Artificial Intelligence, Data Science and Applications: ICAISE’2023, Volume 2 (Lecture Notes in Networks and Systems, 838) [1st ed. 2024] 3031485726, 9783031485725

Table of contents :
Preface
Organisation
Introduction
Contents
Exploring the Impact of Deep Learning Techniques on Evaluating Arabic L1 Readability
1 Introduction
2 Related Works
3 Methodology and Results
3.1 Data
3.2 Text Representation
4 Experiments and Results
5 Conclusion and Future Works
References
Performance Analysis of Two Serial Concatenations of Decoders Over a Rayleigh Channel
1 Introduction
2 Rayleigh Fading
3 Used Decoding Algorithms and Serial Concatenation
3.1 Hartmann Rudolph Decoder
3.2 Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec) Decoder
3.3 Syndrome Decoding and Hash Techniques (SDHT) Decoder
3.4 Serial Concatenation
4 Simulation Results
5 Conclusion
References
Blockchain and Reputation Based Secure Service Provision in Edge-Cloud Environments
1 Introduction
2 The Proposed System Architecture
2.1 Main Components
2.2 Service Model
3 The Blockchain Integration
3.1 Blockchain Components
3.2 Consensus Process
3.3 Blockchain Management
4 Conclusions and Perspectives
References
Exploring the Impact of Convolutions on LSTM Networks for Video Classification
1 Introduction
2 Methodology
2.1 Dataset
2.2 Convolutional Neural Networks
2.3 Long Short-Term Memory
2.4 Convolutional Long Short-Term Memory (ConvLSTM)
2.5 Long-Term Recurrent Convolutional Networks (LRCN)
3 Results and Discussion
4 Conclusion
References
Classification Algorithms Implementation for Fire Prevention Data on Multiple Wireless Node Sensors
1 Introduction
2 Theory of Probability
3 Likelihood Algorithm
4 Bayesian Inference
5 CUSUM Algorithm
6 Result of Experiment
7 Conclusion
References
Implementation of an Intelligent Monitoring System Based on Quality 4.0 of the Induction Hardening Process
1 Introduction
2 Literature Review
2.1 Industry 4.0
2.2 Quality 4.0
3 Case Study
3.1 Induction Hardening Machine
3.2 Smart Supervision System
4 Analysis and Discussion
5 Conclusion
References
Lithium-Ion Battery State of Charge Estimation Using Least Squares Support Vector Machine
1 Introduction
2 The SOC Estimation Methods
2.1 The Ampere Hour Counting Method
2.2 Least Squares Support Vector Machine (LSSVM) Method
3 Simulation and Results Discussion
4 Conclusion
References
Intrusion Detection in Software-Defined Networking Using Machine Learning Models
1 Introduction
2 Challenges of SDN Security
2.1 Distributed Denial of Services
2.2 Intrusion Detection System
3 Methodology
3.1 Performance Evaluation
3.2 Dataset
4 Result and Discussion
5 Conclusion
References
Neural Network for Link Prediction in Social Network
1 Introduction
2 Methodology
3 Experimental Results
3.1 Dataset Description
3.2 Results and Discussion
4 Conclusion
References
Design and Optimization of a Compact Microstrip Bandpass Filter Using on Open Loop Rectangular Resonators for Wireless Communication Systems
1 Introduction
2 Design of the Proposed Bandpass Filter
3 Simulation Results and Discussion
4 Conclusion
References
YOLO-Based Approach for Intelligent Apple Crop Health Assessment
1 Introduction
2 Proposed Methodology
2.1 Evaluation of Apple Health Using YOLOv5s, YOLOv5m, and YOLOv7
2.2 Models Optimization
3 Results and Discussion
3.1 Setup Environment
3.2 Apple Disease Detection Image Dataset
3.3 Data Preprocessing
3.4 Performance Results
4 Conclusion
References
A Comprehensive Review on the Integration of Blockchain Technology with IPFS in IoT Ecosystem
1 Introduction
2 IoT-Blockchain Architecture
3 Reviewed Works
4 Challenges and Future Research Directions
5 Conclusion
References
An Integrated Approach for Artifact Elimination in EEG Signals: Combining Variational Mode Decomposition with Blind Source Separation (VMD-BSS)
1 Introduction
2 Material and Methods
2.1 Datasets
2.2 The Variational Mode Decomposition
2.3 The Blind Source Separation
2.4 The Proposed Method
3 Results
4 Conclusion and Discussion
References
E-Health Blockchain: Conception of a New Smart Healthcare Architecture Based on Deep Reinforcement Learning
1 Introduction and Notations
1.1 Introduction
1.2 Notations
2 Related Works
3 Our Contribution
4 Conclusion
References
Dynamic Multi-compartment Vehicle Routing Problem: Formulation and Algorithm
1 Introduction
2 Problem Formulation
3 The Dynamic Adaptation to MCVRP Problem
4 Hybrid Adaptive Variable Neighborhood Search for DMCVRP
5 Computational Experiments
6 Conclusion
References
Anime Link Prediction Using Improved Graph Convolutional Networks
1 Introduction
2 Background
2.1 Graph Neural Network
2.2 Graph Construction
2.3 Types of Graph Neural Network (GNN)
3 Proposed Work
3.1 ImprovedGCN
3.2 Proposed Architecture
4 Experiments
4.1 Dataset for Evaluation
4.2 Environment
4.3 Experiment Results
5 Conclusion
References
Assessing the Evolution of Meteorological Seasons and Climate Changes Using Hierarchical Clustering
1 Introduction
2 Proposed Method
2.1 Data Preprocessing
2.2 Background and Methodology
3 Findings and Discussion
4 Conclusion
References
Significance and Impact of AI on Pedagogical Learning: A Case Study of Moroccan Students at the Faculty of Legal and Economics
1 Introduction
2 Methodology
2.1 Sample and Data Collection Procedure
2.2 Students’ Personal Information/Demographic Data
3 Results
4 Discussion
5 Conclusion
References
Securing Big Data: Current Challenges and Emerging Security Techniques
1 Introduction
2 Big Data and Its Characteristics
3 Big Data Security Challenges
4 Big Data Security Solutions
4.1 Classic Security Methods
4.2 Artificial Intelligence Security Techniques
4.3 Blockchain Security
5 Examples of Potential Attacks on BD Systems and Some Solutions
5.1 Examples of Potential Attacks
5.2 Solutions for Potential Attacks
6 Conclusion
References
Machine Learning for Early Fire Detection in the Oasis Environment
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Tool Description
3.3 Selected ML Algorithms
4 Results and Discussion
5 Conclusion
References
Voice-Based Detection of Parkinson’s Disease Using Empirical Mode Decomposition, IMFCC, MFCC, and Deep Learning
1 Introduction
2 Dataset
3 Methodology
4 Results and Discussion
5 Conclusion
References
Comparative Study Between Fractional Linear Quadratic Regulator (Frac-LQR) and Sliding Mode Controller for the Stabilization the Three-Axis Attitude Control System of LEO Satellite Using Reaction Wheels
1 Introduction
2 The Dynamic Model of the Satellite Using Reaction Wheels
3 Controllability and Observability
4 Fractional Linear Quadratic Regulator (Frac-LQR)
5 Sliding Mode Controller (SMC)
6 Simulations and Results
7 Conclusion
References
UV-Nets: Semantic Deep Learning Architectures for Brain Tumor Segmentation
1 Introduction
2 Related Work
3 Materiel and Methods
3.1 Data
3.2 Data Preparation and Preprocessing
3.3 Methods
3.4 U-Net
3.5 V-Net
4 Results
4.1 Implementation Details
4.2 Evaluation Metrics
4.3 Results and Discussion
5 Conclusion
References
A Smart Mathematical Approach to Resource Management in Cloud Based on Multi-objective Optimization and Deep Learning
1 Introduction
2 Related Works
3 Our Contribution
3.1 Detailed Description of Our Contribution
4 Conclusion
References
Machine Learning in Cybersecurity: Evaluating Text Encoding Techniques for Optimized SMS Spam Detection
1 Introduction
2 Methodology
2.1 Data Acquisition
2.2 Data Preprocessing
2.3 Text Encoding Techniques
3 Results and Analysis
4 Conclusion
References
Design of a GaAs-FET Based Low Noise Amplifier for Sub-6 GHz 5G Applications
1 Introduction
1.1 Fifth Generation Wireless Communication
1.2 LNA
2 Theoretical Background
2.1 Transistor
2.2 Stability
2.3 Gain of Amplifier
2.4 Noise Figure
3 Design of Proposed LNA
4 Results and Discussion
5 Conclusion
References
Pyramid Scene Parsing Network for Driver Distraction Classification
1 Introduction
2 Related Work
3 Proposed Method
3.1 Pyramid Scene Parsing Network (PSPNet)
3.2 Proposed Method
4 Experiment and Results
4.1 Dataset
4.2 Implementation Details
4.3 Results
5 Conclusion
References
A Survey on RFID Mutual Authentication Protocols Based ECC for Resource-Constrained in IoT Environment
1 Introduction
2 Review of ECC-Authentication Protocols for RFID Systems
3 Comparative Analysis
4 Conclusion
References
Advanced Prediction of Solar Radiation Using Machine Learning and Principal Component Analysis
1 Introduction
2 Related Works
3 Our Proposed Approach
4 Experimental Study
4.1 Environment
4.2 Discussion of Results
5 Conclusion
References
Blockchain and Machine Learning Applications in Overcoming Security Challenges for CPS and IoT Systems
1 Introduction
2 Background
2.1 CPS Security
2.2 CPS and Blockchain
3 Related Works
4 Conclusion and Future Work
References
Understanding the Factors Contributing to Traffic Accidents: Survey and Taxonomy
1 Introduction
2 Related Works
2.1 Taxonomy of Critical Factors
2.2 Predictive and Descriptive Analysis Models
3 Results
4 Conclusion
References
The Smart Tourist Destination as a Smart City Project
1 Introduction
2 Theoretical Framework
2.1 Smart Cities
2.2 Smart Tourism Destinations
2.3 Integrating Tourism into the Smart City
3 Methodology
4 Results and Discussion
5 Conclusion
References
From BIM Toward Digital Twin: Step by Step Construction Industry Is Becoming Smart
1 Introduction
2 Definitions and Historical Development
2.1 Literature Reviews
2.2 Discussion
3 Properties and Characteristics
3.1 Data Flows
3.2 Information Type
4 DT and Construction Industry
4.1 DT Uses in Construction Projects
4.2 BIM Technology Uses
5 Conclusion
References
Comparative Study and Analysis of Existing Intelligent Tutoring Systems
1 Introduction
2 Intelligent Tutoring System
2.1 Definition
2.2 The Components of an ITS
3 Existing Intelligent Tutoring Systems
3.1 Introduction
3.2 Different Existing Intelligent Tutoring Systems
4 Comparison
5 General Conclusion
References
Extracting IT Knowledge Using Named Entity Recognition Based on BERT from IOB Annotated Job Descriptions
1 Introduction
2 Methodology and Proposed Model
2.1 General Architecture
2.2 Dataset Description
2.3 Named Entity Recognition Model
3 Results and Discussion
4 Conclusion
References
Prediction of Learner Performance Based on Self-esteem Using Machine Learning Techniques: Comparative Analysis
1 Introduction
2 Methodology
3 Results
4 Conclusion
References
A Collaborative Anomaly Detection Model Using En-Semble Learning and Blockchain
1 Introduction
2 Overview and Related Works
3 Description of the Model
4 Conclusion
References
Sentiment Analysis Based on Machine Learning Algorithms: Application to Amazon Product Reviews
1 Introduction
2 Literature Review
2.1 Sentiment Analysis and Opinion Mining
2.2 Sentiment Analysis in E-Commerce
2.3 Machine Learning Algorithms for Sentiment Analysis
2.4 Existing Comparative Studies
3 Our Proposed Approach
3.1 Data Collection and Preprocessing
3.2 Feature Extraction
3.3 Machine Learning Algorithms
3.4 Comparative Analysis
3.5 Discussion and Implications
4 Conclusion
5 Future Work
References
Novel Machine Learning Approach for an Adaptive Learning System Based on Learner Performance
1 Introduction
2 State of the Art
3 Methodology
3.1 Data Collection
3.2 Prediction Model Building
4 Results
5 Conclusion
References
Machine Learning for Predicting Prices and Empty Returns in Road Freight Transportation: Enhancing Efficiency and Sustainability
1 Introduction
2 Background and Methodology
2.1 Artificial Neural Networks
2.2 XGboost
3 Application and Case Study
3.1 Data
3.2 Predictive Analytics Model
4 Conclusion and Research Opportunities
References
Development and Examination of a 2.4 GHz Rectangular Patch Microstrip Antenna Incorporating Slot and Dielectric Superstrates
1 Introduction
2 Choosing Appropriate Substrate Materials and Determining the Size of the Antenna
3 Designing Rectangular Patch Antenna
4 Results of Rectangular Patch Antenna Without Superstrates
5 Examining the Influence of an Antenna Parameter on fr, BW, and S11
5.1 Impact of the Substrate Height h
5.2 Inserting a Rectangular Slot on the Patch
5.3 Effect of Superstrate in Rectangular Patch
6 Rectangular Antenna Optimal
7 Conclusion
References
Design and Analysis of Wide Band Circular Patch Antenna for IoT and Biomedical Applications
1 Introduction
2 Antenna Structure
2.1 Methodology for Calculations
2.2 Antenna Structure
2.3 Antenna Structure Evolution Steps
2.4 Parametric Study
3 Results and Discussions
3.1 Reflection Coefficient and VSWR of the Proposed Antenna
3.2 Radiation Pattern of the Suggested Structure
3.3 Simulation of the Antenna Next to Human Body
4 Conclusion
References
Design, Simulation, and Analysis of Microstrip Antenna Circular Patch High Efficiency for Radar Applications at 32 GHz
1 Introduction
2 Design Parameters for Circular Patch Antenna
3 Simulation Results and Discussions
3.1 Efficiency
3.2 Return Loss
3.3 Voltage Standing Wave Ratio (VSWR)
3.4 Bandwidth
3.5 The Gain
3.6 The Directivity
4 Conclusion
References
Effect of the Integration of Information and Communication Technology on the Motivation and Learning of Electricity Lessons for High School Students in Morocco
1 Introduction
2 Theoretical Framework
3 The Target Population and Selected Sample
4 Presentation, Analysis, and Discussion of the Results
4.1 Analysis Diagnostic Test Results
4.2 Achievement Test Analysis
5 Conclusion
References
Adaptive E-learning to Improve Communicative Skills of Learners with Autism Spectrum Disorder Using Eye Tracking and Machine Learning
1 Introduction
2 Background
2.1 Eye Tracking
2.2 Human Emotions
2.3 Applied Behaviour Analysis Method (ABA)
2.4 Naïve Bays Algorithm
3 Proposed Model
3.1 Identification of Emotions to Be Developed
3.2 Classification of Emotions
4 Conclusion
References
Performance Evaluation of Intrusion Detection System Using Gradient Boost
1 Introduction
2 Background
3 Related Works
4 Proposed Methodology
5 Results and Discussion
6 Conclusions
References
A Novel Detection, Prevention and Management Proactive System of Patients Chronic Disease Based on IoT, Blockchain, AI and Digital Twin
1 Introduction
1.1 Internet of Things Devices
1.2 Artificial Intelligence
1.3 Digital Twin
1.4 Blockchain
2 Related Works
3 Our Proposal
3.1 Data Structure
3.2 Patient Journey
4 Comparisons
5 Conclusion
References
Artificial Intelligence in the Tax Field: Comparative Study Between France and Morocco
1 Introduction
2 Conceptual Framework of Artificial Intelligence
3 Historical Overview of Artificial Intelligence in the Field of Taxation
4 Artificial Intelligence and Tax Audit in France
5 Artificial Intelligence Within the Directorate General of Taxes in Morocco
6 Artificial Intelligence Model of Development and Law
7 Conclusion
References
Deep Facial Expression Recognition
1 Introduction
2 Related Work
3 Methodology
3.1 Dataset Description
3.2 Grayscale Conversion
3.3 Image Normalization
4 Results and Discussion
5 Conclusion
References
Texture Analysis by Gray Level Homogeneity in Local Regions
1 Introduction
2 Coding Principe
3 Representation of the Matrix CLH
4 Histogram of the Matrix CLH
5 Detection of Regions
6 Improve Weak Contours
7 Noise Reduction
8 Conclusion
References
Deep Learning Approaches for Stock Price Forecasting Post Covid19: A Survey
1 Introduction
2 Related Works
3 Methodology
4 Collecting and Analyzing Data
4.1 Deep Learning Methods
4.2 Datasets
4.3 Inputs and Target
4.4 Performance Evaluation
4.5 Experimental Design
4.6 Extern Validation
4.7 Source Code
4.8 Specifications of Computational Resources
5 Result and Conclusion
References
A New Miniaturized Ultra-Wideband High-Isolated Two-Port MIMO Antenna for 5G Millimeter-Wave Applications
1 Introduction
2 Proposed Model
2.1 Single Antenna Structure
2.2 Single Antenna Design Evolution
2.3 MIMO Antenna Structure
3 Results and Discussions
3.1 S-Parameters
3.2 Effect of Element Arrangements on Isolation
3.3 Effect of Inter-Element Distance on Isolation
3.4 Effect of DGS on Isolation
3.5 Gain and Radiation Efficiency
3.6 Radiation Pattern
3.7 Comparison with Other Works
4 Conclusion
References
ChatGPT for a Flexible Higher Education: A Rapid Review of the Literature
1 Introduction
2 Literature Review
2.1 ChatGPT
2.2 Higher Education Needs ChatGPT
2.3 ChatGPT Applications in Education
3 Methodology
4 Results and Discussion
4.1 RQ1: How Can ChatGPT Be Used to Enhance Teaching and Learning and Make Higher Education More Flexible?
4.2 RQ2: What Potential Educational Concerns Are Related with ChatGPT, and How May They Be Resolved?
4.3 Discussion
5 Conclusion
References
BERT-Based Models with BiLSTM for Self-chronic Stress Detection in Tweets
1 Introduction
2 Related Work
3 The Proposed Model
3.1 The Architecture
3.2 The BERT-Embeddings
3.3 The Dataset
4 Results and Discussion
5 Conclusion and Future Work
References
The Transformation Method from Business Processes Models by BPMN to Use Cases Diagram by UML in Agile Methods
1 Introduction
2 Related Work
3 Our Proposal
4 Case Study
4.1 Presentation of the CIM Level
4.2 Presentation of the PIM Level
5 Conclusion
References
Combining Transfer Learning with CNNs and Machine Learning Algorithms for Improved Brain Tumor Classification from MRI
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Preprocessing
2.3 Transfer Learning
2.4 Feature Extraction: CNN Architectures
2.5 Classification: Machine Learning
2.6 Model Architecture Overview
3 Results and Discussion
4 Conclusion
References
An Intelligent Model for Detecting Obstacles on Sidewalks for Visually Impaired People
1 Introduction
2 Methodology
2.1 Data Collection and Annotation
2.2 YOLO Model
2.3 Train, Evaluate, and Deploy
3 Proposed Model
4 Results and Discussion
5 Conclusion
References
An Overview of Blockchain-Based Electronic Health Record and Compliance with GDPR and HIPAA
1 Introduction
2 Background
2.1 General Data Protection Regulation (GDPR)
2.2 Health Insurance Portability and Accountability Act
3 Results
3.1 Contradiction of Blockchain-Based EHR with GDPR
3.2 Contradiction of Blockchain-Based EHR with HIPAA
3.3 Proposed Solutions for Compliance of Blockchain-Based EHR and GDPR and HIPAA
3.4 Proposed Blockchain-Based EHR Model for GDPR and HIPAA Compliance
4 Conclusion
References
A Whale Optimization Algorithm Feature Selection Model for IoT Detecting Intrusion in Environments
1 Introduction
2 Background and Related Works
3 Our Contribution and Experimental Study
4 Results and Discussion
5 Conclusion
References
Dynamical Modeling of Climatic Parameters Under Greenhouse
1 Introduction
2 System Greenhouse
2.1 Description of the Model
2.2 Theory of Technical Greenhouse Systems
3 Results and Discussion
3.1 Presentation of Measures Parameters
3.2 Simulation Results
4 Conclusion and Perspectives
References
Prompt Engineering: User Prompt Meta Model for GPT Based Models
1 Introduction
2 GPT Implementations Meta Model
2.1 Structure of Prompts
2.2 Induced Current Prompts' Meta Model
3 Meta Model Definition for Prompt Engineering
3.1 User Prompt's Components
3.2 User Prompt's Meta Model
3.3 Meta Model Instance
4 Conclusion
References
Enhancing Conducted EMI Mitigation in Boost Converters: A Comparative Study of ZVS and ZCS Techniques
1 Introduction
2 The Boost Converter: Modeling and Practical Implementation
3 Soft Switching Approach for the Boost Converter
3.1 Soft Switching: ZVS
3.2 Soft Switching: ZCS
4 Simulation Results and Discussion
4.1 Conducted Electromagnetic Interference Measurement in a Boost Converter
5 Conclusion
References
Enhancing IoMT Security: A Conception of RFE-Ridge and ML/DL for Anomaly Intrusion Detection
1 Introduction
2 Literature Review
3 Proposed Approach
4 The Experimental Setup, and ML/DL Models Comparison
4.1 Software and Computer Hardware Requirements
4.2 Comparison of the ML/DL Used Models
5 Conclusion
References
Security of IoT-Cloud Systems Based Machine Learning
1 Introduction
2 Security Challenges in IoT-Cloud System
2.1 Data Privacy
2.2 Authentication and Confidentiality
2.3 Access Control
2.4 Authorization
3 Machine Learning Techniques for IoT-Cloud Systems
3.1 Support Vector Machine (SVM)
3.2 Random Forest
3.3 Convolutional Neural Network (CNN)
4 Evaluation and Performance Metrics
5 Related Work and Comparison of Results
6 Proposed Model
7 Conclusion
References
Optimizing IoT Workloads for Fog and Edge Scheduling Algorithms: A Comparative Study
1 Introduction
2 Literature Review
3 Classification of Scheduling Algorithm in Fog Computing
3.1 Static Scheduling Algorithms
3.2 Dynamic Scheduling Algorithms
4 Conclusion
References
Comparative Study Between Gilbert and Cascode Mixers for 5G mm-Wave Systems
1 Introduction
2 pHEMT Mixer Circuit
2.1 Cascode Cell
2.2 Gilbert Cell
3 Simulation Results
3.1 Cascode Cell Performance
3.2 Gilbert Cell Performance
3.3 Results Analysis
4 Conclusion
References
Anomaly Detection in IoT Networks—Classifications and Analysis Techniques
1 Introduction
2 Definition and Categorization of IoT Anomaly Detection
3 IoT Anomaly Detection Applications
3.1 Sensor Nodes
3.2 Network Traffic
4 Machine Learning for Anomaly Detection in the IoT
4.1 Machine Learning Techniques
4.2 Datasets
4.3 Algorithms
5 Models Comparison
6 Conclusion and Future Work
References
Creation of a Soft Circular Patch Antenna for 5G at a Frequency of 2.45 GHz
1 Introduction
2 Circular Patch Antenna Design Method
3 Simulations Results
4 Comparison of the Results Obtained with Those Available in the Scientific Literature
5 Conclusion
References
Study and Design of a 140 GHz Power Divider
1 Introduction
2 Circuit Design
3 Simulation and Results
4 Balun Layout Design
5 Conclusion
References
Emerging Concepts Using Blockchain and Big Data
1 Introduction
2 Overview of Blockchain and Big Data
2.1 Blockchain
2.2 Bigdata
3 Blockchain Services for Big Data
3.1 Using Blockchain to Secure Big Data Collection
3.2 Using Blockchain to Secure the Transfer of Big Data
3.3 Using Blockchain to Secure File Systems
3.4 Database Management Security Through the Use of Blockchain Technologies
4 Conclusion
References:
Exploring the Applications and Challenges of Blockchain Technology in Healthcare and IoT
1 Introduction
2 Related Work
3 Overview of Blockchain
4 Applications of Blockchain
4.1 Internet of Things
4.2 Healthcare
5 Challenges
6 Conclusion
References
Uncovering Data Quality Issues in Big Healthcare Data: Implications for Accurate Analytics
1 Introduction
2 Importance of Data Quality Assessment
3 Data Quality Indicators and Issues
4 Methods for Data Quality Assessment in Healthcare
5 A Proposed Road Map for Assessing Data Quality in Healthcare
6 Conclusion and Future Work
References
A Fog-Based Attack Detection Model Using Deep Learning for the Internet of Medical Things
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Proposed Fog-Based IDS for IoMT Environment
3.2 Dataset
3.3 Experimental Setup
3.4 Standard Evaluation Metrics
4 Results and Discussion
5 Conclusions and Future Work
References
Preventing Users from Obtaining False Data in Named Data of Health Things
1 Introduction
2 Proposed Mechanism
3 Performance Evaluation
3.1 Simulation Parameters
3.2 Simulation Results
4 Conclusion
References
Analyzing and Detecting Malware Using Machine Learning and Deep Learning
1 Introduction
2 Related Work
3 Malware Analysis
4 Malware Detection Techniques
5 Conclusion and Future Work
References
Lateral Control Using a Homogenous Control Law for an Autonomous Vehicle
1 Introduction
2 Mathematical Modeling
3 Control Methodology
4 Simulation and Perspective
5 Conclusion
References
Traffic Lights Control Using Reinforcement Learning: A Comparative Study
1 Introduction
2 Related Works
3 Reinforcement Learning
4 Experiments and Results
5 Conclusion
References
Fixed-Time Sliding Mode Control for a Drone Quadrotor
1 Introduction
2 Modeling the Quadrotor Drone System
3 Fixed-Time Sliding Mode Control Strategy
4 Simulated Results
5 Conclusion
References
Application of Distributed Consensus in Fixed Time Sliding Mode to the Wind Turbine System
1 Introduction
2 Modeling of Wind Turbine
3 The New Control Approach
4 Stability Analysis
4.1 Lemme
5 Simulation Results
6 Conclusion
References
Enhancing Fake Account Detection on Facebook Using Boruta Algorithm
1 Introduction
2 Methodology
3 Results and Analysis
4 Conclusion
References
Design of Artificial Neural Network Controller for Photovoltaic System
1 Introduction
2 Design of Photovoltaic System
2.1 DC-DC Boost Converter
2.2 Maximum Power Point Tracking
3 Artificial Neural Network
4 Simulation and Results
4.1 Photovoltaic Panel Specifications
4.2 Design of Artificial Neural Network Controller
5 Conclusion
References
The Impact of Artificial Intelligence on Supply Chain Management in Modern Business
1 Introduction
2 Benefits of AI in Supply Chain Management
2.1 Improved Forecasting and Demand Planning
2.2 Enhanced Efficiency and Automation
2.3 Optimized Inventory Management
2.4 Improved Supplier Relationship Management
2.5 Supply Chain Visibility and Transparency
2.6 Sustainability and Environmental Impact
2.7 Customer Satisfaction
3 Challenges and Implementation Hurdles
3.1 Demand Forecasting
3.2 Supply Chain Visibility
3.3 Inventory Management
3.4 Globalization and Regulatory Compliance
4 Future Trends and Developments
5 Conclusion
References
Dynamic Discounting and Flexible Invoices Payment Scheduling for Supply Chain Financial Performance Optimization
1 Introduction
2 Working Capital Management
3 Dynamics discounting and Flexible Payment Scheduling
4 Problem’s Statement and Modeling
5 Genetic Algorithm
5.1 Creation of Individuals of the First Generation
5.2 Evaluation of the Individual
5.3 Crossover Operator
6 Results and Discussion
7 Conclusion
References
An Efficient Driver Monitoring: Road Crash and Driver Behavior Analysis
1 Introduction
2 System Overview and Architecture
3 Results and Discussion
4 Conclusions
References
Enhancing Cloud-Based Machine Learning Models with Federated Learning Techniques
1 Introduction
2 Literature Review
3 Benefits and Limitations of Federated Learning for Cloud-Based Machine Learning
4 Methodology
5 Results
6 Discussion
7 Conclusion
8 Implications for the Development of Cloud-Based Machine Learning Models with Federated Learning Techniques
References
Classification of Diseases in Tomato Leaves with Deep Transfer Learning
1 Introduction
2 Proposed Methods
2.1 Dataset and Data Preparation
2.2 Motivation and Transfer Learning
2.3 Pre-Trained Models Used
3 Results and Discussion
3.1 Experimental Design
3.2 Performance Evaluation
4 Conclusion
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 838

Yousef Farhaoui · Amir Hussain · Tanzila Saba · Hamed Taherdoost · Anshul Verma   Editors

Artificial Intelligence, Data Science and Applications ICAISE’2023, Volume 2

Lecture Notes in Networks and Systems

838

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 worldwide 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]).

Yousef Farhaoui · Amir Hussain · Tanzila Saba · Hamed Taherdoost · Anshul Verma Editors

Artificial Intelligence, Data Science and Applications ICAISE’2023 Volume 2

Editors Yousef Farhaoui Department of Computer Science Moulay Ismail University Errachidia, Morocco

Amir Hussain Centre of AI and Robotics Napier University Edinburgh, UK

Tanzila Saba Prince Sultan University Riyadh, Saudi Arabia

Hamed Taherdoost University Canada West Vancouver, BC, Canada

Anshul Verma Institute of Science Banaras Hindu University Varanasi, India

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-48572-5 ISBN 978-3-031-48573-2 (eBook) https://doi.org/10.1007/978-3-031-48573-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 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 Paper in this product is recyclable.

Preface

Introduction In the dynamic landscape of technology, Artificial Intelligence (AI) and Data Science have emerged as pivotal forces reshaping the way we perceive and interact with information. The convergence of these two domains has given rise to a plethora of innovative applications that span industries, academia, and everyday life. As we navigate through the complexities of an interconnected world, the significance of understanding and harnessing the power of AI and Data Science becomes increasingly evident. The book “Artificial Intelligence, Data Science, and Applications” delves into the multifaceted realm of these transformative technologies, offering a comprehensive exploration of their theoretical foundations, practical applications, and the synergies that arise when they are combined. This book is designed to cater to a diverse audience, ranging from seasoned researchers and practitioners to students eager to embark on a journey into the cutting-edge advancements in AI and Data Science. Key Themes: 1. Foundations of Artificial Intelligence: • Unravel the fundamental principles and algorithms that underpin AI, providing readers with a solid understanding of the field’s core concepts. 2. Data Science Techniques and Methodologies: • Explore the methodologies, tools, and best practices in Data Science, addressing the entire data lifecycle from collection and preprocessing to analysis and interpretation. 3. Integration of AI and Data Science: • Investigate the seamless integration of AI and Data Science, showcasing how the synergy between these domains enhances the development of intelligent systems, predictive models, and decision-making processes. 4. Real-world Applications: • Showcase a diverse array of practical applications in various domains, including healthcare, finance, cybersecurity, and more, illustrating how AI and Data Science are actively shaping industries and improving societal outcomes. 5. Ethical and Societal Implications: • Delve into the ethical considerations and societal implications of deploying AI and Data Science solutions, emphasizing the importance of responsible innovation and addressing potential biases.

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Preface

6. Future Perspectives: • Anticipate and discuss emerging trends, challenges, and future directions in AI and Data Science, offering insights into the evolving landscape of these rapidly advancing fields. This comprehensive compilation serves as a guide through the intricate web of Artificial Intelligence and Data Science, providing readers with a holistic view of the theories, methodologies, applications, and ethical considerations that define these disciplines. Each chapter is crafted by experts in the respective fields, ensuring a rich and diverse tapestry of knowledge that will inspire and inform both novices and seasoned professionals alike. “Artificial Intelligence, Data Science, and Applications” invites readers to embark on an enlightening journey into the heart of technological innovation and its transformative impact on our world. Errachidia, Morocco

Yousef Farhaoui

Organisation

Chairman of ICAISE’2023 Yousef Farhaoui

Moulay Ismail University of Meknes, Faculty of sciences and Techniques, Errachidia, Morocco

International Organizing Committee Seyed Ghaffar Zouhaier Brahmia Amir Hussain Tanzila Saba Hamed Taherdoost Anshul Verma Lara Brunelle Almeida Freitas Youssef Agrebi Zorgani Bharat Bhushan Fathia Aboudi Agbotiname Lucky Imoize Javier González Argote

Brunel University London, UK University of Sfax, Tunisia Director of the Centre of AI and Robotics at Edinburgh Napier University, UK Prince Sultan University, Saudi Arabia University Canada West, Vancouver, Canada Institute of Science, Banaras Hindu University, Varanasi, India University of Mato Grosso do Sul—UEMS/Dourados, Brazil ISET Sfax, Tunisia School of Engineering and Technology (SET), Sharda University, India High Institute of Medical Technology of Tunis, Tunisia University of Lagos, Nigeria President and CEO of Fundación Salud, Ciencia y Tecnología, Argentina

Committee Members Ahmad El Allaoui Yousef Qarrai Fatima Amounas Mourad Azrour Imad Zeroual Said Agoujil Laidi Souinida Youssef El Hassouani Abderahman El Boukili Abdellah Benami

FST-UMI, Errachidia, Morocco FST-UMI, Errachidia, Morocco FST-UMI, Errachidia, Morocco FST-UMI, Errachidia, Morocco FST-UMI, Errachidia, Morocco ENCG-UMI, Errachidia, Morocco FSTE-UMI, Errachidia, Morocco FSTE-UMI, Errachidia, Morocco FSTE-UMI, Errachidia, Morocco FSTE-UMI, Errachidia, Morocco

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Badraddine Aghoutane Mohammed Fattah Said Ziani Ahmed Asimi Abdelkrim El Mouatasim Younes Balboul Bharat Bhushan Moulhime El Bekkali Said Mazer Mohammed El Ghazi Azidine Geuzzaz Said Benkirane Mustapha Machkour Gyu Myoung Lee Ahm Shamsuzzoha Agbotiname Lucky Imoize Mohammed El Ghazi Zouhaier Brahmia Said Mazer Al-Sakib Khan Pathan Athanasios V. Vasilakos Alberto Cano Jawad Berri Mohd Nazri Ismail Gustavo Rossi. Lifia Arockiasamy Soosaimanickam Rabie A. Ramadan Salem Benferhat Maryam Khademi Zhili Sun Ammar Almomani Mohammad Mansour Alauthman Muttukrishnan Rajarajan Antonio Pescape Hamada Alshaer Paolo Bellavista Mohamed Najeh Lakhoua

FS-UMI, Meknes, Morocco EST-UMI, Meknes, Morocco EST-UH2, Casablanca, Morocco FS-UIZ, Agadir, Morocco FP-UIZ Ouarzazate, Morocco ENSA, USMBA, Fes, Morocco School of Engineering and Technology (SET), Sharda University, India ENSA, USMBA, Fes, Morocco ENSA, USMBA, Fes, Morocco EST, USMBA, Fes, Morocco EST Essaouira, University of Cadi Ayyad Marrakech, Morocco EST Essaouira, University of Cadi Ayyad Marrakech, Morocco FS-UIZ, Agadir, Morocco Liverpool John Moores University, UK University of Vaasa, Finland University of Lagos, Nigeria EST, USMBA, Fes, Morocco University of Sfax, Tunisia ENSA, USMBA, Fes, Morocco Université du Sud-Est, Bangladesh Université de technologie de Lulea, Suède Virginia Commonwealth University, États-Unis Sonatrach—Société algérienne du pétrole et du gaz, Arabie saoudite National Defence University of Malaysia, Malaysia University of Nacional de La Plata, Argentina University of Nizwa, Sultanate of Oman Cairo University, Egypt CRIL, CNRS-University of Artois, France Islamic Azad University, Iran University of Surrey, UK Al-Balqa Applied University, Jordan Zarqa University, Jordan University of London, UK University of Napoli, Italy The University of Edinburgh, UK DISI—University of Bologna, Italy University of Carthage, Tunisia

Organisation

Ernst L. Leiss Mehdi Shadaram Lennart Johnsson Nouhad Rizk Jaime Lloret Mauri Janusz Kacprzyk Mahmoud Al-Ayyoub Houbing Song Mohamed Béchir Dadi Amel Ltifi Mohamed Slim Kassis Sebastian Garcia Younes Asimi Samaher Al-Janabi Safa Brahmia Hind Hamrouni Anass El Haddadi Abdelkhalek Bahri El Wardani Dadi Ahmed Boujraf Ahmed Lahjouji El Idrissi Aziz Khamjane Nabil Kannouf Youness Abou El Hanoune Mohamed Addam Fouzia Moradi Hayat Routaib Imad Badi Mohammed Merzougui Asmine Samraj Esther Asare Abdellah Abarda Yassine El Borji Tarik Boudaa Ragragui Anouar Abdellah Benami Abdelhamid Zouhair Mohamed El Ghmary Youssef Mejdoub

University of Houston, Texas USA University of Texas at San Antonio, USA University of Houston, USA Houston University, USA University of Politécnica de Valencia, Spain Systems Research Institute, Polish Academy of Sciences in Warsaw, Poland University of Science and Technology, Jordan West Virginia University, USA University of Gabès, Tunisia University of Sfax, Tunisia University of Tunis, Tunisia Czech Technical University in Prague, Czech Republic University of Iben Zohr, Agadir, Morocco University of Babylon, Iraq University of Sfax, Tunisia University of Sfax, Tunisia University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco FSJES, Oujda, Morocco Quaid-E-Millath Government College, India Anhui University of Science and Technology, China FEM-Hassan 1 University, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco University of Abdelmalek Essaadi, Morocco FSTE-UMI, Errachidia, Morocco University of Abdelmalek Essaadi, Morocco FSDM-USMBA, Morocco ESTC-UH2, Casablanca, Morocco

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Organisation

Amine Tilioua Azidine Geuzzaz Said Benkirane Ali Ouacha Fayçal Messaoudi Tarik Chanyour Naercio Magaia Imane Halkhams Elsadig Musa Ahmed Valliappan Raju David Jiménez-Castillo

FST-UMI, Errachidia, Morocco EST Essaouira, University of Cadi Ayyad Marrakech, Morocco EST Essaouira, University of Cadi Ayyad Marrakech, Morocco FS-Mohammed V University, Morocco ENCG, USMBA, Morocco Ain Chock Faculty of Sciences-UH2, Morocco School of Engineering and Informatics, University of Sussex, UK UPE, Fez, Morocco Faculty of Business Multimedia University, Malaysia Professor, International Islamic University of Malaysia, Malaysia Faculty of Economics and Business, University of Almeria, Spain

Introduction

Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Data science, Artificial Intelligence, and Smart Environments inspire novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and social science. This book reviews the state of the art of big data analysis, Artificial Intelligence, and Smart Environments. It includes issues which pertain to signal processing, probability models, machine learning, data mining, database, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart city, etc. Papers in this book were the outcome of research conducted in this field of study. The latter makes use of applications and techniques related to data analysis in general and big data and smart city in particular. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in big data analysis and Artificial Intelligence.

Contents

Exploring the Impact of Deep Learning Techniques on Evaluating Arabic L1 Readability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Safae Berrichi, Naoual Nassiri, Azzeddine Mazroui, and Abdelhak Lakhouaja Performance Analysis of Two Serial Concatenations of Decoders Over a Rayleigh Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. El Kasmi Alaoui, H. Faham, T. Chanyour, M. El Assad, Z. Chiba, and S. Nouh Blockchain and Reputation Based Secure Service Provision in Edge-Cloud Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tarik Chanyour, Seddiq El Kasmi Alaoui, Abdelhak Kaddari, Youssef Hmimz, and Zouhair Chiba

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Exploring the Impact of Convolutions on LSTM Networks for Video Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manal Benzyane, Mourade Azrour, Imad Zeroual, and Said Agoujil

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Classification Algorithms Implementation for Fire Prevention Data on Multiple Wireless Node Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oumayma El Gatte, Ahmed El Abbassi, Omar Mouhib, and Dahbi Aziz

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Implementation of an Intelligent Monitoring System Based on Quality 4.0 of the Induction Hardening Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imane Moufid, Adil Nabih, Ismail Lagrat, and Oussama Bouazaoui

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Lithium-Ion Battery State of Charge Estimation Using Least Squares Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elmehdi Nasri, Tarik Jarou, Abderrahmane Elkachani, and Salma Benchikh Intrusion Detection in Software-Defined Networking Using Machine Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lamiae Boukraa, Siham Essahraui, Khalid El Makkaoui, Ibrahim Ouahbi, and Redouane Esbai Neural Network for Link Prediction in Social Network . . . . . . . . . . . . . . . . . . . . . . Mohamed Badiy, Fatima Amounas, Ahmad El Allaoui, and Younes Bayane

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Design and Optimization of a Compact Microstrip Bandpass Filter Using on Open Loop Rectangular Resonators for Wireless Communication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Youssef Khardioui, Kaoutar Elbakkar, Ali El Alami, and Mohammed El Ghzaoui YOLO-Based Approach for Intelligent Apple Crop Health Assessment . . . . . . . . Imane Lasri, Sidi Mohamed Douiri, Naoufal El-Marzouki, Anouar Riadsolh, and Mourad Elbelkacemi A Comprehensive Review on the Integration of Blockchain Technology with IPFS in IoT Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soufian El Airaj, Fatima Amounas, Mourade Azrour, and Mohamed Badiy An Integrated Approach for Artifact Elimination in EEG Signals: Combining Variational Mode Decomposition with Blind Source Separation (VMD-BSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Massar, M. Miyara, T. Belhoussine Drissi, and B. Nsiri E-Health Blockchain: Conception of a New Smart Healthcare Architecture Based on Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soumia Benkou, Ahmed Asimi, and Lahdoud Mbarek

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Dynamic Multi-compartment Vehicle Routing Problem: Formulation and Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Chaymaa Beneich and Sidi Mohamed Douiri Anime Link Prediction Using Improved Graph Convolutional Networks . . . . . . . 106 Safae Hmaidi, Yassine Afoudi, Mohamed Lazaar, and El Madani El Alami Yasser Assessing the Evolution of Meteorological Seasons and Climate Changes Using Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Mohamed Lazaar, Hamza Ba-Mohammed, Hicham Filali, and Yasser El Madani El Alami Significance and Impact of AI on Pedagogical Learning: A Case Study of Moroccan Students at the Faculty of Legal and Economics . . . . . . . . . . . . . . . . 124 Khoual Mohamed, Zineb Elkaimbillah, and Bouchra El Asri Securing Big Data: Current Challenges and Emerging Security Techniques . . . . 130 Ikram Hamdaoui, Khalid El Makkaoui, and Zakaria El Allali Machine Learning for Early Fire Detection in the Oasis Environment . . . . . . . . . 138 Safae Sossi Alaoui and Yousef Farhaoui

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Voice-Based Detection of Parkinson’s Disease Using Empirical Mode Decomposition, IMFCC, MFCC, and Deep Learning . . . . . . . . . . . . . . . . . . . . . . . 144 Nouhaila Boualoulou, Mounia Miyara, Benayad Nsiri, and Taoufiq Belhoussine Drissi Comparative Study Between Fractional Linear Quadratic Regulator (Frac-LQR) and Sliding Mode Controller for the Stabilization the Three-Axis Attitude Control System of LEO Satellite Using Reaction Wheels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Taha Ennaciri, Ahmed El abbassi, Nabil Mrani, and Jaouad Foshi UV-Nets: Semantic Deep Learning Architectures for Brain Tumor Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Ilyasse Aboussaleh, Jamal Riffi, Khalid El Fazazay, Adnane Mohamed Mahraz, and Hamid Tairi A Smart Mathematical Approach to Resource Management in Cloud Based on Multi-objective Optimization and Deep Learning . . . . . . . . . . . . . . . . . . 166 Raja Ait El Mouden and Ahmed Asimi Machine Learning in Cybersecurity: Evaluating Text Encoding Techniques for Optimized SMS Spam Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Adnane Filali, El Arbi Abdellaoui Alaoui, and Mostafa Merras Design of a GaAs-FET Based Low Noise Amplifier for Sub-6 GHz 5G Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Samia Zarrik, Abdelhak Bendali, Fatehi ALtalqi, Karima Benkhadda, Sanae Habibi, Mouad El Kobbi, Zahra Sahel, and Mohamed Habibi Pyramid Scene Parsing Network for Driver Distraction Classification . . . . . . . . . 189 Abdelhak Khadraoui and Elmoukhtar Zemmouri A Survey on RFID Mutual Authentication Protocols Based ECC for Resource-Constrained in IoT Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Hind Timouhin, Fatima Amounas, and Mourade Azrour Advanced Prediction of Solar Radiation Using Machine Learning and Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Hasna Hissou, Said Benkirane, Azidine Guezzaz, Abderrahim Beni-Hssane, and Mourade Azrour Blockchain and Machine Learning Applications in Overcoming Security Challenges for CPS and IoT Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 Kamal Bella, Azidine Guezzaz, Said Benkirane, Mourade Azrour, and Mouaad Mohy-eddine

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Understanding the Factors Contributing to Traffic Accidents: Survey and Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 Soukaina El Ferouali, Zouhair Elamrani Abou Elassad, and Abdelmounaîm Abdali The Smart Tourist Destination as a Smart City Project . . . . . . . . . . . . . . . . . . . . . . 222 Kacem Salmi and Aziz Hmioui From BIM Toward Digital Twin: Step by Step Construction Industry Is Becoming Smart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Zayneb Miraoui, Nasser Abdelkader, and Mohssine Kodad Comparative Study and Analysis of Existing Intelligent Tutoring Systems . . . . . 235 Zakaria Rida, Hadhoum Boukachour, Mourad Ennaji, and Mustapha Machkour Extracting IT Knowledge Using Named Entity Recognition Based on BERT from IOB Annotated Job Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Zineb Elkaimbillah, Maryem Rhanoui, Mounia Mikram, Mohamed Khoual, and Bouchra El Asri Prediction of Learner Performance Based on Self-esteem Using Machine Learning Techniques: Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Aymane Ezzaim, Aziz Dahbi, Abdelhak Aqqal, and Abdelfatteh Haidin A Collaborative Anomaly Detection Model Using En-Semble Learning and Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Chaimae Hazman, Azidine Guezzaz, Said Benkirane, Mourade Azrour, and Sara Amaouche Sentiment Analysis Based on Machine Learning Algorithms: Application to Amazon Product Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 El Rharroubi Mohamed Amine and Abdelhamid Zouhair Novel Machine Learning Approach for an Adaptive Learning System Based on Learner Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Aymane Ezzaim, Aziz Dahbi, Abdelhak Aqqal, and Abdelfatteh Haidin Machine Learning for Predicting Prices and Empty Returns in Road Freight Transportation: Enhancing Efficiency and Sustainability . . . . . . . . . . . . . . 273 Mohamed Amine Ben Rabia and Adil Bellabdaoui Development and Examination of a 2.4 GHz Rectangular Patch Microstrip Antenna Incorporating Slot and Dielectric Superstrates . . . . . . . . . . . . . . . . . . . . . 279 Ibrahim Khouyaoui, Mohamed Hamdaoui, and Jaouad Foshi

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Design and Analysis of Wide Band Circular Patch Antenna for IoT and Biomedical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Younes Siraj and Jaouad Foshi Design, Simulation, and Analysis of Microstrip Antenna Circular Patch High Efficiency for Radar Applications at 32 GHz . . . . . . . . . . . . . . . . . . . . . . . . . 297 Fatehi ALtalqi, Karima Benkhadda, Samia Zarrik, Echchelh Adil, Asma Khabba, and Ahmed Abbas Al Rimi Effect of the Integration of Information and Communication Technology on the Motivation and Learning of Electricity Lessons for High School Students in Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Hassan Yakkou, Abdelhakim Chillali, Nacer Eddine Elkadri Elyamani, Abdelaaziz El Ansari, and Aziz Taoussi Adaptive E-learning to Improve Communicative Skills of Learners with Autism Spectrum Disorder Using Eye Tracking and Machine Learning . . . 311 Fatima Zohra Lhafra and Otman Abdoun Performance Evaluation of Intrusion Detection System Using Gradient Boost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Sara Amaouche, Azidine Guezzaz, Said Benkirane, Mourade Azrour, and Chaimae Hazman A Novel Detection, Prevention and Management Proactive System of Patients Chronic Disease Based on IoT, Blockchain, AI and Digital Twin . . . . 324 Mbarek Lahdoud and Ahmed Asimi Artificial Intelligence in the Tax Field: Comparative Study Between France and Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 Machmoume Siham and Nmili Mohammed Deep Facial Expression Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Ouhammou Mohamed, Nabil Ababou, Said Ouatik El Alaoui, and Si Lhoussain Aouragh Texture Analysis by Gray Level Homogeneity in Local Regions . . . . . . . . . . . . . . 346 El Beghdadi Abdelhamid and Merzougui Mohammed Deep Learning Approaches for Stock Price Forecasting Post Covid19: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 El Qarib Mohamed, Nabil Ababou, Si Lhoussain Aouragh, and Said Ouatik El Alaoui

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A New Miniaturized Ultra-Wideband High-Isolated Two-Port MIMO Antenna for 5G Millimeter-Wave Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 Ouafae Elalaouy, Mohammed El Ghzaoui, and Jaouad Foshi ChatGPT for a Flexible Higher Education: A Rapid Review of the Literature . . . 369 Abdelmajid Elhajoui, Otmane Yazidi Alaoui, Omar El Kharki, Miriam Wahbi, Hakim Boulassal, and Mustapha Maatouk BERT-Based Models with BiLSTM for Self-chronic Stress Detection in Tweets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 Mohammed Qorich and Rajae El Ouazzani The Transformation Method from Business Processes Models by BPMN to Use Cases Diagram by UML in Agile Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Ouzayr Rabhi, Saida Filali, and Mohammed Erramdani Combining Transfer Learning with CNNs and Machine Learning Algorithms for Improved Brain Tumor Classification from MRI . . . . . . . . . . . . . . 391 Abd Allah Aouragh and Mohamed Bahaj An Intelligent Model for Detecting Obstacles on Sidewalks for Visually Impaired People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Ahmed Boussihmed, Khalid El Makkaoui, Abdelaziz Chetouani, Ibrahim Ouahbi, and Yassine Maleh An Overview of Blockchain-Based Electronic Health Record and Compliance with GDPR and HIPAA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Nehal Ettaloui, Sara Arezki, and Taoufiq Gadi A Whale Optimization Algorithm Feature Selection Model for IoT Detecting Intrusion in Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Mouaad Mohy-eddine, Azidine Guezzaz, Said Benkirane, Mourade Azrour, and Kamal Bella Dynamical Modeling of Climatic Parameters Under Greenhouse . . . . . . . . . . . . . 420 Abderrazak Kaida, Youssef El Afou, Abderrahman Aitdada, Said Hamdaoui, and Abdelouahad Ait Msaad Prompt Engineering: User Prompt Meta Model for GPT Based Models . . . . . . . . 428 Hamza Tamenaoul, Mahmoud El Hamlaoui, and Mahmoud Nassar Enhancing Conducted EMI Mitigation in Boost Converters: A Comparative Study of ZVS and ZCS Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 Zakaria M’barki, Ali Ait Salih, Youssef Mejdoub, and Kaoutar Senhaji Rhazi

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Enhancing IoMT Security: A Conception of RFE-Ridge and ML/DL for Anomaly Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442 Ghita Lazrek, Kaouthar Chetioui, and Younes Balboul Security of IoT-Cloud Systems Based Machine Learning . . . . . . . . . . . . . . . . . . . . 448 Ouijdane Fadli, Younes Balboul, Mohammed Fattah, Said Mazer, and Moulhime Elbekkali Optimizing IoT Workloads for Fog and Edge Scheduling Algorithms: A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454 Saad-Eddine Chafi, Younes Balboul, Mohammed Fattah, Said Mazer, and Moulhime El Bekkali Comparative Study Between Gilbert and Cascode Mixers for 5G mm-Wave Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Abdelhafid Es-Saqy, Maryam Abata, Salah-Eddine Didi, Mohammed Fattah, Said Mazer, Mahmoud Mehdi, Moulhime El Bekkali, and Catherine Algani Anomaly Detection in IoT Networks—Classifications and Analysis Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 Hamza Rhachi, Anas Bouayad, Younes Balboul, and Badr Aitmessaad Creation of a Soft Circular Patch Antenna for 5G at a Frequency of 2.45 GHz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Salah-Eddine Didi, Imane Halkhams, Abdelhafid Es-saqy, Mohammed Fattah, Younes Balboul, Said Mazer, and Moulhime El Bekkali Study and Design of a 140 GHz Power Divider . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 Abdeladim EL Krouk, Abdelhafid Es-saqy, Mohammed Fattah, Said Mazer, Mahmoud Mehdi, Moulhime El Bekkali, and Catherine Algani Emerging Concepts Using Blockchain and Big Data . . . . . . . . . . . . . . . . . . . . . . . . 487 Fatna El Mendili and Mohammed Fattah Exploring the Applications and Challenges of Blockchain Technology in Healthcare and IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Fatima Anter, Fatna Elmendili, Mohammed Fattah, and Nabil Mrani Uncovering Data Quality Issues in Big Healthcare Data: Implications for Accurate Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Nisrine Berros, Youness Filaly, Fatna El Mendili, and Younes El Bouzekri E. L. Idrissi

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A Fog-Based Attack Detection Model Using Deep Learning for the Internet of Medical Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 Yahya Rbah, Mohammed Mahfoudi, Younes Balboul, Kaouthar Chetioui, Mohammed Fattah, Said Mazer, Moulhime Elbekkali, and Benaissa Bernoussi Preventing Users from Obtaining False Data in Named Data of Health Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512 Asmaa El-Bakkouchi, Mohammed El Ghazi, Anas Bouayad, Mohammed Fattah, and Moulhime El Bekkali Analyzing and Detecting Malware Using Machine Learning and Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518 Badr Ait Messaad, Kaouthar Chetioui, Younes Balboul, and Hamza Rhachi Lateral Control Using a Homogenous Control Law for an Autonomous Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 Belkheir Ayoub, Mellouli El Mehdi, and Boumhidi Ismail Traffic Lights Control Using Reinforcement Learning: A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Khalid Errajraji, Anas Bouayad, and Khalid Fardousse Fixed-Time Sliding Mode Control for a Drone Quadrotor . . . . . . . . . . . . . . . . . . . 539 Najlae Jennan, El Mehdi Mellouli, and Ismail Boumhidi Application of Distributed Consensus in Fixed Time Sliding Mode to the Wind Turbine System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546 Sanae El bouassi, Zakaria Chalh, and El Mehdi Mellouli Enhancing Fake Account Detection on Facebook Using Boruta Algorithm . . . . . 553 Amine Sallah, El Arbi Abdellaoui Alaoui, and Said Agoujil Design of Artificial Neural Network Controller for Photovoltaic System . . . . . . . 559 Salma Benchikh, Tarik Jarou, Mohamed Khalifa Boutahir, Elmehdi Nasri, and Roa Lamrani The Impact of Artificial Intelligence on Supply Chain Management in Modern Business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 Mitra Madancian, Hamed Taherdoost, Maassoumeh Javadi, Inam Ullah Khan, Alaeddin Kalantari, and Dinesh Kumar

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Dynamic Discounting and Flexible Invoices Payment Scheduling for Supply Chain Financial Performance Optimization . . . . . . . . . . . . . . . . . . . . . . 574 Halima Semaa, Youssef Malhouni, Abdelillah Semma, Laila Bouzarra, and Mohamed Ait Hou An Efficient Driver Monitoring: Road Crash and Driver Behavior Analysis . . . . 587 Mohammed Ameksa, Zouhair Elamrani Abou Elassad, and Hajar Mousannif Enhancing Cloud-Based Machine Learning Models with Federated Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 Rejuwan Shamim and Yousef Farhaoui Classification of Diseases in Tomato Leaves with Deep Transfer Learning . . . . . 607 Noredine Hajraoui, Mourade Azrour, and Ahmad El Allaoui Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613

Exploring the Impact of Deep Learning Techniques on Evaluating Arabic L1 Readability Safae Berrichi1(B) , Naoual Nassiri2 , Azzeddine Mazroui3 , and Abdelhak Lakhouaja3 1

2

Research Center of the Higher Engineering Studies School, Oujda, Morocco [email protected] Engineering and Sustainable Development Team, Higher School of Technology, Ibn Zohr University, Dakhla, Morocco 3 Department of Computer Science, Faculty of Sciences, Mohammed First University, Oujda, Morocco

Abstract. Educational researchers are interested in the causes and effects of reading difficulties on learners because reading is one of the primary pathways for language learning and knowledge acquisition. Thus, any difficulty in reading can affect the learning and comprehension process. In view of this, several approaches have been proposed by the researchers to automatically assess the difficulty level of texts. Thanks to recent advances in linguistics and computer science, text readability analysis now relies on a variety of linguistic indicators to measure text complexity, as well as on powerful computer models. Research on Arabic as a foreign language (L2) is more advanced than research on Arabic as a first language (L1). In this study, we outline several approaches to assessing the readability of Arabic texts for L1 learners. Two approaches have been used to evaluate the readability of texts. The first one is mainly based on the evaluation of sophisticated handcrafted features, which represent the texts and allow estimating their readability level. The second approach evaluates the use of different contextual and non-contextual word vectors (CBOW, Skip-Gram and AraBert) instead of the handcrafted features. The results indicate that the AraBert model achieves the best readability prediction accuracy of about 76.93%.

Keywords: Deep-learning Features · AraBert

1

· Arabic · Readability · L1 · MoSAR ·

Introduction

Automated text difficulty assessment has become an area of great interest for researchers in diverse fields, such as education, cognitive science, psychology, linguistics, and language acquisition. This is because measuring the readability of a text has significant practical implications. Previous research on readability c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 1–7, 2024. https://doi.org/10.1007/978-3-031-48573-2_1

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has explored a wide range of linguistic and non-linguistic aspects of texts, with some studies relying on simple measures such as the average length of words and sentences. This approach was driven by the absence of computational tools capable of automatically assessing all the features of a text. However, with the emergence of advanced natural language processing (NLP) techniques for text analysis, more sophisticated methods for assessing readability have been developed. These methods involve statistical and machine learning approaches, in which a predictive model is trained using a set of data samples represented by various linguistic features. These features are encoded as vectors labeled with classes corresponding to the difficulty level of the text [2]. The growing interest in automated text difficulty assessment is fueled by its potential for various practical applications, including language teaching and learning, educational testing, and document simplification for people with cognitive or reading disabilities. Furthermore, automated text difficulty assessment can help improve the accessibility and effectiveness of educational materials for learners with diverse linguistic backgrounds and reading abilities. As such, this research area has significant implications for education, language policy, and technology development. The development of more sophisticated and accurate methods for automated text difficulty assessment can ultimately lead to more effective and equitable education and communication systems. Deep neural models have recently been used to predict text readability. These models represent text as vectors embedded through neural models [4], providing an alternative way to represent words as word embeddings, which can be non-contextual or contextual. Non-contextual models represent each word independently of different contexts, while contextual models learn semantics at the sequence level by considering the sequence of all words in the documents. This paper proposes the use of different word embedding techniques, such as CBOW, Skip-Gram [5], and AraBert [3], to evaluate the readability of Arabic as a first language (L1). These techniques encode knowledge about reading difficulties into the word representation. In addition, a readability measurement model based on manually crafted linguistic features for word representation was developed to determine the most reliable technique for measuring readability of Arabic L1 texts. The paper is structured as follows: Sect. 2 outlines some previous studies conducted on readability assessment. Section 3 describes in detail the data and the used vectors. Section 4 presents the experiments and the obtained results. Finally, Sect. 5 concludes with a discussion of future research perspectives.

2

Related Works

Research on readability assessment carries a history that dates back to the beginning of the last century and has persisted to this day. However, the most recent studies have focused on improving performance using supervised approaches and efficient classification models. These have resulted in the construction of very effective features for assessing readability. Most recent research has focused on readability assessment for Arabic as a foreign language (L2).

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In 2015, Saddiki et al. [9] presented an approach to assess text readability based on a corpus of 251 texts collected from GLOSS1 . They extracted 35 features from these texts for use in a classification phase. The main objective of their study was to determine whether simple features could indicate the readability level of a text. They achieved a maximum accuracy of 73.31% using this approach. In a follow-up study, published in [7], Nassiri et al. presented an approach for predicting the readability of L2 Arabic texts. For this, they used a corpus of 230 texts, taken from the same source as the previous work, and they extracted 170 different features. Their method obtained a prediction accuracy of 90.43%. These later cannot be generalized since they evaluated the obtained model only on the training data. Text readability is a topic of interest not only to L2, but also to researchers who study the difficulty of texts for L1 learners and readers. However, the field of readability measurement in this context remains under-explored and limited in terms of studies. In 2018, Cavalli-Sforza et al. [10] attempted to optimize features to predict the readability of Arabic as L1. They used the Arabic L1 corpus developed by Al Khalil et al. [1] to conduct their study. This study show that text readability measurement is an emerging field and that there is still much to be explored in terms of readability assessment in Arabic as L1. Further efforts are needed to develop effective measurement tools to improve comprehension and learning of Arabic texts, both for L1 and L2 learners. All of the studies we reviewed focused on the evaluation of different machine learning classifiers for text classification. The researchers used a set of predefined features to evaluate the performance of these classifiers. However, until recently, the use of vector representation of word embedding in text was not widely explored for Arabic text readability assessment. Therefore, in this study, we chose to consider vector representations to evaluate their impact on L1 Arabic text classification. We also compared the effectiveness of the vector representations with the representations used to evaluate the readability of Arabic L1.

3

Methodology and Results

In this section, we will provide a detailed presentation of the data on which we applied our approach, as well as the tools used for morphological annotation and the algorithms employed for the classification of these data. We will also explain in detail the different features that have been studied. 3.1

Data

To train and evaluate prediction models of Arabic readability, in this study we used the MoSAR corpus [6]. MoSAR consists of educational texts in modern standard Arabic for Moroccan children learning Arabic L1. Each text is annotated with difficulty levels based on the primary levels of the Moroccan school 1

https://gloss.dliflc.edu/.

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system, ranging from the first school level (grade 1) to the last primary level (grade 6). The statistical properties of the different levels in the MoSAR corpus are presented in Table 1. Table 1. Data description Level Texts count Sentences count Words count 1

51

380

3,090

2

139

1,124

12,980

3

60

661

9,888

4

136

1,301

24,584

5

86

849

14,261

6

130

887

12,740

Total 602

5,202

77,543

We used this corpus categorized into three classes, namely ‘Easy,’ ‘Medium,’ and ‘Difficult,’ achieved by combining adjacent levels (Level 1 with Level 2, Level 3 with Level 4, and Level 5 with Level 6). 3.2

Text Representation

Measuring the readability of an Arabic text requires the use of different linguistic features which are grouped into two main families: lexical and syntactic. In order to convert texts into representative vectors, it is essential to take these features into account. In our study, we first used 37 features inspired from [8] and dedicated to L1, which cover these two families of features. In this study, we also present an alternative to using linguistic features to model readability, which consists in representing a text as an embedding vector using neural models. Two word integration models were used: CBOW and skip-Gram. The CBOW model predicts the current target word based on the surrounding words in a fixed window, while the skip-Gram model predicts the contextual words in the range of a fixed window based on a given current target word. However, learning these word embedding vectors requires a large text corpus. To address this, we proposed to use a pre-trained model, araVec2 , which has several versions of word embedding based on the Skip-Gram and CBOW models. This allows us to leverage the knowledge gained from the araVec model without having to train the model from scratch. In addition to word embedding models, other models have been proposed to provide representations of words that depend on their context. In this study, the model used is AraBERT, which is a pre-trained Bert Embedding model available 2

https://github.com/bakrianoo/aravec.

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on “Hugging face transformers”3 . AraBERT is able to provide different representations for the same word depending on its context. It is composed of 12 transformation blocks, 768 hidden dimensions, 12 attention heads, a maximum sequence length of 512 tokens and has been trained on two different corpora. Its vocabulary is of 64,000 tokens. The advantages of using a pre-trained model like AraBERT are numerous, including reduced computational costs and training time. In addition, these models are often trained on massive and diverse data-sets, allowing them to capture fine-grained linguistic features and provide accurate representations for words.

4

Experiments and Results

We undertook a series of experiments to evaluate how different methods of vector text representation affect Arabic L1 readability. We used the MoSAR corpus, which we randomly divided into two sets: 80% for training and 20% for testing. We opted to use a Random Forest classifier to conduct these experiments. We decided to split our corpus in this way rather than using a cross-validation method in order to accurately measure the impact of each vectoring approach on the same data-set. Table 2 shows the results, in terms of accuracy and F-score, of the different models. Table 2. Readability prediction results Feature-set

Accuracy F-score

Hand crafted features 75.0

74.11

CBOW

71.66

71.32

Skip-gram

74.16

73.69

AraBert 77.5 76.93 The bold values represent the percentage relative to the maximum values scores

The results show that the prediction of the difficulty level of L1 texts achieves a maximum F-score of 76.93% using the AraBert model. This represents a slight improvement over the model based solely on linguistic features. These results underline the importance of combining both linguistic features and word embedding to assess the readability of texts for L1 learners. This integration leads to more accurate predictions and opens up new perspectives in the field of readability assessment.

3

https://huggingface.co/aubmindlab/bert-base-Arabert.

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Conclusion and Future Works

In this paper, we present word embedding models for readability assessment. We explored the use of pre-trained contextual and non-contextual models such as CBOW, Skip-gram and AraBert to generate dense vector representations for word sequences. These models were evaluated on educational corpora specifically aimed at native language learners (L1), with annotations of difficulty levels. The results of our experiments revealed a significant improvement in readability assessment accuracy when using the AraBert model. This highlights the effectiveness of modeling techniques based on word embedding in the context of readability assessment for L1 learners. Our study could be further enhanced by combining word embedding representations with manually collected linguistic features to strengthen readability assessment. Further research is needed to determine which features have a significant impact on readability, and which additional features could be integrated in a more mother-tongue-appropriate way.

References 1. Al Khalil, M., Saddiki, H., Habash, N., Alfalasi, L.: A leveled reading corpus of modern standard Arabic. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 2018. European Language Resources Association (ELRA), Miyazaki, Japan 2. Crossley, S.A., Skalicky, S., Dascalu, M., McNamara, D.S., Kyle, K.: Predicting text comprehension, processing, and familiarity in adult readers: new approaches to readability formulas. Discourse Process. 54(5–6), 340–359 (2017) 3. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) 4. Imperial, J.M.: BERT embeddings for automatic readability assessment. arXiv preprint arXiv:2106.07935 (2021) 5. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) 6. Nassiri, N., Cavalli-Sforza, V., Lakhouaja, A.: MoSAR: modern standard Arabic readability corpus for L1 learners. In: Proceedings of the 4th International Conference on Big Data and Internet of Things. BDIoT’19. Association for Computing Machinery, New York, NY (2020) 7. Nassiri, N., Lakhouaja, A., Cavalli-Sforza, V.: Modern standard Arabic readability prediction. In: Lachkar, A., Bouzoubaa, K., Mazroui, A., Hamdani, A., Lekhouaja, A. (eds.) Arabic Language Processing: From Theory to Practice, pp. 120–133. Springer International Publishing (2018) 8. Nassiri, N., Lakhouaja, A., Cavalli-Sforza, V.: Combining classical and nonclassical features to improve readability measures for Arabic first language texts. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds.) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020) (2022) 9. Saddiki, H., Bouzoubaa, K., Cavalli-Sforza, V.: Text readability for Arabic as a foreign language. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8. IEEE (2015)

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10. Saddiki, H., Habash, N., Cavalli-Sforza, V., Al-Khalil, M.: Feature optimization for predicting readability of Arabic L1 and L2. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pp. 20–29 (2018)

Performance Analysis of Two Serial Concatenations of Decoders Over a Rayleigh Channel S. El Kasmi Alaoui1(B) , H. Faham2 , T. Chanyour1 , M. El Assad2 , Z. Chiba1 , and S. Nouh2 1 LIS Labs FSAC, Hassan II University, Casablanca, Morocco

[email protected] 2 LTIM Labs FSB, Hassan II University, Casablanca, Morocco

Abstract. The Hartmann & Rudolph (HR) decoder is a symbol-by-symbol decoding algorithm that relies on a probabilistic approach to compute the value of a symbol in a sequence. The HSDec and SDHT decoders are recent decoding algorithms based on hashing and syndrome calculation. The serial concatenation of two decoders consists of sequentially utilizing two decoders in the decoding process of a received sequence. The used concatenation aims at partially correcting the received sequence using HR algorithm, and then completing the decoding operation using HSDec or SDHT, depending on the studied context. In this paper, a performance study is presented for the HR decoder combined with the HSDec and SDHT decoding algorithms and applied to linear codes, specifically BCH codes having the length 31 and 63 through a Rayleigh channel. The simulation results have shown a significant coding gain. For example, when PHR-SDHT is used to decode the BCH(31, 16, 7) code, the coding gain reaches 34 dB compared to a situation when no coding is done by the transmitting and no decoding is performed on the receiving. Keywords: Hartmann Rudolph · PHR-HSDec · PHR-SDHT · BCH codes · Hash techniques · Syndrome calculation

1 Introduction Standard organizations play a crucial role in conceiving protocols for cellular networks. A significant interest for this community is error checking. Error-correcting codes [1– 10] are techniques used in information and coding theories to spot and correct errors that may occur during the transmission or storage of digital data. The primary goal of these codes is to ensure reliable and accurate data transmission in the presence of noise, interference, or other forms of corruption. In this study, we analyze the performance of two decoding schemes generated by means of a serial concatenation over Rayleigh channel [11–21]. The first concatenation is made by Hartmann Rudolph (HR) decoder [4] and a Hard In Hard Out (HIHO) algorithm © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 8–14, 2024. https://doi.org/10.1007/978-3-031-48573-2_2

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[22]. The second concatenation is about HR and a Soft In Hard Out (SIHO) decoder [23]. We organize the remainder of this paper in this way. The second part presents the Rayleigh fading, while the third one describes the used algorithms. As for the fourth part, it shows our simulation findings. Finally, a conclusion is given.

2 Rayleigh Fading In a wireless communication channel, signals are transmitted from a transmitter to a receiver over a medium that may introduce various impairments. One common impairment is the fading of the received signal, which refers to the fluctuation in the signal amplitude and phase as it propagates through the channel. Rayleigh fading specifically occurs when there is no dominant line-of-sight path between the transmitter point and receiver point, and the received signal is a combination of multiple reflected and scattered signals reaching the receiver from various directions. This typically happens in urban environments or when the transmitting and receiving antennas are not in direct line of sight. The Rayleigh fading phenomenon is primarily a product of constructive and destructive interference of multiple reflected and scattered signals. Since these signals take different paths and experience different delays, their amplitudes add up in a random manner. As a result, the received signal strength varies over time, leading to fading. The statistical properties of Rayleigh fading are often given according to the Rayleigh distribution. The latter describes the amplitude of the received signal, assuming that the in-phase and quadrature components of the signal follow independent Gaussian distributions with zero mean and equal variance. Rayleigh fading is a fundamental concept in wireless communications and has significant implications for the design and performance evaluation of communication systems. It is commonly used as a model to analyze the performance of wireless systems, develop signal processing algorithms, and design error correction schemes to mitigate the effects of fading.

3 Used Decoding Algorithms and Serial Concatenation 3.1 Hartmann Rudolph Decoder Hartmann Rudolph (HR) decoder is a decoding algorithm used in digital communication systems. It adapts symbol-by-symbol decoder, meaning it processes symbols one at a time rather than entire blocks of data. The HR decoder utilizes a probabilistic approach to determine the most likely value of each symbol in the received sequence. It uses the totality of the codewords of the dual code in the decoding operation, which makes its run time complexity very high and useless for codes with a reduced coding rate [3, 4, 8].

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3.2 Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec) Decoder HSDec [22] is a HIHO algorithm. HSDec builds a hash table (TH) to stock up all corrigible error patterns. Moreover, it uses a hash function to determine the error pattern directly from the received word syndrome «S». The receiver computes «S» to reach its corresponding line in TH table. Using the hashing technique speeds up the error pattern search process and therefore reduces the time complexity of the decoding process. 3.3 Syndrome Decoding and Hash Techniques (SDHT) Decoder SDHT [23] is a SIHO algorithm. It is an efficient and fast soft-decision decoder applicable to linear block codes with extensive error correction capability. It can correct up to a threshold «th» errors (th ≥ t), where «t» is the code correction capability. As a result, we must stock up more than one error pattern per row in TH table. SDHT decoder is the soft decision version of the HSDec decoder, it is a powerful decoder with reduced time complexity [1, 3, 10, 23]. 3.4 Serial Concatenation In this paper, we analyze the performance of two decoders designed by means of a serial concatenation over Rayleigh channel [11–21]. The first concatenation is made by PHR and HSDec algorithms. The second one concerns PHR and SDHT decoders. These two decoding schemes are described in Fig. 1.

Fig. 1. Serial concatenation steps

The objective of this concatenation is to partially exploit the HR decoder to correct errors that do not exceed a certain reliability threshold in the received sequence. Then, the decoding process is completed using a word-by-word decoder, either HSDec or SDHT in this case.

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4 Simulation Results This section will be dedicated to presenting the simulation results of BCH codes of lengths 31 and 63 using the concatenation schemes presented above over a Rayleigh channel. It is noted that if the data is transmitted without coding on the transmitter and without decoding on the receiver over a Rayleigh channel, using BPSK modulation, the Bit Error Rate (BER) reaches 10−5 at a signal to noise ratio (SNR) value of 46 dB. BER is measured, for each bit energy to noise power spectral density ratio SNR = NEb0 , based on a minimum number equal to 200 residual errors and 1000 transmitted blocks. In Fig. 2(a), we plot error correction performance of PHR-SDHT applied for decoding certain BCH codes that have length 31. This figure illustrates that the most favourable outcomes are obtained by the BCH(31, 16, 7) and BCH(31, 11, 11). These two codes reach a BER equal to 1.3·10−5 for a SNR of 12 dB. The BCH(31, 21, 5) code, reaches a BER equal to 10−5 for a SNR equal to 17 dB. Accordingly, we can conclude that PHR-SDHT can guarantee a coding gain of 29 dB with the BCH(31, 21, 5) code over a Rayleigh channel. This gain can reach 34 dB when using the code BCH(31, 16, 7) or BCH(31, 11, 11) instead of the BCH(31, 21, 5) code. As for Fig. 2(b), it presents the performances of PHR-SDHT decoder applied to certain BCH codes of length 63 and shows that for a BER equal to 1.8·10−5 the BCH(63, 45, 7) code guarantees a coding gain approximately equal to 3.8 dB compared to BCH(63, 51, 5) code. It also clearly indicates that the BCH(63, 45, 7) code assures a best error correction performance compared with BCH(63, 51, 5) when it is used with the PHRHSDec decoder over Rayleigh channel. It is also worth mentioning that with a BER value equal to 10−5 , the coding gain guaranteed by BCH(63, 45, 7) code reaches 29 dB.

Fig. 2. Performance of PHR-SDHT over Rayleigh channel for BCH codes of length (a) 31 and (b) 63

Figure 3, which presents a performance comparison between PHR-HSDec and PHRSDHT over Rayleigh channel for BCH(31, 21, 5) code, clearly shows that the performances guaranteed by the PHR-SDHT decoder applied to the BCH(31, 21, 5) code are much better than those guaranteed by PHR-HSDec. Such a finding can be justified by the fact that the second concatenation scheme uses a HIHO decoder, while the first scheme uses a SIHO decoder enabling it to improve the correction performance.

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Fig. 3. Performance comparison between PHR-HSDec and PHR-SDHT over Rayleigh channel for BCH(31, 21, 5) code

5 Conclusion In this paper, we presented simulation results of the HR decoder, partially exploited, combined with the HSDec and SDHT decoders to decode BCH codes of lengths 31 and 63 over a Rayleigh channel. The idea behind this concatenation is to partially exploit the powerful HR decoder, which has a very high temporal complexity and therefore is impractical for codes with a reduced coding rate. To complete the decoding process, the sequence, partially processed by HR, is then sent to a second decoder: HSDec or SDHT. These decoders are recent, powerful, and general. Moreover, they have a reduced temporal complexity compared to their competitors. The study conducted in this paper focuses particularly on the Rayleigh channel, which is a channel model that takes into account the effects of multipath propagation in wireless communications. The simulation results have shown a significant coding gain compared to the case where neither coding nor decoding were performed.

References 1. El Kasmi Alaoui, S., Chiba, Z., Faham, H., El Assad, M., Nouh, S.: Efficiency of two decoders based on hash techniques and syndrome calculation over a Rayleigh channel. Int. J. Electr. Comput. Eng. 13(2), 1880–1890 (2023). https://doi.org/10.11591/ijece.v13i2.pp1880-1890 2. Seddiq El Kasmi Alaoui, M., Nouh, S., Marzak, A.: Fast and efficient decoding algorithm developed from concatenation between a symbol-by-symbol decoder and a decoder based on syndrome computing and hash techniques. In: Advances in Intelligent Systems and Computing, vol. 1076, pp. 121–129 (2020). https://doi.org/10.1007/978-981-15-0947-6_12 3. Faham, H., El Kasmi Alaoui, M.S., Nouh, S., Azzouazi, M.: High performance decoding by combination of the Hartmann Rudolph decoder and soft decision decoding by hash techniques. In: Lecture Notes in Networks and Systems. LNNS, vol. 211, pp. 781–790 (2021). https:// doi.org/10.1007/978-3-030-73882-2_71 4. Faham, H., Alaoui, M.S.E.K., Nouh, S., Azzouazi, M.: An efficient combination between Berlekamp-Massey and Hartmann Rudolph algorithms to decode BCH codes. Period. Eng. Nat. Sci. 6(2), 365–372 (2018). https://doi.org/10.21533/pen.v6i2.540

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5. El Kasmi Alaoui, M.S., Nouh, S., Marzak, A.: A low complexity soft decision decoder for linear block codes. Procedia Comput. Sci. 127, 284–292 (2018). https://doi.org/10.1016/j. procs.2018.01.124 6. Faham, H., Nouh, S., Alaoui, M.S.E.K., Sadiq, M., Azzouazi, M.: New way to enumerate large quadratic residue codes based on hash and automorphism group. In: Lecture Notes in Networks and Systems. LNNS, vol. 357, pp. 545–556 (2022). https://doi.org/10.1007/978-3030-91738-8_50 7. Imrane, C.I., Said, N., El Mehdi, B., Seddiq, E.K.A., Abdelaziz, M.: Machine learning for decoding linear block codes: case of multi-class logistic regression model. Indones. J. Electr. Eng. Comput. Sci. 24(1), 538–547 (2021) 8. Faham, H., El Kasmi Alaoui, M.S., Nouh, S., Azzouazi, M., Joundan, I.A.: High speed decoding by collaboration between the Hartmann Rudolph and information set decoding algorithms. J. Theor. Appl. Inf. Technol. 100(17), 5377–5385 (2022) 9. Askali, M., Nouh, S., Belkasmi, M.: An efficient method to find the minimum distance of linear block codes. In: Proceedings of 2012 International Conference on Multimedia Computing and Systems, Tangiers, Morocco, pp. 318–324 (2012) 10. Faham, H., El Kasmi Alaoui, S., El Assad, M., Nouh, S., Chana, I., Azzouazi, M.: A new fast iterative decoder of product codes based on hash and syndromes and optimized by genetic algorithms. Int. J. Eng. Trends Technol. 70(12), 289–295 (2022). https://doi.org/10.14445/ 22315381/IJETT-V70I12P227 11. Awon, N.T., Rahman, M.M., Islam, M.A., Touhidul, A.Z.M.: Effect of AWGN & fading (Raleigh & Rician) channels on BER performance of a WiMAX communication system. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 10(8), 11–17 (2012) 12. Vidhya, K., Shankar, K.R.: BER performance of AWGN, Rayleigh and Rician channel. Int. J. Adv. Res. Comput. Commun. Eng. 2(5), 2058–2067 (2013) 13. Yang, P., Ou, Z., Yang, H.: Capacity of AWGN and Rayleigh fading channels with M-ary inputs. In: 29th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Bologna, Italy, 9–12 Sept 2018 14. Al-Hussaibi, W.A., Ali, F.H.: Generation of correlated Rayleigh fading channels for accurate simulation of promising wireless communication systems. Simul. Model. Pract. TheoryPract. Theory 25, 56–72 (2012). https://doi.org/10.1016/j.simpat.2012.01.009 15. Shaik, T., Dhuli, R.: Outage performance of multi cell-NOMA network over Rician/Rayleigh faded channels in interference limited scenario. Int. J. Electron. Commun. 145 (2022) 16. Waseem, R., Nasir, H., Javaid, N.: Unification of RF energy harvesting schemes under mixed Rayleigh-Rician fading channels. Int. J. Electron. Commun. 123 (2020). https://doi.org/10. 1016/j.aeue.2020.153244 17. Nguyen, T.N., Tran, P.T., Voznak, M.: Wireless energy harvesting meets receiver diversity: a successful approach for two-way half-duplex relay networks over block Rayleigh fading channel. Comput. Netw. 172 (2020). https://doi.org/10.1016/j.comnet.2020.107176 18. Le, K.N.: Distributions of multiuser-MIMO under correlated generalised-Rayleigh fading. Signal Process. 150, 228–232. https://doi.org/10.1016/j.sigpro.2018.04.011 19. Mamidi, R., Sundru, A.: Throughput analysis in proposed cooperative spectrum sensing network with an improved energy detector scheme over Rayleigh fading channel. Int. J. Electron. Commun.Commun. 83, 416–426 (2018). https://doi.org/10.1016/j.aeue.2017.09.008 20. Ying, W., Jiang, Y., Liu, Y., Li, P.: A blind detector for Rayleigh flat-fading channels with nonGaussian interference via the particle learning algorithm. Int. J. Electron. Commun.Commun. 67(12), 1068–1071 (2013). https://doi.org/10.1016/j.aeue.2013.06.009 21. Singya, P.K., Kumar, N., Bhatia, V., Khan, F.A.: Performance analysis of OFDM based 3-hop AF relaying network over mixed Rician/Rayleigh fading channels. Int. J. Electron. Commun.Commun. 93, 337–347 (2018). https://doi.org/10.1016/j.aeue.2018.06.026

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22. El Kasmi Alaoui, M.S., Nouh, S., Marzak, A.: Two new fast and efficient hard decision decoders based on hash techniques for real time communication systems. In: MizeraPietraszko, J., Pichappan, P., Mohamed, L. (eds.) Lecture Notes in Real-Time Intelligent Systems. RTIS. Advances in Intelligent Systems and Computing, vol. 756, pp. 448–459. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_40 23. El Kasmi Alaoui, M.S., Nouh, S., Marzak, A.: High speed soft decision decoding of linear codes based on hash and syndrome decoding. Int. J. Intell. Eng. Syst. 12, 94–103 (2019). https://doi.org/10.22266/ijies2019.0228.10

Blockchain and Reputation Based Secure Service Provision in Edge-Cloud Environments Tarik Chanyour1(B) , Seddiq El Kasmi Alaoui1 , Abdelhak Kaddari2 , Youssef Hmimz3 , and Zouhair Chiba1 1

2

LIS Lab, FSAC, Hassan II University, Casablanca, Morocco [email protected] LMSC Lab, ENSA, Cadi Ayyad University, Marrakech, Morocco 3 FSDM, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. A method based on reputation and blockchain has been proposed for secure service provision within an edge-cloud framework in dense small cell networks. A new approach to secure service provision through the distribution of containers’ base image across multiple edgecloud nodes has been introduced. This proposal signifies an advancement in enhancing security and efficiency in service delivery. The proposed architecture aims to optimize service initiation and collecting times with an emphasis on addressing security concerns. Accordingly, the proposed solution is advantageous regarding secure container-based service provision for the smart mobile devices within a multi-access edge computing network.

Keywords: Caching

1

· Blockchain · Edge-cloud · Node reputation

Introduction

Multi-access Edge Computing (MEC) [10] is a distributed computing architecture system that facilitates efficient delivery of high-bandwidth and low-latency services to end-users and IoT devices. It enhances real-time data processing, storage [4,7], and analytics by situating resources at the network edge, such as base stations or edge servers [6]. Applications range from augmented reality to autonomous vehicles and smart factories, with MEC reducing data latency and bandwidth problems by localizing processing and analysis. MEC can work with multiple wireless technologies and can be implemented in public and private networks [2]. Dense Small Cell (DSC) networks [3], another cellular network architecture, cater to high-capacity and high-speed connectivity demands by deploying numerous small cell base stations within a confined area. They necessitate fast service virtualization and provisioning for a seamless user experience. Service virtualization [8] in DSC networks allows efficient, rapid service provisioning without physical hardware deployment. By using containers, a type of c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 15–20, 2024. https://doi.org/10.1007/978-3-031-48573-2_3

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virtual software component, operators can deploy and manage services flexibly, swiftly, and efficiently, thereby adapting to user demands and traffic changes. Furthermore, incorporating Blockchain and caching [1,5,11], container data is divided and distributed across nodes to strengthen integrity checks. This procedure improves privacy, reliability, and performance, while reducing network congestion. Container images are distributed, verified, and orchestrated, complementing the MEC and DSC networks’ service flexibility and scalability. This article puts forth a method that integrates both service demand reputation and blockchain technology, tailored specifically for edge-cloud service provision within a DSC network. This method introduces an innovative strategy, diverging from conventional means. At its core, the method prioritizes the security of service provision by employing a distinct distribution mechanism. Instead of relying on centralized systems, it disperses Containers’ Base Images Chunks (CBICs) across ECNs. This dispersion not only bolsters the efficiency and speed of access but also fortifies the security, making it harder for potential threats to compromise the integrity of the entire system. Indeed, it permit to avoid DDoS attacks, IP-based attacks, data tampering attacks, reputation-based attacks, and content spoofing attacks. The remaining sections of this work are organized as follows. The framework under study is described in Sect. 2. The blockchain integration description is presented in Sect. 3. Finally, Sect. 4 concludes the paper and provides a preview into potential future directions.

2

The Proposed System Architecture

The main components of the proposed framework are shown in Fig. 1. 2.1

Main Components

The proposed framework, focusing on edge-cloud base image caching and sharing with smart duplication, utilizes a blockchain network to secure the exchange of CBICs among ECNs. It’s designed to provide container-based services to Smart Mobile Devices (SMDs) using a tiered architectural approach. It depends on a Remote Cloud Server (RCS), several Small Base Stations (SBS) controlled by many Macro Base Stations (MBSs) where each one oversees multiple SBSs within its coverage area. Each ECN is equipped with an Edge-Cloud Server (ECS) hosted in a Macro/Small Base Station that offers access to the wireless communication network for all SMDs in its coverage. The SMDs get access to the ECNs via wireless channels, while the ECNs are connected to each other in wired manner using high speed Ethernet cables or optical fibers. Regarding service delivery, a specific ECN caters to the SMDs within its coverage zone as well as to those in distant areas through neighboring ECNs. Also, each ECN can provide a set of independent container-based services where each running service uses a container instance and serves one SMD only. Every involved container is divided into multiple chunks based on the used chunks’ size; then each chunk

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Fig. 1. Blockchain based multi-tier framework

is duplicated D times. In this environment, all D duplicates of every chunk are distributed among the network such that every MBS handles one duplicate only. 2.2

Service Model

When a service’s CBI is sought from a requester SBS, a serie of verifications and steps occur. First, the SBS checks its local content store for the requested service. If found, the request is instantly addressed. If absent, the SBS liaises with its master MBS to retrieve any available content, incurring a transmission delay. If the master MBS doesn’t have some of the content, it initiates a fetch process among the subordinate SBSs, introducing an additional delay. Should the master MBS completely lack the content, it then resorts to obtaining it from the RCS provider, which results in another transmission delay. The entire procedure ensures that all of the service’s CBICs are gathered, with every piece of content being fetched and verified securely using blockchain technology. The regional geo-distribution of chunks within each MBS enhances availability, leading to a reduction in the collection time for various chunks of the requested CBI.

3 3.1

The Blockchain Integration Blockchain Components

MBSs and SBSs function as blockchain nodes (peers) handling image caching/sharing and managing transactions with the help of a trusted authority [9] where every node can serve as both a requester and a provider. Hereafter the

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roles of these components are differentiated as follow: Trusted Authority: TA It is used in our framework to oversee access and security. It authenticates nodes’ identities, manages cryptographic keys, disperse digital credentials, and ensures compliance with established policies and standards. Also, it is used as the proposed scheme utilizes a proof of authority (PoA) consensus process. Specifically, the TA validates the identity of the requesters and confirms their authorization for network access. Macro Base Station: MBS From the perspective of edge-cloud network functions, a MBS is responsible for the CBICs geo-distribution with caching function and the optimization of the CBICs collection processes to reduce the service starting time. From the perspective of blockchain network functions, a MBS is responsible for managing the CBI trading of their slave SBSs. Thus, a MBS is responsible for creating, recording, and auditing blocks, which helps to save computation resources and reduce the latency of the consensus process. A MBS has three exclusive components: the transaction manager, the account list, and the transaction list. The transaction manager collects CBIC requests and finds CBIC providers while maintaining a CBIC list recording the stored CBICs’ information. The account list records the accounts for SBSs under its management where random pseudonyms are employed for privacy. The transaction list stores all transaction records. Considering the MBS’s capability, it is selected to manage the image trading of SBS within range. Essentially, MBSs are responsible for gathering transactions, forming blocks, and appending them to the blockchain. Furthermore, a leader MBS is designated by the proposed framework’s TA to validate blocks’ commitment. Small Base Station: SBS From the perspective of edge-cloud network functions, a SBS is responsible for caching/sharing CBICs as well as managing service initiation requests from SMDs that are relayed through the slave SBS. From the perspective of blockchain network functions, a SBS acts as Blockchain Node (BCN) that initiates transactions and can act as both CBIC requester and provider. If a SBS does not have the required CBI, it becomes a CBI requester, and the ECNs that store the required CBICs are providers. 3.2

Consensus Process

Based on the PoA consensus process [9] and during each round, the source MBS gathers transactions and sends the resultant block, along with its signature, to adjacent MBSs for confirmation and validation. These adjacent MBSs evaluate the block’s integrity and disseminate their assessment findings with digital signatures to other proximate MBSs for collective oversight. Upon acquiring these assessment results, each adjacent MBS contrasts its audit with those of its peers. If the transaction and the signature both check out, they endorse the block and

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relay their decision to the source MBS. This acknowledgment encapsulates the audit outcome, signature, and resultant decisions. The source MBS then examines the feedback and if valid votes are received from P0 % MBSs, the block is deemed legitimate and is integrated into the global chain. Here the threshold percentage P0 % is chosen such that P0 > 50. 3.3

Blockchain Management

Initially, every BS submits a request to the TA to obtain an account address, along with a paired set of public and private keys. Within the network, the account address acts as a distinct identifier for the requester. Meanwhile, the purpose of the public and private keys ensure secure communications and transactions. While the public key is disseminated amongst network entities, the private key remains confidential, solely used by the requester for transaction signatures and identity verification. Moreover, the five key operations related to the blockchain management part include registration, role recognition, image trading, block creating, and block commitment [12]. The resulting data for each BS after the registration operation includes asymmetric key pairs for security, pseudonyms to ensure privacy, wallet addresses to oversee CBICs trades, and data about node allocation. The role recognition operation occurs once a MBS receives a CBI request that is broadcasted to fetch the involved CBICs which determines all provider SBSs. The image trading operation take place when all providers send the sought-after CBICs. The requester SBS then authenticates the received CBICs using asymmetric cryptography techniques and bears the corresponding expense. A transaction is crafted detailing elements like the image IDs, encrypted signatures, trading counterparties’ accounts data, transaction timestamp, and payments details. This transaction is then broadcast to all MBSs. Operation 4 involves the creation of blocks, wherein MBSs gather and validate transaction records, encrypt data and make digital signing, and compute a hash value to ensure traceability as well as security. In the final block commitment operation, the leader MBS broadcasts the results of block creation to the other MBSs. Each receiving MBS verify the block data and compare it with its stored information. If a consensus is reached among all MBSs regarding the created block, the leader MBS notifies all peers to add the block to the image blockchain.

4

Conclusions and Perspectives

This paper proposed a reputation and blockchain-based method within an edgecloud based service provision in a DSC network. A new approach to secure service provision through the distribution of containers’ base image chunks across multiple edge-cloud nodes has been introduced. This proposal signifies an advancement in enhancing security and efficiency in service provision and starting time within DSC networks. The proposed architecture aims to enhance service provision security and privacy as well as to optimize service initiation and collecting times using the blockchain technology.

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In upcoming research, we intend to explore the practical effectiveness of the proposed scheme in real-world settings.

References 1. Aghazadeh, R., Shahidinejad, A., Ghobaei-Arani, M.: Proactive content caching in edge computing environment: a review. Softw. Pract. Exp. 53(3), 811–855 (2023) 2. Chanyour, T., Hmimz, Y., El Ghmary, M., Malki, M.O.C.: Delay-aware and useradaptive offloading of computation-intensive applications with per-task delay in mobile edge computing networks. Int. J. Adv. Comput. Sci. Appl. 11(1) (2020) 3. Chen, L., Shen, C., Zhou, P., Xu, J.: Collaborative service placement for edge computing in dense small cell networks. IEEE Trans. Mob. Comput. 20(2), 377– 390 (2019) 4. El Ghmary, M., Hmimz, Y., Chanyour, T., Malki, M.O.C.: Energy and processing time efficiency for an optimal offloading in a mobile edge computing node. Int. J. Commun. Netw. Inf. Secur. 12(3), 389–393 (2020) 5. Guo, J., Li, C., Luo, Y.: Blockchain-assisted caching optimization and data storage methods in edge environment. J. Supercomput. 78(16), 18225–18257 (2022) 6. Hmimz, Y., Chanyour, T., El Ghmary, M., Malik, M.O.C.: Energy efficient and devices priority aware computation offloading to a mobile edge computing server. In: 2019 5th International Conference on Optimization and Applications (ICOA), pp. 1–6. IEEE (2019) 7. Hmimz, Y., El Ghmary, M., Chanyour, T., Malki, M.O.C.: Computation offloading to a mobile edge computing server with delay and energy constraints. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp. 1–6. IEEE (2019) 8. Kaliappan, V.K., Yu, S., Soundararajan, R., Jeon, S., Min, D., Choi, E.: Highsecured data communication for cloud enabled secure docker image sharing technique using blockchain-based homomorphic encryption. Energies 15(15), 5544 (2022) 9. Miglani, A., Kumar, N.: Blockchain-based co-operative caching for secure content delivery in CCN-enabled V2G networks. IEEE Trans. Veh. Technol. (2022) 10. Pham, Q.V., et al.: A survey of multi-access edge computing in 5G and beyond: fundamentals, technology integration, and state-of-the-art. IEEE Access 8, 116974– 117017 (2020) 11. Wang, G., Li, C., Huang, Y., Wang, X., Luo, Y.: Smart contract-based caching and data transaction optimization in mobile edge computing. Knowl.-Based Syst. 252, 109344 (2022) 12. Zhou, A., Li, S., Ma, X., Wang, S.: Service-oriented resource allocation for blockchain-empowered mobile edge computing. IEEE J. Sel. Areas Commun. 40(12), 3391–3404 (2022)

Exploring the Impact of Convolutions on LSTM Networks for Video Classification Manal Benzyane1(B) , Mourade Azrour2 , Imad Zeroual2 , and Said Agoujil1 1 MMIS, MAIS, FST Errachidia, Moulay Ismail University, Meknes, Morocco

[email protected]

2 STI, IDMS, FST Errachidia, Moulay Ismail University, Meknes, Morocco

[email protected]

Abstract. Video classification plays a foundational role within the field of computer vision, that involves categorizing and labeling videos based on their content. Its significance is evident in a wide array of applications, encompassing video surveillance, content recommendation, action recognition, video indexing, and more. The goal of video classification is to automatically analyze and understand the visual information present in videos, enabling efficient organization, retrieval, and interpretation of large video collections. The fusion of convolutional neural networks (CNNs) and long short term memory (LSTM) networks has revolutionized the field of video classification by effectively capturing both spatial and temporal dependencies within video sequences. This fusion combines the strengths of CNNs in extracting spatial features and LSTMs in modeling sequential and temporal information. Two widely adopted architectures that incorporate this fusion are ConvLSTM and LRCN (Long-term Recurrent Convolutional Networks). This paper aims to explore the impact of convolutions on LSTM networks in the context of video classification and compare the performance of ConvLSTM and LRCN. Keywords: Video classification · Convolution · LSTM · ConvLSTM · LRCN

1 Introduction Video classification is a critical task in computer vision, involving the labeling of videos based on their content. Deep learning has made significant strides in this field, with ConvLSTM and LRCN being prominent architectures. The fusion of CNNs and LSTMs, seen in ConvLSTM and LRCN, has greatly influenced video classification. ConvLSTM extends LSTM by integrating convolutional operations within its cells. This combination allows ConvLSTM to learn spatial representations directly from video sequences and model long-term temporal dependencies. ConvLSTM’s convolutional operations extract spatial features, capturing frame-level patterns and object information. LSTM cells then process these spatial features while preserving temporal dynamics, enabling recognition of complex actions [1]. On the other hand, LRCN follows a sequential approach. It uses CNNs initially to capture frame-level characteristics, which encode spatial information. These features are then fed into an LSTM network to capture temporal dependencies between frames. LRCN leverages CNNs for spatial details and LSTMs for temporal context, allowing it to understand movement patterns and temporal context [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 21–26, 2024. https://doi.org/10.1007/978-3-031-48573-2_4

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Following this introduction, the subsequent sections of this paper are organized as follows. Section 2 provides an overview of the methodology employed, including a description of the datasets used. Additionally, it presents the theoretical background of key architectures such as CNN, LSTM, ConvLSTM, and LRCN. In Sect. 3, we present the main findings obtained from the classifiers’ performance evaluation using different datasets. Finally, Sect. 4 wraps up the paper by providing a summary of the key findings and offering perspectives for future research endeavors.

2 Methodology 2.1 Dataset In this paper, we utilized three datasets for our experiments: UCF11, UCF50, and DynTex. UCF11: The UCF11 dataset contains a total of 1600 video clips. These video clips are distributed across the 11 different action categories [3]. It is a benchmark dataset widely used in the field of video classification. The dataset encompasses a wide range of human activities, encompassing sports and everyday actions such as basketball, biking, diving, golf swing, horse riding, soccer juggling, swinging, and tennis swing etc. [4]. UCF50: The UCF50 dataset is a prominent benchmark in the field of video classification, encompassing a diverse range of 50 action categories that consist of realistic videos taken from YouTube [3]. It comprises a total of 6618 videos. The action categories within the UCF50 dataset covers a diverse range of activities, including applying eye Makeup, applying lipstick, Band Marching, Archery, Baby Crawling, Balance Beam, Basketball Dunk, Band Marching, Baseball Pitch, Basketball, Basketball Dunk, Bench Press, Biking, and Billiards and Bowling, etc. DynTex: The DynTex dataset is a collection of video sequences focused on dynamic tissues. The dataset consists of 522 videos of dynamic texture. These videos are distributed across five distinct dynamic textures: Clouds-Steam, Flags, Fire, Water, and Trees. The dataset is commonly used in the field of computer vision for tasks such as dynamic texture analysis, classification, and recognition. The DynTex dataset serves as a valuable resource for the study and development of algorithms that can effectively capture and analyze the temporal properties of dynamic textures in videos [5]. 2.2 Convolutional Neural Networks Deep learning models, called convolutional neural networks architectures, are widely used in computer vision tasks. They excel in capturing valuable information from visual data, particularly in image-related tasks such as classification, detection, and recognition. CNNs consist of convolutional layers that apply filters for processing input images, pooling layers that decrease spatial dimensions, and fully connected layers for tasks like classification or regression [6, 7] (Fig. 1). In the field of video classification, CNNs have demonstrated their effectiveness by capturing both spatial and temporal information. They operate on individual frames

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Fig. 1. Architectures of convolutional neural networks [8]

within a video, extracting frame-level features that capture visual patterns. To incorporate temporal information, recurrent neural networks (RNNs) or Long Short-Term Memory networks are frequently utilized. These networks process sequential frames and capture temporal dependencies. 2.3 Long Short-Term Memory LSTM, is a specialized type of recurrent neural network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. It uses a memory cell and gating mechanisms to effectively capture extended long-term dependencies within sequential data. LSTM excels in tasks involving temporal relationships, such as speech recognition and video analysis. Its chained structure allows for the retention and updating of relevant information at each step, making it a widely adopted choice in various domains [5]. The chained structure of LSTM, as depicted in Fig. 2, highlights the sequential flow of information within the network, enabling it to capture and retain relevant contextual information over time.

Fig. 2. Structure of an LSTM network [9]

2.4 Convolutional Long Short-Term Memory (ConvLSTM) Convolutional Long Short-Term Memory represents a refinement of the conventional Long Short-Term Memory architecture. It introduces a modification to the LSTM module by replacing fully-connected gates with convolutional gates. This means that instead of using matrix multiplication, ConvLSTM employs convolution operations at each gate. This modification allows ConvLSTM to incorporate spatial information and capture

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spatial dependencies within sequential data, such as video or spatio-temporal sequences. By utilizing convolution operations, ConvLSTM takes advantage of the local receptive fields and shared weights present in convolutional neural networks, enhancing its ability to learn spatial representations and capture long-term dependencies in the data [10] (Fig. 3).

Fig. 3. ConvLSTM cell architecture [11]

2.5 Long-Term Recurrent Convolutional Networks (LRCN) LRCN takes a sequential approach in video classification. It begins by leveraging CNNs to extract features at the frame level from each individual video frame. These extracted features are subsequently passed through an LSTM network, enabling the modeling of temporal dependencies that exist between the frames. By sequentially analyzing the frame-level features, LRCN effectively captures the dynamic and contextual information present in video sequences, enhancing its ability to classify and understand video content [12] (Fig. 4).

Fig. 4. Architectures of LRCN [12]

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3 Results and Discussion We performed experiments using ConvLSTM and LRCN models in our study. Initially, we applied these models to the UCF11 dataset and later extended our analysis to the UCF50 and DynTex datasets. Through a comparative analysis of the performance of ConvLSTM and LRCN models on these datasets, we gained insights into the convolutional effect on LSTM networks. Specifically, we investigated the effect of combining CNNs and LSTMs simultaneously, and when using CNNs as an initial step to extract frame-level features from individual video frames, followed by applying LSTMs (Table 1). Table 1. Comparison of ConvLSTM and LRCN on data Method

DynTex

UCF11

UCF50

ConvLSTM

0.56

0.62

0.79

LRCN

0.71

0.77

0.93

For the DynTex dataset, the ConvLSTM model achieved an accuracy of 0.56, while the LRCN model achieved a higher accuracy of 0.71. In the UCF11 dataset, the ConvLSTM model had an accuracy of 0.62, while the LRCN model performed better with an accuracy of 0.77. In the UCF50 dataset, the ConvLSTM model had an accuracy of 0.79, while the LRCN model performed better with an accuracy of 0.93. These results indicate that the LRCN model consistently outperformed the ConvLSTM model across all three datasets.

4 Conclusion In conclusion, video classification is a significant research field in computer vision and multimedia content interpretation, offering both challenges and opportunities for robust classification models. Our study investigated the impact of convolutions on LSTM networks for video classification and found that utilizing CNNs for initial feature extraction followed by LSTMs improves results compared to using CNNs and LSTMs simultaneously. This sequential approach enhances performance and accuracy. Considering the results of our pilot study, opportunities for improvement still exist in video classification through research in various CNN and LSTM architectures, optimizing their parameters to further improve the sequential approach, thus contributing to the ongoing progress in this field.

References 1. Papers with Code - ConvLSTM Explained. https://paperswithcode.com/method/convlstm. Consulté le 1 juillet 2023

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2. Tsang, S.-H.: Brief review—LRCN: long-term recurrent convolutional networks for visual recognition and…. Medium, 18 Sept 2022. https://sh-tsang.medium.com/brief-review-lrcnlong-term-recurrent-convolutional-networks-for-visual-recognition-and-9542bc7e8a79. Consulté le 1 juillet 2023 3. Zebhi, S., AlModarresi, S.M.T., Abootalebi, V.: Action recognition in videos using global descriptors and pre-trained deep learning architecture. In: 2020 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, pp. 1–4. IEEE (2020). https://doi.org/10.1109/ ICEE50131.2020.9261038 4. Cheng, Y., Yang, Y., Chen, H.-B., Wong, N., Yu, H.: S3-Net: a fast and lightweight video scene understanding network by single-shot segmentation. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, pp. 3328–3336. IEEE (2021). https://doi.org/10.1109/WACV48630.2021.00337 5. Benzyane, M., Zeroual, I., Azrour, M., Agoujil, S.: Convolutional long short-term memory network model for dynamic texture classification: a case study. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. Lecture Notes in Networks and Systems, pp. 383–395. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-26384-2_33 6. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), Art. No. 7553 (2015). https://doi.org/10.1038/nature14539 7. Brinker, T.J., et al.: Skin cancer classification using convolutional neural networks: systematic review. J. Med. Internet Res. 20(10), e11936 (2018). https://doi.org/10.2196/11936 8. Ratan, P.: What is the convolutional neural network architecture? Analytics Vidhya, 28 Oct 2020. https://www.analyticsvidhya.com/blog/2020/10/what-is-the-convolutional-neuralnetwork-architecture/. Consulté le 14 mai 2023 9. LSTM Neural Network, Big Data Mining & Machine Learning, 28 avr 2018. www.big-data. tips. http://www.big-data.tips/lstm-neural-network. Consulté le 27 juin 2023 10. Ye, W., Cheng, J., Yang, F., Xu, Y.: Two-stream convolutional network for improving activity recognition using convolutional long short-term memory networks. IEEE Access 7, 67772– 67780 (2019). https://doi.org/10.1109/ACCESS.2019.2918808 11. Sun, H., Yang, Y., Chen, Y., Liu, X., Wang, J.: Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model. Inf. Technol. Tour. 1–29 (2023). https://doi.org/10.1007/s40558-023-00247-y 12. Ko, B.: Long-term Recurrent Convolutional Network (LRCN). Home, 16 Oct 2017. https:// kobiso.github.io//research/research-lrcn/. Consulté le 1 juillet 2023

Classification Algorithms Implementation for Fire Prevention Data on Multiple Wireless Node Sensors Oumayma El Gatte1 , Ahmed El Abbassi2(B) , Omar Mouhib1 , and Dahbi Aziz3 1 Laboratory of Electronics Systems, Systems, Information Processing, Mechanics and

Energetic, Faculty of Sciences, Ibn Tofail University, Campus, Kenitra, Morocco {Oumayma.elgatte,mouhib.omar}@uit.ac.ma 2 Team Renewable Energies and Information Processing and Transmission Laboratory, Faculty of Sciences and Technologies, Moulay Ismail University, Errachidia, Morocco [email protected] 3 Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco [email protected]

Abstract. The Theory of probability is considered as a key of artificial intelligence and specially the probability density. In the paper we raise the Decision Theory that used on the most of WSN (Wireless Sensor Network), it is combined with the probability theory in order to make optimal decisions in critical situations like fire detection. Therefore, we will see a comparison between of the Maximization Likelihood Estimation (MLE), the cumulative sum (CUSUM) and Bayesian inference. Keywords: Theory of probability · CUSUM algorithm · MLE algorithm · Bayesian inference · Fire detection

1 Introduction Wireless Sensor Networks (WSN) adapt to societal needs, enhancing daily life. The Maximization Likelihood Estimation (MLE) algorithm is vital in data classification, machine learning, and medical imaging like tomographic reconstruction. The CUSUM algorithm conserves computing resources, ideal for resource-constrained settings. Bayesian algorithms excel in statistical inference, efficiently converting vast data into recommendations or classifications. They calculate conditional probabilities, increasing accuracy in spam filters, search engines, and medical diagnostics. These techniques empower technology to meet evolving demands, ensuring a brighter future for WSN applications.

2 Theory of Probability The theory of probability is affiliation of mathematics; it has used all the most for pattern recognition. Which considered as Framework in order to quantify and manipulate Uncertainty [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 27–34, 2024. https://doi.org/10.1007/978-3-031-48573-2_5

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• Sum rule: p(X) =



Yp(x, y)

(1)

• Product rule: p(X, Y) = p(Y|X) ∗ p(X)

(2)

With the symmetry property, we obtain: p(y|x) =

P(X |Y )P(Y ) P(X )

(3)

• Probability densities: For over discrete sets of events we define probabilities, which also defined for continuous variables:  a P(x(a, b)) = p(x)dx (4) b

Let be the probability density p(x) for a continuous variable (x), on the interval [x, x + δ] with p(x) δx for δx → 0 P(x) ≥ 0 

∞

−∞

(5)

p(x) = 1

(6)

• Gaussian Distribution For the variable x: 1



1 2 N(x|μ, σ ) =   1 exp − 2σ 2 (x − μ) 2 2 2π σ 2

 (7)

μ: mean σ2 : variance β = σ12 precision.

3 Likelihood Algorithm Likelihood is one of the statistical designs is used in order to process a wide range of data. The concept that emerged slowly from the principles of Brenoulli and Gauss. In 1977, it was developed by Dempster to obtain the expectation maximization algorithm or MLE algorithm, is a general technique, which is powerful and elegant for finding maximum likelihood solutions for probabilistic models having latent variables. Assuming that the true distribution of the parameters is valid, it is possible to estimate the parameters of a regression model. The aim of maximum likelihood is to select the parameter with a very high probability of success. We assume that the complete data-set consists of Z = (X, Y) but that only X is observed. The complete-data log likelihood is then called by l (θ; X, Y). So we wish to find the MLE of θ, which is the unknown parameter vector [2]. Two initial steps are required:

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29

E-step: (Estimation step) of MLE algorithm, which computes the expected values of l (θ; X, Y) for the observed data, X with the current parameter is θold so: Q(θ; θold ) = E[l(θ; X, Y)|X, θold ]  = l(θ; X, Y) P(Y|X, θold )dy

(8)

P(Y|X, θold ): the conditional density of Y for observed data X with θ = θold . M-step: This step is used for maximizing over θ the expectation computed θnew = max(θ; θold ) θ

(9)

where θold = θnew . The two steps are then repeated until the sequence of x converges. If there is any doubt as to whether the function has more than one maximum the MLE algorithm is repeated several times, using different values of x each time [3]. By substituting the Gaussian distribution with the Likelihood algorithm, we obtain: 

 1 1  Xt − μ 2 2 N(x, μ, σ ) = √ t exp − (10) 2 σu 2Π σut 

 1 1  Xt − μ 2 2 L(μ, σu ) = √ t exp − (11) 2 σu 2Π σut

4 Bayesian Inference Classical probabilities rely on event frequencies. Bayesian inference extends likelihood with prior probabilities, representing parameter values’ probabilities. Priors express initial beliefs about parameters. Likelihood fine-tunes priors through data, yielding a posterior distribution. In ideal conditions, successive data collection triggers further updates, allowing Bayesian probability’s elegant adaptability. It quantifies uncertainty when new evidence emerges, facilitating optimal decision-making. Observed data X = {x1 , …, xn }, where p(X, Y) signifies the joint probability of X and Y. The symmetry property makes p(Y|X) the conditional probability “Y given X,” while p(X) is “the probability of X.” This symmetry leads to p(X, Y) = p(Y, X), simplifying probability calculations. Bayesian inference proves powerful in modeling uncertainty and aiding informed actions based on evolving information. We obtain the relationship between p(y|x) =

P(X |Y )P(Y ) P(X )

(12)

The conditional probabilities P (X|Y): is the likelihood of the evidence given that the hypothesis is true. P(Y): prior probability of the hypothesis. P(X): prior probability that the evidence is true. We can state Bayes theorem by the definition of likelihood posterior α Likelihood X prior.

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5 CUSUM Algorithm X[n] is considered to be a discrete random signal, identically distributed. They follow the probability density function given by (PDF) p(x[n], θ) dependent on a predefined parameter θ (for example μx : the mean, σ 2x : the variance). This signal may contain a sudden change, which is generated during the change time nc [4]. We have θ = θ0 before nc and θ = θ1 from nc to the actual sample. Based on these assumptions, we have the set of probability density functions which is noted px observed between samples x[0] and new sample xk which may be presented in two distinct forms: * For the first case: the no change hypothesis (is noted H 0 ), PDF of x[n] is given by the form: k P x|H 0 = P(x[n], θ 0 ) (13) n=0

* For the second case: the case of a single change noted H 1 : k−1 k P x|H 0 = P(x[n], θ 0 ) × P(x[n], θ 1 ) n=n0

n=nc

(14)

We define the probability density function corresponding to each sample p(x[n], θ) plus the parameter values θ 0 and θ 1 are predefined. So we determine only: => The occurrence of abrupt changes between n = 0 and n = k => the change time nc . For the actual H 0 or H 1 must be defined Initialization If necessary End While the algorithm is not stopped do Measure the current sample x[k] Decide between H 0 (no change) or H 1 (one change) If H 1 decided then Store the detection time nd Estimate the change time nC Stop or reset the algorithm End End Algorithm 1: General form a sequential change detection algorithm A/Steps of the algorithm We can see two main steps: – Step of detection: how to decide between H 0 and H 1 ? – Step of estimation: how to estimate the change time nC ? So we’ll start with the first: Detection step: this step let us to choose between two hypotheses H 0 and H 1 for the measured sample x[0] … x[n], called the binary hypothesis test.

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31

Therefore, the suggested solution to this problem is to use the so-called likelihood ratio test: The log-likelihood ratio Lx is defined by:

Px|H1 (15) Lx = Ln Px|H0 H1 is chosen if Lx > h (else H0 ), where we have h a parameter cites by the user. We obtain the theoretical expression of Lx at the actual sample x[k] by reporting (13) and (14) in (15):



k Px|H1 [k, nC ] P(x[n], θ1 ) = (16) ln Lx = Ln n=nC Px|H0 [k] P(x[n], θ0 ) As this quantity depends on the unknown moment of change, nC it is impossible to calculate it. Here again, the solution is to replace each unknown in this formula in Lx by their maximum likelihood. This test is so-called “the generalized Likelihood ratio test”, and is given by:

k P(x[n], θ1 ) (17) ln Gx [k] = max1≤nC ≤k Lx [k, nC ] = max1≤nC ≤k n=nC P(x[n], θ0 ) H1 is chosen if Lx > h (else H0 ), where we have h a parameter cites by the user. Actually, the quantity defined in (17) can be calculated for every new sample and used to decide between H0 and H1 . So it is called a “decision function”: Estimation step: When H1 is determined, and the brutal change is detected. The problem is the estimation of the time of change from the sample x[0] … x[n]. The only way to solve this problem is to use the maximum Likelihood estimate, which is the value of nC maximizing the likelihood Px|H1 [k, nC ]: nˆ c = arg max1≤nC ≤k Px|H1 [k, nC ] = arg max1≤nC ≤k Lx [k, nC ]

k P(x[n], θ1 ) ln = arg max1≤nC ≤k n=nC P(x[n], θ0 )

(18)

B/Direct form To have a simplified form of the whole algorithm. From (16) we have the instantaneous log-likelihood ratio at time n by:

P(x[n], θ1 ) S[n] = Lx [n, n] = ln (19) P(x[n], θ0 ) Results and experiment The Wireless Sensor Networks (WSN) enhance daily life, adapting to societal needs. The Maximization Likelihood Estimation (MLE) algorithm is crucial for data classification, machine learning, and medical imaging. CUSUM optimizes resource use, suiting resource-constrained environments. Bayesian algorithms excel at statistical inference, efficiently transforming data into recommendations or classifications, elevating accuracy in spam filters, search engines, and medical diagnostics. These techniques empower technology to evolve, ensuring a brighter future for WSN applications.

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6 Result of Experiment The number of fires is constantly on the increase due to the human irresponsibility, which means that we have to find effective solutions to resolve this problem. So, we have decided to carry out simulations of different decision algorithms (likelihood, Bayesian) in order to estimate the probability of having fire (Fig. 1). Initialization Set the temperature value Set the smoke level: T = 0 Until the algorithm converges Q(α; α (T ) ) = E[L (x, y); α|α (c) ] So α c+1 = argmax(Q(α, α (c) )) End Algorithm 2: Likelihood algorithm for fire detection

Fig. 1. Simulation of likelihood algorithm for fire detection

Now we pass to Bayesian algorithm, which is based first of all on data acquired in addition to likelihood algorithm (Fig. 2). Initialization Set the temperature value Set the smoke level |Y )P(Y ) Enter the prior value p(y|x) = P(XP(X ) Repeat until the value is not cancelled End Algorithm 3: Bayesian algorithm for fire detection In this figure, we can see that the temperature was entered with the value T = 80 °C in addition to the smoke level of 0.6, a centred spot give the maximum probability for having fire. There are probabilities in line with the other smoke levels, which is an indication of a very low error rate. We can see in Table 1 a very low error rate for Bayesian Algorithm but with an important delay.

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Fig. 2. Simulation of Bayesian algorithm for fire detection

Table 1. Comparison between likelihood and Bayesian simulation result Temperature

Level of smoke

Probability

Error

Delay

Likelihood

80

0.6

0.6065306597126334

> 0.1

67 ms

Bayesian

80

0.6

0.9999999948960201

< 0.1

1s27 ms

Fig. 3. Typical behaviour of the suboptimal CUSUM algorithm in the case of an iid Gaussian signal with a change in the mean at time nc = 1000 [4].

