margin control and voltage droop control techniques consist of two controller stages: The first stage is a fast controller called an “inner controller,” and the second stage is composed of slower controllers called “outer controllers” [14] (Fig. 2).

Fig. 2. Control temple of a VSC-HVDC system

Inner Current Controller The inner current loop, as illustrated in Fig. 3, is typical for the couple margin voltage and voltage droop control techniques. The alternating current grid consists of three phases, which are connected with a converter. Using the Clark inverse transformation for w, where w is the alternating current network voltage angle acquired out of a phase-locked loop, the network link dq model is presented by [15].     Lv wLv  q∗ ki Rv  d ∗ q iv + iv + kp + iv + ivd (1) + vvd ∗ = vfd + Rv ivd ∗ − 2 s T 2

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=

q vf

q∗ + Rv iv

    Lv wLv  d ∗ ki Rv  q∗ q d iv + iv + kp + i v + iv + + 2 s T 2

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(2)

vvd ∗ and vv are voltages in the dq frame at the output of the inner current control, while Rv and Lv are the resistance and inductance of the phase reactor. q∗

Fig. 3. Inner current control loop

Outer Controller The VSC’s outside controllers are a subset of its controller [13] and provide the inner current controller with the current source signals. If the (dq) frame’s d-axis is considered to be in line with the AC network voltage phasor over a phase-locked loop under steady state [16]. The outer system is employed in four control modes: P/Q, P/Vac, Vdc/Q, and Vdc/Vac [10]. Each controller is equipped with a PI controller to reduce the stable state error (Fig. 4).

Fig. 4. Outer control

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2.3 DC Voltage Control Direct current voltage control is assuredly among the most crucial roles assigned to the voltage source converter-based high-voltage direct current system to guarantee power [17]. Taking into consideration the HVDC systems’ operational needs for DC voltage, two primary control methodologies are presented in the literature that could be used in upcoming global networks [18]: the voltage margin method and the voltage droop method. Voltage Margin Control The voltage margin control is a set of active power and direct current voltage controls. In fact, every converter can regulate both the direct current voltage and the active power flow [19].

Fig. 5. Voltage margin control

Figure 5 illustrates the VDC − P characteristics of an HVDC link with “voltage margin control”. As illustrated in this figure, the VSC1 converter controls the DC voltage to follow the set values thus far as it operates within its active power limits. Once these power limits are reached, the VSC1 converter will be unable to control the DC voltage, but it will start to control the power flow. Nevertheless, the VSC2 converter will regulate the DC voltage to ensure that it meets the new specified value. The “voltage margin” is the distinction between the two preset values [19]. Figure 6 illustrates the voltage margin control fundamental design based on [17].

Fig. 6. Fundamental design of voltage margin control

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Voltage Droop Control Voltage droop control is developed for controlling the grid using voltage source converter technology. The voltage droop control works on the following principles: If the converter containing a voltage droop detects a DC voltage variation, this regulator will result in a linear fluctuation of the converter’s power source that aids in system stabilization. Consequently, this avoids lowering the direct current voltage to unsafe levels [20].

Fig. 7. Voltage droop characteristics.

As illustrated in Fig. 7, the characteristics of voltage droop increase in dc voltage leads to an excess of energy in the system; thus, the dc voltage-regulating station is employed to rebalance power. Simultaneously, the DC voltage droop characteristics show a power shortage inside the system, so the DC voltage-regulating station employs methods to increase rectification [21–23]. Figure 8 illustrates the voltage droop control fundamental design. Based on [17].

Fig. 8. Fundamental design of voltage droop control.

Comparison Control The constancy of the direct current voltage is the determining element in the operation of an HVDC system. Furthermore, the power balance in the system will be ensured through DC voltage control. Previously, we discussed two types of control-to-control DC voltage. Starting with the voltage margin control and another proposal command called voltage droop control.

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Table 1 presents the comparison between voltage droop control and voltage margin control with respect to expandability, dynamic response, communication, and flexibility needs [24]. Dynamic response is the velocity of the procedure for reducing the transient response of the DC voltage within a system during power variability; expandability marks the ability to control the DC voltage in HVDC networks, whereas flexibility shows the ability of every direct current voltage control strategy; eventually, communication shows how each control technique requires external data to be entered to perform transmission schemes [25]. Table 1. Comparison of voltage margin control and voltage droop control. Control method

Dynamic Response

Expandability

Flexibility

Communication Requirement

Voltage Margin

Low

High

High

Medium

Droop

High

Medium

Low

Low

In the report of Table 1 [26], voltage margin control has many characteristics, including high extensibility and flexibility.

3 Simulation Results A VSC-based high-voltage direct current system (as illustrated in Fig. 1) was simulated utilizing MATLAB/Simulink. Simulations are used to perform performance analyses of the VSC-based high-voltage direct current system with voltage margin control and voltage droop control techniques. Figures 9 and 10 show how the system performs, while Figs. 9 and 10 show the VSC’s active power flows. The DC grid voltages are shown in Figs. 11 and 12, which show voltage changes to maintain balance. The behavior of the powers is shown in the first two figures; when margin voltage control is used, the power stabilizes at the initial power levels. When we use droop control voltage, however, we see power oscillation, and it takes longer to stabilize. As seen, the DC voltage with a margin voltage method takes longer to recover to the starting voltage values than a droop voltage method; nevertheless, the droop voltage strategy stabilizes the system at approximately 0.5 s. Because of faster transients and fewer oscillations, the VSC-based High-Voltage Direct Current system with the margin control system outperformed the system with voltage droop control.

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Fig. 9. Active power flows with voltage margin control.

Fig. 10. Active power flows with voltage droop control.

Fig. 11. DC voltage with voltage margin control.

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Fig. 12. DC voltage with voltage droop control.

4 Conclusion The control strategies of voltage source converter-based high-voltage direct current systems have been reviewed in this paper. First, we discuss the system description. On the other hand, we are talking about the control of VSC-HVDC. Then, two types of control are studied. Starting with the voltage margin control, which is characterized by the highest flexibility, the limitation of this control strategy is the complexity of implementing it. Another proposal for control called voltage droop control was then presented. The difficulty of this method is finding the voltage droop value that must be assigned to each converter.

References 1. Latorre, H.: Modeling and control of VSC-HVDC transmissions. Doctoral thesis, Royal Institute of Technology School of Electrical Engineering, Electric power systems Stockholm, Sweden (2011) 2. Wang, F., et al.: An overview introduction of VSC-HVDC: state-of-art and potential applications in electric power systems. In: BOLOGNA 2011 (2011) 3. Kim, C.-K., Sood, V.K., Jang, G.-S., Lim, S.-J., Lee, S.-J.: HVDC Transmission Power Conversion Applications in Power Systems. IEEE Press, Wiley (2009) 4. Hoffmann, M., Leowald, K.: A thyristor valve for a peak blocking voltage of 120 Kv, vol. 42(4). Siemens-Zeitschrift (1968) 5. Anwander, E., Etter, P.: Thyristor converter valve for 100 KV DC bridge voltage. Brown Boveri Mitt. 56(2), 79–88 (1969) 6. Gyugyi, L.: Reactive power generation and control by thyristor circuits. IEEE Trans. Ind. Appl. 15(5), 521–532 (1979) 7. Alishah, R.S., Nazarpour, D., Hosseini, S.H., Sabahi, M.: Design of new single-phase multilevel voltage source inverter. Int. J. Power Electron. Drive Syst. 5(1), 45–55 (2014) 8. Lakshmana, B., Venkataratnam, G.: THD and switching losses analysis of multi-level inverter fed ϕ induction motor drive. Int. J. Sci. Eng. Res. 5(1), 2067–2074 (2014) 9. Ding, G., Tang, G.: New technologies of voltage source converter (VSC) for HVDC transmission system based on VSC, Pittsburgh PA, USA (2008). https://doi.org/10.1109/PES.2008. 4596399 10. Dierckxsens, C., Srivastava, K., Reza, M., Cole, S., Beerten, J., Belmans, R.: A distributed DC voltage control method for VSC MTDC systems. Electr. Power Syst. Res. 82(1), 54–58 (2012)

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11. Daoud, A.A., Abouzeid, A.F., Dessouky, S.S.: Offshore wind power integration to support weak grid voltage for industrial loads using VSC-HVDC transmission system. Int. J. Electr. Comput. Eng. (2088–8708) 11(3), 1876 (2021) 12. Shilpa, G.N., et al.: Voltage Source Converter Based HVDC Transmission (2012) 13. Belgacem, M., Khatir, M., Djehaf, M.A., Bouddou, R., Zidi, S.A.: Modeling and control of multi-terminal direct current with voltage margin control strategy. In: 2019 4th International Conference on Power Electronics and their Applications (ICPEA) (2019) 14. Funck, M., et al.: Liaisons HVDC: structure, controle et modelisation. Diss. Master thesis, Université Catholique de Louvain-La-Neuve (2016) 15. Du, C.: VSC-HVDC for industrial power systems. Chalmers Tekniska Hogskola (Sweden) (2007) 16. Teixeira Pinto, R., Rodrigues, S.F., Bauer, P., Pierik, J.: Description and comparison of DC voltage control strategies for offshore MTDC networks: steady-state and fault analysis. EPE J. 22(4), 31–39 (2015) 17. Gonzalez-Longatt, F.M., Rueda, J.L. (eds.): PowerFactory Applications for Power System Analysis. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12958-7 18. Wang, W., Barnes, M.: Power flow algorithms for multi-terminal VSC-HVDC with droop control. IEEE Trans. Power Syst. 29(4), 1721–1730 (2014) 19. Mier, V., Casielles, P.G., Koto, J., Zeni, L.: Voltage margin control for offshore multi-use platform integration. In: The International Conference on Renewable Energies and Power Quality (ICREPQ 2012) (2012) 20. Akkari, S., Dai, J., Petit, M., Guillaud, X.: Interaction between the voltage-droop and the frequency-droop control for multi-terminal HVDC systems. In: IET Generation, Transmission & Distribution (2016) 21. Rouzbehi, K., Miranian, A., Luna, A., Rodriguez, P.: Optimized control of multi-terminal DC Grids using particle swarm optimization. In: 15th European Conference on Power Electronics and Applications (EPE) (2013) 22. Rouzbehi, K., Miranian, A., Candela, J.I., Luna, A., Rodriguez, P.: A generalized voltage droop strategy for control of multiterminal DC grids. IEEE Trans. Ind. Appl. 51(1), 607–618 (2014) 23. Rouzbehi, K., Miranian, A., Candela, J.I., Luna, A., Rodriguez, P.: A generalized voltage droop strategy for control of multiterminal DC grids. IEEE Trans. Ind. Appl. 51(1), 607–618 (2015) 24. Dewangan, L., Bahirat, H.J.: Comparison of HVDC grid control strategies. In: 2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) (2017) 25. Rodrigues, S.: Dynamic modeling and control of VSC-based, Multi-terminal DC networks, M.Sc. thesis, Universidade Tecnica de Lisboa (2011) 26. Sakraoui, M.A., De Jaeger, E., Bekemans, M., Janssens, N.: Gestion du réseau électrique: HVDC et services auxiliaires (2016)

Modelling and Simulation of PV System Grid Connected with 100 KW Rated Power Zohra Lahyan(B) and Ahmed Abbou Electrical Engineering Department, Mohammadia School of Engineers, Mohammed V University of Rabat, Rabat, Morocco [email protected], [email protected]

Abstract. The Principe of injecting photovoltaic energy into the power grid has been widely accepted in these times of renewable energy production. This paper explains a complete study for different components of a photovoltaic system connected to the, such as PV panels, DC/DC converters, PV inverters, transformers, filtering system and grid. Therefore, a general explanation of the different controls, using the incremental conductance method with the integral controller to control the maximum power point tracking (MPPT), as well as the VSC and boost converter controls. Then we used Simulink/MATLAB for simulated a model of grid connected PV system 100kW rated power. Keywords: PV panels · DC/DC converters · Mppt · Inverters · Grid

1 Introduction Rapid development and research in the field of solar energy has progress for photovoltaic systems that are must efficient and reliable, particular for power supply at high, medium and low voltage generation systems at high, medium and low voltage [1]. The modelling of photovoltaic power plant basic to modelling all components of PV farm have three steps: the first to produce electricity from solar energy, second to ensure the connection between the large scale and the grid, third to assure a perfect performance. The grid connected of PV systems generation causes a lot of important technical issues and challenges which effect on the power quality. These variations contains voltage fluctuations, total harmonic distortion (THD), and reactive power capability support and frequency response power factor (PF), grid or load management. Therefore, a grid connected of photovoltaic systems energy source must meet standards and requirements of a power quality [2, 3]. The multiple functionalities of a smart PV include the conversion of the solar source into electricity, and the quality of power devices in the controllability of flexible power, the ability to cross faults, also the functionality of a network support, there is a smart provision on the auxiliary services [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 674–683, 2023. https://doi.org/10.1007/978-3-031-29857-8_67

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For these features to be possible, existing standards and grid requirements for PV systems was be reviewed with update. The control of PV power converters should be reviewed and update be improved by considering the next points (MPPT operation), reactive and active power control, regulation and control of frequency, Harmonic compensation dynamic grid support, capability correction, and improved reliability and efficiency [4, 5]. In this paper we interested by the control of boost converter using by incremental conductance method with Integral regulator and we see the effect of this method for all the components of this grid connected PV system.

2 PV System Modeling 2.1 PV Modules Modeling The general isolated system of this work contains PV generators, DC-DC boost converter and MPPT used is the incremental conductance method (ICM). The Fig. 1 shown a very simple circuit equivalent for the photovoltaic cell (a shunt resistor, diode, series resistor and current source). The output of the current source is proportional directly to the falling light on the cell (photocurrent Iph) [6, 7].

Fig. 1. PV cell equivalent circuit.

We select a PV module with high maximum power 305 W, every module has 96 solar cells of a monocrystalline silicon, our PV array contains (66 parallel strings, and 5 string connected on series), to product in Standard conditions approximately 100.7 KWp of maximum power. The Tables 1 and 2 presents the characteristics of PV module, therefore of PV array. Using MATLAB for show the irradiance and temperature effects of PV module and array in these figures (Figs. 2 and 3). Maximum Power Point Tracking MPPT is a method that allows the controller to operate at the optimal operating point. Maximum power using a simple voltage relationship to a more complex analysis based on multiple samples. The point tracker is a specific type of charge controller that uses the solar panel at its maximum potential. The method of incremental conductance [8, 9] is due to the fact that the slope of the PV generator power curve is zero at the MPP, positive to the left of the MPP and negative to the right (Fig. 4).

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Impp/Isc

5.58/5.96

A

Vmpp/Voc

54.7/64.2

V

Pmax

305.226

W

Solar Radiation

1000

W/M2

Temperature

25

C

Table 2. Array characterization under standards test conditions (STC). Array Specification under STC Impp/Isc

368.6/392.71

A

Vmpp/Voc

273.5/321

V

Pmax

100 650

W

Solar Radiation

1000

W/M2

Temperature

25

C

Fig. 2. Characteristics variation (IV and PV) of PV module with irradiance.

In the following equation: dp = 0, at Mpp dv

(1)

dp > 0, left of Mpp dv

(2)

dp < 0, right of Mpp dv

(3)

dp d(IV) = dv dV

(4)

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Fig. 3. Characteristics variation (IV and PV) of PV array with irradiance.

Fig. 4. ICM model and control simulink.

2.2 Boost Converter Modelling The Boost converter DC-DC circuit comprises of a diode, an inductor, a power switch, capacitor, a switching controller and load [10]. The DC-DC boost converter will increase the output voltage to be higher than the input voltage [10, 11, 12].

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Fig. 5. Boost converter equivalent circuit.

It controlled by the method of incremental conductance, the MPPT control remove the maximum power of a PV array under varying climatic conditions, which their flowchart resuming all operating control enhanced with the mathematical algorithm taped in MATLAB, and the Simulink model are presents in the next Figs. 5 and 6 (Fig. 7).

Fig. 6. Boost converter control MPP in MATLAB.

Fig. 7. ICM control with integral regulator in MATLAB

3 Simulation Results and Discussion For this simulation part, we try to combine between all components for our PV System of 100 KWp rated power. The Fig. 8 present detailed configuration of grid connected to the PV system. The idea of Fig. 9 it is a comparison between theoretical and simulation results of our PV array under varying climatic conditions, voltage and current there is the same. The results, show in Figs. 10 and 11, the boost converter increases the DC voltage from 200 V to 300 V. The boost converter used a MPPT system, which automatically varies the duty cycle in order to generate the voltage required to extract the maximum power. The parameters of the boost converter are not stable, they vary under the effects

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Fig. 8. Configuration of simulation model of grid connected PV System in MATLAB.

Fig. 9. Array voltage, current and diode current.

Fig. 10. Irradiation, Power and voltage of boost converter.

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Fig. 11. The incremental conductance method results.

of climatic conditions, the value of the duty cycle also changes, the most interesting observation here is that the power changes in parallel with the change of the daily irradiance. (Input voltage 274.74 V, Output voltage 300 V, Duty cycle 0.55 A, Lin 5 mH, Cin 100 µF) (Fig. 12).

Fig. 12. Three-phase inverter and LC filter results.

These Figs. 13, 14 and 15 show that, the three-phase transformer used to increase the output voltage inverter to in important voltage equivalent of the grid, for our situation the transformer increase the voltage from 260 V to 25kV. In addition, the transformer contains an internal inductor have harmonic filter effect. The passive filter system used is an LC filter, which provide these parameters. The inverter parameters such as (DC Link Voltage 599.97 V, Switching Frequency 10 kHz, Filter Inductance 250 µh and DC Link Capacitor 12 mF). Therefore, the parameters of PV Interfacing Transformer (MVA Rating 100 KVA, transformer Ration 260\25 V/KV, Grid Voltage 25 kV, Grid Frequency

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Fig. 13. Voltage source control.

Fig. 14. VSC main controller results.

Fig. 15. Current, voltage output.

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Fig. 16. Grid active power.

50 Hz). In addition, the main controller of the voltage source control (VSC) obtains the following results: (rated power it 100 KW, frequency 50 Hz, the nominal and secondary voltage between 260 V and 25 kV, nominal dc bus voltage 500 V. Leakage impedance between 0.0020 and 0.06 LF, choke impedance R 0.002 and L250*-6 H. for the vdc regulation gains, we take the Kp equal to 7 and Ki equal to 800, then we take kp equal to 0.3 and ki equal to 20). The active power injected into the electrical network is not stable Fig. 16, and I confirmed the main objective of this simulation to minimize the reactive power injected in the grid Fig. 16, that it stabilized near to zero VAR during all simulations times.

4 Conclusion This paper studies the different aspects starting with the theoretical part, the modeling and then the simulation of a photovoltaic system connected to the electrical grid with a nominal power of 100 KW simulated in the MATLAB environment. The system design it done to feed the grid with a power of 100 kW. The inverter it controlled to feed the grid with active power. The photovoltaic solar panels efficiency varying under the variation of radiation. When the solar radiation level is high, the output of the solar PV panel is maximum. It is necessary to compare between the instantaneous with the incremental conductance (I/V and I/V) to find the MPPT. Tracking based on the duty cycle D of the boost converter by incremental conductance algorithm, to make the PV array run on at MPP it necessary to adjust the operations of voltage. It is very that the ICT apparatus to stop at the MPP. On the side source, we use a step-up converter connected with photovoltaic panel to improve the voltage of output, this type used on many different applications. In parallel, we can adapt the impedance of the source to that of the load by the boost converter, in particular by varying the duty cycle. Grid integration of photovoltaic systems is the dominant trend for the use of the generated energy. In addition, the photovoltaic system generation becomes an important alternative source. The alternative source varies, starts from kilowatt to megawatt depending on the needs of the application. The characteristics (current, voltage) of the PV system play an important role in monitoring the maximum power generation. IEC and IEEE these grid connected standards, its mandatory to apply in the system, to maintain and keep the quality of power in accordance with the standards defined by the agencies the standards). The quality of the power supplied by the PV system depends on standards and practices,

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with the most influential parameters being harmonics, frequency, voltage, flicker and power factor [1, 14, 15].

References 1. Malek, H.: Control of grid connected photovoltaic system using fractional order operators. Ph.D dissertation (2014) 2. Teodorescu, R., Liserre, M., Rodríguez, P.: Grid Converters for Photovoltaic and Wind Power Systems. Wiley, New York (2011) 3. Ledwich, G., Ghosh, A.: Custom Power Devices for Power Quality Enhancement 4. Yang, Y., Kim, K.A.: Advances in Grid-Connected Photovoltaic Power Conversion Systems. Elsevier, New York (2019). https://doi.org/10.1016/B978-0-08-102339-6.00001-4 5. Braun, M., et al.: Is the distribution grid ready to accept large-scale photovoltaic deployment? State of the art, progress, and future prospects. Prog. Photovolt. Res. Appl. 20(6), 681–697 (2012) 6. Sahin, ¸ M.E., Blaabjerg, F.: A hybrid PVBattery/supercapacitor system and a basic active power control proposal in MATLAB/Simulink. Electronics 9(1), 129 (2020) 7. Abousserhane, Z., Abbou, A., IdKhajine, L., Zakzouk, N.E.: Power flow control of PV system featuring on-grid and off-grid modes. In: 2019 7th International Renewable and Sustainable Energy Conference (IRSEC), 27 April 2019, p. 19572448 (2019) 8. Quaschning, V.: Understanding Renewable Energy Systems. Carl Hanser Verlag GmbH & Co KG, London (2005) 9. Evju, S.E.: Fundamentals of grid connected photovoltaic power electronic converter design. Specialization Project, Department of Electric Engineering, Norwegian University of Science and Technology, December 2006 10. IRENA: Renewable Energy technologies: cost analysis series (Solar Photovoltaics), IRENA, Technical report (2012). 0 Publications/RE Technologies Cost Analysis-SOLAR PV.pdf 11. Taghvaee, M.H., Radzi, M.A.M., Moosavain, S.M., Hizam, H., Marhaban, M.H.: A current and future study on non-isolated DC-DC converters for photovoltaic applications. Renew. Sustain. Energy Rev. 17, 216–227 (2013) 12. Fialho, L., Melıcio, R., Mendes, V.M.F., Viana, S., Rodrigues, C., Estanqueiro, A.: A simulation of integrated photovoltaic conversion into electric grid. Sol. Energy 110, 578–594 (2014) 13. Bennett, T., Zilouchian, A., Messenger, R.: Perturb and observe versus incremental conductance MPPT algorithms. IEEE Trans. Power Syst. 14. Tripathi, R.N.: Two degrees of freedom DC voltage controller of grid interfaced PV system with optimized gains. Int. J. Electr. Power Energy Syst. (IJEPES) 85(15), 87–96 (2017) 15. Evju, S.E.: Fundamentals of grid connected photovoltaic power electronic converter design, Ph.D. dissertation, Department of Electrical Power Engineering, Norwegian University of Science and Technology, January 2007

Development of Geometrical Parameters for a Conical Solar Concentrator – Application for Vapor Generation Firyal Latrache(B) , Zakia Hammouch, Karima Lamnaouar, Benaissa Bellach, and Mohammed Ghammouri Mechanics, Energetics, Systems and Signals Team, Laboratory of Modelization and Scientific Calculus, National School of Applied Sciences, University Mohammed I Oujda, Oujda, Morocco [email protected]

Abstract. Solar concentrators are a technology used to generate electrical energy. They concentrate solar energy along an absorber tube, where it is transformed into useful thermal energy for a heat transfer fluid. The present work develops the geometrical parameters of a conical solar concentrator. It presents a generalized equation for the whole focal distances about this geometrical form of the solar concentrator. In addition, the research below studies the impact of the solar concentrator parameters on the absorber tube to determine the solar concentrator dimensions. By establishing the mathematical equation concerning the heat transfer fluid, the current work shows an application of the conical solar concentrator in vapor generation. Thereafter, the efficiency and concentration ratio of the solar concentrator are determined to detect the production quality and optimize it. Therefore, determining the efficiency distribution over the absorber tube geometry allows us to identify how the direct normal irradiation component is distributed and in this regard improve the output power and solar concentrator efficiency. Keywords: Concentrator · Vapor · Modeling

1 Introduction and Related Work to Solar Concentrators Economic development and sustainability are necessary for creating favorable conditions for citizens and improving environmental quality [1]. In fact, the definition of energy is the ability to perform action, and renewable energy is the capacity to eliminate the damage induced by fossil energy [2]. Furthermore, while fossil energy has fueled economic progress over the past century, it harms the environment and depletes natural resources [3, 4]. Solar energy is regarded as an abundant and clean source of energy that is used for photovoltaic and solar thermal technologies [5]. While solar photovoltaic energy is only utilized to produce electricity, solar thermal energy allows for the simultaneous generation of electricity and water heating [6]. The fundamental operating principle of solar thermal systems is the collection of solar radiations using solar concentrator technology and their reflection on an absorber tube [7]. Working as an evaporator, the absorber tube converts heat into useful thermal energy for a heat © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 684–693, 2023. https://doi.org/10.1007/978-3-031-29857-8_68

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transfer fluid [8]. The most recent solar concentrator application combines heliostats, parabolic, and parabolic-cylinder concentrators to create vapor, in the evaporator, which is then transported through a cycle by a turbine, condenser, and pump [9–11]. The literature lists several applications for solar concentrators: applications for cooking to reduce pollution from fuel used in cooking and applications in hydrogen production [12–14]. The objective of this work is to develop the geometrical parameters of a conical solar concentrator and its application for vapor generation. The nodal method is applied to determine the heat transfer fluid equation, which is discretized by the finite difference method. Then, to optimize energy production, the effect of the geometrical parameters of the solar concentrator on the absorber tube is studied. Finally, the efficiency of the absorber tube is analyzed to optimize energy production.

2 Development of Geometrical Parameters for the Conical Solar Concentrator 2.1 Presentation of the Conical Solar Concentrator The conical solar concentrator is a type of solar concentrator that collects and condenses solar radiation on an absorber tube. As with the other types of solar concentrators, the conical solar concentrator has a thermal storage system, which is not the case for solar photovoltaic technology. Current research has focused only on the optimal angle of the conical solar concentrator. In the figure (Fig. 1), there are 3 possible ranges for the conical angle of the solar concentrator: a) 0◦ < θ < 45◦ , b) θ = 45◦ and c) 45◦ < θ < 90◦ . The red colored part in both cases a) and c) represents the thermal losses: In case a), the reflected solar radiation is blocked by the bottom part of the solar concentrator, and therefore, the solar radiation cannot reach the whole surface of the absorber tube. In case c), the length of the absorber tube is longer than the length of the solar concentrator, and solar radiation is not transmitted to the bottom part of the absorber tube [15]. The heat losses are limited in case b), and all solar radiation reflected by the solar concentrator has reached the entire surface of the absorber tube. The optimal angle of the solar concentrator is approximately 45◦ , where all the solar radiation is concentrated on the absorber tube vertically.

Fig. 1. Description and presentation of the optimal angle for the conical solar concentrator

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2.2 Development of Geometrical Parameters for the Conical Solar Concentrator Focal Distance of the Conical Solar Concentrator The concentration of irradiations using solar concentrator technologies such as the parabolic concentrator is realized on a unique focal point [16]. However, the concentration by the conical concentrator is made on several focal points following a focal axis [15]. The focal distance of the conical concentrator can be determined using the following equation as a generalized expression: fn = (1 + n)(r − H )

(1)

with: n is the order of the focal point H is the height of the conical solar concentrator r is the radius of the conical solar concentrator. Surface of the Conical Solar Concentrator According to the figure (Fig. 1), the surface of the conical solar concentrator is calculated by:  √  (2) Ssc = π r 2 1 + 2 − (r − H )2

Study of the Influence of Solar Concentrator Parameters on the Absorber Tube The diameter of the solar concentrator and the diameter of the absorber tube are correlated, as shown by the following equation: CD = AA + 2H

(3)

The table below shows the effect of the solar concentrator diameter on its height. The impact of these parameters on the absorber tube parameters is studied (Table 1). In this section, the impact of the solar concentrator dimensions on the absorber tube is analyzed. The influence of one geometrical parameter on another is examined while keeping the other parameter constant. The effect of the solar concentrator diameter on its height is estimated by keeping the absorber tube diameter constant. The impact of the solar concentrator diameter on the absorber tube diameter is studied by keeping the solar concentrator height constant. Finally, the effect of the solar concentrator height on the absorber tube diameter is evaluated by keeping the solar concentrator diameter constant. As result, the diameter of the solar concentrator should be at least 4 m. Increasing the diameter of the solar concentrator requires an increase in the diameter of the absorber tube. However, the increase in height of the solar concentrator should not exceed the value of 1.75 m to have an optimal absorber tube diameter of approximately 0.5 m. The study of the influence of solar concentrator parameters on the absorber tube leads to the analysis of the effect of the solar concentrator surface on the absorber tube surface. Then, this study allows us to model the direct normal irradiation component on

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Table 1. Impact of the solar concentrator parameters on the absorber tube Solar concentrator diameter

Solar concentrator height H

Influence of the solar concentrator diameter on the absorber tube diameter

Influence of the solar concentrator Height on the absorber tube diameter

0.5 m

0m

−3.5 m

4m

1m

0.25 m

−3 m

3.5 m

1.5 m

0.5 m

−2.5 m

3m

2m

0.75 m

−2 m

2.5 m

2.5 m

1m

−1.5 m

2m

3m

1.25 m

−1 m

1.5 m

3.5 m

1.5 m

−0.5 m

1m

4m

1.75 m

0m

0.5 m

4.5 m

2m

0.5 m

0m

5m

2.25 m

1m

−0.5 m

5.5 m

2.5 m

1.5 m

−1 m

6m

2.75 m

2m

−1.5 m

6.5 m

3m

2.5 m

−2 m

7m

3.25 m

3m

−2.5 m

7.5 m

3.5 m

3.5 m

−3 m

8m

3.75 m

4m

−3.5 m

the absorber tube geometry. Consequently, identifying the absorber tube zones that are the most exposed to irradiation necessary to optimize vapor production. Dimensions of the Conical Solar Concentrator Based on influence analysis of the concentrator parameters on the absorber tube, the concentrator dimensions are presented in the figure below: (Fig. 2).

Fig. 2. Dimensions of the conical solar concentrator

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3 Application to Vapor Generation: Research Methodology 3.1 Presentation of the Absorber Tube The absorber tube has a geometrical serpentine form coiled on a cylindrical tube. The cylindrical tube functions as thermal storage. Heat storage technology provides a reliable energy supply throughout the day and achieves substantial economies. The diameter and length of the absorber tube are 0.1143 m and 2 m, respectively. The diameter of the serpentine tube is approximately 8 mm. (Fig. 3.)

Fig. 3. Representation of the absorber tube geometry

3.2 Concentration of Solar Irradiation on the Absorber Tube The solar radiations are concentrated along the focal axis of the conical solar concentrator where the absorber tube exists. The solar energy absorbed by the absorber tube is converted into useful thermal energy for the heat transfer fluid. After vaporization, the heat transfer fluid moves to thermal storage. Water is used as the heat transfer fluid in this case. The figure (Fig. 4.) represents the concentration of radiation on the absorber tube: 3.3 Mathematical Modeling of Heat Transfer Fluid The modeling of the heat transfer fluid is based on the nodal method, which decomposes the solid parts of the system into volume elements and products of equations solved manually and numerically [17]. The following hypotheses are proposed to establish the heat balance through the absorber tube: • The ambient temperature around the absorber tube is uniform. • The heat transfer fluid is incompressible. • The heat transfer fluid flow is unidimensional.

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Fig. 4. Concentration of solar irradiation on the absorber tube

• The concentrated solar radiation on the absorber tube is uniformly distributed. • The heat exchange by conduction through the serpentine tube is negligible. The equation of the heat transfer fluid is presented by: V˙ F ∂TF hF ∂TF = + (TF − TS ) ∂t AF ∂z ρF CF

(4)

with: TF is the heat transfer fluid temperature TS is the serpentine tube temperature V˙ F is the volume flow rate of the heat transfer fluid AF is the area of the heat transfer fluid hF is the heat exchange coefficient of the heat transfer fluid ρF is the density of the heat transfer fluid CF is the thermal capacity of the heat transfer fluid. 3.4 Discretization of the Heat Transfer Fluid Equation The heat transfer fluid equation is discretized by the Euler Explicit Decentered Forward of the Finite Difference Method [18]:   hF V˙ F t V˙ F t n hF n+1 n n + TF,k+1 − = 1− t TF,k + tTS,k + εT (5) TF,k AF z ρF CF AF z ρF CF εT is the truncation error defined by:   2 hF ∂ TF V˙ F z ∂ 2 TF εT = − t 2 2 ∂t AF t ∂z 2ρF CF

(6)

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3.5 Expression of Solar Concentrator Efficiency Absorber Tube Efficiency The following expression is used to calculate the absorber tube efficiency:  5.64 × 106 + 17.994853LF (T ) + 0.0144392ρvap,F (T )Cvap,F (T )(TF − 100) γr = ISsc μ (7) LF (T ) is the latent vaporization heat, I is the direct normal irradiation component and μ is the optical efficiency of the solar concentrator. Distribution of the Efficiency on the Absorber Tube Geometry The expression below shows the influence of the real efficiency on the absorber tube geometry: γr =

4.152692 38.396407 − d 2

(8)

d is the distance between 2 focal points, and is considered a height element dH of the solar concentrator. Expression of Mean Concentration Ratio The following expression presents the factor C, which is the mean concentration ratio describing the concentration of solar radiation on the absorber tube: C = 46.822944γr−1

(9)

4 Results of the Concentrator Application and Discussion The study was conducted on a typical day in the summer of 2019: 25 July 2019. Mathematical modeling estimated the outlet temperature of the heat transfer fluid to be approximately 180 °C (Fig. 5). The efficiency of the absorber tube depends on the input and produced powers, and the latent heat decreases when the temperature of vaporization increases, which influences the power produced [19]. According to (Fig. 6), the efficiency of the absorber tube varies between 20% and 48% with a decrease during the midday period of approximately 8%, and the concentration ratio reaches 590 in the same period (Fig. 6). The latent heat of vaporization is 2013.56 KJ.Kg−1 for a temperature of 180 ◦ C. However, the latent heat of vaporization for a temperature of 66 °C is 2340.40 KJ.Kg−1 . This justifies the 8% real efficiency rate at noon. The distribution of the efficiency on the absorber tube geometry as a function of parameter d indicates the distribution of solar − → − →  radiation along the absorber tube geometry. Either z such as: z = Oz with Oz = H k, as z increases the efficiency of the absorber tube increases as well (Fig. 5). As long as the efficiency of the absorber tube is a function of the direct component of the solar radiation, the part with increasing z of the absorber tube geometry is more exposed to

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solar radiation than the lower part of the absorber tube. Practically, our conical solar concentrator has an optimal conic angle on the order of 45◦ . In addition, the diameter and height of the conical solar concentrator are 4.5 m and 2 m, respectively. The exposition of the concentrator to solar illumination during the midday period generates a water vapor temperature of approximately 180 ◦ C. The efficiency of the solar concentrator is optimized by taking advantage of the solar radiation distribution on the absorber tube part with increasing z. Two more conical tubes can be placed in the area of increasing z of the absorber tube and occupy H/4 of the conical solar concentrator. They will be linked to storage and therefore increase the generated quantity of vapor and optimize the energetic production of the solar concentrator.

Fig. 5. Representation of the heat transfer fluid outlet temperature for a typical day on 25 July 2019 (left curve) and the distribution of real efficiency along the absorber tube geometry (dH is an element of the absorber tube height H) (right curve).

Fig. 6. Representation of the concentration ratio with the real efficiency of the absorber tube

5 Conclusions and Perspectives The objective of the present work is to introduce and develop the geometrical parameters of the conical solar concentrator. Then, this solar concentrator geometry is used for the production of electricity and vapor for home applications. This work is generally about the following:

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• Presentation of the conical solar concentrator geometry and its optimal angle for solar irradiation to reach the whole surface of the absorber tube. • Development of the geometrical parameters of the conical solar concentrator: a generalized expression for the focal distances, the concentrator surface and the influence of the concentrator geometry on the absorber tube. • Application of the conical solar concentrator for vapor generation using an absorber tube accompanied by a storage system. • Mathematical modeling for the estimation of the heat transfer fluid outlet temperature. • Determination of the absorber tube efficiency and concentration ratio of the solar concentrator. • Distribution of the efficiency on the absorber tube geometry This work can initiate further studies as perspectives for the evaluation and optimization of the solar concentrator geometry. Moreover, studying the effect of the solar concentrator parameters allows estimating the solar radiation distribution on the absorber tube geometry. The analysis of the direct radiation distribution on the absorber tube geometry facilitates the detection of zones where the solar rays are more concentrated. Therefore, the solar concentrator efficiency is improved.

References 1. Yang, Q., Huo, J., Saqi, N., Mahmood, H.: Modeling the effect of renewable energy and public-private partnership in testing EKC hypothesis: evidence from methods moment of quantile regression. Renew. Energy 192, 485–494 (2022) 2. Shrestha, A., Mustafa, A.A., Htike, M.M., You, V., Kakinaka, M.: Evolution of energy mix in emerging countries: modern renewable energy, traditional renewable energy, and nonrenewable energy. Renew. Energy 199, 419–432 (2022) 3. Li, P., Ng, J., Lua, Y.: Accelerating the adoption of renewable energy certificate: insights from a survey of corporate renewable procurement in Singapore. Renew. Energy 199, 1272–1282 (2022) 4. Zhang, N., Zheng, J., Song, G., Zhao, H.: Regional comprehensive environmental impact assessment of renewable energy system in California. J. Clean. Prod. 376(20), 134349 (2022) 5. Akyol˙Inada, A., Arman, S., Safaei, B.: A novel review on the efficiency of nanomaterials for solar energy storage systems. J. Energy Storage 55(Part C), 105661 (2022) 6. Alshibil, A.M.A., Farkas, I., Víg, P.: Multiaspect approach of electrical and thermal performance evaluation for hybrid photovoltaic/thermal solar collector using TRNSYS tool. Int. J. Thermofluids 16, 100222 (2022) 7. Dehghanimadvar, M., Shirmohammadi, R., Ahmadi, F., Aslani, A., Khalilpour, K.R.: Mapping the development of various solar thermal technologies with hype cycle analysis. Sustain. Energy Technol. Assess. 53(Part B), 102615 (2022) 8. Hongyu, X., et al.: A beam-down solar concentrator with a fixed focus—design and performance analysis. Sol. Energy 241(15), 428–436 (2022) 9. Arbuzov, Y.D., Evdokimov, V.M., Shepovalova, O.V.: Reflectivity of light on axisymmetric radiation receiver of cylindrical parabolic solar energy concentrator. Energy Rep. 7(Supplement 5), 466–480 (2021) 10. Rongji, X., He, Z., Yang, L., Shuhui, X., Wang, R., Wang, H.: Concentration performance of solar collector integrated compound parabolic concentrator and flat microchannel tube with tracking system. Renew. Energy (2022). https://doi.org/10.1016/j.renene.2022.09.107

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11. Grigoriev, V., Milidonis, K., Corsi, C., Blanco, M.: Heliostat fields with a balanced mirror density. Sol. Energy 243(1), 336–347 (2022) 12. Gorjian, A., Rahmati, E., Gorjian, S., Anand, A., Jathar, L.D.: A comprehensive study of research and development in concentrating solar cookers (CSCs): design considerations, recent advancements, and economics. Sol. Energy 245, 80–107 (2022) 13. Munir, Z., Roman, F., Niazi, B.M.K., Mahmood, N., Munir, A., Hensel, O.: Thermal analysis of a solar latent heat storage system using Scheffler concentrator for agricultural applications. Appl. Therm. Eng. 218(5), 119230 (2023) 14. Kumar, S., RaviKumar, K.: Techno economic feasibility study on hydrogen production using concentrating solar thermal technology in India. Int. J. Hydrogen Energy (2022). https://doi. org/10.1016/j.ijhydene.2022.08.285 15. Na, M.S., Hwang, J.Y., Hwang, S.G., Lee, J.H., Lee, G.H.: Design and performance analysis of conical solar concentrator. J. Biosyst. Eng. 43(1), 21–29 (2018). https://doi.org/10.5307/ JBE.2018.43.1.021 16. Arnaoutakis, G.E., Al. Katsaprakakis, D., Christakis, D.G.: Dynamic modeling of combined concentrating solar tower and parabolic trough for increased day-to-day performance. Appl. Energy 323(1), 119450 (2022) 17. Jarrah, I., Rizwan-uddin: Nodal integral methods in general 2D curvilinear coordinates applied to convection–diffusion equation in domains discretized using quadrilateral elements. Int. J. Heat Mass Transfer 187, 122559 (2022) 18. Ureña, F., Gavete, L., Benito, J.J., García, A., Vargas, A.M.: Solving the telegraph equation in 2-D and 3-D using generalized finite difference method (GFDM). Eng. Anal. Boundary Elem. 112, 13–24 (2020) 19. Spence, J., Buttsworth, D., Carter, B.: Energy content, bulk density, and the latent heat of vaporization characteristics of abattoir paunch waste. Energy 248(1), 123645 (2022)

High-Performance MPPT Based on Developed Fast Convergence for PV System-Experimental Validation Abdel Hamid Adaliou1(B) , Abdelhak Lamreoua1 , Ismail Isknan4 , Said Doubabi2 , Mustapha Melhaoui2 , Mostafa El Ouariachi1 , and Kamal Hirech1,3 1 Laboratory of Electrical Engineering and Maintenance, Higher School of Technology,

University of Mohammed I, Oujda, Morocco [email protected] 2 Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakech, Morocco [email protected] 3 Higher School of Education and Training, Mohammed I University, Oujda, Morocco 4 Laboratory of Materials and Renewable Energies, Ibn Zohr University, Agadir, Morocco [email protected]

Abstract. This paper proposes high-performance maximum power point tracking (MPPT) based on a developed fast convergence approach. The methodology developed in this work is based on the rapid convergence of the operating point to the maximum power point. This approach is compared with conventional MPPT techniques, such as perturbation and observation (P&O) and incremental conductance (IC) algorithms. This technique offers better performance in terms of start-up, steady-state and transient characteristics for climatic conditions. Additionally, this algorithm is based on the characteristics of the photovoltaic (PV) model and is also simpler than conventional MPPT techniques. This proposal has been validated by experimental studies, using a DC-DC Boost converter controlled by a control circuit realized in our laboratory, under changing climatic conditions. Keywords: Fast convergence approach · MPPT algorithms · Photovoltaic system

1 Introduction In recent years, the growing need for energy and the pollution caused by using fossil fuels and nuclear fission pushed the public to use renewable energy [1, 2]. In this context, photovoltaic energy is an important renewable energy source that presents a solution to our energy production problems [3, 4]. Moreover, this energy seems the most promising, nonpolluting, and inexhaustible [5]. In addition to being silent, it integrates perfectly into buildings (facades, roofs, etc…), and because it does not include moving mechanical parts, it does not require special maintenance and remains reliable for a long time, which is why it has become a reference in space applications and isolated sites. It is becoming © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 694–703, 2023. https://doi.org/10.1007/978-3-031-29857-8_69

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a safe bet for small and medium energy consumption applications, especially since solar panels are now cheaper and more efficient [6]. The performance and efficiency of a photovoltaic system are directly related to the weather conditions (irradiation and temperature). Indeed, a variation in the weather conditions leads to a variation in the MPP of the PV module, which causes the PV system to not produce the maximum of its power. Therefore, it is necessary and essential to use a mechanism that can extract the maximum power from the PV source as much as possible [7]. This work aims to study the different types of tracking mechanisms to charge a battery from the point of view of efficiency and complexity, through simulations under various atmospheric conditions so that the test is real and practical to detect their advantages and disadvantages over each other and then try to conclude their respective qualities. For the test, we have made a low-cost MPPT charge controller designed to work with a solar panel of a hundred watts. This system will be used to improve the performance of the photovoltaic conversion chain for applications such as battery charging and DC load supply.

2 The Study System To show the efficiency and performance of the method proposed in this work, the PV system shown in Fig. 1 is the system used in the various stages of the study of this system.

Fig. 1. Architecture of the used PV system

This PV system as shown in this figure contains the PV generator which is an assembly in series and/or in parallel of the PV cells, and the adapter between the source and the load which is a boost converter controlled by a control circuit based on different MPPT algorithms and a DC load. The DC load is a battery, to be charged by exploiting the maximum power delivered by the PV generator.

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3 Fast Convergence Approach: Simulation and Results 3.1 Fast Convergence Approach Figure 2 presents the characteristics of a PV module for different irradiations where the maximum power points are marked. A virtual load line is also sketched, which corresponds to the following equation [8]: VPV − rIPV − Vref = 0

(1)

Fig. 2. PV panel characteristics under different irradiations [8]

The operation of the MPPT converter along this line is realized by the simple control loop, where Eq. (1) is realized by a current sensor with an appropriate gain r. Since the MPP locations are not on a straight line, the value of Vref is tuned so that the virtual load line moves to different locations while maintaining its tilt, which is defined by the gain r. In this way, the PV generator is operated at the actual MPP at any given irradiation [9]. To further increase the efficiency and performance of this approach, the temperature of the PV panel is considered in the tracking of the maximum power point. Indeed, if the PV temperature changes, the reference voltage also changes according to the temperature coefficient of the PV module, and consequently, the virtual load right changes its position, as shown in Fig. 3. To be on the characteristic line of the optimum power point taking into consideration the variation of the temperature (Fig. 3), the following condition must be satisfied:       (2) Vref T ◦ = Vref 25◦ − (0.0036 ∗ VOC ) T − 25◦ with Vref (T ◦ ): Reference voltage at a given temperature Vref (25◦ ): Reference voltage ambient temperature 25°

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Fig. 3. Characteristics of the PV panel under changing irradiation and temperature ([8] modified)

(0.0036): Coefficient de température du module PV (−0.36%/°C) VOC : Open circuit voltage To validate and test the robustness of this algorithm, the PV system is subjected to sudden variations in solar irradiation and temperature. 3.2 Simulation of the Fast Convergence Algorithm Under Variation of Irradiation and Temperature First, the PV system is subjected to a variation of irradiation and constant temperature. In this test, the system is subjected to three different irradiations, at the beginning is simulated under 1000 W/m2 , at 0,05 s under 800 W/m2 , at 0,1 s under 500 W/m2 and then returns to 1000 W/m2 after 0.15 s. The results of this simulation are given by the following figure (Fig. 4):

Fig. 4. Simulation results under variable irradiation and constant temperature

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After each change in irradiation, the system is able to follow the point of maximum power, corresponding to this irradiation, with an important speed that was estimated to be approximately 0,2 ms. In a steady state, the system is stable since the fluctuations are approximately 2 W, which corresponds to the stability of the system at approximately 98%. This time, we kept the irradiation constant, and we changed the temperature abruptly to test the performance of this algorithm in MPP tracking. At the beginning, the system is simulated at 25 °C; after 0,1 s the temperature is changed to 40 °C and then returned to 15 °C after 0.2 s. According to the simulation results obtained (see Fig. 5), we note that this approach has the ability to converge quickly to the MPP in a reduced response time. For the change in temperature from 25 °C to 40 °C and from 40 °C to 15 °C, the panel voltage tends toward the voltage corresponding to each temperature, and consequently, the power produced and the system converges toward the corresponding MPP in an estimated time of 0.43 ms, which shows the speed of the algorithm in MPP tracking. This convergence does not cause instability of the system since there are no large fluctuations in the steady state whose stability performance can reach 97%.

Fig. 5. Simulation results under constant irradiation and variable temperature

According to the simulations obtained, under variable irradiation and temperature, the photovoltaic system converges to the optimal operating point through a transient regime that is very reduced, and the estimated convergence time of this method is approximately 0.2 ms, as shown in Fig. 6. This rapid convergence under different external disturbances (weather) shows the robustness and performance of this algorithm.

4 Experimental Validation To experimentally validate the robustness of this approach, it is compared with conventional algorithms such as perturbation and observation (P&O) and incremental conductance (IC). The P&O method is performed based on periodic perturbations (i.e.,

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Fig. 6. Simulation results under variable irradiation and temperature

incrementing or decrementing) of the panel voltage and the difference between the PV power output and that of the previous perturbation cycle [10]. The first step is to measure the initial PV voltage and current and calculate the corresponding power. If a small disturbance in the voltage (V ) or duty cycle (D) of the DC-DC converter in one direction is considered, the corresponding power is calculated and compared with the reference value. The corresponding power is calculated and compared with the previous value of the power. If the change in power (P) is positive, then the disturbance is in the correct direction; otherwise, the direction must be reversed (i.e., decrement V ) [11, 12]. The IC algorithm compares the instantaneous conductance of a PV generator with its incremental conductance and decides to increase or decrease a control parameter accordingly [13]. This MPPT algorithm is based on the fact that the power-voltage curve of a PV array at constant solar irradiance and cell temperature levels normally has only one MPP. At this MPP point, the derivative of power with respect to voltage is zero, which means that the sum of the instantaneous conductance I /V and the incremental conductance dI /dV is zero [14]. On the right side of the MPP, the sum of the instantaneous and incremental conductance is negative, while on the left side of the MPP, the sum is positive [15]. 4.1 The Characteristics of the PV Module Used in Experimental Validation During all the experimental tests of these algorithms, we used the synoptic schema of the PV system given in Fig. 1. This system consists of a PV module, DC-DC boost converter, MPPT controller, and DC load, as presented in Fig. 7. The current sensor and voltage sensor are used to acquire the current and voltage delivered by the PV panel. The efficiency of photovoltaic panels is decreasing due to several factors such as misuse, aging of cells, and fatigue of other components [16]. For these reasons, the real characteristics of the SGM-FL panel are studied by a direct connection of the PV panel with a rheostat. These characteristics are taken under irradiation at 1061 W/m2 , a temperature of 31 °C, and an inclination of 30° toward the south. These characteristics are given in Table 1.

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Fig. 7. Materials used in the experimental part

Table 1. Real electrical characteristics of the SGM - Fl panel Parameter

Value

PMPP

64.34 W

VOC

17.5 V

VMPP

13.72 V

ISC

5.64 A

IMPP

4.69 A

4.2 Experimental Results Under irradiation at 1063 W/m2 and a temperature of 33 °C, we carried out several experimental readings to study the behavior of the PV panel voltage Vpv , the PV panel current Ipv , and the power of the PV panel P. We studied the experimental behavior of the current of the GPV (IPV), its voltage (VPV), and consequently its instantaneous power (PPV) on precise readings obtained with the software of acquisition on short intervals of time. The results of the implementation of MPPT algorithms are given in the following figures: Following the implementation of MPPT algorithms such as P&O, CI and the fast convergence approach, the following interpretations were retained: The P&O algorithm has a transient regime that lasts approximately 9.4 s to reach the PPM because of the initial conditions, namely, the initialization of the duty cycle and the increment step [17]. In a steady state, the algorithm could follow the PPM with an error of 3.62 W and with oscillations around this PPM, which are estimated by fluctuations of 1.61 W (see Fig. 8). The CI algorithm (see Fig. 9) converges to the PPM after a transient regime that lasts approximately 9.2 s with oscillations around this point that are estimated by 1.6 W. In a steady state, the algorithm could follow the PPM with an error of 3.26 W compared to the maximum power due to the complexity of achieving the condition dP/dv = 0.

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Fig. 8. Practical results of the P&O algorithm

Fig. 9. Practical results of the IC algorithm

From the experimental results obtained, the fast convergence approach is efficient in the MPP track, especially at the transient level since it quickly converges to the maximum power of the PV generator after 1,2 s (see Fig. 10). This approach leads to good performance since it has a faster response time. This approach has low oscillations compared to conventional algorithms since it oscillates at approximately 0.97 W. In a steady state, the algorithm could follow the PPM with an error of 1.69 W, which is a small error. A summary of the performances and results of the three algorithms is given in Table 2.

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Fig. 10. Practical results of comparison of the algorithms Table 2. Summary table of the three algorithms Algorithm

Pmax (W)

Response time (s)

Stability

Error

Global Efficiency

P&O

60,72

9,4

97,34%

5,63%

91,18%

IC

61,08

9,2

97,38%

5,1%

92,15%

FC

62,65

1,2

98,45%

2,63%

95,54%

5 Conclusion The PV conversion chain is a very important part of the optimization of energy production by the PV panel. In this work, a fast convergence algorithm was developed to increase the efficiency and robustness of the conversion chain, which consists of a controlled boost converter. Experimental results show that this approach has many advantages over traditional MPPT algorithms. It can track the MPP in an efficient and fast way with an improvement of approximately 4,36% compared to the P&O and 3,39% compared to the IC algorithms, and according to the synthesis of the simulation results and the practical results, it was found that it can reach an efficiency of 95.54% with a stability of 98.45%. The results obtained allowed us to conclude that the implemented algorithm presents very good agreement between the experiment and the simulation. Moreover, during the operation of the photovoltaic energy conversion chain, no discrepancies were observed.

References 1. Priyadarshi, N., Sharma, A.K., Priyam, S.: Practical realization of an improved photovoltaic grid integration with MPPT. Int. J. Renew. Energy Res. 7(4), 1880–1891 (2017) 2. Yap, K.Y., Sarimuthu, C.R., Lim, J.M.Y.: Artificial intelligence based MPPT techniques for solar power system: a review. J. Modern Power Syst. Clean Energy 8(6), 1043–1059 (2020)

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3. Kumar, K., Ramesh Babu, N., Prabhu, K.R.: Design and analysis of an integrated Cuk-SEPIC converter with MPPT for Standalone wind/PV hybrid system. Int. J. Renew. Energy Res. 7(1), 96–106 (2017) 4. Jiang, M., et al.: A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system. Control Eng. Pract. 114, 104880 (2021) 5. Hai, M.A., Moula, M.M.E., Seppälä, U.: Results of intention-behaviour gap for solar energy in regular residential buildings in Finland. Int. J. Sustain. Built Environ. 6(2), 317–329 (2017) 6. Roy, R.B., et al.: A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system. IEEE Access 9, 102137–102152 (2021) 7. Chouial, H., et al.: High performance MPPT controller for solar photovoltaic system under variable solar irradiations. Int. J. Electr. Eng. Inform. 14(3), 682–697 (2022). https://doi.org/ 10.15676/ijeei.2022.14.3.12 8. Sokolov, M., Shmilovitz, D.: A modified MPPT scheme for accelerated convergence. IEEE Trans. Energy Convers. 23(4), 1105–1107 (2008) 9. Tey, K.S., Mekhilef, S., Member, S.: A fast-converging MPPT technique for photovoltaic system under fast varying solar irradiation and load resistance. IEEE Trans. Ind. Inform. 3203(c), 1–11 (2014) 10. Pilakkat, D., Kanthalakshmi, S.: An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions. Sol. Energy 178, 37–47 (2019) 11. Elgendy, M.A., Zahawi, B., Atkinson, D.J.: Operating characteristics of the P&O algorithm at high perturbation frequencies for standalone PV systems. IEEE Trans. Energy Convers. 30(1), 189–198 (2015) 12. Saxena, A.R., Gupta, S.M.: Performance analysis of P&O and incremental conductance MPPT algorithms under rapidly changing weather conditions. J. Electr. Syst. 10(3), 292–304 (2014) 13. Elgendy, M.A., Zahawi, B., Atkinson, D.J.: Assessment of the incremental conductance maximum power point tracking algorithm. IEEE Trans. Sustain. Energy 4(1), 108–117 (2013) 14. Safari, A., Mekhilef, S.: Simulation and hardware implementation of incremental conductance MPPT with direct control method using CUK converter. IEEE Trans. Industr. Electron. 58(4), 1154–1161 (2011). https://doi.org/10.1109/TIE.2010.2048834 15. Ika, R., Wibowo, S., Rifa, M.: Maximum power point tracking for photovoltaic using incremental conductance method. Energy Procedia 68, 22–30 (2015) 16. Lasfar, S., et al.: Study of the influence of dust deposits on photovoltaic solar panels: case of Nouakchott. Energy Sustain. Dev. 63, 7–15 (2021) 17. Motahhir, S., El Hammoumi, A., El Ghzizal, A.: Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation. Energy Rep. 4, 341–350 (2018)

A Comparative Study Between MPC Algorithm and P&O and IncCond the Optimization Algorithms of MPPT Algorithms Chaymae Boubii1(B) , Ismail El Kafazi2 , Rachid Bannari1 , and Brahim El Bhiri2 1 Laboratory Systems Engineering ENSA, Ibn Tofail University Kenitra, Kenitra, Morocco

[email protected] 2 Laboratory SMARTILAB, Moroccan School Engineering Sciences, EMSI Rabat, Rabat,

Morocco

Abstract. Fluctuations in solar radiation and ambient temperatures of Photovoltaic installations require to depend on the MPPT for photovoltaic installations to guarantee permanent gathering of max energy. MPPT is used to obtain the maximum power age point, although temperature, irradiation, or shading have various effects. With this technique, we can manage the energy required with fewer plates, reducing the cost of adding it to the photovoltaic frame. There are many types of regular and uncommon MPPT calculations. The primary objective here in the work is to analyze the MPPT model based on predictive control for photovoltaic systems and compare three algorithms of MPPT (P&O, IncCond, and MPC). Therefore, this article presents a similar approach to the MPPT algorithm using MPC aid MATLAB/SIMULINK. Keywords: Photovoltaic (PV) · Boost · Maximum Power Point Tracking (MPPT) · Model predictive control (MPC) · Perturb & Observe (P&O) · Incremental Conductance Method (IncCond)

1 Introduction Photovoltaic energy has many advantages. It is mainly clean, free, and has unlimited resources. The PV system has nonlinear current-voltage power-voltage characteristics. MPP monitor draws maximum from the solar panel when weather conditions change. Several MPPT techniques have been developed to track maximum power points, and many studies focus on the different MPPT algorithms, for example, traditional techniques (P&O and IncCond) [1, 2]. In [3], the authors suggested using fuzzy logic control to improve the photovoltaic system’s traction precision and power transfer. In recent years, smart methods such as fuzzy [4], neural networks [5], and MPC [6] were developed to achieve max performance of PV systems. The neural networks and predictive control models were developed to achieve the maximum performance of photovoltaic systems. In recent years, studies have focused on the MPC method because it is dynamic and adaptive to different climatic conditions [7, 8], for that, we will concentrate in this paper on the study of the MPC algorithm and we will make a comparison between the MPC and two traditional algorithms (P&O and IncCond). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 704–713, 2023. https://doi.org/10.1007/978-3-031-29857-8_70

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2 Basic Principles of MPC In 80s, researchers began to apply the MPC method in high power with low switching frequency [9]. Now, it cannot be used for higher switching due to the increased analysis time. But due to new developments in microprocessors, the use of the MPC method has become popular [10, 11]. The MPC method is based on sampling over time to choose the future optimal command (x better (k + N ), N = 1). We can present a system in the following form (1) and (2) x(k + 1) = A.x(k) + B.u(k)

(1)

y(k) = C.u(k)

(2)

The cost function (g) represents the desired system behavior. This function considers references, states, and future operations: g = f (x(k), u(k), . . . , u(k + N − 1))

(3)

The cost function defined g is minimized for not predefined in the time horizon N; a sequence of optimal N powers is determined by the controller u(k), and the controller is applied only to the element of the sequence. To present the function g we must base it on the controller u(k) when it is in the process, while the calculated signals are refused [12]. This the exit to the next sample state is already known. So, to minimize the function g, we can write in the form of a vector as Eq. (4) shows. u(k) = [10 . . . 0] arg minu g

(4)

3 Analysis of Boost Converter The chopper is composed by an inductance, diode, transistor, and a capacitor [13] as Fig. 1 shows. Applying Kirchhoff’s voltage law to Fig. 1 we find that: For S = 1 : When S = 0 :

L.

L.

diL (t) = vpv (t) dt

diL (t) = vpv (t) − vc2 (t) dt

(5) (6)

If S = 1 and S = 0 means to convert ON and OFF. And duty ratio :

D =1−

Therefore VC2 (k) =

Vpv (k) VC2 (k)

1 .Vpv (k) 1−D

(7) (8)

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Fig. 1. DC-DC converter (Boost)

4 MPC MPPT Technology The proposed MPPT algorithm appears in Fig. 2. The entry tension and current of the PV system were factored into the algorithm. The derived voltage if the button is turned on and off by (5), (6). The current of the capacitor PV for S ∈ {0,1} is from before. c1 .

dvpv (t) = ipv (t) − iL (t) dt

(9)

Application of the direct Euler method, the discharges in (5), (6), and (9) may be approximated as X (k + 1) − X (k) dx(t) = dt TS

(10)

where: • x: the parameter for discretization. • TS : the sampling periods. • k: discretized t. Act of the controlled variable results from deriving discrete time equations. The control can make a forecast for the future time point of k. With (5), (6), (9) and (10), the discrete-time model of the transducer results from (11)–(14) when S = 1: TS .Vpv (k) + IL (k) L  TS  . Ipv (k) − IL (k) + Vpv (k) Vpv (k + 1) = c1 IL (k + 1) =

(11) (12)

And when S = 0:  TS  . Vpv (k) − Vc2 (k) + IL (k) L  TS  . Ipv (k) − IL (k) + Vpv (k) Vpv (k + 1) = c1 IL (k + 1) =

(13) (14)

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It can be seen from (11) to (14) that there exist four entries Ipv , IL , Vpv and Vc1 . To reduce the number of detectors needed, these equations can be rearranged by reducing the number of entry tags. Using (8), Vc2 can be removed from (13), so for S = 0: IL (k + 1) =

TS TS .Vpv (k) + IL (k) .Vpv (k) − L L.(1 − D)

(15)

Application of Euler’s backward method [14] and (9): Vpv (k) − Vpv (k − 1) =

 TS  . Ipv (k) − IL (k) c1

(16)

With (16), (12) and (14) they can now be represented as follows: Vpv (k + 1) = 2.Vpv (k) − Vpv (k − 1)

(17)

therefore, Ipv is eliminated from (12) and (14). The two matrices (18) and (19) represent the system when the interrupter is ON and is OFF. If S = 1:    TS      IL (k + 1) I (k) 0 1 L . L = + (18) .Vpv (k − 1) Vpv (k + 1) Vpv (k) −1 0 2 And when S = 0:    1 IL (k + 1) = Vpv (k + 1) 0

TS L

−

TS L.(1−D)



2

   0 IL (k) + . .Vpv (k − 1) Vpv (k) −1

(19)

Subsequently, we presented in Fig. 2 these two matrices (18) and (19) in a two-input algorithm IL (k) and Vpv (k). We can notice in Fig. 2 that the future state of the current IL (k + 1) is linked by the state of the switch. The value of S is equal to 1 or to 0. So, to determine the future value of IL we use a small offset value, “d” which is added or subtracted to IL (k) to determine ILref (k + 1). In Fig. 2, ILref (k + 1) is equal to IL (k) when: ∂ppv =0 ∂vpv ∂(iL .vpv ) =0 ∂vpv   (iL .∂ vpv + vpv .∂(iL ) From (21) =0 ∂vpv by using (20)

Using (22) iL +

vpv .∂(iL ) =0 ∂vpv

(20) (21) (22) (23)

with (23) it is determined (24), if Eq. (24) is met, so ILref (k + 1) = IL (k): iL iL (k) =− vpv vpv (k)

(24)

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Instead, to prevent the singularity in (24), the algorithm calculated vpv , iL . We can see the details in Fig. 2. The reference ILref (k + 1) is current to follow at the sampling instant, it is the input of the minimized cost function. The subject minimization of the cost function given by     (25) gS∈{0,1} = ILref (k) − I L (k + 1)S∈{0,1}  Equation (25) represents the cost function after minimization. The complete controller flow is summarized in Fig. 2.

Fig. 2. Flowchart for MPPT by MPC

5 Simulation and Results 5.1 Simulation of a PV System and Converter BOOST with MPC Algorithm The model of a system composed of PV and boost converter with MPPT algorithms is shown in Fig. 3. 5.2 Results Figure 4 shows the voltage in the out of the boost converter with the MPC algorithm. The voltage is increased from the start of the simulation to t = 4 s, where it reaches the maximum value (Vout_MPC = 1150 V). After that, the tension remains constant at its full value until t = 25 s. Then it decreases gradually. In t = 30 s, the voltage drops rapidly. The same thing happened to the power. It remains constant on its maximum value (Pout_MPC = 9470 W) between t = 2 s and t = 25 s. Also, In t = 30 s, the power decreases rapidly, and the curve of power is presented in Fig. 5.

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Fig. 3. Simulation of system PV with boost converter and MPC algorithm of MPPT.

Fig. 4. Voltage Profile of the Boost Converter with MPC algorithm.

5.3 Simulation of a PV System and Converter BOOST with Different Algorithms (P&O, InC and MPC) To better see the effect of using the MPC algorithm, we had to compare the results obtained by this algorithm with the results of the other algorithms. And for this, we have designed the same system as in Fig. 3, which is composed of PV and a BOOST converter and an MPPT programmed by an algorithm, but this time we have used two of the most common algorithms (P&O and IncCond). For the first time, we used the P&O algorithm see Fig. 6. Then, we changed the P&O algorithm to the IncCond algorithm, see Fig. 7. 5.4 Comparison Results Figure 8 represents the results of the voltage obtained by the three algorithms. We can say that from the beginning of the simulation until t = 25 s, the three curves obtained by

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Fig. 5. Power Profile of the Boost Converter with MPC algorithm.

Fig. 6. PV system and converter BOOST with P&O algorithm.

Fig. 7. PV system and converter BOOST with IncCond algorithm.

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the three algorithms are the same. At the beginning of the simulation, the curve begins to increase until it reaches the voltage value (Vout = 1150 V) at t = 2 s. Afterward, the curve stays constant in Vout = 1150 V until t = 25 s. Then, we notice that after the time t = 25 s, the curve obtained by the IncCond algorithm decreases rapidly at t = 27.5 s. Still, for the two other algorithms, we find that the curve obtained by P&O begins to decrease from time t = 25, and at t = 30 s, the curve will be zero. For the MPC algorithm, we notice its voltage curve after time t = 25 s decreases less quickly than the other algorithms and will be equal to t = 34 s. The same thing happened with the power shown in Fig. 9. The power in the out-of-boost converter is just the same for the three algorithms P&O, IncCond, and MPC between t = 0 s and t = 25 s. The power remains constant at its maximum value (Pout = 9470 W) between t = 2 s and t = 25 s. The power in the out-of-boost converter is just the same for the three algorithms P&O, IncCond and MPC between t = 0 s and t = 25 s. The power remains constant at its maximum value (Pout = 9470 W) between t = 2 s and t = 25 s. After t = 25 s, the curves start to decrease. For the IncCond algorithm, the curve decreases quickly at t = 27.5 s and the same for P&O and MPC algorithms. The curve of P&O decrease at t = 30 s but the curve of MPC algorithm decrease at t = 34 s.

Fig. 8. Voltage Profile of the Boost Converter with MPC, P&O and IncCond.

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Fig. 9. Power Profile of the Boost Converter with MPC, P&O and IncCond.

6 Conclusion This article describes the simulation and analysis of the MPC algorithm. The simulation was performed for a photovoltaic system for maximum power under standard test conditions. The results are given in according to the monitoring of the MPP. The comparison between the results obtained by MPC and P&O and IncCond and algorithms discussed in the literature show the advantage of the MPC. With the MPC algorithm, the power output is more compared to other algorithms. Generally, the MPC method is recommended for programming the MPPT.

References 1. Esram, T., Chapman, P.L.: Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans. Energy Convers. 22, 439–449 (2007) 2. Reisi, A.R., Moradi, M.H., Jamasb, S.: Classification and comparison of maximum power point tracking techniques for photovoltaic system: a review. Renew. Sustain. Energy Rev. 19, 433–443 (2013) 3. El Khateb, A., Rahim, N.A., Selvaraj, J., Uddin, M.N.: Fuzzy-logic-controller-based SEPIC converter for maximum power point tracking. IEEE Trans. Ind. Appl. 50, 2349–2358 (2014) 4. Al Nabulsi, A., Dhaouadi, R.: Efficiency optimization of a DSP based standalone PV system using fuzzy logic and dual-MPPT control. IEEE Trans. Ind. Inf. 8(3), 573–584 (2012) 5. Syafaruddin, Karatepe, E., Hiyama, T.: Artificial neural network polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions. IET Renew. Power Gener. 3(2), 239–253 (2009)

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6. Sajadian, S., Ahmadi, R.: Model predictive-based maximum power point tracking for gridtied photovoltaic applications using a Z-source inverter. IEEE Trans. Power Electron. 31(11), 7611–7619 (2016) 7. Holtz, J., Stadtfeld, S.: A predictive controller for the stator current vector of AC machines fed from a switched voltage source. In: International Power Electronics Conference (IPEC) (1983) 8. Sajadian, S., Ahmadi, R.: Distributed maximum power point tracking using model predictive control for solar photovoltaic applications. In: 2017 IEEE Applied Power Electronics Conference and Exposition (APEC), Tampa, FL, pp. 1319–1325 (2017) 9. Holtz, J., Stadtfeld, S.: A predictive controller for the stator current vector of AC machines fed from a switched voltage source. In: International Power Electronics Conference, IPEC, Tokyo, pp. 1665–1675 (1983) 10. Jiefeng, H., Jianguo, Z., Dorrell, D.G.: Model predictive control of grid-connected inverters for PV systems with flexible power regulation and switching frequency reduction. IEEE Trans. Ind. Appl. 51, 587–594 (2015) 11. Kouro, S., Perez, M.A., Rodriguez, J., Llor, A.M., Young, H.A.: Model predictive control MPC’s role in the evolution of power electronics. IEEE Ind. Electron. Mag. 9, 8–21 (2015) 12. Bordons, C., Montero, C.: Basic principles of MPC for power converters: bridging the gap between theory and practice. IEEE Ind. Electron. Mag. 9, 31–43 (2015) 13. Palanisamy, R., Vijayakumar, K., Venkatachalam, V., Narayanan, R.M., Saravanakumar, D., Saravanan, K.: Simulation of various DC-DC converters for photovoltaic system. Int. J. Electr. Comput. Eng. 9(2), 917 (2019) 14. Mohan, N., Robbins, W.P., Undeland, T.M., et al.: Simulation of power electronic and motion control systems-an overview. Proc. IEEE 82, 1287–1302 (1994)

Comparative Analysis of Classical and Meta-heuristic MPPT Algorithms in PV Systems Under Uniform Condition Abdelfettah El-Ghajghaj1(B) , Hicham Karmouni2 , Najib El Ouanjli3 , Mohammed Ouazzani Jamil4 , Hassan Qjidaa5 , and Mhamed Sayyouri1 1 Laboratory-of-Engineering, Systems, and Applications, National School of Applied Sciences,

Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] 2 National School of Applied Sciences of Marrakech, Cadii Ayyad University, Marrakech, Morocco 3 Laboratory of Mechanical, Computer, Electronics and Telecommunications, Faculty of Sciences and Technology, Hassan First University, 26000 Settat, Morocco 4 Laboratoire Systèmes et Environnement Durables, Faculté des sciences de l’ingénieur, Université Privée de Fès (UPF), Fez, Morocco 5 Laboratory of Electronic Signal and Systems of Information, Faculty of Science Dhar El Mahraz, Fez, Morocco

Abstract. This work presents a comparative study and discussion of several techniques to extract the maximum power Point Tracking (MPPT) through photovoltaic systems. This comparison includes meta-heuristic approaches namely Particle Swarm Optimization (PSO), Cuckoo Search (CS) optimization, and the standard Perturb and Observe (P&O) approach. PSO, CS and P&O based tracker performance is provided for irradiation 1000 W/m2 and with 25-degree of temperature. The MATLAB-Simulink software is used to test and analyze the simulation results of these algorithms and therefore show the performance and limits of each algorithm. The results confirm that the methods based on PSO, and CS exert a considerable convergence towards MPP with less oscillations compared to the P&O. Keywords: MPPT · P&O · PSO · CS · MATLAB/Simulink

1 Introduction The growth of the need for energy in the world as well as the political and military conflict between countries Affects the world’s energy supply, especially traditional and fossil sources, pushing the world to improve production based on renewable energies. In this context, photovoltaic energy is one of the remarkable and lower-priced sources facing the problem of production [1, 2]. However, the production of this energy is nonlinear due to the variations of irradiation and temperature. Consequently, several works have centered on photovoltaic systems, there are several MPPT techniques developed and established © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 714–723, 2023. https://doi.org/10.1007/978-3-031-29857-8_71

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in the literature to enhance the effectiveness of PV panel. Among these techniques: Perturb and Observe [3–6], Incremental Conductance (IC) [7], Hill Climbing [8], Fuzzy Logic [9], Particle Swarm Optimization [10]. These algorithms are appropriate to finding MPP according to consistent conditions, where there is a unique MPP into power versus voltage. On the other hand, these algorithms are not applicable for finding the global MPP (GMPP) under partial shading condition (PSC) [11], To solve the problem of PSC, metaheuristic methods present an efficient solution to find the GMPP [12, 13], such as Genetic Algorithm, Cuckoo Search, Particle-Swarm Optimization, Ant Colony Optimization [14, 15]. The principal objective of this work is to specify and compare the performance of two metaheuristic methods and the conventional P&O technique under constant temperature and irradiation. This work is submitted by the coming parts: Sect. 2 is dedicated to the modeling of the PV system. In Sect. 3, an analysis of P&O, PSO and CS algorithms is discussed in detail. Section 4 is centered on the implementation of these algorithms on MATLAB/Simulink. Finally, a conclusion is presented in the Sect. 5.

2 System Modeling The PV system installation is consisted of the following elements: a PV panel, boost converter and an MPPT algorithm as shown in Fig. 1. I1 I2 Panel

V.1

Ipv Vpv

Boost _converter

V.2

Load

Duty cycle (D) MPPT Algorithm

Fig. 1. PV system installation

2.1 Photovoltaic Module The PV panel transforms the rays of light into electrical energy through the photovoltaic effect. Figure 2 illustrate the model of a PV cell [16]. As illustrate in Fig. 2, the current may be indicated with the coming equation [13]:       V + R sI V + Rs I −1 − (1) I = Iph − I0 exp aKTNs Rsh Table 1 indicates the specifications of PV panel.

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I Rss Ipph

Rssh

D

V

Fig. 2. PV cell

Table 1. Specific Parameters for the PV panel. Parameter

Value

Maximum Power, Pm

60.53 W

Open circuit voltage, Voc

21.1 V

Voltage at Pm, Vmp

17.0458 V

Temperature coefficient of Voc −(0.229) %/deg.C Cells per module

36

Short-circuit current, Isc

3.8 A

Current at Pm, Imp

3.5510197 A

Temperature coefficient of Isc

(0.030706) %/deg.C

Light-generated current, IL

3.86 A

Diode saturation current, I0

4.4971e−13 A

Diode ideality factor

0.76717

Shunt resistance, Rsh

124.864 

Series resistance, Rs

0.51439 

2.2 Boost Converter The Boost transform the input voltage to a superior voltage in the output. This converter contain an inductor, two capacitors, IGBT switch and a diode that protects IGBT by obstructing current return [16] (Fig. 3).

L.1

Is V.s

C1

IGBT

Fig. 3. Boost converter

I.0

C2

V .0

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The expression for output voltage and current are designated by the coming equations:  V0 = VS /(1 − D) (2) I0 = Is ∗ (1 − D) where: D is the duty-cycle. The specifics of the boost converter are: C 1 = 1000 * 10–6 , L 1 = 5 * 10–4 and C 2 = 1000 * 10–6 .

3 MPPT Methods There are several working principles of MPPT optimization techniques. A summary of the main existing MPP search modes is presented in this chapter, the theoretical basis and the simulations of these techniques are presented in what follows. Figure 4 shows the I-V and P-V curves with the position of the maximum power.

Fig. 4. P-V and I-V curves

3.1 MPPT Using P&O The principle of the P&O technique consists in making perturbation of low value on the PV voltage, which create a variation in the PV power [3]. The P&O algorithm is detailed in the coming flowchart (Fig. 5): 3.2 MPPT Using PSO Particle swarm optimization is a metaheuristic technique based on the behavior of animals such as flocks of birds to reverse the problems embroiled in the search process. This technique is governed by displacement rules, enabling the particles to move from their random positions to find good local and global optimal positions. The PSO algorithm is based on rules that upgrade the different local and global positions of the particles and the group [10]. The detailed Flowchart of PSO based MPPT is illustrated in Fig. 6.

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Fig. 5. Flowchart of the P&O based MPPT

Fig. 6. Flowchart of the PSO algorithm

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3.3 MPPT Using CS The CS optimization algorithm is a parasitic breeding strategy inspired by cuckoos. For utilizing the CS for MPPT, suitable parameter must be chosen for the search. Primary, they are specified as the values of the duty cycle, i.e., Di (i = 1 , 2....n ). Next the step size, identified by α, the power at MPP is the fitness (J), then J is reliant on the duty cycle, then J = f(D). Firstly, the created models are implemented to the PV modules and the power is set as the first value of fitness. The MPP produced by its equivalent duty-cycle ratio. Thereafter the Lévy flight is affected. Therefore, new duty cycle is created by the coming equation: Dit+1 = Dit ⊕ Levy (λ)

(3)

where: α = α0 ( Dbest + Di ). The Lévy allocation is presented as:   u ( Dbest + Di ) S = α0 ( Dbest + Di ) ⊕ levy (λ) ≈ k × 1 |v| β

(4)

where: β = 1.5, k is the Lévy factor, with u and v are resolute from the distribution curves. u ≈ N ( 0 , σu2 ) v ≈ N ( 0 , σv2 )

(5)

The variable σu and σv are defined as: ⎛

⎞1

× β2 ) ⎟

⎠

⎜ (1 + β) × sin (π σu = ⎝ (( 1+β 2 )) × β × (2)

β−1 2

β

and σv = 1

(6)

The several powers for the novel duty cycle are calculated from the PV modules. By comparing the power values, the MPP is improved by selecting the duty cycle as the new best. Moreover, this best is arbitrarily destroyed with a probability of Pa, such process reproduces the conduct of the host bird anticipating the cuckoo’s eggs and then demolishing those. Subsequently a new random are produced to change the destroyed. Therefore, the powers for all samples are calculated again by measuring J. The iteration continues until all the samples have achieved the MPP [15]. The Flowchart of CS based MPPT is presented in Fig. 7.

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Fig. 7. Flowchart of the CS algorithm

4 Simulation Results To analyze the efficiency of CS and PSO based MPPT and compare it with the P&O method on MATLAB/Simulink, the simulation is carried out on the same conditions which are 25 degrees of a temperature and a 1000 W/m2 of irradiation. These MPPT methods were used as converter controller by generating an appropriate duty cycle to analyze and compare the dynamic response. From Fig. 8, the three algorithms are achieved the MPP with strong tracking. The exhaustive simulation (PV power, PV voltage, PV current and duty cycle) with different MPPT techniques under a uniform condition model are shown in Figs. 9, 10 and 11.

Fig. 8. Output PV power with PSO, P&O and CS techniques

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Fig. 9. Simulation-result with P&O

Fig. 10. Simulation result with PSO

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Fig. 11. Simulation-result with CS

From these results, it is evident that the efficiency of CS−based MPPT is higher than the PSO and P&O in terms of tracking time and less oscillations. Table 2 summarizes a comparison between these optimization algorithms in terms of tracking speed and power performance. Table 2. Comparative studied between MPPT techniques. Technique Power (W) Tracking speed (S) Maximum Power (W) Tracking efficiency % P&O

60.18

0.25

PSO

60.25

0.21

CS

60.30

0.15

99.39 60.53

99.49 99.61

5 Conclusion In this article, a comparative study between P&O and two MPPTs based on metaheuristic optimization algorithms is evaluated, these algorithms comprise PSO and CS methods. The MPPT system is modeled in MATLAB/Simulink with uniform irradiance and temperature, the efficiency of studied MPPT methods is examined and compared. The results validate that the methods based on CS and PSO have strong precision and stability in the extraction of MPP compared to the P&O method. Future work concerns the experimental validation and comparative study of these algorithms under partial shading conditions.

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References 1. Tan, Y.T., Kirschen, D.S., Jenkins, N.: A model of PV generation suitable for stability analysis. IEEE Trans. Energy Convers. 19(4), 748–755 (2004) 2. Mkahl, R., Nait-Sidi-Moh, A., Wack, M.: Modeling and simulation of standalone photovoltaic charging stations for electric vehicles. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. 9(1), 72–80 (2015) 3. Yadav, A.P.K., Thirumaliah, S., Haritha, G., Scholar, P.G.: Comparison of MPPT algorithms for DC-DC converters based PV systems. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 1(1), 18–23 (2012) 4. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron. 20(4), 963–973 (2005) 5. Femia, N., Granozio, D., Petrone, G., Spagnuolo, G., Vitelli, M.: Predictive & adaptive MPPT perturb and observe method. IEEE Trans. Aerosp. Electron. Syst. 43(3), 934–950 (2007) 6. El-Ghajghaj, A., Ouanjli, N.E., Karmouni, H., Jamil, M.O., Qjidaa, H., Sayyouri, M.: An improved MPPT based on maximum area method for PV system operating under fast varying of solar irradiation. In: Motahhir, S., Bossoufi, B. (eds.) International Conference on Digital Technologies and Applications, pp. 545–553. Springer, Cham (2022). https://doi.org/10.1007/ 978-3-031-01942-5_54 7. Errouha, M., Derouich, A., Motahhir, S., et al.: Optimization and control of water pumping PV systems using fuzzy logic controller. Energy Rep. 5, 853–865 (2019) 8. Bahari, M.I., Tarassodi, P., Naeini, Y.M., Khalilabad, A.K., Shirazi, P.: Modeling and simulation of hill climbing MPPT algorithm for photovoltaic application. In: 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), pp. 1041–1044. IEEE, June 2016 9. Cheikh, M.A., Larbes, C., Kebir, G.T., Zerguerras, A.: Maximum power point tracking using a fuzzy logic control scheme. Revue des energies Renouvelables 10(3), 387–395 (2007) 10. Ishaque, K., Salam, Z., Amjad, M., Mekhilef, S.: An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE Trans. Power Electron. 27(8), 3627–3638 (2012) 11. Ishaque, K., Salam, Z.: A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renew. Sustain. Energy Rev. 19, 475–488 (2013) 12. Rezk, H., Fathy, A., Abdelaziz, A.Y.: A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renew. Sustain. Energy Rev. 74, 377–386 (2017) 13. Zafar, M.H., et al.: A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technol. Assess. 47, 101367 (2021) 14. Karmouni, H., Chouiekh, M., Motahhir, S., Qjidaa, H., Jamil, M.O., Sayyouri, M.: Optimization and implementation of a photovoltaic pumping system using the sine–cosine algorithm. Eng. Appl. Artif. Intell. 114, 105104 (2022) 15. Daoui, A., Karmouni, H., Sayyouri, M., Qjidaa, H.: Robust image encryption and zerowatermarking scheme using SCA and modified logistic map. Expert Syst. Appl. 190, 116193 (2022) 16. Yang, Y., Zhou, K.: Photovoltaic cell modeling and MPPT control strategies. Diangong Jishu Xuebao/Trans. China Electrotech. Soc. 26(SUPPL. 1), 229–234 (2011) 17. Mosaad, M.I., Abed el-Raouf, M.O., Al-Ahmar, M.A., Banakher, F.A.: Maximum power point tracking of PV system based cuckoo search algorithm; review and comparison. Energy Procedia 162, 117–126 (2019)

Robust Control of a Wind Power System Based on a Doubly-Fed Induction Generator Using a Fuzzy Controller Mbarek Chahboun(B) and Hicham Hihi Laboratory of Engineering, Systems and Applications, USMBA, ENSAF, Avenue My Abdallah Km 5 Road d’Imouzzer, Fez, BP 72, Fez, Morocco {mbarek.chahboun,hicham.hihi}@usmba.ac.ma

Abstract. The aim of this study is to investigate the behaviour of the vectorcontrolled Doubly-Fed Induction Generator (DFIG) fed by a voltage inverter, used as a wind turbine generator. In order to adjust the active and reactive power, nonlinear control is used in combination with a change in rotor quantities. The design of the proposed technique is based on fuzzy logic. The simulation results obtained by this Fuzzy Logic Controller (FLC) will be compared with those obtained by the classical Proportional-Integral PI controller to judge the performance in both cases, and show the high performance of this control. Utilizing the Matlab application, the simulation is run. Therefore, in order to observe how this system responds to active and reactive power steps, we have subjected it to them. Keywords: DFIG · Fuzzy Logic · PI · turbine · regulator · inverter · renewable energy · wind turbine · control system

1 Introduction Electrical energy from renewable sources, especially wind, is considered an important production alternative in modern electrical energy systems. Indeed, the use of this green source contributes to the reduction of greenhouse gas emissions [1], once again these systems are not dependent on any grid or source and have the advantage of being close to the consumption sites. Indeed, wind energy systems have grown rapidly in recent years and have become increasingly accessible in terms of cost and technology [2]. Indeed, a lot of progress has been made in the field of control, especially for non-linear systems. The doubly-fed induction generator is the basis for the most popular variable speed wind system now being utilised in wind farms (DFIG) [3]. Wind power conversion systems are non-linear and therefore require more robust control techniques. In this context, our work is based on the modelling of a conversion chain, by connecting to the wind turbine an asynchronous machine with double supply, then we will describe a strategy of piloting this machine, it is a vector control with orientation of flow which makes it possible to obtain a separate control of the flow and the torque. After giving a general overview of the theory of the fuzzy logic technique [4], we will detail in the rest of our paper the reasons for the choice of this type of control and its basic principles [5]. The overall control scheme of the system as well as the simulation results will also be presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 724–734, 2023. https://doi.org/10.1007/978-3-031-29857-8_72

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2 Modelling the Wind Energy System The device, which is studied here, consists of a wind turbine comprising a generator driven by three blades of length R through a gain multiplier G. 2.1 Turbine Modelling The part that converts wind kinetic energy into mechanical energy is the wind turbine. The wind turbine rotates and generates mechanical energy on the turbine shaft when the wind, whose speed is v, is supplied to the blades. The equation gives the aerodynamic power at the turbine rotor is: Pt =

1 .Cp (λ, β).ρ.S.v3 2

(1)

The power coefficient is frequently expressed as a function of the angle β (the angle between the plane of the chord in blade section and rotation) to the relative tip speed λ. A wind turbine’s tip speed ratio is described as follows: λ=

t .R u = v v

(2)

where: ρ: is the density of the air, λ: the proportion of the wind speed to the linear speed at the tip of the wind turbine blades, or relative speed, t : the speed of rotation of the turbine, R: the length of the blade, S: the circular area that is swept by the turbine and v: wind speed, Cp : the power coefficient. The latter is a depiction of the turbine’s aerodynamic efficiency and is impacted by a characteristic of the turbine. The Betz limit [6] is a theoretical upper limit. In this study, we will adopt the following approximation of the expression of the power coefficient as a function of the relative speed and the blade tilt angle, the expression of which is specified [7]:   −a 5 a2 − a4 − a3 β e λi + λa6 (3) Cp (λ, β) = a1 λi where: 1 = λi



0.035 1 − 3 λ + 0.08β β +1

 (4)

The expression for the aerodynamic torque is given by: Ctur =

Ptur S.ρ.v3 1 . = Cp . t 2 t

(5)

where: Ptub is the aerodynamic power and the coefficients a1 = 0.51090, a2 = 116.00, a3 = 0.40, a4 = 5.00, a5 = 21.00 and a6 = 0.0068 depend on the turbine considered. Figure 1 displays the features of the Cp coefficient as a function of λ various wedge angle β values.

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Fig. 1. Influence of the setting angle on the power coefficient.

3 DFIG Modelling The literature has already covered a lot of ground with the modelling of the dual-fed asynchronous machine. However, its mathematical model is complex, despite its simple construction, as the phenomena involved are of a mechanical, magnetic and electrical nature, with a multivariable non-linear structure. In a fixed three-phase reference frame linked to the stator, the disadvantage of the DFIG model is that it results in differential equations with variable coefficients as a function of rotor position, and therefore time. 3.1 The Voltage Equations The modelling of the DFIG is described in the two-phase reference frame (Park). The following system of equations describes the overall modelling of the generator. We can establish the expressions for the rotor and stator voltages in the d-q park reference frame: Vds = Rs Ids +

d φds − ωs φqs dt

(6)

Vqs = Rs Iqs +

d φqs + ωs φds dt

(7)

Vdr = Rr Idr +

d φdr − ωr φqr dt

(8)

Vqr = Rr Iqr +

d φqr − ωr φdr dt

(9)

The following are the definitions of the electrical equations that link the stator and rotor currents to the interacting flows in the generator: φds = LS Ids + MIdr

(10)

φqs = LS Iqs + MIqr

(11)

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φdr = Lr Idr + MIds

(12)

φqr = Lr Iqr + MIqs

(13)

4 Vector Control of the DFIG Active and reactive power control can be achieved by rotor flux control of the DFIG. This technique consists in keeping the armature reaction flux in quadrature with the rotor flux. In order to be able to control the stator power independently we have to control the rotor and forward transverse voltages separately in a decoupled mode by introducing compensation terms. By substituting Eq. (8) into Eq. (13): The rotor voltages can be rewritten: d Idr − gωs Lr Iqr dt

(14)

d Lm vs Iqr + gωs σ Lr Idr + g dt Ls

(15)

Vdr = Rr Idr + Lr σ Vqr = Rr Iqr + Lr σ

Figure 3 depicts the block diagram of the condensed DFIG model. By manipulating the stator flux to conveniently control the wind turbine’s power output, we will be able to independently control both active and reactive power. So, here we are: φsd = −Ls .Isd + MIrd

(16)

φsq = −Ls .Isq + MIrq = 0

(17)

So, here is how the electromagnetic torque is described: Ce = p(Iqs φds − Ids φqs ) = −p

M Iqr φds Ls

(18)

where: J: Rotor moment of inertia, P: Number of pole pairs, Ce : Electromagnetic torque and Cm : Mechanical torque One way to express both active and reactive power is as follows: At the rotor: Pr = Vdr .Idr + Vqr .Iqr

(19)

Qr = Vqr .Idr . − Vdr .Iqr

(20)

Qs = −Vqs .Ids . − Vds .Iqs

(21)

Ps = −Vds .Ids . − Vqs .Iqs

(22)

At the stator:

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Using Eq. (21), the active and reactive powers then become: Qs = Vs .

s M M (Vs )2 − Vs . I = − Vs . I Ls Ls rd ws. Ls Ls rd Ps = −Vs .

M Irq Ls

(23)

(24)

5 Power Control Based on Fuzzy Regulators Due to their tactical efficiency, fuzzy controllers are highly recommended when obtaining a mathematical model of a system is extremely difficult or the mathematical model of the system is non-linear. However, these controllers are rapidly gaining popularity, especially in industrial applications. Professor Zadeh originally proposed fuzzy logic, which departs from rigid binary logic (True or False) and a domain where concepts do not conflict with each other (True or False). Instead of truth values, concepts and objects are given a level of membership in such a collection. The aim of fuzzy logic is to convert the fuzzy laws of language into a mathematical representation. It also seeks to describe the reactions and observations of a human operator when controlling a process. Fuzzy logic gives completely deterministic conclusions. Fuzziness should, in theory, be related to the uncertainty present in most of the systems we use in everyday life. A membership function mathematically formalises this uncertainty. Therefore, the fuzzy logic control approach allows for the creation of a very efficient control rule without the need for precise knowledge of the controllable parameters of the system or laborious modelling. The fuzzy logic controller (FLC) does not deal with well-defined mathematical connections as it employs inferences with several rules that are based on linguistic factors. The operators of a particular technological process can draw conclusions using a variety of criteria based on their knowledge. In this work we will present some general basics of the fuzzy logic control technique and the general procedure of designing a setting using this technique. We will present the advantages and disadvantages and we will detail the steps of designing an RLF to control the powers of a DFIG. 5.1 General Principles of Fuzzy Logic Control FLR consists of the four main steps: Knowledge Base, Fuzzification, Inference Engine and Defuzzification. The RLF should turn the numerical values into fuzzy values, process them using fuzzy rules, and then convert the fuzzy value control signal back into physical values in order to apply to this system because it only receives physical quantities. 5.2 Fuzzification In this step, a specific fuzzy subset is associated with the value of a certain variable (current, voltage, etc.). In order to quantify the relative uncertainty of the variables that belong to this set, we use linguistic variables that are represented quantitatively by membership functions. Linguistic variables are outputs or inputs of a system whose values are words from a natural language (Table 1 and Fig. 2).

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Table 1. The basis of the rules for power control

Δe

e

NM

NP

ZE

PP

PM

PG

NG

NG

TG

TG

TG

G

PG

P

ZE

NM

TG

TG

G

G

M

P

TP

NP

TG

M

G

TG

TP

P

TP

ZE

P

PG

M

ZE

M

PG

P

PP

TP

P

TP

TG

G

MG

TG

PM

TP

P

M

G

G

TG

TG

PG

ZE

P

PG

G

GT

TG

TG

Where: ZE : Zero approximately M : Medium TP : Very positive G : Big PG : Positive big TG : Very big P : Small The corresponding language values are characterized by symbols such as: NG: High negative. NM: Medium negative. ZE: Zero approximately. NP: Small negative. PM: Positive medium. PP: Positive small.

Fig. 2. The fuzzy active power controller’s membership functions for the input variables E_P, dE_P, and the output variable dI_rq

5.3 Defuzzification Making a choice, or obtaining a true control from the control acquired as a fuzzy set, is the essence of defuzzification. There are several approaches to reasoning based on fuzzy rule inference, but the one that establishes the centre of gravity of the resulting membership function is the most popular. We can determine this using the following general formula: VR =

∫1−1 Xk UR (Xk )dXk ∫1−1 UR (Xk )dXk

(25)

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Fig. 3. Block diagram of DFIG control with fuzzy logic controllers

5.4 Simulation and Results The MATLAB/SIMULINK software’s numerical simulation was used to validate the power control strategy of the DFIG by fuzzy logic, whose DFIG parameters are listed in the appendix. In order to examine how this system responds to active and reactive stator power steps, we will provide the results of the control simulation in this section. Figures 4, 5 and 6 show the response of the machine.

Fig. 4. Active power response of the stator using the fuzzy controller

The simulation results presented in Figs. 4, 5 and 6 show that the controller provides better tracking of the power reference with low overshoot. In order to better highlight the efficiency of this controller, a comparison between the response of this controller and a conventional one (PI) is proposed.

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Fig. 5. Stator reactive power response with the fuzzy controller

Fig. 6. Rotor current response with fuzzy regulator

6 Control of the DFIG by a Classical PI Controller 6.1 Synthesis of the PI Controller The DFIG’s performance is adequately controlled using a proportional-integral controller that is simple and quick to build. As shown, the open loop transfer function using the PI controller is: ⎞ ⎛    K .P  1 + Kp i ) Kp + KPi K(Kp .P + Ki ) ⎠ = KKi ⎝ (26) = FBO (p) = 1 + τ.P P(1 + τ.P) P(1 + τ.P) We take

τ=

then FBO (p) =

Kp Ki

(27)

KKi P

(28)

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The following is how the closed loop transfer function is expressed: 1 1 + τ.P     1 1 KKi = And we have FBF (p) = => τr = 1 KKi + P KK 1 + Ki K p i FBF (p) =

(29)

(30)

where: τr : The response time of the corrected system must be small enough. Thus, the controller gains can be expressed as follows: Ki =

Kp τ

then τr =

and

(31)

τ K.Kp

(32)

τ K.τr 1 K.τr

(33)

Kp = Ki =

To remove the zero of the transfer function, we select the pole compensation approach for the controller synthesis. τ(5%) = 0.9 ms. 6.2 Simulation and Results

Fig. 7. Stator active power response with the classic PI controller

From these results it can be seen that the system has unsatisfactory dynamics, the system with a remarkable overshoot, with fluctuations (Figs. 7 and 8).

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Fig. 8. Response of the stator reactive power with the classical PI controller

7 Interpretation of the Results From these results we can see, on the one hand, a good tracking of the active and reactive stator powers of the two controllers. On the other hand, the network currents follow their references with a small overshoot. Indeed the difference between the two controls is not very significant in terms of speed (the response time of fuzzy controller is 9.2 ms against the response time of PI controller is 12 ms) we saw the superiority of the fuzzy controller with its results compared to the PI controller except that we notice for the step variations of reactive power we observe a peak on the response of active power and the opposite (the step variations of active power we observe a peak on the response of reactive power); the influence of coupling.

8 Conclusion This study presents a control method for a wind energy recovery system based on a dual-fed asynchronous generator. A model of the turbine is first presented, followed by a model of the generator. For the independent regulation of active and reactive power, the fuzzy logic control technique is also suggested. Simulation results show that unlike the traditional PI controller, the response characteristics of the fuzzy controller produce good results with low overshoot.

Appendix 1 GADA parameters: Nominal power: 2,1 MW Rotor resistance: Rr = 2.87 m Stator resistance: Rs = 2.58 m Mutual inductance: M = 2.45 mH Inductance stator: Ls = 2.579 mH Inductance rotor: Lr = 2.579 mH.

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Appendix 2 Wind turbine parameters: Nominal power: Pn = 2 MW Radius of the blade: R = 35.25 m Number of blades: 3 Multiplier ratio: G = 80.

References 1. Martin, J.: Energies éoliennes. Technique de l’ingénieur 2. Khalfouni, H., Ferroudja, S.: Etude et simulation d’une installation éolienne, Mémoire de Fin d’Etudes de master academique, université mouloud Mammeri deTizi-Ouzou (2018) 3. https://www.natura-sciences.com/s-adapter/la-france-se-met-a-loffshore.html (2022) 4. Guelmine, S., Guasmi, A.: Modélisation et commande du système de conversion éolienne basé sur une GADA. Mémoire de master: Universite Mohamed Boudiaf M’sila, Alger 5. Hihi, H., Rahmani, A., Junco, S., Donaire, A.: On the stability of class 6. Hihi, H.: Modelisation d’une Chaine Eolienne Associee a un Stockaged’Energie, CPI 2013. Tlemcen, Alger (2013) 7. Chahboun, M., Hihi, H.: A comparative study between direct and indirect power control of DFIG within wind power system by the stator flux orientation technique. In: The 3rd International Conference on Electronics, Control, Optimization and Computer Science. Scopus IEEE, Morocco (2022)

A Review Backstepping Control of a DFIG-Based Wind Power System Farah Echiheb1,2(B) , Badre Bossoufi1 , Ismail El Kafazi2 , and Brahim El Bhiri2 1 LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah

University, 30000 Fez, Morocco [email protected] 2 Laboratory SMARTILAB, Moroccan School Engineering Sciences, EMSI Rabat, Rabat, Morocco

Abstract. The focus is always on producing the maximum power from a wind power system and integrating it properly into the grid. In this context, several controls have been developed including the Backstepping control around which this paper is based, it is a systematic method of nonlinear control design that allows us to have an electric power with the desired performances. This paper presents an overview of Backstepping technique’s control, then a description of a nonlinear Adaptive robust control of active and reactive power is discussed and applied on doubly fed induction generator (DFIG). Indeed, the proposed control has performed better than other techniques in the field of renewable energy. Keywords: Doubly Fed Induction Generator DFIG · Wind Energy Conversion System WECS · Backstepping Control BSC · Rotor Side Convertor RSC · Grid Side Convertor GSC

1 Introduction Today, the focus is on wind power as a pleasant, efficient and abundant source of renewable energy, which is growing every year in many countries. Currently, the DFIG-based variable speed wind system is the most widely used in onshore wind farms due to the economic advantage of sizing its three-phase static converters for part of the DFIG’s rated power [1–5]. The three-phase static converters in the electromechanical conversion chain of a wind power system are the main elements whose control allows to monitor and track the variations of active and reactive power fed to the grid as a function of the wind speed applied to the wind turbine blades. However, in order to achieve maximum power at all a wind speed, the wind power system requires a more stable, efficient and parametrically robust control mechanism [6]. Among the most effective control methods that allow us to achieve the desired objectives is the Backstepping technique [7]. The latter was developed in the early 1990s. The advent of Backstepping control gave a new lease of life to the control of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 735–744, 2023. https://doi.org/10.1007/978-3-031-29857-8_73

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non-linear systems, which despite the great progress made, lacked general approaches. The word Backstepping is particularly justified by the recursive process intrinsic to the synthesis. The idea is to choose a Lyapunov function that is positive and that its derivative always remains negative, the objective of which is to construct a control law for which the output of the system tends towards its desired state, in order to cancel the error and obtain the stability and equilibrium of the system [8]. This paper is organized as follows: Sect. 1 presents a review of Backstepping control, Sect. 2 shows the successive steps of the Backstepping control design applied on the rotor side and the grid side, Sect. 3 discusses the performance of this control by a simulation in the Matlab/Simulink environment, Sect. 4 concludes the paper.

2 Backstepping Control 2.1 Review of Backstepping Control The principle of this control, which is generally multivariable and of high order, is to first decompose it into a chain of first-order subsystems, and then determine the virtual control for each subsystem, which is considered as a reference for the following subsystem until the complete control law is acquired. There are two forms of Backstepping control techniques: non-adaptive and adaptive like the one used in this paper. It consists in estimating the different unknown parameters so that they converge towards their own values while keeping the system performance. The classical BSC is known for its “Chattering” drawback. This phenomenon occurs when virtual entries are differentiated several times. Many solutions have been proposed to solve this problem. The improved Backstepping control structures are shown in the following figure (Fig. 1):

Improved Backstepping control

SMC BSC

MPC BSC

Disturbance Compensation

Disturbance observer

Extended state observer

Artificial Intelligent

Integral BSC

Neural Network

Fuzzy Logic

Fig. 1. Summary of BSC enhancement techniques.

In [7], a specific control parameter based on fuzzy rules is discussed. Then Song Shuai [8] chose to use both neurological and fuzzy systems in each subsystem. In [9], a nonlinear adaptive filter with a time-varying positive integral function is proposed to avoid the complexity explosion problem. Then in [10], the authors tried to combine the

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advantages of the perturbation observer with the SMC in the Backstepping framework. While, Loucif Mourad [11] presents the Backstepping controller with integral action applied to the rotor-side converter. While in paper [12], a Backstepping adaptive control strategy with an extended state observer is proposed, it is based on a radial basis function neural network (RBFNN) that adaptively estimates the uncertainties of the nonlinear parameters. In the same context, Yeonsoo Kim [13] integrated Model Predictive Controller optimization with piecewise constant functions and Backstepping as well as the complexity explosion caused by repeated differentiations of the virtual control is discussed (Table 1). Table 1. Various techniques used for BSC control

Backstepping

Techniques

Researchers

Artificial intelligent

Min Wan [7]; Song Shuai [8]

Filter

Yong-Hua Liu [9]

Disturbance observer

Wang Fang [10]

Integral BSC

Loucif, Mourad [11]

SMC-BSC

Echiheb, Farah [14]

2.2 Application of Backstepping Control on the DFIG Wind Power System The following successive steps of this control algorithm, knowing that each step provides references for the next design step [14]. 2.2.1 Applying the Backstepping Control to the RSC To ensure the stability of powers, we define the errors and Lyapunov function [15, 16], such as: ePs = Ps−ref − Ps

(1)

eQs = Qs−ref − Qs

(2)

1 2 e 2 Ps

(3)

1 2 2 2 (e + ePs + eQs ) 2 Vdc

(4)

V1 = V2 =

For the system to be stable, the derivatives of the Lyapunov functions must be negative: 2 ≤0 V˙ 1 = −kPs ePs

(5)

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(6)

We get the vectors:

(7)

Also, we find the virtual reference input: (8)

2.2.2 Applying the Backstepping Control to the GSC On the other hand, to ensure the stability of the DC bus amplitude voltage and to maintain the unit power factor [17, 18] we define the errors as: eVdc = Vdc−ref − Vdc

(9)

ePg = Pg−ref − Pg

(10)

eQg = Qg−ref − Qg

(11)

V1 =

1 2 e 2 Vdc

(12)

 1 2 2 2 eVdc + ePg (13) + eQg 2 The same control procedure as in the rotor side converter control, we obtain: V2 =

2 ≤0 V˙ 1 = −KVdc eVdc

(14)

2 2 2 − KPg ePg − KQg eQg ≤0 V˙ 2 = −KVdc eVdc

(15)

We obtain the vectors and virtual reference inputs such as: ⎧    Lr Rr 1 2 2 ⎪ ˙ ⎪ ⎨ vgd =  3 vˆ KPs ePs + Ps−ref + Lr Ps + ωs Qs − Lr vgd + vgq 2   Lr Rr ⎪ ˙  K = e + Q + Q + ω P v ⎪ gq Qs Qs s s s−ref ⎩ Lr s 3 2 vˆ Pg_ref = Pg P Vs_ref = C.kVg_ref.eV − Pr dc

dc

(16)

(17)

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3 DFIG Parameters Estimation To achieve the performance of robustness to parametric variations and measurement noise of the adaptive nonlinear Backstepping control, we will use observers to estimate the parameters of the machine, which will help us to develop its real model. Then, to design this control, we will replace the vectors of the real DFIG parameters with their estimates. The power expressions will be [17, 18]:

(18)



Rˆ s .Lˆ r + Rˆ r .Lˆ s aˆ 2 = σˆ .Lˆ r .Lˆ s

3 aˆ 5 = 2.σˆ .Lˆ s



 aˆ 3 =

3.(Rˆ r + ωr .Lˆ r 2.σˆ .Lˆ r .Lˆ s



aˆ 4 =

3.M 2.σˆ .Lˆ r .Lˆ s

(19)

Rs , Rr , Ls , Lr , and σ are the varying parameters then M can be deduced easily. Similarly, if Ls and Lr vary, M also varies. Then, using gain adaptation, a new Lyapunov function V2 will be defined: 

a˜ 52 a˜ 12 a˜ 22 a˜ 32 a˜ 42 1 2 C˜ m2 2 2 V2 = + + + + + (20) e + ePs + eQs + 2  γm γ1 γ2 γ3 γ4 γ5 To ensure the stability of the system, the derivative of Eq. (20) is kept equal to zero. We obtained the following control laws (Vrd and Vrq ) [19]:

(21)

The stator and rotor resistances are estimated from (20) and (21) (Fig. 2):

(22)

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The stator and rotor inductances:

(23)

Fig. 2. Synoptic schema of BSC applied to the PFIG-wind energy conversion system

4 Simulations Results In order to visualize the performance of the control in Backstepping mode, we will work on a 10 kW DFIG connected to a 400 V/50 Hz grid dedicated to a wind system with a variable wind profile closer to the real wind evolution. 4.1 Pursuit Tests • Variable speed response As shown in Fig. 3, the powers generated by the DFIG are well disunited and follow their references perfectly, with a low response time compared to the other controls. Similarly, it can be seen that the electromagnetic torque is dependent on the active power because it follows its shape.

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4

x 10

1.5

Psmes Psref

10000

150

5000 0

0.5

-5000 0

0

0.05

Irq

100

X: 0.0338 Y: -410.8

The Rotor Current Irq[A]

The Active Stator Power Ps[W]

1

0.1

0.15

-0.5

50

0

-50

-1 -100

-1.5 0

5

10

15 Times[s]

20

25

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10

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25

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4

x 10

Qsmes Qsref

0 -5000 -10000 -15000 0

0.6 0.4 0.2

0.1

0.2

0 -0.2 -0.4

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-1 0

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10

15 Times[s]

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Cem

g 0.9

200

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The Glissement g

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5

100 80 60 40

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15 Times[s]

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Isa Isb Isc

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600 Vdcref Vdcmes 500 X: 0.2012 Y: 510

600

The Dc Voltage Vdc[V]

-100 0

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40 20 0 -20 -40 -60

The Rotor Currents Ir-abc[A]

-300 0

The Stator Currents Is-abc[A]

The Electromagnetic Torque Cem[N.m]

Ird

400

X: 0.0345 Y: 0

The Rotor Current Ird[A]

The Staror Reactive Power Qs[VAR]

1 0.8

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400 200

300

0

0.2

0.4

0.6

0.8

200

100

0

0

5

10

15 Times[s]

20

25

30

Fig. 3. Results of the Backstepping Mode variable speed control

25

30

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The rotor currents Irq and Ird depend on the active power Ps and reactive power Qs respectively. The operation of the machine in hypo-synchronous mode is proved by the positive sign of the slip g. Furthermore, the results obtained show that the stator currents Is-abc are sinusoidal, with a better quality than those obtained by the other controls. This means that good quality energy is supplied to the grid. The DC bus voltage perfectly follows its reference value of 510 V with almost no error and a lower response time with the proposed non-linear adaptive Backstepping control compared to other techniques. 4.2 Robustness Tests In order to test the robustness of this control, Rr and Rs are increased by 50% of their nominal values. Figures 4 and 5 show the results obtained: 4

4

x 10

1 Psmes Psref

1

4

1

x 10

X: 0.0345 Y: 38.03

-2000 -4000 -6000 -8000 -10000

0

0.5

-1 0

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0.02 0.04 0.06

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20.01 20.02

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-1

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0 X: 0.0354 Y: 0

-5000

0.4 -10000 0

0.2

0.05

0.1

0.15

0 -0.2 -0.4 -0.6 -0.8

9.99

-1.5 0

x 10

0.8

The Reactive Stator Power Qs[VAR]

The Active Stator Power Ps[W]

1.5

10

5

10.01 10

15 Times[s]

20

25

-1 0

30

5

10

15 Times[s]

20

25

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Fig. 4. Robustness test with variation of resistances Rr and Rs for step wind speed 4

x 10

4

1

Psmes Psref

4

The Active Stator Power Ps[W]

x 10 1

1

X: 0.0354 Y: -867.4

0 0.5

0

-1 0

0.05

x 10

0.8

The Reactive Stator Power Qs[VAR]

1.5

0.1

-0.5

-1

0.6 0.4 0.2

Qsmes Qsref

4

x 10 1 0 -1 -2

0

X: 0.0347 Y: 0

0.05

0.1

0 -0.2 -0.4 -0.6 -0.8

-1.5 0

5

10

15 Times[s]

20

25

30

-1 0

5

10

15 Times[s]

20

25

30

Fig. 5. Robustness test with variation of resistances Rr and Rs for variable wind speed

Indeed, the active and reactive powers always remain decoupled which ensures the robustness of our control to the parametric variations of the DFIG, in particular by the good follow-up of the power reference, with almost the same response time at start-up.

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5 Conclusion This paper presents the nonlinear adaptive Backstepping control applied to a WECSDFIG. In order to make the WECS work in better conditions, this control is based on the Lyapunov stability technique. And to verify its effectiveness, the whole system is simulated using the Matlab/Simulink tool which offers satisfactory results$ and shows better performance in terms of tracking and robustness to parametric variations. From this study, it is found that the nonlinear adaptive backstepping control offers several advantages such as good reference tracking at reduced response time, robustness and linearisation of the system. Indeed, the proposed control offers better performance compared to other controls [7–14].

References 1. Mahmoudi, H., Bossoufi, B.: Modelling and simulation of a wind system using variable wind regimes with Backstepping control of DFIG. IOP Conf. Ser. Earth Environ. Sci. 161, 012026 (2018) 2. Ihedrane, Y., El Bekkali, C., El Ghamrasni, M., Mensou, S., Bossoufi, B.: Improved wind system using non-linear power control. Indonesian J. Electr. Eng. Comput. Sci. 14(3), 1148– 1158 (2019) 3. Bossoufi, B., Karim, M., Lagrioui, A.: Observer backstepping control of DFIG-generators for wind turbines variable-speed: FPGA-based implementation. Renew. Energy 81, 903–917 (2015) 4. Loucif, M., Mechernene, A., Bossoufi, B.: Integral backstepping power control of DFIG based nonlinear modeling using voltage oriented control. In: Motahhir, S., Bossoufi, B. (eds.) Proceedings of ICDTA 2021 The International Conference on Digital Technologies and Applications, 29–30 January 2021, Fez, Morocco, pp. 1725–1734. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-73882-2_156 5. Zemmit, A., Messalti, S., Harrag, A.: A new improved DTC of doubly fed induction machine using GA-based PI controller. Ain Shams Eng. J. 9, 1877–1885 (2016) 6. El Ouanjli, N., Derouich, A., El Ghzizal, A., Bouchnaif, J., Taoussi, M., Bossoufi, B.: Realtime Implementation in dSPACE of DTC-backstepping for doubly fed induction motor. Eur. Phys. J. Plus 135(1), 2–9 (2019) 7. Min, W., Liu, Q.: An improved adaptive fuzzy backstepping control for nonlinear mechanical systems with mismatched uncertainties. Automatika 60, 1 (2019) 8. Song, S., Zhang, B., Song, X., Zhang, Z.: Adaptive neuro-fuzzy Backstepping dynamic surface control for uncertain fractional-order nonlinear systems. Neurocomputing 360, 172–184 (2019) 9. Liu, Y.-H.: Adaptive dynamic surface asymptotic tracking for a class of uncertain nonlinear systems. Int. J. Robust. Nonlinear Control 28, 1233–1245 (2018) 10. Wang, F., Guo, Y., Wang, K., Zhang, Z., Hua, C.C., Zong, Q.: Disturbance observer based robust backstepping control design of flexible air-breathing hypersonic vehicle. IET Control Theory Appl. 13(4), 572–583 (2019) 11. Loucif, M., Mechernene, A., Bossoufi, B.: Integral backstepping power control of DFIG based nonlinear modeling using voltage oriented control. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 1725–1734. Springer, Cham (2021). https://doi.org/10. 1007/978-3-030-73882-2_156

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12. Hui, J., Ling, J., Gu, K., Yuan, J.: Adaptive backstepping controller with extended state observer for load following of nuclear power plant. Progress Nucl. Energy 137, 103745 (2021). ISSN 0149-1970 13. Kim, Y., Park, T., Lee, J.M.: Integration of model predictive control and backstepping approach and its stability analysis. IFAC-PapersOnLine 51(18) 405–410 (2018). ISSN 2405-8963 14. Echiheb, F., et al.: Robust sliding-Backstepping mode control of a wind system based on the DFIG generator. Sci. Rep. 12(1) (2022). https://doi.org/10.1038/s41598-022-15960-7 15. Belkhier, Y., Achour, A.: An intelligent passivity-based backstepping approach for optimal control for grid-connecting permanent magnet synchronous generator-based tidal conversion system. Int. J. Energy Res. 45, 5433–5448 (2021) 16. Hui, Z., Guo-bao, Z., Shu-min, F.: Enhanced model reference adaptive backstepping control of permanent magnet synchronous generator equipped wind energy conversion system with stator parameters varying. In: 2011 Chinese Control and Decision Conference (CCDC), 2011, pp. 133–138 (2011) 17. Bossoufi, B., et al.: DSPACE-based implementation for observer backstepping power control of DFIG wind turbine. IET Electr. Power Appl. 14, 2395–2403 (2020) 18. Bossoufi, B., Karim, M., Lagrioui, A.: MATLAB & Simulink simulation with FPGA based implementation adaptative and not adaptative backstepping nonlinear control of a permanent magnet synchronous machine drive. WSEAS Trans. Syst. Control 9, 86–100 (2014) 19. El Mourabit, Y., Derouich, A., El Ghzizal, A., Bouchnaif, J., El Ouanjli, N., Bossoufi, B.: Implementation and validation of backstepping control for PMSG wind turbine using dSPACE controller board. Energy Rep. J. 5, 807–821 (2019) 20. Bossoufi, B., Karim, M., Lagrioui, A., Taoussi, M., El Hafyani, M.L.: Backstepping control of DFIG generators for wide-range variable-speed wind turbines. Int. J. Autom. Control 8(2), 122–140 (2014)

Detection and Prevention of Repetitive Major Faults of a WTG by Analysis of Alarms Through SCADA Mohamed Bousla1(B) , Ali Haddi1 , Youness El Mourabit1 , Ahmed Sadki2 , Abderrahman Mouradi2 , and Abderrahman El Kharrim2 1 National School of Applied Sciences, Abdelmalek Essaadi University, Tetouan, Morocco

[email protected] 2 Higher Normal School of Tetouan, Abdelmalek Essaadi University, Tetouan, Morocco

Abstract. In the renewable energy industry, the wind sector, preventing failures is still a challenge. It is necessary to schedule the maintenance planning and reduce unavailability time (down-times) to prevent faults. We conducted a study on a sample of 40 Wind Turbine Generators (WTG), for two years (2019–2020), based on a sort by WTG. Then we applied an in-depth Pareto analysis to deduce the redundant top critical faults on the WTGs subsystems. The FMEA analysis showed us which part or subsystem the O&M (Operation and Maintenance) team should focus on optimising the failure prevention process. Keywords: SCADA · WTG · FMEA · Optimization · O&M · Wind Farm · National Energetic Strategy · Energetic Mix · Maintenance Cost

1 Introduction Globally, the rational energy transition tends towards progressive independence in its energy mix by following the national energy strategy inaugurated by HM King Med VI in a 2009 speech. The Kingdom of Morocco considers itself among its region’s and continent leaders as the energy ministry has embarked on a program which promises to reach a quota of 20% of the mixed national energy source of wind power by 2030. Several projects are being built in areas with a good source of wind to achieve this goal: the north coast, the atlas, and the regions of the Sahara. According to the production balance sheet of the wind farms for the year 2017, wind farms injected into the National Electricity Network (ONEE) are approximately 261 Keep, which equals 3 035 GWh, with an estimate of losses of about 94 GWh. The parks have been unavailable at around 3% on average until today; failure modes of different categories cause this unavailability. • Grid fault: In most cases, the WTG is subject to network decoupling. • Surrounding fault: The case of wind at speeds outside the operating range of the WTG • Equipment fault: The case of faults at the level of the WTG, which is directly related to the machine’s reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 745–752, 2023. https://doi.org/10.1007/978-3-031-29857-8_74

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The last type of fault – equipment fault – represents the pillar of our analysis and the potential over which the O&M department can control by acting on the processes and the maintenance policy to apply the recommendations to optimise the mean downtime. To identify the most relevant reasons behind the wind farms’ unavailability, we found the following studies answering our question: – Underestimating access restrictions for asset maintenance [1]. – The application of corrective maintenance; is the initiation of maintenance activities reactively after the failure of a component or part of the wind system [2]. – Low reliability of wind turbines [3]. The budget of a study conducted on a maintenance strategy was around 12 million Euros per year for 500 MW of installed power to maintain production availability. The direct budget for corrective maintenance (intervention due to equipment failures) is approximately four times the cost of preventive maintenance during a year [4]. Another factor that keeps this order of ratios variable is the site conditions and the technical characteristics of wind turbines. Also, regular replacement of wearing parts, oil, or grease and responding to unpredictable conditions such as machine failure; all these activities are classified under the preventive maintenance category to empower the efforts to achieve contractual availability compliances. The main target of this work is to define a maintenance plan for the main issues in the wind systems based on operating data of a wind farm in the north of Morocco during the period 2019–2020, considering all the primary assemblies of the Kinematic Chain and the effects of their faults on the WTG performance. At first, the primary data analysis will reveal the main issues causing the WTG suspension (except the external causes and grid faults, which are non-O&M causes), then a macroscopic FMEA analysis to propose a procedure for the prevention of these main faults. The paper is organised as follows: First, a definition of the tools used in this article, PARETO and FMEA analysis; the next part will define the data sample studied, and then the Pareto analysis results. In the next section, the interpretation of the previous analysis to prepare the FMEA procedure proposition describes the wind turbine’s preventive maintenance plan, proposed solutions, improvements, recommendations, and finally, a conclusion.

2 Tools: In-Depth Pareto and FMEA analysis 2.1 Pareto In the 1900, Vilfredo Pareto was an Italian economist who highlighted through several studies that 20% of people in his country held 80% of the wealth. In the 1940, an engineer, Joseph Juran, evokes the 80/20 law facilitating the separation between the “vital few” (the 20% with high impact) and the “trivial many” (the remaining 80%) [5]. A distribution that seems “natural”.

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Indeed, by projection, this principle is verified in many fields. Most of the results, or the impacts, are due to a minority of the causes. 20% of bugs are responsible for 80% of software crashes, 20% of items in stock represent 80% of the cost of storage, and 20% of downtimes in a device are generally the causes behind 80% of losses (in material and energy), etc. Hence the second name: the law of 20/80 (or 80/20). 2.2 FMEA The acronym FMEA stands for Analysis of Failure Modes and Their Effects [6]. Everything is summarised in one title. It is the French equivalent of the original FMEA method developed within the American army in the early 1940 and then used by NASA for the Apollo program. The acronym FMEA stands for Failure Mode and Effects Analysis. The FMEA analysis method applies equally well to the design of a new product, to the development of a manufacturing process or even a process to identify the points of failure likely to penalise performance. This method is preventive. It is a valuable tool to ensure the feasibility of specifications in compliance with customer specifications and regulatory requirements. The reference standard The AFNOR standard: “NF EN IEC 60812”, published in October 2018, is the reference for the FMEA and AMDEC approach.

3 Study Sample An extract from the history of alarms reported on the wind farm’s monitoring and supervision platform (SCADA) for two successive years (from January 2019 to December 2020). 3.1 Method: Prioritisation of Failures (and Risk Prevention) After supplying the data matrix, we must refine the sample before applying the two tools (Pareto and FMEA). In our study, we have received all the alarms about the park; hence, we must only be interested in the category of equipment faults (O&M Category). Therefore, the first filter to apply is eliminating defects from external sources (caused by the environment or network). Second, the fleet contains 40 WTGs; we will verify that the malfunction mode - of a macroscopic vision - is respectively the same on all these forty WTGs; a simple approach is to represent the rate of faults reported on the platform. SCADA showed us that WTGs follow a dysfunction rhythm in an almost similar order, except for a single WTG which suffers many problems and a lot of shutdowns (information confirmed according to the O&M team on site). Therefore, we must not consider WTG number 15 in our following analyses to interpret this result. By applying the distribution of defects to achieve the Pareto analysis, the latter will show us the major failing sub-parts on which we will use the FMEA analysis to go further on the origin of the defect. Subsequently, we will focus on the leading causes behind these failure modes and interpret the recommendations to adopt to avoid or at least optimise them (Fig 1).

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6% 5%

WTG15; 5,2%

4% 3% 2% 1% 0% 0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Fig. 1. Defects distribution by WTG

3.2 Presentation of Experimental Results Pareto analysis (Table 1): Table 1. Pareto analysis Sorting code

Alarme code

Nbr

%

% Cumulative

#1

309

3372

16,096%

16,096%

#2

632

2115

10,096%

26,192%

#3

974

1242

5,929%

32,121%

#4

545

940

4,487%

36,608%

#5

296

604

2,8832%

39,49%

#6

174

532

2,5395%

42,03%

#7

707

519

2,4774%

44,51%

#8

100

508

2,4249%

46,93%

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

#25

879

229

1,0931%

74,06%

#26

493

193

0,9213%

74,98%

#27

808

175

0,8354%

75,81%

#28

546

172

0,8210%

76,63%

#29

895

169

0,8067%

77,44%

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

#168

990

1

0,0048%

100,000%

Detection and Prevention of Repetitive Major Faults of a WTG

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To distinguish zones A, B and C according to the Pareto method, generally, we fix the threshold of 20% in the cumulative percentage; however, it is not always the threshold of 20% valid. In our case, we close the critical zone A as we are going to plot the exponential shape of the unit percentages of the defects and surround area A (the critical area) using the intersection of the percentages with the shape, which is represented on the graph following (next page) (Table 2 and Fig 2):

100%

4000

90%

3500

80% 3000

70% 2500

60% 50%

2000

40%

1500

30% 1000

20% 500

10%

Nbr.

4000 3500 3000 2500 2000 1500 1000 500 0

Fig. 2. Top critical failures

#163

#166

#160

#151

#157

#154

#145

#142

#148

#139

#136

#127

#133

#124

#130

#115

Expon. (Nbr.)

#121

#112

#118

#106

#103

#109

#94

#100

#91

%C

#97

#88

#79

#85

#76

%

#82

#70

#67

#73

#61

#64

#52

#58

#55

#43

#49

#46

#37

#40

#28

#34

#25

#31

#16

#22

#19

#7

#13

#4

#10

#1

y= 0

692,03e-0,04x

0%

750

M. Bousla et al. Table 2. In-depth Pareto analysis

Sorting code

Alarme code

Nbr

%

% Cumulative

Critical

#1

309

3372

16,096%

16,096%

A

#2

632

2115

10,096%

26,192%

#3

974

1242

5,929%

32,121%

#4

545

940

4,487%

36,608%

#5

296

604

2,8832%

39,49%

#6

174

532

2,5395%

42,03%

#7

707

519

2,4774%

44,51%

#8

100

508

2,4249%

46,93%

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

#25

879

229

1,0931%

74,06%

#26

493

193

0,9213%

74,98%

#27

808

175

0,8354%

75,81%

#28

546

172

0,8210%

76,63%

#29

895

169

0,8067%

77,44%

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

#168

990

1

0,0048%

100,000%

B

C

The critical defects noted are as follows (Table 3): Table 3. Top critical failures Code Alarme

Event/Fault

Inspection O&M (on-site)

Interpretation

309

Pause over RCS C = XXXX

Stop for intervention following customer request

No faulty equipment

632

Signal error. PAUSE (a) XX, X

Communication fault

No faulty equipment

974

Stator Filter Overload

Faulty Stator

FMEA

545

High temp, HV trafo: XXXーC,LX

Faulty transformer

FMEA

3.3 Interpretation The first two faults are not equipment faults; alarm n° 309 is triggered when a stop request follows a customer request, and the second n° 632 means a communication fault at the ILN (Industrial Local Network) level of the WTG. Consequently, there remain two alarms which are the most repetitive at the level of the system:

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• Alarme n° 974: Signifies a load fault on the WTG stator. • Alarme n° 545: This means a very high temperature in the transformer, more precisely on a line. To go further on these two faulty subsystems, we will investigate them by completing an FMEA analysis (Table 4). Table 4. FMEA analysis SubSystem

Fonction

Failure mode

Detectability

Failure cause

Failure effect

STATOR

Creates fixed longitudinal magnetisation using coils (inductor) or permanent magnets

Overload phase S Stator

On SCADA

• Protective Stopping relay problem the wind • Mechanical turbine overload of the driven wind turbine (increased torque resistance) • Lower voltage • Blocking at start-up • Short circuit

• Tension measurement, • Cable check and stator protection • Change of manifold • General converter check, • Carrying out tests, • Changing the measurement card and restarting the machine

TRANSFORMER

Raises the Very high voltage of the temperature electric current produced by the alternator so that it can be more easily transported

On SCADA

• The undersizing of the refrigeration system • Weather conditions • The transformer power undersizing

• Forcing of backup refrigeration (external) • Recheck power sizing

Causes overload on the stator phases, and the overloaded stator, in turn, stops the wind turbine

O&M intervention

4 Conclusion After observing the periodic maintenance manual subscribed to by the O&M department of the on-site service provider, it turned out that potential for improvement and optimisation is available at different levels: • At the O&M team level, whether it is their awareness of good practices and methods for anticipating failures or providing training to adopt quality approaches such as Pareto, FMEA, etc., to bypass problematic subsystems and devote more systematic maintenance programs to them.

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• And at the optimal supply of tools to simultaneously intervene at the level of these redundant and energy-consuming problems. Based on the discussion with the O&M manager on-site over the results, we have concluded that the cost of the updated on-site maintenance policy promises savings. The cost of the necessary investment to get involved compared to keeping the same policy, which keeps the same quantity of energy losses, we determine the cost of non-quality in the continuity of production. Glossary

WTG SCADA O&M FMEA

wind turbine generator Supervisory Control And Data Acquisition Operation and Maintenance Failure Mode and Effects Analysis

References 1. Van Bussel, G.J.W., Zaaijer, M.B.: Reliability, availability and maintenance aspects of largescale offshore wind farms, a concepts study. In: Proceedings of MAREC 2001 (2001) 2. Brändli, S., Höft, E., Abdel-Maksoud, M., Düster, A.: Simulation of the interaction of service ships with offshore wind turbine plants. In: Proceedings of the Conference on Maritime Energy, pp. 511–518. Hamburg, Germany (2013) 3. Slot, R.M., Sørensen, J.D., Sudret, B., et al.: Surrogate model uncertainty in wind turbine reliability assessment. Renew. Energy 151, 1150–1162 (2020) 4. Scheu, M.N., Tremps, L., Smolka, U., et al.: A systematic failure mode effects and criticality analysis for offshore wind turbine systems towards integrated condition based maintenance strategies. Ocean Eng. 176, 118–133 (2019) 5. https://www.manager-go.com/gestion-de-projet/dossiers-methodes/la-methode-des-20-80 6. Wu, Z., Liu, W., Nie, W.: Literature review and prospect of the development and application of fmea in manufacturing industry. Int. J. Adv. Manuf. Technol. 112(5), 1409–1436 (2021)

HDL Coder Tool for ECG Signal Denoising Bouchra Bendahane(B) , Wissam Jenkal, Mostafa Laaboubi, and Rachid Latif System Engineering and Information Technology Laboratory, National School of Applied Science, Ibn Zohr University, Agadir, Morocco [email protected], {w.jenkal,mostafa.laaboubi, r.latif}@uiz.ac.ma

Abstract. Denoising is a primordial stage in ECG signal analysis. The hybrid denoising method based on the DWT and the ADTF methods is one of the efficient algorithms developed for ECG signal denoising. The FPGAs integrated circuits have been successfully used in many applications making them unavoidable. However, FPGAs run with HDLs that describe systems as functional circuits at low-level abstraction. Thus, the integration of the system has become more difficult and time-consuming as the system’s complexity increases. Therefore, numerous High-Level synthesis (HLS) tools have been built to address this problem. These tools allow system description at a higher level of abstraction and generate corresponding synthesizable HDL for FPGAs or ASICs. This work presents an HLS description for the DWT-ADTF filter using the Matlab HDL Coder HLS tool. The algorithm is described inside a Matlab user-defined function and a VHDL architecture is generated. The simulation of the obtained VHDL architecture has been carried out using the Modelsim tool. The ECG signal n°103 from the MIT-BIH Arrhythmia database was used for verification where it was corrupted with an input additive white Gaussian noise (AWGN) of 10 dB. Simulation results show that the signal is well denoising. Keywords: ECG · Denoising · HLS · FPGA

1 Introduction The electrocardiogram (ECG) signal reflects the natural phenomenon of the heart’s electrical activity. It is a collection of waves P, Q, R, and S with a specific voltage and duration, resulting from the depolarization of the cardiac cells (auricles and ventricles). This signal is used in heart diagnosis, so the acquisition and recording of a such signal must be done carefully. Due to its high sensitivity to noise, different types of noises contaminate it, so a denoising stage is required. In general, noise infecting the ECG signal is from physiological sources e.g. electromyogram (EMG) signals, and material sources, e.g. power line interference (PLI) and baseline wander (BW). For reducing the noise effect, several solutions have been developed. Classically, analog filters are used [1, 2]. Analog filters are performant with excellent resolution. However, they suffer from the adaptability and flexibility that digital solutions give. Digital solutions allow the implementation of designed filters into embedded systems platforms (CPUs, GPUs, and FPGAs). Therefore, several numerical solutions have been developed namely, digital filters [3, 4], the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 753–760, 2023. https://doi.org/10.1007/978-3-031-29857-8_75

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discrete wavelet transform (DWT) [5, 6], the empirical mode decomposition (EMD) [7, 8], and the artificial intelligence algorithms [9, 10]. To enhance denoising quality, researchers have developed hybrid methods based on the previous techniques [11, 12]. The DWT-ADTF algorithm [11] is one of these methods that gathered the performance of two denoising techniques, the DWT method, and the adaptive dual thresholding filter (ADTF). The DWT is the most used method for denoising tasks, and this is thanks to its ability to decompose signals and show their frequency composition according to the time when these frequencies appear. The ADTF is a recently published ECG signal-denoising algorithm [13]. The embedded systems have covered almost all areas thanks to their performance and the low area. In particular, field programmable gate arrays (FPGAs) have been successfully integrated into different applications replacing central processing units (CPUs) and graphics processing units (GPUs). Biomedicine is a sensitive domain where performance is a necessity. Therefore, embedded systems are largely integrated. The FPGAs represent a complete and low-cost platform for embedded systems study from design to verification and cost evaluation. However, the FPGAs run with hardware description language (HDL), which is a low-level abstraction. Thus, system design complexity is raised since HDLs used for programming require deep knowledge of embedded electronics to design efficient systems [14]. Numerous High-Level synthesis (HLS) tools have been built to address this problem [14]. The HLS tools allow the system design at a higher degree of abstraction using high-level description languages like C and C++, and generate different synthesized HDL descriptions. This paper discusses an HLS description proposed for the DWT-ADTF filter by the use of the Matlab HDL Coder HLS tool [15]. This work is structured as follows: Sect. 2 describes the ECG denoising literature works as well as the process of the denoising algorithm choice. This section also gives the selected algorithm’s essentials. Section 3 gives the HLS description of the chosen algorithm (DWT-ADTF) and the simulation results of the corresponding VHDL architecture. Finally, Sect. 4 concludes this work.

2 ECG Signal Denoising During its acquisition, the ECG signal is infected by several noises, which hide the signal features desired for diagnosis, especially the QRS complex. Therefore, denoising is an important stage in ECG signal analysis. 2.1 ECG Denoising Literature The denoising stage is a crucial step in the ECG signal analysis since it makes appear useful features for correct diagnosis. Due to the importance of this stage, researchers have developed several technics. The most known and used are the digital filters [3, 4] which are simple to design and implement, however pre-recognition of frequencies to be treated by the filter is needed. The discrete wavelet transform (DWT) [5, 6] is an FIR (finite impulse recursive) filter-bank-based decomposition and reconstruction process that allows frequency separation in form of frequency intervals per level. However,

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performance is linked to the appropriate choice of the wavelet mother and the decomposition level which are user-chosen or empirically chosen parameters. The empirical mode decomposition (EMD) [7, 8] technic is based on the local extremum interpolation to separate frequencies so no mathematical function is required. However, it is a massive arithmetic operation technic, and different stopping criteria are used. Artificial intelligence algorithms (AI) [9, 10] have been successfully applied in ECG signal denoising. However, they present a higher complexity in model configuration and implementation in addition to the need for a pre-optimization to increase their implementability. To enhance the denoising quality, researchers developed hybrid methods based on the previous techniques [11, 12]. In [11] a DWT-ADTF-based hybrid method is presented which presents the algorithm purpose of this embedded system design paper. The DWTADTF method gathers the performance of two denoising techniques, the DWT and the ADTF. The DWT is the most used method in denoising tasks, and this is thanks to its ability to decompose signals and show their frequency composition according to the time when these frequencies appear. The ADTF is a recently published ECG signal denoising algorithm [13], the principle of this technique has been inspired by the dual-threshold median filter used in image processing [13]. 2.2 ECG Signal Denoising Algorithm Choice In this work, we aim for the embedding of an ECG signal denoising algorithm. However, there are many efficient algorithms. Therefore, a comparative study has been carried out on these works in terms of performance and algorithmic complexity, which allows for choosing the most efficient and lowest complex one at the same time. The AI models are the most complex algorithms because they demand a massive matrix calculation as well as the need for pre-optimization to increase their implementability. The EMD involves an extremum calculation and interpolation process where the iteration number is unpredictable. Digital filters are simple to design and implement, however they demand huge adders and multipliers in addition to the need for the preknowledge of noise frequency to be removed. The DWT involves a greater or lesser significant number of operations linked immediately to the coefficients number of the selected wavelet mother and the level of decomposition. Thus, the degree of complexity is controllable for this technique in comparison to the others. Therefore, for the choice of the ECG denoising algorithm, only the DWT-based works are considered excluding those where the DWT is combined with the EMD or AI algorithms. In the DWT-based denoising literature, the DWT is used with thresholding, adaptive thresholding, nonlocal means, and other algorithms. We have compared the denoising performance of four of the most powerful and recent works in the literature. Variant ECG signals from the MIT-BIH Arrhythmia database and additive white Gaussian noise were used in the comparison process with an input SNR (signal-to-noise ratio) of 10 dB. The mean square error (MSE) and signal-to-noise ratio improvement (SNRimp) were the basic metrics. The denoising performance comparison showed efficient results of the DWT-ADTF algorithm (Figs. 1 and 2). On the other hand, as this paper’s aim is embedded systems application, we have also interested to compare the execution time required by each of the aforementioned DWT-based techniques where the lowest execution time is given by the DWT-ADTF algorithm with 0.030 s while 0.1189 s, 0.9968 s, and 0.5480 s are given

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MSE

by the DWT-Thresholding, the DWT-NLM, and by the DWT-VMD-NLM respectively. According to these two comparison results, the DWT-ADTF is the chosen algorithm for this embedded system design paper.

0.02 0.018 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 100

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Fig. 2. SNRimp Comparison of four DWT-based methods

2.3 DWT-ADTF Algorithm It is a hybrid algorithm based on the DWT technic and the filter ADTF. This algorithm consists of three steps, the DWT application which consists of the elimination of the two

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first details, the ADTF application which consists of the calculation of two thresholds (HT for high threshold and LT for low threshold) using a moving window of ten samples following the Eqs. (1), (2), and (3). Finally, the highest peaks correction. The method is tested using the recording n°103 from the MIT-BIH Arrhythmia database with an input SNR of 15 dB (Fig. 3).

Fig. 3. Signal 103 denoising with DWT-ADTF Filter

1 S(i) L n+L

m=

(1)

i=n

HT = m + (Mx − m) ∗ β

(2)

LT = m − (m − Mn) ∗ β

(3)

3 Embedded System HLS Design The FPGAs integrated circuits have been successfully used in many applications making them unavoidable devices. However, FPGAs run with HDLs that describe systems as functional circuits at low-level abstraction. Thus, system integration has become more difficult and time-consuming as the system’s complexity increases. To overcome this issue, many HLS tools have been developed.

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3.1 Literature HLS Tools The increasing development in integrated circuits technology has raised the system’s performance in several domains. In particular, with the use of FPGAs, which can run without instructions and operating systems. Therefore, delay and power consumption are significantly reduced compared to CPUs and GPUs making FPGAs unavoidable devices [14]. However, the FPGAs run with HDLs, which are low-level abstractions. Thus, system embedding has been difficult and time-consuming due to the system design complexity that requires deep knowledge of embedded electronics to design efficient systems [14]. To overcome this issue, numerous HLS tools have been developed since the eighties [14]. The HLS tools allow the system design at a higher degree of abstraction using high-level description languages like C and C++, and generate different synthesized HDL descriptions according to the user specifications and the optimization directives of the HLS tools [14]. Among the HLS tools of the literature, we find VevadoHLS, Catapult-C, etc. [14]. The HLS tools share the same design flow, however, they differ in the input and output languages, the target platform (FPGA, ASIC) [14], and the majority are device-dependent and not interchangeable [16]. The HDL Coder tool and the FixedPoint toolbox of MathWorks generate HDL Description that is synthesizable and device-independent [15, 16]. The Matlab HDL Coder has been used in several works for different applications [16–19]. In [18] a general-use Band-Pass FIR Filter with an area, speed, and Power Optimization is Implemented on Xilinx Kintex 7 FPGA using HDL Coder where Verilog code was generated from Simulink model. In [16] and [17] chaotic and hyper-chaotic systems have been modeled using Simulink and VHDL descriptions were generated with optimized signed fixed-point representation. Also in [19], the Sobel edge detector used in image processing has been the subject of a hardware implementation using the HDL Coder tool. The simulation and the synthesis results in the previous works were interesting. 3.2 DWT_ADTF HLS Design In this work, an HLS description for the DWT-ADTF filter is carried out using the HDL Coder tool. The algorithm is described inside a Matlab user-defined function. The auxiliary and elementary operations that the algorithm need are all described as functions and called inside the general function that serves as the target VHDL hardware architecture. The simulation of the generated architecture is done for signal n°103 and AWGN noise with an input SNR of 10dB using the Modelsim software. The results showed that the signal is well-denoised (Fig. 4). 3.3 Target FPGA Device The targeted FPGA platforms in this work are the Cyclone series of the Altera-Intel company. These FPGA ranges were developed to meet low-power and cost-sensitive design requirements [20]. The Cyclone involves five families namely cyclone, cyclone II, III, IV, and V. The cyclone FPGA devices are general-FPGA architecture with important logic elements (LE) and memory blocs. However, the cyclone II family is a highdensity architecture with advanced features namely an important number of embedded

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Fig. 4. ECG signal n°103 denoising with 10 dB using the generated VHDL architecture

multipliers, DSP, memories, and customized soft processor embedding possibility, in addition to the large logic network. With these advanced features and at a cost comparable to application-specific integrated circuits (ASICs), Cyclone II devices may support sophisticated digital systems on a single chip [20]. The other cyclone families are more advanced with more embedded specific elements and are targeted to high-volume systems. Therefore, the cyclone II family devices are the preferred and appropriate platform for signal processing applications. The generated VHDL architecture will be implemented on the Intel-Altera Cyclone II DE1 device and synthesis results will be presented in future works.

4 Conclusion This paper was the subject of an HLS design of the DWT-ADTF algorithm, which is an ECG signal-denoising filter. The HLS description is carried out using the Matlab HDL Coder tool where the algorithm is described inside a Matlab user-defined function and VHDL architecture is generated. The obtained VHDL architecture is simulated using the Modelsim software. The ECG signal n°103 and the AWGN noise are used for testing the obtained VHDL architecture with an input SNR of 10 dB. The results showed that the signal is well-denoised. The synthesis results of the generated VHDL architecture on the Intel-Altera Cyclone II DE1 device will be presented in future works. The DWT-ADTF has been designed using the VHDL language in [21], as future work, the results of the proposed HLS design will be compared to those obtained in [21] which allows judging the efficacy of each method.

References 1. Luo, S., Johnston, P.: A review of electrocardiogram filtering. J. Electrocardiol. 43, 486–496 (2010). https://doi.org/10.1016/j.jelectrocard.2010.07.007 2. Bansal, D.: Design of 50 Hz notch filter circuits for better detection of online ECG. Int. J. Biomed. Eng. Technol. 13, 30–48 (2013) 3. Bhogeshwar, S.S., Soni, M.K., Bansal, D.: To verify and compare denoising of ECG signal using various denoising algorithms of IIR and FIR filters. Int. J. Biomed. Eng. Technol. 16, 244–267 (2014). https://doi.org/10.1504/IJBET.2014.065806 4. Cuomo, S., De Pietro, G., Farina, R., Galletti, A., Sannino, G.: A novel O(n) numerical scheme for ECG signal denoising. Procedia Comput. Sci. 51, 775–784 (2015). https://doi. org/10.1016/j.procs.2015.05.198

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5. Castillo, E., Morales, D.P., García, A., Martínez-Martí, F., Parrilla, L., Palma, A.J.: Noise suppression in ECG signals through efficient one-step wavelet processing techniques. J. Appl. Math. 2013, 1–13 (2013). https://doi.org/10.1155/2013/763903 6. Aqil, M., Jbari, A., Bourouhou, A.: ECG signal denoising by discrete wavelet transform ECG signal denoising by discrete wavelet transform. Int. J. Electron. Commun. Comput. Eng. (2017). https://doi.org/10.3991/ijoe.v13i09.7159 7. Pal, S., Mitra, M.: Empirical mode decomposition based ECG enhancement and QRS detection. Comput. Biol. Med. 42, 83–92 (2012). https://doi.org/10.1016/j.compbiomed.2011. 10.012 8. Liu, S.H., Hsieh, C.H., Chen, W., Tan, T.H.: ECG noise cancellation based on grey spectral noise estimation. Sensors (Switzerland) 19, 1–16 (2019). https://doi.org/10.3390/s19040798 9. Xiong, P., Wang, H., Liu, M., Lin, F., Hou, Z., Liu, X.: A stacked contractive denoising autoencoder for ECG signal denoising. Physiol. Meas. 37, 2214–2230 (2016). https://doi.org/10. 1088/0967-3334/37/12/2214 10. Wang, G., et al.: ECG signal denoising based on deep factor analysis. Biomed. Signal Process. Control 57, 101824 (2020). https://doi.org/10.1016/j.bspc.2019.101824 11. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El, O., Maoulainine, F.M.R.: An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Integr. Med. Res. 36, 499–508 (2016). https://doi.org/10.1016/j.bbe.2016. 04.001 12. Boda, S., Mahadevappa, M., Kumar, P.: Biomedical signal processing and control a hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT. Biomed. Signal Process. Control 67, 102466 (2021). https://doi.org/10.1016/j.bspc. 2021.102466 13. Jenkal, W., Latif, R., Toumanari, A., Elouardi, A., Hatim, A., El’bcharri, O.: Real-time hardware architecture of the adaptive dual threshold filter based ECG signal denoising. J. Theor. Appl. Inf. Technol. 96, 4649–4659 (2018) 14. Huang, L., Li, D.-L., Wang, K.-P., Gao, T., Tavares, A.: A survey on performance optimization of high-level synthesis tools. J. Comput. Sci. Technol. 35(3), 697–720 (2020). https://doi.org/ 10.1007/s11390-020-9414-8 15. Mathworks, C., Drive, A.H.: HDL CoderTM Reference (2020) 16. Senouci, A., Bouhedjeur, H., Tourche, K., Boukabou, A.: FPGA based hardware and deviceindependent implementation of chaotic generators. AEU-Int. J. Electron. Commun. 82, 211– 220 (2017). https://doi.org/10.1016/j.aeue.2017.08.011 17. Bonny, T.: Chaotic or hyper-chaotic oscillator? Numerical solution, circuit design, MATLAB HDL-coder implementation, VHDL code, security analysis, and FPGA realization. Circuits Syst. Signal Process. 40(3), 1061–1088 (2020). https://doi.org/10.1007/s00034-020-01521-8 18. Sikka, P., Asati, A.R., Shekhar, C.: Area, speed and power optimized implementation of a Band-Pass FIR Filter using high-level synthesis. Wireless Pers. Commun. 127, 1869–1878 (2021). https://doi.org/10.1007/s11277-021-08727-2 19. Sikka, P., Asati, A.R., Shekhar, C.: High-speed and area-efficient Sobel edge detector on field-programmable gate array for artificial intelligence and machine learning applications. Comput. Intell. 37, 1056–1067 (2021). https://doi.org/10.1111/coin.12334 20. Corporation, A.: Section I . Cyclone II. History, pp. 1–168 (2008) 21. Mejhoudi, S., Latif, R., Jenkal, W., Saddik, A., El Ouardi, A.: Hardware architecture for adaptive dual threshold filter and discrete wavelet transform based ECG signal denoising. Int. J. Adv. Comput. Sci. Appl. 12, 45–54 (2021). https://doi.org/10.14569/IJACSA.2021. 0121106

Realization of an Electrical Power Quality Analyzer Based on NIDAQ6009 Board Azeddine Bouzbiba(B) , Yassine Taleb, and Ahmed Abbou EREEC Laboratory, Department of Electrical Engineering, Mohammadia School of Engineeringx, Mohammed 5 University in Rabat, Rabat, Morocco {azeddinebouzbiba,yassinetaleb}@research.emi.ac.ma, [email protected]

Abstract. The quality of electrical energy is a critical element for all actors in the energy sector. Among all these actors, the network operator occupies a central position. Its responsibility is to implement the means to ensure the quality of electrical energy within the electrical networks. Among the reasons that define the great importance given by the actors, whether network managers, suppliers, producers, or consumers of electrical energy, are the electrical disturbances in the economic sector. This causes problems such as production stoppages, loss of raw materials, disruption of production quality, premature aging of equipment, etc. This work aims to design a system to evaluate the quality of electrical energy in the VHV(Very High Voltage) and HV (High Voltage) national power grid, to visualize in real time the results obtained, and to record all the parameters. Keywords: Power Quality · Harmonics · LABVIEW · NIDAQ6009

1 Introduction The objective of this work is to design a system of evaluation of the quality of electrical power within the Moroccan power networks similar and more developed than our project realized with the ARDUINO board [1], it can visualize in real time the results obtained and also record all the calculated parameters. Our study of the realization of this qualimeter is based on a study of the electrical network and its components and an understanding of the method of evaluation of the quality of energy according to the standard EN50160 [2]. Then we developed an algorithm that allows us to calculate the parameters necessary for the evaluation of power quality and we realized a graphical interface Labview [3] that allows the evaluation of power quality as well as the visualization in real time and the recording of the obtained results. This work is composed of 3 parts: The first part is devoted to the definition of the quality of electrical energy and the various types of disturbances, as well as their origins, causes and consequences, and we will present the various stages of realization of the project as well as the development of the graphic interface. In the second part, we will present the tests carried out as well as the results obtained for the validation of the functioning of the designed Qualimeter. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 761–772, 2023. https://doi.org/10.1007/978-3-031-29857-8_76

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Finally, we will end our work with a general conclusion affirming all the deductions provided in the previous sections.