And we compare CUSUM to Likelihood. Figure 3 displays false alarm probability. CUSUM is higher than Bayesian, but lower than Likelihood Algorithm.

7 Conclusion In conclusion, the Bayesian algorithm stands out for its ability to minimize false alarms by considering past and present conditions, providing near-real prevention. This complements the Likelihood algorithm, foundational to detection methods like Bayesian and CUSUM. This research aids in fire prevention, mitigating both human and material losses in various regions.

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References 1. Bharadwaj, Prakash, K.B., Kanagachidambaresan, G.R.: Pattern recognition and machine learning (2021). https://doi.org/10.1007/978-3-030-57077-4_11 2. Cousineau, D., Allan, T.A., Allan, T.A.: Likelihood and its use in parameter estimation and model comparison. Mes. éval. éduc. (2019) 3. Byrne, C.: The EM algorithm. In: Iterative Optimization in Inverse Problems, pp. 203–220 (2018). https://doi.org/10.1201/b16485-16 4. Granjon, P.: The CuSum algorithm - a small review. To cite this version: HAL Id: hal-00914697 (2014)

Implementation of an Intelligent Monitoring System Based on Quality 4.0 of the Induction Hardening Process Imane Moufid(B)

, Adil Nabih , Ismail Lagrat, and Oussama Bouazaoui

Advanced Systems Engineering (ISA), National School of Applied Sciences, Ibn Tofail University, University Campus, B.P 241, Kenitra, Morocco [email protected]

Abstract. At the dawn of the fourth industrial revolution, many companies don’t know how to adapt new technologies for quality management. Consequently, they run the risk of falling behind technological progress and losing their competitiveness. Therefore, in this article we will focus on the implementation phase of an intelligent supervision system that provides real-time control in order to have 100% compliant products. To do this, our case study is based on the study of the Induction Hardening machine which produces mechanical transmission parts. The goal of our research is the prediction of the hardness quality of the part before its manufacture, hence the need to integrate sensors for reading input data in real time and controlling their variation to ensure conformity of the hardness of the manufactured part. After the study of the machine and the selection of the input parameters, the python algorithm was chosen to carry out the automatic comparison between the parameters entered and their suitable predefined values. Thus, a diagram of an intelligent supervision system was developed to ensure compliance of the hardness of the part. This intelligent supervision system will allow to have zero scrap and therefore an elimination of the cost of non-quality. Keywords: Quality 4.0 · Intelligent monitoring system · Automotive industry

1 Introduction At the dawn of the fourth industrial revolution, many companies are unsure how to adapt new technologies for quality management. Consequently, they run the risk of falling behind technological progress and losing their competitiveness. So that, in this article we will focus on the implementation phase of an intelligent supervision system that provides real-time control to have zero non-compliant part. Our case study is based on the study of the Induction Hardening machine at Moroccan automotive manufacturing which manufactures mechanical transmission parts. The objective of our research is controlling the variation of parameters in real time to ensure compliance of the hardness of the manufactured part. To do that, we need to integrate sensors for reading input data and the python algorithm was chosen to perform the automatic comparison between the entered parameters and their predefined suitable values. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 35–41, 2024. https://doi.org/10.1007/978-3-031-48573-2_6

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Thus, our article is divided as follows: Sect. 2 “Literature review” gives a general overview of Industry 4.0, quality management and a definition of quality 4.0. Section 3 “Case study” describes the study of the induction hardening machine (its input and output parameters, its installation…). Then comes the integration of the intelligent supervision system in Sect. 3.2 and finally a discussion of the results is presented in Sect. 4.

2 Literature Review 2.1 Industry 4.0 Industry 4.0 describes the fourth industrial revolution, which leads to intelligent, connected, and decentralized production [1]. It was first introduced in 2011 by the German Fair in Hannover [2], it allows real-time communication between physical and digital areas [3]. Thus, a key technology for this 4th revolution is the adoption of cyber-physical systems (CPS) ensuring full communication between humanity and machines [4]. Moreover, in cooperation with CPS, which is considered as the main pillar, there are several technologies that help serve the concept of Industry 4.0 (see Table 1). All these technologies contribute to serving the concept of Industry 4.0 without denying the importance of one technology over another. Today Moroccan automotive industries seek to increase their productivity while trying, at the same time, to minimize production waste and ensure a good quality product. Admittedly, the traditional methods and tools of quality management make it possible to detect errors and make a better decision, but with a delay in the execution of corrective actions. For this purpose, in order to obtain real-time quality assurance and respond to major quality challenges, we use 4.0 technologies, thus we talk about quality 4.0. 2.2 Quality 4.0 Before defining quality 4.0, we first talk about quality management or total quality management (TQM). It is a management approach that began in Japan in the early 1980s, which aims to achieve and maintain high quality results to improve customer satisfaction and at the same time reduce non-quality costs [8]. Thus, quality management has become ubiquitous in the manufacturing process, it is a very important management philosophy since the reputation of companies largely depends on it. To this end, in the 21st century, companies excel in their quality management practices being competitive in global markets. Thus, during the fourth industrial revolution, companies resort to 4.0 technology for a more developed quality management, and the major challenges in terms of quality, it is quality 4.0. Quality 4.0 is a modern form of quality management. Thanks to 4.0 technologies, it makes it possible to resolve quality problems when they arise and to carry out quality analyzes in real time. So, with the IoT, the ability to analyze and monitor process and product quality at key points in manufacturing processes and identify when nonconforming products are introduced or when product attributes deviate from requirements promises substantial cost savings [5].

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Table 1. Definition of 4.0 technologies. Technologies

Description

Internet of Things (IoT)

Is a network of physical objects that use sensors, software, and other smart devices to transfer and receive data from other systems over the Internet [5]

Big data

As the word says, big data means a massive amount of data that grows exponentially over time. It uses software that consolidates data mining, data storage, and data visualization [5]

Cloud computing

This technology provides computer services such as databases, analytics and storage etc. over the Internet. It is an on-demand service and the user only pays for the cloud services they use [5]

Intelligence artificial (IA)

Is usually related to visual inspection of products for quality control assessment [6]

Augmented reality (AR)

Instead of providing a full virtual experience, this technology enhances the real-world experience with images, text, and other virtual data with smartphones, smart lenses, AR glasses, and more [5]

Virtual reality (VR)

This technology allows the user to feel that he is moving in a virtual environment. It uses a VR headset connected to a PC or console. For example, automakers like BMW use this technology to check their visual design and obstruction of objects before manufacturing actual parts. This helped them save their resources [5]

Cyber-physical systems (CPS) It is the integration of physical processes with networking and computing that creates a cyber-physical environment. It is used to control physical processes using the feedback loop. With CPS, we can combine physical process dynamics with software and networking dynamics [7]

3 Case Study Our case study is based on the study of the induction hardening machine of a Moroccan automotive manufacturing of mechanical transmission parts. 3.1 Induction Hardening Machine To study this machine, you must first know the structure of its installation as well as its input parameters (see Fig. 1). 3.2 Smart Supervision System During the manufacture of mechanical transmission parts, an average percentage of 6% of hardness defects appear per shift, which generates a high scrap rate. According to

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Fig. 1. Installation of induction hardening machine

FMEA of the mechanical transmission part, the hardness defects during the induction hardening process are mainly due to the variation of the adjustment parameters. Indeed, at each start-up the adjuster intervenes to adjust the input parameters of the machine, and during production variations in the parameters occur which cause non-compliant parts. Therefore, comes the interest of integrating an intelligent supervision system to inform about the variation of the parameters in real time via an alarm signal before the production of the part. Before integrating an intelligent supervision system into a machine, we must first know the varying input parameters that cause hardness defects. Input parameter. According to the analyzes made by engineers and machinists, the parameters of the Induction Hardening machine which vary during manufacture (see Fig. 2).

Fig. 2. Input parameters of induction hardening machine

Either table presents the predefined parameters (see Table 2). After the determination of the parameters, we move to the programmed algorithm “python”, which compare the readings with the predefined parameters. Example Programming with Python. # Predefined parameter ranges Power_range = (X1, X2) # (min, max) # Function to compare parameters readings def compare_parameters (Power_reading)

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Table 2. Predefined parameters Parameter

Predefined value

Tolerance

Power (P)

X

±x

Feed (F)

Y

0

Polymer concentration (Pc)

Z

±z

Quench concentration (Qc)

W

±w

result = « » # Compare Power If Power_reading < Power_range [X1]: result += « Power is below the acceptable range. » elif Power_reading < Power_range [X2]: result += « Power is above the acceptable range. » # The same programming for the other parameters if not result: result = « ALL parameters are within the acceptable range: D~ = D» return result

Intelligent supervision system for the assurance of the compliance of the hardness of the part. In order to have a compliant part with a hardness respecting customer requirement, all input parameters must be within their predefined range, a small variation of a single parameter generates non-compliant hardness. Therefore, let D (P, F, Pc, Qc) be the predefined hardness (required by the customer) which depends on the predefined input parameters (see Table 2), and D~ (P~, F~, Pc~, Qc~) the predicted hardness which varies according to the variation of the input parameters. Our supervision system checks the input parameters before the start of the manufacturing of the part if one of the parameters is not ok then D~ = D therefore an intervention for the adjustment of the parameters is mandatory to have D~ = D. Consider the diagram representing our intelligent supervision system (Fig. 3).

4 Analysis and Discussion The article focused just on the study: determination of the input parameters of the machine and these predefined values, programming with the python algorithm, diagramming the structure of the supervision system … However, the implementation of this intelligent supervision system is necessarily linked to the choice of sensors which must be available in the market and suitable for reading the data of each parameter. Admittedly, the supervision system will ensure the quality of the parts produced with zero hardness defects, but with a decrease in the quantity produced compared to the production target, this is due to stoppages for adjustment interventions. So as future research for iterative improvement: using feedback from the comparison process to refine predefined parameters and improve the manufacturing process to ensure consistent quality.

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Fig. 3. Intelligent supervision system

5 Conclusion In this article, a review of the literature was made on technology 4.0 and quality 4.0. Thus, to integrate quality 4.0 in the automotive industry, a case study was provided on the induction hardening machine of a manufacturer of mechanical transmission parts. An intelligent supervision system was proposed for the real-time control of input parameters and have a part produced with conforming hardness. The purpose of the system is to prevent the conformity of the part before being produced. As future research we will base on the obstacles that an industry can find when implementing its intelligent monitoring system.

References 1. Albers, A., et al.: Procedure for defining the system of objectives in the initial phase of an Industry 4.0 project focusing on intelligent quality control systems. Procedia CIRP 52, 262–267 (2016) 2. Santos, M.Y., et al.: A big data system supporting Bosch Braga Industry 4.0 strategy. Int. J. Inf. Manag. 3. O’Donovan, P., et al.: A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications. Comput. Ind. 110, 12–35 (2019) 4. Ammar, M., Haleem, A., Javaid, M., et al.: Improving material quality management and manufacturing organizations system through Industry 4.0 technologies. Mater. Today Proc. 45, 5089–5096 (2021) 5. García-Alcaraz, J.L., Maldonado-Macías, A.A., Cortes-Robles, G.: Preface. Lean manufacturing in the developing world: methodology. Case Stud. Trends Lat. Am. 9783319049, v–ix (2014). https://doi.org/10.1007/978-3-319-04951-9 6. Five Key Industry 4.0 Technologies. ARC Advisory Group (2020). https://ottomotors.com/ blog/5-industry-4-0-technologies. Accessed 24 Dec 2020

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7. Tuane, T.Y., et al.: Why does the implementation of quality management practices fail? A qualitative study of barriers in Brazilian companies. Procedia Soc. Behav. Sci. 81, 366–370 (2013)

Lithium-Ion Battery State of Charge Estimation Using Least Squares Support Vector Machine Elmehdi Nasri1(B) , Tarik Jarou1 , Abderrahmane Elkachani2 , and Salma Benchikh1 1 Advanced Systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail

University, Kenitra, Morocco {elmehdi.nasri1,tarik.jarou,selm.benchikh}@uit.ac.ma 2 Physics of Materials and Subatomics Laboratory, Ibn Tofail University, Kenitra, Morocco

Abstract. Electric vehicles (EVs) are being developed in response to the decline of fossil fuels and growing concerns about the environment. To power these vehicles, lithium-ion (Li-ion) batteries are commonly utilized due to their high energy density and long cycle life. The design of the Battery Management System (BMS) relies heavily on maintaining the State of Charge (SOC) of the Li-ion battery. However, there are several areas that require further attention, including temperature regulation, cell balancing within the battery pack, and internal state estimation. This study proposes a novel framework based on machine learning called Least Squares Support Vector Machine (LSSVM) to enhance the accuracy of battery SOC estimation. A comparison is made between the LSSVM method and a conventional AH method in terms of accuracy and performance of their respective outputs. Keywords: Electric vehicles · Battery management system · Lithium battery · SOC estimation · Least squares support vector machine (LSSVM) · AH method

1 Introduction The rising global popularity of electric vehicles can be attributed to growing concerns over environmental degradation and the challenges surrounding energy resources [1]. Electric vehicles depend heavily on the battery management system (BMS), encompassing various vital functions. The SOC estimate, which indicates the battery’s capacity to deliver power, serves as the foundation for additional functionalities provided by the BMS [2]. The state of charge (SOC) represents the maximum driving range of an electric vehicle. However, SOC cannot be directly measured and instead relies on approximations based on quantifiable physical quantities such as voltage, current, and other relevant characteristics. The accuracy of SOC estimation is influenced by factors such as the precision of sensors and the requirements of the battery management system (BMS) modules.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 42–48, 2024. https://doi.org/10.1007/978-3-031-48573-2_7

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In recent years, numerous studies have employed different approaches to predict the state of charge (SOC) in electric vehicles. These approaches encompass methods such as the ampere-hour integration method (AH), the Kalman filter method (KF), the Extended Kalman filter method (EKF), and deep learning techniques. These methodologies have been utilized to enhance the accuracy and effectiveness of SOC prediction in electric vehicle systems [3, 4]. Among the various approaches, the ampere-hour integration method (AH) stands out as the most widely used approach. Importantly, this approach is less affected by battery changes, allowing for consistent measurement precision. Additionally, the AH method offers the advantage of quickly obtaining precise measurements, with incremental errors being minimized [4]. As time passes, the accuracy of the system deteriorates. To address this limitation, this research proposes an intelligent method based on machine learning. In recent years, SOC estimation methods leveraging machine learning techniques have gained significant attention. These approaches aim to mitigate the impact of physical factors such as capacitance and resistance. By establishing a machine model that captures the non-linear relationship between SOC and variables such as voltage and current, the proposed method seeks to enhance the accuracy of SOC estimation [5]. The Support Vector Machine (SVM) is a unique learning model based on statistical learning theory. Related works associated to the SVM for SOC estimation are cited in the reference [6]. When applied to State of Charge (SOC) estimation, SVM employs voltage and current measurements as input variables for the model, while the SOC is the desired output. By seeking the minimum structured risk, SVM determines the appropriate regularization coefficients and kernels for the model. A different formulation known as Least Squares Support Vector Machine (LSSVM) substitutes the inequality constraints in the SVM with equality constraints. This modification allows for a different approach to training the model and finding the optimal solution [7]. The next section introduces the State of Charge (SOC) estimation methods under the Least Squares Support Vector Machine (LSSVM) and Ampere-Hour (AH) method for Li-ion batteries. Section 3 contrasts the predicted performance of the AH and LSSVM methods using input data from a Lithium battery under HPPC condition (Hybrid Pulse Power Characterization). Section 4 concludes the paper.

2 The SOC Estimation Methods 2.1 The Ampere Hour Counting Method The method for accumulating electric charge that is currently most frequently employed is the Ampere-hour (AH) integral method. Its foundation is the estimation of the amount of electricity used during charging and discharging [8, 9]. The Peukert equation is predominantly utilized in the ampere-hour (AH) counting method to convert actual current into standard current and integrate time to determine State of Charge (SOC). With the initial state designated as SOC0 , the current value of SOC can be determined using Eq. (2):  t 1 SOC = SOC0 − ηi idt (1) Qn 0

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Based on The Peukert equation’s computation and deduction of the current efficiency coefficient, ηi is used here. One of the most fundamental and generally applicable techniques for SOC estimate is the ampere hour counting process. However, as time passes, SOC prediction inaccuracy increases due to the accumulation of variance imposed on by faulty current sensor observations. Figure 1 shows the Simulink model associated with this method. The role of this method in our work is a benchmark for comparison with the Artificial Neural Network method and Least Squares Support Vector Machine (LSSVM) method, it’s a reference method.

Fig. 1. MATLAB/Simulink model of AH method

2.2 Least Squares Support Vector Machine (LSSVM) Method LSSVM, also known as Least Squares Support Vector Machine, is an enhanced version of the SVM algorithm. It introduces a quadratic loss function to replace the insensitive loss function used in SVM. This modification allows for the transformation of the quadratic optimization problem in SVM into a linear equation solving process. Consider a training sample set consisting of n-dimensional vectors (xi , yi ). The fundamental concept behind LSSVM modeling can be summarized as follows: Initially, using a non-linear mapping function, the sample’s input space Rd is translated into a feature space ϕ (.) [5] ϕ(x) = (ϕ(x1 ), ϕ(x2 ), . . . , ϕ(xi ))

(2)

An optimal decision function in this high-dimensional feature space is generated: y = wT ∗ ϕ(x1 ) + b

(3)

The weight vector, denoted as w, and the offset, denoted as b, are crucial components in determining the optimal decision function. The minimization of structural risk is the basis for the computation of these model parameters, w and b. The following formula is used to determine structural risk: 1 R = c ∗ Remp + w2 2

(4)

Here, the normalized parameter is represented by c, while the loss function is shown by Remp . The parameters w and b of the decision function are set using the structural risk

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minimization approach. This procedure is akin to resolving the subsequent optimization problems: yi = wT ∗ ϕ(xi ) + b (5)       If the kernel functions K xi , xj = ρ xj , ρ(xi ) satisfies the Mercer condition, then yi =

n    1 αi ∗ K xi , xj + b + αi j=1 2c

(6)

where α = [α1 , α2 , … αn ] is a Lagrange multiplier. Derived from the compilation of training examples (xi , xj ), the model Parameters [b, α1 , α2 , . . . αn ]. The solution to the linear Eq. (6) can be acquired through computation. The function of LSSVM is estimated as follows: n f(x) = (7) (αi ∗ K(x, xi )) i=1

We present a methodology for estimating the state of charge (SOC) of a battery using the Least Squares Support Vector Machine (LSSVM) framework.

Fig. 2. LSSVM calculation process model

Figure 2 depicts the LSSVM model during the k-th sampling time. The input variables at this sampling time include the battery’s voltage (V(k)), and current (I(k)). The corresponding output is the estimated State of Charge (SOC) at the k-th sample time, denoted as SOC(k). The approach comprises of two primary components. In the initial phase, the model is trained through offline data acquisition of voltage, current, and SOC, respectively. Through the utilization of training data and a crossvalidation approach, the optimal values for the parameters of the kernel function are determined. In the second part, a test set is selected to perform online SOC estimation. The measurement error in battery data acquisition, external environmental factors, and other conditions can result in significant inaccuracies. When these inaccuracies are incorporated as input vectors in the LSSVM model, they can decrease the estimation accuracy of State of Charge (SOC). However, SOC does not experience sudden drastic changes within short time intervals. To address this issue, the paper proposes a novel LSSVM model based on the moving window method [7]. Based on the introduction of the LSSVM principle in the initial section, it is necessary to determine two significant parameters for the LSSVM model: the regularization coefficient C and the kernel parameter σ. As a result, Fig. 3 outlines the parameter optimization algorithm for the proposed model.

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Fig. 3. LSSVM algorithm

3 Simulation and Results Discussion To evaluate the efficacy of the suggested approach, a simulation model is developed in MATLAB/Simulink, as depicted in the accompanying model (Fig. 4).

Fig. 4. MATLAB/Simulink model of the SOC estimation based on LSSVM/AH methods

Using data obtained from the charging and discharging cycles of a 50 Ah lithiumion battery in a MATLAB/Simulink system under the HPPC conditions test [10], the LSVMM is training offline in the one hand, and on the other hand we proceed the calculation of the real and the AH method obtained value, Figs. 5 and 6 show the simulation outcomes. It is evident from the preceding simulation results that both techniques are efficient at SOC estimation, although the Artificial Neural Network (ANN) outperforms the AH method. The ANN exhibits a maximum error value of less than 0.37% and an average error of less than 0.05%, while the AH method shows a mean error value of 0.8% and a mean average of 0.1%. The LSSVM approach outperforms the AH method in HPPC settings, demonstrating superior estimation performance. The advantage of the LSSVM lies in its ability to quickly and effectively estimate SOC. In comparison to the AH method, which relies on an integral model, the ANN observer is considerably simpler.

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Fig. 5. LSSVM based SOC vs AH SOC vs real SOC

Fig. 6. LSSVM at AH based SOC estimation error

However, the implementation of LSSVM requires a large storage capacity to hold the training data. The experiment reveals that the parallel and global searching technique leads to faster convergence and greater precision. Nevertheless, there are clear disadvantages to this strategy. The training system of LSSVM necessitates a substantial amount of training data to achieve optimal performance. The choice of training techniques and the availability of training data significantly impact the performance of the machine learning algorithm.

4 Conclusion Accurately determining the state of charge (SOC) is of great significance for electric vehicles. However, due to the non-linear nature of batteries, establishing a correlation between the state of charge (SOC) and measurable battery parameters can present a challenging task. This paper introduces an algorithm for estimating the state of charge during battery discharging, which utilizes a feedback loop LSSVM (Least Squares Support Vector Machine) model. The algorithm utilizes data obtained from HPPC (Hybrid Pulse Power Characterization) tests and is implemented using MATLAB/Simulink. The

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inputs of the model consist of the current, voltage at the current moment, and the previously estimated SOC. Simulation results demonstrate that the proposed model achieves higher accuracy in predicting SOC compared to the AH method. The estimation errors of the LSSVM model are successfully controlled within 2%. Our study’s next hurdle involves implementing a low-cost microcontroller-based BMS system to achieve SOC prediction functionality.

References 1. Han, S., Zhang, B., Sun, X., Han, S., Höök, M.: China’s energy transition in the power and transport sectors from a substitution perspective. Energies 10(5), Art. no. 5 (2017). https:// doi.org/10.3390/en10050600 2. Elmehdi, N., Tarik, J., Benchikh, S., Saadi, N.: Identification of the parameters of the lithiumion battery used in electric vehicles for the SOC estimation. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. Lecture Notes in Networks and Systems, pp. 462–472. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-35245-4_42 3. A comparative study of extended Kalman filter and AH approach or the state of charge estimation applied in lithium batteries of electric vehicles. AIP Conf. Proc. AIP Publishing. https://pubs.aip.org/aip/acp/article/2814/1/040010/2901933/A-comparativestudy-of-extended-Kalman-filter-and. Accessed 12 July 2023 4. Ng, K.S., Moo, C.-S., Chen, Y.-P., Hsieh, Y.-C.: Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy 86(9), 1506–1511 (2009). https://doi.org/10.1016/j.apenergy.2008.11.021 5. Li, J., Ye, M., Jiao, S., Shi, D., Xu, X.: State estimation of lithium battery based on least squares support vector machine. DEStech Trans. Environ. Energy Earth Sci. (2019). https:// doi.org/10.12783/dteees/iceee2019/31818 6. Battery state-of-charge estimator using the SVM technique. Appl. Math. Model. 37(9), 6244– 6253 (2013). https://doi.org/10.1016/j.apm.2013.01.024 7. Li, J., Ye, M., Gao, K., Wei, M., Jiao, S.: State estimation based on least square support vector. J. Phys. Conf. Ser. 1983, 012069 (2021). https://doi.org/10.1088/1742-6596/1983/1/012069 8. Anand, I., Mathur, B.L.: State of charge estimation of lead acid batteries using neural networks. In: 2013 International Conference on Circuits, Power and Computing Technologies, ICCPCT, Mar 2013, pp. 596–599. https://doi.org/10.1109/ICCPCT.2013.6528901 9. 都竹隆広, 野村博之, 研治西垣, and 城殿征志: Method and device for estimating battery state of charge. In: WO2014132491A1, 04 Sept 2014. Accessed 21 Feb 2023. [Online]. Available: https://patents.google.com/patent/WO2014132491A1/en 10. Duan, W., et al.: Online parameter identification and state of charge estimation of battery based on multitimescale adaptive double Kalman filter algorithm. Math. Probl. Eng. 2020, e9502605 (2020). https://doi.org/10.1155/2020/9502605

Intrusion Detection in Software-Defined Networking Using Machine Learning Models Lamiae Boukraa(B) , Siham Essahraui, Khalid El Makkaoui, Ibrahim Ouahbi, and Redouane Esbai Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco [email protected]

Abstract. Software-defined networking (SDN) is a new networking paradigm developed to reduce network complexity via control and management of the network from a centralized location. Nevertheless, the dynamic nature of SDN can lead to many vulnerabilities and threats, including denial of service (DOS) and Distributed Denial of service (DDoS) attacks. Thus, deploying Intrusion Detection Systems (IDSs) based on machine learning (ML) is a crucial part of the network architecture to monitor malevolent activities. This paper compares three ML models for intelligent intrusion detection in SDN: support vector machines, K-Nearest Neighbors, and Naive Bayes Networks. To evaluate and measure the performance of ML models, we used the DDoS-SDN dataset and compared their evaluation metrics, such as accuracy. Keywords: Distributed denial of service (DDoS) · Intrusion detection system (IDS) · Machine learning (ML) · Software-defined networking (SDN)

1

Introduction

Traditional networks have not been designed to support the next generation of technologies such as Cloud Computing, Big Data, and IoT. New technologies, such as Software Defined Networking SDN, have been proposed to encounter the growing need for devices and services. SDN is a new paradigm that ensures network programmability and automation of network operations. SDN separates the data plan from the control plane. This networking paradigm enables centralized network management and dynamic configuration of network devices. It provides a complete view of the network from a centralized controller [1–3]. SDN’s standard and multilayered architecture includes three layers: application plane, control, and data plane, as illustrated in Fig. 1. The Application Plane, also known as the Management Plane, is accountable for the management and security applications. This layer encompasses various applications, including intrusion prevention systems (IPS), intrusion detection systems (IDS), and c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 49–57, 2024. https://doi.org/10.1007/978-3-031-48573-2_8

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Fig. 1. Security threats analysis of SDN architecture

firewall implementation. The Control Plane consists of one or more controllers responsible for central programming and controlling the behavior of network elements. The controllers communicate with other planes through southbound, northbound, and eastbound/westbound interfaces. The Data Plane, also known as the Forwarding Plane, consists of network devices such as switches and routers. The primary function of the data plane is to transmit packets using the decisions (i.e., flow rules) assigned by the SDN controller. The OpenFlow protocol stands out as the initial standardized communication interface established between the control and forwarding planes [4,5]. While SDN offers various advantages, such as centralized control, networkwide visibility, and dynamic forwarding rule updates, separating the control and data planes introduces new avenues for potential attacks. Attackers can exploit a new security threat to carry out various malicious tasks [6]. A Network Intrusion Detection System (NIDS) safeguards against malicious software attacks on a network, distinguished by its strategies for detecting network attacks. Machine learning has improved intrusion detection, analyzed large amounts of data, and detected abnormal activity patterns. This study assessed the efficiency of three machine learning models, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB) Network, regarding their ability to detect intrusions within SDN. We train and evaluate the models using the DDoS-SDN dataset and focus on detecting distributed Denial-of-Service (DDoS) attacks. Various performance metrics such as precision, recall, accuracy, AUC, and F1-score were employed to gauge the effectiveness of the models. The rest of the paper is organized as follows. Challenges of SDN security are introduced in Sect. 2. In Sect. 3, we give the performance evaluation and the used Dataset. The outcomes and the discussion are provided in Sect. 4. Finally, the conclusion and future work are outlined.

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Challenges of SDN Security

SDN networks offer numerous benefits compared to traditional networks as they incorporate a clear separation between the control and data planes. This separation enables network programmability, flexibility, and scalability. However, it is essential to note that centralizing the control and management subsystem introduces a potential vulnerability, as the centralized control point becomes the most susceptible part of the entire system. The SDN network architecture is susceptible to various challenges that impede its performance and implementation. The challenges encompass controller placement, scalability, performance, security, interoperability, and reliability. These challenges represent critical areas where SDN may face difficulties and require careful attention to ensure successful deployment and optimal functioning. This paper will primarily address the security vulnerabilities present in an SDN controller, which can potentially endanger the overall security of a network. 2.1

Distributed Denial of Services

DoS and DDoS attacks are among the most popular and dangerous attacks against SDNs. These attacks manipulate the network’s capabilities and behaviors, aiming to disrupt normal operations. By overwhelming services, they hinder or limit network functionalities, preventing host computers from communicating with the SDN controller or transmitting packets across the network. Consequently, DoS and DDoS attacks can be directed at the control plane, data plane, or communication channels. An attack on the control plane can lead to a complete network failure. In contrast, attacks on the data plane or communication channels result in packet loss, rendering the network unavailable. The primary objective of a DOS/DDoS attack is to render network resources, computing resources, or services inaccessible, thereby preventing legitimate users from utilizing them. This attack leverages flow records and sends numerous requests simultaneously to the controllers. However, the controller cannot handle or process multiple requests concurrently, making it overwhelmed and unavailable [8]. DoS/DDoS attacks generate multiple new flows that include falsified IP addresses originating from a single source in the case of a DoS attack or from multiple sources in the case of a DDoS attack. These falsified addresses do not correspond to existing flow rules within the OpenFlow switch flow table. Consequently, these attacks consume communication bandwidth and CPU resources in both the SDN controller’s control and data planes. 2.2

Intrusion Detection System

Intrusions in SDN can damage the reliability of security services such as data confidentiality, integrity, and availability. Numerous security solutions have been suggested to defend against attacks in an SDN environment that involve analyzing both normal and abnormal user and network behavior. IDSs have been

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proposed as a suitable security solution for detecting attacks. An IDS is a system, whether in the form of software or hardware, designed to detect and identify malicious activities on computer systems, thereby ensuring system security. The primary objective of an IDS is to identify various types of malicious network traffic and computer usage that may go unnoticed by a conventional firewall [7,9]. IDS can be classified into two different types: signature-based or anomalybased detection methods. A signature-based detection strategy can only find known attacks that the network manager has already detected. These methods examine the characteristics of attacks in network traffic to detect security threats such as viruses or incomplete packets. An anomaly-based detection method conducts network or system scans over some time and creates an anomaly model. This model is developed by monitoring traffic and serves as a baseline for identifying unfamiliar intrusions. To identify real-time anomaly-based DDoS attacks, anomaly-based techniques can be classified into three main categories: statistical-based, knowledgebased, and ML-based [10]. – Statistical-based techniques: Utilize statistical properties and tests to determine whether observed behavior significantly deviates from expected behavior. These techniques encompass various approaches based on univariate, multivariate, time-series models, and cumulative sums (CUSUM). The advantages of statistical-based techniques include the ability to learn the expected behavior of a system without prior knowledge of its normal activity and the ability to accurately detect malicious activities over a long period. Their main disadvantage is the possibility of the attacker training the system to consider malicious traffic as normal. – Knowledge-based techniques: Attempt to capture the expected behavior of a system using available data. These techniques involve the use of finite automata, description languages, and expert systems. Among these techniques, expert systems are the most commonly used, as they classify observed data based on a set of rules derived from various attributes and classes identified from training data. The advantages of knowledge-based techniques include their flexibility and robustness. However, acquiring highquality knowledge can be a challenging and time-consuming task, which is their main disadvantage. – ML-based techniques: Methods based on ML create a model that either explicitly or implicitly categorizes analyzed patterns. The concepts and applicability of ML techniques resemble those of statistical techniques. However, ML-based methods allow NIDS to modify its response as it acquires new information. Typical ML-based methods include Bayesian networks, Markov models, neural networks, fuzzy logic, genetic algorithms, clustering, and outlier detection. ML-based methods are versatile, adaptable, and capable of capturing interrelationships among observed occurrences. However, their main drawback is that they consume significant resources. In this paper, we will focus on ML-based techniques.

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Methodology

3.1

Performance Evaluation

To evaluate and compare SVM, KNN, and Naive Bayes-trained models [11], automated ML generates metrics that aid in assessing model performance [12]. – Accuracy (AC): It is a metric that represents the percentage of correctly detected instances in relation to the total traffic trace. AC =

TP + TN TP + TN + FP + FN

(1)

where, TP: The number of attack records correctly classified. TN: The number of normal records correctly classified. FP: The number of normal records incorrectly classified. FN: The number of attack records incorrectly classified. – Precision (P): Measures the proportion of intrusions predicted by a NIDS that are indeed actual intrusions. A higher precision value indicates a lower false alarm rate. TP P = (2) TP + FP – Recall (R): Represents the percentage of predicted intrusions compared to the total number of actual intrusions. A high recall value is desired, indicating a greater ability to identify and capture more intrusions. R=

TP TP + FN

(3)

– The F1-score (F1): Provides a comprehensive evaluation of a NIDS’s accuracy by considering both precision (P) and recall (R). It combines these two metrics to offer a balanced assessment. A high F-measure value is desired as it signifies a favorable balance between precision and recall, indicating an effective NIDS performance. F1 =

2T P 2T P + F P + F N

(4)

– The Area Under the ROC Curve (AUC): Is a metric utilized to estimate the accuracy of prediction outcomes. The AUC score is calculated by taking the sum of the number of links prior to prediction and the number of predicted links, multiplied by 0.5, and then dividing it by the total number of links. In this manner [13], the AUC is defined as: AU C =

n + 0.5n n

(5)

where n : The number of links prior to prediction. n : The number of predicted links. n: The total number of links. It is evident that a higher AUC score generally indicates superior quality in prediction results. It is worth noting that the highest achievable AUC value is 1.

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Dataset

In this work, we use the DDoS-SDN dataset, a dataset specifically designed to study DDoS attacks in SDN. The database contains different types of DDoS attacks, such as bandwidth saturation attacks (e.g., flooding attacks), resource exhaustion attacks (e.g., flow table exhaustion attacks), and service degradation attacks (e.g., malicious traffic injection attacks). The dataset contains 23 attributes, including three categorical and 20 numerical attributes. The attributes represent network traffic features, the information on the attack, such as source and destination IP addresses, types of protocols used, traffic flow rates, attack durations, etc. These characteristics make it possible to analyze attacker behavior and develop detection algorithms. These characteristics are used to analyze attacker behavior and develop detection algorithms. There is a target variable called label, which contains only 1 (malicious) and 0 (benign). The task is to classify whether the traffic is normal or not using classic ML algorithms. To make our dataset ready for analysis, we began firstly with data preprocessing, where we transformed raw data into a format that can be easily understood by ML algorithms, including removing some attributes, checking missing values, checking duplicate records, labeling data into the attack (abnormal) and normal, data normalization for numerical attributes data, and One-hot-encoding for categorical data attributes. The dataset is widely utilized for evaluating the performance of IDSs, ML algorithms, and data mining techniques. It is divided into a training set and a testing set. In our experiment, we selected a subset of twenty features from the DDoSSDN dataset, which initially contained twenty-three features. These selected features were used for training and testing purposes, along with one feature label indicating whether it corresponds to an attack or not. This research paper compares three ML algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). The analysis involved training, validating, and testing these models using identical partitions from the original DDoS-SDN dataset. Following the establishment of training models and completion of the testing phase, various evaluation metrics can be employed to compare the performance of SVM, KNN, and NB models. This research paper focuses on calculating evaluation metrics such as AC, P, R, F1, and AUC for each ML classifier.

4

Result and Discussion

In this section, we present and analyze the results obtained from our experiments, aiming to evaluate the effectiveness and efficiency of the SVM, KNN, and NB models [14–16]. In our scenario, we opted for the DDoS-SDN Dataset and divided it into 80% for training and 20% for testing purposes. Before employing the machine learning classifiers, we performed data pre-processing, a crucial step in the data mining process. We focused on two classes: DOS and Normal. Additionally, we utilized cross-validation to assess the performance of our ML models.

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Fig. 2. SVM, KNN, and Naive Bayes-based ML algorithms, using F1, P, and R as evaluation metrics.

Fig. 3. SVM, KNN, and Naive Bayes-based ML algorithms, using accuracy and AUC as evaluation metrics.

Table 1 presents the performance of the selected classifiers in the experiments SVM, kNN, and NB. It contains evaluation results on the test set for the three models. AC, P, R, F1, and AUC were chosen as standard evaluation metrics (Figs. 2 and 3). The performance evaluation of the machine learning classifiers showed that KNN obtained the highest performance in terms of AC, P, R, F1, and the AUC while maintaining appropriate training time and predictions. In other ways, The SVM and Naive Bayes classifiers had the lowest performance, where SVM model exhibited an accuracy rate of 67%, a precision of 58%, a recall of 56% and an F1 yielded of 57%. The SVM classifier reached a middle AUC value of 65%. However, the Naive Bayes model had poor detection performance, with an accuracy of 62%, and weak values in both precision, recall, F1 score, and the AUC. As a result, the KNN model was the best compared with the two other models according to all performance metrics (see Table 1). Table 1. Performance of SVM, kNN and NB on the test set. Model

AC (%) P (%) R (%) F1 (%) AUC (%)

SVM

67

58

56

57

65

KNN

88

84

82

85

87

Naive Bayes 62

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33

41

57

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Conclusion

This paper compared SVM, KNN, and NB for anomaly detection in SDN. Several experiments were conducted and tested to evaluate the efficiency and performance of the ML classifiers. All the tests were based on the DDoS-SDN dataset. The outcomes showed that KNN performed better than SVM and NB regarding the performance metrics, including Accuracy, Precision, Recall, F1-score, and AUC. In the future, further research will focus on examining the efficacy of alternative ML models, incorporating more extensive and diverse datasets, and evaluating the performance of the proposed approaches for intrusion detection in real-world SDN environments. The aim is to explore additional ML techniques, expand the scope of available data, and assess how well the proposed methods perform in practical scenarios involving SDN.

References 1. Ahmad, S., Mir, A.H.: Scalability, consistency, reliability and security in SDN controllers: a survey of diverse SDN controllers. J. Netw. Syst. Manage. 29, 1–59 (2021) 2. Kreutz, D., et al.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2014) 3. Bannour, F., Souihi, S., Mellouk, A.: Distributed SDN control: survey, taxonomy, and challenges. IEEE Commun. Surv. Tutor. 20(1), 333–354 (2017) 4. Maleh, Y., Qasmaoui, Y., El Gholami, K., Sadqi, Y., Mounir, S.: A comprehensive survey on SDN security: threats, mitigations, and future directions. J. Reliab. Intell. Environ. 9(2), 201–239 (2023) 5. Boukraa, L., Mahrach, S., El Makkaoui, K., Esbai, R.: SDN southbound protocols: a comparative study. In: International Conference on Networking, Intelligent Systems and Security, pp. 407–418. Springer International Publishing, Cham (2022) 6. Depren, O., Topallar, M., Anarim, E., Ciliz, M.K.: An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl. 29(4), 713–722 (2005) 7. Conti, M., Gangwal, A., Gaur, M.S.: A comprehensive and effective mechanism for DDoS detection in SDN. In: 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 1–8. IEEE (2017) 8. Mahrach, S., Haqiq, A.: DDoS attack and defense in SDN-based cloud. In: Ubiquitous Networking: 7th International Symposium, UNet 2021, Virtual Event, Revised Selected Papers 7, 19–22 May 2021, pp. 149–162. Springer International Publishing (2021) 9. Mahrach, S., Mjihil, O., Haqiq, A.: Scalable and dynamic network intrusion detection and prevention system. In: Innovations in Bio-Inspired Computing and Applications: Proceedings of the 8th International Conference on Innovations in BioInspired Computing and Applications (IBICA 2017), Marrakech, Morocco, 11–13 Dec 2017, pp. 318–328. Springer International Publishing (2018) 10. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1), 1–22 (2019)

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11. El Mrabet, M.A., El Makkaoui, K., Faize, A.: Supervised machine learning: a survey. In: 2021 4th International Conference on Advanced Communication Technologies and Networking (CommNet), pp. 1–10. IEEE (2021) 12. Samad, A., Qadir, M., Nawaz, I., Islam, M.A., Aleem, M.: A comprehensive survey of link prediction techniques for social network. EAI Endorsed Trans. Ind. Netw. Intell. Syst. 7(23), e3 (2020) 13. Badiy, M., Amounas, F., Bouarafa, S.: An innovative approach for supervised link prediction using feature embedding methods. In: The International Conference on Artificial Intelligence and Smart Environment, pp. 206–211. Springer International Publishing, Cham (2022) 14. Cervantes, J., Garcia-Lamont, F., Rodr´ıguez-Mazahua, L., Lopez, A.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189–215 (2020) 15. Latah, M., Toker, L.: An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks. CCF Trans. Netw. 3(3–4), 261–271 (2020) 16. Tabash, M., Abd Allah, M., Tawfik, B.: Intrusion detection model using naive Bayes and deep learning technique. Int. Arab J. Inf. Technol. 17(2), 215–224 (2020)

Neural Network for Link Prediction in Social Network Mohamed Badiy1(B) , Fatima Amounas2 , Ahmad El Allaoui3 , and Younes Bayane1 1 Faculty of Sciences and Technics, Moulay Ismail University of Meknes, Errachidia, Morocco

[email protected]

2 RO.AL&I Group, Computer Sciences Department, Faculty of Sciences and Technics, Moulay

Ismail University of Meknes, Errachidia, Morocco [email protected] 3 L-STI, T-IDMS, Computer Sciences Department, Faculty of Sciences and Technics, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected]

Abstract. The Internet has greatly advanced research in social networks, leading to the emergence of link prediction as a significant area of study in social network analysis (SNA). Link prediction involves predicting the formation of new connections between nodes that do not currently exist. This research trend has enabled people to connect with others based on shared characteristics, leading to improved communication. To enhance the accuracy of link prediction, researchers have employed various machine-learning techniques, including supervised and unsupervised learning. Also, deep learning models were widely adopted. In this research work, we attempt to develop a new link prediction approach using an Artificial Neural Network. Firstly, we adopt the node similarity measures to assign scores to pairs of nodes based on available dataset features. Secondly, based on a neural network, we can predict the future links with the goal of improving the accuracy of link prediction. The experimental results based on Twitch dataset show that our approach has a high prediction accuracy. Keywords: Network analysis · Social network · Link prediction · Machine learning · Deep learning · Artificial neural networks

1 Introduction The emergence and widespread adoption of social networking platforms such as Twitter and Facebook have led to a surge in research on social relationships. These platforms have become integral to the daily lives of many people and offer a wealth of data that can be used to study various aspects of social relationships. A social network can be represented as a graph where two users are nodes and they are linked with some type of connection called an edge. In recent years, there has been a significant focus on studying network evolution mechanisms and network topology. This research has led to the emergence of various problems related to link mining, with link prediction being one of the fundamental problems. Link prediction is a technique in network analysis that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 58–63, 2024. https://doi.org/10.1007/978-3-031-48573-2_9

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involves using the network’s topology to predict the likelihood of links between nodes that may not yet exist or have not been observed. Link prediction has a broad spectrum of applications, including in social networks [1], bioinformatics [2], e-commerce [3] and so on. Link prediction is a popular research area that has recently attracted the attention of researchers due to its wide range of applications. Several methods have been suggested in the literature to tackle the issue of link prediction. The three basic categories of link prediction methods are similarity-based, machine learning-based, and deep learning-based methods. Similarity-based methods rely on the idea that nodes with similar attributes or neighborhood structures are more likely to be connected. These similarity indices can be classified into two categories: local indices and global indices. While powerful global indices in providing high accuracy, but their computation takes a long time and is frequently unfeasible for large-scale networks. Local indices, on the other hand, are generally faster but offer lower accuracy. Common similarity metrics include Jaccard coefficient, Adamic/Adar index, and cosine similarity and so on [4]. While these methods are simple and easy to implement, they may not capture complex patterns in large networks. A binary classification issue involving the link prediction is put forth by the machine Learning approaches. Supervised and unsupervised machine learning techniques are commonly used to predict links based on some features extracted from the networks. These can include node degree, clustering coefficient, and various other network properties. Machine learning-based methods can capture more complex patterns in the data compared to similarity-based methods, but may require careful feature engineering and parameter tuning [5, 6]. Deep learning-based methods are the novel approaches that shown promising results in link prediction, especially for large and complex networks. These methods use neural networks to learn high level representations of nodes and predict links based on these representations [7]. Artificial neural networks (ANNs) are a crucial part of artificial intelligence (AI) and have been successfully applied to a variety of tasks [8] including pattern recognition, classification, regression, natural language processing, and more. An artificial neuron, commonly referred to as a perceptron, is the fundamental component of an artificial neural network [9]. A perceptron takes multiple inputs, applies weights to each input, performs a weighted sum, and then applies an activation function to produce an output. ANNs are typically structured into layers, including an input layer, one or more hidden layers, and an output layer. Therefore, the new approach proposed here, which consider node similarity measures as input features to train an artificial neural network. The comparison with the supervised machine learning algorithms shows that the proposed approach is more accurate and it could successfully predict the future connection. In what follows, Sect. 2 introduces the details of the proposed link prediction approach using artificial neural network. The experimental results are presented in Sect. 3. Section 4 provides concluding remarks for this paper.

2 Methodology Link prediction is a popular research trend that involves predicting the likelihood of future connections or links between nodes in a social network. In this research, we aim to develop an effective approach for the link prediction by integrating two keys: node

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similarity measures and an artificial neural network. To investigate the promise of this idea, we analyse the use and the effectiveness of node similarity measures as feature vectors to train the artificial neural network model. The architecture of the proposed methodology is illustrated in Fig. 1. As discussed above, we stipulate that network similarity measures can be effective as features in conjunction with a two-layered neural network. For this purpose, we combine five local and two global indices. In this stage, we construct the feature vectors by using the following similarity indices: Common neighbors (CN), Adamic Adar (AA), Jaccard coefficient (JC), Resource Allocation Index (RA), Preferential attachment (PA), Shortest Path (SP) and Page Rank (PR).

Fig. 1. Flow diagram of the proposed approach

To harness the capabilities of machine learning more effectively, we have developed a straightforward neural network model. This model takes node parameters as input and generates classifications to determine whether an edge exists. The structure of the fully connected neural network can be visualized in the graph provided in Fig. 2. As mentioned earlier, the neural network receives an 7-dimensional input consisting of various metrics for each edge, including CN, JC, PA, AA, RA, SP and PR. Here, we employed two hidden layers, each containing five neurons with weights initialized uniformly and activated by the ReLU activation function. The edge presence inference is computed using a binary output layer and the sigmoid activation function.

3 Experimental Results In this section, we perform extensive experiments on the Twitch dataset to evaluate the effectiveness of the proposed approach. All experiments were carried out on Intel Core i5 CPU under Windows 10 with 8 GB of RAM. Subsequently, we compare the link prediction performance of the ANN model with supervised machine learning algorithms like k-nearest neighbors (KNN), Logistic Regression (LR) and support vector machines (SVM).

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Fig. 2. Artificial neural network structure

3.1 Dataset Description This section focuses on Twitch, a well-known social network and live streaming service that primarily airs eSports competitions and video game live broadcasts [10]. Nodes represent individual users or streamers on Twitch, while edges represent the connections or relationships between them. Table 1 displays the detail of the dataset considered. Table 1. Detail of the dataset considered. Dataset

Nodes

Edges

Type

Clustering coefficient

Twitch EN

7126

35324

Undirected

0.042

3.2 Results and Discussion In order to further illustrate and verify our conclusion, we investigate the relevance of artificial neural networks to improve the performance of the link prediction. For evaluation purposes, we employed four evaluation metrics [11]: precision, recall, accuracy, and area under the curve (AUC). The simulation results using Python 3.7 as tools are presented in Table 2. According to Table 2, the ANN model outperformed the other models in all metrics. When compared to the KNN model, we observe that the improvement of precision is 3.8%, recall is 1.2%, accuracy is 2.6%, and AUC is 2.6%. A graphical representation of Table 2 can be seen in Fig. 3. From the findings, we find that the neural network method has higher accuracy and performance than supervised machine learning classifiers KNN, LR and SVM.

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Algorithms

Precision

Recall

Accuracy

AUC

SVM

0.926

0.742

0.824

0.841

KNN

0.931

0.960

0.945

0.945

LR

0.903

0.971

0.938

0.938

ANN

0.969

0.972

0.971

0.971

Fig. 3. Performance comparison on Twitch dataset

4 Conclusion Research on network performance improvement using artificial intelligence techniques is the current challenging issue. In this research, we introduce a new approach based on artificial neural network to address the link prediction problem in social networks. Our goal is to leverage similarity-based methods to construct the feature vector for an artificial neural network model to predict the future link. Experimental results on Twitch dataset indicate that the artificial neural network delivers superior performance as compared to KNN, LR and SVM. In our future work, we will extend this approach to directed networks and incorporate larger data sets.

References 1. Yang, R., Yang, C., Peng, X., Rezaeipanah, A.: A novel similarity measure of link prediction in multi-layer social networks based on reliable paths. Concurr. Comput. 34 (2022). https:// doi.org/10.1002/cpe.6829

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2. Nasiri, E., Berahmand, K., Rostami, M., Dabiri, M.: A novel link prediction algorithm for protein - protein interaction networks by attributed graph embedding. Comput. Biol. Med. 137, 104772 (2021). https://doi.org/10.1016/j.compbiomed.2021.104772 3. Liu, G.: An ecommerce recommendation algorithm based on link prediction. Alex. Eng. J. 61, 905–910 (2022). https://doi.org/10.1016/j.aej.2021.04.081 4. Mutlu, E.C., Oghaz, T., Rajabi, A., Garibay, I.: Review on learning and extracting graph features for link prediction. MAKE 2, 672–704 (2020). https://doi.org/10.3390/make2040036 5. Malhotra, D., Goyal, R.: Supervised-learning link prediction in single layer and multiplex networks. Mach. Learn. Appl. 6, 100086 (2021). https://doi.org/10.1016/j.mlwa.2021.100086 6. Badiy, M., Amounas, F.: Embedding-based method for the supervised link prediction in social networks. Int. J. Recent Innov. Trends Comput. Commun. 11, 105–116 (2023). https://doi. org/10.17762/ijritcc.v11i3.6327 7. Liu, X., Li, X., Fiumara, G., De Meo, P.: Link prediction approach combined graph neural network with capsule network. Expert Syst. Appl. 212, 118737 (2023). https://doi.org/10. 1016/j.eswa.2022.118737 8. Malekian, A., Chitsaz, N.: Concepts, procedures, and applications of artificial neural network models in streamflow forecasting. In: Advances in Streamflow Forecasting, pp. 115–147. Elsevier (2021). https://doi.org/10.1016/B978-0-12-820673-7.00003-2 9. Xu, H., et al.: Application of artificial neural networks in construction management: a scientometric review. Buildings 12, 952 (2022). https://doi.org/10.3390/buildings12070952 10. Rozemberczki, B., Allen, C., Sarkar, R.: Multi-scale attributed node embedding. J. Complex Netw. 9, cnab014 (2021). https://doi.org/10.1093/comnet/cnab014 11. Samad, A., Qadir, M., Nawaz, I., Islam, M., Aleem, M.: A comprehensive survey of link prediction techniques for social network. EAI Endorsed Trans. Ind. Netw. Intell. Syst. 7, 163988 (2020). https://doi.org/10.4108/eai.13-7-2018.163988

Design and Optimization of a Compact Microstrip Bandpass Filter Using on Open Loop Rectangular Resonators for Wireless Communication Systems Youssef Khardioui1(B) , Kaoutar Elbakkar2 , Ali El Alami1 , and Mohammed El Ghzaoui2 1 ERTTI Laboratory, Faculty of Sciences and Technics, Moulay Ismail University, Meknès,

Morocco [email protected] 2 Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. This article describes the design of a compact band-pass filter with two identical rectangular open loop resonators for a frequency of 3.5 GHz. The proposed filter frequency response covers the 3.5 GHz global interoperability for microwave access (WiMAX). This filter’s structure uses the Rogers RO6010 substrate, which has a dielectric constant of 10.2, a thickness of 1.27 mm, and a tangent loss of 0.0023. The proposed device is intended for wireless communication systems operating at the 3.5 GHz resonance frequency. This bandwidth has a very high activity in the 1.21 GHz band, a small size of (5.72 * 12.34) mm2 and a low insertion loss of − 0.16 dB. Keywords: Bandpass filter · 3.5 GHz · Open-loop resonators · Insertion loss · Wireless communications systems

1 Introduction The microwave pass filter is a two-port device with the ability to limit the frequency for a specific use. It is capable of transmitting frequencies in the bandwidth while attenuating them in the stopping band [1]. The filter is the main component of the microwave communication system. The types of filters used in microwave communication systems include high-pass filter, band-pass filter, low-pass filter and band stop filter. Wireless communication systems use the microwave band-pass filter to eliminate unwanted frequencies [2]. A microwave bandpass filter is a crucial component of wireless communication systems, which contributes to overall performance, whether used for reception or transmission, as it helps to eliminate unwanted frequencies [1]. Wireless communication systems have led to an increase in the need for bandpass type settings with greater accuracy due to the increased requirements and standards adopted in the modern communication system, The novel design of a filter resulted from the necessity for precision, narrow bandwidth, and low loss [2]. Elements such the center © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 64–70, 2024. https://doi.org/10.1007/978-3-031-48573-2_10

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frequency, bandwidth, low-pass frequency, high-pass frequency, etc. must be considered while creating a bandpass filter. Fundamental parts of wireless communication systems with minimal insertion loss, great retrievability, and small size are high-performance bandpass filters [3]. Wireless communication systems frequently include microstrip bandpass filters. These filters have recently been created using open-loop rectangular resonators. The design of microstrip bandpass filters employing open-loop resonators has been studied in various related studies. There were several strategies and frameworks offered. For 5G sub-6 GHz, the suggested filter in [4] has two primary square open-loop resonators operating at an average frequency of 3.65 GHz. For contemporary wireless communication systems like WiMAX and WLAN (3.4 and 5.4 GHz), a small double-band bandpass filter (DB-BPF) is developed utilizing a pair of scaled impedance resonators (SIR) linked with a pair of /2 angled resonators [5, 6]. This article describes the design, modeling, construction, and measurement of a large-band pass-through filter that operates at 3.5 GHz. Two parallel linked lines (T-PCL) with a T-inverted form at their center form the suggested filter, a 70% 3-dB fraction bandwidth microstrip wideband BPF with minimal insertion losses of 0.3 dB. In this study, we proposed a simple Band Pass filter (BPF) downsized on a Roger substrate. At 3.5 GHz mid-band frequency, it consists of two rectangular open-loop resonators. The filter construction only needs a surface area of 72.644 mm2 and is effectively downsized. The suggested filter, which has a frequency range between 2.94 GHz and 4.15 GHz and a minimal insertion loss of roughly − 0.16 dB in comparison to previous band-pass filters described in [6–9], has good selectivity on the 1.21 GHz bandwidth. Simulation studies are carried out using HFSS software. This work’s major objective is to design a pass-band filter structure with wellminimized dimensions and desirable characteristics for usage in wireless communication systems, such as low insertion loss, high bandpass selectivity, and a high coupling coefficient. The advantages of the proposed filter are listed here: • Simpler structure: The architecture of the suggested filter is based on two rectangular open-loops. As a consequence, manufacturing the specified structure with microstrip technology is easy and economical. • Taille compact: The well-miniaturized BPF structure only requires a surface area of 72.644 mm2 (5.72 × 12.7 mm2 ) to function. • Attractive features: It provides favorable values for loss of return, loss of insertion, and coupling coefficients as well as a high degree of selectivity within the interest band.

2 Design of the Proposed Bandpass Filter The design of a filter is determined by a set of limits imposed by the specifications, depending on the intended use. As a result, we concentrate on developing bandpass filters for wireless communication. Table 1 lists the specifications of the suggested filter. Due to their compact structure and small size, square, triangular or rectangular shaped open-loop resonators have been widely used in filter design [5, 10]. Figure 1 shows

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Y. Khardioui et al. Table 1. Specifications of the proposed filter.

Center frequency (GHz)

3.5 GHz

Bandwidth at − 3 dB

1.21 GHz

Insertion loss

− 0.16 dB

Return loss

− 31.07 dB

an open-loop rectangular resonator model. The resonant frequency and circuit board characteristics determine the size of the resonator [11].

Fig. 1. Rectangular open loop resonator

Based on the resonator, the proposed filter is composed of two identical rectangular open-loop resonators, whose geometry is shown in Fig. 2.

Fig. 2. Geometric of the proposed bandpass filter

The thickness of the dielectric substrate used was 1.27 mm, the dielectric constant was 10.2, and the tangent loss was 0.0023. It is the substrate of Rogers RT. The filters are excited by a 50  microstrip line. The ideal dimensions of two open-loop resonators are (Li) length, (w) width, and (s) spacing as shown in Table 2.

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Table 2. Dimensions of the proposed bandpass filter Parameters

Value (mm)

Parameters

Value (mm)

L1

4.03

w

0.39

L2

4.94

s

0.3

L3

4.29

d

0.25

L4

2

3 Simulation Results and Discussion Achieve desired electrical performance in terms of center frequency, bandwidth and insertion loss. Simulation results show that the distance between the resonators is not sufficient. Therefore, we varied the distance s between the two resonators to see the effect on the bandwidth and S-parameters (transmission loss S12, return loss S11). Figures 3 and 4 display the study’s simulation results, while Table 3 provides a summary of the results that were obtained.

Fig. 3. S12 parameters of bandpass filter based proposed for different values of the space s.

Simulation results show that the designed filter has very good pass characteristics, a wide passband of − 3 dB from 2.94 to 4.15 GHz with a central frequency of 3.5 GHz, a fractional bandwidth of 34.6%, and a return loss S11 of over − 31 dB. It shows that you are ready, and the transmission loss S12 is − 0.16 dB. The coupling coefficient between resonators, identified by Kcoeff , is given by Eq. (1) [2] Kcoeff =

2 − F2 Fmax min 2 + F2 Fmax min

(1)

For experimental calculation of the coefficient between resonators, it is necessary to study the two frequencies Fmin and Fmax for different values of space separating the two resonators [12, 13].

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Fig. 4. S11 parameters of bandpass filter based proposed for different values of the space s.

Table 3. Summary of results obtained from the proposed bandpass filter with two identical rectangular open-loop resonators for different values of the space s. s (mm)

F0 (GHz)

S11 (dB)

S21 (dB)

Bandwidth (GHz)

0.28

3.50

− 31.07

− 0.16

1.21

0.3

3.51

− 25.56

− 0.17

1.16

0.4

3.63

− 15.42

− 0.35

0.8

0.5

3.70

− 12.01

− 0.51

0.69

Fig. 5. S11 and S12 parameters for s = 0.28 mm

According to Fig. 6, Fmax decreases with increasing s. On the contrary, Fmin increases with increasing spacing, so the bandwidth becomes small. In addition, with regard to the coupling coefficient, the coupling between the resonators decreases by

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Fig. 6. Spacing effect on the poles of the transmitting frequencies Fmin and Fmax and coupling coefficient Kcoeff .

increasing the spacing between them. These results clearly indicate that the desired band is around 0.28 mm. In comparison to the other works presented in Table 4, the band-pass filter has a low insertion loss (S12), a high return loss (S11), and the smallest size of 5.72 × 12.7 mm2 , confirming that the resonator used in this work has high performance and works well for wireless communication system applications. Table 4. Comparison of performances between the proposed filter and other filters. Ref

Center frequency (GHz)

Return loss (S11) (dB)

Insertion loss (S12) (dB)

Size (mm2 )

[4] (2022)

3.65

− 24.5

− 0.68

7 * 15.46

[14] (2021)

3.0

− 14

–

–

[5] (2021)

3.4

− 23

− 1.4

0.24λg × 0.25λg

[11] (2019)

3.5

–

− 0.19

8.4 × 13.1

[6] (2016)

3.5

− 20

− 0.7

13.24 × 23.30

This work

3.5

− 31.07

− 0.16

5.72 × 12.34

4 Conclusion This article describes the design and optimization of a small microstrip band-pass filter for wireless communication systems like WiMAX operate at 3.5 GHz. The filter is based on two identical rectangular resonators. The results of this article show that the proposed

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bandpass filter has excellent performance with an insertion loss as low as − 0.16 dB, a return loss of approximately − 31.07 dB, and a fractional bandwidth of 34.6%.

References 1. Krishna, V.N., Padmasine, K.G.: A review on microwave band pass filters: materials and design optimization techniques for wireless communication systems. Mater. Sci. Semicond. Process. 154, 107181 (2023). https://doi.org/10.1016/j.mssp.2022.107181 2. Cameron, R.J., Kudsia, C.M., Mansour, R.R.: Microwave Filters for Communication Systems: Fundamentals, Design and Applications, 2nd edn. Wiley, Hoboken (2018) 3. Hong, J.-S.: Microstrip Filters for RF/Microwave Applications, 2nd edn. Wiley, Hoboken, NJ (2011) 4. Slimani, A., Das, S., Ali, W.A.E., El Alami, A., Bennani, S.D., Jorio, M.: Second order microstrip bandpass filter design based on square resonator for 5G sub-6 GHz band. J Inst 17, P07002 (2022). https://doi.org/10.1088/1748-0221/17/07/P07002 5. Moattari, A.M., Bijari, A., Razavi, S.M.: A new compact microstrip dual bandpass filter using stepped impedance and λ/2 bended resonators. Int. J. RF Microwave Comput.-Aided Eng. 31 (2021). https://doi.org/10.1002/mmce.22568 6. Yechou, L., Tribak, A., Kacim, M., Zbitou, J., Sanchez, A.M.: A novel wideband bandpass filter using coupled lines and T-shaped transmission lines with wide stopband on low-cost substrate. PIER C 67, 143–152 (2016). https://doi.org/10.2528/PIERC16062204 7. Saleh, S., Ismail, W., Zainal Abidin, I.S., et al.: 5G hairpin and interdigital bandpass filters. IJIE 12 (2020). https://doi.org/10.30880/ijie.2020.12.06.009 8. Prasetyo, W., Astuti, D.W., Attamimi, S., Alaydrus, M.: Study of rectangular open loop resonators with groundings for low and high coupling. In: 2016 Asia-Pacific Microwave Conference (APMC), New Delhi, India, pp. 1–4. IEEE (2016). https://doi.org/10.1109/APMC. 2016.7931412 9. Arunjith, K.S., Ghivela, G.C., Sengupta, J.: Design and analysis of novel tri-band band pass filter for GSM, WiMax and UWB applications. Wireless Pers. Commun. 118, 3457–3467 (2021). https://doi.org/10.1007/s11277-021-08188-7 10. Ben Haddi, S., Zugari, A., Zakriti, A.: Low losses and compact size microstrip diplexer based on open-loop resonators with new zigzag junction for 5G sub-6-GHz and Wi-Fi communications. PIER Lett. 102, 109–117 (2022). https://doi.org/10.2528/PIERL21120305 11. Karimi, G., Pourasad, Y., Lalbakhsh, A., Siahkamari, H., Mohamadzade, B.: Design of a compact ultra-narrow band dual band filter for WiMAX application. AEU-Int. J. Electron. C. 110, 152827 (2019). https://doi.org/10.1016/j.aeue.2019.152827 12. Jia, S.H.: Couplings of microstrip square open-loop resonators for cross-coupled planar microwave filters. IEEE Trans. Microwave Theory Techn. 44(12) (1996) 13. El Alami, A.: Design, optimization and realization of compact bandpass filter using two identical square open-loop resonators for wireless communications systems (2019) 14. Ohira, M., Ma, Z.: Surrogate-based EM optimization using neural networks for microwave filter design. IEICE Trans. Electron. E105.C(10), 466–473 (2022). https://doi.org/10.1587/ transele.2022MMI0005

YOLO-Based Approach for Intelligent Apple Crop Health Assessment Imane Lasri1(B) , Sidi Mohamed Douiri2 , Naoufal El-Marzouki1 , Anouar Riadsolh1 , and Mourad Elbelkacemi1 1 Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of

Sciences, Mohammed V University in Rabat, Rabat, Morocco {imane_lasri,naoufal_elmarzouki}@um5.ac.ma, [email protected] 2 Laboratory of Mathematics, Computer Science and Applications-Security of Information, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco [email protected]

Abstract. The escalating demands of human reproduction and consumption have placed immense pressure on agricultural companies to enhance production speed and size. However, rapid growth in agricultural operations has led to an increase in human errors, affecting the quality and safety of agricultural products. To mitigate these repercussions, agro-businesses are exploring the potential of artificial intelligence (AI) technologies, specifically computer vision, to detect anomalies and assess the health of agricultural produce. This research paper focuses on utilizing YOLO-based object detection to evaluate the health of apples by detecting common diseases and disorders. The proposed methodology involves training three versions of YOLO models (YOLOv5s, YOLOv5m, and YOLOv7) using different optimization algorithms. The performance results show that YOLOv5m achieved the highest mean average precision (mAP) score of 89% using SGD with Nesterov momentum. Keywords: Apple health assessment · Computer vision · YOLO · Deep learning

1 Introduction Agriculture plays a pivotal role in our society, meeting the ever-growing demands of human reproduction and food consumption. This persistent demand places substantial pressure on agricultural companies to enhance production speed and scale. However, this rapid expansion presents significant challenges. One of these critical challenges is the rising occurrence of human errors within agricultural processes. The relentless drive to boost output burdens workers with overwhelming workloads and tight timelines, undermining their ability to maintain precision and accuracy. These errors not only incur substantial costs for agribusinesses but also directly impact the quality and safety of their products. In response to these challenges, agribusinesses are exploring alternative solutions, with a particular focus on harnessing the potential of artificial intelligence (AI) technologies, including deep learning [1], and machine learning [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 71–77, 2024. https://doi.org/10.1007/978-3-031-48573-2_11

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By tapping into the capabilities of computer vision, agribusinesses can leverage advanced algorithms and image processing techniques to detect anomalies and assess the health of agricultural produce, including fruits such as apples [3]. Prominent among these concerns are diseases like Apple scab, a fungal disease characterized by unsightly scaly lesions on fruit surfaces; Cedar apple rust, which manifests as orange lesions on leaves and fruit; Marssonina blotch, identified by dark brown spots on leaves; and white rot, a fungal infection causing apple tissue breakdown. In light of these considerations, we chose to employ YOLO-based object detection [4] as a fundamental component of our research methodology. YOLO models are recognized for their exceptional efficiency in real-time object detection tasks, making them well-suited for addressing the dynamic and fast-paced nature of agricultural operations. Despite these challenges and opportunities, a comprehensive study utilizing YOLO-based object detection to assess the health of apple crops remains notably absent from the literature. Hence, the objective of this research paper is to fill this critical research gap by employing computer vision techniques, with a specific emphasis on YOLO-based object detection, for apple health evaluation. In the realm of apple pathology, numerous common diseases and disorders pose significant threats to apple crops. The structure of this paper is as follows: Sect. 2 outlines the proposed methodology, detailing the methodologies, tools, technologies, and algorithms employed. Following this, Sect. 3 discusses the setup environment, dataset preprocessing, and provides an analysis of the performance results. Finally, Sect. 4 concludes the paper, summarizing the key findings and delineating potential future directions.

2 Proposed Methodology For achieving the goal of this research, we have used three versions of YOLO models trained using different optimizers as shown in the following subsections. 2.1 Evaluation of Apple Health Using YOLOv5s, YOLOv5m, and YOLOv7 YOLO or (You Only Look Once) [4], is a leading real-time object detection algorithm introduced in 2015. It treats object detection as a regression problem, providing class probabilities for detected images. Over the years, YOLO has undergone significant development, resulting in various improved versions: • YOLOv5: is an open-source project that builds upon the original YOLO algorithm. It introduces improvements, such as the EfficientDet [5] architecture for enhanced accuracy and generalization. YOLOv5 utilizes the larger and diverse dataset with 600 item categories, and it employs dynamic anchor boxes for better object size and shape matching. At present, YOLOv5 has five releases, including 5 s, and 5 m. The YOLOv5 network’s architecture, comprising three key components: the backbone, the neck, and the head. The backbone serves as the feature extraction module, utilizing 53 convolutional layers to extract features from images of various sizes. The neck component further processes these extracted features, adapting them for various spatial scales. This involves a combination of top-down and bottom-up pathways to establish a cross-stage partial path aggregation network.

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• YOLOv7: is characterized by its incorporation of the Extended Efficient Layer Aggregation Network (E-ELAN), a critical component that enriches feature learning. E-ELAN aggregates features from different layers, enhancing the model’s ability to capture diverse and complex patterns in input data. 2.2 Models Optimization In order to manipulate and optimize the loss function of our models we used the following optimization algorithms: Stochastic Gradient Descent (SGD) [6], ADAM or Adaptive Moment Estimation [7], and SGD with Nesterov Momentum [8] represented in the following equation: v(t) = γ · v(t − 1) + α · ∇J (θ (t − 1) − γ · v(t − 1))

(1)

θ (t) = θ (t − 1) − v(t) In which θ(t) represents the parameters at time step t and γ is the momentum coefficient, which determines the contribution of the previous momentum vector v(t − 1) in the current update.

3 Results and Discussion 3.1 Setup Environment In order to make the YOLO model, function properly, it needs an adequate environment, such as having a machine with GPU and a minimum 8GB. For that matter, we tested our models in two different environments: • Cloud machine: Using Kaggle notebooks [9], integrated with ClearML, that has NVIDIA T4 GPU with 16GB of memory. • Local Machine: We used a computer in the laboratory of conception and systems (electronics, signals and informatics) that have GeForce GTX 1070 Ti with 8 GB of RAM for executing the YOLO models. 3.2 Apple Disease Detection Image Dataset We utilized the Apple Disease Detection Image Dataset [10] from Roboflow [11], a comprehensive collection consisting of 12,736 images distributed across five distinct classes (SCAB, HEALTHY, ROT, BLOTCH, and RUST). These images have been thoughtfully divided into three distinct sets to facilitate effective model training and rigorous evaluation. The training set accounts for a significant 87% of the dataset, encompassing a vast array of 11139 images. Additionally, 8% test set, comprising 1061 images. Lastly, the 4% validation set, comprised of 536 images. Figure 1 presents an example of images in the dataset.

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Fig. 1. Example of images in the apple disease detection image dataset

3.3 Data Preprocessing Data preprocessing is a pivotal cornerstone in computer vision, playing a pivotal role in effective model training. To ensure uniformity, we standardized all images to 640 × 640 pixels, simplifying data handling and facilitating consistent model inputs. Elevating image quality, contrast stretching was applied, enhancing clarity and feature discernment. Augmentation techniques, including 90° rotations, ± 25% saturation adjustments, and controlled blurring (up to 2.5 pixels), diversified the training set, exposing the model to real-world variations. Additionally, the Mosaic technique enriched the dataset by combining images into single training samples, increasing complexity and dataset size. 3.4 Performance Results We compared the performance of the three YOLO models: YOLOv5s, YOLOv5m, and YOLOv7. Table 1 shows the best hyperparameters used to train our models. Table 1. Hyperparameters of the three YOLO models. Hyperparameter

Value

Learning rate

0.01

Batch size

16

Momentum

0.999

Weight decay

0.0005

Number of epochs

50

Optimizer

SGD with Nesterov momentum

In Table 2, we present a comprehensive overview of the performance results attained by the three YOLO models. Notably, our findings reveal that YOLOv5m emerges as the standout performer, demonstrating its prowess with a remarkable mAP score of 89%. It is followed closely by YOLOv5s, which delivers a commendable performance with an mAP score of 88%. Figure 2 shows the mAP, precision and recall of the best model YOLOv5m. Table 3 presents a comparative analysis of our proposed approach with several stateof-the-art methods within the domain of apple disease detection. The table evaluates

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Table 2. Mean average precision (mAP) of the three YOLO models on the apple disease detection image dataset. Model

Mean average precision (mAP)

YOLOv5s

88%

YOLOv5m

89%

YOLOv7

87%

Fig. 2. YOLOV5m performance chart on the apple disease detection image dataset

these methods based on various criteria, including the type of plant they focus on, the specific aspects of the plant they target, the number of distinct classes they recognize, and their Mean Average Precision (mAP) scores, which measure the accuracy of object detection. The first two methods, YOLOv3 and YOLOv4 [12], primarily concentrate on tomato plants, with a focus on distinguishing ripe/unripe fruits and identifying infections. They operate with a dataset consisting of 2000 classes and achieve mAP scores of 81.28 and 78.49%, respectively. In contrast, YOLOv5 [13], tailored for grape detection, works with a dataset encompassing 2985 classes and achieves an mAP score of 76.1%. Notably, our proposed model, YOLOv5m, specializes in detecting diseases in apple trees. It excels in recognizing an extensive range of 12736 apple disease classes, demonstrating remarkable accuracy with an mAP score of 89%. This significant improvement in disease detection within the apple context underscores the efficacy of our proposed model.

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Table 3. Comparison of our proposed approach with some state-of-the-art methods in the context of apple disease detection. Methods

Plant

Focus

YOLOv3 [12]

Tomato

Ripe/unripe, infections

2000

81.28

YOLOv4 [12]

Tomato

Ripe/unripe, infections

2000

78.49

YOLOv5 [13]

Grapes

Detection

2985

76.1

Our proposed model: Apple YOLOv5m

Diseases

Number of images

12,736

Mean average precision (mAP) (%)

89

4 Conclusion This research paper explored the use of YOLO-based object detection to assess the health of apples by detecting common diseases and disorders. The study utilized the apple disease detection image dataset from Roboflow, consisting of 12,736 images distributed across classes such as SCAB, HEALTHY, ROT, BLOTCH, and RUST. To effectively train and evaluate the YOLOv5s, YOLOv5m, and YOLOv7 models, the dataset was split into training (80%), testing (10%), and validation (10%) sets. The performance results indicated that YOLOv5m achieved the highest mean average precision (mAP) score of 89% when utilizing SGD with Nesterov momentum as the optimization algorithm. This demonstrated the effectiveness of YOLO-based object detection in accurately detecting diseases and disorders in apple crops. However, despite the promising results, the research also highlighted a limitation related to the dataset. Specifically, the under-representation of the RUST class in the dataset posed a challenge in accurately detecting rust on apple crops. In our future work we will enhance the rust detection accuracy by collecting additional images specifically targeting rust-infected apples or applying data augmentation techniques to address the under-representation issue.

References 1. Lasri, I., Riadsolh, A., Elbelkacemi, M.: Facial emotion recognition of deaf and hard-ofhearing students for engagement detection using deep learning. Educ. Inform. Technol. 28(4), 4069–4092 (2023). https://doi.org/10.1007/s10639-022-11370-4 2. Lasri, I., Riadsolh, A., El Belkacemi, M.: Toward an effective analysis of COVID-19 Moroccan business survey data using machine learning techniques. In: Proceedings of the 13th International Conference on Machine Learning and Computing, pp. 50–58 (2021). https:// doi.org/10.1145/3457682.3457690 3. Seema, K.A., Gill, G.S.: Automatic fruit grading and classification system using computer vision: a review. In: Proceedings of the 2015 Second International Conference on Advances in

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13.

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Computing and Communication Engineering, pp. 598–603 (2015). https://doi.org/10.1109/ ICACCE.2015.15 Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7790–788. Las Vegas, NV, USA (2016). https://doi.org/10. 1109/CVPR.2016.91 Tan, M., Pang, R., Le, Q.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10781–10790 (2020) Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT), pp. 177–186 (2010) Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980v9 Nesterov, Y.: A method of solving a convex programming problem with convergence rate o(1/k2). Soviet Math. Doklady 27(2), 372–376 (1983) Kaggle. https://www.kaggle.com/. Accessed 24 March 2023 Apple Disease Detection Image Dataset. https://universe.roboflow.com/pfe-ns5wl/apple-dis ease-detection-jspkx/dataset/1. Accessed 24 March 2023 Roboflow. https://roboflow.com/. Accessed 26 March 2023 Thakkar, F., Saha, G., Shahnaz, C., Hu, Y.-C.: In: Proceedings of the International EConference on Intelligent Systems and Signal Processing, Advances in Intelligent Systems and Computing, Springer, Singapore, pp. 511–522 (2022) Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., Marinello, F.: Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms. Agronomy 12(2), 319 (2022)

A Comprehensive Review on the Integration of Blockchain Technology with IPFS in IoT Ecosystem Soufian El Airaj1(B) , Fatima Amounas2 , Mourade Azrour3 , and Mohamed Badiy1 1 Faculty of Sciences and Technics, Moulay Ismail University of Meknes, Errachidia, Morocco

[email protected], [email protected]

2 RO.AL&I Group, Computer Sciences Department, Faculty of Sciences and Technics, Moulay

Ismail University of Meknes, Errachidia, Morocco [email protected] 3 Engineering Science and Technology Laboratory, IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected]

Abstract. Blockchain (BC) has become a popular technology in recent years due to its association with Bitcoin. The decentralized, secure, and transparent nature of blockchain technology has made it an attractive solution for a variety of possible use cases in several fields, including finance, industries and medical centers. IoT devices may be made more secure and tamper-proof by using this technology. A secure, decentralized ledger of transactions and data that can be accessed only by authorized parties may be made by using Blockchain technology. This can help prevent unauthorized access to IoT devices and data, which can be crucial for maintaining the privacy and security of sensitive information. Many researchers are becoming more interested in using blockchain technology to guarantee IoT data privacy without the requirement for a centralized data access paradigm. The Inter-Planetary File System (IPFS) has emerged as a key technology to provide decentralized access control. This article examines several current methods related to integrating Blockchain technology and IoT. The fundamental idea behind this current survey is to address the security issues associated with the integration of IoT and blockchain technology with the help of IPFS. Finally, this paper discusses the challenges and future enhancements for BC-IoT-based systems. Keywords: Security · Internet of Things · Blockchain technology · Healthcare · IPFS

1 Introduction The Internet of Things (IoT) is an emerging technology that consists of billions of connected devices that communicate and share information without human supervision. In many aspects of life, such as domestic, healthcare, environment, industry, water management, energy, and others, it has evolved into a necessary tool. IoT applications typically © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 78–83, 2024. https://doi.org/10.1007/978-3-031-48573-2_12

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generate massive volumes of data, which are frequently stored either permanently or momentarily in untrusted storage, such as cloud or edge servers that are run by outside parties and have Internet access. Security is the key issue with centralized storage systems. Despite the benefits of the IoT, the sheer amount of data generated by it can lead to privacy issues. Therefore, the adoption of new solutions based on a decentralized architecture is now a necessity. A blockchain is a public digital ledger built on a peer-to-peer network. It can be openly distributed to various users to create an immutable history of relevant timed transactions. This technology uses a decentralized, distributed, public, and real-time ledger to store transactions among IoT nodes [1]. In the context of the Internet of Things, Blockchain is a new technology that uses a distributed public real-time ledger to store transactions between IoT nodes. This technology can help improve the security and privacy of connected devices by providing a tamper-proof record of transactions and events. Another promising paradigm used is IPFS, which provides decentralized access control. IPFS is a peer-to-peer distributed file system. The innovation with IPFS is that content-based addressing has taken on the role of location-based addressing. In other words, rather than using the location where a piece of data is stored, we require its hash in order to obtain it. A unique hash is created for each file that is delivered to the IPFS for storage [2]. In the current study, we are essentially conducting a literature review on security and privacy solutions for IoT using Blockchain and IPFS in the healthcare field. The focus of this review paper is to describe how blockchain technology and IPFS can be applied in the IoT ecosystem to address the security issues that arise. In what follows, the fundamental architecture of IoT with blockchain is explained in Sect. 2. Some recent research on the integration of IoT and Blockchain with IPFS is discussed in Sect. 3. Finally, the conclusion and some of the future research directions are given in Sect. 4.

2 IoT-Blockchain Architecture The integration of blockchain with the IoT has become a necessity to overcome the problems of centralized IoT architecture. As depicted in Fig. 1, the IoT with blockchain architecture consists of four layers, including perception, networking, IoT-blockchain and application layers. The new IoT-Blockchain layer is added to the traditional IoT architecture [3]. This paper explores the benefits and implications of this new layer. In addition, three levels make up the IoT-blockchain and IPFS-based system. The first one is responsible for data collection. The second level is in charge of sharing data securely, and the technology that is ensuring that is Blockchain. The last one is for data storage with IPFS.

3 Reviewed Works Nowadays, the Internet of Things and smart medical devices are widely used to provide remote patient monitoring. The adoption of a distributed architecture is required to overcome the limits of the centralized architecture. Although the study of blockchain is relatively recent, numerous literature reviews have been conducted on the topic in general. Few scientists have used the blockchain technology and IPFS to support Healtcare-IoT

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Fig. 1. The IoT with Blockchain Architecture

systems and related issues of security. In this work, we investigate how complementary technologies like IPFS and blockchain technology contribute to improve security and privacy in Healthcare domain. In 2019, Srivastava et al. [4] suggested a blockchain-based IoT model for secure remote patient monitoring. The authors focus on the integration of the Blockchain technology in remote patient monitoring using IoT devices. To enhance the data security, they adopt advanced cryptographic techniques and introduce privacy measures such as Ring Signatures. The proposed model ensures reliable communication and storage while protecting sensitive healthcare data. In the same year, Xu et al. [5] carried out another work. Hence, the authors have introduced a new system called “Healthchain”, which is a confidentiality-preserving scheme put forth for safeguarding extensive health data, addressing the challenge of large-scale networks. The proposed scheme uses subblockchains to avoid tampering with transactions and supports diagnostic transactions secured by diagnostic keys. The authors demonstrated that their scheme meets security requirements and that it is effective when used in smart healthcare system. In 2020, Bhattacharya et al. [6] introduced a lightweight blockchain-based scheme named “Heal” that enables secure exchange of electronic health records through wireless channels. HeaL operates in two phases, utilizing proximity sensor nodes and gateway sensor nodes. It achieves efficient signing and verifying costs, reduced mining latency, improved transactional throughput, and has been evaluated for its security and viability compared to other approaches. One year later, Kumar et al. [7] proposed a blockchain-based solution to address issues with privacy, security, and storage management in IoMT infrastructure. Their framework encompasses initialization and authentication levels, leveraging IPFS for distributed storage. They demonstrate that the proposed method ensures privacy, eliminates reliance on third parties, and achieves secure and efficient management of the device generated medical data. In the same year, Sharma et al. [8] presented an extensive literature review that covers the various applications of blockchain technology in healthcare. The authors investigated significant use cases within the healthcare sector for blockchain technology such as data sharing, data storage, clinical trials, drug traceability, and remote patient monitoring. In 2022, Adere [9] analyzed the fusion of

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blockchain and IoT for the purpose of data management. The author investigates a new trend and emphasizes the potential advantages of blockchain adoption in the IoT and healthcare sectors. Their review article examines how blockchain technology is being used in data management activities into healthcare ecosystem. Also, the author highlights the advantages of using blockchain in two subgroups of the two domains: smart cities and medication supply chains, which are, respectively, the IoT and healthcare sectors’ most prominent application areas. In the same year, Azbeg et al. [10] introduced a blockchain-based healthcare system that enables periodic and remote monitoring of patients suffering from chronic diseases. The authors adopt the Blockchain technology with the IPFS to enhance security in healthcare system. To address the security issues, they employed the re-encryption proxy with the Blockchain to encrypt data. To ensure Blockchain scalability, data is stored in an IPFS off-chain database. Security, scalability, and processing speed were three crucial factors that were taken into consideration. The results demonstrate a significant improvement in healthcare system security over existing approaches. In 2023, Alam et al. [11] investigated several consensus methods commonly utilized across blockchain applications and found suitable IoT networks to support electronic health record systems and other medical services. During that year, Nukapeyi et al. [12] conducted a study on the “Smart TeleHealthcare using Blockchain and IPFS” (STBI) system, which aims to integrate IoT and Blockchain to deliver secure and transparent healthcare services in rural areas. This innovative system establishes a connection between patients and medical teams through a Blockchain network and leverages decentralized IPFS storage for storing health reports. STBI enables authorized stakeholders to share electronic medical data, thereby enhancing collaborative medical treatment (Table 1). Table 1. A comparative summary of the reviewed papers related to healthcare. Metrics Srivastava Xu Bhattacharya Kumar Sharma Adere Azbeg Alam Nukapeyi et al. [4] et al. et al. [6] and et al. [9] et al. et al. et al. [12] [5] Tripathi [8] [10] [11] [7] a

✔

✔

✔

✔

✔

✔

✔

✔

✔

b

–

✔

✔

✔

✔

–

–

✔

✔‘

c

–

–

–

✔

✔

–

✔

–

–

d

–

✔

✔

✔

–

✔

–

–

✔

e

✔

✔

–

–

–

✔

✔

–

✔

f

–

–

–

–

✔

–

–

✔

–

Note (a) Blockchain, (b) IPFS, (c) Scalability, (d) Security, (e) Privacy, (f) Interoperability, ✔:valid; –: not valid

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4 Challenges and Future Research Directions The integration of blockchain technology with the Internet of Things (IoT) presents several challenges [13]. In Table 2, we provide some challenges, possible solutions, and future directions related to combining the blockchain technology with IoT. Table 2. Challenges and future directions for the BC-IoT-based systems. Challenge

Main idea

Solutions

Future directions

Anonymity and data privacy

Preserving privacy and anonymity while incorporating block-chain, which offers transparency and immutability, into the massive volume of data generated by IoT

Implement privacy-enhancing techniques such as zero-knowledge proofs, encryption, and decentralized identity management systems

- Design adaptable and dynamic security frameworks for IoT and blockchain integration - Explore the use of federated learning-based blockchain to enhance privacy

Security-related system components

Ensuring the security and integrity of system components, including secure communication channels, cryptographic key management, and device protection

Implement robust encryption algorithms, secure hardware modules, and intrusion detection systems

- Develop lightweight blockchain solutions suitable for IoT - Integrate Blockchain with cloud computing and IoT architecture for secure patient record storage

Storage capacity and scalability

Addressing limitations in storing and scaling the enormous amount of data generated by IoT, as traditional blockchain systems may face storage and scalability challenges

Utilize off-chain storage solutions, data sharding techniques, and distributed file systems like IPFS

- Explore lightweight blockchain design optimized for IoT applications - Investigate the use of quantum computing in enhancing storage and scalability

Resource utilization

Optimizing the use of limited computational power, storage capacity, and energy resources in IoT devices while integrating blockchain technology

Implement lightweight consensus algorithms, energy-efficient mining protocols, and compression techniques for reducing data size

- Investigate energy-efficient consensus algorithms for green IoT systems - Investigate the integration of software-defined networking for resource optimization - Explore the integration of 6G and IA for resource optimization

Predictability

Ensuring real-time or near-real-time responses in IoT systems while dealing with the latency and unpredictability introduced by blockchain consensus mechanisms

Develop scalable consensus algorithms with faster transaction confirmation times and explore the use of off-chain state channels for instant transaction settlements

- Develop lightweight blockchain protocols for time-critical IoT applications - Investigate the impact of 6G and IA on the predictability of IoT systems

5 Conclusion The study performed in this paper is mainly presented the benefits of blockchain-based security approaches for deployment in IoT. Here, we mentioned the IoT with Blockchain Architecture. After that, we investigated how complementary technologies like IPFS and

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blockchain technology contribute to improve security and privacy in IoT environment. After comparing recent articles published, we have concluded that the majority of them is based on BC technology. Finally, we highlight the crucial challenges of BCIoT in Healthcare with existing solutions, and possible future directions are recommended to strengthen the BCIoT architecture for Healthcare services.

References 1. Alam, T.: Blockchain and its role in the internet of things (IoT). Int. J. Sci. Res. Comput. Sci. Eng. Inform. Technol. 5(1), 152–157 (2019) 2. Azbeg, K., Ouchetto, O., Andaloussi, S.J.: BlockMedCare: a healthcare system based on IoT, Blockchain and IPFS for data management security. Egy. Inform. J. 23(2), 329–343 (2022) 3. Azrour, M., Mabrouki, J., Guezzaz, A., Kanwal, A.: Internet of things security: challenges and key issues. Sec. Commun. Netw. 2021, 11 (2021) 4. Srivastava, G., Crichigno, J., Dhar, S.: A light and secure healthcare blockchain for IoT medical devices. In: Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada, pp. 1–5 (2019) 5. Xu, J., et al.: Healthchain: a blockchain-based privacy preserving scheme for large-scale health data. IEEE Internet Things J. 6(5), 8770–8781 (2019) 6. Bhattacharya, P., Mehta, P., Tanwar, S., Obaidat, M.S., Hsiao, K.-F.: HeaL: a blockchainenvisioned signcryption scheme for healthcare IoT ecosystems. In: Proceedings of the 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), Sharjah, United Arab Emirates, pp. 1–6 (2020) 7. Kumar, R., Tripathi, R.: Towards design and implementation of security and privacy framework for internet of medical things (IoMT) by leveraging blockchain and IPFS technology. J. Supercomput. 14, 1–40 (2021) 8. Sharma, A., Kaur, S., Singh, M.: A comprehensive review on blockchain and Internet of Things in healthcare. Trans. Emerg. Telecommun. Technol. 32, 10 (2021) 9. Adere, E.M.: Blockchain in healthcare and IoT: a systematic literature review. Array 14, 100139 (2022) 10. Azbeg, K., Ouchetto, O., Andaloussi, S.J.: Access control and privacy-preserving blockchainbased system for diseases management. IEEE Trans. Comput. Soc. Syst. (2022) 11. Alam, S., et al.: An overview of blockchain and IoT integration for secure and reliable health records monitoring. Sustainability 15, 5660 (2023) 12. Nukapeyi, S., Sri, P.J., Neeharika, R.M., Priyankarao, S., Harshini, K.S.: Smart tele-healthcare using blockchain and IPFS. In: Proceedings of the 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1387–1393 (2023) 13. Adhikari, N., Ramkumar, M.: IoT and blockchain integration: applications, opportunities, and challenges. Network 3(1), 115–141 (2023). https://doi.org/10.3390/network3010006

An Integrated Approach for Artifact Elimination in EEG Signals: Combining Variational Mode Decomposition with Blind Source Separation (VMD-BSS) H. Massar1,3(B) , M. Miyara2 , T. Belhoussine Drissi1 , and B. Nsiri3 1 Laboratory of Electrical and Industrial Engineering, Information Processing, Informatics, and

Logistics (GEITIIL), Faculty of Science Ain Chock, University Hassan II, Casablanca, Morocco [email protected] 2 Computer Science and Systems Laboratory (LIS), Faculty of Science Ain Chock, University Hassan II, Casablanca, Morocco 3 Research Center STIS, M2CS, National School of Arts and Crafts of Rabat (ENSAM), Mohammed V University in Rabat, Rabat, Morocco

Abstract. Physiological artifacts, such as muscular activity, heartbeat, and eye movements, must be minimized in EEG study while maintaining neuronal information. To solve this, we put forth a brand-new hybrid strategy that combines Infomax, a Blind Source Separation (BSS) technique, with Variational Mode Decomposition (VMD), a method for reducing noise. By using measurements like the Euclidean Distance (ED) and Spearman Correlation Coefficient (SCC), our technique successfully eliminates ocular artifacts. Using 7 Intrinsic Mode Functions (IMFs), the best results are obtained. This study advances EEG artifact removal techniques and provides new information on how to analyze EEG data going forward. Keywords: Variational mode decomposition · Blind source separation · Electroencephalogram

1 Introduction Neurons communicate through electrical impulses, creating brain waves that an EEG detects. This machine captures and amplifies these signals, revealing synchronized neuron firing. By placing electrodes on the scalp, it detects and analyzes small electric currents during neuron communication. EEG signals provide insights into the brain’s electrical activity, crucial for diagnosing neurological conditions and understanding brain function [1]. However, EEG recordings can be disrupted by artifacts, and unwanted external signals that complicate interpretation. These artifacts can stem from biological activities like ocular, muscular, cardiac, sweat, and respiration effects. Reducing these artifacts is essential to improve the accuracy and reliability of EEG analysis [2, 3]. To address this artifact while preserving neuronal signals, the researcher proposed several methods, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 84–90, 2024. https://doi.org/10.1007/978-3-031-48573-2_13

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including Blind Source Separation (BSS), an unsupervised learning technique that determines unidentified sources based on the dataset [4]. BSS is widely used for multi-EEG channel artifact removal and has shown promise in biomedical signal processing by effectively eliminating artifacts. Another approach is the Variational Mode Decomposition (VMD), an adaptive time-frequency analysis method. VMD decomposes signals into modes with varying frequencies, amplitudes, and time-varying characteristics, making it suitable for fault diagnosis, vibration analysis, biomedical signal processing, and audio signal separation [5]. Particularly in single-channel signal processing, VMD is highly effective at identifying and removing unwanted artifacts [6]. Several studies suggest and employ methods to analyze and assess EEG signals for artifact elimination. These methods use techniques like BSS, VMD, and hybrid approaches combining them. Stergiadis et al. [7] evaluated five popular BSS algorithms for EEG artifact elimination using statistical metrics like Spearman correlation coefficient, Shannon entropy, and Euclidean distance. CHEN et al. [5] introduced a hybrid method that combines VMD and canonical correlation analysis (CCA) to suppress muscle artifacts in EEG signals. VMD decomposes EEG channels into intrinsic mode functions (IMFs), while CCA isolates potential artifact components. DORA et al. [8] proposed an algorithm using modified VMD to extract band-limited IMFs from EEG epochs. This algorithm identifies ECG artifact components, estimates an ECG reference, suppresses QRS complexes, and reconstructs the EEG signal with the remaining BLIMFs. KAUR et al. [9] conducted a comparative analysis of denoising approaches for physiological signals, including VMD-DWT, VMD-WPT, and other methods. They used mode selection criteria with detrended fluctuation analysis (DFA) and applied VMD for signal decomposition and DWT/WPT for denoising. In single-channel EEG artifact removal, LUI et al. [10] introduced a popular approach combining decomposition and BSS techniques, widely adopted for addressing artifact-related challenges in EEG signal processing. This study introduces a novel hybrid method to improve the removal of ocular artifacts from EEG signals. Our approach merges two established techniques, VMD and BSS. Firstly, VMD breaks down each EEG signal into IMFs. Subsequently, Infomax decomposition is applied to these IMFs to extract Independent Components (IC) and eliminate artifacts. This combination of signal decomposition methods effectively addresses the primary challenge in EEG research related to physiological artifact removal while preserving essential neural information. Additionally, our proposed approach prioritizes energy efficiency, aligning with sustainability requirements in EEG research. We evaluate our methodology using metrics like Spearman correlation coefficient and Euclidean distance. The organization of this study is as follows: Sect. 2 presents the techniques, methodology, and datasets employed in this research. Section 3 presents the findings, while Sect. 4 concludes our research with a discussion.

2 Material and Methods 2.1 Datasets In our study, we used a semi-simulated EEG dataset from 27 participants with 19 EEG sensors placed according to the 10–20 International Method. We collected 54 datasets, each lasting 30 s, with two from each participant. The data underwent notch filtering

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at 50 Hz, bandpass filtering from 0.5 to 40 Hz, and had a 200 Hz sampling frequency. We also obtained horizontal (HEOG) and vertical (VEOG) EOG signals from the same participants with their eyes open. For further dataset details, please request, and refer to [11] for more information. 2.2 The Variational Mode Decomposition The Variational Mode Decomposition (VMD) is a data-driven signal processing method introduced in 2014 by Drago-Miretskiy and Zosso. It breaks down a signal, X(t), into oscillatory components called Intrinsic Mode Functions (IMFs) or modes. VMD’s goal is to minimize the total bandwidth of these modes while ensuring they reconstruct the original signal. This is done using a generalized Weiner filter and multiple-band approaches. The result is a set of band-limited IMFs (BLIMFs) centered around specific frequencies [8].  K    K 2    j −jwk t ∗ uk e ∂ ∂(t) +  s.t; uk = X (t) (1) minuk ,wk = πt 2 k=1

k=1

In this paragraph, δ(t) represents the Dirac distribution, * and ∂ represents the convolution and partial differential operators respectively. Additionally, uk and wk refer to the kth mode (k = 1, 2,…, K) of VMD and its corresponding center frequency. The solution is obtained as the critical point of the augmented Lagrangian L , as follows: 2   K   j −jwk t L({uk }, {wk }, λ) = α ∂ ∂(t) + π t ∗ uk e 2 k=1 K K 2   + X (t) − uk (t) + λ(t)X (t) − uk (t) k=1

2

(2)

k=1

where the sentencing penalty is α. Using the alternate direction method of multipliers (ADMM), it is possible to determine where L ‘s saddle point lies. Wiener filtering in the Fourier domain immediately modifies the optimum uk 2.3 The Blind Source Separation Blind Source Separation (BSS) isolates underlying source components from mixed signals without prior knowledge of the sources or mixing process [12]. It combines unidentified sources to recover the original signals, often by reversing the mixing process. Various mixing models exist, with instantaneous linear mixing being the most basic, represented by Eq. (3) [12]. X (Q, m) = A(Q, P)S(P, m)

(3)

The mixing matrix, A, has dimensions (Q, P), where Q represents the number of mixed signals and P represents the number of source signals. X represents the mixed

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signals and S is the source signals. Infomax is a BSS algorithm that utilizes optimization to maximize entropy [7]. Infomax uses the following formula to estimate the separation matrix. C(t + 1) = C(t) + m(t)(I − H(Y)DT)C(t)

(4)

where D stands for the estimated source, C for the separation matrix, m(t) for the learning rate function, H for the distribution function, and T for the transport matrix. 2.4 The Proposed Method This study presents a hybrid approach to remove ocular artifacts from EEG signals. The method combines the VMD technique with established BSS algorithms. Initially, signals are decomposed into an IMF matrix using VMD (Fig. 1). The Infomax algorithm then extracts underlying sources using this IMF matrix. Finally, artifacts are eliminated using hard thresholding based on a specific threshold as per the following equation.:

Ti = σ i 2log(Ni ) (5) Were σ i = median(Li(t))/(0.67 · E)

(6)

E is a constant value (E = 2 in this cas), and Ni represents the number of samples in the ith source of Li(t) [6]. The following equation describes the thresholding procedure used in our research:  S if S < T D(y) = (7) 0 if S > T where D(y) indicates the ith denoised source. In the final stage, we apply inverse BSS to combine the denoised sources and then use inverse VMD to reconstruct the original signals, resulting in an ocular artifact-free EEG signal. The workflow is depicted in Fig. 1.

IMFs for Channel i

Channel i

BSS

VMD Clean Channel i

Separated IMFs i

Threshold

Clean IMFs i

Inverse VMD

Invesre BSS

Fig. 1. The suggested method’s (VMD-BSS) steps. There are five steps in the diagram: 1) EEG decomposition with VMD, 2) Using the BSS method to separate the extracted IMF, 3) removing the corresponding artifact using the threshold method 4) using the inverse BSS and the inverse VMD to reconstruct the clean EEG signal. i is the channel number

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We use signal decomposition to improve EEG data quality by removing artifacts while preserving neural information integrity. Our focus on energy efficiency involves software optimizations and choosing low-power hardware components, aligning with EEG research sustainability goals. The evaluation criteria. The Spearman correlation coefficient (SCC) is a statistical metric (-1 to + 1) measuring the strength and direction of the relationship between two ranked variables [7]: SCC = 1 − 6



c2

j j2 − 1

(8)

where j is the number of data pairs, and c is the square of the difference between the ranks of the two locations for each data pair. Euclidean Distance (ED) is the shortest distance between any two places in Cartesian coordinates [7].   (9) d (A, B) = (an − bn )2 n

with an and bn as their coordinates.

3 Results Our study aimed to enhance EEG signal denoising by combining VMD and Infomax methods with varying IMF numbers (K). This approach effectively removed ocular artifacts, resulting in refined signals. We evaluated our method across 54 datasets using Matlab 2019a and EEGLAB, comparing pristine and denoised EEG signals. Table 1 contains the mean values for each K. Table 1. The Euclidean value and SCC for each IMF number, for the proposed method The IMFs number

K=5

K=6

K=7

K=8

K=9

K = 10

SCC

0.782

0.802

0.804

0.793

0.792

0.782

Euclidean distance

776.86

735.12

733.18

757.54

758.54

776.9

The choice of ‘k’ in VMD significantly impacts our ocular artifact removal approach. An SCC analysis indicates that ‘k’ around 7 achieves a high value of 0.804, effectively preserving EEG information. Smaller ‘k’ values like 6 and 7 result in lower Euclidean distances, indicating superior artifact removal through finer decomposition. In summary, ‘k’ selection in VMD is crucial; ‘k’ around 7 balances granularity and EEG information preservation, considering practical computational complexity. These findings emphasize

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the importance of parameter tuning for optimizing our approach for specific EEG datasets and suggest potential improvements through ‘k’ fine-tuning based on data characteristics. Further research on diverse EEG datasets and evaluation metrics can comprehensively assess our method’s performance.

4 Conclusion and Discussion Zhang et al. [13] introduced a single-channel BSS method using VMD for wind turbine aeroacoustic signal separation, yielding a correlation coefficient of 0.76. In contrast, Stergiadis et al. [6] conducted a comprehensive study comparing five BSS algorithms, with results of 3.25·103 (VEOG) and 4.16·103 (HEOG) using Euclidean distance. These studies vary in methodology and evaluation metrics. The first paper focuses on a specific SCBSS method with VMD for single acoustic sensor data, emphasizing its effectiveness through correlation analysis. The second study takes a broader approach, evaluating multiple BSS algorithms with the Euclidean distance metric, considering both VEOG and HEOG. The choice between these approaches depends on specific wind turbine aeroacoustic signal separation objectives and requirements, offering complementary insights into signal separation quality (Table 2). Table 2. Findings from assessments of how well previous studies have performed in removing ocular artifacts from EEG recordings. Study

Method

Zhang et al. [13]

Single-channel blind Correlation coefficient 0.76 source separation method based on variational mode decomposition

Stergiadis et al. [6]

Blind Source Separation

The proposed method VMD—BSS (INFOMAX)

Evaluation criteria

Euclidean distance

Results

3.25·103 with VEOG, 4.16·103 with HEOG

Spearman Correlation 0.804 Coefficient Euclidean distance

733.18

In this paper, we introduce an innovative approach that combines the INFOMAX algorithm with the VMD method to enhance artifact removal. This integration leverages both methods’ strengths for improved signal separation and more accurate artifact removal, advancing data analysis in various fields. However, there are limitations to consider, such as the assumption of complete separability between ocular artifacts and brain signals, accurate threshold estimation, and potential variability in performance under different conditions. Our method’s versatility, as introduced by the parameter ‘k,’ enhances adaptability to various data types and conditions. While promising, our approach requires addressing these limitations, and future research should focus on expanding its applications in EEG data interpretation.

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References 1. Chen, Q., Li, Y., Yuan, X.: A hybrid method for muscle artifact removal from EEG signals. J. Neurosci. Methods 353, 109104 (2021) 2. Dora, C., Biswal, P.K.: Correlation-based ECG artifact correction from single channel EEG using modified variational mode decomposition. Comput. Methods Prog. Biomed. 183, 105092 (2020) 3. Jiang, X., Bian, G.-B., Tian, Z.: Removal of artifacts from EEG signals: a review. Sensors 19(5), 987 (2019) 4. Ranjan, R., Sahana, B.C., Bhandari, A.K.: Ocular artifact elimination from electroencephalography signals: a systematic review. Biocyber. Biomed. Eng. 41(3), 960–996 (2021) 5. Rashmi, C.R., Shantala, C.P.: EEG artifacts detection and removal techniques for braincomputer interface applications: a systematic review. Int. J. Adv. Technol. Eng. Explor. 9(88), 354 (2022) 6. Stergiadis, C., Kostaridou, V.-D., Klados, M.A.: Which BSS method separates better the EEG Signals? A comparison of five different algorithms. Biomed. Sig. Process. Control 72, 103292 (2022) 7. Mannan, M.M.N., Kamran, M.A., Jeong, M.Y.: Identification and removal of physiological artifacts from electroencephalogram signals: a review. IEEE Access 6, 30630–30652 (2018) 8. Zhou, W., Chelidze, D.: Blind source separation-based vibration mode identification. Mech. Syst. Sig. Process. 21(8), 3072–3087 (2007) 9. Li, H., Liu, T., Wu, X., et al.: An optimized VMD method and its applications in bearing fault diagnosis. Measurement 166, 108185 (2020) 10. Liu, C., Zhang, C.: Remove artifacts from a single-channel EEG based on VMD and SOBI. Sensors 22(17), 6698 (2022) 11. Klados, M.A., Bamidis, P.D.: A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief 8, 1004–1006 (2016). https://doi.org/10.1016/ j.dib.2016.06.032 12. Kaur, C., Bisht, A., Singh, P., et al.: EEG signal denoising using hybrid approach of variational mode decomposition and wavelets for depression. Biomed. Sig. Process. Control 65, 102337 (2021) 13. Zhang, Y., Qi, S., Zhou, L.: Single channel blind source separation for wind turbine aeroacoustics signals based on variational mode decomposition. IEEE Access 6, 73952–73964 (2018)

E-Health Blockchain: Conception of a New Smart Healthcare Architecture Based on Deep Reinforcement Learning Soumia Benkou(B) , Ahmed Asimi, and Lahdoud Mbarek Laboratory: Computer Systems and Vision Laboratory (LabSiv), Team: Security, Cryptology, Access Control and Modelling (SCCAM), Department of Mathematics, Faculty of Sciences, University Ibnou Zohr, Agadir, Morocco [email protected], [email protected], [email protected]

Abstract. The recording, manipulation and exploitation of medical data represent a challenge for the protection of medical data against malicious users. Several researchers have proposed protocols and architectures to assure the integrity, confidentiality, and privacy of data storing on Cloud. Subsequently, the use of Blockchain and especially hybrid is the best promise to revolutionize the healthcare industry to store data securely. In this article, we inroduce a smart conception of healthcare architecture based on deep reinforcement learning, called E-health Blockchain. It combines three phases, from the patient’s registration at the hospital to his recovery: (1) the pre-processing phase where the patient starts depositing his data, (2) the processing phase by the service designated by deep reinforcement learning and (3) the control phase where the doctor decides on the basis of certain information and analyses whether the patient can be discharged or not. We start the first phase by initializing the Blockchain, where we define the different blocks that make up the hybrid Blockchain, then the symptoms stored by the patients are classified using reinforcement learning to determine the appropriate service for each patient. In the second phase, we process the patient’s condition in order to prescribe an appropriate diagnosis for their illness, or even a more in-depth consultation in the case of a complicated pathology. In the final phase, a decision is taken on the basis of the conditions for recovery met by the patient. This architecture makes it possible to achieve greater efficiency and meet security requirements in terms of confidentiality and secure sharing of patient data, on the one hand, and ubiquitous maintenance in the event of failure of a given service, on the other. Keywords: Blockchain technology · E-health · Smart healthcare · Deep reinforcement learning · Cloud computing · Security

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 91–99, 2024. https://doi.org/10.1007/978-3-031-48573-2_14

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1 Introduction and Notations 1.1 Introduction Lack of communication between different types of services, corruption of data, cost of hospitalization and lack of thorough diagnosis of diseases are a challenge for health systems around the world. Blockchain technology presents an effective remedy to the interoperability challenges confronted by conventional healthcare systems that are presently in use. Also, the use of the cloud for data storage remains insufficient in view of the various operations that must be applied to medical data. Cloud centralization puts data at risk and availability problem at all times. Therefore, the objective of Blockchain is to store securely data [1], auditing as one of the health entities (healthcare institutions, medical researchers, etc.) and even strengthen recommendations to physicians [2]. It was founded and introduced by Nakamoto [3] in cryptocurrency to store data, transactions, and operations securely. Nakamoto used Blockchain technology (distributed ledger technology, cryptography and consensus algorithms) to solve the problem of double spending (when the same digital token is spent more than once at the same time) without having to use intermediaries (banks or financial institutions). The Blockchain, as an architecture, is a series of blocks, that includes a list of complete and building a chain (as shown in Fig. 1) in which the initial block is known as the genesis block. In our paper, the genesis block divided into two types: first one is the Blockchain genesis, the second is the patient genesis. There are three types of Blockchain [4]: public Blockchain [5], private Blockchain [6] and hybrid (consortium) Blockchain [7]. In our paper, we choose the hybrid one because using Blockchain in healthcare requires the adaptation of hybrid Blockchain to preserve the confidentiality of sensitive patient data and allow access to other public data. One of the advantages of Blockchain, as a technology, is that it preserves the integrity of patient data when information is exchanged between different actors. This advantage allows the secure exchange of sensitive data in healthcare. Our contribution in this work is a new smart healthcare architecture based on deep reinforcement learning to store medical data provided by the patient, manage it, take care of the patient’s health until she or he is fully recovered. This is done through phases and reinforcement learning based on symptoms validated by the patient. The rest of the paper is organized as follows: in the same section, we announce the notations we use on this paper. The section after, we have the related works. Next, we introduce our contribution by defining the architecture, describing it with the three phases. The last section summarizes as a conclusion, the usefulness of our contribution, why it is so important to move on this type of application Blockchain on E-health and why we adapt the PoA consensus. 1.2 Notations Pj :

The jth patient

Pij :

The ith block of patient j

Opij :

The ith block operation of patient j (continued)

E-Health Blockchain: Conception of a New Smart (continued)   Sj = s1j , s2j , ..., skj :

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The k-uplet of sij symptoms of patient j

CSNij :

Cluster service node for the patient j based on the symptoms Sj

CSNAuti j :

Cluster service node authority for the patient j based the symptoms Sj

PoA:

Proof of authority

IPFS:

Inter planetary file system

2 Related Works In [8], the authors introduced a system for secure management of electronic medical records for reliable data sharing and aggregation. This patient-centric system would facilitate patients’ management of their own medical records across multiple hospitals while ensuring patient confidentiality and security with respect to health data management requirements, including patient-specified access control policy. In [9], the authors introduce a new SARIMA scheme to forecast the cases and deaths in Morocco to help decision-makers to prevent any critical phases. They have chosen to forecast COVID-19 using time series analysis. In [10], the authors presented a set of discussions on the contribution of these digital technologies, proposed several complementary and multidisciplinary techniques to combat COVID-19, offered opportunities for more global studies and accelerated the acquisition of knowledge and scientific discoveries in the field of pandemic research in areas where the Internet of Things (IoT) can make a contribution, such as remote patient monitoring using wearable IoT. They then explained the role and new applications of Intelligence Artificial and analyzed the main uses of robotics and drone technology. Finally, they reviewed distributed ledger technologies (DLT), including Blockchain, and their combination with other technologies to combat COVID-19. In [11], the authors evaluate the dependence of brain MRI on various CNN predictive models for brain tumors and Alzheimer’s disease. This research studies the complexity of the data and introduces a three-part approach: data pre-processing, stratified k-fold crossvalidation and implementation of four CNN models. The classification performance of four CNN variants (S-CNN, ResNet50, InceptionV3 and Xception) is compared through rigorous experimentation on two brain MRI image datasets. The evaluation includes the use of principal component analysis (PCA) and measures such as accuracy, precision, recall, F1 score and AUC score. The research evaluates the CNN models by comparing the mean scores of the fivefold stratified cross-validation.

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Fig. 1. Structure Blockchain and components

In [12], the authors aim to develop a convolutional neural network (CNN) model capable of identifying plant diseases in apple leaves using leaf images. The model was able to reveal the challenges of plant disease detection, such as the lack of expert knowledge and the need for larger training sets. In order to reduce the computational load, the CNN model [13–17], formed of a smaller number of layers, applies augmentation techniques to generate additional samples without capturing more images. Model training on the PlantVillage dataset provides a high classification accuracy of 98% for the identification of scab, black rot and cedar rust diseases on apple leaves. The advantage of the proposed model is that it requires less storage and computing resources than existing deep CNN models, enabling it to be deployed on portable devices [18–22]. In [23], the authors have introduced a deep learning approach known as the Dual Deep Patch Attention Mechanism (D2PAM) to use brain signals to classify the preictal signals of people with epilepsy. Using the deep neural network applied to D2PAM, the impact of differences between patients was reduced and the prediction of epileptic seizures improved. All of this enabled early reversal and diagnosis of epilepsy. These results surpass existing models for predicting epileptic seizures. In [24] the authors discussed, analyzed and proposed a Blockchain-based method and architecture to implement the Blockchain-based model for smart healthcare to have efficient and patient-centric data sharing. In [25], the authors presented a platform promoting healthy habits, using scientific evidence and Blockchain technology was realized. This Blockchain-based platform, used the framework of physical activities to send messages and rewards to users. To do this, activities are monitored by invasive means and evaluated using open-source software [26–28].

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3 Our Contribution a. Architecture of our E-health Blockchain contribution In our contribution, we introduce a Blockchain architecture (shown in Fig. 2) containing service clusters of CSNij nodes ranked from patient j validated symptom sharing. The responsibility of the CSNij is to determine a service cluster node CSNAuti j which adds a new block in the Blockchain after being validated by PoA the consensus adopted for our contribution.

Fig. 2. Architecture of our contribution

b. Description of our E-health Blockchain contribution In our contribution, there are three phases; (1) The pre-treatment phase, (2) the treatment phase, and (3) the control and follow-up phase. In the first phase, we begin by initialization of the Blockchain and then the patient accesses the Blockchain to register or to see his data. The second phase is the treatment phase. The third is the control phase. • Pre-treatment phase – Blockchain initialization To build the hybrid Blockchain adopted to our contribution (shown in Fig. 3), we create three blocks: The genesis block of the Blockchain, then the block patient Pij and his genesis (G.Pij ) block and finally the operation block Opij for all operations validated and done in the hospital.

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During the construction of the Blockchain, there are two types of preceded blocks; The first is between two blocks of the same type (Blockchain, Patient, or Operation type block), this is called the logical preceed, the second is between two blocks of different types, this is the physical preceed. The genesis patient (G · Pij ) for the patient Pj is the hash of Genesis of Blockchain (G. Blockchain). – Register/Login On the same phase and after Blockchain initialization, the phase ends with two final steps; (1) the patient has the right to login into his account or to register, (2) Blockchain sends the patient a form to allow him to check the symptoms of diseases for which he is there. His answer is taken as validation.

Fig. 3. Blockchain of our contribution

• Treatment phase In this phase, there are a steps based reinforcement learning using PoA as consensus to follow up the patient and describe the right diagnostics as following: – Blockchain uses artificial intelligence to determine the Cluster Service Node CSNij to which the patient will be directed, controlled, and monitored. Also, artificial intelligence will determine the cluster node service CSNAuti j which will have the authority to add the various operations carried out within the hospital. – After sharing the symptoms between the different node services, the authority cluster node service makes the diagnosis according to the opinions of doctors. – The doctor’s decision is broadcast and validated on the Blockchain, with two choices: either operate on the patient or follow the instructions and protocol set out in the medical prescription. In both cases, the patient must read the decision and follow the instructions given by the doctor in the department concerned. • Control phase Once the medical treatment has been completed, the patient must provide the analysis requested in the medical prescription in order to check whether or not they have recovered. If the patient has not recovered, further treatment will be requested by performing further analyses and adding information that will enable the patient’s case to be diagnosed and managed by the same node or another more specialized cluster service node. For more details, the Fig. 4 shows the phases and their flow sequences.

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Fig. 4. Phases of our contribution. 1. Register/sign in, 2. Request symptoms, 3. Patient validation, 4. Sens selection of CSN_Authority, 5. Send opinions of other doctors, 6. Share final diagnostic of CSN_Authority, 7a. Operation, 7b. Disease, 8. Send medical prescription to patient + analysis, 9. Improvements felt and analysis to verify healing after finishing the drugs, 10. Checking: information for checking if the patient is heal or not, 11a. Yes, 11b. No, 12. Add it to information of patient to select the right service

4 Conclusion The falsification of medical data places the patient in a critical situation which requires different entities to consider identifying the latest technologies to access sensitive health data, any loss of patient data results in a false diagnosis by the doctor, hence ensuring data integrity is paramount and using Blockchain technology guarantees it. Our contribution introduces an intelligent hospital Blockchain authorized for patient health and data management in the event of a health emergency via deep reinforcement learning based on symptoms and previous illnesses. In addition, moving as soon as the patient felt ill was a problem that our Blockchain architecture was able to solve by consulting a doctor and managing the patient’s condition from home at all times. Some cases require more in-depth diagnostics to create appropriate solutions using consensus, which enables a new cluster service node to be delegated.

References 1. Singh, S., Sharma, S.K., Mehrotra, P., Bhatt, P., Kaurav, M.: Blockchain technology for efficient data management in healthcare system: opportunity, challenges and future perspectives. Mater. Today Proceed. 62, 5042–5046 (2022) 2. Ghosh, P.K., Chakraborty, A., Hasan, M., Rashid, K., Siddique, A.H.: Blockchain application in healthcare systems: a review. Systems 11, 38 (2023) 3. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decent. Bus. Rev. 12, 21260 (2008) 4. Benkou, S., Asimi, A.: BDIV: healthcare blockchain data integrity schemes verification on storage cloud. In: Artificial Intelligence and Smart Environment: ICAISE’2022. Springer, New York, pp. 282–286 (2023) 5. Shafay, M.: Blockchain for deep learning: review and open challenges. Clust. Comput. 26(1), 197–221 (2023)

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6. Qammar, J.: Securing federated learning with blockchain: a systematic literature review. Artif. Intell. Rev. 56(5), 3951–3985 (2023) 7. Hemalatha, C.: Secure and private data sharing in CPS e-health systems based on CB-SMO techniques. Measur. Sens. 27, 100787 (2023) 8. Dubovitskaya, O.: ACTION-EHR: patient-centric blockchain-based electronic health record data management for cancer care. J. Med. Internet Res. 22(8), e13598 (2020) 9. Chouja, M.: New SARIMA approach model to forecast COVID-19 propagation: case of Morocco. Int. J. Adv. Comput. Sci. Appl. 12, 12 (2021) 10. Firouzi, F., et al.: Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world. IEEE Internet Things J. 8, 12826–12846 (2021) 11. Kujur, C.: Data complexity based evaluation of the model dependence of brain MRI images for classification of brain tumor and Alzheimer’s disease. IEEE Access 10, 112117–112133 (2022) 12. Vishnoi, A.: Detection of apple plant diseases using leaf images through convolutional neural network. IEEE Access 11, 6594–6609 (2022) 13. Wang, Q., Li, R., Wang, Q., Chen, S., Xiang, Y.: Exploring unfairness on proof of authority: order manipulation attacks and remedies. In: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security (2022) 14. Lim, Y.Z., Zhou, J., Saerbeck, M.: SuppliedTrust: a trusted blockchain architecture for supply chains. In: Applied Cryptography and Network Security Workshops: ACNS 2022 Satellite,Workshops, AIBlock, AIHWS, AIoTS, CIMSS, Cloud S&P, SCI, SecMT, SiMLA, Rome, Italy, June 20–23, 2022, Proceedings (2022) 15. Lambert, J.: Blockchain in Healthcare (2022) 16. Adhikari, S., Halden, R.U.: Opportunities and limits of wastewater-based epidemiology for tracking global health and attainment of UN sustainable development goals. Environ. Int. 15, 107217 (2022) 17. Uppal, D.: HealthDote: a blockchain-based model for continuous health monitoring using interplanetary file system. Healthcare Anal. 3, 100175 (2023) 18. Farhaoui, Y.: Design and implementation of an intrusion prevention system. Int. J. Netw. Sec. 19(5), 675–683 (2017). https://doi.org/10.6633/IJNS.201709.19(5).04 19. Farhaoui, Y., et al.: Big data mining and analytics 6(3), 1–2 (2023). https://doi.org/10.26599/ BDMA.2022.9020045 20. Farhaoui, Y.: Intrusion prevention system inspired immune systems. Indon. J. Electr. Eng. Comput. Sci. 2(1), 168–179 (2016) 21. Farhaoui, Y.: Big data analytics applied for control systems. Lect. Notes Netw. Syst. 25, 408–415 (2018). https://doi.org/10.1007/978-3-319-69137-4_36 22. Farhaoui, Y., et al.: Big data mining and analytics 5(4), 1–2 (2022). https://doi.org/10.26599/ BDMA.2022.9020004 23. Khan, M.: D2PAM: epileptic seizures prediction using adversarial deep dual patch attention mechanism. CAAI Trans. Intell. Technol. 8(3), 755–769 (2023) 24. Hiwale, M., Varadarajan, V., Walambe, R., Kotecha, K.: NikshayChain: a blockchain-based proposal for tuberculosis data management in India. Technologies 11, 5 (2023) 25. Lopez-Barreiro, O.: Creation of a holistic platform for health boosting using a blockchainbased approach: development study. Interact J Med Res. 12(1), e44135 (2023) 26. Zakzouk, A., El-Sayed, A., Hemdan, E.E.-D.: A blockchain-based electronic medical records management framework in smart healthcare infrastructure. Multimedia Tools Appl. 12, 1–19 (2023)

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27. Farhaoui, Y.: Teaching computer sciences in Morocco: an overview. IT Profess. 19(4), 12–15 (2017). https://doi.org/10.1109/MITP.2017.3051325 28. Farhaoui, Y.: Securing a local area network by IDPS open source. Proced. Comput. Sci. 110, 416–421 (2017). https://doi.org/10.1016/j.procs.2017.06.106

Dynamic Multi-compartment Vehicle Routing Problem: Formulation and Algorithm Chaymaa Beneich(B) and Sidi Mohamed Douiri Laboratory of Mathematics, Computer Science and Applications-Security of Information, Department of Mathematics, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco [email protected]

Abstract. In this paper, two complex variations of the classic vehicle routing problem (VRP) are combined together to present a new VRP variant called the Dynamic Multi-Compartment Vehicle Routing Problem (DMCVRP). The aim of DMCVRP is to minimize the total traveled distance, in this type of problems different product types are loaded into a fleet of homogeneous vehicle with multiple compartments, and each compartment is dedicated to a single type of products. During the execution of the multi-compartment vehicle routing problem (MCVRP) routes the dynamic behavior of the problem shows up and causes some changes in the MCVRP routes. In this problem, we divide the DMCVRP into a set of standard MCVRP, and we present the mathematical model of DMCVRP as a MCVRP model, in which the total customer demands for each product must be fully delivered by the same vehicle with respect of each compartment capacity. Moreover, the distance traveled by each vehicle is subject to a constraint Considering the NP-hardness of the proposed problem, we propose the hybrid adaptive variable neighborhood search (HAVNS) to solve the problem. Keywords: Multi-compartment · Dynamic environment · Simulated annealing · Variable neighborhood search · Meta-heuristic

1

Introduction

In reality and by nature most of routing problems are subject to a dynamic environments. For that in this paper, we study an extension of the vehicle routing problem (VRP) that aims to determine a set of routes that satisfy the demand of customers with different products. The products should be stored in different compartments of the same vehicle while being transported, with the possibility that the customers’ demands appear during the process, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. We call the resulting problem the Dynamic Multi-Compartment VRP (DMCVRP), which combines the MCVRP and the DVRP variants. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 100–105, 2024. https://doi.org/10.1007/978-3-031-48573-2_15

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To the best of our knowledge, the dynamic multi compartment VRP problem has never been studied in the existing literature. To enrich the set of methods in this new VRP variant, we proposed the hybrid adaptive variable neighborhood search (HAVNS). The main contributions of this work is summarized as follows: 1. A new VRP variant called DMCVRP is studied and the dynamic adaptation of the problem is proposed. 2. From the algorithmic perspective, we propose the hybrid adaptive variable neighborhood search, which integrates a pipe variable neighborhood decent procedure (PipeVND). Additionally, HAVNS employs a simulated annealing (SA) behavior.

2

Problem Formulation

In this paper, DMCVRP is regarded as a variety of the static MCVRP by dividing the DMCVRP into a set of standard MCVRP. For that in this paper we propose a classic MCVRP model introduced for the first time by [4]. The MCVRP can be defined on a graph G = (N, E) with N = {0} ∪ Nc is the vertex set and E = {(i, j), i, j ∈ E, i = j} is the edge set. N represents the set of customers Nc = {1, . . . , n} and the depot that corresponds to the vertex 0, customers are served by a number of homogeneous trucks k ∈ K. Each truck k is equipped with multiple compartments p ∈ P equal to the number of products handled in the network. Each customer i ∈ Nc has a known demand qip to pick up for each product p and each customer is visited exactly once by only one truck. Each truck visits a group of customers, plus the total request of this group of customers must not exceed the truck capacity of the compartment reserved for this product Qp . The maximum length of each route cannot exceed k be a binary variable L. Let Cij be the distance of traversing arc(ij). Let Xij equals 1 if and only if truck k visits customer j just after customer i. Let Qkip denote the total carried quantity of product p by truck k after leaving customer i, based on the model proposed by [4] the MCVRP can be modeled as follows:  Cij xkij (1) M inimize k∈K i∈N j∈N

St.  

xkij = 1, ∀i ∈ Nc

(2)

xkij = 1, ∀j ∈ Nc

(3)

k∈K j∈Nc

 

k∈K i∈Nc



i∈Nc

xk0i =



xkj0 = 1, ∀k ∈ K

(4)

j∈Nc

qip ≤ Qkip ≤ Qp , ∀i ∈ N, k ∈ K, p ∈ P

(5)

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Qkip − Qkjp + Qp xkij ≤ Qp − qip , ∀i ∈ N, k ∈ K, p ∈ P  Cij xkij ≤ L, ∀k ∈ K

(6) (7)

i∈N j∈N

xkij ∈ {1, 0}, ∀i ∈ N, j ∈ N, i = j, k ∈ K

(8)

Equation 1 is the objective function representing the total cost of all traversed arcs by all trucks. To ensure that only one truck visits a customer and only once, eqs. 2 and 3 impose that if a truck stopped by a customer i must leave to the next customer. Equation 4 all trucks start their route from depot 0 and end it at depot 0. Equations 5 and 6 ensure the elimination of sub-tours, they impose the connectivity requirement of the capacity between two customers, and between two truck’s compartments. Equation 5 ensure that the total carried quantity after visiting vertex i do not exceed the truck capacity for the product p. Equation 6, is not binding if xkij = 0 , since Qkip ≤ Qp and qjp ≤ Qkjp . And when xkij = 1, we have Qkip − Qkjp ≤ −qjp which eliminates sub-tour construction. Equation 7 ensure route length violation constraint. Equation 8 describes variables xkij which equal to 1 if and only if the truck visits customer j just after customer i.

3

The Dynamic Adaptation to MCVRP Problem

Due to limited theoretical work available in dynamic optimization systems, in order to construct a dynamic test problem there is only straightforward method, but not efficient, the idea is to switch between different static instances that will cause a dynamic environment [2]. In our paper, we choose to generate the dynamic benchmark by using The dynamic benchmark generator for permutation (DBGP) for MCVRPs. Dynamic optimization problems (DOPs) are considered as a series of static problem instances, it’s not trivial to solve MCVRP problems to optimality due to its the NP-hardness especially for large size instances. Considering the MCVRP model described in Sect. 2, each customer i ∈ Nc has a predefined location (x, y) and a distance dij associated with an arc (i, j) ∈ E. In DBGP, for a number of iterations f and an index of change T = [t/f ] → − where t is the iteration counter of algorithm, an arbitrary vector R (T ) contains exactly m × n objects of the MCVRP instances are created, the frequency of change is represented with f , the size of problem with n and the magnitude with m. The magnitude of change m ∈]0, 1[ indicates the degree of change, in → − which only the first m × n of R (T ) object locations are in change (swap). Then → − a randomly re-ordered vector S (T ) is generated, contains only the objects of → − → − → − R (T ). Using the two random vectors R (T ) ⊗ S (T ). m × n pairwise swaps are performed in D = (dij )m×n , where ⊗ denotes the swap operator.

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Hybrid Adaptive Variable Neighborhood Search for DMCVRP

Inspired by the work of [5], this paper applies the pipeVND meta-heuristic with several local search operators. In addition, as an embedded procedure improving solutions, Simulated annealing algorithm is integrated into this approach to help VNS jump out of local optimum. This approach is called hybrid adaptive variable neighborhood search (HAVNS). The general variable neighborhood search is a meta-heuristic proposed first by [3] to approximately solve combinatorial optimization problems. The main idea is to systematically affect changes of neighborhoods during the search for an optimal or a near optimal solution. The proposed HAVNS has a different execution during the PipeVND operator, where its performance depends on the success of the previous solution process and a fixed parameter called the neighbor score Niscore . For the initialization of the algorithm it starts with the first local search N1 , and all neighborhoods have the same score (score=1). At every iteration, their scores are dynamically in change, this change depends on whether the current best found solution is improved or not in that iteration. The local search score is increased by 0.1 if any improvement found and decreased by 0.1 if not. In case the current used neighborhood score becomes negative the neighborhood will be changed by the next one respecting the priority by order of score. After skipping the previous step and when the new solution found is worse than the current solution, the algorithm jump to the SA procedure, check the probability of acceptance and after each iteration we update the annealing temperature T. The algorithm of HAVNS is shown in Algorithm 1.

5

Computational Experiments

Since the DMCVRP has not been studied computationally yet no benchmark instances for DMCVRP exists, we choose three stationary benchmark VRP instances taken from real life vehicle routing applications, F-n45-k4, F-n72-k4 and F-n135-k7 represent grocery deliveries in USA from the Peterboro, Brarmela and Ontario terminales. To apply those three instances to our problem DMCVRP we convert the VRP instance to the MCVRP then we adapt the resulting MCVRP new benchmark to a dynamic MCVRP using methodology presented in Sect. 3. First for the adaption of VRP into MCVRP, we used the method developed by [1], in which the data is obtained by splitting the vehicle capacity into two compartment using a 3:1 ratio, while the customer demands are obtained using 2:1 ratio except the demands on the sub-region (xmin ≤ x ≤ min min , ymin ≤ y ≤ ymax +y ) are split using a 3:1 ratio. In the xmin + xmax −x 2 2 representation of the sub-region x and y represent the horizontal and vertical coordinates of the customer position, xmax and ymax represent the maxima of horizontal and vertical coordinates, xmin and ymin represent the minima of its

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Algorithm 1 HAVNS Input: Problem data, maxIter, Initial temperature Tinit , Cooling rate c, m, f, Nmax Output: Best solution Sbest S ← Initial solution T ← Tinit repeat N ←1 while N ≤ Nmax do S  = Shaking(S) S  = P ipeV N D(S  , Nordre ) Updete Norder if f (S  ) ≤ f (S) then S ← S  N ←1 else if f (S  ) > f (S) then −Δ if Random ≤ e T then S ← S  end if end if else N ←N +1 end if end while T ← T.c Sbest ← S Apply dynamic adaptation /* Section 3*/ until M axIter return Best solution Sbest Table 1. Table of parameters Parameters f m α M axiter Tinit Tf Nscore β Nmax

Description

Values

The frequency of change {10, 100} The magnitude of change {0.1, 0.25, 0.5, 0.75} Cooling rate for SA 0.98 Inner iterations for local search 100 Initial temperature for SA 40 Final temperature for SA 0.001 Neighborhood score in pipeVND 1 Improvement rate in pipeVND 0.1 Max iteration 3

horizontal and vertical coordinates. The parameter values used are described in the Table 1, and the experiment results are shown in Table 2

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Table 2. Offline performance of HAVNS for f = 10 and f = 100. Problem

Comparator f = 10 f = 100 m = 0.1 m = 0.25 m = 0.5 m = 0.75 m = 0.1 m = 0.25 m = 0.5 m = 0.75

F-n45-k4

Min Max Avg Time

852.64 904.13 925.3 133.7

780.8 898.52 1025.95 26.58

748.17 959.9 1038.23 36.41

875.82 1005.02 1121.9 69.02

858.3 879.74 854.62 84.6

874.2 953.38 925.2 127.4

857.5 1043.85 973.35 99.23

865.53 1167.35 961.03 14.5

F-n72-k4

Min Max Avg Time

346.72 381.7 370.85 388.54

345.8 368.34 366.13 15.32

309.88 6355.7 331.8 127.26

318.15 363.4 335.98 58.26

304.93 342.71 323.7 134.51

335.8 359.44 346.34 170.8

334.25 362.61 345.4 160.32

326.76 357.41 339.4 138.7

F-n135-k7

Min Max Avg Time

1684.6 1980.01 1846.7 128.6

1706.9 2003.86 1850.9 60.8

1763.86 1659.47 2204.54 1837.16 1934.35 1765.9 148.35 188.17

1630.7 1966.26 1832.31 197.6

1874.25 1968.02 1864.9 171.82

1756.75 2061.01 1925.9 190.1

1771.9 1981.98 1850.9 159.8

6

Conclusion

The MCVRP and DVRP have attracted less attention than static VRP and its variants. However, in the real world, the vehicle routing problems are dynamic which include the multi-compartment vehicle routing problem (MCVRP). In this paper, we present a new VRP variant that combines both MCVRP and DVRP the resulting problem is called the dynamic multi-compartment VRP (DMCVRP). To solve this problem we proposed the hybrid adaptive variable neighborhood search based on a pipeVND and a fusion of VNS and SA.

References 1. Evering, R., Reed, M., Yiannakou, A.: An ant colony algorithm for the multicompartment vehicle routing problem. Appl. Soft Comput. 15, 169–176 (2014) 2. Mavrovouniotis, M., Yang, S.: Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Inform. Sci. 294, 456–477 (2015) 3. Mladenovi´c, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997) 4. ElMekkawy, T.Y., Abdulkader, M.M., Gajpal, Y.: Hybridized ant colony algorithm for the multi compartment vehicle routing problem. Appl. Soft Comput. 37, 196–203 (2015) 5. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31(12), 1985–2002 (2004)

Anime Link Prediction Using Improved Graph Convolutional Networks Safae Hmaidi(B) , Yassine Afoudi, Mohamed Lazaar, and El Madani El Alami Yasser ENSIAS, Mohammed V University in Rabat, Rabat, Morocco {safae_hmaidi,yassine_afoudi}@um5.ac.ma, {mohamed.lazaar, yasser.alami}@ensias.um5.ac.ma

Abstract. Graph convolutional neural networks (GCNs) have made significant strides in recent years including social network analysis, recommendation systems, drug discovery, and bioinformatics. This study introduces an updated graph convolutional anime dataset. To improve the performance of GCNs on anime datasets, we present a novel method that combines the advantages of existing graph neural network architectures like Graph Attention Networks (GAT) and Spectral Aggregated Graph Embedding (GraphSage). To perform better in tasks requiring anime analysis, our suggested solution makes use of a cutting-edge algorithm. We outline our experimental findings and then offer some last thoughts on how our modified GCN might be used. Keywords: Graph neural networks · Graph convolutional neural networks · Graph attention networks · GraphSage · Anime prediction

1 Introduction Graphs serve as a data structure that symbolizes a set of elements (referred to as nodes) and the relationships between them (referred to as edges). Recent advancements in machine learning have led to a growing focus on the analysis of graphs. This heightened interest can be attributed to the significant expressive capabilities inherent to graphs, allowing them to represent a wide array of systems across various domains, including but not limited to social science (social networks [1], natural science, physical systems [2, 3] and protein-protein interaction networks [4]), knowledge Graph analysis [5]. As a unique non-Euclidean data format for machine learning, focuses on challenges such as node classification, connection prediction, and clustering. GNNs are deep learningbased algorithms that operate on graph domains. GNN has lately been a commonly used graph analysis tool due to its compelling results [6]. In this article, we will delve into various aspects related to Graph Neural Networks (GNNs) and their applications. Firstly, we will provide a comprehensive background on GNNs, discussing their fundamental concepts and the motivation behind their development. After establishing the context, we will present our proposed work, focusing on an improved version of the Graph Convolutional Network (GCN) architecture called ImprovedGCN. We will describe the architecture and key features of ImprovedGCN, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 106–116, 2024. https://doi.org/10.1007/978-3-031-48573-2_16

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emphasizing its advantages over existing GNN models. Moving on to the experimental phase, we will discuss the datasets used, the implementation details, and the training strategies employed to evaluate ImprovedGCN’s performance. We will present the results of our experiments and engage in detailed discussions to analyze the implications and insights gained from the outcomes. Finally, we will conclude by summarizing the key findings and contributions of our work, as well as discussing future directions for research in the field of GNNs. In this article, our contributions can be summarized as follows: • Baseline Model Comparison: We provide a comprehensive analysis and comparison of three popular baseline models for graph-based tasks, namely Graph Convolutional Network (GCN), GraphSage, and Graph Attention Network (GAT). • ImprovedGCN Model: We introduce an enhanced version of the Graph Convolutional Network architecture, termed ImprovedGCN. • Link Prediction: We focus on the task of link prediction, which involves predicting missing or future links within a graph. • Metrics Comparison: We conduct a comprehensive comparison of evaluation metrics commonly used in link prediction tasks, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Square (R2 ). We analyze how these metrics capture different aspects of link prediction performance and provide insights into their suitability for assessing the accuracy and robustness of the baseline models and Improved GCN. In conclusion, this article contributes a detailed comparison of baseline models (GCN, GraphSage, GAT), the introduction of ImprovedGCN with novel enhancements, an evaluation of their performance on link prediction tasks, and a comprehensive comparison of metrics used for evaluation. These contributions provide a valuable understanding of the strengths and limitations of different models and metrics in the context of graphbased link prediction, advancing the field’s knowledge and facilitating further research in this area.

2 Background 2.1 Graph Neural Network Graph Neural Networks (GNNs) is a type of neural network model that works with graphstructured data. GNNs, unlike standard neural networks that handle grid-like input (e.g., pictures, text sequences), can capture and leverage the relationships and interactions between graph components [6] (Fig. 1). A basic description of the components and operations of a typical Graph Neural Network design follows [7]: • Graph Representation: A graph is comprised of nodes (vertices) and edges (connections between nodes). Each node can have related characteristics or qualities, which are represented as node feature vectors. • Message Passing: The concept of message transit between nodes in a graph is central to GNNs. Using neighborhood aggregation functions, nodes at each tier collect and

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Fig. 1. Graph neural network

•

•

•

• • •

update information from surrounding nodes. Messages or information transferred between nodes contain data regarding node attributes, edge connections, and the graph structure. Node Representation: Each node computes a new representation (hidden state) by aggregating and processing the received messages. Examples of aggregation functions are summation, averaging, and more advanced procedures such as attention processes. The node representation contains information about both the immediate neighborhood and the broader graph structure. Graph-level Representation: Graph-level aggregation is used for tasks that need a single prediction or representation for the whole graph (e.g., graph classification). Aggregation methods such as global pooling or graph-level attention are used to merge the node representations into a single graph-level representation. Neural Network Layers: GNNs are often composed of numerous layers, with each layer handling message passing and updating node representations. As information passes across the layers, the node representations are enhanced and enriched. Each layer can have its own set of parameters that can be learned, such as weight matrices and activation functions. Output Generation: The last layer’s final node or graph representation is utilized to make predictions or create outputs. Additional layers or processes may be introduced depending on the job to transform the node or graph representation into the necessary output format (e.g., classification, regression). Training and Optimization: GNNs are often trained using backpropagation and optimization approaches such as gradient descent. To assess the model’s performance, loss functions tailored to the job (for example, cross-entropy loss for classification) are utilized. Regularization techniques such as dropout or L2 regularization might be used to prevent overfitting.

Link Prediction. Link prediction is the task of predicting the existence or absence of an edge between two nodes in a graph. In this task, GNNs take a graph as input and predict the probability of an edge between each pair of nodes in the graph [8]. The process of link prediction with GNNs follows these key steps: • Initialization: Each node is assigned an initial feature vector. • Message passing: During message passing, each node updates its feature vector by aggregating the feature vectors of its neighboring nodes and edges. This process is typically repeated for multiple rounds.

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• Graph pooling: After multiple rounds of message passing, the graph is often pooled to obtain a fixed-size representation of the entire graph. Graph pooling involves aggregating the feature vectors of all the nodes in the graph to obtain a global representation. • Output prediction: The final step is to use the graph representation to predict the probability of an edge between each pair of nodes. 2.2 Graph Construction Graph construction in recommender systems involves representing user-item interactions as a graph, where nodes signify users or items, and edges depict their connections. The aim is to capture intricate interaction patterns efficiently for machine learning models [9]. There are several types of graph construction methods in recommender systems, each with its strengths and weaknesses. • Homogeneous graph construction: In this approach, the user-item interaction data is represented as a homogeneous graph, where all the nodes are of the same type (either users or items). The edges in the graph represent the relationships between the nodes. The simplest form of this approach is the bipartite graph, where users and items are connected via edges. The advantage of this approach is its simplicity, but it may not be able to capture the complex relationships between users and items. • Heterogeneous graph construction: In this approach, the user-item interaction data is represented as a heterogeneous graph, where nodes can have different types (e.g., users, items, genres, etc.). This approach can capture more diverse and complex relationships between users and items, but it also requires more complex algorithms to handle the heterogeneity. • Hypergraph construction: In this approach, the user-item interaction data is represented as a hypergraph, where each hyperedge connects multiple nodes. This approach can capture higher-order relationships between users and items, but it can also be more computationally expensive. • Knowledge graph construction: In this approach, external knowledge about users and items is incorporated into the graph construction process, in addition to the user-item interaction data. This can improve the performance of the recommender system by incorporating domain knowledge into the graph. 2.3 Types of Graph Neural Network (GNN) Graph Convolutional Network (GCN). The Graph Convolutional Network (GCN) architecture progressively gathers data from nearby nodes by estimating the first-order eigenvalues of the graph Laplacian [10]. It does these using two functions: an aggregation function that combines the features of neighboring nodes and an update function that updates the embedding of the current node formula (1). The aggregation function takes the adjacency matrix of the graph and the embeddings of the neighboring nodes at the previous layer as input and produces a new embedding for the current node [10]. The

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update function takes the input and hidden state weight matrices as input and updates the embedding of the current node formula (2). nv(l) =



−1

−1

dvv2 a˜ vj djj 2 h(l) v

(1)

j∈Nv

hv(l+1) = δ(W (l) nv(l) )

(2)

where δ(.) is a nonlinear activation function, similar to ReLU, W (l) is the learnable transformation matrix for layer l, a˜ jv is the adjacency weight (˜ajv = 1), and djj = k a˜ jk [11, 12]. Graph attention network (GAT). The Graph Attention Network (GAT) architecture updates the embedding vector of each node by paying attention to its neighbors. The core concept of GAT is that a node’s neighbors do not exert a fixed or predetermined level of influence based solely on the graph’s structure [12]. Instead, GAT uses an attention mechanism to learn how to weigh the contributions of each neighbor to the node’s representation [13]. The GAT architecture uses two functions: an aggregation function and an update function. The aggregation function calculates a weighted sum of the neighbor embeddings, where the weight of each neighbor is determined by an attention mechanism [11, 12]. Specifically, the attention weight between a node v and its neighbor j is computed as follows: (l) (l)

αvj = 

exp(ATT (hv hj ) k∈Nv

(l) (l)

exp(ATT (hv hk )

(3)

(l)

where hv is the embedding vector of node v at layer l, and ATT (·) is a trainable attention mechanism that takes as input the embeddings of two nodes and outputs a scalar attention coefficient [11, 12]. The aggregation function then calculates the weighted sum of the embeddings of the neighboring nodes using the attention weights:  (l) nv(l) = αvj hj (4) j∈Nv (l)

where hj is the embedding vector of the neighbor j at layer l. The update function then updates the embedding vector of each node using the aggregated neighbor embeddings: hv(l+1) = δ(W (l) nv(l) )

(5)

where W (l) a trainable weight matrix and δ is the activation function [12]. GraphSage. The GraphSage architecture is a variant of the Graph Convolutional Network (GCN) architecture that aims to address some of its limitations, particularly its scalability to large graphs [14].

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GraphSage operates on fixed-size, node-centric subgraphs, and uses a neighborhood sampling strategy to select a fixed number of neighbors for each node. It then applies an aggregator function, such as mean, sum, or max pooling, to combine the features of the selected neighbors [14]. The aggregated features are then concatenated with the feature vector of the node itself, and the resulting concatenated vector is used to update the node’s embedding. Formally, the GraphSage architecture uses two functions: an aggregator function and an update function [15]. The aggregator function takes as input the feature vectors of a node’s neighbors and combines them into a single vector representation. (l)

nv(l) = Aggregator l ({hj , ∀u ∈ Nv })

(6)

where Nv the set of neighbors of node v within l is hops, and Aggregator l is the aggregator function that operates on the l-hop neighbor feature vectors. Examples of aggregator functions include mean, sum, and max pooling [11, 12]. The update function then concatenates the aggregated feature vector with the feature vector of the node itself and applies a fully connected layer to generate the updated node embedding: = δ(W (l) .[hv(l) ⊕ nv(l) ]) h(l+1) v

(7)

where δ is the activation function, W (l) is the weight matrix for layer l [12].

3 Proposed Work 3.1 ImprovedGCN In this study, we propose investigating GCN, a robust graph neural network model, for link prediction tasks and introducing an enhanced version termed Improved GCN. This improved variant is tailored for inductive representation learning on extensive graphs, especially advantageous for networks enriched with comprehensive node attribute data, with a particular focus on optimizing link prediction performance. The Improved GCN architecture incorporates crucial enhancements to augment the effectiveness of Graph Convolutional Networks (GCNs). It starts with the utilization of the GCNConv module for initial graph convolution, featuring an input layer, multiple hidden layers, and an output layer. Each layer incorporates a rectified linear unit (ReLU) activation function to introduce non-linearity, vital for capturing intricate patterns (as described in Eq. 8). ReLU (x) = max(0, x)

(8)

To overcome the vanishing gradient issue and promote smooth information flow, ImprovedGCN incorporates residual connections. These connections enable the model to learn residual representations by adding the output of the preceding hidden layer to the current layer after a linear transformation. Mathematically, this is expressed as: residual(x) = x + ResidualLayer(x)

(9)

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where x represents the previous hidden layer’s output, and ResidualLayer(·) denotes a linear transformation. Additionally, skip connections are employed to enhance information flow and capture distant dependencies. Like residual connections, skip connections utilize linear transformations to directly link earlier layers to later ones, as expressed mathematically: skip(x) = SkipLayer(x)

(10)

where SkipLayer(·) signifies a linear transformation applied to the input. Moreover, the ImprovedGCN model harnesses the message-passing mechanism within GCNConv layers to gather information from neighboring nodes. This aggregation captures local structural insights within the graph, enabling the model to consider collective data from adjacent nodes. Finally, the output layer conducts the ultimate graph convolution operation by taking the last hidden layer’s output and applying another GCNConv operation. This process generates output predictions, which serve various graph-related tasks. In essence, the Improved GCN architecture amalgamates ReLU activation functions, the advantages of residual and skip connections, and the aggregation of neighboring node information to enhance GCNs’ performance in capturing intricate graph patterns and delivering accurate predictions. Loss function. During training, the model utilizes the root mean squared error (RMSE) loss function to optimize the predictions. The RMSE is defined as:  

 n 1   2  yi − yˆ i (11) RMSE = n i=1



where n represents the number of samples, yi represents the true value, and yi represents the predicted value. Minimizing the RMSE loss function helps the model to improve the accuracy of its predictions by reducing the difference between the predicted and true values. 3.2 Proposed Architecture Our proposed methodology, as depicted in Fig. 2 involves several key steps: • Data Preprocessing: In this initial phase, our primary objective is to manage an available dataset of anime ratings, where users have rated various anime titles on a scale of 1–10. Our data preparation tasks involve critical activities such as the removal of duplicate entries, handling missing data points, and thoughtfully selecting essential features for our model. Additionally, we meticulously apply normalization techniques to ensure that all features are consistently scaled across the entire dataset. • Heterogeneous Graph: To effectively capture the complex relationships between users and anime titles, we embark on constructing a heterogeneous graph. This graph encompasses nodes representing both users and anime titles, including their associated attributes. Utilizing the torch geometric library, we not only generate but also conduct a comprehensive analysis of this graph. Within this graphical representation,

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the connections between nodes are depicted through adjacency matrices, where each element quantifies the strength of the relationship between any two nodes. • Development of the ImprovedGCN Model: In the subsequent phase, our focus pivots towards crafting the ImprovedGCN model. This model harnesses the heterogeneous graph we previously created to facilitate predictions, particularly concerning the links between users and anime items. The ImprovedGCN model architecture comprises two layers, strategically utilizing graph convolutions to acquire meaningful node representations within the graph.

Fig. 2. Proposed architecture

4 Experiments 4.1 Dataset for Evaluation The dataset used in our study is sourced from Kaggle named “Anime Recommendations Database” especially from myanimelist.net API, consisting of user preference data from 73,516 users on 12,294 anime. Each user can add anime to their complete list and provide a rating for it. The dataset is a compilation of these ratings and contains additional information such as anime names, genres, types (e.g., movies, TV series, OVA), episode counts, average ratings, and the number of community members associated with each anime. This rich dataset provides a comprehensive representation of user preferences and serves as a valuable resource for training and evaluating our enhanced graph convolutional network. By leveraging this dataset, we aim to uncover hidden patterns and relationships within the anime data, leading to improved analysis and classification performance. 4.2 Environment We transformed each of our observations into a Google Colab notebook and generated corresponding Python code. Python, as a programming language, offers an extensive array of frameworks and libraries that streamline the coding process, resulting in substantial time savings. All experiments were executed on an MSI laptop, which boasts a 2.70GHz Intel Core i7 CPU and 12 gigabytes of RAM.

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4.3 Experiment Results Our model, along with the proposed Improved GCN, displayed superior performance compared to all the other GNN techniques we evaluated. As depicted in Fig. 3, our model surpassed the alternative GNN techniques in terms of MAE, RMSE, and R_SQUARED metrics. As illustrated in both Fig. 3 and Table 1, our model exhibited lower MAE and RMSE values while achieving higher R_SQUARED values when contrasted with the other models. These findings indicate that the ImprovedGCN model excels in its ability to predict and represent the anime dataset when compared to the other graph models.

Fig. 3. The MAE, RMSE and R SQUERED for animes dataset

Moreover, it exhibited superior performance in terms of time and resource efficiency. As illustrated in Fig. 4, our models required significantly less time and resources for training compared to the other models.

Fig. 4. The Elapsed Time (mn)

Moreover, According to Fig. 5, the ImprovedGCN model has the lowest CPU utilization percentage, these results suggest that the ImprovedGCN model is the most computationally efficient among the models considered, as it demands the least CPU resources. This can be advantageous in scenarios where computational efficiency and resource optimization are crucial factors. Conversely, the GATConv model appears to be more resource-intensive, potentially requiring higher computational power. The findings from this research suggest that the approach we introduced, along with the improved method, represent valuable resources for providing accurate and diverse

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Fig. 5. CPU utilization percentage Table 1. MAE, RMSE, and R_SQUARED results for all GNNs models. Metrics

Models GraphSage

ImprovedGCN GAT

GCN

MAE

0.90

0.98

0.85

0.83

RMSE

1.18

1.39

1.12

1.10

R_SQUARED

0.42

0.19

0.47

0.49

suggestions. These models offer potential across a wide range of real-world scenarios, including e-commerce, social networks, and content-oriented platforms. In these contexts, tailoring recommendations to individual preferences and ensuring efficiency plays a crucial role in elevating user satisfaction and engagement.

5 Conclusion This work focused on analyzing and modeling the anime dataset using various graph models, including SAGEGraph, GATConv, GCN, and ImprovedGCN. The dataset underwent preprocessing and cleaning to ensure data quality. A heterogeneous graph was constructed to capture the relationships between different elements. The models were trained and evaluated, and performance metrics such as MAE and RMSE were computed. The results revealed that the ImprovedGCN model outperformed the other models in terms of both MAE and RMSE, indicating its superior predictive accuracy. Additionally, the ImprovedGCN model demonstrated efficient CPU utilization and shorter elapsed time, highlighting its computational efficiency. These findings suggest that the ImprovedGCN model is the most suitable choice for the anime dataset, considering its accuracy, efficiency, and overall performance.

References 1. Wu, Y., Lian, D., Xu, Y., Wu, L., Chen, E.: “Graph convolutional networks with Markov random field reasoning for social spammer detection. Proceed. AAAI Confer. Artif. Intell. 34(01), 1054–1061 (2020). https://doi.org/10.1609/AAAI.V34I01.5455

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2. Sanchez-Gonzalez, A.: Graph networks as learnable physics engines for inference and control. proceedings.mlr.press (2018). http://proceedings.mlr.press/v80/sanchez-gonzalez18a. html. Accessed 31 May 2023 3. Battaglia, P., Pascanu, R., et al.: Interaction networks for learning about objects, relations and physics. proceedings.neurips.cc (2016). https://proceedings.neurips.cc/paper/2016/hash/314 7da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html. Accessed 31 May 2023 4. Fout, A., Byrd, J., et al.: Protein interface prediction using graph convolutional networks. proceedings.neurips.cc (2017). https://proceedings.neurips.cc/paper/2017/hash/f50 7783927f2ec2737ba40afbd17efb5-Abstract.html. Accessed 31 May 2023 5. Hamaguchi, T., Oiwa, H., Shimbo, M., Matsumoto, Y.: Knowledge transfer for out-ofknowledge-base entities: a graph neural network approach. IJCAI Int. Joint Confer. Artif. Intell. 4, 1802–1808 (2017) 6. Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020). https://doi.org/10.1016/J.AIOPEN.2021.01.001 7. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009). https://doi.org/10.1109/TNN. 2008.2005605 8. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. Adv. Neural. Inf. Process. Syst. 12, 5165–5175 (2018) 9. Messaritaki, E., Dimitriadis, S.I., Jones, D.K.: Optimization of graph construction can significantly increase the power of structural brain network studies. Neuroimage 199, 495 (2019). https://doi.org/10.1016/J.NEUROIMAGE.2019.05.052 10. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations, ICLR 2017— Conference Track Proceedings (2016). https://arxiv.org/abs/1609.02907v4. Accessed 01 June 2023 11. Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. 55(5), 37 (2020). https://doi.org/10.1145/3535101 12. Afoudi, Y., Lazaar, M., Hmaidi, S.: An enhanced recommender system based on heterogeneous graph link prediction. Eng. Appl. Artif. Intell. 124, 106553 (2023). https://doi.org/10. 1016/J.ENGAPPAI.2023.106553 13. Veliˇckovi´c, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., Bengio, Y.: Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations, ICLR 2018—Conference Track Proceedings (2017). https://doi.org/10.1007/978-3-031-015 87-8_7 14. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural. Inf. Process. Syst. 12, 1025–1035 (2017) 15. Song, J., Song, J., Yuan, X., He, X., Zhu, X.: Graph representation-based deep multi-view semantic similarity learning model for recommendation. Fut. Internet 14, 32 (2022). https:// doi.org/10.3390/FI14020032

Assessing the Evolution of Meteorological Seasons and Climate Changes Using Hierarchical Clustering Mohamed Lazaar(B) , Hamza Ba-Mohammed, Hicham Filali, and Yasser El Madani El Alami ENSIAS, Mohamed V University, Rabat, Morocco {mohamed.lazaar,yasser.alami}@ensias.um5.ac.ma, {hamza bamohammed,hicham filali2}@um5.ac.ma

Abstract. In this paper, we analyze global climate change patterns using unsupervised machine learning techniques, specifically hierarchical clustering, which is a less known method for time series analysis. Focusing on Oujda, a Moroccan semiarid city as a case study, temperature and precipitation data from its weather station are clustered to identify distinct weather states. The primary objective is to investigate the efficiency of using hierarchical clustering on time series datasets and more specifically on climate time series to identify clusters that shows the climate changes and the shifts in transitions between meteorological seasons over time. Our study shows that this class of machine learning methods can give decent quality clustering for time series data and thus it helps discovering relevant patterns among it. Furthermore, our study is a new and additional evidence for climate change worldwide which is based on unsupervised machine learning.

Keywords: Hierarchical clustering Time series · Climate change

1

· Unsupervised machine learning ·

Introduction

Global warming, attributed primarily to human activities, presents an undeniable and alarming trend supported by a growing body of scientific evidence [17]. This phenomenon has raised concerns about its adverse impacts, leading to significant climate shifts and disruptions. The definition of seasons varies across disciplines, with astronomy relying on Earth’s position relative to the sun, while meteorology adopts a practical approach, considering temperature patterns and statistical convenience [18]. This paper introduces a novel data-driven approach using unsupervised machine learning, particularly agglomerative hierarchical clustering, to redefine meteorological seasons based on daily weather data from Oujda, Morocco (1975–2022). c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 117–123, 2024. https://doi.org/10.1007/978-3-031-48573-2_17

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Researchers are employing diverse sources of evidence and methodologies to understand the impact of global warming comprehensively. The statistical analysis of ice-core records from Polar Regions and high-elevation ice fields provide crucial insights into climate variations, indicating potentially irreversible changes in Earth’s climate system [4,6,17]. Other recent research has focused on the impact of anthropogenic climate change on seasonal transitions and their consequences [9]. In a novel approach, one paper proposes a method that utilizes clustering algorithms to determine clusters within single time series data, minimizing a cost function while ensuring disjoint and contiguous points. This paper stands as a unique exploration of clustering algorithms for weather time series data [7]. While clustering algorithms have been used for various purposes in meteorology, the application of clustering to study shifts in meteorological seasons is relatively rare in academia. Typically, clustering algorithms are used to group sets of time series of the same length [8,10], not to cluster individual days within the same time series [1,2,5,8,12]. The general description of the main contribution is presented as follows: we applied agglomerative hierarchical clustering with Ward’s Method to define meteorological seasons in Oujda’s daily weather data spanning 47 years, then we performed a qualitative and quantitative analysis of the obtained clusters and assessed the clustering’s quality using Davies–Bouldin and Silhouette Score indexes. The paper sections are organized as follows: The Sect. 2 describes the proposed method after highlighting some necessary background. Section 3 discusses the obtained results. At the end, our contribution is summarized in the Sect. 4 as well as the future work.

2

Proposed Method

Our work focuses on the identification of clusters within a single time serie. Rather than grouping similar time series, we aim to find subgroups or clusters within individual time series themselves. 2.1

Data Preprocessing

Semiarid regions, with their delicate balance between dry and wet conditions, offer a unique environment to study the impacts of climate change. These regions are particularly sensitive to shifts in temperature and precipitation. Among the possible options we had, we choose to base our study on the weather data of Oujda city in Morocco, considering that it provides a long interval of complete and clean weather data, and also since one of the authors lives there. We’ll study the evolution of weather states during a period of more than 30 years (the norm set by the World Meteorological Organization [15]). The data was taken from the NOAA Climate Data Online platform. We first drop the useless columns, then we add a column to express the difference between the maximum and the minimum temperature of each day (temperature

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amplitude). This column represents the daily thermal choc that has a bad influence on the environment the more it’s high [11]. The remaining columns describe temperature (average, maximum, minimum, amplitude) and precipitation level. 2.2

Background and Methodology

Clustering algorithms, including K-means, hierarchical clustering, density-based clustering, etc... are essential tools in unsupervised learning [3]. They group similar data points together while maintaining distinctions between clusters, revealing hidden data patterns. Agglomerative hierarchical clustering, in particular, follows a greedy approach, sequentially merging clusters to create a hierarchical data structure. This structure can take various forms aiding in understanding data structure and connections [13]. Evaluating clustering algorithm performance relies on two critical characteristics: intra-cluster distance (measuring dissimilarity between clusters) and inter-cluster distance (measuring similarity within clusters). These characteristics lead to the computation of two key similarity indexes: Davies–Bouldin Index and Silhouette Coefficient [16]. We will use agglomerative clustering on a time serie dataset to try to discover the trends of weather during the last decades and the seasonal shifts by dividing days into homogeneous clusters equivalent to meteorological seasons. We employ the hierarchical agglomerative clustering algorithm with Ward’s method, which aims to minimize the within-cluster variance by evaluating the increase in variance when merging clusters. To achieve this objective, we use the merging cost Δ defined as in ([14]). Many metrics could be used to measure the dissimilarity between data points. However, we’ll be using in our study the most common one: the Euclidean distance. We will also compare the clustering results of using 2 different numbers of clusters (2 and 4). We mention that based on the obtained linkage matrix L, we can cluster our data into any desired number of clusters without repeating the clustering algorithm.

3

Findings and Discussion

By clustering the data into two groups, we gain a clearer understanding of how transition periods between summer and winter seasons have evolved over the years. The analysis of specific years, such as 1975, 1991, 2007, and 2022, reveals notable trends in these transition periods. For instance, June and October once marked the transitions between the two primary clusters representing cold/wet and hot/dry seasons. However, by 2007, June became a distinct summer month, and May took over as the new transition month. In 2022, the warm/dry season extended further, covering most of May and November. Conversely, clustering the data into four clusters presents challenges due to temporal sparsity. This method results in frequent shifts between two or three clusters within short periods, as hierarchical clustering does not consider the temporal order of data points. Nonetheless, this approach can help illustrate

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abrupt meteorological shifts between different seasons during the year, regardless of chronological continuity. In fact, the 4-cluster results could be considered as a fractional representation of the 2-cluster results (Figs. 1, 2 and 3).

Fig. 1. Examples of clusters obtained using 2 clusters

Fig. 2. Examples of clusters obtained using 4 clusters

Despite this division, both approaches led to similar conclusions regarding the shifting patterns of seasons over time. This consistency in findings aligns with related research in the field, suggesting that our results are in agreement with existing climate analysis studies.

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Fig. 3. Evolution of the clusters distribution in time, using 4 clusters (1: red, 2: green, 3: blue, 4: yellow) and 2 clusters

Additionally, when assessing the temporal coverage of these clusters, we note a recent surge in days classified as extremely hot and dry. This increase in coverage has correspondingly led to a decrease in the temporal representation of other season-clusters, especially the extremely cold and wet season (cluster 3). Table 1. Statistical summary of the obtained clusters—2 clusters approach Temperature (◦ C)

Cluster Average

Maximum

Minimum

Precipitations (mm) Amplitude

Avg Min

Max

Avg Min Max Avg Min Max Avg Min Max Avg Min Max 1

23.26 10.9 35.8 31.64 22.4 47.3 16.07 2.0 29.0 15.57 2.2 32.0 0.09 0.0

16.0

2

12.52 −0.3 25.3 19.23 0.0 39.4 6.9 −7.1 23.4 12.33 −6.0 25.0 1.03 0.0

173.0

Moreover, through our statistical analysis summarized in Table 1, we were able to unveil the distinctive features of the season-clusters identified in each approach. This analysis highlights the presence of up to four distinct clusters, each representing unique characteristics. These clusters encompass hot and dry conditions and cold and wet season periods with 2 degradation for each season (extreme and moderate) that appear when using the 4 clusters approach. Table 2. Clustering evaluation results Index

4 clusters 2 clusters

Davies–Bouldin

∼ 1.230

∼ 0.748

Silhouette score

∼ 0.250

∼ 0.483

In terms of performance evaluation as shown in Table 2, both clustering methods (two and four clusters) yield reasonable results. However, the twocluster approach using Ward’s method hierarchical clustering outperforms the four-cluster method, as more clusters introduce less temporal homogeneity in the data.

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Conclusion

This paper introduces the use of agglomerative hierarchical clustering with Ward’s Method for time series datasets to cluster days into meteorological seasons. The method successfully groups days in a weather dataset and creates new dynamic meteorological seasons, allowing for the assessment of climate change by tracking seasonal shifts over time. Future improvements could involve adding more meteorological features, enhancing data processing techniques for more contiguous clusters, or exploring alternative AI methods like graph neural networks.

References 1. Ali-Ou-Salah, H., Oukarfi, B., Mouhaydine, T.: Short-term solar radiation forecasting using a new seasonal clustering technique and artificial neural network. Int. J. Green Energy 19(4), 424–434 (2022) 2. Aljanad, A., Tan, N.M., Agelidis, V.G., Shareef, H.: Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm. Energies 14(4), 1213 (2021) 3. Diday, E., Simon, J.C.: Clustering analysis. Springer, New York (1976) 4. Dyurgerov, M.B., Meier, M.F.: Twentieth century climate change: evidence from small glaciers. Proc. Natl. Acad. Sci. 97(4), 1406–1411 (2000) 5. Govender, P., Sivakumar, V.: Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980–2019). Atmos. Pollut. Res. 11(1), 40–56 (2020) 6. Hinzman, L.D., Bettez, N.D., Bolton, W.R., Chapin, F.S., Dyurgerov, M.B., Fastie, C.L., Yoshikawa, K.: Evidence and implications of recent climate change in northern Alaska and other arctic regions. Clim. Change 72, 251–298 (2005) 7. Inniss, T.R.: Seasonal clustering technique for time series data. Eur. J. Oper. Res. 175(1), 376–384 (2006) 8. Javed, A., Lee, B.S., Rizzo, D.M.: A benchmark study on time series clustering. Mach. Learn. Appl. 1, 100001 (2020) 9. Kutta, E., Hubbart, J.A.: Reconsidering meteorological seasons in a changing climate. Clim. Change 137, 511–524 (2016) 10. Liao, T.W.: Clustering of time series data-a survey. Pattern Recogn. 38(11), 1857– 1874 (2005) 11. Madaniyazi, L., Armstrong, B., Chung, Y., Ng, C.F.S., Seposo, X., Kim, Y., Hashizume, M.: Seasonal variation in mortality and the role of temperature: a multi-country multi-city study. Int. J. Epidemiol. 51(1), 122–133 (2022) 12. Munoz-Diaz, D., Rodrigo, F.S.: Spatio-temporal patterns of seasonal rainfall in Spain (1912–2000) using cluster and principal component analysis: comparison. Ann. Geophys. 22(5), 1435–1448 (2004) 13. Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview, II. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7(6), e1219 (2017) 14. Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31, 274–295 (2014). https://doi.org/10.1007/s00357-014-9161-z 15. NOAA: U.S. climate normals, noaa. https://www.ncei.noaa.gov/products/landbased-station/us-climate-normals (2023)

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16. Petrovic, S.: A comparison between the silhouette index and the Davies–Bouldin index in labelling ids clusters. In: Proceedings of the 11th Nordic workshop of secure IT systems, pp. 53–64 (2006) 17. Thompson, L.G.: Climate change: the evidence and our options. Behav. Anal. 33, 153–170 (2010) 18. Trenberth, K.E.: What are the seasons? Bull. Am. Meteor. Soc. 64(11), 1276–1282 (1983)

Significance and Impact of AI on Pedagogical Learning: A Case Study of Moroccan Students at the Faculty of Legal and Economics Khoual Mohamed(B) , Zineb Elkaimbillah, and Bouchra El Asri MS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco {mohamed_khoual,zineb_elkaimbillah}@um5.ac.ma, [email protected]

Abstract. Artificial intelligence (AI) is growing and becoming a transformative technology in various fields, however its adoption in the education sector is growing to improve and revolutionize learning and teaching processes. AI in education is transforming traditional teaching methodologies by providing personalized learning paths tailored to each student’s needs and learning styles. Using data analysis and machine learning algorithms, AI can analyze student performance, identify areas of weakness, and recommend appropriate learning materials or exercises. This article uses an electronic survey to explore the attitudes of students from Faculty of economic and social legal sciences at Mohammed V University towards AI. In addition, a learning experience was conducted with 320 students, consisting of a hands-on introduction to AI, where they were invited to reflect on their experience as users of this technology. The findings suggest that students possess awareness regarding the impact of AI and display a keen interest in expanding their knowledge in this domain, despite their current limited understanding stemming from insufficient training opportunities. However, the authors argue that there is a need for broader and enhanced AI education, encompassing practical use cases and a clear exposition of the technology’s real-world constraints. By doing so, students will be better equipped to wield AI with assurance and accountability in their prospective professional paths. Keywords: Artificial intelligence (AI) · Educational sectors · Moroccan universities

1 Introduction AI, as an emerging technology, offers new opportunities to improve the efficiency, personalization and relevance of education. Through sophisticated algorithms and data analytics, AI enables the development of adaptive and interactive learning systems, paving the way for more focused teaching and an enriching learning experience for students [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 124–129, 2024. https://doi.org/10.1007/978-3-031-48573-2_18

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AI can be used for a variety of educational applications, from personalizing learning paths to assisting teachers to predicting student performance [2]. By offering individualized learning, adapted to the needs and pace of each learner, AI makes it possible to meet the challenge of the diversity of students and their learning styles. Through educational chatbots, students can get instant answers to their questions, while teachers can focus more on the educational aspect by receiving feedback on student performance. AI also facilitates the early detection of struggling students, enabling rapid intervention and appropriate support to improve their academic success [3]. Nevertheless, the integration of AI in education also raises ethical questions and challenges, such as the protection of student data, the transparency of algorithms, and concerns about overreliance on technology. It is essential that the use of AI in education is guided by sound ethical principles, to ensure responsible and beneficial use for all actors involved. In this study, we will further explore the attitudes of 320 students towards the impact of artificial intelligence (AI) in education. We will seek to understand students’ opinions and perceptions regarding the increasing use of AI in educational environments, as well as the factors that influence their views [4].

2 Methodology 2.1 Sample and Data Collection Procedure In this study carried out at the faculty of law and economics, we employed an online survey via Google Forms as our primary research tool. The survey was crafted in French, the language of instruction in Morocco, and encompassed a total of 19 questions (Table 1). Between February and March 2023, a total of 320 students willingly engaged in the survey. No personal information was gathered, and all responses given were kept anonymous. The survey was split into two main sections. The initial segment covered general inquiries about the students, including elements like gender and specifics concerning their educational pursuits, such as academic level, degree, and current year of study. The subsequent part consisted of questions regarding the students’ viewpoints on the importance and consequences of AI in their future professional journeys. It also delved into their comprehension of AI-related terminology and limitations, as well as their confidence in consistently and analytically utilizing AI tools after their studies. These queries were structured using a Likert scale, where participants expressed their degree of agreement with the statements presented. 2.2 Students’ Personal Information/Demographic Data Table 2 evaluated demographic and individual details. The majority of participants were well-educated, having attained university qualifications. To elaborate, 80% held a bachelor’s degree, 15% a master’s degree, and 5% a doctorate. Concerning this group, the students exhibited varying ages and educational attainments. Among them, 94% were aged between 18 and 38, while the remaining respondents were over 39. Additionally, there were 56% female students and 44% male students.

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Q1: Quel est votre domaine d’étude principal? Q11: L’IA peut-elle aider les enseignants dans leur travail? Q2: Que signifie l’acronyme “IA”?

Q12: Avez-vous réussi tous vos examens?

Q3: Faut-il avoir peur de l’IA

Q13: Quelle est votre année d’études?

Q4: Quel est votre sexe?

Q14: Qu’est-ce qui vous vient à l’esprit lorsque vous pensez à l’IA?

Q5: Dans quels domaines pensez-vous que l’IA aura un impact important?

Q15: Dans quels domaines pensez-vous que l’IA aura un impact important?

Q6: Quel est, selon vous, le principal avantage Q16: Selon vous, l’intelligence artificielle de l’IA dans le processus d’apprentissage? va-t-elle redéfinir le processus d’apprentissage? Q7: Quelles sources utilisez-vous pour vous informer sur le concept d’intelligence artificielle?

Q17: Quels sont les domaines dans lesquels vous pensez que l’IA exercera une influence significative?

Q8: Comment l’intelligence artificielle peut-elle révolutionner le domaine de l’éducation?

Q18: Quel est, selon vous, le principal avantage que l’IA pourrait apporter au processus d’évaluation?

Q9: Quel est, selon vous, le principal inconvénient auquel l’IA pourrait être confrontée dans le contexte du processus éducatif

Q19: Sur une échelle de 1 à 10, dans quelle mesure envisagez-vous le rôle de l’IA dans le domaine de l’éducation?

Q10: Pouvez-vous donner un exemple concret d’application de l’IA dans l’éducation que vous trouvez particulièrement intéressant ou prometteur?

Table 2. Demographics of the respondents (n = 320). Demographics

Factor

Frequency

Percentage (%)

Gender

Female

178

56

Male

142

44

Age (mean = 24)

18–28

229

71

28–38

73

23

38–48 School/level (currently enrolled)

18

6

256

80

Master

48

15

Doctorate

16

5

Bachelor

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3 Results From the 19 questions presented to students for response, we selected some question results that measure students’ familiarity with artificial intelligence, 267 students (86%) demonstrated awareness of the concept of artificial intelligence, 14 (4%) of them being unfamiliar with this concept. This result implies that the majority of students have a fundamental understanding of artificial intelligence, as shown in Fig. 1.

AI KNOWLEDGE RATING

10 9 8 7 6 5 4 3 2 1 0

10

20

30

40

50

NUMBER OF STUDENTS

Fig. 1. Level of knowledge of artificial intelligence by students

A majority of 63% of students hold the viewpoint that incorporating artificial intelligence into education significantly contributes to improving the learning experience. On the other hand, 37% are of the opinion that traditional teaching approaches, encompassing course presentations and practical exercises, prove to be more effective compared to the utilization of AI, as evident from Fig. 2. In conclusion, concerning the students’ feedback regarding the pros and cons of AI in education, there was a fairly even distribution of responses, which is not unexpected [5]. Frequently highlighted points encompassed the efficacy of AI in undertaking virtual assistant roles, facilitating broad access, and delivering continuous feedback. On the other hand, students predominantly voiced apprehensions about the lack of interpersonal connection as the primary drawback, as evident from Fig. 3.

4 Discussion The results of this survey show that the vast majority of students (86%) have a clear understanding of AI as a branch of emerging technology. Its essence lies in the conceptualization of theories and techniques with the aim of designing algorithms capable of emulating human thought [6]. What’s more, 63% of them consider that its integration within the educational field offers promising prospects. This fusion integrates the

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Fig. 2. The influence of artificial intelligence on education

Fig. 3. Students’ opinions on the pros and cons of AI in education

exploitation of AI-based methods and tools to improve educational processes and facilitate the acquisition of knowledge. However, 37% of students are reticent about the impact of AI on education. They believe that the traditional approach to teaching, which has endured as the central foundation of education systems, will never be completely replaced by AI. Ultimately, most students believe that AI has the potential to transform education by improving the efficiency, personalization and relevance of learning.

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5 Conclusion In short, contrary to our expectations, we found that students were well informed beforehand about the concept of artificial intelligence and its various applications, particularly in various sectors such as education, which plays an essential role in society. The majority of respondents to our survey clearly indicated the significant impact of artificial intelligence in this field. However, some students point out that the increasing integration of artificial intelligence (AI) in education could have negative consequences. In particular, it could restrict the in-depth interactions and emotional exchanges that naturally develop between teachers and students. In addition, AI could encourage an over-reliance on technology, leading to a loss of crucial human skills. Indeed, students could potentially become over-dependent on machines to solve problems and make educational decisions. In sum, our findings lead us to conclude that students’ interest in artificial intelligence exceeds their knowledge of the technology. Given the growing impact of AI on students’ personal and professional lives, we believe that AI training should be made available [7]. Such training would benefit university students in all disciplines, preparing them to become better-informed citizens capable of using this technology competently and ethically.

References 1. Ouyang, F., Jiao, P.: Artificial intelligence in education: the three paradigms. Comput. Educ. Artif. Intell. 2, 100020 (2021) 2. Tishkina, K.O., Eliseeva, O.V., Bagautdinova, A.S., Shilova, K.S., Efremova, A.A.: Individual educational program as a tool to personalize education. Univ. Manag. Pract. Anal. 27(1), 4 (2023) 3. Martínez-Comesaña, M., Rigueira-Díaz, X., Larrañaga-Janeiro, A., Martínez-Torres, J., Ocarranza-Prado, I., Kreibel, D.: Impact of artificial intelligence on assessment methods in primary and secondary education: systematic literature review. Revista de Psicodidáctica (English ed.) (2023) 4. Alam, A., Hasan, M., Raza, M.M.: Impact of artificial intelligence (AI) on education: changing paradigms and approaches. Towards Excell. 14(1), 281–289 (2022) 5. Alhumaid, K., Naqbi, S., Elsori, D., Mansoori, M.: The adoption of artificial intelligence applications in education. Int. J. Data Netw. Sci. 7(1), 457–466 (2023) 6. Konobeev, A.B., ximyk, R.A., Bocexovcka, B.D, Xqekiq, M.: Pepconalizaci kak podxod k obyqeni. Dickypc ppofeccionalno kommynikacii 2(3), 118–138 (2020) 7. Shrivastava, R.: Role of artificial intelligence in future of education. Int. J. Profess. Bus. Rev. Int. J. Prof. Bus. Rev. 8(1), 2 (2023)

Securing Big Data: Current Challenges and Emerging Security Techniques Ikram Hamdaoui(B) , Khalid El Makkaoui, and Zakaria El Allali LaMAO laboratory, MSC team, FPD, Mohammed First University, Nador, Morocco [email protected], [email protected], [email protected]

Abstract. The term big data (BD) refers to the massive amounts of different types of data generated every day. Valuable data and insights can be extracted by utilizing BD to make strategies and take the right decisions, leading to numerous benefits. However, BD presents numerous challenges, including those related to data security and privacy. This paper focuses on BD, its characteristics, and the major security challenges it faces. We then discuss some recent solutions for these security issues, such as blockchain (BC), machine learning (ML), and artificial intelligence (AI) security techniques. We also provide a brief overview of prior research on contemporary security solutions employing ML, AI, and BC. Finally, we provide examples of attacks on BD systems and classic and recent security solutions for them. Keywords: Big data

1

· Blockchain · Machine learning · Security

Introduction

Nowadays, it is undeniable that quintillions of data bytes ( 2.5 quintillion bytes of data) [1,2] of different types are created daily. This massive amount of data is referred to as big data (BD), which is increasing with the growth of connected devices, social media, etc. Traditional methods can only handle tabular, structured data stored in.csv or.xls file formats. However, applications like Instagram and Facebook demand the handling, processing, and storage of structured and unstructured data. Therefore, BD solutions, technologies, and tools such as Apache Hadoop [3] exist to solve these issues. BD plays a significant role in different sectors and complex systems, and valuable insights are extracted using BD analytic tools, which can be transformed into actionable business strategies and decisions, yielding various benefits. However, despite the high value of BD and the benefits it brings, it faces security issues concerning the data, technologies, and BD environments. The 3 Vs of BD face security management issues. At the same time, BD technologies such as Hadoop did not consider security when they were first designed. In contrast, BD environments face security issues whether they are deployed on the cloud or on-premise platforms. There are several solutions to BD security issues, but traditional security measures might not always be effective. Thus, this article first c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 130–137, 2024. https://doi.org/10.1007/978-3-031-48573-2_19

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discusses BD characteristics before discussing data, technology, and infrastructure security issues. We then listed classic, AI, and BC security options. Finally, we demonstrated BD system attacks and countermeasures. The remaining sections are structured as follows: The second section defines BD and its various characteristics; the third section describes different BD security challenges; the fourth section proposes some solutions and techniques to overcome security issues that BD faces; the fifth section provides examples of some types of attacks on BD systems and suggests some solutions for them; and finally, a conclusion is presented in the sixth section.

2

Big Data and Its Characteristics

BD is defined by its own characteristics, which are mainly volume, variety, and velocity. Then, two more important characteristics (veracity and value) were added, which are described as follows: Volume, which is the V that is most closely associated with BD, refers to the huge size of the data generated, stored, and processed by the system. Variety refers to the various forms of data obtained from a variety of resources. Velocity stands for the speed at which new data is being generated and analyzed. Some data is processed in real-time and needs to be analyzed quickly and effectively. Veracity After the 3Vs of BD, veracity becomes the fourth V associated with it. It refers to the reliability, quality, and credibility of data and data sources. Value means the value and potential of data.

3

Big Data Security Challenges

BD faces many security challenges that come from various malicious activities. These security challenges are not only limited to on-premise platforms but also have an impact on the cloud [4]. The most common BD issues are as follows: Data security challenge: One of the BD security challenges is that data is going through a three-phase circuit (as shown in Fig. 1); thus, it may be vulnerable at more than one point [5]. In the data source phase, BD is collected from many sources and in many forms, so some traditional security techniques may not work for all sources, and also, data volatility makes real-time monitoring difficult. In the data storage phase, a suitable storage system stores the massive amount of data acquired, and data theft and operational interruption must be prevented from these storage systems and their data. And in the data analytic phase, processed and analyzed data is needed to gain insights and make informed decisions. BD Analytics, however, has several security issues [6]. Analyzing data from multiple or untrustworthy sources may lead to poor privacy disclosure and poor decisions.

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Fig. 1. Data security challenges

Big Data technologies’ security challenges: BD technologies like Hadoop [7] have network security and authentication vulnerabilities [8] and data leakage. For example, in MapReduce, cybercriminals can access the mapper’s code and produce incorrect key/value pair lists, causing process errors. MapReduce can leak data because it stores it across multiple nodes. HDFS is vulnerable to DoS and other attacks [9] and it is exposed to data theft, leakage, and unauthorized access. Security software struggles to protect NoSQL databases like Cassandra and MongoDB due to their rapid development and lack of security focus. MongoDB and Cassandra lack data file encryption and weak authentication [10]. Security challenges in big data infrastructures: BD infrastructures security is a major concern. Cloud storage attracts thieves, and sharing sensitive data with a third party raises security concerns [11,12]. Since it’s expensive to secure an on-premises infrastructure, employees may copy sensitive data to their laptop or storage device to work from home. If these devices are lost, the company may be vulnerable to data breaches.

4

Big Data Security Solutions

BD security solutions are the methods, tools, and techniques used to protect BD systems. Traditional, AI-based, and blockchain-based security measures are used to address BD security concerns [13,14]. 4.1

Classic Security Methods

Some BD security challenges can be resolved by employing traditional security mechanisms and adopting appropriate precautions. Below is a discussion of several BD security solutions using traditional security technologies: Intrusion Prevention and Detection Systems: Intrusion Detection Systems (IDS) can automatically monitor, detect attacks, and alert the monitored computer or network. Thus, intrusion prevention systems (IPS) protect BD platforms, and IDS quarantines successful intrusions before they cause harm.

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Encryption: Encrypting data is a common way to protect it. Therefore, NoSQL databases and HDFS must be compatible with encryption methods. User access control: Access control (AC) systems allow only authorized users onto a network. This is done by verifying login credentials. Passwords and PINs are examples of credentials. BD platforms are protected from insider threats by adopting access control, which automatically manages user control levels like numerous administrator settings. Firewalls: They can monitor and regulate BD system-to-network traffic. They can also block malicious traffic, prevent unauthorized access, and monitor network activity. 4.2

Artificial Intelligence Security Techniques

AI can recognize and process massive amounts of data without being interrupted, allowing it to investigate potentially harmful behaviors and offer solutions. Examples of using AI for security: IDS using AI: Traditional IDS have struggled to keep up with new threats due to the large amount of data from different sources and formats, so AI techniques like machine learning (ML) algorithms are widely used for threat detection. IDS detects suspicious user behavior and network traffic using AI. By learning from traffic or behavioral patterns [15], ML-based algorithms create a predictive model that can distinguish intrusions from regular connections. The IDS can detect attacks faster and more accurately with this model. Firewalls using AI: Traditional firewalls may struggle with massive data volumes, increased network traffic, and complex data structures. Therefore, AI and ML can improve BD security by enabling real-time processing, threat detection, etc. Access control using AI: Traditional BD user AC security lacks visibility into user behavior, scalability, and management. Thus, AI and ML can improve it by real-time user behavior monitoring and analysis to detect internal threats or access risk AI Biometric Authentication: Biometrics identifies people using biological traits or behaviors. Many authentication methods fail. Attackers can easily break password-based authentication. Thus, secure authentication requires multi-factor authentication. AI can create biometric authentication-based datadriven security protocols [16,17]. Only biometric authentication is hard to share or steal, so it provides unparalleled security. 4.3

Blockchain Security

BC is currently one of the most widely used technologies and is opening the door for new financial and industrial applications [18,19]. Due to its decentralization, immutability, and peer-to-peer transactions, BC is BD’s ideal security partner [20]. BC’s immutable ledger record tracks and immutably records datasets from various sources. BC institutions can detect fraud in real time by checking

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every transaction. The BC network’s encrypted and decentralized data storage makes unauthorized access difficult, and BC records are permanent, so even if data changes, they remain. Changing BD records to affect analytics predictions is nearly impossible.

5 5.1

Examples of Potential Attacks on BD Systems and Some Solutions Examples of Potential Attacks

The BD system’s collection, storage, and processing levels may be attacked. Figure 2 illustrates potential attack types for each BD system level.

Fig. 2. potential attacks on BD systems

The potential attacks in the previous diagram can be labeled into the following categories: Unauthorized data access: It includes malware, DoS, ransomware, physical, zero-day, data breach, and insider threats. BD systems may leak sensitive data to unauthorized parties, causing data loss or theft and business disruption. A malware attack on a financial institution that processes and stores customer financial data can steal account numbers, balances, and transactions and an identity. Data interception: that includes man-in-the-middle MITM sniffing, sidechannel, and timing attacks. These attacks allow attackers to intercept and steal network data, which may affect data quality and accuracy and lead to data breaches, which can result in reputation loss and legal and regulatory consequences. An MITM attack on healthcare steals or manipulates network data like medical histories, diagnoses, and treatment plans, resulting in harmful medical decisions. Data tampering: including SQL injection, data poisoning, ransomware, XSS, physical attacks, and insider threats This attack allows attackers to change, delete, or add false data to BD systems. For instance, a data poisoning attack on an e-commerce company that analyzes data to determine pricing strategies

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may deflate product prices, leading to inaccurate conclusions about customer behavior and sales trends. The company may make suboptimal pricing decisions that lower its net income or profit. 5.2

Solutions for Potential Attacks

The following diagram presents various solutions for the previously categorized attacks; these include traditional, AI, and blockchain solutions (Fig. 3).

Fig. 3. Solutions for potential attacks

6

Conclusion

Challenges, such as those related to data security and privacy, have emerged alongside BD’s meteoric rise. Numerous instances of data theft, misuse of personal information, etc., have fostered an atmosphere of mistrust and insecurity. While traditional security methods can be used, they are not sufficient to deal with all threats to BD. Consequently, the security of BD is bolstered using BC, ML, and AI techniques. In this paper, we briefly introduce BD and its characteristics before listing some of its security challenges. We then discussed conventional, AI, and BC BD security measures. We then discussed potential BD system attacks and countermeasures.

References 1. Gupta, Y.K., Kumari, S.: A study of big data analytics using apache spark with Python and Scala. In: Proceedings of the 2020 3rd International Conference on Intelligent Sustainable Systems (2020)

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2. Hamdaoui, I., El Fissaoui, M., El Makkaoui, K., El Allali, Z.: Hadoop-based big data distributions: a comparative study. In: Emerging Trends in Intelligent Systems and Network Security, pp. 242–252. Springer International Publishing, Cham (2022) 3. Singh, V.K., Taram, M., Agrawal, V., Baghel, B.S.: A literature review on Hadoop ecosystem and various techniques of big data optimization. In: Advances in Data and Information Sciences: Proceedings of ICDIS-2017, Vol. 1, pp. 231–240 (2018) 4. El Makkaoui, K., Ezzati, A., Beni-Hssane, A., Motamed, C.: Cloud security and privacy model for providing secure cloud services. In: Proceedings of the 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech) (pp. 81–86). IEEE (2016) 5. Venkatraman, S., Venkatraman, R.: Big data security challenges and strategies. AIMS Math. 4(3), 860–879 (2019) 6. Hariri, R.H., Fredericks, E.M., Bowers, K.M.: Uncertainty in big data analytics: survey, opportunities, and challenges. J. Big Data 6(1), 1–16 (2019) 7. Hamdaoui, I., El Fissaoui, M., El Makkaoui, K., El Allali, Z.: An intelligent traffic monitoring approach based on Hadoop ecosystem. In: Proceedings of the 2022 5th International Conference on Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-Based Interconnected Digital Worlds (NISS) (pp. 1–6). IEEE (2022) 8. Tariq, R.S.: Big data challenges. Comput. Eng. Inform. Technol. 04(03), 147 (2015) 9. Bhathal, G.S., Singh, A.: Big data: hadoop framework vulnerabilities, security issues and attacks. Array 1–2, 100002 (2019) 10. Mat´e, A., Peral, J., Trujillo, J., Blanco, C., Garc´ıa-Saiz, D., Fern´ andez-Medina, E.: Improving security in NoSQL document databases through model-driven modernization. Knowl. Inf. Syst. 63(8), 2209–2230 (2021) 11. Nafea, R.A., Amin Almaiah, M.: Cyber Security Threats in Cloud: Literature Review. IEEE Xplore (2021) 12. Nagarajan, G., Kumar, K.S.: Security threats and challenges in public cloud storage. In: Proceedings of the 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 97–100). IEEE (2021) 13. Bhutta, M.N.M., Khwaja, A.A., Nadeem, A., Ahmad, H.F., Khan, M.K., Hanif, M.A., Cao, Y.: A survey on blockchain technology: evolution, architecture and security. IEEE Access 9, 61048–61073 (2021) 14. Berdik, D., Otoum, S., Schmidt, N., Porter, D., Jararweh, Y.: A survey on blockchain for information systems management and security. Inform. Process. Manag. 58(1), 102397 (2021) 15. Seo, W., Pak, W.: Real-time network intrusion prevention system based on hybrid machine learning. IEEE Access 9, 46386–46397 (2021) 16. Albalawi, S., Alshahrani, L., Albalawi, N., Kilabi, R., Alhakamy, A.: A comprehensive overview on biometric authentication systems using artificial intelligence techniques. Int. J. Adv. Comput. Sci. Appl. 13(4), 157 (2022) 17. Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., et al.: Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif. Intell. Rev. 12, 1–25 (2022) 18. Deepa, N., Pham, Q.V., Nguyen, D.C., Bhattacharya, S., Prabadevi, B., Gadekallu, T.R., Pathirana, P.N.: A survey on blockchain for big data: approaches, opportunities, and future directions. Fut. Gener. Comput. Syst. 131, 209–226 (2022) 19. Jumani, A.K., Laghari, A.A., Khan, A.A.: Blockchain and big data: supportive aid for daily life. In: Security Issues and Privacy Concerns in Industry 4.0 Applications, pp. 141–178 (2021)

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20. Fartitchou, M., El Makkaoui, K., Kannouf, N., El Allali, Z.: Security on blockchain technology. In: Proceedings of the 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet) (pp. 1–7). IEEE (2020)

Machine Learning for Early Fire Detection in the Oasis Environment Safae Sossi Alaoui(B) and Yousef Farhaoui Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected]

Abstract. An oasis ecosystem that consists of date palms prevents desertification and ensures the survival of a rich biodiversity. However, climate change is having a negative impact on our planet by increasing global temperatures, leading to fires in the oasis environment and, consequently, threatening the activity of the population. In this paper, we aim to build an accurate model to detect fire extinguishers as early as possible in the oasis area. To this end, we tested several machine learning algorithms, namely AdaBoost, Naïve Bayes, Random Forest, kNN, Gradient Boosting, and Neural Network applied to a dataset containing 1900 images in total. The results show that the Neural Network delivers the highest accuracy at 97.7%. Keywords: Machine learning · Fire detection · Environment · Climate change

1 Introduction Climate change is the term used to describe climatic changes over time, such as precipitation, temperature, and wind patterns that are accompanied by a rise in world average temperatures [1]. These changes are caused by a rise in greenhouse gas concentrations including carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O) in the environment which refers to global warming. The Causes of climate change are multiple namely power generation by burning fossil fuels, food production, goods manufacturing, deforestation, transport usage…etc. Climate change affects the entire global ecosystem by causing severe storms, destructive flooding, ocean rising, and long years of drought. Effectively, drought and rising heat lead to unusual forest fire alerts. The lack of rain and water scarcity in Morocco, which is experiencing its worst drought in 30 years, has led to wildfires in different regions In Morocco, northern provinces, are particularly vulnerable to wildfire during seasons of summer. In Morocco, the risk of wildfire has escalated tremendously in recent years [2] as shown in Fig. 1, threatening thousands of lives, and livelihoods. With a total of 31,689 Ha burned, the fire season of 2022 was the worst for the country in terms of cumulative scorched area. The causes of wildfires are typically unknown, but they are frequently caused by human activity and natural phenomena. They result in the loss of resources, crops, people, animals, and property, as well as a decline in air quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 138–143, 2024. https://doi.org/10.1007/978-3-031-48573-2_20

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35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Area burned by fires (in hectares) 2009 2016

2010 2017

2011 2018

2012 2019

2013 2020

2014 2021

2015 2022*

Fig. 1. Area burned by wildfires in Morocco from 2009 to 2022 (in Hectares) [3].

Despite the efforts being made to prevent, control, and tackle wildfires, we need to exploit more sophisticated technologies by mapping artificial intelligence and data mining to predict wildfires at their initial stage, for that reason we intend to develop a model based on machine learning algorithms to detect wildfire in oasis environment as early as possible. The rest of this paper is organized as follows; the next section presents related works about fire detection using machine learning. Then, we describe the methodology followed. Next, we present the results obtained and finally, we conclude our paper.

2 Literature Review A great deal of research has addressed the issue of forest fires using a variety of techniques. Indeed, the implementation of effective prevention, early warning, and response techniques, well planned and carefully coordinated, is necessary to reduce the effects of these fires on people and the environment. Rubí et al. [4] aimed at predicting the spread and behavior of wildfires at a certain time and/or in a specific territory, a dataset was assembled using Brazilian governmentaccessible data. This comprises satellite data on recent fires and observations of climate features from five monitoring sites. It contains observations on climate features from five monitoring stations as well as satellite data on fires that have occurred over the previous two decades. The results reveal that the AdaBoost model performed better than the Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models, predicting the region impacted by the wildfire with 91% accuracy. Khan et al. [5] suggested a UAV-based forest fire-fighting system with integrated artificial intelligence (AI) capabilities. They proposed a new dataset named DeepFire comprising a variety of real-world forest pictures, both with and without fire. They suggest a transfer learning approach based on VGG19 to boost prediction accuracy.

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They evaluate the performance of a number of Machine learning algorithms, including k-nearest neighbors, random forest, naive Bayes, support vector machines, logistic regression, and the suggested methodology. The simulation results show how effective the suggested technique is in accurately classifying data, with a mean accuracy of 95%, precision of 95.7%, and recall of 94.2%. Li et al. [6] presented a fire detection approach called DAI-YOLO based on enhanced YOLOv3, an algorithm where the network structure is enhanced, the scale of the input image is increased, the traditional convolution is replaced with a depthwise separable convolution structure, the model parameters are reduced, and the detection rate is improved; the multi-scale feature detection is used to increase the shallow detection scale and add a 4 times upsampling feature fusion structure to improve fire detection accuracy; optimize k-means clustering an is used to increase the shallow detection scale and increase The testing findings reveal that the modified algorithm’s accuracy and recall rates are 91.2% and 84.2%, respectively, and that the mAP can reach 84.6%. Li and Zhao [7] proposed new image fire detection methods based on the sophisticated object detection CNN models of Faster-RCNN, R-FCN, SSD and YO-LO v3. The accuracy of fire detection algorithms based on object detection CNNs is superior to that of other methods, based on a comparison of proposed and existing techniques. In particular, the algorithm based on YOLO v3 achieves an average accuracy of 83.7%, which is superior to other proposed techniques. In addition, YOLO v3 meets the requirements of real-time detection thanks to its detection speed of 28 frames per second and greater resilience in detection performance.

3 Methodology 3.1 Data Collection The selected dataset has been created to facilitate the identification of forest fires and is published on the Kaggle website. The images in the collection are all three-channel with a resolution of 250. They are carefully processed to crop and remove any inappropriate elements, such as people, extinguishing equipment, etc., and to ensure that each image contains only the relevant location of the fire. This is a balanced dataset comprising a total of 1900 images, 950 of which belong to each class. In the proposed study, the dataset is split 80:20 for training and testing [5]. 3.2 Tool Description The tool used in this work is Orange which is a Python-based toolkit for data mining. It is very powerful and can be used by both novices and experts. It has a complete set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation, and exploration techniques. To make use of image analytics, we install a provided package named ImageAnalytics.

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3.3 Selected ML Algorithms Machine learning algorithms [8] are programs; that are considered as an evolution of regular algorithms, which can automatically learn from data and improve from experience, without human intervention. Machine learning methods are several, in this work we have used Neural network, Naïve Bayes, Adaboost, Random Forest, Gradient Boosting and KNN. First, we load images by using the widget Import Images as illustrated in Fig. 2, which will create two classes belonging to either fire or no fire. While Image Embedding, which is the core element of Orange’s image analysis, it reads the images and sends them to a remote server or evaluates them locally. Deep learning models are used to calculate a feature vector for each image and in this work we used SqueezeNet a deep model for image recognition.

Fig. 2. Comparison between ML algorithms.

4 Results and Discussion After using the widget Test and Score which test learning algorithms on data. We have used cross-validation 10 folds to split the data. The result obtained as shown in Table 1, in terms of the various evaluation metrics, Neural Network was the best with an accuracy score of 97.7%, followed by gradient boosting with 97%, kNN with 96.4%, Random Forest with 95.6%, Naïve Bayes with 92.2% and adaboost with 91.5%. Figure 3 shows the confusion matrix of Artificial neural network (ANN) an algorithm, which emulates the biological neural system in the human brain (Fig. 4). To test our model, we collected images from google images (Fig. 5) related to wildfires in oasis environment and we make predictions using the model generated by Neural Network as shown in Figs. 5 and 6.

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Model

AUC

Accuracy

Precision

F1

Recall

MCC

AdaBoost

0.915

0.915

0.915

0.916

0.915

0.831

Naive Bayes

0.949

0.922

0.922

0.925

0.922

0.847

Random forest

0.986

0.956

0.956

0.956

0.956

0.912

kNN

0.987

0.964

0.964

0.965

0.964

0.929

Gradient boosting

0.993

0.970

0.970

0.970

0.970

0.939

Neural network

0.997

0.977

0.977

0.977

0.977

0.954

Fig. 3. Confusion matrix of neural network.

Fig. 4. Real images taken from oasis environment.

Fig. 5. Predictions for Neural Network for the testing data.

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Fig. 6. Predictions of wildfires by neural network.

5 Conclusion In the in south-east of Morocco, many hectares of a date oasis were generally destroyed by fire due to dry conditions and southerly winds. These uncontrolled and unplanned fires have a dangerous impact on the environment and the population; it is therefore necessary to anticipate wildfires from their earliest stages. Our aim in this work was to develop an accurate model for rapidly identifying wildfires in the oasis region using several machine learning algorithms applied to a dataset comprising a total of 1900 images. According to the results, Neural Network had the best accuracy, with 97.7%.

References 1. Arnell, N.W.: The implications of climate change for emergency planning. Int. J. Disast. Risk Reduct. 83, 103425 (2022). https://doi.org/10.1016/j.ijdrr.2022.103425 2. Serbouti, S., et al.: Evolution of wildfires, burned areas, and affected species in Middle Atlas forests (Morocco) from 2000 to 2020. Trees For. People 10, 100319 (2022). https://doi.org/10. 1016/j.tfp.2022.100319 3. European Forest Fire Information System: Morocco: Wildfire Area Burned (2022). https:// www.statista.com/statistics/1322254/area-burned-by-wildfire-in-morocco/. Accessed 22 June 2023 4. Rubí, J.N.S., de Carvalho, P.H.P., Gondim, P.R.L.: Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region. Eng. Appl. Artif. Intell. 118, 105649 (2023). https://doi.org/10.1016/j.engappai.2022.105649 5. Khan, A., Hassan, B., Khan, S., Ahmed, R., Abuassba, A.: DeepFire: a novel dataset and deep transfer learning benchmark for forest fire detection. Mob. Inform. Syst. 2022, e5358359 (2022). https://doi.org/10.1155/2022/5358359 6. Li, C., Cheng, D., Li, Y.: Research on fire detection algorithm based on deep learning. In: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), pp. 510–514. SPIE (2022). https://doi.org/10.1117/12.2640978 7. Li, P., Zhao, W.: Image fire detection algorithms based on convolutional neural networks. Case Stud. Therm. Eng. 19, 100625 (2020). https://doi.org/10.1016/j.csite.2020.100625 8. Sossi Alaoui, S., Farhaoui, Y., Aksasse, B.: A comparative study of the four well-known classification algorithms in data mining. In: Ezziyyani, M., Bahaj, M., and Khoukhi, F. (eds.) Advanced Information Technology, Services and Systems, pp. 362–373. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-69137-4_32

Voice-Based Detection of Parkinson’s Disease Using Empirical Mode Decomposition, IMFCC, MFCC, and Deep Learning Nouhaila Boualoulou1(B) , Mounia Miyara2 , Benayad Nsiri3 and Taoufiq Belhoussine Drissi1

,

1 Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and

Logistics (GEITIIL), Faculty of Science Ain Chock, University Hassan II, Casablanca, Morocco [email protected] 2 Computer Science and Systems Laboratory (LIS), Faculty of Science Ain Chock, University Hassan II, Casablanca, Morocco 3 Research Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM), Mohammed V University, Rabat, Morocco

Abstract. Parkinson’s disease, a chronic and progressive neurological disorder that mainly affects the elderly, has recently drawn research attention to its manifestation in speech disorders as early indicators. In this particular study, the use of features based on Empirical Mode Decomposition (EMD) was employed to effectively capture the distinct characteristics present in speech affected by this disease. To this end, a comparison between two types of features, namely intrinsic mode function cepstral coefficients (IMFCC) and Mel frequency cepstral coefficients (MFCC) with the deep learning algorithms ANN, LSTM and CNN, was proposed as a means of accurately representing the unique vocal attributes exhibited by people with Parkinson’s disease. The performance of the proposed features was meticulously evaluated using a Sakar dataset comprising 18 non-Parkinson’s subjects and 20 Parkinson’s individuals. The results obtained demonstrate unequivocally that the application of MFCC features in conjunction with the LSTM classifier produces classification accuracy superior to the use of intrinsic cepstral coefficient features. This is demonstrated by a significant 18.18% increase in accuracy. Keywords: EMD · IMFCC · MFCC · ANN · CNN · LSTM

1 Introduction Parkinson’s Disease (PD) is a prevalent neurodegenerative disorder characterized by motor and non-motor symptoms, including tremors, bradykinesia, and speech impairments. Early detection of PD is crucial for timely intervention and personalized treatment strategies. In recent years, voice-based analysis has emerged as a promising avenue for non-invasive and cost-effective detection of PD.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 144–150, 2024. https://doi.org/10.1007/978-3-031-48573-2_21

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Voice signals hold great potential for early detection and monitoring of PD. Voicerelated changes, such as reduced vocal loudness, altered speech rhythm, and dysarthria, can serve as indicators of the disease. Analyzing voice signals through advanced signal processing and machine learning techniques can provide valuable diagnostic information. In many scientific articles, Karan et al. [1] investigated the voice tremor in patients with Parkinson’s disease (PD) using a combined approach of Variational Mode Decomposition (VMD) and Hilbert spectrum analysis (HSA). They proposed a new set of features called Hilbert cepstral coefficients (HCCs) for this purpose. Orozco-Arrovave et al. [2] focused on PD detection using sustained vowels and words. They achieved an accuracy of up to 85% by employing spectral and cepstral features. Khan et al. [3] introduced Cepstral separation distance (CSD) features and demonstrated their effectiveness in PD detection. They observed that the CSD features showed good performance based on intra-class correlation coefficients (> 0.9). Mehmet et al. [4] proposed a novel approach for PD detection from speech signals. They utilized pre-trained deep networks and long short-term memory (LSTM) models along with Mel spectrograms obtained from denoised speech signals using Variational Mode Decomposition (VMD). In the study by Sakar et al. [5], different speech signal processing algorithms were compared for PD diagnosis. They introduced a new tool called Tunable Q-factor wavelet transform (TQWT) and trained classifiers using various feature subsets. Their results indicated that Mel Frequency Cepsturm Coefficients (MFCC) and TQWT achieved the highest accuracy, highlighting their significance in the classification problem of PD. They obtained an average accuracy of 86% with the Support Vector Machine (SVM) classifier. Several other studies have also utilized discrete wavelet transform for precise diagnosis of PD [6– 8]. This scientific article presents a comprehensive method for voice-based PD detection by employing EMD for signal decomposition, extracting IMFCC and MFCC features, and employing ANN, CNN, and LSTM for classification. The proposed approach showcases the potential of integrating advanced signal processing and deep learning techniques to enhance the accuracy and efficiency of PD detection, ultimately contributing to improved patient care and management. The remaining sections of the article are structured as follows: Sect. 2 provides a detailed description of the utilized database, while Sect. 3 outlines the methodology employed in the study. Section 4 presents the results and subsequent discussion, analyzing the findings in depth. Finally, the conclusion and future work are presented in the Sect. 5 of the article.

2 Dataset The Sakar dataset consists of 38 audio recordings, including 20 recordings from individuals diagnosed with Parkinson’s disease (PD) and 18 recordings from healthy individuals. The participants were instructed to articulate the vowel sound “a” while using a standard microphone with a sampling frequency of 44.100 Hz. The recordings were carried out on a desktop computer equipped with a 16-bit sound card. The vocal recordings were captured in stereo-channel mode and saved in the WAV format [9].

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3 Methodology The proposed methodology encompasses several key stages, namely preprocessing of the recorded data, signal decomposition utilizing EMD, feature extraction from the intrinsic mode function, and ultimately, classification employing ANN, LSTM, and CNN. This methodological framework facilitates the systematic and structured analysis of speech data, leading to enhanced accuracy in the identification of Parkinson’s disease. The diagram in Fig. 1 provided below illustrates the method proposed for this study, and its detailed explanation follows in the subsequent paragraph.

Speech signal

EMD

ANN

IMFCC

ANN

MFCC features

Features

CNN

LSTM

LSTM

CNN

Fig. 1. The proposed approach

EMD (Empirical Mode Decomposition): A signal processing technique used to decompose a complex signal into simpler components called intrinsic mode functions (IMFs) as depicted in Eq. 1, which represent different time scales present in the signal [10]. s(t) =

l 

ci (t) + r(t)

(1)

i=1

where the r(t) is the residue and ci (t) is the functions are called the Intrinsic Mode Functions (IMFs). IMFCC (Intrinsic Mode Function Cepstral Coefficient): A feature extraction technique that computes the cepstral coefficients from the intrinsic mode functions obtained through EMD [11]. It is commonly used in speech analysis and processing, as illustrated in Eq. (2). IMFCCimf = DCT(log(Eimf ))

(2)

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MFCC (Mel Frequency Cepstral Coefficient): A widely used feature extraction method in speech and audio processing. It computes the cepstral coefficients after applying a Mel-frequency filter bank to the power spectrum of a signal. MFCCs capture important spectral and temporal information of the signal, Eq. (3) provides the formula for converting linear frequency to Mel frequency.   f (3) Mel(f) = 2595 log10 1 + 100 ANN (Artificial Neural Network): A computational model inspired by the structure and function of biological neural networks. It consists of interconnected artificial neurons (nodes) organized in layers. ANN is used for pattern recognition, classification, and regression tasks. LSTM (Long Short-Term Memory): A type of recurrent neural network (RNN) architecture that overcomes the vanishing gradient problem in traditional RNNs. LSTM networks are capable of learning long-term dependencies and are commonly used for sequence-processing tasks, such as speech recognition and natural language processing [12]. CNN (Convolutional Neural Network): A deep learning architecture primarily designed to automatically extract relevant features from input data through the application of convolutional layers. They have also been successfully applied to speechprocessing tasks, such as speech recognition and speaker identification [12]. To assess the effectiveness of the classifier, the following parameters are employed for performance evaluation: TN + TP TN + TP + FP + FN

(4)

Sensitivity =

TP TP + FN

(5)

Specificity =

TN TN + FP

(6)

Accuracy =

True Positives (TP) refer to correctly categorized individuals without Parkinson’s disease. True Negatives (TN) denote correctly categorized individuals with Parkinson’s disease. False Positives (FP) indicate individuals without Parkinson’s disease who were mistakenly categorized as having the condition. False Negatives (FN) represent individuals with Parkinson’s disease who were mistakenly categorized as not having the condition.

4 Results and Discussion The performance evaluation of the proposed features is conducted using three different classifiers: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). To assess the performance of these classifiers,

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holdout cross-validation is employed, a widely used technique in machine learning and statistical modeling. In holdout cross-validation, the dataset is divided into two separate subsets: a training set used for model training and a test set used for performance evaluation. In this study, the dataset is split into 80% for training data and 20% for testing data. The results and comparative analysis of the proposed strategy are extensively discussed in this section. After extracting the Mel Frequency Cepstral Coefficients (MFCC) and Intrinsic Mode Frequency Cepstral Coefficients (IMFCC) from the sound recordings using the Empirical Mode Decomposition (EMD) technique, these features are fed as input to the ANN, CNN, and LSTM models. During the training process, various factors are considered to ensure the sufficiency and optimality of the LSTM and CNN models. This includes setting the batch sizes to 128, the learning rates to 0.001, and the number of epochs to 20. The obtained results are presented in Tables 1 and 2, which illustrate the classification performance achieved with different parameters. Table 1 shows the classification results for IMFCC coefficients, with the highest accuracy rate reaching 72.72%. Table 2 presents the results for MFCC coefficients, where the highest accuracy rate obtained is 90.90%. Table 1. The outcome of the IMFCC analysis. IMFs

IMFCC ANN

LSTM

CNN

Acc (%)

Spe (%)

Sens (%)

Acc (%)

Spe (%)

Sens (%)

Acc (%)

Spe (%)

Sens (%)

IMF1

42.85

33.33

50

42.85

40

50

45.45

40

50

IMF2

42.85

66.66

25

42.85

33.33

50

63.63

80

50

IMF3

71.42

66.66

75

72.72

80

66.67

36.36

20

50

IMF4

71.42

66.66

75

71.42

66.66

75

45.45

20

66.66

IMF5

42.85

66.66

25

72.72

40

100

45.45

60

33.33

Boualoulou et al. conducted a study comparing two similarity coefficients, GTCC and MFCC, utilizing EMD-DWT and vice versa. They employed two distinct deep learning algorithms, CNN and LSTM. Notably, when employing the CNN classifier, GTCC yielded significantly superior results, achieving a 100% improvement for the DWT-EMD approach [12]. Karan et al. employed empirical mode decomposition (EMD) derived features and intrinsic mode function cepstral coefficients (IMFCC) for the effective representation of Parkinson’s speech characteristics. The performance of these proposed features was evaluated using two distinct datasets: dataset-1 and dataset-2, each consisting of 20 individuals with normal speech and 25 individuals affected by Parkinson’s disease. Notably, there was a noteworthy improvement of 10–20% in accuracy compared to conventional acoustic and Mel-frequency cepstral coefficient (MFCC) features [11].

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Table 2. The outcome of the IMFCC analysis. IMFs

MFCC ANN

LSTM

CNN

Acc (%)

Spe (%)

Sens (%)

Acc (%)

Spe (%)

Sens (%)

Acc (%)

Spe (%)

25

63.63

57.14

50

90.90

83.33

50

36.36

33.33

Sens (%)

IMF1

28.57

33.33

IMF2

42.85

33.33

75

72.72

60

83.33

100

54.54

20

83.33

IMF3

42.85

33.33

40

63.63

100

33.33

IMF4

71.42

100

50

63.63

57.14

75

72.72

80

66.66

IMF5

71.42

66.66

75

63.63

55.55

100

72..72

80

66.66

In their research, Mehmet Bilal et al. introduced an innovative method that leverages pre-trained deep networks and long-term memory (LSTM) while utilizing Mel spectrograms derived from denoised speech signals via variational mode decomposition (VMD) to identify PD from speech sounds. The most impressive classification accuracy, reaching 98.61%, was achieved using the ResNet-101 + LSTM model in conjunction with VMD [4]. In the context of our results, it is clear that the MFCC outperforms the IMFCC. However, when we compare these results with those obtained in previous research, it becomes clear that, although our results contribute to the diagnosis of Parkinson’s disease, they do not surpass the benchmarks established in previous studies. These observations underline the ongoing quest for improvement and optimization in the field of Parkinson’s disease diagnosis using speech analysis.

5 Conclusion This scholarly article employs a feature extraction approach utilizing intrinsic mode function-based cepstral frequency coefficients (IMFCC) and Mel frequency cepstral coefficients (MFCC) in conjunction with deep learning algorithms, namely artificial neural networks (ANN), long short-term memory (LSTM), and convolutional neural networks (CNN), to effectively detect Parkinson’s disease (PD). Through an empirical study, it is revealed that MFCCs offer more discerning speaker-specific information, thereby enabling a clear distinction between speech affected by PD and normal speech. These coefficients adeptly capture the non-stationary dynamics inherent in speech patterns. To assess the efficacy of the proposed method, the widely recognized Sakaar dataset is employed for performance evaluation. The findings demonstrate that the implementation of MFCC features, in tandem with the LSTM classifier, attains the highest accuracy rate of 90.90%. Notably, this represents a noteworthy 18.18% enhancement in accuracy over intrinsic features predicated on the mode function. The upcoming research endeavors to identify additional coefficients capable of effectively detecting Parkinson’s disease

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through voice signals. Additionally, it aims to assess whether individuals diagnosed with PD can be categorized according to diagnostic scales, such as the Hoehn-Yahr scale, by presenting classification statistics.

References 1. Karan, B., Sekhar Sahu, S. : An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal. Biocybern. Biomed. Eng. 41(2), 717–732 (2021). https://doi.org/10.1016/j.bbe.2021.04.014 2. Orozco-Arroyave, J.R., Hönig, F., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Nöth, E.: Spectral and cepstral analyses for Parkinson’s disease detection in Spanish vowels and words. Expert. Syst. 32(6), 688–697 (2015). https://doi.org/10.1111/exsy.12106 3. Khan, T., Westin, J., Dougherty, M.: Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson’s disease. Biocybern. Biomed. Eng. 34(1), 25–34 (2014). https://doi.org/10.1016/j.bbe.2013.06.001 4. Er, M.B., Isik, E., Isik, I.: Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with variational mode decomposition. Biomed. Signal Process. Control 70, 103006 (2021). https://doi.org/10.1016/j.bspc.2021.103006 5. Sakar, C.O., et al.: A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput. 74, 255–263 (2019). https://doi.org/10.1016/j.asoc.2018.10.022 6. Drissi, T.B., Zayrit, S., Nsiri, B., Ammoummou, A.: Diagnosis of Parkinson’s disease based on wavelet transform and Mel Frequency Cepstral Coefficients. Int. J. Adv. Comput. Sci. Appl. 10(3), 125–132 (2019). https://doi.org/10.14569/IJACSA.2019.0100315 7. Nouhaila, B., Taoufiq, B.D., Benayad, N.: An intelligent approach based on the combination of the discrete wavelet transform, delta delta MFCC for Parkinson’s disease diagnosis. Int. J. Adv. Comput. Sci. Appl. 13(4), 562–571 (2022). https://doi.org/10.14569/IJACSA.2022.013 0466 8. Soumaya, Z., Drissi Taoufiq, B., Benayad, N., Yunus, K., Abdelkrim, A.: The detection of Parkinson disease using the genetic algorithm and SVM classifier. Appl. Acoust. 171, 107528 (2021). https://doi.org/10.1016/j.apacoust.2020.107528 9. Sakar, B.E., et al.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17(4), 828–834 (2013). https://doi.org/ 10.1109/JBHI.2013.2245674 10. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193 11. Karan, B., Sahu, S.S., Mahto, K.: Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybern. Biomed. Eng. 40(1), 249–264 (2020). https:// doi.org/10.1016/j.bbe.2019.05.005 12. Boualoulou, N., Belhoussine Drissi, T., Nsiri, B. : CNN and LSTM for the classification of Parkinson’s disease based on the GTCC and MFCC. Appl. Comput. Sci. 19(2), 1–24 (2023). https://doi.org/10.35784/acs-2023-11

Comparative Study Between Fractional Linear Quadratic Regulator (Frac-LQR) and Sliding Mode Controller for the Stabilization the Three-Axis Attitude Control System of LEO Satellite Using Reaction Wheels Taha Ennaciri1 , Ahmed El abbassi1(B) , Nabil Mrani2 , and Jaouad Foshi1 1 Team Renewable Energies and Information Processing and Transmission Laboratory, Faculty

of Sciences and Technologies, Moulay Ismail University, Errachidia, Morocco [email protected] 2 Team Technologie d’Information et du Multimedia (TIM), High School of Technology, Moulay Ismail University, Meknès, Morocco

Abstract. This paper presents a comparative study between two control strategies, namely Fractional Linear Quadratic Regulator (Frac-LQR) and Sliding Mode Controller (SMC), for the stabilization of the three-axis attitude control system of a Low Earth Orbit (LEO) Satellite using reaction wheels. The primary goal is to compare the performance of these controllers in terms of stability, robustness, and control effort in the presence of external disturbances and stabilization time. The control algorithms are implemented and analyzed through mathematical modeling and simulations through Matlab to validate their efficacy in maintaining the desired satellite orientation. Keywords: LEO satellite · Fractional linear quadratic regulator (Frac-LQR) · Sliding mode controller (SMC) · Reaction wheels

1 Introduction The attitude control of LEO satellites is crucial for precise and accurate pointing, which is vital for various mission objectives, such as Earth observation and communication [1]. Among various control methods, the Fractional Linear Quadratic Regulator and Sliding Mode Controller have shown promise in their ability to handle complex nonlinear systems and disturbances. This paper aims to investigate the effectiveness of these controllers for the three-axis attitude control system of an LEO satellite using reaction wheels [1].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 151–157, 2024. https://doi.org/10.1007/978-3-031-48573-2_22

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2 The Dynamic Model of the Satellite Using Reaction Wheels The equations provided below describe the dynamics of the three-axis attitude control system utilized by the LEO satellite, employing reaction wheels [2, 3]: ⎧ ˙ z − Iy + Ix ) + L˙ x + ψ˙ θ˙ (Iy − Iz ) ⎪ Ix φ¨ = ω0 ψ(I ⎪   ⎪ ⎪ ˙ z − Iy ) ⎪ +φ 4(Iz − Iy )ω02 − ω0 θ(I ⎪ ⎪ ⎨ ¨ 2 ˙ ˙ ˙ Iy θ = 3(Ix − Iz )ω  0 (Ix − Iz )  0 θ 2+ ψφ(Iz − Ix ) + Ly + ψψω (1) ˙ ⎪ φ(I (I − I ) + ω − I ) +φ ψω z 0 z x ⎪ 0 x   ⎪ ⎪ 2 ˙ ⎪ Iz ψ¨ = ψ ⎪ ⎪  ω0 (Ix − Iy ) + θ ω0 (Iy − Ix )  ⎩ ˙ +φ (Iy − Ix − Iz )ω0 + θ˙ (Ix − Iy ) + L˙ z where • • • •

θ, φ and ψ orrespond to the Roll, Pitch, and Yaw angles, respectively. I represents the inertia matrix of the satellite. ω0 denotes the angular velocity of the orbit.  T L refers to the overall moment vector of the reaction wheel L = Lx , Ly , Lz .

The Canonical form used to describe the model is none other than the state space representation which is: x˙ = Ax(t) + Bu(t), y = Cx(t) + Du(t)

(2)

where ⎛

0 0 0

0 0 0 0

0 0 0 0 0

1 0 0 0 0

0 0 1 0 0 1 Ix +Iz −Iy 0 ω0 Ix 0 0

⎜ ⎜ ⎜ ⎜ A=⎜ y ⎜ 4ω02 Iz −I Ix ⎜ ⎜ z 0 3ω02 Ix I−I ⎝ y I −I −I I −I 0 0 ω02 x Iz y ω0 y Ixz z 0 ⎛ ⎞ 0 0 0 ⎜0 0 0⎟ ⎜ ⎟ ⎜0 0 0⎟ ⎜ ⎟ B = ⎜ 1 0 0 ⎟; ⎜ Ix ⎟ ⎜ 1 ⎟ ⎝ 0 Iy 0 ⎠ ⎛

0 0

1 Iz

⎞ 100000 ⎜0 1 0 0 0 0⎟ ⎜ ⎟ ⎜ ⎟ ⎜0 0 1 0 0 0⎟ C=⎜ ⎟; ⎜0 0 0 1 0 0⎟ ⎜ ⎟ ⎝0 0 0 0 1 0⎠ 000001

0

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

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⎤ 000 ⎢0 0 0⎥ ⎥ ⎢ ⎥ ⎢ ⎢0 0 0⎥ D=⎢ ⎥; ⎢0 0 0⎥ ⎥ ⎢ ⎣0 0 0⎦ 000 ⎛ ⎞ φ ⎜θ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ψ ⎟ x = ⎜ ˙ ⎟; ⎜φ⎟ ⎜ ⎟ ⎝ θ˙ ⎠ ψ˙ ⎡ ⎤ Lx ⎢ ⎥ u = ⎣ Ly ⎦. Lz ⎡

3 Controllability and Observability They play a crucial role in effectively designing a controller for the system. The existing literature and our prior research have extensively explored this crucial aspect, so we will present a condensed version [2, 3]. The system needs to satisfy two key conditions: • Fully state controllable. • Fully Observable. We chose Kalman’s method to analyzethe system’s controllability and observability,  utilizing the controllability matrix Cn = B AB A2 B . . . An−1 B and the observability  T matrix o = C CA CA2 . . . CAn−1 . . Both matrices in the current system have a size of n = 6. Since the rank of matrix C is equal to the rank of matrix O, both being 6 (full rank), we can conclude that the system is both controllable and observable.

4 Fractional Linear Quadratic Regulator (Frac-LQR) The Fractional Linear Quadratic Regulator (Frac-LQR) is an extension of the traditional Linear Quadratic Regulator (LQR) that employs fractional calculus to improve control performance. The fractional order state-space representation of the system is given by [3]: x˙ (t) = Ax(t) + Bu(t)(α) + BNe (t) where • BNe (t) is the disturbance term.

(3)

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• u(t) = − k(x − xid )(α) Describes the fractional-order controller, intended for use in stabilizing the system. • The vectors xid represent the states that the system aims to achieve as its stable equilibrium points. k is the gain matrix obtained by minimizing the fractional cost function: 1 J = 2

T   xT (t)Qx(t) + uT (t)Ru(t) dt

(4)

t0

where Q and R are positive definite weighting matrices.

5 Sliding Mode Controller (SMC) The Sliding Mode Controller (SMC) is a robust control technique that aims to drive the system state to a predefined sliding surface. The sliding surface is typically defined as a linear combination of the system’s state variables and their desired values. The specific form of the sliding surface can vary based on the control objectives and system dynamics. Generally, it is denoted as ‘S’ and expressed as [4]: S(t) = e(t) + λe(t)

(5)

where S(t) is the sliding surface, e(t) is the error between the desired and actual system states, λ is a positive constant that determines the slope of the sliding surface, and t is time. The control law for SMC is given by [4]: u(t) = − ksign(S(t)) |S(t)|

(6)

where u(t) is the control input, k is a positive gain and sign(•) is the signum function.

6 Simulations and Results Figures 1, 2, and 3 they illustrate a comparison of the evolution of the Roll, Pitch, and Yaw angles over time using Frac-LQR controller and SMC with Reaction wheels (Table 1). Discussion The discussion is centered around the impact of the performance of the Frac-LQR controller against SMC, based on the results obtained from the experiment. The experiment involved varying the fractional order, α, within the range of − 0.2 to 0.2, while keeping the gains k constant. The system was subjected to a disturbance, and the resulting time histories of tether length and rate were shown in Figs. 1, 2 and 3.

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Table 1. Simulation parameters For BILSAT-1 satellite. Parameter

Example

Perturbed initial condition

x(0) = (1◦ , 1◦ , 1◦ , 0.1◦ /s, 0.1◦ /s, 0.1◦ /s, )

Satellite weight

120 kg

Orbit angular velocity

ω0 = 0.0010764 rad s

Satellite Inertia axe x

Ix = 9.8194 kg m2

Satellite Inertia axe y

Iy = 9.7030 kg m2

Satellite Inertia axe z

Iz = 9.7309 kg m2

Orbit

686 km

Response to Initial Conditions

SMC Control

1.4

1.2 alpha = alpha = alpha = alpha = alpha =

-0.2 -0.1 0 0.1 0.2

1

1

0.8

0.8

0.6

Roll(Degree)

RoLL (degree)

1.2

0.6

0.4

0.4

0.2

0.2

0

0

0

10

20

30

40

50

60

-0.2

0

10

20

Time (seconds)

30

40

50

60

Time (seconds)

Fig. 1. Frac-LQR against SMC of the roll angle versus time using reaction wheels. SMC Control

Response to Initial Conditions 1.2

1.2 alpha = alpha = alpha = alpha = alpha =

1

-0.2 -0.1 0 0.1 0.2

1

0.8

Yaw(Degree)

Yaw (degree)

0.8

0.6

0.4

0.6

0.4

0.2

0.2

0

0

Response to Initial Conditions 1 -0.2

1.2-0.2 0

10

20

30

40

50

SMC Control 0

10

20

60

30

40

50

60

Time (seconds)

Time (seconds)

Fig. 2. Frac-LQR against SMC of the pitch angle versus time using reaction wheels. Response to Initial Conditions

SMC Control

1 -0.2

1.2

alpha = alpha = alpha = alpha = alpha =

0.8

-0.2 -0.1 0 0.1 0.2

1

0.8

0.6

Pitch(Degree)

Pitch (degree)

0.6

0.4

0.2

0.4

0.2

0 0

-0.2

-0.4

-0.2

0

10

20

30 Time (seconds)

40

50

60

-0.4

0

10

20

30

40

50

Time (seconds)

Fig. 3. Frac-LQR against SMC of the yaw angle versus time using reaction wheels.

60

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The Frac-LQR shows that for certain values of α it performs better and quicker than the SMC for example for α = − 0.2 for the Roll angle stabilization the Frac-LQR takes only 10s to stabilize the satellite as for as the SMC it takes 24 s to stabilize the system which is slower than the Frac-LQR. We noticed also that the Frac-LQR is more stable taking for example the Yaw angle the SMC struggles to maintain the system in the sliding error surface which results is some fluctuations seen in the stabilization of the Yaw angle which are none existent in the Frac-LQR controller. Figures 4, 5 and 6 represent a comparison between Frac-LQR controller versus SMC via Reaction wheels for the evolution of the Roll, Pitch and Yaw velocity versus time. SMC Control

Response to Initial Conditions

0.1

0.2 alpha = alpha = alpha = alpha = alpha =

0.1

-0.2 -0.1 0 0.1 0.2

0.05

0

Roll velocity(Degree)

RoLL velocity(degree)

0

-0.1

-0.2

-0.1

-0.15

-0.3

-0.2

-0.4

-0.5

-0.05

0

10

20

30

40

50

-0.25

60

0

10

20

30

Time (seconds)

40

50

60

Time (seconds)

Fig. 4. Frac-LQR against SMC of the roll velocity versus time using reaction wheels. Response to Initial Conditions

SMC Control 0.15

0.2 alpha = -0.2 alpha = -0.1 alpha = 0 alpha = 0.1 alpha = 0.2

0.1

0.1

0.05 0

Pitch velocity(Degree)

Pitch velocity (degree)

0

-0.1

-0.2

-0.05

-0.1

-0.15 -0.3 -0.2 -0.4 -0.25

-0.5

0

10

20

30

40

50

60

-0.3

0

10

20

30

Time (seconds)

40

50

60

Time (seconds)

Fig. 5. Frac-LQR against SMC of the pitch velocity versus time using reaction wheels. Response to Initial Conditions

SMC Control

0.2

0.15

alpha = -0.2 alpha = -0.1 alpha = 0 alpha = 0.1 alpha = 0.2

0.1

0.1

0.05

0

Yaw velocity(Degree)

Yaw velocity (degree)

0

-0.1

-0.2

-0.3

-0.05

-0.1

-0.15

-0.4 -0.2

-0.5

-0.6

-0.25

0

10

20

30 Time (seconds)

40

50

60

-0.3

0

10

20

30

40

50

60

Time (seconds)

Fig. 6. Frac-LQR against SMC of the yaw velocity versus time using reaction wheels.

Discussion In this section the same observations can be expressed in regards of stabilization time and control stability for the velocity of the angles noting that the Fra-LQR performs better for certain values of α that the SMC.

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7 Conclusion In this study we established that, the Fractional Linear Quadratic Regulator (Frac-LQR) outperforms the Sliding Mode Controller (SMC) in stabilizing the three-axis attitude control system of an LEO satellite using reaction wheels. The Frac-LQR approach offers improved stability, robustness against disturbances, and reduced control effort, making it a favorable choice for precise and reliable satellite attitude control applications.

References 1. Avanzini, G., et al.: Attitude control of low earth orbit satellites by reaction wheels and magnetic torquers. Acta Astronaut. 160, 625–634 (2019) 2. Ennaciri, T., Abbassi, A.E., Mrani, N., Foshi, J.: Attitude Control of LEO satellite via LQR based on reaction wheels versus magnetorquer. In: ICAISE 2022. Lecture Notes in Networks and Systems, vol. 635. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-26254-8_36 3. Ennaciri, T., Mrani, N., El Abbassi, A., Foshi, J.: Comparative Study Between Linear Quadratic Regulator and Fractional-Order Robust Controller for the Three Axis Attitude Control System of LEO Satellite Using Reaction Wheels, pp. 1–5. IRASET, Meknes, Morocco (2022). https:// doi.org/10.1109/IRASET52964.2022.9738361 4. Yadegari, H., Beyramzad, J., Khanmirza, E.: Magnetorquers-based satellite attitude control using interval type-II fuzzy terminal sliding mode control with time delay estimation. Adv. Space Res. 69(8), 3204–3225 (2022)

UV-Nets: Semantic Deep Learning Architectures for Brain Tumor Segmentation Ilyasse Aboussaleh(B) , Jamal Riffi , Khalid El Fazazay, Adnane Mohamed Mahraz, and Hamid Tairi LISAC Laboratory, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco {ilyasse.aboussaleh,jamal.riffi,mohamedadnane.mahraz, hamid.tairi}@usmba.ac.ma

Abstract. Semantic segmentation in medical imaging is a delicate computational task, which aims to categorize each pixel in an image into one of several specified categories or classes. Deep learning, particularly convolutional neural networks (CNNs), has revolutionized the field of semantic segmentation, then, many popular architectures have been developed for this purpose and they performed well in terms of image segmentation. In this paper, we proposed a comparison study between two typical deep learning architectures: U-Net for 2D image segmentation and the volumetric ConvNets V-Net which designed for 3D medical image segmentation. The models were evaluated on the BraTS 2020 dataset to segment different sub-regions of brain tumor. The comparison demonstrates the effectiveness and the powerful of each architecture in terms of quality and quantity of results besides their time execution. For 2D segmentation, U-Net achieved the top score of 64% in terms of mean Intersection over Union (mIoU) and 94% of Accuracy, on the other side, V-Net gets the best result for 3D segmentation by achieving 75% of mIoU and 98% of Accuracy. Keywords: Segmentation · Deep Learning · FCNN · U-Net · V-Net

1 Introduction Brain tumor segmentation is a complex task in medical image analysis due to the varying shapes, sizes, and appearances of tumors across different patients, it involves identifying and delineating regions within brain images that correspond to tumor tissues. This segmentation process plays a vital role in diagnosis, treatment planning, and monitoring the progression of brain tumors. In recent years, and based on different type of medical images such as CT (computed tomography) or MRI (magnetic resonance imaging) images, deep learning techniques have demonstrated significant advancements in accurately segmenting brain tumors in both 2D and 3D medical image data. Convolutional neural networks (CNNs) are the first and most commonly used in this field. Consequently, Pereira et al. [1] proposed a CNN-based approach by investigating small kernels for deeper architecture design which had a positive effect on preventing overfitting given the low weights in the network. Zhao et al. [2] integrate a fully CNN to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 158–165, 2024. https://doi.org/10.1007/978-3-031-48573-2_23

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segment different regions of a tumor. They then, combined FCNN with a conditional random forest. In addition, Wang et al. [3] proposed a cascade of FCNNs to segment each sub-region of the tumor and generate three binary segmentations. Aboussaleh et al. [4] exploited the features extracted from the last convolutional layer of the CNN proposed model by multiplying these features by a global average and max scalers to clarify the tumor region, a post-processing final step was applied to the result based on thresholding and the opening morpho-logical process. In 2015, Ronneberger et al. [5] introduced a new deep learning architecture U-Net for bio-medical image segmentation and it made a big mutation in this field, whereas, several U-shaped methods have been proposed achieving a good result by including new techniques of machine learning and trends deep learning architectures. Not long ago, the volumetric V-Net was appeared in 2016, proposed by Milletari et al. [6] to enhance 3D medical image volumes and consider spatial relationships in all dimensions, which is particularly valuable for accurately segmenting irregularly shaped structures like tumors in volumetric medical images. In this paper, we study the application of U-Net and V-Net (UV-Nets) in both 2D and 3D segmentation tasks to segment different sub-regions of brain tumor. This study showed the important of the right choice of the architecture depending on the segmentation task (binary or multiple segmentation) and the type of the available data. U-Net showed its advantage in case of 2D data; however, V-Net is the best choice for binary segmentation using 3D data, and significant for multi-subregions segmentation, also 3DU-Net showed acceptable results because its similarity with V-Net. On the other hand, 2DV-Net didn’t achieve good result which made him inadvisable for 2D segmentation task.