2 Methods and Materials The quality of electrical energy depends on four essential parameters, namely: – – – –

The frequency (50 Hz) ; The symmetry (120° phase shift); The waveform (sinusoidal); The amplitude (declared voltage).

On the distributor’s side, the quality of supply of the network is highly regulated, in particular by the EN50160 standard. This European standard lists the different types of disturbances and the parameters to be calculated. 2.1 European Standards EN 50160/61000 The disturbances on the HV/VHV network according to the EN 50160 standard and the test and measurement techniques according to the EN 61000 standard are: Variation of the Frequency The frequency of the HV/VHV transmission network is fixed at 50Hz. According to the EN50160 standard: Under normal operating conditions, the average value of the fundamental frequency measured by periods of 10 s must be within the following ranges 50 Hz + 4% / − 6% (i.e. from 47 Hz… 52 Hz) for 100% of the time.

Fig. 1. Frequency fluctuations [4]

Unbalanced A three-phase system is said to be unbalanced when the three voltages are not equal in amplitude and/or are not out of phase with each other by 120°. The degree of unbalance is defined using the Fortescue component method which is defined by the ratio of the inverse (U1i ) (or homopolar (U1o )) component of the

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Fig. 2. Unbalanced three-phase system

fundamental to the direct (U1d ) component of the fundamental. The following formulas are used to calculate the direct, inverse and homopolar components: The ratio of the inverse component to the direct component is given by the parameter named “Ui ”: Ui (%) =

|Vi | × 100 |Vd |

(1)

The ratio of the homopolar component to the direct component is given by the parameter “U0 ”: U0 (%) =

|V0 | × 100 |Vd |

(2)

Vo: Homopolar component. Vd: Direct component. Vi: Inverse component. V1 , V2 and V3 are RMS values. According to the EN50160 standard: “Under normal operating conditions, for each period of one week, it is recommended that 95% of the RMS values averaged over 10 min of u2 are between 0% and 2% of the forward component. Voltage Dip According to the standard, a voltage dip is a sudden drop in voltage at a point of an electrical network, to a value between 90% and 1% of a reference voltage (Uref) followed by a recovery of the voltage after a short period of time between the half fundamental period of the network (10 ms at 50 Hz) and one minute. The reference voltage is generally the nominal voltage for low voltage networks and the declared voltage for medium voltage and high voltage networks. Voltage Interruptions Voltage interruptions are a special case of voltage dips with a depth greater than 99%. They are characterized by a single parameter: duration, and they are divided into two types:

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Fig. 3. Voltage dips

– Short interruptions are of less than 3 min duration, they are caused in particular by automatic reclosing intended to avoid long interruptions. – Long interruptions are the result of the definitive isolation of a permanent fault by the protection devices or the voluntary or untimely opening of a device.

Fig. 4. Voltage interruption

Swells Electrical surges are sudden increases in supply voltage, usually due to switching or disconnection of loads. According to the standard, the threshold of the overvoltage is equal to 110% of the reference voltage. Voltage Fluctuation or Flicker Voltage fluctuations cause variations in the luminance of the lighting, which produces the ocular phenomenon called flicker. Above a certain threshold, the flicker becomes annoying. This discomfort increases rapidly with the amplitude of the fluctuation. This is the result of amplitude variations within a range not exceeding ± 10% of the nominal voltage. Harmonic Voltages Harmonics come mainly from non-linear loads whose characteristic is to absorb a current that does not have the same shape as the voltage that supplies them. To evaluate the waveform, the total harmonic distortion rate (THD) is calculated, which gives a measure of the distortion of the signal. The individual rate of each harmonic compared to the fundamental is: Yh ∗ 100 Y1

(3)

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To calculate the total THD, the following formula is used:   ∞  2  Yh THD =  Y1

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(4)

h=2

2.2 Description of System In this chapter we are going to present the different steps of realization of the qualimeter as well as the components used and the programming tools necessary to the elaboration of the graphic interface [5, 6, 7, 8, 9]. System Architecture The qualimeter is composed of: – A reducer module which contains three voltage transformers (VT) and three current transformers (CT) in order to reduce the voltages and currents received in input. The three voltage transformers have a transformation ratio m = 57.73 V/1 V and three current transformers have a ratio of 800 A/1 A. – A conditioning board which ensures mainly, the conversion of the electric quantity received in input into an electric quantity exploitable by the processing unit. In order to eliminate the electromagnetic noises and to make the signals exploitable by the acquisition board, we used a conditioning board which gives a voltage image of the reduced currents, eliminates the electromagnetic noises and also amplifies the signals before the phase of treatment by the acquisition board. – An acquisition board which will carry out the calculation of the parameters necessary to the evaluation of the voltage. The acquisition board used is of type NI DAQ 6009 [10] built by the company National Instrument, it is equipped with: – Eight analog input channels (AI) asymmetrical. – Two analog output channels (AO) The NI DAQ 6009 is programmable with LabVIEW graphical programming software. – A graphic interface for the visualization of the obtained results. The programming tool used for the elaboration of the code is the programming software LabVIEW [11] which is composed of two parts: “Block diagram”: is used for the realization of the program implemented on the NI DAQ 6009 board, it is composed of several blocks allowing the calculation, the evaluation and the recording of the measurements carried out. – 12 digital input/output channels (DIO) as well as a 32-bit counter and a USB interface. “Front panel”: it is the graphic interface used to display the results obtained from the calculations carried out by the program, it is composed of several indicators and control which corresponds to the code carried out on the part "Block diagram". Code In this part of the code we visualize in real time:

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Fig. 5. Architecture of the qualimeter

– The injected voltages and currents, – The RMS value of the voltages and currents, – The frequency

Fig. 6. Calculation of: voltage, current, frequency and power

– Harmonics

Fig. 7. Calculation of harmonics

This part of the code allows the calculation in real time of the THD as well as the RMS value of the harmonics, we also display the voltage and current spectrum. – Calculation of the parameters in compliance with the EN50160 standard This part of the code performs the calculation of parameters according to the evaluation method indicated by the EN50160 standard. There are two types of parameters:

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• The parameters calculated every 10 s are displayed in the “10-s values” section. • The parameters calculated every 10 min, are displayed in the section 10-min values”. – Recording This part of the code allows you to save the following values on an Excel file: • The RMS value of voltages and currents • The frequency and THD • The recording is done according to a step (Time interval) given by the user in seconds, in order not to have large files.

3 Simulation Results of Power Quality Analyzer To validate the correct operation of the power quality analyzer, it is necessary to perform a set of tests for each calculated parameter. In this chapter we will simulate the measurement of a three-phase electrical network and the evaluation of the voltage quality from the measurements made. 3.1 Test Bench

Fig. 8. Test bench

The evaluation of the operation of the qualimeter was carried out using a test bench consisting essentially of an injection box (Type: CMC 256) [12] which is an equipment for testing electrical protections, it can generate voltages and currents adjustable in RMS value, phase and frequency, it can also generate harmonics adjustable in relative amplitude. The injection box is controlled by the “Test Universe” software which allows the insertion of the necessary settings for each test. The configuration inserted on the software “Test Universe” will be sent to the injection box via an Ethernet cable, then the injection box will provide the voltages and currents to the qualimeter by respecting the configuration sent. And finally the results obtained from each test will be displayed on the graphic interface of the qualimeter.

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Fig. 9. Configuration of the 1st test on “Test Universe”

3.2 Results Voltage Waveforms “Window” After inserting the initial parameters, we launch the graphic interface, on the “Voltage Waveforms” window. We notice the appearance of three balanced voltages with an RMS value of 58 v in LV and 60 kV in VHV. The detected frequency has a value of 50 Hz for the 3 phases.

Fig. 10. Measured parameters on “Voltage Waveforms”

The “RMS” and “Frequency” graphs show the variation of the RMS value and the frequency as a function of time for the three phases. Current Waveforms Window In the “Current Waveforms” window. We notice the appearance of three balanced currents with an RMS value of 1A on the LV (low voltage) side and 200 A on the VHV side. The frequency is equal to 50 Hz for the three phases.

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Fig. 11. Parameters measured on “Current Waveforms”

Fig. 12. “Frequency” graph on “Current Waveforms”

Measurement THD on Voltage Spectra “Window” In the “Voltage Spectra” window we visualize in real time the voltage spectra of each phase up to rank 25. We notice the appearance of the fundamental at 50 Hz with an RMS value of 60 kV. According to the visualized spectrum, the harmonics have a negligible amplitude compared to the fundamental which is justified by the THD which has a value of 0%. The indicator “Harmonic RMS value” displays the RMS value of each harmonic in real time.

Fig. 13. Phase spectrum (L1) and THD measured on "Voltage Spectra”

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Fig. 14. The “THD” graph shows the variation of the THD calculated as a function of time for the three phases.

Measurement THD on Current Spectra “Window” In the “Current spectra” window we visualize in real time the current spectra of each phase up to rank 25. We notice the appearance of the fundamental at 50 Hz with an RMS value of 200 A. According to the visualized spectrum, the harmonics have a negligible amplitude compared to the fundamental which is justified by the calculated.

Fig. 15. Spectrum of the phase (L1) and THD measured on "Current Spectra”

Fig. 16. “THD” graph on “Current Spectra”

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The “THD” graph shows the variation of the THD calculated as a function of time for the three phases. Detection of Voltage Variations on the Network (60 kV, 200 A, 50 Hz) To simulate the voltage variations within a network, we will use the “Step/Ramp” option of the “Test Universe” software. In order to verify the detection of harmonics, we will use the option “Harmonics” of the software “Test Universe” which is used to inject harmonics adjustable in relative amplitude. The configuration inserted consists in putting the relative amplitude of the harmonic of rank 2 to 20% and add 10% for each successive harmonic until rank 10 which will have a relative amplitude of 100%. According to the value of harmonics, the software indicates that the THD must have a value of 196%.

Fig. 17. Spectrum of the "L1" phase and measurement of the RMS value and THD

In the “Voltage Spectra” window we can see that the configuration inserted was detected, the THD calculated has a value of 196% for the three phases. In this chapter we have presented the test bench used for the validation of the operation of the quality meter and the tests carried out in terms of measurements, evaluation and recording of the parameters indicated by the standard EN50160.

4 Conclusion The work carried out in this project of end of studies has for objective the realization of a qualimeter for the measurement of the electric quantities and the evaluation of the quality of energy within the networks of transport VHV and HV of the ONEE. The realization of the solution was carried out on the basis of the NI DAQ 6009 board programmed by LabVIEW software. The designed quality meter allows the evaluation of the power quality by the EN50160 standard as well as the visualization in real time and the recording of all the measurements carried out through a graphic interface. The majority of the problems encountered during the realization of the solution were related to the programming by the LabVIEW software, in particular at the level of the compatibility between the blocks of calculations of the parameters as well as at the level of the organization of the tasks like the visualization in real time and the recording of measurements.

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The validation of the qualimeter operation was carried out via a test bench which simulates all the disturbances which can appear within a transport network. The quality meter designed during this work can be advantageously improved, either by the use of less bulky components in order to have a more compact system and easy in terms of portability, or by adding an option that allows to control the quality meter remotely via the Internet network in order to ensure the supervision of the electrical network from any remote machines.

References 1. Design a power quality analyzer using an ARDUINO board and display signals in the LABVIEW environment. ICDTA 2022 : Technologies et applications numériques pp 706–717 2. European standard NF EN 50160 version 2010 3. LNCSHomepage. https://www.ni.com/en-lb/shop/labview/labview-details.html 4. Electrcial Power Systems Quality, Second Edition 5. Fuchs, E.F., Massoum, M.A.S.: Power quality in power systems and electrical machines -Academic Press (2008) ISBN: 9780128009888 6. Voltage Power Quality Disruption Guide-section 5.2.3 7. Kusko, A., Sc.D., P.E., Thompson, M.T.: Ph.D .Power Quality in Electrical 8. Philippe FERRACCI quality of electrtical energy-schneider electric9. Kusko, A., Thompson, M.T.: Ph.D. Power Quality in Electrical Systems. The McGraw-Hill Companies-ISBN 0–07–151002–8 10. https://www.ni.com/docs/en-US/bundle/usb-6008-6009-feature/page/introduction.html 11. LNCSHomepage. https://www.ni.com/en-lb/support/downloads/software-products/dow nload.labview.html#460283 12. LNCS Homepage, https://www.omicronenergy.com/en/products/cmc-356/

OpenCL Kernel Optimization Metrics for CPU-GPU Architecture Latif Rachid, Jahid Khadija(B) , and Saddik Amine Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University, 80000 Agadir, Morocco [email protected], [email protected], [email protected]

Abstract. In this work, we demonstrate the implementation of two algorithms: the addition of two vectors and the matrix multiplication, using the C + + programming language, the Open Computing Language (OpenCL) framework, and its kernel optimization metrics. In embedded systems, the processing time of most algorithms is a challenge that we attempt to address in this paper by switching from sequential to parallel implementation using high-level synthesis (HLS) tools. The main purpose of the work has been to propose optimization methods for the heterogeneous embedded CPU-GPU architecture. The primary goals of these methods are to reduce resource use and processing time. The methods applied in our work are based on global memory for communication between the host and the device. We also have a local memory in our system that enables us to restrict access to the global memory, which gives us an advantage in terms of latency and processing speed. We have specifications for the number of workgroups, unrolling loops, and computing units that allow us to optimize both the amount of time it takes to process data and the number of resources used by a kernel. The results demonstrate that the parallel implementation of our algorithms, through the use of OpenCL and these kernel optimization metrics, provides better processing speed than the sequential implementation. Keywords: Optimization Metrics · Heterogeneous Systems · HLS · CPU-GPU · Open CL

1 Introduction Heterogeneous systems represent the closure of embedded companies in recent years, in order to gain more performance in embedded systems since we have reached the atomic limit of transistors in addition to the miniaturization of logic circuits that are becoming more and more complex [1], but these performances still not the best solution we need more and more acceleration and optimization on the used resources and processing time. This led us to think about the high-level synthesis (HLS) techniques which are a new technology that allows applications to be built using high-level programming languages such as Open Computing Language (OpenCL) [2]. These techniques allow heterogeneous programming which means that the system is divided into two parts that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 773–781, 2023. https://doi.org/10.1007/978-3-031-29857-8_77

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makes it possible to treat tasks in parallel: the host part which is always executed by the central processing unit (CPU) and the device part which performs the processing which can be a CPU or a graphic processing unit (GPU) or field programmable array gate (FPGA) or other microprocessor design [3]. It has various advantages, including debugging and advanced simulation of the system under development. This increases development time and reduces development costs. Where our choice of OpenCL comes from. The OpenCL framework is a new industry standard that has been developed by the Khronos group, is a parallel programming language that is built upon C/C++ and hence can be ported very easily from C/C++ [4]. It aims to support the portability of parallel algorithms across different heterogeneous platforms with minimal transcoding on a variety of modern CPUs, GPUs, DSPs, and FPGAs. Several vendors provide implementations of the OpenCL standard, including Intel, NVIDIA, AMD, ARM, and more. A typical OpenCL program consists of host code and kernel code. The host code contains code for multiple OpenCL APIs to manipulate Device buffers, launch OpenCL kernels, transfer data between devices Hosts and OpenCL devices, etc.… [5]. The term GPU appears for the first time to designate the graphics processor of the PlayStation 1, released in 1994. It is an electronic circuit that interprets and executes special instructions for the needs of the computer. The GPU is composed of a very large number of cores and these cores are used to process data. A GPU is composed of a very large number of cores and they have a reduced instruction set compared to CPUs, but they can still perform common mathematical operations quickly. GPUs are used in many areas, such as desktop computers, game consoles, computer systems, etc.… since there are too many cores, the strategy for optimizing an algorithm on a GPU is to use a large number of different instances (a large number of cores) of the same program, which means that the problem to be solved can be divided into more groups than the total number of computing units [6]. The objective of this paper is to realize an acceleration of two algorithms since in embedded systems we work on signal and image processing, we tested two basic algorithms, the addition of two vectors and matrix multiplication, considering that a vector is a numerical representation of a signal and a matrix represents an image. This paper is divided into four parts: In the section titled “Methodology” we explained the motivation for this study, and we provided a brief description of the various optimization metrics we used. In the section titled “Image Processing and Vision,” we presented the algorithms we used for testing. In the section titled “Results and discussion,” we presented the results after implementing the algorithms on a laptop. Finally the conclusion.

2 Methodology The main motivation for conducting this research, namely the optimization of data processing time that we can obtain by using GPUs rather than CPUs. Also, the optimization metrics of the OpenCL kernel are already used for the FPGA board [7–10]. The optimization metrics of the kernel used in our work:

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2.1 Using Global Memory Data is sent from the host to the device and then stored there in the global memory of the device. Data that is transported in the other direction is also kept in global memory, but this time it is kept on the host. The qualifier “__global” (two underscores!) denotes that information related to a pointer is kept in global memory. The Syntax of using global memory: __global float *A. With: A: a vector. 2.2 Using Local Memory Local storage injection is an optimization technique that allows the limitation of global memory accesses, an OpenCL kernel operation or loop is called the external memory only once and transfers data to local memory, rather than executing a transaction in each iteration to receive a new input to retrieve. In the OpenCL kernel the entire working group has access to local memory, which means that it is shared among all of the working units in this group and is not available to any other working groups. The Syntax of using local memory: __local float *A. With: A: a vector. 2.3 Unrolling Loop Loop unrolling, is a loop optimization technique, which attempts to optimize the execution speed of a program at the expense of its binary size, an approach called space-time tradeoff. The conversion can be done manually by the programmer or by an optimizing compiler. The goal of loop processing is to increase the speed of the program by reducing or eliminating the statements that control the loop, such as Pointer arithmetic and “end of loop” testing in each iteration, reducing branch penalties. And hide the latency, including the latency of reading data from memory. To eliminate this computational overhead, the loop can be rewritten as a repeating sequence of independent statements [11]. The body of the loop is duplicated to avoid repeating the jump instruction. When unrolling is enabled, the optimizer determines and applies the best unrolling factor for each loop [12]. # Pragma unroll N. With: N: unrolling factor. 2.4 Specification of the Size of the Work Group Allows to explicitly define the size of the workgroup, or to specify the ND Range of the OpenCL kernel. This specification allows us to further optimize the use of kernel hardware resources without involving excess logic. It also allows us to make sure that

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the size of the work group is correct and without this specification, the compiler takes a default value, which depends on the compile time and execution constraints. X and Y and Z represent each dimension of a three-dimensional matrix specifying the size of the workgroup for the kernel [13]. It is recommended by the programming guide [14] to explicitly set the required or maximum work-group size in the OpenCL kernel using one of these attributes. __attribute__ ((reqd_work_group_size(X, Y, Z))). With: X, Y, Z: ND Range dimension. 2.5 Specification of the Number of Computing Units This specification is made in the case of an ND Range kernel, where the number of work items is necessarily greater than 1. Increasing the number of computational cores has the advantage of being able to share the elements in local memory among all the work items in the same group. In general, it further increases the efficiency of data processing in an OpenCL kernel. It is possible to ask the AOC compiler to generate multi-kernel computational units. Each computational unit is capable of running several workgroups simultaneously. By specifying the number of computational units, the compiler distributes the workgroups to the different computational units [15]. __attribute__ ((num_compute_units (N))). With: N: number of desired computing units.

3 Image Processing and Vision For image processing and vision we work on vectors and matrices, the reason is that a vector is a table of values and the same case for a signal so to process a signal we use a vector as a digital representation of that last. Also, an image consists of a set of pixels, and each pixel has two indices x, y, or row and column which is the same case for a matrix. In signal and image processing we have for example in new embedded systems to enable ECG signal processing, programmers use vectors for processing and storing the necessary values [16], etc. Thus, in precision agriculture to create an application that allows processing images that are taken from soils or agricultural fields to obtain different vegetation indices (NDVI, NDWI…) programmers use the matrix in their code as a representation of an image [17–19], etc… It’s always the same in other fields; this is why we have chosen to test and evaluate two algorithms: the addition of two vectors and the multiplication of two matrices. In this section, we will present the pseudo-code of the two basic algorithms as well as the different results obtained. The following is the pseudo-code of the two algorithms: Algorithm 1 (In Fig. 1) addition of two vectors, and algorithm 2 (In Fig. 2) multiplications of two matrices. An array of n real numbers is used to represent a vector. To add two vectors, a program allows you to cycle through each element of the array using a for loop. Then, using an index, you can add the elements of the two vectors at this index and store the result at the same index in the result vector.

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Fig. 1. Flowchart of the addition of two vectors algorithm

Fig. 2. Flowchart of the matrix multiplication algorithm

An array of two or n dimensions is used to represent a matrix in code. Thus, to multiply two matrices, we create a code that allows us to browse each of the arrays on

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the two dimensions x (row), and y (column) and initialize the output matrix C on these indices by 0 after it adds to this value the result of multiplication of the two matrices A, B on the same indices x, y this treatment is done by two nested loops.

4 Results & Discussion In this section, we will detail the different results we got after the sequential and parallel implementation of the two algorithms addition of two vectors, and the multiplication of two matrices, thus the study of these results. After testing and evaluating algorithm 1(In Fig. 1) is the addition of two vectors and algorithm 2 (In Fig. 2) is the multiplication of two matrixes on my laptop characterized by an Intel 11 generation core i7 processor and an NVIDIA GeForce MX330 graphics card. Here are the results of the processing times for the sequential code in C and the parallel code in OpenCL by using the different optimization metrics of the kernel (In Fig. 3, Fig. 4, Fig. 5, and Fig. 6). 4.1 The Results of Implementing the Two-Vector Addition Algorithm Figure 3 displays the results of the processing time of Algorithm 1 (In Fig. 1) using the sequential programming language C and parallel programming in OpenCL.

Fig. 3. Results of the processing times of algorithm 1 by OpenCL (global memory) and C++

For algorithm 1 of the addition of two vectors, we have for results of times of treatment: for the sequential code the average time of treatment is 0.006 ms and for the average times of OpenCL global memory we have 0.000025 ms. We can conclude that the parallel code gives better performance in terms of processing time than the sequential code with a factor of 0.005 ms. The Fig. 4 shows the results of processing times of algorithm 1 with the use of different OpenCL optimization metrics. From Fig. 4, we can conclude that the parallel implementation on the GPU and more optimized than the sequential implementation on a CPU. The optimization factor is higher than 0.005 ms.

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Fig. 4. Results of the processing times of algorithm 1 by the different optimization metrics of the OpenCL kernel

4.2 The Results of Implementing the Matrix Multiplication Algorithm Figure 5 presents the outcome of the Algorithm 2 (see Fig. 2) processing time utilizing the sequential programming language C and parallel programming in OpenCL.

Fig. 5. Results of the processing times of algorithm 2 by OpenCL (global memory) and C++

For the image processing algorithm “multiplication of two matrices,” we have for results of processing times: for the sequential code the average processing time is 0.33 ms and for the average times of the OpenCL global memory we have 0.0004 ms then the parallel code gives better performance in term of processing time than the sequential code with a factor of 0.3296 ms. The Fig. 6 shows the results of processing times of algorithm 2 with the use of different OpenCL optimization metrics.

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Fig. 6. Results of the processing times of algorithm 2 by OpenCL and its kernel optimization metrics

For OpenCL and its optimization metrics we have for the global memory 0.0004 ms, for the local memory and NDRK specification 0.00018 ms, for the unrolling loop 0.00015 ms, and for the specification of the number of computational units we have 0.00017 ms. Therefore, Parallel implementation on the GPU with the use of the unrolling loop metric gives us better performance when compared with other kernel optimization metrics of OpenCL. After studying the different results of processing times for each of the optimization metrics of the OpenCL kernel, we can deduce that the use of global memory makes the code slower than the use of local memory, which can be justified by the fact that the use of local memory limits the latency (the comings and goings in the global memory) and the data is sent to the kernel only once. Of all these OpenCL kernel optimization metrics for this code, the one that gives us the best optimization is unrolling loops, we get a speedup factor of 99.95%. After studying the results of the processing times of the two algorithms we can see that the sequential implementation of the two codes in C/C++ takes more time than the parallel implementation with the OpenCL framework and the use of these kernel optimization metrics. The latter gives a better optimization that is presented by the speedup factor that gives us for each of the OpenCL kernel optimization metrics more than 90%. Also, we concluded that the local memory of OpenCL is more optimized than the global memory.

5 Conclusion In this paper, we examined the acceleration of algorithms that are the addition of two vectors and multiplying matrices—by adopting the OpenCL kernel’s optimization metrics for CPU-GPU architecture, and we got successful results specifically for the multiplication code of two matrices, which we tested with extremely large matrices. One of the conclusions we reached was that these measures only apply to processing large amounts

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of data, which is the case for image processing. We will work on these metrics and some new ones for an embedded image processing application in our future work, and we’ll try to validate them on several heterogeneous systems CPU-GPU and CPU-FPGA.

References 1. PAPON Pierre, La loi de Moore anticipe l’avenir de l’électronique, Futuribles, 2017/2 (N° 417), pp. 79–84. https://doi.org/10.3917/futur.417.0079 2. Sun, Y., Wang, G., Yin, R., Cavallaro, J.R., Ly, T.: Chapter 8 - High-level design tools for complex DSP applications. In: Oshana, R., (ed.) DSP for Embedded and Real-Time Systems, Newnes, pp. 133–155 (2012), ISBN 9780123865359 3. Stone, J.E., Gohara, D., Shi, G.: OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems. Comput Sci Eng. 12(3), 66–72 (2010) 4. Struyf, L., De Beugher, S., Van Uytsel, D.H., Kanters, F., Goedemé, T.: The battle of the giants: a case study of GPU vs FPGA optimisation for real-time image processing. Proc. PECCS 1, 112–119 (2014) 5. Shata, K., Elteir, M.K., El-Zoghabi, A.A.: Optimized implementation of OpenCL kernels on FPGAs. J. Syst. Architect. 97, 491–505 (2019) 6. Rouzaud-Cornabas, J.: Calcul gpu – cours 1 : Introduction.” https://www.calcul.math.cnrs. fr/attachments/spip/IMG/pdf/coursgpu1.pdf 7. Domingo, R., et al.: High-level design using Intel FPGA OpenCL: A hyperspectral imaging spatial-spectral classifier. In: 2017 12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), pp. 1–8. IEEE (July 2017) 8. Parker, S.J., Chouliaras, V.A.: An OpenCL software compilation framework targeting an SoC-FPGA VLIW chip multiprocessor. J. Syst. Archit. 68, 17–37 (2016) 9. Tang, Q.Y., Khalid, M.A.S.: Acceleration of k-means algorithm using altera SDK for OpenCL. ACM Trans. Reconfigurable Technol. Systl 10(1), 1–19 (2016), Article 6 10. Tang, Q.Y.: FPGA based acceleration of matrix decomposition and clustering algorithm using high level synthesis” Electronic Theses and Dissertations, p. 5669 (2016) 11. https://en.wikipedia.org/wiki/Loop_unrolling, (Accessed 12 Nov 2022) 12. :https://www.intel.com/content/www/us/en/docs/programmable/683176/181/unrollingloops-opencl-standard.html, (Accessed 12 Nov 2022) 13. :https://www.intel.com/content/www/us/en/docs/programmable/683846/214/specifyingwork-group-sizes.html, (Accessed 15 Nov 2022) 14. Intel FPGA SDK for OpenCL Programming Guide. Quartus version 18.1, (Retrieved Sep 27 2018). https://www.altera.com/en_US/pdfs/literature/hb/opencl-sdk/aocl_program ming_guide.pdf, (Accessed 12 Nov 2022) 15. URL:https://www.intel.com/content/www/us/en/docs/programmable/683846/214/specif ying-number-of-compute-units.html, (Accessed 12 Nov 2022) 16. Mejhoudi, S., Latif, R., Jenkal, W., Elouardi, A.: Real-time ECG Signal Denoising Using the ADTF Algorithm for Embedded Implementation on FPGAs. In: 2019 4th World Conference on Complex Systems (WCCS), pp. 1–5 (2019) https://doi.org/10.1109/ICoCS.2019.8930771 17. Saddik, A., Rachid, L., El Ouardi, A., Alghamdi, M.I., Elhoseny, M.: Improving sustainable vegetation indices processing on low-cost architectures. Sustainability 14(5), 2521 (2022) 18. Saddik, A., Latif, R., El Ouardi, A..: Low-Power FPGA architecture based monitoring applications in precision agriculture. J. Low Power Electron. Appli. 11(4), 39 (2021) 19. Saddik, A., Latif, R., Elhoseny, M., El Ouardi, A.: Real-time evaluation of different indexes in precision agriculture using a heterogeneous embedded system

Embedded System of Signal Processing on FPGA: Implementation OpenMP Architecture Mhamed Hadji1,2(B) , Abdelkader Elhanaoui1,4 , Rachid Skouri3 , and Said Agounad4 1 REPTI Laboratory, Faculty of Sciences and Technology, BP 509, Boutalamine, Errachida,

Morocco [email protected] 2 Moulay Ismail University of Meknes, Meknes, Morocco 3 High School of Technology, Moulay Ismail University Meknès, Km 5, Road of Agouray, N6, 50040 Meknès, Morocco 4 Laboratory of Métrologie et Traitement de l’Information, Faculty of Sciences, Ibn Zohr University, 80000 Agadir, Morocco

Abstract. The purpose of this paper is to study the phenomenon of acoustic scattering by using a new method. The signal processing (FFT iFFT BESSEL functions) is widely applied to obtain information with high precision accuracy. Signal processing has a wider implementation in general-purpose processors Our interest was focused on the use of FPGAs (Field-Programmable Gate Arrays) in order to minimize the computational complexity in single processor architecture then be accelerated on FPGA and meet real time and energy efficiency requirements.General-purpose processors are not efficient for signal processing. We implemented the acoustic backscattered signal processing model on the DE1SOC FPGA and compared it to Odroid xu4.By comparison, the computing latency of Odroid xu4 and FPGA are 60 s, and 20 s respectively. The detailed SoC FPGA-based system has shown that acoustic spectra are performed at up to 3 times faster than the Odroid xu4 implementation. FPGA-based system of processing algorithms is realized with an absolute error about 10–2 .This study underlines the increasing importance of embedded systems in underwater acoustics, especially in non-destructive testing. It is possible to obtain information related to the detection and characterization of submerged cells. So we have achieved good experimental results in real time and energy efficiency. Keywords: DE1 FPGA · acoustic scattering · Form function · signal processing · Non-destructive testing

1 Introduction In many fields of science and engineering, a wide range of recent research work related to the processing of ultrasonic acoustic signals has been carried out [1, 2]. These studies, which are centered on acoustic scattering by thin elastic immersed tubes, touch important research axes such as non-destructive testing [3], characterization, and detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 782–788, 2023. https://doi.org/10.1007/978-3-031-29857-8_78

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Many different sectors, including practical engineering, the storage and production of renewable energy, and the transportation of fluids and gases, make extensive use of cylindrical structures. It is necessary to maintain control over these structures because of the environmental circumstances in which they are employed.Several studies on the acoustic scattering from cylindrical tube are offered in the literature [4, 5].Gerard Maze [6] developed an experimental Method for Identification and Isolation of Resonance (MIIR) of a cylinder and tube.Mitri [7] analyzed the acoustic backscattering enhancements from cylinders for incidence angles…However, few works have called upon the use of FPGAs in order to remedy the problem of the complexity of the calculations performed [8, 9]. Indeed, the Bessel functions represent the fundamental member in the resolution of such a problem [10]. This paper focuses on solutions for the processing of acoustic signal backscattered by a metal tube, using the DE1 FPGA board. The design system has been developed with OpenMP architecture, and it has been built in three blocks.These will allow us to obtain backscattering spectra, characteristic of the studied structure.The results obtained are compared with those calculated by Odroid XU4 architecture.

2 The Studied Problem 2.1 Backscattering Response An air-filled metallic tube immersed in water is considered.The scattering of an ultrasound plane wave from the target, is done using the solution of the wave equation, and the boundary conditions [1]. Figure 1 shows the polar coordinate (r, θ) orientation and the direction of the incident wave, characterized by the incident wave number k1 (k1 = ω/cW); ω is the angular frequency and cW is the acoustic velocity in the external fluid (water).

Fig. 1. Acoustic Scattering from a Thin Cylindrical Elastic Tube.

The scattering pressure in far field is given by the summation of the normal modes.This summation takes into account different contributions of the incident wave, the reflected echo, and the circumferential waves. The general form of the scattered pressure field at normal incidence is formulated as [11]: Pdiff (r, θ) = P0 n [Rn ωεn H(1) n (k1 r)] cos (nθ)

(1)

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where εn is the Neumann factor (if n=0, εn =1 else εn =0), and P0 is the amplitude of the plane incident wave.The scattered coefficients Rn (ω) are computed from the boundary conditions at interfaces r = a and r = b.The function H n (1) is called the Hankel function of the first kind [1, 10]. The module of the Hankel function in a far-field can be expressed as: |Hn(1) (k1 r)| ≈ (2/π k1 r)1/2

(2)

The module of the backscattered pressure in a far-field, denoted by F ∞ , is then obtained by the following equation: F∞ (v) ≈ (4/π k1 r)1/2 |n (1)n Rn εn |

(3)

This expression is computed versus the frequencyν. 2.2 Use of Field-Programmable Gate Arrays The FPGAs (Field-Programmable Gate Arrays) are parallel electronic circuits that allow us to develop applications that are increasingly powerful in terms of execution speed and greedy in terms of hardware resources [15]. In our work, we implement on FPGAs, some signal processing algorithms and we study the feasibility of processing in a minimum of time, thus lifting the complexity of calculations, due to the presence of Bessel functions in the resolution of the problem of ultrasonic acoustic diffusion. The Fig. 2 shows the DE1 FPGA board and the open MP architecture that we used in this work.

Fig. 2. (a) Cyclone V DE1; (b): Open MP architecture

3 Acoustic Backscattering Response We first consider a single-layer cylindrical stainless Aluminum cell with outer radius a = 27mm and inner radius c whose radius ratio is equal to 0, 94.The mechanical parameters of this material are the density ρ2 = 1500 kg.m−3 , the longitudinal polarization

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wave velocity cL = 2500 m.s−1 and the transverse polarization wave velocity cT = 1200 m.s−1 .The external fluid (water) is characterized by its density ρW = 1000 kg.m-3 and by the speed of sound cW = 1470 m.s−1 . The Fig. 3 presents the acoustic backscattering response of the studied cell using a Odroid xu4 architecture and DE1 FPGA board. Figure 3 shows the successions of maxima and minima, which are linked to the resonances of the circumferential waves. Their processing by the inverse Fourier transform (IFFT) provides the total time signal.A series of echoes thus appears in Fig. 4.The echoes are indicated by an arrow on the Fig. 4.

Fig. 3. Function form: (a): ODROID XU4; (b): cyclone V DE1 processing

Fig. 4. Temporal signal: (a): ODROID XU4; (b): cyclone V DE1 processing

The treatment this time by FFT provides a spectrum where the resonances of circumferential waves (A0, S0, A1) are manifested in the form of amplitude peaks, and this according to the Fig. 5. In the following, we present the results of this study.

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Fig. 5. Resonance Spectrum: (a): ODROID XU4; (b): cyclone V DE1 processing.

4 Results and Discussion The filtered acoustic signal backscattered from the tube is obtained when the reflected echo shown in Fig. 4 is replaced by zeros. Figure 5 presents the correlation between filtered temporal signals that are computed by an Odroid xu4 and cyclone v DE1 respectively. A good agreement is then obtained (Fig. 6).

Fig. 6. Correlation between filtered temporal Signals of the Shell

In addition, we can use the resonance spectra in Fig. 5 to derive the value of the cutoff frequency of the A1 wave. For the studied single layer tube we find the cutoff frequency respectively using Odroid xu4, DE1 SOC and Theory [12–14]: 25,6; 28,2 and 27,5. The findings are very comparable to those of the scientific literature; indeed, the absolute error is estimated about 10–2 . Results justify well the contribution of our method.

5 Conclusion The results obtained from this study on the acoustic scattering of a plane wave by an elastic cylindrical shell, show that the acoustic resonances of the shell are related to its

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physical and geometrical properties. We have demonstrated the increasing importance of embedded systems in the study of the acoustic scattering by a thin elastic immersed tube. The useful data is obtained by the analytical method. Calculations carried out during the signal processing using the Soc DE1 FPGA are compared to those computed by Odroid xu4.Results show that acoustic spectra is performed at up to 3 times faster than the Odroid xu4 implementation. In addition, FPGA-based system of processing algorithms is realized with an absolute error about 10–2 .In this approach, we proposed the algorithm heterogeneous implementation using a parallel programming via OpenMP on heterogeneous architecture that offer an optimization in the resources of our card, but the proposal remains the best at the level of resource optimization and processing time.

References 1. Agounad, S., Aassif, E.H., Khandouch, Y., Gérard, M., Décultot, D.: Investigation into the bistatic evolution of the acoustic scattering from a cylindrical shell using time-frequency analysis. J. Sound Vib. 412, 148–165 (2018) 2. Elhanaoui, A., Aassif, E., Gérard, M., Décultot, D.: Acoustic scattering by a two-layer cylindrical tube immersed in a fluid medium: existence of a pseudo wave. Ultrasonics 65, 131–136 (2016) 3. Agounad, S., Aassif, E.H., Khandouch, Y., Elhanaoui, A.: Signal Processing Techniques of Circumferential Waves for Characterization of Bilaminated Cylindrical Shells 4. Naum, D.: Veksler. Berlin, Springer-Verlag, Resonance acoustic spectroscopy.Springer Series on Wave Phenomena (1993) 5. Agounad, S., Aassif, E., Khandouch, Y., Décultot, D., Maze, G.: Application of spectral and time-frequency analyses to study an acoustic signal backscattered from composite cylindrical shell. In: IEEE, 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (2017) 6. Maze, G.: Acoustic scattering from submerged cylinders.miir im/re: Experimental and theoretical study. J. Acoustical Soc. Am. 89(6), 2559–2566 (1991) 7. Mitri, F.G.: Acoustic backscattering enhancements resulting from the interaction of an obliquely incident plane wave with an infinite cylinder. Ultrasonics 50, 675–682 (2010) 8. Nane, R., Sima, V.M., Pilato, C., Choi, J., Bertels, K.: A survey and evaluation of FPGA highlevel synthesis tools. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 35, 1591–1604 (2016) 9. Turqueti, M., Saniie, J., Oruklu, E.: MEMS acoustic array embedded in an FPGA based data acquisition and signal processing system. In: Proceedings of the 2010 53rd IEEE International Midwest Symposium on IEEE Circuits and Systems (MWSCAS), Seattle, WA, USA, 1– 4, pp. 1161–1164 (August 2010) 10. .Abramovitz, M., Stegun, I.A.: Handbook of Mathematical Functions. National Bureau of Standards, Washington, DC, pp. 435–442 (1964) 11. Dariouchy, A., Aassif, E.H., Decultot, D., Maze, G.: Acoustic characterization and prediction of the cut-off dimensionless frequency of an elastic tube by neural networks, IEEE. Trans. Ultrason. Ferroelectr. Freq. Control. 54, 1055–1064 (2007) 12. Younes, K., Aassif, E.H., Agounad, S., Gérard, M.: Development of an artificial neural network model for estimating the radius ratio of a one-layered cylindrical shell 13. Agounad, S., Aassif, E.H., Younes, K., Elhanaoui, A.: Analysis of the prediction of a bilayered cylindrical shell’s reduced cutoff frequency with data driven-approaches

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14. Agounad, S., Aassif, E.H., Khandouch, K., Décultot, D., Maze, G., Elhanaoui, A.: Acoustic scattering from immersed composite cylindrical shells: Existence of zero group velocity circumferential waves. Compos. Struct. 182, 12–24 (2017) 15. Boutros, A., Betz, V.: FPGA architecture: principles and progression. IEEE Circ. Syst. Mag. 21(2) 4–29 (2021). https://doi.org/10.1109/MCAS.2021.3071607

LWR Application on Calculating Travel Time in Urban Arterials with Traffic Lights Hamza El Ouenjli(B) , Anas Chafi, and Salaheddine Kammouri Alami Faculty of Science of Fez, Sidi Mohammed Ben Abdellah University, Imouzzer Road, B.P. 2202, Fez, Morocco {hamza.elouenjli,Salaheddine.kammourialami}@usmba.ac.ma

Abstract. Calculating travel time in urban arterials with or without traffic lights is a fundamental task for all transport planners in order to adapt the system settings in all conditions. In order to have reliable and exact values, we propose the use of the variational theory (VT) applied to the Lighthill-Witham-Richards (LWR) first order traffic model and in the particular case of a triangular fundamental diagram. Research confirm that this method provides exact travel time values at the boundaries of an arterial and with the use of a minimum number of nodes and edges (Sufficient Variational Graph – SVG) and taking into account traffic signal settings. The output of the method are the accurate macroscopic fundamental diagram (MFD) and travel time distribution able to be applied for all dynamic traffic conditions. Keywords: Macroscopic fundamental diagram · urban arterials · traffic lights · variational theory · traffic flow

1 Introduction The value of travel time estimation represents a key component of any traffic management system and are the basic information to be disseminated to users to inform them of the traffic conditions on their routes. Different data sources can be used. Some studies focus on vehicle tracing (Lagrangian sensors) such as buses, taxis, or telephones [1–3]. They provide direct access to travel times and allow the derivation of empirical travel time distributions [4]. However, such data is still rare and unavailable to the traffic manager. Therefore, other methods consist in estimating instantaneous travel times from information coming from fixed sensors (Eulerian). The most classical are the electromagnetic loops which provide speed and flow measurements at certain points of the boulevard. The knowledge of traffic light parameters is also very important for the accurate estimation of travel times [5–10]. These data are perfectly accessible by traffic managers. However, they do not directly provide realized and/or instantaneous travel times. For this, assumptions on the dynamics of traffic flow must be made. The best-known traffic model is the LWR model [11, 12], often combined with a triangular fundamental diagram (FD) [13]. This very simple model correctly reproduces the overall traffic dynamics, especially in urban networks © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 789–798, 2023. https://doi.org/10.1007/978-3-031-29857-8_79

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where traffic dynamics are mainly induced by traffic lights [14]. The relevance of this model has been experimentally validated in many works. To estimate travel times, a first approach is to calculate the solution of the LWR model on the whole boulevard for a given situation (demand and supply). A simple method is to directly calculate the vehicle trajectories according to the Newell model [15] (Newell, 2002). Numerical solutions based on spatiotemporal discretization [16] or exact resolution schemes [17] also provide solutions of the LWR model. Other works use the very convenient concept of Cumulative Vehicle Curve (CVC) to calculate the solution only at each intersection rather than on the whole boulevard [18] (Qian et al., 2012). The main shortcoming of these methods is that they do not detect queue upsets at upstream intersections. Neglecting these backups can lead to significant errors in travel time estimation. Numerous studies seek to solve this complex problem [19–21]. All these methods compute the solution for a given situation depending on the input demand (throughput) and output supply (maximum possible throughput). Theoretical distributions of travel times can then be obtained. Recently, Daganzo [22] introduced the concept of variational theory (VT). It allows the calculation of the solution of the LWR model on an urban boulevard from the boundary conditions. Among other things, it is used to analytically calculate the macroscopic fundamental diagram (MFD) characteristic of an urban boulevard [23–25]. The MFD is an elegant and attractive tool to describe the characteristics of all possible homogeneous traffic states (flow and average concentration). The use of VT for its calculation allows the detection of queue ups and the correct estimation of capacity reductions due to the proximity of traffic lights. Finally, this tool can be refined to take into account the impact of buses on traffic [26]. This paper resorts on extended VT to estimate the dynamic evolution of travel times corresponding to the kinetic waves (KW) solutions. The paper is organized as follows. Section 2 provides background elements about LWR model and variational theory (VT). Section 3 presents its application in travel time calculation in the case of a boulevard with traffic lights and analyzes results before concluding and giving openness to further applications.

2 Basic Theories and Formulations 2.1 Formulation of the LWR Model for First Order Traffic Flow The Lighthill-Witham Richards model (LWR) [27] simulates the dynamic of vehicles travelling along a road, it represents traffic not as a discrete set of vehicles, but as a continuous flow. This follows the so-called FIFO (first in first out) rule, i.e. neither lane changes nor overtaking are considered. In its original version, it also does not reproduce acceleration and deceleration phenomena either. Leclercq (op. cit.) nevertheless shows how to overcome this limitation. The LWR model is characterized by a fundamental diagram (FD). There are two possible formulations: the first one is called variational formulation, relates the variations of the function N (number of vehicles) in time and space:  ∂N  ∂N (1) ∂t (x, t) = FD − ∂x (x, t)

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This formula is a partial differential equation of the Hamilton-Jacobi type. A large number of mathematicians tend to solve this type of equation. One particular method is the calculation of variations of the function N in the (t, x) plane. Applied to traffic, it is the basis of the variational theory (VT) introduced by Daganzo, Daganzo and Geroliminis, Leclercq and Geroliminis (op. cit.) and viability theory by Mazaré et al. Different forms have been used to characterize the FD. In particular, it must be concave. The simplest form is the triangular FD, illustrated in Fig. 1.a: q = {u.k pourk ∈ [0; kc ]w(K − k) pourk ∈ [kc ; K]

(2)

where k is the concentration, k c is the critical concentration associated with the maximum possible flow rate (capacity) Q = u.k c , K is the maximum concentration representing stopped vehicles, u [m/s] is the desired vehicle speed, and -w [m/s] is the (negative) speed of the restarting waves of vehicles after stopping. The latter two are the slopes on both sides of the triangular FD. The FD is entirely determined by the definition of three of the above parameters. For example, Q = N lanes .w.u.K/(w + u) is derived from parameters u, w and K. In the rest of this paper, it is assumed that the traffic dynamics is consistent with the LWR model with a triangular FD whose parameters are detailed above. 2.2 Solving the LWR Model for the Simple Case Around a Traffic Light The second formulation of the LWR model describes the traffic states. The solution of the model is then well represented by a TSD (time space diagram). Figure 1.b shows the traffic conditions around a traffic light for a given demand qin and no output restrictions. It appears that several traffic states can coexist in time and space. Each is associated with a point in the FD. In general, four traffic states appear. The white areas O correspond to the absence of a vehicle, light grey ones correspond to a generic fluid state A and the dark grey for vehicle’s queues J. Figure 1.c shows the traffic conditions around the same traffic light when the supply qout is reduced. It imposes a constraint on inter-vehicle times on exit. To meet them, vehicles travel at a reduced speed vB determined by the FD just downstream of the exit. A new generic congested state B then appears, also shown in light grey. In contrast, the generic fluid state A no longer appears. It represents the trajectories of vehicles in this traffic. On a TSD, two adjacent traffic states A and B are separated by a wave. Its speed vAB verifies the Rankine-Hugoniot condition [28]: vAB =

qB − qA kB − kA

(3)

2.3 Practical foundations of the Variational Theory LWR equation expresses the relationship between the variations of the function N in time and space. The VT is based on this expression to define the local cost r(v) [veh/s] associated with an instantaneous speed v. Let us consider a mobile observer moving along

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Fig. 1. (a) FD. Resolution of the associated LWR model at the approach to a traffic light for a (b) fluid (c) congested situation.

the corridor independently of the traffic and counting the number of vehicles along the corridor. When this observer moves at speed v, the cost r(v) expresses the maximum flow rate that can pass him (passing rate). This can be read on the (k, q) plan as the y-intercept of the line with slope v tangent to the FD. This relation is consequently linear for a triangular FD. In this case, Leclercq and Geroliminis (op. cit.) that it is sufficient to consider bidirectional mobile observers only moving locally in space only at -w, 0 and u speeds. This makes it possible to reconstruct any average speed. In particular, we have: r(u) = 0 since no vehicle is moving faster than the free speed, r(0) = Q which represents the maximum possible flow through a point, and r(-w) = wK. On a corridor with traffic lights, the red phases play a crucial role since no vehicle can pass them in the absence of an incoming movement at the intersections. In this case: r(0) = 0 [29]. Considering speeds other than those in the interval [-w, u] would not make physical sense since the information is propagated in the traffic at speeds v between -w (wave ascent speed) and u (free flow speed). More generally, the VT associates a bidirectional moving observer traveling a path given in the diagram (t, x) the maximum flow that can cross it, also called its cost. The latter is then the integral over this path of the local costs r(v). A sufficient variational graph (SVG) consisting of nodes and arcs in the (t, x) plane gathers a set of paths of minimal size that guarantees an exact computation of the solution at these nodes. The definition of such a network allows an efficient set of solutions. An example is shown in Fig. 2.a. The SVG allows full connectivity between all nodes located at intersections. The zero-velocity arcs are only located at the intersections and correspond to the phases of the traffic lights. The original nodes correspond to the ends of these arcs. The u and -w speed arcs start from the ends of red. A new node is created each time such an arc meets an intersection. The arcs grouped in the SVG are oriented in the direction of increasing t. They have a crucial physical importance. They are the waves separating the traffic states of the TSD which do not depend on the traffic conditions on the boulevard. This is for example the case for the separations between O and J, between O and C, and between J and C in Fig. 1.b. They do not depend on the actual flow rate, unlike the separation between A and J which depends on the arrival of vehicles in the queue. The SVG does not depend on traffic conditions either.

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The SVG is therefore also not dependent on traffic conditions and is determined solely from the parameters of the traffic lights and the FD.

Fig. 2. (a) SVG and (b) eSVG of an homogeneous arterial (M = 4, lm = 100 m, cm = 90 s, gm = 60 s, om = 8 s, u = 15 m/s, w = 5 m/s, K = 0.2 veh/m) (c) Samples of paths between two nodes of eSVG

3 Application to Travel Time Calculation When traffic conditions are dynamic, various phenomena can occur. They are summarized in Fig. 3. The determination of the input and output cumulative count curves (CCCs) according to demand and supply takes all these phenomena into account in a precise order. It is carried out in four steps.

Fig. 3. Presentation of the phenomena leading to dynamic traffic conditions.

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(0) Calculation of the theoretical starting CCC. It depends only on the demand. This step is trivially described by the expression of (4) t

q(t) = ∫ qin (τ )d τ = qin .(t − t0 )

(4)

t0

(1) Calculation of the theoretical arrival CCC. The difficulty is to take into account the route of the vehicles on the boulevard and their possible delays at the traffic lights encountered. (2) Calculation of the actual CCC of exit from the boulevard. Without any restriction of the exit offer, it is equal to the theoretical arrival CCC. On the other hand, if qout is less than the arrival flow, congestion appears at the output. The difficulty in this step is to correctly determine the speed at which the congestion can be relieved. (3) Calculation of the effective CCC of entry to the boulevard. The entry of vehicles may be constrained by queues from downstream. These can be caused either by saturation of a huge demand or a congestion by the offer reduction. The difficulty of this step is to determine the time when these queues reach the entrance. The four phenomena to be taken into account are (a) the path of vehicles on the boulevard, (b) the congestion relief at the exit, (c) saturation and (d) the propagation of congestion to the entrance. In its original presentation, the SVG does not have nodes at the entrance and exit. This personalized method consists first of all of creating connections between the SVG and the boulevard ends. As shown in Fig. 2.b, new arcs connect the SVG terminals to the beginnings of the red phases, while others follow the existing oblique arcs. Four types of nodes appear. They are presented in Table 1. The origins and destinations of the new arcs are indexed by arcs are indexed by i and j. The extended variational network is called eSVG (extended SVG). Table 1. Nodes added to SVGs at the input and output level. Node Position Characteristics Ai

Entry

Origin of a new arc at speed u

Bj

Exit

Destination of a new arc at speed u

Ci

Exit

origin of a new arc at speed -w

Dj

Entry

Destination of a new arc at speed -w

3.1 Travel Cost Calculation Hypothesis The VT is used to calculate the costs between the points of the eSVG, and in particular between the different nodes at terminals Ai , Bj , C i and Dj . The different ways of connecting these nodes allow us to distinguish four types of paths illustrated in Fig. 4. Each is associated with one of the four phenomena mentioned above.

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The costs between pairs of nodes at the terminals increase the variations of N. The least action principle of VT then allows us to determine the exact solution of the LWR model at a given point. For example, the solution of N at a point Bj is deduced from the values of N at points Ai (initialized at step 0) and the costs RAiBj :   (5) NBj = mini NAi + RAi Bj This equation also applies to other pairs of nodes. All costs of interest for the implementation of the method can be compiled into four matrices RAB , RCB , RAD , and RCD according to the type of path to which they relate. These matrices are independent of the input and output conditions. We can therefore calculate the solution by a kind of matrix multiplication. The use of VT for the cost calculation allows the solution of the LWR model to be determined only at the end nodes. The CCCs are only known at the associated dates. Four physical considerations expressed in the form of constraints allow them to be reconstructed from partial knowledge. (i) (ii) (iii) (iv)

The instantaneous flow is always less than the local road capacity Q; N is a continuous and increasing function; Vehicles cannot travel at a speed greater than u: N(t, L) ≤ N(t − L/u, 0); Congestion cannot propagate at a speed greater than w: N(t, 0) ≤ N(t − L/w, L) + LK.

Fig. 4. Shortest variational path between (a) one input node and one output node (b) two output nodes (c) two input nodes (d) one output node and one input node.

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3.2 Detailed Steps and Results The four steps of the method and the associated formulas are rigorously detailed in Hans et al. [30]. Figure 5 illustrates this for a particular case whose SVG is shown in Fig. 5.a. The blue curve in Fig. 5.b represents the theoretical starting CCC. The red dashed curve represents the theoretical arrival CCC. The red solid curve in Fig. 5.c refers to the actual output CCC. The red area between the two downstream CCCs represents delays due to congestion. In Fig. 5.d, the blue solid line curve refers to the effective input CCC. The blue area between the two CCCs calculated at the entrance represents the time that vehicles spend upstream of the entrance. Note that depending on the objectives of modelling, especially when one only wants to estimate the delay of the vehicles, it is better not to consider this step 3. Finally, the physical constraints mentioned above are represented by black lines. The input and output N values calculated in steps (2) and (3) respectively correspond to the exact solutions of the LWR model. In addition to its accuracy, an interest of the method is that it only requires the calculation of a cost matrix between the extreme nodes of the eSVG. These costs include all the internal dynamics due to traffic lights such as delays, queuing or saturation. They can be calculated once for a boulevard using a shortest path algorithm, e.g. a Dijkstra. Then, the travel times can be determined very quickly (matrix calculation and application of constraints) for many different demand and supply scenarios.

Fig. 5. (a) eSVG, (b) steps 0 and 1, (c) step 2 and (d) step 3 of the method.

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4 Conclusion This paper focused on the use of variational theory in the traffic domain. We saw that creating a sufficient variational grid in the case of a boulevard with traffic lights lead to solving the LWR model and deducting the exact macroscopic fundamental diagram and travel time at the arterial extremums in all dynamic conditions. Taking into consideration all phenomena that can occur and in particular the spread of congestion and congestion relief, this model presents an exhaustive overall way to detect queue ups and also the estimated capacity reductions due to the proximity of traffic lights. This opens up new perspectives to the application of this method to estimate travel time for special vehicles like buses and put an optimal traffic light settings in order to develop the quality of their service and reduce the impact on other categories and also adapt their offer and schedule to real time traffic conditions.

References 1. Tantiyanugulchai, S., Bertini, R.L.: Arterial performance measurement using transit buses as probe vehicles. In: Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, vol. 1, pp. 102–107. IEEE, October 2003 2. Zornoza, A., et al.: Long-range hybrid network with point and distributed Brillouin sensors using Raman amplification. Opt. Express 18(9), 9531–9541 (2010) 3. Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via structured multi-task sparse learning. Int. J. Comput. Vision 101(2), 367–383 (2013) 4. Ramezani, M., Geroliminis, N.: On the estimation of arterial route travel time distribution with Markov chains. Transp. Res. Part B: Methodol. 46(10), 1576–1590 (2012) 5. Dion, F., Rakha, H., Zhang, Y.: Evaluation of potential transit signal priority benefits along a fixed-time signalized arterial. J. Transp. Eng. 130(3), 294–303 (2004) 6. Skabardonis, A., Geroliminis, N.: Real-time estimation of travel times on signalized arterials (No. ARTICLE, pp. 387–406) (2005) 7. Liu, L., Cui, X., Qi, L.: Simulation of electromagnetic transients of the bus bar in substation by the time-domain finite-element method. IEEE Trans. Electromagn. Compat. 51(4), 1017–1025 (2009) 8. Viti, F., Van Zuylen, H.J.: Probabilistic models for queues at fixed control signals. Transp. Res. Part B: Methodol. 44(1), 120–135 (2010) 9. Zheng, F., Van Zuylen, H.: Modeling variability of urban travel times by analyzing delay distribution for multiple signalized intersections. Transp. Res. Rec. 2259(1), 80–95 (2011) 10. Liu, J., Zhao, Y., Yuan, Y., Luo, W., Liu, K.: Vehicle capturing and counting using a new edge extraction approach. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 62–66. IEEE, October 2011 11. Lighthill, M., Whitham, G.: On kinematic waves I. Flood movement in long rivers. In: Proceedings of the Royal Society, vol. 229A, pp. 281–316, London, May 1955 12. Richards, P.I.: Shock waves on the highways. Oper. Res. 4, 42–51 (1956) 13. Chiabaut, N., Leclercq, L.: Wave velocity estimation through automatic analysis of cumulative vehicle count curves. Transp. Res. Rec. 2249(1), 1–6 (2011) 14. Papageorgiou, M.: Some remarks on macroscopic traffic flow modelling. Transp. Res. Part A: Policy Pract. 32(5), 323–329 (1998) 15. Newell, G.F.: Memoirs on highway traffic flow theory in the 1950s. Oper. Res. 50(1), 173–178 (2002)

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16. Godunov, S.K.: A finite difference method for the numerical computation of discontinuous solutions of the equations of fluid dynamics. Matematicheskii Sbornik 47, 271–306 (1959) 17. Daganzo, C.F.: The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp. Res. Part B: Methodol. 28(4), 269–287 (1994) 18. Mazaré, P.E., Dehwah, A.H., Claudel, C.G., Bayen, A.M.: Analytical and grid-free solutions to the Lighthill–Whitham–Richards traffic flow model. Transp. Res. Part B: Methodol. 45(10), 1727–1748 (2011) 19. Qian, S., Ai, Y.: Electrokinetic Particle Transport in Micro-/Nanofluidics: Direct Numerical Simulation Analysis, vol. 153. CRC Press, Boca Raton (2012) 20. Geroliminis, N., Skabardonis, A.: Identification and analysis of queue spillovers in city street networks. IEEE Trans. Intell. Transp. Syst. 12(4), 1107–1115 (2011) 21. Qi, X., Wu, G., Boriboonsomsin, K., Barth, M.J.: Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions. Transp. Res. Part D: Transp. Environ. 64, 36–52 (2018) 22. Daganzo, C.F.: A variational formulation of kinematic waves: basic theory and complex boundary conditions. Transp. Res. Part B: Methodol. 39(2), 187–196 (2005) 23. Geroliminis, N., Daganzo, C.F.: Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transp. Res. Part B: Methodol. 42(9), 759–770 (2008) 24. Geroliminis, N., Boyacı, B.: The effect of variability of urban systems characteristics in the network capacity. Transp. Res. Part B: Methodol. 46(10), 1607–1623 (2012) 25. Leclercq, L., Geroliminis, N.: Estimating MFDs in simple networks with route choice. Procedia Soc. Behav. Sci. 80, 99–118 (2013) 26. Xie, X., Chiabaut, N., Leclercq, L.: Macroscopic fundamental diagram for urban streets and mixed traffic: cross comparison of estimation methods. Transp. Res. Rec. 2390(1), 1–10 (2013) 27. El Ouenjli, H., Chafi, A., Alami, S.K.: Numerical resolution of the LWR method for first order traffic flow model. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications. ICDTA 2022. LNNS, vol. 455, pp. 727–736. Springer, Cham (2022). https://doi.org/10.1007/ 978-3-031-02447-4_75 28. LeVeque, R.J.: Numerical Methods for Conservation Laws, vol. 214. Birkhäuser, Basel (1992) 29. Daganzo, C.F., Menendez, M.: A variational formulation of kinematic waves: Bottleneck properties and examples (2005) 30. Hans, E., Chiabaut, N., Leclercq, L.: Applying variational theory to travel time estimation on urban arterials. Transp. Res. Part B: Methodol. 78, 169–181 (2015)

Analysis of the Driver’s Overspeed on the Road Based on Changes in Essential Driving Data Mohammed Karrouchi1(B) , Ismail Nasri1 , Kamal Kassmi1 , Abdelhafid Messaoudi2 , and Soufian Zerouali2 1 Electrical Engineering and Maintenance Laboratory, High School of Technology, Mohammed

First University, BP. 473, Oujda, Morocco [email protected] 2 Energy, Embedded Systems and Information Processing Laboratory, National School of Applied Sciences, Mohammed First University, Oujda, Morocco

Abstract. With more people owning cars, mobile pollution has grown to be a significant source of air pollution, which makes it more difficult for Morocco to regulate air pollution. According to the French Petroleum Institute, driver behavior plays a major role in air pollution. Several studies have been conducted in the context of vehicle monitoring and quantification of air pollution based on driving behavior. One of the most frequent reasons for car accidents is aggressive and erratic driving, which also contributes to air pollution from exhausts. This article provides an overview of research into driving habits and engine behavior, based on a practical demonstration of vehicle data collection and analysis. The paper describes a proposed driving type recognition based on variations of RPM and throttle position. We also include a comparison of recently published research in terms of accuracy, reliability, hardware requirements, and intrusiveness. Each approach has its own set of advantages and disadvantages. This study will provide a summary solution of a hybrid system that combines various strategies to make the system more efficient, more resilient, and more accurate in defining driving style. Keywords: Aggressive driving behavior · air pollution · Over speed engine · RPM variation · OBD-2 · CAN Bus

1 Introduction The industry of transportation is and quickly growing, with a growth in the number of customized cars [1], It is one of the main contributors to air pollution [2, 3], as well as traffic, accidents, injuries, fatalities and financial losses are just some examples of the negative consequences of this development. These cars’ high amounts of pollution are mostly to blame for respiratory conditions like lung cancer, asthma, etc. [4]. Additionally, the type of engine used in an automobile affects its emission profile; either a diesel engine or a gasoline engine. Most exhaust emissions are due to driving actions and human factors [5] such as careless driving (high speed and pressure on the accelerator pedal), © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 799–808, 2023. https://doi.org/10.1007/978-3-031-29857-8_80

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and general carelessness [6–9] and anger also affects driving behavior [10]. Driver and vehicle data can all contribute to improving driving safety and reducing greenhouse gas emissions. Too much or too little throttle will result in incomplete fuel combustion and air pollution [11]. So, there is a strong link between the way of driving and the mobile pollution unit [12]. To this end, some academic researchers have proposed several approaches to assess and analyze a driver’s driving behavior and style. In terms of road safety, driving styles can be divided into two groups: safe and aggressive [13]. Aggressive driving, as opposed to safe driving, is a control style that increases air pollution and the risk of road crashes (sudden braking, acceleration, lane changes, etc.) For dependable travel agencies and intelligent transportation networks, it is imperative that a system be in place that can monitor driver behavior and identify crucial driving events that may be used to determine whether or not they are aggressive. If a system needs costly and complicated sensors, electricity, and processing, some applications could not be possible. The dangers of aggressive and risky driving behavior are decreased when a driver is observed and their driving habits are recorded, according to extensive study done in this area over the preceding two decades [14]. The majority of scientific research use a variety of sensors made from inertial measurement units (IMUs), such as three-axis linear accelerometers, angular accelerometers (gyroscopes), and occasionally magnetometers. For taxi or rental car companies, some use data recording devices to monitor each driver and make sure they are adhering to the approved routes and not exceeding the speed limit [15]. Others opt to use a smartphone with a variety of sensors in place of the on-board data logger, which offers similar capability [16]. Due to the high expense of the necessary sensors, it is unlikely that these technologies will be adopted into more affordable cars anytime soon. Smartphones have a number of drawbacks, including battery drain, erratic position in the automobile, and inconsistent data across several travels with various means of transportation, etc. [17]. The most accurate option, however, is to gather car characteristics data via the OBD-2 port, which does not require an additional power supply [18]. The current research proposes an algorithmic toolbox based on onboard technologies to make use of the vehicle’s data gathering capabilities and create the driver behavior information to describe the nature of driving. In order to complete our project, we faced two challenges: the first was focusing on the source of the complete, precise, and meaningful data that we would analyze, and the second was building an intelligent device that was both dependable and inexpensive, as well as putting the algorithm into practice. In this paper, we propose and study an inexpensive method and an effective for analyzing driver behavior to identify driver aggressiveness on the roads, based on clean and real data, obtained from Real Driving Studies. Experiments are carried out on our cars using the information extracted via the OBD-2 port, as well as a hardware and software design that is designed to justify the feasibility of the approach and ensure the proper functioning of the proposed system. The remainder of this work is structured in the following manner: the second portion of this paper, defining the CAN Bus communication protocol. The third part presents the proposed system and the strategies followed. The experimental results are discussed in the fourth section. Finally, a conclusion for our work.

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2 Methodology 2.1 Definition of the CAN Bus Protocol The CAN bus (Control Area Network) is a local and multi-master broadcast serial bus standard for connecting electronic control unit peripherals, or microcontroller devices. Each node is able to send and receive messages [19, 20]. When a unit fails, it does not affect others. Figure 1 shows us the topology of the CAN module as well as describes the structure of the OBD2 connector. This standard port is a door to access the CAN bus, it’s a protocol that is implemented in most of the vehicles which are manufactured lately [21]. The OBD2 standard covers only the exchange of diagnostic data [22, 23]. The CAN bus pins used are pin 6 (CAN high), pin 14 (CAN low), pins 4 and 5 (chassis/signal ground) and the pin 16 (battery power).

Fig. 1. Structure of the OBD-2 connector and its connection to the physical CAN bus

2.2 System Block Diagram The below section has a more detailed description of the system we’re using to evaluate the data we’ve gathered in order to uncover leadership actions. The electronic board, which is made up of a PIC microcontroller and other components, serves as a target device for collecting data from the Vehicle’s OBD-2 socket, and save it to the SD card for analysis. The structure of the system and its components are depicted in Fig. 2.

PIC18F4580 Microcontroller

SD Card

MCP2551 CAN Transceiver

Fig. 2. The recommended system’s functioning architecture

The whole platform’s functionality is fundamentally controlled by an electrical circuit. It is primarily made up of a PIC18f4580 microcontroller that is used to regulate and

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monitor component interactions. The microcontroller series is used to generate CAN Bus (Control Area Network) connection, this particular PIC incorporates a CAN controller that maintains the rules for transmitting and receiving signals. The second device is an MCP2551 CAN network signal adapter that serves as a link between the microcontroller management and the practical CAN bus. The last component is an SD memory card, which is intended to record the data collected from the vehicle in real time. This block captures data circulating on the vehicle’s genuine CAN bus, which includes information on the vehicle’s speed (V), engine speed (RPM), instantaneous fuel consumption, and throttle position (T.P) from the OBD-2 port. This data is identified and filtered based on its PID (Parameter Identifier). The CAN bus in the automobile has a transfer rate of 500 kbps. In order to construct a final algorithm that identifies the driving norms, we’ll need a computer to read the data, evaluate it, and track it. The data collection tests are carried out on a Dacia Dokker equipped with an OBD2 port, which is available at our Oujda high technical school. Figure 3 show some recorded vehicle data.

Fig. 3. Test info; (a) Recorded RPM data; (b) Recorded Throttle Position data

We conducted various tests using several volunteers in our local laboratory to support our analysis. The same individual operates a single car type under identical circumstances, then examines every driving-related activity that was recorded. At the RPM change, we asked the participants to drive in their own way in the aggressive and normal states (The degree of strain on the accelerator pedal). The purpose of this step is to know the difference in RPM margin for each driving style. We can see that the volunteers have comparable characteristics but with different intensities. Figure 4 shows an extract of the average variance of the RPM data over time for normal and overspeed operations. Our civil experiences show also that in normal driving, fuel consumption is mainly low between 3.5L/100 km and 9 L/100 km. Unlike for aggressive driving, which evolves up to 18 L/100 km in the transient regime. Figure 5 describes the variation of instantaneous fuel consumption in aggressive driving as a function of engine speed.

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Fig. 4. The average variation of RPM data over time for engine overspeed driving and rational driving activities

Fig. 5. The average change in fuel consumption and RPM in real time for aggressive driving

Similarly, we have dealt with the exhaust gases (CO, HC and CO2 ) and the quantity of polluting gases generated in the two targeted styles. Figure 6 summarizes the quantity of greenhouse gases released by the exhausts. Based on the degree of pressure on the accelerator pedal (variation of RPM and T.P), our analysis aims at reducing fuel consumption and lowering the generation of polluting gases in driving mode. As demonstrated in Figs. 4, 5, and 6, the outcomes of this examination were highly significant. Keep in mind that more aggressive driving

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Fig. 6. Generation of CO, HC and CO2 gases in terms of engine speed (RPM).

styles result in a large rise in fuel consumption (in liters per 100 km), as well as an increase in CO, HC and CO2 emissions. According to Fig. 6, we can clearly see that the percentage of emission of polluting gases such as CO, CO2 and HC become intense with the progression of the RPM. For a variation of RPM from 870 rpm to 3200 rpm, the emission percentages of CO and CO2 do not exceed 1% and 6.5% respectively, as well as the HC gas particles do not surpass 170 particles per million. In contrast, when the RPM exceeds 3500 RPM, the quantity of pollutant gases becomes more intense, especially CO and CO2 , which reach 1.98% and 10.07% successively. 2.3 Software Structure Based on our analyses, and after a long discussion to decide whether the driving style is normal and respectful of the environment (eco-driving) or not, a standard variation of the RPM was proposed and which does not exceed 3300 tr/min. The last step is to convert these results into a final program, which is defined by the same steps followed, but with a setting of limits not to be exceeded. We have added the position of the Throttle Position (T.P) to solidify the analysis and take a precise action. Figure 7 illustrates the suggested method for routine data collection and analysis. The code was written in MicroC Pro for Pic and then uploaded to the microcontroller using WinPic.

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START

If RPM> 3300Tr/min & T.P>38%

Initialize the collected factors: V, RPM, T.P RPM data collecting

Speed data collecting

T.P data collecting

If V > 0

No

No

Yes Display a message on the driver's LCD panel alerting him or her that they are over-revving the vehicle's engine and that they need to adjust their driving style.

Yes

END

Fig. 7. Software structure for final program

3 Results and Discussion This section contains the values obtained and the techniques utilized. After confirming that the flowchart and order of the proposed scenarios are adequate, the second step consists of the assignment of the printed circuit of the treatment, a reliable and fast system that will be connected to the mission’s peripherals. A PIC18f4580 microcontroller is used in our circuit. Figure 8 (a) depicts our own system, which was built using the identical components as in Fig. 2 as well as a 12 V to 5 V adaptor block adapted to the car connection. The Altium Designer program was used to design the circuit board. We ran many checks to confirm that our system was working properly. We were able to retrieve and see the messages, or electrical impulses, that make up the CAN communication frames. Figure 8 shows all of the stages that went into making our project a success. The instantaneous information on speed, throttle position, and engine speed was detected, displayed on the LCD screen, and analyzed according to the limits set by the proposed algorithm (Fig. 7). These limit values can be changed for each case and for which type of engine is treated. So, we were able to know and define if there is an overspeed at the engine level or not, based on our own system constitutes logically and material. The driver will be notified on the screen that he has over-revved the engine. Figure 8 (d) and (e) show the results obtained.

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Fig. 8. (a) Hardware structure of our system; (b) Lifting the car for testing. (c) The car’s OBD-2 PORT. (d) and (e) are demonstrations of correct reading of vehicle data.

4 Conclusion Eco-driving is a type of driving that is both ecologically and economically friendly. It’s simple to learn and has a big influence on consumption, the environment, and safety. The goal of this form of driving is to keep the engine running at a low speed while traveling smoothly and at a constant pace to prevent as much rapid acceleration and braking as feasible. This paper focuses on the hardware and software design of a monitoring system, that identifies if the vehicle engine is over-speeding or not. For the collection of vehicle engine data and analysis, we used a hardware platform consisting of a PIC18f4580 microcontroller and MCP2551 Transceiver, which collects the data of speed, RPM and throttle position and alerts the driver in the event of engine overspeed. The system offered at low cost, can be transformed into a compact format and scalable.

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Mechatronic, Industry 4.0 and Control System

The Use of Industry 4.0 Technologies in Maintenance: A Systematic Literature Review Safaa Essalih1(B) , Zineb El Haouat1 , Mohamed Ramadany2 , Fatima Bennouna3 , and Driss Amegouz1 1 Higher School of Technology Sidi Mohammed Ben Abdellah University, 30050 Fez, Morocco

[email protected]

2 Faculty of Sciences and Techniques Sidi Mohammed, Ben Abdellah University, 30050 Fez,

Morocco 3 National School of Applied Sciences, Sidi Mohammed Ben Abdellah University, 30050 Fez,

Morocco

Abstract. In the Industry 4.0 era, where competitiveness in the industrial sector is increasingly tough, maintenance optimization is an undeniable tool to stand out in this fierce context. To minimize costs, increase productivity, improve quality and facilitate decision-making in maintenance activities, companies are resorting to the deployment of digital technologies of the fourth industrial revolution, including the Internet of Things (IoT), Big Data, Additive Manufacturing (AM), Augmented Reality (AR), Cloud Computing, etc. The main goal of this paper is to assess the impact of Industry 4.0 in maintenance, to identify which technologies are used by companies in maintenance, what are the reasons that push companies to use these tools, and what are their benefits. Keywords: Industry 4.0 · IoT · big data · cloud computing · predictive maintenance

1 Introduction The term "Industry 4.0" was initially used in 2011 at the Hannover Fair for Industrial Technology. Industry 4.0 involves the deployment of numerical technologies such as big data, the internet of things, and cyber-physical systems (CPS). These innovations can change a traditional plant toward a "smart" plant. Industry 4.0 provides close cooperation between production and maintenance planning to supply timely and seamless maintenance services [1]. This allows businesses to build a manufacturing system that is both efficient and cost-effective. One of Industry 4.0’s primary value drivers, along with "asset utilization" and "service and after sales," is maintenance [2]. Based on [3], the majority of firms perceive maintenance management as an early action in the implementation of Industry 4.0, with a significant move from breakdowns and regular maintenance to predictive maintenance policies to reach economic and technical benefits. This study intends to define which Industry 4.0 technologies are currently used by companies in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 811–821, 2023. https://doi.org/10.1007/978-3-031-29857-8_81

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maintenance segment to achieve better performance and to explore how smart maintenance or maintenance 4.0 can enhance industrial processes and opportunities and their impact on business economics. Our work is divided as below: in the initial section, we give a literature review regarding industry 4.0 technologies, in the ongoing second section, we describe the research methodology; third, we explain how we gathered the materials; and finally, we give the study’s findings in the results and discussion.

2 Industry 4.0 Technologies According to the analysis of the literature, Industry 4.0 technologies can be summarized as follows: The Internet of Things (IoT) allows the interconnection of physical objects through sensors by using standardized internet protocols. The Internet of Things (IoT), in the words of [4], is the basis for cyber-physical systems (CPS), which are systems interconnected via the internet. They enable the local or global exchange of data without requiring human interaction [5]. According to [6], big data is specified as “the volume of data that just exceeds the efficiency with which technology can be stored, managed, and processed.“ In a setting that uses industrial 4.0, CPSs collect a considerable volume of real-time digital data. In addition, big data analytics and machine learning tools are a result of the proliferation of data generated by sensors and IoT. These tools have a wide range of applications, such as analyzing trends, monitoring processes, predicting and controlling quality, diagnosing faults and classifying them, detecting soft issues in real-time, and controlling processes [7]. A highly dynamic system is created by horizontal and vertical system integration, which presupposes complete supply chain connectivity [8]. Simulations are a digital method that can assist in the design of production systems and result in efficient maintenance. They are also ideal for optimizing intelligent CPS systems with real-time data [9]. The rise of cloud computing makes it possible to share processing resources and devices as needed [10]. This technology also facilitates the communication of information between systems, ranging from a single production line to the entire plant [11]. Augmented reality (AR) is a technique that enables humans and machines to interact cohesively by superimposing digital data on reality [12]. Autonomous robots incorporate many applications in a smart factory that are suitable for a variety of services, helping operators in their work and communicating with other robots [13]. Additive manufacturing (AM) enables the conversion of a digital design (i.e., 3D CAD) into a physical object through 3D printing [9]. This technology is adapted to the small lot production of personalized products [14]. Finally, cybersecurity is a tool capable of protecting shared information and SPCs from cyberattacks [15].

3 Research Methodology As a methodology, we used the Mayring method [16]. This method allowed us to thoroughly analyze the gathered materials through theme-specific structural dimensions and analytical categories (Fig. 1).

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Formulation of the research question: What are the Industry 4.0 technologies implemented in maintenance?

Collecting materials from Scopus and WoS

Descriptive analysis

Material evaluation

Fig. 1. Research methodology. Source: Authors’ elaboration

4 Collection of Materials Our papers were collected using two major databases, Scopus and Web of Science. The search criteria used were “Title, abstract, author keywords” on Scopus, and on WoS, we started the search process through the use of the subject. “Industry 4.0” and “Maintenance” were the search terms utilized. On Scopus, there were 1805 articles associated with these terms, while there were 1416 on WoS. We then focused our search on “Industry 4.0 technologies” AND “maintenance” as a result. After applying our new filter, we discovered 98 articles in total: 79 in Scopus and 62 in WoS. To select only articles coherent with the research activity to be conducted, we excluded articles: 1) not related to maintenance (NR) and 2) absence of full text (NF). The collection of 98 articles was reduced to 36 articles for the literature review. Figure 2 illustrates the process of identifying relevant articles. WoS

SCOPUSS Step 1 Keywords: 1805 papers

Step 2 Specify Keywords: 79 papers

43 Duplicates

98 Papers

Step 1 Keywords: 1416 papers

Step 2 Specify Keywords: 62 papers

NF= 29; NR=33 36 Papers

Fig. 2. Selection process of pertinent articles. Authors’ elaboration

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5 Results The study of digital technologies and maintenance is growing, and according to an analysis of the papers’ publication dates, it peaked in 2021 with 13 publications, as shown in Fig. 3.

14 12 10 8 6 4 2 0

2018

2019

2020

2021

2022

Fig. 3. Number of publications by year. Source: Authors’ elaboration

Additionally, “Procedia Computer Science” and “Sensors” are the journals with the most publications connected to the search terms we used, and “IFAC-PaperOnLine” is the publisher of conference papers. These journals are followed by “Applied Science”, “Journal of Cleaner Production”, “Advanced Engineering Informatics”, “Computers in Industry”, and “Sustainability”. We have determined one manuscript for each of the remaining journals, as shown in Fig. 4, and we have recognized it.

Procedia CIRP Journal of Applied Research and Technology Journal of Industrial Engineering and Management Procedia Manufacturing International Journal of Production Economics Designs International Journal of Emerging Technology and Advanced Engineering Sustainability Computers in Industry Advanced Engineering Informatics Journal of Cleaner Production Applied Science Sensors Procedia Computer Science IFAC-PaperOnLine

0

1

2

Fig. 4. Publications by journal. Source: Authors’ elaboration

3

4

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According to the research methodology, the 36 papers were divided into five major categories, as shown in Fig. 4. The most popular articles were reviews (27%) and application articles (33%) (Fig. 5).

Survey Conceptual Case study Review Application 0

2

4

6

8

10

12

14

Fig. 5. Research methodologies employed. Source: Authors’ elaboration

Figure 6 shows that Italy (25%) and the United Kingdom (16%) account for the majority of the primary author’s national origins. 10 9 8 7 6 5 4 3 2 1 0

Fig. 6. Papers published in each of the first author’s countries. Source: Authors’ elaboration

Finally, Table 1 highlights what technologies of Industry 4.0 the authors have mentioned in their articles. As seen in the table, every paper has referenced at least one industry 4.0 technology. IoT, CPS, big data, AM, and AR are the technologies that are most frequently mentioned in publications. Furthermore, the fact that these technologies and maintenance directly interact is a key factor in every article’s use of this term.

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Table 1. Authors who discussed Industry 4.0 technologies and maintenance. Source: Authors’ elaboration Authors

Industry 4.0 tools

Jónasdóttir et al. [17]

Big Data, IoT, AR, CPS

Tortorella et al. [18]

cloud computing, three-dimensional printing technology, CPS, IoT, Internet of Services (IoS), Big Data

Caterino et al. [19]

cloud computing, CPS, IoT

Burdack et al. [20]

cloud computing, machine learning (ML)

Justus et al. [21]

CPS, IoT, IoS, cloud computing

Di Bona et al. [22]

CPS, IoT, IoT, Cloud Computing, and Big Data Analytics

Onur et al. [23]

IoT, Big Data, ML

Di Capaci et al. [24]

Data mining, cloud computing, IoT

Di Carlo F et al. [25]

CPS, IoT, Digital Twin

Forcina et al. [26]

cloud computing, IoT, CPS, AR, Big Data Analytics, simulation, AM, Cyber Security, Autonomous robots

Gallo et al. [27]

CPS, robots, Big Data Analytics, IoT

Hardt et al. [28]

CPS

Titmarsh et al. [29]

IoT, Big Data

Aheleroff et al. [30]

IoT, Big Data Analytics, CPS, cloud computing, Digital Twin

Aheleroff et al. [31]

Digital Twin, IoT, AR, Cloud computing, ML, Big Data, Mixed Reality (MR), Virtual Reality (VR), Extended Reality (XR)

Silvestri et al. [32]

(IoT), Big Data and Analytics, Horizontal and vertical system integration, Simulation, Cloud computing, AR, Autonomous Robots, AM, and Cyber Security

Chen et al. [33]

IoT, AM, Big Data Analytics, ML

Kerin et al. [34]

IoT, Big Data, cloud computing, Digital Twin

Wang et al. [35]

CPS, IoT

Vargas et al. [36]

AR, MR, IoT, Big Data, robotics, CPS, Digital Twin

Nordal et al. [37]

Big Data, cloud computing, CPS, IoT, (continued)

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Table 1. (continued) Authors

Industry 4.0 tools

Butt [38]

IoT, CPS, Big Data Analytics, Cyber Manufacturing (CM), Horizontal and Vertical Integration, Cyber Security, cloud computing, Autonomous Robots, AR, AM, Simulation and Modeling Techniques, Digital Twin

Pech et al. [39]

ML, IoT, CPS, Big Data, Artificial Intelligence, SCADA, Digital Twin

Tortorella et al. [40]

IoT, Big Data, Cloud Computing, AR, simulation, ML, AM, robots

Turner et al. [41]

IoT, Digital Twin

Giada et al. [42]

IoT

Wippel et al. [43]

IoT, Big Data, cloud computing, AR, AM, CPS

Di Nardo et al. [44]

IoT, AR, CPS, Digital Twin, Big Data, cloud computing, AM

Cortés-Leal et al. [45]

IoT, Wireless Sensor Networks (WSN)

Drakaki et al. [46]

IoT, Big Data analytics, CPS

Turner et al. [47]

IoT

Alejandro Cortés-Leal et al. [48]

IoT

Malgorzata Jasiulewicz-Kaczmarek et al. [49] IoT, Cloud Computing, Big data analytics, Digital Twin, AM, AR, Artificial Intelligence (AI) Mariya Guerroum et al. [50]

digital twins, CPS, cloud computing, big data

San Giliyana et al. [51]

IoT, Cloud Computing, Big Data and Analytics, AR, CPS, ML, Artificial Intelligence

Sachini Weerasekara et al. [52]

Advanced simulation, autonomous robots, system integration, AM, big data, AR, IoT, cloud computing, cybersecurity

6 Discussion After analyzing the results, we realized that industry 4.0 has brought some relevant related challenges to manufacturing processes and systems, including maintenance policies, maintenance management approaches, maintenance tasks, and operator roles in maintenance. Industry 4.0 technologies answer the criteria for a predictive, proactive, or prescriptive maintenance policy. Indeed, Industry 4.0 technologies enable innovative solutions, including remote maintenance or self-maintenance, allowing industry practitioners to move away from traditional policies to more appealing and compelling policies, for

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example, autonomous maintenance [22]. Using industrial IIoT technology, it is becoming feasible to develop predictive maintenance algorithms that enhance efficiency and quality while minimizing costs [22]. By using digital technologies, such as augmented reality and autonomous robots, as well as efficient big data analysis tools, effective remote maintenance is becoming increasingly practical [22]. A new model is proposed by [19] for the selection of the maintenance policy improved by the use of CPS, IoT, and cloud computing, and its application is performed using MATLAB code [19] with Industry 4. 0, it becomes possible to achieve the optimization of production processes by technical surveillance of the machines and their production processes by creating an integrated, interactive, web-based machine learning system, which enables the representation of the footprint of every machine, every part of the machine in a central master data management and comprises a project system where several machine learning strategies can be determined, evaluated and put into production [20]. A new predictive maintenance assessment matrix was proposed by [37] based on Industry 4.0 technologies. It confirms industry predictions of the ability to increase present levels of detection, diagnostics, and prognostics by demonstrating that multivariate analysis can help [37]. Furthermore, the smart and intelligent predictive maintenance (SIPM) system for the intelligent factory proposed by [39] contains the following four core subsystems: planning, production, monitoring, and maintenance. Utilizing current IoT and cloud technologies, these subsystems communicate and work together. Their key advantage is real-time planning and management to reduce financial expenditures brought on by production interruptions [39]. Technological improvements alone will not support industry 4.0 without the human element [53]. Therefore, industry 4.0 is not about the disappearance of the people component from businesses. Instead, it is a major opening to show the chance for humans to investigate new approaches to work among organizations. Employees must be able to connect their experience and traits to evolving technologies because they are innovators. As a result, the “maintenance operator” must adapt to a technological environment in which he or she must be able to supervise automated manufacturing as well as enhanced supervision systems and user interfaces. Simultaneously, innovations such as smart devices can help the operator obtain more convenient, efficient, and real-time data, contributing to the growth of safe and effective maintenance [22]. The capabilities and performance of maintenance specialists should improve over time. Meanwhile, the training process will be accelerated by Industry 4.0 technologies such as smart appliances and virtual reality [27].

7 Conclusion The major goal of this study was to discover which digital technologies are used in maintenance and how maintenance is optimized with industry 4.0. 36 publications that were published between 2018 and 2022 were checked and analyzed. Throughout the analysis of the different articles, the IoT and big data are generally embedded as enabling technologies in each study reviewed. However, AR, AM, and CPS appeared to be the most used Industry 4.0 technologies by companies in their operations to outperform their competitors. The results show that Industry 4.0 tools could be integrated into maintenance so that companies are able to optimize their maintenance function through the

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benefits brought by these digital technologies, which are increasing the reliability of systems, reducing costs, saving time, increasing quality assurance, and increasing employee safety. By way of perspectives, we will conduct an empirical survey on the integration of digital technologies in Moroccan companies to identify the different obstacles and barriers and then develop a model of deployment of these technologies in the process of optimization of the maintenance function considering the results of the empirical study.

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Adoption of Smart Traffic System to Reduce Traffic Congestion in a Smart City Oluwasegun Julius Aroba(B) , Phumla Mabuza, Andile Mabaso, and Phethokuhle Sibisi ICT and Society Research Group, Information System and Information Technology Department, Durban University of Technology, Durban 4001, South Africa [email protected]

Abstract. Cities across the world suffer significantly from traffic congestion. Governments are trying to harness the power of today’s computing, networking, and communication technologies to build system that can improve the efficiency of current road traffic and conditions. The study investigated the purpose efficiencies of intelligent system to assess their performance. Considering the findings, it can be said that traffic flow forecasting (TFF) possibilities are numerous, involve a variety of technologies, and can significantly reduce most traffic issues in smart cities. The studies were later evaluated to find similarities, content, benefits, and disadvantages of traffic congestion. By applying the project management tools such as the performance metrics and SQERT model were used to evaluate and prioritize the state-of-the-art methods. A classical model was proposed to improve upon and determine the traffic dangers that affect road users and aggregate the information about traffic from vehicles, traffic lights, and roadside sensors. These on-road sensors (ORS) performance are used for analyses such are vehicle classification, speed calculations, and vehicle counts. Keywords: Congestion · Deep Learning · Forecasting · Smart City · Scope Quality Effort Risk and Timing (SQERT) · Traffic System Sensors

1 Introduction 1896 was one of the most significant years in the world. Henry Ford designed and assessed the first-ever car in the world. Since the automobile’s large-scale production in 1908, traffic congestion has increased tremendously and is the biggest problem in large cities worldwide [1]. There are many factors to these traffic congestions, including the construction of roads, stalled vehicles, and motor accidents. This stall of cars has brought problems such as air pollution. The negative effect on the economy is that businesses lose profits to such an extent that others close due to delayed deliveries. Accurate traffic flow forecasting (TFF) is vital for achieving traffic control, releasing effective traffic organization strategies, alleviating traffic congestion, and improving the urban environment [2]. These countless algorithms can be classified into two categories such as parametric and nonparametric techniques [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 822–832, 2023. https://doi.org/10.1007/978-3-031-29857-8_82

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Similar state-of-the-art approaches and algorithms have been deployed in tackling traffic congestion. These algorithms are practical for calculations and appropriate for stationary and linear time series. It attempts to identify past information similar to the state at prediction time, which leads to an easily implemented nature [2]. However, this has been a challenge as the solution to this intelligent system will need to defeat the dynamic nature of traffic flow. From the year 1990, researchers all around the world have tried to come up with algorithms to reduce traffic flow to be more efficient [3]. Nonparametric approaches have been shown by some researchers to perform well in general because of their potent ability to capture the complicated nonlinearity and non-determinism of traffic time series [4]. However, the technique displayed poor traffic balance, which in turn restricted its scope. In the literature, the application of a deep learning technique for detecting traffic congestion. He proposed the mix of deep learning and k-shortest path algorithm to detect traffic congestion [4]. De Souza (2016) [5] submitted an application of a cooperative rerouting algorithm that uses the prediction technique called K-Nearest Neighbour to prevent congestion and improve traffic by using vehicle-to-infrastructure communication. In this study, intelligent traffic system must be improved because they should provide the benefits of reducing vehicle accidents, thus saving lives in our society. They should reduce vehicle waiting time so tasks can be done efficiently [5]. Lastly, to prevent traffic obstruction so that there is an ease of flow. A key element to having an efficient Intelligent Traffic system is to produce algorithms that will detect traffic congestion efficiently with minimal errors [6]. This research objective is to adopt intelligent traffic system to minimize traffic congestion in a modernized smart city. Another strategy to reduce power consumption was suggested by Ahmad et al. (2013) [6]. The study arrangement is as follows, Sect. 1 is the introduction, Sect. 2 is the relevant literature review, Sect. 3 is the methodology, and the Sect. 4 is a section for conclusion and recommendation.

2 Related Work 2.1 Recent Advances Recent advances in wireless sensor networks (WSN) and the low cost of sensors reinforce the rethinking of the creation of intelligent traffic management system. Governments are trying to harness the power of today’s computing, networking, and communication technologies to build strategies that can improve the efficiency of current road and traffic conditions. Cities worldwide can collect traffic-related data from vehicles, including traffic lights and roadside sensors [7–12]. They can aggregate this data to identify traffic hazards affecting the road. The ever-increasing number of vehicles leads to recurring traffic management problems. Increasing infrastructure growth is a solution, but expensive in terms of time and effort. Similarly, an intelligent traffic management system will ensure that data collection and analysis across the city are connected while alerting you when immediate action and preventive measures are required. The studies of traffic patterns and driving behavior using AI-enhanced video sensor technology will provide law enforcement with the data they need to reduce traffic accidents and congestion and improve citywide mobility. Sensors will be used in traffic system to collect real-time information on traffic flow,

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traffic congestion, and more [13]. As a result, sensor networks can map entire cities and collect the tiniest details with minimal time and cost overhead. These on-road sensors (ORS) perform analyses such as vehicle classification, speed calculations, and vehicle counts. The system recommends the best route considering travel time, cost, and distance [14]. 2.2 The Collection of Traffic Data One of the most common functions in managing traffic is the collection of traffic data. This data is essential because all traffic management methods rely on accurate traffic prediction. There are several data collection methods related to traffic and road conditions. The first approach to collecting traffic data was recording sounds from the vehicles’ honks to estimate the congestion level. The multiple vehicular honks created an error in the accuracy of the data. “The development of machine learning and artificial intelligence changed the design of sensors.” On the other hand, they are developing a sensor for collecting data to detect traffic congestion using machine-learning technology. The Received Signal Strength Indicator (RSSI) represents the total signal power in the channel bandwidth. RSSI includes valid signals, background noise, and interference [11–14]. These sensors were placed at different toll gates to record data. This system successfully determined traffic congestion; however, the plan was costly to implement [9–11, 15]. The development of a technique to use wireless networks to detect traffic congestion. They used Received Signal Strength Indicator (RSSI) to detect vehicles and classify their types. The adaptive architecture framework to gather traffic data. Khan argued that because many citizens use their private cars, it directly impacts congestion. A perfect balance could be achieved if potential public transport issues are solved [16]. This technique also proved helpful in collecting accurate data but could not be applied because of high costs. A piezoelectric sensor is a device that uses a piezoelectric effect to measure the changes in pressure, acceleration, temperature, strain, or force by converting them. However, this method has complications of short-range problems. Table 1 above further analyses the different approaches used for traffic congestion. In addition, the traffic management procedure uses a channel-switching approach to reduce communication expenses. Radio Frequency Identification is another technology that tracks goods using tags transmitting radio signals. An RFID-based Traffic management system to determine traffic movement was done [18]. The intelligent traffic light control system (ITLCS) uses Radio Frequency Identification to detect the number of vehicles that pass by main roads. It also sees how long cars pass the intersection during the green light period. The Zig Bee module was used, which is an IEEE 802.15 protocol. This module sent real-time data. Furthermore, the sensor can detect traffic congestion; however, it is limited to proper weather conditions [19]. Most often, different approaches from Saqib and Lee (2010) [20] estimated the vehicle location and speed by employing Wireless Sensor Networks seemed to prove better than the previous. However, low data transfer rates and vast security issues were its major drawbacks that limited its scale and electrical charge [22, 23]. Friesan (2014) created an algorithm where vehicular count data is collected using Bluetooth, which is forwarded to a master node that handles all

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Table 1. Summary of methods used for collecting and managing traffic congestion. Author

Tech

Method

Advantages

Disadvantages

Perez-Murueta et al. (2019) [4]

Deep learning, k Shortest Path algorithm

This algorithm uses deep learning techniques to detect traffic congestion in real-time. Vehicles are redirected to another route to mitigate traffic congestion using entropy-balanced k Shortest Path

Inexpensive, easy to implement

Not scalable

De Souza et al. (2016) [5]

KNN

Traffic is classified to reroute traffic and avoid congestion. Average road speeds are used as input to detect the density of vehicles and see congestion

Reduces average, distance, and waiting time

Unfair traffic control

Ahmad et al. (2013) [6]

WSN

Vehicles are put nodes to communicate with receivers on the road. A switching method reduces the duration of response and connecting issues

Reduces communication cost and power used

Quick channel interference, security issue

Fan et al. (2018) Big Data [15]

Sensors attached economical to vehicles produce real-time data

Accuracy decreases as more cars are added (continued)

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Author

Tech

Method

Advantages

Disadvantages

Khan (2017) [16]

Adaptive architecture framework

Data Mining techniques and Machine Learning algorithms to monitor traffic

Monitors data in real-time No alterations needed

New data sources will change the data performance and life cycle at large

Haferkamp et al. WSN (2017) [17]

Three transmitters High classifying and receivers are accuracy used to detect and classify vehicle types

Utilization of directional antennae increases the cost of implementation

Chao and Chen (2014) [18]

The RFID system, ZigBee wireless network communication

RFID tags detect traffic flow. Receivers analyze data traffic control

Scalable Reduces traffic accidents, power savings

low data transfer rates, which affect the accuracy

Rivas et al. (2017) [19]

Piezoelectric acceleration sensors

Algorithms analyze the amplitudes and frequency range of vibrations

Economical High accuracy

Weather conditions could affect the efficiency

Saqib and Lee (2010) [20]

WSN

This algorithm used two nodes to give the location as soon as a moving vehicle came within the operational range of the nodes

Economical Robust Requires less computation

Not scalable

Sen et al. (2009) Sound recording [21] (microphones)

Sounds from the honks of vehicles are recorded to estimate the traffic congestion level

Economical, Strives in unorganized areas

Overlapping affects results High speeds affect accuracy

Zhou et al. (2013) [22]

A user-dependent strategy to gather traffic data. Vehicles are routed based on various user needs

Reduced power consumed on sensors Delay in delivering data

The data transfer rate is low Data transfer is a security risk

WSN

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the received data through an IEEE 802.15.4 protocol. After a 5-min interval, the data is sent to a server through a cell phone [24]. 2.3 Algorithms Deployed for Traffic Congestions As discussed above in Table 1, there are two main categories for traffic flow forecasting, parametric and nonparametric solutions. Parametric uses a set of fixed readings x at a given time t. The aim is to determine a probability model using machine learning, looking for temporal patterns in historical traffic information and use these findings to forecast. The method generated better results compared to other parametric solution such as moving average or the exponential smoothening. Later, The Kalman Filtering approach, which is an iterative mathematical process that keeps track of the estimated state of the system and its variance. It was applied to the mobile stochastic problems to compact deterioration issues and reduce variance for arriving at optimal results [25, 26]. Parametric Solution In parametric solutions, a technique called Random Forest based on Near Neighbour (RFNN) method was standardized using location and time obtained from Geographical Positioning System (GPS). This method used real time location to measure bus travel time. This new boosted Random Forest proved to be faster, with greater accuracy in the rise of data [28]. Nonparametric Solution There is an ability of communication of information between vehicles and infrastructure to determine traffic congestion. The technique to predict urban travel time. The details within the network were combined with current travel time to predict future travel time [29]. This technique uses Probabilistic Principal Component Analysis (PPCA). Using data gather for four months in China, the results indicated that PPCA prediction outperformed KNN prediction with a higher accuracy [30]. Machine Learning This section describes some key approaches proposed in the literature, which use machine-learning approaches. Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Effective rerouting strategies are necessary when traffic networks are congested. An algorithm CARTIM that uses a fuzzy logic-based system for effective traffic congestion [28]. However, it had challenges of not scaling the intensity of traffic congestion. The model to predict travel time based on ANN and Support Vector Machine (SVM) [30]. Artificial Neural Networks (ANNs) model traffic data mathematically. ANNs play a significant role in machine learning (ML) artificial intelligence (AI) technology. Donavan, (2022) [3] recognize patterns and offer deep learning functionalities. However, these methods limited sample space, limited its use. A traffic congestion prediction approach which combined ANN and data fusing. The framework considers real time unforeseen occurrences which influence traffic jams such as traffic accidents and GPS information [31]. Figure 1 below show the prediction technique, advantages, and disadvantages used for traffic congestion (Table 2).

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Proficiency

Performance Matrix High Medium Low

Kalman Filtering, Random Forest ARIMA KNN Low

Support Vector PCA

ANN

Medium

High

Complexity Handling

Fig. 1. Performance methods

Table 2. Advantages and disadvantages of each method Types

Authors

Prediction Techniques

Advantages

Disadvantages

Parametric approaches

Ahmed and Cook (2015) [14]

ARIMA

Ease of use, Minimal computations required

It only works on a static model Difficulty updating new observations

Xie et al. (2017) [25], Wang et al. (2018) [26]

Kalman Filtering

Allows variables updating without disruption

Accuracy is not stable Difficulty managing unstable traffic conditions

Mallek, Klosa and Random Forest Busken (2022) [27]

Simple implementation. Adapts to new data fast

Only predicts the range provided on training data

Jenelius and Kotsiopoulos (2017) [29], Tian (2018) [30]

KNN, PCA, and Support Vector

Can handle noise. Can not manage Adapts to new Spatial and data easily temporal modeling

Elleuch et al. (2020) [31]

Deep Learning: ANN

Manages non-linear approximation

Manages complex problems poorly

Araújo et al. (2014) [28]

Fuzzy logic

Minimizes traffic congestion

It cannot detect accidents Traffic information is not readily available

De Souza et al. (2016) [5]

k-Nearest Neighbour (KNN)

Reduces average A poor traffic ka trip time, stop balancer time, and distance

Nonparametric approaches

Based on the performance Matrix above in Fig. 1 illustrated that ANN is the most suitable method.

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3 Methodology In addition of cameras, sensor, and GPS tracking system to improve this method. The following SQERT in Fig. 2 will be used for the development of the traffic congestion system as the proposed model.

Fig. 2. SQERT Process Model

The following Use Case Diagram in Fig. 3 illustrates the process of the proposed solution.

Fig. 3. Use-Case Diagram

The proposed system in Fig. 2 and Fig. 3 will compose of IoT traffic lights that will be placed in each lane. In Fig. 4 and Fig. 4, the use of GPS mapping software enables the information to be collected of the number of vehicles that will pass through the road.

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Fig. 4. Sample of Prototype Model

4 Conclusion and Recommendation This paper aimed to determine the effectiveness of essential traffic flow forecasting (TFF) techniques currently in use. The report assessed studies on TFF’s point in terms of functional efficiencies. The study conducted a literature review to determine the effectiveness of the most important existing TFF for regulating traffic flow. The study investigated the purpose efficiencies of intelligent system to assess their performance. In light of these findings, it can be said that TFF possibilities are numerous, involve a variety of technologies, and can significantly reduce the majority of traffic issues in smart cities. It was considering the environment in which the traffic must be controlled. The embedded camera sensors and GPS data should be employed by the location’s characteristics since this would shorten travel times and lines and improve safety and productivity. Future directions is for improvement in data collection. Managing vast amounts of data and data Privacy concerns.

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The Contribution of an ERP System in an LCA Analysis: A Case Study Zineb El Haouat1(B) , Safaa Essalih1 , Fatima Bennouna2 , Mohammed Ramadany3 , and Driss Amegouz1 1 Higher School of Technology, Sidi Mohammed Ben Abdellah University, 30050 Fez, Morocco

[email protected]

2 National School of Applied Sciences, Sidi Mohammed Ben Abdellah University, 30050 Fez,

Morocco 3 Faculty of Sciences and Techniques, Sidi Mohammed Ben Abdellah University, 30050 Fez,

Morocco

Abstract. With the emergence of Industry 4.0, the concept of sustainability has been adopted in many manufacturing companies. This concept aims to integrate management systems by respecting all aspects of sustainability, including the aspect of environmental impact assessment of the life cycle of a product that integrates enterprise resource planning (ERP) software. Since the collection of information can be exploited from an environmental point of view, this paper has gone even further in trying to describe the architecture and application of a Life Cycle Assessment (LCA) and show that more than 53.33% of the full data and 24.23% of the partial data are available in the ERP systems, which serves as a basis for the realization of the Life Cycle Inventory (LCI) phase, the most time and energy consuming phase. The realization of LCA is beneficial to organizations to the same degree as a commercial discipline that will be able to focus on ERP data and reduce the collection of data from other sources. Keywords: LCA · LCI · Industry 4.0 · ERP · BOM · digitalization of manufacturing · MES

1 Introduction The use of ERP systems in the manufacturing sector has become crucial, and to make the manufacturing industry more environmentally related, life cycle assessment (LCA) is presented as an efficient evaluation tool to help determine the use of resources required to manufacture a product with a low environmental impact. The goal of this research is to analyze to what extent computer systems today can support the realization of an LCA. Today, the data of small and medium-sized companies are managed by different computer systems. Moreover, Dubai is defending itself until 2021 by wanting to become one of the first cities in the world with a paperless administration and therefore favoring intelligent information systems that manage various modalities, including the collection, exploitation and analysis of data from the design (CAD) to the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 833–844, 2023. https://doi.org/10.1007/978-3-031-29857-8_83

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delivery of the finished product to the customer, through the various departments and collaborators in interaction. It is therefore admissible that there is a link between the databases of the computer system and the data of the realization of an LCA in view that no aspect escapes from these information systems. The ISO organization has taken a first step toward standardizing LCA. It published an environmental management standard in the 14000 series and proposed a formal definition of LCA: “Life cycle assessment (LCA) is a standardized evaluation method (ISO 14040 and 14044) that makes it possible to carry out a and multistage environmental assessment of a system (product, service, company or process) over its entire life cycle. It is the compilation and evaluation of the inputs, outputs, and potential environmental impacts of a product system over its life cycle (ISO 2006)” [1]. According to the international technical series standards ISO 14040 and 14044, an LCA consists of four stages: (i) goal and scope definition, (ii) life cycle inventory (LCI), (iii) life cycle impact assessment (LCIA), and (iv) interpretation [2]. LCA is a robust tool to determine and assess the potential energy and environmental performance of a product [3]. Its use makes it possible to achieve product transparency in environmental impacts throughout its life cycle and to compare the impacts of different products on the same functional unit (FU) [4]. Emissions to water, air and soil are well identified and quantified to achieve a proven and certain environmental inventory. The life cycle inventory (LCI) for the second phase of the LCA is regarded as the most refined of all phases since it results in the creation of an analogical model of reality that must accurately capture the interactions between the various industrial process phases [5]. It is therefore necessary to collect input and output data for the sequence and flow of the processes of the system under analysis to execute the life cycle analysis assessment (LCIA). By evaluating and comparing the system process under study (scaling stage: UF) as well as these inputs and outputs, the environmental effects are well assessed. LCA therefore limits energy consumption from the product design stage. This method is further developed to consider a wide variety of environmental impacts and indicators, including greenhouse gas (GHG) emissions, effects on human toxicity, acidification, and aquatic ecotoxicity [1, 2]. The quick digital change that is taking place, often known as Industry 4.o, has made live LCA possible. The development of digital technology, especially low-cost detectors and processors, has made it possible to collect and monitor data in almost real time [6]. When performing it, it is important to separate company-specific master data (ERP systems) from data related to an LCA inventory database. Moreover, the greatest caution is required when using these data, as they are only valid for a specific geographical area and for a defined period [7]. We should therefore try to perform an LCA with as much relevant data as possible that is already available within an organization. We can conclude that a typical ‘perfect’ LCA includes relevant background data from the ERP systems and global data from the product manufacturing process, as represented in Fig. 1. Enterprise resource planning (ERP) systems are designed to provide users with the necessary information to manage the different modules within an organization from finance, human resources management, supply chain management and control to delivery

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Fig. 1. Flowchart of product life cycles, highlighting influences differing environmental impacts of product

to the end customer through sales and production (information capture: BOM, production test, manufacturing range). Thus, we will present an evaluation of LCA and ERP systems, and then we will study the methodology of integrating an ERP system with advanced LCA computer applications. Finally, we will present a schematic case study overview.

2 Why ERP Systems as Primary Databases for LCA The low frequency of life cycle studies in the manufacturing setting today is puzzling given the emergence of the so-called fourth industrial revolution, or Industry 4.0 [8], and multiple tools have been made available. The implementation of an MES (Manufacturing Execution System) is part of the technology of industrial companies [9], a piece of software that interfaces with planning systems (ERP) [10] and gathers and analyses strategic data to assist management in monitoring and enhancing a plant’s output. The origin of the basic data is the primary source; they are obtained directly from the production site. Therefore, to obtain a credible inventory, it is necessary to use primary source data for the parts of the system related to the product OR/and service. The complexity of LCA studies in manufacturing companies lies in the realization of the LCI, as it requires experts who can execute the analysis, a strong management collaborative mindset, and plant staff who can efficiently conduct primary data collection [11]. In this way, and due to the challenges in collecting information that requires a lot of time, the environmental assessment is only carried out eventually by the companies. Therefore, we note that LCA, unlike KPIs, remains an empirical method that has not yet

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passed the methodical and rational stage to take place in the manager system (QMS) of manufacturing companies. LCAs are mainly carried out to reach a clear need at an exact or specific moment in time, as a goal for academic research (in the field) or to earn an environmental certification (in the business sector). The authors [12] have noted that the scientific literature has not fully addressed the issue of how often a model of the assessment of environmental impacts should be performed. One of the crucial aspects in the environmental impact study is the possibility that LCA offers to use auxiliary information in the database to achieve similar results of environmental estimation. In any case, the aim is to encourage the widespread use of LCA. With ERP systems, most businesses have the essential inventory data needed to perform LCA. The complexity, constraints, and inconsistencies of common data in LCA inventory information can be solved by using genuine ERP data streams [13]. Rational documentation is rich in terms of linking ERP systems and LCA tools. According to the authors [14], a specific ERP solution for LCA should be developed to match main process data with databases for life cycle inventories and tools for life cycle impact assessments. To employ the ERP system’s informative capacity, scientists [15] have identified a set of sustainability performance measures, 12 of which are associated with environmental performance. The authors [16] provide a comprehensive viewpoint to help decision-makers create companies that are sustainable and sustainably connected. The authors [12] discuss the design and implementation of the dynamic LCA system, which links an ERP system with a specially designed LCA tool using business intelligence (BI) software. These tools are suitable for fortifying the incorporation of ecological variants in managing a business system. Actually, in the manufacturing context, ERP systems have become indispensable for all companies (regardless of their size). Nevertheless, it can be identified that the largest segment of ERP users in 2021 will be in North America and that 88% of companies in France in 2021 consider their ERP implementation to have been a success [17]. “Enterprise resource planning (ERP) is defined as being able to provide a comprehensive set of developer tools” [18]. It can be defined as a software package for the internal management of a company that allows for the optimal and precise management of the various specific processes of the industry. Therefore, what are the advantages and barriers of the ERP system? (Refer to Table 1). For this reason, the development of its ERP systems inside the organizations, also for small and medium-sized companies, expresses ‘extraordinary’ capacities to support and standardize the information of the company in a systematic way. They are configurable to the specific needs of each organization. Integrating an MES is typically required for manufacturing organizations to become digital [19], a software that interfaces with planning systems (ERP) [20], analyzes and employs important data to help management in managing and improving a plant’s output. Certainly, by using actual ERP data streams, the difficulties, inconsistencies and boundaries of shared data can be achieved in environmental footprint inventory data.

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Table 1. Advantages and boundaries of an ERP system Benefits

Boundaries

ERP can integrate costs, profits, and Resistance to change by operators, which information about realized sales revenues, and explains the unsuccessful implementation of it can be presented in a detailed way. Enterprise an ERP system within companies [10, 24] Resource Planning can also be responsible for changing the way a product is manufactured [21] Ability to have meaningful plant monitoring, increased data and process integration, and meaningful sharing of information between business levels of organizational and manufacturing organizational functions [22] Improved integration and flexibility with other software [15] Information centralization Increased data availability for control and planning [23]

The time to respond to quality issues is addressed later

Reducing the time and cost of business processes [24]

3 Methodology of Affiliation of an ERP System for the Realization of an LCA (Specifically LCI) As noted earlier, ERP systems bring many benefits to companies, and they provide shared planning information on all company resources. In developing an environmental assessment framework that operates digital data from Industry 4.0, the study is organized following the four phases of LCA, organized as suggested by the ISO 14040 standard. 3.1 Schematic Case Study During execution of the empirical part of the research, the cookware sector was recommended as a case study in one of the industrial areas of Morocco. A manufacturing company that produces aluminum and stainless-steel cooking utensils for the final consumer and for the hospital, hotel and restaurant sector. This company has been known on a national scale since the 1990s. Today, this sector has emerged with important investments. Since 2016, the company has started the systematic implementation of an ERP that is convenient in both an associated and thematic way. With the help of the ERP, the company was able to create a database of great importance without duplicates and redundancies. To assess the nature of the organization’s digital maturity, several tools for evaluating and checking the consistency and reliability of the data are integrated into the working environment. These tools allow the analysis of the manufacturing organization on several dimensions (see Fig. 2).

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The manufacturing process of kitchen utensils involves a set of steps, phases and operations. These sets of actions can be either dependent or independent from each other. From a selected category of raw material, semifinished products and finished products can be produced. The manufacturing process starts with the storage of raw material in the form of discs or sheet metal from different suppliers (local or imported); these discs are sorted and then passed into the stamping molds of hydraulic presses. This process transforms the product (stamped part) into the desired shape, thickness and suitable tenacity.

Fig. 2. Tools to evaluate the manufacturing system

Then, the spinning phase is performed to achieve the proper deformation of the stamped part. The lubrication of the spinning tools with an oil mixture is now essential to facilitate the change of the part. Gradually, the product is machined from the contour to remove burrs and then satinized and brushed by semiautomatic machines from the inside and outside by steel wools. The polishing phase is now absolutely necessary with the help of vibrating machines to make the product smooth and shiny. A firing test is carried out later to check if the product undergoes any physical-chemical reactions and transformations. The last step of the production process is the assembly of the accessories OR/AND of the pieces, sorting and packing which has the following objective: • Disposal of nonconforming parts; • Separation of 1st quality products from 2nd quality products; • Packaging by unit and by quantity.

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The arrangement of emerging technologies such as sensors and electrical signals vs. the traditional technology mentioned above can evolve from a ‘classical’ factory into a digitally efficient one. PLCs attached to production lines, collecting the possibility of using data for production management and even for LCI [21]. Nevertheless, to transform these innovative data into assets, the MES system is busy in the field. It allows the shop floor to be closely synchronized with customer requirements while dealing with problems that arise during manufacturing [19]. MES is required for ERP integration because it represents the ‘meeting’ of various data sources between the decision stage and the manufacturing stage. Due to these major points of convergence, ERP is a powerful software for performing LCA. The manufacturing process was examined and broken down into several phases and operations to fully understand and distinguish each stage of the production cycle. The life cycle of the production process of a cooking utensil used in this study is shown in Fig. 3.

Fig. 3. Inventory data related to the production of a stainless-steel cooking utensil

For all the data needed to carry out the LCA, this analysis consists of specifying the source of the data and its degree of availability on the ERP system studied. The results are summarized in Table 2. The availability of the data is indicated in 3 different levels: (Lvl 1: fully internal data: the searched data are intact and complete in the ERP system; Lvl 2: incomplete data: the searched data are partial; Lvl 3: external data: the searched data are not available in the ERP system).

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The approach used to evaluate the impact on the environment on the result chain and the approach that takes into account the impact of domestic emissions related to serious damage affect the category end-point as well as the impacts on the quality of ecosystems and land use and the impacts on climate change. This case study focuses on 4 phases: the supply phase, the production phase, the distribution phase and the end-of-life phase. For the raw material procurement phase, the ERP system contains material data in the purchase requisitions. Focusing on the production phase, all the data needed for manufacturing are basically in the ERP system. With reference to the distribution phase, the data are partial, and thanks to the existing customer data sheets in the ERP system, as well as the sales delivered to customers and the logistics part that manages the transport, we can easily calculate the exact distances, hence the environmental impact of external transport. For the internal transport flow (movement of raw material, circulation between production sections, sending of products for storage), we can easily estimate the median distance. Finally, the end-of-life phase is reduced to external data of level 3, which we will perhaps evaluate later in our next studies. The analysis’s conclusions are reported in Table 2 for each stage of the business process by identifying the 4 phases integrating the cradle-to-grave aspect of the life cycle. Table 2. Life cycle phase vs. Information provision on ERP system Process

LCA phase

Category

Detail

Level

Support

Raw material supply

Ram material

Direct components

Lvl1

Packaging

Final Lvl1 components (product BOM)

Accessories

Internal (semifinished product) and external accessories

Lvl1

Consumables

Machine processing products

Lvl1

Indirect components

Lvl1

Energy

Electricity, gas, Lvl2 fuel (continued)

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Table 2. (continued) Process

LCA phase

Category

Realization

Product production (from assembly to packaging)

Moving the raw material to Pallet trucks, the production lines carts

Lvl1

Assembly, finishing and packaging of the product

Production lines (conveyors or carts between phases)

Lvl1

Operating time

Lvl1

Mass used

Lvl1

Pallet trucks, carts, lifts

Lvl1

Moving the finished product for storage Support

-

MANAGEMENT (ISO 9000 and ISO 14001 standards)

Distribution

Utilization

End of life

Detail

Shipment of the finished Mode of product to the points of sale transportation

Level

Lvl1

Distance

Lvl1

Digitalization of road transport

Lvl2

Shipment of the finished product to supermarkets/wholesalers

Transport mode Lvl1

Shipment of the finished product to the final consumer

Transport mode Lvl2

Use of the product

Distance

Lvl2

Digitalization of road transport

Lvl3

Distance

Lvl2

Digitalization of road transport

Lvl3

Client finale

Lvl3

Customer feedback Survey

Lvl1

Product handling at the end None

Lvl3

Waste management

Discharge of waste from the plant

Lvl2

Production waste

Lvl2

Packaging waste

Lvl2

Distribution waste

Lvl3

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There is no longer a need for organizations in general to collect data from many systems and transform it into a single format and value [25]. For the effectiveness of Industry 4.0 technologies and models, the integration of data into a single database is crucial, especially for manufacturing plants. Therefore, the majority of the primary data are included in the ERP database essential for conducting the LCA. Figure 4 summarizes the LCI data in the ERP system in terms of the percentage of nuances according to the life cycle stages. It can be seen that more than 75% of the data collected are available in full or in part in the ERP system. Thus, the production stage represents 100% of the relevant information available intact in the ERP. Similarly, the distribution and supply stage reflect high percentages that are perfectly and completely applicable in the ERP system.

Fig. 4. Availability of ERP data by LCA stage

4 Conclusions and Discussion However, all information available in an ERP system is not sufficient for environmental impact analysis purposes. Practically, all the transaction data (work plans, work orders, inventories, work components) or basic process data (product sheet, BOM, manufacturing range) for the production phase are implemented in the ERP system. On the other hand, the data used for management approaches are very limited. The end-of-life cycle phase, which has practically zero information, can subsequently be muted if a mandate is issued to the cookware manufacturing industry to cover the end-of-life processing of their product. In fact, the huge opportunity to integrate data from ERP systems with IoT technologies and LCA software is definitely overwhelming. From a business and ecological standpoint, performing an LCA can provide many benefits to the organizations, as much as a commercial LCA instruction, which can reduce data collection from other sources and focus on the baseline data referred to on the ERP. • Outlooks:

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• Technological innovations in the sphere of production systems have the possibility of providing a high volume of raw data in the strict meaning that only these data are completely available in the production stage. Therefore, it is necessary to use other data sources by taking ownership of the ERP system to collect inventory data. • In this study, both main and auxiliary data were used, especially for production. However, it is important to remember the scope of all phases of product life cycle assessment, especially for end-of-life processing.

References 1. ISO, “14040: Environmental management–life cycle assessment—Principles and framework,” Int. Organ. Stand. (2006) 2. ISO, “14044: Environmental management - Life cycle assessement - Requirements and guidelines, International Organization for Standardization,” Int. Organ. Stand. (2006) 3. Bouyarmane, H., El amine, M., Sallaou, M.: Environmental assessment in the early stages of product design. In: 2019 5th International Conference on Optimization and Applications (ICOA), pp. 1–5 (2019) 4. Hagen, J., Buth, L., Haupt, J., Cerdas, F., Herrmann, C.: Live LCA in learning factories: real time assessment of product life cycles. Procedia Manufact. 45, 128–133 (2020) 5. Patouillard, L., Collet, P., Lesage, P., Tirado Seco, P., Bulle, C., Margni, M.: Prioritizing regionalization efforts in life cycle assessment through global sensitivity analysis: a sector meta-analysis based on ecoinvent v3. Int. J. Life Cycle Assessm. 24(12), 2238–2254 (2019). https://doi.org/10.1007/s11367-019-01635-5 6. Kumara, R., et al.: Live life cycle assessment implementation using cyber physical production system framework for 3D printed products. Procedia CIRP 105, 284–289 (2022) 7. Hauschild, J., Jeswiet, L.A.: Fromlife cycle assessment to sustainable production: status and perspectives. CIRP Ann. Manuf. Technol. 54(2), 1–21 (2005) 8. Frank, A.G., Dalenogare, L.S., Ayala, N.F.: Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 210, 15–26 (2019) 9. Yue, L., Wang, L., Niu, P., Zheng, N.: Building a reference model for a Manufacturing Execution System (MES) platform in an Industry 4.0 context. J. Phys. Conf. Ser. 1345(6), 062002 (2019) 10. Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., Barata, J.: Digital transformation of manufacturing through cloud services and resource virtualization. Comput. Ind. 108, 150–162 (2019) 11. Linhares, T.B., Pereira, A.F.: Sustainable buildings and interior design. Blucher Design Proceed. 3(6), 82–87 (2017) 12. Ferrari, A. M., Volpi, L., Settembre-Blundo, D., García-Muiña, F.E.: Dynamic life cycle assessment (LCA) integrating life cycle inventory (LCI) and enterprise resource planning (ERP) in an Industry 4.0 environment. J. Cleaner Product., 125314 (2020) 13. Meinrenken, C. J., Garvan, A.N., Lackner, K.S.: “Fast LCA” to apply life cycle methodologies and supply chain management at scale. In: 7th International Society for Industrial Ecology Biennial Conference (June 2013). http://isie2013.ulsan.ac.kr/data/ISIE2013_Proceeding.pdf 14. De Soete, W.: Towards a multidisciplinary approach on creating value: Sustainability through the supply chain and ERP systems. Systems 4(1), 16 (2016) 15. Hasan, M. S., Ebrahim, Z., Mahmood, W.W., Ab Rahman, M.N.: Sustainable-ERP system: a preliminary study on sustainability indicators. J. Adv. Manufact. Technol. (JAMT), 11(1 (1)), 61–74 (2017)

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16. Chofreh, A.G., Goni, F.A., Klemeš, J.J.: Evaluation of a framework for Sustainable Enterprise Resource Planning systems implementation. J. Clean. Prod. 190, 778–786 (2018) 17. https://moovago.com/blog/10-statistiques-sur-les-erp-2021/ 18. Eric, M.: Utilisation D’un Système ERP Pour Soutenir La Réalisation D’une ACV. Département de mathématiques et de génie industriel école polytechnique de Montréal (2010) 19. Enterprise Resource Planning (ERP). https://www.gartner.com/en/information-technology/ glossary/enterprise-resource-planning-erp 20. Van Nuijs, S.: Conduire avec succès la transformation de la joint-venture American Express Global Business Travel dans le cadre de l’implémentation du système ERP Cloud « Netsuite OneWorld » au sein des différentes entités européennes. Louvain School of Management, Université catholique de Louvain (2017) 21. Fatima, B., Sarah, A., Driss, A.: The Manufacturing Executing System instead of ERP as shop floor management. In: 2020 IEEE 13th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA) (2020) 22. Exforsys Inc. The Advantages and Disadvantages of ERP (2009). [Online] http:// www.exforsys.com/tutorials/erp/the-advantages-and-disadvantages-of-erp.html [Consulté le 10/10/2022] 23. Morgan, J., O’Donnell, G.E.: Cyber physical process monitoring systems. J. Intell. Manuf. 29(6), 1317–1328 (2015). https://doi.org/10.1007/s10845-015-1180-z 24. Sumner, M.: Enterprise Resource Planning, pp. 2–17, 116–118. Pearson, New Jersey (2005) 25. Costa, C.J., Ferreira, E., Bento, F., Aparicio, M.: Enterprise resource planning adoption and satisfaction determinants. Comput. Hum. Behav. 63, 659–671 (2016)

The Impact of Blockchain Technology and Business Intelligence on the Supply Chain Performance-Based Tracking Process Khadija El Fellah1(B) , Adil El Makrani2 , and Ikram El Azami2 1 Laboratory of Research in Informatics, FS, UIT, Kenitra, Morocco

[email protected] 2 Department of Informatics, Laboratory of Research in Informatics, FS, UIT, Kenitra, Morocco

Abstract. The holy grail of business intelligence is to gather and produce justin-time data, combine it with current data assets, channel it through on-demand modeling to identify critical operational and strategic activities, and immediately distribute valuable information across the organization. Since blockchain provides users with a mechanism to track the data that are stored in its encrypted ledger-based database, which is already more secure than any centralized database now in use, business intelligence can tremendously benefit from it. Not only does Blockchain improves the traceability, transparency, and trust of data shared throughout a shared network. However, it also brings cost savings through new efficiencies. In this article, we illustrate how integrating blockchain with business intelligence may enhance the performance of the supply chain, which requires gathering various sorts of data from various resources for their process by making real-time data analytics available. On the other hand, we discuss how customers can profit from tracking information to ensure that their items have not been tampered with. Keywords: Blockchain · Tracking · Supply chain · Business intelligence · Performance

1 Introduction Consumers increasingly request information about the origin of their products, and they need to be sure the products are not falsified. This was a major concern following many food scandals. Consumers have lost confidence in the food they purchase and are seeking more concrete information about its origins. A recently developed technology, referred to as “blockchain,” could potentially revolutionize the tracking of supplies [1]. Because of this media exposure, many people have at least heard of this technology. Many people consider the benefits of this technology to be that it is decentralized and secure. It also has no authority or owner that can be altered. This is why businesses are considering utilizing blockchain technology in their processes [2]. This technology was first used to create a digital currency, specifically the famous “Bitcoin”. Then, several other networks were created, including “Ethereum”, which, thanks to smart contracts, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 845–854, 2023. https://doi.org/10.1007/978-3-031-29857-8_84

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has expanded the possibilities of use in many areas of activity such as pharmaceuticals [3], food [4], and energy. In the latter one, researchers have focused on developing MPPT (Maximum Power Point Tracking) technology for efficient energy harvesting [5], such as solar photovoltaic energy, which is a decentralized renewable energy grid aimed at integrating renewable energy into the energy industry [6]. Since the blockchain system defines the collection of raw data, and stores it in an immutable ledger, we suggest taking advantage of those features to perform business intelligence in supply chain management to highlight the impact of blockchain technology and business intelligence on the supply chain performance-based tracking process. We will discuss the proposed idea in more detail in the following sections.

2 Methodology To carry out our work, we adopt the following methodology: The first part will address the definition of the essential concepts connected to blockchain technology (BCT), supply chain, tracking, and business intelligence (BI), while the second part will address the information gathering behind that conceptual model. We emphasize the importance of blockchain in corporate analytics and supply chains. Furthermore, we outline examples of blockchain application in several sectors so that you can see how it impacts supply chain performance. Then we wrap up by talking about the outcomes of that work (Fig. 1).

Fig. 1. Conceptual Research Model (Source: Prepared by the authors)

3 Literature Review 3.1 Blockchain A blockchain is a fully decentralized system based on a peer-to-peer network. Each object in the network keeps a copy of the ledger to avoid having a single point of failure. All copies are updated and validated simultaneously. The initial objective of the creation of the blockchain was the resolution of the problem of multiple crypto-currencies (virtual money). This technology can be explored in many use cases and used as a secure way to manage and protect any kind of data (monetary or not). The ledger is composed of a set of blocks. Each block contains two parts. The first part represents the body of the block. It contains the transactions, also called facts, that the

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database must record. These facts can be monetary transactions, medical data, industrial information, system logs, etc. The second part is the header of the block. This contains information about the block, such as the timestamp and the hash of the transactions. In addition, the hash of the previous block. Thus, all the existing blocks form a chain of linked and ordered blocks. The longer the chain is, the more difficult it is to falsify. Indeed, if a malicious user wants to modify or exchange a transaction on a block, he must modify all the following blocks since they are linked by their hashes. Then, he has to change the version of the blockchain that each participating object stores. Blockchain technology is mainly characterized by six major elements: decentralized (no central authority to make decisions), transparent (fully traceable and much easier to maintain, secure (all records in the BC are individually encrypted), immutable (permanent and unalterable network), autonomous (no central leadership), open source (anyone can be part of the BC network), and anonymous (a person’s identity is unknown and their actions are not traceable). Each new transaction that is made requires verification by a person who is a part of this network. As each network node verifies a search transaction in a blockchain block, its immutability increases. The blockchain has two different types of participating objects: the first can only read facts (passive mode), while the second can read and write facts (active mode) called miners. Miners are tasked with creating new blocks on the chain through a process called mining. Mining is the process used to create new blocks by solving increasingly difficult computational puzzles; in exchange, they are rewarded with tokens or bitcoins. To prove the honest validation of a block, there are many validation mechanisms. The most commonly used mechanisms are the proof of work (PoW) mechanism and the proof of stake (PoS) mechanism [7]. There are three types of blockchains: the first type is public blockchain which is highly accessible; the second type is private blockchain, which is restricted to a small number of users; and the last type is permissioned blockchain, also known as consortium blockchain, where only a few predefined nodes are invited to participate and all transactions are public [7]. Finally, for complex interactions on the blockchain rather than just storing or transferring value, the idea of a smart contract comes into the picture. A smart contract is a software that executes predefined conditions on each of the nodes of a blockchain network, and the contract is verified automatically [8] without using a third party. If the conditions of the contract are met, the contract will be executed. As long as smart contracts are stored in a blockchain, they are immutable which means that once a smart contract is created, it cannot be modified. These contracts are also distributed, as the result is validated by all the participants of the blockchain network, so in case someone tries to act on the smart contract, the other people will be informed, and the action will not be validated [9]. 3.2 Supply Chain and Logistics Supply chain and logistics exist in all areas of activity, whether as part of a company or as its main activity. A supply chain, also known as a logistics chain, consists of a

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series of activities, methods, processes, and actors involved in the journey of goods from raw materials to the sale of finished products. The main goal of a supply chain is to optimize ordering, production, and delivery processes to create a competitive advantage and best meet customer expectations: “A supply chain is the integration of vital business processes from the end customer to the original supplier to provide customers and other stakeholders Products, services and information that add value.” [10]. The supply chain is made up of three flows: the information flow, which refers to all the information that circulates in the supply chain. This flow contains for example the characteristics of a product, information related to suppliers, supply strategies such as lead times and prices, information related to service providers (customs), sales history, etc. Next, we find the physical flow that concerns all the movements of the goods, storage, and transport of the goods. Finally, there is the financial flow, which concerns the movement of funds around suppliers and within the company, Proper coordination of these three flows will ensure that customer needs are met; Key participants in the supply chain include suppliers, subcontractors, manufacturing units, assembly units, distributors, logistics providers, customers, and end customers [10] (Fig. 2).

Fig. 2. Supply chain Diagram (Source: Supply Chain Management, 2019, p.33 [10])

Logistics is a part of the supply chain that consists of controlling the physical and information flows in a company, thanks to available IT resources, methods, and processes. Logistics is essentially focused on bringing finished products to their final consumer at the lowest cost and with the best quality. Among the activities concerning logistics, we will find the reception, storage, preparation, and shipment of products. Logistics is the “technology of controlling the physical flow of materials and goods transferred within the supply chain between several industrial, commercial and service companies”. 3.3 Tracking According to Rousse, traceability is defined as the “Possibility of following a product at its production, transformation, and marketing, especially in the food industry” (Rousse definition). The most widely used techniques for tracking the process in real time are as follows: • Internet of Things (IoT): Asset-tracking IoT solutions are systems used to track the location and status of valuable assets in real time using sensors and other IoT devices. [11].

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• RFID: RFID enables the identification of objects utilizing radio waves without the requirement for direct physical or visual contact. Memory use and identification processing time are crucial factors in the identification process [12]. • GSM framework: Within intricate networks, GSM trackers provide exact insights into delivery, handover, sorting, and logistics activities [13]. 3.4 Business Intelligence The term “business intelligence” (BI) has different meanings in different fields. From a technical perspective, BI refers to the process of extracting, transforming, managing, and analyzing business data to support decision-making. This process is primarily based on large data sets, including data warehouses, to disseminate information or knowledge throughout the organization, from strategic to tactical and operational levels [14]. Business intelligence improves supply chain management by making real-time data analytics accessible. The business data that are extracted must be affordable and safe. Due to several benefits of blockchain technology, such as low cost, perfect security, and high-level automation, blockchain technology is currently the driving force behind the growth of business intelligence. 3.5 Blockchain in the Supply Chain Blockchain has many advantages in the supply chain. Indeed, this technology can be applied in several activities of the supply chain, and the applications that would present a major interest would be the traceability of a product since its manufacture [15]. Supply chains will benefit from the immutability and transparency of the blockchain. In a blockchain-based supply chain, suppliers and traders are miners. When the product status is updated, miners send new transactions with other miners. Validation of transactions must be checked by everyone involved in the product lifecycle [9].

Fig. 3. Traditional supply chain vs. supply chain using blockchain (Source: CHOWDHURY Mohammad et COLMAN Alan, 2018) [16])

All information shared between parties is recorded on a distributed ledger, as seen in the graphic above (Fig. 3), which contrasts the representation of interactions in a conventional supply chain on the left with a blockchain implementation on the right. Transactions can only be accessed by network users, such as suppliers, manufacturers, assembly facilities, and distribution centers [16].

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3.6 Blockchain in Business Intelligence Historical data are crucial to creating long-term decisions thanks to their key role in strategic analysis. By gathering historical data, an operational organization can perform accurate analyses and draw more profitable business intelligence. Since businesses need real-time data to run their operations, blockchain technology provides instant access to huge amounts of information. This makes it an ideal tool for chasing business intelligence. Some of the advantages of using blockchain for this are low cost, perfect security, and automation of collecting data [8]. In the case of supply chains, users can use smart contracts to filter out all irrelevant data from blocks and perform quantitative analysis by removing all irrelevant data. Smart contracts store information related to the progress or current state of the supply chain. Then turns these data into analytics and business intelligence reports. In this case, the blockchain system defines the data model, collects raw data, stores it in an immutable block, and finally helps perform BI by executing one or more carefully crafted smart contracts, and finally converts these raw data into analytical reports and profitable BI. In this case, using Ethereum’s public blockchain allows for the seamless authorization of properties from multiple sources. It ensures that authorization data are immutable and helps build trust between everyone involved in the product lifecycle [8]. The possibility of using blockchain to improve the process of business intelligence appeared with the help of the Delivchain Framework, which is proposed to address the drawbacks of the traditional OTIF (on time in full) model, which is the most widely used metric for delivery performance in supply chain management. It is also used to solve the lack of transparency and trust problem in traditional SCM [8]. The capability of DelivChain is used to gather all the information about the latest status or progress throughout the supply chain and then to convert that raw information into analytical reports and valuable business intelligence [8]. 3.7 Blockchain Implementation Cases in Industries Here are some examples of industries that have implemented blockchain in their supply chains: Food Industry It should be noted that following food frauds that have caused scandals such as that of 2013 relating to a retailer who sold beef containing horsemeat, some companies in the sector have implemented blockchain technology, with the aim of traceability of food products, so that the brands become transparent. In addition, the implementation of the blockchain prevents any modification or deletion of information concerning the stages of manufacture from production to sale, so the monitoring of data in the blockchain is done in an encrypted manner that promotes transparency between the actors in the chain and increases the security of the data, taking the example of TE FOOD, IBM Food Trust, Provenance and Ambrosus [10]. Pharmaceutical Industry The pharmaceutical supply chain aims to combat counterfeit medicines. According to

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research conducted by the OMS in 2017, 10% of medicines in circulation in low-and middle-income countries are of inferior quality or falsified. The objective of implementing blockchain in clinical trials is to promote data and traceability and to fight against the modification of the information collected. In addition, another technology combined with blockchain is the Internet of Things (IoT), which aims to collect patient data. Adding that the traceability and immutability of the blockchain would allow the pharmaceutical industry to record a digital trail of every action performed on the drugs from manufacturing to distribution, which will allow the actors of the pharmaceutical supply chain to verify the authenticity of the products. Thus, Blockpharma registers each box of medicine produced by a laboratory in a chain of private blocks and tracks these boxes of medicine throughout the supply chain cycle. This company, therefore, offers an application to its consumers to scan the QR code of a box of medicine from their smartphone and instantly have on-screen all the information on the product, which allows the consumer to verify the authenticity of the drug [10].

4 Discussion and Results The movement of products is controlled by a complex system known as the supply chain. Currently, transparency, relatively low transaction costs, and immediate applications of technology might help product security. The blockchain’s robust and decentralized capability is typically utilized for financial systems, but it may be easily extended to contracts and other processes such as supply chain tracking. Business intelligence software offers several benefits to different organizations. In addition to improving performance, increasing efficiency, planning resources, and forming relationships with suppliers and buyers, research shows that implementing BI systems can reduce costs and give businesses an advantage over competitors. This is because they can use tracking and tracing tools to collect data from multiple sources without having to rely on multiple spreadsheets. These tools include tracking KPIs and supply chain-related goals. BI software provides teams with reports and dashboards to help them visualize their supply chains. This software also allows teams to track goals, such as key performance indicators, using blockchain-based tracking. This guarantees data confidentiality and trust through an immutable ledger of transactions. Despite the range of opportunities that the adoption of blockchain technology brings to the industry, the shortcomings of blockchain are still worthy of discussion. We discuss 3 challenges that the industry currently faces when integrating blockchain into the existing SCM system and that are urgently awaiting a solution to facilitate large-scale adoption of the technology [8]. • Scalability: Once a blockchain system has been implemented, the overall number of transactions will significantly increase. Each network participant must maintain an independent copy of the distributed registry due to the distributed registry’s immutability to verify transactions and extract new blocks, resulting in an unavoidable data redistribution [8].

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• Performance: The advancement of Internet of Things (IoT) technology is responsible for the success of digital transformation in SCM. It was capable of gathering various types of data and uploading it to the network in a controlled manner. The most recent IoT technology offers industry users a lightweight deployment option at a cheaper cost and reduced power consumption compared to conventional programmed devices. However, one of the major obstacles to blockchain integration is comparably poor performance [8]. • Privacy: Key pairs used in blockchain systems are used to identify participants. In a distributed network, other users cannot immediately determine their real identity by reading the ledger. However, anonymity does not imply untraceability. The traditional blockchain was unable to completely protect user privacy since it is still possible to determine the identity by looking at one or more fixed transaction patterns from the ledger [8]. Finally, industrial organizations need a powerful application and systematic system capable of operating in real time, providing insightful tracking of supply chains, logistics, and operations that are closely linked to sales tracking applications, hourly, daily, and monthly production tracking, financial data tracking and many other business data sources for business performance management purposes. Combining technologies such as RFID and IOT can achieve that kind of real-time tracking data.

5 Conclusion According to several academics, blockchain technology is positioned as a future technology in multiple industries, as it provides benefits to users while trying to advance their business operations regardless of the industry. If blockchain has thus far succeeded in bringing together numerous stakeholders (participants or users), this will be their greatest challenge, not to mention security issues and blockchain’s ability to handle ever-increasing data volumes. Supply chain businesses can use blockchain to document production updates to a single shared ledger, providing total data visibility and a single source of truth. Companies can access a product’s status and location at any moment because transactions are constantly time-stamped and current. Therefore, problems such as fake goods, noncompliance, delays, and waste are lessened. Additionally, the ledger audit trail ensures regulatory compliance and allows for quick responses to situations (such as product recalls). Moreover, supply chains can automate the monitoring of the circumstances of production, transportation, and quality control by merging blockchain with intelligent technologies such as the Internet of Things. As a tool to confirm the legitimacy of products and moral supply chain procedures, businesses can also decide to share track and trace data with their clients. Developing a blockchain-based tracking system integrated with robust technology for tracking data from different resources such as IoT or RFID can help to mitigate the spread of falsified or modified data. And promotes trust, transparency, and traceability and streamlines the communication between stakeholders in the network. We suggest that these tracking data from blockchain-based tracking systems can be used in the business intelligence process. by executing one or more well-designed

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smart contracts and finally transforming these raw data into analytical reports, therefore it will affect the performance of the supply chain because, with the help of supply chain management dashboards, an organization can achieve their goals by tracking crucial KPIs (such as cash-to-cycle time, perfect order rate, customer order cycle time, and inventory turnover), digging into the key metrics with a detailed analysis in every widget, and identifying the risks in their process and mitigating those risks with action plans.

References 1. Biggs, J., Hinish, S.R., Natale, M.A., Patronick, M.: Blockchain: Revolutionizing the Global Supply Chain by Building Trust and Transparency 2. Zhang, Y., Zhang, C.: Improving the application of blockchain technology for financial security in supply chain integrated business intelligence. Secur. Commun. Netw. 2022, 1–8 (2022). https://doi.org/10.1155/2022/4980893 3. Abbas, K., Afaq, M., Ahmed Khan, T., Song, W.-C.: A blockchain and machine learningbased drug supply chain management and recommendation system for smart pharmaceutical industry. Electronics 9(5), 852 (2020). https://doi.org/10.3390/electronics9050852 4. Rejeb, A., Keogh, J.G., Zailani, S., Treiblmaier, H., Rejeb, K.: Blockchain technology in the food industry: a review of potentials, challenges and future research directions. Logistics 4(4), 27 (2020). https://doi.org/10.3390/logistics4040027 5. Narasipuram, R.P., Somu, C.: Efficiency analysis of maximum power point tracking techniques for photovoltaic systems under variable conditions 6. Oyekola, A.: Decentralized Solar Photovoltaic Distributed Generation Integrated With Blockchain Technology: A Case Study In 7. Khettry, A.R., Patil, K.R., Basavaraju, A.C.: A detailed review on blockchain and its applications. SN Comput. Sci. 2(1), 1–9 (2021). https://doi.org/10.1007/s42979-020-00366-x 8. Meng, M.H., Qian, Y.: The blockchain application in supply chain management: opportunities, challenges and outlook, EasyChair, EasyChair Preprints (Oct 2018). https://doi.org/10.29007/ cvlj 9. Su, S., Wang, K., Kim, H.S.: Smartsupply: Smart contract based validation for supply chain blockchain. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Halifax, NS, Canada, pp. 988–993 (juill. 2018). https://doi.org/10.1109/Cybermatics_2018.2018.00186 10. Juré, C.: Quel est le rôle de la technologie Blockchain dans la Logistique et la Supply Chain ? : le cas d’une entreprise de logistique à Genève, p. 63 11. Ramson, S.R.J., Vishnu, S., Shanmugam, M.: Applications of Internet of Things (IoT) – An Overview. In: 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India, pp. 92–95 (mars 2020). https://doi.org/10.1109/ICDCS48716. 2020.243556 12. Radovi´c. I., Baˇcanin Džakula, N.: Blockchain Application in Rfid Domain. In: Proceedings of the International Scientific Conference - Sinteza 2021, Beograd, Serbia, pp. 333–337 (2021). https://doi.org/10.15308/Sinteza-2021-333-337 13. Hilger, N.: 5 good reasons to use live GSM trackers. Spectos (17 avril 2019). https://www. spectos.com/en/supply-chain-monitoring-live-gsm-trackers/ (consulté le 27 octobre 2022) 14. Herschel, R.T., Jones, N.E.: Knowledge management and business intelligence: the importance of integration. J. Knowl. Manag. 9(4), 45–55 (2005). https://doi.org/10.1108/136732 70510610323

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15. Caro, M.P., Ali, M.S., Vecchio, M., Giaffreda, R.: Blockchain-based traceability in Agri-Food supply chain management: A practical implementation. In: IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany). Tuscany 2018, pp. 1–4 (mai 2018). https://doi.org/ 10.1109/IOT-TUSCANY.2018.8373021 16. Chowdhury, M.J.M., Colman, A., Kabir, M.A., Han, J., Sarda, P.: Blockchain versus database: a critical analysis. In: 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), New York, USA, août 2018, pp. 1348–1353 (2018). https://doi.org/10.1109/TrustCom/BigDataSE.2018.00186

Design of an Integral Sliding Mode Control Based on Reaching Law for Trajectory Tracking of a Quadrotor System Mouna Lhayani(B) , Ahmed Abbou, Yassine El Houm, and Mohammed Maaroufi Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco {mounalhayani,yassineelhoum}@research.emi.ac.ma, {abbou, maaroufi}@emi.ac.ma

Abstract. To stabilize the trajectory of an unmanned aerial vehicle (UAV) quadrotor system, a nonlinear adaptive integral sliding mode controller based on the Reaching law is proposed in this paper. First, the mathematical model of the quadrotor was developed using the Newton-Euler formalism, and then the design of an ISMC with three different types of reaching laws (reaching law with constant rate, exponential reaching law, and reaching law with power rate) and an analysis of their properties were investigated in this work. The proposed controllers’ performances were evaluated using the MATLAB/Simulation environment. The simulation results indicate that the ISMC-based exponential reaching law ensures fast convergence of the sliding manifold and attenuates the chattering phenomena in the sliding phase. Keywords: Quadrotor · Integral sliding mode controller · Chattering · Reaching law

1 Introduction An unmanned aerial vehicle, or drone, is a type of aircraft that can fly without the presence of a pilot and is able to complete a mission in the military and civilian sectors. These vehicles are used in a wide range of real-world applications, including aerial photography and videography, surveillance and package delivery [1]. The utilization of UAVs in any of these application areas necessitates the planning of optimal trajectories; therefore, the problem of developing a control strategy allowing UAVs to realize autonomous flights following preprogrammed trajectories has attracted the interest of researchers around the world in recent years. The main type of multirotor unmanned aerial vehicle studied in the control literature is quadcopters. Due to their simple mechanical structure, ease of manufacturing, and low maintenance costs, they provide an excellent platform for testing and validating different control technologies. However, the high nonlinearity of its dynamics, coupled dynamics properties, unknown parameter uncertainties, external disturbances, and unmodeled dynamics make quadcopter trajectory tracking a challenging task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 855–864, 2023. https://doi.org/10.1007/978-3-031-29857-8_85

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Quadrotor position and attitude stabilization have been addressed using different control technologies, including proportional integral derivative (PID) [2], linear quadratic regulator (LQR) [3], feedback linearization control [4] and back-stepping control [5]. Among these technologies, sliding mode control (SMC), a very popular nonlinear control technology, has been demonstrated to be one of the most prospering strategies due to its accuracy, robustness against uncertainties and disturbances and finite-time convergence [6]. The development of a nonlinear controller based on SMC for quadrotor trajectory tracking has received increasing attention. The chattering phenomena are considered the main disadvantage of SMC. It can degrade the tracking performance of the quadcopter controller. To attenuate this phenomenon and enhance the controller’s tracking performance in the presence of time-varying disturbances, the authors in [7] propose a nonlinear fast sliding mode controller combined with a modified supertwisting algorithm. In the same context, the work developed in [8] proposes adaptive backstepping sliding mode control to address the quadcopter path tracking control problem based on actuator faults and external disturbances. The study presented in [9] addresses the problems of modeling and control of the quadrotor with time-varying mass and external disturbances. A mathematical model with a time-varying mass was developed first, and then, based on sliding mode theory, a robust control scheme was designed. The proposed controller is capable of rejecting external disturbances as well as uncertainties caused by the time-varying mass. The authors in [10] suggest implementing multichannel time-varying sliding mode control to control the attitude of a quadcopter system for nuclear decommissioning applications. The parameters of the investigated UAV are always time-varying and uncertain since they are imposed by constant radiation. The authors in [11] developed a fast terminal sliding mode controller (FTSMC) for the control and stabilization of an unmanned aerial vehicle’s trajectory in the presence of input saturation and external disturbances, which are estimated using a finite-time nonlinear disturbance observer (NDO). The authors in [12] propose to use a neural network-based SMC to stabilize the attitude and position of a quadcopter in the presence of external disturbances. In this proposed control scheme, the SMC gains are tuned using the backpropagation rule, and the external disturbances are estimated and compensated through a disturbance observer. In this paper, an integral sliding mode controller was proposed for quadrotor trajectory tracking. The proposed controller was designed, and an integral sliding surface was chosen for the attitude and position subsystems. To enhance the robustness of the controller and ensure fast convergence of the sliding manifold while minimizing chattering phenomena, reaching control laws were added to the equivalent control laws obtained using ISMC for position and attitude subsystems, and a comparative study of the three most popular kinds of reaching laws, reaching law with constant rate, reaching law with power rate, and exponential reaching law, was investigated in our study. The remainder of this paper is structured as follows: The quadcopter mathematical model is presented in Sect. 2. The controller design is given in Sect. 3. Simulation results are presented in Sect. 4. Finally, Sect. 5 concludes the paper.