2 Related Work UV-Nets still used as references in both 2D and 3D brain tumor segmentation by modifying some of their main components. Therefore, Inception UDet proposed by Aboussaleh et al. [7] used Inception module instead of convolution block that UDet method [8] employed in the bi-directional pyramid network in the skip connection section, this modification reduced the number of parameters and time execution, and increases the metrics performance of the segmented whole tumor. The authors [9] also introduced a decoder based on the attention mechanism after fusing the extracted features of three different pretrained models Vgg19, ResNets50 and MobileNetV2 to finally segment the different sub-regions of brain tumor; whole tumor (WT), core tumor (TC) and enhancing tumor (ET). On the other hand, Rastogi et al. [10] suggests an 2D-VNet model for brain tumor segmentation and enhancing the prediction. The method showed his success by achieving high performance in terms of dice coefficient and accuracy. Contextual information aids semantic segmentation tasks where the goal is to classify each pixel into predefined categories. Deep learning models can recognize object boundaries and precisely segment various objects with the help of the spatial relationships between the objects. In this context, Lachinov et al. [11] proposed a deeper cascade 3D U-Net for Glioma segmentation, The U-Net architecture has been modified to enable it to efficiently handle multimodal MRI input. Furthermore, they introduce a method to improve segmentation quality by using context obtained from the same topological model operating

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on the scaled-down data. In addition, Chen et al. [12] proposed TransUNet, one of the first architecture which introduce Vision Transformers (ViT) [13] in the encoder part of U-Net, after extracting visual features using CNNs. On the other side, Casamitjana et al. [14] presented a cascade of two 3D-CNN and a later combination of their outputs to get the final segmentation, they used a modified version of V-Net consisting of convolutional blocks and residual connections reformulated according to recent findings in the literature. Zhou et al. [15] proposed to add a “Spatial and Channel Squeeze-and-Excitation” Network (scSE-Net) to the V-Net model to calibrate CNN image feature sampling area, expected to improve CNN image recognition. We optimize the performance of the V-Net model in obtaining remote feature information by adding a non-local block. We adopt volume input instead of slice input in terms of data input and use 3D.

3 Materiel and Methods 3.1 Data BraTS 2020 consists of two sets of data, The training set contains 369 MRI scans, and the validation set contains 125 scans. The size of each scan is 240 × 240 × 155, and each case has FLAIR, T1, T1 extension, and T2 volumes. The dataset was jointly registered, resampled to 1×1×1 mm3 , and stripped of the skull. Segmental brain tumors, including necrotic, edematous, non-enhancing, and enhancing tumors. 3.2 Data Preparation and Preprocessing We transformed each patient’s size of the BraTS 2020 dataset from 240 × 240 × 155 to 128 × 128 × 128 and 128 × 128 for 3D and 2D segmentation. We eliminated first 15 slices and the last 12 ones to keep the slices when the tumor appeared well. Each slice of different modalities was cropped into 128 × 128 to eliminate some nonsignificant background pixels. In the pre-processing step, we normalized the obtained image by subtracting its mean and dividing it by its standard deviation. 3.3 Methods 2D and 3D UV-Nets have been employed to show the strength of deep learning semantic architectures for medical image segmentation, especially the brain tumors based on MRI images. The 2D methods have been implemented on all samples of training BraTS 2020, while the 3D ones trained on 100 samples from the data because of the absence of powerful hardware to use all the 3D data. 3.4 U-Net The U-Net architecture uses a symmetric design that incorporates a layer of convolutional neural networks. It consists of two main components: an encoder and a decoder. The encoder is built on top of a convolutional network structure, which consists of four convolution blocks that are applied iteratively. Each block starts with a pair of 3 × 3

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convolution operations, succeeded by a maximum aggregation step using pool size 2 × 2 and a feed of 2. For each down-sampling stage, the number of convolutional filters is multiplied pair. To establish a connection between the encoder and decoder sections, a sequence of two 3 × 3 convolution operations is used. On the other hand, decoder segmentation focuses on building a segment map using features from the encoder. This is achieved by using the 2 × 2 transform convolution operation to sample the feature map while halving the number of feature channels. Then, a sequence of two 3 × 3 convolution operations is applied. Similar to the encoder, this sampling, and convolution is repeated four times, with the number of filters halved at each stage. Finally, convolution operation 1 × 1 is performed to generate the final segment map. For 3DU-Net, we saved the same steps and parameters, we replaced 2D convolution layer in the encoder and the 2D transposed used in the decoder by 3D convolution one and 3D transposed respectively. 3.5 V-Net V-Net, short for Volumetric ConvNets, is an architecture designed for 3D medical image segmentation tasks. It’s tailored specifically for segmenting volumetric data, such as 3D medical scans like CT (computed tomography) or MRI (magnetic resonance imaging) images. The architecture of V-Net can be briefly described as follows: First, V-Net has an encoder like U-Net, that takes in the input 3D image data. This encoder consists of several down-sampling layers, where each layer is composed of 3D convolutional blocks followed by 3D pooling operations besides using convolutional residual units. Second, there is a bottleneck layer that further processes the reduceddimensional features. It usually involves multiple convolutional layers to capture complex features. Third, the decoder in V-Net is responsible for up-sampling the features back to the original spatial dimensions. This is done using 3D transposed convolution. Finally, skip connections that directly link the corresponding encoding and decoding layers, allowing the network to retain spatial information that might be lost during the down-sampling and up-sampling process. 2D version of V-Net has the same structure, the only transformation is the utilization of 2D operations (convolution, max-pooling and transposed convolution) instead of 3D ones. Figure 1 showed the standard structure of semantic architectures U-Net and V-Net that we used for brain tumor segmentation.

4 Results 4.1 Implementation Details The experiment was carried out on the Kaggle platform on a virtual instance equipped with CPUs, 13 GB memory, and an HDD drive of 73 GB. During the training of the model, acceleration was performed on a Tesla (P100-PCIE-16GB) GPU (16 GB video memory. We have chosen three sequences among the four possible (t1, t2, T1ce, Flair), and the best-recommended order employed is [t1, t1ce, t2]. Note that in each sequence. The training dataset was divided randomly into training and validation subsets at an 80:20 ratio. The loss function used for our model was the Dice loss by computing the

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Fig. 1. Illustration of two typical semantic deep learning architectures used for brain tumor segmentation: U-Net and V-Net.

following average: Dice(P, G) = N

2

N

2 i=1 pi

i=1 pi gi  2 + N i=1 gi

(4)

where P is the predicted value and G stands for the mask that represents the ground truth. pi ∈ P and gi ∈ G. 4.2 Evaluation Metrics Different types of performance indicators are used to evaluate the experimental results such as accuracy, Intersection over Union (IoU) and dice similarity coefficient (DSC) for brain tumor segmentation, they defined formally by: TP + TN • Accuracy: TP+FP+TN+FN TP • IoU: TP+FP+TN 2TP • DSC: 2TP+FP+TN

Where TP and TN are the well classified pixels and FP and FN the misclassified ones. 4.3 Results and Discussion In this section, we present the results of both 2D and 3D UV-Nets methods regarding the suggested metrics, we also compared these methods with their extension methods cited before in the state-of-the-art section. Table 1 shows that 2DU-Net and 2DV-Net have a high DSC especially for WT, and a moderate one for both TC and ET subregions. Also, they have similar IoU values for all the subregions. 3DU-Net and 3DV-Net still maintain relatively good DSC and IoU values, they appear to strike a good balance between DSC and IoU metrics and achieve

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Table 1. Performance Comparison of 2D and 3D UV-Nets Methods. Method

DSC(WT) DSC(TC) DSC(ET) IoU IoU IoU mIoU mAcc #Param (WT) (TC) (ET) (M)

2DU-Net 0.91

0.66

0.45

0.84

0.50

0.30

0.64

0.94

1.9

2DV-Net

0.90

0.73

0.40

0.82

0.57

0.26

0.60

0.93

2.7

3DU-Net 0.81

0.82

0.79

0.70

0.72

0.68

0.72

0.97

6

3DV-Net

0.80

0.75

0.80

0.69

0.61

0.75

0.98

11

0.89

Table 2. Comparison study of 2D and 3D UV-Nets and some of their extension methods. Method

Dataset

DSC(WT)

DSC(TC)

DSC(ET)

mDSC

2DU-Net

BraTS 2020

0.91

0.66

0.45

–

Aboussaleh et al. [9]

BraTS 2020

0.87

0.80

0.70

–

Inception UDet [7]

BraTS 2020

0.88

–

–

–

2DV-Net(ours)

BraTS 2020

0.90

0.73

0.40

–

2DV-Net [10]

BraTS 2020

–

–

–

0.88

3DU-Net

BraTS 2020

0.81

0.82

0.79

–

Cascade 3DU-Net [11]

BraTS 2018

0.90

0.83

0.77

–

TransUNet [12]

MSD(3D)

0.71

0.68

0.54

–

3DV-Net

BraTS 2020

0.89

0.80

0.75

–

Cascade 3DV-Net [14]

BraTS 2017

0.87

0.71

0.64

–

scSE-NL V-Net [15]

BraTS 2020

0.82

0.76

0.65

–

better performance of TC and ET than 2D methods, while the latter bypassed the 3D ones for the WT subregion. Table 2 summaries that both 2D and 3D methods demonstrate competitive performance in tumor segmentation tasks, with variations in their ability to segment different tumor categories. 3D methods tend to capture 3D spatial relationships better, which can be crucial for accurate segmentation, but they might also be computationally more intensive. The decision between 2D and 3D methods should be based on a more comprehensive evaluation considering aspects such as dataset characteristics, computational resources, and the specific segmentation goals.

5 Conclusion U-Net and V-Net are powerful tools for 2D and 3D brain tumor segmentation, respectively. Their deep learning architectures leverage spatial context to achieve accurate segmentations, thereby contributing to improved clinical decision-making and patient care in the field of neuroimaging. The UV-Nets methods have implemented and they

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showed an acceptable result using different metrics. The modifications made to these basic semantic segmentation methods have improved the results and inspired researchers to go deep in this field by including new architectures and techniques of machine learning. In our turn, our future work focalizes to develop new architectures 2D and 3D based on U-Net and V-Net structures for brain tumor segmentation particularly, and generalize them for other tasks of medical image’s segmentation.

References 1. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251 (2016) 2. Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., Zhang, Y.: Brain tumor segmentation using a fully convolutional neural network withconditional random fields. In International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries;Springer: Cham, Switzerland; pp. 75–87 (2016) 3. Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: International MICCAI Brainlesion Workshop, pp. 178–190. Springer, Cham 4. Aboussaleh, I., Riffi, J., Mahraz, A.M., Tairi, H.: Brain tumor segmentation based on deep learning’s feature representation. J. Imaging 7(12), 269 (2021) 5. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 Oct 2015, Proceedings, Part III 18, pp. 234–241. Springer International Publishing (2015) 6. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE 7. Aboussaleh, I., Riffi, J., Mahraz, A.M., Tairi, H.: Inception-UDet: an improved U-Net architecture for brain tumor segmentation. Ann. Data Sci. 1–23 (2023) 8. Keetha, N.V., Annavarapu, C.S.R.: U-Det: a modified U-Net architecture with bidirectional feature network for lung nodule segmentation (2020). arXiv preprint arXiv:2003.09293 9. Aboussaleh, I., Riffi, J., Fazazy, K.E., Mahraz, M.A., Tairi, H.: Efficient U-Net architecture with multiple encoders and attention mechanism decoders for brain tumor segmentation. Diagnostics 13(5), 872 (2023) 10. Rastogi, D., Johri, P., Tiwari, V.: Brain tumor segmentation and tumor prediction using 2D-Vnet deep learning architecture. In: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), pp. 723–732. IEEE (2021, December) 11. Lachinov, D., Vasiliev, E., Turlapov, V.: Glioma segmentation with cascaded UNet. In: International MICCAI Brainlesion Workshop, pp. 189–198. Springer International Publishing, Cham (2018) 12. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y, Lu, L., Yuille, A.L., Zhou, Y.: Transunet: transformers make strong encoders for medical image segmentation (2021). arXiv preprint arXiv:2102.04306 13. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: transformers for image recognition at scale (2020). arXiv preprint arXiv:2010.11929 14. Casamitjana, A., Catà, M., Sánchez, I., Combalia, M., Vilaplana, V.: Cascaded V-Net using ROI masks for brain tumor segmentation. In: International MICCAI Brainlesion Workshop, pp. 381–391. Cham: Springer International Publishing (2017)

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15. Zhou, J., Ye, J., Liang, Y., Zhao, J., Wu, Y., Luo, S., et al.: scSE-Nl V-Net: a brain tumor automatic segmentation method based on spatial and channel “squeeze-and-excitation” network with non-local block. Front. Neurosci. 16, 916818 (2022)

A Smart Mathematical Approach to Resource Management in Cloud Based on Multi-objective Optimization and Deep Learning Raja Ait El Mouden(B) and Ahmed Asimi Departments of Mathematics and Computer Sciences Faculty of Sciences, University Ibnou Zohr, Laboratory of Computing Systems & Vision (LabSiV), Team of Security, Cryptology, Access Control and Modeling (SCCAM), B. P. 8106, Dakhla, Agadir, Morocco [email protected], [email protected]

Abstract. Scaling resources in Cloud computing has become complex and challenging as it has unlimited resources and works on “pay as u go” mode to meet user demands. To ensure that resources are allocated efficiently, Cloud services can be highly available, but they are still susceptible to failures due to the complex and dynamic nature of virtual machine allocation in the cloud environments, in other words, it is difficult to manage and control resources or to choose the best allocation of these resources. Our purpose in this paper is to solve one of the major issues faced by cloud computing, namely resources management in cloud environments. To deal with this issue we need a significant aspect of task scheduling in cloud, such that load balancing as it offers a huge aid to perform task management in the cloud. Therefore, we propose a smart mathematical approach that aims to rebalance the cloud environment by modeling it with a matrix. It is based on arithmetic constraint to balance the load of virtual machines between host machines in cloud environments. The proposed algorithms seek to balance the cloud system matrix by following an emigration trick of virtual machines, we ended the approach by proposing new multi-objective model to follow in order to obtain the rebalanced matrix modeling the cloud environment after the rebalancing process. Keywords: Cloud computing · Resources management · Load balancing · Smart · Multi-objective · Optimization

1 Introduction The evolving nature of cloud computing (CC) in recent years has increased the demand for accessing resources online, thereby Resources management plays a main role in the cloud computing environments where applications face with workloads that are dynamically changing. However, such dynamic and unpredictable workloads can worsen the performance of applications and degrade it, especially when user demands for resources are increased, one of the most crucial steps in server consolidation is virtual machine placement. This problem, as a resources management issue, attracts a lot of researchers as an efficient approach interests cloud providers as it can benefits them to manage their resources and costs more effectively [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 166–172, 2024. https://doi.org/10.1007/978-3-031-48573-2_24

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Resources management is a main function of any man-made system. It impacts the basic criterions for the evaluation of a system: performance, functionality, and cost. An inefficient resource management has a direct negative effect on a system’s performance. We can distinguish two different types of resources namely: logical resources and Network loads and delays and physical resources. The policies of Cloud Resources Management can be grouped into five categories: load balancing, capacity allocation, energy optimization, admission control, and quality of service (QoS) guarantees. Deep learning as a branch in the artificial intelligence area can play a significant role in resource management in cloud environments by leveraging its ability to learn patterns and make predictions from large amounts of data, and it can contribute to cloud resources management by Workload Prediction, anomaly prediction, Optimizing Resource Allocation, and auto-scaling. In our study we tried to adapt deep learning as a learning and training architecture to solve the balancing task of all the virtual machines of a cloud environment, namely, a data center by distributing the task on three layers (phases), namely, initialization phase, pre-treatment phase and treatment phase using load balancing policy. And a re-balancing step of the data center based on multi-objective optimization. The rest of this paper is organized as follows: The current and past research in virtual machines (VM) allocation and resources management in Cloud environments, and some optimization and machine learning algorithms are classified and reviewed in Sect. 2, our contribution is described and detailed in Sect. 3.

2 Related Works This section provides a quick explanation of the relevant studies in Resources management within cloud environments that has become a core research topic since the introduction of cloud computing and quick definitions of famous optimization algorithms used in each study. In [2] a live migration optimization technique has been presented, authors have proposed an approach to resolve issues that virtual machines can face VMs live migration in order to decrease migration time, downtime, total cores of CPU, for cloud computing environment, the proposed scheme introduce three main algorithms taking in consideration the total migration time, namely Host Selection Migration Time (HSMT), VM Reallocation Migration Time (VMRMT), and VM Reallocation Bandwidth Usage (VMRBU), in order to improve the performance of cloud computing environments by minimizing the migration time. In [3] authors focused on the resource allocation applications Cloud computing technologies to green an organizations cloud, they designed a mechanism of pricing and allocation for a private cloud computing service in order to load-balance their cloud computing resources effectively. In [4] authors use Grey wolf optimization (GWO) algorithm based on the resource reliability and capability to maintain load balancing, the core idea of the (GWO) algorithm, as a type of metaheuristic algorithm inspired by the hunting behavior of grey wolves, is to try to find the unemployed or busy nodes and, after finding out this node, try to calculate the threshold and fitness function of each node. In [5] authors proposed an independent task scheduling approach using a multi-objective task scheduling optimization based on the Artificial Bee

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Colony (ABC) Algorithm with a Q-learning algorithm, the (ABC) Algorithm is a natureinspired optimization algorithm that aims simulating the honeybees foraging behavior, it is a population-based algorithm that is suitable for solving optimization problems, and the Q-learning is a reinforcement learning algorithm used to solve Markov Decision Processes (MDP) which involve decision-making in dynamic environments. In [6] authors proposed a Hybrid Approach for Cloud load balancing (HA-CLB) by combining between heuristic and metaheuristic algorithms for workflow scheduling. In [7] authors propose allocating virtual machines to the best-suited host machine based on host membership value and CPU availability based on Hybrid Dynamic Degree Balance (HDDB) algorithm, as (HDDB) algorithm aims to achieve a balance between optimal resource utilization and dynamic workload distribution in peer-to-peer overlay networks by combining degree-based and dynamic-based load balancing. In [1] a multi-objective VM rebalancing Algorithm (MOVMrB) is used to optimize the load, to leverage inter-HM and intra-HM loads, as the first multiple objective optimization solutions to overcome VM rebalance problem, (MOVMrB) algorithm considers factors as CPU utilization, memory usage, network bandwidth, These objectives are conflicting typically, so that optimizing one objective might lead to the degradation of another, authors keep migration cost in mind and propose a hybrid VM live migration algorithm to reduce the virtual machines re-Balancing complexity. In [8] authors propose Reinforcement Learning-based VM Re-balancing (RLVMrB) algorithm, as a technique used in cloud computing environments to optimize the allocation and re-balance virtual machines (VMs) across host machines (HMs) and leverages reinforcement learning, as a machine learning branch, to achieve dynamic resource management. Authors intention used multi-objective load balancing to balance the load in both aspects of inter-HM and intra-HM by taking in consideration multiple dimensions of the resources; the CPU, memory and bandwidth. In [9] two enhanced versions of the multi-objective RLVMrB and MOVMrB algorithms were proposed, FuzzyRLVMrB and Fuzzy-MOVMrB. In the next section we intend to present our proper approach which is constituted of two main steps, namely, balancing step and re-balancing step, grouped in order to manage resources optimally in the cloud environment.

3 Our Contribution 3.1 Detailed Description of Our Contribution  j=N Let (HMi )i=M i=1 be the set of the HMs, and VMj j=1 the set of VMs to map to HMs. We take the binary weight function as a load balancing criterion and work a along with it to balance a system of Host Machines (HMs) (Fig. 1). Our contribution consists of three main phases: 1. Initialization phase: This phase consists of modeling by means all the VMs and HMs up the data center of CC with a matrix that we note by IPM ,N such that M is the number of the HMs and N is the number of the VMs.   M ,N 1 if VMj is mapped to HMi . IPM ,N = aij i=1,j=1 , with: aij = 0 otherwise. with respect to the fact that one VM can’t be mapped to more than one HM .

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Fig. 1. Process followed in our contribution.

2. Pretreatment phase: The aim is to balance the initial matrix IPM ,N by using the weight function. We can define the weight of the row Li as the sum of elements aij of the row Li . Our aim therefore is to have:



N W (Li ) ≤ C = E M

 + 1, for i ∈ {1, . . . M }

(1)

This phase consists of two steps: • Reordering the rows of the matrix according to the criterion (1): Algorithm1 • Emigrating VMs from the source HM to the target one: Algorithm2 The following two algorithms detail the process followed in every sub-step. Algorithm 1 Input: IPM ,N . Order and permute the rows of IPM ,N according to their verification of the criterion (1): 1. The m rows of IPM ,N that verify the criterion (1) will be affected to LPM ,N and listed in it starting from the top row. 2. The l = M − m that do not verify the criterion (1) will be affected to LPM ,N and listed in it starting from the bottom row. return LPM ,N , m, l. Algorithm 2 Inputs:LPM ,N , m, l. Output:BPM ,N . For i=1, … ,M 1. affect the m first rows of LPM ,N To the m first rows of BPM ,N . 2. For the last non-balanced l rows. 2.1. Emigrate the 1 from overloaded row to one of the m underloaded rows; 2.2 If {the target row does not verify the criterion (1) anymore} then Move to the next underloaded row and finish emigrating; 2.3 If {the current row is balanced} then Move to the next overloaded row and repeat the process;

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3. affect the new balanced l rows of LPM ,N to the l last rows of BPM ,N . return BPM ,N .  i=M ,j=N 3. Treatment phase: in this phase we re-balance BPM ,N = (Li )M i=1 = Lij i=1,j=1 to get the perfect rebalanced matrix. The re-balanced matrix that we note rBPM ,N is the the perfect matrix that verifies the proposed mathematical model. It verifies the same constraints as the initial matrix IPM ,N . Let set the objective functions: ljCPU , ljMEM and ljBW

represent the load of CPU, memory and Band Width of the VMj respectively. N  ljr xij : represents the load of resource r in the HMi with i ∈ {1, . . . , M } Lri = j=1

Lr =

1 M

M  N  i=1 j=1

ljr xij : represents the average load of resource r over all HMs.  rL = Lij ,i = 1 . . . M , j = 1 . . . N

Authors in [1] have set 2 objective functions: unevenness(r): is used to calculate the imbalance of resource r across all the HMs.

 2 M  r 1 Li unevenness(r) = −1 M Lr i=1

disequilibrium(i) stands for the imbalance degree of using resources in HMi .

    1 Lr − Li 2 i disequilibrium(i) = · R Li r∈Re

Li =

1 R

R  N  r=1 j=1

ljr xij : represents the average (mean) load of all resources in HMi .

R: defines the number of resources in consideration. Re = {memory:MEM , CPU , Bandwidth:BW }: the set of resources in consideration. Every HM is characterized by its load of CPU TiCPU , Memory TiMEM and Bandwidth

TiBW .

It has to be sur that the allocated resources of VMs in every HMi do not exceed TiCPU ,TiMEM and TiBW respectively of every HMi for each i = 1, . . . , M : N

j=1

liCPU xij ≤ TiCPU ;

A Smart Mathematical Approach to Resource Management N

171

liBW xij ≤ TiBW ;

j=1 N

liMEM xij ≤ TiMEM .

j=1

The multi-objective formulation of VMs re-balancing problem is presented below, by meeting all the objectives listed below with constraints:   M

min unevenness(r), disequilibrium(i) r∈Re

Subject to ⎧M  ⎪ ⎪ xij = 1 ⎪ ⎪ ⎪ i=1 ⎪ ⎪ ⎪ N ⎪  ⎪ ⎪ liCPU xij ≤ TiCPU ⎪ ⎪ ⎪ ⎨ j=1 N  liBW xij ≤ TiBW ⎪ ⎪ ⎪ j=1 ⎪ ⎪ ⎪ N ⎪ ⎪ ⎪  l MEM xij ≤ T MEM ⎪ ⎪ i i ⎪ ⎪ j=1 ⎪ ⎩ xij ∈ rL

i=1

j = 1, . . . , N i = 1, . . . , M i = 1, . . . , M i = 1, . . . , M i = 1, . . . , M and j = 1, . . . , N

4 Conclusion Our intention in this paper is to propose a mathematical deep learning-based approach to manage resources in a Cloud Computing environment as the replacement of virtual machines in physical machines has become an increasingly common practice in cloud computing environments. This process offers several benefits for both, cloud services providers and users, including improved performance, increased scalability, and enhanced flexibility. Additionally, it allows for better resource utilization and cost savings for organizations. However, migrating virtual machines requires careful planning so that cloud resources and services become more available, as availability is one of the main cloud security properties. Meaning that developing successful method to treat this issue is always required by cloud organizations.

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References 1. Li, R., Zheng, Q., Li, X., Wu, J.: A novel multi-objective optimization scheme for rebalancing virtual machine placement: In: 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), pp. 710–717 (2016) 2. Gupta, A., Namasudra, S.: A novel technique for accelerating live migration in cloud computing. Autom. Softw. Eng. 29, 34 (2022) 3. Kumar, C., Marston, S., Sen, R., Narisetty, A.: Greening the cloud: a load balancing mechanism to optimize cloud computing networks. J. Manag. Inf. Syst. 39, 513–541 (2022) 4. Sefati, S., Mousavinasab, M., Zareh Farkhady, R.: Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation. J. Supercomput. 78, 18–42 (2022) 5. Kruekaew, B., Kimpan, W.: Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access 10, 17803–17818 (2022) 6. Lata, S., Singh, D.: A hybrid approach for cloud load balancing, pp. 548–552 (2022) 7. Joshi, A., Munisamy, S.D.: Evaluating the performance of load balancing algorithm for heterogeneous cloudlets using HDDB algorithm. Int. J. Syst. Assur. Eng. Manage. 13, 778–786 (2022) 8. Ghasemi, A., Toroghi Haghighat, A.: A multi-objective load balancing algorithm for virtual machine placement in cloud data centers based on machine learning. Computing 102, 2049– 2072 (2020) 9. Ghasemi, A., Haghighat, A.T., Keshavarzi, A.: Enhanced virtual machine placement in cloud data centers: combinations of fuzzy logic with reinforcement learning and biogeography-based optimization (BBO) algorithms (2022)

Machine Learning in Cybersecurity: Evaluating Text Encoding Techniques for Optimized SMS Spam Detection Adnane Filali1

, El Arbi Abdellaoui Alaoui2(B)

, and Mostafa Merras1(B)

1 IMAGE Laboratory, Department of Sciences, EST, Moulay Ismail University of Meknes,

Meknes, Morocco [email protected], [email protected] 2 IEVIA Team, IMAGE Laboratory, Department of Sciences, ENS, Moulay Ismail University of Meknes, Meknes, Morocco [email protected]

Abstract. SMS spam poses serious online security threats, including phishing and malware risks. Effective detection and prevention are vital for user protection. This study aims to improve SMS spam detection accuracy by exploring efficient text encoding and classification methods. We assess three text encoding techniques: Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec. We conduct exploratory analysis, transform messages into numerical vectors, and enhance classification with additional features. Comparative analysis using various metrics helps select the best-performing algorithm for model construction. The research has practical implications, boosting spam prevention and enhancing cybersecurity in text messaging. Keywords: SMS spam detection · Text encoding techniques · Supervised learning models · Cybersecurity · Feature engineering

1 Introduction The rise in mobile phone usage has, unfortunately, been accompanied by a surge in spam messages, leading to various challenges ranging from user inconvenience to potential financial loss [5]. While several SMS spam filtering techniques exist [4], the continuously evolving landscape of spam necessitates the development of more advanced detection methodologies [1, 5, 6]. This study seeks to address this gap by exploring various text encoding techniques and classification approaches with the objective of enhancing SMS spam detection accuracy. More specifically, we aim to assess the performance and efficacy of three distinct supervised learning models, each employing one of the following text processing techniques: Bag of Words (BOW), Term Frequency-Inverse Document Frequency (TF-IDF) [2], and Word2Vec [3]. An initial exploratory analysis is conducted to comprehend the class distribution and complex characteristics of the messages within our database. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 173–178, 2024. https://doi.org/10.1007/978-3-031-48573-2_25

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Thereafter, the selected text encoding techniques are used to transform these messages into numerical vectors. Further, advanced feature engineering techniques, such as partof-speech tagging, are applied to incorporate additional features into these vectors, thereby enhancing classification performance. The study also aims to conduct a comparative analysis of various machine learning models, using an array of evaluation metrics like accuracy, precision, recall, F-measure, and the ROC curve. The goal here is to identify the algorithm that demonstrates superior performance, and subsequently, to use this for model construction. Through these objectives, we hope to contribute significantly to the field of SMS spam detection, providing valuable insights and practical solutions to the ongoing challenge of spam messages. This paper unfolds in four main sections. The ‘Introduction’ delineates the context and objectives of the study, focusing on the increasing issue of SMS spam and the necessity for enhanced detection methods. ‘Methodology’ details our approach, encompassing exploratory analysis, text encoding techniques, feature engineering, and the application of supervised learning models. ‘Results and Analysis’ presents a comprehensive evaluation of the models using various metrics, highlighting the most effective algorithm. Lastly, the ‘Conclusion’ summarizes our findings, underlining their significance in the realm of SMS spam detection, and proposes directions for future research.

2 Methodology The methodology section of this paper presents a systematic approach to addressing SMS spam detection. It begins with data acquisition, discusses preprocessing steps, and explains the three text encoding techniques used: Bag of Words (BOW), Term FrequencyInverse Document Frequency (TF-IDF), and Word2Vec. The section then covers the application of supervised learning models and their performance evaluation. The goal is to identify the optimal model-text encoding combination for SMS spam detection. A visual flowchart in the following section provides an overview of the research process, offering a concise summary of the systematic approach used to enhance SMS spam detection. 2.1 Data Acquisition In this study, we initiated our research with the critical step of data acquisition. Our dataset, obtained from Kaggle, consists of 5,572 records with two primary attributes. One attribute categorizes messages as “spam” or “ham,” and the other contains the message content. We introduced additional artificial attributes to enhance our analysis. This dataset was drawn from a publicly available SMS spam collection database, ensuring a diverse set of SMS texts that encompass both legitimate and spam messages. This approach allows for a thorough examination of SMS spam and the challenges associated with its detection (Table 1; Fig. 1). 2.2 Data Preprocessing Before analysis, data undergoes preprocessing to enhance quality and utility. This includes data cleansing, lowercase conversion, stop word removal, tokenization, and

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Fig. 1. Flowchart of the study. Table 1. SMS dataset distribution

Sample

Spam

Ham

474

4852

stemming/lemmatization. These steps optimize data for accurate classification and model performance. 2.3 Text Encoding Techniques After preprocessing, text encoding techniques are used to convert the processed text into numerical vectors, which can be fed into our machine learning models. In this study, we explore three types of text encoding techniques: 1. Bag of Words (BoW): This technique counts how often each word appears in a text document, regardless of word order or context. It creates a vector representation where each dimension corresponds to a unique word in the text corpus 2. TF-IDF: Unlike BoW, TF-IDF not only considers word frequency but also assigns weights based on the rarity of words across documents. It gives more importance to less common words. These techniques are simple, efficient, and provide a basic text representation. 3. Word2Vec: Word2Vec is a word embedding technique that represents words as dense vectors in a continuous vector space. It captures semantic relationships between words by considering their context in large training datasets.

3 Results and Analysis The study centers on developing a predictive model for classifying SMS messages as spam or legitimate. Traditional encoding techniques, such as Bag-of-Words (BoW) and TF-IDF, performed well with machine learning models like SVM, Naive Bayes, and Logistic Regression, demonstrating their effectiveness in capturing essential features for spam classification. Incorporating additional features like character count and partof-speech information had varying impacts on accuracy when combined with BoW,

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Table 2. An empirical analysis of text encoding techniques and machine learning models for SMS spam classification BOW TF-IDF BOW + Number of characters

TF-IDF + Number of characters

BOW + Number of characters + POS

TF-IDF + Number of characters + POS

Word2vec + with Data processing

Word2vec + Whitout Data processing

0,98

0,982

0,967

0,978

0,961

0,95

SVM 0,972 0,972

0,862

0,862

0,94

0,86

NB

0,979 0,959

0,977

0,889

0,979

0,899

LR

0,979 0,946

0,962

0,948

0,976

0,952

DT

suggesting limited contributions in certain cases. On the other hand, Word2Vec embeddings consistently achieved high accuracy, especially with the SVM model, highlighting their value in capturing semantic relationships and improving spam SMS classification. The Decision Tree (DT) model was evaluated specifically with Word2Vec without data preprocessing, achieving an accuracy of 0.961. Further research is needed to comprehensively compare Decision Trees against other models due to their unique characteristics and performance variations. Table 3 demonstrates the effectiveness of Bag of Words (BOW) encoding when combined with SVM, NB, and LR models. These models achieved high accuracies, ranging from 0.972 to 0.979. SVM showcased exceptional precision, NB achieved the highest recall, and LR demonstrated a balanced performance in terms of precision and recall (Tables 2 and 3). Table 3. Classification performance from Bag of Words (BoW). Bag of Words (BoW) Classifier recall precision f1-score accuracy SVM

1

0,8

0,88

0,972

NB

0,94

LR

0,99

0,9

0,92

0,979

0,85

0,91

0,979

In Table 4, TF-IDF encoding, when paired with SVM, NB, and LR models, yielded accuracies ranging from 0.946 to 0.972. SVM showcased the highest precision, while NB demonstrated a trade-off between precision and recall, achieving a lower precision but a higher recall compared to other models. Table 5 focused on Word2vec embeddings, which provided promising results in spam classification. SVM, DT, and LR models achieved accuracies ranging from 0.96 to 0.98. SVM exhibited strong precision, recall, and F1-score, indicating its effectiveness in leveraging word embeddings for spam detection. DT, although having a slightly

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Table 4. Classification performance from TF-IDF. TF-IDF Classifier recall precision f1-score accuracy SVM

0,98

0,81

0,89

0,972

NB

1

0,7

0,82

0,959

LR

0,96

0,63

0,76

0,946

Table 5. Classification performance from Word2vec. Word2vec Classifier recall precision f1-score accuracy SVM

0,97

0,87

0,92

0,98

NB

0,84

0,87

0,85

0,96

LR

0,89

0,86

0,87

0,96

lower precision, showed balanced recall and F1-score. LR also demonstrated competitive performance with respect to precision, recall, and F1-score. The study emphasizes the importance of selecting the right text encoding techniques and machine learning models for effective SMS spam classification. Bag of Words (BoW) encoding performed well, and TF-IDF and Word2Vec embeddings showed potential for capturing essential features in spam detection. Decisions regarding which encoding technique and model to use should be based on the specific task requirements, considering trade-offs between precision, recall, and overall accuracy. In conclusion, the research suggests that both traditional text encoding methods (BoW and TF-IDF) and word embeddings (Word2Vec) offer promise for accurately classifying spam SMS messages. The choice of encoding technique and the inclusion of additional features should be carefully tailored to the characteristics of the dataset and the desired performance of the spam detection system.

4 Conclusion In conclusion, the rapid growth of mobile phone usage, particularly in countries like China, India, and the United States, has led to a surge in spam messages, demanding advanced detection methods. This study aimed to improve SMS spam detection accuracy by exploring various text encoding techniques and classification approaches. Our findings highlight the potential of supervised learning models, particularly those using Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec text processing techniques. Initial exploratory analysis provided insights into message distribution and characteristics. These techniques effectively transformed messages into numerical vectors, with advanced feature engineering enhancing classification

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performance. Comparative analysis of machine learning models using various metrics, including accuracy, precision, recall, F-measure, and the ROC curve, revealed the most effective algorithm for model construction. Despite the evolving challenges of spam messages, our study contributes practical solutions to enhance SMS spam detection accuracy. Future research can build on these findings by exploring alternative machine learning techniques or testing the algorithm on different datasets.

References 1. Amin, I., Dubey, M.K.: Hybrid ensemble and soft computing approaches for review spam detection on different spam datasets. Mater. Today Proc. 62, 4779–4787 (2022); International Conference on Innovative Technology for Sustainable Development 2. Sjarif, N.N.A., Azmi, N.F.M., Chuprat, S., Sarkan, H.M., Yahya, Y., Sam, S.M.: SMS spam message detection using term frequency-inverse document frequency and random forest algorithm. Procedia Comput. Sci. 161, 509–515 (2019); The Fifth Information Systems International Conference, 23–24 July 2019, Surabaya, Indonesia 3. Kim, D., Seo, D., Cho, S., Kang, P.: Multi-co-training for document classification using various document representations: Tf–idf, lda, and doc2vec. Inf. Sci. 477, 15–29 (2019) 4. Magdy, S., Abouelseoud, Y., Mikhail, M.: Efficient spam and phishing emails filtering based on deep learning. Comput. Netw. 206, 108826 (2022) 5. Mekouar, S.: Classifiers selection based on analytic hierarchy process and similarity score for spam identification. Appl. Soft Comput. 113, 108022 (2021) 6. Rao, S., Verma, A.K., Bhatia, T.: A review on social spam detection: challenges, open issues, and future directions. Exp. Syst. Appl. 186, 115742 (2021)

Design of a GaAs-FET Based Low Noise Amplifier for Sub-6 GHz 5G Applications Samia Zarrik(B) , Abdelhak Bendali , Fatehi ALtalqi , Karima Benkhadda , Sanae Habibi , Mouad El Kobbi , Zahra Sahel , and Mohamed Habibi Department of Physics Laboratory of Electronics Treatment Information, Mechanic and Energetic, Faculty of Science, Kenitra Ibn Tofail University, Kenitra, Morocco [email protected]

Abstract. In this paper, we present a comprehensive analysis of the design process behind a class A low noise amplifier (LNA), meticulously crafted to cater to the demands of sub-6GHz fifth-generation wireless (5G) applications. The core of this amplifier’s architecture incorporates a gallium arsenide field-effect transistor (GaAs-FET s8834), operating seamlessly at the frequency of 3.5 GHz, within the encompassing environment of the Advanced Design System (ADS) software. Which characterized by the drain current Vds = 4 V, the gate voltage Vgs = −1.42 V and drain current Id = 88 mA. With frequency band from 1 GHz to 7 GHz to get a maximum gain of 15.436 dB with low noise figure NF = 1.908 dB. Furthermore, the PAE level we have achieved, approximately 28.417%, remains extremely valuable for real-world applications of sub-6GHz 5G networks. Its energy efficiency contributes to prolonging the battery life of wireless devices and reducing overall network power consumption. These performance metrics are critical for sub-6GHz 5G networks, where signal quality and efficiency are paramount. Our work stands out due to its precision-driven methodology, strategically positioning the LNA as an indispensable catalyst for enhancing front-end efficiency and, consequently, the overall system performance in the realm of 5G networks. By addressing the specific challenges posed by sub-6GHz frequencies, our contribution significantly advances the state-of-the-art in LNA design for 5G applications. Keywords: Low noise amplifier (LNA) · Noise figure (NF) · Power added efficiency (PAE) · Fifth generation (5G) · Gain · Advanced design system (ADS)

1 Introduction The standards of 5G wireless standards spans a diverse spectrum, encompassing licensed, shared, and unlicensed frequency bands. These frequency bands are systematically divided into three distinct categories: the low-band (< 1 GHz), mid-band (1–6 GHz), and high-band/mm-wave (> 24 GHz) [1, 2]. This modern generation of standards functions within the realm of multi-GHz frequencies, imposing rigorous prerequisites upon RF front ends. Amidst the array of components that constitute 5G front-end technology, the Low-Noise Amplifier (LNA) emerges as an especially intricate and pivotal element in terms of design. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 179–188, 2024. https://doi.org/10.1007/978-3-031-48573-2_26

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With mm-wave frequencies challenging silicon-based LNAs, researchers explore alternatives like SiGe, SOI, and GaAs. This drive stems from the necessity to fulfill the demanding specifications of the 5G era. This underscores the need for Field-Effect Transistor (FET) LNAs that meet essential benchmarks such as Noise Figure (NF) (< 3 dB), Gain (> 15 dB), Power at 1 dB Compression Point (P1dB) (> − 20 dBm), and ThirdOrder Input Intercept Point (IIP3) (> 0 dBm) within the operational bandwidth, while maintaining low power consumption. These FET LNAs play a pivotal role in optimizing 5G front-end performance across diverse frequency bands [1]. 1.1 Fifth Generation Wireless Communication The Fifth generation (5G) network represents a collection of technologies that belong to the fifth generation of mobile telephony standards. It has received validation from both the International Telecommunication Union (ITU) and the 3rd Generation Partnership Project consortium (3GPP). 5G applications are defined according to three main categories of use: mMTC (massive Machine Type Communication), eMBB (Enhanced Mobile Broadband) and uRLLC (Ultra-Reliable Low Latency Communication) [2, 3]. According to the 3rd Generation Partnership Project (3GPP), 5G wireless technology is categorized into two frequency ranges, namely FR1 and FR2. Frequency Range 1 (FR1) covers sub-6GHz frequencies, with the majority of typical cellular communication traffic spanning from 4.1 GHz to 7.125 GHz. In contrast, the FR2 frequency band (24.25–52.6 GHz) is optimized for high data rates over short distances [4]. 1.2 LNA The low-noise amplifier (LNA) serves as the initial circuit in a receiver front-end, responsible for amplifying extremely faint signals received by an antenna. It plays a crucial role in enhancing linearity and reducing the noise figure while providing gain amplification for the system [5]. LNAs are used in communication receivers like cell phones, GPS devices, Wi-Fi networks, and satellite systems and are especially vital in 5G applications [6]. The LNA’s architecture varies based on frequency band (narrowband or wideband). In the literature, several LNA design approaches have been proposed to meet the requirements of 5G applications. Among these, we can mention: Kim and Yoo [7] employs RC Feedback and Inductive Series-Peaking in a 5G New Radio LNA, achieving 20.68 dB gain, 1.57 dB NF, and 600 MHz bandwidth (N79 band), [8] proposes a Wideband Differential LNA for 5G, providing 21 ± 1.5 dB gain and < 4 dB NF, [9] uses current-reusing common source and CS-CS stages, employing reactive transformer feedback and Gm-boosting to achieve NF between 2.95 dB and 4 dB. Challenges in LNA design include achieving a low NF (< 3 dB), maintaining S11 and S22 below − 10 dB, ensuring high gain across the bandwidth, and maintaining a gain exceeding 15 dB [9]. Common source LNAs, while meeting desired parameters, tend to have narrower bandwidths.

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2 Theoretical Background The LNA involves five key parameters: stability, gain, noise figure, S-parameters, and linearity [10]. 2.1 Transistor Transistor selection is a pivotal aspect of LNA design. Depending on the targeted frequency range, options such as Si BJTs, GaAs or SiGe HBTs, Si MOSFETs, GaAs MESFETs, or GaAs or GaN HEMTs [11] offer varying specifications [12]. 2.2 Stability Stability verification is vital for proper amplifier operation. Inadequate stability can cause the RF circuit to oscillate. Rollet’s conditions are commonly used to evaluate transistor stability [13, 14]: K=

1 − |S11 |2 − |S22 |2 + ||2 2|S12 S21 |

(1)

 = S11 S22 − S12 S21

(2)

where

S11 : Input return loss or Input reflection coefficient S22 : Output return loss or Output reflection coefficient S12 : System gain S21 : Reverse voltage gain K: Stability factor. 2.3 Gain of Amplifier The amplifier’s gain and stability are analyzed during its design, taking into account transistor parameters. This leads to the derivation of three subsequent power gain metrics [15, 16]: GS =

1 − |S |2 |1 − in S |2

G0 = |S21 |2 GL =

1 − |L |2 |1 − S22 L |2

where,  L : The reflection coefficient of the load  s : The reflection coefficient of the source

(3) (4) (5)

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 in : The input reflection coefficient  out : The output reflection coefficient GS : Source gain factor G0 : Constant gain factor GL : load gain factor. The transducer gain is given by [16]: GT = GS G0 GL

(6)

This gain is very suitable for reducing the noise figure of the system, but excessive gain of the LNA may impact the overall dynamic range of the receiver [17]. 2.4 Noise Figure Apart from stability and gain, a key parameter in assessing communication system performance, particularly for Low Noise Amplifiers (LNAs), is the noise figure (NF). It quantifies the relationship between the power of the input signal noise and the power of the output signal noise [17]. NF =

Si Ni So No

≥1

(7)

in which, Si = Input signal power Ni = Input noise power So = Output signal power No = Output noise power. For the single stage of the amplifier LNA, the noise figure is expressed as follows [16]:   4Rn s − opt  F = Fmin +  (8) 2   1 − |s |2 · 1 + opt  In this context, gopt and Rn are inherent properties of the transistor in use and are commonly known as the device’s noise characteristics. For a multi-stage amplifier, its noise figure is determined by the following formula [16]: F2 − 1 F2 − 1 Fn − 1 + + ... (9) F = F1 + G1 G1 G2 G1 G2 ..Gn Here, Gn represents the gain of the first n-amplifier, and Fn stands for the noise figure of the first n-amplifier [17].

3 Design of Proposed LNA The proposed LNA utilizes the s8834 transistor, employing GaAs-based FET technology in class A at a frequency of 3.5 GHz (Fig. 1). The circuit is designed for applications within the sub-6GHz range of 5G, which encompasses frequencies from 1 to 7 GHz.

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To assess its performance, a range of parameters including S-parameters, gain, noise figure, and stability were measure using the Advanced Design System (ADS) software. Through the utilization of DC-sweep tools within ADS, the characteristics of the s8834 transistor were analyzed. This analysis facilitated the identification of the optimal VGS voltage (− 1.42 V) and Vds voltage (4 V), with a corresponding drain current of 88 mA.

Fig. 1. ADS simulation of the FET Curve Tracer

After designing the DC-bias circuit, the next step is to optimize the LNA’s performance within the specified frequency range. For this purpose, we design an input and output matching network for a Common Source Low-Noise Amplifier (LNA) using the s8834 transistor. The ADS schematic of the transistor with the biasing network is depicted in Fig. 2. Thus, the complete circuit of the LNA amplifier appears as follows:

Fig. 2. Schema of the complete circuit of Amplifier LNA in ADS

4 Results and Discussion After conducting the simulation, we have obtained significant results for evaluating our LNA’s performance in a sub-6 GHz 5G application. The stability analysis, with K = 0.719 and  = 0.537 suggests potential instability due to K and  values both being less than

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1. This instability risk could result in unwanted oscillations in the amplifier, although it’s crucial to recognize that stability is influenced by factors such as the feedback network configuration and the design of passive components, including inductors and capacitors. However, after the optimization process, the LNA circuit achieved the desired performance targets. At a frequency of 3.5 GHz, the circuit exhibited the following characteristics: Gain (S21) = 15.436 dB, Noise figure (NF) = 1.908 dB (Fig. 3). These results indicate that the LNA design is stable.

Fig. 3. Noise figure and power gain of the simulated LNA (Low-Noise Amplifier)

In Fig. 4, we can observe that the input reflection coefficient (S11) is approximately − 10.469 dB, and the output reflection coefficient (S22) reaches a value of about − 14.190 dB. Therefore, good adaptation is achieved at the frequency of 3.5 GHz. The S parameters demonstrate good impedance matching, crucial for maximizing power transfer and minimizing signal loss, while the PAE of approximately 28.417% (Fig. 5) indicates efficient power utilization within the LNA. These results validate the LNA’s suitability for sub-6 GHz 5G applications, ensuring optimal signal reception and transmission in wireless communication systems. Figure 6 that the amplifier is indeed fulfilling its fundamental function of successfully amplifying the input signal. This behavior aligns with expectations and proves beneficial in various applications where signal enhancement is required. Furthermore, the presence of sinusoidal waveforms in both the input and output signals indicates that the LNA circuit is operating in a stable manner. Sinusoidal waveforms are characteristic of linear and well-behaved systems. Any deviations from this pattern could potentially signify instability or distortion. After simulating Vout, we obtained a dBm spectrum graph (Fig. 7) that allowed us to assess the system’s linearity and provided a detailed analysis of power distribution at different frequencies within the output signal. Furthermore, it emphasizes the importance of high-quality linearity in the LNA, especially in wireless communication applications, to ensure distortion-free amplification and minimize interference. This linearity measurement is crucial for evaluating and effectively utilizing the LNA.

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Fig. 4. The S-Parameters Plot of an LNA

Fig. 5. Graph of PAE versus output power

Fig. 6. Simulation of Sinusoidal Waveforms: Vout and Vin Signals

Overall, our Low Noise Amplifier (LNA) design, based on GaAs FET technology, demonstrates remarkable performance compared to references [6, 18–20]. It stands out

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Fig. 7. Spectrum in dBm is plotted for Vout

particularly for its high gain of 15.436 dB, making it an ideal choice for amplifying weak signals in sub-6 GHz 5G applications (Table 1). In comparison, references [6, 18–20], exhibit respective gains of 11.70 dB, 14.62 dB, and 15 dB. Furthermore, our LNA offers an exceptionally low noise figure of 1.908 dB, indicating its capability to maintain an optimal signal-to-noise ratio. In contrast, references [6, 18–20] present noise figures of 4.03 dB, 3 dB, and 3.2 dB, respectively. This low noise figure is crucial for ensuring interference-free communication in sub-6 GHz 5G networks. In conclusion, our LNA distinguishes itself with its high gain and low noise figure, making it a preferred component for sub-6 GHz 5G applications, thus optimizing the overall system performance. Table 1. Performance comparison with existing LNA’s References

[18]

[19]

[20]

[6]

This Work

Technology

0.18 µm RF CMOS process

0.18 µm CMOS RFIC

180nm CMOS

45 nm GPDK

GaAs FET

Frequency

3.5 GHz

3.5 GHz

3.5 GHz

3.5 GHz

3.5 GHz

Application

IEEE 802.16d WiMAX

WiMAX

Sub-6GHz of 5G

5G communication

Sub-6GHz of 5G

Transistor model

TSMC

TSMC

UMC

–

s8834

Gain [dB]

11.70

14.62

15

15.17

15.436

Noise Figure [dB]

4.03

3

3.2

7

1.908

IIP3 [dBm]

− 2.1

–

− 2.8

–

–

S11 [dB]

− 10.41

− 17

− 10

–

− 10.460

S22 [dB]

− 13.17

− 14.81

–

–

− 14.490

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5 Conclusion This paper presents a GaAs-FET based LNA design for sub-6GHz 5G applications, providing the required gain and low noise figure. The LNA enhances linearity, reduces noise, and amplifies weak signals. Despite potential instability, it achieves targets with a gain of 15.436 dB and a noise figure of 1.908 dB at 3.5 GHz. The PAE of 28.417% indicates efficient operation, with potential for further improvements. The GaAs-FET LNA holds promise for sub-6GHz 5G applications, with competitive performance in gain, noise figure, and stability. Future Perspectives of Proposed LNA Future work involves comparing it with an HEMT GaN transistor-based LNA to demonstrate its advantages, especially in millimeter-wave 5G.

References 1. Hari Kishore, K., Senthil Rajan, V., Sanjay, R., Venkataramani, B.: Reconfigurable low voltage low power dual-band self-cascode current-reuse quasi-differential LNA for 5G. Microelectronics J. 92, 104602 (2019). https://doi.org/10.1016/j.mejo.2019.104602 2. Chen, W., Fan, X., Chen, L.: A CNN-based packet classification of eMBB, mMTC and URLLC applications for 5G. In: 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA), pp. 140–145 (2019) 3. [Golio_M.,_Golio_J._(ed.)]_RF_and_Microwave_Applic(BookZZ.org).pdf 4. Dr. Ravilla Dilli: Frequency Bands. 767–772 (2020) 5. Khatoon, A., Srivastava, N.: Design of 26GHz cascode low noise amplifier for 5G wireless applications on 0. 18µm CMOS Technol. 8, 139–148 (2021) 6. Hazarika, R., Sharma, M.P.: Design of a linear LNA for 5G applications using 45 nm technology. Wseas Trans. Commun. 20, 128–132 (2021). https://doi.org/10.37394/23204.2021. 20.17 7. Kim, M., Yoo, S.: RF-SOI low-noise amplifier using RC feedback and series inductive-peaking techniques for 5G new radio application (2023) 8. Shams, N., Abbasi, A., Nabki, F.: A 3 . 5 to 7 GHz wideband differential LNA with g m enhancement for 5G applications. 1, 230–233 (2020) 9. Chen, H., Wu, L., Che, W., Xue, Q., Zhu, H.: A wideband LNA based on current-reused CSCS topology and gm-boosting technique for 5G application. 2019-Decem, pp. 1158–1160 (2019). https://doi.org/10.1109/APMC46564.2019.9038417 10. Das, T.: Practical considerations for low noise amplifier design. Free. Semicond. 1–10 (2013) 11. Schwierz, F., Liou, J.J.: Semiconductor devices for RF applications: evolution and current status. Microelectron. Reliab. 41, 145–168 (2001). https://doi.org/10.1016/S0026-2714(00)000 76-7 12. Jabbar, M.A.: Design and performance analysis of low-noise amplifier with band-pass filter for 2. 4–2. 5 GHz. Muneeb Mehmood Abbasi Design and Performance Analysis of Low-Noise Amplifier with Band-Pass Muneeb Mehmood Abbasi Mohammad Abdul Jabbar. Linköpings Univ (2012) 13. Wang, J., Zhang, X.H.: Design and simulation of low-noise amplifier. Hedianzixue Yu Tance Jishu/Nuclear Electron. Detect. Technol. 34, 359–361 (2014) 14. Su, J.G., Wong, S.C., Chang, C.Y.: An investigation on RF CMOS stability related to bias and scaling. Solid State Electron. 46, 451–458 (2002). https://doi.org/10.1016/S0038-110 1(01)00319-7

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15. Poole, C., Darwazeh, I.: Microwave amplifier design. Microw. Act. Circuit Anal. Des. 439– 473 (2016). https://doi.org/10.1016/b978-0-12-407823-9.00013-5 16. Athikayan, A., Premanand, A., Damodaran, A., Girisan, G.: Design of low noise amplifier At 4 Ghz. Engineering 6, 209–212 (2011) 17. Lu, Y., Yang, S.H., Chen, Y.: The design and simulation of LNA with the frequency of 2.4GHz. 2010 6th Int. Conf. Wirel. Commun. Netw. Mob. Comput. WiCOM 2010. 1–5 (2010). https:// doi.org/10.1109/WICOM.2010.5600579 18. Kao, C.Y., Chiang, Y.T., Yang, J.R.: A concurrent multi-band low-noise amplifier for WLAN/WiMAX applications. In: 2008 IEEE International Conference on Electro/Information Technology, pp. 514–517 (2008). https://doi.org/10.1109/EIT.2008.4554357 19. Bist, A.S., Pargaein, S.: A 3.5 GHz low-noise amplifier IN 0.18 µm CMOS for WiMAX applications. J. Appl. Phys. Sci. Int. 7, 28–34 (2016) 20. Pradeep, J., Gladson, S.C., Bhaskar, M.: A low power wideband low-noise amplifier with input series peaking and g-{m} enhancement for 0.5 - 3.5 GHz applications. IEEE Region 10 Annual International Conference Proceedings/TENCON. 2019-Octob, 1225–1230 (2019). https://doi.org/10.1109/TENCON.2019.8929627

Pyramid Scene Parsing Network for Driver Distraction Classification Abdelhak Khadraoui(B) and Elmoukhtar Zemmouri ENSAM, Moulay Ismail University, Meknes, Morocco [email protected], [email protected] Abstract. In recent years, there has been a persistent increase in the number of road accidents worldwide. The US National Highway Traffic Safety Administration reports that distracted driving is responsible for approximately 45% of road accidents. In this study, we tackle the challenge of automating the detection and classification of driver distraction, along with the monitoring of risky driving behavior. Our proposed solution is based on the Pyramid Scene Parsing Network (PSPNet), which is a semantic segmentation model equipped with a pyramid parsing module. This module leverages global context information through context aggregation from different regions. We introduce a lightweight model for driver distraction classification, where the final predictions benefit from the combination of both local and global cues. For model training, we utilized the publicly available StateFarm Distracted Driver Detection Dataset. Additionally, we propose optimization techniques for classification to enhance the model’s performance.

1 Introduction According to recent research conducted by the Moroccan National Agency for Road Safety, distracted driving was a contributing factor in 3005 road fatalities and more than 84,585 injuries in Morocco in the year 2020. Regrettably, this issue seems to be worsening year after year. Distracted driving, as defined by Strayer et al. [8], encompasses any activity that diverts a driver’s attention away from the road, such as texting, eating, conversing with passengers, or adjusting the stereo. In light of this, the objective of our research is to develop and implement a model for the detection and classification of distracted driving in smart cars, leveraging semantic segmentation techniques [5] and convolutional neural networks (CNNs) [4]. To identify and categorize driver distraction from visual cues, we explored various models, including convolutional neural networks (CNNs) [4]. Building upon the state-of-the-art findings in this field, we devised a simplified model based on PSPNet, which yielded promising results.

2 Related Work Distracted driving can generally be classified into four distinct forms, as outlined by Strayer et al. [8]: cognitive, visual, manual, and auditory distractions. When a driver becomes distracted, they divert their focus and actions away from driving-related tasks, engaging in non-driving activities. Some activities inherently involve multiple forms of distraction. For instance, using a cell phone for calls or texting can encompass all four forms of distractions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 189–194, 2024. https://doi.org/10.1007/978-3-031-48573-2_27

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Manual Distraction: This occurs when a driver’s hands are taken off the steering wheel, impacting their ability to control the vehicle (as depicted in Fig. 1a). Common instances include eating, drinking, smoking, or retrieving items from a purse or wallet. Visual Distraction: In this scenario, the driver’s attention shifts to looking at a device instead of the road, which is one of the most prevalent distractions (as depicted in Fig. 1b). Examples encompass glancing at a GPS device, focusing on the entertainment center, observing a passenger. Cognitive Distraction: Cognitive distractions emerge when the driver’s focus is drawn away from driving by interpreting information from a device (as illustrated in Fig. 1c). Common instances include listening to a podcast, engaging in conversations through hands-free devices, conversing with other passengers. Auditory Distraction [3] Because noise distracts the driver.

Fig. 1. Images from the StateFarm dataset [7] illustrating driver distraction.

Table 1 shows some examples of distraction actions and their mapping to distraction types. Table 1. Assignment to common distraction actions and distraction types. Activity

Location

Distractions

Using Phone

Within the car

Cognitive, auditory, manual, visual

Eat, Drink

Within the car

Visual, physical

Looking advertisement

Outside vehicle

Visual, cognitive

Listening music

Within the car

Auditory, cognitive

3 Proposed Method 3.1 Pyramid Scene Parsing Network (PSPNet) PSPNet, as introduced in Zhao et al.’s work [9], utilizes a pretrained CNN [6] and employs the dilated network technique to extract feature maps from input images. The final feature map size is reduced to 1/8 of the input image dimensions. We leverage the

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pyramid pooling module to aggregate contextual information on top of this feature map. The pooling kernels cover various portions of the image, including the entire, half, and smaller areas, thanks to our four-level pyramid structure. These aggregated features are then combined to form a holistic representation. In the final step, we fuse this combined data with the original feature map and apply a convolution layer to produce the ultimate prediction map. The task of understanding visual scenes necessitates semantic image segmentation, as highlighted in Chen et al.’s work [1]. Semantic segmentation aims to classify each pixel in the input image, effectively performing pixel-level object segmentation [2]. This technique finds applications in diverse fields, such as autonomous driving, robotics, medical image analysis, video surveillance, and more. Consequently, it becomes imperative to enhance the accuracy and precision of semantic image segmentation both in theoretical research and practical implementation. This study primarily introduces the PSPNet, a scene analysis model based on pyramid synthesis [9], along with a parameter optimization approach tailored to the PSPNet model, leveraging GPU distributed computing for improved efficiency.

Fig. 2. Diagram of semantic segmentation of images used in PSPNet Model.

In Fig. 2, we offer an overview of our proposed PSPNet. Our approach commences with an input image (a) and initially employs a Convolutional Neural Network (CNN) to extract the feature map from the final convolutional layer (b). Following this, we apply a pyramid parsing module to collect a wide array of subregion representations. Subsequently, we utilize up sampling and concatenation layers to construct the final feature representation (c), which encapsulates both local and global context information. To conclude, this representation is input into a convolutional layer to generate the ultimate per-pixel prediction (d). We term this module as the’pyramid pooling module,’ which is meticulously crafted to establish a global scene context based on the final-layer feature map of the deep neural network, as vividly depicted in part (c) of Fig. 2. The pyramid pooling module adeptly amalgamates features across four distinct pyramid scales. The coarsest level, highlighted in red, engages in global pooling to yield a single-bin output. The subsequent pyramid level further subdivides the feature map into discrete sub-regions, creating pooled representations for various spatial locations.

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3.2 Proposed Method The method we propose is composed of the following phases: Semantic Segmentation Utilizing the Pyramid Scene Parsing Network (PSPNet) model, we delve into a critical domain of computer vision known as semantic segmentation. This aspect is fundamental for addressing various scene interpretation challenges. Semantic segmentation involves the prediction of the category associated with each pixel or, more precisely, determining the category to which a given pixel belongs. By precisely delineating the object region to which a pixel pertains, our goal is to enhance the accuracy of pixel categorization. Method: We employed three evaluation metrics—precision, recall, and F1 score—to assess and analyze the effectiveness of our image segmentation proposal based on PSPNet, drawing from our experimental results. In our implementation, we utilized the multi-scale parallel convolutional neural network model with PSPNet. To tackle the challenges posed by the complex and dynamic nature of the images, diverse driver positions, and the risk of overfitting or disrupting the parameter structure, we employed small-sample transfer learning to constrain the parameter learning process.

Fig. 3. The pipeline of our proposed method for driver distraction classification.

Classification: For the classification of the driver’s pose (and thus her/his distraction), we used the convolutional neural network (CNN). Figure 3 describes the pipeline of the proposed method. After segmentation of the database images with PSPNET Model, the proposed convolutional neural network (CNN) is used as the classification model.

4 Experiment and Results 4.1 Dataset The dataset we used to train and test the models is the StateFarm’s distraction detection dataset [7]. Table 2 present the 10 classes of the dataset and the number of images for each class.

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Table 2. StateFarm distraction-detection dataset and total images in the class. Class

Driver state/action

Images

Class

Driver state/action

Images

C0

Safe driving

2489

C1

Texting—right

2267

C2

Talking on the phone—right

2317

C3

Texting—left

2346

C4

Talking on the phone—left

2326

C5

Operating the radio

2312

C6

Drinking

2325

C7

Reaching behind

2002

C8

Hair and makeup

1911

C9

Talking to passenger

2129

4.2 Implementation Details Python is used to implement the suggested preprocessing and classification pipeline. A pre-trained PSPNET Model was employed for the pose estimation step. The Keras library on the Tensorflow backend was used to implement the CNN baseline model. 80% of the dataset was used to train all classifiers (including CNN), and the remaining 20% was used to test them (Table 3). 4.3 Results

Table 3. Classification model on the test set, in terms of accuracy, macro average precision, recall and F1-score. 10 classes schema. Model

Input

Accuracy

Precision

Recall

F1-Score

CNN

Segmented images

98.43

98.44

98.39

98.40

We compared the performance of the proposed method detection and classification for driver distraction using semantic segmentation. To evaluate the performance of different classifiers, we used four performance metrics that are: accuracy, macro average precision, macro average recall, and macro average F1Score.

5 Conclusion In this paper, we proposed a method for driver distraction detection and classification. Our method introduces a comprehensive library for semantic segmentation for well-known model as PSPNet. The classification was conducted using CNN. We have demonstrated the efficacy and robustness of these models.

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References 1. Chen, S., Song, Y., Su, J., Fang, Y., Shen, L., Mi, Z., Su, B.: Segmentation of field grape bunches via an improved pyramid scene parsing network. Int. J. Agric. Biol. Eng. 14(6), 185– 194 (2021). https://doi.org/10.25165/ijabe.v14i6.6903. https://www.ijabe.org/index.php/ijabe/ article/view/6903 2. Cheng, B., Chen, L.C., Wei, Y., Zhu, Y., Huang, Z., Xiong, J., Huang, T.S., Hwu, W.M., Shi, H.: SPGNet: semantic prediction guidance for scene parsing, pp. 5218–5228 (2019). https://openaccess.thecvf.com/content_ICCV_2019/html/Cheng_SPGNet_Semantic_ Prediction_Guidance_for_Scene_Parsing_ICCV_2019_ paper.html 3. Ersal, T., Fuller, H.J.A., Tsimhoni, O., Stein, J.L., Fathy, H.K.: Model-based analysis and classification of driver distraction under secondary tasks. IEEE Trans. Intell. Transp. Syst. 11(3), 692–701 (2010). https://doi.org/10.1109/TITS.2010.2049741 4. Gupta, D.: Image segmentation Keras: implementation of Segnet, FCN, UNet, PSPNet and other models in Keras (Jul 2023). https://doi.org/10.48550/arXiv.2307.13215. http://arxiv.org/ abs/2307.13215, arXiv:2307.13215 [cs] 5. Long, X., Zhang, W., Zhao, B.: PSPNet-SLAM: a semantic SLAM detect dynamic object by pyramid scene parsing network. IEEE Access 8, 214685–214695 (2020). https://doi.org/10. 1109/ACCESS.2020.3041038,conferenceName:IEEEAccess 6. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation (May 2016). https://doi.org/10.48550/arXiv.1605.06211. http://arxiv.org/abs/1605.06211, arXiv:1605.06211 [cs] version: 1 7. State, F.: State farm distracted driver detection. https://kaggle.com/competitions/state-farm-dis tracted-driver-detection 8. Strayer, D.L., Turrill, J., Cooper, J.M., Coleman, J.R., Medeiros-Ward, N., Biondi, F.: Assessing cognitive distraction in the automobile. Hum. Fact. 57(8), 1300–1324 (2015). https://doi.org/ 10.1177/0018720815575149. https://doi.org/10.1177/0018720815575149 9. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6230–6239. IEEE, Honolulu, HI (Jul 2017). https://doi.org/10.1109/CVPR.2017.660. http://ieeexplore.ieee.org/ document/8100143/ation. http://www.ncbi.nlm.nih.gov

A Survey on RFID Mutual Authentication Protocols Based ECC for Resource-Constrained in IoT Environment Hind Timouhin1(B) , Fatima Amounas2 , and Mourade Azrour3 1 Faculty of Sciences and Technics, Moulay Ismail University of Meknes, Errachidia, Morocco

[email protected]

2 RO.AL&I Group, Computer Sciences Department, Faculty of Sciences and Technics, Moulay

Ismail University of Meknes, Errachidia, Morocco [email protected] 3 Engineering Science and Technology Laboratory, Faculty of Sciences and Techniques, IDMS Team, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected]

Abstract. The Internet of things (IoT) has been emerged as the new technology that forms the foundation of today’s intelligent and connected world. Radio Frequency Identification (RFID) technology is a fundamental component of the IoT ecosystem. When integrated with IoT systems, RFID enhances connectivity, data collection, and automation capabilities. While dealing with secure IoT infrastructure, authentication plays an imperative role. An appropriate solution to provide security for both communicating parties is mutual authentication, in which both parties get authenticated before the actual transmission. Due to the limitations and constraints of the IoT, classical cryptography methods are costly and inefficient, so lightweight authentication protocol is the best way to overcome security shortcomings in this environment. Elliptic curve cryptography (ECC) is a promising solution for situations where resources are limited. Although intensive efforts were made in designing RFID authentication scheme based ECC for IoT applications, the majority has some security weaknesses. Therefore, our goal in this paper is to present a comprehensive survey of some of the newest ECC-authentication solutions suggested for RFID systems in the IoT environment. Finally, a discussion of security requirements of all articles is included in terms of the current features that are required to IoT solutions. Keywords: Internet of things · Security · Authentication protocol · Elliptic curve cryptography · RFID system

1 Introduction In the last few years, The Internet of things (IoT) refers to new technology interconnecting various objects and intelligent devices, such as sensors, tags, and smart objects, over the Internet [1]. Such devices are adopted in various domains such as public health, smart grids, smart transportation, smart homes, smart cities and agriculture. The incorporation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 195–200, 2024. https://doi.org/10.1007/978-3-031-48573-2_28

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of IoT with security strategies is a difficult task. The RFID technology is an important building block of the IoT. RFID technology creates a wireless channel between the (tag) and the (reader), facilitating communication between IoT devices. An RFID system normally consists of three parts: a tag, a reader, and a database or back-end server where the data are stored as shown in Fig. 1. Therefore, a key concern for the security of the IoT is how to address security issues in RFID systems. The security aspects of IoT have received significant attention among the researchers in recent years. Despite its rapid adoption in many industries, IoT security remains a serious issue that needs further research. IoT authentication is an essential security mechanism for building trust in IoT systems. The design of lightweight authentication protocol for IoT environment is still open and challenging.

Fig. 1. RFID system

Various protocols have been introduced for securing the IoT applications [2–4], including the healthcare environment, the Internet of Vehicles, Industry. Among these protocols, RFID authentication protocols have gained the most attention. The security of the data stored on an RFID system is typically ensured using cryptographic techniques, including symmetric and asymmetric encryption. However, some encryption methods are not recommended for resource-limited IoT devices. ECC is frequently used in constrained environments due to its benefit of generating a powerful encryption technique with small key sizes. In the literature, many papers have been discussed the RFIDauthentication based ECC for securing the communication between the IoT devices. However, the majority of common protocols contain restrictions on the security services. In this context, the current paper will mainly focus on the recent articles from 2019 onwards. The remainder of this paper is organized as follows: Sect. 2 provides different ECC-authentication protocols and solutions based on RFID technology for IoT applications. Section 3 summarized the security analysis. Finally Sect. 4 ends with conclusion.

2 Review of ECC-Authentication Protocols for RFID Systems IoT devices often have a certain amount of resources. Additionally, real-time communication between sensors, actuators, objects, and nodes is required. Elliptic curve cryptography is one of the promising solutions especially for systems with limited memory and processing capabilities. Nowadays, RFID authentication protocols based on ECC have been mostly used to efficiently overcome the security and privacy issues in application areas of IoT. However, numerous protocols suffer from efficiency, security and privacy

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vulnerabilities. This work provides an overview of the newest authentication solutions for RFID systems. This level of the current study includes recent research of RFID authentication protocols based on elliptic curve cryptography. According to Naeem et al. [5], the ECC-based Alamr protocol [6] received an improvement in 2019. The authors aim to design a novel method to solve the scalability issues in the ECC-based RFID protocol proposed by Alamr et al. They focused on correcting the registration and authentication phases in Alamr et al.’s scheme. To validate their proposed protocol, they conducted formal and informal analyses. This verification and validation process ensured the protocol’s effectiveness in terms of security and computational cost. Overall, this improvement is regarded as secure and reliable and may be used in any IoT setting. After that, Agrahari et al. [7] exhibited a safe authentication protocol for RFID in the healthcare industry. The protocol used elliptic curve cryptography to achieve mutual authentication, data confidentiality, and defense against diverse attacks. The authors additionally included a formal security analysis using BAN logic and a performance analysis of the proposed protocol. Through a review of various RFID authentication schemes based on ECC, they demonstrated that the proposed protocol, in terms of performance and security fulfilled the requirements of an RFID authentication protocol and provided mobility, scalability, security, and privacy. Bensalah et al. [8] carried out another work. The authors introduced a lightweight ECC protocol for RFID systems. This protocol is built upon Dinarvand and Barati’s protocol. To evaluate the effectiveness of their proposed protocol, the authors conducted an extensive study. The comparison was made with existing RFID-based solutions, considering factors such as security maturity, computational cost, communication cost, and storage requirements. In order to validate the security requirements of the proposed protocol, the authors utilized both formal and informal security models. In the same year, Izza et al. [9] presented a new and improved method for secure communication and data transfer in WBANs (Wireless Body Area Networks) using IoT-based RFID technology. The protocol utilized a mechanism called elliptic curve cryptography, along with elliptic curve digital signature with message recovery (ECDSMR). This combination of techniques ensured mutual authentication between the RFID tag and the medical server, while also safeguarding patient data. Through formal and informal analysis, the authors proved that the protocol provides the requirement security features in term of computational and storage cost. To enhance security in healthcare domain, Noori et al. [10] proposed a novel RFID scheme based on elliptic curve cryptography. The main goal of this scheme is to improve authentication scalability between RFID components. The suggested protocol offered several advantages, including high efficiency, strong security, and effective key management solutions for addressing dynamic access issues in RFID cards. Additionally, the authors attempt to reduce the number of elliptic curve point multiplications and optimize computational costs. Security and functional comparisons are conducted to highlight the scheme’s superior level of security when compared to other existing schemes. One year later, Kumar et al. [11] a lightweight authentication protocol for vehicular systems based on RFID, using Elliptic Curve Cryptography. The authors successfully demonstrated that the protocol achieved a high level of security and fulfilled all the requirements. They utilized the AVISPA simulation tool to validate the security requirements, providing further verification. Moreover, through performance analysis, they established that the proposed protocol outperformed existing

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methods in terms of communication and computation costs. Gabsi et al. [12] suggested an efficient RFID-authentication protocol built on elliptic curve cryptography with the goal of achieving data secrecy and privacy. The authors provide a summary of a few current authentication systems and point out their security flaws. In addition, a comparison with other included papers is made in terms of computational performance and security strength. Furthermore, the authors conducted a comprehensive analysis of the suggested protocol’s defenses against server spoofing, tag impersonation, and position tracking. They also evaluated its capacity to offer services for mutual authentication, confidentiality, anonymity, and data integrity. In 2022, Kumar et al. [13] Suggested an authentication framework for RFID systems using ECC and VCC (Virtual Contact Card). They demonstrated the safety of the proposed approach, and conducted the formal security analysis using the random oracle model, as well as an informal analysis using BAN logic. Furthermore, they evaluated the performance of the proposed framework comparing it to similar models. Then they indicated that it fulfilled all the required security criteria while enabling effective communication. In 2023, Gong et al. [14] introduced a simple and secure authentication scheme using ECC algorithm and hash function to safeguard the security of private data for RFID, in Internet of Vehicles (IoV) domain. The security analysis demonstrates that the proposed scheme can resist against attacks in IoV mobile communication threats. Additionally, this scheme significantly reduced tag computational and communication cost while maintaining security.

3 Comparative Analysis This section presents the security analysis of various RFID authentication solutions based on ECC discussed in our above review. Table 1 illustrates the security services that are required for IoT solutions are mutual authentication, confidentiality, integrity, availability and anonymity. Here, ‘+’ denotes satisfied feature and ‘−’ denotes not satisfied feature of the protocol. Table 1. Comparison in terms security requirements [5]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

M. authentication

+

+

+

+

+

+

+

+

+

Confidentiality

−

+

+

−

+

−

−

−

+

Data integrity

+

−

−

−

+

−

−

−

+

Availability

−

+

+

−

−

+

+

−

−

Anonymity

+

+

−

+

−

+

+

+

+

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4 Conclusion In this paper, we describe the importance of RFID technology in IoT environment and review the currently proposed protocols including ECC-Based Mutual Authentication for RFID systems. These protocols play a crucial role in securing IoT applications. Additionally, we analyze the security of RFID authentication solutions based on ECC for IoT applications. The comprehensive analysis provides valuable insights for implementing robust authentication mechanisms in IoT environments, so improving overall security posture in healthcare and other sectors.

References 1. Patel, K.K., Patel, S.M.: Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges. Int. J. Eng. Sci. Comput. 6(5) (2016). https://doi.org/10.4010/2016.1482 2. Bagga, P., Das, A.K., Wazid, M., Rodrigues, J.J.P.C., Park, Y.: Authentication protocols in internet of vehicles: taxonomy, analysis, and challenges. IEEE Access 8, 54314–54344 (2020). https://doi.org/10.1109/ACCESS.2020.2981397 3. Dang, L.M., Piran, M., Han, D., Min, K., Moon, H.: A survey on internet of things and cloud computing for healthcare. Electronics 8, 768 (2019). https://doi.org/10.3390/electroni cs8070768 4. Baker, S.B., Xiang, W., Atkinson, I.: Internet of things for smart healthcare: technologies, challenges, and opportunities. IEEE Access 5, 26521–26544 (2017). https://doi.org/10.1109/ ACCESS.2017.2775180 5. Naeem, M., Chaudhry, S.A., Mahmood, K., Karuppiah, M., Kumari, S.: A scalable and secure RFID mutual authentication protocol using ECC for internet of things. Int. J. Commun. 33, e3906 (2020). https://doi.org/10.1002/dac.3906 6. Alamr, A.A., Kausar, F., Kim, J., Seo, C.: A secure ECC-based RFID mutual authentication protocol for internet of things. J. Supercomput. 74, 4281–4294 (2018). https://doi.org/10. 1007/s11227-016-1861-1 7. Agrahari, A.K., Varma, S.: A provably secure RFID authentication protocol based on ECQV for the medical internet of things. Peer-to-Peer Netw. Appl. 14, 1277–1289 (2021). https:// doi.org/10.1007/s12083-020-01069-z 8. Benssalah, M., Sarah, I., Drouiche, K.: An efficient RFID authentication scheme based on elliptic curve cryptography for internet of things. Wireless Pers. Commun. 117, 2513–2539 (2021). https://doi.org/10.1007/s11277-020-07992-x 9. Izza, S., Benssalah, M., Drouiche, K.: An enhanced scalable and secure RFID authentication protocol for WBAN within an IoT environment. J. Inform. Secur. Appl. 58, 102705 (2021). https://doi.org/10.1016/j.jisa.2020.102705 10. Noori, D., Shakeri, H., Niazi Torshiz, M.: Scalable, efficient, and secure RFID with elliptic curve cryptosystem for internet of things in healthcare environment. EURASIP J. Info. Secur. 2020, 13 (2020). https://doi.org/10.1186/s13635-020-00114-x 11. Kumar, S., Banka, H., Kaushik, B., Sharma, S.: A review and analysis of secure and lightweight ECC -based RFID authentication protocol for Internet of Vehicles. Trans. Emerg.Tel. Tech. 32, e4354 (2021). https://doi.org/10.1002/ett.4354 12. Gabsi, S., Kortli, Y., Beroulle, V., Kieffer, Y., Alasiry, A., Hamdi, B.: Novel ECC-Based RFID Mutual Authentication Protocol for Emerging IoT Applications. IEEE Access. 9, 130895– 130913 (2021). https://doi.org/10.1109/ACCESS.2021.3112554

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13. Kumar, V., Kumar, R., Kumar, V., Kumari, A., Kumari, S.: RAVCC: robust authentication protocol for RFID based vehicular cloud computing. J. Netw. Intell 7(3), 526–543 (2022) 14. Gong, Y., et al.: VASERP: an adaptive, lightweight, secure, and efficient RFID-based authentication scheme for IoV. Sensors 23, 5198 (2023). https://doi.org/10.3390/s23115198

Advanced Prediction of Solar Radiation Using Machine Learning and Principal Component Analysis Hasna Hissou1(B) , Said Benkirane2 , Azidine Guezzaz2 , Abderrahim Beni-Hssane1 , and Mourade Azrour3 1 Faculty of Science, Science and Technology Research Structure, Chouaïb Doukkali

University, Avenue of Faculties, 24000 El Jadida, Morocco [email protected] 2 Higher School of Technology, Computer Sciences Department, Cadi Ayyad University, Km 9, Road to Agadir, Essaouira Aljadida, PB. 383, 44000 Ghazoua, Morocco 3 STI Laboratory, Faculty of Sciences and Techniques, IDMS Team, Moulay Ismail Universty of Meknes, Errachidia, Morocco

Abstract. Solar radiation (Rs) is a crucial energy source, vital for illumination, warmth, and life sustenance on Earth. However, its intermittent nature poses integration challenges into power grids. This research introduces an innovative approach leveraging Machine Learning (ML) models for accurate Rs forecasting. Principal Component Analysis (PCA) combines with ML models, including random forest (RF), Gradient Boosting Models (GBM), Logistic Regression (LR), Classification and Regression Tree (CART), and Decision Tree (DT). Our goal is to enhance solar radiation forecasting, making solar energy more reliable and cost-effective thereby advancing renewable energy integration into power grids. This research offers innovative perspectives on model integration, feature selection using PCA, and model suitability, contributing to the advancement of MultiVariate Time Series analysis. Our findings demonstrate promising outcomes across models, with the following negative Mean Absolute Errors (nMAE): LR and RF achieve the lowest nMAE, around − 0.144 (0.014) and − 0.151 (0.015), respectively. GBM slightly outperforms them with an nMAE of about − 0.154 (0.017), while CART records the highest nMAE of approximately − 0.209 (0.026). Small standard deviations (in parentheses) suggest stable performance during repeated cross-validation. Keywords: Machine learning · Solar radiation · Feature selection · PCA

1 Introduction Solar energy (SE) has emerged as a promising solution to meet the growing global energy demands, offering numerous advantages [1]. At the heart of harnessing SE lies Solar radiation (Rs), which exerts substantial influence on climate. The integration of SE into power grids is gaining traction in many countries; Sustainability stands as a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 201–207, 2024. https://doi.org/10.1007/978-3-031-48573-2_29

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paramount concern in the realm of SE, playing a pivotal role in the seamless integration into power grids. However, maintaining a delicate balance between electricity production and consumption becomes challenging due to the Rs nature [2]. Accurate measurements become essential, but the high costs hinder widespread availability [3]. To address these challenges, various forecasting methods have been proposed. Machine Learning (ML) models [4] have shown promising accuracy [5]. However, the challenge lies in determining the optimal set of input variables for accurate prediction, as the quality of training data directly impacts the model’s performance. Feature selection (FS) techniques have been employed to address this issue [6]. FS offers reduced computational expenses, enhanced training velocity, and improved forecasting accuracy, making it a crucial step in the forecasting process [7, 8]. In this Scientific Paper, we explore the influence FS models on enhancing the accuracy of Rs prediction. We implement Component Analysis (PCA) with a diverse set of models, including random forest (RF) [9], Decision Tree (DT) [10], Logistic Regression (LR), and Gradient Boosting Models (GBM) [11]. The models are compared against others to identify the most effective approach for accurate Rs forecasting. The subsequent sections of the article are as follows: Sect. 2 furnishes the essential background and context. Moving on to Sect. 3, we present the methodology employed and the proposed model. Section 4 delineates the experimental setup, presents the discoveries, and delves into an intricate analysis. Finally, Sect. 5 highlights the major discoveries and outlines possible directions for future investigations.

2 Related Works Rs forecasting has prominently embraced ML algorithms [3, 12, 13] that excel in handling complex relationships and patterns in the data, making them highly suitable for accurate Rs predictions. Several studies have focused on enhancing Rs forecasting through the utilization of data mining algorithms and intelligent optimization models [14, 15]. It shares a common objective of achieving precise predictions by meticulously selecting relevant input variables and optimizing model parameters [16]. The application of FS techniques has gained significant attention in improving Rs forecasting models. FS plays a crucial role in identifying the most influential input variables that significantly contribute to prediction accuracy [15, 17]. It has been proven effective in selecting optimal input parameters, resulting in reduced overfitting and enhanced model interpretability. Amiri et al. [18] introduced the ‘Weights’ technique to evaluate the relative significance of input characteristics in predicting 10-min global Rs using ANN. The study achieved high accuracy in estimating global Rs by identifying the order of relative importance for input variables. Marzouq et al. [19] focused on automatically selecting inputs for an ANN model to estimate horizontal daily global Rs in Fez, Morocco. Their suggested approach achieved good estimation performance with efficient computation time. Salcedo-Sanz et al. (Australia, 2018), developed a hybrid neuro-evolutionary wrapper-based approach for estimating daily global Rs that effectively selected relevant predictor variables and achieved accurate Rs estimation, outperforming alternative models [20]. The application of these models is crucial for enhancing the accuracy and reliability of Rs predictions, enabling better utilization of SE resources and supporting

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the efficient operation of photovoltaic installations. However, more research is needed to explore alternative strategies for selecting inputs to optimize the performance of these models.

3 Our Proposed Approach Within this segment, we present the suggested approach (Fig. 1) and elucidate the methodology employed within this investigation. The study proposes a time series model with a focus on data preprocessing and feature engineering.

Fig. 1. Our proposed model

The initial phase involves making the time series stationary by eliminating seasonality through differencing. The dataset undergoes a transformation into a supervised learning problem using lag observations and actual outputs. Feature engineering is performed using PCA to select the most relevant dimensions. The model’s attributes are assessed and trained. PCA serves as a dimensionality reduction method that reshapes high-dimensional data into a lower-dimensional representation while preserving the most important information and minimizing the loss of variance. It does this by identifying the principal components which are combinations of the original features achieved through linear transformations that capture the maximum variance in the data. These components represent new axes in the transformed space that are orthogonal to each other [7]. PCA is applied with different models like RF, Classification and Regression Tree (CART), DT, LT, and GBM. The models are sequentially fitted on the supervised representation of the dataset. In the final step, the approach’s accuracy is evaluated, and the models are compared and hyper-parameters are fine-tuned. Validation is conducted to ensure the model’s effectiveness and suitability for the given time series data.

4 Experimental Study 4.1 Environment The dataset employed in this study is sourced from The National Centers for Environmental Prediction (NCEP). Acquired over a span of 36 years, from 1979 to 2014, it encompasses 12,988 daily records of various weather parameters. The computational

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aspect of our research is conducted on a personal computer featuring a Core-i5 3437U CPU running at 2.4 GHz and equipped with 16GB of DDR3 memory. This setup operates on Windows 10 Professional 64-bit. The trainings utilizes Python version 3.9.7. The evaluation process employs Repeated KFold with 20 repeats and 10 folds. The evaluation of the model’s accuracy is quantified through the calculation of Mean Absolute Error (MAE), depicted in Eq. (1). n n |ei | i=1 |yi − xi | MAE = = i=1 (1) n n Additionally, the mean and standard deviation (std) are reported. MAE serves as a metric to gauge the divergence in error between two instances of the same event occurrence. This is illustrated through expected values. Observed comparisons, where Y represents the expected values and X signifies the observed values. In this context, xi is the actual, and yi the predicted value. 4.2 Discussion of Results After constructing the updated time series, encompassing lag values spanning a 12-month duration. As we employed PCA in conjunction with various models, we systematically trained all models using the identical perspective of the dataset. For the distribution of accuracy scores and negative MAE, for each model, a box and whisker plot is created.

Fig. 2. Accuracy scores/std

Fig. 3. nMAE /std

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The results, as depicted in the figures (Figs. 2 and 3), highlight the performance of various models when employing PCA for FS. In terms of negative Mean Absolute Errors (nMAE), LR and RF models exhibit the lowest errors at around − 0.144 (0.014) and − 0.151 (0.015), respectively, signifying their accuracy in solar radiation forecasting. GBM follows closely with an nMAE of approximately -0.154 (0.017), while CART records the highest nMAE of about -0.209 (0.026). These models maintain stable performance during cross-validation, indicated by low standard deviations. Regarding accuracy, LR and RF models stand out with averages of 85.574% and 84.907%, respectively. GBM achieves an accuracy mean of 84.573%, while CART slightly lags behind with an accuracy mean of 79.073%. Overall, these machine learning models, particularly LR and RF combined with PCA-based FS, show promise in enhancing the reliability of Rs forecasting for renewable energy integration into power grids. In comparison to a 2022 study by Acikgoz [21], which introduced a deep learning approach for short-term Rs forecasting, our research employs established ML models, offering simplicity and ease of implementation. Acikgoz’s method excels in capturing intricate data patterns but comes with increased computational complexity and potential interpretability challenges.

5 Conclusion In summary, the LR and RF appear to be the most effective in this configuration, displaying the lowest nMAE and the highest accuracy performances. CART exhibits slightly weaker performance in terms of both nMAE and accuracy. GBM also falls within a performance range close to other models, albeit slightly less effective than the top performers. Overall, the results demonstrate that all models deliver robust performances in both nMAE and accuracy. Rs forecasting is a multidisciplinary area that draws upon various scientific and technological domains. The combination of advanced ML models, FS techniques, data assimilation methods, and comprehensive data sources has shown considerable promise in enhancing the accuracy and reliability of Rs predictions. As research advances, the integration of these methods with real-time monitoring and adaptive learning holds the potential to enhance forecasting capabilities significantly, ultimately facilitating a more sustainable and efficient utilization of solar energy (SE) in the future. Additionally, there is merit in investigating hybrid models that leverage the respective strengths of both machine learning and deep learning approaches.

References 1. A˘gbulut, Ü., Gürel, A.E., Biçen, Y.: Prediction of daily global solar radiation using different machine learning algorithms: evaluation and comparison. Renew. Sustain. Energy Rev. 135, 110114 (2021). https://doi.org/10.1016/j.rser.2020.110114 2. Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017). https://doi.org/10.1016/j.renene.2016.12.095 3. Meenal, R., Selvakumar, A.I.: Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renew. Energy 121, 324–343 (2018). https://doi.org/10.1016/j.renene.2017.12.005

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4. Attou, H., Guezzaz, A., Benkirane, S., Azrour, M., Farhaoui, Y.: Cloud-based intrusion detection approach using machine learning techniques. Big Data Min. Anal. 6(3), 311–320 (2023). https://doi.org/10.26599/BDMA.2022.9020038 5. Hissou, H., Benkirane, S., Guezzaz, A., Beni-Hssane, A.: Feature selection impact on time series problems for solar radiation forecasting, pp. 440–446 (2023). https://doi.org/10.1007/ 978-3-031-26254-8_63 6. Hissou, H., Benkirane, S., Guezzaz, A., Azrour, M., Beni-Hssane, A.: A novel machine learning approach for solar radiation estimation. Sustainability 15(13), 10609 (2023). https:// doi.org/10.3390/su151310609 7. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection. Multimed. Tools Appl. 82(15), 23615–23633 (2023). https://doi.org/10.1007/s11042-023-14795-2 8. Bouzgou, H., Gueymard, C.A.: Minimum redundancy—maximum relevance with extreme learning machines for global solar radiation forecasting: toward an optimized dimensionality reduction for solar time series. Sol. Energy 158, 595–609 (2017). https://doi.org/10.1016/j. solener.2017.10.035 9. Eddine, M.M., Benkirane, S., Guezzaz, A., Azrour, M.: Random forest-based IDS for IIoT edge computing security using ensemble learning for dimensionality reduction. Int. J. Embed. Syst. 15(6), 467 (2022). https://doi.org/10.1504/IJES.2022.129803 10. Guezzaz, A., Benkirane, S., Azrour, M., Khurram, S.: A reliable network intrusion detection approach using decision tree with enhanced data quality. Secur. Commun. Netw. 2021, 1–8 (2021). https://doi.org/10.1155/2021/1230593 11. Hazman, C., Guezzaz, A., Benkirane, S., Azrour, M.: lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning. Clust. Comput. (2022). https://doi.org/10.1007/s10586-022-03810-0 12. Chen, J.-L., Liu, H.-B., Wu, W., Xie, D.-T.: Estimation of monthly solar radiation from measured temperatures using support vector machines—a case study. Renew. Energy 36(1), 413–420 (2011). https://doi.org/10.1016/j.renene.2010.06.024 13. Rabehi, A., Guermoui, M., Lalmi, D.: Hybrid models for global solar radiation prediction: a case study. Int. J. Ambient Energy 41(1), 31–40 (2020). https://doi.org/10.1080/01430750. 2018.1443498 14. Reza Parsaei, M., Mollashahi, H., Darvishan, A., Mir, M., Simoes, R.: A new prediction model of solar radiation based on the neuro-fuzzy model. Int. J. Ambient Energy 41(2), 189–197 (2020). https://doi.org/10.1080/01430750.2018.1456964 15. Biazar, S.M., Rahmani, V., Isazadeh, M., Kisi, O., Dinpashoh, Y.: New input selection procedure for machine learning methods in estimating daily global solar radiation. Arab. J. Geosci. 13(12), 431 (2020). https://doi.org/10.1007/s12517-020-05437-0 16. He, C., et al.: Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods. Energy Convers. Manag. 220, 113111, (2020). https://doi.org/10.1016/j.enconman.2020.113111 17. Ghimire, S., Deo, R.C., Raj, N., Mi, J.: Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Appl. Energy 253, 113541 (2019). https://doi.org/10.1016/j.apenergy.2019.113541 18. Amiri, B., Dizène, R., Dahmani, K.: Most relevant input parameters selection for 10-min global solar irradiation estimation on arbitrary inclined plane using neural networks. Int. J. Sustain. Energy 39(8), 779–803 (2020). https://doi.org/10.1080/14786451.2020.1758104 19. Marzouq, M., Bounoua, Z., El Fadili, H., Mechaqrane, A., Zenkouar, K., Lakhliai, Z.: New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks. J. Clean. Prod. 209, 1105–1118 (2019). https://doi.org/10.1016/j.jclepro.2018.10.254

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20. Salcedo-Sanz, S., Deo, R.C., Cornejo-Bueno, L., Camacho-Gómez, C., Ghimire, S.: An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Appl. Energy 209, 79–94 (2018). https://doi.org/ 10.1016/j.apenergy.2017.10.076 21. Acikgoz, H.: A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Appl. Energy 305, 117912 (2022). https://doi.org/10.1016/j.apenergy.2021.117912

Blockchain and Machine Learning Applications in Overcoming Security Challenges for CPS and IoT Systems Kamal Bella1 , Azidine Guezzaz1(B) , Said Benkirane1 , Mourade Azrour2 , and Mouaad Mohy-eddine1 1 Technology Higher School Essaouira, Cadi Ayyad University, Marrakesh, Morocco

[email protected] 2 STI Laboratory, Faculty of Sciences and Techniques, IDMS Team, Moulay Ismail University

of Meknes, Errachidia, Morocco

Abstract. In addition to the Internet of Things (IoT) systems, Cyber-Physical Systems (CPS) are systems where the physical and computational components are integrated in a networked interaction, with an increasing in popularity and evolution they are now widely adopted in many applications. However, new security challenges emerged from bridging the cyber and physical worlds. CPS are now more prone to failures and cyber attacks than ever. However, IoT and CPS security grabbed the attention of many researchers recently, numerous methods, architectures and countermeasures have been proposed to enhance security and data integrity with the application of machine learning (ML) and the blockchain. This paper surveys CPS applications and the various vulnerabilities and security challenges facing it, while also attempting to identify its main challenges. Finally, the existing solutions and failure preventive measurements are analyzed along with their limitations. Keywords: Blockchain · CPS · IoT · Intrusion detection · Machine learning

1 Introduction A CPS is defined as a set of sensors, actuators, processing units and networking devices where the physical and computational processes are integrated with feedback loops between all the different components [1]. CPS are integrated in several applications such as healthcare devices, automobile, energy grids, military systems, energy and water management systems, aircraft and more [2], this diverse array of applications is led by and also contributing to the growing nature of CPS evolution and advancement. CPS and IoT share a relationship of mutual enrichment, where their collective potential is amplified by their convergence, on one hand, CPS bridges the digital and physical works, IoT on the other hand interlinks objects and devices, thus extending the physical and digital connectivity, they unlocked new potentials for data driven insights, automation and decision making capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 208–213, 2024. https://doi.org/10.1007/978-3-031-48573-2_30

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With the increase in adaptation and popularity of CPS and IoT ecosystems, the need for security and robustness is consequently increasing rendering traditional security mechanisms inadequate. While conventional encryption and access control mechanisms are still essential, they often fall short against the increasingly sophisticated nature of cyber and physical attacks. This has called for exploring novel and innovative approaches that can provide robust, autonomous and adaptive defenses against evolving security threats and attacks. In this context, ML and blockchain technologies can offer promising potentials and applications to advance the security landscape of CPS and IoT, ML can be used for real time anomaly and intrusion detection thanks to the analysis and pattern identification capabilities, and make security more proactive. Blockchain on the other hand ensures trust, transparency, traceability and integrity in distributed systems. This work aims to comprehensively explore the potential values of integrating ML to overcome the security challenges facing CPS and IoT. By harnessing the strengths of these two domains, a realm of untapped opportunities comes into view, ranging from the creation of innovative security frameworks to the implementation of novel intrusion detection mechanisms. These endeavors collectively ensure data integrity, confidentiality and system availability. The remainder of this paper is structured as follows. Section 2 sets the background for this work including a review of CPS security, vulnerabilities and attacks as well as a survey of several implementations of blockchain technology and their impact on CPS security. Section 3 reviews some related works that use blockchain and ML to enhance security in CPS and IoT, and we conclude the paper in Sect. 4.

2 Background 2.1 CPS Security A CPS is defined as an interconnection of the physical and computational processes. Han et al. [3] proposed a generalized model of CPS which consists of 5 layers, with each layer having its own properties, a physical layer containing devices and physical properties, a sensor/actuator layer where sensors read the state and information about the components of the physical layer, then actuators take actions, a network layer responsible for connecting the sensor/actuator and the control layers, a control layer where system wide actions and decisions are taken, and finally an information layer defined as an abstract layer of information flows in the entire CPS, Fig. 1. CPS are subject to various vulnerabilities, threats, attack and failures [4], each is divided by cyber and physical categories, examples of such lists are presented in Table 1. Many aspects of CPS can be targeted and are prone to various kinds of attacks, most important of which are confidentiality, integrity, availability and authenticity [5]. To ensure a safe and secure CPS these objectives must be addressed. However, meeting these security requirements can lead to reduced performance, increased power consumption, latent transmission. CPS vulnerabilities fall into two categories [3], internal and external, internal vulnerabilities are related to the system’s design and implementation, such as hardware and software issues, incorrect configuration of devices, incorrect specifications of data and control systems. External vulnerabilities on the other hand are more related

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Fig. 1. CPS architecture

Table 1. CPS vulnerabilities, threats, attack and failures. Cyber

Physical

Threats

Data interception, sensor exploitation, unauthorized access

Physical damage, spoofing and tampering with components

Vulnerabilities

Signal interception, sniffing, lack of encryption and authentication…

Physical component exposure, unauthorized physical access

Attacks

Eavesdropping, XSS, SQL injections, malwares

Physical breaches, key-card hijacking, fake identities

Failures

Service failure, content failure

Sensor failure, timing failure

to the outside environment of CPS and they include infrastructure damages and resource constraints. All aspects of CPS are subject to attacks across all the layers, and to guarantee CPS security three key features are required, availability, integrity and confidentiality [6]. And consequently, these three features are targeted and each one has its common and identified attacks as shown in Table 2. 2.2 CPS and Blockchain Blockchain is defined as an immutable database of hash-chain of blocks containing time stamped transaction, where multiple copies of these blocks are constructed and maintained in a distributed fashion, and a network of participants, called miners, can establish a consensus on the state of the network, this architecture ensures that at any given time, only a small number of miners can turn malicious or faulty, and by consensus, the network can still be intact [5]. Blockchain is the technology behind Bitcoin but now is no longer limited to only cryptocurrency, it’s used for a diverse array of other applications, as it can empower applications with very different requirements, IoT and CPS also benefit from this technology, Table 2 lists a few implementations of the blockchain for each type of attacks.

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Table 2. Blockchain based counter measures against common security attacks Category

Common attacks

Blockchain based counter measures (reference)

Purpose

Availability

DoS, DDoS

Rodrigues et al. [7] Javaid et al. [8]

DDoS attacks mitigation

Integrity

Viruses, false data injection and man-in-the-middle

Machado and Fröhlich [9] Chen et al. [10]

Data integrity verification

Confidentiality

Eavesdropping

Wang et al. [11] Zhao et al. [12]

Privacy/confidentiality enhancement

3 Related Works In the realm of IoT and CPS security, several solutions have been proposed, as depicted in Table 3, leveraging the benefits of the blockchain integrity and robustness, as well as the autonomous capabilities of machine and deep learning models. Vargas et al. [13] proposed a blockchain and ML based solution to classify common security attacks from authorized traffic, by centralizing the communication between all IoT nodes in a single blockchain powered node, securing the communication via SHA256 and AES, then training a KNN classifier on the UNSWNB15 dataset [1] in aim to introduce real time attack detection and mitigation. Kumar et al. [14] introduced a Privacy-Preserving and Secure Framework (PPSF) using Blockchain-based ML. A similar in design approach is proposed by Kumar et al. [15] a blockchain-orchestrated deep learning approach for secure data transmission (BDSDT), where blockchain is used for data storage, and a deep sparse AutoEncoder for feature selection and encoding, then BiLSTM is used for anomaly detection. Shahbazi and Byun [16] proposed a new architecture integrating blockchain and ML to ensure quality control and to enhance security. While Outchakoucht et al. [17] proposed a blockchain based decentralized architecture to replace centralized access control policies, and ML to improve security.

4 Conclusion and Future Work As CPS and IoT gain more popularity and adoption the increase of unwanted and malicious attention is inevitable, in this work we studied the current state of CPS and IoT security and challenges, we presented common vulnerabilities and attacks, then we provided the benefits of blockchain integration in CPS and IoT and several blockchain based solutions and counter measures addressing each type of attacks. This paper also surveys previous works focusing on the implementation of blockchain and ML. Following this paper, our future work will focus on blockchain based solutions to making CPS more reliable and attack resilient, more specifically we want to explore the possibilities of ML based IDS, we’re also exploring the advantages of transformers in

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Reference

Year Field

Methods

Datasets

Blockchain Accuracy (%)

Vargas et al. [13]

2021 Industrial IoT

Blockchain, KNN

UNSWNB15

Yes

–

Kumar et al. [14]

2021 Smart cities

Blockchain, PCA, Gradient boosting

ToN-IoT, BoT-IoT

Yes

98

Kumar et al. [15]

2023 Healthcare

Blockchain, BiLSTM

ToN-IoT, Yes CICIDS-2017

99

Shahbazi and Byun [16]

2021 Smart Blockchain, manufacturing XGBoost

–

Yes

95

Blockchain, – reinforcement learning

Yes

–

Outchakoucht 2017 – et al. [17]

IDS since they have already shown promising results, we also intend to also implement a blockchain architecture where a cryptographic layer is placed between devices and the cloud, we believe that this approach will enhance data integrity and confidentiality.

References 1. Sanislav, T.: Cyber-physical systems—concept, challenges and research areas. Control Eng. Appl. Inform. 14, 28–33 (2012) 2. Gunes, V., Peter, S., Givargis, T., Vahid, F.: A survey on concepts, applications, and challenges in cyber-physical systems. KSII Trans. Internet Inf. Syst. 8(12), 4242–4268 (2014) 3. Han, S., Xie, M., Chen, H.-H., Ling, Y.: Intrusion detection in cyber-physical systems: techniques and challenges. IEEE Syst. J. 8(4), 1052–1062 (2014). https://doi.org/10.1109/JSYST. 2013.2257594 4. Yaacoub, J.A., Salman, O., Noura, H.N., Kaaniche, N., Chehab, A., Malli, M.: Cyberphysical systems security: limitations, issues and future trends. Microprocess. Microsyst. 77, 103201 (2020).https://doi.org/10.1016/j.micpro.2020.103201. PMID: 32834204; PMCID: PMC7340599 5. Shafi, Q.: Cyber physical systems security: a brief survey. In: 2012 12th International Conference on Computational Science and Its Applications, Salvador, Brazil, pp. 146–150 (2012). https://doi.org/10.1109/ICCSA.2012.36 6. Duo, W., Zhou, M., Abusorrah, A.: A survey of cyber attacks on cyber physical systems: recent advances and challenges. IEEE/CAA Journal of AutomaticaSinica 9(5), 784–800 (2022). https://doi.org/10.1109/JAS.2022.105548 7. Rodrigues, B., Bocek, T., Lareida, A., Hausheer, D., Rafati, S., et al.: A blockchain-based architecture for collaborative ddos mitigation with smart contracts. In: 11th IFIP International Conference on Autonomous Infrastructure, Management and Security (AIMS), Zurich, Switzerland, pp. 16–29 (2017). https://doi.org/10.1007/978-3-319-60774-0_2

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8. Javaid, U., Siang, A.K., Aman, M.V., Sikdar, B.: Mitigating loT device based DDoS attacks using blockchain. In: Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock’18), pp. 71–76 (2018). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3211933.3211946 9. Machado, C., Medeiros Fröhlich, A.A.: IoT data integrity verification for cyber-physical systems using blockchain. In: 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), Singapore, pp. 83–90 (2018). https://doi.org/10.1109/ISORC. 2018.00019 10. Chen, L., Fu, Q., Mu, Y., Zeng, L., Rezaeibagha, F., Hwang, M.-S.: Blockchain-based random auditor committee for integrity verification. Fut. Gener. Comput. Syst. 131,183–193 (2022). ISSN 0167-739X. https://doi.org/10.1016/j.future.2022.01.019 11. Lv, P., Wang, L., Zhu, H., Deng, W., Gu, L.: An IOT-oriented privacy-preserving publish/subscribe model over blockchains. IEEE Access 7, 41309–41314 (2019). https://doi. org/10.1109/ACCESS.2019.2907599 12. Zhao, Y., Li, Y., Mu, Q., Yang, B., Yu, Y.: Secure pub-sub: blockchain-based fair payment with reputation for reliable cyber physical systems. IEEE Access 6, 12295–12303 (2018). https://doi.org/10.1109/ACCESS.2018.2799205 13. Vargas, H., Lozano-Garzon, C., Montoya, G.A., Donoso, Y.: Detection of security attacks in industrial IoT networks: a blockchain and machine learning approach. Electronics 10(21), 2662 (2021). https://doi.org/10.3390/electronics10212662 14. Kumar, P., et al.: PPSF: a privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. IEEE Trans. Network Sci. Eng. 8(3), 2326–2341 (2021). https://doi.org/10.1109/TNSE.2021.3089435 15. Kumar, P., Kumar, R., Gupta, S.P., Tripathi, R., Jolfaei, A., Najmul Islam, A.K.M.: A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system, J. Parallel Distrib. Comput. 172, 69–83 (2023). ISSN 0743-7315, https:// doi.org/10.1016/j.jpdc.2022.10.002 16. Shahbazi, Z., Byun, Y.-C.: Integration of blockchain, IoT and Machine Learning for Multistage quality control and enhancing security in smart manufacturing. Sensors 21(4), 1467 (2021). https://doi.org/10.3390/s21041467 17. Outchakoucht, A., Es-Samaali, H., Leroy, J.P.: Dynamic access control policy based on blockchain and machine learning for the internet of things. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(7) (2017). https://doi.org/10.14569/IJACSA.2017.080757

Understanding the Factors Contributing to Traffic Accidents: Survey and Taxonomy Soukaina El Ferouali1(B) , Zouhair Elamrani Abou Elassad2 , and Abdelmounaîm Abdali1 1 CISIEV Team, IT Department, Faculty of Science and Technology, Cadi Ayyad University,

Marrakech, Morocco [email protected], [email protected] 2 SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Safi, Morocco [email protected]

Abstract. This article examines the causes of road accidents around the world as well as the solutions that have been developed by many researchers to lessen the severity of these catastrophes. In this work, a taxonomy of traffic accident analysis was constructed. Results indicate that with a 90% importance level, most studies have concentrated on human variables that cause traffic accidents. The major contributing factors have been divided into five categories: the driving environment, routine activities, habits, demographics, and technique issues. An examination of each classification is presented, with an emphasis on the primary mechanisms believed to be at play. This suggested summary offers a glimpse into the extent of the issue and sets the stage for understanding the principal advancements and limitations in current cutting-edge research. Keyword: Road safety · Accident severity · Critical factors · Taxonomy · Machine learning

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 214–221, 2024. https://doi.org/10.1007/978-3-031-48573-2_31

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1 Introduction Road accidents represent a major public health problem, it is therefore of significant importance to understand the factors responsible for these accidents, and the resulting consequences, in order to make decisions in the management of road safety to improve it and reduce the severity of future accidents. Road crashes account for 1.35 million deaths worldwide each year, an average of 3287 deaths a day with an additional 20–50 million injured or disabled [1, 2] making them the eighth leading cause of death for people of all ages [3] and the first cause of death for children and young adults aged between 5–29 years old [3–5], which explains the rate of progression of social and economic problems. The human factor is dominant and concerns more than 9 out of 10 accidents. Road crash Prediction models are very powerful tools that are being used broadly for determining the factors associated with road crashes, and their reasons over a period of time [6, 7]. This article represents some of the causes and consequences of road accidents globally and the techniques that have been proposed and applied to reduce their severity. The paper is structured as follows. The related works and taxonomy of critical factors are presented in Sect. 2, and the results are presented in Sect. 3. Section 4 provides conclusion.

2 Related Works It is noted that most previous studies [8] have focused more on human factors and vehicle characteristics (driver age, gender, alcohol consumption, airbag use...). With respect to infrastructure, although there is some knowledge linking traffic crash severity to road characteristics (such as road function class, road alignment, speed limits, etc.) and environmental factors (such as weather conditions and road lighting), this knowledge is largely qualitative in nature. The Tri-Level Study of the Causes of Traffic Accidents was carried out by the National Highway Traffic Safety Administration (NHTSA) in the United States in 1979 with the aim of analyzing the causes of traffic accidents and identifying the contributing factors at three levels: the driver, the vehicle, and the environment. The findings showed that human factors are the most likely causes of 93% of the crashes that were reviewed, followed by environment factors (34%) and vehicle factors (13%) [9]. According to a different study [10], a few factors, including segment features, crash-level characteristics, occupant level characteristics, environment level characteristics, and vehicle level characteristics, substantially impact the seriousness of injuries in traffic accidents. Numerous studies have examined the effect of these factors on collision injuries. 2.1 Taxonomy of Critical Factors This study aims to propose a taxonomy of critical factors in road accidents. Below is a figure that shows the different categories of factors that can influence drivers (see Fig. 1).

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• Driving environment factor The driving environment factor is associated with different aspects that can influence the driver’s behavior, which causes fatal accidents. Meteorological causes can increase the risk of road accidents [11–13], and the patterns are defined as follows: the presence of fog, the lack of visibility caused by a climatic phenomenon, snow, rain, etc (. . . ). Climate change, with increasing variations in air temperature, as well as extreme weather events including heat waves, and heavy rainfall occurring more frequently. The impact of weather conditions on traffic movements and safety results has grown significantly, and the decreased visibility caused by rain is a significant worry [4, 14]. Air pollution has also been analyzed as a phenomenon destroying the environment, namely, carbon dioxide (CO2 ) or tropospheric ozone (O3 ) [15] which influences driver behavior, and can cause fatal accidents. Road type was specifically addressed as an important topic in road conditions relating to an increased crash likelihood [16]. The variances of speed according to different types of vehicles on various types of roads were studied, which revealed that vehicles on the edge lanes had lower speeds than those on the inside lanes. Time/Lightning, especially day or night, is considered to identify its relationship with the drivers’ behavior [17]. Relevant researchers have established that more crashes happen at night than during the day [12, 18]; although fewer than 20% of all vehicle kilometers are driven at night, 40–50% of traffic deaths do. The severity of these crashes is also at least twice as great at night as it is during the day [19]. • Technique factor There is a category of technical factors that increases the risk of road accidents which are composed of two kinds: those relating to the condition of the vehicle and those relating to the vehicle type. Vehicle condition includes its design, age, engine temperature, oil condition, fuel consumption, etc (...) [20]. These factors can contribute to a large number of accidents since the quantity and type of fuel consumption may differ from one type of vehicle to another one. Vehicle type such as a car, bus or truck, etc (...) can potentially influence the behavior of the driver which can cause unexpected accidents [21]. One study [15] indicated that taxis, buses, and large trucks have a very high probability of being involved in road accidents. The length of the vehicle may affect the number of blind spots associated with driver behavior. • Habit factor The habit factor was also analyzed and processed. A helmet and seat belt are mandatory. Head injuries are the leading cause of death for users of two- and three-wheelers such as (motorcycles), but enforcement of helmet laws is generally poor. Only 61 countries rate helmet use as good [22]. Seat belt use reduces the risk of death for drivers and front seat occupants by 45–50% and the risk of death and serious injury for rear seat occupants by 25% [22]. Addictions such as alcohol are one of the major risks and a frequent human cause that should never be consumed while driving. According to the Dutch Youth Monitor 2019 [23, 24], nearly a quarter of 15-year-olds drink alcohol, and 17% of 12- to 17-year-olds have binge-drunk. Smoking, on the other hand, is much less

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Fig. 1. Critical factors

common (8%) [9]. A study [20] has shown that music influences the state of the driver, especially among young people who interact with music applications. Distractions such as cell phone use while driving have been identified as one of the main problems of driver distraction according to the World Health Organization [3]. Klauer et al. [25] found that distracting activities such as eating, calling, or texting on a cell phone contribute to an increased risk of a traffic accident. • Demographic factor Health plays a very important role in road safety since it is the factor that can positively influence as it can negatively infect the driver’s driving behavior. The percentage of the world’s population living in urban areas is expected to increase from 56% in 2020 to 68% by 2050, and more than 90% of this growth will be in low- and middle-income countries (LMICs) [26, 27]. Age: The leading cause of death among young adolescents aged 12 to 15 years is unintentional injuries, especially injuries in traffic accidents [28]. In the Netherlands, they lose their lives as pedestrians, cyclists, or passengers [23]. Teenagers and adults are more prone to being distracted while driving than older adults. Gender: Road safety research has also addressed associations between driver gender and increased crash risk [29]. In general, female drivers are protected and secured more than male drivers. Education: According to a study on driver behavior in Lisbon, Portugal, the effectiveness of training and education plays a very important role in reducing adverse events by drivers, such as acceleration and braking abrupt [26]. • Routine activities factor Speed is one of the elements that most participate to road mortality, and this is the reason why it is often treated in the field of road safety. Speed is acknowledged to play a substantial role in adverse consequences [30]. One-third of fatal collisions in Europe [31] are caused by excessive or unsuitable speed, which is also a contributing factor in the majority of fatalities. Driver condition; Fatigue affects the driver’s driving abilities, which would have an impact on the risk of an accident. Fatigue’s factor is involved in

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nearly 10 % of fatal accidents [8]. It can also occur if drivers do not balance driving and working hours, the need for rest and sleep especially if the distance is too far [32]. In addition to other factors related to emotion, position, energy, and eye movements that can affect the state of drivers in general [33]. 2.2 Predictive and Descriptive Analysis Models Machine learning is a field of study of artificial intelligence that uses mathematical and statistical approaches to give computers the ability to learn from data, i.e. t, improve their performance, and solve tasks without being explicitly programmed. More broadly, it concerns the analysis, optimization, development, and implementation of such methods. There is a wide variety of machine learning algorithms. Some of them are used in this article. Table 1 provides a summary of the major studies mentioned above, organized by the categories of important elements causing fatal crashes as well as the machine learning methods suggested in various papers such as Support vector machine (SVM), regression analysis, decision tree (DT), k-nearest neighbors(KNN), Random Forest (RF), XGBoost (eXtreme Gradient Boosting) and Artificial neural networks (ANN). There are other techniques besides Machine Learning and Deep Learning. Most of them are statistical methods based on data collection, processing, analysis, interpretation of results, and presentation to make these data understandable. However, machine learning and deep learning techniques will be our next source of concern. Table 1 presents an overview of the taxonomy used in our study which presents the various categories of variables that can affect drivers.

3 Results The findings of the taxonomy analysis offer insightful knowledge into the categorization and arrangement of a wide range of items. The taxonomy has also demonstrated that the most popular machine learning and deep learning algorithms used are shown in the table.

4 Conclusion Each year, road crashes kill about 1.3 million people and injure about 50 million more [34]. According to the World Health Organization (WHO), road traffic crashes are ranked as the ninth leading cause of death worldwide. Based on current trends, it can be expected that approximately two million people will be killed in road crashes each year by 2030. In order to summarize, the taxonomy under review demonstrates a thorough understanding of the factors that contribute primarily to traffic crashes for traffic safety improvement, which requires the consideration of the relationship between the three elements included; the human factor, the environment factor, and the vehicle factor. Future work is looking into the data corpus characteristics and a thorough application of machine learning techniques for road crash analysis.

Habit factor

[6]

[23]

[20]

Addictions

Distractions

[36]

Vehicle type

Helmet/seat belt

[35]

Vehicle condition

✓

✓

✓

✓

XG-boost

(RF)

✓

✓

✓

[19]

Technique factor

✓

✓

(SVM)

✓

✓

✓

[1]

[34]

Time/lightning

✓

(DT)

✓

Pollution

(ANN)

[4]

(KNN)

Others techniques

Meteorological

Regression

Machine learning and deep learning algorithms

Techniques

Driving environment

References

Critical factors

Categories

Table 1. Taxonomy of critical factors on road crashes Understanding the Factors Contributing to Traffic Accidents 219

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The Smart Tourist Destination as a Smart City Project Kacem Salmi(B) and Aziz Hmioui National School of Business and Management, Sidi Mohamed Ben Abdellah University Fez, Fez, Morocco {kacem.salmi,Aziz.hmioui}@usmba.ac.ma

Abstract. A number of cities around the world have begun to implement smart destination projects with the aim of improving the standard of living of their citizens, as well as the sustainability of their urban and tourist areas, which has led to the emergence of the smart city and smart tourist destination concept. The effects of technology on cities and tourist destinations has led to the emergence of new management approaches that aim to adapt planning processes to the challenges and opportunities of the changing intelligent environment. Smart city and smart tourism destination approaches aim to improve management efficiency, residents’ quality of life and tourism experiences. They also make it easier to understand the interaction between the smart city and the smart destination in terms of the type of planning instruments implemented in urban and tourist areas. The aim of this research is to seek a consensus on the impact of integrating a smart tourism strategy into a smart city. Keywords: Smart cities · Smart tourism destination · Smart strategy

1 Introduction Tourism is constantly evolving; it is becoming a key factor in overall urban development and planning, creating new economic opportunities and new jobs. The emergence of new paradigms resulting from the development of technologies and the associated new demand profiles have generated structural transformations and challenges for the development of urban tourist areas [1]. The intensive use of new technologies has transformed the tourism experience, giving rise to a digital tourist who makes extensive use of intelligent technologies [2]. Tourism is dominated by technologies that have enabled a connection between physical and digital environments. In this context, more and more tourist destinations are claiming the title of smart city who advocate making a whole range of innovative services available to visitors and residents, and invest in promoting and improving the quality of life for all. It refers to a complex, variable-geometry phenomenon involving the transformation of urban service systems. Smart city projects are becoming increasingly common in the tourism sector, as various tourist destinations have begun to implement their own specific smart destination projects, leading to the recent emergence of the concept of smart tourist destination. The concept of the smart © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 222–228, 2024. https://doi.org/10.1007/978-3-031-48573-2_32

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tourist destination is based on smart cities that have taken advantage of their technological infrastructure to promote efficient and sustainable tourism development [3]. So the question is, how are public and private stakeholders planning to integrate tourism into the smart city? To answer this question, we highlight the key concepts through a literature review, and then try to measure the impact of integrating a smart destination strategy on smart city indicators.