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2 Quadrotor Dynamics The mathematical model of the quadrotor system based on the Newton-Euler formalism is developed in this section. Quadrotor system is a rigid body and has six degrees of freedom.

Fig. 1. A structure diagram of the quadrotor

To obtain the dynamic model of the quadrotor, two frames are established, as illustrated in Fig. 1, the body frame {B} and the earth frame {E}, which are represented by  T (OB , B, Xb , Yb , Zb ). (OE , E, Xe , Ye , Ze ) Assume that the vectors ξ = x, y, z and η = [φ, θ, ψ]T represent the position and attitude orientation of the quadrotor expressed in E, where (φ, θ, ψ) are the Euler angles known as yaw, pitch, and roll. Based on the work developed in [7], the dynamic equations of a quadrotor system in the presence of time-varying disturbances are expressed as: ⎧   ˙ − k4 φ˙ 2 + duφ + dφ (t) ˙ Jy − Jz − Jz θω ⎪ φ¨ = J1x θ˙ ψ ⎪ ⎪  ⎪ ⎪ ˙ z − Jx ) − Jr φω ˙ − k5 θ˙ 2 + duθ + dθ (t) θ¨ = J1y φ˙ ψ(J ⎪ ⎪ ⎪   ⎨¨ ˙ J x − Jy − k 5 ψ ˙ 2 + fuψ + dψ (t) ψ = J1z θ˙ ψ (1) dx (t) k1 1 ⎪ ⎪ ⎪ x¨ = m (sin θ cos φ cos ψ + sin ψ sin φ)um − m x˙ + d m(t) ⎪ y ⎪ k 1 ⎪ ⎪ y¨ = m (sin θ cos φ sin ψ + sin φ cos ψ)um − m2 y˙ + m ⎪ ⎩ d (t) k z¨ = −g + m1 (cos θ cos φ)um − m3 z˙ + zm where Ji (i = x, y, z, r) ∈ R+ represents the moment of inertia, ki for (i = 1, 2, 3, 4, 5, 6) represents the positive aerodynamic constants, di (t) ∈ R for i = (x, y, z, φ, θ, ψ) The external disturbances, ui (i = m, φ, θ, ψ) corresponds to the control inputs, d, f, g the arm length of the vehicle, the scaling factor from force to moment, and the gravitational acceleration, respectively [7].

3 Quadrotor Position and Attitude Controller Design In this part, the proposed controllers are designed. The proposed controllers are used ˙ ψ, ψ) ˙ follows the desired ˙ θ, θ, to ensure that each state variable (x, x˙ , y, y˙ , z, z˙ , φ, φ, ˙ d ) in a short finite time. trajectory (xd , x˙ d , yd , y˙ d , zd , z˙ d , φd , φ˙ d , θd , θ˙ d , ψd , ψ

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Fig. 2. Block diagram for quadrotor control structure

A quadrotor has four control inputs and has to control six states. As shown in Fig. 2, which depicts the strategy adopted to control the quadcopter, the proposed methodology is a double loop control. The outer loop (the position loop) generates virtual controllers using the proposed approach for calculating tilting angles (φd , θd ) and total thrust um , whereas the inner loop (the attitude loop) is used to calculate pitching, yawing, and rolling torques uφ , uθ , uψ . First, consider the following tracking errors of position and attitude: ⎧ ⎨ eφ = φ − φd (2) e = θ − θd ⎩ θ e ψ = ψ − ψd ⎧ ⎨ ex = x − xd (3) e = y − yd ⎩ y ez = z − zd The PID sliding surfaces are given as ⎧ ⎨ sφ = λpφ eφ + λdφ e˙ φ + λφI ∫ eφ dt s = λpθ eθ + λdθ e˙ θ + λθI ∫ eθ dt ⎩ θ sψ = λpψ eψ + λdψ e˙ ψ + λψI ∫ eψ dt ⎧ ⎨ sx = λpx ex + λdx e˙ x + λxI ∫ ex dt s = λpy ey + λdy e˙ y + λyI ∫ ey dt ⎩ y sz = λpz ez + λdz e˙ z + λzI ∫ ez dt

(4)

(5)

where λpj , λdj , and λij for j = (x, y, z, φ, θ, ψ) are positive parameters to be chosen. Taking the time derivatives of sj , one obtains



⎧ 1 ˙ ˙ 2 ⎪ ˙ ¨ ˙ ˙ ⎪ Sφ = λpφ e˙ φ + λdφ θψ Jy − Jz − JI θω − k4 φ + duφ + dφ (t) − φd + λφI eφ ⎪ ⎪ Jx ⎪ ⎪ ⎪

⎨  ˙ z − Jx ) − Jr φω ˙ − k5 θ˙ 2 + duθ + dθ (t) − θ¨ d + λθI eθ ˙Sθ = λpθ e˙ θ + λdθ 1 φ˙ ψ(J ⎪ Jy ⎪ ⎪



⎪ ⎪ 1 ˙ ˙ ⎪ ⎪ ¨ d + λψI eψ ˙ 2 + fuψ + dψ (t) − ψ ⎩ S˙ ψ = λpψ e˙ ψ + λdψ θφ Jx − Jy − k6 ψ Jz (6)

Design of an Integral Sliding Mode Control

⎧  dx (t) ⎪ s ˙ v ¨ d + λxI ex = λ e ˙ + λ − x ˙ + ⎪ x px x dx x ⎪ m −x ⎨  dy (t) s˙y = λpy e˙ y + λdy vy − y˙ + m − y¨ d + λyI ey ⎪  ⎪ ⎪ ⎩ s˙z = λpz e˙ z + λdz vz − z˙ + dz (t) − z¨ d + λzI ez m

859

(7)

Assuming that the time derivative of each sliding surface is equal to 0 s˙i = 0 and considering that di = 0, then. The equivalent control laws for the inner loop (attitude loop) are given as:

⎧ Jx ¨ 1  1 ˙ ˙  2 ⎪ ˙ ˙ ⎪ u θω θ ψ J = − e ˙ + λ e − J − k λ − − J φ φ pφ φ φI φ y z r 4 d ⎪ ⎪ φ,eq d λdφ Jx ⎪ ⎪ ⎪

⎨ Jy 1 ˙ ˙ 1  ˙ − k5 θ˙ 2 θ¨ d − uθ,eq = λpθ e˙ θ + λθI eθ − φψ(Jz − Jx ) − Jr φω (8) ⎪ d λdθ Jy ⎪ ⎪

⎪ ⎪ 1  Jz ¨ 1  ⎪ 2 ⎪u ˙ ˙ ˙ ⎩ θφ Jx − Jy − k6 ψ λpψ e˙ ψ + λψI eψ − ψd − ψ,eq = f λdψ Jz The equivalent control laws for the outer loop (position loop) are given as: ⎧  k1 1 ⎪ ⎨ vx,eq = m x˙ + x¨ d − λdx λpx e˙ x + λxI ex vy,eq = km2 y˙ + y¨ d − λ1dy λpy e˙ y + λyI ey ⎪  ⎩ vz,eq = km3 z˙ + z¨ d − λ1dz λpz e˙ z + λzI ez

(9)

To enhance the controller robustness and avoid the chattering phenomenon, a discontinuous component us (reaching law) is added to the equivalent control laws. 3.1 Reaching Law with a Constant Rate In the case of ISMC-CR, the discontinuous component us is given by Eq. (10). us = − ks sin(s), ks > 0

(10)

By adding us to the equivalent control laws, the total control laws for the inner loop (attitude loop) are given as:

⎧  Jx ¨ 1  1 ˙ ˙  ⎪ ˙ − k4 φ˙ 2 ⎪ φd − λpφ e˙ φ + λφI eφ − kφS sin sφ − θψ Jy − Jz − Jr θω ⎪ uφ = ⎪ d λdφ Jx ⎪ ⎪ ⎪

⎪  ⎨  Jy 1 1 ˙ z − Jx ) − Jr φω ˙ − k5 θ˙ 2 φ˙ ψ(J λpθ e˙ θ + λθI eθ − kθs sin(sθ ) − uθ = θ¨ d − ⎪ d λdθ Jy ⎪ ⎪

⎪ ⎪  ⎪ 1 ˙ ˙  Jz ¨ 1  ⎪ ˙2 ⎪ ψd − λpψ e˙ ψ + λψI eψ − kψs sin sψ − θφ Jx − Jy − k6 ψ ⎩ uψ = f λdψ Jz

The total control laws for the outer loop (position loop) are given as: ⎧  k1 1 ⎪ x) ⎨ vx = m x˙ + x¨ d − λdx λpx e˙ x + λxI ex − kxs sin(s  vy = km2 y˙ + y¨ d − λ1dy λpy e˙ y + λyI ey − kys sin sy ⎪  ⎩ vz = km3 z˙ + z¨ d − λ1dz λpz e˙ z + λzI ez − kzs sin(sz )

(11)

(12)

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3.2 Exponential Reaching Law In the case of ISMC-EXPO, the discontinuous component us is given by Eq. (13). us = −ks sin(s) − bs s , ks > 0 bs > 0

(13)

By adding us to the equivalent control laws, the total control laws for the inner loop (attitude loop) are given as:

⎧  Jx ¨ 1  1 ˙ ˙  ⎪ ˙ − k4 φ˙ 2 ⎪ φd − λpφ e˙ φ + λφI eφ − kφs sin sφ − bφs sφ − θψ Jy − Jz − JI θω uφ = ⎪ ⎪ d λdφ Jx ⎪ ⎪ ⎪

⎪  ⎨  Jy 1 1 ˙ z − Jx ) − Jr φω ˙ − k5 θ˙ 2 φ˙ ψ(J λpθ e˙ θ + λθI eθ − kθs sin(sθ ) − bθs sθ − uθ = θ¨ d − ⎪ d λdθ Jy ⎪ ⎪

⎪ ⎪   ⎪ 1 ˙ ˙  J 1 z ⎪ ¨d− ˙2 ⎪ ψ λpψ e˙ ψ + λψI eψ − kψs sin sψ − bψs sψ − θφ Jx − Jy − k6 ψ ⎩ uψ = f λdψ Jz

(14) The total control laws for the outer loop (position loop) are given as: ⎧ k1 1  ⎪ vx = x˙ + x¨ d − λpx e˙ x + λxI ex − kxs sin(sx ) − bxs sx ⎪ ⎪ ⎪ m λdx ⎪ ⎪ ⎨  k2 1  vy = y˙ + y¨ d − λpy e˙ y + λyl ey − kys sin sy − bys sy m λdy ⎪ ⎪ ⎪ ⎪ ⎪  k ⎪ ⎩ vz = 3 z˙ + z¨ d − 1 λpz e˙ z + λzI ez − kzs sin(sz ) − bzs sz m λdz

(15)

3.3 Reaching Law with Power Rate In the case of ISMC-PR, the discontinuous component us is given by Eq. (16). us = −ks |s|α sin(s), ks > 0, 1 > α > 0

(16)

By adding us to the equivalent control laws, the total control laws for the inner loop (attitude loop) are given as:

⎧  α  1 ˙ ˙  Jx ¨ 1  ⎪ ˙ − k4 φ˙ 2 ⎪ φd − λpφ φ˙ φ + λφI Iφ − kφs sφ  φ sin sφ − θψ Jy − Jz − Jr θω uφ = ⎪ ⎪ d λdφ Jx ⎪ ⎪ ⎪

⎪  ⎨  Jy 1 1 ˙ z − Jx ) − Jr φω ˙ − k5 θ˙ 2 φ˙ ψ(J λpθ e˙ θ + λθI eθ − kθs |sθ |αθ sin(sθ ) − uθ = θ¨ d − ⎪ d λdθ Jy ⎪ ⎪

⎪ ⎪     ⎪ J 1 1 ˙ ˙  z α ⎪ ¨d− ˙2 ⎪ ψ λpψ e˙ ψ + λψI eψ − kψs sψ  ψ sin sψ − θφ Jx − Jy − k6 ψ ⎩ uψ = f λdψ Jz

(17) The total control laws for the outer loop (position loop) are given as: ⎧ k1 1  ⎪ vx = x˙ + x¨ d − λpx e˙ x + λxI ex − kxs |sx |αx sin(sx ) ⎪ ⎪ ⎪ m λdx ⎪ ⎪ ⎨  α  k2 1  λpy e˙ y + λyI ey − kys sy  y sin sy vy = y˙ + y¨ d − m λdy ⎪ ⎪ ⎪ ⎪ ⎪  k ⎪ 3 ⎩ vz = z˙ + z¨ d − 1 λpz e˙ z + λzI ez − kzs |sz |αz sin(sz ) m λdz

(18)

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Table 1. Quadrotor parameters Parameter g(m/s2 ) m(kg) Jx (kg.m2 ) Jy (kg.m2 ) Jz (kg.m2 ) Jr (kg.m2 )

Value

Parameter

Value

9.81

k1 (N/m/s)

5.5670e−4

0.486

k2 (N/m/s)

5.5670e−4

3.8278e−3

k3 (N/m/s)

6.3540e−4

3.8278e−3

k4 (N/m/s)

5.5670e−4

7.6566e−3

k5 (N/m/s)

5.5670e−4

2.8385e−5

k6 (N/m/s)

6.3540e−4

Table 2. Controller parameters ISMC base on Reaching law with constant rate

ISMC base on Exponential reaching law

ISMC base on Reaching law with power rate

λpφ , λpθ , λpψ 10,10,2

λpφ , λpθ , λpψ 10,10,10

λpφ , λpθ , λpψ 10,10,10

λpx , λpy , λpz

λpx , λpy , λpz

λpx , λpy , λpz

2,3,1

2,3,1

2,3,3

λdφ , λdθ , λdψ 2,2,1

λdφ , λdθ , λdψ 1,1,1

λdφ , λdθ , λdψ 1,1,1

λdx , λdy , λdz

1,1,1

λdx , λdy , λdz

1,1,1

λdx , λdy , λdz

1,1,1

λIφ , λIθ , λIψ

0.07,0.03,0.03 λIφ , λIθ , λIψ

0.001,0.001, 0.001

λIφ , λIθ , λIψ

0.02,0.02,0.01

λIx , λIy, λIz

0.03,0.01,0.07 λIx , λIy, λIz

0.001,0.001, 0.005

λIx , λIy, λIz

0.01,0.01,0.05

kφs , kθs , kψs

10,10,1

kφs , kθs , kψs

0.2,0.2,0.2

kφs , kθs , kψs

10,10,10

kφs , kθs , kψs

1,1.5,1

kφs , kθs , kψs

0.005,0.5,0.01 kφs , kθs , kψs

bφs , bθs , bψs

15,15,15

αφs , αθs , αψs

0.9,0.9,0.9

bxs , bys , bzs

3,3,4.5

αxs , αys , αzs

0.8,0.8,0.8

3,3,3

4 Simulation and Results In this section, a simulation using the MATLAB/Simulink environment was performed to verify the effectiveness of the suggested controller and compare its performance by adding three kinds of reaching laws to the equivalent laws. In this comparative study, we will focus on two major points: tracking accuracy and chattering attenuation. The quadrotor parameters used in this simulation are listed in Table 1, while the parameters of the three simulated controllers obtained are presented in Table 2. The simulation results obtained are depicted in Figs. 3, 4, 5, 6, 7 and 8. Figures 3 and 4 show that the ISMC-Exponential reaching law (ISMC-EXPO) shows good performance tracking in the position loop and attitude loop. From the results presented in Fig. 5, we can see the performance superiority of ISMC-EXPO in terms of precision and rapidity compared to ISMC-CR and ISMC-PR.

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In terms of chattering attenuation, the controllers’ input signals are presented in Figs. 6, 7 and 8. In Fig. 8, the input signals obtained using ISMC-PR exhibit low chattering dynamics compared to the results obtained using ISMC-CR. The control input signals depicted in Fig. 7 are characterized by a chattering-free smooth response.

Fig. 3. Quadrotor position tracking

Fig. 4. Quadrotor attitude tracking

Fig. 5. Quadrotor 3D trajectory tracking

Design of an Integral Sliding Mode Control

Fig. 6. Control inputs based on ISMC-CR

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Fig. 7. Control inputs based on ISMC-EXPO

Fig. 8. Control inputs based on ISMC-PR

5 Conclusion In this work, an integral sliding mode controller was suggested for the trajectory tracking of a quadrotor. The proposed controller was designed first with three different reaching laws: reaching law with constant rate, exponential reaching law, and reaching law with power rate. Then, its performance was evaluated by simulation using the MATLAB/SIMULINK environment. Simulation results presented previously show that the ISMC-based exponential reaching law has provided satisfactory performance in both accuracy and chattering attenuation. In reality, quadrotors are subject to external disturbances. In our future work, a state observer/disturbance observer will be integrated into the controller to estimate and handle undesirable external perturbations on UAVs.

References 1. Mahmoud, M.S., Oyedeji, M.O., Xia, Y.: Path planning in autonomous aerial vehicles. In: Advanced Distributed Consensus for Multiagent Systems. pp. 331–362. Academic Press (2021) 2. Najm, A.A., Ibraheem, I.K.: Nonlinear PID controller design for a 6-DOF UAV quadrotor system. Eng. Sci. Technol. Int. J. 22(4), 1087–1097 (2019) 3. Martins, L., Cardeira, C., Oliveira, P.: Linear quadratic regulator for trajectory tracking of a quadrotor. IFAC-Pap. 52(12), 176–181 (2019)

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4. Zhou, Q.-L., Zhang, Y., Rabbath, C.-A., Theilliol, D.: Design of feedback linearization control and reconfigurable control allocation with application to a quadrotor UAV. In: Conference on Control and Fault-Tolerant Systems (SysTol), pp. 371–376, October 2010 5. Koksal, N., An, H., Fidan, B.: Backstepping-based adaptive control of a quadrotor UAV with guaranteed tracking performance. ISA Trans. 105, 98–110 (2020) 6. Falcón, R., Ríos, H., Dzul, A.: Comparative analysis of continuous sliding-modes control strategies for quad-rotor robust tracking. Control Eng. Pract. 90, 241–256 (2019) 7. El Houm, Y., Abbou, A., Labbadi, M., Cherkaoui, M.: Optimal new sliding mode controller combined with modified supertwisting algorithm for a perturbed quadrotor UAV. Int. J. Aerosp. Eng. 2020, e9753870 (2020) 8. Huang, S., Huang, J., Cai, Z., Cui, H.: Adaptive backstepping sliding mode control for quadrotor UAV. Sci. Program. 2021, 1–13 (2021) 9. Wu, X., Xiao, B., Qu, Y.: Modeling and sliding mode-based attitude tracking control of a quadrotor UAV with time-varying mass. ISA Trans. 124, 436–443 (2022) 10. Nemati, H., Montazeri, A.: Analysis and design of a multi-channel time-varying sliding mode controller and its application in unmanned aerial vehicles. IFAC-Pap. 51(22), 244–249 (2018) 11. Huang, D., Huang, T., Qin, N., Li, Y., Yang, Y.: Finite-time control for a UAV system based on finite-time disturbance observer. Aerosp. Sci. Technol. 129, 107825 (2022) 12. Nguyen, N.P., Mung, N.X., Thanh, H.L.N.N., Huynh, T.T., Lam, N.T., Hong, S.K.: Adaptive sliding mode control for attitude and altitude system of a quadcopter UAV via neural network. IEEE Access 9, 40076–40085 (2021)

Product Family Formation for Reconfigurable Manufacturing Systems Chaymae Bahtat(B)

, Abdellah El Barkany, and Abdelouahhab Jabri

Mechanical Engineering Laboratory, Faculty of Science and Technology, Sidi Mohammed Ben Abdellah University, B. P. 2202 – Route d’Imouzzer, Fes, Morocco [email protected]

Abstract. Reconfigurable manufacturing systems, or RMS, are a new concept in production systems. Their goal is to assure the feasibility of numerous generations of products by making quick adjustments to existing systems in order to meet the increasing evolution of markets while maintaining a high quality of products at a low cost. An RMS is designed around a family of products with enough flexibility to manufacture them all. These products are grouped into families based on certain common characteristics, such as modularity, components, sequence of manufacturing operations, etc. These characteristics differ depending on the production system studied and the products that need to be integrated in order to have an optimal formation of product families. These characteristics differ according to the production system studied and the products that must be integrated in order to have an optimal formation of product families. With this objective, this paper presents a structured and updated literature review on product family formation for RMS, highlighting key application areas and tools. Through this literature review, we were able to construct a general clustering algorithm that can be applied to any production system. Keywords: Reconfigurable Manufacturing Systems · product family formation · clustering algorithm

1 Introduction In the literature, the concept of grouping products into families was first introduced by Group Technology (GT) [1]. Cellular Manufacturing (CM) is built on the notion of GT. Part family creation procedures utilized in CM (e.g., Classification and Coding Systems [2] and Production Flow Analysis (PFA) [3]) have given rise to methodologies specifically addressing product family development for RMS. The product family concept has been extended from multiple perspectives to support the development of new product families such as a family of services [4], an evolutionary family [5], a modular product family [6], a meta-product family [7], a universal product family [8], a sustainable product family [9], and an eco-product family [10]. It is important to note that there are rich and diverse studies on the design of product families for RMSs. However, to our knowledge, there is no document that gathers the different © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 865–872, 2023. https://doi.org/10.1007/978-3-031-29857-8_86

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points of view presented in this research axis. In this work, we first present an enriching bibliographical study on the formation of product families, and then we propose a hierarchical clustering algorithm that can be used in any production system. To do this, this research is carried out using different databases (Science Direct, Springer, etc.). After filtering the different documents provided according to their importance, more than 50 articles related to the design of product families were selected. The remainder of this paper is organized as follows: Sect. 2 introduces a generalization on RMSs, Sect. 3 presents product grouping methods and applications, Sect. 4 provides the algorithm for hierarchical clustering of products and the last section gives a perspective on the work performed.

2 Reconfigurable Manufacturing System According to [11–14], an RMS is a production system that allows the change and adaptation of the system’s hierarchical structures according to the production demand, while modifying the capacity, functionality and configuration. From [15, 11], an RMS provides adequate agility for a specific product family, and it will have an open architecture that can be enhanced, upgraded, and re-configured instead of exchanged. Per [16, 17, 13], an RMS is a production system that can be constructed by integrating fundamental modules (physical and logical) that can be reordered or changed out rapidly and safely. Reconfiguration allows for the addition, deletion, or modification of module capabilities, control systems, etc., in order to effectively respond to changing market conditions. For the design of an RMS, [21] mention that it is necessary to apply a long-term view of the production system, in order to ensure the economic feasibility of several product generations and market situations. In other words, the system should be designed to be changeable in terms of functionality and capacity. An RMS has certain key characteristics that allow a high degree of reactivity of the system to the requirements in the marketplace. According to [15, 18–20], these characteristics, six in number, must be incorporated into the reconfigurable system from the design phase to enable a high level of the reconfigurability. These features are: Customization, Convertibility, Scalability, Modularity, Integrability and Diagnosability.

3 Background and Related Work 3.1 Poduct Family Formation An RMS is designed around a family of products with enough flexibility to manufacture them all. These products are grouped into families based on certain shared characteristics such as modularity, sequence of manufacturing operations, etc. Each product family requires a system configuration to produce them, and switching from one product to another or more generally from one product family to another requires reconfiguring the system. According to [18], Designing around a family of products instead of a single specific product allows designers to plan a system that supports different variations of the same product family with minimal changes to the production system [21–23].

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3.2 Literature Review For more than a decade, including theory and practice have focused on the establishment of product families [24]. Different approaches might be taken to define what comprises a product family [25]. However, because the RMS is based on product families to decrease flexibility and promote efficiency, product families must be designed alongside the production system [26]. Three issues have been raised in the literature in this regard: grouping items into product families, developing the matching ideal configuration, and adjusting system configurations to changing product families [27, 28]. Several techniques to addressing these challenges are available in the literature. Table 1 highlights the many publications on product family design found in the literature. Table 1. Review on product family formation. Reference

Year

Keywords

[29]

2000

commonality index of components

[30]

2000

the component communality and process communality

[31]

2002

customer needs

[25, 32]

2004; 2007

Operational Similarity, AHP analytical hierarchical processing

[33]

2007

modularity, commonality, compatibility, reusability, and demand

[34]

2008

Tool and direction

[35]

2007; 2009

the component level

[36]

2011

Bill of materials BOM

[37]

2014

cell configuration

[1]

2014

the assembly sequences, product demand and commonality

[38]

2016

Bill of materials BOM, components and assembly structure

[39]

2016

bypassing moves and idle machines

[40]

2016

Component, interface synergy

[41]

2018

Datum Flow Chain DFC

[42]

2018

Pareto, commonality and modularity

[43]

2020

LPCS, ALC, D-RMS system

[44]

2020

dynamic expression

[45]

2021

MBPF

[46]

2022

Differential Evolution (DE)

[47]

2022

D-RMS, machine learning, K-medoids, LPCS

As shown in Table 1, in the literature several methods deal with the formation of product families, namely commonality [1, 29, 30, 33, 42], by passing moves using LCS & SCS [39, 43], Bil Of Materiel (BOM) [36, 38], MCDA [25, 38, 47], etc. The decision between these approaches is determined by the production system under consideration;

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each system has a set of criteria that must be considered when computing the coefficients of similarity. The main criteria are; commonality, customer demand, assembly sequence, machining sequence, bill of materials, tools, modularity, etc. Despite the fact that each method was examined independently, it appears that the majority of the approaches give the data acquired in the form of a matrix enabling comparison between the different products. In this context, the goal of this study is to reformulate a general algorithm for grouping products into product families independent of the system or criteria used. 3.3 Hierarchical Clustering A clustering algorithm is designed to construct a hierarchical clustering tree (Dendogram) for the grouping of products into product families. This phase follows the generation of matrices for each criterion utilized. The comparison matrices associated with each criteria are present in the input data. The clustering process is then depicted in Fig. 1. The matrix of similarity Sij is the normalized sum of the matrices associated with each criteria. After determining the pair of products with the maximum Sij, the rows and columns containing its two pairs will be deleted and combined into a single family of products; subsequently, the similarity matrix is then recalculated using Eq. 1. Where i and j are

Fig. 1. ClusterinAlgorithm for formation product.

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the products of the p and q families, Np and Nq are the number of products in the p and q families, and Sij are the coefficients of similarity between i and j.   i∈p j∈q Sij (1) Spq = NpNq

4 Conclusion and Perspectives In this work, a literature review on the formation of product families for the design of reconfigurable RMS manufacturing systems is presented. For this purpose, several grouping methods are selected. Components, common sequences, production tools and production demand are the main parameters involved in product grouping. Multi-criteria decision support methods such as AHP and ELECTRE hierarchical analytical treatment are also used. The diversity of these methods, as well as the non-adaptability of a specific method of grouping products, confirms the absence of a systematic methodology that addresses all types of production systems. For the rest of this paper, we propose a method for clustering products depending on comparative criteria which should be chosen by the experts of the company. In our next work, we will try to propose some examples of the comparison criteria as well as the methods of calculation adequate for each criterion. To help experts in the calculation of individual matrix. Thus, from our point of view, in a world characterized by the 4th industrial revolution “Industry 4.0”, artificial intelligence, the Internet of Things and smart factories. There is a need to leverage all of these technologies to improve the proposed bundling method, to be able to meet all products and different types of systems. And this requires a long-term vision to ensure the economic feasibility of several generations of products and market situations. But the question that arises is this; how can we leverage Industry 4.0 in RMS design? This question presents a first step to expand our knowledge of RMSs, and methods of entity clustering to support the industry of the future in its transition to the reconfigurable production of tomorrow and to the so-called factories 4.0.

References 1. Kashkoush, M., Elmaraghy, H.: Product family formation for reconfigurable assembly systems. Procedia CIRP 17, 302–307 (2014). https://doi.org/10.1016/j.procir.2014.01.131 2. Opitz, H.: A classification system to describe workpieces. Pergamon Press, Oxford (1970) 3. Burbidge, J.L.: Production Flow Analysis for Planning Group Technology. Clarendon Press Oxford, New York (1989) 4. Seung, K.M., Simpson, T.W., Shu, J., Kumara, S.R.T.: Service representation for capturing and reusing design knowledge in product and service families using object-oriented concepts and an ontology. J. Eng. Des. 20(4), 413–431 (2009) https://doi.org/10.1080/095448209031 51723 5. ElMaraghy, H.A.: Changing and evolving products and systems – models and enablers. In: ElMaraghy, H.A. (ed.) Changeable and Reconfigurable Manufacturing Systems, pp. 25–45. Springer London, London (2009). https://doi.org/10.1007/978-1-84882-067-8_2

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A New Method for Mobile Robots to Learn an Optimal Policy from an Expert Using Deep Imitation Learning Abderrahim Waga1(B) , Younes Benhouria1 , Ayoub Ba-Ichou1 , Said Benhlima1 , Ali Bekri1 , and Jawad Abdouni2 1 Department of Computer Science, Moulay Ismail University of Meknes, Meknes, Morocco {a.waga,younes.benhouria,a.baichou}@edu.umi.ac.ma, {s.benhlima, a.bekri}@umi.ac.ma 2 Advanced Systems Engineering Laboratory, National School of Applied Sciences of Kenitra, Kenitra, Morocco [email protected]

Abstract. Mobile robots must be able to learn the navigation policy from an expert like human beings. This aspect remains a challenge for mobile robots primarily when relying only on sensory inputs like ultrasound or mobile robots have limited resources. This aspect becomes complex, especially in mapless navigation. In this paper, we present a new technique based on deep imitation learning that serves to develop an efficient and robust policy and transmit it to the mobile robot. The proposed method relies on the collection of a set of data sets from several training environments. Then this dataset is used to train an intelligent model using a Convolution neural network (CNN). Extensive experiments on simulation environments of different complexity for the validation of the trained model show the effectiveness of the proposed approach in terms of success rate. Keywords: Autonomous navigation · Imitation Learning · Convolution neural network · Deep learning · Knowledge learning

1 Introduction Navigation is a basic task for mobile robots, many researchers try to make this task as easy as possible, but it remains a challenge for researchers because it still requires heavy programming or human intervention every time the environment changes. The traditional map-based navigation system [1] is composed of several subtasks such as mapping [2], planning [3], and tracking. The first component has the role of building a global map of the unknown environment, the second component is used to give our mobile robot an optimal trajectory to reach the final goal by avoiding the obstacles [4] and the third subsystem is used to control the robot to follow the trajectory generated by the second component. This heavy process influences the performance of the whole system since it relies on the quality of the map. This board is very sensitive to sensor noise and the size of the environment. This requirement can make the implementation of this system in robots with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 873–882, 2023. https://doi.org/10.1007/978-3-031-29857-8_87

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limited resources or dynamic environments a bit difficult. To simplify this process and with the rapid development of artificial intelligence, we proposed a robust and efficient map-less navigation system based on deep imitation [5, 6] which uses a neural network model to predict the motion control commands for the mobile robot to reach the target while avoiding obstacles. In general, the proposed technique has two major advantages over the traditional approach, firstly our approach does not need a map of the environment since it is based on the sensory inputs and decisions made by the neural network, this advantage makes our system independent of the map but depends on the precession of our model generated. Secondly, our approach directly uses sensor information to predict a suitable action, this advantage makes our system independent of the mobile robot or the hardware and makes our robot react in real-time. Several approaches have been studied using different kinds of sensors. In general, in the literature, there are three main categories, namely vision-based approaches, approaches based on sensory inputs such as ultrasonic sensors or lidar, and approaches that merge the two types of sensors [7–9]. The first category uses RGB cameras to record images of the environment and a CNN model to learn the correspondence between the image and the appropriate action taken by the mobile robot to bring the robot to its final destination even if in unknown or dynamic environments. In this study, [10] the authors have tried to minimize the navigation process by proposing a system based on deep imitation. The proposed technique uses lidar as a sensor and a CNN model to find the correspondence between the state and the action taken. To increase the precession of the generated model, they used the data augmentation technique to increase the number of training samples in the training data set. The proposed system showed its effectiveness especially since the model was tested twenty times on four environments, the robot has no prior information about its environments, and it reached 75% as a means of success. In this innovative study, [11] the authors combined two major machine learning techniques namely imitation learning and reinforcement learning. The approach is tested on a simulation environment and validated on a real robot. In this research [12], the authors tried to use a traditional reinforcement learning algorithm called q-Learning. The motion planner takes only 10-dimensional lidar telemetry data and relative navigation goal data as input, and five possible actions for the mobile robot as output. After training, the generated model successfully navigated in new environments even though the generated path is not optimal. Another research [13] is similar to the third one since it is based on the q-Learning technique but it added two essential criteria to speed up the navigation process. He introduced a Safe state i.e. no obstacle is close to the robot and an almost Safe state i.e. at least one sensor has detected a very close obstacle. This mechanism increased the speed of navigation. On the other side, several approaches have developed innovative techniques using the camera namely [14], in this research the authors tried to merge two modes to make a robot autonomous in terms of navigation. The first mode is the approach mode which has the role of directing the mobile robot to the target direction based on the name and also the image of the landmark. The other positioning mode moves the mobile robot with a precession towards the target based on the image captured at each moment. The method is effective in real robots and first-time environments. Similar research in references [15, 16]. The rest of the article is organized as follows: In the second section, we will see the background and the tools used in this paper. In the third section, we will start

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the experiments done to validate our approach, then the next section is dedicated to the discussion of the obtained results and the last section is for the conclusion and the possible future works.

2 Background The mobile robot must be able to learn the navigation policy from an expert. This skill will make the robot intelligent since it will not need to be reprogrammed from scratch every time the environment changes. The Dijkstra expert will navigate in 40 environments. Our robot will try to repeat the same trajectory and generate a dataset for each environment. These datasets will be used as training data for our deep learning technique. 2.1 Dijkstra Method The Dijkstra technique [17] tries to decompose the graph problem into subproblems by computing the shortest path from one vertex to another neighbor. The latter will be given priority in the queue and now the new vertices in a minimum priority queue and store a single intermediate node so that only one shortest path can be found. The Dijkstra algorithm relies on a greedy strategy for path planning but optimality remains the strong point of this technique. Several improvements have been proposed to overcome these drawbacks and each improvement has uses in specific domains. We have chosen to work with the traditional technique since we do not need the speed in the generation of the path but especially the optimality of the generated path. – A Node can be considered as a point in a network or the intersection between two graph lines. – A path is a finite sequence of stops, such as the set of nodes from a vertex to its final destination. – A weighted graph is a graph that has stops with their weights. – Directed graph: a graph with a set of nodes connected to form a network. The operation of the Dijkstra algorithm can be divided into four steps. 2.2 Data-Set Generation We mentioned that the generated dataset is only correspondence between a specific state and a suitable action taken. So, the dataset must be composed of a pair of useful states and actions for the mobile robot, in other words, the trajectory generated by the Dijkstra expert. In our case, the Dijkstra expert will navigate in 40 environments, and all the data retrieved from the trajectories will be used as demonstrations. Giving 40 demonstrations, therefore, the representation of the trajectories will be as follows:   (i) (i) (1) D(i) = Xt , Yt We have taken as a model a mobile robot with two wheels and eight ultrasonic sensor inputs distributed as shown in the Fig. 1.

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Fig. 1. Mobile Robot.

The dataset collected will be composed of eight values returned by the eight ultrasonic sensors -DS- in addition to two other values, namely the region where the goal is located -RG- and the remaining Euclidean distance in each state of the trajectory generated -ED-. We can schematize the state as a vector of ten values. ⎡ ⎤ DSt (2) Xt = ⎣ RGt ⎦ ∈ R10∗1 EDt For example, if the goal is in the zone 7, the last value is 7, but if the goal is in the zone T

5, the last value is 5. We have constructed a vector Yt = y1 , y2 , y3 , y4 , y5 for the five possible actions shown in Table 1. Table 1. Possible actions for the mobile robot. Yt

Action

[1, 0, 0, 0, 0]

Right

[0, 1, 0, 0, 0]

Left

[0, 0, 1, 0, 0]

Left front

[0, 0, 0, 1, 0]

Front

[0, 0, 0, 0, 1]

Right front

2.3 Deep Learning Deep learning [18] is the trend of machine learning. At first, it showed that it can reach the same level as a human being but lately it can be said that it has overcome the capabilities of the human being. Deep learning is used in different fields such as object recognition, machine translation, voice recognition, and many others fields. The structure of the deep learning network is the best simulation of the human cerebral cortex. The convolutional

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neural network (CNN) is a kind of feed-forward neural network with convolutional computation and deep structure [19]. It is one of the representative algorithms of deep learning. CNN has been successfully used in image classification. This paper focuses on the application of a deep neural network in the autonomous navigation of mobile robots.

Fig. 2. Proposed architecture.

Figure 2 shows the proposed architecture to properly transmit the navigation policy to the mobile robot. After the collection of datasets from the Dijkstra expert from different environments. We apply several convolution filters, Maxpooling, flatten and dense with the last layer with a soft-max activation function so that the probability generated for each action is between 0 and 1.

3 Experiences Several experiments were done to collect the dataset. We built 40 environments of size 53x53 pixels. The simulations are run on a PC with an Intel(R) Core (TM) i3-2348M CPU @2.30GHz and 8GB of internal RAM, the code was written in python. To offer a diversity of data to our approach we chose two start and two finish points to check the robustness of our method. The starting and target points are respectively (2, 2), (49, 2) and (48,48), (3,50).

Fig. 3. Training environments.

Figure 3 shows some data collection environments for our mobile robot from the Dijkstra expert. We mentioned that we built 40 environments to collect useful data for

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our robot, and that we chose two starting points and two arrival points. The blue point in Fig. 3 represents the departure of the robot, and the other green point represents the final goal to reach. The environments are divided to respect the degree of complexity: easy, medium, and difficult. The idea behind this division is to offer a diversity of data to the robot during training.

4 Results and Discussion In this section, we will see the results obtained after training the model and the results obtained in the validation phase. 4.1 Training Result The Fig. 4 shows the precession of the model in training phase for test and training data.

Fig. 4. Accuracy during the training phase.

The results according to this figure show that after 200 epochs the two curves do not diverge much, i.e., there is no overfitting or underfitting. From epoch 50, the two curves exceed 90% of precession for both data, which shows that the model can learn very well even for a small number of epochs. We see that the learning rate increases for both types of data during the training phase of the model. The elapsed time in this training phase is 71 min. From Fig. 5, the two curves are very close to each other and, during training, converge to 0, which shows that for both sets of data, most predictions are correct. It can be seen that the loss value for the training data decreases up to 13% but for the test deals it remains stable during training Figure 6. According to the confusion matrix, we notice that the model has well predicted the totality of the actions, especially for the action -right front- with a percentage of 95.45%. On the other hand, the action -front- has managed to classify 79 right actions and 19 wrong actions, which shows that our model has difficulties predicting this action well.

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Fig. 5. Loss function during the training phase.

Fig. 6. Confusion matrix.

4.2 Validation of the Obtained Model To validate our approach, several experiments have been done in 98 validation environments, with different forms of obstacles and different levels of difficulty. We introduce an essential metric: the success rate in these 98 environments TR (Table 2). Figure 7 shows some examples of navigation using our approach. The two figures D and F show that our robot did not succeed in reaching the goal, but the rest of the figures A, B, C and E show that our robot succeeded in reaching the goal and with an almost optimal trajectory.

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Value

Number of environments

98

Successful environments

88

Failed environments

10

The success rate

89.8%

Fig. 7. Navigation of the mobile robot using our approach.

5 Conclusion and Future Work In this paper, a new approach is proposed to facilitate the navigation process of mobile robots. The major goal of this approach is to make the mobile robot autonomous and independent of factors such as map and sensor uncertainty and human intervention whenever the environment changes. The proposed technique is based on deep imitation, and it has shown its robustness and effectiveness in environments seen for the first time with a high percentage of success. Our next work will focus on adding another sensor, such as a camera, to overcome the weaknesses of this approach and increase the percentage of success in new environments.

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2. Labesse-Jied, F., Brown, H., Dimnet, J.: Robotic modelling of the spine based on threedimensional radiography during a standing to sitting movement. ITBM-RBM 21(6), 367–375 (2000) 3. Ying, Y., Li, Z., Ruihong, G., Yisa, H., Haiyan, T., Junxi, M.: Path planning of mobile robot based on improved RRT algorithm. In: 2019 Chinese Automation Congress (CAC), pp. 4741– 4746. IEEE (November 2019) 4. Waga, A., Lamini, C., Benhlima, S., Bekri, A.:Fuzzy logic obstacle avoidance by a NAO robot in unknown environment. In: 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), pp. 1-7 (2021). https://doi.org/10.1109/ICDS53782.2021.9626718 5. Liu, B., Xiao, X., Stone, P.: A lifelong learning approach to mobile robot navigation. IEEE Robot. Autom. Lett. 6(2), 1090–1096 (2021). https://doi.org/10.1109/LRA.2021.3056373 6. Hussein, A., Gaber, M.M., Elyan, E., Jayne, C.: Imitation learning: a survey of learning methods. ACM Comput. Surv. (CSUR) 50(2), 1–35 (2017) 7. Cèsar-Tondreau, B., Warnell, G., Stump, E., Kochersberger, K., Waytowich, N.R.: Improving autonomous robotic navigation using imitation learning. Front. Robot. A I, 8 (2021) 8. Wigness, M., Rogers, J.G., Navarro-Serment, L.E.: Robot navigation from human demonstration: learning control behaviors. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1150–1157. IEEE (May 2018) 9. Pokle, A., Martín-Martín, R., Goebel, P., Chow, V., Ewald, H.M., Yang, J., Vázquez, M.: Deep local trajectory replanning and control for robot navigation. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 5815–5822. IEEE (May 2019) 10. Tsai, C.Y., Nisar, H., Hu, Y.C.: Mapless LiDAR navigation control of wheeled mobile robots based on deep imitation learning. IEEE Access 9, 117527–117541 (2021) 11. Pfeiffer, M., et al.: Reinforced imitation: sample efficient deep reinforcement learning for mapless navigation by leveraging prior demonstrations. IEEE Robot. Autom. Lett. 3(4), 4423– 4430 (2018) 12. Qiang, L., Nanxun, D., Huican, L., Heng, W.: A model-free mapless navigation method for mobile robot using reinforcement learning. In: 2018 Chinese Control and Decision Conference (CCDC), pp. 3410–3415. IEEE (June 2018) 13. Zuo, B., Chen, J., Wang, L., Wang, Y.: A reinforcement learning based robotic navigation system. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3452–3457. IEEE (October 2014) 14. Kanayama, H., Ueda, T., Ito, H., Yamamoto, K.: Two-mode mapless visual navigation of indoor autonomous mobile robot using deep convolutional neural network. In: 2020 IEEE/SICE International Symposium on System Integration (SII), pp. 536–541. IEEE (January 2020) 15. Xue, T., Yu, H.: Model-agnostic metalearning-based text-driven visual navigation model for unfamiliar tasks. IEEE Access 8, 166742–166752 (2020) 16. Zhu, Y., et al.: Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3357–3364. IEEE (May 2017) 17. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959) 18. Kim, Y.H., Jang, J.I., Yun, S.: End-to-end deep learning for autonomous navigation of mobile robot. In: 2018 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6. IEEE (January 2018) 19. Galvez, R.L., Bandala, A.A., Dadios, E.P., Vicerra, R.R.P., Maningo, J.M.Z.: Object detection using convolutional neural networks. In: TENCON 2018–2018 IEEE Region 10 Conference, pp. 2023–2027. IEEE (October 2018) 20. Alzubaidi, L., et al.: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021)

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Modeling and Simulation of a BLDC Motor Speed Control in Electric Vehicles Mariem Ahmed Baba1(B) , Mohamed Naoui2 , and Mohamed Cherkaoui1 1 Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of

Engineers, Mohammed V University in Rabat, Rabat, Morocco [email protected] 2 Research Unit of Energy Processes Environment and Electrical Systems, National Engineering School of Gabes, University of Gabés, Gabes, Tunisia

Abstract. In comparing the different electric motors used in electric vehicles, the Brushless motor achieved the highest efficiency, reaching 95% . After its success in taking over from DC motors, the Brushless motor has also become the main competitor to induction motors. This work consists of modeling and simulating the speed control of a BLDC motor for electric vehicles. First, the simulation of the global model was carried out under Matlab, and a PI controller ensured the speed control then, during the comparison, the PID type controller and the fuzzy logic were implemented to obtain a better performance according to the results obtained for each control. Keywords: BLDC motor · Hall position · PI controller · PID controller · Fuzzy logic controller

1 Introduction The automobile industry developed considerably in the period between the 1830s and 1900, when the first model of a simple electric vehicle was made by Robert Anderson in 1834 [1]. Currently, EVs are receiving international support in the hope that they will succeed in replacing fuel-based cars to reduce carbon dioxide emissions. The electric transportation sector has received a lot of attention from manufacturers recently, making it the most promising means of transportation. Research in this area is focused on increasing the autonomy of EVs so that they can run for longer periods to keep pace with the age of speed that the world is currently witnessing. Therefore, research in this area focuses on improving electric motors’ performance and battery control. The electric motors generally used in EVs are [2] the DC Motor, Induction Motor, PM Brushless DC Motor, and Switched Reluctance Motor. In a comparison of motors for electric vehicles in [2–4], it was well indicated that the Brushless motor has the highest efficiency rate, reaching 95%, followed by the induction motor, which offers about 90% efficiency. BLDC motors are becoming increasingly popular in industrial applications due to their advantageous capabilities, including high efficiency, high power density, low weight, and low maintenance [5]. These types of motors are frequently used in several sectors © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 883–895, 2023. https://doi.org/10.1007/978-3-031-29857-8_88

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such as automotive, aerospace, household appliances, and medical devices. The BLDC is also considered the major competitor of the induction motor after having been remarkably adopted as an alternative to DC motors. The Brushless motor has proven itself in the automotive field, especially in electric and hybrid vehicles. Hence Toyota adopted this motor in its Toyota Prius model, which is considered among the most famous hybrid vehicles [6]. In addition, the car company Peugeot has launched a hybrid electric vehicle (Dynavolt) by adopting the brushless motor in its model. The BLDC motor is characterized by permanent magnets alternating around the rotor and windings located on the stator, a DC voltage source ensures its power supply while the switching times depend on the position of the rotor which is ensured by employing sensors in the case of the adoption of the system with the sensor or by following the techniques replacing the sensor, for example, the method based on electromotive forces. These types of motors are frequently used in several sectors such as automotive, aerospace, household appliances, and medical devices. Fig. 1 is the descriptive diagram of the BLDC motor. The types of controllers applied to BLDC motors are many, Controllers (PI, PID) and fuzzy controllers have been implemented in a lot of studies to compare these types to get the best speed control performance. Although the PID control technique is old, it still attracts the attention of researchers [7], for example in [8] two-speed control systems have been presented which are the PID controller and the Fuzzy-PID controller applied to a BLDC motor. Therefore, in [9] the speed control method is based on PI and PID controllers. Combining the fuzzy controller with other control strategies often gives satisfactory results. This approach was applied in [10] by using a hybrid fuzzy logic control system and moth-flame fuzzy logic controller (MFOFLC). A similar principle is employed in [11], which used a fuzzy control system and a deep learning neural network for better speed control. The paper [12] for example, examines a comparison of two types of efficient control of a BLDC motor which are the PI controller andthe fuzzy logic controller, the results showed the advantage of fuzzy logic controller over PI controller. Therefore, the work presented in [13] indicates the use of three control modes which are the PI, the fuzzy controller, and the hybrid PI-Fuzzy controller to maintain the speed control of a brushless motor, the simulations of the various systems proved the superiority of the hybrid controller. Another comparative study was presented in [14] which compared the PI controller with several combined fuzzy techniques such as the integrated fuzzy logic controller and the hybrid fuzzy logic controller. This paper is organized as follows. First, a general introduction is presented, then the mathematical modeling part is examined, which indicates the different equations of the treated model. Therefore, a part describing the various control techniques used in this work (PI, PID, fuzzy logic) is presented while describing the principle of operation of each control process by its respective diagram. In the end, the last part corresponds to the analysis results of the simulation obtained during the comparison between the different control methods.

2 Modeling and Control of BLDC Motor A trapezoidal EMF of the permanent magnet synchronous motor powered by a voltage inverter and controlled by a position sensor is included in the system to be modeled. The synoptic diagram of the assembly is shown in Fig. 2.

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Fig. 1. Schematic diagram of three BLDC motor

Vdc

Control

Position sensor

Inverter

M

Control signals

BLDC

Fig. 2. Block diagram of BLDC motor

It is useful to know the six instants of phase switching in the case of the operation of a brushless motor. Several techniques can be proposed, but the Hall effect sensors are implemented in this case. The position of the rotor plays an essential role in the choice of power supply to the windings. This operation generally requires three Hall effect sensors inside the stator [15, 16]. Table 1 shows the different values of the output signals delivered by the Hall effect sensors. Table 1. Output signals from the Hall effect sensor θe

ha

hb

hc

−180 < θ e < −120

−1

0

1

−120 < θe < −60

0

−1

1

−60 < θe < 0

1

−1

0

0 < θ e < 60

1

0

−1

60 < θ e < 120

0

1

−1

120 < θ e < 180

−1

1

0

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2.1 Mathematical Modeling of BLDC Motor The equation of a BLDC motor is represented by its two electrical and mechanical models. Electric Model of the BLDC The basic voltage equations of armature winding for BLDCM can be represented as follows:   dia (1) Va = RIa + L + ea dt   dib Vb = RIb + L + eb (2) dt   dic Vc = RIc + L + ec (3) dt The vector of voltages across the three phases according to Eqs. (1, 2, 3) can be represented in the following matrix form: ⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤⎡ ⎤ R00 ia ea Va Laa Lab Lac ia d ⎣ Vb ⎦ = ⎣ 0 R 0 ⎦⎣ ib ⎦ + ⎣ Lba Lbb Lbc ⎦⎣ ib ⎦ + ⎣ eb ⎦ dt Vc ic Lca Lcb Lcc ic ec 00R ⎡

(4)

The speed is given by the equation: W=

dθr dθ =p = pwr dt dt

(5)

P: the number of pole pairs. 8: The rotor position. The electromotive forces of phases (a, b, c) are given as: ea = ke wr fa (θ )

(6)

eb = ke wr fb (θ )

(7)

ec = ke wr fc (θ )

(8)

where ke is the coefficient of the electromotive force. ea = ke wf (θe )

(9)

eb = ke wf (θe − 2/3)

(10)

eb = ke wf (θe + 2/3)

(11)

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According to (4) and after a simplification, the current equation can be written as: dib 1 = (−Vab + Vbc − 3Rib + ea − 2eb + ec ) dt 3Lm

(12)

The following form represents the corresponding current equations: dia 1 = (2Vab + Vbc − 3Ria + PK(−2ea + eb + ec )) dt 3Lm

(13)

dib 1 = (−Vab + Vbc − 3Rib + PK(ea − 2eb + ec )) dt 3Lm

(14)

dia dib dic = −( + ) dt dt dt where ea, eb, and ec are the electromotive forces of the phases. Vab , Vbc : are the voltages between phases. : Mechanical rotation speed. The converter block was created using the following equations:

(15)

Va =

(S4 )Vd (S1 )Vd − 2 2

(16)

Vb =

(S6 )Vd (S3 )Vd − 2 2

(17)

Vc =

(S5 )Vd (S2 )Vd − 2 2

(18)

Mechanical Model of the BLDC The Mechanical Equation of Motion is given in the form below: J

d = Ce − f  − Cr dt

(19)

where: : angular speed of motor, Ce: the electromagnetic torque. Cr: load torque. J:moment of inertia of the rotating parts of the motor. f: coefficient of friction. The expression of the electromagnetic torque can be established from the following relationship: Ce =

Pe 

(20)

Pe: Represents electromagnetic power. ea ia + eb ib + ec ic (21)  The simulation model under Matlab-Simulink is represented by the following Fig. 3. Ce =

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Fig. 3. Flow chart of the simulated machine model

3 Control The general goal of a controller is to limit or minimize as much as possible the deviation between the actual output and the desired output, where the actual output is the output obtained after controlling the system. In this work, a PI controller and a PID controller are exploited to compare the two, continue the comparison process by introducing a fuzzy logic controller, and finally, interpret the various results provided by these correctors. As the BLDC motor is considered non-linear, its control must be assigned to one of the developed controllers [17]. There are a lot of advanced controllers in the framework of the speed control of BLDC, for example, in [18], an adaptive and fuzzy PID controller is applied to achieve the speed control of a BLDC motor. Furthermore, in the case of BLDC, [19] examines the combination of fuzzy control and PID control to obtain the proposed fuzzy PID method for controlling a BLDC motor. The authors in [20] performed a comparative study supported by simulation results between two control systems: the self-tuning fuzzy PID controller and the model reference adaptive control (MRAC) with a PID compensator. This document presents three control systems of a BLDC motor: the PI controller, then the PID controller, and the fuzzy logic controller. 3.1 PI Controller A PI’s operating mode consists of approximating the value of the actual speed of an electric motor to the value of the reference speed established by the feedback process, where the corresponding PI inputs are the real speed and the reference speed. The transfer function of the most basic form of the PI controller is written: s wi (22) PI (S) = KP . .(1 + ) s wi where: Kp = K1, and wi =

k1 k2

=

1 Ti .

3.2 PID Controller The PID controller is a corrector whose role is to correct and improve the performance of the system, its abbreviation is the source of three terms that are respectively (P:

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Proportional, I: Integral, D: Derivative). In fact, the purpose of PID implementation is summarized in three functions, the first of which is performed by the proportional term that reduces the error response of the system to disturbances, then the integral term that eliminates the static error, and finally, the derivative term makes the system more stable. The PID controller has been widely applied in industrial fields due to its simple use, adequate structure, and efficiency (Fig. 4).

Fig. 4. Block diagram of PID controller

The corresponding PID equations: C(S) = Kp +

KI + Kd S S

C(S) = (Kp S + KI + Kd S 2 )/S

(23) (24)

where KP = Proportional gain, KI: Integral gain, and KD: Derivative gain. Where: Kp = 0.8; Ki = 48; Kd = 0.01. The diagram below (Fig. 5) describes the control system of a PID implemented under Matlab Simulink.

Fig. 5. Block Diagram of PID Controller system

3.3 Fuzzy Logic Controller Fuzzy logic has imposed its existence among the most powerful control strategies. Its principle is based on four main phases: Fuzzification, the knowledge base (including the

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database and the rule base), the fuzzy reasoning mechanism, and the defuzzification unit. Despite the popularity of fuzzy logic in various industrial systems, it remains the most suitable to control the BLDC engine because the latter has a non-linear system [19]. The control by FLC for a BLDC engine consists in knowing the system’s general behavior and allows the controller’s actions to be specified thanks to linguistic rules formed by “if-else” instructions tools (Fig. 6).

Fig. 6. Fuzzy logic controller diagram

The selection of fuzzy inference rules characterizes the fuzzy logic control process. Table 2 shows the different fuzzy inference rules used. Table 2. Rule base of fuzzy logic controller e/ce

NB

NM

NS

ZE

PS

PM

PB

NB

NB

NB

NB

NB

NM

NS

ZE

NM

NB

NB

NB

NM

NS

ZE

PS

NS

NB

NB

NB

NM

ZE

PS

PM

ZE

NB

NM

NS

ZE

PS

PM

PB

PS

NM

NS

ZE

PS

PM

PB

PB

PM

NS

ZE

PS

PM

PB

PB

PB

PB

ZE

PS

PM

PB

PB

PB

PB

The fuzzy membership functions, in this case, are defined as follows: NG: Negative Large, EZ: About Zero, et PG: Positive Large. e: error, ce: change in error.

4 Simulation Results The BLDC motor was simulated using MATLAB/Simulink. The proposed model was validated under different conditions related to all statuses of the motor and its parameters. The motor specifications used in simulations are listed in Table 3.

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Table 3. BLDC parameters Rated speed

3800 rpm

Stator inductance

58.9 μH

Supply voltage

12 v

Magnet excitation flux

0.0030225 Wb

Number of pairs of poles

4

E.m.f. constant (Ke)

0.05

Moment of inertia of rotating parts (J)

0.0000528 N.m.s2

stator resistance

0.39

Fig. 7. Results of PI controller (Speed, current, rotor position)

The simulation results obtained by implementing a PI controller are shown in Fig. 7, which shows the current, speed, and rotor position curves during the starting of the motor under load.The motor speed reaches the value of 3000 rpm with overshoot and with a stability time of less than 0.19 s.The current reaches a maximum value of Imax = 100

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A before returning to a value of 25 A in a steady state. The torque reaches a maximum value of 600. The simulation results obtained by implementing a PID controller in Fig. 8 show the current, speed, and rotor position curves during the starting of the motor under load. The motor speed reaches the value of 2500 rpm with overshoot and with a stability time of less than 0.1 s. The current reaches a maximum value of Imax = 100 A before returning to a value of 30 A in a steady state. The torque reaches a maximum value of 60.

Fig. 8. Results of PID controller (Speed, current, rotor position)

The simulation results obtained by the implementation of a fuzzy logic controller: The simulation of this model made it possible to obtain Fig. 9 which shows the curves of the rotor’s current, speed, and position during the starting of the motor under load.The motor speed reaches the value of 2000 rpm with overshoot and with a stability time less than 0.09 s. The current reaches a maximum value Imax = 30A before returning to a value of 20 A in a steady state. The torque reaches a maximum value of 600.

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Fig. 9. Results of fuzzy logic controller

5 Results and Discussion The simulation results showed the effectiveness of the PID controller compared to PI but the fuzzy logic controller was the best compared to the latter two. We notice that in the speed curve of each case, a disturbance appears at T = 0.3 s and the PI and PID controller were able to reduce the effect of this disturbance significantly as indicated by the speed curves above but the fuzzy logic controller presented the best correction of this disturbance.

6 Conclusion In this paper, the detailed modeling of the brushless motor has been presented, and then a description of the proposed control systems which are PI, PID, and fuzzy logic controllers to achieve the speed control objective of the BLDC motor. During the simulation, a comparison was made between the three control systems used and the results proved that the fuzzy logic control provided more effective results than the PI and PID controllers.

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The future work consists in combining neural network and fuzzy logic when comparing different speed controls of a BLDC motor. Acknowledgment. This work was accomplished due to the grant provided by Organization for Women in Science for the Developing World (OWSD) and Swedish International Development Cooperation Agency (SIDA). The author wishes to express his gratitude to OWSD and SIDA for the opportunity and the support given.

References 1. Rajesh, R.G., Balaji, C: Speed control of bldc motor using PID. Int. J. Adv. Res. Electr. Electron. Instrumentation Eng. (2014) 2. Hashernnia, N., Asaei, B.: Comparative study of using different electric motors in the electric vehicles. In: 2008 International Conference on Electrical Machines 3. Yildirim, M., Polat, M., Kürüm, H.: A survey on comparison of electric motor types and drives used for electric vehicles. In: 16th International Power Electronics and Motion Control Conference and Exposition 4. Bhatta, P.N., Mehar, H., Sahajwanib, M.: Electrical motors for electric vehicle – a comparative study. In: International Conference on “Recent Advances in Interdisciplinary Trends in Engineering & Applications”, SSRN-ELSEVIER (2018–19) 5. Toda, H., Xia, Z., Wang, J., Atallah, K., Howe, D.: Analysis of motor loss in permanent magnet brushless motors. IEEE Trans. Magnetics (2004) 6. Dorrell, D.G., Hsieh, M-.F., Knight, A.M.: Alternative rotor designs for high performance brushless permanent magnet machines for hybrid electric vehicles. IEEE Trans. Magn. 48(2) (2012) 7. Amieur, T., Bechouat, M., Sedraoui, M., Kahla, S., Guessoum, H.: A new robust tilt-PID controller based upon an automatic selection of adjustable fractional weights for permanent magnet synchronous motor drive control. Electr. Eng. 103(3), 1881–1898 (2021). https://doi. org/10.1007/s00202-020-01192-3 8. Maghfiroh, H., Ramelan, A., Adriyanto, F.: Fuzzy-PID in BLDC motor speed control using matlab/simulink. Journal of Robotics and Control (JRC) 3(1), 8–13 (2022) 9. Usha, S., Dubey, P.M., Ramya, R., Suganyadevi, M.V.: Performance enhancement of BLDC motor using PID controller. Int. J. Power Electron. Drive Syst. 12(3), 1335 (2021) 10. Kamalapathi, K., et al.: A hybrid moth-flame fuzzy logic controller based integrated cuk converter fed brushless DC motor for power factor correction. Electronics 7(11), 288 (2018) 11. Gobinath, S., Madheswaran, M.: Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor. Soft. Comput. 24(13), 10161–10180 (2019). https://doi.org/10.1007/s00500-019-04532-z 12. Usman, A., Rajpurohit, B.S.: Speed control of a BLDC motor using fuzzy logic controller. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–6. IEEE (July 2016) 13. Abidin, M.F Z., Ishak, D., Hassan, A.H.A.: A comparative study of PI, fuzzy and hybrid PIFuzzy controller for speed control of brushless dc motor drive. In: 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE), pp. 189–194. IEEE (December 2011) 14. Kalavathi, M.S., Reddy, C.S.R.: Performance evaluation of classical and fuzzy logic control techniques for brushless DC motor drive. In: 2012 IEEE international power modulator and high voltage conference (IPMHVC), pp. 488–491. IEEE (June 2012)

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15. Seol, H.S., Lim, J., Kang, D.W., Park, J.S., Lee, J.: Optimal design strategy for improved operation of IPM BLDC motors with low-resolution hall sensors. IEEE Trans. Industr. Electron. 64(12), 9758–9766 (2017) 16. Alaeinovin, P., Jatskevich, J.: Filtering of hall-sensor signals for improved operation of brushless DC motors. IEEE Trans. Energy Conver. 27(2) (2012) 17. Wang, B., Yang, L., Wu, F., Chen, D.: Fuzzy predictive functional control of a class of non-linear systems. IET Control Theory Appl. 13(14), 2281–2288 (2019) 18. Kandiban, R., Arulmozhiyal, R.: Speed control of BLDC motor using adaptive fuzzy PID controller. Procedia Eng. 38, 306–313 (2012) 19. Jing, J., Wang, Y., Huang, Y.: The fuzzy-PID control of brushless DC motor. In: 2016 IEEE International Conference on Mechatronics and Automation, pp. 1440–1444. IEEE (August 2016) 20. El-Samahy, A.A., Shamseldin, M.A.: Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control. Ain Shams Eng. J. 9(3), 341–352 (2018)

Smart Supply Chain Management: A Literature Review Nabila Bouti1(B) and Fatima El Khoukhi2 1 IA Laboratory, FS Meknes, Moulay Ismail University of Meknes, Meknes, Morocco

[email protected]

2 IA Laboratory, FLSH Meknes, Moulay Ismail University of Meknes, Meknes, Morocco

[email protected]

Abstract. Industry 4.0 (I4.0) is an innovative way of improving organizations’ production methods by using new technologies that revolutionize the Supply Chain (SC). Traditionally, SC managers focused on simple tasks such as delivering products to customers and assuring that a company maintains a sufficient supply of raw materials to sustain ongoing operations. However, with the fast progress in logistics, SC Management (SCM) has become a complicated process involving forecasting demands, establishing lucrative partnerships, and optimizing business performance. To overcome this challenge, Smart Supply Chain Management (SSCM) uses several technologies such as Big Data (BD), the Internet of things (IoT), Blockchain, Artificial Intelligence (AI), and Advanced Robotics (AR) to analyze data, and identifies trends and opportunities in the market that enhance the effectiveness of logistics, whether inside or outside of the company. This paper examines the available literature on SSCM. It aims to assess the impact of new technologies on SSCM. Keywords: Smart Supply Chain Management · Industry 4.0 · Artificial Intelligence · Digitalization of Supply Chain · Literature Review

1 Introduction The SC regroups several professionals: suppliers, distributors, manufacturers, logistics, and the final customer to create an optimal collaboration among them. Their ultimate goal is to satisfy the clients by offering them the product they require in less time, with lower costs, and by allowing them to personalize their order and track it in real-time without intermediaries. After all, their opinions are becoming very important today and can influence many people via social networks. SC growth can be the foundation of a company’s innovative competitive strategy. This growth is driven by the development of outsourcing, greater efficiency in global operations, and increased logistics service to customers. Since SC is a major source of revenue for the company, it is essential to manage it properly [1]. The concept of SCM first appeared in the literature in the mid-80s, building on existing assumptions about inter-organizational operations management, systems integration, and information © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 896–904, 2023. https://doi.org/10.1007/978-3-031-29857-8_89

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exchange [2]. The main objective of this concept is to satisfy customer needs while optimizing costs in terms of inventory, resources and processes in the network. Many factors determine whether or not this goal is achieved, such as improving customer orientation and satisfaction, increasing delivery capacity and reducing lead times [3]. Although SCM is a complex process, proper management ensures the efficient operation of many SCs. Hence, the use of SSCM where products or services are administered using new technologies. With the changes brought by I4.0 and especially the use of Smart devices, Humans can interconnect using information obtained from different sources, including direct communications between machines. The Digitalization of SC (DSC) saves time and money for all SC stakeholders. Therefore, organizations are using new technologies to plan based on supply and demand, in addition to the communication between each SC actor [4]. While scholars and practitioners have articulated the concept of SSC in many ways, there are common ideas in those studies: advanced technology has the potential to influence an automated process within a system, thereby improving the visibility of the SC [5]. The objective of this paper is to provide a review of the literature on SSCM and to highlight some future directions for researchers interested in the field. This paper is structured as follows: the next section (Sect. 2) presents I4.0 and its role in SCM. Section 3 details the literature review. Section 4 includes a conclusion and an overview of future work.

2 Role of Industry 4.0 in Supply Chain Management Humanity has experienced three revolutions that lasted nearly 200 years. The first began in the early 18th century with the mechanization of production through water power and steam engines. The second revolution began at the end of the 19th century by introducing electricity production and assembly lines. The third or “Digital” industrial revolution began in the 1970s with the introduction of information technology and electronics. Since its appearance, we can automate some production processes without human assistance. However, automation has always been partial because humans cannot automate any process or task, and machines could not exchange information with each other. The fourth and final industrial revolution (I4.0) emerged in 2011. Known as I4.0 or the “Industry of the future,” it incorporates many emerging technologies that collect and leverage industrial data clusters to drive manufacturing and SC automation, deliver insights in real-time, and provide a way to close the communications feedback loop to accelerate manufacturing business decisions [6]. This important Industrial Revolution allowed the consumer to experience a different perspective. It impacts both the functioning of the enterprise and the nature of what it produces. 2.1 Classic Supply Chain While classic SCM is the union of four independent but unrelated businesses, such as marketing, procurement, warehouse management and transportation, it is not a single entity. SCM’s role is to establish and maintain the connections between the various

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organizations within the company that are responsible for everything from raw material acquisition to delivery of the final product to the end customer [7]. Most SCs are known for their numerous supply or demand incompatibility issues, including overstocking, shortages, and late deliveries, due to complexity, uncertainty, and other factors. Furthermore, as the business environment has become more competitive, so have customers’ requirements. These challenges and issues prompt organizations to address those problems to optimize their business operations and working methods. To address these growing problems efficiently, SCs need to become more efficient and intelligent. 2.2 Smart Supply Chain I4.0 encourages a manufacturing shift toward an intelligent factory; it is a flexible system that is enabled by networked manufacturing components and an effective manufacturing system. Such a system can optimize itself, adjust and learn from new situations in near or real-time, and it can also manage the production process independently. A Smart factory strives to handle management concerns such as asset efficiency, quality, cost, safety, and sustainability. It differs from a typical factory in that it is connected, optimized, transparent, proactive, and agile. However, Smart Factory production requires a SSC, which, like industry, is confronted with a challenging and constantly changing market. Consequently, it requires the latest technology in all its processes in order to create a more flexible and efficient logistics network. Smart SCM can reduce costs, improve profitability, and enable a competitive advantage for many organizations. It uses many emerging technologies, including BD, IoT, Blockchain, and AI.

3 Literature Review of Smart Supply Chain Management To study how new technologies could affect SSCM and to determine future research directions, this paper will review 32 publications from the literature that are relevant to SSCM (9 literature reviews and 23 research articles). It will also classify the technologies that have an impact on it. Militello et al. developed a theoretical framework for SC4.0 in their systematic literature review [9] to identify its major elements and the outcomes they achieved in various industrial fields. Meanwhile, the Authors in [10] examined the existing literature and currently available courses on SSCM to uncover its different challenges. They then presented a conceptual framework with some of the most important factors for a successful SSCM. A recent study adopted an experimental methodology to track the application of I4.0 concepts across several SC levels. It recommended a multi-stage implementation strategy with a special emphasis on organizational enablers including culture, a cross-functional approach, and continuous improvement projects. Other technologies such as augmented reality and autonomous robots were not taken into account in this study [11], which only looked at a small number of tools (Sensor Technology, IoT, and AI). According to Abdirad and Krishnan [12], a careful examination of the I4.0 literature highlighted both the advancement and knowledge gaps in SSCM. The findings of this research showed

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that the Production Society of China believes that I4.0 would significantly affect its manufacturing process. The importance of I4.0 in SC must be emphasized to understand the advantages and challenges of SC digitalization [13] and how I4.0 may increase opportunities for SC and Logistics Management to advance [14]. In their review, Büyüközkan, and Göçer [15] identified gaps in the existing literature and determined the features of previous research by conducting a study of several publications and developing a DSC framework based on the strengths, limits, and weaknesses of the current DSC literature. For Zekhnini et al., it was required to review SCM 4.0 Literature in order to construct a Conceptual Framework that allows managers to better comprehend its objective [16]. BD and BD Analytics (BDA) have a powerful role in managing SSC [17]. According to Awwad et al. [18], adopting BD with SC is mostly motivated by its size, and they then investigated the procedures and activities involved in the growth of BD in the SC, learning that the integration of complex data from SC operations and BD’s scope has practical applications that may handle some of the most urgent issues the SC has been dealing with recently. By developing a classification framework, Authors in [19] displayed how SCM benefits from BD by specifying the areas that apply BDA, BDA models, and the techniques used to develop them. For organizations, BDA facilitates benchmarking of performance across various areas of logistics and SCM. Additionally, it helps businesses continuously track these metrics, resolve issues of bad performance, and identify their underlying problems, resulting in more effective business decisions. This review’s outcomes [20] can serve as a foundation for further research and discussion for both researchers and practitioners. To provide an illustrative framework connecting the aspects of Smart Cities, BDs, and Supply Networks in this research, Tachizawa et al. [21] rely on theories of organization. Furthermore, they claimed that the Smart Cities idea itself has little capacity to enable novel SCM arrangements. This study showed that a straightforward linear model cannot adequately represent the relationships between supply networks, smart cities, and business development. Authors, therefore, proposed an integrated approach that may be applied in upcoming empirical investigations to assess the effects of Smart Cities and BD on SCM. Because of its capacity to identify business patterns, learn business phenomena, find information, and conduct intelligent data analysis, artificial intelligence (AI) has been able to enhance human decision-making processes and productivity. Despite this, its use in SCM is still quite restricted. To fully grasp the potential advantages of AI for SCM, the authors in [22] investigated numerous AI subfields that are most suited for resolving realworld issues relevant to SCM. By fusing SCM operations with Smart and Sustainable technologies, this research [23] proposes a distinctive hybrid approach that combines the Best-Worst Method (BWM) and Quality Function Deployment (QFD) to measure the maturity level of the Digital Transformation. By combining I4.0 technology with SC sustainable procedures, the results demonstrate some of the most important smart technologies and sustainability KPIs. To consider the interactions between the different participants in the Blood SCM through the network connection, [24] provided an IoT-based paradigm for the Blood SCM. In the same way, a conceptual IoT-based architecture has been suggested by

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Authors [25], who provided instructions for information exchange and resource usage in SCM. As one of the first papers in the literature, this research [26] describes the detailed development and testing of an RFID-based drone on inventory tasks. The Authors proposed the theoretical Design and Practical Implementation of a flexible, scalable, and modular UAV-based architecture for cybersecurity purposes. Furthermore, the Inventory Data collected by drones can also be validated, trusted, and accessed by stakeholders using a Blockchain in the proposed system. To facilitate cybersecurity, the first conceptual and practical design and construction of an adaptable, modular, and scalable drone-based infrastructure was described in [27]. It was developed with the goal of obtaining the inventory data acquired by drones, confirming it, guaranteeing its correctness, and making it accessible to the right people. Their proposed solution may make use of a Blockchain. A variety of Smart Logistics Research projects on the latter Technology exists. In fact, there is considerable interest in Blockchain applications, which is why many companies are developing them [28]. For the Smart Manufacturing SC, the academics in [29] presented a Blockchain-based Architecture. They then investigate how Blockchain technology might improve trust management by looking at information flow, logistics, and cash flow. In order to emphasize the significance and advantages of having such a system, they Scenario presented a manufacturing scenario that used Hard Mining Equipment as a case study. The Authors in [30] provided a case study on a number of Machine Learning (ML) models that forecast Retail Sales in the Fashion Industry. This case study highlighted how the Blockchain method is advantageous for SCM, particularly in the case of the COVID-19 epidemic. The retailer is unreliable and might not give the manufacturer accurate demand information, even though the manufacturer and retailer are both participants in the supply chain. In the context of intelligent supply chain management, this study recommends a ML approach for on-demand forecasting. The usage of RFID enables real-time tracking and tracing of each product’s movement within the inventory, while demand is forecasted using Long-Short-Term Memory (LSTM) to reduce overstock or understock issues [31]. By comparing the efficiency of conventional approaches with AI, ML algorithms help to pick and segment suppliers, forecast SC risks, and estimate demand and sales, production, Inventory Management, and other SCM applications [32]. Because of advancements in risk assessment, supply and demand predictions, effective networking across several supply channels, and machine learning, a strong SC has been built. Fundamentally, employing this state-of-the-art technology has opened doors for long-term financial success and increasing competitiveness. As a result, the primary outcome of the digital revolution and ML has been quite encouraging [33]. The new technologies are changing business practices in all sectors. The SC industry is no exception, and six technologies are particularly promising. Indeed, In Table 1; we note that the most commonly used technologies are BD, IoT, Blockchain, RFID, AI and ML. Our review identified six key technologies that have a significant impact on SCM. Figure 1 illustrates how the reviewed articles were distributed according to the technologies that have an influence on SCM. It demonstrates that the majority of Authors (24%)

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Table 1. Technologies that impact Supply Chain Management. Technologies

Authors

BD

Zhang [8] -Tamym et al. [17]- Awwad et al. [18] - Nguyen et al. [19] -Tachizawa et al. [21]-

BDA

Tamym et al. [17]- Awwad et al. [18] - Nguyen et al. [19] - Wang et al. [20]

IoT

Zhang [8] - Shao et al. [11] - Valan et Raj [24] - Bhaveshkumar Pasi et al Rane [25]

Blockchain

Zhang [8]- Frazzon et al. [26]- Fernández-Caramés et al. [27]- Issaoui et al. [28]- Wu et Zhang [29]- Nguyen et al. [30]

RFID

Frazzon et al. [26] - Sardar et al. [31]

AI/ML

Nguyen et al. [30]- Sardar et al. [31]- Tirkolaee et al. [32]- Wisetsri et al. [33]

RFID 8%

AI / ML 16%

BD 20% BDA 16%

Blockchain 24% IoT 16%

BD

BDA

IoT

Blockchain

RFID

AI / ML

Fig. 1. Technologies that impact Supply Chain Management.

used “Blockchain,” which was followed by “BD” (20%), “BDA,” “IoT,” and “AI/ML” (16% each).