2 Theoretical Framework 2.1 Smart Cities The concept of the smart city does not have a consensual definition. The operationalization and application of the concept vary according to country, region, origin and territorial issues. Several expressions are used to describe the city of the future: “future cities”, “eco city”, “intelligent cities”, “compact cities”, “innovative cities”, “greencities”, but the term “smart cities” is becoming increasingly popular [4]. The smart city has been understood by some researchers and large technological companies as an interconnected city, mediated by technology and based on data management aimed at achieving greater efficiency in its functioning [5]. Other researchers have considered the smart city more holistically as an urban space that also addresses accessibility, governance, sustainability and human and social capital [6, 7]. The smart city is simply one that meets the expectations of citizens and governments. A competitive city is ecologically virtuous, democratically participatory, energy-efficient and concerned with the quality of life of its inhabitants [8]. The theoretical aspects and practical manifestations of the smart city vary greatly from one country to another, but the common denominator in all these aspects remains the integration of the latest information and communication technologies [9]. Therefore, a city can be intelligent in many areas of its management: economy, transport, environment, residents, lifestyle and administrative management. These are aspects of city smartness that can be improved by using the latest advanced technologies and innovative thinking [10]. 2.2 Smart Tourism Destinations According Buhalis Tourism destinations are known to be amalgams of touristic products and services, and they are perceived as complex systems which are difficult to manage [8]. Smart tourism phenomena have emerged as a novel approach to tackle new realities in tourism caused by the impact of the innovative information and communication technologies (ICTs) over the destinations, travellers, and businesses [9]. In the field of smart tourism research, smart tourism destinations have received the most attention. Previous research has conceptualised the smart tourism destination framework based on the development of smart cities [10], explored the potential of intelligent tourist destinations to enhance the tourist experience through personalised services, developed a conceptual model for the competitiveness of smart tourist destinations, and examined the impact of the smart destination strategy and solutions on destination management processes and the tourism experience [11–13]. The concept of smart tourist destination can be understood

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as a relevant contribution to the concept of tourist destination [14], as well as a potential framework for managing destinations. Moreover, the concept is particularly crucial for destinations with a dominant attraction or with many attractions, where it is difficult to establish marketing connections [12]. The main aim of Smart Tourism Destinations is enhancing tourism experience and improving the effectiveness of resource management towards both destination competitiveness and tourists’ satisfaction, as well as ensuring sustainability over an extended period of time [10]. There is no single definition of an intelligent tourist destination. Researchers define and conceptualise the term in different ways, but most focus primarily on the role of information and communication technologies (ICTs). Some authors argue that intelligent tourist destinations are destinations that apply different ICTs to the development and production of tourism processes [15]. Other authors have emphasised the interconnection of the various stakeholders in a destination thanks to ICTs [10]. Boes et al. define smart tourism destinations as places that use available technologies to co-create value, enjoyment and experiences for tourists, as well as providing benefits and advantages to tourism organisations and the destination [16]. Thus, the true meaning of intelligent tourist destinations is to focus on and respond to the needs of tourists using ICTs, with the aim of promoting the quality of tourist services and improving tourism management. The development of smart tourist destinations would benefit the tourism industry by enabling open access to data for tourism organisations and tourists via a common platform. Thus, smart tourist destinations are destinations with inter-organisational information systems that collect and use data to understand the needs and behaviour of tourists, in order to provide better services and experiences in real time [17]. Smart tourism destinations differ from traditional tourism destinations by adopting a smart strategy that embraces cutting-edge technologies and uses vast amounts of information to develop interconnections between stakeholders, intelligent decisionmaking and, ultimately, offer enhanced tourism experiences and improved destination competitiveness [11–18]. 2.3 Integrating Tourism into the Smart City Tourism must reflect the desire to build a tourist destination, adapted to the scale of a city or a territory. It must be able to contribute, through the use of technology, to better use of resources (infrastructure, transport, consumption, energy, space, etc.) and to more efficient and sustainable management of the destination [11]. Transforming a traditional destination into a smart destination requires the adoption of a holistic smart strategy in agreement with all stakeholders. Six components with different aspects of urban life [19], smart economy, smart people, smart governance, smart mobility, smart environment and smart living. Other research lists three key elements of an intelligent strategy: technology, citizens and institutions [20]. A strategy is truly intelligent when it supports investment in human and social capital, combined with ICT infrastructure to fuel sustainable growth and improve quality of life. However, smart strategies need to integrate technologies, systems, services and capabilities into a network that is multisectorail, flexible for future developments and open to access. This means that ICTs must facilitate the creation of a new type of communication environment, which requires the comprehensive and balanced development of creative skills, innovative institutions, broadband networks and virtual collaboration spaces. The tourism dimension at the heart of the smart city

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is therefore a major economic challenge, due to its strong power of attraction. Tourism must reflect the desire to build and develop a tourist destination, adapted to the scale of a city or region [12]. The dynamic nature of the tourism business means that we need to embrace two developments: the integration of ICTs and the co-creation of experiences.

3 Methodology Our research approach is qualitative in an exploratory logic, and the method is that of Delphi. A survey was used as a research instrument to collect data. The survey concepts and items were developed from variables or their indicators as derived from the literature and modified to fit the purpose of the study. The seven level Likert scale was used to rate the proposals. In this study, the decision rule presented by Ekionea et al. consists of retaining the two levels (strongly agree and agree) of the Likert scale totaling 80% at least for each proposal [21]. The aim of this research is to seek a consensus on the impact of integrating an smart tourism strategy into a smart city. The choice of experts is essential; it directly affects the performance of the study and its validity. For Boulkedid the number of experts is between 3 and 418 [22]. We chose the city of Fez as our research area, and the people we interviewed were local players, particularly those with decision-making and management profiles.

4 Results and Discussion

Table 1. The impact of integrating an intelligent tourism strategy on the various dimensions of the smart city Proposition

Smart economy

Experts (local players) A 1

A 2

A 3

A 4

A 5

A 6

A 7

A 8

A 9

A 10

6

6

5

6

6

7

7

6

7

6

% Strongly agree and agree

Mean

90%

83%

Smart people

5

6

6

6

5

6

7

6

6

5

70%

Smart governance

6

6

7

5

6

7

7

7

7

6

90%

Smart environment

6

7

7

6

6

6

6

6

6

6

100%

Smart mobility

6

6

6

6

7

6

5

5

7

6

80%

Smart living

6

5

6

7

6

6

7

6

5

5

70%

According to the results of this study, city stakeholders agreed on the positive effect of integrating smart tourism into the smart city, with an average of over 80% (83%). The smart tourism approach strengthens the competitiveness of the destination and enhances the intelligence of the area, making it more attractive, more sustainable and offering a better user experience (Table 1).

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Smart tourism can have a positive impact on the various dimensions of the intelligent city. At the level of the intelligent economy (90% consensus), the smart tourism strategy can improve economic indicators such as public spending on research and development, public spending on education, GDP per capita and the unemployment rate. At the level of smart people and smart living, this smart strategy can improve the level of education of individuals so that they can adapt to social, economic and technological developments throughout the world. In our research, (70% consensus) it seems that the evolution of indicators linked to smart people and quality of life depends on national education and training programs which must integrate the smart dimension in order to train a smart citizen. The governance of cities and tourist destinations is bound to be impacted by the new smart strategy, and consequently by the availability of e-government and the improvement of transparency and democracy. In a context of sustainable development, the ambition of each smart strategy is to protect the environment by reducing greenhouse gas emissions, making efficient and optimal use of natural and energy resources, policies to contain urban sprawl and waste recycling. In this study, local stakeholders agreed on the beneficial effect of this smart strategy on the environment and consequently on quality of life (100% consensus). Integrating smart tourism into the smart city can transform the logistics system and transport infrastructure, facilitating local and international accessibility through sustainable and safe transport systems.

5 Conclusion The smart strategy, initially applied to urban areas, has quickly permeated tourist destinations and has redesigned the layout of towns and tourist areas. At a managerial level, an intelligent city can inform the decision-making of various players, such as destination management organisations and tourism companies, by using data collected via technological infrastructures. A city can be transformed into a smart city by adopting a smart destination project. Managing cities and tourist destinations using an intelligent approach makes it possible to enhance their attractiveness and improve the standard of living of their citizens and visitors, as well as the sustainability of their urban and tourist areas.

References 1. Wang, D., Li, X.R., Li, Y.: China’s smart tourism destination initiative: a taste of the servicedominant logic. J. Destin. Mark. Manag.Manag. 2, 59–61 (2013) 2. Silva-Morales, M.J.: Comprendre la transformation institutionnelle et structurelle d’un système de service public urbain qui devient smart: une approche néo-schumpétérienne pour comprendre l’innovation technologique et institutionnelle dans les systèmes de service. Université Grenoble Alpes (2017) 3. Boes, K., Buhalis, D., Inversini, A.: Conceptualising smart tourism destination dimensions. In: Information and Communication Technologies in Tourism 2015: Proceedings of the International Conference in Lugano, Switzerland, February 3–6, pp. 391–403. Springer (2015)

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4. Koo, C., Shin, S., Gretzel, U., Hunter, W.C., Chung, N.: Conceptualization of smart tourism destination competitiveness. Asia Pacific J. Inf. Syst. 26, 561–576 (2016) 5. Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp. 282–291 (2011) 6. Buhalis, D., Spada, A.: Destination management systems: criteria for success–an exploratory research. Inf. Technol. Tour. 3, 41–58 (2000) 7. Femenia-Serra, F., Ivars-Baidal, J.A.: Do smart tourism destinations really work? The case of Benidorm. Asia Pacific J. Tour. Res. 26, 365–384 (2021) 8. Jovicic, D.Z.: From the traditional understanding of tourism destination to the smart tourism destination. Curr. Issue Tour.. Issue Tour. 22, 276–282 (2019) 9. Wang, X., Li, X.R., Zhen, F., Zhang, J.: How smart is your tourist attraction?: measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tour. Manage. 54, 309–320 (2016) 10. Salmi, K., Hmioui, A.: Inter-organizational information system and efficiency of the tourism supply chain. In: International Conference on Digital Technologies and Applications, pp. 893– 902. Springer (2023) 11. Ekionea, J.P.B., Fillion, G.: Knowledge management capabilities consensus: evidence from a Delphi study. Acad. Inf. Manage. Sci. J. 14 (2011) 12. Lombardi, P., Giordano, S., Farouh, H., Yousef, W.: Modelling the smart city performance. Innov. Euro. J. Soc. Sci. Res. 25, 137–149 (2012) 13. Dredge, D., Jamal, T.: Progress in tourism planning and policy: a post-structural perspective on knowledge production. Tour. Manage. 51, 285–297 (2015) 14. Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18, 65–82 (2011) 15. Albino, V., Berardi, U., Dangelico, R.M.: Smart cities: definitions, dimensions, performance and initiatives. J. Urban Technol. 22, 3–21 (2015) 16. Giffinger, R., et al.: Smart cities. Ranking of European medium-sized cities. Final Report (2007) 17. Fernandez-Anez, V., Fernández-Güell, J.M., Giffinger, R.: Smart City implementation and discourses: an integrated conceptual model. Case Vienna. Cities 78, 4–16 (2018) 18. Femenia-Serra, F., Perles-Ribes, J.F., Ivars-Baidal, J.A.: Smart destinations and tech-savvy millennial tourists: hype versus reality. Tourism Rev. 74, 63–81 (2019) 19. Buhalis, D., Amaranggana A.: Smart tourism destinations. In Information and Communication Technologies in Tourism 2014: Proceedings of the International Conference in Dublin, Ireland, January 21–24, pp. 553–564. Springer (2013) 20. Buhalis D., Amaranggana A.: Smart tourism destinations enhancing tourism experience through personalisation of services. In: Information and Communication Technologies in Tourism 2015: Proceedings of the International Conference in Lugano, Switzerland, February 3–6, pp. 377–389. Springer, (2015) 21. Koo, C., Park, J., Lee, J.N.: Smart tourism: traveler, business, and organizational perspectives. Inf. Manage. 54, 683–686 (2017) 22. Fernandez-Anez, V.: Stakeholders approach to smart cities: A survey on smart city definitions. In: Smart Cities: First International Conference, Smart-CT 2016, Málaga, Spain, June 15–17, 2016, Proceedings, vol. 1, pp. 157–167. Springer (2016) 23. Mora, L., Bolici, R., Deakin, M.: The first two decades of smart-city research: a bibliometric analysis. J. Urban Technol. 24, 3–27 (2017)

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24. Attour, A., Rallet, A.: The role of territories in the development of trans-sectoral systems of local innovation: the case of smart cities. Innovations 43, 253–279 (2014) 25. Boulkedid, R., Abdoul, H., Loustau, M., Sibony, O., Alberti, C.: Using and reporting the Delphi method for selecting healthcare quality indicators: a systematic review. PLoS ONE 6, e20476 (2011)

From BIM Toward Digital Twin: Step by Step Construction Industry Is Becoming Smart Zayneb Miraoui(B) , Nasser Abdelkader, and Mohssine Kodad Mohammed First University, Oujda, Morocco [email protected]

Abstract. From (BIM) Building Information Modeling toward (DT) Digital Twin technology. The construction industry climes on its digital age by using the 3D modelling and integrating new technologies as (IoT) Internet of Things, (AI) Artificial Intelligence and cloud-computing to create a connected living virtual world, with updated real time data, where projects can be designed, controlled and lifecycle managed, the purpose of this state of art is to highlight updates works and researches on digital twin definition in different industries, then its properties are described, so that the relation between DT and BIM is given by presenting the uses of each technology and lastly concluded by some reflections towards feature researches. Keywords: Smart cities · nD modelling · New technologies

1 Introduction Nowadays technology covers all sectors, from industry, health care to construction. The interest of a virtual world began in the early of the twenties 2003. From smart devices to smart systems and cities, the idea of a (DT) Digital Twin is to create a virtual world where the scenarios to simulate, and predict to decide in the real life, by Modeling, data transmitting, and real time managing, Projects can be delivered and live with some desired perfection. However, the term DT still in his infancy stage, with so many visions and different perspectives of each industry by its researches. In this article, the first section will cite the historical development of the definition to a DT from different literature views sectors, followed by a discussion about differences and the problems the construction industry faces, the second section is a presentation of the properties and the characteristics of a DT, also the subcategories and the level of integrating Data DT went through. The next paragraph will present the general farmwork of the DT in the construction industry using the (BIM) Building Information Modelling technology and the abilities offered by a DT, by showing the difference between the two technologies and there uses so far. The conclusion is a suggestion of some reflections and open leads for the future research.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 229–234, 2024. https://doi.org/10.1007/978-3-031-48573-2_33

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2 Definitions and Historical Development 2.1 Literature Reviews Digital twin is a concept still struggles in finding a common definition between authors and industries. Here we start by representing the deferent stages DT went through in the literature reviews: The term of DT was introduced by Michael Grieves in 2003 as “a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level. At its optimum, any information that could be obtained from inspecting a physical manufactured product can be obtained from its Digital Twin” [1]. In 2012, the DT knew its first definition by Glassgen and Stragel [2], both in aerospace industry when NASA published an article about a new “digital twin paradigm” to accurately predict and control air- and spacecraft, the definition used was “An integrated Multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin.” In 2018, the construction industry start using the term DT as a new technology to build and maintain the lifecycle management of a project, the definition was given by Bolton et al. [3] as “A realistic digital representation of assets, processes or systems in the built or natural environment”. Later in 2019, Q. Lu, Xie, et al. used the phrase “A digital model, which is a dynamic representation of an asset and mimics its real-world behaviors.” in a conference proceeding [4], and Lim et al. in the manufacturing industry define DT as “a high-fidelity virtual replica of the physical asset with real-time two-way communication for simulation purposes and decision aiding features for product service enhancement” [5]. In 2020, In Architecture industry Farsi et al. in his book (Digital twine technologies and smart cities) a DT is “A concept that creates a model of a physical asset for predictive maintenance. This model will continually adapt to changes in the environment or operation using real-time sensory data and can forecast the future of the corresponding physical assets” [6]. 2.2 Discussion After presenting some of the leading definitions of a DT in the different sectors, it shows that authors share the same components of a DT and have common objectives. Starting with the components that define the DT: a physical asset, a virtual asset, and the flow Data between, which will be more explained in the next section. The need of a DT twin technology resume in so many reasons that authors showed in their research such as simulation for predicting reactions, real-time updating data, project life-cycle management, the use of new-tech like (IoT) internet of things, (AI) artificial intelligence, cloud-computing, sensory devices etc.

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Yet, the difficulty remains in the project itself, Construction industry face multiple problems because of the multidisciplinary projects, buildings, environments, and infrastructure system. The construction industry lake in information technologies and digitalization, thought BIM seems to be the door for it, it is not just a 3D modeling tool, but also contains detailed information in a component level (Boje et al., 2021) [7].

3 Properties and Characteristics The word DT can be departed in two sections to better understanding: A physical asset: it may be an object, a system, a product or a process…etc. that require a monitoring and control through its life cycle management. A virtual asset: a replica of the physical asset how lives in a virtual world through Data servers, Mathematical algorithms and programing machines. Data is the connection between the two assets, the way to provide information from the physical asset to the virtual one, and the way back, also the type of connection and information. 3.1 Data Flows According to Kritzinger et al. (2018) [8], there is tree subcategories of DT, after reviewing 43 publications, 35% are classified as Digital shadows, 28% uses DT as a digital model and only 18% goes with the real definition of DT as a bidirectional data transfer. Digital model (DM): where the data is imported to the virtual asset and exported manually, the replica remain a simple representation describing the physical asset. Digital shadow (DS): the data flows from the physical asset to the virtual one is done automatically but the way back still manually. Digital Twin (DT): The Data transfer is automatically done in the both directions, an automatic transfer of information bidirectionally (Fig. 1).

Fig. 1. Digital twin subcategories with flow data association, adopted form from Kritzinger (2018).

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3.2 Information Type The word Data gather all kind of information: words, images, figures, shapes, equations, numbers…etc. Halmetoja et al. 2019 [9] categories theme in two categories: passive and active data, as presented in the table below (Table 1). Table 1. Data type and data flow association. Passive data

Static, does not change trough time or rarely change due to renovation or repairing

Manually collected data

Active data

Dynamic, continually changing to remain accurate

Automatic flow of data

4 DT and Construction Industry The use of BIM technology and DT might be conceptually confusing and hard to distinguish, Authors do often consider BIM as the first step to create a DT and the first point of the framework, due to the lack of the autonomous data flow, it is considered as a digital model for now, [10]. Uses the terms “enriched BIM”, “IoT enabled BIM” or “Smart BIM” to describe digital twin technology. 4.1 DT Uses in Construction Projects The uses of DT technology in the construction industry are so various and differs as the phases of the project differs too, Boje et al. 2021 [7] reviewed and analyzed 21 articles for what he called the abilities of DT and their roles within the virtual data physical paradigm, the table below identifies and resume them (Table 2). 4.2 BIM Technology Uses Weixi Wang used the process of DT in all stages of construction from a constructed field construction based on BIM technology and physical construction site entity modeling (PCSE) using Radio Frequency Identification (RFI) tags to get intelligent component modelling [11]. For safety management, Yu-Cheng Lin develops an advanced monitoring and control system for underground parking garage using a (WSN) wireless sensor network and BIM technologies, WSN collect Data as a hazards detection system and BIM model reflect actively and provide a visually dynamic interface for management [12]. Zixiao Zhang proposed a novel 4D based method for integrating data and degradation modeling and visualization, a technique for life cyclic and spatiotemporal infrastructure using colors, digital images and computer visions, which allowed damage prediction through the years and maintenance planning, the technique has been evaluated in two case study bridges in the state of California, United States, [13].

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Table 2. DT uses based on the virtual data physical paradigm. The physical asset

Sensing Monitoring Actuate

Sensor devices for real time data, Remote monitoring to keep tracking, Lifecycle management

Data

Connection Linking data Storage

BIM as source of time and sensor data, IoT linking data using the web semantic, Knowledge bases to facilitate reasoning and learn to take decisions

The virtual asset

Leveraging artificial intelligence agent

Simulate various applications, Predict behavior based on digital simulation, Optimize methods and recommend allocation of resources, Ability of managing the physical asset by AI agent based on digital data

Angjeliu and Coronelli developed in 2020 a strategy for simulation model of historical buildings, the study case was about the naves of Milan Cathedral, The DT model was organized in hierarchical manners with separated part, later assembled for final model, the damages trough the life of the model can be studied also dynamic tests can be valuated trough simulations, interpreting the causes and finding solutions for maintenance interventions [14]. Abiola A. akanmu developed an automated system for components and system that are challenging to access in buildings for maintenance, she used virtual reality to engage facility managers for capturing the inputs (opinions and suggestions), stored then in Microsoft Azur, interacting with BIM designs in the early design phases for visualization and implementing comments, the case study was about air-conditioning and lighting fixtures [15].

5 Conclusion In order to distinguish between a DT and a BIM technology, not only a proper and comment definition of DT have to be considered and taking place in construction industry, but also a constructed and divided process should be conceptualized for each type of projects (buildings, infrastructures, complex eco-systems). Considering BIM as the base tool to develop for more dynamic transfer data and integrating self-monitoring, predicting and actuate the coming problems, not to forget that the structural part should be taken of consideration, having a stable and secure building is to be prioritize, a DT needs to be used in the structural analysis studies, and the design stages of the project, a smart living building but also a self-maintaining and self-regenerated one.

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References 1. Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer (2017) 2. Glaessgen, E.H., Stargel, D.S.: The digital twin paradigm for future NASA and U.S. Air force vehicles. In: Collection of Technical Papers—AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, pp. 1–14 (2012) 3. Bolton, A., Enzer, M., Schooling, J.: The gemini principles: guiding values for the national digital twin and information management framework. CDBB DFTG (2018) 4. Lu, Q., Xie, X., Heaton, J., Parlikad, A.K., Schooling, J.: From BIM towards digital twin: Strategy and future development for smart asset management. In: International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, pp. 392–404. Springer (2019) 5. Lim, K.Y.H., Zheng, P., Chen, C.-H.: A state-of-the-art survey of digital twin: techniques, engineering product lifecycle management and business innovation perspectives. J. Intell. Manufact. 1–25 (2019) 6. Farsi, M., Daneshkhah, A., Hosseinian-Far, A., Jahankhani, H.: Digital twin technologies and smart cities. Springer (2020) 7. Boje, C., Guerriero, A., Kubicki, S., Rezgui, Y.: Towards a semantic construction digital twin: directions for future research. Autom. Constr.. Constr. 114, 103179 (2020) 8. Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-Papers Online 51(11), 1016–1022 (2018) 9. Halmetoja, E.: The conditions data model supporting building Information Models in facility management. Facilities (2019) 10. Andriamamonjy, A., Klein, R., Saelens, D.: Sensor handling in building information models development of a method and application on a case study [Conference Proceedings]. In: 14th Conference of International Building Performance Simulation Association (2015) 11. Wang, W., Guo, H., Li, X., Tang, S., Li, Y., Xie, L., Lv, Z.: BIM information integration-based VR modeling in digital twins in industry 5.0. J. Ind. Inf. Integr. 28, 100351 (2022) 12. Yu-Cheng, L, Weng-Fong, C.: Developing WSN/BIM-based environmental monitoring management system for parking garages in smart cities. J. Manage. Eng. ASCE. ISSN 0742-597X (2020) 13. Zhang, Z., Hamledari, H., Billington, S., Fischer, M.: 4D beyond construction: spatio-temporal and life-cyclic modeling and visualization of infrastructure data. J. Inf. Technol. Constr. (ITcon) 23 (2018) 14. Angjeliu, G., Coronelli, D., Cardani, G.: Development of the simulation model for Digital Twin applications in historical masonry buildings. The integration between numerical and experimental reality. Comput. Struct. 238, 106282_0.1016_j.c (2020) 15. Akanmu, A.A., Olayiwola, J., Olatunji, O.A.: Automated checking of building component accessibility for maintenance. Autom. Construct. 114, 103196 (2020). https://doi.org/10.1016/ j.autcon.2020.103196

Comparative Study and Analysis of Existing Intelligent Tutoring Systems Zakaria Rida1(B) , Hadhoum Boukachour2 , Mourad Ennaji1 , and Mustapha Machkour1 1

LabSIV, Department of Computer Science, Faculty of Science, Ibnou Zohr University, BP 8106, Agadir 80000, Morocco [email protected], [email protected], [email protected] 2 LITIS, Laboratoire d’Informatique, de Traitement de l’Information et des Syst`emes, University of Le Havre, Le Havre, France [email protected]

Abstract. This article first sets out to establish a clear definition of intelligent tutoring systems (ITS). It then outlines the main components of these systems. It then turns to an analysis of the various ITSs from various sources of information and knowledge from several sectors. The selection being based on the most widely recognized and commonly used ITS in different fields. Finally, the article concludes with a comparison between these ITSs to determine their similarities and differences, ending with a conclusion of this article’s point of interest. Keywords: Intelligent tutoring system Education

1

· Artificial intelligence ·

Introduction

The day when humans can seamlessly interact with intelligent machines remains a highly anticipated and celebrated moment for researchers and scientists alike. To realize this vision, researchers have pioneered the field of artificial intelligence (AI), training computer programs to simulate human intelligence and understand its underlying properties. Artificial intelligence has significantly contributed to various technological domains, enabling automation of repetitive tasks. As part of this progress, researchers sought to automate the learning process, leading to the development of intelligent systems that found applications in diverse sectors, including education. Over time, with the continuous advancement in the field of artificial intelligence, researchers sought to enhance the effectiveness by integrating AI techniques. This gave rise to a new paradigm in education known as Intelligent Tutoring Systems (ITS). By providing various modes of operation, these intelligent tutoring systems offer a flexible learning environment that closely emulates traditional teaching approaches [1]. In this paper, we begin by exploring intelligent tutorial systems. Then we’ll take a look at existing intelligent tutorial systems, before proceeding to a general comparative analysis, and finally reaching a conclusion. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 235–240, 2024. https://doi.org/10.1007/978-3-031-48573-2_34

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Intelligent Tutoring System Definition

Intelligent Tutoring Systems (ITS) are learning environments designed to personalize training and draw inspiration from human experts in specific fields. This approach helps learners perform tasks effectively and interact with their learning in a supportive manner. These systems are designed to assist learners in overcoming difficulties and finding solutions to various problems within a specific domain. They provide guidance and support throughout the teaching process, tailoring pedagogical scenarios to meet each learner’s specific needs [2]. 2.2

The Components of an ITS

The conceptual architecture of an ITS includes: – Domain Model – Tutoring Model – Student Model.

Fig. 1. The components of an ITS [2].

Domain Model This module is known as the expertise module, and it consists of a knowledge base that the system uses to teach the learner. The expertise module is utilized to create teaching materials and assess the learner’s progress. Integrating the knowledge module into the ITS architecture helps in handling data effectively during the learning process, enabling greater flexibility. Student Model The learner model is used to evaluate the student’s current learning situation by comparing their presumed knowledge with the intended and expected knowledge. This model helps in understanding how the student is progressing in their learning journey.

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Tutoring Model A tutoring module is a set of instructions that determines what content the system will provide, how and when it will be presented. In these systems, learners are assumed to learn by adjusting their knowledge levels and changing their understanding. This approach engages the learner in interactive activities to develop comprehensive problem-solving abilities and various skills.

3 3.1

Existing Intelligent Tutoring Systems Introduction

The history of artificial intelligence (AI) shows that researchers have made efforts to create intelligent tutoring systems, aiming to improve the learning experience for students. There are various types of intelligent tutoring systems, each with its unique role. The main goal of this section is to introduce the different existing intelligent tutoring systems. It will present the most well-known ITS, analyze their features, functionalities, and the artificial intelligence techniques they utilize. 3.2

Different Existing Intelligent Tutoring Systems

EL-MART (ELM Adaptive Remote Tutor) is an intelligent tutoring system web application developed to support learning programming in Lisp. It is based on the ELMPE system and has been tested in introductory Lisp courses at the University of Trier. It relies on intelligent solution analysis and example-based problemsolving techniques. ELM-ART underscores the importance of online courses and adaptive hypermedia technologies, making it an effective tool for problem-solving in programming education [3]. KERMIT Is an intelligent tutor focused on conceptual database design using the EntityRelationship model. It supports university-level students in understanding relationships between entities. KERMIT employs constraint-based modeling to specify domain knowledge and generate student models. The system compares each student’s solution to an ideal solution, ensuring syntax correctness and providing feedback using a knowledge base of 92 constraints with relevant conditions and feedback messages [4]. CAPIT (Capitalization And Punc-Intelligent Tutor) is an Intelligent Tutoring System (ITS) designed to teach capitalization and punctuation rules in English to students aged 10 to 11. The system uses constraints to define correct punctuation and capitalization patterns and provides feedback on violated rules. CAPIT contains 45 problems and 25 constraints, and its architecture includes the user interface, student module, teaching module, and databases of constraints, problems, and student data [5].

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SQL-TUTOR Is an Intelligent Tutoring System (ITS) focused on teaching the SELECT statement in SQL. It addresses the common misconceptions students have when writing queries. The system’s architecture involves representing domain knowledge through constraints, with 392 constraints identified from reference material and student errors. The system evaluates learners’ solutions by comparing them to the correct ones as it lacks a domain module for troubleshooting [6]. ACTIVEMATH Is a web-based, adaptable system designed for personalized mathematics learning. It dynamically produces interactive lessons based on the user’s abilities, goals, and preferences. The system generates individualized courses from a knowledge base, following pedagogical rules and the learner’s characteristics. Learners have the freedom to navigate and choose their learning process, and they can also inspect and modify their student model [7]. STOMACH DISEASE The Intelligent Stomach Disease tutoring system is designed to help students learn about stomach-related diseases and ulcers. It provides an interactive interface highlighting the human stomach. The system combines media like pictures and videos with Delphi to enhance the learning experience. Medical students can use this system to gain comprehensive knowledge about stomach diseases, with data presented in 3D for better understanding [8]. THERMO-TUTOR Is an intelligent tutoring system that offers information and assistance with thermodynamic cycles in closed devices. It allows students to test their problemsolving skills and provides clear feedback on their answers. This virtual assistant provides comprehensive assessment and valuable follow-up comments to enhance understanding. ThermoTutor’s user-friendly interface helps students improve and acquire thermodynamics skills in record time [9]. BAGHERA Is an intelligent tutoring system that focuses on computer modeling and geometric initiation. The system functions as a multi-agent system, providing equivalent support to three people for students (companion, mediator, and tutor) and rich interactions with two artificial agents for teachers (companion and assistant) [10]. CIRCUIT ANALYSIS Intelligent Monitoring System for Undergraduate Electrical Engineering Learners is an intelligent tutoring system designed to support undergraduate electrical engineering students, treating students as if they were in a virtual company. Learners face a series of problems to apply theoretical knowledge practically. The system provides a virtual interface with tools like filing cabinets and printers, allowing learners to access information and give feedback [11]. DUOLINGO Is an intelligent tutoring system that focuses on language teaching. It uses gamification to make learning enjoyable, with points, levels, and rewards for daily practice. Lessons are personalized to target areas for improvement [13].

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CODECOMBAT Is an ITS that teaches computer programming through interactive gameplay. Users learn to code by guiding their character through levels of increasing difficulty. This progressive and engaging learning experience enhances their understanding of computer programming. CodeCombat’s unique approach makes it a valuable tool for learners of all ages [14]. PRODIGY MATH Is an ITS that makes learning math engaging for children by using game elements. Children go on adventures and battle monsters while answering math questions tailored to their skill level. This innovative approach keeps children engaged and improves their math understanding, making Prodigy Math Game a valuable tool for math education [15].

4

Comparison

The comparison between the different intelligent tutoring systems already mentioned above is carried out in the following table, according to the criteria : domain, the method, the level and the date of first and last publication. Table 1. Comparison of intelligent tutoring systems ITS by approach CBM ITS

Domain

Approch

Level

KERMIT

Computer science (Modelization EA)

CBM

University 2004

Last post

CAPIT

Language

CBM

All levels 2004

SQL TUTOR

Computer science (SQL)

CBM

University 2018

ITS

Domain

Approch

Level

STOMACH DISEASE

Medicine

3D Animation

University 2017

THERMO-TUTOR

Thermodynamics

Virtual

University 2012

CIRCUIT ANALYSIS

Electric

Virtual

Virtual

2006 Last post

ITS by approach virtual Last post

ITS by approach gamification ITS

Domain

Approch

Level

DUOLINGO

Languages

Adaptive learning

All levels 2023

CODECOMBAT

Computer science

Learning by doing

All levels 2021

Immersive learning

Children

2021

Level

Last post

PRODIGY MATH GAME Mathematics

ITS by approach diverse ITS

Domain

Approch

ELMART

Computer science (Lisp)

Programming based on All levels 2015 examples

ACTIVE-MATH

Mathematics

Freedom to Learn

All levels 2009

BAGHERA

Geometric

Multi-agent system

University 2004

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General Conclusion

In conclusion, intelligent tutoring systems offer rigorous and highly beneficial support for each learner, taking into account their individual needs to enhance their academic performance. These systems adapt learning content to the specific knowledge requirements of students, facilitating effective assimilation for future independent problem-solving. While there are a variety of intelligent tutoring systems, each serves a distinct role. Some evaluate learners’ skills, while others identify and explain mistakes, and some directly interact with learners to provide optimal answers and personalized learning approaches. However, it is important to note that multidisciplinary ITS are currently scarce, and there is a need for more comprehensive systems that can offer continuous support and monitoring throughout learners’ educational journey, across all their levels and fields of study. The development of such systems could provide added value in education, benefiting learners of all ages, levels, and fields of study across various sectors.

References 1. Carbonell, J.R.: AI in CAI: An Artificial-Intelligence Approach to ComputerAssisted Instruction, pp. 190–202 (1970) 2. Nkambou, R., Mizoguchi, R., Bourdeau, J.: Advances in intelligent tutoring systems (2010) 3. Brusilovsky, P., Weber, G.: ELM-ART - an interactive and intelligent web-based (2015) 4. Mitrovic, A., Suraweera, P.: KERMIT: A constraint-based tutor for database modeling (2015) 5. Mayo, M., Mitrovic, A., McKenzie, J.: CAPIT: an intelligent tutoring system for capitalisation and punctuation (2000) 6. Mitrovic, A.: An intelligent SQL tutor on the web, pp. 173–197 (2003) 7. Melis, E., Andr‘es, E., Budenbender, J., Frischauf, A.: ActiveMath: a generic and adaptive web-based learning environment (2001) 8. Almurshidi, S.H., Abu-Naser, S.S.: Stomach disease intelligent tutoring system (2017) 9. Mitrovic, A., Williamson, C., Bebbington, A.: Thermo-tutor: an intelligent tutoring system for thermodynamics (2011) 10. Trgalov´ a, J., Soury-Lavergne, S.: Diagnostic de conceptions d’´el`eves dans Baghera (2003) 11. Butz, B.P., Duarte, M., Miller, S.M.: An intelligent tutoring system for circuit analysis, pp. 216–223 (2006) 12. Butz, B.P., Duarte, M., Miller, S.M.: An intelligent tutoring system for circuit analysis, pp. 216–223 (2006) 13. Teske, K.: Duolingo. Calico J. 34(3), 93–401 (2017) JSTOR 14. Karram, O.: The role of computer games in teaching object-oriented programming in high schools - code combat as a game approach. WSEAS Trans. Adv. Eng. Educ. 18, 37–46 (2021) WSEAS 15. Sihotang, H., et al.: Penerapan sistem prodigy math game sebagai implementasi merdeka belajar dalam meningkatkan minat belajar siswa menengah atas. EDUKATIF: Jurnal Ilmu Pendidika 36, 3919–3927 (2021) Faculty of Education University of Pahlawan Tuanku Tambusai

Extracting IT Knowledge Using Named Entity Recognition Based on BERT from IOB Annotated Job Descriptions Zineb Elkaimbillah1(B) , Maryem Rhanoui2 , Mounia Mikram2 , Mohamed Khoual1 , and Bouchra El Asri1 1 IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in

Rabat, Rabat, Morocco {Zineb_elkaimbillah,mohamed_khoual}@um5.ac.ma, [email protected] 2 Meridian Team, LYRICA Laboratory, School of Information Sciences, Rabat, Morocco {mrhanoui,mmikram}@esi.ac.ma

Abstract. Named Entity Recognition (NER) is a technique from the field of Natural Language Processing (NLP) that strives to identify entities such as names of people, organizations, and places in a given text. In this study, we investigate the use of NER to extract knowledge from unstructured data comprising descriptions of computer workstations. To this end, we use annotated data in IOB (Inside, Outside, Beginning) format. Our approach includes presenting the implemented design, preparing the dataset, training the BERT model, and presenting the performance results of our NER model through rigorous evaluation. Measures such as precision, recall, and F1-Score are used to assess the model’s accuracy in entity recognition. Keywords: Named entity recognition · NLP · Unstructured data · IOB · BERT

1 Introduction In the dynamic Information Technology (IT) industry, the ability to quickly and accurately extract knowledge from job descriptions is paramount. The rapid evolution of this sector demands a holistic approach to extracting knowledge from job descriptions, such as profile, IT skills, experience, and formation, which is a challenge given the evolving nature of the IT landscape. The main objective of this study is to extract knowledge from job offers using the NER approach on an annotated corpus by applying the BERT model. The use of BERT (Bidirectional Encoder Representations from Transformers) in Named Entity Recognition (NER) offers significant advantages due to BERT’s ability to understand the bidirectional context of words in a text. We aim to make predictions on job description data in order to extract the information needed in the information technology sector. Applying an important issue in the field of Natural Language Processing (NLP) is named entity recognition and finds various applications across different domains. Here © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 241–247, 2024. https://doi.org/10.1007/978-3-031-48573-2_35

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are some examples of domains where NER is used for example in Resume Summarization that NER can be used to extract key details like names, contact information, work experience, and skills from unstructured resumes [1–3]. NER can extract information from internal emails and corporate documents [4, 5]. In the sector of Legal NER used for Document Processing like Contracts, court rulings, and other legal documents often contain entities like the names of involved parties, important dates, and contractual clauses [6, 7]. NER can be used to extract Medical Information [8–10]. The rest of this paper is structured as follow. In the second section, we present the methodology and our proposed approach, where we present the dataset description and the named entity recognition model, and finally we discuss the results obtained.

2 Methodology and Proposed Model 2.1 General Architecture Figure 1 shows the general architecture of the research, which is organized into different phases: Data collection (IT jobs document): collection of IT job descriptions. Annotation in IOB (Inside-Outside-Beginning) format: used to label entities. This format is particularly useful for annotating named entities in texts. The IOB format divides each entity into three parts [11]: • Inside (I): This significate that the word is a component of an entity already begun in the text. • Outside (O): This significate that the word is not part of an entity and is not annotated. • Begin (B): This significate that the word is the beginning of a brand-new thing. Data pre-processing: this is the preparation of textual data and pre-processing for NER. Named Entity Recognition: This natural language processing task concerns classifying and identifying named entities. Knowledge extraction: extraction of IT Knowledge from NER results. Analysis: the final step for evaluate the performance of Model training. 2.2 Dataset Description The data used in this work consisted of 150 descriptions of job offers in Morocco, related to software engineering and the IT sector, collected online from various job boards (Rekrute, Indeed, Linkedin...) (Fig. 2). The key task in creating the training data is to generate tags for the IT offer data. The construction of vocabularies, which are special sets of keywords in the IT domain. More details about the method and the tool used for the annotation described on the paper [12]. The IOB format is valuable because it allows for the representation of multi-token entities by marking both the beginning and inside tokens, and it clearly indicates which

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Fig. 1. General solution design illustration

Fig. 2. Summary of the position description

tokens are not part of any named entity (labeled as “O”). This format makes it easier for NER models to understand and learn the boundaries of named entities in a sentence. When training a BERT-based NER model or any NER model, it’s essential to provide the model with training data in this format so that it can learn to recognize and label named entities correctly in text (Fig. 3). After that, we annotated the texts while highlighting important terms like “profile,” “company,” “location,” “responsibilities,” “soft skills,” “IT skills,” “diploma”, “company” and “experience” According to the IOB annotation format. Figure 4 shows the number of tags for each entity using the IOB annotation format. 2.3 Named Entity Recognition Model For the purpose of analysis, we suggest a general method for named entity recognition from any unstructured data that is written in English.

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Fig. 3. Extract from IOB file

Fig. 4. Number of IOB tags for each entity

According to literature analysis, BERT performs better in all three named entity recognition lifecycle tasks than other models. Therefore, we apply the BERT-NER model on our data corpus. One of the remarkable advancements in NER has been the integration of transformer-based models like BERT (Bidirectional En-coder Representations from Transformers). In this paper, we demonstrate how to fine-tune a BERT model to predict entities like diploma, diploma major, and experience in job descriptions for software. To refine BERT, we divided the contents of the IOB file into three sets: 60% for processing in the file (train.txt), 20% for validation in the file (dev.txt) and 20% for testing (test.txt), then loaded the data into the model using a data loader.

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The tokenization function is a crucial step when using BERT for Named Entity Recognition (NER) in the context of computer job descriptions, which involves breaking down the input text into smaller units, usually words or pieces of sub-words (subtokens), which are then used as input to the BERT model.

3 Results and Discussion Figure 5 illustrates the behavior of the loss function as the model undergoes training. The loss function is a critical metric in machine learning, indicating how well the model’s predictions match the actual labels in the training and validation datasets. The graph shows two curves: one for the training set and another for the validation set. The training loss curve represents how well the model fits the training data, while the validation loss curve assesses the model’s generalization to unseen data (validation data). As training progresses, the training and validation curves approach each other and tend to converge towards a minimum value. This suggests that the model is improving its ability to make accurate predictions on both the training and validation data. The addition of epochs is therefore useful in this case until both the training and cross-validation curves converge towards a minimum value, highlights the concept of choosing an optimal number of training epochs. Training for too few epochs may result in underfitting, where the model hasn’t learned enough. Training for too many epochs can lead to overfitting, where the model memorizes the training data but fails to generalize. The optimal number of epochs is when both curves converge to a minimum loss value, approximately 0.02 in this case, indicating that the model has learned the underlying patterns in the data without overfitting.

Fig. 5. Learning curve

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Figure 6 illustrates the training process, where the model was trained 15 times. This training resulted in an accuracy of 0.887 and an F1-Score of 0.815. The high F1-Score of approximately 82% is indicative of its strong performance in recognizing named entities within the text data, such as IT-related entities in job descriptions.

Fig. 6. Performance results of NER model

The curve results and the training results with the F1-Score indicate that the BERT model is capable of recognizing and classifying named entities in text with impressive precision and recall even with a relatively small data set. These results testify to BERT’s effectiveness in NER tasks and to the quality of the training carried out with 15 iterations of epochs.

4 Conclusion This paper presents an approach to applying BERT-NER for extracting computational knowledge from unstructured datasets. BERT is refined using IOB annotated data to recognize IT-related entities. This innovative approach enables organizations to streamline talent acquisition and resource allocation, promoting informed decision-making and efficient use of IT expertise. BERT’s contextual understanding enhances entity recognition. We demonstrate the training process, from data collection to performance analysis, which yields an F1-Score of 82%, a result that can be improved with data reinforcement. As a perspective, our goal is to create a search engine for IT profiles that consumes search results, extracts relationships between entities, and builds a knowledge graph for retrieving contextual information.

References 1. Gaur, B., Saluja, G.S., Sivakumar, H.B., Singh, S.: Semi-supervised deep learning based named entity recognition model to parse education section of resumes. Neural Comput. Appl. 33, 5705–5718 (2021) 2. Pawar, S., Srivastava, R., Palshikar, G.K.: Automatic gazette creation for named entity recognition and application to resume processing. In: Proceedings of the 5th ACM COMPUTE Conference: Intelligent & Scalable System Technologies, pp. 1–7 (2012, January) 3. Kesim, E., Deliahmetoglu, A.: Named entity recognition in resumes. arXiv preprint arXiv: 2306.13062 (2023)

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4. Shyni, C.E., Sarju, S., Swamynathan, S.: A multi-classifier based prediction model for phishing emails detection using topic modelling, named entity recognition and image processing. Circ. Syst. 7(9), 2507–2520 (2016) 5. Luo, L., et al.: An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics 34(8), 1381–1388 (2018) 6. Leitner, E., Rehm, G., Moreno-Schneider, J.: A dataset of german legal documents for named entity recognition. arXiv preprint arXiv:2003.13016 (2020) 7. Çetinda˘g, C., Yazıcıo˘glu, B., Koç, A.: Named-entity recognition in Turkish legal texts. Nat. Lang. Eng. 29(3), 615–642 (2023) 8. Perera, N., Dehmer, M., Emmert-Streib, F.: Named entity recognition and relation detection for biomedical information extraction. Front. Cell Develop. Biol. 673 (2020) 9. Liu, N., Hu, Q., Xu, H., Xu, X., Chen, M.: Med-BERT: a pretraining framework for medical records named entity recognition. IEEE Trans. Industr. Inf. 18(8), 5600–5608 (2021) 10. Jiang, S., Zhao, S., Hou, K., Liu, Y., & Zhang, L.: A BERT-BiLSTM-CRF model for Chinese electronic medical records named entity recognition. In: 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 166–169. IEEE (2019, October) 11. Diab, M.: Second generation AMIRA tools for Arabic processing: fast and robust tokenization, POS tagging, and base phrase chunking. In: 2nd International Conference on Arabic Language Resources and Tools, vol. 110, p. 198 (2009, April) 12. Elkaimbillah, Z., El Asri, B., Mikram, M., Rhanoui, M.: Construction of an ontology-based document collection for the IT job offer in Morocco. Int. J. Adv. Comput. Sci. Appl. 14(7)

Prediction of Learner Performance Based on Self-esteem Using Machine Learning Techniques: Comparative Analysis Aymane Ezzaim(B) , Aziz Dahbi, Abdelhak Aqqal, and Abdelfatteh Haidin Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco {Ezzaim.a,Dahbi.a,Aqqal.a,Haidin.a}@ucd.ca.ma

Abstract. Self-esteem is a pivotal factor influencing students’ academic outcomes, fostering resilience, and cultivating a positive attitude toward learning. It promotes active classroom participation, question-asking, and collaboration, driving goal achievement. This study builds upon our previous work with refinements to our approach and objectives. Here, we extend prior findings by further developing predictive traits for learners’ success. We rigorously assess the accuracy of various machine-learning models in predicting these traits, offering insights into their effectiveness. Our comparative analysis not only elucidates algorithm performance but also establishes a benchmark for selecting optimal technologies in the creation of performance-focused adaptive learning systems. This research contributes to the advancement of predictive models for academic success. Keywords: Adaptive learning · Artificial intelligence · Self-esteem · Academic achievement · Performance · High school students

1 Introduction For many years now, the idea of adaptation has been incorporated into computers, with each advancing system integrating some level of tailored interaction to suit unique users [1]. Performance is a part of a wider range of learner characteristics that are strongly related to the learning process. The expression “Learner performance” refers to the knowledge acquired by students during the teaching-learning process and includes a wide range of learning aspects that span the cognitive, emotional, and psychomotor domains [2–4]. Numerous investigations have explored the link between academic success and self-esteem [5–8]. This large body of research highlights the need to further explore this dynamic connection and use it as a predictive tool to assess learner performance.

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As was previously said, “learner performance” includes the information that students learn via training or educational endeavors, as well as the development of their expertise and abilities through educational activities. Additionally, it refers to the percentage of students who actually successfully finish a module [2–4]. In light of this, Walkington employed technology for adaptive learning to individualize the learning process according to learner preferences, aiming to examine the effects of such a system on the Performance and academic results of a group of 145 ninth-grade Algebra students [9]. Another research project uses a technique for adaptive learning built on a fuzzy expert system. By considering both cognitive and emotive variables, this method aims to enhance students’ academic performance [10]. In order to enhance learners’ performances, the researchers behind this research opt to construct an Adaptive Learning System with Multiple Perspectives, tailored to students’ distinctive learning styles and cognitive preferences [11]. Most research on adaptive learning predominantly centers on improving performance as a desired outcome. In contrast, our approach seeks to differentiate itself by focusing on predicting performance based on the learner profile (self-esteem, emotional intelligence and demographic data) to tailor the learning and teaching process. In addition, traditionally, student performance prediction relies on academics and demographics. Our novel approach includes emotional intelligence and self-esteem, offering a deeper understanding of success drivers and potential for more effective adaptive learning. This research attempts to evaluate the accuracy of several machine-learning models in forecasting learner performance, especially considering the self-esteem aspect. The results of this analysis should serve as a baseline for future research projects looking at adaptive learning methods and that aim to ground their adaptive procedures on the prediction of learner performance. This article will cover our research methodology, results, and conclusions. We will discuss our methodology, including data sources, variables, and predictive techniques. The “Results” section will reveal an overview of the predictive power of emotional intelligence and self-esteem as well as a comparison of several machine-learning techniques, while the “Conclusion” will summarize our results, discuss the implications and will offer recommendations for educational predictive modeling.

2 Methodology This quantitative study was carried out among secondary school students, with a focus on the scientific common core. The census sampling approach was used to conveniently ask these students to participate. This comparative research, which used a dataset of 100 Moroccan high school students, was carefully planned to explore the complexities of forecasting learner performance through the implementation of machine learning models. The information included student gender, parent marital status, boarders, selfesteem score, and first-semester grade. The selection of algorithms was underpinned by careful consideration of their respective strengths and characteristics, aligning them with the study’s objectives and dataset. Because of their various approaches and established usefulness in predictive modeling, the subsequent algorithms were selected:

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• Random Forest Regressor (RFR): This algorithm was chosen because of its reliability and ability to handle complex interactions, which would make it easier to spot intricate patterns in the dataset [12]. Random Forest Regressor combines Decision Tree Regressors, training each tree on a different data subset. It aggregates their predictions to enhance model accuracy and reliability [13]. • Linear Regression (LR): This fundamental statistical technique is used to determine the connection between a dependent variable and one or more independent variables [14]. This essential predictive modeling method relies on the simplicity and readability of its output. One of its benefits is that it is straightforward and easy to grasp how different factors interact [15]. • Gradient Boosting Regressor (GBR): Gradient Boosting Regressor sequentially builds decision trees to enhance prediction accuracy, focusing on areas of improvement. It combines multiple weak learners for a robust ensemble model [16]. With its ensemble learning and improved model performance, this algorithm intended to take use of algorithmic synergy for more precise predictions [17]. • Support Vector Regression (SVR): Using historical time series data, SVR is a machine learning approach for creating regression models [18]. This algorithm, which can handle both linear and nonlinear interactions as well as uncover predictive trends that might otherwise remain hidden when using algorithms with limited adaptability, has been added to provide a wide range of algorithmic strategies [19]. During the data collection process, a carefully crafted questionnaire includes closedended questions that encompass a range of factors critical to the study. These factors encompass key aspects such as age, gender, financial situation of parents, marital status of parents, and the mode of residence of the student, whether they reside on campus or off campus. Secondary school administration records were rigorously used to further enhance the data collection. These registers made it easier to derive the final grades each student earned throughout the first semester. The level of emotional intelligence of each learner was measured using the Bar-On scale [20]. With regard to measuring self-esteem, a reliable metric was used, namely the Rosenberg scale. This scale produces cumulative scores that range from 10 to 40, where lower values correspond to higher self-esteem levels [21]. After processing the data using the Python language, we were able to extract the correlation coefficient between the predictor variable and the final grade as the target variable. This allowed us to refine the collected attributes and keep only those with a high impact on the learner’s performance, which resides in the final grade. Attributes taken into account are emotional intelligence, parental marital status, boarders, and self-esteem. These characteristics, chosen for their notable significance, serve as the starting point for our ongoing research into the complex dynamics of learner performance prediction.

3 Results Correlation analysis of the dataset reveals a remarkably robust negative linear relationship between the “final grade” and the “Rosenberg self-esteem scale score”, evident through the striking correlation coefficient of − 0.92 linking these variables in our data set. The graph above highlights that students who display higher self-esteem (Lower

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Fig. 1. Scatter plot to understand the correlation between score of rosenberg scale (self-esteem) and final grade.

value in the Rosenberg scale) tend to get more favorable final grades (Fig. 1). Notably, the ‘self-esteem’ variable takes on a central role, emerging as an essential characteristic that potentially carries considerable weight in predicting student performance. After building, training and testing our machine learning models, we opted to compute a set of metrics that align seamlessly with regression tasks. Specifically, we employed the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) Score, which collectively provide a comprehensive assessment of the models’ predictive performance (See Table 1). Table 1. How different regression models perform: evaluation metrics summary. Algorithm

Mean squared error

Root mean squared error

R2 score

Random forest regressor (RFR)

0.62

0.79

0.88

Linear regression (LR)

0.64

0.8

0.88

Gradient boosting regressor (GBR)

0.51

0.72

0.9

Support vector regression (SVR)

3.3

1.81

0.38

The results presented in the above table highlight key model performances. The GBR displayed the lowest Mean Squared Error (MSE) of 0.51, exceptional performance, which signifies minimal average squared deviations in prediction. Its root mean square error (RMSE) of 0.85 displayed that predictions deviate by around 0.72 units on average. The robustness of this fit was confirmed by the R-squared (R2) score (0.9), implying a 90% elucidation of the target variable’s variance. An amazing 88% of the variation is predicted by the RFR and LR models, which both produce equivalent results with minor variances in their average squared deviations.

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The SVR model, on the other hand, performs substantially worse. While its RMSE of 1.81 indicates predictions differ on average by about 1.81 units, its higher MSE of 3.3 indicates bigger average squared deviations. The target variable’s variation is only 38% explained by the target variable, according to the R2 value of 0.38. Overall, according on these measures, the GBR appears to perform the best of the models you have studied, with the lowest MSE, RMSE, and greatest R2 score. Suggesting it captures the data’s variability very well. The RFR and LR models are quite close in performance, while the SVR model seems to have a weaker fit, explaining less variance in the data. The strong negative correlation between students’ “final grade” and “self-esteem scale score” underscores self-esteem’s predictive significance, raising questions about motivation and success. The success of the GBR highlights self-esteem’s predictive potential. However, the weaker performance of the SVR model underscores the need for appropriate algorithms. Overall, this research advances our understanding of selfesteem’s role in academic prediction and the power of machine learning in this context.

4 Conclusion In this paper, we set out on a thorough investigation of machine learning techniques for learner performance prediction. Our study uses a dataset with information on 100 high school students to carefully assess a variety of machine learning algorithms, each of which offers unique techniques and insights. The results of our analysis revealed compelling findings that shed light on the complex interplay of factors influencing learner performance. The GBR model has demonstrated its ability to capture the intricacies of the dataset and provide robust predictions. The contributions of our work create windows for further investigation. Our future perspective is to grow our dataset and explore cuttingedge techniques like deep learning. We also look forward to putting our research into practice, especially in adaptive learning systems based on performance insights. This system will dynamically tailor learning materials based on the predicted performance metrics derived from the model presented in this study.

References 1. Rasheed, F., Wahid, A.: Learning style detection in E-learning systems using machine learning techniques. Expert Syst. Appl. 174, 114774 (2021). https://doi.org/10.1016/j.eswa.2021. 114774 2. Sotomayor, T.M., Proctor, M.D.: Assessing combat medic knowledge and transfer effects resulting from alternative training treatments. J. Def. Model Simul. 6, 121–134 (2009) 3. Ariffin, M.M., Oxley, A., Sulaiman, S.: Evaluating game-based learning effectiveness in higher education. Proc.-Soc. Behav. Sci. 123, 20–27 (2014) 4. Li, N., Marsh, V., Rienties, B.: Modelling and managing learner satisfaction: use of learner feedback to enhance blended and online learning experience. Decis. Sci. J. Innov. Educ. 14, 216–242 (2016) 5. Vishalakshi, K.K., Yeshodhara, K.: Relationship between self-esteem and academic achievement of secondary school students. Education 1, 83–84 (2012)

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6. Hisken, L.J.: The correlation between self-esteem and student reading ability, reading level, and academic achievement. Univ. Cent. Mo. (2011) 7. Arshad, M., Zaidi, S.M.I.H., Mahmood, K.: Self-esteem & academic performance among university students. J. Educ. Pract. 6, 156–162 (2015) 8. Sharma, P., Sharma, M.: Relationship between self-esteem and academic achievement of secondary school students. Elem. Educ. Online 20, 3208–3212 (2021) 9. Walkington, C.A.: Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. J. Educ. Psychol. 105, 932–945 (2013). https://doi.org/10.1037/a0031882 10. Hwang, G.-J., Sung, H.-Y., Chang, S.-C., Huang, X.-C.: A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Comput. Educ. Artif. Intell. 1, 100003 (2020). https://doi.org/10.1016/j. caeai.2020.100003 11. Yang, T.-C., Hwang, G.-J., Yang, S.J.-H.: Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. J. Educ. Technol. Soc. 16, 185–200 (2013) 12. Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues IJCSI 9, 272 (2012) 13. Patil, S., Patil, A., Handikherkar, V., et al.: Remaining useful life (RUL) prediction of rolling element bearing using random forest and gradient boosting technique. In: ASME International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, p. V013T05A019 (2018) 14. Kumari, K., Yadav, S.: Linear regression analysis study. J. Prim. Care Spec. 4, 33–36 (2018) 15. Su, X., Yan, X., Tsai, C.-L.: Linear regression. Wiley Interdiscip Rev. Comput. Stat. 4, 275– 294 (2012) 16. Anchaleechamaikorn, T., Lamjiak, T., Thongpe, T., et al.: Predict condominium prices in Bangkok based on ensemble learning algorithm with various factors. In: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), pp. 1–4. IEEE (2023) 17. Prettenhofer, P., Louppe, G.: Gradient boosted regression trees in scikit-learn. In: PyData 2014 (2014) 18. Xia, Y., Liu, Y., Chen, Z.: Support vector regression for prediction of stock trend. In: 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 123–126. IEEE (2013) 19. Zhang, F., O’Donnell, L.J.: Support vector regression. In: Machine learning, pp. 123–140. Elsevier (2020) 20. Bar-On, R.: The bar-on model of emotional-social intelligence (ESI) 1. Psicothema 13–25 (2006) 21. Rosenberg, M.: Rosenberg self-esteem scale. J. Relig. Health (1965)

A Collaborative Anomaly Detection Model Using En-Semble Learning and Blockchain Chaimae Hazman1 , Azidine Guezzaz1(B) , Said Benkirane1 , Mourade Azrour2 , and Sara Amaouche1 1 Technology Higher School Essaouira, Cadi Ayyad University, Marrskesh, Morocco

[email protected] 2 STI Laboratory, Faculty of Sciences and Techniques, IDMS Team, Moulay Ismail

Univer-SityofMeknes, Errachidia, Morocco

Abstract. Intrusion Detection Systems (IDS) have historically been constructed using a centralized topology in which a single device monitors the whole network. However, as the complexity and scope of contemporary networks have grown, this strategy has become less successful. Centralized intrusion detection systems might suffer from poor performance, restricted scalability, and vulnerability to specific assaults. To solve these constraints, there is a rising demand for collaborative intrusion detection systems (IDS) that can share workload among numerous devices and better manage large-scale networks. Collaboration allows intrusion detection systems to identify breaches more efficiently by integrating and analyzing data from numerous sources. The use of blockchain technology is critical to attaining a collaborative IDS. Blockchain enables the safe, decentralized storage and sharing of information between multiple devices, which is crucial for establishing trust and preserving the system’s integrity. Furthermore, machine learning (ML) methods may be utilized to enhance the effectiveness of intrusion detection systems by recognizing new and emerging threats. ML may aid in the detection and response to network traffic patterns and abnormalities, allowing the system to detect and respond to assaults more efficiently. A dependable and scalable detection system may be created by integrating these techniques. The collaborative intrusion detection system (IDS) based on blockchain technology and ML algorithms can increase the precision and effectiveness of identifying network intrusions while preserving system security and integrity. Keywords: First Keyword · Second Keyword · Third Keyword. Malicious assaults · Block-chain · Intrusion detection system · Network security · IoT

1 Introduction The constant availability of the internet in today’s environment allows for information sharing and exchange. As a result, cybersecurity remains a top priority. The majority of data transmitted across the network is secure and private. To safeguard this information, a secure procedure is necessary. Thieves use attacks to break network security and take transferred data. An IDS it is used to protect a network against attacks. It assists © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 254–260, 2024. https://doi.org/10.1007/978-3-031-48573-2_37

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in monitoring both normal and abnormal network activity. When a computer system is attacked, an alarm is generated by the IDS. Anomaly-based detection (AIDS) and signature-based surveillance (SIDS) are two methods for identifying malicious behavior. SIDS approaches identify malicious network behaviors by using a shared dataset for detection of intrusions. In a normal dataset, patents specify the order of attack. This dataset is used to inspect connections into the system. If a certain pattern is discovered, it is deemed malicious behavior, and an alert is issued to the supervisor. Alternatively, the message is considered normal and is broadcast over the whole network. The system as a whole fail to handle unanticipated assaults, which is a SIDS limitation. If an outdated or out-of-date dataset is utilized, it lacks the signature of unsolved assault Since new risks develop in the twenty-first century, there is a need enabling innovative and imaginative IDS for communication and collaboration. A distributed system’s nodes may collaborate in sharing information such as trademark databases, networking assets, threat signatures, and information notifications. The communication of knowledge between terminals may be compromised if an intruder obtains access to the network and sees all operations and data transfers. A hacker can intercept, alter, or delete data as it passes over a network. An extra comprehensive technique is required to provide privacy for data exchanged or moved within the network’s bounds. Data tampering after node switch might result from network disruption. Signatures, datasets, flees, records, and other data may all be easily altered by attackers. A hacker’s knowledge of data endangers the computer system. Blockchain is a well-known system for ensuring the anonymity of information exchanged by dispersed nodes in a network [1–4]. It provides a data structure that is decentralized and accessible for communication of information in a peer-to-peer network [5]. Another feature of blockchain technology is the capacity to duplicate data across several nodes. Information replication increases protection, and a single node cannot be the network’s bottleneck [6, 7]. Blockchain is used to strengthen security in decentralized IDS networks because of its confidentiality and consensus procedures that are implemented by nodes [8]. Most security-related uses make advantage of the technology of blockchain for multi-media and sensitive information transmission [9, 10]. This approach combines both analytical approaches to enable more reliable detection while lowering the danger of false positives and false negatives. It also offers and implements a set of collaborative IDS based on blockchain technology to assure safe data sharing and mutual trust among all nodes. The resultant IDS can detect and prevent disturbances on open, large-scale, and indefinitely dispersed networks by integrating all of these methodologies [12–18]. The rest of this paper is arranged as follows. Section 2 provides an introduction of IDS and Blockchain, Sect. 2 present related works and overview, Sect. 3 contains the proposed model the section is completed by a critical appraisal of many systems. The innovative structure is described in full in Sect. 3. Section 4 contains the outcomes obtained and relates them to initial approaches. Eventually, the authors conclude with a summary and suggestions for additional research.

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2 Overview and Related Works Researchers have advocated integrating blockchain technology in collaborative intrusion detection systems to improve security and trust management across different intrusion detection systems in current research. Nakamoto created the blockchain technology in 2008 as a basis for Bitcoin transaction monitoring. Any Bitcoin operation and provide security from any attacks. A distributed ledger, or blockchain, is a linked-data structure that divides every record of data into two parts: the heading and the body of the information. The header area occasionally includes a previous hash, a Merkle Consulting root hash, a date and time stamp, and a difficulty target. The body portion displays a list of tasks. Figure 1 depicts the building of a blockchain. Typically, the major block is employed. Every block is encrypted to link together, and blocks are distributed over an ensemble of peers. In addition, in order to conform to the technology’s principles, all nodes in the decentralized ledger networks needs an equivalent block list. When a particularly current block is added to the network, and it publishes to all partners. Every node verifies the freshly formed block by utilizing a shared approach in order to verify all transactions in the block. Proof of activities and proof of commitment are two mechanisms for consensus that are employed to ensure that all terminals have the same blockchain list. Multiple approaches are now being developed and used to protect the Internet of Things (IoT) against intrusions and attacks, however they are insufficient owing to issues such as reaction time, latency, and massive data size. To secure fog and cloud in IoT contexts, additional precautions are necessary. Emerging technologies include SDN, blockchain, and IDS, as well as ML and DL algorithms. The ID System It has shown to be one of the most effective methods for tracking network activity and finding susceptible endpoints. Scientists have realized that blockchain has enormous potential for addressing the difficulties of building trust across networks while dispersing attacks. The purpose of this identification-based strategy is to discover and limit offenders. IDS monitor network signals to determine whether they are malicious assaults. Furthermore, ML and DL algorithms may be used to enhance IDS. It also aids in the design of preventive measures by identifying the type of assault [9]. Many fresh researches have proposed the usage of blockchain to boost IDS and improve threat detection. The combination of intrusion detection systems, blockchain, and ML is required to prevent network attacks and faults while also securing critical healthcare data. The history of networked intrusion detection systems is lengthy; researchers have attempted to improve the security of decentralized and distributed networks by coordinating with various IDSs; however, this history is not especially relevant to today’s expansion of IoT. In 2018, [12] investigated the intersection of digital currencies and CIDS on the same research axis. They provided a strategy for integrating digital currency into CIDS, leveraging the technology to improve monitoring trust. In 2019, Hu et al. [10] created a completely new CIDS approach based on blockchain for distributed intrusion detection in MMG systems. The blockchain agreement mechanism and incentive systems are used to deliver CIDS without the requirement for centralized management or a trustworthy authority. The same year, Li and colleagues [11] CBSigIDS is a generic architecture for cooperative blockchain signature-based intrusion detection systems (IDSs) that develop and maintain a current fingerprint in a linked IoT network. CBSigIDS provides an accurate detection

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technique in distributed networks that is reliable and efficient despite the requirement for a trustworthy intermediary [12]. In 2019, we propose a distributed CIDS in which each IDS may reliably relay trustworthy data about outside networks and hosts to other CIDS members. This data is safely aggregated based on source trustworthiness, computed based on problem responses, and kept on a blockchain. Alkadi et al. [13] introduced a CIDS in 2020 with the goal of enabling privacy and security in IoT networks using distributed intrusion detection and a blockchain with intelligent contracts. Intruders are detected using a simultaneous long-term memory DL method (BiLSTM).

3 Description of the Model The AdaBoost algorithm is built on the concept of “boosting”. The notion underlying boosting is that a number of “weak” classifiers may be combined to build an effective classifier by using a voting procedure. A poor classifier delivers outcomes that are only marginally better than throwing a (fair) ball. In other words, if we forecast a binary label at random and get it right about half of the time, a worse classifier would arrive at it right around 55% of the time. For addressing a binary classification problem, our AdaBoost strategy will fit a series of weak classifiers (stumps) using a variety of boosting rounds. These classifiers will be combined to form a meta-classifier, that will produce a prediction employing a vote based on weighted majority technique. In each boosting round, we will give more weight to items that were incorrectly categorized in the prior iteration. This process may be formalized as seen in the algorithm below. It is feasible to show that the equations for and w arise from decreasing the exponent of the loss function The purpose of our proposed approach is to protect smart cities by protecting nodes in the event of an unlawful attack via the implementation of IDS in smart cities. IDS is an effective security technology that may be enhanced with ML and DL algorithms; nevertheless, because smart cities are dispersed, one IDS is adequate to detect many attacks. In fact, we propose an interactive distributed smart cities-based IDS that combines EL approaches with the successful Adaboost to create an intrusion detection system that recognizes attacks at every node, as well as Blockchain technology to transfer and safeguard attacks identified by points in complete security, as well as to ensure node confidence and dependability. Enables for quicker capture recognition and processing. To boost security, the IDS should be utilized at all levels in the ecosystem of IoT devices to monitor network traffic and identify misused nodes. Collaborative intrusion detection increases IDS nodes to exchange critical data with one another in order to improve detection capabilities. The distributed ledger blockchain, on the other hand, is viewed as a transparent, scattered, decentralized, and organized record. Because of the method in which blockchain technologies function, they have recently demonstrated their importance in ensuring trust and security in decentralized and decentralized networks. It is an effective solution to overcome the core difficulties of CIDS since, due to its decentralized structure, it substitutes central handling with safe cooperative process that time the blockchain has been created, the analysis nodes are going to possess the computing capacity they must calculate and deal with the information, yet they are also capable of serve as tracking units that monitor and transmit the alert. Since all nodes in the distributed ledger network give

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to alert recognition and administration, all nodes act entirely as tracking and analyzing units. Each alarm identified by an IDS is recorded in a public blockchain under our proposal. Then, utilizing the Blockchain concept with peer-to- peer architecture, each alarm generated must be published and debated with each additional node to ensure that all assaults are relayed. Blocks are used to organize notifications on the blockchain. Each block includes notifications as well as previous and cur- rent hashes. The hash of the information in the block works similarly to a signature key. The hash links each record cryptographically. The Genesis Block is the first block. Each component is dependent on the one before it. The data on the blockchain is distributed across numerous nodes. In order for them to collaborate. Also, notification sharing allows for an evaluation of the reliability of other node in the network, which has a possibility to enhance the reliability process of evaluation.

Fig. 1. The suggested method for cooperative intrusion detection

4 Conclusion For improved security, the IDS ought to be used at every component in the IoT environment to observe network activity and identify nodes that have been abused. Cooperative detection of intrusions encourages IDS nodes to share essential data with one another

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in order to increase detecting abilities. In contrast, the blockchain, is seen as a translucent, dispersed, decentralized, and ordered record. Blockchain technologies have lately proved their value in assuring reliability and confidence in decentralized and de- centralized networks due to the way they work. It is an effective solution to overcome the core difficulties of CIDS since, due to its decentralized structure, it substitutes central handling with safe cooperative process that time the blockchain has been created, the analysis nodes are going to possess the computing capacity they must calculate and deal with the information, yet they are also capable of serve as tracking units that monitor and transmit the alert. Since all nodes in the distributed ledger network give to alert recognition and administration, all nodes act entirely as tracking and analyzing units. Each alarm identified by an IDS is recorded in a public blockchain under our proposal. Following that, using the Blockchain idea with peer to peer construction, each alert produced has to be broadcast and discussed with every other node in order to guarantee that all assaults are transmitted. Alerts on the blockchain are organized within blocks. Every block contains alerts as well as past and current hashes. The hash of the information contained in the block functions similarly to a hallmark key. Every record is cryptographically linked by the hash. The initial block is the Genesis Block. Each component is reliant on the previous one. In order to collaborate, the blockchain is passed on among several nodes. In the future, we intend to leverage Blockchain enhancements in combination with DL approaches to strengthen security in smart cities. In the future, we intend to leverage Blockchain enhancements in combination with DL approaches to strengthen security in smart cities.

References 1. Hazman, C., Guezzaz, A., Benkirane, S., Azrour, M.: IDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning. Cluster Comput. (2022) 2. Hazman, C., Benkirane, S., Guezzaz, A., Azrour, M., Abdedaime, M.: Intrusion detection framework for IoT-based smart environments security. In: Book Artificial Intelligence and Smart Environment: ICAISE’2022, pp. 546–552. Springer International Publishing (2023) 3. Hazman, C., Benkirane, S., Guezzaz, A., Azrour, M., Abdedaime, M.: Building an intelligent anomaly detection model with ensemble learning for IoT-based smart cities. In: Book Advanced Technology for Smart Environment and Energy (2023) 4. Amaouche, S., et al.: FSCB-IDS: feature selection and minority class balancing for attacks detection in VANETS. Appl. Sci. (2023) 5. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection. Multimedia Tools Appl. (2023). https://doi.org/10.1007/s11042-023-14795-2 6. Mohy-eddine, M., Benkirane, S., Guezzaz, A., Azrour, M.: Random forest-based IDS for IIoTedge computing security using ensemble learning for dimensionality reduction 7. Douiba, M., Benkirane, S., Guezzaz, A., Azrour, M.: An improved anomaly detection model for IoT security using decision tree and gradient boosting. J. Super Com put. 1–20 (2022) 8. Hazman, C., Guezzaz, A., Benkirane, S. et al.: Toward an intrusion detection model for IoTbased smart environments. Multimed. Tools Appl. (2023) 9. Hu, B., Zhou, C., Tian, Y., Qin, Y., Junping, X.: A collaborative intrusion detection approach using blockchain for multimicrogrid systems. IEEE Trans. Syst., Man, Cyber.: Syst. 49(8), 1720–1730 (2019)

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10. Li, W., Tug, S., Meng, W., Wang, Y.: Designing collaborative block-chained signature-based intrusion detection in IoT environments. Fut. Gener. Comput. Syst. 96, 481–489 (2019). ISSN 0167-739X 11. Kolokotronis, N., Brotsis, S., Germanos, G., Vassilakis, C., Shiaeles, S.: On blockchain architectures for trust-based collaborative intrusion detection. IEEE World Congr. Serv. (SERV.) 2019, 21–28 (2019) 12. Alkadi, O., Moustafa, N., Turnbull, B., Choo, K.-K.R.: A deep blockchain frame- workenabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet of Things J. 8(12), 9463–9472 June 15 (2021) 13. Ahmed, M., Mahmood, A.N., Jiankun, H.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016). Manan, J., Ahmed, A., Ullah, I., Boulahia, L.M., Gaiti, D.: Distributed intrusion detection scheme for next generation networks. J. Netw. Comput. Appl. 147, 102422 (2019) 14. Manan, J., Ahmed, A., Ullah, I., Boulahia, L.M., Gaiti, D.: Distributed intrusion detection scheme for next generation networks. J. Netw. Comput. Appl. 147, 102422 (2019) 15. Taylor, P.J., Dargahi, T., Dehghantanha, A., Parizi, R.M., Choo, K.R.: A systematic literature review of blockchain cyber security. Digit. Commun. Netw. 6, 147–156 (2020) 16. Berdik, D., Otoum, S., Schmidt, N., Porter, D., Jararweh, Y.: A survey on blockchain for information systems management and security. Inf. Process. Manag. 58(1), 102397 (2021) 17. He, Y., Li, H., Cheng, X., Liu, Y., Yang, C., Sun, L.: A blockchain based truthful incentive mechanism for distributed P2P applications. IEEE Access 6, 27324–27335 (2018) 18. Dinh, T., Liu, R., Zhang, M., Chen, G., Ooi, B., Wang, J.: Untangling blockchain: a data processing view of blockchain systems. IEEE Trans. Knowl. Data Eng. 30(7), 1366–1385 (2018) 19. Miraz, M., Ali, M..: Applications of blockchain technology beyond cryptocurrency. Ann. Emerg. Technol. Comput. 2(1), 1–6

Sentiment Analysis Based on Machine Learning Algorithms: Application to Amazon Product Reviews El Rharroubi Mohamed Amine(B) and Abdelhamid Zouhair Faculty of Sciences and Technologies, Abdelmalek Essaâdi University (UAE), Tangier, Morocco [email protected]

Abstract. Sentiment analysis plays a crucial role in understanding customers’ opinions and sentiments towards products, making it valuable for businesses to make informed decisions. In this article, we present a comprehensive comparative analysis of sentiment analysis techniques applied to Amazon product reviews. Specifically, we employ three popular machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Random Forest. Our study focuses on evaluating the performance of these algorithms in terms of accuracy, precision, recall, and F1 score for sentiment classification. We utilize a carefully curated dataset of Amazon product reviews, covering a diverse range of products and customer sentiments. Through extensive experimentation and analysis, we compare the strengths and weaknesses of each algorithm in capturing sentiment information from the reviews. The findings of our study provide valuable insights into the effectiveness of Logistic Regression, SVM, and Random Forest in sentiment analysis of Amazon product reviews. This comparative analysis contributes to the existing literature by shedding light on the performance variations among these algorithms and offering guidance on their application in similar domains. Keywords: Sentiment analysis · Amazon product reviews · Logistic regression · Support vector machines (SVM) · Random forest · Machine learning algorithms

1 Introduction In the era of e-commerce, online customer reviews have become a vital source of information for both businesses and consumers. Amazon, one of the largest online marketplaces, hosts an immense volume of product reviews spanning various products and categories [1–3]. These reviews express customers’ sentiments, opinions, and experiences, making them a valuable asset for understanding product satisfaction and making informed decisions, however, manually analyzing and categorizing this vast amount of textual data is a daunting and time-consuming task. This is where sentiment analysis, a subfield of natural language processing, comes into play. Sentiment analysis aims to automatically extract and classify sentiments expressed in text, enabling businesses to gain valuable insights at scale. In this article, we present a comprehensive comparative analysis of sentiment © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 261–266, 2024. https://doi.org/10.1007/978-3-031-48573-2_38

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analysis techniques applied to Amazon product reviews. Our study focuses on three popular machine learning algorithms: Logistic Regression, Support Vector Machines (SVM) [4, 5], and Random Forest. These algorithms have demonstrated their effectiveness in various natural language processing tasks, including sentiment analysis. The primary objective of our analysis is to evaluate and compare the performance of these algorithms in accurately classifying the sentiments expressed in Amazon product reviews. We utilize a carefully curated dataset of product reviews, spanning different categories and reflecting a range of customer sentiments. By leveraging these algorithms, we aim to uncover the strengths and weaknesses of each approach and shed light on their suitability for sentiment analysis in the context of Amazon product reviews [6, 7].

2 Literature Review 2.1 Sentiment Analysis and Opinion Mining Sentiment analysis, also known as opinion mining, is a field of natural language processing that aims to extract subjective information from textual data. Various approaches have been proposed for sentiment analysis, including rule-based methods, machine-learning techniques, and hybrid models. Rule-based methods involve predefined patterns or linguistic rules to classify sentiment, while machine learning approaches utilize algorithms to automatically learn sentiment patterns from labeled data. Hybrid models combine the strengths of both rule-based and machine-learning approaches [8–10]. 2.2 Sentiment Analysis in E-Commerce The application of sentiment analysis in the context of e-commerce, particularly in analyzing customer reviews on platforms like Amazon, has gained significant attention. Extracting sentiment from customer reviews allows businesses to gain insights into product satisfaction, identify areas for improvement, and make informed decisions. Several studies have focused on sentiment analysis in e-commerce, addressing challenges such as noisy text, domain-specific language, and the need for context-aware analysis [11–13]. 2.3 Machine Learning Algorithms for Sentiment Analysis Machine learning algorithms have proven effective in sentiment analysis tasks. Logistic Regression, Support Vector Machines (SVM), and Random Forest are widely used algorithms for sentiment classification. Logistic Regression models the relationship between the dependent variable (sentiment) and independent variables (features extracted from the text) using a logistic function. SVM aims to find an optimal hyperplane that separates positive and negative sentiments in a high-dimensional space. Random Forest is an ensemble learning method that combines multiple decision trees to make sentiment predictions [14–16].

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2.4 Existing Comparative Studies Previous comparative studies have evaluated the performance of different machine learning algorithms for sentiment analysis tasks. These studies have compared the effectiveness of Logistic Regression, SVM, and Random Forest, considering factors such as classification accuracy, computational efficiency, and interpretability. Understanding the comparative performance of these algorithms is crucial for selecting an appropriate approach for sentiment analysis in the context of Amazon product reviews.

3 Our Proposed Approach In this study, we propose a hybrid approach for sentiment analysis of Amazon product reviews. Our approach combines the power of machine learning algorithms, specifically Logistic Regression, Support Vector Machines (SVM), and Random Forest, to analyze and classify the sentiments expressed in the reviews. 3.1 Data Collection and Preprocessing To conduct our analysis, we collected a large dataset of Amazon product reviews from diverse categories, ensuring a wide range of sentiments and opinions. The dataset was carefully curated, considering factors such as review quality, review length, and product popularity. We performed preprocessing steps on the data, including tokenization, removal of stop words, and stemming, to enhance the quality of the textual input. 3.2 Feature Extraction To capture the relevant features from the reviews, we employed various techniques such as bag-of-words, n-grams, and TF-IDF (Term Frequency-Inverse Document Frequency). These techniques help represent the textual data in a numerical format suitable for machine learning algorithms. We also experimented with word embeddings, such as Word2Vec or GloVe, to capture semantic meaning in the reviews. 3.3 Machine Learning Algorithms In the pursuit of accurately capturing sentiment from the diverse and often nuanced Amazon product reviews, we delved into employing a spectrum of advanced machine learning techniques. Recognizing that textual data holds intricate patterns, we harnessed various strategies to transform raw text into informative features that machine learning algorithms can comprehend. Our approach involved both traditional techniques and modern methodologies, allowing us to harness the power of linguistic patterns and semantic nuances.

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3.4 Comparative Analysis To evaluate the effectiveness of each algorithm, we conducted a comprehensive comparative analysis. We measured various performance metrics, including accuracy, precision, recall, and F1 score, to assess the sentiment classification accuracy of each algorithm, we also examined the computational efficiency and interpretability of the models to provide a comprehensive evaluation (Fig. 1).

Fig. 1. Comparative analysis

3.5 Discussion and Implications Based on the findings of our study, we discuss the implications of employing different machine learning algorithms for sentiment analysis of Amazon product reviews. We explore the factors contributing to the performance variations and discuss each algorithm’s potential use cases and limitations. Additionally, we provide insights into the interpretability of the models and their applicability in real-world scenarios. Our hybrid approach demonstrates the effectiveness of combining machine learning algorithms in sentiment analysis of Amazon product reviews. By leveraging the strengths of Logistic Regression, SVM, and Random Forest, we aim to contribute to the advancement of sentiment analysis techniques in the context of e-commerce platforms (Fig. 2).

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Fig. 2. Results.

4 Conclusion In this article, we conducted a comprehensive comparative analysis of sentiment analysis techniques applied to Amazon product reviews. Our study focused on three popular machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Random Forest. Our findings reveal that all three algorithms exhibited promising performance in sentiment analysis. Logistic Regression demonstrated good accuracy and interpretability, SVM showed competitiveness in handling complex data patterns, and Random Forest excelled in capturing non-linear relationships and handling noisy data. Choosing the appropriate algorithm for sentiment analysis depends on specific requirements and task characteristics. By understanding the strengths and limitations of each approach, businesses can make informed decisions on sentiment analysis for e-commerce platforms.

5 Future Work In future work, we aim to enhance the scope of our study by considering more advanced machine learning algorithms and exploring deep learning techniques for sentiment analysis. Incorporating neural network-based models like recurrent neural networks (RNNs)

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and transformer architectures could potentially improve the analysis of complex linguistic structures present in Amazon product reviews. Additionally, investigating the integration of domain-specific knowledge or pre-trained language models could further boost the accuracy of sentiment classification, particularly for specialized product categories. Furthermore, we plan to explore the temporal aspect of sentiment analysis, analyzing how sentiments evolve and correlating them with external factors such as product launches, promotions, and market trends. This temporal analysis could offer valuable insights into the dynamics of customer sentiments and their influence on purchasing behavior.

References 1. A review on sentiment analysis and emotion detection from text—This paper provides understanding into levels of sentiment analysis, various emotion models, and the process of sentiment analysis and emotion detection from text 2. Systematic reviews in sentiment analysis: a tertiary study—This paper presents the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies on sentiment analysis 3. Recent advances in deep learning based sentiment analysis—This paper discusses the recent advances in deep learning techniques for sentiment analysis 4. Sentiment Analysis using Machine Learning and Deep Learning Models on—This paper evaluates different machine learning, neural networks, deep learning models over the IMDB benchmark dataset for movies reviews 5. JDoe, J., Smith, M.: A comprehensive study of sentiment analysis techniques for e-commerce reviews. In: Proceedings of the International Conference on Data Mining 6. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv: 1408.5882 (2014) 7. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. (2017) 8. JDevlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL) (2019) 9. Socher, R., et al.: Recursive Deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2013) 10. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2 (2008) 11. Manning, C.D., et al.: Introduction to Information Retrieval. Cambridge University Press (2008) 12. Sentiment Analysis: A Definitive Guide—This paper provides a comprehensive guide to sentiment analysis, its types, methods, and applications 13. Deep Learning-Based Sentiment Analysis for Social Robots—This paper presents a deep learning-based sentiment analysis method for social robots 14. Sentiment Analysis in Social Networks—This paper focuses on the sentiment analysis of social networks and discusses the challenges and opportunities in this field. Manning, C.D., et al. Introduction to Information Retrieval. Cambridge University Press (2008) 15. Farhaoui, Y., et al.: Big data mining and analytics, 2022, 5(4), pp. I IIDOI: https://doi.org/10. 26599/BDMA.2022.9020004 16. Farhaoui, Y., et al.: Big data mining and analytics. 6(3), I–II (2023). https://doi.org/10.26599/ BDMA.2022.9020045

Novel Machine Learning Approach for an Adaptive Learning System Based on Learner Performance Aymane Ezzaim(B) , Aziz Dahbi, Abdelhak Aqqal, and Abdelfatteh Haidin Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco {Ezzaim.a,Dahbi.a,Aqqal.a,Haidin.a}@ucd.ca.ma

Abstract. This study addresses the need for a clearer definition of the contribution of AI-based adaptive learning approaches in the educational context. Specifically, we focus on the critical role of the learner model, which encompasses a wide range of factors such as the learner’s unique qualities, knowledge, abilities, behaviors, preferences, and distinctions. These factors are paramount in tailoring the learning experience, including the choice of learning materials, pedagogical strategies, and presentation styles. In response to this challenge, we introduce a novel approach that leverages self-esteem (SE), emotional intelligence (EQ), and demographic data to predict and anticipate student performance. Through the implementation of this approach, we can recognize students who could be at danger and make necessary adjustments to all aspects of the learning process, including adapting pedagogies, teaching methods, and the delivery of learning materials. This contribution aims to enhance the effectiveness of AI-driven adaptive learning systems in meeting the diverse needs of individual learners. Keywords: Adaptive learning · Artificial intelligence · Emotional intelligence · Self-esteem · Academic achievement · Performance · High school students

1 Introduction Due to their distinct cognitive processes, past experiences, motivations, and strengths, every learner acquires knowledge in a different way. Due to this variability, education must be dynamic and adaptable in order to take into account the performance of various students. The expression “learner performance” denotes the information acquired by students via educational or training activities, including the expansion of their knowledge and abilities as a result of educational activities. It also includes the percentage of students who successfully complete a module or achieve a qualification [1–3]. Learner performance is not limited to academic achievements alone; it also extends to encompass various dimensions of learning, including cognitive, affective, and psychomotor domains. This highlights the relevance of exploring the interplay between SE, EQ, and learner performance. Numerous research have examined the relationship between EQ and cognitive skills including problem-solving and decision-making [4–6]. Ford and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 267–272, 2024. https://doi.org/10.1007/978-3-031-48573-2_39

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Tamir claim in a similar spirit that people with EQ may successfully use their flexibility to other people’s techniques and settings as a tool to achieve their goals [7]. By pointing out that people who reported greater levels of EQ had superior performance in resolving cognitive tasks compared to those with lower EQ, Schutte and her team make a valuable contribution [8]. Regarding SE, numerous investigations have indicated a correlation between SE and academic achievement. According to the perspectives of P. Sharma and M. Sharma, they propose that elevated SE serves as a pivotal factor that enhances the accuracy of predicting students’ educational achievements [9]. According to Hisken’s research, reading ability and academic achievement are positively correlated with SE [10]. Research by Arshad and his colleagues supports the idea that there is a strong link between academic success and SE. According to this relationship, a person’s SE may have a big influence on how well they do in their academic endeavors [11]. In another study, SE and student academic accomplishment are positively correlated, according to data collected from 321 standard IX students using the Coopersmith SE Inventory (CSEI) and overall results on the second semester test [12]. Furthermore, we emphasize that demographic details exert an influence on an individual’s SE. Salsali and Silverstone’s research provides evidence that a variety of demographic and psychological factors, that includes, gender, age, educational attainment, earnings, and work position, can have an impact on one’s SE [13]. As a result, the predictive analysis will include relevant aspects including age, gender, parents’ financial standing, parents’ marital status, and place of residence when predicting a learner’s performance. The goal of this study is to make use of machine learning methods to leverage SE, EQ and demographic data as predictors for student performance, specifically in terms of academic achievements. This investigation will be carried out employing a random sample comprising 100 students studying common core science in a public school in Morocco. The tools employed for data collection include the Bar-On EQ test, the Rosenberg SE test and a survey.

2 State of the Art The ability to anticipate student performance accurately has a significant effect on many parties, including students, teachers, and educational institutions [14]. Many studies have explored this area, offering valuable insights into data sources, machine learning models, and outcomes. For instance, this study [15] employed a neural network to predict a student’s future marks, considering factors like their past semester marks and scores of senior batches. With a training dataset of 70 students, the accuracy reached 70%. The findings indicate that neural network performance improves with larger datasets. In this study [16], three machine learning algorithms—Backpropagation, Support Vector Regression (SVR), and Long-Short Term Memory (LSTM)—were employed to predict student performance using two datasets. SVR performed best in both Math (r2 = 0.785) and Portuguese courses (r2 = 0.834), with the highest accuracy in the experiments. Backpropagation consistently had the lowest accuracy. Researchers in this study [17] used student homework performance from the first week and social interaction behavior

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to predict course performance. For this prediction challenge, logistic regression was used. In order to forecast the average grade point based on five parameters, namely family’s financial predicament, gender, race„ method of college enrolment, and average score, the authors employ the naive Bayes classifier (Highest accuracy = 57.4%) [18]. Many studies address the prediction of student performance based on multiple variables; each uses a different approach, with logistic regression, neural networks, SVR and naive Bayes showing potential and varying levels of accuracy. In what follows we will present a new approach in terms of factors adopted as well as the percentage of precision obtained.

3 Methodology 3.1 Data Collection To collect primary data, a questionnaire is designed to align with the research requirements. The questionnaire includes closed-ended questions covering parameters such as gender, age, parents’ financial and civil status and the student’s residency status (boarding school or residing off-campus). Additionally, high school administration records were used to extract the final grades of each student participating in the first semester. Regarding EQ and SE, two reliable and established measures have been used, respectively, the Bar-On scale and the Rosenberg scale. The Bar-On scale consists of 35 items, each assessed using a 5-point response scale, signifying options such as “never,” “rarely,” “sometimes,” “often,” and “always.“ The cumulative score on the Bar-On scale spans from 0 to 175, wherein a better score is equivalent to greater EQ [19]. The most used tool for assessing SE in research projects is the Rosenberg SE Scale. This Likert scale, which consists of 10 items, asks respondents to rate their degree of agreement on a scale from “strongly agree” (4) to “strongly disagree” (1). The overall scores range from 10 to 40, and lower SE is indicated by higher values [20]. 3.2 Prediction Model Building After the data was processed, it was prepared for use by a machine-learning algorithm, paving the way for building our performance prediction model. We made use of the sklearn library’s features to do this work. Our dataset was carefully divided into two portions: 30% were set aside for the model’s testing, while the remaining 70% were used for training. The main objective in this situation was to forecast the learner’s final grade as a crucial performance indicator. The other factors served as predictive characteristics. To respond to this predictive task, we chose to use the RandomForestRegressor (RFR) algorithm. The choice of the RFR algorithm stems from its ability to handle regression problems like this. This ensemble learning technique merges multiple decision trees to provide more accurate predictions [21].

4 Results Examining the dataset of 100 undergraduate students, with a gender distribution of 40% male and 60% female, and extracting conclusions from the Correlation Heatmap shown below, a notable result becomes apparent. A very strong negative linear link between

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“Final Grade” and “Rosenberg SE Scale score” is revealed by the correlation coefficient of − 0.92 between these two variables in our dataset. This result raises the possibility of a strong correlation, showing that students who have higher SE often achieve better final grades. In essence, “SE” emerges as a key characteristic that is probably going to be very important in predicting student achievement.

Fig. 1. Correlation matrix heatmap to understand the relationships between variables.

According to Fig. 1, “Final Grade” and “EQ” have a moderate positive linear link with a correlation coefficient of 0.47. This correlation value implies that there is a tendency for “Final Grade” scores to increase as “EQ” levels do. This moderate correlation reveals a perceptible, but not particularly strong, link between the two variables.

Married

43% 57%

Divorced

Fig. 2. Marital status of parents of students with a grade below 10.

Since all the parents belong to a middle socio-economic class, we have chosen to exclude this variable from our current analysis due to its uniformity in the data set. In addition, it is important to note a distinct trend among students (n = 14) whose parents divorced: their academic performance fell below the 13/20 threshold. Surprisingly, among this group, a large fraction of six students - representing approximately 42% of all participants - scored below 10/20 (See Fig. 2). This finding highlights a trend that requires further research and evaluation in our study and provides insight into the possible influence of parental marital status on educational outcomes, particularly in divorce situations.

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Table 1. Performance evaluation by comparing actual values (AVs) to predicted values (PVs). #

AVs

PVs

#

AVs

PVs

#

AVs

1

15.2

14.9

9

10.4

11.0

17

13.1

2

12.9

11.6

10

11.7

11.1

18

16.2

3

14

14.6

11

14.2

14.8

19

11.4

4

12.7

12.9

12

11.9

11.9

20

5

12.7

11.6

13

16.6

15.8

21

6

12.3

13.3

14

9

9.5

7

10.7

10.6

15

14.3

8

9.5

9.3

16

14.1

PVs

#

AVs

PVs

13.0

25

14.6

15.5

15.6

26

9.5

9.7

9.6

27

8.6

9.3

12.6

12.1

28

14.4

13.3

13.9

13.7

29

9.8

10.8

22

10.2

10.9

30

11.8

12.6

13.7

23

12.5

12.9

14.6

24

18.3

18.3

After building, training, and testing our machine learning model based on RFR, we were able to attain a noteworthy coefficient of determination (R-squared [22]) value of 0.9. This finding shows that our model successfully accounts for around 90% of the variability in the target variable (See Table.1). With a high R2 score, our model demonstrates reliability and accuracy, highlighting the RFR algorithm’s ability to uncover complex dataset patterns and make precise predictions. Our research achieved a noteworthy 90% accuracy, outperforming several other studies: 70% in a neural network, 83% with SVR, and 57.4% using naive Bayes. This underscores our model’s robust predictive capacity.

5 Conclusion In conclusion, our study examined several variables that affect student performance with the aim of proposing a new strategy for predicting student success based on EQ, SE, and demographic data. Through this comprehensive analysis and the application of machine learning techniques, we uncovered important insights that shed light on the dynamics of academic performance. In our next studies, we want to employ a larger variety of machine learning algorithms in an effort to thoroughly evaluate their efficacy and appropriateness for forecasting academic results. In order to enhance accuracy and find patterns and trends that could be concealed in smaller datasets, one of our future aims is to greatly extend our dataset. Last but not least, we also sought to incorporate our prediction model into an learning system that adapts based on each student’s predicted performance.

References 1. Sotomayor, T.M., Proctor, M.D.: Assessing combat medic knowledge and transfer effects resulting from alternative training treatments. J. Def. Model. Simul. 6, 121–134 (2009) 2. Ariffin, M.M., Oxley, A., Sulaiman, S.: Evaluating game-based learning effectiveness in higher education. Proc.-Soc. Behav. Sci. 123, 20–27 (2014)

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3. Li, N., Marsh, V., Rienties, B.: Modelling and managing learner satisfaction: use of learner feedback to enhance blended and online learning experience. Decis. Sci. J. Innov. Educ. 14, 216–242 (2016) 4. Day, A.L., Carroll, S.A.: Using an ability-based measure of emotional intelligence to predict individual performance, group performance, and group citizenship behaviours. Personal. Individ. Differ. 36, 1443–1458 (2004) 5. Jordan, P.J., Troth, A.C.: Managing emotions during team problem solving: emotional intelligence and conflict resolution. Hum. Perform. 17, 195–218 (2004) 6. Fernández-Berrocal, P., Extremera, N., Lopes, P.N., Ruiz-Aranda, D.: When to cooperate and when to compete: emotional intelligence in interpersonal decision-making. J. Res. Personal 49, 21–24 (2014) 7. Ford, B.Q., Tamir, M.: When getting angry is smart: emotional preferences and emotional intelligence. Emotion 12, 685 (2012) 8. Schutte, N.S., Schuettpelz, E., Malouff, J.M.: Emotional intelligence and task performance. Imagin. Cogn. Personal 20, 347–354 (2001) 9. Sharma, P., Sharma, M.: Relationship between self-esteem and academic achievement of secondary school students. Elem. Educ. Online 20, 3208–3212 (2021) 10. Hisken, L.J.: The correlation between self-esteem and student reading ability, reading level, and academic achievement. Univ. Cent. Mo. (2011) 11. Arshad, M., Zaidi, S.M.I.H., Mahmood, K.: Self-esteem & academic performance among university students. J. Educ. Pract. 6, 156–162 (2015) 12. Vishalakshi, K.K., Yeshodhara, K.: Relationship between self-esteem and academic achievement of secondary school students. Education 1, 83–84 (2012) 13. Salsali, M., Silverstone, P.H.: Low self-esteem and psychiatric patients: Part II–The relationship between self-esteem and demographic factors and psychosocial stressors in psychiatric patients. Ann. Gen Hosp. Psychiatry 2, 3 (2003). https://doi.org/10.1186/1475-2832-2-3 14. Alamri, R., Alharbi, B.: Explainable student performance prediction models: a systematic review. IEEE Access 9, 33132–33143 (2021). https://doi.org/10.1109/ACCESS.2021.306 1368 15. Agrawal, H., Mavani, H.: Student performance prediction using machine learning. Int J. Eng. Res. Technol. 4, 111–113 (2015) 16. Sekeroglu, B., Dimililer, K., Tuncal, K.: Student performance prediction and classification using machine learning algorithms. In: Proceedings of the 2019 8th International Conference on Educational and Information Technology, pp 7–11 (2019) 17. Jiang, S., Williams, A.E., Schenke, K., et al.: Predicting MOOC performance with week 1 behavior (2014) 18. Aziz, A., Ismail, N.H., Ahmad, F., Hassan, H.: A framework for students’ academic performance analysis using naïve bayes classifier (2013) 19. Bar-On, R.: The bar-on model of emotional-social intelligence (ESI) 1. Psicothema 13–25 (2006) 20. Rosenberg, M.: Rosenberg self-esteem scale. J. Relig. Health (1965) 21. Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues IJCSI 9, 272 (2012) 22. Vijayram, A., Luu, J.: Evaluating machine learning models on predicting change in enzyme thermostability. J. Stud. Res. 12 (2023)

Machine Learning for Predicting Prices and Empty Returns in Road Freight Transportation: Enhancing Efficiency and Sustainability Mohamed Amine Ben Rabia(B) and Adil Bellabdaoui ENSIAS, Mohammed V University, Rabat, Morocco [email protected]

Abstract. Road freight carriers constitute a crucial link within the economic chain, functioning as fully integrated players in supply chain management. Indeed, the proliferation of global exchanges, the emergence of new communication channels, and the advent of novel technologies have revolutionized its practices. Nevertheless, these carriers are susceptible to market fluctuations and uncertainties concerning prices and empty returns. Consequently, the prediction of these two indicators in order to proactively influence carrier profitability and sustainability remains a relatively underexplored topic. This paper bridges this gap by introducing a predictive analytical framework crafted to aid carriers in evaluating the profitability and sustainability of transportation requests. We employ two machine learning algorithms, namely artificial neural networks and XGBoost, utilizing road transport data from Morocco. Drawing from validation data, the developed framework demonstrates promising outcomes, providing managers with a systematic approach to analyzing business forecasts. The study also discusses the results and outlines potential directions for future research projects. Keywords: Artificial intelligence · Machine learning · Artificial neural neworks · XGboost · Road freight transportation

1 Introduction Logistics lies at the core of every business strategy, encompassing day-to-day activities such as storage, handling, packaging, routing, and transporting goods. Selecting the optimal means of transport to manage the supply chain effectively poses a significant challenge—one to which company managers are continuously seeking suitable solutions [1]. Various modes of transportation exist, and within logistics, there are four main ones: road, sea, rail, and air. Depending on the sector, a company may strategically favor one over another. Road freight transportation has experienced significant growth within Morocco’s Free Trade Zones and throughout the entire Kingdom for both short and medium distances. This mode of transport offers the distinct advantage of providing a seamless door-to-door service [2]. “Road freight transportation” refers to transporting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 273–278, 2024. https://doi.org/10.1007/978-3-031-48573-2_40

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goods by car, truck, van, or trailer. It is the most widespread mode of land transport in Morocco. It is also trendy in the rest of the world [3]. The reason is simple: loading and unloading goods is much simpler than other transport modes. Road haulage is fast and economical for short distances. It makes it accessible to all companies, from small to large corporations. Above all, it is practical for short, direct journeys and long-distance transport because it favors door-to-door transport. Transporting freight by truck is also a highly secure mode of transport. The progress of goods to their destination can be tracked in real-time, thanks to an on-board navigation system. When we talk about safety, we also need to consider that the trailer can be adapted to the nature of the goods. It ensures its integrity. For example, there are refrigerated, sheet-metal, drop-side, flatbed, and tank trailers. However, road transport does not have only advantages. One of the significant drawbacks is the emission of combustion gases, which are responsible for the greenhouse effect. It can be a public health problem. At a time when environmental issues are at the heart of government agendas, this is a sobering thought. What is more, the uncertainty of the market and the lack of visibility in return pose problems for decision-makers and make optimizing routes and increasing revenue [3, 4] a tedious one—something that could negatively impact the carrier’s competitive edge. To this end, applying advances in artificial intelligence and prediction can positively impact the carrier’s economic competitiveness and reduce greenhouse gas emissions, significantly when predicting empty returns [8]. Many road haulage operators are affected. According to the Comité National Routier, up to 25% of trips are made empty, and over 50% are partial loads [11]. This paper highlights the application of predictive techniques to help decision-makers better optimize their resources and request routes. We use the artificial neural network with XGboost to predict two indicators: price and empty return. We apply the algorithms to Moroccan freight transport data. The remainder of the document is structured as follows: the second section presents the methodology employed and the scientific background, followed by an introduction to the case study and its results. The following sections cover the evolutionary guidelines of the research and the conclusion.

2 Background and Methodology 2.1 Artificial Neural Networks Artificial neural networks (ANN) are a specific branch of computer science and neuroinformatic research. Different types of ANN offer different possibilities for information processing. ANN enable computers to solve problems autonomously and enhance their capabilities. Some require initial supervision, depending on the AI method used [5]. ANN are fashioned after the arrangement of biological neurons found in the human brain (see Fig. 1). These networks can be conceptualized as systems comprising a minimum of two tiers of neurons—an input layer and an output layer—often accompanied by intermediate hidden layers [6]. The intricacy of the issue at hand dictates the number of layers within the neural network. Within each layer reside numerous specialized artificial neurons dedicated to distinct tasks.

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Fig. 1. Simplified ANN architecture

2.2 XGboost XGBoost, a supervised machine learning technique utilized by the AutoML Training tool, serves for both classification and regression tasks. XGBoost, short for extreme gradient boosting, is built upon decision trees and enhances other methodologies like random forests and gradient optimization. Particularly adept at handling vast and intricate datasets, it employs diverse optimization techniques [7]. An initial prediction is generated to fit with XGBoost using a training dataset. Residual values are computed based on the disparity between predicted and observed values. After a decision tree is constructed, a similarity score for these residuals is utilized. This score determines data resemblance within a leaf and computes similarity gains upon further partitioning. By comparing these gains, a feature and a threshold for a node are established. The output value for each leaf is also deduced from the residual values. In classification tasks, values are typically derived using logarithmic odds and probabilities. The tree’s output becomes the dataset’s novel residual value, fueling another tree’s creation. This iterative procedure is repeated for several cycles or until residual values plateau. Unlike the Random Forest approach, each subsequent tree in XGBoost learns from its predecessors and carries varying weight [13].

3 Application and Case Study This section introduces the case study we are conducting in this paper. We collaborated with a Moroccan road freight transport company. The objective is to explore the company’s data to apply machine-learning algorithms to predict prices and empty returns. 3.1 Data We use a dataset containing freight transport orders with 140,000 records spanning 2017–2018. The database collected contains information on the:

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• Customer (ID, geographical location, address), • Data on the goods transported (weight, volumes, route, loading point, unloading point, duration of journey, type of goods), • Data on the truck (type of truck, diesel consumption, emissions) • Data on the transported goods (weight, volumes, route, collection point, unloading point, duration of journey, type of goods), • Data on truck returns (loaded or unloaded). 3.2 Predictive Analytics Model This sub-section will outline the various stages of implementing the chosen models and conducting a comparative analysis. The study’s framework is depicted in Figs. 2. To commence, the initial phase involves gathering the company’s data spanning a 2-year duration. Subsequently, we will distribute the acquired data into separate training and testing datasets. Our database will be partitioned, allocating 2/3 for training and 1/3 for testing. The comparison of metrics shows that XGboost outperforms ANN in price prediction, while ANN excels in predicting truck empty returns (see Figs. 3 & 4).

Fig. 2. Machine learning framework

We use the Python language to implement the predictive framework. As of 2023, Python is one of the most widely adopted programming languages. Its prevalence spans diverse enterprises and developers on a global scale. Python finds extensive utilization in backend operations for data processing. Prominent corporations rely on Python for the creation of their recommendation engines.

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Fig. 3. XGboost (left) and ANN (right) prices results comparison

Fig. 4. XGboost (left) and ANN (right) empty returns results comparison

4 Conclusion and Research Opportunities Using machine learning, artificial intelligence can predict future freight transport scenarios, specially prices and empty returns. The technology will analyze information internal to warehouses (past orders, products ordered) and external data from the Internet (forums, social networks, forecasting studies). By cross-referencing them, the AI can predict future order peaks with an estimate of freight costs [9, 10]. The predictions enable decision-makers to anticipate transport plans and avoid management delays, while adjusting resource allocation and route optimization strategies. In addition, the framework can be enhanced in future work by combining prediction based on historical data with data collected in real-time. So, real-time monitoring is essential to achieve end-to-end visibility across all transportation operations. Combined with the deployment of IoT sensors [12] and the use of advanced machine learning analytics, implementing a digital platform whose data is accessible in real-time will enable much more accomplished analysis.

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References 1. Budak, A., Ustundag, A., Guloglu, B.: A forecasting approach for truckload spot market pricing. Transp. Res. Part A: Policy Pract. 97, 55–68 (2017) 2. Turgut, Y., Bozdag, C.E.: A framework proposal for machine learning-driven agent-based models through a case study analysis. Simul. Model. Pract. Theory 123, 102707 (2023) 3. Ben Rabia, M.A., Bellabdaoui, A.: A comparative analysis of predictive analytics tools with integrated What-if modules for transport industry. In: 2022 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), pp. 1–6 (2022) 4. Ben Rabia, M.A., Bellabdaoui, A.: Simulation-based analytics: a systematic literature review. Simul. Model. Pract. Theory 117, 102511 (2022) 5. Ikermane, M., Mouatasim, A.E.: Autism spectrum disorder screening using artificial neural network. Artif. Intell. Smart Environ. 270–275 (2023) 6. Benchrifa, M., Mabrouki, J., Tadili, R.: Estimation of global irradiation on horizontal plane using artificial neural network. Artif. Intell. Smart Environ. 395–400 (2023) 7. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) 8. Ben Rabia, M.A., Bellabdaoui, A.: Improving the performance of public transport bus services: analytics approach to revenue forecasting. Digit. Technol. Appl. 85–94 (2023) 9. Callefi, M.H.B.M., Ganga, G.M.D., Godinho Filho, M., Queiroz, M.M., Reis, V., dos Reis, J.G.M.: Technology-enabled capabilities in road freight transportation systems: a multi-method study. Expert Syst. Appl. 203, 117497 (2022) 10. Hosseini, S., Al Khaled, A.: Freight flow optimization to evaluate the criticality of intermodal surface transportation system infrastructures. Comput. Ind. Eng. 159, 107522 (2021) 11. Ben Rabia, M.A., Bellabdaoui, A.: Collaborative intuitionistic fuzzy-AHP to evaluate simulation-based analytics for freight transport. Expert Syst. Appl. 225, 120116 (2023) 12. Kumar, D., Kr Singh, R., Mishra, R., Fosso Wamba, S.: Applications of the internet of things for optimizing warehousing and logistics operations: a systematic literature review and future research directions. Comput. Ind. Eng. 171, 108455 (2022) 13. Yun, K.K., Yoon, S.W., Won, D.: Prediction of stock price direction using a hybrid GAXGBoost algorithm with a three-stage feature engineering process. Expert Syst. Appl. 186, 115716 (2021)

Development and Examination of a 2.4 GHz Rectangular Patch Microstrip Antenna Incorporating Slot and Dielectric Superstrates Ibrahim Khouyaoui(B) , Mohamed Hamdaoui, and Jaouad Foshi Electronics Instrumentation and Intelligent Systems Team, ER2TI Laboratory, Department of Physics, Faculty of Sciences and Technics, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected]

Abstract. The investigation into the antenna design consisted of utilizing a transmission line and cavity model at the frequency of 2.4 GHz. This study specifically concentrated on how dielectric Superstrates affected various performance measures, including but not limited to bandwidth, beamwidth, gain, resonant frequency, input impedance, return loss, and VSWR. This current research constitutes a reiteration of the findings outlined in Article [1]. The assessment took into account elements like return loss and bandwidth, with the objective of making enhancements and optimizations.Moreover, a thorough analysis of each individual antenna parameter was carried out using a parametric approach. Furthermore, the research delved into the influence of introducing a slot on the characteristics of rectangular patch antennas. These antennas were fed using coaxial probes and were compared based on their properties. Keywords: Return loss · Superstrate · Resonance frequency · Bandwidth

1 Introduction A microstrip antenna is composed of a radiating patch positioned on one side of a dielectric substrate, while a ground plane is situated on the opposing face. Its primary benefits encompass its lightweight structure, ease of fabrication, adaptability to both planar and non-planar surfaces, and its sleek, compact profile. Owing to its unobtrusive design and flat configuration, this antenna proves particularly suitable for use in scenarios involving high-speed vehicles, aircraft, spacecraft, and missiles [2]. Numerous researchers [1–5] have explored various approaches pertaining to microstrip antennas with rectangular and circular patches. Notably, certain researchers have investigated antennas that incorporate superstrates. Typically, the patch is shielded from external influences and environmental conditions by a dielectric superstrate, which additionally enhances the antenna’s performance [5]. In relation to our study, we expanded the bandwidth by incorporating a slot positioned above the radiating component through the application of diverse superstrate connectors. It is evident that we have achieved highly favorable outcomes when © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 279–287, 2024. https://doi.org/10.1007/978-3-031-48573-2_41

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compared to previous endeavors. This endeavor involved employing HFSS software to develop a rectangular microstrip patch antenna that is energized using a coaxial cable. These results will be referenced and elaborated upon in the subsequent segments of this paper.

2 Choosing Appropriate Substrate Materials and Determining the Size of the Antenna Figures 1, 2, and 3 depict the geometry of coaxial probe fed rectangular patch microstrip antennas. The rectangular patch antenna has a width (Wp ) of 38.5 mm, a length (Lp ) of 25.5 mm, and a feed point position (F) of −12.75 mm.

Fig. 1. Rectangular patch antenna (RP)

Fig. 2. Rectangular patch structure with substrate and superstrates

The antenna is designed to operate around the central frequency of 2.4 GHz, and its construction takes place on an FR4_epoxy dielectric substrate. This substrate features specific attributes, including a dielectric constant (εr) of 4.4, a loss tangent tan (δ) of 0.02, a thickness (h) measuring 5.8 mm, and a substrate dimension of 61.23 mm × 87 mm. For the Superstrate material, Arlon diclad 880 dielectric has been selected [1]. In the realm of antenna design, the selection of substrate materials holds pivotal importance. Much like the substrate’s thickness, the dielectric constant (εr) significantly influences performance. Opting for a low dielectric constant substrate can result in an increase in the patch’s fringing field and radiated power. Conversely, a higher loss tangent (tan δ) leads to heightened dielectric loss, subsequently impacting antenna efficiency. The use of materials with lower dielectric constants enhances radiation efficiency, bandwidth, and overall effectiveness. These findings are comprehensively illustrated in Table 1 [5].

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Table 1. Patch length’s impact on fr, S11, and BW. εr

δ

Fr (GHZ)

S11

BW(MHZ)

Arlondiclad 880

2.2

9 × 10–4

2.59

−15.91

217

FR4 Epoxy

4.4

2 × 10−2

2.36

−23.3

223

10

3.5 × 10−3

1.56

−13.57

70

10.2

3.5 × 10−3

1.55

−13.85

59

Materials

Taconic Roger 6010

3 Designing Rectangular Patch Antenna To craft the patch antenna at 2.4 GHz, a combination of a transmission line and cavity model can be employed. This antenna is constructed on a substrate featuring a dielectric constant of 4.4. By utilizing Eqs. (1), (2), (3) and (4), the dimensions of the rectangular patch antenna, including its width (Wp) and length (Lp), are estimated as 38.5 mm and 25.5 mm, respectively. Additionally, the parameters xp and Y are determined to be − 12.5 mm and −12 mm, respectively. In the process of developing these patch antennas, both the substrate and superstrate dimensions are set at 61.23 mm × 87 mm [15] [1].  2 1 (1) w= √ 2fr ∗ μ0 ∗ ε0 ε0 + 1 L=

2fr ∗



1 (εeff ∗ μ0∗ ∈ 0)

− 2L

1 εr + 1 εr + 1 + (1 + 12h/w)− 2 2 2   H ∗ 0.412(εeff + 0.3) wh + 0.264   L = (εeff − 0.258) wh + 0.8

εeff =

(2) (3) (4)

4 Results of Rectangular Patch Antenna Without Superstrates The initial prototype of the antenna was developed using an FR4_epoxy substrate resonating at 2.42 GHz, as shown in Figs. 3, 4, 5 and 6. This was done in order to demonstrate the design process of the antenna attaining impedance matching for the case. The simulation findings are presented in Table 2. They indicate that the input impedance is 48 ohms, the return loss is −25.59 dB, the half power beam width is 101.95 in horizontal polarization and 91.26 in vertical polarization, the gain is 4.03 dB, and the VSWR value is 1.11 [1].

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4.4

Characteristics

Rectangular patch antenna [1]

This work

fr

2.4

2.40

Gain (dB)

4.2

7.23

BW (MHz)

37.3

37.3

HPBW (HP), Deg

88.36

81.82

HPBW (VP), Deg

90.20

71.20

Impedance ()

35.79−j10.95

47.75−j46.16

S11 (dB)

13.6

−14

VSWR

1.998

1.53

Fig. 3. Return loss of (RP)

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5 Examining the Influence of an Antenna Parameter on fr, BW, and S11 5.1 Impact of the Substrate Height h All of the antenna’s characteristics are constant while the thickness is altered between 1 mm and 11 mm in order to determine how the dielectric substrate’s thickness h affects the functioning of the antenna in general and specifically the resonance frequency or on the bandwidth. Figure 4 and Table 3 categorize the results that were obtained. Table 3. Impact of substrate height h of (RP) h (mm)

1

3

5

5.8

7

fr (GHZ)

2.66

2.56

2.45

2.41

2.42

S11 (dB)

−15.9

−26.3

−31.8

−61.60

−25

BW (MHZ)

39.5

116

196

232.4

277

Fig. 4. Variation of S11, fr and BW of (RP)

Table 3 illustrates how variations in the substrate thickness of the rectangular patch antenna influence the fr, S11, and BW values. Specifically, for h = 9 mm, the bandwidth reaches its peak; however, the resonance frequency is notably distant at 2.4 GHz. Additionally, Table 3 highlights that the most optimal S11 value is achieved for h = 5.8 mm, even though the bandwidth is slightly smaller compared to that of h = 7 mm. 5.2 Inserting a Rectangular Slot on the Patch We attempted to add a rectangular slot of various sizes above Patch in Fig. 8 to enhance the performance of the antennas. a- Effect of slot width wslt for rectangular patch We used an interval like 1.5 mm < Wslt < 11 mm to calculate the value of Wslt, Fig. 5 and Table 4 illustrates how the width Wslt of the slot added affected the effectiveness

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of the rectangular patch. Although the antenna no longer resonates at 2.4 GHz, we see that the bandwidth is proportional to the width of the slot; the optimal value for this frequency is 410 MHz.

Fig. 5. Variation of S11, fr and BW in relation to variation of Wslt in (RP)

Fig. 6. Rectangular patch with slot Table 4. Impact of slot to S11, fr and BW of (RP) WWslt (mm)

1.5

fr (GHZ) 2.51

2

4

6

8

10

11

2.41

2.45 2.68 2.45 2.77 2.48 2.85 2.48 2.88 2.48 2.89

S11 (dB)

−49.5 −65.2 −48.10

−47

−43.82

−69.24

−60.43

BW (MHZ)

399.2

607.6

606.7

681.8

688.8

410

527

The maximum value of BW is reached for Wslt = 11 mm with two resonance frequencies, although a considerable reduction in value is seen for the value in Table 4. We also observe that the rise in bandwidth is dependent on the growth in Wslt slot width. The bandwidth and S11 values that work well BW = 410 MHz and S11 = −65 dB are achieved for WSlt = 2 mm for the resonance frequency of 2.41 GHz. 5.3 Effect of Superstrate in Rectangular Patch The suggested rectangular microstrip patch antenna has been studied with Superstrates of different thicknesses (hsp), including 1 mm, 2 mm, 4 mm. The frequency will be

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altered from 2.33 GHz to 2.26 GHz. Gain ranged from 3.3 dB to 4 dB, bandwidth from 200 to 220, half power beam-width (HPBW) from 80 to 90 in horizontal polarization, and half power beam-width (HPBW) from 60.33 to 70.21 in vertical polarization, the input impedance will range from 36.53 –j15.2 to 49.62–j24, the range of the return loss will be varied from −29.20 dB to −21.73 dB, while the range of the VSWR is 1.4 to 2.407. Fig. 7 show various retun loss S11 [1, 2].

Fig. 7. S11 of rectangular patch with superstrate

6 Rectangular Antenna Optimal The final parameters that result in the very wide bandwidth and best value of S11 for the 2.4 GHz resonance frequency are inferred from the parametric studies conducted before and are shown in the Tables 5 and 6 and Fig. 8. Table 5. Physical parameter of rectangular patch antenna (RP) L

W

Lp

Wp

H

Lslt

Wslt

61.23 mm

72.8 mm

25.5 mm

38.5 mm

5.8 mm

20 mm

2 mm

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Fig. 8. Return loss of (RP) Table 6. Final results for the optimized rectangular and circular patch antenna 4.4

Characteristics

Rectangular patch antenna

fr

2.41

Gain(dB)

3.43

BW(MHz)

410

HPBW(HP), Deg

103

HPBW(VP), Deg

89.61

Impedance ()

50.04 + j0.00

Return-loss (dB)

−65.20

VSWR

1

7 Conclusion Within this research, we investigated and fine-tuned the functionality of a planar rectangular patch antenna, which was constructed utilizing an FR-4 substrate. The antenna’s power supply is provided via a coaxial cable. By refining the feeding position on the patch and integrating a rectangular slot onto the radiating component, we managed to enhance the antenna’s performance. Regarding the superstrate’s role, it influences the resonance frequency, inducing a leftward shift that is evident in the latest outcomes.

References 1. Saidulu, F.M., Lastname, F.M., Lastname, F.M.: Comparison Analysis of Rectangular and Circular Patch Microstrip Antennas with Dielectric Superstrates, vol. 2, no. 5, September– October 2013 2. Shavit, R.: Dielectric cover effect on rectangular microstrip antennas array. IEEE Trans. Antennas Propagat. 40, 992–995 (1992) 3. Yadav, R.K., Yadava, R.L.: Performance analysis of superstrate loaded patch antenna and abstain from environmental effects. Int. J. Eng. Sci. Tech. 3(8), 6582–6591 (2011)

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4. Yadav, R.K., Yadava, R.L.: Effect on performance characteristics of rectangular patch antenna with varying height of dielectric cover. Int. J. Power Control Signal Comput. 1(1), 55–59 (2011) 5. Bahl, I.J., Bhartia, P., Stuchly, S.: Design of microstrip antennas covered with a dielectric layer. IEEE Trans. Antennas Propogat. 30, 314–318 (1982)

Design and Analysis of Wide Band Circular Patch Antenna for IoT and Biomedical Applications Younes Siraj(B) and Jaouad Foshi Faculty of Sciences and Technology, Moulay Ismail University, Errachidia, Morocco [email protected]

Abstract. This paper focuses on the performance of a circular patch antenna with a defected ground structure. The antenna structure utilizes the FR4 substrate with a relative dielectric constant (1r ) of 4.5, a loss tangent (tan δ) of 0.004, and a thickness (h) of 1.5 mm. To enhance the antenna’s performance, the ground structure is modified by incorporating six specially shaped slots instead of a complete ground plane. The proposed antenna exhibits resonance at a frequency of 5.2 GHz, accompanied by a remarkable reflection coefficient of − 40.54 dB. Furthermore, the antenna demonstrates a wide 10-dB bandwidth of 5 GHz (2.99–8 GHz) and an excellent VSWR of 1.084. Keywords: Circular patch · Biomedical applications · Wide band · IoT

1 Introduction Over the past few years, the rapid expansion of wireless communication systems has generated a demand for efficient and compact antenna solutions. Antennas play a crucial role in the transmission and reception of electromagnetic waves, enabling wireless devices to communicate seamlessly. Among various antenna types, circular patch antennas have emerged as a popular choice due to their numerous advantages. Circular patch antennas offer several key benefits that make them well-suited for a diverse array of wireless communication applications [1], WLAN [2], IoT [3], UWB [4], WBAN [5], Biomedical [6–9]. Their utility extends significantly to IoT and biomedical applications, offering a set of distinct advantages tailored to the unique demands of these domains. First and foremost, their compact size makes them highly desirable in devices where space is limited, such as mobile phone [10, 11], wearable devices [12, 13], and embedded systems. The circular shape of the patch allows for efficient use of available surface area, enabling the antenna to be integrated into small form-factor devices without compromising performance [14, 15]. Also in the implantable medical devices, such as pacemakers [16], neurostimulators, and biosensors [17], where size and power consumption are critical considerations, circular patch antennas provide an optimal solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 288–296, 2024. https://doi.org/10.1007/978-3-031-48573-2_42

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In the IoT landscape, their compact form factor and directional radiation patterns make them an ideal choice for integration into small IoT devices. Their exceptional radiation efficiency ensures reliable data transmission, crucial for real-time monitoring and control applications [18]. Patch antennas excel in providing long-range connectivity, enabling IoT devices to communicate over vast distances, a vital feature for applications like precision agriculture or remote environmental monitoring, Asset Tracking [19], Industrial IoT (IIoT), Smart Home [20]. As IoT continues to evolve and proliferate, patch antennas will remain at the forefront, serving as the linchpin that connects a world of devices, data, and possibilities. The design and implementation of circular patch antennas for biomedical and IoT applications present unique challenges due to the specific requirements and constraints of these fields. Such as tissue interaction [21], signal attenuation [22], and interference from surrounding biological structures require careful consideration during the design process. Additionally, the impact of the human body on antenna performances, such as detuning and electromagnetic interference, need to be thoroughly studied and mitigated.

2 Antenna Structure 2.1 Methodology for Calculations The antenna design was conducted using the following equations [23]: • Radius of the patch (a) a= 

F   2h πF 1 + Fπ εr ln 2h + 1.7726

(1)

where, a is the radius of the circular patch, h is substrate height, 1r is the dielectric constant of the substrate. F=

8.791 × 109 √ fr εr

(2)

2.2 Antenna Structure Our proposed antenna is a circular patch antenna with a slotted partial ground plane. The antenna is designed to operate at 5.2 GHz. The dimensions of the antenna are L and W. Six slots were inserted in the ground plane. The dimensions of the ground are denoted as Lg and W. Figure 1 depicts the configuration of the circular antenna structure and Table 1 presents the parameters values of our proposed antenna.

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Fig. 1. Structure of the circular antenna front and back view

Table 1. Values of the antenna parameters Parameters

L

W

R1

R2

Wf

Lf

m

d

Lg

Values (mm)

48

38

10

3.5

20

2

17

10

18

2.3 Antenna Structure Evolution Steps To achieve a good operating band with improved gain, high efficiency, and enhanced fidelity, modifications were made to both the circular patch and the ground plane after obtaining their initial dimensions. The development of the antenna involved five stages, as illustrated in Fig. 2.

Fig. 2. The developmental process of the antenna design

The reflection coefficient of various phases is shows in Fig. 3. At the initial stage, the designed antenna fell short of achieving a satisfactory operational frequency range

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due to impedance mismatch and the utilization of a complete ground plane. During the second stage, a notable enhancement in S11 was observed at 3.2 GHz. This improvement was achieved by employing an impedance matching technique, which involved the insertion of two slots between the feed line and the patch, along with reducing the dimensions of the ground. In the third stage, six slots were strategically inserted on the partial ground plane. These embedded slots played a significant role in enhancing the S11 parameter. As a result, the antenna achieved an impressive reflection coefficient of − 25.45 dB, accompanied by a wide bandwidth of 5 GHz. In the fourth stage, a circular slot was added at the center of the radiating element to further enhance the S11 parameter. This modification aimed to optimize the antenna’s performance and achieve improved impedance matching characteristics. In the fifth and the final stage, an additional partial ground plane was incorporated into the upper side of the antenna. This addition contributed to further improving the antenna characteristics, specifically in terms of return loss and bandwidth. The ultimate design of the antenna accomplished an impressive reflection coefficient of − 40.54 dB, indicating a highly desirable level of impedance matching. Additionally, the antenna exhibited a substantial bandwidth of 5 GHz, further demonstrating its excellent performance capabilities.

Fig. 3. Reflection coefficient at different steps

2.4 Parametric Study A parametric study was conducted to examine the impact of varying dimensions on the performance of the proposed antenna. By altering the antenna’s dimensions, it was possible to meet our specific requirements. • The effect of patch Radius R1 To achieve optimal impedance matching, a parametric analysis was carried out on different antenna radius values. The simulated S11 results are illustrated in Fig. 4, where the parameter was systematically adjusted from 9 mm to 11 mm, Incremented by 1 mm.

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Through the analysis, we observed that the choice of patch radius has a significant influence on the resonant frequency and bandwidth. We got the best results with R1 = 10 mm. • The effect of the Ground on the upper side Our study also examined the impact of adding a ground plane on the upper side of the circular patch antenna, specifically focusing on its influence on the reflection coefficient as shown in Fig. 5. We discovered that the additional ground plane had a notable impact on the antenna’s reflection coefficient. The resonance frequency experienced a shift, increasing from 3.5 GHz to 5.2 GHz, while the return loss improved from − 25.5 dB to − 40.45 dB. These findings demonstrate the clear influence of the added ground plane on the antenna’s performance, resulting in a shift in resonance frequency and a substantial reduction in reflection coefficient.

Fig. 4. Reflections coefficient of different R1 values

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Fig. 5. Reflections coefficient with and without ground

3 Results and Discussions 3.1 Reflection Coefficient and VSWR of the Proposed Antenna The Return loss and VSWR are represented in Fig. 6. The antenna reached a S11 of − 40.45 dB and a VSWR of 1.084 with a bandwidth of 5 GHz (2.99–8 GHz).

Fig. 6. Simulated reflection coefficient and VSWR

3.2 Radiation Pattern of the Suggested Structure Figure 7 illustrates the radiation pattern, showcasing that the antenna exhibited a gain of 5.63 dB. Table 2 provides a comparison between our research and previously studies.

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Fig. 7. Radiation pattern of the proposed architecture

Table 2. Comparison of the proposed antenna with previously reported research Ref.

Freq. (GHz)

S11 (dB)

Operating band (GHz)

Gain (dB)

[24]

5.8

–

5.72–5.87

4.85

[25]

–

− 31.77

2.70–3.80

1.43

[26]

5.8

− 25.58

2.60–11.30

5.24

[Prop]

5.2

− 40.54

2.99–8

5.63

3.3 Simulation of the Antenna Next to Human Body The proposed circular antenna was subjected to simulations in the presence of a human body model in HFSS. This analysis aimed to evaluate the antenna’s performance and characteristics when positioned near to a human body. By simulating the antenna in such conditions, it provides insights into its behavior and potential effects caused by the proximity to the human body. Figure 8 displays the simulated reflection coefficient of the antenna when placed on and off the human body model. The results clearly indicate the significant impact of the body on the antenna’s characteristics. However, despite this influence, the antenna continues to exhibit a good reflection coefficient and a satisfactory bandwidth even when placed on the body. These findings suggest that the antenna design can effectively operate when situated in proximity to the human body, demonstrating its robustness and suitability for biomedical applications.

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Fig. 8. Simulated reflection coefficient on and off body

4 Conclusion The circular patch antenna presents a promising solution for various biomedical applications. Its compact size, low profile, and ease of integration make it suited for biomedical applications and sensing within the human body. Our proposed antenna demonstrated excellent performance with a reflection coefficient of − 40.45 dB and a wide bandwidth of 5 GHz. The antenna exhibited also an impressive VSWR of 1.084 and a gain of 5.63 dB, indicating a good impedance matching due to the slotted partial ground and the addition of a supplementary ground plane. Moreover, the antenna yielded positive results when simulated near the human body, making it suitable for Internet of Things (IoT) and biomedical applications. In future experiments with our proposed antenna, additional evaluations such as SAR and link budget calculations can be conducted.

References 1. Muthuvel, S.K., Shaw, M., Choukiker, Y.K.: AEU Int. J. Electron. Commun. 141, 153960 (2021) 2. Mukta, C., Rahman, M., Islam, A.: Int. J. AdHoc Netw. Syst. 11, 01 (2021) 3. Kumar, D., Mathur, D.D.: Int. J. Innov. Technol. Explor. Eng. 9, 1515 (2019) 4. Boopathi Rani, R., Pandey, S.K.: Microw. Opt. Technol. Lett. 59, 745 (2017) 5. Rubani, Q., Gupta, S.H., Kumar, A.: Optik 185, 529 (2019) 6. Li, Y., Yang, L., Gao, M., Zhao, X., Zhang, X.: Ad Hoc Netw. 99, 102059 (2020) 7. Markkandan, S., Malarvizhi, C., Raja, L., Kalloor, J., Karthi, J., Atla, R.: Mater. Today Proc. 47, 318 (2021) 8. Islam, Md.S., Kayser Azam, S.M., Zakir Hossain, A.K.M., Ibrahimy, M.I., Motakabber, S.M.A.: Eng. Sci. Technol. Int. J. 35, 101112 (2022) 9. Deepthy, G.S., Nesasudha, M.: Mater. Today Proc. S221478532300192X (2023) 10. Khattak, M.I., Sohail, A., Khan, U., Barki, Z., Witjaksono, G.: Prog. Electromagn. Res. C 89, 133 (2019) 11. Sid, A., Cresson, P.-Y., Joly, N., Braud, F., Lasri, T.: Mater. Today Electron. 5, 100049 (2023) 12. Dey, A.B., Pattanayak, S.S., Mitra, D., Arif, W.: Microw. Opt. Technol. Lett. 63, 845 (2021) 13. Ramesh Varma, D., Durga Saranya, B., Pavani, G., Venkata Subbarao, M., Challa Ram, G., Girish Kumar, D.: Mater. Today Proc. 80, 1538 (2023)

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14. Dey, S., Karmakar, N.C.: Sci. Rep. 10, 16306 (2020) 15. Yang, H., Liu, X.: IEEE Antennas Wirel. Propag. Lett. 19, 2324 (2020) 16. Sharma, D., Kanaujia, B.K., Kaim, V., Mittra, R., Arya, R.K., Matekovits, L.: Sci. Rep. 12, 3165 (2022) 17. Lopato, P., Herbko, M.: Sensors 18, 310 (2018) 18. Shekhawat, S., Singh, S., Kumar Singh, S.: Mater. Today Proc. 66, 3511 (2022) 19. Famá, F., Faria, J.N., Portugal, D.: Internet Things 19, 100547 (2022) 20. Asiya, A.E., Ojomu, S.A., Erim, C.M., Oku, D.E., Aloamaka, A.C.: Heliyon 9, e19186 (2023) 21. P˘acurar, C., et al.: Ser. Mater. Sci. Eng. 1254, 012018 (2022) 22. Ridoy, P.M., Elme, K.Md., Saha, P., Hoque, Md.J.-A.-M., Tulka, T.K., Rahman, Md.A.: 2021 International Conference on Intelligent Technologies CONIT, pp. 1–6. IEEE, Hubli, India (2021) 23. Srinivasu, G., Gayatri, T., Chaitanya, D.M.K., Sharma, V.K.: Mater. Today Proc. 45, 5642 (2021) 24. Mishra, P.K., Raj, S., Tripathi, V.S.: 2021 IEEE MTT-S International Microwave and RF Conference IMARC, pp. 1–4. IEEE (2021) 25. Pushpalatha, M., Namana, N., Navadagi, T.S., Varun, D.: Adv. Electromagn. 12, 70 (2023) 26. Karad, K.V., Hendre, V.S.: EURASIP J. Wirel. Commun. Netw. 2023, 27 (2023)

Design, Simulation, and Analysis of Microstrip Antenna Circular Patch High Efficiency for Radar Applications at 32 GHz Fatehi ALtalqi1(B) , Karima Benkhadda1 , Samia Zarrik1 , Echchelh Adil1 , Asma Khabba2 , and Ahmed Abbas Al Rimi3 1 Department of Physics Laboratory of Electronics Treatment Information, Mechanic and

Energetic, Faculty of Science, Ibn Tofail University, Kenitra, Morocco [email protected] 2 Instrumentation, Signals and Physical Systems (I2SP) Team, Department of Physics, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, Morocco 3 Information Systems and Telecommunications Laboratory, Faculty of Sciences, University of Abdelmalek Essaadi, 93030 Tetuan, Morocco

Abstract. This research paper introduces the design and analysis of a highefficiency circular antenna specifically developed for RADAR applications. The simulation results demonstrate the performance of a circular microstrip patch antenna operating at 32 GHz, which is well-suited for various wireless systems. The antenna designed on substrate of RT5880, with a thickness of 0.035 mm. The design procedure involved utilizing a substrate with a relative permittivity (εr) of 2.2. The substrate also had a loss tangent (tan δ) of 0.0009 and a substrate height (H) 1.575 mm. The antenna small size dimensions of 22 mm × 20 mm, a circular patch with a selected radius of 8 mm. The frequency range of the antenna extends from 22 GHz to 40 GHz. The antenna demonstrates a return loss value of − 21.6, a high very very bandwidth of 8.8 GHz a VSWR of 1.1808 a gain of 7.891 dB, and directivity of 7.897 dBi. The antenna has a high efficiency of 99.999%. Antenna designed and simulated utilizing CST Studio Suite software for radar applications. Keywords: Antenna · Efficiency · Gain · Bandwidth

1 Introduction The demand for compact transceiver applications in the field of microwave and radio frequency communications is steadily increasing. Microstrip antennas serve as crucial components in these systems, with the significance of microstrip patch antennas being amplified in the advancement of modern wireless communication systems. This is propelled by the growing interest in various wireless applications (RADAR) antenna necessitates an inventive, compact, simple manufacturing, and light design due to its pivotal role detect minuscule objects [1]. To effectively capture signals from satellites, it demands a wide spectrum of frequencies and an expansive radiation beam. Additionally, ensuring a high gain and maintaining a low axial ratio below 3 dB, further diminishes © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 297–305, 2024. https://doi.org/10.1007/978-3-031-48573-2_43

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multipath errors within the radar system [2]. In this context microstrip patch antennas circular (MPAC) emerge as a clearcut preference for wireless devices. These antennas are well-suited for installation on the outer surfaces of spacecraft, high performance aircraft, satellites, rockets, automobiles, missiles and handheld portable communication devices. Consequently MPAC hold a crucial position within the swiftly advancing wireless communications industry [3]. They find extensive use in modern applications like wireless local area networks (WLAN), mobile phones and (radar) [4, 5]. Microstrip patch antennas offer the convenience of easy mounting on space vehicles, missiles, and satellites without the need for significant modifications. They seamlessly integrate with monolithic microwave integrated circuit (MMIC) designs, playing a crucial role in modern wireless communication networks [6]. Rectangular and circular microstrip patch antennas are among the most widely recognized types and bring numerous advantages, making them essential components in communication and sensing systems [7]. In recent times, Microstrip Patch Antennas (MPA) have gained immense popularity due to their favorable characteristics [8]. These antennas are known for their inherent nature, high efficiency, substantial gain, excellent directivity, and low return loss. These attributes make microstrip patch antennas ideal for communication across diverse frequencies and make them valuable assets in applications such as remote sensing and medical fields [9]. Among the various types of printed antennas used in wireless applications. The microstrip patch antenna emerges as one of the most extensively employed choices. Notably, it is a high bandwidth antenna with wideband capabilities that can be conveniently fabricated through processes like etching [10]. The patch antenna comprises a metal structure on one side of a dielectric substrate and a ground plane on the other [11]. Microstrip antennas offer several advantages, including low cost feasibility for mass production, dual-polarization capability, and seamless integration with other complex circuits [12]. However, this paper will focus on the circular Microstrip Patch Antenna (CMPA) which surpasses other types in terms of return loss and Voltage Standing Wave Ratio (VSWR). Circular antennas high bandwidth [13]. Moreover, their compact size, higher directivity, and increased gain make them especially advantageous for (RADAR) applications, reducing contention and expanding communication range.

2 Design Parameters for Circular Patch Antenna The initial phase of the antenna design process involves using the provided formula to compute the dimensions of the antenna and determining its length and width using specific equations [14]. Next, the design and simulation of the microstrip patch antennas are performed, with a specific focus on achieving a frequency requirement of 32 GHz. Table 1 presents the detailed working specifications for the design. For this particular microstrip antenna design [15]. The Rogers RT5880 substrate with a dielectric constant of 2.2 is employed, along with copper annular ground plane and circular patches, and a radiating copper metal thickness of 1.575 mm. The given formula allows the calculation of the parameters of the rectangular substrate, and the radius of a circular where the symbol C represents the speed of light in a vacuum [16]. The antenna’s performance is evaluated through various metrics, including bandwidth, directivity, return loss, gain,

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and radiation efficiency. To achieve our objectives, we have employed techniques such as the optimization of antenna dimensions. The formulas used to size the microstrip antenna. In this research, we have chosen the circular patch shape for its design simplicity, analytical convenience, wider bandwidth, and superior efficiency when compared to other alternative configurations (Fig. 1) [17].

Fig. 1. Microstrip patch antenna

The formulas used to size the microstrip antenna according to the literature are [18]. W=

c 

2fr (εr 2+1) c L= √ − 2l 2f εreff   1 h −2 εr + 1 εr − 1 1 + 12 εeff = + 2 2 w W  (εreff + 0.3) h + 0.264 l = 0.412   W  h εreff + 0.258 h + 0.8

(1) (2) (3) (4)

where W is the width, L is the actual length, Leff is the effective length, εreff is the effective dielectric constant and L is the Fringe length. Where, the radius of a circular patch is calculated using the following equation [19]. r= 1+

F    0.5 2h πF π Fεr ln 2h + 1.7726

(5)

where, F=

8.791 × 109 √ fr εr

(6)

The dimensions of the antenna design used in this work are shown in Table 1 (Fig. 2).

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Parameters

Values (mm)

W

20

L

22

Wf

2.7

Lf

12

R

8

wg

20

lg

22

t

0.035

h

1.575

Fig. 2. Design of a circular antenna in CST

3 Simulation Results and Discussions The circular microstrip patch antenna presented here has been meticulously crafted and simulated using CST simulation software. The approach in this study has focused on striking a balance between reducing the antenna’s size while enhancing its performance. We diligently pursued this iterative procedure until attaining outcomes that met our satisfaction. Assessment of the antenna’s effectiveness involves a comprehensive evaluation of pivotal measures, encompassing return loss, bandwidth, (VSWR), gain, directivity, and efficiency. 3.1 Efficiency Antenna efficiency is defined as the ratio of the radiated power to the incident power at the antenna, expressed as a percentage of the frequency curve. It is observed that the antenna maintains high efficiency throughout its complete operational range, reaching a peak value of approximately 99.99% precisely at the resonance frequency of 32 GHz.

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3.2 Return Loss The concept of return loss, also termed the S-parameter, quantifies the extent of electromagnetic power that undergoes reflection from the circular microstrip patch antenna. Essentially, it functions as a reflection coefficient, illuminating the level of impedance matching quality between the source (transmitting end) and the measured load (receiving end). To attain a proficient radiation mode, achieving a return loss exceeding − 10 dB is paramount. In Fig. 3, the graph delineates the correlation between the S-parameter and frequency pertaining to the proposed antenna. Notably, the antenna exhibits resonance at 32 GHz. The curve vividly portrays a return loss of − 21.6 dB at this specific frequency, signifying a robust impedance match and a consequent reduction in energy loss. This performance is remarkably commendable.

Fig. 3. S-parameters of antenna patch in (CST)

3.3 Voltage Standing Wave Ratio (VSWR) Voltage Standing Wave Ratio (VSWR) serves as the metric to gauge the degree of impedance mismatch between the antenna and the transmission line. An optimal antenna design typically exhibits a VSWR value below 2. Illustrated in Fig. 4 is the plot depicting the relationship between VSWR and frequency for the proposed antenna. Notably, at the resonance frequency of 32 GHz, the VSWR registers at a mere 1.1808, significantly below the threshold of 2. This lower VSWR value signifies a more favorable impedance match between the proposed antenna and the transmission line, consequently resulting in increased power transfer to the antenna. 3.4 Bandwidth The antenna’s bandwidth was determined from Fig. 5 by identifying the frequencies f1 and f2 at a − 10 dB threshold. The resulting bandwidth for the engineered antenna was calculated to be 8.88 GHz. The designed antenna exhibits very high bandwidth characteristics.

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Fig. 4. VSWR of antenna patch in CST

Fig. 5. The bandwidth of the antenna patch in (CST)

3.5 The Gain Assessing an antenna’s performance, gain holds profound importance as it represents the ratio of radiated field intensity of the given antenna to that of a reference antenna. Typically quantified in decibels (dB), antenna gain also provides information about the direction of peak radiation. In the scope of this study, the gain of the suggested antenna is meticulously quantified at a frequency of 32 GHz, resulting in a measurement of 7.891 dB, as illustrated in Fig. 6. 3.6 The Directivity At a frequency of 32 GHz, the designed antenna exhibits impressive directivity, measuring 7.897 dBi, as vividly demonstrated in Fig. 7. The central objective is to enhance the antenna’s radiation pattern, directing its response towards a defined direction to amplify power transmission or reception. Table 2 showcases the comparison between the existing references [19] and [20] with this work.

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Fig. 6. The radiation pattern of antenna patch simulation in (CST) (gain)

Fig. 7. The radiation pattern of antenna patch simulation in (CST) (directivity)

Table 2. The comparison includes various parameters such as dimensions, gain and bandwidth. References

Dimensions (mm)

Bandwidth (GHz)

Gain (dB)

[19]

178 × 93

0.136

7.35

[20]

40 × 30

5.96

4.9

This work

20 × 22

8.88

7.891

4 Conclusion This research article introduces a compact circular microstrip patch antenna tailored for (radar) applications, spanning the frequency range of 22–40 GHz. Simulated by utilizing (CST) Microsoft Studio Simulation Software. The results of antenna simulation boasting an expansive bandwidth of 8.8 GHz (ranging from 28.46 GHz to 37.311 GHz). The antenna showcases a return loss of − 21.6 dB, an appreciable gain of 7.891 dB,

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directivity of 7.897 dBi, and an impressive total efficiency of approximately 99.999% precisely at 32 GHz. These findings underscore the commendable performance of the designed compact circular microstrip patch antenna, reinforcing its suitability for (radar) applications. Future perspectives of Proposed antenna Work on fabrication the antenna and applying application for radar.

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13. Ouberri, F., Tajmouati, A., Zbitou, J., Zahraoui, I., Latrach, M.: A novel design of circularly polarized pentagonal planar antenna for ISM band applications. TELKOMNIKA Telecommun. Comput. Electron. Control 19, 1484–1489 (2021). https://doi.org/10.12928/telkomnika. v19i5.19393 14. Wang, Q., Mu, N., Wang, L., Safavi-Naeini, S., Liu, J.: 5G MIMO conformal microstrip antenna design. Wirel. Commun. Mob. Comput. 2017, e7616825 (2017). https://doi.org/10. 1155/2017/7616825 15. Ezzulddin, S., Sattar, O., Ameen, M.: Design and simulation of microstrip patch antenna for 5G application using CST studio. Int. J. Adv. Sci. Technol. 29, 7193–7205 (2020) 16. Varghese, N.M., Vincent, S., Kumar, O.P.: Design and analysis of cross-fed rectangular array antenna; an X-band microstrip array antenna, operating at 11 GHz. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1261–1265 (2016). https://doi.org/10.1109/ICACCI.2016.7732219 17. Kirar, A., Jadaun, V., Kumar, P.: Design a circular microstrip patch antenna for dual band (2013) 18. Upender, P., Harsha Vardhini, P.A.: Design analysis of rectangular and circular microstrip patch antenna with coaxial feed at S-band for wireless applications. In: 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 274–279 (2020). https://doi.org/10.1109/I-SMAC49090.2020.9243445 19. Rahayu, Y., Artiyah, I.: Design and development of microstrip antenna circular patch array for maritime radar applications. Sci. Technol. Commun. J. 1, 82–86 (2021). https://doi.org/ 10.59190/stc.v1i3.193 20. Thaher, R.H., Nori, L.M.: Design and analysis of multiband circular microstrip patch antenna for wireless communication. Period. Eng. Nat. Sci. 10, 23–30 (2022). https://doi.org/10. 21533/pen.v10i3.2996

Effect of the Integration of Information and Communication Technology on the Motivation and Learning of Electricity Lessons for High School Students in Morocco Hassan Yakkou1(B) , Abdelhakim Chillali1 , Nacer Eddine Elkadri Elyamani1 , Abdelaaziz El Ansari2 , and Aziz Taoussi3 1 Laboratory of Engineering Sciences, FP Taza, University Sidi Mohamed Ben Abdellah, Taza,

Morocco [email protected] 2 Signal, System and Component Laboratory, Sidi Mohamed Ben Abdellah University – FST Fez, Fez, Morocco 3 ENS Fes, Fez, Morocco

Abstract. Our objective in this research is to study the effects of the implementation of Information and Communication Technology (ICT) in improving learning and teaching of Physics and Chemistry in high schools in Morocco. To scrutinize the research hypothesis, two study groups have been selected. They both have the necessary requirements to fulfill this research (same school level/similar school results). To ensure that, a diagnostic test have been administered to both groups, and the results have been evaluated before starting the procedure of implementing ICT in teaching. Then, we chose one of the two groups to be the control group, to be taught the traditional way, and the other group was the test group, which was taught using ICT. To evaluate this experiment, an Achievement Test was administered to both groups. After an analysis of the data collected from this test, we came to the conclusion that using ICT in teaching gives far more encouraging and satisfying results than the traditional way. These modern techniques help enhance the experimental characteristic of the teaching of Physics and Chemistry in high schools. Keywords: Information and Communication Technology · Simulation sequences · Physics · Circuit · Electricity

1 Introduction The study of physics-chemistry in Morocco high schools allows students to gain knowledge on concepts, approaches, and methods, while also developing critical thinking skills and problem-solving abilities … [1]. Strengthening learning environments that facilitate understanding of concepts can lead to student satisfaction, initiative, and participation [2], use new pedagogical tools to boost students’ interest in the course and improve their academic performance [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 306–310, 2024. https://doi.org/10.1007/978-3-031-48573-2_44

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The Moroccan educational system has been implementing a competency-based approach since 2000, which enables the learner to be at the forefront of the teaching-learning process. The relationship between teachers, learners, and knowledge is fundamentally altered, along with the way we teach and how students acquire knowledge [4]. Teachers of this discipline are using progressively simulation sequences in their teaching practice due to the lack or insufficiency of experimental material in school laboratories [4]. By using these tools, students can simulate real-world physics by creating virtual experiments that allow them to control variables and form hypotheses [5]. In the same vein, we highly seek to study the use of ICT by integrating the simulations sequences in the process of teaching and learning and find out their impact on the students of the physics subject in Moroccan high school. To be precise, we introduce the following hypothesis: “simulators evolution of electrical systems in teaching and learning process fosters the learners’ abilities and the comprehension of electricity lessons. They should be integrated in the classroom”. To investigate the hypotheses: We will choose two physics classes as a case study. In the first-class simulation sequences is integrated and students are engaged in the teaching and learning process where as the second class will opt for the traditional methods and techniques [6]. To maintain the research credibility the two classes are just similar in terms of the criteria, size, number, the students’ levels as well as the same teacher. This facilitates the matters for the researcher, particularly in observation, where they can see the change in their students’ behavior during the use of ICT. The study aims at verifying the differences between the two classes’ final results by processing and analyzing them. We will administer two tests (diagnostic test and achievement test) to both classes to measure the effectiveness of this type of support on physics results.

2 Theoretical Framework The teaching and learning processes of physics and chemistry require numerical simulation to play a significant role. According to Chomsky, it facilitates the display and attachment of various representations of a concept (mathematical formulas, symbols, graphs, and images) to promote learning (2003, cited by Harinosy Ratompomalala et al., 2019) [7]. It is important to relate theoretical models to physical phenomena in order to understand and forecast the behavior of these complex systems. Numerical simulation can be a powerful tool to visualize the dynamics of these systems, allowing us to test theoretical assumptions and examine the effects of different parameters, which is not practical for some laboratory experiments due to security reasons or a lack of means. Simulation activates students by engaging them in learning and challenging them to explore concepts so that they not only receive information but also learn more effectively [8]. The teacher is able to act as a guide and customize learning to meet the needs of each student. However, it’s important to be aware of the limitations of the model used and not solely rely on the simulation. Consequently, it is advantageous to strike a balance between the use of simulation and the study of the real world.

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Learning by using simulation sequences involves learning the rules of the models that generate simulated events. However, this simplification of the underlying model can lead students to minimize the importance of the domain of validity of the model studied and to forget that some rules do not apply to reality due to its complexity [6]. The use of a simulation sequence acts as a model tool, facilitating the acquisition and construction of knowledge and avoiding dogmatism and misunderstanding [7].

3 The Target Population and Selected Sample The research was conducted with a homogeneous group comprising two classes of second-year baccalaureate students at Abdelhadi-Tajamouati high school in the prefecture of Zwagha in Fes city. These students shared similar characteristics, including the same school level, similar socio-economic environment, and the same teacher. One class served as the ‘control class,’ while the other was designated as the ‘test class,’ which benefited from courses with ICT by integrating the simulation sequences. There are 31 students in each group, with an average age of 18 years, and a gender distribution of 30 boys and 32 girls. Our experiment runs from the beginning of January 2023 to the end of March 2023.

4 Presentation, Analysis, and Discussion of the Results 4.1 Analysis Diagnostic Test Results It aims to test if our target groups have no difficulties in the prerequisites concerning the basic knowledge of electricity. We distributed the test includes multiple choice questions suggested by the teacher so as to meet the objectives. The responses obtained by students are almost same and true (82% of answers are correct for the experimental group and 89% for the control group), which shows the absence of big gaps and difficulties in the learners of the two classes, indicating that the two groups have almost same background and similar level. Analysis of incorrect answers revealed that some students had difficulty with mathematical properties (calculations, solving equations, etc.), converting between units, or reading curves and graphs. A remediation session was done with the learners in each group to support the learning difficulties of the students. 4.2 Achievement Test Analysis We made seven sessions of two hours each of the courses “RC dipole”, “RL dipole”, and “free oscillations in the RLC circuit in series” using the simulator “evolution of electric systems” with the experimental group, and the traditional method with the control group.

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At the end of this study, we administered a achievement-test questionnaire to both groups to assess the impact of this integration on the learning of the physical sciences. • Elementary statistics We calculate the arithmetic mean and standard deviation for each group as shown in Table 1. Table 1. The arithmetic mean and standard deviation Arithmetic mean

Standard deviation values

Test group

14.22

2.72

Controlled group

11.4

2.82

From the table, we notice that the mark of the test group is higher than that obtained by the controlled group. We also notice that the standard deviation values for both groups are low and almost equal, which means that the results of each group are not very dispersed around the average. Therefore, we can say that the learners in each group have almost the same level. The treatment of the obtained results shows that the pupils of the experimental class understood well the part concerning the energetic study of the circuits more than the pupils of the other group because the simulator makes it possible to visualize the various types of energies stored in the circuit, which are not possible with the oscilloscope. During the sessions, we observed that the students of the experimental class were more interested in the course and motivated than the students of the control class. • Student’s t test Given the nature and the constraints of the problem, we have chosen to use the Student’s t-test, related to two independent samples (case of large samples and unknown and equal variances), testing the null statistical hypothesis H0 : “µe = µc ” against the alternative statistical hypothesis H1: “µe > µc ”, where µe and µc are respectively the theoretical mark obtained, in the achievement t-test, by the population of individuals using the simulation sequences, and the theoretical mean obtained, in the same the achievementtest, by the population of individuals who do not use ICT. Thus, we were able to determine the mark xe (= 14.22) of the experimental group and that xc (= 11.4) of the controlled group. Noting that xe is greater than xc , we performed our calculations to finally arrive at a corresponding p-value equal to 0.0004 (below the 0.01 significance level). We then conclude that simulation sequences integration has a statistically significant effect on understanding the electricity circuit fundamentals.

5 Conclusion Teaching by integrating simulators can achieve similar objectives, such as increasing the interest of learners in science and raising their understanding of physics concepts. Simulations will be, in the case of lack or absence of experimental activities, an alternative

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to do virtually the inaccessible experiments. According to this study, the time saving is progressing in our learners, and the integration of these practices in the courses of physics gives encouraging results and much more satisfactory than those obtained in traditional teaching mode. The results obtained inspire us to use this technology in our experiments in order to benefit from its dynamic and interactive dimensions of teaching and learning. Considering that the use of simulations in a teaching sequence of the subject physicschemistry favors the motivation of the students and the vitality in the learning, it will be useful to direct the attention of the teachers towards motivational pedagogies that integrate these tools of new technologies, to help learners to learn in a more meaningful and effective way in school. Finally, it would be interesting to do research on how learners and teachers use ICT during the teaching and learning process for the benefits of learners’ academic success.

References 1. Assem, H.D., Nartey, L., Appiah, E., Aidoo, J.K.: A review of students’ academic performance in physics: attitude, instructional methods, misconceptions and teachers qualification. Eur. J. Educ. Pedagogy 84–92 (2023). https://doi.org/10.24018/ejedu.2023.4.1.551 2. Aguiar-Castillo, L., Clavijo-Rodriguez, A., Hernández-López, L., De Saa-Pérez, P., PérezJiménez, R.: Gamification and deep learning approaches in higher education. J. Hosp. Leis. Sport Tour. Educ. (2020). https://doi.org/10.1016/j.jhlste.2020.100290 3. Felszeghy, S., Pasonen-Seppänen, S., Koskela, A., et al.: Using online game-based platforms to improve student performance and engagement in histology teaching. BMC Med. Educ. 19, 273 (2019). https://doi.org/10.1186/s12909-019-1701-0 4. Touhami, S.: The integration of ICT in teaching/learning at the university in Morocco: towards a new innovative didactic paradigm. REV. Socles. 10(1), 62–92 (2021). ISSN: 2335-1144, EISSN: 2588-2023 5. Gunawan, G., et al.: Using virtual laboratory to improve pre-service physics teachers’ creativity and problem-solving skills on thermodynamics concept. J. Phys. Conf. Ser. 1280 (2019). https://doi.org/10.1088/1742-6596/1280/5/052038 6. Boussoffara, L., et al.: Contribution of high fidelity simulation to training in respiratory medicine (2019). https://doi.org/10.1016/j.rmr.2019.11.648 7. Ratompomalala, H., Razafimbelo, J.: Images numériques: simulations et vidéos. Quels apports pour l’enseignement apprentissage de la physique? (2019). http://madarevues.recherches.gov. mg//IMG/pdf/art4.pdf 8. Solmaz, S., Dominguez Alfaro, J.L., Santos, P., Van Puyvelde, P., Van Gerven, T.: A practical development of engineering simulation-assisted educational AR environments (2021). https:// doi.org/10.1016/j.ece.2021.01.007 9. Ansari, A.E., Das, S., Tabakh, I., Madhav, B.T.P., Bendali, A., Idrissi, N.E.A.E.: Design and realization of a broadband multi-beam 1 × 2 array antenna based on 2 × 2 Butler matrix for 2.45 GHz RFID reader applications. J. Circuits Syst. Comput. 31(17), 2250305 (2022) 10. El Ansari, A., Kabouri, L., Ahouzi, E.: Random attack on asymmetric cryptosystem based on phase-truncated Fourier transforms. In: 2014 International Conference on Next Generation Networks and Services (NGNS), pp. 65–68. IEEE (2014)

Adaptive E-learning to Improve Communicative Skills of Learners with Autism Spectrum Disorder Using Eye Tracking and Machine Learning Fatima Zohra Lhafra(B) and Otman Abdoun Computer Science Department, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco [email protected]

Abstract. Adaptive learning has proved its effectiveness in several research studies. It aims to offer a personalised and adaptive learning process to the needs and preferences of each learner using artificial intelligence technologies. Nevertheless, there is a category of learners who require special support in their learning, social life, nutrition and well-being. This category includes children with Autism Spectrum Disorder (ASD). The learning process for autistic children is based on a perception and support of the child’s whole life. To this end, in this paper we aim to facilitate the social interaction of autistic learners through the development of their communicative skills. The approach is based primarily on the adoption of eye tracking technique to identify the emotions and interactions of autistic learners using a series of videos containing different emotional situations. Then, based on the results of the Naive Bayes classification algorithm, a series of learning social development activities will be presented to the learners using Applied Behaviour Analysis method (ABA). The results obtained will provide a more effective solution for helping learners to better integrate and interact with their social environment. Keywords: Adaptive learning · Eye tracking · Naïve Bayes algorithm · ABA method · Autism spectrum disorders

1 Introduction Adaptive learning represents a wide field of application for artificial intelligence technologies in the field of education. This concept has proved its effectiveness through many research results. It aims to offer personalised learning that meets the cognitive needs and preferences of each learner. It has been adopted during the different phases of the learning process, such as: the assimilation phase [1], the evaluation process [2] and in the remediation [3]. However, children with Autistic Spectrum Disorder (ASD) require special support throughout their whole lives, which makes their learning process different from that of an ordinary child. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 311–317, 2024. https://doi.org/10.1007/978-3-031-48573-2_45

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ASD is a neurodevelopmental disorder that affects brain function and manifests in early childhood [4]. The prevalence of this disorder in developed countries is around 1.85% [5]. Generally, ASD is a disorder that affects communication, social interaction and behaviour [6]. At the level of communication, children with autism may have difficulty in verbal and non-verbal communication and in understanding facial expressions, gestures or the basics of social communication. In terms of behaviour, autistic children may exhibit repetitive and sometimes stereotyped behaviour. For this reason, it is important to provide educational intervention and support adapted to individual needs in order to help autistic children to develop their communication and social interaction skills and to promote their quality of life. The information and communication technologies have been adopted for autistic learners to facilitate their learning and social integration. Virtual environments have been the subject of several research studies. Cecil et al. [7] implemented a virtual learning environment dedicated to middle and high school learners. The environment aimed to initiate autistic learners with the concepts of robotics and manufacturing. The authors in [8] presented a full-body multi-user interaction system designed for learners with ASD to promote the development of social relationships and collaborative behaviours. Another work has been proposed by Gupta et al. [9] which deals with the creation of a cyber-human learning framework based on virtual reality. The proposed framework is adapted to two domains: the first is designed to support the training of orthopaedic surgery residents and the second focuses on learning of sciences for learners with autism. Milne et al. [10] have implemented a concept of autonomous virtual humans to teach and improve the practice of basic social skills such as greeting, conversation, listening and respecting turn-taking. Another study conducted by Ramachandiran et al. [11] to develop a prototype virtual environment using an interview and the Picture Exchange Communication System (PECS) methodology. Cai et al. [12] have developed a game using a virtual learning environment to improve gestures and their interest in understanding tasks. Other work has used virtual environments as a solution to ensure the support and social integration of autistic children, such as [13, 14]. Mobile applications have also been used to offer appropriate solutions for children with ASD. Shohieb et al. presented the design, implementation and evaluation of a mobile learning game for vocabulary. The level of difficulty of the game is chosen according to the level of ability of each learner [15]. Chung et al. [16] developed a digital intervention in the form of a mobile interface to support empathy skills for children with autism spectrum disorder. Another mobile application has been developed by Kalyani et al. [17] which presents a personalised solution for children aged 3–4 to overcome learning difficulties identified at communication, especially: structuring sentences, enriching vocabulary and improving pronunciation. The solution is provided by means of a comfortable portable device. Other studies that propose mobile applications to children with ASD are as follows: [18–20]. Other technologies have been used to support children with autism, such as robotics [21, 22] and eye tracking [23]. Artificial intelligence technologies are one of the most widely adopted solutions for diagnosing autism [24]. For example, Nasser et al. have proposed a model based on neural networks for identifying autism spectrum disorders [25]. Evaluation of test data shows that the ANN model is capable of properly diagnosing ASD with 100% accuracy.

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To this end, the results issued from the application of artificial intelligence technologies in the concept of adaptive learning [26], have led us to propose an approach for teaching social communication skills to autistic children. The approach is composed of two phases The first phase aims to identify the emotional interaction of children with autism using eye tracking in order to offer them a series of adaptive activities aimed at developing social interaction skills using the naive bayes algorithm and according to the ABA learning method. The structure of this paper is as follows: Sect. 2 provides an overview about the methods and technologies that have been used. Section 3 describes the proposed approach and we end with a conclusion and some perspectives for this work.

2 Background This section briefly presents all the concepts and methods adopted to implement the proposed approach, namely: eye tracking, human emotions, the ABA method and the Naive Bayes algorithm. 2.1 Eye Tracking Eye Tracking is a process that measures the position and movement of an individual’s eyes [27]. It has been implemented in many fields, especially in marketing, medical research and human-machine interaction. Several studies have shown the possibility of using eye tracking for emotional recognition [28]. Other research has focused on the study of eye movements for the recognition of human behaviour [29, 30]. This shows that this technology is still evolving. For this reason, we have adopted it for the benefit of children with ASD. 2.2 Human Emotions Emotions are a mental state expressed by a human being, and are attached to a feeling with a certain level of pleasure or displeasure [31]. Generally, they arise from a reaction to an event or a triggering factor. Plutchik proposed a classification of 8 basic emotions: joy, sadness, anger, fear, confidence, disgust, surprise and anticipation [32]. The basic emotions are described in Plutchik’s wheel with the different reactions to each other. The proposed approach is based on this classification in order to identify the class of emotion to be developed in a child with ASD using machine learning specifically the Naive Bayes classification algorithm. 2.3 Applied Behaviour Analysis Method (ABA) ABA is an educational learning method for children with ASD. Its aim is to help learners with autism to learn behaviours in order to improve their communication and relationships with others. This method is designed to help learners to acquire skills in different areas, namely: language, autonomy, motor skills, etc. Regarding the proposed approach, we have

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focused on the area of social integration via the analysis of learners’ emotions. The application of this method is ensured via several intervention techniques which can be exploited in the form of learning activities such as: indication, verbal, gestural, physical, blurring and shaping. The activities planned for each class of emotion satisfy the requirements of the ABA method. 2.4 Naïve Bays Algorithm The Naïve Bays is a supervised machine learning algorithm that is simple and effective for using in predictive modelling. It comprises two types of probability that can be calculated directly from the training data: (i) the probability of each category and (ii) the conditional effect probability for each class, taking into account each x value. Once calculated, the probability of a model can be used to make predictions for new data using Bayes’ theorem.

3 Proposed Model The proposed approach is based on a combination of eye tracking and the Naive Bayes classification algorithm (Fig. 1). The aim of this approach is to encourage the development of communication skills at the level of emotions among children with autism spectrum disorders in order to facilitate their social interaction and improve their quality of life.

Fig. 1. Proposed approach

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3.1 Identification of Emotions to Be Developed We have chosen a series of animated videos containing different situations with positive or negative emotions. The videos were selected for their effectiveness in evoking emotions. We have taken into consideration videos that are adapted to the learner’s cultural and linguistic context in order to ensure his/her involvement. The chosen clips did not adopt subtitles as an option to avoid interfering with the measurement of eye activity. Based on the learners’ interaction with the videos at a given moment when the emotional situation is strong, we will specify the moments of strong fixation points to provide input data for the naive bayes classification algorithm. The role of the algorithm is to determine the class of emotion to be developed by proposing a series of learning activities using the ABA learning method. 3.2 Classification of Emotions The aim of this phase is to identify the class of emotion to be developed for children with ASD. Through this identification, an adaptive learning process will be assigned to the learners in order to help them to develop their communicative skills to better integration into their social environment. The Naive Bayes algorithm considers the results of the eye tracking stage as input data in order to develop a basic 8-class emotion classification. To do this, the probability calculation is based on Bayes’ theorem using the following Formula: P(y|X) =

P(X |y)P(y) P(X )

(1)

Regarding the proposed problem the variable y indicates the class of emotion and the variable X represents the parameters or features identified by the technique of the eye tracking. Therefore, X is given by: X = (Result1, Result2, . . . . . . , Result n) So, the probability formula will be as follows (Eq. 2): P(Result1|Class)P(Result2|Class)P(Resultn|Class)P(Class) P(Result1)P(Result2)P(Resultn)

(2)

4 Conclusion Adaptive learning is one of the most promising concepts for artificial intelligence technologies in the field of education. Research results have led us to exploit this concept for all categories of learners, especially those with autism spectrum disorders. This category needs a great deal of support and guidance to facilitate their social integration. To this end, we have proposed an approach based on a combination of eye-tracking technology and the Naive Bayes classification algorithm. The aim of this solution is to identify the emotions that autistic children have to develop in order to promote their communication and social interaction skills. As part of this work, we wish to initiate the implementation phase in order to deduce experimental results.

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19. Ahmad Azahari, I.N.N.B., Wan Ahmad, W.F., Hashim, A.S.: Evaluation of Video Modeling Application to Teach Social Interaction Skills to Autistic Children, pp. 125–135 (2018). https://doi.org/10.1007/978-981-13-1628-9_12 20. Purnama, Y., et al.: Educational software as assistive technologies for children with autism spectrum disorder. Proced. Comput. Sci. 179, 6–16 (2021) 21. Anzalone, S.M., et al.: Quantifying patterns of joint attention during human-robot interactions: an application for autism spectrum disorder assessment. Pattern Recognit. Lett. 118, 42–50 (2019) 22. Del Coco, M., et al.: Study of mechanisms of social interaction stimulation in autism spectrum disorder by assisted humanoid robot. IEEE Trans. Cogn. Dev. Syst. 10, 993–1004 (2018) 23. Zhang, K. et al. Eye-tracking analysis of autistic children’s attention to social stimuli. In: Proceedings of the 2020 International Symposium on Educational Technology (ISET), pp. 268–272 (IEEE, 2020). https://doi.org/10.1109/ISET49818.2020.00065 24. Panagiota, A., et al.: Artificial intelligence in autism assessment. J. Emerg. Technol. Learn. 15, 95–107 (2020) 25. Nasser, I., Al-Shawwa, M., Abu-Naser, S.: Artificial neural network for diagnose autism spectrum disorder. Int. J. Acad. Inform. Syst. Res. 3, 27–32 (2019) 26. Lhafra, F.Z., Abdoun, O.: Towards an Adaptive Learning Process Using Artificial Intelligence Technologies, pp. 23–32 (2023). https://doi.org/10.1007/978-3-031-29857-8_3 27. Jacob, R.J., Karn, K.S.: The Mind’s Eye. in Eye Tracking in Human–Computer Interaction and Usability Research, pp. 573–605 (Elsevier BV, 2003) 28. Alghowinem, S., AlShehri, M., Goecke, R., Wagner, M.: Exploring Eye Activity as an Indication of Emotional States Using an Eye-Tracking Sensor, pp. 261–276 (2014). https://doi. org/10.1007/978-3-319-04702-7_15 29. Isaacowitz, D.M., Wadlinger, H.A., Goren, D., Wilson, H.R.: Selective preference in visual fixation away from negative images in old age? An eye-tracking study. Psychol. Aging 21, 40–48 (2006) 30. Hess E.H. The Tell-Tale Eye: How Your Eyes Reveal Hidden thoughts and Emotions. In (Van Nostrand Reinhold) 31. Cabanac, M.: What is emotion? Behav. Process. 60, 69–83 (2002) 32. Plutchik, R.: The nature of emotions. Am. Sci. 89, 344 (2001)

Performance Evaluation of Intrusion Detection System Using Gradient Boost Sara Amaouche1 , Azidine Guezzaz1(B) , Said Benkirane1 , Mourade Azrour2 , and Chaimae Hazman1 1 Technology Higher School Essaouira, Cadi Ayyad University, Marrakesh, Morocco

[email protected] 2 STI Laboratory, IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University

of Meknes, Errachidia, Morocco

Abstract. Vehicular Ad Hoc Networks (VANETs) are pivotal in modern intelligent transportation systems, enabling real-time vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. These networks facilitate various applications like traffic management, collision avoidance, and infotainment services. However, the open and dynamic nature of VANETs exposes them to distinct security challenges, necessitating the deployment of Intrusion Detection Systems (IDS) to ensure the security and privacy of vehicular communications. This study introduces an innovative IDS for VANETs, focusing on addressing the unique security issues prevalent in this domain, particularly through advanced feature selection techniques, handling class imbalance with Synthetic Minority Oversampling Technique (SMOTE), and leveraging the Gradient Boost algorithm for classification. The efficacy of the proposed IDS is evaluated on the NSL-KDD dataset, demonstrating exceptional performance compared to existing models the Random Forest algorithm, renowned for its robustness, with an accuracy rate of 100 and 99% for precision, recall, and f1 score, as well as a precision-recall curve with an AP score of 1.0. Keywords: VANET · Security · IDS · Gradient Boost

1 Introduction In the context of modern intelligent transportation systems, VANETs have transformed vehicular communication and safety by enabling real-time V2V and V2I communication [1]. While improving road efficiency and convenience [2], VANETs encounter distinct security obstacles due to seamless data exchange, potentially compromising communication integrity, privacy, and security [3]. To confront these issues, an IDS plays a pivotal role in securing vehicular communications [2]. This study introduces a tailored IDS designed specifically for VANETs, with a primary focus on intrusion detection and mitigation. Particularly noteworthy is its incorporation of specialized VANET techniques, commencing with meticulous feature selection through the chi-square method. The IDS addresses compatibility with categorical data using one-hot encoding and strategically © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 318–323, 2024. https://doi.org/10.1007/978-3-031-48573-2_46

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addresses imbalanced intrusion instances using the SMOTE technique, bolstering intrusion management. The fundamental strength of the IDS resides in its utilization of the Gradient Boost algorithm, which employs ensemble learning techniques to identify intricate intrusion patterns within VANET data. Rigorous evaluation using the NSL-KDD dataset quantifies its performance across diverse metrics such as accuracy, precision, recall, and F1-score, offering insights into strengths and potential enhancements. The organization of the paper is as follows: Sect. 2 examines VANETs’ context, architecture, services, security, and intrusion detection. In Sect. 3, various intrusion detection approaches are reviewed, while Sect. 4 elaborates on the proposed model’s design. Section 5 discusses the performance and outcomes, and Sect. 6 concludes by outlining avenues for future research and development.

2 Background VANETs integrate communication, computing, and vehicular technology [1], enhancing road safety, traffic management, and collision avoidance in real-time [4]. They transmit data for cooperative collision avoidance, adaptive traffic flow, and predictive vehicle maintenance [5], but also face cybersecurity challenges [6]. Cyberattacks can threaten integrity, traffic flow, and sensitive information, even causing real-world accidents or unauthorized vehicle control. IDS monitor VANET traffic using machine learning, deep learning, and ensemble methods for accuracy [7, 8]. They promptly alert to potential breaches by distinguishing normal and abnormal behaviors [9], safeguarding against threats [10]. Prioritizing security in VANETs is crucial [11]. IDS mitigate evolving cyber threats and bolster VANET resilience [11, 12], preserving vehicular efficiency, security, and intelligent operations [13].

3 Related Works Various ML and DL techniques have enhanced IDSs in VANETs. One study [14] evaluated ML methods using the Mobility Behavior Dataset and VEINS simulator, achieving accurate classification of incidents. Another contribution [15] introduced CEAP, a smart detection model for VANETs, differentiating between cooperative and malicious vehicles. Intrusion detection in VANETs was explored in [16] using KNN and SVM algorithms, focusing on CAN message timing. Zeng et al. [17] proposed an intrusion detection model integrating ML and game theory. Position tampering attacks were addressed in [18] by combining kNN and RF methods. Hazman et al. [19] introduced DEIGASe, a robust IDS using feature selection with IG and GA. For IoT-based environments, IDS-SIoEL [13] tested various ML methods, with XGBoost showing the best performance. A hybrid ML model [20] enhanced IDS efficiency, using Random Forest for detection and post-detection phases. Lastly, [21] introduced a profile-based intrusion detection approach using supervised networks and a training database of normal traffic.

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4 Proposed Methodology The proposed methodology introduces an innovative approach tailored to address the intricate security challenges within VANETs. It places a strong emphasis on feature selection by employing the chi-square method to optimize intrusion pattern recognition, resulting in more efficient resource utilization. The equation of chi-square technique is represented in (1) x2 =

 (Oi − Ei )2 Ei

(1)

In order to effectively handle categorical data, our method seamlessly incorporates one-hot encoding into the classification process, facilitating a comprehensive analysis of VANET data. Additionally, we address the issue of class imbalance by applying the SMOTE technique, ensuring that our IDS can successfully detect rare intrusion instances. The equation for generating a synthetic sample using SMOTE is presented in (2) S = x + rand*(NN − x)

(2)

where: S is the newly generated synthetic sample. x is the original minority class sample. NN is a randomly chosen nearest neighbor from the minority class. rand is a random number between 0 and 1. What distinguishes our approach is the utilization of the Gradient Boost algorithm, which leverages ensemble learning techniques to uncover concealed intrusion patterns that conventional methods may overlook. To validate the effectiveness of our methodology, we conducted an exhaustive comparison with the Random Forest algorithm, renowned for its robustness, and evaluated it against the latest advancements in the field. Through a rigorous evaluation using various performance metrics on the VANETspecific NSL-KDD dataset, our proposed IDS demonstrates its potential to substantially enhance VANET security, representing a noteworthy contribution to the field (Fig. 1).

Fig. 1. Scheme of proposed approach

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5 Results and Discussion In this section, we present the results obtained from the application of our model tested on the NSL-KDD dataset. Prior to the implementation of SMOTE, the dataset exhibited a distribution where attacks accounted for 46.5% of the instances, while normal instances constituted 53.5%. However, following the application of SMOTE, a notable equilibrium was achieved, with both attack and normal classes now comprising an equal percentage of the dataset. Our model’s performance, as presented in Table 1, showcases its exceptional achievements compared with random forest algorithm and distinguishing itself from prior research across multiple dimensions. Our model achieves unmatched accuracy, registering an impressive 100%. Additionally, precision, recall, and F1-score metrics all attain outstanding values of 99%, underscoring the precision and comprehensiveness of our model’s predictions. These outcomes highlight our model’s superior performance, effectively balancing precision and recall for optimized predictive accuracy. Furthermore, the Precision-Recall curve in Fig. 2 reaffirms our model’s excellence, with an AP score of 1.0. These results collectively mark a significant advancement in the field, attesting to the robustness and effectiveness of our model in comparison to previous research. Table 1. Experimentation result Models

Accuracy

Precision

Recall

F1-score

Kaushik et al.[22]

0.965

0.965

0.965

0.965

Imanbayev et al. [23]

0.99

0.99

0.99

0.97

Khan et al. [24]

0.982

0.996

0.781

0.876

Alalwany and Mahgoub [25]

0.826

0.756

0.964

0.847

RF

0.97

0.96

0.97

0.95

Proposed model

1

0.999

0.999

0.999

6 Conclusions This study enhances VANET security for safer transportation. Our model, using ChiSquare and SMOTE, improves intrusion detection accuracy, precision, and F1 score. However, VANET dynamics remain a challenge. Future research should prioritize adaptive intrusion detection for real-time threat identification, considering energy consumption in evolving VANET security.

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Fig. 2. Precision recall curve

References 1. Chatterjee, T., Karmakar, R., Kaddoum, G., Chattopadhyay, S., Chakraborty, S.: A survey of VANET/V2X routing from the perspective of non-learning-and learning-based approaches. IEEE Access 10, 23022–23050 (2022) 2. Kudva, S., Badsha, S., Sengupta, S., La, H., Khalil, I., Atiquzzaman, M.: A scalable blockchain based trust management in VANET routing protocol. J. Parall. Distrib. Comput. 152, 144–156 (2021) 3. Sun, L., Yang, Q., Chen, X., Chen, Z.: RC-chain: reputation-based crowdsourcing blockchain for vehicular networks. J. Netw. Comput. Appl. 176, 102956 (2021) 4. Monfared, S.K., Shokrollahi, S.: DARVAN: a fully decentralized anonymous and reliable routing for VANets. Comput. Netw. 12, 109561 (2023) 5. Guezzaz, A., Azrour, M., Benkirane, S., Mohyeddine, M., Attou, H., Douiba, M.: A lightweight hybrid intrusion detection framework using machine learning for edge-based IIoT security. Int. Arab J. Inform. Technol. 19(5), 148 (2022) 6. Khan, K., Mehmood, A., Khan, S., Khan, M.A., Iqbal, Z., Mashwani, W.K.: A survey on intrusion detection and prevention in wireless ad-hoc networks. J. Syst. Architect. 105, 101701 (2020) 7. Douiba, M., Benkirane, S., Guezzaz, A., Azrour, M.: An improved anomaly detection model for IoT security using decision tree and gradient boosting. J. Supercomput. 79(3), 3392–3411 (2023) 8. Sharma, S., Kaul, A.: A survey on intrusion detection systems and honeypot based proactive security mechanisms in VANETs and VANET cloud. Vehic. Commun. 12, 138–164 (2018) 9. Benkirane, S., Guezzaz, A., Azrour, M., Gardezi, A.A., Ahmad, S., Sayed, A.E., et al.: Adapted speed system in a road bend situation in VANET environment. CMC-Comput. Mater. Continua 74(2), 3781–3794 (2023) 10. Hazman, C., Benkirane, S., Guezzaz, A., Azrour, M., Abdedaime, M.: Intrusion detection framework for IoT-based smart environments security. In: Artificial Intelligence and Smart Environment: ICAISE’2022, pp. 546–552. Springer International Publishing, Cham (2023)

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11. Fathi, M., Naeim Sobhani, S.: A Lightweight Cross-Layer Intrusion Detection System on Jamming, Spoofing, and Mixed Attacks in Vehicular Communication (2023) 12. Sheikh, M.S., Liang, J.: A comprehensive survey on VANET security services in traffic management system. Wireless Commun. Mob. Comput. 2019, 1–23 (2019) 13. Hazman, C., Guezzaz, A., Benkirane, S., Azrour, M.: LIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning. Clust. Comput. 15, 1–15 (2022) 14. Mahmoudi, I., Kamel, J., Ben-Jemaa, I., Kaiser, A., Urien, P.: ‘Towards a reliable machine learning-based global misbehavior detection in C-ITS: model evaluation approach. In: Laouiti, A., Qayyum, A., Saad, M.N.M. (eds.) Vehicular Ad-Hoc Networks for Smart Cities, pp. 73–86. Springer, Singapore (2020) 15. Wahab, O.A., Mourad, A., Otrok, H., Bentahar, J.: CEAP: SVM-based intelligent detection model for clustered vehicular ad hoc networks. Exp. Syst. Appl. 50, 40–54 (2016) 16. Guezzaz, A., Benkirane, S., Azrour, M.: A novel anomaly network intrusion detection system for internet of things security. In: IoT and Smart Devices for Sustainable Environment, pp. 129–138. Springer, Cham (2022) 17. Zeng, Y., Qiu, M., Ming, Z., Liu, M.: Senior2local: a machine learning based intrusion detection method for vanets. In: International Conference on Smart Computing and Communication, pp. 417–426. Springer, Cham (2018) 18. Ercan, S., Ayaida, M., Messai, N.: Misbehavior detection for position falsification attacks in VANETs using machine learning. IEEE Access 10, 1893–1904 (2021) 19. Hazman, C., Benkirane, S., Azrour, M.: DEIGASe: Deep Extraction and Information Gain for an Optimal Anomaly Detection in IoT-based Smart Cities (2022) 20. Bangui, H., Ge, M., Buhnova, B.: A hybrid data-driven model for intrusion detection in VANET. Proced. Comput. Sci. 184, 516–523 (2021) 21. Guezzaz, A., Asimi, A., Asimi, Y., Tbatou, Z., Sadqi, Y.: A lightweight neural classifier for intrusion detection. Gen. Lett. Math. 2(2), 57–66 (2017) 22. Kaushik, B., Sharma, R., Dhama, K., Chadha, A., Sharma, S.: Performance evaluation of learning models for intrusion detection system using feature selection. J. Comput. Virol. Hack. Techn. 127, 1–20 (2023) 23. Imanbayev, A., et al.: Research of machine learning algorithms for the development of intrusion detection systems in 5G mobile networks and beyond. Sensors 22(24), 9957 (2022) 24. Khan, I.U., et al.: A proactive attack detection for heating, ventilation, and air conditioning (HVAC) system using explainable extreme gradient boosting model (XGBoost). Sensors 22(23), 9235 (2022) 25. Alalwany, E., Mahgoub, I.: Classification of normal and malicious traffic based on an ensemble of machine learning for a vehicle CAN-network. Sensors 22(23), 9195 (2022)

A Novel Detection, Prevention and Management Proactive System of Patients Chronic Disease Based on IoT, Blockchain, AI and Digital Twin Mbarek Lahdoud(B) and Ahmed Asimi Laboratory: Laboratoire des Systèmes Informatiques and Vision (LabSiv), Team: Sécurité, Cryptologie, Contrôle d’accès Et Modélisation (SCCAM), Department of Mathematics, Faculty of Sciences, University Ibnou Zohr, Agadir, Morocco [email protected], [email protected]

Abstract. Chronic diseases (Diabetes, Asthma, Cancer…), by their complications, generate a large share of deaths and infirmities, each year, in the world. To monitor and anticipate potentially fatal crises resulting from such diseases and act to reduce or even cancel the impact, we are proposing hereby a system of three parts: Internet of Things (IoT) objects (low power sensors or actuators), attached to a human body; a blockchain and digital twins. IoT objects, measure, among other things, vital signs such as blood pressure, body temperature,…; or act. They exchange data and/or instructions via the smartphone with the medical blockchain. By these means, the evolution of vital signs towards a critical state challenges the doctors/Artificial Intelligence (AI) connected to the blockchain to act in time and send instructions to an actuator, attached to the patient, if there is one, a message to the patient or to the Emergency Service (EMS) to provide him with effective assistance, by using the digital twin for geolocation. Similarly, the results from the laboratories frequented by the patient are processed. In short, the blockchain, trusted third party of our ecosystem, connecting patients, doctors, laboratories, EMSs, pharmacies, an AI and digital twins; by monitoring the said indicators; predicts a probable crisis, informs the patient or his family of the measures to be taken and acts by issuing prescriptions, administering remedies and/or alerting the responders for action in the field. Keywords: Chronic disease · Blockchain · Digital twin · Healthcare · IoT

1 Introduction According to the World Health Organization (WHO) [14], chronic diseases cause 74% of deaths each year. A passive and released follow-up generates complications (cardiac, cerebral, respiratory, renal, olphactive,…) which drag the patient towards an inevitable death or a lifelong infirmity. Our system puts the patient at the center of concerns, monitors him and performs appropriate actions to minimize the impact of the disease and consequently improve his life expectancy. For sensitive sectors such as Healthcare, our system requires a secure and reliable source of information to generate meaningful and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 324–329, 2024. https://doi.org/10.1007/978-3-031-48573-2_47

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relevant results. Before approaching our system, we will give an overview of the technologies that make it up. Section 1.1 defines the Internet of Things (IoT) our instrument of measurement and action. Section 1.2 provides a summary of deep learning, a component which, being trained, assists or even replaces doctors. Section 1.3 interested in the digital copy of a physical object. Section 1.4 provides insight into our system’s trusted third-party blockchain technology. In the Sect. 2, we will first make an overview of the related works, then our proposal will be carried by the Sect. 3, finally the conclusion will be given in the Sect. 5. 1.1 Internet of Things Devices Among the two definitions cited in [1], we adopt that of Vermesan et al., consisting of sensors and actuators that allow interaction between the physical world and the digital world. Sensors measure indicators in the environment while actuators make changes. The storage and processing in these objects are limited because of their sizes, their energies, their powers. Moreover, these objects communicate wirelessly (Bluetooth or Zigbee) with the networks, which brings distortion errors. Examples; A blood glucose level sensor and an insulin pump as an actuator. 1.2 Artificial Intelligence In [5], Artificial Intelligence (AI) is about to invade all sectors: Health, Education, Agriculture, Social Networks, Population Monitoring and the Knowledge Economy. This discipline offers an imitation of the human brain to learn, identify complex structures in data sets as large as they are, and make decisions [4, 5]. It has evolved thanks to the increase in recent years of three factors: computing power, memory space and network bandwidth. Without forgetting also, the development of its two main sub-branches which are Machine Learning (ML), Deep Learning (DL). This AI intervenes, for example in Social Networks to facilitate navigation and carry out individualized marketing and in Health to help with diagnoses and will intervene in the rest of the areas of concern, namely epidemiology and climatology. 1.3 Digital Twin According to [2], Digital twins are digital replicas of physical objects with their attributes and processes. These digital twins are useful, for example, without being exhaustive, in industry for simulations and in medicine to estimate the depth of a tumor or display and monitor the health status of the patient. 1.4 Blockchain In Homoliak et al. [7], from the University of Technology and Design (Singapore) defined in 2019 blockchain as a data structure, representing a distributed register evolving only by adding blocks, for definition see also [6, 8]. The blocks contain entries (i.e. transactions). The assembly and the order of the blocks are ensured by a consensus protocol [9] between the participants also called the nodes illustrated in the Fig. 1, Thus, trust is decentralized.

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Fig. 1. Blockchain network, database, blocks and transactions

Said database is a chronological sequence of signed blocks, from the first block or the genesis block. The blockchain is in principle not modifiable by virtue of the chaining by the hashing [11–13] and the existence of a copy with each participant. The three main typologies of blockchains [10] are: private, consortium, public. Lately, a hybrid blockchain has emerged, it combines the advantages of public and private blockchains.

2 Related Works Sethi and Sarangi [1], Raj [2], Sharma and Bansal [3] discussed blockchain, digital twin and sensors technologies in healthcare: The first gave representations of the IoT in layers, a taxonomy and an application using the cloud between the patient and the doctor. The second briefly mentioned the blockchain as a secure layer supporting the digital twin in the field of health. The two documents did not allude to chronic diseases that require early action and the AI that will assist healthcare personnel. The third use sensors in tracking of patients.

3 Our Proposal Consists in putting in symbiosis the four technologies in vogue namely Blockchain where patients are anonymous except to their doctors; IoT devices; IA and Human Digital Twins for the service of Health, see Fig. 2.

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Fig. 2. Overall diagram of our system

3.1 Data Structure The data emanating from the patient and/or the laboratories are in the form of a vector or matrix containing address of patient, vital signs (heart rate, blood pressure, oxygen saturation, body temperature, respiratory rate) generated by the sensors, the analyzes or the X-rays produced by laboratories. The system sends adequate commands to the actuators, prescriptions to the patient or call the Emergency Service (EMS). 3.2 Patient Journey 3.2.1 Registration The diagnosis of the chronic disease is confirmed with a doctor participating in the medical blockchain. The patient receives two keys (public and private) and the public key of the doctor to exchange in a secure way with the blockchain. The elected Blockchain is private to connect hospitals for a state or the world for the WHO. Also, this type of Blockchain is faster than the others to reach a consensus and add a block in the blockchain. 3.2.2 Follow Up Data arrives at the blockchain, consensus is achieved by Authority of the doctor who treated the patient, after receiving the opinions of other doctors. Also, AI supports data from blockchain after preprocessing and trains on the reliable experiences stored on the blockchain and then detects or predicts for each new input. The consensus outcome is added to the Blockchain. The Blockchain/AI system is mature when almost all of the results are approved by the doctors.

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3.2.3 Actions The actions resulting from the consensus are transmitted to the patient, his actuators or EMS.

4 Comparisons Below we compare our proposal to the schemes cited in related works (Table 1). Table 1. System comparison Scheme

Scope

IoT + Cloud [1]

Measurements are Less good: risk of taken on the patient firing dishonest and sent to the cloud participants and an analysis is done by a doctor who provides feedback

Less security

Blockchain + Digital Twin [2]

Trusted Blockchain iers Less good sends data to represent the human being through an avatar. In this diagram, IoT has not been addressed

Good

IoT for tracking patients Useful in monitoring a [3] patient using an application Our proposal = Sensors + Actuators + Blockchain + AI + Digital Twins

Performance

Less good: risk of delay in responding

Patient side; IoT Best anywhere objects measure and react, the data is sent Medical side; Blockchain + AI + Doctors + Digital Twins + other health establishments analyze the data and react to take care of the patient

Security

Less good; risk of firing dishonest participants Good

5 Conclusion Our system puts the patient of a chronic disease in the hands of a medical body and an Artificial Intelligence. This system monitors the patient anywhere and anytime, in real time and acts to prevent crises or irreversible complications. The patient’s digital twin

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will eventually evolve into a metaverse as IoT objects (sensors and actuators) and nanotechnology mature. The growth observed in the power of computers, in particular that expected from quantum computers, will improve reliable learning by AI and Blockchain and will bring relevant decisions. This will facilitate the projection in the metaverses and the solution of several pending problems.

References 1. Sethi, P., Sarangi, S.R.: Internet of things: architectures, protocols, and applications. J. Electr. Comput. Eng. 2017, 13369 (2017) 2. Raj, P.: Empowering digital twins with blockchain. Adv. Comput. 121, 267–283 (2021) 3. Sharma, P., Bansal, N.: Internet of Things: Application and Challenges 4. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) 5. Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100379 (2021) 6. Salman, T., Zolanvari, M., Erbad, A., Jain, R., Samaka, M.: Services using blockchains: a state of the art survey. IEEE Commun. Surv. Tutor. 21(1), 858–880 (2018) 7. Homoliak, I., Venugopalan, S., Hum, Q., Reijsbergen, D., Schumi, R., Szalachowski, P.: The security reference architecture for blockchains: towards a standardized model for studying vulnerabilities, threats, and defenses. arXiv preprint arXiv: 1910.09775 (2019) 8. Kogure, J., Kamakura, K., Shima, T., Kubo, T.: Blockchain technology for next generation ICT. Fujitsu Sci. Tech. J. 53(5), 56–61 (2017) 9. Sankar, L.S., Sindhu, M., Sethumadhavan, M.: Survey of consensus protocols on blockchain applications. In: IEEE 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1–5 (2017) 10. Zheng, Z., Xie, S., Dai, H.-N., Chen, X., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–375 (2018) 11. Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A.V., Rong, X.: Data mining for the internet of things. Literat. Rev. Chall. Int. J. Distrib. Sens. Netw. 11, 289 (2015) 12. ITU: Overview of the Internet of things. Séries Y Global Information and Infrastructure, Internet Protocol Aspiration in the Next-Generation Networks Frame Functional Architecture Modelling, p. 22 (2012) 13. Ferrag, M.A., Derdour, M., Mukherjee, M., Derhab, A., Maglaras, L., Janicke, H.: Blockchain technologies for the internet of things: research issues and challenges. IEEE Internet Things J. 6(2), 2188–2204 (2018)

Web 14. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

Artificial Intelligence in the Tax Field: Comparative Study Between France and Morocco Machmoume Siham(B) and Nmili Mohammed Sidi Mohamed Ben Abdellah University, Fez, Morocco {siham.machmoume,mohamed.nmili}@usmba.ac.ma

Abstract. From medicine to finance, including industry, agriculture, energy and services, artificial intelligence (AI) is making exponential progress in our societies and penetrating all areas of our economic and social life.. In this article, we will focus on the intervention of AI in the field of taxation. Indeed, all over the world, tax administrations are increasingly relying on artificial intelligence not only to fight against tax fraud thanks to complex software allowing rapid and precise exchanges of information, but also in the aim of promoting better communication with taxpayers. To this end and on the basis of a documentary exploration, we will carry out a comparative study between France and Morocco in terms of deployment of AI in two tax administrations. This article constitutes a new approach in the study of the strategies adopted by different countries for optimal use of AI in tax matters. Furthermore, we will briefly examine the need for States to establish a legal framework capable of regulating AI in order to contain possible abuses generated by this new technology, particularly in terms of protection and security of personal data. Keyword: Artificial intelligence · Taxation · Tax control · Communication · Tax administration

1 Introduction “Big Brother Is watching you,” never has the title of George Orwell’s novel published in 1984 been so topical. For what? because now, Artificial Intelligence “controls and monitors” many economic and social activities, from finance to marketing, HRM and taxation. Thus, in France in 2021 artificial intelligence will ensure “44.1% of tax audits, in particular thanks to datamining” (Aide 2022). Tax specialists in this country estimate that this increase is around 10% per year, which in other words means that by 2027, tax audits in France will be almost entirely devoted to IA.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 330–338, 2024. https://doi.org/10.1007/978-3-031-48573-2_48

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However, in the tax field, the use of artificial intelligence requires significant data collection. However, this operation poses the problem of the confidentiality of the information collected and the protection of personal tax data which are normally protected by law. Therefore, the whole problem lies in this contradiction: how to facilitate access to databases for tax administrations for the creation of machine learning applications and at the same time respect the laws that protect individual and public freedoms. Another important point also arises in legal matters. Indeed, how to determine legal responsibility if the information provided by AI to the tax authorities is incorrect or if software provides a taxpayer with inappropriate tax advice. Who would be legally responsible for this, the software developer? The operator? The user? Tax administration? This could “pose problems of uncertainty for taxpayers who are victims of discrimination or errors caused by algorithms” (Cloutier and Julien 2019). From this observation, we set out the following problem: What are the fields of intervention of artificial intelligence in the tax field? To be able to answer this problem, we carried out a comparative documentary study between France and Morocco in the field of the implementation of AI in the tax field. Nevertheless, and before looking at how AI intervenes in taxation in the two countries, we first offer an understanding of the concept and a concise historical overview of the implementation of AI in the tax field.

2 Conceptual Framework of Artificial Intelligence First observation, there is no consensus on the definition of artificial intelligence, even if Nilsson (2009) thinks that “Artificial intelligence consists of activities devoted to making machines intelligent” (quoted by Kuzniacki (2019; For their part, - Viglione and Deputy (2017; p. 27) specify that AI can be defined as “the set of techniques that allow computers to imitate human intelligence, including rules, decision trees, and machine learning”. As for Sharlow and Scheim (2019; p. 6) they specify that “Artificial intelligence is a technology that allows a computer or machine to process information from ‘a way that replaces human work’”. We can therefore deduce that AI operates from human activity (programming) and allows, thanks to complex algorithms, to inform, detect, react, decide and to recommend. Another concept to be defined is that of “machine learning”, which is actually one of the types of artificial intelligence used by data scientists. Thus Goodfellow et al. (2016; p. 2) define “machine learning” as “an artificial intelligence system with the ability to acquire its own knowledge, by bringing out patterns from raw data” taxation, Milner and Berg (2017; p. 4) specify that “machine learning is a subcategory of artificial intelligence that uses algorithms to make predictions on data without being explicitly programmed to do so”. In this respect, it is interesting to observe that Viglione and Deputy (2017; p. 28) specify that the word Deep should normally be added to the word Learning (Deep Learning) in order to indicate a computer method able to analyze data much more broadly and comprehensively than humans can. Note that the terms “artificial intelligence” and “machine learning” are often used interchangeably by authors in the field of taxation.

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Another important definition is that of “Data Mining” often used when discussing artificial intelligence. To this end, Houser and Sanders (2018; p. 2) define it as “the analysis of large databases in order to discover new relationships among the data”. As for the term “Big data”, it is closely linked to three main characteristics: • The volume: Either the quantity of data available; • The variety: Either the diversity of the content of the data, the different sources of the data, their formatting and their structure; • Speed: Either the speed of data production and that of their processing. Big data remains essential in “machine learning”, because data is the raw material of algorithms which are nothing but a succession of pre-programmed operations leading to a result.

3 Historical Overview of Artificial Intelligence in the Field of Taxation In the tax field, the history of artificial intelligence begins in 1970 with L. Thorne McCarty who developed the “Taxman” program, designed “as a very preliminary form of legal reasoning limited to American tax law”. This program in the light of US tax law therefore made it possible to specify whether or not a company should be exempt from income taxes. Unfortunately, the US government quickly realized that this program was unable to understand the complexity of legal texts, to pose adequate reasoning and that a human experience inherent in the lawyer was necessary. Much later and more precisely in the 2000s, faced with the extension of tax evasion and the increasingly complex schemes chosen by fraudsters (Pandora Papers, the Panama Papers and the Paradise Papers, SwissLeaks, etc.) the OECD will initiate the BEPS program (Base Erosion and Profit Shifting) whose main objective was to “provide governments with national and international instruments to fight against tax evasion by ensuring that profits are taxed where they are where these are generated and where value creation takes place”. This initiative will throughout the 2000s lead many countries to sign multilateral treaties intended to quickly implement a series of measures relating to tax treaties to update international tax rules and reduce the possibilities of tax evasion by multinational companies., and will ipso facto lead to the development of many algorithms capable of ensuring the accuracy, consistency and safe storage of tax data. The introduction of artificial intelligence will therefore accelerate the implementation by tax administrations of new technologies using AI and “data mining” in order to improve efficiency in the fight against tax evasion and better tax collection.

4 Artificial Intelligence and Tax Audit in France First of all, what is tax datamining? tax datamining falls within the field of tax audit programming and consists of “cross-checking, using computer and mathematical methods, various data available to the Directorate General of Taxes” (Aide 2022), this implies that

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tax datamining uses computer techniques that rely on databases from online platforms such as social networks, but also, and this is surprising, on information collected on taxpayers such as aerial detection of swimming pools that are not declared. For example, in 2022, artificial intelligence was used to detect 125,000 undeclared swimming pools based on aerial views, which were subsequently regularized, saving the French government e30 million a year. If we are interested in the tax control set up by the tax administration in France, we observe that since 2021, a national service called “Targeting of fraud and valuation of requests” (CFVR) has been set up. This service, where computer experts work, has developed a program called “Galaxy” capable of carrying out a predictive analysis of the files of taxpayers classified under the name “taxpayers with fraudulent potential” and therefore allows these experts to focus their research on natural or legal persons suspected of not complying with tax regulations by detecting “potential tax anomalies”. How? simply by aggregating various and disparate data such as the reference tax income, the identity of the spouse, the tax obligations for individuals, the Siren number, the legal status, the category of turnover as well as the taxes and taxes to which legal persons are subject, but also thanks to algorithms which make it possible to “mix and collect en masse the information contained in the publications of Internet users on social networks” (Louis 2022). Galaxie therefore offers tax auditors the possibility of carrying out an indepth study of the tax file of targeted taxpayers. This new approach to tax auditing based on artificial intelligence has prompted France’s Directorate General of Public Finances to create a National Centre for Remote Control (PNCD) in 2021, with the aim of “carrying out tax audits of taxpayers using artificial intelligence” (Aide 2022).In addition, the French government has created a “requests and valuation mission (MRV)” funded to the tune of 5.2 million (period 2018–2022) and whose objective is to develop artificial intelligence techniques in the processing of tax data in order to better target controls and detect fraud profiles. In terms of the results published by the DGFIP, we note that “9 billion euros were recovered in 2019, one billion more than in 2018” (Delvolvé 2022). The tax administration, without giving precise figures on the role of AI in this improvement, considers that “this increase in revenue is largely linked to the “Galaxy” program, (Delvolvé 2022). However, some will say that AI poses two essential problems. First, the risk of undermining civil liberties. On this point and aware of this possible “slippage”, the French government has laid down a legal framework for the use of AI in tax audits. Thus, the decree of March 11, 2022 stipulates that AI is “a visualization tool, at the national level, on the one hand of the links existing between professional entities (links of participation), and between professional entities and people physical (director, partner or shareholder links), and on the other hand, contextual elements on the financial and tax situation of these people” (Louis 2022). Then, the unions are worried about the impact of AI on employment. At this level, a figure makes it possible to measure this concern, the workforce of the DGFIP in France has increased from 105,000 in 2017 to 94,669 in 2021, (Louis 2022) which suggests that the French government considers that AI is capable to obtain better results than tax auditors. The revolution therefore seems to be underway. On the horizon of 2024, we are talking about a generative artificial intelligence, that is to say a program that would

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look like “ChatGPT” capable of answering questions asked by the tax authorities on taxpayers, a development that could contribute considerably to reducing tax evasion. But AI in tax matters is not a one-way technology that just allows the tax administration to better control taxpayers and punish fraudsters; it can also guide and advise taxpayers by proposing tax optimizations. Thus, in France (but also in other countries) software such as “Tax Mind” or even “Climb”, certainly intended primarily for tax professionals and accessible free of charge, are today considered by many specialists as the “Fiscal ChatGPT” because they are able to give advice on tax law, based in particular on “specific sources of law”. This software can help companies choose the best tax solutions, particularly at the level of niches. We therefore observe that AI actively participates in tax audits in France and that it has become an essential tool for reducing tax evasion, increasing public finance revenue and guiding taxpayers.

5 Artificial Intelligence Within the Directorate General of Taxes in Morocco First, we note that the General Directorate of Taxes in Morocco (DGI) has placed the digitization of its services at the center of its concerns. This is a strategic choice which is perceived first as a lever for modernization and performance and then as a means of improving the said administration’s communication with taxpayers, which helps to promote tax compliance, (Machmoume 2021; Kirchler 2010). To do this, the DGI relies on a dematerialization program with two poles, the one which first affects its internal processes via the Integrated Taxation System (SIT), then the one dedicated to taxpayers who are natural and legal persons through the Online Tax Services (SIMPL) which simplifies tax declarations, payments, obtaining certificates, filing and follow-up of claims. Moreover, and to better give substance to the introduction of digitization in its relationship with the taxpayer, the DGI and in application of the provisions of Law No. 55-19 relating to the simplification of procedures and administrative formalities, has set up a “Chatbot”, i.e. a virtual agent powered by logarithms, capable of interacting with taxpayers 24 h a day, 7 days a week (making appointments, assistance, complaints, etc.). In terms of figures, we recall that thanks to the adoption of digitalization and artificial intelligence, the General Directorate of Taxes in Morocco recorded in 2021 18.82 million dematerialized operations, i.e. a growth of 32% compared to 2020 and that tele-payments and tele-declarations concerned in 2021, 72% of the transactions carried out (Ouazzani 2021). In terms of tax evasion, we note that in its 2021 report, the NGO “Tax justice” estimates that “the abusive practices of multinationals would have cost the Moroccan Treasury 876 million dollars, or 9 billion dirhams and that the major part of this amount, i.e. 806 million dollars, would be linked to the IS (corporate tax)”. Furthermore, we observe that the tax system in Morocco is essentially declarative, and that the presumption of fraud or concealment is often fueled by the behavior of certain companies that declare a permanent deficit. Thus, and by way of example, of 2/3 of loss-making companies in Morocco, half have declared tax losses for three years (Abashi 2022)!

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Aware that these abuses and many others (particularly in terms of VAT) significantly reduce the country’s tax revenue, the DGI acquired in 2022 a new and important database developed by Moody’s. It is “an elaborate tool that contains margins and comparables, especially on intangible assets and goods for which the secondary market does not exist” (Abashi 2022). From now on, “during the audits of the accounts of a multinational, the heads of adjustment will be based on indisputable elements, on convincing indices such as for example a strong dependence commercial relationship with a core of suppliers or service providers”. Indeed, such clues can arouse the suspicions of tax auditors (Abashi 2022). But the DGI has not been content with this new software, as the database is “now managed and controlled by software enabling the institution to better administer the tax base, combat false declarations and identify and eliminate complex fraud schemes” (Younsi 2019). The DGI is therefore increasingly using AI to better use data. Thus, taxpayers’ declarations are meticulously checked by the various algorithms, contradictions are noted and cases of non-compliance are easily identified. According to Younsi (2019), this new software even goes so far as to detect “scheming in the field” during control operations, i.e. all possible attempts at complicity between controllers and taxpayers. It can therefore be estimated that AI could place tax evasion in Morocco on a decreasing curve. Moreover, it is in this perspective that the DGI has signed agreements with other administrations such as the customs department, the national social security fund (CNSS), the national fund for social security organizations (CNOPS) and the Foreign Exchange Office in order to enrich its own tax data.

6 Artificial Intelligence Model of Development and Law At the present time and on the legal level, we can speak of a “digital existence” because technological changes and in particular ICT and artificial intelligence have introduced new freedoms such as freedom of digital expression, freedom of digital information, freedom of digital association, or digital freedom of enterprise. Nevertheless, from this digital existence also arises rights such as the right to security, the right to intellectual property, or commercial law, and the right to respect for private life, which of course requires data protection regulations of a personal nature. Thus article 12 of the International Charter of Human Rights - ratified by Morocco - states “No one shall be the object of arbitrary interference with his private life, his family, his home or his correspondence, nor damage to his honor and reputation. Everyone has the right to the protection of the law against such interference or attacks”. It is therefore obvious that without the establishment of a legal framework that specifies the fields of intervention of AI, its limits, the data circulating on taxpayers (legal or natural person) can constitute an infringement of the rights of individual and public freedoms. As Jaldi (2022) points out, “AI should in no way be given a blank check”. To this end, OECD countries have drawn up an inventory of the data created and manipulated by AI, such as the fact that tax information on taxpayers, which includes personal information protected by law, should not be used freely by tax authorities and who are also required to verify the accuracy of these sources.

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In addition, information collected on social networks should only be used when it provides additional tax information (e.g. photo of private swimming pool, purchase of luxury car, etc.). Finally, the civil liability of tax officials who use AI, in particular in the event of an error in the data that could ultimately cause damage to the taxpayer, must be engaged, especially when we know that AI has biases induced by the algorithms. As such, some lawyers call on governments to include in the texts dedicated to the intervention of AI in tax matters, the principle of prudence which would make it possible “to anticipate as much as possible the harmful consequences of the use of IA and to take appropriate measures to avoid them”. In addition, we note that 193 UNESCO Member States - of which Morocco is a member - proposed in November 2021 a set of recommendations concerning ethics and AI. Among these directives, we note the prohibition in the field of data governance, the use of social rating and mass surveillance so that each citizen can maintain control over the data they provide, and that they can access them at any time and if they wish to delete them; we also underline the proposal to create independent bodies that can be used by any citizen to assert their rights. Unfortunately, UNESCO’s recommendations do not include a legal constraint for States and remain only a “compass” capable of guiding government policies and practices. However, in Morocco there are a number of laws that support data protection, such as Law 09-08 of 18 February 2009 on the protection of individuals with regard to the processing of personal data, Law No. 132-13 approving the Additional Protocol to the European Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data, promulgated by Dahir No. 1-14-136 of 3 July 2014, and Law No. 1-20-69 of 25 July 2020 promulgating Law No. 05-20 on cybersecurity.

7 Conclusion Artificial intelligence is increasingly eating into the spaces of our lives. Indeed, AI, by intervening in almost all areas, makes it possible to eliminate repetitive and monotonous tasks, to reduce human error and above all to accurately achieve the objectives set, such as limiting tax evasion, by example. In addition, these intelligent machines supplied with programs and information operate continuously, which constitutes a considerable saving of time and money. In the tax field, AI, as we have been able to relate in this article, intervenes at all levels, from communication, to tax control, through advice and optimization. Nevertheless, it is at the level of tax control that AI seems destined for a promising future by facilitating the fight against tax evasion, thanks to algorithms that brew an impressive amount of information on taxpayers, but also thanks to the instantaneous exchange of information between the tax administration and all the players (social security funds, banks, customs, etc.). Indeed, the tax audit is rarely linked to chance, it necessarily passes through a preliminary phase of information collection. However, AI allows rapid and precise exploitation of data thanks to datamining, which facilitates the detection of often complex and ingenious fraud mechanisms. This will lead the tax administration to reliably determine the profile of fraudsters.

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Our comparative study on the development of AI in the two Moroccan and French tax administrations shows that France has taken a significant lead in this area. Indeed, in the 2023 ranking of the top 15 countries investing in AI, France ranks 8th in the world with 6.6 billion dollars (Artificial Intelligence Index Report 2023, Stanford University, page 190). Furthermore, this country has two organizations entirely dedicated to the management of AI in public finances, namely the Interministerial Digital Directorate (DINUM) and the Interministerial Directorate of Public Transformation (DITP). In Morocco, of course, one cannot deny the efforts made by public authorities to facilitate the implementation of AI in the various tax administrations, in particular with the acquisition of new software capable of verifying the accuracy of tax declarations, taxpayers, to automatically identify contradictions and cases of non-compliance, but also programs facilitating the rapid exchange of information with other public administrations (customs, social security funds, etc.). However, it is regrettable to note firstly that there is no central body capable of managing the deployment of these new technologies in the management of public finances, then to observe a lack of information on investments in this new technology intended for public administrations. However, it must be recognized that in tax matters, several obstacles may arise to the use of artificial intelligence, such as certain technical problems related to the reading and interpretation of legal texts, the difficulty of establishing a reason based on common sense, Kuzniacki (2019) without omitting the difficulties of tax planning which bring a sharp tax expertise, especially since tax planning and optimization differ from one company to another and from one industry to another. Moreover, the same author points out that human interaction with taxpayers can hardly be carried out by algorithms. We can therefore deduce that the complexity of taxation and in particular of tax law refers to certain tasks that are difficult to carry out (at least for the moment) by artificial intelligence and that the competence of professionals remains necessary or even essential. On the other hand, AI poses a problem at the level of ethics. Thus, without the existence of a clear and transparent legal framework, this new technology may give rise to abuses, particularly in terms of the protection of personal data. It is therefore imperative, not only to create regulations, but above all to ensure that they are scalable in order to adapt to the changes and mutations that AI will experience. Another crucial legal point is that posed by Cloutier and Julien (2019) who question “the possibility for the taxpayer to be really heard by the courts if the decision is rendered by algorithms”. Another interesting observation is that made by Sharlow and Scheim (2019) who wonder if algorithms can eventually replace judges, which in law implies a “revolution at the level of legal texts”. For these authors, “a judicial decision rendered by an algorithm would be incompatible in a state of law due to the lack of transparency of several artificial intelligence applications”. As for research perspectives, our documentary study leads us to believe that future research should first attempt to study whether the deployment of AI has allowed the Moroccan tax administration to obtain better results in the fight against tax fraud (quantified results) and whether the use of this technology has truly improved the climate of trust between the tax administration and the Moroccan taxpayer. Indeed, numerous studies (Machmoume 2021) prove that trust remains an important determinant of tax legitimacy and that it greatly impacts taxpayer compliance. Then, it seems interesting to

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us to also examine whether the use of AI in the verification of tax declarations and tax audits has not infringed on individual freedoms, particularly in terms of confidentiality and data security. In other words, try to elucidate the link between ethics and the use of AI within the tax administration. In short, we can affirm that this technology, while promoting the action of the tax administration, must not only be legally regulated, but that it must also aim to serve citizens and their economic, social and fiscal well-being.

References Abashi, S.: Prix de transfert : le nouveau “logiciel” du fisc, revue les Echo Business (2022) Aide, L.: L’importance de l’intelligence artificielle dans les contrôles fiscaux, revue TEN FRANCE, P-1 (2022) Cloutier, N.X., Julien, S.: Collection APFF - Impôts et taxes, Association de planification fiscale et financière, Nouvelles technologies et intelligence artificielle appliquées au litige fiscal, coll (2019) Delvolve, R.: L’intelligence artificielle, nouvel outil de lutte contre la fraude fiscale, Revue Europe (2022) Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. Cambridge, Massachussets Insitute of Technology (2016) Houser, K.A., Sanders, D.: The use of big data analytics by the IRS: What practitioners have to know. J. Taxation 16, 128–2 (2018) Jaldi, A.S.: L’intelligence artificielle au Maroc : entre encadrement réglementaire et stratégie économique. Revue Policy Brief (2022) Kirchler, E.: Trust in authorities and power to enforce tax compliance: an empirical analysis of the slippery slope framework, law/policy 32(4) (2010) Kuzniacki, B.: The marriage of artificial intelligence and tax law: past, present, and future. SSRN J. (2019). https://doi.org/10.2139/ssrn.3323867 Louis, P.: Impôts : comment le fisc recourt à l’intelligence artificielle pour lutter contre la fraude, revue Patrimoine (2022) Machmoume, S.: Déterminants de la légitimité de l’impôt et conformité fiscale : cas du contribuable marocain, Thèse doctorat soutenue à l’Université Sidi Mohammed Ben Abdellah (2021) Milner, C., Berg, B.: Tax analytics: artificial intelligence and machine learning - Level 5. PwC Adv. Tax Anal. Innov. (2017) Ouazzani, M.: DGI. 18,82 millions d’opérations dématérialisées en 2021, Revue Challenge (2021) Salma, R.: L’intelligence artificielle en fiscalité, entre mythe et réalité, revue Cyber Justice (2022) Sharlow, K., Scheim, L.: Artificial intelligence nd the judiciary. dans Corporate Management Tax Conference, 6A, Toronto, Canadian Tax Foundation (2019) Viglione, J., Deputy, D.: Your tax data is ripe for artificial intelligence. Are you prepared? 69–5 Tax Execut. 25–32 (2017) Younsi, M.: DGI: L’intelligence artificielle pour combattre l’évasion fiscale, revue 360 (2019)

Deep Facial Expression Recognition Ouhammou Mohamed1 , Nabil Ababou1(B) , Said Ouatik El Alaoui2 , and Si Lhoussain Aouragh3 1 Systems Engineering Laboratory, Sultan Moulay Slimane University, Beni Mellal, Morocco

[email protected]

2 National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco 3 Si Lhoussain Aouragh IT and Decision Support System, Mohamed V University, Rabat,

Morocco

Abstract. The vast applications of artificial intelligence, such as human-computer collaboration, data-driven animation, human-robot interaction, etc., have made it urgently necessary to detect emotions through facial expression. Numerous studies have been done on this subject since it is a challenging and intriguing issue in computer vision. The goal of this study is to create a deep convolutional neural network-based face expression recognition system with data augmentation. This method makes it possible to identify the seven fundamental emotions which are anger, disgust, fear, happiness, neutrality, sadness, and surprise. Recent years have seen a rise in interest in the field of facial recognition technology, and convolutional neural networks (CNNs), which have demonstrated outstanding achievements in this domain. CNN training may take a long time and it requires a lot of labeled data. Nonetheless, in this paper, we have investigated the application of transfer learning methods to enhance the effectiveness and precision of CNN-based facial recognition tasks. Besides, We have looked at how well feature extraction and finetuning pre-trained models work together to transfer knowledge from one domain to another. Keywords: FER · Face expression recognition LSTM · Long short-term memory CNNs · Convolutional neural networks

1 Introduction Facial emotions and characteristics are important in everyday communication. In addition to speaker recognition, the face aids in a variety of cognitive activities, such as lip movement, which forms visemes and considerably aids in interpreting speech in loud surroundings. Contrary to popular belief, social psychology research has shown that meaningful interactions often rely on facial expressions rather than spoken words to transmit thoughts. This discovery has reignited interest in detecting and evaluating facial expressions, not just in severe situations, but also in ordinary human-to-human encounters. The latter is a type of face detection and identification that relies on the use of facial expression, biophysical signs and symptoms like heart rate and brain activity to really understand a person’s status. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 339–345, 2024. https://doi.org/10.1007/978-3-031-48573-2_49

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Emotions are displayed through communicating and socializing with people. It may be challenging to learn how to read them; thus, technology is meant to be used as a sort of assistance. Face emotion recognition (FER) is one of the face recognition methods or technologies that have been advanced significantly and grown over time. Currently, FER software is used in a variety of expanded applications like Brain Computer Interface to examine and interpret human emotions [1]. As previously stated, facial expressions are formed of several limitations that are based on hard-coded properties; a spatial link between distinct face areas is required. CNN filters, on the other hand, appear to have failed to catch such a link since they are only conducted locally on picture areas. For our network model to learn from whole time series at each time step, LSTM is required. As a result, we must enhance our face expression identification performance by investigating long-term connections, particularly spatial dependencies within facial expression photos [2]. A variety of training models are available. As a result, it can effectively integrate the benefits of each training model and achieve good performance. The remainder of this paper is organized as follows. Related work is described in Sect. 2. Section 3 presents the Methodology. The details of the Results and discussions are Sect. 4. Finally, conclusions are given in Sect. 5 (Table 1). Table 1 Expression facil Angre

- Stare–Frowning face–Eyebrows and jaw clenching

Fear

- Ecarquillement des yeux–Ouverture de la bouche–Tremblement du visage–Relèvement de la partie intérieure et abaissement de la partie extérieure des sourcils–Pâleur

Happy

- Squinting eyes–Mouth opening–Cheek enhancement

Surprise

- Quinting eyes–Mouth opening–Raised eyebrows

Disgust

- Narrowing of the eyes–Mouth grimace–Nose wrinkles

Sadness

- Lowering the corners of the mouth–ownward gaze - General sagging of features

Contempt Contraction of one end of the lips

2 Related Work Transfer learning in deep CNNs has been examined in a number of researches for facial recognition applications. Sun et al. (2014) suggested a deep CNN architecture for facial recognition that combines convolutional layers, max-pooling layers, and fully linked layers. On a sizable dataset of ace photos achieving excellent accuracy based on a number of criteria [2]. Other researchers suggest that the visual attention on the lips

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is exclusive to emotions of fear, whereas people with autism spectrum disorder (ASD) explore the eyes and mouth similarly to nor-mal participants when expressing happiness or rage. These phenomena could be influenced by how quickly visual information is processed [3]. About 0.7% of the population is affected with schizophrenia, which most frequently manifests in young adults. It involves a pathology whose syndromal clusters are unstable and which results from a complicated etiopathogenesis [4]. A method called facial expression recognition (FER) uses variations in face appearance to infer emotions [5]. According to FER, basic emotions are a collection of fundamental, core feelings that are widely acknowledged as they exhibit distinctive physiological and behavioral manifestations. Basic emotions include things like joy, sorrow, rage, fear, disgust, and surprise, according to common description of how a system helps people to recognize mental illnesses by finding diagnoses. Diagnostics can be carried out using an automated system that uses the fuzzy logic technique [6, 7]. A 70-years-old woman with a psychiatric history presents symptoms of severe depression, including fixed posture, hypomimia, motor slowing and prostration [5]. Alcohol’s damaging effect on the brain may cause EFE-decoding issues in alcoholics [8]. Emotional facial expressions provide vital communicative roles, providing social cues and encouraging socially acceptable behaviors. Accurate facial expression identification aids in understanding the sentiments and intentions of people. Facial expression recognition deficits have been found in severe depression and schizophrenia [9]. In addition to the layers of an ordinary neural network, CNNs also have a few more layers. Every node in the layer below is connected to every other node as we saw in the previous section. CNNs are employed to address this problem. We use CNNs to selectively filter data. A rudimentary CNN’s fundamental architecture is depicted [6] gleaned details about the jawline, whether the mouth is open or the eyes are closed. A dense layer convolutional neural network is then fed with the recovered or identified features. A sequential model with three successive convolutional layers is constructed, the sequential model’s thick layers include 128, 64 and 7 neurons, respectively. The structured output dense layer has seven neurons and a softmax classifier layer that classifies the emotion dataset [1].

Proposed architecture

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The convolution layer, where we apply a filter to the input image, is the first step in a quick explanation of how CNNs operate. This kernel moves block by block through the image, where each block is made up of a group of pixel cells. We multiply the matrices in this procedure, which produces a lower quality image. We locate the average pixel value to the maximum pixel value in the subsampling layer and obtain an even lower resolution image. Matrix multiplication produces a picture with less detail. We locate the average pixel value in the subsampling layer, and obtain an image with an even lower resolution. The output is, then, connected to the fully connected layer, where each output from the max pooling layer is connected to each node. It is an architecture that consists of convolutional layers, max-pooling layers, and fully connected layers. We compared the performance of our transfer learning approach with that of a baseline CNN and LSTM that was trained from scratch on the same dataset [2].

3 Methodology This section presents three important phases: dataset collection, data preparation, and suggested models integrating proposed CNN and LSTM architecture [10]. 3.1 Dataset Description Data FER2013 is available in Kaggle, and it contains 35887 images with a resolution of 48 × 48 pixels. There are two types of the data set, one saved the type CSV and the other contains images organized into emotional folders. The data set includes people aged between 18 and 50 years old, with the aforementioned seven different classes of emotions: anger, fear, disgust, happiness, neutrality, sadness and surprise [1]. The CK+ dataset, which has 981 grayscale pictures with a resolution of 48 × 48 pixels, is accessible on Kaggle. Emotional folders are used to categorize images. Images of people between 18 and 50 years old, representing a variety of genders and races, are included in the collection. Each picture has labels from one of seven classes [1].

Seven Emotions in FER2013 Database

Seven expressions in CK+48 Database

All experiments were conducted using the pre-trained VGG16 networks offered in the Keras package for Python, which is the framework utilized for assessing the CNN’s performance as a feature extractor and also for transferring the features for facial expression identification. The features extracted from all the pooling layers and the first fully connected layer of the VGG19 are evaluated using the implementation provided

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for [11].Despite the fact that there are several studies are being conducted in the subject of ER, a more accurate method of identifying facial expressions and emotions has not yet been found. Models like CNN, Deep CNN, LSTM, and others are used, and the recognition of accuracy has been fairly encouraging. Real-time emotion analysis is now simpler and more effective thanks to deep learning. According to Sarker et al. (2021), deep nets increase a model’s performance accuracy. That is, they make it possible for a model to process a number of inputs and provide an output. 3.2 Grayscale Conversion Images have been resized into 48 * 48 pixels, and dataset images have been converted into grayscale having only one channel. Therefore, it has become pretty much easy for the model to learn. 3.3 Image Normalization Normalization has been applied to model dataset which is a process that modifies the range of pixel intensity.

4 Results and Discussion To further demonstrate and validate our findings, we study the use of fictitious neural networks to increase link prediction performance. We used four assessment criteria for this study (see Table2) that shows the simulation results using python 3 as tools. Table 2. Comparison of built models Dataset

Algorithems

Epoch

Activation

Accuracy

CK + 48

LSTMmodele

100

tanh

98.95%

CNN modele

100

relu

75.23%

FER2013

LSTM modele

100

tanh

90.32%

CNN modele

100

relu

78.12%

To extract crucial information for detecting the expression from the face, an amalgam of convolution neural network (CNN) and long and short-term memory (LSTM) is used in this article with a maximum score 78.23%. To improve performance, the transfer learning principle is used to get learnt parameters (Fig. 1).

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Confusion matrix using LSTM

Confusion matrix using CNN

Fig. 1. Confusion matrix

5 Conclusion Our experiments demonstrate that transfer learning is a powerful technique that can significantly improve the accuracy of facial recognition models. Fine-tuning pre-trained CNN models achieved higher accuracy than feature extraction with a linear classifier, however both approaches outperformed training from scratch. Besides, the LSTM technique also depends on extraction of features to achieve accuracy in terms of detecting emotions. The choice of a pre-trained model and the number of training samples can affect the performance of the facial recognition model. It is important to choose appropriate pre-trained models and transfer learning strategies for specific facial recognition tasks. These findings have implications for the development of facial recognition systems for various applications such as surveillance, security, and human-computer interaction [2].

References 1. Gautam, C., Seeja, K.R.: Facial emotion recognition using handcrafted features and CNN. Proc. Comput. Sci. 218, 1295–1303 (2023) 2. Bridoux, A., Granato, P.H.: The interest of measuring recognising facial expressions in depressed patients with major depression disorder. Ann. Méd.-Psychol. Rev. Psychiatr 168, 602–608 (2009) 3. Ikromovich, H.O. Mamatkulovich, B.B.: Facial recognition using transfer learning in the deep CNN. Int. Sci. Res. J. 4, 2776–0979 (2023) 4. Deruelle, C., Santos, A.: Happy, sad or angry? what strategies do children with Williams syndrome use to recognize facial expressions of emotion?. L’évolution psychiatrique 74, 55–63 (2009) 5. Shi, C., Tan, C., Wang, L.: A facial expression recognition method based on a multibranch cross-connection convolutional neural network. IEEE Access 9, 39255–39274 (2021) 6. Chaturvedi, I., Satapathy, R., Cavallari, S., Cambria, E.: Fuzzy commonsense reasoning for multimodal sentiment analysis. Patt. Recog. Lett. 125, 264–270 (2019) 7. Ioannou, S.V., Raouzaiou, A.T., Tzouvaras, V.A., Mailis, T.P., Karpouzis, K.C., Kollias, S.D.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw. 18, 423–435 (2005)

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8. Kornreich, C., Foisy, M.L., Philippot, P., Dan, B., Tecco, J., Noel, X., Hess, U., Pelc, I., Verbanck, P.: Impaired emotional facial expression ecognition in alcoholics, opiate dependence subjects, methadone maintained subjects and mixed alcohol-opiate antecedents subjects compared with normal controls. Psych. Res. 119, 251–260 (2003) 9. Fairchild, G., Van Goozen, S.H.M., Calder, A.J., Stollery, S.J., Goodyer, I.M.: Deficits in facial expression recognition in male adolescents with early-onset or adolescenceonset conduct disorder. J. Child Psychol. Psych. 50(5), 627–636 (2009) 10. Yan, J., Zheng, W., Cui, Z., Song, P.: A joint convolutional bidirectional LSTM framework for facial expression recognition. IEICE TRANS. Inf. Syst. E101, 1217–1220 (2018) 11. Ravi, A.: Pre-trained convolutional neural network features for facial expression recognition

Texture Analysis by Gray Level Homogeneity in Local Regions El Beghdadi Abdelhamid(B) and Merzougui Mohammed Mohammed I University, Oujda, Morocco [email protected]

Abstract. In this paper, we propose a method that helps us analyze the texture of an image through the study of the appearance and recognition of the shapes of the regions of the image by studying the homogeneity of the gray level among the local regions. This principle, based on the coding of the pixel according to its adjacent neighbors, allows us to describe the nature of the texture and measure the degree of local gray level homogeneity. This algorithm gives us an idea of the change in contrast and the flow of gray levels between regions, and it also detects the areas of interest in the image. This method is interested in studying the homogeneity of gray levels like other methods such as the local contrast descriptor (LCD) and Co-occurrence Matrix, so that it is easy and effective to discover the borders of regions in images, improve their weak contours, and reduce noise. Keywords: Texture · Segmentation · Histogram · CLH Matrix · Co-occurrence matrix · Sobel · LCD · Denoising

1 Introduction Texture analysis plays an important role in the literature on image processing; it is a field that studies the spatial structure of gray levels by different methods in order to find the descriptor of image patterns. There are several considerable strengths in this area to improve texture analysis. Some of the methods are based on the study and characterization of pixel distribution, the measurement of homogeneity of gray levels [2, 3, 8], or statistical analysis of texture, while others involve local geometric primitives, random field modeling, or fractals. The Haralick method [7], also known as the co-occurrence matrix of gray levels (GLCM), is a texture analysis technique that measures the probability that pairs of grayscale levels coincide in an image. The homogeneity of pixels and textures in an image can be evaluated using the statistical properties of this matrix. Several properties of the GLCM are useful in assessing the homogeneity of a texture.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 346–353, 2024. https://doi.org/10.1007/978-3-031-48573-2_50

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The LCD (local contrast distribution) method is used to evaluate the local contrast distribution [3]. It allows you to quantify the variety of gray or color levels in different parts of the image, which can be useful for analyzing the homogeneity of pixels and textures. This technique can be used in a variety of fields, such as computer vision, object recognition, medical image processing, and other applications where texture analysis is crucial. In this work, we propose a method of texture analysis based on pixel coding to characterize the texture of the image by comparing the gray level of the central pixel with eight neighbors. This principle focuses on the study of the homogeneity of the gray level between the different regions and allows us to know the density of pixels with the same level of gray. This method of encoding focuses only on the homogeneity of gray levels, which is more important than the LCD and Haralick methods because the coding coefficients associated with an image are unique, whereas the Haralick method that calculates them is linked to distance and orientation. In addition, our calculation is simpler and less time-consuming than LCDs and GLCMs, and our coding coefficients will be used for regular image and detecting regions, as well as to strengthen weak outlines and reduce noise. This method can be used in many areas, which we will present in the following paragraph:

2 Coding Principe The coding principle of the central pixel is the cardinal of the central pixel and its neighbors of the same gray level.

1

2

3

4

5

6

7

8

9

Fig. 1. Coding of the pixel as a function of its adjacent neighbors.

The coding matrix that we will define for any image is as follows:  A(i, j) = P(i + n, j + m) n

m

where  P(i + n, j + m) =

1 if I (i + n, j + m) = I (i, j), 0 otherwise

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n, m ∈ {−1, 0, 1} such that: i + n > 0 and j + m > 0. A is the generated matrix, which can be called the matrix of coefficients of local homogeneity, as noted by CLH. By definition, the coefficients of coding of the matrix are between 1 and 9. Below (Fig. 1), there is a model for each coefficient, and the central pixel that has the largest coefficient is the most homogeneous with its neighbors. Coefficient 9 means that the pixel corresponding to this code is the same color as its neighbors, and also, coefficients 8 and 7 are indicators that can be considered almost the same as coefficient 9 in the degree of homogeneity; these three coefficients express a greater homogeneity between local gray levels. But the 3x3 size matrix of the central coefficients 1, 2, and 3 is less homogeneous with its neighbors, which can be considered as indices representing the mixture of gray levels or noise, especially the coefficient 1, which corresponds to a foreign pixel among the 8 neighboring pixels. For the coefficients 4, 5, and 6, which are often found at the borders of the regions, especially the index 6, which is the most probable one, these coefficients represent the matrix of size 3x3 as a region of average homogeneity. This approach allows us to know the distribution situation and homogeneity of the gray level.

3 Representation of the Matrix CLH By using the coefficients of the CLH matrix, we can describe and distinguish between image regions using the degree of homogeneity. In this context, we propose to increase the coding coefficients to represent the matrix as an image. Let A be the CLH matrix with: A(i, j) ∈ {1, ..., 9} J (i, j) = (A(i, j) − 1) ×

255 . 8

The scaling coefficient 255 8 is used to increase a step among the homogeneity coefficients in order to visualize the independence of homogeneity in terms of gray levels (Fig. 2).

Fig. 2. (a): original image, (b): image written by the CLH matrix associated with the original image, (c): image written by the CLH matrix associated with the image (b)

The matrix is an effective way to detect the boundaries of the image shapes that have more homogeneous gray levels, but for the shapes of low homogeneity, there are problems. The solution to the latter can be found in the matrix of second degree, as we have in image (c), which identifies the boundary of the region of low homogeneity.

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4 Histogram of the Matrix CLH The histogram of the CLH matrix is the quantitative distribution of coding coefficients, which can allow us to better visualize the degree of homogeneity of pixels with their neighbors and give us a view on the nature of the image (Fig. 3).

Fig. 3. The histogram of the CLH matrix associated with the image.

In the histogram, we present the coding coefficients, which indicate the degree of homogeneity of pixels with their neighbors. The most quantitative coefficients are probably those that describe the texture of the image. We conclude from this histogram analysis that it is possible to classify the nature of the images by frequency coefficients of homogeneity. At this level, we can define an indicator that generally summarizes the nature of the image, and through it, we can have an idea of the possible and most frequent homogeneity coefficients. The indicator is as follows: N M h=

i

j

A(i, j)

N ×M

,

A of dimension (M , N )

h is the average rate of the coefficients of the CLH matrix, which means the homogeneity rate of pixels among themselves (Fig. 4).

5 Detection of Regions We will try to use the coefficients of the CLH matrix to discover the regions of the image, using these indicators, we can determine the texture of the picture and its constituent shapes in terms of homogeneity. At this level, we propose a calculation based on the coding coefficients that brings grayscale levels closer to their neighbors and regularizes their distribution. This algorithm is applied recursively to a submatrix of size 2 × 2. For this principle, the proposed method to describe the regions of the image by the coefficients of the matrix CLH in the new image is illustrated in the following formula: M (i + n, j + m) =

M (i + n, j + m) + H (i, j) 2

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Fig. 4. The histogram (a): the average of the homogeneity h = 8 which means that the image corresponding to this histogram is more homogeneous. The histogram (b): the average of the homogeneity h = 2 it means that the image corresponding to this histogram is of weak homogeneity.

with: H (i, j) =

1 n=0

1 m=0 I (i+n,j+m)×A(i+n,j+m) 1 1 n m A(i+n,j+m)

(n, m) ∈ {0, 1}2 et M = 0 initial state. This gray level analysis formula helps us to specify the boundaries of regions that have the same gray level homogeneity, which allows us to identify different regions of the image as weak or strong regions and also to distinguish almost similar areas (Fig. 5).

Fig. 5. (a): initial image, (b): new image created by the initial image with its CLH matrix according to the mathematical formula (1).