4 Conclusion SSCM offers a wide range of opportunities to optimize business processes and increase revenue. Technologies such as BD, IoT, Blockchain, and AI will improve the business and make it easier to process. In addition, these technologies will enable businesses to significantly improve efficiency at all levels of service delivery. This paper presents a

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literature review of 32 papers that exposes the different technologies cited in the literature and their impact on SCM. After close examination of all the papers reviewed, potential areas for further research were identified. To our knowledge we note the following: • Absence of technology and tools to handle SC issues in a digital environment; • No papers discussed the challenges of SC transformation; • Lack of information on the challenges of SC digitalization using I4.0 technologies.

References 1. Ballou, R.H.: The evolution and future of logistics and supply chain management. Eur. Bus. Rev. 19, 332–348 (2007). https://doi.org/10.1108/09555340710760152 2. Cooper, M.C., Lambert, D.M., Pagh, J.D.: Supply chain management: more than a new name for logistics. Int. J. Logist. Manag. 8, 1–14 (1997). https://doi.org/10.1108/095740997108 05556 3. Arnold, J.R.T., Chapman, S.N., Clive, L.M.: Introduction to materials management. Pearson Prentice Hall, Upper Saddle River, N.J (2008) 4. Ng, T.C., Lau, S.Y., Ghobakhloo, M., Fathi, M., Liang, M.S.: The application of industry 4.0 technological constituents for sustainable manufacturing: a content-centric review. Sustainability 14, 4327 (2022). https://doi.org/10.3390/su14074327 5. Montabon, F.L., Pagell, M., Wu, Z.: Making sustainability sustainable. Journal of Supply Chain Management. 52, (2016) 6. Bai, C., Dallasega, P., Orzes, G., Sarkis, J.: Industry 4.0 technologies assessment: A sustainability perspective. Int. J. Production Econ. 229, 107776 (2020). https://doi.org/10.1016/j. ijpe.2020.107776 7. van Goor, A.R., van Amstel, M.J.P., van Amstel, W.P.: Trends in supply chain management. In: European distribution and supply chain logistics, pp. 45–75. Routledge (2019) 8. Zhang, G.: Supply chain opportunities in industry 4.0. In: The 6th international Asia Conference on Industrial Engineering and Management Innovation (2015) 9. Militello, M., Camperlingo, L., Bortoleto, W.C.: Supply Chain 4.0 Results: A Systematic Literature Review. Presented at the Online Platform October 14 (2020) 10. Lee, S.J.: Review pf Literature and Curricula in Smart Supply Chain & Transportation, p. 26 (2018) 11. Shao, X.-F., Liu, W., Li, Y., Chaudhry, H.R., Yue, X.-G.: Multistage implementation framework for smart supply chain management under industry 4.0. Technol. Forecasting Social Change 162, 120354 (2021). https://doi.org/10.1016/j.techfore.2020.120354 12. Abdirad, M., Krishnan, K.: Industry 4.0 in logistics and supply chain management: a systematic literature review. Eng. Manag. J. 33, 187–201 (2021). https://doi.org/10.1080/10429247. 2020.1783935 13. Elkazini, R., Hadini, M., Ali, M.B., Sahaf, K., Rifai, S.: Impacts of adopting Industry 4.0 technologies on supply chain management: Literat. Rev. 31, 7 (2021) 14. Witkowski, K.: Internet of Things, Big Data, Industry 4.0 – Innovative solutions in logistics and supply chains management. elsevier. Proc. Eng., 763–769 (2017) 15. Büyüközkan, G., Göçer, F.: Digital Supply Chain: Literature review and a proposed framework for future research. Comput. Ind. 97, 157–177 (2018). https://doi.org/10.1016/j.compind. 2018.02.010

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16. Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benghabrit, Y., Garza-Reyes, J.A.: Supply chain management 4.0: a literature review and research framework. BIJ 28, 465–501 (2020). https:// doi.org/10.1108/BIJ-04-2020-0156 17. Tamym, L., Benyoucef, L., Moh, A.N.S.: Big data for supply chain management in industry 4.0 context : A comprehensive survey. In: 3th International Conference on Modeling, Optimization and Simuation - MOSIM 2020, p. 11 (2020) 18. Awwad, M., Kulkarni, P., Bapna, R., Marathe, A.: Big data analytics in supply chain: A Literat. Rev., 9 (2018) 19. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in supply chain management: A state-of-the-art literature review. Comput. Oper. Res. 98, 254–264 (2018). https://doi.org/10.1016/j.cor.2017.07.004 20. Wang, G., Gunasekaran, A., Ngai, E.W.T., Papadopoulos, T.: Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 176, 98–110 (2016). https://doi.org/10.1016/j.ijpe.2016.03.014 21. Tachizawa, E.M., Alvarez-Gil, M.J., Montes-Sancho, M.J.: How “smart cities” will change supply chain management. Supply Chain Manag. Int. J. 20, 237–248 (2015). https://doi.org/ 10.1108/SCM-03-2014-0108 22. Min, H.: Artificial intelligence in supply chain management: theory and applications. Int J Log Res Appl 13, 13–39 (2010). https://doi.org/10.1080/13675560902736537 23. Gunduz, M.A., Demir, S., Paksoy, T.: Matching functions of supply chain management with smart and sustainable Tools: A novel hybrid BWM-QFD based method. Comput. Ind. Eng. 162, 107676 (2021). https://doi.org/10.1016/j.cie.2021.107676 24. Valan, J.A., Raj, Dr.E.B: Machine learning and big data analytics in iot based blood bank supply chain management system. IJAEMS 4, 805–811 (2019). https://doi.org/10.22161/ija ems.4.12.4 25. Bhaveshkumar Pasi, Rane, S.B.: Smart supply chain management: a perspective of industry 4.0. Int. J. Adv. Sci. Technol. 29, 3016–3030 (2020). https://doi.org/10.13140/RG.2.2.29012. 01920 26. Frazzon, E.M., Rodriguez, C.M.T., Pereira, M.M., Pires, M.C., Uhlmann, I.: Towards supply chain management 4.0. BJO&PM 16, 180–191 (2019). https://doi.org/10.14488/BJOPM. 2019.v16.n2.a2 27. Fernández-Caramés, T.M., Blanco-Novoa, O., Froiz-Míguez, I., Fraga-Lamas, P.: towards an autonomous industry 4.0 Warehouse: A UAV and blockchain-based system for inventory and traceability applications in big data-driven supply chain management. Sensors 19, 2394 (2019). https://doi.org/10.3390/s19102394 28. Issaoui, Y., Khiat, A., Bahnasse, A., Ouajji, H.: Smart logistics: study of the application of blockchain technology. Proc. Comput. Sci. 160, 266–271 (2019). https://doi.org/10.1016/j. procs.2019.09.467 29. Wu, Y., Zhang, Y.: An integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing. Adv. Eng. Inform. 51 (2022) 30. Nguyen, T.H., Nguyen, H.D., Tran, K.D., Nguyen, D.D.K., Tran, K.P.: Enabling smart supply chain management with artificial intelligence. In: Machine Learning and Probabilistic Graphical Models for Decision Support Systems, pp. 294–310. CRC Press, Boca Raton (2022) 31. Sardar, S.K., Sarkar, B., Kim, B.: Integrating machine learning, radio frequency identification, and consignment policy for reducing unreliability in smart supply chain management. Processes 9, 247 (2021). https://doi.org/10.3390/pr9020247 32. Tirkolaee, E.B., Sadeghi, S., Mooseloo, F.M., Vandchali, H.R., Aeini, S.: Application of machine learning in supply chain management: a comprehensive overview of the main areas. Math. Probl. Eng. 2021, 1–14 (2021). https://doi.org/10.1155/2021/1476043

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33. Wisetsri, W., Donthu, S., Mehbodniya, A., Vyas, S., Quiñonez-Choquecota, J., Neware, R.: An investigation on the impact of digital revolution and machine learning in supply chain management. Materials Today: Proceedings. 56, 3207–3210 (2022). https://doi.org/10.1016/ j.matpr.2021.09.367

A Dynamic MAS to Manage a Daily Planning for a Team Operating Theater Oumaima Hajji Soualfi1(B) , Abderrahim Hajji Soualfi2 , Khalid Chmali3 , Abdelmajid Elmrini3 , Abdellah Elbarkany1 , and Bilal Harras1 1 Faculty of Sciences and Techniques, Mechanical Engineering Laboratory, Sidi Mohamed Ben

Abdellah University, Fez, Morocco {oumaima.hajjisoualfi,abdellah.elbarkany, bilal.harras}@usmba.ac.ma 2 Faculty of Sciences and Techniques, Departement of Science Computer, Moulay Ismail University, Errachidia, Morocco 3 Department of Orthopedic Surgery B4, Faculty of Medicine and Pharmacy of Fez, Hassan II University Hospital, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. Surgical planning is a preponderant step in the management of operating theaters, which becomes more and more solicited; considering the multiplicity and the complexity of the human and material components, which intervene there; in order to face the various disturbances hindering the normal course of the surgical activity. It is a subject widely discussed in the literature with the realization of several solutions and applications, but which remain globally incompatible with the realities of the surgical process. For this reason, we propose a daily surgical planning realized with a multi-agent system (MAS) based on distributed artificial intelligence (DAI). We describe some basic architectural entities of MAS in relation with the surgical planning, before presenting their application on a real case of the orthopedic surgery department B4 of the CHU Hassan II of Fez-Morocco. The objective of this work is to elaborate a daily, dynamic and real time surgical program answering the various possible and frequent disturbances altering the process of the operating theater. Keywords: multi-agent system · distributed artificial intelligence · operating theater planning

1 Introduction The management of health care structures aims at optimizing their processes and increasing their overall performance, while ensuring a certain rapid local reactivity in case of disturbances such as emergencies, annulments, delays and unavailability of resources [1]. The operating theater is one of the most complex components of a hospital [2]. The Operating Room is a key resource of all major hospitals, but it also accounts for up 40% of resource costs [3]. The management of its function remains delicate because multiple constraints must be taken into account [4]. It operates according to a provisional © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 905–915, 2023. https://doi.org/10.1007/978-3-031-29857-8_90

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planning of the surgical interventions to be performed during a predefined time interval. The elaboration of an operating program is an important step, widely discussed in the literature. The multi-agent system (MAS) paradigm can be involved in order to optimize the surgical planning and to achieve the set objectives [5]. The objective of this work is to elaborate a daily, dynamic and real time operating program responding to the various disturbances that can prevent the normal course of surgical activities in the operating theater.

2 Problematic The problem with the approaches to operating theater management already proposed in the literature lies in the incompatibility of the theoretical models with the many functional realities of the process. Many simplifying assumptions are made to make the model mathematically tractable. Even the often complex stochastic models, which are supposed to take into account uncertain events, only consider a reduced subset of these events. Indeed, incomplete and rigid approaches with unrealistic assumptions have led to inefficient management in the field [1]. We conducted a study at the orthopedic surgery department B4 of the Hassan II University Hospital of Fez - Morocco aiming at developing a dynamic method for surgical planning that takes into consideration the frequent disturbances affecting this process. To solve this problem, we propose the use of a multi-agent system based on distributed artificial intelligence.

3 MAS and Operating Activity 3.1 Operating Process The elements that make an operating theater function properly or not are the result of a fluid planning activity and good management [6]. The planning activity affects the entire surgical process, which is divided into three phases [7, 8]: – The pre-operative phase corresponds to the management of the patient from the moment he/she enters the hospital until he/she arrives in the operating room. – The intra-operative phase defines the period of the operation that takes place in the operating rooms, from the patient’s arrival in the operating room to his or her discharge from the room. – The post-operative phase covers all care after the operation, i.e. when the patient is discharged from the operating room. Our intervention concerns the automatic management of the planning of the operating theater in the pre-operative phase. 3.2 MAS MAS is a new programming paradigm that is increasingly taking an important place among the technologies for the development of complex systems and distributed and

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heterogeneous systems where the solution is the result of the interaction of several entities [9]. MASs are at the intersection of several scientific fields: distributed computing and software engineering, artificial intelligence, artificial life. They also draw on studies from other related disciplines including sociology, social psychology, cognitive science and many others [10]. 3.3 Multi-agent Platforms Natural evolution of Multi-Agent Systems (MAS) has leaded them to migrate from the research laboratories to the software industry. This migration is good news for the entire MAS community, but it also leads to new expectations and new questions about multiagent system methodologies, tools, platforms, reuse, specification, and so on [11]. By analogy with the development tools for systems based on the object paradigm, development tools for systems based on the agent paradigm have appeared, notably generic platforms. Among these platforms, we mention Development and Implementation of Multi-Agent Systems (DIMA), Java Agent DEvelopment Framework (JADE) and MultiAgent Development Kit (MADKIT) [12]. This list is not exhaustive, there are also other platforms that have been used for the implementation of several applications [12]. Some criteria were taken into consideration for the choice of the platform for the development of the multi-agent system, namely: Conformity of the interaction protocols with the Foundation for Intelligent Physical Agents (FIPA) specifications, Flexibility, Distribution and Extensibility [12]. Taking into account these criteria, we have chosen the JADE platform to implement our distributed architecture. JADE is a software framework to make easy the development of multi-agent applications in compliance with the FIPA specifications. JADE can then be considered a middle-ware that implements an efficient agent platform and supports the development of multi agent systems. JADE agent platform tries to keep high the performance of a distributed agent system implemented with the Java language [13]. In addition, this platform supports mobility and scalability. It also offers the possibility of integrating web services, distribution to different servers and communication between several JADE platforms [12].

4 Application 4.1 Role of the Different Agents MAS is composed of four agents: – Manager AGENT is the master agent who cooperates with all other agents to ensure the planning of the operating room, taking into account the predefined constraints – Surgeon AGENT represents a team of surgeons of a given specialty. This agent asks the Manager AGENT to do the daily planning of his team – Room AGENT represents a surgical room on a given day. Each Room AGENT is responsible for establishing his individual plan in collaboration with the Manager AGENT – DataBase AGENT is a database that contains all the necessary informations for each scheduled intervention. This data can be extracted in a predefined format.

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4.2 Constraints The Manager AGENT must take into consideration certain constraints; to achieve the main objective of our work; namely: – Minimize the waiting time between two surgical procedures performed in the same room. – Each surgical team must operate a single patient in the same time slot. – Maximize the occupation of the operating rooms in order to alleviate the long waiting lists of the patients. 4.3 MAS Algorithm When the Manager agent receives a reservation request from a Surgeon agent (Team A for example), it enriches the request with information obtained from the DataBase agent, relating to preferences, constraints and the type of room. It launches a Call For Proposal to the Room agents. Each Room calculates the virtual cost of each of these available time slots that may be suitable to respond to the Agent Manager by indicating the corresponding virtual cost. The Agent Manager analyzes the received answers and decides to choose the one that proposes the lowest virtual cost and that respects the constraints related to our objective. It informs the Room agents of its decision and asks the winner to include this reservation, then confirms (or not) the reservation of the Surgeon Team agent. When there is an incident that causes a delay or even a cancellation of the intervention, the DataBase agent informs the agent manager so that he can reschedule the interventions or even the operating rooms (if a room has become unavailable for example). The Manager agent will reissue a new call for tenders to the Room agents and then send a notification to the Surgeon agent to indicate the new planning. 4.4 Results To test the efficiency of our algorithm, we took the case of the orthopedic surgery department B4 of the Hassan II University Hospital of Fez (Morocco). This department contains four teams of surgeons using three rooms: Main Room, Ambulatory Room and Emergency Room. We are going to realize the daily operative planning; of a single surgical team; responding to the possible incidents. The platform used (JADE) is composed of containers of agents that can be distributed on a network. The agents live in the containers which are java processes that provide all the necessary services for hosting and running the agents. The platform must have a main container that represents the starting point (Fig. 1):

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Fig. 1. Agents of JADE Multi Agent System.

Each team can request a planification through the agent “Surgoen”, just by indicating the name of his team (Fig. 2):

Fig. 2. Surgeon Team AGENT of MAS.

We have taken as an example two days of the week (Monday and Tuesday) in order to realize the operating planning of a surgical team “Team A”. When the Agent Manager receives the request from the Surgeon agent (“Manage” button in Fig. 2), it will search for the best possible slot in the agent Room to satisfy this request and give the operative planning (P1) for the requested day (Fig. 3): Note (in the “Human Resources Intervention” section of the intervention details) that: “su": surgeon; an": anaesthetist; nu”: nurse; tr”: medical transport officer. In response to possible incidents such as: urgent cases, absence of personnel, insufficient of equipment suitable for surgery, absence of the patient…, occurring during the same day of surgery, the surgical team receives a notification that informs them of the change imposed on the operating plan (P1) (Fig. 4). The surgical team will consult the new surgical planning (P2) (Fig. 5). This change is made in order to optimize the planning of the operating rooms. This is due to the SMA with dynamic and reactive criteria in real time, which will substitute all types of losses (dead time between interventions, insufficient human and material resources…). It can be noted that the third intervention (lasting one and a half hours) and the fourth intervention (lasting 45 min) were replaced by a single intervention (lasting two hours). The cause was the declaration of absence of the patient planned for the third intervention. Therefore, the MAS rescheduled this procedure for Tuesday morning and the fourth procedure for Wednesday evening. The MAS therefore took advantage of the afternoon to schedule a single two-hour intervention. It should also be noted that the surgical team performed its second intervention even though the medical transport officer was absent. We can therefore tolerate the absence of this personnel.

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Fig. 3. Operating planning (P1) of Team A on Monday.

Fig. 4. Notification of the change in the operating planning.

Regarding Tuesday’s planning (Fig. 6), the first intervention went through as planned by the MAS, while the second was performed in the main room since the ambulatory room became unavailable.

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Fig. 5. Operating planning (P2) of Team A on Monday.

A third intervention lasting two and a half hours was planned for the following day, but since this intervention requires the presence of all the team members, while the incident of two nurses will be absent, the MAS took the decision to substitute this intervention with two interventions where the absence of these nurses will not have much impact as in the first case (Fig. 7). We can notice that the operating planning (P2) obtained responds to unforeseen events, respects the defined constraints and keeps its dynamic and reactive criterion in front of the various disturbances arriving in real time, which agrees with the targeted objective. 4.5 Discussion The operating planning established by conventional methods with non-real data does not take into account the weekly variability, however common, of surgeons’ preferences regarding their availability for a given time period of the week, the number of hours granted to a given team of surgeons or to a given specialty, the duration of vacations, and the availability of a specific operating room for a specific team. Indeed, some authors [14] discuss the characteristics of the Mixed Integer Linear Programming (MILP) procedure

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Fig. 6. Operating planning (P1) of Team A on Tuesday.

to establish an operating room (OR) block schedule. They then propose an alternative approach based on distributed artificial intelligence (DAI) to establish an optimal surgical program. Simulation tests on a test case using real data are performed by both methods. The results lead to the conclusion that the (DAI) model is superior [14]. The work of [15] focuses on a multi-agent system for integrated dynamic planning of an operating room. The agents cooperate and coordinate their actions to find a globally efficient quasi-optimal scheduling, capable of maintaining the initial planning optimization while taking into account the disturbances caused by the occurrence, in real time, of unexpected emergencies. The proposed control model is designed to simultaneously improve OR efficiency and responsiveness in emergency surgery practice [15]. Other authors [16] present and compare two operating theatre planning method. The two methods consider different type of constraints but the target is to propose an operating theatre planning for the OR managers. To eliminate the additional costs a flexible date of hospitalisation can be proposed. A mixed method of the Method1 and Method2 can be suggested. The results obtained depend on the quality of the used data set. A no good information about the operating theatre planning can not perform a good planning. Our work is based on the construction of a daily, automatic, dynamic, real-time planning of the operating theater, which takes into account the various possible and frequent incidents and disturbances

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Fig. 7. Operating planning (P2) of Team A on Tuesday.

affecting the ordinary sequence of the operating process. Our planning, obtained with the help of a MAS based on distributed artificial intelligence, optimizes the performance of the operating room, thus contributing to the improvement of patient care.

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5 Conclusion and Perspectives Our work focuses on the management of the operating planning of each surgical team separately, thus facilitating the consideration of disturbances in the normal course of the operating planning and giving solutions to keep a real time, dynamic and reactive planning. The main limitation of our work is its tiring character linked to the daily management of the operating planning. To solve this problem, we propose to build a weekly operating plan taking into account the surgical activity of all the teams of the studied department, this will be the subject of a future work.

References 1. Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: A cooperative control model for operating theater scheduling. In: 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 302–308. IEEE (2018) 2. Persson, M., Persson, J.: Optimization modelling of hospital operating room planning: analyzing. In: Operational Research for Health Policy: Making Better Decisions. In: Proceedings of the 31st Annual Conference of the European Working Group on Operational Research Applied to Health Services 2007, Peter Lang, p. 137 (2007) 3. Barbagallo, S., Corradi, L., de Ville de Goyet, J., et al.: Optimization and planning of operating theatre activities: an original definition of pathways and process modeling. BMC Med. Inform. Decis. Mak. 15, 38 (2015) 4. De Winne, M.R.: Vers un outil d’aide à la planification et à l’ordonnancement des blocs opératoires. Doctoral dissertation, Université de Technologie de Troyes (2006) 5. Saleh, B. B.: Approche Intelligence Artificielle Distribuée pour une planification réactive et une aide à la conduite du processus de blocs opératoires hospitaliers .Doctoral dissertation, Université Bourgogne Franche-Comté; Université Libanaise (2019) 6. Bonvoisin, F.: Evaluation de la performance des blocs opératoires : du modèle aux indicateurs. Doctoral dissertation, Université de Valenciennes et du Hainaut-Cambresis (2011) 7. Saadani, N.H., Guinet, A., Chaabane, S.: Ordonnancement des blocs opératoires. InMOSIM: Conférence francophone de Modélisation et Simulation, vol. 6 (2006) 8. Féniès, P., Gourgand, M., Tchernev, N.: Une contribution à la mesure de la performance dans la supply chain hospitalière : l’exemple du processus opératoire. In: 2ème Conférence francophone en Gestion et Ingénierie des Systèmes Hospitaliers (GISEH). (September 2004) 9. Marir, T., Mokhati, F., Bouchlaghem-Seridi, H., Acid, Y., Bouzid, M.: QM4MAS: a quality model for multi-agent systems. Int. J. Comput. Appl. Technol. 54(4), 297–310 (2016) 10. Chaib-Draa, B., Jarras, I., Moulin, B.: Systèmes multi-agents: principes généraux et applications. Edition Hermès 242, 1030–1044 (2001) 11. Ricordel, P. M., Demazeau, Y.: From analysis to deployment: A multi-agent platform survey. In: International Workshop on Engineering Societies in the Agents World, pp. 93–105. Springer, Berlin (August 2000) 12. Benhajji, N.: Système multi-agents de pilotage réactif des parcours patients au sein des systèmes hospitaliers. Doctoral dissertation, Université de Lorraine (2017) 13. Bellifemine, F., Poggi, A., Rimassa, G.: Developing Multi-agent systems with JADE. In: Castelfranchi, C., Lespérance, Y. (eds.) ATAL 2000. LNCS (LNAI), vol. 1986, pp. 89–103. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44631-1_7 14. Saleh, B.B., Saleh, G.B., Barakat, O.: Operating theater management system: blockscheduling. In: Masmoudi, M., Jarboui, B., Siarry, P. (eds.) Artificial Intelligence and Data Mining in Healthcare, pp. 83–98. Springer, Cham (2021). https://doi.org/10.1007/978-3-03045240-7_5

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15. Bou Saleh, B., El Moudni, A., Hajjar, M., Barakat, O.: A multi-agent architecture for dynamic scheduling of emergencies in operating theater. In: Proceedings of SAI Intelligent Systems Conference. pp. 1256–1272. Springer, Cham (September 2018) 16. Chaabane, S., Meskens, N., Guinet, A., et al.: Comparison of two methods of operating theatre planning: application in Belgian hospital. J. Syst. Sci. Syst. Eng. 17, 171–186 (2008)

Green Transportation Balanced Scorecard: A VIKOR Fuzzy Method for Evaluating Green Logistics Initiatives Badr Bentalha1(B)

, Aziz Hmioui1 , and Lhoussaine Alla2

1 National School of Business and Management, Sidi Mohammed Ben Abdellah University,

Fez, Morocco [email protected] 2 National School of Applied Sciences, Sidi Mohammed Ben Abdellah University, Fez, Morocco

Abstract. Green Transport is an organisational approach to minimising waste and improving the ecology of supply chains. This vision contributes to better sustainability of chains and a harmonised consideration of ethical and environmental considerations with standard economic prerogatives. The integration of the multiple facets of the Balanced Scorecard performance allows for a multidimensional and holistic analysis of the parameters of green transport. This study aims to develop a Green Transport approach to evaluate and compare possible alternatives by combining Fuzzy and VIKOR approaches. The basic idea of this work is the combination of a fuzzy method integrated with VIKOR by the principles of the Balanced Scorecard. The aim is to integrate a categorization of the facets of sustainability with respective sub-criteria and weightings while contextualizing our study with current and empirical data. The study focused on Green Transport alternatives and the optimal choice between these alternatives in a multi-criteria optimisation framework for a drug distribution company. The study revealed that the electric bicycle as a green mode of transport allows an improvement of the efficiency of the supply chain and respects the chosen weighting of the criteria of the Balanced scorecard. Fuzzy weighting of the indicators and the selection of alternatives by the VIKOR method seem to be two complementary methods for a multi-criteria optimisation of green transport. Keywords: Green transportation · Fuzzy approach · method VIKOR · MCMD · Supply chain · Sustainable Supply chain · Green logistics

1 Introduction Green supply chain management is a widely discussed topic among researchers [1]. Indeed, the solicitation and adoption of sustainability principles in supply chains are mainly aimed at achieving efficiency, innovation, and cost reduction [2]. It aims at achieving a sustainable competitive advantage in terms of quality, minimum waste, reduced pollution, improved brand awareness, and high return on investment [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 916–925, 2023. https://doi.org/10.1007/978-3-031-29857-8_91

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The transport sector is crucial for the development and growth of the economy. However, this sector is also responsible for pollution and the exploitation of natural resources. Thus, sustainable sustainability requires a balanced and homogeneous development. The Balanced Scorecard is developed to show the purpose through a complete fixed of performance signs. It integrates several facets of performance to combine economic, social, and societal considerations. Our problematic addresses the understanding of the best choices for green transport using both the literature review, the Fuzzy method, and the VIKOR approach. Using a previous literature review on the topic of green transport and a hierarchy of Fuzzy indicators [4] we want to deepen the reflection by offering a drug distribution company the most optimal choice of green transport by integrating the VIKOR approach with the fuzzy indicators of sustainable transport. After defining the list of green transport indicators, we presented a list of seven possible alternatives for the managers of the drug distribution company. The aim is to choose the most optimal sustainable transport mode according to a multiple weighting of the criteria. The plan followed in our work will be chained on three levels. First, a theoretical background is discussed to analyse green transport’s theoretical and conceptual contours. Secondly, the VIKOR method will be presented to identify its methodological contours. Finally, our empirical results will be presented to discuss the selected best practice of green transport.

2 Theoretical and Conceptual Framework 2.1 Green Logistics Initiatives Green logistics management is an executive approach that targets to assimilate economic and environmental variables [5]. A sustainable supply chain is a seamless integration of facilities, operations, people, and transport operations. The objective of these different components is to work in harmony to achieve the logistics objectives and also to reduce the environmental impacts of these chains. It then encompasses all phases of the life cycle. The green or sustainable chain integrates responsible purchasing, green production, green distribution, and also green reverse logistics operations management. In these responsible and ecological chains, processes, people, and organisations complement each other to achieve a fast, efficient, and sustainable delivery to the customer [6]. Nevertheless, the size and forms of these different organisational entities differ according to size, activity, or sector. Green logistics practices are initiatives undertaken to comply with environmental laws, respect the company’s strategic orientations, and voluntarily reduce the influence of its processes and actions on the environment. These include green purchasing, eco-engineering, environmental partnership, and reversal logistics. The four practices mentioned are the pillars of a sustainable supply chain. In a broader view, all factors (transport, warehousing, packaging, etc.) must be taken into account to improve the environment.

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2.2 Green Transportation Every day a huge quantity of goods and materials and an enormous total of people are continuously transported. This transport sector is of great importance for economic growth. It is transport infrastructure and networks that make these transport operations possible. Having an adequate transport system allows for a high degree of market integration and consequently increases development prospects. Transportation consumes 28% of US energy [7]. Under current policy and technology trends, carbon dioxide (CO2) emissions are projected to be 50% of total emissions by 2030 and 80–130% by 2050 compared to 2007 levels. Thus, it is clear that transport is responsible for a significant proportion of current pollution. For this reason, several initiatives are being considered in the context of green and sustainable transport. Green transport includes modes of transport that do not depend on declining natural resources such as fossil fuels. It, therefore, relies on renewable energy sources. [8] defines Green Transportation as: “Transportation service that has a lesser or reduced negative impact on human health and the natural environment when compared with competing transportation services that serve the same purpose”. Many countries and companies have chosen green transport because of the benefits it offers. These include a reduction in the negative effects of pollution, especially greenhouse gas emissions, and a remarkable increase in the social and image-related benefits to society. Thus, it is essential to raise awareness of the importance of using renewable energy sources such as wind, solar and hydro power. 2.3 Green Transportation Balanced Scorecard The Balanced Scorecard (BS) is developed to show the purpose and strategy through performance indicators. Performance is then assessed from a broad time perspective as the performance factors considered are both long and short term. “By combining financial, customer, internal process, innovation, and organisational learning perspectives, the Balanced Scorecard helps managers to understand, at least implicitly, the many interrelationships” [9]. The idea of comprehensiveness and balance conveyed by the Balanced Scorecard is supported by the assumption that there is a universal model of performance. These are both strategic and operational indicators. The BS measures four performance factors [10]: customer competitiveness, competitive and societal positioning (sustainable products, reputation, image, and brand); operational efficiency (supply chain, governance, risk management); internal competencies (innovation capacity and human capital) and financial performance (actual and potential revenues and costs, cost of non-compliance, cost of conflict with stakeholders, attractiveness to investors). The Balanced Scorecard, as an instrument for implementing strategy and operational mechanisms, aims to fulfill three main functions: Firstly, it is a means to communicate the company’s strategy. The flexibility of the firm requires a high speed in aligning the structure with the strategy and therefore requires communication and ownership of the strategy by the whole organisation [11]. The Balanced Scorecard has several advantages, but does not have a consensus. The tool focuses on monitoring the contribution of employees and suppliers and not enough

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on the importance of intangible and immaterial aspects. These aspects are of great importance in the current digital age. Also, the Balanced Scorecard does not integrate the environment and does not take into account the impact of external variables on the company’s performance [12]. It is also criticised for creating a hierarchy between the four axes and for subordinating the first three axes to the financial axis. [13] presented three possible mechanisms for mainstreaming environmental and social aspects into the Balanced Scorecard. These are to take standard measures without modification, to take standard measures, and complement them with specific green transport measures, and finally to create completely specific green transport measures. It is therefore possible to integrate green transport measures into a balanced scorecard approach.

3 Object, Field, and VIKOR Method Our problematic deals with an understanding of the best choices for green transport using both the literature review, the Fuzzy method, and the VIKOR approach. We have already carried out a comprehensive literature review on the subject of green transport and a hierarchy of fuzzy indicators [4]. The purpose of this study is to complete the reflection by offering a company the most optimal choice of green transport using the VIKOR approach. Thus, the approach followed is schematised in Fig. 1.

Phase 1 : Problem definion (Green transportaon with a Balanced scorecard model)

Phase 2 : Criteria evaluaon (Fuzzy Approach)

Phase 3 : Construcon of decision matric to determinate the best green transportaon (Vikor method) Fig. 1. VIKOR optimisation process of Green transportation

The list of green transportation indicators is derived from our literature review with an operationalisation using the Fuzzy method [4]. In this previous work, we classified the dimensions of green transportation with an operationalisation of the measurement tools. In the present work, we will answer the question of the optimal choice of sustainable transportation mode for a drug distribution company. This company wants a green investment that covers all facets of sustainability. Indeed, the issues of last mile logistics

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and drug distribution have received great importance, especially during the period of COVID ([14, 15]). For this reason, we have presented the dimensions of green transport developed and the weight of each criterion obtained by the Fuzzy method. It is a matter of choosing between seven green transport alternatives respecting optimisation according to the weight of the green transport indicators developed by the Fuzzy approach. To

Fig. 2. Diagram of VIKOR method used [17]

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solve the performance evaluation of the Green Transportation techniques according to the Balanced Scorecard approach, we try to use the VIKOR method. The VIKOR method is a variant of multi-criteria decision support approaches (MCDM). Multi-criteria decision making approaches are useful in such problems to obtain a compromise solution. The final objective is to find a solution considered optimal among several probable alternatives. The aim is therefore to reduce the loss of profit associated with a non-optimal choice. It is therefore a matter of choosing the best solution from a set of possible solutions according to previously defined and weighted criteria. The method was introduced by Serafim Opricovic and developed in several other works [16]. It can be translated as Multi-Criteria Optimisation and Compromise Solution. Figure 2 displays a graph of the VIKOR method offered by [17].

4 Results and Discussions Several possibilities exist for green transport. The list of green transport indicators retained is taken from our literature review with a validation following the Fuzzy method [4]. The main question of the present work is to choose the best green transport alternative by respecting the indicators and the weightings retained in a Balanced Scorecard framework. For greater validity of the model, we have kept only those indicators with an acceptance threshold above 4.5 of the center of the class. For this reason, we have summarised the indicators retained and the global and partial weighting of the indicators (Table 1) (Table 2 and 3). After defining this list of green transport indicators, we have presented a list of seven possible alternatives for managers of a drug distribution company. The aim is to choose the most optimal sustainable transport mode for a drug distribution company. The seven alternatives proposed are as follows: 1. 2. 3. 4.

Bicycle (GT1): This is an E. 520 city bike with a 6-speed clevis gear. Electric bike (GT2): E. 28 electric bike with 4 assistance levels. Electric vehicles (GT3): This is the 225 C. Ami. Electric motorbikes (GT4): This is a Y.3 electric motor with a direct drive transmission. 5. Electric Drone (GT5): V.UAV 5kg. 6. Photovoltaic Drone (GT6): S.P.x 7. Hybrid car (GT7): T.Y. The seven Green Transport alternatives were evaluated taking into account the four main criteria of the Balanced Scorecard and the 17 sub-criteria according to the opinions of the selected experts. The Green Transport initiatives were ranked using the VIKOR methodology. In the last step of VIKOR, the acceptable benefit (Condition 1) and the acceptable stability (Condition 2) under the decision making conditions were studied. For the DQ (1/(7–1) = 0.1667) with alternative 1 - alternative 2 = 0.29181 > 0.1667. Therefore, the acceptable benefit was met. For the second condition, the Si and Ri values of alternative GT4 were the minimum values, which means that the alternative obtains the best rank for

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B. Bentalha et al. Table 1. Green transport indicators by the weighting of each item and group Subcriteria Reduction of external costs (price) A1 Customer satisfaction A2 Respect of deadlines A3 Indicators

Learning, growth and innovation dimension

Internal process dimension

Dimensions Stakeholder

Financial dimension

Level of transparency

B1

Cost of transportation and inventory

B2

Green partnership platform with all stakeholders Sustainable waste management Eco-efficient network optimization Total energy usage Implementation of environmental standards (e.g. ISO 14001) Maximum utilization of transportation capacity Use of alternative fuels and engines Knowledge sharing among supply chain members Development of R&D activities

B3 C1 C2 C3 C4 C5 D1 D2 D3

Development of green technologies in partnership with supply chain members

D4

Eco-efficient driver incentive system

D5

Sophisticated software for route optimization

D6

Local Weight Prices 6,124 Satisfaction index 6,662 Average speed 5,079 of information 5,994 available Average energy 5,148 consumption Presence of part4,699 nership Pollution 7,094 Possible network 7,004 Energy storage 5,661 capacity Presence of stand5,548 ards Maximum capac5,019 ity Used technology 7,186 Information 7,094 shared with members Possibility of de7,004 velopment unit of measure

Possibility to ex6,915 change technologies Motivations of 6,829 HR Presence of soft5,728 ware

Weight Global % Weight 5,84% 17,05% 6,36% 4,85% 5,72% 4,91% 15,12% 4,48% 6,77% 6,68% 5,40%

28,94%

5,29% 4,79% 6,86% 6,77% 6,68% 6,60%

38,89%

6,52% 5,47%

the Si and Ri values as well. Therefore, the second condition for checking the obtained rank was fulfilled. The results obtained from the VIKOR method show that the green transport mode that meets the criteria identified and weighted according to the Fuzzy logic is the electric bicycle. The most convincing mode and the closest to the optimal solution is the electric motor. The result obtained by the VIKOR method is reasonable compared to previous studies on the evaluation of green transport initiatives. The VIKOR technique presents an optimal trade-off solution that represents a maximum multi-criteria optimisation solution. The VIKOR technique proposed and used in this paper can help a drug retail company to select an appropriate valid and sustainable transportation approach. This technique can also be used by different companies in other sectors, as it is extremely flexible and fast. The sustainability of transportation is a multidimensional issue that requires the adequacy of several parameters combined together to achieve an economic, social and societal balance. The solution proposed by the Fuzzy method and VIKOR seems to solve this dilemma by offering a homogeneous and valid multi-criteria optimization.

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Table 2. Alternatives and attributes for green transportation selection

D 1

D2

6,86%

6,77%

6,68%

6,60%

6,52%

5,47%

Development potential (scale of 10)

Possibility of exchanging technologies (scale of 10)

HR motivations (scale of 10)

Presence of software (scale of 10)

C5

D3

D4

D5

D6

600

7

35

7

0

4

0

5

0

7

10

10

5

2

6

1

4

GT2

1100

9

45

9

1,4

7

2

7

0,25

8

30

8

8

8

7

8

7

GT3

10300

6

45

4

28

4

12

6

40

8

200

9

9

9

4

8

6

GT4

1430

8

45

4

18

5

8

4

25

5

60

9

4

7

7

6

7

GT5

24000

5

40

3

8

1

8

1

15

2

5

9

4

2

1

6

4

GT6

40000

8

45

2

0

1

0

1

0

2

5

9

2

3

4

2

2

GT6

13880

8

60

9

1,6

9

4

8

40

6

200

4

3

3

3

2

5

fi+

600

9

60

9

0

9

0

8

0

8

200

10

9

9

7

8

7

fi-

40000

5

35

2

28

1

12

1

40

2

5

4

2

2

1

1

4

Max capacity (Kg)

GT1

Pollution (mg en Km)

Presence of standards (scale of 10)

4,79%

C 4 5,29%

C3 5,40%

6,68%

C2

Average consumption Energy storage capacity (kWh)

4,48% Presence of partnerships (scale of 10)

6,77%

4,91% Average energy consumption kWh

C1

Possible network (scale of 10)

5,72% % of available information (scale of 10)

B3

Average speed

6,36% 4,85%

B2

Satisfaction (scale of 10)

5,84%

B1

Price $

Alternatives

A A3 2

Used technology (scale of 10) Information shared with members (scale of 10)

Attributes A1

Table 3. Valuesof ideal solution “Qi” and ranking (v = 0.5) Alternatives

Sj

Rj

Qi

Rang

GT1

0,4451

0,0668

0,50255

4

GT2

0,1485

0,0418

0,00000

1

GT3

0,4126

0,0677

0,48431

3

GT4

0,4165

0,0484

0,29181

2

GT5

0,7438

0,0668

0,75341

6

GT6

0,6894

0,0912

0,95431

7

GT7

0,452828

0,0686

0,52722

5

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5 Conclusion The selection of green transportation initiatives requires the consideration of several parameters and indicators. It is a matter of integrating technical, commercial, ethical, social, and environmental considerations to take the best option. It is therefore by definition a multi-criteria optimization problem. The optimal solution sought among the list of alternatives selected requires the application of a multi-criteria method such as VIKOR which ranks the possibilities according to the given weighting. To carry out this weighting of the selected criteria we used the Fuzzy technique with the VIKOR method. In this paper, we make several contributions. First, we have integrated the Fuzzy method with VIKOR by the principles of the Balanced Scorecard. This is indeed a combination of several numerical methods in a robust managerial framework. Secondly, we integrated a broad categorisation of sustainability facets with respective sub-criteria and weightings. Finally, we contextualised our study with current and empirical data. This work can be extended in the future with comparative studies of alternative rankings obtained with other multi-criteria optimisation methods. The effect of changes in the weights of the criteria can also be analysed. Finally, an introduction of new probabilistic variants of the VIKOR method is possible.

References 1. Khan, S.A.R., Yu, Z., Golpira, H., Sharif, A., Mardani, A.: A state-of-the-art review and meta-analysis on sustainable supply chain management: Future research directions. J. Cleaner Product. 278(1) (2021) 2. Kumar, S., Teichman, S., Timpernagel, T.: A green supply chain is a requirement for profitability. Int. J. Prod. Res. 50(5), 1278–1296 (2012) 3. Dubey, R., Gunasekaran, A., & Papadopoulos, T. Green supply chain management: theoretical framework and further research directions. Benchmarking: An International Journal. 24(1), 184–218 (2017) 4. Bentalha, B.: Green Transportation balanced scorecard model: a fuzzy-delphi approach during COVID-19. In: Lahby, M., Al-Fuqaha, A., Maleh, Y. (eds) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham. https:// doi.org/10.1007/978-3-030-96429-0_5. (2022) 5. Tseng, M.L., Islam, M.S., Karia, N., Fauzi, F.A., Afrin, S.: A literature review on green supply chain management: Trends and future challenges. Resour. Conserv. Recycl. 141, 145–162 (2019) 6. Hmioui, A., Bentalha, B., Alla, L.: Service supply chain: A prospective analysis of sustainable management for global performance. In: 2020 IE. In 2020 IEEE 13th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), pp. 1–7. IEEE (2020) 7. EIA, Energy Information Administration Monthly Energy Review, April 2022, preliminary data (2022). https://www.eia.gov/energyexplained/use-of-energy/transportation.php (last accessed in 26 November 2022) 8. Bjorklund, M.: Influence from the business environment on environmental purchasing — Drivers and hinders of purchasing green transportation services. J. Purch. Supply Manag. 17(1), 11–22 (2011) 9. Kaplan, R., Norton, D.: The balanced scorecard: measures that drive performance. Harvard Bus. Rev. 70(1). 71–79 (1992)

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10. Asiaei, K., Bontis, N.: Using a balanced scorecard to manage corporate social responsibility. Knowledge and Process Management. Knowl. Process. Manag. 26(4), 371–379 (2019) 11. Zahoor, A., Sahaf, M.A.: Investigating causal linkages in the balanced scorecard: an Indian perspective. Int. J. Bank Market. 36(1), 184–207 (2018) 12. Nørreklit, H., Kure, N., Trenca, M.: Balanced scorecard. In: The International Encyclopedia of Strategic Communication, pp. 1–6 (2018) 13. Sidiropoulos, M., Mouzakitis, Y., Adamides, E., Goutsos, S.: Applying sustainable indicators to corporate strategy: the eco-balanced scorecard. Environ. Res. Eng. Manag. 1(27), 28–33 (2004) 14. Bentalha, B., Hmioui, A., Lhoussaine, A.L.L.A.: Last mile logistics applied to the distribution of COVID-19 vaccines: A prospection of good practices. Alternatives Managériales Economiques 3(3), 41–61 (2021) 15. Alla, L., Bentalha, B., Bouhtati, N.: Assessing supply chain performance in the covid 19 context: a prospective model. In: 2022 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), pp. 1–6. IEEE (2022) 16. Opricovic, S., Tzeng, G.H.: Multicriteria planning of post-earthquake sustainable reconstruction. Comput.-Aided Civil Infrastruct. Eng. 17(3), 211–220 (2002) 17. Anvari, A., Zulkifli, N., Arghish, O.: d. Int. J. Adv. Manuf. Technol. 71(1), 829–841 (2014)

Digital Healthcare

Diabetic Retinopathy Prediction Based on Transfer Learning and Ensemble Voting Mohammed Oulhadj1(B) , Jamal Riffi1 , Chaimae Khodriss1,2 , Adnane Mohamed Mahraz1 , Ahmed Bennis2 , Ali Yahyaouy1 , Fouad Chraibi2 , Meriem Abdellaoui2 , Idriss Benatiya Andsaloussi2 , and Hamid Tairi1 1 LISAC Laboratory, Faculty of Science Dhar El-Mahraz, Sidi Mohamed Ben Abdellah

University, Fes, Morocco [email protected] 2 Ophtalmology Department, Hassan II Hospital, Sidi Mohammed Ben Abdellah University, Fes, Morocco

Abstract. Diabetic retinopathy is a disease that is linked to the leakage of blood vessels in the retina which can lead to loss of sight. This disease affects a wide range of people, especially those who suffer from diabetes. The worst aspect of this disease is that it comes without any symptoms in its early stage, which makes the task of detecting it in the early stage very difficult task. This forces ophthalmologists to rely on early detection to monitor and ensure that the lesion does not worsen. However, the early detection of diabetic retinopathy is still a difficult task even with the present tools and methods. In this paper, we shall propose a comparison of five convolutional neural network methods of automatic detection of the severity stage of diabetic retinopathy with an ensemble voting architecture for the same task. We present a comparison between five transferred learning convolutional neural network (Xception, InceptionV3, VGG16, DenseNet121, Resnet50) methods and ensemble voting. The ensemble voting makes its decision based on the output of the five CNNs. The proposed work was trained and tested on the APTOS Kaggle dataset. Keywords: Diabetic Retinopathy · Transfer Learning · Ensemble Voting · Image Classification · Convolutional Neural Network

1 Introduction Diabetic retinopathy (DR) is an eye disease caused by damage to the retina. The damage is related to blood vessels, either by blocking them or by leaking on the surface of the retina. As a result, this disease can lead to blindness [1]. DR is considered one of the major causes of blindness in the global working-age population, especially for patients who suffer from diabetes. According to [2], as long as a patient has diabetes, they are at an increased risk of developing diabetic retinopathy, which is greater than the risk of developing diabetic retinopathy over time in more than half of the people with diabetes. In general, according to [2] diabetic retinopathy has two main stages: nonproliferative diabetic retinopathy (NPDR) which is the early stage of DR that presents © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 929–937, 2023. https://doi.org/10.1007/978-3-031-29857-8_92

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with mild or no symptoms, and Proliferative diabetic retinopathy (PDR). The NPDR stage is divided into three different stages, where the first stage is the mild stage, the moderate stage, and the severe stage, depending on how serious the blood vessel problem is. Proliferative diabetic retinopathy (PDR) is the forward stage where serious problems are displayed, such as the growth of new fragile blood vessels, and a block of blood vessels which may ultimately cause loss of sight at the end. Detection of the severity stage of DR in the early stage is still a challenging task for ophthalmologists. The manual process to identify DR requires experienced trained clinicians to analyze fundus retina images [3], which is time-consuming. With the rise in the number of patients suffering from DR, and the difficulty of detecting this disease especially in the early stage, finding a new and fast method has become an urgent necessity. The main focus of this research is to present a comparison of five CNN automated methods for the early detection of diabetic retinopathy with the result achieved for an ensemble voting method that is based on the previous CNN architectures. The ensemble voting merges the prediction of the five models on the test set to give a new prediction based on voting by major technique, unlike the five CNN models each model works individually to make its decision. The rest of this article is organized as follows: The Sect. 2 examines the related work. The Sect. 3 is customized for the materials and methods used in our approach. Sect. 4 explains the proposed approach. Section 5 forms an explanation of the experimental results of the proposed method. The Sect. 6 is a discussion section. The paper’s conclusion is presented in Sect. 7.

2 Related Work Deep learning has demonstrated excellent effectiveness in the task of processing the image medical and classification [4]. This is evident in the various types of research that have been proposed in the field of automated detection of diabetic retinopathy based on deep learning. Oulhadj et al. [5] proposed a diabetic retinopathy detection method that is based on deformable registration and an ensemble voting technique consisting of four convolutional neural networks (CNNs). The ensemble voting achieved an accuracy of 85.28% better than any single model of the five CNN networks. Bodapati et al. [6] proposed a work that used a multi-model fusion module, based on multi-pre-trained ConvNet to extract features. The features extracted are used for two tasks. The first was to identify DR, which means a binary classification (with or without DR), where they attained accuracy and kappa scores of 97.41% and 94.82%, respectively. The second was used to train a model for detecting the severity levels of DR, where they attained kappa and accuracy scores of 71.1% and 81.7%. In addition, Zhuang et al. [7] presented two different DR classification approaches. In the first one, they used transfer learning where they retrained the Efficientnet-B3 last layer. In the second one, they proposed a new shallow neural network. The first approach showed a better performance than the other one, where they achieved a test accuracy of 77.87%, whereas 67.05% for the second method. In addition, Kumar et al. [8] presented a diabetic retinopathy identification network architecture (CapsNets) based on the Capsule network. The proposed

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architecture achieved an accuracy of 80.59% on the validation set and an accuracy of 78% on the test set. In addition, Kassani et al. [9] presented a new convolutional neural network model based on Xception named modified Xception. The modified Xception is used for the detection of DR severity stages. The developed methods realized an accuracy of 83.09%. They compared this result with the result obtained from the original Xception (79.59%), and they found that the modified Xception performed better than the original Xception. Recently, Sharma et al. [10] have proposed an automatic method for diagnosing DR in colored fundus images of the retina. The deep convolutional neural network model that has been developed, is used for the task of classifying the image dataset into five labels. The proposed method achieves an accuracy score of 74.04%. Finally, Gangwar et al. [11] presented a Hybrid Inception-ResNet-v2 architecture. They have added a custom block of CNN to a pre-trained Inception-ResNet-v2. They achieved an accuracy score of 82.18%.

3 Material and Methods Xception: Francois Chollet [12] introduced Xception, which is a CNN model influenced from the inception and based on depthwise separable convolution layers. Depthwise separable convolutions are designed to reduce computational cost and memory requirements. The Xception architecture has 36 convolutions arranged into 14 modules. InceptionV3: is a type of CNN inspired by the first generation of the inception module. Szegedy et al. [13] proposed InceptionV3 by inspiring from InceptionV1 by adding convolution factorization which consists of replacing larger convolutions with multiple smaller convolutions, and auxiliary classifiers in the network. VGG16: the VGG16 is a CNN architecture that was proposed by K. Simonyan and A. Zisserman [14]. The 16 belong to the number of layers that have weights. This model was the winner of the 2014 ILSVRC competition with a test accuracy of 92.7%. DenseNet121: the DenseNet architecture presented in ‘Densely connected Convolution Network’ [15]. The appearance of this new architecture solves many problems such as the redundant layer, by connecting all layers. This connection makes each layer also the input layer feed information to the output layer. ResNet50: The ResNet architecture introduced in the deep residual learning network by He et al. [16]. The residual block solved the degradation problem and avoided the explosion of parameters. The residual block is based on adding the input of the first block to the second block. The number 50 presented the number of layer blocks consisting of a convolutional layer. Ensemble Voting (EV): EV consists of training several models on the same dataset and applying a technique of voting to achieve a new result. Many ensemble techniques can be used in this regard such as majority voting, and averaging voting. In this work, we used majority voting, where each model produces its outcome, and the final prediction is made based on the most repeated.

4 Proposed Approach In this study, we implemented an ensemble voting classification framework based on five transferred and fine-tuned CNN models. The five models are Xception, InceptionV3,

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VGG16, DenseNet121, and ResNet50. The five models that we used are pre-trained on the ImagesNet dataset, which is a large dataset that contains 14 million images associated to 1000 classes. We used the transfer learning and fine-tuning technique to retrain these five pre-trained models on the new task of diabetic retinopathy severity detection. First, we adapted these models with the task of classifying retinal fundus images into five classes. The adaptation was performed by omitting the top layer of the five CNN architectures and adding a Global Average Pooling2D layer, a dropout layer in addition to a fully connected layer with 1048 units using ReLU activation, followed by a dropout layer, and a fully connected layer with 5 units using softmax activation. Every single model (Xception, InceptionV3, VGG16, DenseNet121, and ResNet50) trained and validated individuals on the APTOS Kaggle dataset and made its prediction on the test set. In the training and validation step: rather than training the five models from scratch, we used the transfer-learning technique by freezing the top weights of the fine-tuned model and updating the weight of newly added layers. In the test step: each model makes its own prediction on the test set, and then we feed this prediction to an ensemble voting. The ensemble voting adopted works by the majority voting technique, where it merges the results obtained by the five models to admit the most duplicated decision and produce the final prediction (Fig. 1).

Fig. 1. Proposed architecture.

5 Experiments and Results 5.1 Evaluation Metrics To demonstrate the performance of our method, we used several measure metrics such as Accuracy (Eq. 1), which is defined as the number of images that are correctly classified

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and represented by (TP + TN) corresponding to (true positives and true negatives) divided by all the images predicted, represented by (FP + TP + FN + TN), which represent false positives, true positives, false negatives, and true negatives. Besides, the MSE (Eq. 2), known as the average squared error represents the difference between the true values of the dataset and the values predicted by the model. TP + TN FP + FN + TP + TN 1 n MSE = (Yi − Pi )2 i=1 n

accuracy =

(1) (2)

where Yi, Pi, and n produce the true values, the predicted values, and the number of total predictions, respectively. 5.2 Train Test Dataset We used the APTOS competition dataset in our paper which was taken from the Kaggle platform [17]. We used a holdout method to separate the dataset into training, validtion, and test sets. The selection of the percentage of each category was random; as a result, we split the dataset into 80%, for training, and 5% for validation, and we reserved 15% for the testing step. The images in the dataset have different and high-resolution sizes. Therefore, we resized all the images to the same size (299, 299). 5.3 Result We used the accuracy and the MSE to measure the efficiency of the models during the test phase, Table 1 shows the performance of each model. Where we observe that the Xception model provided the highest accuracy of 81.81% and MSE of 0.410. As shown in (Fig. 2.a) the Xception model achieved a correct prediction of 254 images out of 258 for the No_DR stage (label 0), 32 out of 54 for the Mild stage (label 1), 120 out of 161 for the Moderate stage (label 2), 15 images out of 34 for the severe stage (label 3), and 24 images out of 43 for the Proliferative stage (label 4). Additionally, the DenseNet121 model attained a marvelous accuracy of 80.36% and an MSE of 0.44. as presented in (Fig. 2.d) the DenseNet121 model shown a correct prediction of 256 images out of 258 for the No-DR stage, 40 images out of 54 for the Mild stage, 102 images out of 161 for Moderate stage, 14 out of 34 for the severe stage, and 30 out of 43 images for the proliferative stage. Similarly, The ResNet50 model has achieved a good accuracy of 80.36% and an MSE of 0.421. As presented in (Fig. 2.e) the ResNet50 model made a correct prediction of 254 images out of 258 for the No-DR stage, 44 out of 54 images for the Mild stage, 108 images out of 161 for the Moderate stage, 14 out of 34 images for the Severe stage, and 22 images out of 43 for the Proliferative stage. In addition, the InceptionV3 model has provided a good accuracy of 79.81% and an MSE of 0.454. As shown in (Fig. 2.b) the InceptionV3 model made a correct prediction of 254 images from 258 for the No-DR stage, 29 images out of 54 for the Mild stage, 120 out of 161 for the Moderate stage, 15 out of 34 for the Severe stage, and 21 out of 43 for Proliferative-DR. Finally, The VGG16 model also showed an acceptable accuracy of 79.45% and an MSE

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of 0.443. As shown in (Fig. 2.c) the VGG16 model achieved a correct prediction of 257 images out of 258 for the No-DR stage, 23 out of 54 for the Mild stage, 117 out of 161 for the Moderate stage, 15 out of 34 for the Severe stage, and 25 images out of 43 for the Proliferative stage. Table 1. The prediction result of each model in the test step. Metrics/Models

Xception

InceptionV3

VGG16

DenseNet121

ResNet50

Accuracy

81.81%

79.81%

79.45%

80.36%

80.36%

0.410

0.454

0.443

0.44

0.421

MSE

Fig. 2. Confusion matrix of each model in the test step

We have adopted an ensemble voting algorithm that combines the decisions of these five models (Xception, InceptionV3, VGG16, DenseNet121, and ResNet50) to prove that the performance obtained from ensemble voting is better than that of any single model can perform. As shown in Table 2, the ensemble voting by major improved the performance obtained in the testing phase where we achieved a test accuracy of 83.63%, a weighted f1-score of 83%, a weighted recall of 84%, a weighted precision of 83%, and a test MSE of 0.372. As we notice in Table 2 and (Fig. 3), our approach has shown the best result for the No-DR stage by correctly predicting 254 images from 258. Followed by the moderate stage, our model correctly predicted 130 images out of 161. Besides, our model has correctly achieved 36 images out of 54 for the mild stage, 25 out of 43 images for the proliferative stage, and 15 out of 34 images for the severe stage. In order to describe the performance of our approach, we compared the result we obtained with other proposed work. The works that we compare with are using the same APTOS dataset and working on the same task of DR severity detection. Table 3 resumes the comparisons of the proposed approach with other related works. All the works compared with are explained and detailed in the related work section. As we can see in Table 3 our approach achieved a good result.

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Table 2. Classification result of the ensemble voting. Precision

Recall

F1-score

Label 0

0.97

0.98

0.98

Label 1

0.62

0.67

0.64

Label 2

0.81

0.81

0.81

Label 3

0.48

0.44

0.46

Label 4

0.64

0.58

0.61

Accuracy

-

-

0.84

Macro avg

0.71

0.70

0.70

Weighted avg

0.83

0.84

0.83

Fig. 3. Confusion matrix of the ensemble voting.

Table 3. Comparison with previous work. Architectures

Approach

Test accuracy

Bodapati et al. [6]

multi-modal

81.7%

Gangwar et al. [11]

Inception-ResNet-v2

82.18%

Kassani et al. [9]

Modified Xception

83.09%

Proposed Approach

Transfer learning + Voting

83.63%

6 Discussion The detection of diabetic retinopathy by ophthalmologists remains a difficult task that requires time and tools. In order to assist Ophthalmologists, many methods for the automatic detection of diabetic retinopathy have been proposed. Despite this, early detection of DR and detection of DR severity levels remain challenging tasks. In this proposed approach, we used transfer learning and voting technique to combine the decisions made by five deep learning methods using an ensemble voting technique. As we can see from the results section, this voting technique achieved better performance than that obtained with

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a single model. Meanwhile, the suggested approach might be subject to some drawbacks related to the time consumed in training each model individually.

7 Conclusion In this paper, we present an ensemble voting architecture and compare its result with five transferred learning models (Xception, InceptionV3, VGG16, DenseNet121, and Resnet50). The proposed architecture is composed of three steps. In the training step, we transferred and fine-tuned each pre-trained model (Xception, InceptionV3, VGG16, DenseNet121, Resnet50), where each model was pre-trained on the ImageNet dataset. After training each model is subjected to the testing step, where we test each model on the test set by making its own prediction. Then, in the ensemble voting step, we feed each prediction of each model to the ensemble voting algorithm. This ensemble voting uses the major voting technique to predict the result, where it merges the output of all models and takes the output most repeat. This approach achieved a better accuracy compared to any single model used in this architecture, where we achieved an accuracy of 83.63%. In future work, we will try to take into consideration the multi-modalities of the retina image, where each model of these five models will be trained on a different modality of the retina image. Acknowledgments. This work is under a project named the Khwarizmi Program to Support Research in the Field of Artificial Intelligence and Its Applications (AL-KHAWARIZMI), through the support of Morocco’s National Center for Scientific and Technical Research and Innovation (CNRST).

References 1. Solomon, S.D., et al.: Diabetic retinopathy: a position statement by the American Diabetes Association. Diabetes Care 40(3), 412–418 (2017) 2. Mishra, A., Singh, L., Pandey, M.: Short Survey on machine learning techniques used for diabetic retinopathy detection. In: 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 601–606. IEEE (February 2021) 3. Ghosh, R., Ghosh, K., Maitra, S.: . Automatic detection and classification of diabetic retinopathy stages using CNN. In: 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 550–554. IEEE (February 2017) 4. Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221 (2017) 5. Oulhadj, M., et al.: Diabetic retinopathy prediction based on deep learning and deformable registration. Multimedia Tools Applicat. , 1–19 (2022). https://doi.org/10.1007/s11042-02212968-z 6. Bodapati, J.D., et al.: Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics 9(6), 914 (2020) 7. Zhuang, H., & Ettehadi, N.: Classification of diabetic retinopathy via fundus photography: Utilization of deep learning approaches to speed up disease detection (2020). arXiv preprint arXiv:2007.09478

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8. Kumar, G., Chatterjee, S.K., Chattopadhyay, C.: Drdnet: Diagnosis of diabetic retinopathy using capsule network (workshop paper). In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pp. 379–385. IEEE (September 2020) 9. Kassani, S.H., Kassani, P.H., Khazaeinezhad, R., Wesolowski, M. J., Schneider, K.A., Deters, R.: Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE International Symposium on Signal Processing And Information Technology (ISSPIT), pp. 1– 6. IEEE (December 2019) 10. Sharma, H.S., Singh, A., Chandel, A.S., Singh, P., Sapkal, P.: Detection of diabetic retinopathy using convolutional neural network. In: Proceedings of International Conference on Communication and Information Processing (ICCIP) (May 2019) 11. Gangwar, A.K., Ravi, V.: Diabetic retinopathy detection using transfer learning and deep learning. In: Bhateja, V., Peng, S.-L., Satapathy, S.C., Zhang, Y.-D. (eds.) Evolution in Computational Intelligence. AISC, vol. 1176, pp. 679–689. Springer, Singapore (2021). https:// doi.org/10.1007/978-981-15-5788-0_64 12. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017) 13. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016) 14. Simonyan, K., & Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556 15. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 4700–4708 (2017) 16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770– 778 (2016) 17. APTOS: Kaggle diabetic retinopathy detection competition. (Accessed 18 Mar 2020)

Machine Learning Algorithms for Early and Accurate Diabetic Detection Hanae Chaaouan1(B) , Mohamed Bouhadda2 , Rachid El Alami1 , Abdelouahed Essahlaoui2 , Mohammed El Ghzaoui1 , Hassan Qjidaa1 , and Mohammed Ouazzani Jamil3 1 Faculty of Sciences Dhar El Mehraz, LESSI, Sidi Mohammed Ben Abdellah University, BP

1796, Fes-Atlas, Morocco [email protected] 2 Engineering Sciences Laboratory (LSI), Multidisciplinary Faculty, Sidi Mohammed Ben Abdellah University, Taza, Morocco 3 LSEED Laboratory, UPF, Fez, Morocco

Abstract. The diagnosis of diabetes requires many physical and chemical tests, as well as many other tests, although untreated and undiagnosed diabetes causes the destruction of body organs such as the eyes, heart, kidneys, feet, and nerves and may result in loss of life. Therefore, early detection and analysis of diabetes can help reduce mortality rates. In this paper, we develop accurate machine learning models for detecting diabetes. These models are based on three algorithms: the first is Logistic Regression (LR), the second is Support Vector Machine (SVM) and the third is Random Forest Classifier (RFC). They are performed on Diabetes data known as PIDD, which is obtained from the website Kaggle. Thereafter, the performance of predictive models is evaluated using accuracy, sensitivity, and F1 score measures. The model based on predictive learning of random forests emerged as one of the best performing models with 0.96 accuracy, 0.99 sensitivity, and 0.97 F1-score. Keywords: machine learning algorithms · diabetes detection · predictive model · anomaly detection model

1 Introduction Diabetes is on the rise. Long the preserve of wealthy countries, the spread of diabetes is increasing everywhere, especially in middle-income countries [1]. In the absence of effective policies to create healthy living environments and quality health care services, measures to prevent and treat diabetes are sorely lacking, especially among people with limited resources. The impact of uncontrolled diabetes on human health and wellbeing is serious. Diabetes and its complications weigh heavily on the financial resources of patients and their families, as well as on the economies of nations. Without the availability of affordable insulin, people with diabetes who depend on insulin for survival are sacrificed. Diabetes is a noncommunicable chronic disease that affects a lot of people. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 938–948, 2023. https://doi.org/10.1007/978-3-031-29857-8_93

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Approximately 425 million people have been diagnosed with diabetes, based on statistics from 2017 [1]. Nearly two to five million patients lose their lives each year due to diabetes [2]. By 2045, this number is expected to increase to 629 million [3]. Predictive analysis is a field of technology that encompasses a variety of computationally intelligent, statistical, and data mining methods that use current and historical data to discover knowledge and forecast future events [4]. Important conclusions and precise predictions can be made by using predictive analysis of health care data. Machine learning (ML) and regression approaches can be used for predictive analysis. Predictive analytics aims to maximize resources, enhance patient care, and enhance clinical outcomes while making disease diagnoses as accurate as feasible. A major component of artificial intelligence, machine learning (ML) enables the creation of computer systems that can learn from the past without the need for custom programming in each situation [5]. ML algorithms are viewed as a crucial requirement in the current environment to allow automation with the fewest possible flaws and reduce human labor [6]. Laboratory tests such as fasting blood glucose and oral glucose tolerance are widely used to diagnose diabetes. The objective of this article is to develop a diagnostic model for diabetes using ML techniques. This paper is divided as follows: A summary of the prior research on earlier diabetes prediction is provided in Section 2. Section 3 gives the proposed model for detecting diabetes. Section 4 presents the ML algorithms used for detecting diabetes. Section 5 defines the classification evaluation metrics. Section 6 gives the obtained results followed by the conclusion and references.

2 Related Works Diverse prediction models have been developed and implemented by many researchers using variants of statistical methods, ML algorithms, or a combination of these approaches. Aiswarya Iyer et al. employed clustering methods to address the masked patterns in of diabetes the data set [7]. The Naïve Bayes and decision tree algorithms were used in this model. The comparison was made for algorithm performance and the effectiveness of the two algorithms was demonstrated accordingly. Nongyao et al. [8] applied a classification that detects diabetes. The authors used four ML algorithms: the first is decision tree, the second is artificial neural networks, the third is LR, and the fourth is NB. To improve the strength of the predicted bagging model and improve the methods used. Saravana Kumar N M et al. [9] developed a Hadoop and Map-reduce system for diabetes data analysis. This system makes it possible to predict the type of diabetes and the associated risks. The system is built on Hadoop and is cost-effective for any healthcare organization. Diabetes was predicted by Deepti Sisodia et al. using classification algorithms including Naive Bayes, decision tree, and SVM [10]. For the following metrics; accuracy, recall, F-measure, and precision the Naive Bayes is the highest ranked. K. Rajesh et al. [11] used the classification technique C4.5 decision tree to find hidden patterns in diabetes the data set and classify them efficiently. A diabetes prediction model based on artificial neural networks in combination with fuzzy logic was used in [12]. To separate the effects of diabetes on a patient’s wellbeing, the DT algorithm is utilized [13]. B.M. Patil et al. [14] introduced a prediction model based on the application of a simple K-means algorithm on to classified data. To set up classifiers,

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the C4.5 decision tree algorithm is used. In [15], to predict diabetes behavior, the authors suggested an approach based on the RFC.

3 Diabetes Detection Model The Diabetes data set PIDD is used as the basis for the suggested techniques, and the proposed model is assessed using accuracy, sensitivity, and F1-score metrics [16] from a variety of classifiers, including SVM [17], RFC [18], and LR [19]. Figure 1 presents our proposed model diagram, which explains how the model classifies the data, and the performance evaluation of the implemented algorithms.

Fig. 1. Diabetes detection model workflow.

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4 Machine Learning for Diabetes Detection Classifications are one of the main problems that many researchers encounter when working on disease detection problems. We will compare three major techniques: Random Forest Classifier (RFC), Support Vector Machines (SVM) and Linear Regression (LR). 4.1 Random Forest Classifier The RFC algorithm is known as the procedure that expands decision trees by referring all the trees to be a superior choice. We extract N data points from the data set and then consolidate them to obtain a stable choice. After these operations, the average of all the predictions is taken if the predictions are higher. The RFC algorithm is used to solve classification and regression problems [18]. Furthermore, RFC has been well integrated into the diabetes detection process [16]: the RFC includes basic variables and is flexible in processing various sorts of data attributes. In the case of using training data having different sizes, the RFC algorithm can easily run, because this algorithm divides the data set into samples and then starts the training process. Moreover, in the presence of noise and various anomalies, RFC performs very well and works very well when the data sets are poorly labeled [20]. Its robustness is influenced by the connections between the subtrees as well as the structure of the individual trees. Figure 2 depicts how RFC functions.

Fig. 2. Shows how RFC works.

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4.2 Support Vector Machines (SVM) A linear model is transformed into a higher-dimensional feature space via the classification algorithm SVM [17]. The most relevant aspect of SVMs is that they rely on boundary cases to create a separation curve. Additionally, it can handle nonlinear decision boundaries, and the use of boundary cases aids in the management of missing data [17]. In this model, we take each observation as a point in the n dimensions of space, and the coordinate value of every point is specific. Then, the classification is performed by determining a hyperplane that differentiates very well the classes: Not Diabetic = 0 and diabetic = 1. Figure 3 shows how SVM works.

Fig. 3. Shows how SVM works (Class A = diabetic, Class B = not diabetic).

4.3 Linear Regression (LR) The LR algorithm model is a statistical technique for classifying binary data that performs regression on a set of variables using a linear model, which is also known as a logistic or logit model. This is a widely practiced approach to forecasting patterns in data with numerical or unclear characteristics [16]. Several input vectors and a dependent response variable are used in logistic regression, and the natural logarithm is used to determine the probability that the outcome falls into a specific category.

5 Metrics for Evaluating Performance We provide an outline of the performance indicators used to assess a diabetic classification issue/disease in this section. These metrics are the F-score, sensitivity, and global accuracy [16]. The performance metrics are based on the following terms:

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• • • •

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True Negative (TN) = number of patients who were predicted to not have diabetes. True Positive (TP) = number of patients who are predicted to have diabetes. False Positive (FP) = number of non-diabetics who were predicted to develop diabetes. False Negative (FN) = diabetics number predicted who are not diabetic

5.1 Accuracy The most logical performance metric is accuracy, which is calculated as the ratio of properly predicted observations to all observations. It is expressed as: Accuracy = TP + TN/TP + FP + FN + TN

(1)

5.2 Sensitivity The proportion of correctly predicted positive observations to all other observations in the actual class-true is known as recall (Sensitivity). It’s outlined as follows: Recall = TP/TP + FN

(2)

5.3 Precision Precision is calculated as the positive correct outcomes divided by the positive outcomes predicted by the classifier. It can be defined as: Precision = TP/(TP + FP)

(3)

5.4 F1-Score F1-score is the weighted average of precision and recall. It is defined as: F1 − Score = 2 ∗ (Recall ∗ Precision)/(Recall + precision)

(4)

6 Results Analysis In this section, we explore and visualize the data used to perform the ML models for diabetes detection. Next, we describe the technology used to train the models. In the last subsection, we discuss the results obtained.

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6.1 Data Exploration and Visualization Using class 0 as a patient without diabetes and class 1 as a patient with diabetes, the PIDD data set has 16 attributes and two class outputs (0 and 1). The data set includes 520 samples of medical reports, 200 of whom do not have diabetes and 320 of whom do, representing both male (63.08%) and female (36.92%) patients. The class distribution of the patients who are diabetic (positive) and not diabetic (negative) is illustrated in the Fig. 2. The data set is imbalanced; the number of patients who are diabetic is small than the number of the patient that are not. Table 1 and Fig. 3 present successively the definition of attributes and the correlation between them (Fig. 4 and 5).

Fig. 4. Class distribution of diabetes patients.

Table 1. Present the attribute definition. Attributes

Type

Description

Age

Num

Age of patients

Gender

Categorical Male or female

Polyuria

binary

Polydipsia

binary

sudden weight loss binary

production of abnormally large volumes of dilute urine abnormally great thirst Weight loss of 10 lb or more

weakness

binary

The state or condition of lacking strength

Polyphagia

binary

Frequent drinking of water

Genital thrush

binary

High sugar levels lead to better conditions for the yeast to grow (continued)

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Table 1. (continued) Attributes

Type

Description

visual blurring

binary

optical noise

Itching

binary

Is often a symptom of diabetic polyneuropathy, which is a condition that develops when diabetes leads to nerve damage

Irritability

binary

The quality or state of being irritable

delayed healing

binary

Delayed recovery at the level of wounds

partial paresis

binary

A condition of muscular weakness caused by nerve damage or disease

muscle stiffness

binary

Is when the muscles feel tight and difficult to move

Alopecia

binary

The total or partial absence of hair in body areas in which it grows naturally; Baldness

Obesity

binary

The condition of being grossly fat or overweight

class

binary

Class variable: Response variable (1 = Positive and 0 = negative)

Fig. 5. Correlation among different attributes.