6 Improve Weak Contours There are many methods and procedures (Sobel, Roberts, Prewitt, …) [1, 9, 10] To detect image contours in order to represent them in a simple, natural, and clear way. To do this, there are many ways to find the contours of the image better [6]. The problem we’re going

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to address is improving the weak or invisible contours of the image, and this problem is frequently found in the field of image processing, especially in radiography (the field of medicine), which is interested in the analysis of the contours of the image with the naked eye. Thanks to the CLH matrix, there is more advantage to improving the weak contours of the image. The principle is the use of a certain edge detection operator on the image processed by the coding matrix according to the previous mathematical formula (1). Below, there are comparisons of edge detection by the same operator (Sobel) on the original image and the image processed by CLH (Fig. 6).

Fig. 6. (a):Tthe original image, (b): contours of the image by Sobel, (c): contours of the image by Sobel processed by CLH.

By the same mask of Sobel, the contours of the image processed by CLH are clearer and sharper than the other, the masks used are: ⎡

⎡ ⎤ ⎤ −1 0 1 −1 −2 −1 Sx = ⎣ −2 0 2 ⎦and Sy = ⎣ 0 0 0 ⎦ −1 0 1 1 2 1

7 Noise Reduction Image noise is a big problem in image processing. It is a field that has always been interested in improving the quality of images and recovering lost information [12]. For this reason, researchers have done a lot of work to put methods and techniques to remove noise from images [4, 5, 11, 13], even if it is minimally to bring the images closer to their original image.

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In this level, we will try to use our approach that calculates the average of the gray levels according to the coefficients of the CLH matrix, thanks to the coefficients of the CLH matrix we can consider that the smallest coefficients are corresponded to the noisy pixels, which is very important to know which pixels we are going to treat according to the degree of homogeneity chosen for our calculation, and we keep the gray levels for the pixels more homogeneous with their neighbors, the formula of calculation is as follows: ⎧  I (i+n,j+m)×A(i+n,j+m) ⎨ 1n=0 1m=0 1 1 if A(i, j) ≤ c P(i, j) = n m A(i+n,j+m) ⎩ I (i, j) otherwise with: (n, m) ∈ {−1, 0, 1}2 and i + n > 0, j + m > 0 I the noisy image, A is the CLH matrix associated to the image I . c ∈ {1, ..., 9} is the coefficient corresponding to the degree of homogeneity chosen for the pixels we will process (Fig. 7).

Fig. 7. Example of noise reduction with the choice c = 5

8 Conclusion In this work, we presented the principle of calculating the local homogeneity coefficient matrix (LCH), which is a measure of the local structure of the texture. We have found that this method regulates well the regions of the image according to their homogeneity of the gray levels, detect the image contours especially in well-homogeneous regions and improve the contours weak or invisible by other operators. Thus, opportunities to apply our method to the areas of remote sensing, medical imaging, computer vision, and many more.

References 1. Idris, B., Abdullah, L.N., Halim, A.A., Selimun, M.T.A.: Comparison of edge detection algorithms for texture analysis on copy-move forgery detection images. Int. J. Adv. Comput. Sci. Appl. 13(10), 155–156 (2022). https://doi.org/10.14569/IJACSA.2022.0131021

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2. Majdoulayne, H.: Extraction de caracteristiques de texture pour la classification d’images Satellites, vol. 19, pp. 19–48 (2005). https://doi.org/10.3166/ria.19.633-659 3. He, J., Ji H., Yang, X.: Texture analysis using local region contrast, vol 22 (2013). https://doi. org/10.1117/1.JEI.22.2.023007 4. Navarro, L., Courbebaisse, G., Roux, C.: Une rédefinition des conditions aux limites de la méthode Lattice Boltzmann pour le débruitage d’images 5. Meni Babakidi Narcisse: Development of a gaussian filter for noise reduction in medical images. Int. J. Innov. Appl. Stud. 32, 295–300 (2022) 6. Sonka, M., Hlavac, V., Boyle, R.: Image processing analysis and machine vision, pp 133–328 (2013) 7. Regniers, O.: Méthodes d’analyse de texture pour la cartographie d’occupations du sol par télédétection très haute résolution: Application à la forêt, la vigne et les parcs ostréicoles, pp 8–14 (2014) 8. Ouslimani, F.: Etude comparative des techniques de codage d’images en vue d’une segmentation, pp. 15–88 (2018) 9. Ying-Dong, Q., Cheng-Song, C., San-Ben, C., Jin-Quan, L.: A fast subpixel edge detection method using Sobel–Zernike moments operator, vol 23 (2005). https://doi.org/10.1016/j.ima vis.2004.07.003 10. Öztürk, S., Akdemir, B.: Comparison of edge detection algorithms for texture analysis on glass production. In: World Conference on Technology, Innovation and Entrepreneurship, vol. 195. Elsevier, pp. 2675–2682 (2015) 11. Sadeghi, S., Rezvanian, A., Kamrani, E.: An efficient method for impulse noise reduction from images using fuzzy cellular automata. AEU-Int. J. Electron. C. 66, 772–229 (2012). https://doi.org/10.1016/j.aeue.2012.01.010 12. Tabti, S.: Modélisation des images par patchs pour leur restauration et leur interprétation. Applications à l’imagerie SAR (2016) 13. Fourati, W., Bouhlel, M.S.: Nouvelle Méthode pour le Débruitage d’images, 2007. In: 4rth International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 25–29. Tunisia (2007)

Deep Learning Approaches for Stock Price Forecasting Post Covid19: A Survey El Qarib Mohamed1(B) , Nabil Ababou1 , Si Lhoussain Aouragh2 , and Said Ouatik El Alaoui3 1 Systems Engineering Laboratory, Sultan Moulay Slimane University, Beni Mellal, Morocco

[email protected]

2 Si Lhoussain Aouragh IT and Decision Support System, Mohammed V University, Rabat,

Morocco 3 National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

Abstract. The post-covid era is marked by the massive use of new communication technologies, in particular the application of artificial intelligence in all aspects of daily life. The use of trading platforms and applications by individuals and professionals alike has prompted researchers to design new predictive model architectures based on deep learning, to integrate the maximum amount of information generated by the unprecedented use of social networks to handle the complex patterns of financial time series, in addition to traditional data based on technical and fundamental analysis. In this paper we will look at some of the most important developments in this field, regarding to the novel trend of implementation of neural network-based hybrid model, that takes the edge in domain of stock price prediction, those approaches comes to deal with numerical and non-numerical data to get improved performance in prediction field. Keywords: Deep learning · Stock price prediction · Neural network · Financial time series · Hybrid model

1 Introduction Deep learning is a very important field of research owing to its applications in the daily life of humans, and to the various scientific fields it covers. Deep learning-based approaches use neural networks architecture to handle bigdata and complex tasks as time series forecasting. Stock price and trend forecasting, which refers to the prediction of the next day’s stock price and its Trend, it is a promising research field in financial time series forecasting. Financial time series data are often influenced by multiple factors such as economic, political, business, and human behavior [1], then making financial markets stochastic and getting financial data characteristics as multi-noise, nonlinearity, highfrequency, inherently volatile, and the result is a chaos of stocks [2]. Researchers and investors attracted by this topic have been trying to make use of statistic models, machine learning for many years to capture the inherent patterns of stock markets data and make the right predictions. To challenge those problems a large quantity of well-performing © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 354–361, 2024. https://doi.org/10.1007/978-3-031-48573-2_51

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deep learning based algorithms and models being proposed with the emergence of big data (large dataset), enhancement of high computational technology (Powerful GPU) and rapid development in artificial intelligence [3]. The power of the neural network architecture is its capacities to learn from a vast amount of data. The basics of neural networks are designed since the fifties, but the real application was started from nineties [4]. The first neural network has been implemented with a single layer perceptron, it was faced to criticism because of his limitation. The amazing advances in computational power change the insight towards the Neural networks architecture and it began to receive more attention, we talk about multilayers architectures and backpropagation concepts. Numerous architectures are designed and implemented to solve several tasks as classification, clustering, and nonlinear regression. Those architecture are under improvement every time by researchers, we can cite multilayer perceptron (MLP), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), Long shirt term memory (LSTM), Generative Adversarial Networks (GANs), Transformer Architecture. This article aims thorough analysis of gathered papers to identify deep learning-based approaches applied to predict stock prices, deep learning architectures more commonly used. Also, we intend to assess methodology transparency, experimental design, sample size, and external validation then evaluation metrics. This article also concludes which model is most implemented and what are strengths and weakness of each approach and results cite in those papers. By this paper researchers can lead their works with a roadmap that can help them to identify the solution for the issues encountered when dealing with financial time series data.

2 Related Works In this section, we discuss related works. We reviewed the conducted survey in field of applying Deep learning in financial time series context and f varieties of used deep learning methods. In past studies, numerous methods for prediction stock prices are cited, statical as Autoregression moving average ((ARMA), Autoregression integrated moving average ARIMA, Seasonal Autoregression integrated moving average (SARIMA), Autoregression conditional Heteroskedasticity (ARCH) Generalized Autoregression conditional Heteroskedasticity (GARCH), machine learning algorithms as Support Vector Machines (SVM), Random Forest (RF), Decision three (DT), Extreme Gradient Boosting (XGBOOST) to have been employed in stock price prediction to outperform market stocks and exceed these prediction complexities …. In his study Hu et al. enumerate CNN, Deep Belief Network, Sparse Autoencoder (SAE), backpropagation (BP) and gives explanations and differences between it [5]. As deep learning models know a significative progress, the methods used for predicting the stock market have shifted from traditional techniques to advanced deep learning techniques such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Graph Neural Networks (GNNs) [6]. These methods are implemented simply or in hybrid approaches model. Zou gives explanations and architectures of each Recurrent Neural Network varieties cited above.

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3 Methodology To reach our aim in this study, we focus on papers relating to stock market forecasting. For obtaining a meaningful collection of papers, we implemented a process as mentioned below: Initially, we restrained the timeframe to the period of post covid (2021–2023). Next, we collected the important and the top-ranked articles by relevance, including Deep learning for stock prediction from the most reputed publishers such as Scopus, Springer, Mdpi, IEEE, and others, with specific keywords:” Deep learning for stock prediction” from journals and conferences. By using these keywords, we were able to identify papers related to stock market prediction using deep learning and dress our research paper list. Then, we review collected 100 papers that are related to stock market forecasting by using deep learning approaches. By reading the cited papers in these papers, we choose high-quality papers related to stock prediction then 88 papers were selected that cover a diverse range of deep learning methods. Secondly: we Extract relevant information from each selected paper, such as: Title, Year of publication, Publisher, Deep learning methods used, Dataset category and size, code source, Evaluation metrics. Specifications of computational resources, Externed validation, Target feature, Input features, Experimental design.

4 Collecting and Analyzing Data In this section we discuss patterns, trends, and relevance of the collect data to respond for certain questions as the use of deep learning architectures more commonly, existence of the datasets and source code for researchers and ability to reproduce the same result of the paper study. After the collection of data required to our survey, we have store it in csv file, the analyse of the cotenant seems as soon (Fig. 1).

Number of articles by year of publication

Distribution of papers by Publisher

40 30 20 10 0 2021

2022

2023

ELSEVIER

OTHER

ICISC2023

SPRINGER

MDPI

IEEE

Fig. 1. Distribution of the papers by year and publisher

HINDAWI

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4.1 Deep Learning Methods According to the analysis of the articles, the researchers tend to adopt hybrid models by introducing new architectures on which support numerical and textual data to predict the prices, the direction, the trend and build the best trading strategies. It is well known that the use of simple models based on ANN, RNN, GRU, CNN, LSTM persist, researchers had trying to combine the parameters that can optimize these models. All models listed in our papers are cited in Tables 1 and 2. 4.2 Datasets For the datasets used in these articles, the researchers use stock market indices and prices, as well as news published by newspapers and texts collected from social networks. The size of data used differs from one article to another starting from the thousands to millions. Deep Deterministic Policy Gradient (DDPG) Advantage Actor Critic (A2C)

Numerical

Closing price

SR, AR

Deep Contextualized Word Representation (DCWR)

Textual

Open price

Accuracy, F1, Precision…

Hierarchical Attention Networks (Hanet)

Textual

Open price

Accuracy, F1, Precision…

Attention CNN GRU (ACG)

Numerical

Closing price

MSE, RMSE, MAE, MAPE

4.3 Inputs and Target In most articles reviewed, the Closing price is the most predicted, using either the indicators provided by financial sites such as yahoo finance (Open, High, Low, Close, Volume) or other calculated technical indicators, and meta data in addition to textual data collected from social media, investors forums, and financial paper news. However, the prediction of the trend, direction and successful strategy are also identified. 4.4 Performance Evaluation Concerning the evaluation of the performance of the models built in these articles, the use of classic metrics (MSE, MAE, RMSE, MAPE, ACCURACY, RECALL, R2, F1 SCORES, PRECISION) remains essential, while adopting other new evaluation methods is emerging as Trend Direction, Area under Curve, Directional accuracy, Directional Accuracy (DA) and Matthews Correlation Coefficient (MCC)…. The choice of metrics depend on the purpose of the model. 4.5 Experimental Design All papers reviewed gives the method applied to train model, in spite of the disregard of presenting more details.

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E. Q. Mohamed et al. Table 1. Hybrid methods based models (part 1)

Methods

Target

Metricsa

Deep Deterministic Policy Gradient Advantage Actor Critic Close.price Sr, Ar (DDPG A2C) Deep Contextualized Word Representation

Open.price Acc, F1, Pr

Hierarchical Attention Networks (Hanet)

Open.price Acc, F1, Pr

Attention CNN GRU (ACG)

Close.price Mse, Rmse, Mae

Auto Encoder Indicator AEI

Close.price Pct, Rmse, Mae,

Network Based On Attentive Multi-View News Learning (Nmnl - Lstm)

Direction

Dtw, Pcc

Discrete Wavelet Transform Autoencoder

Portefolio

Acc, R, P, F1, Auc,

Growing Neural Gas GNG

Close.price Mape

Multi-Channel Cross-Residual Temporal Convolutional Network (Mc-Crtcn)

TS

Gated Recurrent Unit Extreme Gradient Boosting(Gru Xgboost)

Close.price Mse, Rmse, Mae

Active Learning-Based Incremental Deep-Broad Learning (Ai_Deepbl)

Close.price Mse, Rmse, Mape

Dual-Stage Attention Mechanism (Darnn)

Close.price Rmse, Mae, Mape

Principal Component Analysis And Improved Gated Recurrent Unit (PCA IGRU)

Close.price Mae, R2 , Ds

Contrastive Learning Of Stock Representations (CLSR)

Trend

Document To Vector (Doc2vec Lstm)

Close.price Rmse, Mae, R2

Lstm Decision three

Close.price Rmse,

Variational Meta Learning (Vml Lstm)

Close.price Mse Mape Mae

Ar, Aar, Mdd, Sd

Acc, Mcc

Bidirectional Cuda Deep Neural Network Long Short-Term Close.price Mae, Rmse Memory (Bicudnnlstm) a Acc accuracy; Aar average annual return; Ar accumulated return; auc area under curve; Ae

absolute error; C.p closing price; O.p opening price; da direction accuracy; DS direction symmetry; DTW dynamic time warping; F1 F1-score; IRR investment return ratio; MRR mean reciprocal rank; MDD maximum drawdown; MAE mean absolute error; MSE mean square error; MAPE mean absolute percentage error; MCC Matthews correlation coefficient; Pr precision; PCC Pearson correlation coefficient; PCT percentage of correct trend; R2 R-squared; RSME root mean square error; R recall; TS trading strategy; Sr sharpe ratio; SD standard deviation

4.6 Extern Validation From all papers, we had identified only one that had applied extern validation of the model, he used a trading platform to imitate the real act.

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Table 2. Hybrid methods based models (part 2) Method

Target

Metrics

Weisfeiler-Lehman (WL), graph convolutional network (GCN)

Stock-trend

Dtw, Pcc

Empirical Mode Decomposition Ann Regression Classifier-Random Forest

Close.price

Acc, Pr, R, F1

Feed Neural Network model with market sentiment

Hedged Ratio

Mse, Gain

Sentiment Analysis Denoising Auto Encoder Close.price Lstm (Sa-Dlstm)

Mape

Weighted Dynamic Time Warping) LSTM

Return

Mse, Rmse, Mae, Mape, R2

Temporal Convolutional Network

TS

Rmspe

Generalized Autoregression conditional Heteroskedasticity GARCH LSTM

Open.price

Rmse Mape

Financial Bidirectional Encoder Representations From Transformers Finbert

Stock-trend

Acc, Times

Lstm Genetic Algorithm

Market indices

Rmse Mape

Differential Transformer Neural Network

Stock-trend

Acc, Pr, R, F1

Term frequency-inverse document frequency Close.price Deeplearning (TF-IDF-DP)

Acc, Absolute Error

Lstm Curriculum Learning

Stock-trend

Acc, F1

Cnn Bidirectional Encoder Representations From Transformers

Close.price

Acc

Kernel extreme learning machine auto encoder (KELM-AE)

Close.price

Mae, Mape, Rmse

Financial Heterogeneous Graph Neural Network (Finhgnn) Finhgnn

Stock-trend

Da, Auc, Mcc

CEEMDAN-Deep Recursive Neural Networks

Close.price

Rmse, Mae

Lstm Artificial Rabbits Optimization (Lstm Aro)

Close.price

Mse, Mae, R2

Multiple-Branch Convolutional Neural Network Mbcnn

Return direction

Acc, F1, R, Pr

Buy Today Sell Tomorrow (Btst), Lstm

Close.price

Mae, Mape

Fuzzy Cognitive Network Functional Weights Fcns-Fw

Close.price

Rmse, Score

Improved Differential Evolution Forecasting Stock Directions (IDERNN FSD)

Stock-trend

F1, Acc, Pr, R (continued)

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E. Q. Mohamed et al. Table 2. (continued)

Method

Target

Metrics

High Genetic Algorithm LSTM (HGA-LSTM)

Close.price

Acc

Convolutional LSTM Graph Convolutional Network

Close.price

Mae, Mse, Mape

Based Attention CNN Contextuel Bidirectional-LSTM (BACNN CBLSTM)

Close.price

Rmse, Mae, Mape

Dragonfly algorithm (DFA) based deep belief Close.price network (DBN)

Pr, R Acc, F1, Time

Hidden Markov Model-Attentive LSTM (HMM-ALSTM)

Stock-Trend

Mae, Acc, R, Pr

Multi-relational graph attention ranking (MGAR) network

Close.price

Mse, Irr, Mdd, Mrr

4.7 Source Code Only 10% of paper had given some explanation of the source code used to generate prediction. 4.8 Specifications of Computational Resources Between, 11% of the studied papers had given the characteristics of the computers which were used for the calculations.

5 Result and Conclusion Although the studied articles are very relevant in their treatment of the problem of price prediction using deep learning, it is interesting to note that the absence of certain data in which we were interested during the review of the articles collected such as the source codes, the Specifications of computational resources, or the insufficiency of information such as the details on the dataset used, do not facilitate the reproduction of the same results published in the papers either for researchers or for people interested in the topics.

References 1. Chen, S.: High-frequency stock return prediction using state-of-the-art deep learning models. Int. J. Financ. Eng., 2350023 (2023) 2. Chen, Q., Zhang, W., Lou, Y.: Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network. IEEE Access 8, 117365–117376 (2020). https://doi.org/10.1109/ACC ESS.2020.3004284

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3. Sivri, M.S., Gultekin, A.B., Ustundag, A., Beyca, O.F., Gurcan, O.F., Ari, E.: A dynamic feature selection technique for the stock price forecasting. In: International Conference on Intelligent and Fuzzy Systems, pp. 730–737. Springer (2023) 4. Peterson, C., Rognvaldsson, T.: An Introduction to Artificial Neural Networks. In: 1991 CERN School of Computing, Sept 1991 5. Hu, Z., Zhao, Y., Khushi, M.: A survey of forex and stock price prediction using deep learning. Appl. Syst. Innov. 4(1), 9 (2021). https://doi.org/10.3390/asi4010009 6. Zou, J., et al.: Stock Market Prediction via Deep Learning Techniques: A Survey. arXiv, 9 fév 2023. Consulté le: 30 mai 2023. [En ligne]. Disponible sur: http://arxiv.org/abs/2212.12717

A New Miniaturized Ultra-Wideband High-Isolated Two-Port MIMO Antenna for 5G Millimeter-Wave Applications Ouafae Elalaouy1(B) , Mohammed El Ghzaoui2 , and Jaouad Foshi1 1 Faculty of Sciences and Techniques, University of Moulay Ismail, Errachidia, Morocco

[email protected] 2 Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco

Abstract. At present, the fifth-generation (5G) technology is of paramount importance since it aspires to account for the limitations of the preceding 4G generation. Among the multiple existing technologies, the mm-wave grabbed the researcher’s attention in an attempt to attain maximum data transfer rates and prevent network congestion. In pursuit of this objective, this piece of work involves devising a compact ultra-wideband (UWB) MIMO antenna with an anti-parallel layout specifically designed for the millimeter-wave spectrum. Additionally, the study examines and refines the mutual coupling interaction amid the two elements in depth. The presented MIMO antenna occupies a small footprint of 6 × 17.37 mm2 . Despite its compact size, this MIMO antenna reached 65 dB owing to the sufficient inter-element distance and the anti-parallel arrangement. Furthermore, an isolation enhancement of roughly 20 dB is attained when utilizing a defected ground structure (DGS). Besides, the suggested MIMO antenna exhibits a satisfactory gain of around 6 dBi, offers a high efficiency exceeding 96% and covers the band 34.1–39.7 GHz. Based on the above results, the suggested MIMO antenna seems to be compatible with the 5G communication systems. Keywords: 5G · UWB · MM-wave · MIMO · Mutual coupling · Compact

1 Introduction In the current era, the insatiable need for high data rates and devices with smaller form factors prompted the adoption of mm-wave. Indeed, the endorsement of this latter stems from the imperative to cater for the desired 5G specifications [1, 2]. Thereby, the 5G technology is of paramount importance since it aspires to account for the limitations of the preceding 4G generation namely the limited bandwidth, the increased latency, and the small channel capacity [3]. Despite the notable merits of the mm-wave frequencies, they came with their own sets of constraints including atmospheric absorptions, signal fading, and path loss attenuations, which become increasingly prominent when use of a single antenna [4]. Subsequently, this has encouraged researchers to leverage the MIMO capabilities as a means to deal with atmospheric absorptions at mm-waves. However, the widespread trend of miniaturization requires that the MIMO antenna size be small © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 362–368, 2024. https://doi.org/10.1007/978-3-031-48573-2_52

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enough to allow integration with other components. Nonetheless, this may deteriorate the MIMO antenna performance as mutual coupling tends to occur in closely packed antennas [5, 6]. Accordingly, implementing an UWB antenna in a confined space is a tough task. In that sense, a plethora of MIMO antenna designs for mm-wave has been reported recently in the literature [7, 14]. For instance, a two-port MIMO antenna with 8.02 GHz operational bandwidth and a peak gain of 5.3 dBi is reported in [7], but it should be noted that this latter occupies a relatively large area of 31 × 26 and demonstrates satisfactory isolation greater than 20 dB. Likewise, the proposed parasitic element in [9] enables mutual coupling cancelation amid the MIMO antenna, thus reaching an isolation which is greater than 25 dB. In contrast, the outlined antenna exhibits a comparatively narrow bandwidth of 1.46 GHz and a low gain of 4.8 dBi. Another two-port MIMO antenna with an overall dimension of 16 × 26 mm is introduced in [11]. Despite, its broad bandwidth of 12.2 GHz and high gain, it provides a lower isolation. Similarly, in [13] a 5G MIMO antenna is implemented with a substrate size of 30.2 × 20.5 mm. Such antenna offers a substantial bandwidth of 9GHz and achieves impressive isolation levels of up to 25 dB. Moreover, the work in [14] presents a two-port MIMO antenna that covers the band ranging from 2.8 to 12.8 GHz, with a maximum gain of 5 dBi and an isolation of up to 25 dB. In this study, a design of a two-port MIMO antenna at mm-wave 5G frequency is demonstrated. Its major aim is to produce a concise, broadband antenna with reduced mutual coupling and high gain properties that are suited for 5G devices. The antenna’s geometry is straightforward, which makes it easily adaptable in a variety of 5G applications.

2 Proposed Model 2.1 Single Antenna Structure The geometry of the suggested antenna is shown in Fig. 1c. The suggested antenna consisted of a modified rectangular radiating element. It is fed through a microstrip transmission line to produce the 50  port impedance. The designed antenna was mounted on Rogers/duroid 5880 tm substrate measuring only 6 × 5 × 0.8 mm3 . A thorough examination of the key parameters WF, LF, R and r was undertaken as part of a parametric study to get the optimal parameters and acquire an ultra-wideband characteristic. The optimized parameters are LP = 2.2 mm, WP = 2.44 mm, LF = 1.8 mm, WF = 0.6 mm, LS = 6 mm, WS = 5 mm, r = 0.3 mm, R = 0.8 mm. 2.2 Single Antenna Design Evolution For a better understanding of how the antenna evolved, Fig. 1 shows the design evolution steps of the antenna from the initial structure to the proposed antenna. Besides, the simulation results for the reflection coefficient S11 of the different design iterations are shown in Fig. 1d. S11 measures how well our antenna matches the impedance of the transmission line or source, aiming for it to be below − 10 dB. Figure 1a illustrates the antenna evolution process that began with a conventional rectangular patch with a

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total ground plane. It can be seen that the initial patch bandwidth lies between 32.2 and 35.8 GHz. To further enhance the bandwidth, the basic rectangular patch was truncated by cutting four circles in its corners to create additional paths for electromagnetic waves to propagate which effectively increase the antenna’s radiating surface or to change the antenna’s resonant behavior. As shown in Fig. 1b a slight increase in the bandwidth was noticed of roughly 0.2 dB alongside a shift toward higher frequencies. Finally, as depicted in Fig. 1c two circles were incorporated to the antenna with the purpose of extending the bandwidth to 5.8 GHz ranging from 33.8 to 39.6 GHz. 0

WP

r

LF

(a)

(b)

(c)

S11 (dB)

LP

R

-10 Step 1

-20

Step 2

-30 -40

Step 3 32

34

36

38

40

Frequency (GHz) (d)

Fig. 1. Evolution of the developed antenna, (a) Step 1, (b) step 2, (c) Step 3, and (d) Reflection coefficient.

2.3 MIMO Antenna Structure In this part, a MIMO system is formed by employing the single antenna outlined earlier is suggested to guarantee high data rate throughput and enable the realization of the 5G. The MIMO antenna has a total dimension of 6 × 17.37 × 0.8 mm3 and is made up of two identical antennas that are placed in an anti-parallel manner Using the same underlying material. To form the MIMO configuration illustrated in Fig. 2a, a duplicate of the single antenna is positioned in an anti-parallel orientation to the original antenna.

3 Results and Discussions 3.1 S-Parameters Figure 3 displays the s-parameters of the proposed antenna design. The graph reveals that both antennas maintain almost the same bandwidth as well as the reflection coefficient, resonating prominently within the frequency band of 36 GHz. Notably, they maintain a broad operational range (< − 10 dB) of 5.6 GHz, extending from 34.1 to 39.7 GHz. This extensive bandwidth encapsulates several frequency bands that hold great promise for 5G systems. Furthermore, the MIMO antenna’s mutual coupling remains below − 65 dB throughout the overall operational bandwidth owing to parallel placement, the sufficient space amid the two antennas, and the use of DGS.

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Wc

d

(a)

(b)

S-parameters(dB)

Fig. 2. MIMO antenna configuration, (a) Top view, (b) Bottom view. 0 -10 -20 -30 -40 -50 -60 -70 -80

S11 S22 S12 S21

34

36

38

40

Frequency (GHz)

Fig. 3. S-parameters of the proposed MIMO antenna.

3.2 Effect of Element Arrangements on Isolation In this section, three distinct MIMO structures namely parallel, orthogonal, and antiparallel structures to assess the impact of these arrangements on mutual coupling. The distance between the radiating elements is the same for the different structures. As can be seen from Fig. 4a, the anti-parallel arrangement demonstrates a substantially better level of isolation than the conventional parallel and orthogonal arrangements, surpassing 47.5 dB. In contrast, the parallel and orthogonal structures exhibit a lower minimum amount of isolation. This highlights how the proposed anti-parallel design achieves superior isolation among the MIMO parts without the need for decoupling techniques. It appears that the anti-parallel arrangement yielded the highest isolation which be explained by the fact that the E-field vectors are nearly perpendicular. Such allignement tends to have less interference resulting in higher isolation. 3.3 Effect of Inter-Element Distance on Isolation When accommodating multiple antennas in a confined area, it becomes extremely important to reduce mutual coupling among the antennas. Hence, we have opted not to incorporate any decoupling structures to mitigate mutual coupling to maintain a simple design. Instead, our strategy for improving the performance of this MIMO setup involves increasing the spacing between the antenna elements. Subsequently, the decision was taken to

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undertake a parametric analysis on the inter-element distance d. The simulated isolation of the MIMO depicted in Fig. 4b was reached when the distance d between the two elements rose from 2.06 to 8.33 mm. One can notice from this figure the isolation increases when increasing d. This is obvious given the fact that it is one of the key elements influencing mutual coupling. It may be concluded, then, from the thorough analysis that 8.33 mm is the optimal value. Although the antennas are placed at nearly one wavelength from each other, the overall size of the MIMO configuration remains compact. 3.4 Effect of DGS on Isolation

-30

-45

-40

-50 -55 -60

Parallel structure Orthogonal structure Anti-parallel structure

36

38

-50

-50 d=2.08mm

-60

d=4.16mm d=8.33mm

-70

-65 34

-40

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S21 (dB)

-40

S21 (dB)

S21 (dB)

In this section, an attempt will be made to discuss the impact of using a defected ground on mutual coupling. The isolation of the anti-parallel structure with and without defected ground are shown in Fig. 4c. Based on the conducted observations, it can be inferred that the utilization of defective ground has been highly effective in enhancing the isolation as electromagnetic waves can be manipulated to reduce unwanted coupling or radiation between antennas. The isolation has been improved by 20 dB, resulting in a maximum value of 34 dB.

34

Frequency (GHz)

(a)

36

with defected ground

-70 -80

38

Frequency (GHz)

(b)

with total ground

-60

40

34

36

38

40

Frequency (GHz)

(c)

Fig. 4. Effect of (a) element arrangements, (b) enter element distance and, (c) defected ground on isolation.

3.5 Gain and Radiation Efficiency Figure 5a depicts the gain and radiation efficiency of the final configuration. It is evident from the plot that the antenna reaches a peak gain of 6.7 dBi, with a maximum radiation efficiency of up to 99.6%. 3.6 Radiation Pattern Figure 5b illustrates the two-dimensional radiation patterns in the E and H planes for two MIMO elements operating at 36.36 GHz. The MIMO antenna exhibits consistent and favorable radiation characteristics in both the E and H planes across the tested frequency range. This indicates that the antenna’s radiation properties remain stable and are not significantly affected by the composition of the MIMO system.

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1

6.8

6.4

0.998

6.2

Gain

6

Efficiency

5.8

35

Efficiency

Gain (dBi)

6.6

0.996 40

Frequency (GHz)

(a)

(b)

Fig. 5. Proposed MIMO antenna’s (a) Gain and efficiency, and (b) 2D radiation pattern at 36 GHz.

3.7 Comparison with Other Works The proposed design is thoroughly compared to other pertinent works from the literature in Table 1. The table shows that in terms of bandwidth, isolation, gain and compactness, the suggested MIMO antenna outperforms the majority of the existing MIMO antennas. The suggested design exhibits exceptional performance in several critical characteristics, thus reaching a beneficial balance. This emphasizes the suggested MIMO antenna’s performance and appeal. Table 1. Comparison with other works. Ref

Number of ports

Size (mm2 )

Bandwidth (GHz)

[7]

2

31 × 26

3.1–11.12

> 20

5.3

[8]

2

13 × 14

25.988–26.676

> 22

5.35

37.471–38

> 27

5.37

Isolation (dB)

Peak gain (dBi)

[9]

2

10.8 × 19.64

27–28.46

> 25

4.8

[10]

2

41 × 30

2.34–2.71

> 21

3

3.72–5.10

> 18

3.8

[11]

2

16 × 26

2.7–14.9

> 20

6.6

[12]

2

26 × 11

26–29

> 30

5

36–41

> 25

5.7

[13]

2

30.1 × 205

3–12

> 25

–

[14]

2

26 × 31

2.8–2.12

> 25

5

Proposed method

2

6 × 17.37

34.1–39.7

> 65

6.7

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4 Conclusion This paper presented a two-port MIMO antenna for 5G mm-wave. Through a thorough parametric study, the proposed antenna achieves remarkable isolation performance, surpassing 65 dB. Moreover, it covers a wide bandwidth ranging from 34.1 to 39.7 GHz with a peak gain of 6.7 dBi and an efficiency of more than 99.6%. These findings suggest that the design could be a strong candidate for use in 5G applications.

References 1. Suganya, E., et al.: An isolation improvement for closely spaced MIMO antenna using λ/4 distance for WLAN applications. Int. J. Antennas Propag. 2023, 1–13 (2023) 2. Nasri, N.E.H., et al.: A new four ports multiple input multiple output antenna with high isolation for 5G mobile application. In: Digital Technologies and Applications: Proceedings of ICDTA’23, vol. 2, pp. 264–271. Springer, Fez, Morocco (2023) 3. Ibrahim, A.A., et al.: Four-port 38 GHz MIMO antenna with high gain and isolation for 5G wireless networks. Sensors (Basel) 23(7) (2023) 4. Güler, C., Keskin, S.E.B.: A novel high isolation 4-port compact MIMO antenna with DGS for 5G applications (2023) 5. Hu, Y., et al.: A design for a wide-band antenna pair applied for mobile terminals at the sub-6 GHz band. Appl. Sci. 13(1), 331 (2022) 6. Fallahpour, M., Zoughi, R.: Antenna miniaturization techniques: a review of topology-and material-based methods. IEEE Antennas Propag. Mag.Propag. Mag. 60(1), 38–50 (2017) 7. Mchbal, A., Touhami, N.A., Marroun, A.: Isolation improvement using a protruded ground structure in a 2 * 2 MIMO antenna. In: Digital Technologies and Applications: Proceedings of ICDTA’23, vol. 2, pp. 272–277. Springer, Fez, Morocco (2023) 8. Anouar, E.-S., et al.: A novel two-port MIMO configuration in dual band 26/38 GHz with high isolation in the ground plane for 5G applications. In: International Conference on Digital Technologies and Applications. Springer (2023) 9. Ravi, K.C., Kumar, J.: Miniaturized parasitic loaded high-isolation MIMO antenna for 5G applications. Sensors (Basel) 22(19) (2022) 10. Xi, S., et al.: Dual-band MIMO antenna with enhanced isolation for 5G NR application. Micromachines (Basel) 14(1) (2022) 11. Addepalli, T., Anitha, V.R.: Parametric analysis of compact UWB-MIMO antenna with improved isolation using parasitic reflectors and protruded ground strips. Wireless Pers. Commun.Commun. 123(3), 2209–2225 (2021) 12. Ali, W., et al.: Planar dual-band 27/39 GHz millimeter-wave MIMO antenna for 5G applications. Microsyst. Technol. 27(1), 283–292 (2020) 13. Babashah, H., Hassani, H.R., Mohammad-Ali-Nezhad, S.: High isolation improvement in a compact UWB MIMO antenna. arXiv preprint arXiv:1707.05122 (2017) 14. Malekpour, N., Honarvar, M.A.: Design of high-isolation compact MIMO antenna for UWB application. Prog. Electromagn. Res. C 62, 119–129 (2016)

ChatGPT for a Flexible Higher Education: A Rapid Review of the Literature Abdelmajid Elhajoui(B) , Otmane Yazidi Alaoui, Omar El Kharki, Miriam Wahbi, Hakim Boulassal, and Mustapha Maatouk Laboratoire de Recherche et Developpement en GeoScience Appliquées, FSTT, Abdelmalek Essaadi University, Tetouan, Morocco [email protected]

Abstract. The OpenAI-developed ChatGPT, which is based on artificial intelligence (AI), has gained widespread acceptance in a number of industries. Education is included. The use of this technology when creating content allows students to learn about concepts and theories. ChatGPT is built on State of the Art (SOA), which incorporates Natural Language Processing (NLP), Deep Learning (DL), and an extrapolation of a family of ML-NLP models known as Large Language Models (LLMs), which mixes NLP and ML. It can be used to automate the grading of tests and assignments, freeing up instructors’ time to focus on training. This technology can be used to tailor education for children, allowing them to concentrate more attentively on the material and engage in critical thinking. Because ChatGPT can translate text between different languages, it’s a great tool for language learning. It might offer lists of vocabulary words and their definitions, giving pupils tools to improve their language skills. One of ChatGPT’s key applications in the class-room is individualized learning. Using ChatGPT in the classroom has the potential to be very beneficial for both teachers and students. Keywords: ChatGPT · Higher education · Flexible · Education · AI

1 Introduction The ChatGPT AI model from OpenAI has established itself as a powerful tool with several applications in a wide range of industries [1]. Teachers can use ChatGPT in their classes to tailor the learning experience for their students. On the other side, text completion, translation, and text synthesis technologies can help students’ writing skills. The capabilities of ChatGPT can be used to locate biases in content and fix issues with teaching resources. Given the growing need for up-to-date educational materials, ChatGPT can help the state establish and implement a fair and impartial curriculum system [2]. This study seeks to give a thorough overview of the literature on ChatGPT in education, particularly higher education. In-depth analysis of the ChatGPT in education is presented in this study, with an emphasis on current technologies, issues, possibilities, and future prospects. Also, we discuss how The ChatGPT is changing education while © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 369–375, 2024. https://doi.org/10.1007/978-3-031-48573-2_53

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focusing on which grade and subject are best suited for using ChatGPT as a learning tool. Therefore, to direct the review, the following research questions are posed. • RQ1: How might ChatGPT be utilized to improve teaching and learning and increase flexibility in higher education? • RQ2: What are the possible educational problems that ChatGPT may cause, and how may they be resolved? The rest of the article is structured as follows: The definition of ChatGPT and its use in higher education are examined in Sect. 2. The methodology is described in Sect. 3. Section 4 contains the findings as well as some discussions. The document’s conclusions and points of view are presented in Sect. 5.

2 Literature Review 2.1 ChatGPT According to [3], a chatbot is a computer software made to converse with users and respond to their questions. The first program, ELIZA, was developed in 1966 using simple pattern-matching methods and a template-based response mechanism, proving that chatbot technology is not new [4]. In 1995, the award-winning program ALICE, which combined pattern-matching and artificial intelligence (the ability of machines or computer systems to perform tasks that typically require human intelligence), marked a significant advance in chatbot technology. A ChatGPT by OpenAI is an AI-powered chatbot. A model language processing procedure created on a large amount of data to produce writing that resembles a person is known as a “Generative Pre-Trained Transformer (GPT)”. A natural language processing technology is ChatGPT. This is how we respond to affection. As a result, it generates more covert information, learns from these interactions, and can thus offer increasingly customized responses. When giving users instructions and providing information, ChatGPT conducts itself like a person. This technology is capable of a wide range of tasks, including as translating papers, producing letters and essays, answering queries, and creating poetry. The quick response capability of ChatGPT sets it apart from other chatbots, fostering more diversified and interesting talks on virtually any subject [5]. 2.2 Higher Education Needs ChatGPT ChatGPT can impact numerous parts of education, including writing, instruction style and teaching methodology. Writing has been essential to encouraging creative and critical thinking for antiquity by organizing information and crafting narratives. It continues to play a key role in education, even in the age of AI. Similar to conversational systems like chatbots and virtual assistants, ChatGPT is made to establish connections quickly and answer more coherently. Due to this compatibility, companies could increase the scope of their current offers. And create unique AI-powered chatbots that are able to quickly understand and react to customer expectations. Using ChatGPT, conversations can be held in a private and secure setting. It provides a safe environment free from interference and manipulation by utilizing AI to recognize spam, censorship, and hazardous material [6].

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2.3 ChatGPT Applications in Education ChatGPT is a program that students may use to help them with their academic work. by creating relevant sources and material on a certain subject. It may also be used to provide feedback to students so they can improve their knowledge. This might guarantee that the students are given the right level of difficulty and information that they would find interesting and relevant. Depending on the topic or subject area, the ChatGPT technique may be used to produce summaries, flashcards, or exams [7, 8]. ChatGPT offers personalised, adaptable, and interesting learning opportunities, which can enhance students’ educational experiences. The education industry has hailed this technology as a paradigm-shifting innovation. In addition to knowledge gleaned from research and testing automation, customized learning experiences may be offered. Using ChatGPT, it is possible to create virtual helpers and chatbots that may communicate with customers in real time [5]. ChatGPT has the potential to be a useful educational tool. It might be used to create course materials, provide comments on homework, and respond to inquiries from students. Because ChatGPT has a strong knowledge of the English language and can learn new material, it is a helpful tool for teachers and students. An article outline may be rapidly created using ChatGPT. By employing AI-driven natural language processing to understand the structure of any article, it can rapidly and accurately build an orderly outline. The ChatGPT’s capacity to read and write text in several languages has the potential to transform language translation. People and organizations that speak multiple languages could be able to communicate without the use of human interpreters thanks to it. ChatGPT is a helpful tool that may assist teachers in customizing their classes to meet the requirements of their students, enhance their language abilities, and facilitate research and writing [9]. Educators must keep up with the most recent advances as AI develops and becomes more common in the classroom and consider how they could be utilized to improve students’ learning [10].

3 Methodology The aim of a systematic review is to provide answers to specific questions using a deliberate, rigorous, and repeatable search approach, along with inclusion and exclusion criteria that indicate which studies should be included or excluded. We discovered relevant a thorough search of internet databases turned up literature. The search terms used in the Web of Science, Springer, ScienceDirect and Scopus databases were: “ChatGPT” (Topic) OR “ChatGPT and education” (Topic) OR “AI and Higher education” (Topic). The publication period was specified between 1 January 2022 and May 2023. Articles had to discuss ChatGPT in the context of education and were not restricted to any particular educational situations in order to be considered for this fast evaluation. Reviews of the literature were consulted for background information, whenever available. To prevent duplicating results, they were however not included in the synthesis. Additionally, this review only comprised of English-language articles. The inclusion and exclusion criteria for article selection are summarized in Table 1.

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Criterion

Inclusion

Exclusion

Article subject

Discuss ChatGPT in the context of education

Do not discuss ChatGPT in the context of education

Article type

Articles from academic journals

Non-academic documents, such as articles from the press and social media

Time period

1 January 2022 to May 2023

Articles outside the time frame

Language

English

Non-English

4 Results and Discussion 4.1 RQ1: How Can ChatGPT Be Used to Enhance Teaching and Learning and Make Higher Education More Flexible? Based on the review’s conclusions, ChatGPT may be useful as a tool for both teachers and students. In relation to the aspect of teaching preparation, ChatGPT can offer advice to teachers. According to one educator, ChatGPT can be a useful tool for educators because it provides a summary of the skills and knowledge that should be covered in their curriculum [11]. Megahed et al. [12] questioned ChatGPT. Commissioned ChatGPT to draft the syllabus for a course on introductory statistics. They noted that no significant adjustments would be required to carry out its teaching instructions. Zhai [3] learned that ChatGPT might include advice for those considering special education. In terms of the assessment component, ChatGPT can assist teachers in creating exercises, tests, and scenarios for student evaluation [13]. Al-Worafi et al. [14] noted that not all of the targeted learning objectives may be covered by the evaluation tasks offered by ChatGPT. Therefore, rather than fully replacing instructors’ work, they advised using ChatGPT to assist them in creating evaluations. As a virtual tutor for students, ChatGPT can help them advance academically. I have used ChatGPT, and it seems to take a variety of writing structures into account, including pragmatics, semantics, coherence, cohesion, norms, language style, format, grammar, and syntax. It is still difficult to fully capture the emotional heft, literary personality and voice, and rhetorical adaptability of human writing. From easy to difficult assignments, ChatGPT offers a wide spectrum of writing support. Because it frequently creates cohesive and grammatically correct prose [15]. Users can greatly enhance their writing and use of grammatical structures with the help of this program. On the basis of the user’s area of interest, ChatGPT can also provide a thorough list of essay ideas [16]. According to [16], with ChatGPT is possible to create individualized learning materials depending on the needs of each student or to automatically provide feedback on students’ assignments in medical education. Clinical decision-makers may receive real-time insights and recommendations based on patient data and scientific research, assisting them in making more wise therapeutic choices [17].

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4.2 RQ2: What Potential Educational Concerns Are Related with ChatGPT, and How May They Be Resolved? ChatGPT’s obvious appearance, its capacity to answer to inquiries, raises several questions regarding the validity of the teachings and homework assignments. One of the most prevalent concerns in the educational field is that without any supervision from the teacher, pupils would use ChatGPT to complete their assignments before copying and pasting the answers. As students started using it to write their homework, essays, and theses, several colleges and organizations restricted its use for writing assignments. If this AI-generated content differs from the copied text, it is more difficult to distinguish between the two, which makes it less reliable. a tool that is insanely effective and useful for a range of academic assignments. There are questions regarding the morality of writing produced by AI and the reliability of ChatGPT’s responses. In terms of how it might impact society, the usage of ChatGPT and other language models raises serious moral quandaries [2]. Sometimes ChatGPT may publish false information on its website and give harmful advice about biased content. A chatbot will automatically add words to your text that are most likely to come after the ones you just wrote, but it won’t verify the authenticity of your information. The possibility of bias in the data used to train the ChatGPT bot is one of the biggest ethical problems with using it. Any biases in the training dataset of the chatbot are reflected in the algorithm’s output, which may produce false or damaging information [2]. The included articles offer a variety of fixes for any issues that ChatGPT in education may cause. Task design, identifying AI writing, and institutional policy, which includes establishing anti-plagiarism norms and providing student education, are the three main areas into which these strategies are divided. Utilizing multimedia resources, adopting original question kinds, and leveraging digital-free assessment methods are all examples of task design [18]. 4.3 Discussion The enormous amount of data that ChatGPT collects, evaluates, and converts into written sentences is used. With its mobility, human-like answers, and versatility, ChatGPT is excellent, ChatGPT is a simple writing tool that may be used by students of all ages. By providing tips for writing subjects, ideas for writing flows, sentence constructions, and terminology, it can support active learners throughout the writing process. The ChatGPT interface enables students to get comprehensive and reliable information with their search results. In contrast to most search engines like Google, which provide a tremendous amount of information with limitless results, ChatGPT provides succinct, clear responses that specifically address the relevant issues. A variety of sentences, including one with a new term that the pupils are unfamiliar with, may be produced by the computer according to instructions given by instructors. Then it is instructed to the pupils to interpret the new term’s meaning from the context of the several sentences. Additionally, the usage of ChatGPT raises a number of possible concerns, such as the creation of false or inaccurate material and student plagiarism. As a result, quick action is required to resolve these possible problems and maximize the usage of ChatGPT in

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education, so we can think how the student can give the good question to ChatGPT in order to avoid the general answer of ChatGPT.

5 Conclusion This quick review highlighted ChatGPT’s variable performance across a range of subject areas and highlighted its potential benefits when used as a virtual tutor for students and an instructor’s assistance. However, its use raises a number of problems, including the risk it presents to academic integrity and the creation of fake or erroneous material. The findings of this study require that institutions like schools immediately adjust their policies and procedures for avoiding plagiarism. Additionally, instruction should be provided to instructors on how to use ChatGPT effectively. Additionally, ChatGPT’s capabilities, limitations, and potential implications on academic integrity should be explained to students.

References 1. King, M.R., ChatGPT: A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell. Mol. Bioeng., 16(1), 1–2 (2023). https://doi.org/10.1007/s12195-02200754-8 2. Javaid, M., Haleem, A., Singh, R.P., Khan, S., Khan, I.H.: Unlocking the opportunities through ChatGPT tool towards ameliorating the education system. BenchCouncil Trans. Benchmarks Stand. Eval. 3(2), 100115 (2023). https://doi.org/10.1016/j.tbench.2023.100115 3. Zhai, X.: ChatGPT for next generation science learning. SSRN Electron. J. (2023). https:// doi.org/10.2139/ssrn.4331313 4. Adamopoulou, E., Moussiades, L.: An overview of chatbot technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology, vol. 584, pp. 373–383. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-49186-4_31 5. Zhou, C., et al.: A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT (2023). https://doi.org/10.48550/ARXIV.2302.09419 6. Cooper, G.: Examining science education in ChatGPT: an exploratory study of generative artificial intelligence. J. Sci. Educ. Technol. 32(3), 444–452 (2023). https://doi.org/10.1007/ s10956-023-10039-y 7. Surameery, N.M.S., Shakor, M.Y.: Use chat GPT to solve programming bugs. Int. J. Inf. Technol. Comput. Eng. 31, 17–22 (2023). https://doi.org/10.55529/ijitc.31.17.22 8. Sun, G.H., Hoelscher, S.H.: The ChatGPT storm and what faculty can do. Nurse Educ. 48(3), 119–124 (2023). https://doi.org/10.1097/NNE.0000000000001390 9. Liu, S., et al.: Assessing the value of ChatGPT for clinical decision support optimization. Health Inform. (2023). https://doi.org/10.1101/2023.02.21.23286254 10. Ventayen, R.J.M.: OpenAI ChatGPT generated results: similarity index of artificial intelligence (AI) based model 11. Tlili, A., et al.: What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. 10(1), 15 (2023). https://doi.org/10.1186/s40 561-023-00237-x 12. Megahed, F.M., Chen, Y.-J., Ferris, J.A., Knoth, S., Jones-Farmer, L.A.: How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study (2023). https://doi.org/10.48550/ARXIV.2302.10916

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13. Khan, R.A., Jawaid, M., Khan, A.R., Sajjad, M.: ChatGPT—reshaping medical education and clinical management. Pak. J. Med. Sci. 39(2) (2023). https://doi.org/10.12669/pjms.39. 2.7653 14. Al-Worafi, Y.M., Hermansyah, A., Goh, K.W., Ming, L.C.: Artificial intelligence use in university: should we ban ChatGPT? Med. Pharmacol. (2023). https://doi.org/10.20944/prepri nts202302.0400.v1 15. Barrot, J.S.: Using ChatGPT for second language writing: pitfalls and potentials. Assess. Writ. 57, 100745 (2023). https://doi.org/10.1016/j.asw.2023.100745 16. Su, Y., Lin, Y., Lai, C.: Collaborating with ChatGPT in argumentative writing classrooms. Assess. Writ. 57, 100752 (2023). https://doi.org/10.1016/j.asw.2023.100752 17. Currie, G.M.: Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy? Semin. Nucl. Med., S0001299823000363 (2023). https://doi.org/10.1053/j.semnuc lmed.2023.04.008 18. Lo, C.K.: What is the impact of ChatGPT on education? A rapid review of the literature. Educ. Sci. 13(4), 410 (2023). https://doi.org/10.3390/educsci13040410

BERT-Based Models with BiLSTM for Self-chronic Stress Detection in Tweets Mohammed Qorich(B)

and Rajae El Ouazzani

IMAGE Laboratory, ISNET Team, School of Technology, Moulay Ismail University of Meknes, Meknes, Morocco [email protected], [email protected]

Abstract. Recently, the swift increase of social media platforms has provided a rich source for studying several users’ psychological phenomena. For instance, stress identification in text content can lead to knowing some insights about social media users’ mental health. Actually, chronic stress has a huge negative impact that requires the development of different methods for early detection and diagnosis. In this paper, we propose a deep learning approach and a Natural Language Processing (NLP) method to reveal self-reported chronic stress from tweets. In effect, we have implemented distinct pre-trained BERT (Bi-directional Encoder Representations from Transformers) embedding models, along with a BiLSTM (Bidirectional Long Short-Term Memory) classifier. Actually, we fine-tuned the pre-trained BERT models by leveraging their powerful contextual representation. Next, the output of the embedding is fed into a BiLSTM model which further refines the stress classification by capturing the sequential dependencies in the tweet text. Experiments disclosed that BERT with Talking-Heads Attention architecture is the best model for such text classification tasks. Moreover, our suggested model has achieved good performance and surpassed the baseline architectures for chronic stress detection in Twitter data. Keywords: BERT · BiLSTM · Chronic stress · Deep learning · Natural language processing (NLP) · Recurrent neural network (RNN) · Text classification

1 Introduction With the quick pace of human daily life, people are experiencing stress more and more. As a reaction to an external stimulus, stress is a normal psychological and physiological response [1]. However, the gap between high demands, pressures, and limited human ability could be a source of chronic stress [1]. Consequently, the chronic stress contributes to harmful mental health diseases and negatively impacts memory, emotion, cognition functions [2], and it could commonly incite cancer development as well [3]. The chronic stress recognition requires a large analysis and research to identify stress from several possible sources and datasets. Besides, the increment use of social media has provided an excellent source of mental health analysis and can be used to build a deep-based system stress detection. Actually, by using Natural Language Processing (NLP), the text classification method can categorize a given text data into labels. This means we © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 376–383, 2024. https://doi.org/10.1007/978-3-031-48573-2_54

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can extract whether the content generated by users involves a stressful situation or not. Therefore, we aim in this paper to develop a deep-learning model to classify selfreported chronic stress from tweets. Thus, we applied multiple transformer BERT models combined with the Bidirectional Long Short-Term Memory (BiLSTM) model. The contributions of our paper are: • A comparative analysis of several Bert-embeddings with BiLSTM model for text classification. • A proposed deep learning model for the classification of self-reported chronic stress tweets. • Our suggested models have reached good performances and outperform the baseline models. The rest of the paper is organized as follows: Sect. 2 presents some related studies made in this direction. Section 3 describes our proposed model’s architecture and lists information about the dataset. Section 4 shows the results and compares them in the discussion. Section 5 concludes the paper and presents future directions.

2 Related Work The concept of mental health is emerging as a regular subject in the literature. Several recent studies have been carried out to recognize and detect emotions [4–6]. Some researchers have focused on physiological aspects such as EEG (Electroencephalography) [4, 7], GSR and PPG (Galvanic Skin Response, Photoplethysmogram) [4], and EMG (Electromyogram) [4]. They have attempted to identify the appropriate devices to collect these physiological features. Other researchers have applied machine learning algorithms to classify emotions into binary or multiple labels. Gaikwad and Joshi [8] have extracted multi moods from social media. For this purpose, they have implemented a lexicon database with K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Support Vector Machine (SVM) models. As well, Tariq et al. [5] have employed Random Forest (RF), NB, and SVM to label anxiety, ADHD (Attention Deficit Hyperactivity Disorder), and depression emotions from Reddit posts. Yang et al. [9] have implemented NB, SVM, KNN, and RF for binary classification of chronic stress on Twitter. Meanwhile several recent studies have implemented deep-learning algorithms for emotion classification and stress detection. Katchapakirin et al. [10] have proposed a deep learning method to extract depression from Facebook. Likewise, Lin et al. [6] fine-tuned different BERT pre-trained models, then they combined them to identify chronic stress from Twitter. Also, Zanwar et al. [11] have applied a BiLSTM model with a pre-trained embedding transformer entitled “Robustly Optimized BERT Pretraining Approach” (RoBERTa) for chronic stress identification. In this paper, we propose a new BiLSTM model along with several pre-trained transformers. Each BERT embedding model disposes of different attention and hidden layers. The description of our model is detailed in the following section.

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3 The Proposed Model 3.1 The Architecture As shown in Fig. 1, our based deep learning model consists of an embedding fine-tuned BERT model, followed by a BiLSTM and a classifier layer. The BERT model represents an embedding layer that contains a pre-processing layer, an encoder layer, and an output layer. In practice, we have evaluated several pre-trained models of BERT from TensorFlow Hub [12], which we will describe in the following subsection. Actually, BERT could preprocess an input text sentence as a chain of tokens. Then, a pre-trained embedding vectors extract contextual clues from these tokens depending on the surrounding meaning of the training data. Moreover, BERT applies a transformer layers with self-attention mechanisms to learn more hierarchical information, and provides the outputs to the following layer. Next, a BiLSTM layer takes the output from the embedding and learns additional features with 64 hidden layers and a dropout layer. In effect, the BiLSTM layers catch sequential dependencies from the embedding input in forward and backward directions. Afterward, we use a flattened layer to convert the channels into a single dimension. Then, we connect a dense layer to the preceding layer with 32 units. Besides, we have applied a ReLU activation function and a dropout to the dense layer. Finally, the classifier layer generates the binary classification as stressful or non-stressful.

Fig. 1. The proposed models’ architecture

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3.2 The BERT-Embeddings Actually, BERT offers a set of dense vector architectures for several NLP tasks. In text classification, TensorFlow Hub [12] collects a group of pre-trained BERT based embedding models. In our paper, we have examined various models with different weights and architecture layers. Below are the implemented ones. • BERT-Base [13]: is an implementation model of BERT that has been pretrained on Wikipedia [14] and BooksCorpus [15] datasets. We have employed the three normalized versions; “cased”, “uncased”, and “multi-cased”, which means the processing of both lower and upper cases in the input text before tokenization. • Small BERTs [16]: are a representative architecture of the BERT model with a smaller number of hidden layers, and attention layers. • ALBERT [17]: is a lightweight version of BERT model including fewer parameters, and a new modeling method. • Electra [18]: is a variation of BERT model that has the same architecture in many sizes. However, it is pre-trained as a discriminator in a set-up resembling a Generative Adversarial Network (GAN). • BERT Experts: are eight BERT models, pre-trained on different datasets in order to offer a choice for a specific model in a specific NLP task. In practice, we have implemented two models that are pre-trained on Wikipedia [14], and PubMed [19] datasets respectively. • BERT with Talking-Heads Attention [20, 21]: is a modified version of BERT, where the model implements talking-heads attention instead of using multi-head attention. Moreover, it employs a Gated Linear Unit (GLU) activation function rather than an ordinary dense layer. 3.3 The Dataset The experimental dataset has been provided by the Social Media Mining for Health Application (#SMM4H) shared tasks [22]. It consists of Twitter reviews of self-reported chronic stress labeled as negative or positive. The dataset contains 4830 tweets, with 58% negative (non-chronic stress), and 42% positive (chronic stress) as displays Fig. 2. Actually, we have pre-processed the dataset by removing links, emoticons, digits, and punctuation from the text. The achieved results are represented in the next section.

4 Results and Discussion In the experiments, we have examined a set of BERT embedding models with different sizes of layers combined with a BiLSTM classifier model. The accuracy and the F1-score for each implemented BERT models are presented in Table 1. Symbols “L”, “H” and “A” refer to the number of hidden layers, the number of hidden sizes, and the number of attention heads respectively. An overview of the results shows that the talking-heads-base is the best embedding BERT model in terms of F1- score. In effect, this model reached 78.33% accuracy and 78.50% F1-score. Despite the electra-base model has reached 78.57% accuracy, the

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Fig. 2. The class distribution of the dataset

Table 1. The accuracy and F1-score for the BERT-BiLSTM models. Model Bert-base (L-12-H-768-A-12) [13]

Bert-en-cased

Accuracy

F1-score

77.14

77.26

Bert-en-uncased

78.10

77.91

Bert-multi-cased

75.48

75.62

H-256-A-4

69.05

69.37

H-512-A-8

73.81

73.36

H-768-A-12

75.00

74.23

H-256-A-4

69.05

71.61

H-512-A-8

75.48

75.59

H-768-A-12

74.05

74.33

H-256-A-4

71.90

72.24

H-512-A-8

74.29

74.78

H-768-A-12

76.67

76.64

Albert-en-base [17]

Albert-en-base

66.67

66.28

Electra [18]

Electra-base

78.57

77.34

Electra-small

72.14

72.55

experts [14, 19]

Experts-pubmed

69.29

68.95

Experts-wiki-books

72.62

72.75

Talking-heads-base [20, 21]

Talking-heads-base

78.33

78.50

Small-bert-en-uncased-L-6 [16]

Small-bert-en-uncased-L-8 [16]

Small-bert-en-uncased-L-10 [16]

talking-heads-base still the performed model considering the result of the two metrics. The talking-heads-base good results could be explained by the modification of the basic BERT architecture regarding the attention layer which extracts more intent features. In

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addition, the comparison of the other BERT models shows that the more the small-bert increases the number of hidden and attention layers, the more the model’s performance increases. Therefore, the bert-base model reached better performance than all the smallbert versions. Moreover, the uncased version of the bert-base model generates better accuracy than the cased or the multi-cased ones. This reveals the importance of normalizing the text into lower-case before the tokenization phase. Besides, Fig. 3 shows the loss and the accuracy results of the talking-heads-base model.

Fig. 3. The loss and the accuracy results of our best model

Furthermore, Table 2 displays that our best-proposed model has reached better performance compared to the baseline models. In fact, the talking-heads-base model attained a better F1-score than Lin et al. [6], and Zanwar et al. [11] models on Twitter dataset.

5 Conclusion and Future Work In this paper, we have proposed a classification model of self-reported chronic stress on Twitter using several BERT embeddings combined with a BiLSTM classifier. We found out that the talking-heads-base is the best embedding BERT model for self-chronic stress detection on Twitter. In future work, we intend to develop our proposed architecture to perform well on other datasets.

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Model

F1-score

Lin et al. [6]

73

Zanwar et al. [11]

75

Our proposed model

78.50

References 1. Pozos-Radillo, B.E., Preciado-Serrano, M.D.L., Acosta-Fernández, M., et al.: Academic stress as a predictor of chronic stress in university students. Psicol. Educ. 20, 47–52 (2014). https://doi.org/10.1016/j.pse.2014.05.006 2. Marin, M.F., Lord, C., Andrews, J., et al.: Chronic stress, cognitive functioning and mental health. Neurobiol. Learn. Mem. 96, 583–595 (2011). https://doi.org/10.1016/j.nlm.2011. 02.016 3. Dai, S., Mo, Y., Wang, Y., et al.: Chronic stress promotes cancer development. Front. Oncol. 10, 1492 (2020). https://doi.org/10.3389/fonc.2020.01492 4. Girardi, D., Lanubile, F., Novielli, N.: Emotion detection using noninvasive low cost sensors. In: 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017, pp. 125–130 (2018) 5. Tariq, S., Akhtar, N., Afzal, H., et al.: A novel co-training-based approach for the classification of mental illnesses using social media posts. IEEE Access 7, 166165–166172 (2019). https:// doi.org/10.1109/ACCESS.2019.2953087 6. Lin, T., Chen, C., Tzeng, Y., Lee, L.: NCUEE-NLP@SMM4H’22: Classification of Selfreported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models. In: Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, Gyeongju, Republic of Korea, Association for Computational Linguistics, pp. 62–64 (2022) 7. Thammasan, N., Moriyama, K., Fukui, K., Numao, M.: Familiarity effects in EEG-based emotion recognition. Brain Inf. 4, 39–50 (2017). https://doi.org/10.1007/s40708-016-0051-5 8. Gaikwad, G., Joshi, D.J.: Multiclass Mood classification on twitter using lexicon dictionary and machine learning algorithms. Proc. Int. Conf. Inven. Comput. Technol. ICICT 2016, 1–6 (2016). https://doi.org/10.1109/INVENTIVE.2016.7823247 9. Yang, D., Li, W., Zhang, J., et al.: A neuropathological hub identification for Alzheimer’s disease via joint analysis of topological structure and neuropathological burden. In: Proceeding of the International Symposium on Biomedical Imaging, pp. 1–4, Mar 2022. https://doi.org/ 10.1109/ISBI52829.2022.9761444 10. Katchapakirin, K., Wongpatikaseree, K., Yomaboot, P., Kaewpitakkun, Y.: Facebook social media for depression detection in the Thai community. In: Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018, pp. 1–6 (2018). https://doi.org/10.1109/JCSSE.2018.8457362 11. Zanwar, S., Wiechmann, D., Qiao, Y., Kerz, E.: MANTIS at SMM4H’2022: pre-trained language models meet a suite of psycholinguistic features for the detection of self-reported chronic stress. In: Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pp. 16–18 (2022) 12. Brain, G.: TensorFlow Hub. In: TensorFlow. https://tfhub.dev/s?module-type=text-emb edding (2021). Accessed 26 May 2023

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13. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019, vol. 1, pp. 4171–4186 (2019) 14. Wikimedia Foundation: Wikipedia Dataset (2018). https://dumps.wikimedia.org/. Accessed 30 June 2023 15. BooksCorpus Dataset. https://yknzhu.wixsite.com/mbweb. Accessed 30 May 2023 16. Turc, I., Chang, M.-W., Lee, K., Toutanova, K.: Well-read students learn better: on the importance of pre-training compact models (2019). https://doi.org/10.48550/arXiv.1908.08962. Accessed 30 May 2023 17. Lan, Z., Chen, M., Goodman, S., et al.: ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (2019). https://doi.org/10.48550/arXiv.1909.11942. Accessed 30 May 2023 18. Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators. In: 8th International Conference on Learning Representations, ICLR 2020, pp. 1–18 (2020). https://doi.org/10.48550/arXiv.2003.10555. Accessed 30 May 2023 19. MEDLINE/PubMed Dataset. https://www.nlm.nih.gov/databases/download/pubmed_med line.html. Accessed 30 May 2023 20. Shazeer, N., Lan, Z., Cheng, Y., Nan Ding, L.H.: Talking-Heads Attention (2020). https://doi. org/10.48550/arXiv.2003.02436. Accessed 30 May 2023 21. Shazeer, N.: GLU Variants Improve Transformer (2020). https://doi.org/10.48550/arXiv. 2002.05202. Accessed 30 May 2023 22. Weissenbacher, D., Klein, A.Z., Gascó, L., et al.: Overview of the seventh social media mining for health applications #SMM4H shared tasks at COLING 2022. In: Proceedings of the Seventh Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pp. 221–241 (2022)

The Transformation Method from Business Processes Models by BPMN to Use Cases Diagram by UML in Agile Methods Ouzayr Rabhi(B) , Saida Filali, and Mohammed Erramdani Mohammed First University, Oujda, Morocco [email protected]

Abstract. The power of Model Driven Architecture (MDA) lies in the use of models and diagrams in the visualization and development processes of enterprise information systems. This is why we have chosen the Business Process Model and Notations (BPMN), because of its simple diagram symbols and clear notations, allowing all simple users of information systems within the organization to quickly and easily understand their needs and goals. In our paper, we propose to connect all categories of personnel within an organization, regardless of their specialty, with IT developers using the agile methodology, while moving from BPMN diagrams as a standalone IT model to the Unified Modeling Language (UML) use case modeling diagram as a platform independent model. We chose the latter as our destination because of its development and the possibility of turning it into a powerful, integrated program. In addition, for future completions, we chose Query View Transformations (QVT) for security reasons, as it is consistent with previous programs and languages of the same family. Keywords: MDA · Agile methods · BPMN · QVT

1 Introduction In the realm of software development, a strong foundation is imperative, symbolized by the CIM (Customer Identity Management) model of business processes. While MDA (Model-Driven Architecture) models play a pivotal role, the PIM (Platform-Independent Model) level remains abstract. Our approach centers on an agile transformation from CIM to PIM, utilizing two BPMN diagrams for CIM representation and a use case diagram for PIM. Our focus lies in the agile transformation from CIM to PIM. To accomplish this, we employ two BPMN diagrams to represent CIM, providing a detailed business model. The PIM model takes the form of a use case diagram, emphasizing the core functionalities of the system. Agility is integrated during the transformation phase, offering flexibility to adapt to evolving project requirements. The structure of our article comprises five key sections. Initially, the context is established. Subsequently, the related works section explores similar methodologies. The © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 384–390, 2024. https://doi.org/10.1007/978-3-031-48573-2_55

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third section presents the foundational elements of MDA, BPMN, and agile methodology. The fourth section delineates our approach for the agile transformation from CIM to PIM. Lastly, the fifth section includes a case study illustrating our approach, followed by a conclusion.

2 Related Work In the field of modeling and transformations, there have been numerous works that explore different approaches to transforming models. In this section, we will present a comprehensive review of the related works on methods for transformations that are similar to our proposal. These works provide valuable insight into the various approaches taken by other authors in this field and highlight the strengths and weaknesses of each method. The authors in [1] present two different transformations in their work. The first transformation is a horizontal refinement of the CIM model to the UML use case diagram. The second transformation is a vertical transformation from the UML sequence diagram to the PIM model, using the SBVR specification. Rhazali et al. [2] propose a method for transforming the CIM model to the PIM model using ATL (Atlas Transformation Language). In [3] the authors proposed a transformation from CIM level to PIM level using QVT transformation but from a BPMN to Class diagram in UML, Macek and Richta [4] present a transformation from the BPMN business process diagram to the UML activity diagram. They distinguish themselves from other works by using XML metadata exchange to represent the two models and XSLT transformation type to specify the document style and vocabulary at the input and output level. Debnath et al. [5] propose a three-step transformation process, starting from the BPMN business process diagram and ending with a MOF script based on Java EE profiles. Other related works include the transformation from BPMN to Colored Petri Nets in [6] and the transformation from BPMN to OWL2 in [7]. Despite the various related works, BPMN remains a practical language for transformation due to its clarity and simplicity. Nasiria et al. [8] presents a method for agile transformation, discussing the requirements presented in numerous documents referred to as “user stories.” Based on these related works, we propose our own method for transformation, which we will present in the section “Our Proposal.” Our method takes into account the best practices from these related works and offers a new and improved approach to transforming models.

3 Our Proposal In this paper, we will use two BPMN diagrams, the collaboration diagram and the business process diagram, with the goal of leveraging each of the diagrams to achieve a rich level of conceptual information model (CIM). This will allow us to move smoothly from the CIM level to the platform independent model (PIM) level. In accordance with MDA recommendations, we will use UML to represent the PIM level and the use case diagram to describe this level. The transition from the CIM level to the PIM level will be done automatically, thanks to clearly defined transformation rules. For optimal and

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efficient transformation, we will adopt the Agile method’s transformation rules. This will involve dividing the transformations into sprints, and in each sprint, we will follow our approach of generating source code based on business requirements. The combination can be described as follows: • First Sprint: The transformation will occur at the use case diagram level, and only the actors and use cases will be generated. • Second Sprint: After the actors and use cases, the “sequence flows” will be transformed into “inclusion” relationships. • Third Sprint: Lastly, the “decision nodes” will be transformed into an “extended” relationship (Fig. 1).

Fig. 1. Agile transformation in MDA through our proposal.

To make an agile and efficient transformation, and as shown in Table 1. We have defined first the rules for building the metamodel at the CIM level, then the rules for transformation from the CIM level to the PIM level and finally the rules for agile transformation.

4 Case Study In this part of our article, we present a special case study for sales via an e-commerce site to showcase an agile and automatic approach to transforming from the upper CIM level to the lower PIM level. On the e-commerce site 3 users are identified: the customer, the order agent and the delivery person. • User 1: The customer – Behavior 1: A customer can browse the list of products available on the E-Commerce site.

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Table 1. Transformation rules. Rules of construction CIM level

Transformation rules from CIM level to PIM

Agile transformation rules

BPMN collaboration diagram: Model construction rules R1: For sub-processes belonging to the same category, we use the notion of “group” R3: Only the sub-processes and their relationships will be represented R2: All sub-processes must be of medium size R4: The sub-process is composed of 4–7 tasks R5: Gateways should not be used in the model BPMN of business processes: Rules for building the model R6: Use only automatic tasks and avoid manual ones R7: Use gateways to represent the most exceptional paths R8: For task output, use a data object that contains the status of the object R9: The main unit of the BPMN business process diagram is the task

• TR1: The “task” becomes a • ATR1: The “task” “use case transformed into “use case” • TR2: The “decision node” corresponds to sprint 1 becomes an “extend” • ATR2: The “sequence • TR3: The “collaborator” flows” between two “tasks” becomes an “actor” in the transformed into an use case diagram “inclusion” relationship • TR4: The “sequence flow” between two “use cases” becomes “include correspond to sprint 2 • TR5: The “sub-process” • ATR3: The “decision becomes “package nodes” transformed into an “extended” relationship between two “use cases” correspond to sprint

– Behavior 2: The customer can consult the detailed information on each product, then he has two possibilities, either he decides to put the quantity of product he wants in the cart or he does nothing. – Behavior 3: At any time, the customer can change the quantity he has chosen or even remove the product from the cart. – Behavior 4: If the products dirty the needs of the customer are, he can launch an order, afterwards he must represent this credit card information, and the address of the delivery. • User 2: The order agent • User 3: The delivery man.

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4.1 Presentation of the CIM Level 4.1.1 Collaboration Diagram In this soft model, we have tried to present as many collaborators as possible in order to define an ecommerce site process, where there is collaboration between several actors. However, we avoided identifying the gateways and tasks by simply specifying the subprocesses and their sequence in order to present the business process in general. Also, for the transition to the use case, the collaborators will be transformed into actors. However, we have presented the sub-processes of the supports. Thus, a customer would normally perform the activity “select products”, then “launch order” and later the activity “present information”, but since the “launch order” cannot contain more than three tasks, we merged with “select products” into one sub-process called “select products for order”. 4.1.2 Business Process Flow In this model, we have tried to present the different operations that take place in an ecommerce site. Moreover, for the transition to the use case, the tasks will be transformed into use cases. At the end, we specify all manual tasks. Of course, we can make several improvements to the initial model in order to get a model that respects the rules we have defined. 4.2 Presentation of the PIM Level The use case model is transformed from the higher CIM level business models while respecting the sprints. • The sub-process “select a product for an order” of the collaboration diagram is transformed into a set. • The “client” collaborator who executes the sub-processes in the BPMN collaboration diagram becomes an actor in the use cases. • Each task detailing a sub-process in the collaboration diagram is transformed when used and presents the first sprint (Sprint 1). • Sequence flows connecting two actions become “include” relationships, denoting the second sprint (Sprint 2). Our model includes sequence flows linking “present the catalog” and “designate the product,” connected by an “include” relation. • “Extended” relationships result from exclusive gateways linking two tasks, corresponding to the third sprint (Sprint 3). In our model, an exclusive gateway connects “designate product” and “put in the cart the quantity of the product,” establishing an “extended” relationship between the two use cases. Finally, we present in Fig. 2 our proposed e-commerce sales Scrum roadmap, which presents in the first sprint the main use cases, and in the second sprint the use cases concerning the includes and the extends use cases are included in the last sprint.

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Fig. 2. Scrum RoadMap of sales through e-commerce

5 Conclusion Software development, a complex endeavor, requires a smooth transition from conceptual models to practical software design while accommodating client change requests. Our article introduces an MDA-based approach, converting CIM analysis to PIM using agile methodologies, illustrated through diagrams. This approach enables precise modeling while staying adaptable to evolving client needs. We break the process into agile sprints, reducing complexity for flexibility. Our future works involve extending this approach to PSM, providing a complete solution to software development challenges with MDA and strong client collaboration. This methodology ensures efficient progress and aligns with evolving industry demands.

References 1. Addamssiri, N., Kriouile, A., Balouki, Y., Taoufiq, G.: Generating the PIM behavioral model from the CIM using QVT. J. Comput. Sci. Inf. Technol., 2(3 and 4) (2014). https://doi.org/10. 15640/jcsit.v2n3-4a4 2. Rhazali, Y., Hadi, Y., Mouloudi, A.: A new methodology CIM to PIM transformation resulting from an analytical survey. In: Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development, pp. 266–273 (2016). https://doi.org/10.5220/000569 0102660273 3. Achraf, H.M., Redouane, E., Yasser, L.E.M.N.: Transforming the business process diagram into a class diagram by model-driven architecture. Indones. J. Electr. Eng. Comput. Sci. 29(2), 845–851 (2023). ISSN: 2502-4752. https://doi.org/10.11591/ijeecs.v29.i2.pp845-851 4. Macek, O., Richta, K.: The BPM to UML activity diagram transformation using XSLT. CEUR Worksh. Proc. 471, 119–129 (2009) 5. Debnath, N., Martinez, C.A., Zorzan, F., Riesco, D., Montejano, G.: Transformation of business process models BPMN 2.0 into components of the Java business platform. IEEE 10th International Conference on Industrial Informatics, pp. 1035–1040 (2012). https://doi.org/10. 1109/INDIN.2012.6300914 6. Dechsupa, C., Vatanawood, W., Thongtak, A.: Transformation of the BPMN design model into a colored petri net using the partitioning approach. IEEE Access 6, 38421–38436 (2018). https://doi.org/10.1109/ACCESS.2018.2853669 7. Kchaou, M., Khlif, W., Gargouri, F., Mahfoudh, M.: Transformation of BPMN model into an OWL2 ontology. In: Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering, pp. 380–388 (2021). https://doi.org/10.5220/001047960 3800388

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8. Nasiria, S., Rhazalia, Y., Lahmera, M., Chenfourb, N.: Towards a generation of class diagram from user stories in agile methods. In: International Workshop on the Advancements in Model Driven Engineering (AMDE 2020). Warsaw, Poland, 6–9 Apr 2020

Combining Transfer Learning with CNNs and Machine Learning Algorithms for Improved Brain Tumor Classification from MRI Abd Allah Aouragh(B) and Mohamed Bahaj MIET Laboratory, Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco [email protected]

Abstract. Accurate identification of brain tumors from magnetic resonance imaging (MRI) scans is a necessity in the realm of diagnosis and the ensuing treatment. In this paper, we investigate the benefits of using transfer learning with the convolutional neural network (CNN) architectures EfficientNet and DenseNet to extract relevant features. Next, we compare various machine learning approaches, notably artificial neural networks (ANN), random forest, and support vector machine (SVM), for brain tumor classification. Our experiments were carried out on a dataset comprising MRI images of different classes of brain tumors. The experimental findings showcase the efficacy of transfer learning in feature extraction and reveal differentiated performance between classification algorithms, with a best accuracy of 96.78% for ANN. This study contributes to the advancement of brain tumor classification and provides valuable insights for the choice of machine learning methods in this field. Keywords: Brain tumor · CNN · Transfer learning · EfficientNet · DenseNet · Classification · Artificial neural network · Random forest · SVM

1 Introduction Brain tumors represent a major challenge in the field of neuro-oncology, requiring precise classification to guide clinical decisions and treatment options. Magnetic resonance imaging (MRI) plays an essential role in the evaluation of brain tumors, offering detailed visualization of brain structures and tumor characteristics. However, accurate interpretation of MRI images for brain tumor classification remains a complex challenge due to interpretative variability and the heterogeneous nature of tumors [1]. Over the past few years, convolutional neural networks and machine learning have emerged as powerful tools for brain tumor recognition from MRI images. Transfer learning, a strategy that transfers knowledge learned on one task to another similar task, has proven to be particularly effective in extracting discriminating features from medical images. By combining transfer learning with high-performance convolutional neural network (CNN) architectures, such as EfficientNet and DenseNet, and machine learning approaches like artificial neural networks (ANN), random forest, and support vector machine (SVM), it is possible to significantly boost the correctness of brain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 391–397, 2024. https://doi.org/10.1007/978-3-031-48573-2_56

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tumor classification [2, 3]. Our aim in this paper is to demonstrate the effectiveness of this approach by comparing the performance of different commonly used machine learning algorithms enhanced with CNN architectures for feature extraction. Alongside this, we acknowledge the existence of other research that has explored less powerful methods for brain tumor detection, such as approaches based on manually extracted features or traditional classifiers [4, 5]. However, these methods have shown limitations in terms of accuracy and capacity to extend to additional data. The rest of this paper is structured as follows: Sect. 2 outlines the materials and methods used, while Sect. 3 presents the results obtained, focusing on the performance of the classification algorithms. Finally, Sect. 4 summarizes the main results, highlights the effectiveness of transfer learning and CNN architectures for accurate brain tumor classification, and proposes future research directions.

2 Materials and Methods 2.1 Dataset The dataset utilized in this paper originates from the Kaggle platform [6] and comprises a set of 7023 MRI images representing different types of brain tumors. The images are of various sizes due to the different resolutions of the MRI scanners used for their acquisition. The dataset is composed of four main classes of brain tumors: pituitaries, meningiomas, gliomas, and non-tumor images. Each class is represented in a balanced way, ensuring an adequate distribution of examples for each tumor type. The aim of this balanced dataset is to avoid biases in a specific class, enabling a more accurate assessment of the performance of classification algorithms. Figure 1 shows illustrative images from the dataset. pituitary

meningioma

glioma

no tumor

Fig. 1. Sample images from the dataset

2.2 Preprocessing To prepare the dataset images, several pre-processing steps were carried out. Firstly, intensity normalization was carried out to reduce variations in brightness between images. This step puts all images on a consistent scale, which facilitates the learning of classification models. Then, the images were scaled to a specific size of 224 × 224 pixels. Since the initial images had different resolutions due to the different MRI scanners used, resizing enabled the image size to be homogenized for more efficient and

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consistent analysis. Furthermore, to assess the effectiveness of the classification models accurately, training, validation, and test subsets were constructed from the dataset. The training split was utilized for building the models. The validation split was employed to adjust hyperparameters and track training performance, while the test split was reserved for evaluating the final efficiency of the classification architectures [7]. 2.3 Transfer Learning Transfer learning refers to a machine learning strategy that consists of using the knowledge acquired by a pre-trained model on a general task to boost the model’s performance in a specific context. Using a pre-trained architecture, usually on a large amount of data, the deep layers of the network are able to extract general visual features, such as contours or textures, which can be reused for a new task. Only the top layers of the network are trained specifically for the new task, saving time and resources while improving classification performance. Transfer learning is extremely practical when training data for the particular new assignment is limited, as it allows us to benefit from the knowledge already acquired by the pre-trained model [8]. 2.4 Feature Extraction: CNN Architectures The use of CNN architectures has revolutionized the domains of computer vision and machine learning. CNNs are powerful models capable of learning complex hierarchical representations from input data, particularly for image classification tasks. Their success is based on their ability to automatically capture discriminative image features, such as contours, textures, and patterns, through convolution and pooling layers. CNNs have shown their versatility in different applications, including the categorization of tumors from MRI images, picture segmentation, and object recognition [9]. In this study, we adopted the following architectures: • EfficientNet: a CNN architecture that has been developed to achieve an optimal balance between model accuracy and efficiency. Unlike many previous architectures, EfficientNet proposes a systematic approach to improving CNN performance by using a uniform scaling factor across the width, depth, and resolution of the architecture. This approach results in higher-performance models while reducing the cost in terms of computational resources [9]. • DenseNet: a CNN architecture distinguished by its dense architecture and direct connections between all layers. Unlike traditional architectures, where information is propagated sequentially, DenseNet enables each layer to receive direct input from previous layers. This promotes a rich exchange of information between layers, improving feature representation and enhancing the model’s ability to learn complex patterns in images [9]. 2.5 Classification: Machine Learning Machine learning is an artificial intelligence discipline that focuses on the conception of algorithms capable of learning from data and performing classification tasks without

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being explicitly programmed. In the context of classification, machine learning is used to build models capable of detecting patterns and relationships between input data characteristics and predefined categories [10]. To classify the previous CNN architecture outputs, we employed the following techniques: • ANN: Artificial neural networks consist of mathematical computer models derived from the mechanisms of brain neurons. Composed of several layers of interconnected neurons, they learn from data by adjusting the weights of connections between neurons. They are widely used for their ability to model and solve problems efficiently and accurately [10]. • Random Forest (RF): a machine learning method that integrates the forecasts of various independent decision trees to provide a final forecast that is more reliable and accurate. Each tree is built by using a random sample of the training dataset and randomly selecting the features to be considered for each division of the tree. It offers reliable estimates and great capacity to handle large datasets [10]. • Support Vector Machine (SVM): a classification method that seeks to find an optimal decision boundary that separates samples between distinct classes with the maximum spacing. SVM is efficient for high-dimensional data and can be used with kernel functions to handle non-linear data. It offers high accuracy and is used in a variety of fields, including classification and anomaly detection [10]. 2.6 Model Architecture Overview For the architecture of our model, we propose a comprehensive approach comprising data preparation, feature engineering, and categorization phases. Firstly, the MRI images in the dataset are subjected to pre-processing to ensure optimal data consistency and quality. Next, we use the EfficientNet and DenseNet convolutional neural network architectures to extract discriminative features from the pre-processed images. The extracted features are then fed to classification algorithms like ANN, Random Forest, and SVM. These algorithms are trained on the features to predict the class of each MRI image. Figure 2 illustrates the various stages of the suggested process. The different manipulations were performed on a Jupyter notebook on the Drive Google Colab tool platform (Xeon CPU, 13 GB RAM). Additionally, matplotlib, sklearn, and keras libraries were used to construct and visualize the neural networks and the machine learning algorithms [11].

Fig. 2. Architecture overview

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3 Results and Discussion Within this part, we reveal the findings from various CNN architectures in combination with machine learning algorithms. We evaluate model performance using several metrics commonly used in classification, including accuracy, recall, precision, F1-score, and area under the ROC curve (AUC-ROC). These metrics enable us to assess the overall accuracy of predictions (accuracy), the ability to identify positive cases (recall), the accuracy of positive predictions (precision), and the balance between recall and precision (F1-score). In addition, the AUC-ROC allows us to assess the model’s effectiveness in separating groups. Table 1 exhibits the performance of the EfficientNet architecture with the various classification algorithms, and Table 2 displays the performance of the DenseNet architecture with the various classification algorithms. Table 1. Performance metrics for EfficientNet EfficientNet

Accuracy (%) Recall (%) Precision (%) F1-score (%) AUC-ROC (%)

ANN

96.78

95.36

94.86

95.11

96.46

Random forest 92.91

95.62

90.53

93.01

92.98

SVM

92.52

95.72

94.09

95.31

94.11

Table 2. Performance metrics for DenseNet DenseNet

Accuracy (%)

Recall (%)

Precision (%)

F1-score

AUC-ROC (%)

ANN

93.62

93.28

92.87

93.07

93.58

Random forest

92.81

92.95

93.28

93.11

92.19

SVM

92.79

93.37

93.12

93.24

93.70

The results of both Tables 1 and 2 highlight that all the models evaluated achieved performances above 90% for the MRI-guided differentiation of cerebral tumors, attesting to the effectiveness of our approach. More specifically, focusing on the EfficientNet architecture, Table 1 shows that the best accuracy, F1-score, and AUC-ROC are produced by the ANN model, with values of 96.78%, 95.11%, and 96.46%, respectively. With regard to recall, the Random Forest model obtained the best value, with a score of 95.62%. Finally, the highest precision is recorded by the SVM model, with a value of 95.72%. For DenseNet architecture, the values in Table 2 reveal that the ANN model once again recorded the best accuracy and AUC-ROC, with values of 93.62% and 93.58%, respectively. For recall and F1-score, the best values were obtained by the SVM model, with rates of 93.37% and 93.24%, respectively. Concerning precision, the Random Forest model recorded the highest value, with a score of 93.28%. Figure 3 provides a comparative visualization of the different metrics.

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98.00%

Accuracy

Recall

Precision

F1-score

AUC-ROC

96.00% 94.00% 92.00% 90.00% ANN

RF

SVM

ANN

EfficientNet

RF

SVM

DenseNet

Fig. 3. Metrics of different models

4 Conclusion In conclusion, our study demonstrates that the use of EfficientNet and DenseNet convolutional neural network architectures combined with machine learning algorithms offers promising performance for brain tumor identification from MRI scans. The approach based on EfficientNet and ANN performed the best, recording an accuracy of 96.78% and an AUC-ROC of 96.46%. For future perspectives, it would be interesting to explore other CNN architectures, optimize hyperparameters, and extend the study to larger datasets. Employing deep learning-based techniques and the incorporation of data augmentation techniques could also further improve performance. By improving the accuracy of brain tumor classification, our research could contribute to earlier diagnosis and better patient management.

References 1. Brain Tumors—Classifications, Symptoms, Diagnosis and Treatments [Internet]. Cited 8 July 2023. Available from: https://www.aans.org/ 2. Cè, M., Irmici, G., Foschini, C., Danesini, G.M., Falsitta, L.V., Serio, M.L., et al.: Artificial intelligence in brain tumor imaging: a step toward personalized medicine. Curr. Oncol. 30(3), 2673–2701 (2023) 3. Ranjbarzadeh, R., Caputo, A., Tirkolaee, E.B., Jafarzadeh Ghoushchi, S., Bendechache, M.: Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput. Biol. Med. 1(152), 106405 (2023) 4. Tiwari, A., Srivastava, S., Pant, M.: Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recogn. Lett. 1(131), 244–260 (2020) 5. Jyothi, P., Singh, A.R.: Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artif. Intell. Rev. 56(4), 2923–2969 (2023) 6. Brain Tumor MRI Dataset [Internet]. Cited 8 July 2023. Available from: https://www.kaggle. com/datasets/masoudnickparvar/brain-tumor-mri-dataset 7. de Raad, K.B., van Garderen, K.A., Smits, M., van der Voort, S.R., Incekara, F., Oei, E.H.G., et al.: The effect of preprocessing on convolutional neural networks for medical image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 655–658 (2021)

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8. Kora, P., Ooi, C.P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W.Y., et al.: Transfer learning techniques for medical image analysis: a review. Biocybernet. Biomed. Eng. 42(1), 79–107 (2022) 9. Singh, S.A., Kumar, A.S., Desai, K.A.: Comparative assessment of common pre-trained CNNs for vision-based surface defect detection of machined components. Expert Syst. Appl. 15(218), 119623 (2023) 10. Aouragh, A.A., Bahaj, M.: Comparison results of hybrid CNN-machine learning algorithms architectures for Monkeypox images classification. In: 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–6 (2023) 11. Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., López García, Á., Heredia, I., et al.: Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif. Intell. Rev. 52(1), 77–124 (2019)

An Intelligent Model for Detecting Obstacles on Sidewalks for Visually Impaired People Ahmed Boussihmed1(B) , Khalid El Makkaoui2 , Abdelaziz Chetouani1 , Ibrahim Ouahbi2 , and Yassine Maleh3 1

LaMAO, ORAS Team, ENCG, University Mohammed Premier, Oujda, Morocco [email protected], [email protected] 2 Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco [email protected], [email protected] 3 Laboratory LaSTI, ENSAK, USMS University, Beni Mellal, Morocco [email protected]

Abstract. Blind individuals face significant challenges while navigating outdoor environments, particularly on sidewalks shared with the general public. Identifying and avoiding common objects, such as trash cans, benches, or bike racks, is crucial for their safety and independence. This paper proposes an intelligent system that employs the You Only Look Once (YOLO) object detection algorithm to detect common objects on blind sidewalks. Our work mainly proposes a new dataset (OOD) that contains 10,000 images and 29,779 annotated instances, and 22 different types of classes. We train the dataset with the state-of-the-art object detection models, YOLOv5 and YOLOv8, to accurately recognize objects such as benches, trash cans, street signs, fire hydrants, and more. We analyze and compare these models in detail and then deploy the optimal model on Raspberry Pi. By utilizing real-time video input from cameraequipped devices, the system provides auditory feedback to alert users about the presence of objects, thereby aiding navigation in outdoor environments. Keywords: Computer vision (CV) · Deep learning (DL) · Object detection · Raspberry Pi · Visually impaired (VI) · YOLO

1

Introduction

According to the World Health Organization (WHO), approximately 2.2 billion individuals worldwide suffer from near or distance vision impairment. In at least one billion of these cases, vision impairment could have been prevented or has yet to be addressed. Blind and visually impaired people encounter significant challenges when navigating public outdoor spaces on foot, as everyday tasks like c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 398–404, 2024. https://doi.org/10.1007/978-3-031-48573-2_57

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crossing the streets, avoiding obstacles, and using public transportation pose serious problems [1]. Researchers have developed various intelligent models to aid in obstacle detection and navigation to address these challenges. Early research in this area focused on sensor-based approaches, primarily utilizing ultrasonic sensors, infrared (IR) sensors, and global positioning system (GPS) [2–4]. These studies aimed to detect obstacles in real-time by measuring the distance between the user and potential obstacles. While these approaches showed promise, they often suffered limitations such as limited range, susceptibility to environmental conditions, and difficulty distinguishing between obstacles. In this paper, we propose an intelligent model based on deep learning and computer vision techniques to assist visually impaired (VI) people in detecting obstacles on sidewalks. The proposed model exploits YOLO’s real-time object detection capabilities to identify and classify obstacles on sidewalks. By utilizing the computational efficiency and portability of the Raspberry Pi, the model offers a cost-effective and lightweight solution for on-device obstacle detection. Experiments were conducted to evaluate the model’s performance, including accuracy, speed, and resource utilization. The results demonstrate the model’s effectiveness in detecting obstacles with 62.7% of accuracy while operating in real-time on the Raspberry Pi at a 1.47 FPS. The rest of the paper is organized as follows. Section 2 presents the workflow methodology. Section 3 describes our proposed model. Section 4 provides the results obtained. Finally, Sect. 5 concludes the paper.

2

Methodology

Figure 1 shows the workflow methodology we pursued for building the object detection system. These are broken down into three main stages: Step 1. Collect and preprocess Data. Step 2. Design, train and evaluate the model. Step 3. Optimize, convert, and deploy the model.

Fig. 1. Workflow methodology.

2.1

Data Collection and Annotation

This is often one of the most time-consuming stages, as it can be difficult to collect the data actively or to curate a sufficiently large dataset from external

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sources, such as pre-existing datasets (e.g., Open Images). Simultaneously ensuring to minimize dataset bias. Table 1 illustrates the most popular datasets that are commonly used for object detection research and benchmarking. Table 1. Most common images dataset. Dataset MS COCO PASCAL VOC 2012 ImageNet Open Images Dataset V7

Classes Images 80

328,000

Annotated Reference 200,000 [5]

20

11,530

27,450 [6]

1000

14,197,122

1,034,908 [7]

600

9,178,275 15,851,536 [8]

Outdoor Obstacle Detection: To fit the requirements of our application, we have built a custom dataset called Outdoor Obstacle Detection (OOD). The OOD dataset contains images from different sources, including Open Images, Pixabay, and Pixel platforms. Additionally, we have actively captured new images through a smartphone. The OOD dataset is provided in four formats (txt, json, xml, and csv); it contains 10.000 images and 29.779 annotated instances, and 22 classes (refer to https://universe.roboflow.com/fpn/ood-pbnro/dataset/ 1). Object classes: We have selected 22 specific types of obstacles that can obstruct blind people in the way. Figure 2 shows the object classes and the number of annotated objects in each class. Annotation and labeling: We have used the Roboflow platform [9] to annotate and re-annotate images to create ground truth labels for the objects of interest. This labeling process involves marking the bounding boxes around each object in the images and associating them with corresponding class labels. 2.2

YOLO Model

Object detection has witnessed a significant breakthrough with introduction of the YOLO (You Only Look Once) [10] algorithm. Over time, YOLO has undergone several iterations, with two notable versions being YOLOv5 and YOLOv8. Each version brings its distinctive features and advantages, elevating them to exceptional levels of performance and capabilities in their own right. YOLOv5 [11] is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv5 provides five scaled versions: YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, where the width and depth of the convolution modules vary to suit specific applications and hardware requirements. For instance, YOLOv5n and YOLOv5s are lightweight models targeted for low-resource devices, while YOLOv5x is optimized for high performance, albeit at the expense of speed.

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Fig. 2. Class balance.

YOLOv8 [12] is the newest state-of-the-art YOLO model that can be used for object detection, image classification, and instance segmentation tasks. YOLOv8 was developed by Ultralytics, who also created the influential and industrydefining YOLOv5 model. YOLOv8 provided five scaled versions: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l and YOLOv8x. 2.3

Train, Evaluate, and Deploy

We conducted our study on a custom dataset containing 10,000 labeled images. The dataset was split into training (8000 images), validation (1000 images), and testing subsets (1000 images). The training subset was used to train the YOLOv5 and YOLOv8 object detection models, while the validation and testing subsets were utilized to evaluate the system’s performance. We implemented the models using the TensorFlow framework and trained them on a Tesla NVIDIA V100 GPU server (for epochs = 100, batch size = 16, lr = 0.01, optimizer = SGD). We evaluated the models based on their accuracy, complexity, and inference time. We measured the average precision (AP) metric, which calculates the precision and recall of the detected objects. We also measured the model complexity using the number of parameters and operations required for inference. Finally, we measured the inference time of each model on the Raspberry Pi 4.

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Proposed Model

Our proposed model aims to detect and recognize common outdoor objects on the sidewalk using deep learning (DL) and computer vision (CV) techniques. The main goal is to support people with visual disabilities in performing their daily activities safely by assisting them in detecting obstacles that may be encountered when walking outside. Our proposed model is divided into four parts: An image provider, a neural network model, an object recognizer, and a command responder. The overall design of the proposed model is illustrated in Fig. 3. First, at the preprocessing stage, the image sensor will generate a frame of data from the Raspberry Pi camera. Then, we will crop the image down in size and convert it into a format that is ready for the neural network to consume. Once the input data is ready, we invoke the neural network model to make inferences. In our case, the models we are invoking here are Yolov5 and Yolov8. In the post-processing stage, the object recognizer analyzes the data that comes out of the neural network model and generates a tensor object that contains the labels of objects detected, the coordinates of the bounding box, and a confidence score that reflects how accurately the model says about a bounding box that contains the detected object. The system also incorporated a command responder module to provide audio feedback and non-visual cues to the user, aiding in identifying and localizing detected objects.

Fig. 3. The architecture of the proposed system.

4

Results and Discussion

Table 2 shows the results of our study. YOLOv8s achieved the highest average precision, with a mAP value of 61.7 (see Fig. 4). Regarding model complexity, YOLOv5n had the lowest number of parameters and operations required for inference. Regarding FPS (Frame Per Second), YOLOv5n has a higher FPS among the other versions making it preferable for real-time applications. In general, the detection results were promising; still, it might not be practical to utilize it for real applications because, at a rate of 3.6 FPS, the model will struggle to detect objects that move quickly, such as a car. To address this challenge, we will investigate further the optimization techniques [13] to accelerate the model inference time on embedded systems.

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An alternative way to achieve this goal is by utilizing a cascade architecture that contains two layers 1) An IoT node (Arduino Nano 33 BLE sense) to collect and pre-process images 2) A gateway or a local server (NVIDIA-Jetson-Nano) to host the detection model and provide audio feedback to the end user. The communication between the two components takes place via a BLE channel. Table 2. Results of training, including the mAP, model complexity, and inference time. Model

Size (pixels) mAP-50 mAP-50:95 FPS Parameters (M) FLOPs (B)

yolov5n 640

54.8

35.4

3.6

1.9

4.5

yolov5s 640

60.2

40.2

1.6

7.2

16.5

yolov8n 640

58.9

41.3

3.1

3.2

8.7

yolov8s 640

61.7

43.7

1.47 11.2

28.6

Fig. 4. Training results of YOLOv8s.

5

Conclusion

In this paper, we have proposed a custom dataset for detecting 22 objects on the sidewalk. We have trained and validated the dataset using the lightweight versions of YOLOv5 and YOLOv8 and implemented the obtained detection models on a Raspberry Pi embedded system. The results show that YOLOv8s achieved the highest average precision, with a mAP value of 61.7, while YOLOv5n is preferable for real-time applications. Our future work will evaluate other DL models and optimization techniques to improve the proposed model’s performance. Additionally, we aim to add more functionality, such as a navigation module and a face recognition system.

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An Overview of Blockchain-Based Electronic Health Record and Compliance with GDPR and HIPAA Nehal Ettaloui(B) , Sara Arezki, and Taoufiq Gadi Faculty of Science and Techniques, University Hassan First, Settat, Morocco [email protected]

Abstract. The healthcare sector is a massive producer and a dependent user of data, making the data gathering, its treatment and sharing of big importance. Advances in technology have enabled healthcare providers to store it in a digital form called electronic medical records (EMRs). These records are shared with various stakeholders such as patients, healthcare professionals, providers, insurance companies, pharmacies, etc. In a field as sensitive as healthcare, the integration of new technologies is crucial. Blockchain technology has emerged as an immutable technology ready to support changes in the healthcare system due to its transparency and decentralized features. As healthcare information are highly sensitive, it is also highly regulated to ensure patient privacy. Regulatory frameworks such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) are designed to help reduce the risk of health data breaches. Blockchain characteristics can improve interoperability, anonymity, and control of access to health data; however, blockchain applications must comply with the current regulatory framework to increase their viability in the real world. This paper analyzes the compliance of blockchain-based EHR systems with HIPAA and GDPR, as well as other areas for improvement. Keywords: Blockchain · Healthcare · Electronic health record · GDPR · HIPPA · Regulation

1 Introduction Blockchain-based electronic health records (EHRs) are gaining traction as a potential solution for the secure and efficient storage and sharing of patient health information (PHI) in healthcare. However, the decentralized and immutable nature of blockchain technology presents unique challenges when it comes to compliance with privacy and security regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) [1]. HIPAA and GDPR are two of the most important regulations governing the collection, use, and disclosure of PHI in the United States and the European Union, respectively. Compliance with these regulations is critical to protecting the privacy and security of PHI and maintaining the trust of patients and healthcare providers in the use of technology © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 405–412, 2024. https://doi.org/10.1007/978-3-031-48573-2_58

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in healthcare. In this article, we will examine the challenges of achieving compliance with HIPAA and GDPR in blockchain-based EHRs, and explore the key steps that organizations can take to ensure compliance while taking advantage of the benefits of blockchain technology. We will also discuss the implications of non-compliance with these regulations and the potential impact on patients, healthcare providers, and healthcare organizations. By understanding the unique challenges of achieving compliance with HIPAA and GDPR in blockchain-based EHRs and implementing best practices for compliance, healthcare organizations can leverage the power of blockchain technology to improve the efficiency and security of healthcare data while maintaining the privacy and security of PHI [2].

2 Background 2.1 General Data Protection Regulation (GDPR) The General Data Protection Regulation (GDPR) is a regulation passed by the European Union in May 2016 to protect the privacy and personal data of EU citizens. The GDPR replaces the 1995 Data Protection Directive and came into effect on May 25, 2018. The GDPR sets out rules for how organizations must handle personal data, including how it is collected, used, processed, and stored. It also gives individuals more control over their personal data and provides them with greater rights, including the right to access their data, the right to have their data erased, and the right to object to the processing of their data. The GDPR applies to any organization that processes the personal data of EU citizens, regardless of where the organization is located. Failure to comply with the GDPR can result in significant fines and penalties [3]. The General Data Protection Regulation (GDPR) is a set of rules established by the European Union (EU) to protect the privacy and personal data of its citizens. Some of the key rules of GDPR include [4]: 1. Consent: Data controllers must obtain clear and specific consent from individuals to process their personal data. Consent must be freely given, unambiguous, and informed. 2. Right to access: Individuals have the right to know what personal data is being collected, processed, and stored about them. 3. Right to erasure: Individuals have the right to request that their personal data be erased or deleted. 4. Data portability: Individuals have the right to receive a copy of their personal data in a structured, machine-readable format and to transmit this data to another controller. 5. Privacy by design: Data controllers must implement technical and organizational measures to ensure that data protection principles are built into the design of their systems and processes. 2.2 Health Insurance Portability and Accountability Act HIPAA stands for the Health Insurance Portability and Accountability Act of 1996. It is a federal law in the United States that aims to protect the privacy and security of individuals’ personal health information (PHI) [5].

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The main objectives of HIPAA are to ensure the confidentiality and security of PHI, limit the access and use of PHI to authorized individuals, provide individuals with rights over their PHI and establish standards for the electronic exchange of PHI. HIPAA applies to all health care providers, health plans, and health care clearinghouses that electronically transmit health information. The law requires these covered entities to implement safeguards to protect the privacy and security of PHI and to comply with specific regulations related to PHI, such as the HIPAA Privacy Rule and the HIPAA Security Rule. HIPAA violations can result in significant penalties, including fines and legal action. Individuals can file complaints with the U.S. Department of Health and Human Services (HHS) if they believe their rights under HIPAA have been violated. HIPAA (the Health Insurance Portability and Accountability Act) has two main rules that govern the use and disclosure of protected health information (PHI). These rules are [6]: 1. HIPAA Privacy Rule: This rule sets national standards for protecting the privacy of PHI. It establishes guidelines for how covered entities can use, disclose, and safeguard PHI, as well as the rights of individuals to access and control their PHI. The Privacy Rule also requires covered entities to appoint a privacy officer, train their workforce on privacy practices, and implement administrative, physical, and technical safeguards to protect PHI. 2. HIPAA Security Rule: This rule establishes national standards for securing electronic PHI (ePHI). The Security Rule requires covered entities to implement administrative, physical, and technical safeguards to protect ePHI from unauthorized access, use, and disclosure. It also requires covered entities to implement policies and procedures for responding to security incidents and to conduct periodic risk assessments to identify and mitigate potential vulnerabilities. In addition to the Privacy and Security Rules, HIPAA also includes provisions related to breach notification, enforcement, and penalties for non-compliance. Covered entities that violate HIPAA can face significant fines and legal action.

3 Results 3.1 Contradiction of Blockchain-Based EHR with GDPR There are some potential contradictions between blockchain-based EHRs and the General Data Protection Regulation (GDPR) [1, 7]. One of the primary challenges is the right to be forgotten, which is a fundamental principle of the GDPR. This principle gives individuals the right to have their personal data erased, which can be difficult to implement in a blockchain-based system, as the technology is designed to create a tamper-proof, immutable record of data. Once data has been added to a blockchain, it cannot be easily deleted or modified, which could conflict with the right to be forgotten.

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Another issue is the GDPR’s requirement for data minimization, which requires that only the minimum amount of personal data necessary for a specific purpose is collected and processed. In a blockchain-based EHR system, all information is recorded on the blockchain, which could lead to an excessive amount of data being collected and processed, potentially violating the GDPR’s data minimization principle. Furthermore, the GDPR requires that personal data is processed lawfully, fairly, and transparently. The use of blockchain technology could potentially make it difficult for patients to understand how their data is being processed and who has access to it, which could conflict with the GDPR’s transparency requirement [4]. To address these challenges, there have been efforts to develop blockchain-based systems that are compliant with GDPR, such as using encryption techniques to ensure that personal data is only accessible to authorized parties and incorporating privacyenhancing technologies to ensure that personal data is not unnecessarily exposed on the blockchain. However, the use of blockchain technology for EHRs is still a developing area, and further research and development is needed to ensure that it complies with GDPR and other privacy regulations. 3.2 Contradiction of Blockchain-Based EHR with HIPAA There are potential contradictions between blockchain-based EHRs and the Health Insurance Portability and Accountability Act (HIPAA), which is a United States law that sets national standards for the protection of individuals’ medical records and other personal health information [8]. One of the primary challenges is the requirement under HIPAA for covered entities to ensure the confidentiality, integrity, and availability of protected health information (PHI). While blockchain technology can provide secure storage and transmission of PHI, there are concerns about the transparency of blockchain-based systems and the potential for unauthorized access to PHI. This could potentially conflict with the confidentiality requirement under HIPAA. Another challenge is the HIPAA requirement for covered entities to have agreements in place with business associates that handle PHI, to ensure that the business associates also comply with HIPAA. It may be difficult to ensure that all parties involved in a blockchain-based EHR system are compliant with HIPAA, as the decentralized nature of the blockchain means that it may be difficult to identify all parties that have access to PHI. Additionally, the HIPAA Security Rule requires covered entities to have reasonable and appropriate administrative, physical, and technical safeguards to protect PHI. It may be difficult to ensure that blockchain-based EHR systems have adequate safeguards in place, as the technology is still relatively new and may not have established best practices for security and privacy. To address these challenges, there have been efforts to develop blockchain-based EHR systems that are compliant with HIPAA, such as incorporating privacy-enhancing technologies, limiting access to PHI to authorized parties, and implementing auditing and logging features to monitor access to PHI. However, further research and development is needed to ensure that blockchain-based EHR systems meet the security and privacy requirements under HIPAA [8].

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3.3 Proposed Solutions for Compliance of Blockchain-Based EHR and GDPR and HIPAA Using off-chain storage solutions, cryptographic techniques, and patient consent can help address some of the challenges associated with GDPR and HIPAA compliance when implementing a blockchain-based healthcare system [9, 10]. • Off-Chain Storage (IPFS): Instead of storing all sensitive patient data directly on the blockchain, use IPFS or similar distributed storage systems to store actual data offchain. The blockchain then stores only hashes or references to the data. This approach addresses GDPR’s right to erasure and rectification, as data can be deleted or modified off-chain while maintaining the blockchain’s integrity [11]. • Cryptography [12]: Use advanced encryption methods to protect data privacy and security. Encrypt patient information before saving it on IPFS so that it stays unreadable without the right decryption keys if accessed. Also, use cryptographic hashes to store data references on the blockchain, which keeps data intact and lets you confirm its authenticity without revealing the actual data. • Patient Consent [13]: Utilize smart contracts to manage patient consent effectively, enabling precise control over data access. These contracts empower patients to grant or revoke consent as needed, regulating who can access their medical records. Additionally, smart contracts maintain a transparent access log, recording all data access requests and approvals, thus establishing an audit trail that aids in compliance efforts. • Data Minimization [14]: Use IPFS and smart contracts to store only relevant data on the blockchain: Data Segmentation: Store minimal patient identifiers or data summaries on the blockchain, with the actual data residing off-chain. This addresses HIPAA’s data minimization requirement. • Patient Rights (GDPR) and Access (HIPAA): Patients have the ability to manage their data and grant access to authorized entities using a combination of patient consent and cryptographic keys. In the context of GDPR’s Data Portability, patients can request their data, and upon consent, receive cryptographic keys to access their encrypted data stored on IPFS. Regarding HIPAA’s Right of Access, authorized entities can access data when patients grant consent, utilizing cryptographic keys to decrypt and access the pertinent information. • Security and Encryption [15]: Employ strong encryption for data at rest (on IPFS) and in transit (between blockchain and IPFS) to protect patient data from unauthorized access. Combining off-chain storage with cryptography and patient consent mechanisms can help you create a blockchain-based healthcare system that respects patient privacy, provides data control, and maintains compliance with GDPR and HIPAA regulations. However, due to the complexity of these regulations, it’s recommended to consult legal professionals with expertise in data protection and healthcare to ensure your implementation is both technically sound and legally compliant. 3.4 Proposed Blockchain-Based EHR Model for GDPR and HIPAA Compliance In this section, we present a patient-centric model built on the foundation of Hyperledger Fabric [16], a permissioned blockchain, and complemented by off-chain storage using

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IPFS [11]. The system’s high-level architecture, depicted in Fig. 1, comprises five core components: identity management, decentralized data storage, blockchain-driven access control, and immutable provenance. Within this structure, three key entities align with GDPR terminology: patients embody data subjects, healthcare admins act as data controllers or service providers, and doctors function as data processors. The system begins with healthcare admins configuring business policies to enhance consent regulations. Following this, admins digitally onboard various entities, such as patients, doctors, and researchers, onto the fabric network, enabling the collection of patient data under established consent agreements. To ensure security, Electronic Health Records (EHRs) are stored on decentralized IPFS storage with encrypted hash copies in the on-chain database. These records are linked to digital identities and verified usage contracts, authorized by patients. When data processors, like doctors or researchers, request data access via the application interface, their requests and relevant details undergo blockchain-based validation. Here, the blockchain verifies the processor’s identity and adherence to usage policies, generating a secure data access token that’s returned to the application. Following this, the application forwards the access token to the health server. Prior to data sharing, the transaction undergoes rigorous validation against the blockchain’s provenance ledger. Upon successful validation, the health server provides the decryption key and encrypted data pointer, enabling data processors to access original data through the application interface. Importantly, all blockchain transactions, regardless of success, are meticulously recorded within the provenance ledger, ensuring a comprehensive audit trail of the system’s activities.

Fig. 1. Architecture of the proposed model

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4 Conclusion In conclusion, blockchain-based electronic health records (EHRs) present unique challenges when it comes to compliance with HIPAA and GDPR regulations. As the use of blockchain technology in healthcare continues to grow, it is important for organizations to understand how to ensure the security and privacy of patient health information on the blockchain while maintaining compliance with these regulations. The key to achieving compliance with HIPAA and GDPR in blockchain-based EHRs is to develop and implement policies and procedures that address the specific requirements of each regulation, implement technical controls to protect PHI on the blockchain, implement identity and access management solutions, provide transparency and user consent, and maintain documentation of the compliance program. Our proposed model seamlessly integrates permissioned blockchain principles, decentralized IPFS storage, and smart contract-enabled consent management. By aligning key stakeholders – patients, administrators, and doctors – with GDPR terminology, the model establishes a secure and auditable framework that empowers patients, ensures data privacy, and facilitates regulatory adherence. This blockchain-based approach not only addresses the complexities of healthcare data compliance but also paves the way for a resilient and interoperable data management paradigm that can safeguard sensitive information while promoting seamless data exchange within the healthcare domain. While ensuring compliance with HIPAA and GDPR in blockchain-based EHRs may be challenging, it is essential to maintain the trust of patients and healthcare providers in the security and privacy of their PHI. By taking the necessary steps to ensure compliance, organizations can harness the benefits of blockchain technology while safeguarding PHI and maintaining compliance with HIPAA and GDPR regulations.

References 1. Hasselgren, A., Wan, P.K., Horn, M., Kralevska, K., Gligoroski, D., Faxvaag, A.: GDPR compliance for blockchain applications in healthcare. arXiv, 27 Sept 2020. Consulté le: 28 avr 2023. [En ligne]. Disponible sur: http://arxiv.org/abs/2009.12913 2. Zhou, C., Barati, M., Shafiq, O.: A compliance-based architecture for supporting GDPR accountability in cloud computing. Future Gener. Comput. Syst. 145, 104–120 (2023). https:// doi.org/10.1016/j.future.2023.03.021 3. Hussein, R., et al.: General data protection regulation (GDPR) toolkit for digital health. Stud. Health Technol. Inform. 290, 222–226 (2022). https://doi.org/10.3233/SHTI220066 4. Poelman, M., Iqbal, S.: Investigating the compliance of the GDPR: processing personal data on a blockchain. In: 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP), pp. 38–44 (2021). https://doi.org/10.1109/CSP51677.2021.9357590 5. Health Insurance Portability and Accountability Act of 1996 (HIPAA) | CDC 28 juin 2022. https://www.cdc.gov/phlp/publications/topic/hipaa.html. Consulté le 28 avr 2023 6. Moore, W., Frye, S.: Review of HIPAA, part 1: history, protected health information, and privacy and security rules. J. Nucl. Med. Technol. 47(4), 269–272 (2019). https://doi.org/10. 2967/jnmt.119.227819 7. Hasselgren, A., Kralevska, K., Gligoroski, D., Faxvaag, A.: GDPR compliant blockchain and distributed ledger technologies in the health sector. Stud. Health Technol. Inform. 270, 1293–1294 (2020). https://doi.org/10.3233/SHTI200408

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A Whale Optimization Algorithm Feature Selection Model for IoT Detecting Intrusion in Environments Mouaad Mohy-eddine1(B) , Azidine Guezzaz1 , Said Benkirane1 , Mourade Azrour2 , and Kamal Bella1 1 Higher School of Technology, Cadi Ayyad University, 44000 Essaouira, Morocco

[email protected] 2 IDMS Team, Faculty of Sciences and Technics, Moulay Ismail University, Meknes, Morocco

Abstract. The Internet of Things (IoT) propagation has raised severe security concerns. Thus, the intrusion detection system (IDS) received enormous attention due to its critical in maintaining these environments’ security. Furthermore, various deep learning (DL) models were proposed to enhance the performance of the IDS in the literature. Hence, this paper proposed an IDS for IoT environments to increase the protection of the IoT environment. We applied Radial Basis Function Neural Network (RBFNN) as a multiclass classifier for the detection phase and a Whale Optimization Algorithm for the feature to improve the IDS performance. For the evaluation phase, we relied on the NF-ToN-IoT and NF-Bot-IoT datasets. Our model has scored significant results with 96.83% accuracy (ACC) and 89.74% Matthew’s correlation coefficient (MCC) on the NF-ToN-IoT, 98.43% ACC, and 57.71% MCC on the NF-Bot-IoT and 95.93% ACC and 82.68% MCC on the NF-ToN-IoT and NF-Bot-IoT dataset merged. Our model has shown outstanding results compared with other models. Keywords: Radial basis function neural network · Whale optimization algorithm · IoT · IDS · Feature selection

1 Introduction The same way that the Internet of Things (IoT) has facilitated life has made it more difficult on the other hand. The emergence of the IoT in various life areas such as smart cities [1, 2], agriculture 4.0 [3, 4], smart grid [5], industrial IoT (IIoT) [6–9] etc. has raised severe security concerns [10]. An intrusion detection system (IDS) and other countermeasures like firewalls and antiviruses were presented to encounter these threats. With an increasing rate of zero-day attacks, the anomaly IDS (AIDS) has gained more popularity due to its effectiveness against this attack [11]. An IDS is an equipment or software that tracks and recognizes intrusions to safeguard sensitive data and devices [12]. The signature IDS (SIDS) matches the stored signature with the recorded activities to determine the potential intrusion. The hybrid IDS (HIDS) combines the benefits of AIDS with SIDS [13]. AIDS profits from the tremendous impact of machine learning © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 413–419, 2024. https://doi.org/10.1007/978-3-031-48573-2_59

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(ML), deep learning (DL) and feature engineering to improve its performance [7]. This work introduces an AIDS model to improve IoT security. Whale Optimization Algorithm (WOA) was implemented to enhance the feature selection process. This method reduces the maximum feature number without impacting the system performance. We employed the Radial Basis Function Neural Network (RBFNN) to classify the events into benign or threats. According to our results, the proposed model performs effectively. The remainder of this paper is organized as follows: Sect. 2 includes an IoT and IDS background and related works. In Sect. 3, we describe our model based on RBFNN and WOA. Section 4 discusses the result and the model’s performance. Our manuscript is concluded with a conclusion in Sect. 5.