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6.2 Used Technology We used Python language programming to develop the model proposed in this paper. Python is the main ML, big data, and data science coding language currently used [21]. Python is a programming language that can be used in many contexts and adapt to any type of use through specialized libraries. The specification hardware used to perform ML classifiers for diabetes detection is presented in Table 2. Table 2. Hardware specification to perform diabetes detection algorithms Reference

CPU

RAM

SSD

GPU

Dell precision 5520

i7

32 Go

1 To

Nvidia Quadro pro (4 Go)

6.3 Results In this subsection, we review the results obtained after the experiments on the data set. In this study, we divided the data set into two subsets. A training set (representing 70% of the original data set) and a test set (30% of the original data set) are included in the first subset of data, which is used to train and test ML classifiers. The main objective of this study is to compare ML algorithms to find a more appropriate algorithm to detect diabetes. Table 3. Presents the results of diabetes detection based on ML algorithms. ML methods

Accuracy

Sensitivity

F1-score

RFC

0.96

0.99

0.97

SVMs

0.94

0.98

0.96

LR

0.93

0.93

0.93

According to Table 3, the performance of RFC detection is the highest among the other algorithms. The RFC reaches an accuracy of 0.96, while the SVM and LR reach 0.94 and 0.93, respectively. Additionally, in terms of diabetes sensitivity, RFC achieves 0.99, compared to 0.98 for SVM and 0.93 for LR. In addition, the F1 score measure confirms the ability of the RFC to detect diabetes with a score of 0.97, whereas SVM scored 0.96 and LR reached 0.93. RFC is considered among the most powerful ML algorithms, especially for binary classification problems such as diabetes detection as our results indicate.

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7 Conclusion In this research, several ML algorithms were developed and run on a real-world data set. The classification was performed using LR, SVM, and RFC. The results of the comparison show that RFC can achieve the best accuracy in diabetes prediction in terms of all metrics used despite that data being imbalanced. The model will help doctors and medical staff diagnose and predict diabetes more accurately and quickly in patients suspected of having diabetes than the traditional techniques used. This work can be extended to identify the risk that people without diabetes will develop this disease in the years to come.

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17. Wang, P.W., Lin, C.J.: Support vector machines. In: Data Classification: Algorithms and Applications (2014). https://doi.org/10.1201/b17320 18. Rigatti, S.J.: Random forest. J. Insurance Medicine (N.Y.) (2017). https://doi.org/10.17849/ insm-47-01-31-39.1 19. Hope, T.M.H.: Linear regression. In: Machine Learning: Methods and Applications to Brain Disorders (2019). https://doi.org/10.1016/B978-0-12-815739-8.00004-3 20. Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: A comparative study. Decis. Support Syst. 50, 602–613 (2011). https://doi.org/10.1016/j.dss. 2010.08.008 21. Stancin, I., Jovic, A.: An overview and comparison of free Python libraries for data mining and big data analysis. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 977–982 (2019). https://doi.org/ 10.23919/mipro.2019.8757088

Blood Glucose-Insulin Dynamics in Type-1 Diabetic Patients for the Mitigation of Hyperglycemic Conditions: A PID Controller with a Step Response Abubakar Isah Ndakara(B) , Moad Essabbar, and Hajar Saikouk Euromed Research Center, Euromed University of Fes, Fez, Morocco [email protected], {m.essabbar, h.saikouk}@insa.ueuromed.org

Abstract. It has been noted that eating habits, physical exercise, and anxiety are some of the elements influencing blood sugar levels. After a diabetic person has eaten, a hyperglycemic condition can be detected right away. In a healthy person, insulin is produced by the pancreatic beta cell, which regulates the body. However, in a diabetic person, the pancreatic beta cell is damaged, making it unable to accomplish normal regulation. The incorporation of a controller to ensure the system operates properly is one of the few solutions to this problem. Bergman’s mathematical equations and a PID controller were both used in this investigation, which was conducted in the MATLAB environment. According to the findings, the rising time and settling time for the tuned response were measured at 2.2 and 3.91 s, respectively, while the block response of the controller was measured at 0 and 5.65 s. The derivative gain was adjusted to enhance stability and reduce overshoot. Keywords: Artificial Pancreas · Diabetes · Glucose · Hyperglycemia · Insulin · PID · Step response

1 Introduction Modern technological developments enable little human intervention while providing advanced preventive measures and medical supervision [1] to those in need. An organ of the body located next to the stomach called the pancreas makes insulin and fluid to help with food digestion [2]. In essence, food digestion is handled by the exocrine, whereas sugar or glucose control is handled by the endocrine system [3]. A chemical constituent called insulin regulates the amount of glucose in the bloodstream [4]. The body requires sugar, also known as glucose, which is a monosaccharide and a type of carbohydrate, for energy. Its molar mass is 180 g/mol, and its chemical formula is C6 H12 O6 mol [5]. According to [6], a healthy patient’s overnight fasting glucose level should be between 70 and 180 mg/dl, or 3.9 and 10 mmol/l. The blood glucose concentration rises after a meal, but in healthy patients, it normally returns to fasting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 949–956, 2023. https://doi.org/10.1007/978-3-031-29857-8_94

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levels in 2–3 h [7]. At least once in their lives, people with type 1 diabetes may experience hypoglycemia, which is when their blood glucose level falls below the normal range [8]. Contrarily, hyperglycemia happens when the level of glucose rises above the permissible range [9]. Diabetes is a medical condition that affects digestion and raises blood sugar levels as a result of reduced insulin synthesis and usage [10]. To maintain a balanced livelihood, diabetic individuals must either manually administer insulin using a syringe or pen three (3) to five (5) times a day [11] or utilize an insulin pump to administer the medication automatically [12]. Meals, sleep, stress, and exercise are some of the factors that have been found in studies to have a harmful impact on the body’s glucose concentration [13]. As reported by the IDFA [14], 783 million people will have diabetes worldwide by the year 2045. In the 20–79-year-old adult population, diabetes impacted twenty-four (24) million adults in 2021; by 2030 and 2045, that figure is expected to rise to thirty-three (33) million and fifty-five (55) million, respectively. These findings are alarming and warrant concern in order to minimize the health concerns faced by patients with type 1 diabetes, as well as designing an artificial pancreas (APS) [15]. In this paper, the Bergman Minimal Model, an equation describing the physiology of the body, is used to modulate the glucose levels of individuals with diabetes using a PID controller with a step reaction. The rest of the article is organized as follows: Sect. 2 describes a review of related research, followed by the methodology in Sect. 3. Section 4 focuses on results and Discussions. Then, Sect. 5 gives the conclusion and recommendations for future work.

2 Review of Related Research [16] suggested a Lyapunov-based control strategy. Bergman’s paradigm [17], on which the model has been established, viewed the glucose efficiency factor as an ambiguous parameter. The control system performed adequately when compared to the fuzzyPI architecture in terms of dealing with food disruptions. [18] proposed a technique for diabetes regulations. It was noted that the Tolic concept was employed for insulin administration and the Bergman framework was utilized to regulate sugar levels. The feedback gains of the fuzzy controllers were determined using linear matrix inequalities and numerical convex methods. The control systems were also used with the nonlinear systems when there were numerous food interruptions. [19] recommended a tracking system for diabetic individuals. The unscented Kalman filter (UKF) was utilized to produce forecasts for unquantified system parameters from the actual cutaneous glucose level, while the UVA/Padova simulator was used. Genetic algorithms were also applied as optimization algorithms. [20] put in place a PID control system for diabetic control. Because it only included one glucose segment, it is hypothesized that the patient’s insulin will influence the net glucose by acting on a different compartment. A nonlinear system algorithm that simulates insulin infusion into the bloodstream was utilized to calculate the inflow of glucose and the exogenic insulin that was infused. Plasma glucose concentration served as the (FLC) input, while the output was the rate of insulin infusion. Last but not least, [21] suggests a management scheme and its use for controlling glucose levels. Two controllers make up the system. The system made use of the biogeographybased optimization (BBO) method. For some individuals with time-varying parameters,

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external noise disruptions, and meal upsets, the revised Bergman’s model was evaluated, and it was discovered to function satisfactorily. 2.1 Blood Glucose Dynamics and Equations The minimal model is a simple physiological model consisting of three stages with identifiable parameters. It addresses the consequences of glucose efficacy in the body and the delay in insulin action given in Eqs. (1), (2), and (3). Dg(t) D(t)

  = −p1 g(t) − g(b) − x(t) g(t) + p(t)

(1)

  Dx(t) = −p2 x(t) + p3 I(t) − I(b) D(t)

(2)

  DI(t) = −n I(t) − I(b) + γ (G − h)t + u(t) D(t)

(3)

3 Methodology To see the behavioral pattern of typical patients, the ordinary differential equations (ODEs) described in (1), (2), and (3) were modeled in the MATLAB SIMULINK environment using individual block model format, as shown in Table 1. The parameters used were taken from [22]. The PID technique’s block diagram for how insulin is administered into the patient’s body is shown in Fig. 1. Table 1. The parametric values of the normal patient using the Bergman Minimal Model. Serial Number

Parameters

Values for Normal Patient

1

P1

0.0317

2

P2

0.0123

3

P3

4.92 × 10–6

4

n

0.2659

5

Gb

70.0

6

Ib

7.0

7

G0

291.20

8

I0

364.80

9

γ

0.0039

10

h

79.0353

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Fig. 1. Control Architecture of the Insulin Delivery to the Patient.

4 Results and Discussions 4.1 Considering the Simulink Model Without the Effect of a Controller This section of the paper presented the findings. Figure 2. (a) (b) and (c) shows a graph representing plasma glucose accumulation, plasma insulin aggregation, and insulin action of the normal patient without the effect of the controller at a sampling time of 600 s (10 min). It can be observed that as G(t) increases, I(t) also increases, whereas X(t) has the opposite pattern which decreases up to a point it starts increasing to a stability point because the pancreatic beta cell that does the blood regulation in the human body does exist in the normal patient. In essence, it depicts the real action of insulin in the body system of the normal patient. 4.2 Considering the Simulink Model with a PID Controller Likewise considering the effect of the PID controller as demonstrated in Fig. 3. (a) (b) and (c). It was observed that the plasma glucose accumulation didn’t have any effect since the data adopted is for a normal patient. However, the controller was able to increase the insulin action and plasma insulin aggregation respectively. Perhaps, this scenario is because the diabetic patient lacks γ(G − h)t which happens to be the pancreatic beta cells that do the monitoring of sugar in humans. In essence, the controller acts primarily as a substitute for the pancreatic beta cells seen in a normal patient. Inclusively, Table 2 shows the performance and robustness of the PID controller concerning the tuned and block responses.

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Fig. 2. (a)(b)(c) shows the graph of plasma glucose accumulation, plasma insulin aggregation, and insulin action against time without the influence of the PID, respectively.

Fig. 3. (a)(b)(c) depicts a graph of plasma glucose accumulation, plasma insulin aggregation, and insulin action against time with the influence of the PID.

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Fig. 3. (continued) Table 2. Performance and robustness of the PID System. Tuned

Block

Rise Time

2.2 s

0s

Settling Time

3.91 s

5.65 s

Overshoot

0%

0%

Peak

1

1

Closed Loop Stability

Stable

Stable

5 Conclusion and Recommendation In this research work, the implementation of the Bergman minimal model considering the PID controller with a step response was depicted. The data of the normal patient was accessed considering its control parameters and performance with and without the effect of a control architecture. However, this system only makes use of the single hormonal system for its purposes. Hence, the employment of the Sorenson model for the treatment of hyper- and hypoglycemic rates in a diabetic patients can be seen as a future perspective since it incorporates the glucagon segment into its model. Furthermore, other controllers such as the MPC, Fuzzy Logic, and SMC may also be used to enhance the performance of the system.

References 1. Ortega-Navas, M.d.C.: The use of New Technologies as a Tool for the Promotion of Health Education. Procedia - Social Behav. Sci. 237 23–29 (2017)

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2. Whitley, E.M.: Comparative Pancreatic Pathology. In: McManus, L.M., Mitchell, R.N. (eds.) Pathobiology of Human Disease, pp. 1101–1123. Academic Press, San Diego (2014) 3. Orlando, G., et al.: Transplantation, Bioengineering, and Regeneration of the Endocrine Pancreas: vol. 1. Elsevier Science (2019) 4. Kosasih, F.R., Bonavida, B.: Chapter 16 - YY1-mediated regulation of type 2 diabetes via insulin. In: YY1 in the Control of the Pathogenesis and Drug Resistance of Cancer, B. Bonavida, Editor. 2021, Academic Press. pp. 271–287 (2021) 5. Jamnongwong, M., et al.: Experimental study of oxygen diffusion coefficients in clean water containing salt, glucose or surfactant: consequences on the liquid-side mass transfer coefficients. Chem. Eng. J. 165(3), 758–768 (2010) 6. Nadia Ahmad, N.F., Nik Ghazali, N.N., Wong, Y.H.: Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: a review. Biosens. Bioelectron. 189 113384 (2021) 7. Cinar, A., Turksoy, K.: Advances in Artificial Pancreas Systems: Adaptive and Multivariable Predictive Control. Springer International Publishing (2018) 8. Shi Min Ko, M., et al.: A cross-sectional study on risk factors for severe hypoglycemia among insulin-treated elderly type 2 diabetes mellitus (T2DM) patients in Singapore. Diabetes Res Clin. Pract. 185 109236 (2022) 9. Davidson, M.B.: Historical review of the diagnosis of prediabetes/intermediate hyperglycemia: Case for the international criteria. Diabetes Res. Clin. Pract. 185, 109219 (2022) 10. Bonora, E., et al.: Incidence of diabetes mellitus in Italy in year 2018. A nationwide populationbased study of the ARNO Diabetes Observatory. Nutrition, Metab. Cardiovasc. Diseases 31(8), 2338–2344 (2021) 11. Kesavadev, J., Saboo, B., Krishna, M.B., Krishnan, G.: Evolution of insulin delivery devices: from syringes, pens, and pumps to diy artificial pancreas. Diabetes Ther. 11(6), 1251–1269 (2020). https://doi.org/10.1007/s13300-020-00831-z 12. Danne, T., et al.: Insulin treatment in children and adolescents with diabetes. Pediatr. Diabetes 15(S20), 115–134 (2014) 13. Marik, P.E.: Chapter 76 - Endocrinology of the Stress Response During Critical Illness. In: Ronco, C., et al. (eds.) Critical Care Nephrology (Third Edition), pp. 446–454. e4. Elsevier, Philadelphia (2019) 14. Cho, N.H., et al.: IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 138, 271–281 (2018) 15. Bulantekin Düzalan, Ö., ˙Inkaya, B.: Psychometric properties of Turkish version of insulin delivery device satisfaction (IDSS) scale in patients with type 2 diabetes. Primary Care Diabetes, (2022) 16. Ahmad, I., Munir, F., Munir, M.F.: An adaptive backstepping based non-linear controller for artificial pancreas in type 1 diabetes patients. Biomed. Signal Process. Control 47, 49–56 (2019) 17. Bergman, R.N., et al.: Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest 79(3), 790–800 (1987) 18. Farahmand, B., Dehghani, M., Vafamand, N.: Fuzzy model-based controller for blood glucose control in type 1 diabetes: An LMI approach. Biomed. Signal Process. Control 54, 101627 (2019) 19. Khodakaramzadeh, S., Batmani, Y., Meskin, N.: Automatic blood glucose control for type 1 diabetes: a trade-off between postprandial hyperglycemia and hypoglycemia. Biomed. Signal Process. Control 54, 101603 (2019) 20. Ibbini, M., Masadeh, M.: A fuzzy logic based closed-loop control system for blood glucose level regulation in diabetics. J. Med. Eng. Technol. 29(2), 64–69 (2005)

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21. Jaradat, M.A., Sawaqed, L.S., Alzgool, M.M.: Optimization of PIDD2-FLC for blood glucose level using particle swarm optimization with linearly decreasing weight. Biomed. Signal Process. Control 59, 101922 (2020) 22. Kaveh, P., Shtessel, Y.B.: Blood glucose regulation using higher-order sliding mode control. Int. J. Robust Nonlinear Control: IFAC-Affiliated J. 18(4–5), 557–569 (2008)

Fine-Tuning Transformer Models for Adverse Drug Event Identification and Extraction in Biomedical Corpora: A Comparative Study Chanaa Hiba(B) , El Habib Nfaoui, and Chakir Loqman LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. Adverse Drug Events (ADEs) are potentially fatal problems that patients can deal with only if they have a solid awareness of them. With the available amount of unstructured textual data from biomedical literature, electronic records, and social media (e.g., tweets), early detection of unfavorable reactions and sharing them with biomedical experts, pharma companies, and healthcare professionals is a necessity, as this can prevent morbidity and save many lives. The Biomedical Named Entity Recognition (BioNER) task can be considered the initial step toward resolving this issue. In this paper, we present an empirical evaluation experiment by fine-tuning pretrained language models for detecting biomedical entities (e.g., drug-names and symptoms). We fine-tuned five transformer models: BERT (Bidirectional Encoder Representations from Transformers), SpanBERT, BioBERT, BlueBERT, and SCIBERT, on two well-known biomedical datasets, CADEC and ADE-corpus. The evaluation results demonstrate that BioBERT which was pretrained on both general and domain-specific (biomedical domain) corpora outperformed all other models on both datasets and reached 90.3% and 68,73% on the F1-score in the ADE and CADEC corpora, respectively. Keywords: Adverse Drug Event · Bio-Named Entity Recognition · Deep Learning · Natural Language Processing · Fine-tuning · Transformer Models

1 Introduction Human health is the greatest wealth that life can bestow upon us. Unfortunately, this wealth is at risk of being spoiled by the occurrence of numerous adverse events, which currently represent one of the most worrying topics in the biomedical field. These bad events are countless and diverse and have been addressed by a huge number of scholars and practitioners; however, some of them suffer from conceptual ambiguity; for example, clinicians are frequently unable to identify or handle cases of drug-related damage, which can result in significant morbidity and mortality [1]. Therefore, researchers on [2] used a case study of a patient who experienced many adverse drug events to identify terms such as adverse event, adverse drug reaction, medication error, and side effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 957–966, 2023. https://doi.org/10.1007/978-3-031-29857-8_95

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According to the “International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use” an adverse event (AE) could be any unintended medical incident that may occur when using a medication but does not necessarily have a causal relationship with this medication. Thus, an AE can be any undesirable, accidental indication, symptom, or illness that occurs while using a medicinal product, whether or not it is regarded as related to it [3, [2]. Moreover, the “International Conference on Harmonization” defines adverse drug reactions as all noxious and unintended responses to a medicinal product related to any dose. Thus, an adverse drug reaction is an adverse event that has a direct connection to a drug. An adverse medication reaction, such as renal failure caused by ibuprofen, happens at typical doses and is brought on by the drug’s effect [2]. As a potential challenge in Natural Language Processing (NLP), Named Entity Recognition (NER) has drawn much attention in recent years [4]. The primary goal of the NER task is to identify and categorize references of named entities in a given unstructured text into predefined semantic categories. These entities often relate to a person’s name, nationality, and institution in traditional NER tasks [5]. In this work, we will focus on biomedical NER (BioNER), a field that seeks to extract specific medical entities from the medical literature. These entities include symptoms, diseases, drugs, etc. In this work, we proposed to explore various pretrained language models, some of which are built using the global English domain (i.e., BERT [6] and SpanBERT [7]) and others are built on biomedical domain-specific models (i.e., BioBERT [8], SCIBERT [9] and BlueBERT [10]) to accomplish the BioNER task using ADE and CADEC corpora. The remainder of the paper is structured as follows. Section 2 highlights the problem formulation of this study and discusses the models used during our experiments. Section 3 illustrates the dataset used in this study and discusses the obtained results. Finally, sect. 4 summarizes the conclusions of this study.

2 Materials and Methods 2.1 Problem Formulation ADE identification is a sequence labeling problem (e.g., BioNER task). The ultimate goal of this task is to identify entities such as “Disease-name,” “Drug-name,” and “Symptoms,” from the input sentences. Given an input sequence of words X = {x1, x2, ..., xi}, the task is to label each word xi in the sequence with a tag yi which is a part of the tag set Y = {y1, y2, ..., yi}. 2.2 BioNER Utilizing Pretrained Language Models Named Entity Recognition (NER) is the method of defining and categorizing entities in a given text. Biomedical NER (BioNER) is a hot topic in health coverage since it is the first step in performing relation extraction and clinical decision-making tasks. [11]. BioNER is complicated when compared to NER in other domains since labeled data in the biomedical domain are limited in quantity and are expensive to collect, and

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it necessitates the detection of complex entities that are not prevalent in other fields [12]. Deep learning techniques that use massive volumes of unstructured data, such as Bi-LSTM with CRF [13] and BERT fine-tuning [14], have recently been used to obtain State-Of-The-Art (SOTA) findings concerning the BioNER task. Lately, another work has reached SOTA performance by combining BioBERT word embeddings with BiLSTM-CNN-Char and making certain architectural adjustments: a) removing lexical characteristics such as POS tags and introducing new character level features. b) Generating token feature maps that capture information such as spelling and casing using a 1D convolution layer composed of 25 filters with kernel size 3. These added features proved to be useful while dealing with misspellings and out-of-vocabulary tokens. The most prevalent NER annotation system is BIO tagging, where ‘B’ stands for Beginning of entity, ‘I’ stands for Inside of entity, and ‘O’ stands for Outside of entity. Figure 1 presents the overall flowchart of the BioNER task utilizing pre-trained language models. First, we extract the sentences from the dataset and tokenize each sentence. Second, we applied sentence tokens labelling by means of which we assign to each word a BIO (Begin, Inside, and Outside) labels to denote the position and type of the token inside an entity mention. Third, the input sentences were then processed by inserting a ([CLS]) and ([SEP]) tokens at the start and the end of the text, respectively. Then, the generated inputs were fed into the model after tokenization based on its vocabulary. Eventually, the NER function is completed by putting a further linear layer on top of the contextual representations outputted by the model to predict token tags.

Fig. 1. The global flowchart of the BioNER task utilizing pre-trained language models.

2.3 Pre-trained Language Models In this study we examined five pre-trained language representation models for the general and biomedical domains: BERT, SpanBERT, BioBERT, SCIBERT and BlueBERT.

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BERT: (Bidirectional Encoder Representations from Transformers) [6] is an embeddings model trained with bidirectional transformers [15]. It was trained on large unlabeled text corpora to design deep bidirectional embeddings to better represent sequences of text. It employs two main training objectives: masked language modeling objective and next sentence prediction. Therefore, bidirectional representation (instead of unidirectional) of text sequences is critical to better encode words in natural language [6]. Thus, because of complicated interactions between biomedical terms we anticipate that BERT bidirectionality is essential in biomedical text analytics [15]. BioBERT: Is the first language representation model that has been pre-trained for the biomedical field [8]. Because biomedical documents contain a substantial number of domain-specific words that are largely understood by biomedical professionals. Consequently, NLP models that were built for general-purpose language comprehension frequently perform poorly in biomedical text mining applications. Therefore, BioBERT had previously been trained on corpora from the field of biomedicine (PubMed abstracts and PMC full-text articles). It revealed good results on three well-known NLP tasks (NER, RE, and QA), however in this study, we will illustrate its effectiveness on the BioNER task using both ADE and CADEC datasets. SciBERT: Is a deep neural network training tool for NLP applications that requires a large amount of labeled data. It was pretrained from scratch using a random sample of 1.14 M documents from Semantic Scholar [16]. This corpus contains 18% publications from the subject of computer science and 82% papers from the broad biomedical sector. They used the entire text of the papers rather than simply the abstracts. The average length of a paper is 154 sentences (2,769 tokens), resulting in a corpus size of 3.17 billion tokens, which is similar to the 3.3 billion tokens on which BERT was trained [9]. SpanBERT: Is a pre-trained model for improving the representation and prediction of text spans. It differs from the original BERT in two points: 1) The masking mechanism: instead of masking random individual tokens it masks an entire contiguous span of tokens. 2) It uses a new span-boundary objective to train the model to predict the entire masked span based on tokens in the boundary [7]. BlueBERT [10]: Is a biomedical domain-specific model. It was initialized with BERT weights and then pre-trained utilizing a large clinical and biomedical domain (i.e., PubMed abstracts and clinical notes MIMIC- III).

3 Experiments and Results 3.1 Data In this paper, we explored two corpora, ADE (Adverse Drug Event) and CADEC (CSIRO Adverse Drug Event Corpus). Table 1 shows the statistics of both corpuses. They follow the same basic annotation scheme, whereas the CADEC corpus has more entities than the ADE corpus.

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Table 1. Overall statistics of both CADEC and ADE corpuses. Corpus

Origin

Type

Size

Entities

ADE [17]

MEDLINE

Literature

400 Abstracts (6821)

Disease, Adverse effects

CADEC[19]

AskaPatient

Medical Forum

1253 posts (7398 sentences)

Drug, adverse effect, disease, symptom, finding

ADE-Corpus (version 2) [17] respects many features that should be taken into account, the most significant ones are the corpus’s domain appropriateness and the target user population. For the biomedical domain, medical case reports are the most relevant data sources because they contain essential information about individual patients’ symptoms, signs, diagnosis, therapy, and so on. More significantly, case reports might act as an early warning signal for drugs with unreported or unexpected side effects. MEDLINE articles were utilized because of their free public availability. As a result, the ADE corpus is a subset of MEDLINE case reports. This dataset is used to determine whether a sentence is related to an ADE or not. CADEC dataset [19] is a novel, richly annotated corpus of patient-reported ADEs from medical forums. The dataset is derived from social media posts and comprises content that is mostly written in colloquial English and frequently veers away from traditional English grammar and punctuation norms. Annotations include references to terms such as medications, negative effects, symptoms, and diseases that are associated with terms with the same names in restricted vocabularies. This corpus is crucial for studies on information extraction, or much more broadly text analytics, from social networks to extract potential adverse drug events from first-hand patient experiences (NER task).

3.2 Experiments Further examination of the ADE corpus reveals that sentences are frequently repeated to identify different combinations of drugs and adverse reactions. This is not suitable in an NER setting because if we assigned one set of token labels per row in this dataset as-is, we would end up labeling the same tokens differently in the same sentences. This would confuse the model during fine-tuning, so we must first consolidate all of the variations provided for each unique sentence before labeling all known entities. For the CADEC corpus, as the text documents are separated from the annotations, we had to manually annotate each sentence in a given document based on the indices presented in the associated annotations file. In the first stage, for both datasets we apply the NER sequence labeling task in which each token in the sequence is assigned a predetermined IOB tag, where “B” corresponds to the beginning of an entity, “I” means inside an entity, and “O” represents all other nonentity words. This results in 11 possible classes for each token in the CADEC dataset which are the following: (’O’ - outside any entity we care about, ’B-ADR’ - ’I- ADR’

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- ’B-Drug’ - ’I-Drug’ - ’B-Symptom’ - ’I-Symptom’ - ’B-Finding’ - ’I-Finding’ - ’BDisease’ - ’I-Disease’), and in 5 possible classes for the ADR corpus (’O’ - ’B-Drug’ - ’I- Drug’, ’B-Effect’ - ’I-Effect). Second, the input sentences were tokenized by the default tokenizer using the vocabulary of the pre-trained language models and adding the special [CLS] token at the start of the sentence and a [SEP] token at the end and then fed as input into the model. Finally, the NER process is completed by predicting token labels using an additional linear classification layer on top of the pre-trained language model Fig. 2 shows an example of a sequence labeling performed by the fine-tuned BioBERT model). To handle the out-of-vocabulary (OOV) issue, transformer models often break original words into many pieces of sub-words (WordPiece tokenization) by inserting a specific tag “##” in front of the following sub-words. The Hugging Face transformers library [18] (implemented in PyTorch) was used to obtain all transformer and tokenizer models. Table 2 shows the optimal hyperparameters that were utilized for all the models to obtain good performance based on a simple grid search with a small search space.

Fig. 2. An example of sequence labeling performed by the fine-tuned BioBERT model.

Table 2. Hyperparameters employed for all models. Hyperparameters Search space

Optimal Value

training epochs

[5–15, 15]

8

Learning rates

[1e-6-1e-2]

1e-5

Training Batch size

[4, 8, 16, 32, 64] 8

The effectiveness of all transformer-based NER models was assessed using four evaluation metrics, including precision, recall, F1-score [19] and accuracy measures. Formally: Accuracy = (TP + TN )/(TP + TN + FP + FN )

(1)

Precision = TP/(TP + FP)

(2)

Recall = TP/(TP + FN )

(3)

Fine-Tuning Transformer Models for Adverse Drug Event

F1_score = 2 × (Pr ecision × Recall)/(Pr ecision + Recall)

963

(4)

where TP (True Positives) is the number of data points properly categorized as positives; TN (True Negatives) refers to the number of data points properly classified as negatives; FP (False Positives) is the data points misclassified as positives; and FN (False Negatives) is the number of data points misclassified as negatives. Because the dataset’s categories are unbalanced, we utilized the F1-score measure, which better indicates performance in this case. 3.3 Results Table 3 shows the overall performance of fine-tuning the five pre-trained language models for the BioNER task on the CADEC dataset using Precision, Recall, F1-score and Accuracy measures. Among all models, we observe that BioBERT achieved the highest score on precision with an improvement of 7.6%. It also acquires the best outcomes with F1-score and accuracy by reaching up to 68.73%, and 91.9%, respectively. On the other hand, SpanBERT outperforms all the other models on the recall metric. Table 3. Overall performance results on the test set of the CADEC dataset. Models

Precision Recall F1 (%) (%) (%)

Accuracy (%)

BERT

60.1

68.9

91.0

SpanBERT 65.4

72.3

68.71 91.7

BioBERT

65.8

71.8

68.73 91.9

SciBERT

61.4

70.9

65.8

BlueBERT

58.2

68.7

63.03 91.3

64.2

91.5

Table 4 shows the detailed results of fine-tuning the five pre-trained language models for the BioNER task on the ADE dataset using Precision, Recall, F1-score and Accuracy measures. BioBERT considerably surpasses all other models, ranking highly on all measures. Particularly, it surpasses the competing models by 3% on recall and achieves 90.3% and 96.3 on F1-score and accuracy, respectively. All measures show a considerable improvement, with the exception of accuracy, where the SpanBERT model obtains a score equivalent to BioBERT. 3.4 Discussion In this study, we conducted experiments with a variety of pre-trained language models as previously stated to evaluate the effectiveness of fine-tuning them to address the BioNER challenge on biomedical corpora. Experimental analysis demonstrates the relevance of all five embedding pre-trained models. Indeed, the deep architecture of the pre-trained

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Precision (%)

Recall (%)

F1 (%)

Accuracy (%)

BERT

81.6

89.4

85.3

91.0

SpanBERT

88.4

91.6

90.0

95.8

BioBERT

88.4

92.4

90.3

96.3

SciBERT

87.0

90.7

88.8

96.1

BlueBERT

84.1

89.9

86.9

95.6

models (which contain 12 transformer encoder blocks, each of which involves fully connected layers, multi-head attention layers and output layer) allows them to capture contextual information between words in the given sequence of text. As mentioned above, BERT and SpanBERT were pre-trained utilizing global English corpuses from English Wikipedia and Books Corpus. On the other hand, BioBERT and BlueBERT are built on top of BERT and have been fine-tuned with additional biomedical domain corpora. SciBERT, however, was trained from scratch utilizing biomedical corpora that provide relevant and representative word embeddings for domain-specific tasks. We hypothesize that the nature of the study corpora (ADE and CADEC) which include both general and biomedical domains is the main reason why BioBERT reveals good results over other competing models since it was fine- tuned based on original BERT utilizing biomedical corpora (PubMed Abstracts, PMC Full-text articles Number). As shown in Tables 3, 4 SpanBERT slightly reveals better performance than SciBERT and BlueBERT. The first reason might be the mechanism of masking spans of tokens (instead of individual tokens as in the original BERT) and then predicting the entire masked span based on the tokens at the span’s boundary. The two corpora exhibited remarkable variance in performance, demonstrating the intrinsic distinctions between them (e.g., F1 scores of 68.73% and 90.3% on CADEC and ADE datasets, respectively, for the same BioBERT model). Eventually, this study highlights the utility of fine tuning large pre-trained models to achieve satisfactory results on the BioNER task using CADEC and ADE corpuses that are approximately close to state- of-the art [20],which is based on the BiLSTM-CNN-Char architecture [21] combined with BioBERT embeddings.

4 Conclusion and Future Work In this study, we demonstrate the effectiveness of fine-tuning pre-trained language models to accomplish the NER task on biomedical corpora. The findings show that all models obtain satisfactory performance, with BioBERT outperforming the other models. This is because the pre-training corpora involved both general and biomedical domains. Furthermore, disparities in performance between the CADEC and ADE corpora were observed, which indicates that the natural dataset had a significant role in achieving good results. In the future, we plan to apply these models to other NLP tasks such as relation extraction and combine them with other pertinent models to obtain the best results.

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References 1. Classen, D.C., Pestotnik, S.L., Evans, R.S., Classen, C.: Computerized surveillance of adverse drug events in hospital patients*. Qual Saf Heal. Care 14, 221–226 (2005). https://doi.org/10. 1136/qshc.2002.002972 2. Schroeder, S.A.: How Many hours is enough? an old profession meets a new generation. Ann. Intern. Med. 140(10), 838–839 (2004). https://doi.org/10.7326/0003-4819-140-10-200 405180-00017 3. Agency, E.M.: ICH E2A - clinincal safety data managements: definitions and standards for expedited reporting. Drug News 23(1), 71 (2010) 4. Zhang, R., Zhao, P., Guo, W., Wang, R., Lu, W.: Medical named entity recognition based on dilated convolutional neural network. Cogn. Robot., 12, 13–20, (2022) https://doi.org/10. 1016/j.cogr.2021.11.002 5. Sundheim, B., Road, G., Diego, S., Grishman, R., York, N.: Message U n d e r s t a n d i n g C o n f e r e n c e - 6: A Brief History Ocean Surveillance Center Evaluation Division (NRaD) Short-term subtasks Portability. 6. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.,: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Oct. 2018 http://arxiv.org/abs/1810.04805 7. Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: Spanbert: Improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64–77 (2020). https://doi.org/10.1162/tacl_a_00300 8. Lee, J., et al.: BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020). https://doi.org/10.1093/bioinform atics/btz682 9. Beltagy, I., Lo, K., Cohan, A.: SCIBERT: A pretrained language model for scientific text. In: EMNLP-IJCNLP 2019 - 2019 Conf. Empir. Methods Nat. Lang. Process. 9th International Joint Conference Natural Language Processesing Proceedings Conference, pp. 3615–3620, (2019) https://doi.org/10.18653/v1/d19-1371 10. Peng, Y., Yan, S., Lu, Z.: Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets. In: BioNLP 2019 - SIGBioMed Work. Biomed. Nat. Lang. Process. Proc. 18th BioNLP Work. Shar. Task, no. iv, pp. 58–65, (2019). https://doi.org/10.18653/v1/w19- 5006 11. De Bruijn, B., Martin, J.: Getting to the (c)ore of knowledge: mining biomedical literature. Int. J. Med. Inform. 67(1–3), 7–18 (2002). https://doi.org/10.1016/S1386-5056(02)00050-3 12. Dai, X.: Recognizing complex entity mentions: A review and future directions. In: ACL 2018 - 56th Annu. Meet. Assoc. Comput. Linguist. Proc. Student Res. Work., pp. 37–44 (2018) https://doi.org/10.18653/v1/p18-3006 13. Li, F., Zhang, M., Tian, B., Chen, B., Fu, G., Ji, D.: Recognizing irregular entities in biomedical text via deep neural networks. Pattern Recognit. Lett. 105, 105–113 (2018). https://doi.org/ 10.1016/j.patrec.2017.06.009 14. Sharma, R., Chauhan, D., Sharma, R.: Named Entity Recognition System for the Biomedical Domain. In: Proc. 17th Conf. Comput. Sci. Intell. Syst FedCSIS 2022, vol. 30, pp. 837–840 (2022) https://doi.org/10.15439/2022F63 15. Vaswani, A., et al.: Attention Is All You Need, Jun. 2017 http://arxiv.org/abs/1706.03762 16. Cariello, M.C., Lenci, A., Mitkov, R.: A Comparison between Named Entity Recognition Models in the Biomedical Domain. 76–84 (2022). https://doi.org/10.26615/978-954-452071-7_009 17. Ammar, W. et al.: Construction of the literature graph in semantic scholar. In: NAACL HLT 2018 - 2018 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 3, pp. 84–91, (2018) https://doi.org/10.18653/v1/n18-3011

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18. Gurulingappa, H., Rajput, A.M., Roberts, A., Fluck, J., Hofmann-Apitius, M., Toldo, L.: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports J. Biomed. Inform. 45(5) 885–892 (2012)https:// doi.org/10.1016/J.JBI.2012.04.008 19. Karimi, S., Metke-Jimenez, A., Kemp, M., Wang, C.: Cadec: A corpus of adverse drug event annotations. J. Biomed. Inform. 55, 73–81 (2015). https://doi.org/10.1016/j.jbi.2015.03.010 20. Wolf, T., et al.:Transformers: State-of-the-Art Natural Language Processing. pp. 38–45 (2020) https://doi.org/10.18653/v1/2020.emnlp-demos.6 21. Goutte, C., Gaussier, E.: A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3540-31865-1_25 22. Ul Haq, H., Kocaman, V., Talby, D.: Mining Adverse Drug Reactions from Unstructured Mediums at Scale. 2022, Accessed: Oct. 03 2022. www.aaai.org 23. Kocaman, V., Talby, D.: Biomedical Named Entity Recognition at Scale. vol. 19958 (2019)

Serious Games for Improving Training and Skills Acquisition in Medicine and Health Professions Asmae Lamnai(B) and Abderrahim El mhouti ISISA, FS, Abdelmalek Essaadi University, Tetouan, Morocco [email protected]

Abstract. The medical field is one in which treatments (and technologies) are constantly evolving. The objective of serious games is to minimize medical errors and costs and to use video game technology for practical purposes while bringing a playful and involving dimension to the learning process. The advantage of video games is that they can be used to train at a distance and adapt to the patient’s pace and needs. The main objective of this article is to review the current situation of serious games in the field of medical education and to propose some guidelines for their development. We explain in this article the importance of the use of such games in the health field for training, prevention, rehabilitation, and patient follow-up. We also mention some examples of games that allow the acquisition of skills in the technical, gestural, cognitive (clinical judgment, decision-making, etc.) and social (communication, leadership, teamwork, etc.) domains. Keywords: Serious games · medicine · training · skills

1 Introduction A recent World Health Organization (WHO) report estimated that there will be a global shortage of 18 million health workers by 2030 [1]. One of the reasons for this huge shortage is the lack of access and scalability of formal training programs for health workers and the limited scalability of formal training programs for health care providers (HCPs). To address this gap, there is a need to develop and implement interventions that can lead to greater efficiency through effective training [2]. In addition, healthcare professionals need to be kept informed of the latest developments in their respective fields. Innovative educational approaches, training, and courses linked to continuing professional development (CPD) and continuing medical education (CME) are essential to create a dynamic learning environment with simulators [2]. Simulation-based training for healthcare professionals has a major impact on the evolution of their knowledge (“know”: basics and debriefing), skills (“know-how”: how to manage an intervention in collaboration with other healthcare professionals) and behavior (“know-how”: communication with patients and families) [3]. Virtual patient © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 967–975, 2023. https://doi.org/10.1007/978-3-031-29857-8_96

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simulation or serious game is a case-based process in which learners assume the roles of a physician who must assign the roles of other health care professionals, use appropriate communication, manage stress and emotion, consider a patient’s medical history, perform clinical examinations, debrief and manage patients. Serious games are somewhat similar to surgeries using a virtual patient, but the technique has advanced to the point of being fun [4]. High-quality clinical care involves a series of analysis and decisionmaking steps that constitute the clinical reasoning process (“diagnosis,” “request for further tests,” “therapy,” “follow-up and reassessment,” etc. Recently, educational institutions all over the world have had to switch to distance learning for their students. The use of modern technologies in the virtual classroom is now the most important part of the educational process. Efficient modern facilities, user-friendly platforms, and proper pedagogical teaching methodology are the keys to excellent training. The inclusion of virtual teaching methods and serious educational games contributes to the conduct of high-level training and training of future doctors in the knowledge, skills, and experiences required for the formation of professional skills [5]. The aim of this article is to present a synthesis of studies on this topic and to assess the impact of a serious game on the ability of medical students to solve situations faced by patients. The remainder of this article is structured as follows: The second section is about the background of the study, which reviews some recent studies related to the concepts of serious games and SG for medicine and problem training. The third section reviews the contributions of serious games in medicine. This section also presents some suggestions and areas for improvement. The fourth section concludes the study and describes future work.

2 Research Background 2.1 Some Definitions of Serious Games Serious games are currently in vogue [6]. For Alvarez and Djaouti, a serious game is “a computer application whose initial intention is to combine serious aspects (Serious) such as, in a nonexhaustive and nonexclusive way, teaching, learning, communication, or information and knowledge, with ludic springs from the video game (game)” [7]. Noah Falstein, president of the Serious Game Summit, describes serious games as games that “make learning fun and immersive” [8]. In this paper, serious games are identified as a mode of technology innovation: engaging, interactive videogames with the primary goal of teaching specific skills and knowledge [9]. Virtual reality games, such as Second Life, are not serious games, as there are no real rules, goals to reach, modes or levels, competition or construction of knowledge that is useful in the real world [10]. Without “learning objectives” and without an educative aspect, they do not have some of the essential features of serious games [11].

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2.2 Categorization of Serious Games Serious games, as a new educational format and innovative technology tool, have aroused the interest of many researchers in different areas. Depending on the application areas, serious games can be categorized as shown in Table 1 [12]. Table 1. Categorization of serious games Category

Description

Military Games

Games like American Army are training simulations that are used in the training and recruitment of soldiers

Government Game

Training and simulation within the government range from a municipal level to a national level. Games may concern a number of different kinds of tasks and situations, like different types of crisis management

Educational Games Games designed for students to cultivate their knowledge and practice their skills through overcoming numerous hindrances during gaming Corporate Games

Game designed for employees to train skills that their corporations need, like people skills, job-specific skills, and communication skills

Healthcare Games

• Games for the professional area of doctor training, to teach an operation or to impact specialist knowledge; • Games as a training measure for patients who acquire knowledge about their clinical pictures and possible therapy options

2.3 Simulators: Serious Games in Medicine “Never the first time on the patient” is the slogan written in gold lettering at the entrance of the Center for Medical Simulation (CMS) in Boston (MA, USA), a mot-to repeated in French in January 2012 by the rapporteurs of the health simulation mission asked by the Haute Autorité de Santé (HAS) to Pr. Granry and Dr. Moll [13]. In healthcare, simulation is an innovative active teaching method based on experimental training and reflexive practice (Guide de bonnes pratiques de la simulation en santé, HAS, December 2012). Serious gaming corresponds to the use of tools such as manikins or procedural simulators, virtual reality or standardized patients to recreate a situation or an environment of care [14]. Serious games are widely used in the medical world. They can be used to train, prevent, assist and monitor the state of patients. Health serious games are very developed in Anglo-Saxon countries. Let us mention “The Arnold and Blema Steinberg Center for medical simulation” [15], an initiative of Canadian universities dedicated to the training of paramedical and medical personnel through simulation. Another example is the “Epidemiology Laboratory” [16], which trains medical students to develop solutions to prevent the spread of nosocomial infections in hospitals [3]. Other games were developed in the field of medical science, for instance, EHPAD’Pani. is dedicated to the remote training of the personnel in EHPAD (establishment of accommodation for dependent old people). It allows the player to develop

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communication skills and adapted behavior when dealing with Alzheimer’s patients [17] (Fig. 1).

Fig. 1. The serious game EHPAD’Panic

BreakAway Games vHealthCare (BreakawayGames) has developed several games related to healthcare worker training. Like Pulse! in which healthcare professionals can train in a simulated 3D emergency room to practice complicated medical procedures and learn skills to better respond to injuries sustained in incidents (Fig. 2). In this application, virtual patients are examined and diagnosed (in the form of fillable forms) by the physician. It is a game that allows you to create custom cases by allowing you to change the virtual environment, pathology and appearance of the virtual patient and edit scripts to specify a scenario and patient behavior [18].

Fig. 2. The serious game Pulse

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In 2015, SoFraSimS 1, an academic society devoted to simulation in healthcare, was established. The Haute Autorité de Santé (HAS) recommended this type of learning and described the good practices to follow in a 2012 report [HAS, 2012]. They enable the simulation of various types of medical procedures. For example, in surgical procedures, they can encourage the physical and mental activity of the patient; in physiotherapy, they enable patients to evaluate their health status or to understand the treatment plan. In addition, they inform about the actions to take, for example, when a patient has a long-standing illness [19]. Moreover, a trial that combined gamified video games (“driving,” “piloting,” “golf,” “etc.“”) with learning to perform a hybrid laparoscopic operation halted the improvement of young surgeons’ performance on standardized rating scales. As a consequence, the operators who were playing more than three hours per week for pure recreation and were included in the training program made 37% fewer mistakes and 27% quicker than the senior surgeons, and the score was 42% greater. And/or not play video games for fun. This performance improvement can be explained in part by the increased practice of vision and the easier availability of graphical (screen) remote environments (such as laparoscopic or another video endoscopic surgery) and improved coordination. This “fun” gaming device is incorporated in its training programs, and indeed, in this area, the marketing aspects of financing the development of such games have been given much attention [20]. Another implemented project that is more fully developed is MacCoy Critical, which focuses on the issue of soft skills training in obstetrics practice. The game is aimed at training experts in the field (midwives, obstetricians, and anesthesiologists). The project’s approach draws its originality from a real-time process that can be divided into three points. – The diagnosis: the online assessment of the soft skills of the trainee, based on a specific situation and via the recovery of behavioral, eye tracking, and physiological data; – The Decision: based on the diagnostic and in charge of supplying in real-time situations that are adapted to the profile and the skill level of the learner; – Feedback: based on the suggestions of the decision model in which instructions are sent to a script engine that has generated new life situations. The main objective is to enhance the soft skills of the deficient trainee [21]. In a general sense, serious health games can be aimed at patients or doctors [22].

3 Synthesis and Discussion Serious gaming as a simulation tool is a learning method that offers many advantages. First, it allows respecting a basic ethical principle “never the first time on the patient”, while allowing health experts to be confronted to a clinical situation close to reality, in which they can practice their skills and soft skills in an environment without danger for the patient, especially in case of strong emotional situations. They allow doctors to train before an important surgical intervention, to simulate situations to avoid errors and to ensure safety in practice. They will also modify the learner’s behavior to decrease his

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workload because he will be able to develop skills while playing. The learner will then be able to make decisions easily, detect diseases and react more quickly. Serious games can be an excellent learning tool. For example, it can help prepare students for emergency situations: a patient’s health condition can quickly deteriorate, and you have to react quickly, usually with little information. These stressful situations are difficult to handle and have mental and psychological characteristics. Students can practice thinking about hospital emergencies using mannequins and real equipment. This method is effective, but it is also programmed and time consuming. Serious games, on the other hand, promise to provide a similar set of educational outcomes but have the advantage of being commendable and user-friendly, especially for less seasoned learners. Compared to other learning methods such as classical methods, the effect is much more variable. The important thing, however, is not to have a higher impact on equivalent learning but to enable a quality training experience based on a safe and effective experience. The serious games mentioned above are intended to train doctors in a wide range of areas. Like all simulation-based approaches, the learning in such games is enhanced by repeat use, elaborate and frequent feedback, and embedding. Of course, they gather usage rates, logins, and percentages of learners completing the scenario. They also monitor user performance via a score and change over time, but we would suggest a few things to be improved: • Improving the feedback to enhance motivation: take the learner’s motivational or emotional characteristics into consideration. • Customize the players using avatars: The avatar is more interesting in multiplayer games because it represents the player in the view of the other players. To the extent that the avatar can be a means to showcase the player to other players, it can be the object of rewards for the player. • Reward the player for his performance: – Propose to the player a new and more complicated challenge according to his profile (evaluation activity for perfectionists). – To add points: the point quantifies the success of the player, enabling him to unblock the levels, the badges, and the awards. • To add ratings: Leaderboards are yet another way to give the player a goal. By enabling them to compare themselves to other players, we increase their engagement in the game and push them to improve their standing in front of their peers. In addition, it must not deter the player from playing the game; otherwise, it will be counterproductive. The top of the leaderboard must be shown, but not the lower one. • Promote collaboration with the team of healthcare specialists: Improving communication among healthcare specialists and with the patients and their family members.

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The relational approach with the patient and family needs to be trained and requires individual and group training. However, the staff is not trained in this respect in the classic curriculum, either in the medical or paramedical field. Most disagreements and even conflicts are the result of a lack of communication. It is important to address this issue through quality communication, especially in the emergency department, and through the education of all health care personnel. We need to focus on improving life skills, i.e., how to deal with a patient and family’s aggression in the ED and how to deal with an unexpected serious event in the ED, such as a death in the ED and the incidental finding of a brain tumor. This is possible by the addition of educational scenarios that represent real situations of interaction with patients: We propose to include patients of various age groups in serious games: children, adolescents, and elderly individuals, each of whom has particular characteristics that need an appropriate consultation. More special patients can also be modeled, such as those with speech problems and those of different cultural backgrounds. Last, it is possible to show, by means of scenarios, situations where the doctor must manage the family of the patient or his close friends. Doctors must be faced with various types of virtual patients to increase their awareness of the variety of real situations. We recommend also defining or modifying some characteristics of the patient, such as age, gender, mood of the moment, culture or health background, with the doctor in serious games. We also should focus on enhancing the system feedback via those crucial situations, as a suggestion: – Insert a Quiz with feedback on the answers of the player: this can be an encouragement, a smiling face, congratulations, the success specified under the words “yes”, “true”, “or exact”, the appreciations like “very good”, “continue like that”. – To reward the players with badges: “Best Doctor of the Day”, “Best specialist”, “future Cardiologist"… In addition to educational objectives, the multiplayer feature should be enhanced to enable greater communication, manage crises, and encourage leadership. Debriefing is critical following a game. In the serious game, the player receives feedback on his or her score at the end of every round. To enhance this feedback and therefore the learning, we can, in collaboration with trainers and experts, propose debriefings that are adapted to the learning outcomes in each clinic situation and thereby focus on the fundamental aspects needed. Our suggestion is directed at medical students, but the cases can be adapted or developed for other healthcare professionals (nursing assistants, residents, and doctors).

4 Conclusion The training of junior doctors is a long, complicated and sensitive process, which reflects the difficulty of medical practice. It is a mix of knowledge, skills, and attitudes that should be learned, maintained, and developed by each doctor over the course of his or her career.

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Training skilled doctors is both a need and an ongoing challenge for society. Einstein said that knowledge is acquired from experience, all the rest is information, and this is even more true in the medical field. The doctor spent many years in the faculty to store the colossal sum of information needed for his or her practice. Then, he or she is faced with patients in the hospital to gain knowledge through experience. However, while others train on inanimate elements, the doctor trains on fragile things such as the person’s health. The necessity to create “simplified” training and simulation scenarios that are accessible to the largest number of doctors, such as serious games, is essential. The present generation of healthcare learners has always been digital. A study has demonstrated that medical students have a highly positive opinion of training using new technologies such as serious games. Furthermore, the withdrawal from lecture halls makes it essential to change teaching methods, and the development of digital simulation seems to be an appropriate and suitable strategy. Finally, as the number of healthcare professionals is increasing, this type of education tool has the benefit of requiring much less time and training than a simulation session on a real patient. Simulation-based training for doctors has been linked to a meaningful impact on the knowledge, skills, and behavior of future healthcare professionals. Virtual patient simulation is a software-based clinical case scenario process in which learners take on the role of a healthcare professional and are invited to take a patient story, perform a physical examination, diagnose, and make decisions. To some extent, the serious game is analogous to the virtual patient but with an extreme enhancement of technology and a playful element. The meta-analysis by Cook et al. found that the use of virtual patients had a positive impact on learning and clinical reflection compared to a situation in which no specific training was provided. Compared to other learning methods, such as simulation with standardized patients, the effect was much more variable.

References 1. World Health Organization.: World health statistics 2022: monitoring health for the SDGs, sustainable development goals (2022) 2. Ijaz, A., Khan, M.Y., Ali, S.M., Qadir, J., Boulos, M.N.K.: Serious games for healthcare professional training: A systematic review. Europ. J. Biomed. Inform. 15(1) (2019) 3. Blanie, A.: MISE EN PLACE D’UN JEU SERIEUX EN MEDECINE 4. DE, M.D.D.U., SIMULATEUR, M.S.: Simulation Humaine Et Patient Standardise: Analyse De Deux Sessions De Formation Destinees Au Personnel Du Sau De L’hopital Saint Camille 5. Parent, F. et Jouquan, J.: Inscrire la formation dans le cadre d’une approche par compétences. Dans T. Pelaccia (dir.), Comment (mieux) former et évaluer les étudiants en médecine et en sciences de la santé? (p. 107–124). Bruxelles, Belgique : De Boeck supérieur (2016). 6. Bopp, M.M.: Storytelling and motivation in serious games. Part of the Final Consolidated Research Report of the Enhanced Learning Experience and Knowledge Transfer-Project ELEKTRA, 27986 (2008) 7. Alvarez, J., Djaouti, D.: An introduction to serious game definitions and concepts. Serious Games Simul. Risks Manage. 11(1), 11–15 (2011)

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8. Allal-Cherif, O., Bajard, A.: Serious games as a basis for human resource management: examples from the banking sector (2011) 9. Prensky, M.: Fun, play and games: what makes games engaging, in: Digital Game-Based Learning, McGraw-Hill (2001) 10. Salen, K., Zimmerman, E.: Rules of Play: Game Design Fundamentals. The MIT Press, Cambridge, Mass (2003) 11. Marsh, T.: Serious games continuum: Between games for purpose and experiential environments for purpose. Entertainment Comput. 2(2), 61–68 (2011) 12. Djaouti, D.: De l’utilité de l’appellation «Serious Game». Le jeu est-il l’apanage du divertissement?. Interfaces numériques, 3(3), 409–429 (2014) 13. Levraut, J., Fournier, J.P.: Jamais la première fois sur le patient! Annales françaises de médecine d’urgence 2(6), 361–363 (2012) 14. Singh, H., Kalani, M., Acosta-Torres, S., El Ahmadieh, T. Y., Loya, J., Ganju, A.: History of simulation in medicine: from Resusci Annie to the Ann Myers Medical Center. Neurosurgery, 73(suppl_1), S9-S14.) (2013) 15. Pickering, J., Grignon, M.: McGill University faculty of medicine. Acad. Med. 85(9), S639– S643 (2010) 16. Gonçalves, C., Ney, M., Balacheff, N.: Les étudiants jouent mais à quel jeu jouent-il?. In : Actes du workshop Jeux Sérieux de la conférence EIAH 2009 , pp. 10 (2009) 17. Chauveau, L.A., Szilas, N., Luiu, A.L., Ehrler, F.: Dimensions of personalization in a narrative pedagogical simulation for Alzheimer’s caregivers. In: 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH) (pp. 1–8). IEEE (2018) 18. Ma, M., Zheng, H.: Virtual reality and serious games in healthcare. In Advanced computational intelligence paradigms in healthcare 6. Virtual reality in psychotherapy, rehabilitation, and assessment (pp. 169–192). Springer, Berlin, Heidelberg (2011) 19. L’Her, E., et al.: Simulation-based teaching in critical care, anesthesia and emergency medicine. Anesth. Critical Care Pain Med. 39(2), 311–326 (2020) 20. L’HER, E., CROGUENNEC, Y. Des jeux au service de l’apprentissage: les «serious games» 21. Marshall, R.L., Smith, J.S., Gorman, P.J., Krummel, T.M., Haluck, R.S., Cooney, R.N.: Use of a human patient simulator in the development of resident trauma management skills. J Trauma 51(1), 17–21 (2001) 22. Chon, S.H., et al.: Serious games in surgical medical education: a virtual emergency department as a tool for teaching clinical reasoning to medical students. JMIR Serious Games 7(1), e13028 (2019)

A New Compartmental Model for Analyzing COVID-19 Spread Within Homogeneous Populations Touria Jdid1(B) , Mohammed Benbrahim1 , Mohammed Nabil Kabbaj1 , Mohamed Naji2 , and Mohamed Badr Benboubker3 1 LIMAS, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

[email protected]

2 LPAIS, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco 3 LTI, High School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. Including the specific dynamics of COVID-19 in the modeling remains vital to determine the severity and potential of the burden and thus implement appropriate intervention strategies for effectively reducing the disease in human societies. Here, we introduce a new epidemiological model with a vaccination strategy, particularly tailored to analyze, predict, and control the course of COVID19 propagation within homogeneous populations. We fit the proposed model to Morocco’s reported COVID-19 data and then evaluate the impact of implementing a vaccination program on the COVID-19 epidemic progression. The numerical simulations and performance metrics show consistency between the proposed model and the COVID-19 data. Keywords: Epidemic models · SARS-CoV-2 · Infectious diseases · Vaccination · Homogeneous population · COVID-19

1 Introduction The emergence of COVID-19 continues to lead to more deaths, overwhelming countries’ healthcare systems, and having a dramatic impact on social and economic activities due to the response measures taken to contain the pandemic. The severity of COVID-19 pushed university research groups, pharmaceutical industries, international health organizations, and governments to develop, among others, vaccines, control strategies, and mathematical models to prevent and predict the course of the Covid-19. In the context of epidemics, mathematical modeling provides a powerful tool for parsing the dynamics of contagious diseases and implementing preventive measures to minimize disease transmissibility in human societies [1, 2]. In this regard, various research based on compartmental models have been conducted to model COVID-19 dynamics, predict its course, and update public health measures [3–9]. However, most of these models did not consider hospitalization, quarantine, and vaccination. Recently, some works have tried to add some compartments in their models to have a more accurate model. Thereby, Biao Tang et al. proposed a general SEIR epidemic model based © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 976–985, 2023. https://doi.org/10.1007/978-3-031-29857-8_97

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on the epidemiological state of people, the clinical development of the disease, and response measures for estimating the control reproduction number and assessing the public health measures applied by the authorities of China in Wuhan [10]. In [11], a seven-compartment model was proposed, in which an optimal vaccination strategy that mitigates the SARS-CoV-2 burden based on vaccine profile and natural immunity period was described. In [12], an eight-compartment model was formulated that evaluates the influence of an imperfect hypothetical COVID-19 vaccine on controlling the COVID-19 outbreak in the United States. The model assumed that the vaccine-induced immunity did not wane during the simulation period. This study proposes a new epidemic model that contains nine compartments to parse, predict, and monitor COVID-19 propagation within homogeneous populations. The model includes the fundamental factors related to COVID-19 and addresses vaccination, hospitalization, and quarantine issues. We apply the provided model to COVID19 confirmed cases data for Morocco, and numerical results demonstrate its proficient application. The organization of the paper is as follows. We describe our proposed epidemic model in Sect. 2. We calibrate the model and estimate its parameters in Sect. 3. Numerical results of evaluating the effects of vaccination programs on the COVID-19 infection counts are presented in Sect. 4. The conclusion is given in Sect. 5.

2 Mathematical Model Formulation We assume that the human population of interest is constant, homogeneous, and uniformly mixed. We partition the entire population in line to the following epidemiological classes: Susceptible (S), Exposed (E), Symptomatically Infectious (IS ), Asymptomatically Infectious (IA ), Hospitalized (H), Quarantined (Q), Recovered (R), Dead (D), and Vaccinated (V). Figure 1 represents the transition of individuals between different classes. The number of individuals at time t in each class is labeled by: S(t), E(t), IS (t), IA (t), H(t), Q(t), R(t), D(t), and V(t), respectively. Due to the progression of the illness, the size of each class changes over time, and the total population size at time t, denoted by N(t), has the following expression: N (t) = S(t) + E(t) + IS (t) + IA (t) + H (t) + Q(t) + R(t) + V (t)

(1)

We assume that all individuals are initially equally susceptible to catching the SARSCoV-2 infection and can move randomly within the community. Susceptible persons get infected upon effective contact with symptomatic or asymptomatic infectious individuals, at the contact rates βS and βA , respectively. Exposed individuals remain exposed for the incubation period and display no symptoms. At the end of the incubation time, exposed individuals become infectious and exit their class at a removal rate α. Symptomatic individuals can be hospitalized or quarantined at home based on the disease severity they show, at the transition rates δH and δQ , respectively. Asymptomatic carriers are assumed to recover naturally from the illness at a rate γA . A fraction f of them will manifest symptoms with a time delay of 1/ν days and join the symptomatic class. Hospitalized patients are assumed to be isolated, treated, and can not infect other people. They recover from COVID-19 disease with a removal rate γH or die because of it at

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a rate dH . Quarantined patients at home are monitored and hospitalized immediately when their disease status progresses, at a rate σ. They leave their class upon recovery from COVID-19 disease at a rate γQ or die because of it at a rate dQ . On recovery from natural infection, healed individuals may lose their immunity and become susceptible again at a rate μR . The deceased class represents individuals who die from the disease and does not contribute to the transmission dynamics of COVID-19. The model does not incorporate births and natural deaths. The epidemiological parameters involved in this model are described in Table 1. In vaccination programs, a fraction of the susceptible population becomes vaccinated against the SARS-CoV-2 infection at a rate of θV . Vaccinated individuals were assumed to receive one or two doses of the vaccine according to the anti-COVID-19 vaccine profile. To our knowledge, all approved COVID-19 vaccines are imperfect. Therefore, a percentage of vaccinated individuals related to the vaccine efficacy denoted here by EV , with 0 < EV < 1, remains subject to catching the SARS-CoV-2 virus with a probability (1 - EV ). In our formulation, we assume that vaccine-induced immunity is temporary. Thereby, immune individuals could lose their immunity and become susceptibles again at a rate μV .

Fig. 1. Flow diagram of the epidemiological model with vaccination strategy. The total population is stratified into nine compartments, namely susceptible (S), exposed (E), Symptomatically Infectious (IS), Asymptomatically Infectious (IA), hospitalized (H), quarantined (Q), recovered (R), dead (D), and vaccinated (V).

Based on the hypotheses mentioned and the flow chart in Fig. 1, the dynamics of A IA (t) and COVID-19 are provided by the differential system (2), Where  = βS IS (t)+β N (t) V = (1 − EV ) are the forces of infection for susceptible and vaccinated individuals,

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respectively. All epidemiological parameters implicated in this modeling are positive. ⎧ S˙ = μR R + μV V − ( + θV )S ⎪ ⎪ ⎪ ⎪ E˙ = S + V V − αE  ⎪ ⎪  ⎪ ⎪ ⎪ I˙S = (1 − ε)αE + pνA − ρδH + (1 − ρ)δQ IS ⎪ ⎪ ⎪ ⎪ ⎨ I˙A = εαE − (pν + (1 − p)γA )IA (2) H˙ = ρδH IS + (1 − f1 − f2 )σ Q − (rdH + (1 − r)γH)H ⎪ ⎪ ˙ = (1 − ρ)δQ IS − f1 γQ + f2 dQ + (1 − f1 − f2 )σ Q ⎪Q ⎪ ⎪ ⎪ ⎪ R˙ = (1 − p)γA A + f1 γQ Q + (1 − r)γH H − μR R ⎪ ⎪ ⎪ ⎪ ˙ = rdH H + f2 dQ Q D ⎪ ⎪ ⎩˙ V = θV S − (μV + V )V

3 Model Fitting In the estimation process, to reduce the complexity of the system (2), we set some of its parameters from the literature or assumed them based on public information related to COVID-19 (Table 2). Additionally, we assumed the initial conditions for all state variables except the susceptible population (Table 4). For the unknown parameters (Table 3), we estimated them by fitting the daily and cumulative confirmed infections provided by the model (2) to the daily and cumulative infection counts of Moroccan COVID-19 data obtained from the Center for Systems Science and Engineering (CSSE) repository at Johns Hopkins University (JHU) [13]. We note that the relationship between the number of daily infections, IS (t), and the number of cumulative infections, indicated by Ccum (t), is provided by the following recurrent expression: Ccum (t) = Ccum (t − 1) + IS (t)

(3)

As data on Morocco’s COVID-19 vaccination program are still not publicly available, we fitted model (2) without vaccination (θV = 0 and V(0) = 0) from March 2 to June 10, 2020. In the model calibration, we used the Nelder-Mead optimization method to minimize the sum of squared residuals, denoted by SSR , of the data points from the curve. In our case, SSR is expressed by  SSR ( ) = (4) (dk − Mk ( ))2 where dk is the kth COVID-19 observation, n is the sample size of COVID-19 data points, Mk ( ) is the model output of reported cases, and is the set of unknown p parameters such that = {βS , βA , f, ρ, δQ , f1 , f2 , r}. We performed system (2) simulations using the LMFIT package in Python. The values of the fixed and assumed parameters are displayed in Table 2, the best-fit values of the unknown parameters, their standard deviations, and their 95% confidence bands are given in Table 3, and the initial population sizes are tabulated in Table 4. Figure 2 depicts the results of fitting model (2) curves to COVID-19 confirmed cases data for Morocco with their respective confidence intervals.

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T. Jdid et al. Table 1. Description of epidemiological parameters.

Parameter

Description

N

Population size

βS

Effective contact rate between symptomatic and susceptible individuals

βA

Effective contact rate between asymptomatic and susceptible individuals

α

Incubation rate

ε

Fraction of asymptomatic population

f

Fraction of asymptomatic persons who transit to the symptomatic class

ν

Transition rate from asymptomatic to symptomatic individuals

ρ

Fraction of symptomatic people who are hospitalized with COVID-19

δQ

Transition rate from symptomatic to quarantined class

δH

Transition rate from symptomatic to hospitalized class

σ

Transition rate from quarantined to hospitalized class

f1

Fraction of quarantined individuals who recover from COVID-19

f2

Fraction of quarantined individuals who die because of COVID-19

γA

Recovery rate of asymptomatic individuals

γQ

Recovery rate of quarantined individuals

γH

Recovery rate of hospitalized individuals

dQ

Disease-induced death rate due to quarantined class

dH

Disease-induced death rate due to hospitalized class

r

Proportion of hospitalized individuals who die because of COVID-19

μR

Rate at which healed individuals lose their natural immunity

θV

Vaccination rate

EV

Vaccine efficacy

μV

Waning rate of the vaccine

To assess the goodness of fit between the actual COVID-19 data and model output, we computed three criteria: Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), and Root Mean Squared Error (RMSE). These criteria are specified in the bellow Eq. (5), Eq. (6), and Eq. (7), respectively: BIC = nln(SSR/n) + pln(n)

(5)

AIC = nln(SSR/n) + 2p

(6)

RMSE =



SSR /(n − p)

(7)

The calculation results of these criteria give a BIC of 550.335, an AIC of 529.493, and an RMSE of 13.587. It is noteworthy from these scores that AIC, BIC, and RMSE

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have small values. Moreover, the best-fit values of the estimated parameters (Table 3) make scientific sense, and their 95% confidence bands are reasonably narrow. Thereby our model is likely to be correct and fits better the COVID-19 data points. Table 2. Fixed, and assumed parameters values. Parameter

Mean value

α

0.1923 day−1

95% CI

Reference

[0.1429, 0.2439]

[14]

ε

0.1790 (dimensionless)

[0.1550, 0.2020]

[15]

γA

0.1398 day−1

[0.0701, 0.2094]

[10]

γQ

0.1162 day−1

[0.0388, 0.1937]

[10]

γH

0.0714 day−1

[0.0476, 0.0909]

[10]

μR

0.0050 day−1

-

Assumed

ν

0.5000 day−1

-

Assumed

σ

0.1429 day−1

-

Assumed

δH

0.0314 day−1

-

Assumed

dQ

0.0001 day−1

-

Assumed

dH

0.0010 day−1

-

Assumed

Table 3. Estimated parameters values and their 95% confidence bands. Parameter

Mean value

Standard deviation

95% CI

βS

0.205226 (dimensionless)

0.012842

[0.179542, 0.230910]

βA

0.896239 (dimensionless)

0.110057

[0.676125, 1.116353]

f

0.298536 (dimensionless)

0.040334

[0.217868, 0.379204]

ρ

0.332519 (dimensionless)

0.046305

[0.239909, 0.425129]

δQ

0.050899 day−1

0.000117

[0.050665, 0.051133]

f1

0.400844 (dimensionless)

0.052614

[0.295616, 0.506072]

f2

0.099986 (dimensionless)

0.016325

[0.067336, 0.132636]

r

0.097607 (dimensionless)

0.009760

[0.078087, 0.117127]

The analysis of the estimated parameters in Table 3 shows that the effective contact rate of asymptomatically-infectious humans is significantly higher than that of symptomatic infectious individuals. Accordingly, our estimate suggests the major role of asymptomatic carriers in the dynamics of COVID-19 transmission in Morocco. One can explain this result by the unawareness of asymptomatic individuals of their infectious state, so they can not be avoided by susceptible people, unlike symptomatic individuals, who can easily be detected, hospitalized, or likely to choose self-quarantine.

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T. Jdid et al. Table 4. Initial conditions of state variables.

Initial state

Description

Value

S(0)

Initial size of susceptibles

3.6 × 107

Reference [16]

E(0)

Initial size of exposed population

4

Assumed

IS (0)

Initial size of symptomatic infected population

1

Assumed

IA (0)

Initial size of asymptomatic infected population

1

Assumed

H(0)

Initial size of hospitalized population

1

Assumed

Q(0)

Initial size of quarantined population

0

Assumed

R(0)

Initial size of recovered population

0

Assumed

D(0)

Initial size of deaths

0

Assumed

V(0)

Initial size of vaccinated population

0

Assumed

Fig. 2. Fitting system (2) curves to COVID-19 reported cases data for Morocco. (a) System (2) fitted to daily reported infections, (b) System (2) fitted to cumulative reported infections. The blue dots represent actual data points from March 2, 2020, to June 10, 2020, the solid magenta curving shows model estimations, and the light magenta area depicts the 95% confidence band (CI).

4 Vaccination Impacts In this section, we develop a scenario to examine the impact of implementing vaccination on the evolution of the COVID-19 burden in Morocco. In the construction of this scenario, we assume a baseline vaccination rate of 0.019 per day (i.e., θV = 0.019), a baseline vaccine profile with an efficacy of 79% (i.e., EV = 0.79), and a vaccine-induced immunity period of 183 days (μV −1 = 183). The vaccine-induced immunity is assumed to be equal to natural immunity. Values of the remaining parameters are fixed as in Tables 2 and 3, respectively. We simulate model (2) with vaccination dynamics from the starting date of the epidemic in Morocco, March 2, 2020, to July 20, 2020. To evaluate the effect of vaccination rate, we simulated model (2) first with no vaccination, second by considering a baseline vaccination, and further increasing the

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vaccination rate from its baseline value (θV = 0.019) by 20% and 50%. Figure 3 gives the simulation results based on the vaccination rate. In the case of vaccine efficacy, we simulated model (2) by considering three different COVID-19 vaccine types approved for use in Morocco: AstraZeneca, Sinopharm, and Pfizer. These vaccines have an efficacy of 70%, 79%, and 95%, respectively. Figure 4 illustrates the impact of the efficacy of the three COVID-19 vaccines considered in this study on the evolution of daily and cumulative confirmed infections.

Fig. 3. Effect of vaccination rate on COVID-19 epidemic control. Numerical model simulations show the change in (a) daily confirmed cases and (b) cumulative infections per day as a function of vaccination rate (θV ) for a baseline vaccine with an efficacy of 0.79.

Fig. 4. Effect of vaccine efficacy on COVID-19 epidemic control. Numerical model simulations show the change in (a) daily infections and (b) cumulative infections as a function of the efficacy of vaccine (VE ) with a baseline vaccination rate of 0.019.

Analysis of Fig. 3 shows that implementing a baseline vaccination could protect people from infection by reducing the epidemic peak of daily new cases from 164 to 94. Further, increasing the vaccination rate from its baseline value by 20% and 50% could diminish the epidemic peak incidence to 84 and 72, respectively. A similar analysis

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of Fig. 4 shows that vaccinating a population with a vaccine of 95% efficacy (EV ) by keeping constant the vaccination rate (θV ) at 0.019 could result in a decrease in the pandemic peak incidence from 94 to 73. In contrast, vaccinating a population with a vaccine of 70% efficacy could decrease the epidemic incidence but less than the baseline vaccine with 79% efficacy. The simulations in Fig. 3(b) and Fig. 4(b) show a significant reduction in recorded cumulative infections per day at the pandemic peak when we vary the vaccination rate and vaccine efficacy. Remarkably, the projected cumulative infection simulations are consistent with the daily new cases. Therefore, the greater the vaccine efficacy and vaccination rate, the more vaccination program could protect people from COVID-19 contagion.

5 Conclusion In this work, we constructed an adequate compartmental model to analyze, predict, and control COVID-19 spread within homogeneous populations. The model included the fundamental factors related to COVID-19 and addressed vaccination, hospitalization, and quarantine issues. We simulated our model on daily and cumulative reported COVID19 data and evaluated the vaccination impacts on the COVID-19 epidemic in Morocco. The simulations and score metrics showed how the proposed framework is likely to be accurate, consistent with the observed data points, and suitable for studying SARS-CoV-2 propagation in a homogeneous population. In future work, we will extend our proposed epidemic model to study the dynamics of COVID-19 in heterogeneous populations.

References 1. Hethcote, H.W.: The Mathematics of infectious diseases. SIAM Rev 42, 599–653 (2000). https://doi.org/10.1137/S0036144500371907 2. Hethcote, H.W.: Three Basic Epidemiological Models. In: Levin, S.A., Hallam, T.G., Gross, L.J. (eds.) Applied Mathematical Ecology, pp. 119–144. Springer, Berlin, Heidelberg (1989) 3. Iboi, E., Sharomi, O.O., Ngonghala, C., Gumel, A.B.: Mathematical Modeling and Analysis of COVID-19 pandemic in Nigeria (2020) 4. Samui, P., Mondal, J., Khajanchi, S.: A mathematical model for COVID-19 transmission dynamics with a case study of India. Chaos, Solitons Fractals 140, 110173 (2020). https:// doi.org/10.1016/j.chaos.2020.110173 5. Cooper, I., Mondal, A., Antonopoulos, C.G.: Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic. Chaos, Solitons Fractals 139, 110298 (2020). https://doi.org/10.1016/j.chaos.2020.110298 6. Peng, L., Yang, W., Zhang, D., Zhuge, C., Hong, L.: Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv:200206563 [q-bio] (2020) 7. Ramezani, S.B., Amirlatifi, A., Rahimi, S.: A novel compartmental model to capture the nonlinear trend of COVID-19. Comput. Biol. Med. 134, 104421 (2021). https://doi.org/10. 1016/j.compbiomed.2021.104421 8. Senapati, A., Rana, S., Das, T., Chattopadhyay, J.: Impact of intervention on the spread of COVID-19 in India: A model based study. J. Theor. Biol. 523, 110711 (2021). https://doi.org/ 10.1016/j.jtbi.2021.110711

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9. Acuña-Zegarra, M.A., Santana-Cibrian, M., Velasco-Hernandez, J.X.: Modeling behavioral change and COVID-19 containment in Mexico: A trade-off between lockdown and compliance. Math. Biosci. 325, 108370 (2020). https://doi.org/10.1016/j.mbs.2020.108370 10. Tang, B., et al.: Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J. Clin. Med. 9, 462 (2020). https://doi.org/10.3390/jcm9020462 11. Acuña-Zegarra, M.A., Díaz-Infante, S., Baca-Carrasco, D., Olmos-Liceaga, D.: COVID-19 optimal vaccination policies: aA modeling study on efficacy, natural and vaccine-induced immunity responses. Math. Biosci. 337, 108614 (2021). https://doi.org/10.1016/j.mbs.2021. 108614 12. Iboi, E.A., Ngonghala, C.N., Gumel, A.B.: Will an imperfect vaccine curtail the COVID-19 pandemic in the U.S.? Infectious Disease Modell. 5, 510–524 (2020). https://doi.org/10.1016/ j.idm.2020.07.006 13. CSSEGISandData (2022) COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University 14. Li, Q., et al.: Early Transmission dynamics in Wuhan, China, of novel Coronavirus-Infected Pneumonia. N. Engl. J. Med. 382, 1199–1207 (2020). https://doi.org/10.1056/NEJMoa200 1316 15. Mizumoto, K., Kagaya, K., Zarebski, A., Chowell, G.: Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance 25, 2000180 (2020). https://doi.org/10.2807/ 1560-7917.ES.2020.25.10.2000180 16. Population by Country (2022) - Worldometer. https://www.worldometers.info/world-popula tion/population-by-country/. Accessed 8 Apr 2022

The Use of Artificial Intelligence and Blockchain in Healthcare Applications: Introduction for Beginning Researchers Majda Rehali1(B) , Merouane Elazami Elhassani2 , Asmae El jaouhari3 , and Mohammed Berrada1 1 Artificial Intelligence, Data Science and Emerging Systems Laboratory, National School of

Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] 2 Scientific Research National Center (CNRS), University of Montpellier, Montpellier, France 3 Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. The healthcare sector is evolving daily thanks to fast evolution and rapid advancements. It benefits from novel technologies such as Artificial Intelligence (AI) and Blockchain. These technologies are causing significant revolutions in various areas of healthcare. This paper addresses the fundamentals and key concepts of AI and Blockchain and focuses on their main applications in healthcare. Then, it presents some research studies that use their integration. This work aims to demystify AI and Blockchain for readers. It will help especially beginning researchers to get started with these technologies and have an overview of their applications in healthcare. Keywords: Artificial Intelligence · Machine Learning · Deep Learning · Blockchain · Healthcare

1 Introduction In a matter of years, the healthcare industry has seen rapid progress. This is due to the use of new technologies such as Blockchain and AI. Blockchain is a decentralized database capable of securely transmitting and storing transactions conducted in a peer-to-peer network [1]. Transactions are structured into blocks and processed in a distributed ledger without any intermediary party intervention [2]. AI refers to the methods, techniques, and algorithms that allow machines to simulate human minds in learning and analysis. Hence, it can improve decision-making and problem-solving [3]. The implementation of these technologies has further brought immense revolutions in different fields of healthcare through various applications such as telemedicine and health monitoring [4, 5], electronic medical records management [4], drug discovery and traceability [6, 7], and disease analysis and prediction [8]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 986–997, 2023. https://doi.org/10.1007/978-3-031-29857-8_98

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This paper presents an overview of the use of AI and Blockchain applications in healthcare. It also outlines some cutting-edge research studies combining these two technologies. Then, it highlights the key benefits brought via this integration. The rest of this work is organized as follows: In the next section, we provide an explanation of Blockchain technology, its key features, and its different types. Then, we present the consensus protocols and smart contracts. Section 3 shows some of the main applications of Blockchain in healthcare. In Sect. 4, we present AI, Machine Learning (ML), and Deep Learning (DL). This section also includes a brief description of the four approaches of ML: Supervised Learning (SL), Unsupervised Learning (USL), Semi-Supervised Learning (SSL), and Reinforcement Learning (RL). In Sect. 5, we provide some usage scenarios of AI in healthcare. Then, we address some research studies that combine AI and Blockchain in healthcare in Sect. 6. Finally, we conclude the paper.

2 Blockchain Overview 2.1 What is Blockchain? The introduction of Bitcoin in 2008, the first cryptocurrency invented by Satoshi Nakamoto, sparked the adoption of Blockchain technology [4]. It can be defined as a decentralized database or distributed ledger that comprises transactions shared over a peer-to-peer network [9]. It allows entities to communicate within the network without relying on a trusted third party [10]. Blockchain term describes the way transaction data are stored. It is structured into "blocks" that are linked together. Jointly, they form a “chain” [11]. Following their acceptance into the Blockchain, these blocks are cryptographically connected to previous and future blocks. 2.2 Key Features The following are the main Blockchain features (see Fig. 1): Decentralization. A key feature of Blockchain is decentralization. This means that no central authority has control over the data shared in the Blockchain [12]. Instead, entities interacting in the Blockchain must reach an agreement about events in a peer-to-peer network. It is accomplished using a variety of consensus mechanisms. Privacy. The Blockchain ensures the privacy of transactions given by the pseudonymity feature [10]. Immutability. The core idea, aside from being tamper-proof, is that the information must remain unchangeable. It is impossible to erase the information after it is accepted into the Blockchain. Thanks to the distributed ledger, which is held on numerous nodes [13]. Cryptography. It is another key characteristic of the Blockchain. It adds another level of security to entities in the network. All data on the Blockchain are hashed cryptographically to secure data sharing.

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Audit and Traceability. It is accomplished by linking every newly created block with the existing precedent block via the hash function. As a result, the chain of blocks provides a complete history of all transactions [2]. Security. All the above features allow us to enhance Blockchain security.

Fig. 1: Blockchain features.

2.3 Types of Blockchain Blockchain can be categorized into three different types: Public Blockchain. It is utilized in cryptocurrencies such as Bitcoin and Ethereum. In this type of Blockchain, the ledger is globally accessible. Thus, everyone can send and check transactions [9]. All nodes can participate freely in the consensus protocol. Consortium Blockchain. Like Hyperledger, it can be regarded as partially centralized, but with only a few selected participants, who are authorized to view and participate in the consensus protocol [10]. Private Blockchain. Without authorization, no one may join this Blockchain. Only the selected nodes managed by a single organization participate in the consensus protocol [2].

2.4 Consensus Protocols One of the major appealing components of the Blockchain resides in the way that the distributed consensus protocol authorizes entities in the network, and how it validates the transaction data. In the literature, many of these consensus protocols have been suggested, but the most commonly used in the healthcare sector are as follows:

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Proof of Work (PoW). The fundamental concept of the PoW mechanism is that miners are required to computationally resolve a mathematical problem (sometimes called a puzzle) to determine a matching target hash. The first miner who calculates this hash value, validates the transaction (or other entries) inside the block and receives a reward [10]. Proof of Stake (PoS). This consensus protocol relies on the assets of the entities. The entities, also known as validators, have more chances to be selected for validating the block if their proportion of assets is higher in the Blockchain network [14]. Practical Byzantine Fault Tolerance (PBFT). This consensus protocol allows a distributed network to agree on the same value even when malicious nodes are present. To be functional, a PBFT system tolerates the maximum number of malicious nodes to be less than or equal to 1/3 of all nodes in the system. The system becomes more secure as the number of nodes increases [15].

2.5 Smart Contracts Smart contracts (SC) are programs stored on the Blockchain [13]. They are self-executing contractual agreements that run when predetermined conditions are met without any third party’s or intermediate’s involvement [2]. Smart contracts offer the advantage of eliminating intermediaries and supporting features such as auto-executing, self-verification, accuracy, transparency, and security.

3 Blockchain Applications in Healthcare Research studies were conducted on Blockchain and its potential applications to overcome security and privacy concerns in healthcare systems. This section gives an overview of Blockchain applications in healthcare: Electronic Medical Records Management. Currently, several works have used Blockchain to manage and safely exchange Electronic Medical Records (EMR), which are the patient’s medical information collected and digitally stored [12]. For instance, “MedRec” is a prototype that utilizes a decentralized record management system to manage EMRs using Blockchain technology [16]. It offers patients a full immutable log, as well as quick access to their medical information from different providers and treatment centers. MedRec enables authenticity, privacy, security, and data sharing using only Blockchain features, all of which are critical concerns when dealing with sensitive data. Securing Remote Patient Monitoring Devices. Remote Patient Monitoring (RPM) enables patients to be monitored outside medical centers. RPM’s key feature is patient monitoring via wearable devices, as well as the transmission of medical data for diagnosis and therapy. As the usage of RPM grows, critical data are exposed to third-party manipulation. Many academic types of research have recommended using the benefits of Blockchain to address this issue [4].

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Supply Chain Management and Drug Traceability. Supply chain management is another rising area of interest for Blockchain applications due to its capacity to guarantee the traceability of medical products by offering a transparent decentralized tracking system. Because of the immutability and timestamps of Blockchain transactions, drug manufacturers can easily track their products and ensure that the information included inside the block cannot be changed [6]. Tracking Infectious Disease, COVID-19 Pandemic. Several companies around the world have employed Blockchain to build systems and platforms to fight against the COVID-19 epidemic. For instance, IBM has developed its COVID-19 Blockchainpowered Digital Health Pass, which enables users to save their digital health data and have them checked to physically get back to work[4].

4 Artificial Intelligence 4.1 What is Artificial Intelligence? Artificial intelligence (AI) is a branch of computer science that aims to create systems and machines that can work independently and intelligently. AI can also be defined as the set of methods, techniques, and algorithms that allow machines to mimic human intelligence [9]. The following is a very brief explanation of the AI approaches used in the healthcare literature (see Fig. 2). 4.2 Classical Machine Learning Machine Learning (ML) is a subset of AI that focuses on building predictive models capable of learning directly from data without being programmed to do so. Generally, ML algorithms can be grouped into four categories of learning: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning [17]. Supervised Learning. SL is a ML approach that consists of using labeled datasets to train the algorithms. The model is provided with a set of samples and their corresponding labels, from which it learns to identify the correct label [18]. As such, this task allows the model to learn and improve its accuracy over time. Generally, Classification and Regression are the most common methods used to solve SL problems. The classification algorithms are tasked with assigning the data into specific categories or labels to discover more advanced and sophisticated knowledge [19]. A few of the most popular Classification algorithms are Support Vector Machine (SVM), Random Forest, Decision Tree, and Naïve Bayes. Regression is another SL technique helping recognize the relationships that may exist between a dependent and one or more independent variables [20]. Regression helps make predictions about continuous numbers [21]. Some of the common Regression algorithms are Logistic Regression, Linear Regression, and Polynomial Regression. In both, Classification and Regression algorithms, we are looking for a model that can do correct mapping between the input and the output.

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SL algorithms are largely used in medical research for prognostic studies and disease diagnoses such as cancer [3]. They are also used in the early detection of Alzheimer’s disease and heart disease prediction [18]. Unsupervised Learning. As opposed to SL, USL makes use of unlabeled datasets to train the algorithms. Its key concept is to detect hidden relationships that can group or cluster data under given features. Clustering and Dimensionality Reduction (DR) are the most common USL methods [3] [21]. The task of Clustering algorithms is to group similar inputs into clusters. Common Clustering algorithms comprise Hierarchical Clustering, Gaussian Mixture Clustering, and K-Means Clustering. On the other hand, DR is a way of reducing the input features while ensuring that the information of the original data remains the same. Some popular algorithms of DR are Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). AI applications in healthcare most frequently use SL algorithms, since they give more significant results. USL is generally recognized for feature extraction. As a result, it can be employed, as a preprocessing phase, to reduce the dimensionality of the input or to determine subgroups, which in turn makes the SL phase more efficient [3]. Semi-Supervised Learning. SSL is the combination between SL and USL [22]. SSL is employed when the number of labeled data is relatively limited, compared to the number of unlabeled data [17]. In this case, the labeled data help SSL algorithms use the unlabeled data more effectively and can produce relevant enhancement in the performances. Reinforcement Learning. RL is a trial process in which the model is not given the desired action. It has to attempt various actions in different circumstances to determine the best actions leading to the maximum rewards [23]. Unlike other learning approaches, RL is specifically adapted to unknown environments. In healthcare, RL has been used in various applications such as sequential decision-making task analysis [17].

4.3 Deep Learning: A New Epoch of Machine Learning Deep Learning (DL) is a subfield of ML characterized by the use of Artificial Neural Networks (ANN), which are organized in multiple layers of interconnected nodes. DL models are particularly known for having more hidden layers, which allow them to automatically extract more complex features and patterns from data to generate more accurate predictions [18]. DL is largely used in healthcare applications, especially for medical imaging analysis, as it can naturally handle the complex and high volume of images [3]. Some of the famous DL architectures used in healthcare include Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Deep Belief Network (DBN), and Autoencoders [24, 25].

5 AI Applications in Healthcare The use of AI in healthcare has shown exponential growth due to its massive advantage. This section provides an overview of the main AI-based applications:

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Fig. 2. Relationship among AI, classical ML, and DL.

Disease Diagnosis and Predictive Analytics. Throughout the history of medical informatics, AI techniques have been employed for precise diagnosis, management, and even disease prediction such as cancer, nervous system disease, and cardiovascular disease. For instance, using AI in diagnostic medical imaging has demonstrated great performance and sensitivity in the detection of imaging abnormalities [8]. AI to Mitigate the COVID-19 Pandemic. AI and especially ML techniques appear to be the best technology for combating COVID-19 [12]. AI tools have been investigated by many researchers for diagnosis, screening, classification, drug repurposing, and prediction of COVID-19 [26]. Robotics and AI. In healthcare, robots have sometimes been used to replace human workers, increase human capacities, and assist human healthcare providers. They help in a variety of ways, such as robots utilized for surgeries, robotic assistants for rehabilitation and patient assistance, robots that are integrated into implants, and robots used to help doctors and other medical staff with their missions [27]. Telemedicine and Health Monitoring. Telemedicine is another area of interest for AI. As such, AI can help care for patients remotely by monitoring their data through sensors such as wearable devices and alerting health professionals if anything is abnormal [5]. Drug Discovery. AI has also touched the pharmaceutical industry influencing the drug development process as well as the product’s full lifecycle. Using the most recent AI tools will not only shorten the time required for the products to reach the market, but will also increase their quality, and offer better utilization of available resources while remaining cost-effective [7].

Authors and Years

Chamola, 2022

Ghazal, 2022

Qamar, 2022

Hasanova, 2022

Manocha, 2023

N

1

2

3

4

5

This work proposes a digital twin framework for elder people to predict unusual events that may happen in their daily lives, using IoT and deep learning technologies

This work proposes a framework for heart disease prediction that combines blockchain and machine learning. Blockchain ensures secure storage for patient records, which are used by Machine learning-based meta-heuristic algorithm to perform the prediction of heart disease

In this work, a new framework is introduced for cyber security-based electronic health records analysis using Blockchain and Deep learning techniques. Initially, Deep Learning models are responsible for feature selection and input classification. Whereas, a cryptographic cloud-based cyber blockchain model is utilized to improve network security

In this work, a framework to secure health monitoring systems is proposed with the help of a blockchain-based encryption architecture and computational intelligence approach

This work introduces an AI-assisted blockchain-based framework, in which the medical records are stored and processed using various AI techniques. The objective is to generate a single report about the patient’s medical history. This report precisely comprises crucial information, which is securely stored over a decentralized blockchain

Main contribution

Elder patient monitoring Eldercare

Patient data management Heart disease prediction

Electronic health records

Electronic health monitoring Electronic health records

Electronic Health Records

Applications Domain

Data acquisition and processing, security

Security and prediction

Selection, classification, and security

Security

Security and scalability

Target

Consortium blockchain

Ethereum

Ethereum, private blockchain

Private blockchain

Ethereum

Type/Platform of Blockchain used

Reputation-based Byzantine fault tolerant (RBFT)

Proof of Work (PoW)

Not specified

Not specified

Not specified

Consensus protocol used

No

Yes

Yes

Yes

Yes

Smart contract is applied

Convolutional Neural Network (CNN), Gated Recurrent Unit Network (GRU)

Sine Cosine Algorithm weighted K-Nearest Neighbour (SCA_WKNN)

Kernel-based gradient boosting neural network (Ker_GBNN), Stochastic convolutional neural network (St_ConVolNet)

Support Vector Machine (SVM)

Optical Character Recognition (OCR) Machine learning techniques (Not specified)

AI Techniques applied

Table 1. Presentation of some recent articles that use AI and Blockchain integration in healthcare.

IoT Digital Twin Fog Cloud

N/A

Cloud

IoT Cloud

5G IPFS

Other used technologies

(continued)

[32]

[31]

[30]

[29]

[28]

Reference

The Use of Artificial Intelligence and Blockchain in Healthcare Applications 993

Authors and Years

Hassija, 2022

Singh, 2022

Kumar, 2023

Kumar, 2022

Amponsah, 2022

N

6

7

8

9

10

A novel model for fraud detection and prevention is proposed for health insurance claim processing that uses blockchain and machine learning technologies

In this work, a blockchain-based federated-learning framework is introduced. It allows the collaboration of numerous hospitals that participate in training the federated learning models. Data privacy is ensured with the help of a homomorphic encryption scheme that ciphers the weights of the locally trained model

Authors propose BDSDT, a new Secure Data Transmission mechanism for IoT-enabled healthcare system that is based on Blockchain and Deep learning. This new framework provides two layers for ensuring security. The first layer consists of a blockchain architecture, where all IoT devices are registered, checked then inserted into the blockchain network. The other layer contains a deep learning architecture that extracts features from the data, which are used to detect security breaches in the network. To face the scalability challenge, the authors adopted IPFS-based off-chain storage

In this work, an advanced architecture for privacy-preserving in smart Healthcare is proposed using Blockchain and Federated Learning. The Blockchain mechanism ensures secure data collaboration for IoT devices, while Federated Learning guarantees privacy preservation of healthcare data sources

An innovative framework for minor medical consultations is designed using blockchain and machine learning. The proposed model can predict the score of a professional who is part of the blockchain network, based on different parameters, including reputation, expertise, and detailed orientation… As a result, the patient can benefit from the best consultation

Main contribution

Healthcare fraud

Covid-19 medical images

Securing IoT devices

Securing IoT devices

Minor medical consultations

Applications Domain

Prediction and decision-making

Security and privacy

Security, privacy, and scalability

Security, privacy, scalability, and interoperability

Security, privacy, and cost-effective solution

Target

Ethereum

Permissioned blockchain

Ethereum, private blockchain

Consortium blockchain

Ethereum

Type/Platform of Blockchain used

Table 1. (continued)

Not specified

Proof of Work (PoW)

Zero Knowledge Proof (ZKP), estimable Proof of Work (ePoW)

Proof of Work (PoW)

Not specified

Consensus protocol used

Yes

Yes

Yes

No

Yes

Smart contract is applied

Decision tree (DT)

Federated learning

Deep Sparse AutoEncoder (DSAE), Bidirectional Long Short-Term Memory (BiLSTM)

Federated learning

Word2Vec, TF-IDF, Naive Bayes (NB), Logistic Regression (LR)

AI Techniques applied

N/A

N/A

IoT IPFS

IoT Cloud

N/A

Other used technologies

[37]

[36]

[35]

[34]

[33]

Reference

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6 AI and Blockchain Integration in Healthcare Integrating AI with Blockchain can boost healthcare and solve potential challenges facing this sector. Some of its key benefits include security and privacy, high efficiency, prediction, decision-making, and distributed management for different types of applications in healthcare [5, 1]. Various research studies have been conducted aiming to propose novel architectures and frameworks combining the latest advances of these two technologies. In this section, we choose to outline some of them (see Table 1).

7 Conclusion AI and Blockchain technologies have proven to have great potential for healthcare applications, as they provide many advantages, such as data analysis and prediction, security, and privacy. In this paper, we briefly presented the key concepts of these technologies and looked at some of their healthcare applications. The contribution of academic research to this subject is rapidly increasing, especially in terms of combining these two technologies with other emerging fields such as the Internet of things (IoT) and Cloud computing. Combined, they offer distinct advantages and benefits that would solve various healthcare problems.

References 1. Ekramifard, A., Amintoosi, H., Seno, A.H., Dehghantanha, A., Parizi, R.M.:A Systematic Literature Review of Integration of Blockchain and Artificial Intelligence, pp. 147–160 (2020) 2. Hussien, H.M., Yasin, S.M., Udzir, N.I., Ninggal, M.I.H. and Salman, S.: Blockchain technology in the healthcare industry: trends and opportunities. J. Ind. Inf. Integr. 22, 100217, (2021) https://doi.org/10.1016/j.jii.2021.100217 3. Jiang, F., et al.: Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017). https://doi.org/10.1136/svn-2017-000101 4. Azbeg, K., Ouchetto, O., Andaloussi, S.J., Fetjah, L.: A taxonomic review of the use of iot and blockchain in healthcare applications a taxonomic review of the use of IoT and blockchain in healthcare applications. IRBM (2020) https://doi.org/10.1016/j.irbm.2021.05.003 5. Tagde, P., et al.: Blockchain and artificial intelligence technology in e-Health. Environ. Sci. Pollut. Res. 28(38), 52810–52831 (2021). https://doi.org/10.1007/s11356-021-16223-0 6. Vervoort, D., Guetter, C.R., Peters, A.W.: Blockchain, health disparities and global health. BMJ Innov. 7(2), 506–514 (2021). https://doi.org/10.1136/bmjinnov-2021-000667 7. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., Tekade, R.K.: Artificial intelligence in drug discovery and development. Drug Discov. Today 26(1), 80–93 (2021). https://doi.org/ 10.1016/j.drudis.2020.10.010 8. Rong, G., Mendez, A., Bou, E., Zhao, B., Sawan, M.: Artificial intelligence in healthcare : review and prediction case studies. Engineering 6(3), 291–301 (2020). https://doi.org/10. 1016/j.eng.2019.08.015 9. Fetjah, L., Azbeg, K., Ouchetto, O., Andaloussi, S.J.: Towards a smart healthcare system : an architecture based on iot , blockchain , and fog computing. 16(4), 1–18 (2021) https://doi. org/10.4018/IJHISI.20211001.oa16

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10. Hasselgren, A., Kralevska, K., Gligoroski, D., Pedersen, S.A., Faxvaag, A.: Blockchain in healthcare and health sciences—a scoping review. Int. J. Med. Inform., 134, 104040 (2020) https://doi.org/10.1016/j.ijmedinf.2019.104040 11. Chattu, V.K.: A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health. Big Data Cogn. Comput., 5(3), 41 (2021). https://doi. org/10.3390/bdcc5030041 12. Imran, M., Zaman, U., Imtiaz, J., Fayaz, M. and Gwak, J.: Comprehensive Survey of IoT , Machine Learning , and Blockchain for Health Care Applications : A Topical Assessment for Pandemic Preparedness , Challenges , and Solutions. Electronics. 10(20), 1–37, (2021) 13. Atlam, H.F., Alenezi, A., Alassafi, M.O., Wills, G.: Blockchain with Internet of Things : Benefits , Challenges , and Future Directions. Int. J. Intell. Syst. Appl. 10(6), 40–48 (2018) https://doi.org/10.5815/ijisa.2018.06.05 14. Al-Joboury, I.M., Al-Hemiary, E.H.: Consensus algorithms based blockchain of things for distributed Healthcare. Iraqi J. Inf. Commun. Technol., 3(4), 33–46 (2020) https://doi.org/10. 31987/ijict.3.4.116 15. Wang, W., Hoang, D.T.: A survey on consensus mechanisms and mining strategy management in blockchain networks. IEEE Access 7, 22328–22370 (2019). https://doi.org/10.1109/ACC ESS.2019.2896108 16. Azaria, A., Ekblaw, A., Vieira, T. and Lippman, A.M.: MedRec : Using Blockchain for Medical Data Access and Permission Management (2016) https://doi.org/10.1109/OBD.201 6.11 17. Verma, V.K., Verma, S.: Machine learning applications in healthcare sector : an overview Machine learning. Mater. Today Proc. 57, 2144-2147 (2022) https://doi.org/10.1016/j.matpr. 2021.12.101 18. Garg, A., Mago, V.: Role of machine learning in medical research: a survey. Comput. Sci. Rev. 40, 100370 (2021). https://doi.org/10.1016/j.cosrev.2021.100370 19. Sharma, S., Agrawal, J., Agarwal, S. and Sharma, S.: Machine Learning Techniques for Data Mining : A Survey no. I (2013) 20. Asante, D., Omar, T., Ganat, A., Gholami, R., Ridha, S.: Journal of petroleum science and engineering application of supervised machine learning paradigms in the prediction of petroleum reservoir properties : comparative analysis of ANN and SVM models. J. Pet. Sci. Eng., 200, 108182, (2021) https://doi.org/10.1016/j.petrol.2020.108182 21. Nahavandi, D., Alizadehsani, R., Khosravi, A., Acharya, U.R.: Application of artificial intelligence in wearable devices: opportunities and challenges. Comput. Methods Programs Biomed. 213, 106541 (2022). https://doi.org/10.1016/j.cmpb.2021.106541 22. Castiglioni, I., et al.: AI applications to medical images: From machine learning to deep learning. Phys. Medica 83(February), 9–24 (2021). https://doi.org/10.1016/j.ejmp.2021. 02.006 23. Elhassani, M.E., et al.: Deep Learning concepts for genomics : an overview. EMBnet J. 27, 990 (2022) 24. Kumar, P., Kumar, Y., Tawhid, M.A.: Machine Learning, Big Data, and IoT for Medical Informatics (2021) 25. Shamshirband, S., Fathi, M., Dehzangi, A.: A review on deep learning approaches in healthcare systems : taxonomies , challenges , and open issues. J. Biomed. Inform 113, 103627 (2021) https://doi.org/10.1016/j.jbi.2020.103627 26. Khan, M., et al.: Applications of artificial intelligence in COVID-19 pandemic : a comprehensive review. Expert Syst. Appl., 185, 115695 (2021)https://doi.org/10.1016/j.eswa.2021. 115695 27. Bohr, A., Memarzadeh, K.: The rise of artificial intelligence in healthcare applications. INC (2020)

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28. Chamola, V.: Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management. Neural Comput. Appl. 7, (2022) https://doi.org/10.1007/s00521-022-070 87-7 29. Ghazal, T.M., et al.: Private blockchain-based encryption framework using computational intelligence approach. Egypt. Inform. J. 23(4), 69–75 (2022). https://doi.org/10.1016/j.eij. 2022.06.007 30. Qamar, S.: Healthcare data analysis by feature extraction and classification using deep learning with cloud based cyber security ✩. Comput. Electr. Eng., 104(PA), 108406 (2022) https:// doi.org/10.1016/j.compeleceng.2022.108406 31. Hasanova, H., Tufail, M., Baek, U.J., Park, J.T., Kim, M.: A novel blockchain-enabled heart disease prediction mechanism using machine learning ✩. Comput. Electr. Eng. 101, 108086 (2022) https://doi.org/10.1016/j.compeleceng.2022.108086 32. Manocha, A., Afaq, Y., Bhatia, M.: Knowledge-based systems digital twin-assisted blockchain-inspired irregular event analysis for eldercare. Knowl.-Based Syst. 260, 110138 (2023). https://doi.org/10.1016/j.knosys.2022.110138 33. Hassija, V., Ratnakumar, R., Chamola, V., Agarwal, S., Mehra, A.: Sustainable computing : informatics and systems a machine learning and blockchain based secure and cost-effective framework for minor medical consultations. Sustain. Comput. Informatics Syst.35, 100651 (2022) https://doi.org/10.1016/j.suscom.2021.100651 34. Singh, S., Rathore, S., Alfarraj, O., Tolba, A., Yoon, B.: A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Futur. Gener. Comput. Syst. 129, 380–388 (2022). https://doi.org/10.1016/j.future.2021.11.028 35. Kumar, P., Kumar, R., Gupta, G.P., Tripathi, R., Jolfaei, A., Islam, A.K.M.N.: Journal of parallel and distributed computing a blockchain-orchestrated deep learning approach for secure data transmission in iot-enabled healthcare system. J. Parallel Distrib. Comput. 172, 69–83 (2023). https://doi.org/10.1016/j.jpdc.2022.10.002 36. Kumar, R., et al.: Computerized Medical Imaging and Graphics Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images. 102 (2022) 37. Amponsah, A.A., Adekoya, A.F., Weyori, B.A.: A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology. Decis. Anal. J., 4, 100122 (2022) https://doi.org/10.1016/j.dajour.2022.100122

Color Medical Image Encryption Based on Chaotic System and DNA Ahmed E. L. maloufy1(B) , Hicham Karmouni2 , Mohamed Amine Tahiri3 , Hassan Qjidaa3 , Mhamed Sayyouri1 , and Mohamed Ouazzani Jamil4 1 Engineering, Systems and Applications Laboratory, National School of Applied Sciences, Sidi

Mohamed Ben Abdellah University, Fez, Morocco {ahmed.elmaloufy,mhamed.sayyouri}@usmba.ac.ma 2 National School of Applied Cadi Ayyad University, Marrakech, Morocco [email protected] 3 CED-ST, STIC, Laboratory of Electronic Signals and Systems of Information LESSI, Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco [email protected] 4 Laboratoire Systémes et Environnement Durable (SED), Faculté Des Sciences de L’Ingénieur (FSI), Universite Privee de Fes (UPF), Fés, Maroc [email protected]

Abstract. The discipline of information security known as deoxyribonucleic acid (DNA) cryptography is one of the newest and most promising areas. In this context, we suggest a new color medical imaging algorithm that combines the biogenetic principle of DNA with a four-dimensional, extremely chaotic system. This program encrypts the image blocks using the hyper-chaotic system’s chaotic output, then determines the encoding, decoding, and DNA calculation of each image block to increase key space and resist image processing attacks. Simulation results exhibit the strength and efficiency of the suggested algorithm against different types of image processing attacks compared to other encryption algorithms. Keywords: Color image · Encryption · Decryption · Chaotic systems · DNA

1 Introduction Because of the quick progress of the web and media technology, images as an important information medium are increasingly disseminated and stored on the network [1,2]. Therefore, the problem of transmission security of storing images has attracted the attention of researchers proposing encryption of stored and transmitted data as a fundamental solution [3,4]. Indeed, many schemes, such as encryption schemes based on chaos theory, have been sequentially for encrypted images to be able to increase the anti-attack as well as increase efficiency of Encryption All by responding to digital images [5]. The severe chaotic systems sensitivity to beginning, worth ergodicity and, certainty, high random key, and inherent benefits in image encryption are some of the traits that define chaotic systems [6]. The chaotic system of low dimension has a small key, which limits © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 998–1007, 2023. https://doi.org/10.1007/978-3-031-29857-8_99

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its resistance to attacks [5]. High-dimensional chaotic systems with high randomness and complexity have good resistance to image processing attacks [7,8]. Since the characteristics of chaotic systems meet the requirements of cryptography, researchers have proposed combining chaotic systems and cryptography to develop robust and efficient encryption schemes. Indeed, Shannon proposed a confusiondiffusion criterion based on chaos characteristics for the design of a cryptographic system for digital image encryption [9]. In terms of confusion, the position of each pixel in the image is blurred, but in terms of diffusion, chaotic alters the original image’s pixel values [10, 11]. Compared to confusion, the anti-attack performance of diffusion is stronger, but from the human vision point of view, the chaotic degree of the encrypted image is far from the confusion effect [12, 13]. The algorithm is built based on the criterion of which complements the advantages of confusion and scattering, and allows to achieve good encrypted visual effects by blocking the outside world based on the confusion the diffusion [14]. As the above information shows, Researchers are being enticed to develop algorithms based on these systems by the advantages and successful results of the picture encryption strategy based on high-dimensional chaotic systems. [15] a fresh method for encrypting medical images is proposed in this work. It is founded on combination of the hyper-chaotic Chen system, the chaotic logistics system and DNA coding and decoding operations. The simulation results demonstrated the effectiveness of the suggested picture encryption method as well as robustness to different image processing attacks such as differential attacks and noise attacks [16, 30]. The remainder of the parts of the document are arranged as shown. Section 2 presents some preliminary results of this work. Section 3, presents the suggested encryption method. In Sect. 4, we present some metrics for the suggested method. Finally, Sect. 5 presents the conclusion.

2 Preliminaries We will present a general overview of chaotic systems and DNA coding and decoding. 2.1 The Chaotic Logistics Map The logistic chaotic map (Eq. 1) is a time-independent dynamic system that is sensitive to the initial condition. xn+1 = μ(1 − xn )

(1)

xn represents the population size of a species and µ the control parameter. 2.2 Chen’s Hyperchaotic System The System is First Proposed by Chen in [18] by the Following Dynamic Equation: x˙ 1 = −x2 − x3 + x4 , x˙ 2 = x1 + a1 x2 , x˙ 3 = x1 x3 − a3 x3 + a2 , x˙ 4 = a4 x1

(2)

where x˙ 1 , x˙ 2 , x˙ 3 , x˙ 4 are the state variables, and a1 , a2 , a3 , a4 are positive real constants. The numerical simulation of the above system (see Fig. 1) displays a hyper-chaotic attractor according to the projections of the phase portrait on the planes [18, 19].

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Fig. 1. Phase portrait projections of the system

2.3 Deoxyribonucleic Acid (DNA) Sequence Thymine (T), guanine (G), cytosine (C),adenine (A) and are the four nucleic acid bases contained in a DNA sequence. With G and C complementary, A and T are also. Out of a total of twenty-four coding schemes, eight are shown in Table 1 as meeting the Watson-Crick complement criteria [20]. Table 1. DNA sequence rule encoding and decoding systems. 1

2

3

4

5

6

7

8

A

00

00

01

01

10

10

11

11

C

11

11

10

10

01

01

00

00

G

01

10

00

11

00

11

01

10

T

10

01

11

00

11

00

10

01

Using the XOR operator rule, the original image will be encoded in this article (see Table 2). Table 2. XOR algorithms for DNA coding rule 4. ⊕⊕

A

T

C

G

A

C

G

A

T

T

G

C

T

A

C

A

T

C

G

G

T

A

G

C

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3 Proposed Medical Color Image Encryption System Following an explanation of Chen’s chaotic system and the basic idea behind DNA coding and decoding, we will suggest a brand new algorithm for encrypting and decrypting color images. 3.1 The Proposed Process for Encryption and Decryption The encryption algorithm of the images which has been proposed presented in the form of a flowchart (see Fig. 2). It is based on a combination of a hyper-chaotic system increasing the key space, and DNA encoding and decoding resisting cropping attacks, in the purpose of improving performance of encryption algorithms.

Fig. 2. Encryption algorithm flow chart

The procedure for developing our suggested algorithm for color medical images includes the following steps:

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Algorithm: The key steps that implement the proposed algorithm for image encryption. Step 1: Make three two-dimensional matrices from the original image: R, G, and B.. Step 2: Obtain the chaotic succession by defining the initial number 0 and the parameter , and continuously iterating the logistic map Step 3: Transform the resulting sequence { } into a two-dimensional matrix × of the same size as a level, and transform the matrix values into the range of 0 to 255, and finally we perform a DNA operation. Step 4: Using the Runge-Kutta ode45 function embedded in Matlab to compute a highly chaotic Chen system and obtain four sequences. Step 5: DNA coding of each sub-block of the array. Step 6: Perform DNA decoding on the template block after the DNA operation. Step 7: We follow the same steps as the second one to obtain two logistic chaotic sequences. Step 8: Combine the three two-dimensional matrices that have undergone row and column permutation into a three-dimensional matrix to obtain a cipher text image.

Figure 2. Above summarizes the proposed image encryption implementation steps. The decryption process consists of performing the reverse operation of the encrypted image, which is the exact opposite of encryption, and decrypted must be using exactly the same key encryption. It is important to remember that the correct decoded image must be indistinguishable from of the decrypted image.

4 The Simulation Results Obtained Proving our algorithm’s security and ruggedness against various attacks is essential to establish its value. A color medical image of 1024 × 1024 pixels used for the study of security and performance proposed encryption scheme [21]. 4.1 Histogram Analysis

encrypted image

Image original, ('Brain').

The first test is histogram analysis, it is a good indicator of correct operation of the encryption system it displays the frequency the dissemination of the pixel intensity the image’s values. Fig. 3 demonstrates the channel histograms R, G, B original images and their encrypted image.

Fig. 3. The encrypted and unencrypted histograms of medical images.

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In Fig. 3 above, the graph of the encoded image is differs greatly from the graph of the original image, as the former is regular and the latter is random, which confirms that the DNA coding scheme can definitively mask the original pixel distribution [22, 23]. 4.2 Entropy of Information Table 3 displays the three-channel information entropies encrypted images (R, G, and B) and shows that they are all extremely near to 8 [24]. It is clear that the proposed algorithm yields good outcomes in contexts of entropy of the processed information, especially if we consider that this last concept is defined according to Eq. (3): H =−

i 

p(i) log2 p(i)

(3)

i=0

Table 3. Entropy comparison of the original and encrypted images. Entropie d’image Original

Entropie d’image chiffré

Image de test

R

G

B

R

G

B

‘Brain’

7.2089

7.1318

6.9787

7.9999

7.9999

7.9999

Table 4. Comparison of the correlations. Image Original « Brain»

Image chiffrée

R

G

B

R

G

Horizontal

0.99383

0.99317

0.99244

-0.01752

-0.01026

Vertical

0.99596

0.99564

0.99347

0.0067924

0.00091583

-0.004156

Diagonal

0.99088

0.98978

0.9879

0.011336

-0.0051848

-0.007936

B 0.0095527

Table 5. The Results of the average NPCR and UACI comparison NPCR (%)

UACI (%)

Image de test

R

G

B

R

G

B

‘Brain’

99.753%

99.714%

99.717%

33.583%

33.462%

33.603%

4.3 The Test for Correlation Between Two Neighboring Pixels Figure 4 [25] displays the horizontal correlation, vertical correlation, and diagonal correlation of two nearby pixels in the color image. Knowing that “brain” is characterized

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by the following relationship [26]: r(X , Y ) =

cov(X , Y ) σ (X )σ (Y )

(4)

Figures (4(a), (b), and (c)) depict correlation between two neighboring pixels in the R, G, and B channels of the original image, respectively. In the encrypted image, the correlations between nearby pixels are minimized, as seen in Figs. 4(d), (e), and (f). Table 4 also displays more results.

(a) horizontal

(d) horizontal

(b) vertical

(e) vertical

(c) diagonal

(f) diagonal

Fig. 4. Correlation point map of the in the R channel.

The data in Table 4 lead to the conclusion that somehow the correlation coefficient in the encrypted image’s adjacent pixels R, G, and B channels is very close to zero. To put it another way, the proposed image encryption method has a high degree of resistance to statistical attacks. 4.4 Analysis of the Differential Attacks Table 5 displays the outcomes of the suggested encryption technique for the test picture in terms of UACI (R, G, B) and NPCR (R, G, B) [8]. N M   1 D(i, j) M ×N

(5)

N M   |C1 (i, j) − C2 (i, j)| 1 N ×M 255

(6)

NPCR =

i=1 j=1

UACR =

i=1 j=1

NPCR denotes the range of change in pixel count, while UACI denotes the uniform average change in intensity [18, 27]. We discover that the mean NPCR of the suggested scheme is superior to that of previous ways, the NPCR is larger than 99%, and the UACI stands for greater than

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33%, demonstrating the excellent robustness of the suggested method against differential attacks [28]. The results of the noise attack test on the images produced by adding Gaussian noise with densities of 5, 10, and 20 to each of the three channels of the encrypted and then decrypted image. The primary information of the original image can still be differentiated visually even though the mean square error grows with the addition of Gaussian noise, demonstrating that the encryption process has a high ability against Gaussian noise [28]. 4.5 Security Analysis The sensitivity of an algorithm to keys is tested by changing the value of one of the keys slightly during decryption. Figure 5(a) is the decrypted picture below the incorrect key obtained by changing X(0), one of the keys used to encrypt the ’Brain’ image, from 0.4953,3 used in encryption to 0.4953000000000001 during decryption. The decoded image as illustrated in Fig. 5(b). Under the wrong key obtained by changing one of the keys (0) in Fig. 5 from 0.7825 used in encryption to 0.7824999999999999 in decryption [29].

Fig. 5. Image decrypted with a wrong key

Although the original image cannot be viewed from the decoded image, we note that the range of change of both keys is 10–16 , this shows that the keys in this method demand an extremely high level of sensitivity. Decryption produces an output that is entirely distinct from the original image, making it possible to completely resist off exhaustive key attacks. This holds true even if only a minor change is made to one of the keys during decryption [29].

5 Conclusion The results of a scientific investigation of DNA cryptography are presented in this work. This is a fresh and fascinating development in the realm of cryptography. These methods enable the use of DNA in its unaltered condition in a biologically relevant laboratory setting. Indeed, they can use computers and the digital structure of DNA as tools in their job. Then, in this study, we put forth a scheme for encrypting color medical images that draws on chaotic systems as well as DNA coding and decoding processes. Simulation studies have demonstrated that our suggested technique for encrypting color medical images performs better than previous techniques.

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References 1. Tahiri, M.A., Karmouni, H., Azzayani, A., Sayyouri, M., Qjidaa.: Fast 3D Image Reconstruction by Separable Moments based on Hahn and Krawtchouk Polynomials. In: 4th International Conference. Intelligent Computing Data Science ICDS 2020, (2020) https://doi.org/10.1109/ ICDS50568.2020.9268685 2. Bencherqui, A., Amine, T.M., Daoui, A., Mohammed, A.: Optimization of Meixner moments by the Firefly algorithm for image analysis Optimization of Meixner moments by the Firefly algorithm for image analysis (2022) 3. Hosny, K.M., Darwish, M.M.: Robust color image watermarking using multiple fractionalorder moments and chaotic map. Multimed. Tools Appl. 81(17), 2434–24375 (2022)https:// doi.org/10.1007/s11042-022-12282-8 4. Ahmadi, S.B.B., Zhang, G., Rabbani, M., Boukela, L., Jelodar, H.: An intelligent and blind dual color image watermarking for authentication and copyright protection. Appl. Intell. 51(3), 1701–1732 (2020). https://doi.org/10.1007/s10489-020-01903-0 5. Duan, S., Wang, H., Liu, Y., Huang, L., Zhou, X.: A novel comprehensive watermarking scheme for color images. Secur. Commun. Netw. 2020, 1–2 (2020) https://doi.org/10.1155/ 2020/8840779 6. Shen, Y., Tang, C., Xu, M., Chen, M., Lei, Z.: A DWT-SVD based adaptive color multiwatermarking scheme for copyright protection using AMEF and PSO-GWO. Expert Syst. Appl. 168 114414 (2021) https://doi.org/10.1016/j.eswa.2020.114414 7. Ge, R., Yang, G., Wu, J., Chen, Y., Coatrieux, G., Luo, L.: A novel chaos-based symmetric image encryption using bit-pair level process. IEEE Access 7, 99470–99480 (2019). https:// doi.org/10.1109/ACCESS.2019.2927415 8. Liu, Q., Liu, L.: Color image encryption algorithm based on DNA coding and double chaos system. IEEE Access 8, 83596–83610 (2020). https://doi.org/10.1109/ACCESS.2020.299 1420 9. Hu, T.: Discrete chaos in fractional henon map. Appl. Math. 05(15), 2243–2248 (2014). https://doi.org/10.4236/am.2014.515218 10. Tahiri, M.A., Karmouni, H., Sayyouri, M., Qjidaa, H.: 2D and 3D image localization, compression and reconstruction using new hybrid moments. Multidimens. Syst. Signal Process. 33(3),https://doi.org/10.1007/s11045-021-00810-y 11. Tahiri, M.A., Karmouni, H., Sayyouri, M., Qjidaa, H.: Stable Computation of Hahn Polynomials for Higher Polynomial Order. In: 2020 International Conference Intelligent System Computer Vision ISCV 2020, pp. 0–6, 2020 https://doi.org/10.1109/ISCV49265.2020.920 4118 12. Tahiri, M.A., Karmouni, H., Azzayani, A., Sayyouri, M.: Fast 3D Image Reconstruction by Separable Moments based on Hahn and Krawtchouk Polynomials. (2020) 13. Rabab, O., Tahiri, M.A., Bencherqui, A., Amakdouf, H., Jamil, M.O., Qjidaa, H.: Efficient localization and reconstruction of 3d objects using the new hybrid squire moment. In: 2022 International Confernce Intelligent System Computer Vision ISCV (2022) https://doi.org/10. 1109/ISCV54655.2022.9806086 14. Qayyum, A., et al.: Chaos-based confusion and diffusion of image pixels using dynamic substitution. IEEE Access 8, 140876–140895 (2020). https://doi.org/10.1109/ACCESS.2020. 3012912 15. Karmouni, H., Sayyouri, M., Qjidaa, H.: A novel image encryption method based on fractional discrete Meixner moments. Opt. Lasers Eng., 137, 106346 (2021) https://doi.org/10.1016/j. optlaseng.2020.106346 16. Wu, J., Guo, F., Zeng, P., Zhou, N.: Image encryption based on a reality-preserving fractional discrete cosine transform and a chaos-based generating sequence. J. Mod. Opt. 60(20), 1760– 1771 (Nov.2013). https://doi.org/10.1080/09500340.2013.858189

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17. Loan, N.A., Hurrah, N.N., Parah, S.A., Lee, J.W., Sheikh, J.A., Bhat, G.M.: Secure and robust digital image watermarking using coefficient differencing and chaotic encryption. IEEE Access 6, 19876–19897 (2018). https://doi.org/10.1109/ACCESS.2018.2808172 18. Wei, X., Guo, L., Zhang, Q., Zhang, J., Lian, S.: A novel color image encryption algorithm based on DNA sequence operation and hyper-chaotic system. J. Syst. Softw. 85(2), 290–299 (2012). https://doi.org/10.1016/j.jss.2011.08.017 19. Suri, S., Vijay, R.: A synchronous intertwining logistic map-DNA approach for color image encryption. J. Ambient. Intell. Humaniz. Comput. 10(6), 2277–2290 (2018). https://doi.org/ 10.1007/s12652-018-0825-0 20. Ben Slimane, N., Aouf, N., Bouallegue, K., Machhout, M.: A novel chaotic image cryptosystem based on DNA sequence operations and single neuron model. Multimedia Tools Appl. 77(23), 30993–31019 (2018). https://doi.org/10.1007/s11042-018-6145-8 21. Chai, X., Fu, X., Gan, Z., Lu, Y., Chen, Y.: A color image cryptosystem based on dynamic DNA encryption and chaos. Signal Process. 155, 44–62 (Feb.2019). https://doi.org/10.1016/ j.sigpro.2018.09.029 22. Yamni, M., Karmouni, H., Sayyouri, M., Qjidaa, H.: Robust zero-watermarking scheme based on novel quaternion radial fractional Charlier moments. Multimedia Tools Appl. 80(14), 21679–21708 (2021). https://doi.org/10.1007/s11042-021-10717-2 23. Yamni, M., Karmouni, H., Sayyouri, M., Qjidaa, H., Flusser, J.: Novel octonion moments for color stereo image analysis. Digit. Signal Process. A Rev. J. 108, 102878 (2021). https://doi. org/10.1016/j.dsp.2020.102878 24. Wu, X., Kurths, J., Kan, H.: A robust and lossless DNA encryption scheme for color images. Multimedia Tools Appl. 77(10), 12349–12376 (2017). https://doi.org/10.1007/s11042-0174885-5 25. Sayyouri, M., Hmimid, A., Qjidaa, H.: A fast computation of charlier moments for binary and gray-scale images. In: Cist 2012 – Proceeding of the 2012 Colloquium Information Science Technology, pp. 101–105, (2012) https://doi.org/10.1109/CIST.2012.6388071 26. Wu, X., Wang, K., Wang, X., Kan, H., Kurths, J.: Color image DNA encryption using NCA map-based CML and one-time keys. Signal Process. 148, 272–287 (Jul.2018). https://doi.org/ 10.1016/j.sigpro.2018.02.028 27. Yamni, M., Daoui, A., Karmouni, H., Sayyouri, M., Qjidaa, H.: Influence of Krawtchouk and Charlier moment’s parameters on image reconstruction and classification. Procedia Comput. Sci. 148 Icds 2018, pp. 418–427 (2019) https://doi.org/10.1016/j.procs.2019.01.054 28. Kumar, V., Girdhar, A.: A 2D logistic map and Lorenz-Rossler chaotic system based RGB image encryption approach. Multimedia Tools Appl. 80(3), 3749–3773 (2020). https://doi. org/10.1007/s11042-020-09854-x 29. Chen, L., Yin, H., Yuan, L., Machado, J.T., Wu, R., Alam, Z.: Double color image encryption based on fractional order discrete improved Henon map and Rubik’s cube transform. Signal Process. Image Commun., 97, 116363 (2021) https://doi.org/10.1016/j.image.2021.116363 30. Tahiri, M.A., Karmouni, H., Bencherqui, A., Daoui, A., Sayyouri, M., Qjidaa, H., Hosny, K.M.: New color image encryption using hybrid optimization algorithm and Krawtchouk fractional transformations. The Visual Comput., 1–26 (2022)https://doi.org/10.1007/s00371022-02736-3

Classification of EEG Signal Based on Pre-Trained 2D CNN Model for Epilepsy Detection Fatima Edderbali1(B) , Mohammed Harmouchi1 , and Elmaati Essoukaki2 1 FST, Laboratory of Radiance Mater and Instrumentation, Hassan First University of Settat,

Settat, Morocco [email protected] 2 Laboratory of Health Sciences and Technology, Hassan First University of Settat, Settat, Morocco

Abstract. Epilepsy is a chronic neurological disease due to extreme electric discharge in the brain. Epileptic seizures gravely affect the social life and psychology of patients, knowing the lack of neurologists and the fact that analyzing EEG signals is time-consuming and must be done by an expert; therefore, it is important to correctly diagnose patients with epilepsy automatically. In this work, we start with preprocessing raw EEG data, and we extract the spectrogram of every channel. In the next step, we propose an image category classification method that combines the extractor of the feature pattern, which is AlexNet, which is a pretrained convolutional neural network (CNN), with a trainable classifier support vector machine (SVM). Alex-Net passed the feature vectors to the SVM. The spectrogram images that we extracted are used as the input layer. The number of classes is two: one for normal cases and the second for epileptic cases. We split the training and test samples. Finally, spectrogram images are trained by many classifiers, and we find that the SVM in this study had the best mean accuracy, reaching 97.52%, which means that this model is effective for epilepsy classification. Keywords: Epilepsy · EEG · Deep learning · CNN · AlexNet · SVM

1 Introduction Epilepsy is considered the most common neurological disease and affects more than 50 million people worldwide [1]. Electroencephalogram (EEG) is recognized as an important way to identify and analyze brain activity in humans during epileptic seizures. It has been identified as an important tool for researchers to study neurological disorders [2]. CNN convolutional neural networks are a great technique in deep learning. It has been used for image classification and character recognition in machine learning [3]. ConvNets have shown tremendous results in numerous applications, such as image classification, object detection, natural language processing, and medical image analysis © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 1008–1016, 2023. https://doi.org/10.1007/978-3-031-29857-8_100

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[4]. Features were chosen to represent a profound impact on the success of a machine learning system. Better results can be achieved by identifying which aspects of the problem need to be included [5]. Nevertheless, to train the neural network, we need enormous, labeled data [6]. Without investing time and effort, we used a pretrained model [7, 8]. In this work, we made these contributions. First, we extract our spectrogram images from EEG dataset signals EDF after preprocessing and filtering raw data, which are practical for leveraging the accuracy of classification. We combined a pretrained CNN and SVM, which had the best mean accuracy after comparing it with other classifiers such as KNN and Discriminant; other classifiers were used in practice to improve the classification rate [9]. In the start, we focus on the first section, Method, to show the different steps that we went through, giving examples of spectrogram images that we extracted from EEG raw data and different types of use of deep learning, such as pattern recognition and classification of images. The last step in this part of our work is the structure of the chosen model. In the next section, we experimentally evaluate its performance by the accuracy value and the confusion matrix, and we show how our model could correctly identify the normal and epileptic spectrograms. In the last part, there is a discussion about the accuracy of each classifier and which one has the least and the best value.

2 Method 2.1 Citation for the Dataset For this EEG dataset of 6 epileptic signals, the recording was at the monitoring of the Epilepsy unit of the American University of Beirut Medical Center between January 2014 and July 2015. The measurements of 21 scalp electrodes are shown in Fig. 1. With the 10–20 electrode system, due to artifact constraints, some channels have been omitted from recordings. This work was made possible by the Qatar National Research Fund (a member of Qatar Foundation).

Fig. 1. EEG channels

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2.2 Deep Learning Classification As shown by the enhanced state-of-the-art accuracy that has been verified. A comparison of the studies has been made and is given in Table 1 Zhu et al. [10]. They achieved an accuracy of 84% with delay permutation entropy (DPE), a classifier by Sharma et al. based on SVM [11], they differentiated classes with 87% accuracy by an LS-SVM classifier with six entropy measurements from EMD. Das et al. [12] used log energy entropy, EMD, and DWT. They said that their study reached classification with 89.40% accuracy. U. Rajendra Acharya EEG-based seizure detection. A 13-layer deep learning CNN algorithm is implemented for automated EEG analysis. An average accuracy of 88.7% is obtained with a specificity of 90% and a sensitivity of 95% [13]. Table1. Latest studies of classification of epilepsy waves. Study

Model

Accuracy(%)

Zhu et al. 2010 [10]

Delay permutation entropy/SVM

84.00

Sharma et.al. 2015 [11]

EMD and entropy measures/LS-SVM

87.00

Das et al. 2016 [12]

Log-energy entropy-EMD-DWT/KNN 89.40

U. Rajendra Acharya. 2017 [13]

CNN 1D model

90.33

This study

2D CNN model based on scalogram images

97.52

2.3 Spectrogram Used for Classification Starting with the extraction of the spectrogram from the EDF file of EEG to perform classification, which is composed of a CNN extractor associated with many different classifiers. The EEG EDF dataset is used to create spectrogram images, after which we have a new dataset of the image that has two classes in Fig. 2. Images were labeled epileptic and normal with splitting test and training images; images are 1045*570 pixels in color. We used 168 images for training per class and 72 images for testing per class [14].

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Fig. 2. Examples of spectrogram images

2.4 Proposed Model for Classification We see in Fig. 3 and Fig. 4 the common technique to recognize patterns or classifications based on two modules [15]. The feature extractor is the first one; the trainable classifier is the second [16][17]. To evaluate our model, we used accuracy to compare the performance of six classifiers. The results demonstrated that SVM is the best classifier, with an accuracy of 97.52%, outperforming all the other classifiers.

Raw image

Feature Extractor

Trainable classifier

Class score

Feature vector Fig. 3. Pattern Vector structure recognition

Spectrogram

Pre trained Convolutional Neural Network

Support Vector Machine

Image features Fig. 4. Classes identification structure

2 classes

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Fig. 5. Model Structure

Fig. 6. First layer

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3 Results and Discussion We used MATLAB [19] for preprocessing the raw EEG dataset and extracting the spectrogram of every channel followed by splitting spectrogram images into training and testing images. We present the structure of the model in Fig. 5 and the first layer weight in Fig. 6. Then, the accuracy obtained was compared with different classifiers. 3.1 Accuracy of Each Classifier As test samples, seventy-two images from the 300 images of the spectrogram were extracted. The 168 training images were picked randomly from the dataset. The accuracy is presented in Table 1. The best accuracy is 97.52%. The mean accuracy of five tests in Fig. 10 shows the comparison of test accuracy. 3.2 Confusion Matrix Classified testing images are presented in Fig. 9. Healthy image (left) and the true predicted class in the first line the same for seizure (right) are displayed below. It is vividly described in true class as a seizure spectrogram. Therefore, we present an example of a confusion matrix for the best classifier in Fig. 7 and Fig. 8. We conclude with different values of the accuracy of each classifier, showing that it ranges from 66.63 to 97.52% in Fig. 10.

Fig. 7. Confusion training matrix

Test1 = ‘The loaded image belongs to a healthy class. Test2 = ‘The loaded image belongs to an epileptic class.

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Fig. 8. Confusion test matrix

Test1= ‘The loaded image belongs to a healthy class. Test2 =' The loaded image belongs to an epileptic class.

Fig. 9. Examples of classified test images successfully.

3.3 Discussion About Classification Accuracy As explained in related work, the proposed method shows obviously that we did have the best accuracy after using many classifiers, where SVM has the best result. Figure 10 shows the result of each classifier.

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Fig. 10. Comparison of training and test accuracy.

The accuracy for testing and training of our model is represented in the figure above, which shows the accuracy of each classifier, as our finding result varied from the lowest, which is 66.62%, to the highest, which is equal to 97.52%, with the SVM classifier.

4 Conclusion In the conclusion, let me sum up everything that has been stated thus far. We showed that using a pretrained CNN and extracting spectrogram images of EEG EDF raw data actively demonstrates and shows the potential result, especially when combined with classifiers, and the support vector machine showed the best result of accuracy that reached 97.53% in combination with AlexNet CNN for the detection of an epileptic seizure. I would like to draw attention to the point that we need to admit that performance is affected by the amount of data, if we include more the performance will increase even more, another constraint, indeed the classification is important but it would be more beneficial to classify seizure depending on the zone where it occurred, for this reason, it is recommended to work with a greater number of data and work on a more specific classification of seizure type.

References 1. The World Health Organization https://www.who.int/detail/epilepsy. Accessed 01 Oct 2022 2. Omerhodzic, I., Avdakovic, S., Nuhanovic, A.: Energy distribution of EEG Signal Components by wavelet transform (2012)

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3. Nain, N., Vipparthi, S,.K., Raman, B.: Computer vision and image processing. In: 4th International conference. Edition P309 (2020) 4. Wani, A., Bhat, F.: Advances in Deep Learning, p. 13 (2020) 5. Wani, M.A., et al.: Advances in Deep Learning, p. 57 (2020) 6. Vedaldi, A., Bischof, H.: Computer vision. in: 16th European Conference, p. 413 (2020) 7. Wilson, M.D.: Support vector machines. In: Encyclopedia of Ecology (2008) 8. Zappos, I.S., Dondi, R.: Kernel methods: support vector machines. In: Encyclopedia of Bioinformatics and Computational Biology (2019 9. Gopi, E.: Digital signal processing for medical imaging using Matlab (2012) 10. Zhu, G.: Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. In: AIP Conference, pp. 31–36 (2013) 11. Sharma, R.: Application of entropy measures on intrinsic mode functions for the automated classification EEG (2015) 12. Das, A., Huiyan, B.: Discrimination, and classification of the focal and non-focal seizure (2016) 13. Acharya, U.: Deep convolutional neural network for the automated detection of epilepsy. In: Computers in Biology and Medicine (2017) 14. Sun, W., Zhong, H.: An efficient unconstrained facial expression recognition algorithm based on stack binarized auto-encoders and binarized neural networks (2017) 15. Basul, J.K., Charyya, D.B., Kim, T.H.: Use of artificial neural network in pattern recognition. In: Computer Science and Engineering Department Heritage Institute of Tec (2017) 16. Suen, L., Suen, C.Y., Bloch, G.: A Trainable Feature Extractor for Handwritten recognition, pp. 1816–1824. Elsevier, Amsterdam (2007) 17. Lecun, Y.: Gradient-based learning applied to document recognition, vol. 86, no. 1 pp. 2278– 2324. IEEE (1998) 18. Matlab Academy. https://matlabacademy.mathworks.com/details/machine-learning-onramp/ machinelearning. Accessed 20 Nov 2022 19. Matlab Academy. https://matlabacademy.mathworks.com/details/deep-learning-onramp/dee plearning. Accessed 20 Nov 2022 20. Swamynathan, M.: Mastering machine learning with python in six steps a practical implementation guide to predictive data analytics using python (2019) 21. Swersky, K.: Multi-task bayesian optimization. In: NIPS 2013, vol. 2, pp. 2004–2012 (2013) 22. Yang, S.: Deep representation learning with target coding. In: Proceedings of AAAI 29th In Conference on Artificial Intelligence, pp. 3848–3854. (2015) 23. Kim, P.: MATLAB deep learning with machine learning, neural networks and artificial intelligence (2017) 24. Edderbali, F., Harmouchi, M., Essoukaki, E.: Epilepsy detecting based on eeg signal decomposition using wavelet transform. SSRN: https://ssrn.com/abstract=4213055 or https://doi. org/10.2139/ssrn.4213055

Author Index

A Abbou, Ahmed 674, 761, 855 Abbou, Fouad Mohammed 326 Abdelkader Elhanaoui, 782 Abdellah, Hadj Ali 337 Abdellaoui, Meriem 929 Abdoun, Otman 23 Abdouni, Jawad 873 Abid, Abdellah 504 Abida, Rabeb 355 Aboudrar, Imad 62 Adaliou, Abdel Hamid 694 Addaali, Bouthayna 474 Adefisan, Elijah 84 Adnani, Younes 3 Adou, Kablan Jérôme 164 Agounad, Said 782 Ahessab, Hajar 241, 653 Aissaoui, Karima 376 Ajjaj, Souad 299 Akkader, Souad 261 Alami, Rachid El 326, 536 Algani, Catherine 318 Alj, Zakaria 421 Alla, Lhoussaine 916 Amane, Meryem 376 Amegouz, Driss 811, 833 Amine, Saddik 773 Andsaloussi, Idriss Benatiya 929 Aneli, Stefano 125 Annaki, Ihababdelbasset 54 Annich, Afafe 494 Anoune, Kamal 622 Aroba, Oluwasegun Julius 348, 822 Assoufi, Ibtissam 97 Ayikpa, Kacoutchy Jean 164 B Baba, Mariem Ahmed 883 Baghdad, Abdennaceur 261

Bahri, Hicham 664 Bahtat, Chaymae 865 Ba-Ichou, Ayoub 873 Ballo, Abou Bakary 164 Bannari, Rachid 612, 704 Barj, Sara 208, 230 Barkany, Abdellah El 865 Behlouli, Mohamed 105 Bekri, Ali 873 Belghith, Emna Hachicha 355 Belkassem, Tidhaf 175 Bellach, Benaissa 684 Ben Chakra, Fatima Zohra 543 Benabdelaziz, Kawtar 62 Benaboud, Aziza 664 Benaissa, Soukayna 561 Benali, Abdelhamid 153 Benboubker, Mohamed Badr 976 Benbrahim, Mohammed 976 Benchekroun, Youssef 43 Bendahane, Bouchra 753 Bendix, Joerg 84 Benhlima, Said 873 Benhouria, Younes 873 Benlghazi, Ahmad 153 Bennis, Ahmed 929 Bennouna, Fatima 811, 833 Benslimane, Mohamed 516 Bentalha, Badr 916 Benyacoub, Bouchra 337 Berrada, Mohammed 376, 986 Berthoz, Alain 54 Bibang Bi Obam Assoumou, Stahel Serano 643 Bikrat, Mohamed 279 Bossoufi, Badre 735 Bouayad, Anas 421 Boubii, Chaymae 612, 704 Bouhadda, Mohamed 326, 938 Boujenoui, Anouar 579, 590

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 668, pp. 1017–1021, 2023. https://doi.org/10.1007/978-3-031-29857-8

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Author Index

Boulakhber, Mouaad 62 Boumhidi, Jaouad 552 Bourhaleb, Mohammed 54 Bourja, Omar 561 Bousalem, Zakaria 189 Bousla, Mohamed 745 Bouti, Nabila 896 Bouyghf, Hamid 261 Bouzbiba, Azeddine 761 Bouzi, Mostafa 252 Bri, Seddik 279 Bybi, Abdelmajid 579, 590 C Castilla, Alexander 54 Chaaouan, Hanae 938 Chafi, Anas 789 Chahboun, Mbarek 724 Chahdi, Meryem Ouazzani 494 Chenari, Hossein 366, 441 Cherkaoui Malki, Mohammed Ouçamah Cherkaoui, Mohamed 632, 883 Chetouani, Abdelaziz 153 Chihab, Lamyaa 397, 405 Chmali, Khalid 905 Chraibi, Fouad 929 Cleve, Anthony 355 Cohen, Bernard 54 D Dada, Chaimae 13 Daidai, Fouad 218 Daoui, Achraf 453 Das, Sudipta 289 Dhassi, Younes 73 Dhuli, Raju 366, 441 Diop, Ahmadou Moustapha Doubabi, Said 694 Doumbia, Boubacar 84

318

E Echiheb, Farah 735 Ed-Dahhak, Abdelali 13 Edderbali, Fatima 1008 El Akkad, Nabil 516, 543 El Alami, Rachid 289, 309, 938 El Azami, Ikram 845 EL Bekkali, Moulhime 318 El Bhiri, Brahim 612, 704, 735 El Borji, Yassine 412

421

El Boussaki, Hoda 464 El Fellah, Khadija 845 El Fouas, Chaimae 125 El Ghzaoui, Mohammed 289, 938 El Guabassi, Inssaf 189 El Hadadi, Benachir 653 El Hamidi, Younesse 252 El Haouat, Zineb 833 El Hassouni, Loubna 197 El Houm, Yassine 855 El Houssaini, Mohammed-Alamine 299 El Houssaini, Souad 299 El Kaddouhi, Samir 504 El Kafazi, Ismail 612, 704, 735 El Kharrim, Abderrahman 745 El Khoukhi, Fatima 896 El Maimouni, Lahoucine 590 El Makrani, Adil 845 El Manssouri, Oussama 125 El Mhouti, Abderrahim 397, 412 El Mhouti, Abderrahime 405 El Mourabit, Youness 745 El Ouakili, Hajar 536 El Oualy, Hanane 133 El Ouanjli, Najib 714 El Ouariachi, Mostafa 694 El Ouenjli, Hamza 789 El Ougli, Abdelghani 175 El Rhayami, Mounia 412 EL Rhezzali, Nabila 431 El Youbi El Idrissi, Mohmed Achraf 114 Elbarkany, Abdellah 905 Elenga, Rolains Golchimard 643 El-Ghajghaj, Abdelfettah 714 Elgouri, Rachid 3 Elhassani, Merouane Elazami 986 Elmousaid, Rachida 3 Elmrini, Abdelmajid 905 Errafik, Youssef 73 Essabbar, Moad 949 Essahlaoui, Abdelouahed 326, 938 Essaid, Mohamed 504 Essalih, Safaa 811, 833 Essoukaki, Elmaati 1008 Ezzouhairi, Abdellatif 270

F Fakchich, Abdeslam 326 Farissi, Ilhame El 97 Fattah, Mohammed 318

Author Index

1019

Fazazy, Khalid El 33 Fkihi, Sanaa El 561 G Gaga, Ahmed 241, 653 Gagliano, Antonio 125 Galas, Elbert M. 366 Ghammouri, Mohammed 684 Ghazi, Mohamed 622 Ghazouani, Mokhtar 622 Ghzaoui, Mohammed El 326, 536 Gmili, Anouar 33 Gmira, Faiq 526 Gouton, Pierre 164 Guerbaoui, Mohamed 13 H Habib, Mohammed 484 Hadadi, Benachir El 241 Haddi, Ali 745 Haddouch, Khalid 43 Hadji, Mhamed 782 Hain, Mustapha 299 Haj, Abdellatif 189 Hajji, Bekkay 125, 133 Hakam, Youness 241, 653 Halkhams, Imane 309 Hamdane, Karima 397, 405 Hamdaouy, Achour El 3 Hami, Youssef 552 Hamidane, Hafsa 13 Hammouch, Zakia 684 Haouat, Zineb El 811 Harmouchi, Mohammed 1008 Harras, Bilal 905 Hiba, Chanaa 957 Hihi, Hicham 724 Hilal, Imane 431 Hirech, Kamal 694 Hmioui, Aziz 916 Hnida, Meriem 431 Hraoui, Said 526 I Isknan, Ismail

694

J Jabri, Abdelouahhab 865 Jamil, Mohamed Ouazzani 998

Jamil, Mohammed Ouazzani 453, 536, 714, 938 jaouhari, Asmae El 986 Jarjar, Abdellatif 504 Jarjar, Mariem 504 Jdid, Touria 976 Jeghal, Adil 114 Jenkal, Wissam 753

289, 309, 326,

K Kabbaj, Mohammed Nabil 976 Kammouri Alami, Salaheddine 789 Karim, Awatif 552 Karmouni, Hicham 453, 714, 998 Karrouchi, Mohammed 799 Kassmi, Kamal 799 Kenzi, Adil 73 Kerkeb, Mohamed Larbi 385 Khadija, Jahid 773 Khalil, Sara 632 Khamjane, Aziz 33 Khodriss, Chaimae 929 Khoulji, Samira 385 Kiouach, Fatima 289 Kousksou, Tarik 62

L Laabab, Ilham 270 Laaboubi, Mostafa 753 Laaouina, Loubna 114 Labzour, Nouhaila 561 Lachhab, Abdeslam 13 Lahcen, Oughdir 143 Lahyan, Zohra 674 Lakbib, Abdchafia 579, 590 Lakhdairi, Chaimaa 664 Laknizi, Azzeddine 622 Lamnai, Asmae 967 Lamnaouar, Karima 684 Lamreoua, Abdelhak 694 Lamsellak, Oussama 153 Lanuza, Maryann H. 366, 441 Larsari, Vahid Norouzi 366, 441 Latif, Rachid 464, 474, 753 Latrache, Firyal 684 Lhafra, Fatima Zohra 23 Lhayani, Mouna 855 Loqman, Chakir 552, 957

1020

Author Index

M Maaroufi, Mohammed 855 Mabaso, Andile 822 Mabuza, Phumla 822 Madani, Hamid 133 Mahraz, Adnane Mohamed 929 Mahraz, Mohamed Adnane 33 Maimouni, Lahoucine El 579 maloufy, Ahmed E. L. 998 Mamad, Mohamed 105 Mamadou, Diarra 164 Mannino, Giovanni 125 Marah, Rim 189 Margoum, Safae 125 Massar, Mohammed 397, 405 Mazer, Said 318 Melhaoui, Mustapha 694 Mencou, Siham 601 Messaoudi, Abdelhafid 799 Mezrioui, Abdellatif 208, 230 mhouti, Abderrahim El 967 Mnguni, Sanele Baldwin 348 Mokhtari, Khadija 133 Mouradi, Abderrahman 745 Moussaoui, Hanae 516

N Naji, Mohamed 976 Naoui, Mohamed 883 Nasri, Ismail 799 Ndakara, Abubakar Isah Nfaoui, El Habib 957

949

O Omari, Mouhsine 133 Omotosho, Jerome 84 Ouaddah, Aafaf 208, 230 Oubail, Youssef 62 Ouchekkir, Ali 197 Oulhadj, Mohammed 929 Oumidou, Naima 632 Ounejjar, Youssef 484

P Polleux, Jean-Luc 318

Q Qjidaa, Hassan 289, 309, 326, 453, 536, 714, 938, 998 Qobbi, Younes 504 R Rachid, Latif 773 Rahmoune, Mohammed 54 Ramadany, Mohamed 811 Ramadany, Mohammed 833 Rehali, Majda 986 Reskal, Hayat 579, 590 Reyes-Chua, Ethel 366 Riffi, Jamal 33, 929 Rouah, Imane 385 S Saddik, Amine 464, 474 Sadki, Ahmed 745 Saikouk, Hajar 949 Salim, Salmi 143 Sario, Jay A. 366 Satori, Khalid 494 Sayyouri, Mhamed 453, 714, 998 Sebbani, Ilham 62 Sekhra, Salma 484 Senba, Hanae 43 Sibisi, Phethokuhle 822 Skouri, Rachid 782 Slimani, Ilham 97 Sodjinou, Sovi Guillaume 164 Soualfi, Abderrahim Hajji 905 Soualfi, Oumaima Hajji 905 T Tahiri, Mohamed Amine 998 Tairi, Hamid 114, 929 Talbi, Kaoutar 175 Talea, Mohamed 664 Taleb, Yassine 761 Tamnine, Larbi 218 Tannouche, Adil 484 Tazi, El Bachir 601 Thies, Boris 84 Tina, Giuseppe Marco 125 Touil, Hamza 543

Author Index

W Waga, Abderrahim 873 Wildová, Radka 366 wildová, Radka 441

Y Yahyaouy, Ali 929 Yakhlef, Majid Ben 601

1021

Yamni, Mohamed 453 Youssef, Amraoui 309 Z Zaki, Moncef 114 Zaoui, Mohamed 54 Zazi, Malika 62 Zennayi, Yahya 561 Zerouali, Soufian 799