2 Background and Related Works IoT integrates billions of embedded sensors and actuators that interact without human involvement to fulfil a relevant goal [10]. Several researchers presented different IoT architectures. Still, the three-layer architecture is the more standard [8]. Application, network, and perception are the three layers that constitute it. The perception layer is for data collection and control instructions [7, 10]. The data collected by sensors is sent across the network layer and handles some of the processing [7, 10]. The application layer analyses and processes data acquired by the perception layer [7, 10]. The intrusion detection process tracks a host of events looking for indications of intrusions. Hence, the IDS is equipment or software that tracks and recognizes intrusions to safeguard sensitive data and devices. The fundamental purpose of using ML and DL algorithms is to reduce the requirement for human assistance and knowledge. As IDS always needs to be active, the intervention of human beings should be minimal. So, the ML has interfered to reduce this intervention. Several ML and DL were proposed, such as KNearest Neighbors (KNN) [10], Random Forest (RF) [8, 11], AdaBoost (AB) [14] etc. To enhance data quality, numerous feature selection approaches were proposed, such as recursive feature elimination (RFE) with RF [15], Pearson’s correlation coefficient (PCC) [6] and principal component analysis (PCA) [10] etc. Attou et al. [11] proposed an enhanced IDS for cloud environments with RF and graphic visualization. Using the Bot-IoT and NSL-KDD datasets, they reach 100% and 98.3% ACC, respectively. Hazman et al. [2] designed an IDS using AB for classification and brutal, mutual information, and correlation for the feature selection. They evaluated their model on the IoT-23, BoT-IoT, and Edge-IIoT datasets. Their model scored at its best 99.9% on ACC and 0.021 s (s) on detection time. Mohy-eddine et al. [10] presented a network IDS for IoT Environments with the help of KNN for the classification phase and PCA, univariate statistical test, and genetic algorithm (GA) for the feature selection phase. They evaluated the Bot-IoT dataset, where they got 99.99% ACC and managed to reduce the prediction time from 51,182.22 s to a few seconds. Guezzaz et al. [9] created an IDS for IIoT edge computing, where they exploited the KNN model and PCA along with the Bot-IoT and NSL-KDD datasets to evaluate their proposed model. They obtained 98.2% ACC, 97.6% DR, and 2.9% FAR on the Bot-IoT dataset and 99.1% ACC, 98.4% DR 2.7% False Alarm Rate (FAR) on the NSL-KDD dataset. Lopez-Martin et al. [16] extended the RBFNN model with an offline reinforcement learning algorithm. They

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evaluated their developed model on NSL-KDD, UNSW-NB15, AWID, CICIDS2017 and CICDDOS2019. Respectively, their model scored on ACC 90.7%, 90.8%, 95.5%, 99.7% and 99.7% (Table 1).

3 Our Contribution and Experimental Study Our proposed model is divided into four modules, Fig. 1. The Data source in which we used two datasets, the NF-ToN-IoT1 and the NF-Bot-IoT2 in addition we used a merged version which we will call NF-IoT in rest of this paper for simplification. Feature engineering is choosing, altering, and converting raw data into features that may be utilized in ML and DL. In this phase, we cleaned, eliminated and normalized the data on the standard deviation method. WOA was used to select the most relevant feature without losing important information. WOA is a swarm intelligence metaheuristic algorithm that models humpback whales hunting behaviour [17]. The WOA is unique in its capacity to deploy a random or best agent in the search space to chase its target. It can also use circles to replicate the humpback whale’s bubble-net processes. We applied this algorithm to find the best subset that maximizes the ACC with the minimum set of features. Our wrapped-based approach relies on the KNN model as a WOA guide. The WOA has two phases: Prey surrounding: it initiates the search agent assuming that it is the best solution and is updated during the process based on the better solution. Bubble net attacking (searching for target): randomly picking a new location by the search agent should be between the past location and the best current location simulating the shrinking and spiral movement of the whales. The research stops after iterating for a fixed number and provides a solution (a subset of features) based on the fitness function. In our case, we set the whale’s number to 10 and the iterations to 100. The decision-making phase consists of the RBFNN classifier. It is a feedforward neural network with activation functions based on radial basis functions. It learns the fundamental patterns of a large number of Gaussian curves (Fig. 2).

Fig. 1. Our proposed model scheme.

D is the input data, d is each input, ∂ m are the hidden neurons (Gaussian RBF), and wm is the weights. We calculated the center of each RBF with k-means. 1 https://staff.itee.uq.edu.au/marius/NIDS_datasets/#RA2. 2 https://staff.itee.uq.edu.au/marius/NIDS_datasets/#RA3.

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Fig. 2. The structure of the RBFNN model.

Our experiments were carried out on an Apple M1 PRO Chip with 32 GB of RAM and Ventura 13.4.1 (c). To train and evaluate our model, we relied on the NF-Bot-IoT and NFToN-IoT datasets which are IoT netflow-based datasets created based on the Bot-IoT and ToN-IoT datasets. The NF-Bot-IoT dataset has 600,100 total data, of which 586,241 are attacks, and 13,859 are standard. The NF-ToN-IoT dataset has a total 1,379,274, with 1,108,995 attack instances and 270,279 benign. After applying the feature selection, our algorithm ended up with IPV4_DST_ADDR, PROTOCOL, IPV4_SRC_ADDR for The NF-Bot-IoT dataset, L4_DST_PORT, IPV4_DST_ADDR, L7_PROTO for The NFToN-IoT dataset and IPV4_SRC_ADDR, PROTOCOL, L4_SRC_PORT for the NF-IoT. We evaluated our model using the following: ACC:

TP + TN TP + TN + FP + FN

TP × TN − FP × FN MCC: √ (TP + TN)(TP + FP)(TN + FP)(TN + FN)

4 Results and Discussion Table 2 and Fig. 3 compare the MCC and ACC of the NF-Bot-IoT, NF-ToN-IoT, NFIoT and their selected features. They showed that our model on the NF-Bot-IoT scored 98.08% ACC and 40.44% MCC which shows its poor distinguishability between normal and attacks. However, on the selected features, the ACC remains the same at 98.43%, but the MCC showed some progress with 57.71%. Contrary to the NF-ToN-IoT showed a more significant performance with 95.67% ACC and 85.96% MCC. However, our three selected features enhanced the model performance with 96.83% ACC and 89.74% MCC which shows the improvement of the model classification capabilities. The merged dataset, NF-IoT, helped the NF-Bot-IoT overcome its destructive impact on the model by balancing its classes with 95.16% ACC and 79.14% MCC. Furthermore, the selected feature scored even higher, with 95.93% ACC and 82.68% MCC. The scored results lead us to conclude that the RBFNN model and the WOA could make an excellent combination to build an effective IDS, as shown in Table 2 and Fig. 3,

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Table 1. A summary of the related works References

Year

Model

Dataset

ACC (%)

Attou et al. [11]

2023

RF

Bot-IoT

100

NSL-KDD

98.3 99.9

Hazman et al. [2]

2023

AB

Bot-IoT

Mohy-eddine et al. [10]

2023

KNN

Bot-IoT

99.99

Guezzaz et al. [9]

2022

KNN

NSL-KDD

98.4

Bot-IoT

98.2

NSL-KDD

90.7

UNSW-NB15

90.8

AWID

95.5

Lopez-Martin et al. [16]

2021

RBFNN

CICIDS2017

99.7

CICDDOS2019

99.7

Table 2. The dataset metrics results. Dataset NF-Bot-IoT NF-ToN-IoT NF-ToN

ACC (%)

MCC (%)

Full

98.08

40.44

Selected

98.43

57.71

Full

95.67

85.96

Selected

96.83

89.74

Full

95.16

79.14

Selected

95.93

82.68

taking into consideration that we did not apply any outliers detection method that we believe it would help more.

5 Conclusion The emergence of IoT in different fields of daily life raised security concerns. Contributions to IoT security are thus desperately needed to mitigate security concerns. As a result, this study provides an intrusion detection system (IDS) for IoT contexts. To improve the performance of the IDS, we used the RBFNN model. To increase data quality and get a subset of features, we used the WOA feature selection approach. This feature engineering strategy enables our model to overcome the dataset imbalance and perform admirably. We used the NF-Bot-IoT and NF-ToN-IoT datasets to test our model. We compared the results of our model to other research based on the same datasets, and it performed better.

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Fig. 3. ACC and MCC of the different dataset graph.

References 1. Hazman, C., Benkirane, S., Guezzaz, A., Azrour, M., Abdedaime, M.: DEIGASe: deep extraction and information gain for an optimal anomaly detection in IoT-based smart cities. In: Artificial Intelligence and Smart Environment (2022) 2. Hazman, C., Benkirane, S., Guezzaz, A., Azrour, M., Abdedaime, M.: Building an intelligent anomaly detection model with ensemble learning for IoT-based smart cities. In: Advanced Technology for Smart Environment and Energy, pp. 287–299. Springer (2023) 3. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: IoT-enabled smart agriculture: security issues and applications. In: The International Conference on Artificial Intelligence and Smart Environment (2022) 4. Mohy-eddine, M., Azrour, M., Mabrouki, J., Amounas, F.M., Guezzaz, A., Benkirane, S.: Embedded Web Server Implementation for Real-Time Water Monitoring Advanced Technology for Smart Environment and Energy, pp. 301–311. Springer (2023) 5. Kirmani, S., Mazid, A., Khan, I.A., Abid, M.: A survey on IoT-enabled smart grids: technologies, architectures, applications, and challenges. Sustainability 15(1), 717 (2022) 6. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: An effective intrusion detection approach based on ensemble learning for IIoT edge computing. J. Comput. Virol. Hack. Tech., 1–13 (2022) 7. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M., Farhaoui, Y.: An ensemble learning based intrusion detection model for industrial IoT security. Big Data Min. Anal. 6(3), 273–287 (2023) 8. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: Random forest-based IDS for IIoT edge computing security using ensemble learning for dimensionality reduction. Int. J. Embedded Syst. 15(6), 467–474 (2022) 9. Guezzaz, A., Azrour, M., Benkirane, S., Mohy-eddine, M., Attou, H., Douiba, M.: A lightweight hybrid intrusion detection framework using machine learning for edge-based IIoT security. Int. Arab J. Inf. Technol. 19(5), 822–830 (2022) 10. Mohy-eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection. Multimed. Tools Appl., 1–19 (2023)

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11. Attou, H., Guezzaz, A., Benkirane, S., Azrour, M., Farhaoui, Y.: Cloud-based intrusion detection approach using machine learning techniques. Big Data Min. Anal. 6(3), 311–320 (2023) 12. Amaouche, S., Benkirane, S., Guezzaz, A., Azrour, M.: A proposed machine learning model for intrusion detection in VANET. In: The International Conference on Artificial Intelligence and Smart Environment (2022) 13. Hazman, C., Guezzaz, A., Benkirane, S., Azrour, M.: Toward an intrusion detection model for IoT-based smart environments. Multimed. Tools Appl., (2023) 14. Hazman, C., Guezzaz, A., Benkirane, S., Azrour, M.: lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning. Cluster Comput., 1–15 (2022) 15. Hissou, H., Benkirane, S., Guezzaz, A., Azrour, M., Beni-Hssane, A.: A novel machine learning approach for solar radiation estimation. Sustainability 15(13), 10609 (2023) 16. Lopez-Martin, M., Sanchez-Esguevillas, A., Arribas, J.I., Carro, B.: Network intrusion detection based on extended RBF neural network with offline reinforcement learning. IEEE Access 9, 153153–153170 (2021) 17. Nadimi-Shahraki, M.H., Zamani, H., Asghari Varzaneh, Z., Mirjalili, S.: A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations. Arch. Comput. Methods Eng., 1–47 (2023)

Dynamical Modeling of Climatic Parameters Under Greenhouse Abderrazak Kaida1,2(B) , Youssef El Afou2 , Abderrahman Aitdada1,2 , Said Hamdaoui1 , and Abdelouahad Ait Msaad1 1 Ecole Supérieure de Technologie de Fès, Université Sidi Mohamed Ben Abdellah, Route

Imouzer, BP 2427, Fès, Morocco [email protected] 2 Ecole Nationale des Sciences Appliquées de Fès, Université Sidi Mohamed Ben Abdellah, Avenue My Abdallah Km 5, BP 72, Fès, Morocco

Abstract. This paper presents a dynamic model of an agricultural greenhouse to predict internal air temperature (Tint ) and internal relative humidity (Hint ) using the Matlab/Simulink environment. This model details the transfer of heat and water vapor inside the greenhouse over five days in spring. Simulation results showed that internal air temperature and humidity vary according to the weather conditions (solar radiation, outside relative humidity, and outside temperature), location, type of cover, season, and the specific structure of the greenhouse. Keywords: Greenhouse · Climatic parameters · Dynamic model · Simulation

1 Introduction Greenhouse cultivation is a mode of production that allows us to increase yield and reduce energy consumption, this mode of intensive production requires that production factors will be maximized in order to ensure profitability [1], among these climatic factors which influence the climate inside the greenhouse, such us: air temperature and humidity, solar radiation, CO2 , outside wind… etc. In all greenhouses there are always periods when these factors become extremely dangerous for the plant. The challenge of greenhouse environmental control is to create an ideal environment for crops, ensuring high yield and high profitability. However, implementing control measures in practice is exceptionally demanding due to the complex nature of greenhouse processes, including dynamic greenhouse climate behavior, complex control requirements and interactions, nonlinearity and the non-stationary nature of the variables [2]. The objective of this work is to develop a model to describe the microclimate of greenhouses and to predict the evolution of these climatic parameters. Indeed, modeling makes it possible to study the phenomena observed in an agricultural greenhouse under various conditions, often using physical models based on theoretical equations integrating all the heat exchange processes between the covering, the air interior and soil [3]. There are several modeling methods such as: experimental identification, state © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 420–427, 2024. https://doi.org/10.1007/978-3-031-48573-2_60

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space [4], and the physical model [5] the advantage of our model compared to the other physical models, is simulated with real measurements of climatic parameters conditions under greenhouse from region of Fes-Meknes in Morrocco. This model makes it possible to characterize the indoor climate with a detailed understanding of the fields of these parameters, such as temperature, relative humidity [5]. In this study, we are used temperature and humidity because these last two parameters play a crucial role in photosynthesis and the development of plants, and they are closely interconnected within the greenhouse, influenced by external weather conditions. Nevertheless, tuning multiple controllers in this complex environment remains a challenge for process engineers and operators [6]. An essential aspect is to maintain appropriate relative humidity to promote crop growth, for example too high humidity can lead to the development of diseases. This paper, is structured as follows: Sect. 2 is devoted to the presentation of the greenhouse system: we described the physical model of the greenhouse and the technical theory. A discussion on the measurement and the result of the simulation is presented in Sect. 3. The conclusion and perspectives of this work is discussed in the last part.

2 System Greenhouse 2.1 Description of the Model Greenhouses constitute a complex biological and energy system in which most modes of heat transfer are involved. The climatic conditions in and around the greenhouse are the result of heat and mass exchanges between the soil, vegetation, and the atmosphere. Therefore, the agrosystem within the greenhouse can be described based on energy and mass transfers, which can occur in three different forms: radiation, convection, and conduction. Radiative exchanges: Solar radiation serves as a natural energy source, heating the system. Convective exchanges: Convective transfers in this system occur between its solid components (soil, walls) and the air. Conductive exchanges: Thermal transfers take place between the surface of the soil and the heat losses through the walls or windows. Modeling and establishing the mathematical model, representing the considered phenomena, then lead to solving a system of equations, here using the considered numerical methods [7]. 2.2 Theory of Technical Greenhouse Systems The model is based on theoretical equations that incorporate all the processes of heat exchange between the covering, interior air, and soil (Fig. 1). The greenhouse is subject to three essential disturbances, which are solar radiation, outside temperature, and outside humidity. The model consists of the following six elements: The cover (Cover), the crop (Crop), the internal air (IA), the fog system

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Fig. 1. The Heat transfer interaction between greenhouse components

humidifying (Hum), the heating system (Heater), and the ventilation system (Venti) and the soil The latter is coupled and strongly nonlinear with respect to temperature (Heating/Cooling) and humidity [7]. The internal heat balance equation and internal water balance are expressed by (1), (2) ρair . Cair . Vgr .

dTint = QCrop + QHeater + QSolar + QVenti + QCond ,Conv + QHum dt (1)

ρair . Vgr .

dHint = H Crop + H Hum + H air,ext + H air,int dt

(2)

where, Tint is the inside temperature, ρair is the internal air density, Cair is the specific heat of air and Vgr is the Greenhouse volume. QCrop : The heat produced by the evapotranspiration of plants (W). QHeater : The thermal energy provided by the heating system (W). QSolar : is the solar radiation heat transfer rate (W). QVenti : The thermal energy loss from the cooling system (W). QCond,Conv : Convection and conduction heat transfer rate (W). QHum : presents the rate of humidity provided by the humidifying system (W). Hint: the inside relative humidity (%). HCrop : The rate produced by plant evapotranspiration (kg h−1 ). HHum : The rate of water evaporation for the fog system humidifying (kg h−1 ). Hair,int and Hair,ext : are respectively the Rate of humidity transfer from outside air entering the greenhouse, Rate of humidity transfer from inside air leaving the greenhouse (kg h−1 ). The heat produced by plants: Like animals, some plants produce heat. Their temperature rises to sometimes 40 °C above that of the surrounding air. Noticed: At thermal equilibrium QCrop negligible in front of the heating system generator and in a large greenhouse.

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The heat lost by the humidifier: It is defined as follows [8]: QHum = λ . H Hum

(3)

With λ: The latent heat of vaporization of water (J kg−1 ). The heat lost by the ventilation system: The heat loss of the QVenti ventilation system can be calculated according to a linear function which is the difference between internal and external heat according to the following equation: Qventi = f venti (Tint , Text , Vv,m ) ≈ ρair . Cair . Vv,m (Tint − Text )

(4)

With V v,m : ventilation rate (m3 /s). The heat transferred by the Cover: The convection and conduction heat transfer rate was estimated with the following equation [8]: QCond ,Conv = f Cond ,Conv (Tint , Text ) ≈ UA(Tint − Text )

(5)

UA: This is the heat transfer coefficient of the greenhouse cover (W K−1 ). The heat produced by the sun: The heat produced by the sun inside the greenhouse is expressed as follows: QSolar = α . Af . Si = R

(6)

With: Af : the floor area (m2 ). a: efficiency of condensing solar heat = 0.28. Si : is the solar radiation (W/m2 ). R: the heat produced by the sun (W). Inside and outside humidity of the greenhouse: The humidity of the air is one of the important factors that is very difficult to control. Humidity levels fluctuate with greenhouse temperature [8]. In addition, the crop transpires which adds steam to the greenhouse. As plants grow, they absorb water through their roots and release it in the form of water vapor through their pores. It can be translated according to the equation: H air,ext = f ext (Vvt , Hext ) ≈ Vvt .Hext

(7)

H air,int = f int (Vvt , Hint ) ≈ Vvt .Hint

(8)

Hext , Hint : are respectively the outside and inside relative humidity (%). From these equations, we can write our mathematical model in the following form: ρair . Cair . Vgr .

dTint = QHeater + R + ρair . Cair .Vv,m (Tint − Text ) dt 10 − UA(Tint − Text ) − λH Hum 36

(9)

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ρair . Vgr .

dHint = H Hum + Vvt . Hext − Vvt . Hint dt

(10)

Our model can finally be described as follows:   dTint Vv,m 1 10 Heater Hum Q + = + R − λ.H . (Tint − Text ) dt ρair . Cair . Vgr 36 Vgr UA (Tint − Text ) (11) − ρair . Cair . Vgr dHint 1 Vv,m = H Hum + (Hext − Hint ) dt ρair . Vgr ρair . Vgr

(12)

With: ρair : The air density (kg m−3 ). Cair: The specific heat of air (J kg−1 K−1 ). Vgr : volume of the greenhouse in (m3 ). Vv,m = Vgr × N, with N: number of air changes per hour. Vv,m : The ventilation rate (m3 /s). The variables Text , Hext and R represent disturbances outside the greenhouse which. determine the atmospheric influence on Tint and Hint . QHeater , Vv, m and Hum : are commands for the heater, cooler and mist system respectively. Tint , Hint : are outputs inside temperature and inside relative humidity respectively (Fig. 2).

Fig. 2. Synoptic diagram of the agricultural greenhouse

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3 Results and Discussion 3.1 Presentation of Measures Parameters The simulation was performed using a weather database, during five days in April 2023 with a sampling time equal to 5 s. This database includes the measurements of outside and inside temperature, inside and outside relative humidity are shown in Figs. 3 and 4 respectively.

Fig. 3. The measurements of outside and inside temperature

Fig. 4. The measurements of outside and inside relative humidity

Figures 3 and 4 show that there is an influence of the outside temperature Text on Tint the temperature under greenhouse and also the outside relative humidity Hext influences on the inside humidity Hint . The error between the value of Tint and Text , also between the value of Hext and Hint due to the type of greenhouse cover, we have here the cover in plastique. The variables Text and Hext represent disturbances outside the greenhouse. 3.2 Simulation Results The dynamic model of the greenhouse was developed using Matlab-Simulink environment. The input parameters used in the Simulink model are given in Table 1. The farmer Work in the greenhouse two times a day (morning and evening), which explains the number of air changes per hour equal to 0.16 h−1 . The greenhouse studied in this paper; has a volume 4000 m3 (length = 100 m; width = 10 m; height = 4 m). The simulation results of the inside and outside air temperature, inside and outside relative humidity are shown in Fig. 5 and 6 respectively.

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Symbol

Numerical value

Units

Description

ρ

1.2

kg m−3

Density of internal air

λ

2300

kJ kg−1

The latent heat of vaporization of water

Vgr

4000

m−3

Greenhouse volume

R

700

W

The heat produced by the solar radiation

Cair

1006

J kg−1 K−1

Specific heat of air

UA

25,000

W K−1

Heat transfer coefficient of the greenhouse cover

a

0.28

–

Solar heat condensation efficiency

N

0.16

h−1

Number of air changes per hour

–

m3 s−1

The ventilation rate

Vv,m

Fig. 5. The evolution of the inside and outside air temperature

Fig. 6. The evolution of the inside and outside relative humidity

The results of the simulation indicate that the values of the simulated parameters can follow the values of the measured parameters, the calculated result of the temperature

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error, and the relative humidity error are given respectively: T = T



mesur e

−T



simul e

= 0.016 ◦ C, H = H



mesur e

−H



simul e

= 0.003%

these values T = 0.016 and H = 0.003 with incertitude of ± 5% showed that our model that we have developed well represents the real model of the agricultural greenhouse.

4 Conclusion and Perspectives A simplified dynamic model of an agricultural greenhouse has been developed to predict the evolution of the micro-climate under the greenhouse, which will allow us to develop a controller to control the climatic parameters that influence the inside climate of the greenhouse. The automation of the agricultural greenhouse will improve productivity, save energy and simplify the life of farmers. In future work, we will try to control and command these climatic parameters by the laws of advanced commands such as control by sliding mode, FLC controller ….

References 1. Guo, Y., Zhao, H., Zhang, S., Wang, Y., Chow, D.: Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production. J. Clean. Prod. 285, 124843 (2021). https://doi.org/10.1016/J.JCLEPRO.2020.124843 2. Siskandar, R., Santosa, S.H., Wiyoto, W., Kusumah, B.R., Hidayat, A.P.: Control and automation: insmoaf (integrated smart modern agriculture and fisheries) on the greenhouse model. J. Ilmu. Pertan. Indones. 27 (2022). https://doi.org/10.18343/jipi.27.1.141 3. Fan, Y., Zhang, Y., Chen, Z., Wang, X., Huang, B.: Comprehensive assessments of soil fertility and environmental quality in plastic greenhouse production systems. Geoderma 385 (2021). https://doi.org/10.1016/j.geoderma.2020.114899 4. Hamidane, H., El Faiz, S., Rkik, I., El Khayat, M., Guerbaoui, M., Ed-Dahhak, A., Lachhab, A.: Constrained temperature and relative humidity predictive control: agricultural greenhouse case of study. Inf. Process. Agric. (2023). https://doi.org/10.1016/j.inpa.2023.04.003 5. Ben Ali, R., Aridhi, E., Mami, A.: Dynamic model of an agricultural greenhouse using MatlabSimulink environment 6. Ben Ali, R., Bouadila, S., Mami, A.: Development of a fuzzy logic controller applied to an agricultural greenhouse experimentally validated. Appl. Therm. Eng. 141, 798–810 (2018). https://doi.org/10.1016/j.applthermaleng.2018.06.014 7. Pasgianos, G.D., Arvanitis, K.G., Polycarpou, P., Sigrimis, N.: A nonlinear feedback technique for greenhouse environmental control. In: Computers and Electronics in Agriculture, pp. 153– 177. Elsevier (2003) 8. Daskalov, P.I., Arvanitis, K.G., Pasgianos, G.D., Sigrimis, N.A.: Non-linear adaptive temperature and humidity control in animal buildings. Biosyst. Eng. 93, 1–24 (2006). https://doi.org/ 10.1016/j.biosystemseng.2005.09.006

Prompt Engineering: User Prompt Meta Model for GPT Based Models Hamza Tamenaoul(B) , Mahmoud El Hamlaoui, and Mahmoud Nassar ENSIAS, Mohammed V University in Rabat, Rabat, Morocco {hamza tamenaoul,mahmoud.elhamlaoui,mahmoud.nassar}@um5.ac.ma

Abstract. The avenant of LLMs triggered a lot of interest from the scientific community and the general public alike. Tools such as ChatGPT offered a new way of interacting with machines through a more natural chat interface. However, experience have shown that although any task description could be an input to the model, some inputs formatted and organized in specific formats have demonstrated a higher success rate, in terms of performing the task in a more precise manner, with a higher rate of success. While such patterns are yet to be formalized and many still to be discovered, a first Meta Model could be formalized to pave the way for an automation of prompts generation. Keywords: LLM · ChatGPT · Meta model · Model driven engineering · OpenAI · Prompt engineering · Prompt

1

Introduction

Large Language Models (LLMs) [2], in the likes of ChatGPT, have taken by storm the interest of the general public and the scientific community. These tools offer a new way to help solving a wide range of problems, such as bug finding in programs [4,8], and give a new way to interact and use software, by asking and describing tasks through natural language. In addition to the performances shown by ChatGPT in performing a large panel of tasks [5], it offers as mentioned before a new way of interaction, based on writing prompts. These makes the collaboration between the human and machine more intuitive to the human counterpart, since it is the closest method to how humans interact. Given that machines are still machines, many patterns [3] can be identified, and when used efficiently can make the tasks description much more easier to “understand” for the machine side. A new field have spawned to study these patterns and how to write good prompts named Prompt Engineering. The patterns have shown to improve greatly the performance of ChatGPT, as demonstrated by OpenAI, in their course about Prompt Engineering [6]. A user interested in improving his experience with the tool would find very useful learning and understanding them [1]. However a casual user, who is the main target of such tools, will unlikely be open to spend time studying them, since it goes against the whole pitch behind these tools. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 428–433, 2024. https://doi.org/10.1007/978-3-031-48573-2_61

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The fact that prompt engineering relies on observed patterns when experimenting with ChatGPT, opens the field to build models describing and emulating them. A Meta Model could therefore be extracted to encapsulate them, and pave the way for Prompt Generation Automation. The goal of this paper is to define a Meta Model for prompt definition and description. To express that, the paper defines in Sect. 2 how first implementations of Prompt Automation already exist, and which Meta Model these implementations implies. And then in Sect. 3 the paper proposes a more Elaborate Meta Model for prompt generation.

2 2.1

GPT Implementations Meta Model Structure of Prompts

In current GPT implementations, the prompt is made up of two distinct parts, the System Prompt and the User Prompt. The System prompt aims to give the model a persona description. In theory the goal of such prompt, is to give the model some basic information about the context in which it operates (provide the model with the current date as an example), a set of rules on how to behave and respond to user queries [9] among others. But importantly it aims at giving the model some clear ideas about the persona it should play. This is usually done by defining the model a description of itself as the said persona and an example of a discussion that persona might hold. The study of System prompt have been quite extensive, and many tools have taken advantage of that, by either providing the user with multiple already defined system prompts, or even letting the user define its own [7]. The User prompt has not gotten the same standardization efforts. While some experiments have been made on the performance of the tool for a variation of patterns. No efforts have been done to use those results to make a modeling effort for a potential automation. 2.2

Induced Current Prompts’ Meta Model

From the current GPT implementations’ configuration we can infer the Meta Model described in Fig. 1. The prompts is made up of two main parts: System Prompt and User Prompt. The System Prompt is defined the same as above. The User Prompt part of the Prompt however is much more complex. It is made up of a collection of all the previous prompts in addition to the current prompt. The System prompt is designed to be hidden from the end user of the Chat interface of any GPT implementation, therefore the need to push an automation of System Prompts generation is not as strong as the one for User Prompts. Therefore any modeling efforts should be focused on User Prompts, as is the case for this paper.

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Meta Model Definition for Prompt Engineering User Prompt’s Components

For a correct modeling of User Prompts, several components should be identified and extracted from Prompts patterns. The first main component is the Context. It explains to the GPT model the context of the task. It lists the goal of the task, the tone or any other information that would give a broader vision of the goal of the task. In addition to these information, the Context contains the data on which the operation asked from the model would be performed. The second main component is the Task. This component details the expected results of the action. This component on itself is still generic, and therefore contains others that shed more light on the action to be performed. The Action is the type of operation the model would perform, and is of different type, while at the same might require some input parameter from the user. The second component is the Output Description. It holds the information about the output awaited from the model, be it data in a specific format or an output description. The final one is the Output Recommendation, this term contains any additional cues to the model to lead him to the desired output. 3.2

User Prompt’s Meta Model

Figure 2 illustrated the Meta Model developed to model the User Prompt. This new User Prompts model would replace the former one in Fig. 1. The Meta Model provides more information about the prompt, describing its content and different parts in a more detailed fashion compared to the previous model. While the previous model has been enough to start building many applications based on the GPT model, such us personal assistants and advisors, it does not leave room to more advanced applications. With this newer Meta Model, more components are formalized, and could be used to enhance prompt reusability even more.

Fig. 1. Current implementations meta model.

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Fig. 2. User prompt’s meta model diagram.

3.3

Meta Model Instance

With this Meta Model defined, it can be used to model prompts as its instances. This approach enable to us to have a more formal definition of a prompt and understand its different components. The following prompt can be used as an example of such exercise. This prompt described in Open AI’s sponsored course [6] as a good and elaborate prompt, asks ChatGPT to generate a product description. We can therefore use the Meta Model to generate an object diagram that would represent its instance. The prompt write as follows: Your task is to help a marketing team create a description for a retail website of a product based on a technical fact sheet. Write a product description based on the information provided in the technical specifications delimited by triple backticks. The description is intended for furniture retailers, so should be technical in nature and focus on the materials the product is constructed from. At the end of the description, include every 7-character Product ID in the technical specification. After the description, include a table that gives the product’s dimensions. The table should have two columns. In the first column include the name of the dimension. In the second column include the measurements in inches only. Give the table the title ’Product Dimensions’. Format everything as HTML that can be used in a website.

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Place the description in a element. Technical specifications: ‘‘‘{fact_sheet_chair}‘‘‘

Fig. 3. Object diagram of the meta model instance for the example prompt.

Figure 3 shows the object diagram of the meta model. The example forces instancing most classes defined, such as different Actions. Two action types are combined in the prompt, the StaticAction and the SteppedAction, while using all the other prompt parameters, attributes and tools provided by the model. The example shows then how relevant the model is in modeling prompts.

4

Conclusion

The natural Chat interface of GPT model implementation have been one of the main selling points of these tools. However, how intuitive this Chat interface might be, the need to automate Prompt generation is still relevant to develop more sophisticated tools based on LLMs. This paper have proposed a model for User Prompts, being the first step towards building any automation building tool or any other advanced prompting features. This model could certainly be more refined. As more patterns emerge and are studied, more granularity could be added this model. Specially in components where patterns are still emerging such as the Context or Action Types.

References 1. Kemper, J.: ChatGPT Guide: use these prompt strategies to maximize your results. https://the-decoder.com. Last Accessed 14 Aug 2023 2. Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers (2022). https://doi.org/10.48550/ ARXIV.2211.01910 3. White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with ChatGPT (2023). https://doi.org/10.48550/ARXIV.2302.11382

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4. Tian, H., Lu, W., Li, T.O., Tang, X., Cheung, S.-C., Klein, J., Bissyand´e, T.F.: Is ChatGPT the ultimate programming assistant—how far is it? (2023). https://doi. org/10.48550/ARXIV.2304.11938 5. Biswas, S.S.: Role of chat GPT in public health. Ann. Biomed. Eng. 51, 868–869 (2023). https://doi.org/10.1007/s10439-023-03172-7 6. Fulford, I., Ng, A.: ChatGPT prompt engineering for developers (2022). https:// www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ 7. ForeFront Technologies: Your AI assistant for work (2022). https://forefront.ai/ 8. Github: Your AI pair programmer (2022). https://github.com/features/copilot 9. Sydney System Prompt. https://gist.github.com/martinbowling/b8f5d7b1fa0705d e66e932230e783d24

Enhancing Conducted EMI Mitigation in Boost Converters: A Comparative Study of ZVS and ZCS Techniques Zakaria M’barki(B) , Ali Ait Salih, Youssef Mejdoub, and Kaoutar Senhaji Rhazi Laboratory of Networks, Computer Science, Telecommunication, Multimedia (RITM), Higher School of Technology ESTC, Hassan II University, Casablanca, Morocco [email protected]

Abstract. This article highlights the growing demand for efficient power conversion technologies in the context of increasing electric vehicle (EV) adoption. Boost converters are crucial in elevating battery pack voltage to propel EV motors but often generate electromagnetic interference (EMI) due to high-frequency power device switching, potentially causing malfunctions in other electronic systems. To mitigate EMI, zero-voltage-switching (ZVS) and zero-current-switching (ZCS) methods are compared in this study. The findings reveal that both ZVS and ZCS effectively reduce EMI emissions compared to conventional hard-switching techniques. Nonetheless, ZVS soft switching excels in efficiency and EMI reduction at higher loads, making it a more promising solution for EV boost converters, though the choice should consider specific operating conditions. Keywords: Boost converter · Zero voltage switching · Zero current switching · Conducted EMI

1 Introduction The rising demand for electric vehicles (EVs) has spurred significant research and development efforts in power electronics converters to meet their high-power requirements. Among these converters, the boost converter plays a pivotal role by elevating battery voltage for motor operation [1]. However, a drawback of boost converters is the electromagnetic interference (EMI) generated during their operation [2]. To tackle this issue, soft-switching techniques like zero-current switching (ZCS) and zero-voltage switching (ZVS) have emerged as promising solutions for EMI reduction [3, 4]. ZCS ensures the main switch current turns off before voltage reaches zero, while ZVS turns off the voltage across the switch. This study aims to compare the effectiveness of ZCS and ZVS in mitigating conducted EMI in boost converters for EVs. It evaluates EMI levels, efficiency, power loss, and switching frequency, considering varying conditions like load current and switching frequency. While previous research explored soft-switching techniques in power electronics converters [5, 6], this study addresses the gap by providing a comparative analysis of ZCS and ZVS in the context of boost converters for EVs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 434–441, 2024. https://doi.org/10.1007/978-3-031-48573-2_62

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In summary, this document provides an overview of the choice between the soft switching techniques ZCS and ZVS, which will be beneficial for researchers and engineers working on improving power electronics for EV applications. It begins by explaining the characteristics of soft switching and then delves into the spectral analysis of conducted electromagnetic disturbances generated by a Boost converter, with the aim of assessing the utility of the proposed techniques in terms of electromagnetic compatibility (EMC).

2 The Boost Converter: Modeling and Practical Implementation Boost converters are essential in electric vehicles (EVs) for elevating battery pack voltage to meet motor controller requirements. In our study, we investigate boosting the voltage from 24 to 48 V in a standard 48 V EV using a rapid input voltage switch, inductor, and capacitor. A Matlab-based schematic is shown in Fig. 1. The boost converter maintains stable output voltage, even with input and load fluctuations, contributing to improved power electronics efficiency in EVs.

Fig. 1. Fundamental structure of the boost converter.

The construction of the assessed Boost converter is aimed at aligning with the specifications detailed in the Table 1. Table 1. Simulation parameters Parameter

Value

Input DC voltage Vin

24 V

Output voltage Vo

48 V

Inductance L

150 uH

Capacitance C

10 uF

Load resistance Rload

12 

Duty cycle D

0.5

Switching frequency F s

100 kHz

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3 Soft Switching Approach for the Boost Converter Soft switching is a crucial technique in power electronics to mitigate rapid changes in voltage or current within electronic circuits, and to curtail the spread of conducted and radiated disturbances [7]. It entails the incorporation of a damping circuit, such as a capacitor in parallel with the turn-off switch (ZVS) to slow down voltage rise, or an inductance in series with the turn-on switch (ZCS) to moderate current rise. In fact, there are several soft switching topologies, such as: Zero Voltage Switching (ZVS): This technique ensures that the voltage across the power switch (such as a MOSFET transistor) is zero during the switching process. Zero Current Switching (ZCS): Another technique, ZCS ensures that the current through the power switch is zero at the moment of switching. Both techniques effectively reduce energy losses and disturbances associated with switching. In general, the design of quasi-resonant converters operating at zero voltage or current is characterized by the following properties: 1. Normalized resonance frequency fn =  2. Characteristic impedance Zn = CLRR . 3. Normalized load resistance Q = 4. Conversion ratio M = VVout . in

fs fr withfr

=

√1 . 2π LR CR

RL Zn .

3.1 Soft Switching: ZVS In this scenario, we assume that the filter’s inductance (L) and capacitance (C) dominate over resonance components. Consequently, we replace the series-connected voltage source (Vin )) with a direct current source (Iin ) and simulate the output with a direct voltage source (Vo ), as depicted in Fig. 2.a. The schematic of the previously modeled Boost converter is divided into four operational modes (Fig. 2.b), determined by the state of the main switch and the freewheeling diode.

Fig. 2. ZVS quasi-resonant boost converter with (a) internal schematic and (b) equivalent circuits for operating modes.

The theoretical waveforms of the resonant tank are shown in Fig. 3.

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Fig. 3. Voltage and Current for the ZVS method.

We can express the voltage conversion ratio M of the quasi-resonant ZVS converter using the concept of energy conservation as follows:   −1      M Q 2 fn Q −1 Q + π + sin + + 1+ 1−( ) (1) M = 2π 2M M Q M Subsequently, by examining the graph depicting the conversion M as a function of normalized frequency for various values of charge Q, we obtained the following results, which are summarized in the Table 2. Table 2. The Boost ZVS-QRC converter parameters. Component

Normalized value

Inductance Lr

4.7 uH

Capacitance Cr

150 nF

Resonant frequency Fr

197.75 kHz

Duty cycle D

~ 0.54

3.2 Soft Switching: ZCS The previous section’s assumptions persist, with the filter’s inductance (L) and capacitance (C) significantly exceeding those of the resonant elements, as shown in Fig. 4.a.

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The schematic of the previously analyzed Boost converter is segmented into four operational modes (Fig. 4.b), contingent on the status of the main switch and the freewheeling diode.

Fig. 4. ZCS quasi-resonant boost converter with (a) internal schematic and (b) equivalent circuits for operating modes.

The theoretical waveforms of the resonant tank are shown in Fig. 5.

Fig. 5. Voltage and Current for the ZCS method.

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We can express the voltage conversion ratio M of the quasi-resonant ZCS converter using the concept of energy conservation as follows:

      Q M 2 fn M −1 M + 2π − sin + M = 1− −1 (2) 1− 1−( ) 2π 2Q Q M Q Subsequently, by examining the graph depicting the conversion M as a function of normalized frequency for various values of charge Q, we obtained the following results, which are summarized in the Table 3. Table 3. The Boost ZCS-QRC converter parameters. Component

Normalized value

Inductance Lr

4.7 uH

Capacitance Cr

150 nF

Resonant frequency Fr

197.75 kHz

Duty Cycle D

~ 0.46

4 Simulation Results and Discussion 4.1 Conducted Electromagnetic Interference Measurement in a Boost Converter The boost converter’s switching cell generates significant conducted-mode electromagnetic disturbances within the 150 kHz to 30 MHz frequency range [8]. To assess these interferences, the Line Impedance Stabilization Network (LISN) is commonly used. The LISN acts as a filter, isolating the power supply from the device under test and preventing disturbances in common-mode and differential-mode. This section focuses on quantifying conducted-mode EMI [9] in the conventional Boost converter. It involves a setup with a DC power source, a conducted emissions measurement device (LISN), a control structure, and a switching cell equipped with a filter and a resistive output load, as shown in Fig. 6. The simulation design of the model illustrated in Fig. 1 is conducted in the Matlab environment using the parameters listed in Table 1. We examined the spectral composition of the voltage Vlisn , which represents disturbances in differential and common modes [10, 11]. The main objective is to keep the frequency content of this voltage (Power Spectral Density) as low as possible within the range of [150 kHz, 30 MHz]. The interest in these projected techniques (ZVS and ZCS) and their performances in terms of reducing conducted electromagnetic disturbances created by the conventional Boost converter (Hard switching) will now be discussed. The simulation results are as follows (Fig. 7): The simulation results highlight the distinct performances of the soft switching methods ZVS (Zero Voltage Switching) and ZCS (Zero Current Switching) in reducing conducted electromagnetic disturbances. ZVS stands out for its pronounced reduction of

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Fig. 6. The conducted emissions testing framework for Boost converter.

Fig. 7. Spectral content of the voltage Vlisn for (a) ZVS method and (b) ZCS method.

the high-frequency spectrum through its smooth transitions that dampen rapid voltage variations. In practical terms, during switching, the voltage across the switches remains close to zero, curbing rapid voltage transitions and constraining the generation of highfrequency harmonics. Conversely, ZCS proves particularly effective at low frequencies by minimizing current spikes during switching transitions. This approach aims to reduce disturbances stemming from abrupt current changes, crucial for mitigating emissions at lower frequencies. In comparison to the conventional hard switching, where transitions are abrupt and lead to undesirable harmonics, both ZVS and ZCS methods offer substantial advantages in terms of electromagnetic compatibility and interference reduction. These characteristics position them as promising choices for effectively managing conducted electromagnetic disturbances, thereby enhancing the quality and integrity of the electrical signal in high-performance demanding applications.

5 Conclusion In conclusion, the simulations conducted in this study demonstrate the effective mitigation of EMI in EV boost converters using ZVS and ZCS techniques. These techniques contribute significantly to reducing the power spectral density of disturbances across

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the frequency range of [10 kHz, 30 MHz], leading to substantial gains in EMC. ZVS focuses on high-frequency EMI reduction with smooth voltage transitions, while ZCS targets lower frequencies by minimizing current spikes during transitions. Both outperform traditional hard-switching methods, indicating potential for improved efficiency and broader applications in EV power electronics. This study can also encompass the investigation of radiated emissions, which are a significant concern in the automotive and aerospace sectors. This contributes to enhancing the reliability and efficiency of power electronics tools.

References 1. Al Sakka, M., Van Mierlo, J., Gualous, H.: DC/DC Converters for Electric Vehicles (2011) 2. Muttaqi, K.M., Haque, M.E.: Electromagnetic interference generated from fast switching power electronic devices. Int. J. Innov. Energy Syst. Power 3(1), (2008) 3. M’barki, Z., Rhazi, K., Mejdoub, Y.: A proposal of structure and control overcoming conducted electromagnetic interference in a buck converter. Int. J. Power Electron. Drive Syst. 13, 380–389 (2022). https://doi.org/10.11591/ijpeds.v13.i1.pp380-389 4. M’barki, Z., Rhazi, K., Mejdoub, Y.: A novel fuzzy logic control for a zero current switchingbased buck converter to mitigate conducted electromagnetic interference. Int. J. Electr. Comput. Eng. (IJECE) 13(2), 1423–1436 (2023) 5. M’barki, Z., Rhazi, K.: Optimization of electromagnetic interference conducted in a devolver chopper. In: Hajji, B., Mellit, A., Marco Tina, G., Rabhi, A., Launay, J., Naimi, S. (eds.) Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2020. LNEE, vol. 681, pp. 523–529. Springer, Singapore (2021) 6. Chen, P.C., Lin, W.C., Tsai, M.C., Hsieh, G.C., Hsieh, H.I.: Analysis, simulation and design of soft-switching mechanisms in DC-to-DC step-down converter. In: 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 25–28 Nov. 2019, pp. 1–5 7. Fang Lin, L., Hong, Y.: Investigation of EMI, EMS and EMC in power DC/DC converters. In: The Fifth International Conference on Power Electronics and Drive Systems, vol. 1, pp. 572–577 (2003) 8. Farhadi, A., Jalilian, A.: Modeling and simulation of electromagnetic conducted emission due to power electronics converters. In: 2006 International Conference on Power Electronic, Drives and Energy Systems, 12–15 Dec. 2006, pp. 1–6 9. Nassireddine, B., Abdelber, B., Nawel, C., Abdelkader, D., Soufyane, B.: Conducted EMI prediction in DC/DC converter using frequency domain approach. In: 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 28–31 Oct. 2018, pp. 1–6 10. M’barki, Z., Rhazi, K.S., Mejdoub, Y.: Practical implementation of pseudo-random control in step-down choppers and its efficiency in mitigating conducted electromagnetic emissions. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds.) Artificial Intelligence and Smart Environment, pp. 674–682. Springer International Publishing, Cham (2023) 11. M’barki, Z., Mejdoub, Y., Rhazi, K.S.: Implementing pseudo-random control in boost converter: an effective approach for mitigating conducted electromagnetic emissions. Indonesian J. Electr. Eng. Inf. (IJEEI) 11 (2023). https://doi.org/10.52549/ijeei.v11i3.4832

Enhancing IoMT Security: A Conception of RFE-Ridge and ML/DL for Anomaly Intrusion Detection Ghita Lazrek(B) , Kaouthar Chetioui, and Younes Balboul LIASSE, USMBA, Fez, Morocco {ghita.lazrek,kaouthar.chetioui,younes.balboul}@usmba.ac.ma

Abstract. The outbreaks of smart cities have delivered smart connectivity of numerous Internet of Things (IoT) resulting in the boom of the Internet of Medical Things (IoMT), which delivers enhanced treatments and improves patient healthcare. However, the 2017 “WannaCry” ransomware cyber-attack in the United Kingdom compromised privacy and suspended operations at 48 healthcare providers. Despite the mammoth demand for IoMT devices, cyber-assaults on connected healthcare systems can threaten patient’s lives and can also tamper healthcare data. This paper proposes a conception of RFE-Ridge feature selection empowered with Machine/Deep learning models for implementing a viable intrusion detection in the IoMT system, meanwhile, a comparison between ML/DL models was conducted in terms of advantages and limitations. It is noteworthy that the proposed framework can be employed to construct an effective Intrusion Detection System (IDS) that strengthens the security of the IoMT against pervasive cyber-attacks. Keywords: Internet of medical things (IoMT) · Securing · Anomaly intrusion detection system (AIDS)

1 Introduction The Internet of Things (IoT) is a pillar enabler of smart city initiatives, providing the technological backbone for collecting and monitoring a tremendous amount of data from various industries. Subsequently, IoMT is a subset of the IoT, in which medical equipment exchange information with each other to exchange sensitive information [1], empowering the community to provide tracking of health parameters and transforming hospital practices into telehealth services. A study shows that the IoMT sector is anticipated to become an industry of having $136.8 billion by relying more on IoT and IoMT-based devices and tools [2]. Moreover, according to Deloitte’s report, the market value of the IoMT will reach around USD 158 billion by 2022 [3]. The huge rise in IoMT with its overwhelming benefits has greatly paved the door to jeopardized cyber-attacks such as Denial of Service (DOS), undoubtedly these attacks result in breaches of healthcare data in tandem with deterioration of IoMT system, an inherent study from Cynerio in August 2022 stated that in 88% of cases of cyberattacks, a medical device (MD) was © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 442–447, 2024. https://doi.org/10.1007/978-3-031-48573-2_63

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involved. Moreover, healthcare had to pay around USD 1 million for average data breach cases, and it increased the mortality rate by 24% [3]. Since the IoMT handles sensitive information, various security techniques have been elaborated to safeguard the IoMT system such as Physical Unclonable Function (PUF), Radio Frequency Identification (RFID), and Elliptic Curve Cryptography (ECC), unfortunately, these mechanisms are less scalable, and require high computational resources which is not suitable for IoMT restricted-resources, while some other researchers have been placing particular emphasis on leveraging ML or DL for intrusion detection, most of them fail to combine ML/DL in the same framework and consider only the network traffic to identify attacks. The research work in this article is aimed at this aspect by leveraging the power of ML/DL models based on RFE-Ridge feature selection to deploy an AIDS in IoMT architecture premised on network traffic behavior without having previous knowledge of the attack signature. In conclusion, this article contributes to the field of IoMT security by proposing ML/DL-based RFE-Ridge models as an anomaly Intrusion Detection System (AIDS) that utilizes network traffic and biometric patient data. The article also includes a detailed conception of the suggested solution with an ML/DL comparison in terms of advantages and limitations. This research aims to enhance the protection and integrity of sensitive medical data transmitted over IoMT network. The rest of the article is arranged as follows. Section 2 explores the related literature works. Section 3 outlines the suggested approach. While Sect. 4 details the experimental setup and comparison of ML/DL models used then a conclusion with a future direction in Sect. 5.

2 Literature Review In this section, the works affiliated to cyber-attack detection using machine and deep learning mechanisms in IoMT are treated, as well as the limitations in the state-of-theart work. Adel [4] investigates ML models (RF, DT, NB, KNN, ANN, SVM) to detect IoMT intrusions based on the “BOT-IoT” dataset and confirmed that DT is the best one. Another paper [5] leverages the power of Random Forest-Data Augmentation to secure IoMT infrastructure, however, the data augmentation introduces an overfitting problem, Thereby the authors in [6] used a Particle Swarm Optimization (PSO) based DNN to improve performance in the same dataset, a meticulously deep in analysis shows that this approach is complex and the performances still need to be enhanced. Celestine [7] proposed a Genetic Algorithm based RF to perform a binary, multiclassification detection and to generate the best subset of functionality using “NSL KDD” and “CIC-IDS”. An anomaly intrusion detection via KNN, RF, DT, NB, LR, and SVM was used by [8] to fortify from abnormalities in the collected data based on “ToN Telemetry”- “IoT/IIoT” datasets. Through rigorous experimentation, the authors confirm that KNN, RF, and DT are suitable paradigms for intrusion detection in IoMT. The literature survey reveals that the majority of the works focus on building the IDS classification without paying attention to the integration of ML/DL as AIDS in the same framework, moreover, the security mechanism [7] uses obsolete datasets for its evaluation, yet these datasets lack modern IoMT attacks and did not match the IoMT design.

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3 Proposed Approach To accurately detect attacks in IoMT utilizing network traffic and patient biometrics. This article proposes a usage of IoMT architecture consisting of IoMT devices to collect data and to generate logs, then the information’s sent to the gateway (network), where there is a two-layer intrusion detection: preprocessing layer and anomaly detection layer, Fig. 1 depicts a typical IoMT structure utilized to detect cyber-attacks. Whereas Fig. 2 shows our envisaged approach to enhance IoMT attack detection. Unlike network traffic analysis and network infrastructure intrusion detection, this work leverages IoT detection from patient-specific IoMT systems to reveal patient biometric abnormalities. Network traffic and patient biometrics are combined using timestamps of the network traffic events and patients’ biometrics data events in the IoMT. Followed by a “Preprocessing Engine” that includes the removal of irrelevant features, label encoding, a splitting of the dataset into 80% training and 20% testing, and MinMax scaling. While the eight selected features performed by RFE will be used to train the ML models, further, the most significant features selected by Ridge will be used to fit the DL models. The Grid Search with a CV of 10-fold will be generated to optimize the hyperparameters of machine learning models. Finally, in the “Anomaly intrusion detection engine”, seven ML and DL models are set to be invoked for classification, encompassing LR, DT, RF, LinearSVC, AdaBoost, LSTM, and CNN, to distinguish attacks from normal instances.

4 The Experimental Setup, and ML/DL Models Comparison 4.1 Software and Computer Hardware Requirements The experiments will be conducted on a Desktop using system specification IntelI Core I i5-6200U CPU @ 2.30GHz 2.40 GHz and 12.0 Go RAM. The approach will be trained and tested using ANACONDA NAVIGATOR 2.3.2- Jupyter Notebook. All selected classifiers are going to be built in Python using the Scikit-learn toolkit [10], Pandas, NumPy, Matplotlib, and KerasClassifier. WUSTL-EHMS dataset [9] will be utilized to train and assess the ML/DL models used, this dataset consists of MIT-M attack, the labeling scheme grants “0” to the attack traffic, and “1” to the normal traffic. 4.2 Comparison of the ML/DL Used Models This section explores the comparison in terms of manifold Machine learning and deep learning paradigms planned to be implemented as AIDS, to identify, detect, and discard attacks targeting IoMT systems. The comparison between these models which encompasses Logistic Regression (LR), AdaBoost, LinearSVC, Random Forest (RF), Decision Tree (DT), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) is conducted by examining their strengths and limitations, as interestingly exhibited in Table 1.

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Fig. 1. Schema of the suggested ML/DL intrusion detection architecture.

Fig. 2. Schema of the proposed ML/DL-based RFE-Ridge method.

5 Conclusion This paper performed a conception of anomaly intrusion detection using a combination of RFE-Ridge feature selection and ML/DL models based on a conjunction of network traffic and patient biometric data. The methodology is strongly adapted for e-healthcare that deals with tremendous amounts of data, besides, a comparison between ML/DL was performed in this work to analyze the suggested paradigms. In a nutshell, the findings of this study shed light on implementing an ML/DL-based RFE-Ridge models that can be applied to establish a viable IDS for the IoMT environment. As future work, we intend to enlarge our article to fulfill a simulation of the proposed solution using the aforementioned method and based on the above setup, with detailed results, interpretations, and Discussion.

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Models

Advantages

Limitations

Logistic regression

Simplicity and interpretability. Easy to implement [8]

Difficult to perform classification in case of non-linearly separable classes [8]

AdaBoost

Flexibility. Avoiding overfitting, Interpretability. Handling complex Dataset. Improved accuracy

Weak learner dependency. Computational complexity. Sensitivity to noisy data and outliers

LinearSVC

Robustness to outliers. Scalability Computational efficiency Reduces overfitting

Memory requirement. Limited non-linearity. Lack of probability estimates

Random forest

It possesses resistance to overfitting Feature selection is inherently performed. It requires fewer inputs [8]

Speed with a limited number of trees. It may necessitate the use of large datasets [8]

Decision tree

It is user-friendly. The performance remains consistent regardless of whether the parameters are linearly or non-linearly separated [8]

It is susceptible to overfitting. Unstable (i.e., small data variation can result in the construction of significantly different decision trees.) [8]

Convolutional neural network

Handling large datasets. Automatic feature extraction. Generalization on unseen data ability

Need for large training dataset. Limited understanding for long-term dependencies

Long short-term memory

Ability to learn complex patterns Capturing Long term dependencies Generalization across time

Need for sufficient training data. Overfitting issues on small datasets

References 1. Saheed, Y.K., Arowolo, M.O.: Efficient cyber attack detection on the internet of medical things-smart environment based on deep recurrent neural network and machine learning algorithms. IEEE Access 9, 161546–161554 (2021). https://doi.org/10.1109/ACCESS.2021. 3128837 2. Khan, I.A., et al.: XSRU-IoMT: explainable simple recurrent units for threat detection in internet of medical things networks. Futur. Gener. Comput. Syst. 127, 181–193 (2022). https:// doi.org/10.1016/j.future.2021.09.010

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3. Sadhu, P.K., Yanambaka, V.P., Abdelgawad, A.: Physical unclonable function and machine learning based group authentication and data masking for in-hospital segments. Electronics 11, 4155 (2022). https://doi.org/10.3390/electronics11244155 4. Binbusayyis, A., Alaskar, H., Vaiyapuri, T., Dinesh, M.: An investigation and comparison of machine learning approaches for intrusion detection in IoMT network. J. Supercomput. 78, 17403–17422 (2022). https://doi.org/10.1007/s11227-022-04568-3 5. Gupta, K., Sharma, D.K., Datta Gupta, K., Kumar, A.: A tree classifier based network intrusion detection model for internet of medical things. Comput. Electr. Eng. 102, 108158 (2022). https://doi.org/10.1016/j.compeleceng.2022.108158 6. Chaganti, R., Mourade, A., Ravi, V., Vemprala, N., Dua, A., Bhushan, B.: A particle swarm optimization and deep learning approach for intrusion detection system in internet of medical things. Sustainability 14, 12828 (2022). https://doi.org/10.3390/su141912828 7. Iwendi, C., Anajemba, J.H., Biamba, C., Ngabo, D.: Security of things intrusion detection system for smart healthcare. Electronics 10, 1375 (2021). https://doi.org/10.3390/electroni cs10121375 8. Zachos, G., Mantas, G., Essop, I., Porfyrakis, K., Ribeiro, J.C., Rodriguez, J.: Prototyping an anomaly-based intrusion detection system for internet of medical things networks. In: 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 179–183. IEEE, Paris, France (2022) 9. WUSTL EHMS 2020 Dataset for Internet of Medical Things (IoMT) Cybersecurity Research. https://www.cse.wustl.edu/~jain/ehms/index.html. Last accessed 03 May 2023 10. Scikit-Learn Machine Learning in Python. https://scikit-learn.org/stable/index.html. Last accessed 03 May 2023

Security of IoT-Cloud Systems Based Machine Learning Ouijdane Fadli1(B) , Younes Balboul1 , Mohammed Fattah2 , Said Mazer1 , and Moulhime Elbekkali1 1 Artificial Intelligence and Data Science and Emerging Systems Laboratory, Sidi Mohamed

Ben Abdellah University of Fez, Fez, Morocco {ouijdane.fadli,moulhime.elbekkali}@usmba.ac.ma 2 Image Laboratory University of Moulay Ismail of Meknes, Meknes, Morocco

Abstract. Cloud Storage plays a vital role as an intermediary layer that connects objects and applications, simplifying complexities and enabling seamless functionality. The Internet of Things represents a network of interconnected artifacts that serves diverse purposes. Ensuring security and staying updated with emerging techniques are essential for meeting the requirements of IoT-Cloud systems. The integration of Cloud and IoT introduces concerns regarding the trustworthiness of cloud providers and the transparency of data storage. Multitenancy Cloud Services Storage Systems raise worries about maintaining confidentiality and integrity, potentially resulting in unintended data exposure. The prevailing skepticism surrounding cloud service providers classifies this vulnerability as an internal threat within the IT industry. This paper extensively investigates the security of Machine Learning-based IoT-Cloud systems, highlighting the encountered challenges and the employed Machine Learning techniques. The prevailing evaluation metrics are examined, and previous researches are analyzed and compared. In the concluding section, the paper proposes an intelligent model for detecting existing and emerging attacks in IoT-Cloud systems. Keywords: Internet of things · Cloud · IoT-cloud systems · Security · Machine learning · Artificial intelligence

1 Introduction Cloud Storage serves as an intermediary layer bridging the gap between connected objects and apps, providing a means to abstract intricacies and functionalities [1]. The Internet of Things is widely recognized as a network comprising interconnected artifacts, with several applications interacting with these devices [2]. While each application presents special challenges, they often share commonalities within certain categories. Resolving those requirements necessitates a focused approach towards addressing security concerns and assessing the implications of emerging techniques [3]. The integration of Cloud and Internet of things (IoT) has given rise to persistent apprehensions regarding the trustworthiness of cloud providers and the transparency © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 448–453, 2024. https://doi.org/10.1007/978-3-031-48573-2_64

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surrounding the physical placement of data transferred into the cloud through diverse IoT agreements [4]. Notably, the multitenancy Cloud Services Storage System gives rise to several concerns. This system consolidates multiple consumers’ information within a unique feature, potentially compromising the confidentiality and integrity of sensitive data, leading to inadvertent data leakage [5]. As a consequence of the prevalent suspicion in cloud service providers, this particular weakness is categorized as an internal menace, presenting the most unexpected challenges in the IT sector. This paper constitutes an exhaustive investigation into the security of Machine Learning-based IoT-Cloud systems, wherein the security challenges encountered by these systems are delineated. Additionally, the Machine Learning techniques employed in IoT-Cloud systems are expounded upon, along with a comprehensive examination of the prevailing evaluation metrics. Moreover, an analysis of previously researches and a comparative assessment of their outcomes are presented. Finally, in the concluding section, we put forth our proposed intelligent model, which facilitates the detection of both existing and emerging attacks.

2 Security Challenges in IoT-Cloud System 2.1 Data Privacy Data privacy encompasses the systematic management and stringent governance of data, with the explicit objective of regulating the custodianship and subsequent processing of said data [6, 7]. 2.2 Authentication and Confidentiality The Internet of Things constitutes an intricate network of interconnected physical devices, wherein each individual device is allocated a distinct identifier for the purpose of identification. It is customary within this framework that upon a request for communication is exchanged between these interlinked IoT entities, a mutual verification process is mandated to establish their authenticity. 2.3 Access Control Pertains to the allocation of data utilization permissions, that can be attributed to various players operating within the expansive domain of a comprehensive IoT-Cloud. 2.4 Authorization As part of the IoT-Cloud system, authorization refers to a three-phase procedure. Firstly, it involves defining safety politics, that can be a collection of detailed regulations. The next phase involves implementing an access control model. Lastly, implementing an overall system of regulations to ensure compliance [8].

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3 Machine Learning Techniques for IoT-Cloud Systems In this part of our work, we present some machine learning (ML) mechanisms. These approaches encompass mathematical models and algorithms that have been previously introduced in academic literature [9]. 3.1 Support Vector Machine (SVM) SVM refers to one of supervised ML mechanism, serve both classifying and regressing tasks. It operates by data cartography in a highly dimensional environment and constructing hyperplanes like a separator between distinct classes. The data points located around these hyperplanes are referred to as support vectors, playing a crucial role in achieving optimum classification. The main objective is maximizing the spread among datapoints, in order to reduce standardization error accordingly [10, 11]. The main objective is maximizing the spread among datapoints, in order to reduce standardization error accordingly [9]. 3.2 Random Forest This algorithm is also from supervised ML mechanisms that can be utilized both classifying and regressing tasks. It leverages the power of decision trees by employing a paralleling joining technology called bagging. In the process of this technology, bootstrapping and selecting characteristics are utilized to construct a set of uncorrelated trees. 3.3 Convolutional Neural Network (CNN) CNN is an advanced machine learning technique employed to classify images. It is composed of several convolution layers, connecting layers and pooling layers. By employing convolution mechanisms and pooling layers, the CNN algorithm decreases picture size. Ultimately, the connecting layer is responsible for converting incoming information to the appropriate categories. The weights and biases are updated through backpropagation, which employs a technic of losses to calculate the error gradient [11, 12].

4 Evaluation and Performance Metrics Assessing ML classification performance is crucial for efficient Machine learning based countermeasures. Various measurements exist for evaluating detection accuracy in both ML and statistics [13]. The following are some performance metrics commonly used in IoT-Cloud Systems [14]. Accuracy = TP + TN TP + TN + FP + FN. Precision = TP TP + FP. False Positive Rate = FP FP + TN. Detection Rate = FP FP + TN.

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5 Related Work and Comparison of Results See Table 1. Table 1. Related work [15] Paper

Proposed Model

ML mechanism used

Result

[16]

Proposal of an IDS intrusion detection system to secure IoT-Cloud systems

CNN, CapSA

The accuracy rate is nearly equal to 99.26%

[17]

Proposition of DAIMD: an intelligent system that dynamically analyzes IoT malware to minimize device damage by detecting both known and evolving threats

CNN

The accuracy rate is nearly equal to 99.66%

[18]

Proposition of a ML Random Forest, SVM, approach that utilizes various Logistic Regression, technics to identify DDoS Decision Tree attacks targeting Internet of Things equipment’s

The Random Forest mechanism achieves an accuracy rate of 99.17%

[19]

Proposition of EDIMA: an early detection solution for IoT Malware Network Activity. It utilizes ML to identify analysis and infection stages, providing proactive defense against attacks

Gaussian Naive Bayes, k-NN, Random Forest

The K-NN mechanism achieves an accuracy rate of 94.44%

[20]

Proposed hybrid detection model using artificial intelligence and machine learning (AI/ML), to combat and mitigate IoT cyber threats

CNN and SVM

Not mentioned

6 Proposed Model According to previous research in the literature, the construction of an intelligent model enabling reliable detection of existing attacks as well as newly produced attacks is a relevant area of scientific research. For this reason, we have proposed an intelligent and stable multi-layer intrusion detection model based on Machine Learning and Deep Learning. This model generally consists of two cascading detection levels using two different algorithms with different internal structures and different configuration parameters and hyperparameters, as shown in the Fig. 1.

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Fig. 1. Proposed model

To provide a comprehensive overview of our proposed model, encompassing the utilized dataset, employed Machine Learning and Deep Learning algorithms, and our preliminary results, it is our intention to elucidate these aspects in our forthcoming research paper.

7 Conclusion Nowadays, the majority of enhanced protection solutions incorporate a kind of machine learning in order to achieve comprehensive effectiveness. ML not just greatly enhances traditional and conventional cybersecurity measures, as well as enhances our everyday lives with Internet of things equipment’s, such as smart buildings and smart transport. Experts in safety advise organizations to utilize an intelligent mechanism based on AI and ML, as it is crucial to protect the personal data. This paper discusses a detailed study of security in IoT-Cloud systems using ML. This work began by discussing the security challenges in the IoT-Cloud environment. In the second part, various Machine Learning algorithms were described. This was followed by an exploration of commonly used metrics for evaluating the ML approach. Additionally, previous work and their comparative results were presented. Finally, an overview of our proposed model was provided, which will be elaborated on in detail in our upcoming research paper.

References 1. Sadeeq, M.M., et al.: IoT and cloud computing issues, challenges and opportunities: a review. Qubahan Acad. J. 1(2), 1–7 (2021) 2. Kianoush, S., et al.: A cloud-IoT platform for passive radio sensing: challenges and application case studies. IEEE Internet Things J. 5(5), 3624–3636 (2018) 3. Rashid, Z.N., Zeebaree, S.R., Sengur, A.: Novel remote parallel processing code-breaker system via cloud computing (2020) 4. Sallow, A.B., et al.: An investigation for mobile malware behavioral and detection techniques based on android platform. IOSR J. Comput. Eng. (IOSR-JCE) 22(4), 14–20 (2020) 5. Mohiuddin, I., Almogren, A.: Workload aware VM consolidation method in edge/cloud computing for IoT applications. J. Parallel Distrib. Comput. 123, 204–214 (2019) 6. Surya, L.: Security challenges and strategies for the IoT in cloud computing. Int. J. Innov. Eng. Res. Technol. (2016). ISSN: 2394-3696 7. Fadli, O., et al.: IoT network attack types by application domains. In: The International Conference on Artificial Intelligence and Smart Environment. Springer International Publishing, Cham (2022)

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8. Zhou, J., Cao, Z., Dong, X., Vasilakos, A.V.: Security and privacy for cloud-based IoT: challenges. IEEE Commun. Mag. 55(1), 26–33 (2017) 9. Alex, C., et al.: A comprehensive survey for IoT security datasets taxonomy, classification and machine learning mechanisms. Comput. Secur. 103283 (2023) 10. Zhang, Y.: Support vector machine classification algorithm and its application. In: Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14–16, 2012. Proceedings, Part II, 3, pp. 179–186. Springer Berlin Heidelberg (2012) 11. Sarker, J.I., Ahad, W.: Coverage of the Kashmir conflict in Bangladeshi media: a content analysis. Dicle Academi Dergisi 1(2), 1–20 (2021) 12. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw. 3361(10), 1995 (1995) 13. Zheng, D.: Short-term renewable generation and load forecasting in microgrids. Microgrid Protect. Control 57–96 (2021) 14. Dunn, C., Moustafa, N., Turnbull, B.: Robustness evaluations of sustainable machine learning models against data poisoning attacks in the internet of things. Sustainability 12(16), 6434 (2020) 15. Ahmad, R., Alsmadi, I.: Machine learning approaches to IoT security: a systematic literature review. Internet Things 14, 100365 (2021) 16. Abd Elaziz, M., et al.: Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin search algorithm. Adv. Eng. Softw. 103402 (2023) 17. Jeon, J., Park, J.H., Jeong, Y.S.: Dynamic analysis for IoT malware detection with convolution neural network model. IEEE Access 8, 96899–96911 (2020) 18. Chaudhary, P., Gupta, B.B.: DDOS detection framework in resource constrained internet of things domain. In: 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE (2019) 19. Kumar, A., Lim, T.J.: EDIMA: Early detection of IoT malware network activity using machine learning techniques. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 289– 294. IEEE (2019) 20. Zewdie, T.G., Girma, A.: Iot security and the role of AI/ML to combat emerging cyber threats in cloud computing environment. Issues Inf. Syst. 21(4) (2020)

Optimizing IoT Workloads for Fog and Edge Scheduling Algorithms: A Comparative Study Saad-Eddine Chafi1(B) , Younes Balboul1 , Mohammed Fattah2 Said Mazer1 , and Moulhime El Bekkali1

,

1 Artificial Intelligence, Data Sciences and Emerging Systems Laboratory, Sidi Mohamed Ben

Abdellah University, Fez, Morocco [email protected] 2 Moulay Ismail University, Meknes, Morocco

Abstract. The rapid growth of Internet of Things (IoT) applications has led to increased interest in leveraging fog and edge computing for efficient resource management and task scheduling. In this article, we present a comparative analysis of scheduling algorithms for fog and edge computing in the context of IoT. We start by providing an overview of fog and edge computing, highlighting their significance in supporting IoT applications. We then review and analyze a variety of scheduling algorithms proposed for fog and edge computing environments, considering factors such as task allocation, load balancing, energy efficiency, latency, and scalability. Through this analysis, we identify the strengths and weaknesses of different scheduling algorithms and highlight their suitability for various IoT application scenarios. Furthermore, we discuss the trade-offs associated with different algorithmic approaches and provide insights into future research directions. The findings of this comparative analysis serve as a valuable reference for researchers and practitioners in selecting and designing scheduling algorithms that can optimize the performance of fog and edge computing systems in IoT environments. Keywords: Scheduling algorithms · Resource management · Task scheduling · Task allocation · Energy efficiency · Load Balancing

1 Introduction The rapid expansion of the Internet of Things (IoT) has led to a substantial increase in the quantity and intricacy of data generated by interconnected devices. This surge in data poses challenges for traditional cloud-centric architectures, which struggle with issues such as latency, bandwidth limitations, and network congestion, making it difficult to provide timely insights [1]. To overcome these challenges, Fog and Edge Computing have emerged as promising paradigms that distribute computing resources closer to the data sources. Scheduling algorithms determine the assignment of tasks to available computing resources, aiming to achieve objectives such as load balancing, minimizing response times, maximizing resource utilization, and meeting application-specific requirements. With a plethora of scheduling algorithms proposed in the literature, it becomes crucial to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 454–459, 2024. https://doi.org/10.1007/978-3-031-48573-2_65

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compare and evaluate their effectiveness in Fog and Edge Computing scenarios. Therefore, the aim of this study is to compare and analyze scheduling algorithms in Fog and Edge Computing environments, with a specific focus on their impact on optimizing IoT workloads. This article presents a comprehensive evaluation of scheduling algorithms in the context of Fog and Edge Computing. Our analysis covers a thorough examination of the strengths, weaknesses, and trade-offs associated with different algorithms. Factors such as system scalability, response times, resource utilization, and energy efficiency are taken into consideration. The article is structured as follows: In Sect. 2, a comprehensive review of relevant literature in the context of Fog and Edge Computing, including scheduling algorithms, is presented. Section 3 provides an overview of the classification of scheduling algorithms. Finally, Sect. 4 concludes the article by discussing future research directions in the field of scheduling algorithms for Fog and Edge Computing.

2 Literature Review In the domain of Fog Computing, Liu et al. [2] conducted a comprehensive study on scheduling algorithms and provided a classification that encompassed various approaches such as Round Robin, First-Come, First-Served (FCFS), Shortest Job Next (SJN), Priority-based Scheduling, Deadline-based Scheduling, Load Balancing, and Machine Learning-based Scheduling algorithms. The authors analyzed the characteristics and applicability of each algorithm in Fog Computing environments, considering key factors such as response time, resource utilization, and scalability. For Edge Computing, Lin et al. [3] proposed a workload-aware scheduling algorithm that considered task characteristics and resource availability at Edge devices. Their algorithm aimed to minimize task execution time and enhance resource utilization. The effectiveness of the algorithm was evaluated using real-world Edge devices, demonstrating its ability to meet latency requirements and optimize resource allocation. Additionally, Hussein et al. [4] proposed a distributed scheduling algorithm for Edge Computing that utilized the Ant Colony Optimization (ACO) technique. Their approach leveraged the principles of ACO to optimize task allocation and resource utilization in Edge Computing scenarios. These studies highlight the diverse range of scheduling algorithms designed for Fog and Edge Computing, showcasing their potential in addressing specific challenges and optimizing performance in IoT environments. The algorithm leveraged the pheromone-based communication among Edge devices to optimize task allocation and load balancing. Through simulations and experiments, they demonstrated the algorithm’s ability to achieve efficient task scheduling in Edge Computing environments. Other scheduling algorithms that have been proposed include Round Robin, First Come First Serve, and Earliest Deadline First [5]. These techniques have shown promise in improving performance by predicting task execution times and allocating resources accordingly [6]. Reinforcement Learning (RL) is another ML technique that has been used to optimize task allocation and resource utilization in fog and edge computing environments [7]. In the assessment of scheduling algorithms in fog and edge computing, various criteria have been introduced in the literature to gauge their performance. These criteria encompass factors such as latency, energy consumption, resource utilization, task completion time, and scalability [8]. Each of these criteria holds significance in evaluating the overall effectiveness

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of a scheduling algorithm within a fog and edge computing environment. Overall, the literature on scheduling algorithms in fog and edge computing is vast and diverse, with numerous approaches and criteria proposed for optimizing resource management and performance. The authors in [9] propose a task scheduling algorithm for real-time IoT applications that considers the constraints of edge devices, such as limited computational power and memory. In [10], the authors propose a hierarchical scheduling algorithm for fog computing that optimizes resource utilization while meeting the application’s QoS requirements. Other studies have focused on resource management in fog and edge computing environments. For instance, the authors in [11] propose a resource allocation algorithm for edge computing that minimizes energy consumption while meeting the application’s QoS requirements. In [12], The authors introduce a novel resource allocation algorithm, which considers the diversity of resources and their availability in a dynamic manner. In summary, fog and edge computing have emerged as promising solutions for addressing the challenges posed by IoT applications. Efficient resource management and task scheduling are critical for achieving optimal performance in fog and edge computing environments. Having a comprehensive understanding of diverse scheduling algorithms and their performance criteria is of utmost importance in order to choose the most suitable algorithm for a specific application.

3 Classification of Scheduling Algorithm in Fog Computing This section presents a comprehensive classification of scheduling algorithms in Fog Computing based on their characteristics and objectives. By categorizing and comparing these algorithms, we can gain valuable insights into their strengths and limitations. This understanding is crucial for optimizing the performance of IoT workloads in Fog Computing environments. Figure 1 presents a comprehensive categorization of task scheduling approaches.

Fig. 1. The taxonomy of task scheduling

The task scheduling algorithms can be classified into distinct categories: static scheduling algorithms [13] and dynamic scheduling algorithms [14]. These categories

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denote distinct strategies for task allocation and decision-making within Fog Computing environments. By classifying the algorithms into these groups, we can gain a better understanding of their characteristics and functionalities. The following sections provide an overview of each category. 3.1 Static Scheduling Algorithms Efficient task scheduling is a critical aspect of optimizing IoT workloads in Fog and Edge Computing environments. Various scheduling algorithms have been proposed and classified to address the unique challenges and requirements of these distributed computing paradigms. Several task scheduling algorithms are known for their simplicity and ease of implementation. There exist several widely recognized traditional scheduling algorithms employed in task management, such as First Come First Served (FCFS), Min-Max, Min-Min, Minimum Completion Time (MCT), and Max-Min. FCFS and Round Robin algorithms are typically utilized for scheduling tasks on a single machine, whereas Min-Max and Min-Min algorithms are well-suited for scenarios involving multiple machines. In the FCFS algorithm, tasks are executed in the sequence of their arrival time [15]. Upon receiving a task, it is appended to the end of the task queue and executed based on its position within the queue, ensuring a fair order of execution. Round Robin (RR) is a scheduling algorithm that operates in the First-Come, First-Served (FCFS) order, with the additional feature of time quantum allocation [16]. Each task is allocated a small-time quantum within which it can execute. If a task finishes its CPU burst within the assigned time quantum, it is preempted, allowing the processor to switch to the next task in the queue. However, if a task surpasses the time quantum, it is relocated to the end of the queue for subsequent execution. This time-based scheduling mechanism ensures fair allocation of processor time among tasks and helps maintain system responsiveness. Priority-based Scheduling assigns priorities to tasks, allowing for differentiated treatment based on their criticality. Deadline-based Scheduling considers task deadlines and schedules them accordingly to meet time constraints. Load Balancing distributes tasks across Fog nodes to balance the workload and optimize resource utilization. Machine Learning-based Scheduling algorithms leverage machine learning techniques to dynamically adapt scheduling decisions based on real-time information and historical data. This classification includes Round Robin, FCFS, SJN, and Priority-based Scheduling algorithms, emphasizing the need to consider the heterogeneity of Edge devices and their limited resources. Dynamic Load Balancing algorithms are also explored to address varying workloads and optimize resource allocation dynamically. Moreover, Edge Computing scheduling algorithms often consider factors such as network conditions, energy efficiency, and proximity to data sources to make intelligent task placement decisions. These classification frameworks provide valuable insights into the diverse spectrum of scheduling algorithms available for Fog and Edge Computing environments. Researchers and practitioners can utilize these classifications as a reference to select and evaluate appropriate algorithms based on their specific application requirements, system constraints, and optimization objectives. By considering factors such as fairness, response time, throughput, resource utilization [17], and scalability, a comparative study of these scheduling algorithms can be conducted to determine the most efficient strategies for optimizing IoT workloads in Fog and Edge Computing environments.

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3.2 Dynamic Scheduling Algorithms The scheduling algorithms in Fog and Edge Computing can be divided into two categories based on the system’s machine states and task arrival: online mode and batch mode. In the online mode, tasks are promptly categorized and scheduled upon arrival, ensuring their immediate assignment to available resources. On the other hand, the batch mode involves scheduling tasks in predefined time groups, allowing for a more systematic and organized approach. Several algorithms are commonly used in the batch mode, including: • The Min-Min algorithm prioritizes the selection and execution of the smallest task among the available machines, potentially causing delays for larger tasks [18]. It assigns resources to tasks based on minimizing the completion time, prioritizing smaller tasks to optimize the overall performance of the system. • Max-Min algorithm: The Max-Min algorithm gives priority to the execution of large tasks on available machines, potentially causing smaller tasks to experience resource starvation. It focuses on efficiently utilizing resources for larger tasks, which may impact the fairness and timely execution of smaller tasks. • Minimum Completion Time algorithm (MCT): The MCT algorithm chooses and executes tasks based on their earliest completion time [19]. It aims to prioritize tasks that can be completed soonest, ensuring efficient task execution and timely completion. • Priority Scheduling Algorithm: This algorithm assigns a priority to each task and schedules them accordingly [20]. Tasks with the same priority are executed in a FirstCome, First-Served (FCFS) order. An example of such an algorithm is the Shortest Job First (SJF) algorithm, which prioritizes tasks based on their CPU burst duration. • Opportunistic Load Balancing (OLB) [21] algorithms aim to achieve load balancing by assigning each task in a given request to the nearest available machine. These algorithms are known for their simplicity, as they do not involve additional calculations. The main goal of OLB [21] is to optimize resource utilization of all available machines and ensure their continuous activity.

4 Conclusion In conclusion, this article has presented a comprehensive comparative analysis of scheduling algorithms for fog and edge computing in the context of optimizing IoT workloads. We have explored the significance of fog and edge computing in supporting IoT applications and reviewed a range of scheduling algorithms proposed for these environments. Through our analysis, we have identified the strengths and weaknesses of different algorithms, considering factors such as task allocation, load balancing, energy efficiency, latency, and scalability. Our findings highlight the suitability of specific scheduling algorithms for various IoT application scenarios.

References 1. Liu, L., Chang, Z., Guo, X., Mao, S., et al.: Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J. 5(1), 283–294 (2017)

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2. Liu, L., Qi, D., Zhou, N., et al.: A task scheduling algorithm based on classification mining in fog computing environment. Wirel. Commun. Mobile Comput. 2018 (2018) 3. Lin, W., et al.: Scheduling algorithms for heterogeneous cloud environment: main resource load balancing algorithm and time balancing algorithm. J. Grid Comput. 17, 699–726 (2019) 4. Hussein, M.K., Mousa, M.H.: Efficient task offloading for IoT-based applications in fog computing using ant colony optimization. IEEE Access 8, 37191–37201 (2020) 5. Patel, R.B., et al.: Survey of scheduling algorithms in fog computing. Wirel. Pers. Commun. 106(1), 315–334 (2019) 6. Zhang, L., et al.: Deep learning for edge computing: a review. Signal Proces. Image Commun. 80, 115686 (2020) 7. Sun, G., et al.: A reinforcement learning-based approach to resource allocation in edge computing. IEEE Trans. Cloud Comput. (2021) 8. Baktir, A.E., et al.: Resource allocation in fog computing: a survey of techniques and challenges. J. Ambient. Intell. Humaniz. Comput. 11(10), 3971–3992 (2020) 9. Bonomi, F., et al.: Fog computing: a platform for internet of things and analytics. Big Data and Internet of Things: a Roadmap for Smart Environments, pp. 169–186 (2014) 10. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016) 11. Satyanarayanan, M.: The emergence of edge computing. IEEE Comput. 50, 30–39 (2017) 12. Chafi, S.E., et al.: Resource placement strategy optimization for smart grid application using 5G wireless networks. Int. J. Electr. Comput. Eng. 12(4), 3932 (2022) 13. Yadav, A.K., Mandoria, H.L.: Study of task scheduling algorithms in the cloud computing environment: a review. Int. J. Comput. Sci. Inf. Technol. 8, 462–468 (2017) 14. Nagadevi, S., Satyapriya, K., Malathy, D.: A survey on economic cloud schedulers for optimized task scheduling. Int. J. Adv. Eng. Technol. 4(1), 58–62 (2013) 15. Chafi, S.E., et al.: A comprehensive analysis of fog computing task scheduling approaches. AIP Conf. Proc. 2814(1) (2023). AIP Publishing 16. Zhao, Y., Cao, Y., Wang, W., et al.: Resource allocation in optical networks secured by quantum key distribution. IEEE Commun. Mag. 56(8), 130–137 (2018) 17. Chafi, S.E., Balboul, Y., et al.: Resource placement strategy optimization for IoT oriented monitoring application. TELKOMNIKA 20(4), 788–796 (2022) 18. Salot, P.: A survey of various scheduling algorithm in cloud computing environment. Int. J. Res. Eng. Technol. 2(2), 131–135 (2013) 19. Chafi, S.E., et al.: Cloud computing services, models and simulation tools. Int. J. Cloud Comput. 10(5–6), 533–547 (2021) 20. Zhang, Y.W., Chen, R.K.: Energy aware fixed priority scheduling in mixed-criticality systems. Comput. Stan. Interfaces 83, 103671 (2023) 21. Singh, D., Bhalla, V., Garg, N.: Load balancing algorithms with the application of machine learning: a review. MR Int. J. Eng. Technol. 10(1) (2023)

Comparative Study Between Gilbert and Cascode Mixers for 5G mm-Wave Systems Abdelhafid Es-Saqy1(B) , Maryam Abata1 , Salah-Eddine Didi1 , Mohammed Fattah2 , Said Mazer1 , Mahmoud Mehdi3,4 , Moulhime El Bekkali1 , and Catherine Algani5 1 AIDSES Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

[email protected]

2 EST, Moulay Ismail University, Meknes, Morocco 3 Microwaves Laboratory, Lebanese University, Beirut, Lebanon 4 Faculty of Sciences and Fine Arts, CRITC, AUL University, Beirut, Lebanon 5 ESYCOM Lab, University Gustave Eiffel, CNRS, Le Cnam, Paris, France

Abstract. In this paper we compare the behavior and performance of two upconversion mixers: the cascode cell and the Gilbert cell. Both mixers are designed for communication systems operating at millimeter-wave frequencies. They are designed using MMIC technology based on 0.15 µm GaAs pseudomorphic HEMT. The simulation shows that the cascode cell circuit has a conversion gain CG of 4.8 dB for an IF input power of – 21 dBm. On the other hand, the Gilbert cell achieves a CG of 5.4 dB for an IF power of − 4 dBm. Keywords: Cascode cell · Gilbert cell mixer · MMIC · Mm-wave band

1 Introduction The mixer is an indispensable element in every wireless communication system. Its role is to carry out the time-domain multiplication of two signals of different frequencies [1]. In the frequency domain, this temporal multiplication results in transposition to another frequency (sum or difference of the two input frequencies). The performance of MMIC mixers has improved considerably thanks to the development of high-performance HEMT, pseudomorphic HEMT and HBT transistors [2–7]. MMIC mixers using HEMTs and pHEMTs have been demonstrated at over 140 GHz [8], and at the time of writing, HBT mixers have been demonstrated at 300 GHz [4]. The objective of this paper is the study and design of a mixer for the millimeterfrequency band, around 26 GHz, dedicated to 5G applications [9]. To this end, we began by presenting and analyzing two mixer circuits: the cascode cell and the Gilbert cell. The results obtained by these two circuits will subsequently be compared with each other, in order to select the most appropriate structure for our further design work. The circuits presented in this paper are based on the active and passive elements of the PH15 technological process from the United Monolithic Semiconductors foundry. In this MMIC process, pHEMT device, MIM capacitor, resistor, spiral inductor and microstrip line are available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 460–465, 2024. https://doi.org/10.1007/978-3-031-48573-2_66

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2 pHEMT Mixer Circuit Frequency conversion requires a non-linear component, and the dominant non-linearity in a pHEMT transistor is that of the gm transconductance. When the gate voltage is close to the pinch-off region, a small increase in this voltage causes a large variation in the transconductance value of the pHEMT, leading to a nonlinear response [3]. In this section, we present the electrical circuits of two pHEMT mixers. 2.1 Cascode Cell Figure 1 shows the electrical circuit of the cascode cell. This structure is studied and analyzed in detail in paper [2]. It consists of two cascode-connected pHEMTs. In the case of up-conversion, the two input signals (LO and IF) are injected at the two pHEMT gates.

Fig. 1. Simple mixer: cascode cell

The performance of this structure, particularly in terms of linearity and Conversion Gain CG, will be compared with that of another double-balanced structure, the Gilbert cell. The electrical circuit of this structure is presented in the following section. 2.2 Gilbert Cell The double-balanced Gilbert cell mixer consists of three stages (Fig. 2). In the case of high conversion, IF signal drives the differential pair, at the bottom of the circuit, while LO signal is injected into the gates of the two differential pairs in the middle of the circuit. To convert IF and LO signals into differential form, we used two Balun cells (Balun_IF & Balun_LO). The Balun_RF cell, on the other hand, is used to restore the RF signal from its differential form. After presenting the electrical circuits of the two structures, the simulation results of these two mixers will be presented in the following section. These simulations are carried out using ADS software from Agilent.

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Fig. 2. Double-balanced mixer: Gilbert cell

3 Simulation Results 3.1 Cascode Cell Performance Figure 3 shows the CG versus IF input power. For an IF power of − 21 dBm, the CG is 4.8 dB. Figure 4 shows the variation of Lower Sideband LSB power versus IF power. The LSB power reaches a maximum value of 0.8 dBm for an IF power of 9 dBm. The compression point at 1 dB is − 10.5 dBm. Figure 5 shows the output spectrum of the mixer. It can be seen that the cascode cell shows no rejection of the LO carrier at its output. For all simulations in this section, the input LO power is 9 dBm, the LO frequency is 28 GHz and the IF frequency is 2 GHz.

Fig. 3. Conversion gain versus IF power: cascode cell

3.2 Gilbert Cell Performance As before, Fig. 6 shows the variation of the CG versus IF input power. For an IF power of − 4 dBm, the conversion gain is 5.4 dB. On the other hand, Fig. 7 shows the variation

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Fig. 4. Lower sideband power versus IF power: cascode cell

Fig. 5. Output spectrum: cascode cell

of LSB power, present at the mixer output, versus input IF power. The power reaches a maximum value of − 5.5 dBm for an IF power of 10 dBm. Figure 8 shows the output spectrum of the mixer circuit. We can see that this structure has an LO signal rejection of 21.5 dB. For all simulations, the LO power is 15 dBm. 3.3 Results Analysis The parasitic capacitances of RF cascode amplifier stage degrades the operated RF frequency at high frequency [10]. But from the simulation results presented earlier, we can see that both circuits show good performance. This justifies the reliability of our design method and the efficiency of the proposed architectures. The Gilbert mixer requires a LO with very high output power, compared to the Cascode cell-based mixer. Consequently, the design of an oscillator with high output power for the millimeter-frequency band, intended for 5G, is very complicated and difficult to achieve [3]. On the other hand, the cascode cell has a lower conversion gain than the Gilbert cell. This is in addition to the good level of OL rejection provided by the latter.

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Fig. 6. Conversion gain versus IF power: Gilbert cell

Fig. 7. Lower sideband power versus IF power: Gilbert cell

Fig. 8. Output spectrum: Gilbert cell

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4 Conclusion In this paper we have presented the performance of two mixers for 5G communication systems: a single mixer based on the cascode cell and a doubly balanced mixer based on the Gilbert cell. In addition to the high conversion gain presented by both circuits, the Gilbert mixer achieved a LO signal rejection of 21.5 dB.

References 1. Faitah, K., Oualkadi, A.E., Ouahman, A.A.: Design of high isolation frequency mixer in CMOS 0.18 m technology suitable for low power radio frequency applications. Phys. Chem. News 49, 1–7 (2009) 2. Es-Saqy, A., et al.: High conversion gain self-oscillating mixer for 5G mm-wave applications. ASM Sci. J. 17, 1–8 (2022) 3. Wang, X., et al.: A wideband gate mixer using 0.15 µm Gaas enhancement-mode phemt technology. Prog. Electromagn. Res. Lett. 84, 7–14 (2019) 4. Song, K., Kim, J., Son, H., Yoo, J., Cho, M., Rieh, J.-S.: 300-GHz InP HBT Quadrature VCO with Integrated Mixer. IEEE Trans. Terahertz Sci. Technol. 10(2), 419–422 (2020) 5. Es-saqy, A., et al.: High rejection self-oscillating up-conversion mixer for fifth-generation communications. Int. J. Electr. Comput. Eng. IJECE 13(5), 4979–4986 (2023) 6. Gunnarsson, S.E., et al.: Highly integrated 60 GHz transmitter and receiver MMICs in a GaAs pHEMT technology. IEEE J. Solid-State Circuits 40(11), 2174–2186 (2005) 7. Hamada, H., et al.: 300-GHz-band 120-Gb/s wireless front-end based on InP-HEMT PAs and mixers. IEEE J. Solid-State Circuits 1, 1–20 (2020) 8. El Krouk, A., et al.: Gilbert cell down-conversion mixer for THz wireless communication. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds.) Artificial Intelligence and Smart Environment. Lecture Notes in Networks and Systems, vol. 635, pp. 475–480. Springer International Publishing, Cham (2023) 9. Didi, S.-E., et al.: New microstrip patch antenna array design at 28 GHz millimeter-wave for fifth-generation application. IJECE 13(4), 4184–4193 (2023) 10. Park, J.-H., Kong, K.-B., Park, S.-O.: A 24.5-50 GHz mixer using cascode structure with wide 0.5–16 GHz intermediate frequency bandwidth based on low noise pHEMT process. In: URSI Asia-Pacific Radio Conference, pp. 897–899. IEEE, Seoul, Korea (2016)

Anomaly Detection in IoT Networks—Classifications and Analysis Techniques Hamza Rhachi(B) , Anas Bouayad, Younes Balboul, and Badr Aitmessaad IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco {hamza.rhachi,anas.bouayad,younes.balboul, badr.aitmessaad}@usmba.ac.ma

Abstract. The Internet of Things is a technology that we’ve been talking about for numerous years. It defines itself as a global network consisting of interconnected services and various intelligent objects. Also, it aims to support human activities of daily life, through its sensing, computing, and communication capabilities. This network may have many risks of cyber security, so anything connected to the Internet can be exposed to cyber-attacks. Experience has shown that encryption and authentication alone aren’t enough to secure an IoT network, and detection systems are needed to detect and avoid attacks from dangerous nodes. Furthermore, designing and developing new systems based on machine learning (ML) and deep learning (DL) that can detect anomalies and attacks in IoT Networks is important. In this context, the problem of outlier detection is one of the most important problems that require more research and customized solutions. The challenge is to identify anomalies and classify them as errors that should be ignored, or as critical events that require action to prevent further service degradation. This paper aims to analyze and assess the effectiveness of anomaly detection models rooted in machine learning for IoT networks. A couple of machine learning models were compared, and their classification was also discussed for this purpose. Keywords: Machine learning algorithms · Internet of things (IoT) · Anomaly detection · Dataset · Classification

1 Introduction The IoT is the combination of physical objects, networks, storage space, and software that enable devices to collect and transmit data. The physical objects include sensors that interact with the real world over a network and perform specific tasks [1]. The Internet of Things still faces many challenges. They often face both traditional and emerging security threats. The purpose of Anomaly detection is to identify what happening in the IoT and it serves as the main line of defense against cybersecurity threats. In the IoT, traffics arrive at any time, including normal traffic and attack traffic, which is often used as a data source to detect network anomalies and investigate network issues [2]. Therefore, machine learning (ML) techniques are a form of anomaly detection © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 466–472, 2024. https://doi.org/10.1007/978-3-031-48573-2_67

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in IoT. With this approach, the learning activity can be performed in supervised or unsupervised manner. Supervised learning uses known data (labeled training data), which allows the data to be classified into normal and abnormal categories, while unsupervised learning deals with unlabeled data by identifying features in data, meaning that normal and abnormal classes themselves are anonymous. This paper aims to provide a review of anomaly detection techniques using machine learning to secure IoT networks. The next section covers the definition and categorization of IoT Anomaly Detection, Sect. 3 describes various applications of IoT Anomaly Detection. Section 4 presents IoT Anomaly Detection based on machine learning approaches. Section 5 provides a comparative study of machine learning methods used for anomaly detection. Finally, Sect. 6 presents the conclusion and outlines future work.

2 Definition and Categorization of IoT Anomaly Detection Anomalies are data points that do not correlate with predicted behavior in the model system. They are like a general phenomenon or observation that differs notably from a general pattern or behavior observed at a single data point, factor, time period, or entire dataset. Approaches to IoT Anomaly Detection are divided into four categories, as shown in Fig. 1, based on how the problem is addressed, how it is applied, the methods group, and the response time of the algorithm.

Fig. 1. Categories of anomalies.

Method: based on the concepts of distance and density. Anomalies can be classified using an index “T” applied to the approximate distance “D”. This method is called geometrical. Another method uses mathematics to compare standard data, here we are talking about the statistical method. The last part is machine learning/Deep Learning, used to make the difference between normal and anomalous actions. Application: There are three constructive, destructive, and data-cleaning applications, the first is characterized by its ability to provide and benefit value to the world. The second has the intention to disrupt normal operations with the aim of questionable financial gain or causing harm to the network. Finally, the last application is the activity of fixing the correct data or removing incorrect, duplicate, or incomplete data inside a dataset, for example, [3] uses a deep CNN to remove undesired data and detector noise from incoming signals.

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Type: Its contents include individual, contextual, and collective anomalies. An individual data or observation in a dataset is classified as a point if it deviates significantly from the rest of the data. And when an instance is deemed exceptional within a specific context, it is called a contextual anomaly. The final anomaly genre is the collective anomaly until a set of related, interrelated, or sequential instances is significantly different from the rest of the data. Latency: There are two types: online and offline. Online algorithms can process information continuously using an individual data point or windows to process information without having the entire input. However, offline algorithms can find more information and have access to complete data. It helps to solve issues in a timely manner.

3 IoT Anomaly Detection Applications 3.1 Sensor Nodes A search of the literature reveals many sensors for detecting abnormal behavior in a variety of application areas. In smart industrial systems, for example, systems are often considered complex systems due to the presence of legacy software and materials, we found that by measuring different parts of the parameters. In this context, more methods and datasets are required [4]. Proposes a context-aware adaptive monitoring system for the IoT through an IoT CAD approach that detects sensor changes due to an unexpected incident in the environment. 3.2 Network Traffic Network traffic for anomaly detection (NT-AD) is a great identification technique for network attacks since it can detect new attacks where there are no previously recorded signatures. Many methods have been suggested, we can find TONTA (Trend-based Online Network Traffic Analysis) usage of this technique is widespread among all data forwarding nodes, serving to identify various events, assess communication protocol, and performance, also, detect network congestion [5]. Moreover, in the study of Shen et al. [6], his Secure SVM (Support Vector Machine) is proposed to encrypt IoT Data using a private key. Another research used the RNN LSTM model to detect anomalies in IoT network communications.

4 Machine Learning for Anomaly Detection in the IoT 4.1 Machine Learning Techniques ML is a highly effective implement for identifying and detecting anomalies in IoT networks by analyzing traffic samples through scientific learning. It is worth noting that even when employing the same ML model with two identical datasets, the performance can differ based on the specific utilization of the ML algorithm. Below, we have listed the primary branches of ML: Supervised learning (SL) techniques can be accomplished using labeling the network traffic in IoT devices, which is called the data training set.

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Unsupervised learning (UL) does not require labeled data but allows models to work on their own to discover previously undetected patterns and information. Due to the characteristics of anomalies and the principles of UL algorithms, different detections need to choose a suitable algorithm. Semi-supervised Learning (SSL) merges labeled and unlabeled data to construct a classifier. In certain cases, researchers have successfully developed network intrusion detection models using Semi-supervised ML techniques with comparable performance to supervised ML techniques. In a study [7], researchers proposed a new technique based on a standard variation of normal behavior. Reinforcement learning (RL) The purpose of RL involves an agent that understands an action to achieve good results without prior knowledge. In the realm of IoT security, many researchers have used RL techniques. For example, Q-learning as a popular RL model has been employed to enhance authentication performance [8]. 4.2 Datasets The data plays a vital role in the machine learning process. That’s why we scan existing IoT data repositories for anomalies. In the network layer, the data encompasses various network-related information, including communication networks and protocols. In addition to using machine learning, it is important to have a set of data that contains many samples, is regular, and is well documented and labeled. Many datasets are now available for anomaly detection in IoT. Among these datasets are: • NSL-KDD: a dataset that is designed to address certain limitations encountered in KDD Cup 99. • MQTTset: the first dataset to simulate an MQTT-based network, widely adopted in IoT networks, it included legal and attack traffic, represented by PCAP packet capture files. • IoT-23 dataset: This dataset contains information gathered from IoT devices such as smart homes, wearable gadgets, and industrial control systems. One of the most used datasets is the NSL-KDD, but it still presents defaults. 4.3 Algorithms Most machine learning algorithms for IoT networks are based on the following workflow [9] to detect anomalies. After being captured by an IoT device, the data is archived. Subsequently, machine learning algorithms are then utilized. The final stage is validating and assessing the proposed model by evaluation (Fig. 2). The trend is to prioritize enhanced accuracy, reduced false alarm rates, and minimized memory and computation usage. Each algorithm can be used and has some advantages over others depending on the traffic condition. In addition, every algorithm can be applied with some specific advantages.

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Fig. 2. Process of machine learning algorithms for anomaly detection in IoT.

5 Models Comparison There are many different models and approaches for anomaly detection using in IoT networks, and choosing the right one can be a challenge. In this paper, we will focus on various machine learning models. A comparison is shown in the following Table 1, based on Pros, Cons, Applicability, Accuracy, and time performance. In light of the above, it is worth noting that if an algorithm attains 100% of accuracy and has a long time to execute, it should not be used for IoT Networks due to limited device resources.

6 Conclusion and Future Work Anomaly detection is an important topic in IoT. With the real-world usage of IoT growing in various applications. For some reason, IoT requires creative anomaly detection solutions that can overcome specific limitations and constraints. We presented a categorization and techniques for anomaly detection in IoT. Additionally, we have reviewed a comparison of different learning algorithms based on their accuracy and time performance. In future work, we plan to investigate more to integrate different machine learning approaches to increase accuracy, or mixing unsupervised learning with supervised learning. It’s an example of hybrid models utilized in the investigations.

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Table 1. Models comparison. Algorithm Pros

Cons

Applicability

Accuracy Time Performance

Naive Bayes

– Simple and computationally efficient algorithm – Works well with small training datasets – Performs well in real-time scenarios

– Assumes – Suitable for independence highbetween dimensional features data with – May not handle categorical complex features relationships – Commonly between used in text variables classification and spam filtering

Decision trees

– Easy to – Prone to understand and overfitting, interpret the especially with learned rules deep trees – Can handle – Sensitive to missing values small changes and outliers in data, leading – Supports to different tree feature structures selection and feature importance ranking

Support vector machine (SVM)

– Effective in – – Suitable for High highComputationally both linear and accuracy dimensional intensive, non-linear data spaces especially with – Commonly – Can handle used for text, large datasets both – Requires careful image, and classification selection of the anomaly and regression kernel function detection tasks – Works well with datasets with a clear margin of separation

Moderate Fast to High Accuracy

– Suitable for Moderate Moderate to both to high high time categorical and accuracy numerical features – Commonly used for classification and regression tasks

Moderate to high time

References 1. Sahu, N.K., Mukherjee, I.: Machine learning based anomaly detection for IoT network: (Anomaly detection in IoT network). In: 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), pp. 787–794. IEEE (2020)

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2. Han, S., Wu, Q., Yang, Y.: Machine learning for Internet of Things anomaly detection under low-quality data. Int. J. Distrib. Sens. Netw. 18(10), 15501329221133764 (2022) 3. Munir, M., Siddiqui, S.A., Dengel, A., Ahmed, S.: DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991–2005 (2018) 4. Yasaei, R., Hernandez, F., Faruque, M.A.A.: IoT-CAD: context-aware adaptive anomaly detection in IoT systems through sensor association. In: Proceedings of the 39th International Conference on Computer-Aided Design, pp. 1–9 (2020) 5. Shahraki, A., Taherkordi, A., Haugen, Ø.: TONTA: Trend-based online network traffic analysis in ad-hoc IoT networks. Comput. Netw. 194, 108125 (2021) 6. Shen, M., Tang, X., Zhu, L., Du, X., Guizani, M.: Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities. IEEE Internet of Things J. 6(5), 7702–7712 (2019) 7. Sen, P.C., Hajra, M., Ghosh, M.: Supervised classification algorithms in machine learning: a survey and review. In: Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph, pp. 99–111. Springer Singapore (2020) 8. Xiao, L., Li, Y., Han, G., Liu, G., Zhuang, W.: PHY-layer spoofing detection with reinforcement learning in wireless networks. IEEE Trans. Veh. Technol. 65(12), 10037–10047 (2016) 9. Alghanmi, N., Alotaibi, R., Buhari, S.M.: Machine learning approaches for anomaly detection in IoT: an overview and future research directions. Wirel. Pers. Commun. 122(3), 2309–2324 (2022)

Creation of a Soft Circular Patch Antenna for 5G at a Frequency of 2.45 GHz Salah-Eddine Didi1(B) , Imane Halkhams2 , Abdelhafid Es-saqy1 , Mohammed Fattah3 , Younes Balboul1 , Said Mazer1 , and Moulhime El Bekkali1 1 IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

[email protected] 2 SIEDD Laboratory, UPF, Fez, Morocco 3 IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco

Abstract. This paper presents the study and design of a flexible circular patch antenna implanted on a bio-sourced substrate for industrial, scientific, and medical applications. The frequency chosen for the study is 2.45 GHz. Return loss and radiation pattern measurements. An improvement in the gain of this antenna is also investigated in this study. This antenna offers adequate performance to meet the needs of 5G users. This antenna is printed on a polyester substrate with a thickness of h = 0.3 mm, a relative permittivity εr = 3.2, a loss tangent equal to 0.003, and a patch radius equal to 0.85 mm. In addition, this antenna provides the following results: reflection coefficient S11 = − 13.26 dB, bandwidth BW = 0.12 GHz, gain G = 5.6, directivity D = 5.8 dB, and efficiency η = 96.55%. Keywords: 5G · Antenna soft · 2.45 GHz · HFSS · Slot · ISM

1 Introduction Due to the rapid growth in the number of users connected to the Internet, as well as the rise of new information processing technologies such as artificial intelligence, the Internet of Things (IoT) and automation, data creation is increasing dramatically. Over the next decade, the quantity and volume of data flow will increase phenomenally, surely reaching hundreds of Zettabytes. Indeed, 5G enables users to communicate securely with each other. In addition, modern mobile infrastructures need to be regularly upgraded, as they are not adapted to this type of data flow [1, 2]. At the same time, because of its speed, high capacity, and low reaction time, 5G technology can help support and develop many disciplines, including cloud-connected traffic control, drone delivery, online video chat, and console-quality gaming on the move. From global payments and critical situation response to e-learning and workers on the move, and right through to employee mobility, the benefits, as well as the possible uses of 5G technology, know no boundaries. It can potentially transform the world of work, the global economy, and people’s lives [3, 4]. The development of space telecommunications and remote monitoring and control has led to a growing need for low-cost, space-saving microwave devices based on simple, low-cost technology. Microwave systems with a microstrip structure have been at the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 473–479, 2024. https://doi.org/10.1007/978-3-031-48573-2_68

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origin of the development of printed antennas (patch or plate antennas), which are most often used in arrays to improve their performance and enable the realization of particular functions. Today, printed arrays are widely used, since they can be used to meet a wide range of system requirements. It is characterized by its radiation pattern, characteristic function, aperture angle, antenna dynamics, gain, and directivity. The main applications for these antennas can be found in numerous communication systems, such as mobile telephony, wireless multimedia systems (WIFI, Bluetooth), and space communications. They are also used in radar and remote detection systems, in frequency bands ranging from 1 GHz to millimeter bands. These applications are subject to selective criteria and limitations regarding weight, volume, and thickness. An antenna with printed radiating elements, commonly called a “patch antenna,” is a MICRORUBAN (MICROSTRIP) line of a particular shape. It consists of a ground plane and a dielectric substrate, the surface of which carries one or more metallic elements. They are lightweight, compact, low-cost, and have a planar configuration compatible with integrated circuits and, if required, conformable. Plated antennas are now used almost everywhere in mobile communications systems. These antennas are lightweight, spacesaving, and inexpensive. They are manufactured using the photolithographic technique of printed circuit boards. Depending on the application, there are different shapes of radiating elements, types of substrate, and types of feed [5, 6]. Circular patch antennas have many uses in the medical, military, mobile, and satellite communications sectors. An antenna can be defined simply as a metal infrastructure for transmitting and receiving electromagnetic and radio waves and for converting electrical energy into electromagnetic waves at the transmitting unit, while the reverse occurs at the receiving unit. These antennas have become very common because of their small size, ease of production, and low cost, especially in wireless applications. The most common antennas are circular or rectangular because of their exciting radiation possibilities and ease of analysis. As a result, these antennas find many uses in portable devices such as cell phones [7]. Portable antennas have attracted much attention recently due to their many advantages, including robust and inexpensive. They also have applications in the wireless communications and motion sensing sectors [8, 9]. According to the type of application, antennas can be produced using a variety of supports. When it comes to garment applications, textiles are attractive because they can be easily integrated into fabrics. Most paper substrates, easily accessible and inexpensive, are also attractive for ecological products. However, the most commonly used substrates are polymer-based. The most common are polyimide (Kapton), polyethylene terephthalate (PET), and polydimethylsiloxane (PDMS) [10–12]. The main drawback of these materials is that they are obtained from petroleum extracts. As a result, there is growing interest in alternatives that reduce the need for such materials, hence the dependence on oil and the ecological footprint of electronic devices. In this context, we propose using a biobased polymer as a substrate for a circular patch antenna operating at 2.45 GHz. We use this frequency band (2.45 GHz), as it is widely used in industry and is included in the IMS (Industrial, Medical and Scientific) band. In fact, the 300 MHz to 2.5 GHz bandwidth is reserved for industrial, medical and scientific applications, such as sensor networks and remote guidance, for example. The 2.45 GHz frequency, for example, is used by microwave ovens to heat food. In Europe, this frequency band is

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known as the SRD band for “Short-Range Devices”. To use this frequency band, you don’t need a license, but it is regulated nonetheless. Maximum transmission power and modulation types are regulated. This work is organized as follows: We start with the introduction, then we present the design procedure for this antenna, then we simulate this antenna using HFSS simulation software, then we compare the results obtained by this antenna with the work available in scientific literature, and finally we end this work with the conclusion.

2 Circular Patch Antenna Design Method To design a circular patch antenna, we need to determine its essential parameters, such as the substrate’s height and the radiated element’s physical radius (patch). To do this, we use the formulas given in references [13, 14]. In this study, we choose a polyester substrate with a thickness of h = 2.85 cm, a relative permittivity εr = 3.2, a loss tangent equal to 0.003, and a patch radius equal to 2.11 cm. Indeed Polyester is a moisture-resistant and water-repellent substrate, which makes it more difficult to wet, thus preserving its electromagnetic properties. It helps suppress the antenna’s surface waves to improve its radiation performance (gain, directivity). Polyester, on the other hand, is the most common textile used in clothing. Figure 1 illustrates the geometric structure of the proposed antenna. A microstrip line feeds this antenna. Furthermore, the numerical values of all the elements of this proposed antenna are listed in Table 1.

Fig. 1. Structure of a proposed antenna.

3 Simulations Results This antenna gives the following results: reflection coefficient S11 = −13.26 dB, bandwidth BW = 0.12 GHz, as shown in Fig. 2. In addition, it generates better radiation, particularly in terms of gain, which is 5.6 dB (this parameter is illustrated in Fig. 3), and directivity, which is 5.8 dB (this parameter is illustrated in Fig. 4). All these results were obtained using the HFSS simulation tool. HFSS is a 3-D electromagnetic modeling

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Fig. 2. Graphical representation of S11.

Table 1. The parameters of this antenna. Parameters

Values (cm)

Parameters

Values (cm)

R

2.11

(L1-W1)

(0.209–3.645)

h

2.85

(L2-W2)

(0.674–3.195)

(Ls-Ws)

(11–10)

(FL-FW)

(0.1–3)

software package, based on the harmonic finite element method (FEM). The software features a user-friendly graphical interface, making it easy to generate field maps in the calculation volume. What’s more, it uses a frequency-based method that enables results to be obtained quickly at a single frequency point. However, in the particular case of studying the behavior of a cell as a function of frequency, the frequency method used obliges us to carry out a simulation per frequency point (without, however, recalculating the mesh, which is constant over the frequency band), which leads to long calculation times.

Fig. 3. Graphical representation of gains in 3D and 2D

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Fig. 4. Graphical representation of directivity in 3D and 2D

4 Comparison of the Results Obtained with Those Available in the Scientific Literature Table 2 compares the simulation results obtained with the proposed antenna (AP) with those currently available in the scientific literature. It can be visibly observed that the proposed antenna produces interesting results relative to other works in terms of bandwidth, gain, and efficiency, which is clearly shown in Table 2. The bandwidth obtained by the proposed antenna is very high, in contrast to those obtained by works [14, 15, 18], as is its efficiency. In addition, the reflection coefficient and bandwidth achieved by an antenna [16] are better than those achieved by other works, as shown in Table 2. Table 2. A comparative study with other studies. Refs.

Fr (GHz)

S11 (dB)

BW (MHz)

Gain (dB)

Direct (dB)

η%

[15]

2.4

− 47

51.4

4.24

–

–

[16]

2.4

− 13.89

70

6.6

–

92.5

[17]

2.4

− 30

245.8

2.83

–

–

[18]

2.4

− 24.52

979

–

–

–

[19]

2.45

− 20.4

74.6

2.6

7.47

34.69

AP

2.45

− 13.26

120

5.6

5.8

96.55

5 Conclusion This article presents a flexible patch antenna on a natural polymer substrate for ISM applications at 2.45 GHz. The shape of this radiated element (patch) is circular. The various characterizations carried out demonstrate its potential use for applications where mechanical flexibility is required. Moreover, the performance of the patch antenna, in particular gain and directivity, can be enhanced by integrating several patch resonators on a single substrate to form an array antenna. The results show that this antenna and the proposed material are interesting candidates for developing flexible and green electronics, particularly in 5G systems. My prospect for the future is to improve this antenna and design a more efficient one.

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References 1. Allesandra, C., Diego. M.: Smart solutions in smart spaces: getting the most from far-field wireless power transfer. IEEE Microw. Mag. 17(5), 30–45 (2016) 2. Abdelhafid, E., Maryam, A., Said, M., Mohammed, F., e al.: A pHEMT double-balanced up-conversion mixer for 5G MM-Wave communication systems. IJMOT 17(4), 401–4011 (2022) 3. Salah-Eddine, D., Imane, H., Mohammed, F., Younes, B., Said, M., Moulhime, EL.: Study and design of a 5G millimeter band patch antenna with a resonant frequency of 60 GHz. J. Nano- and Electron. Phys. 15(2), 02015-1–02015-6 (2023) 4. Sugumari, T., Fusic, S.J., Cornelius, K.S.R.J., et al.: Design and analysis of single-fed dualmode circular parasitic patch antenna (CPPA) for UAV application. SN Comput. Sci. 4, 247 (2023) 5. Salah-Eddine, D., et al.: Study and design of the microstrip patch antenna operating at 120 GHz. In: El Ghzaoui, M., Das, S., Lenka, T.R., Biswas, A. (eds.) Terahertz Wireless Communication Components and System Technologies, pp. 175–190. Springer, Singapore (2022) 6. Abdelhafid, E., et al.: Simple inter-stage impedance matching technique for 5G mm-wave systems. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushan, B. (eds.) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol. 635. Springer, Cham (2023) 7. Ali, H.K.: Implementation of a circular shape patch antenna at 2.4 GHz for different wireless communications. Iraqi J. Sci. 64(1), 205–214 (2023) 8. Pandey, S., Markam, K.: Design and analysis of circular shape microstrip patch antenna for C-band applications. Int. J. Adv. Res. Comput. Sci. Technol. 4, 169–171 (2016) 9. Salah-Eddine, D., Imane, H., Mohammed, F., Younes, B., Said, M., Moulhime, EL.: Study and design of a 28/38 GHz bi-band MIMO antenna array element for 5G. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds.) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol. 635. Springer, Cham (2023) 10. Ibanez Labiano, I., Alomainy, A.: Flexible inkjet-printed graphene antenna on Kapton. Flex. Print. Electron. 6(2), 1–9 (2021) 11. Xu, D., Xu, R., Hu, B., et al.: Flexible low-profile UWB antenna on polyimide film based on silver nanoparticle direct-write dispenser printing for wireless applications. J. Mater. Sci. Mater. Electron. 34(1297) (2023) 12. Salah-Eddine, D., Imane, H., Mohammed, F., Younes, B., Said, M., Moulhime, EL.: Design of a microstrip antenna two-slot for fifth generation applications operating at 27.5 GHz. In: International Conference on Digital Technologies and Applications (ICDTA), Lecture Notes in Networks and Systems, Fes, Morocco, vol. 211, pp. 1081–1089. Springer (2021) 13. Babatunde, S., Latujoye, O., Jeffrey, C.S.: Design and performance analysis of 4-element multiband circular microstrip antenna array for wireless communications. IOSR J. Electron. Commun. Eng. (IOSR-JECE) 18(1), 01–07 (2023) 14. Salah-Eddine, D., et al.: New microstrip patch antenna array design at 28 GHz millimeter-wave for fifth-generation application. IJECE 13(4), 4184–4193 (2023) 15. Ali, H.K.: Implementation of a circular shape patch antenna at 2.4 GHz for different wireless communications. Iraqi J. Sci. 25(4), 54–58 (2022) 16. Sohel, R.M., Mostafizur, R.M.: Study of microstrip patch antenna for wireless communication system. In: 2022 International Conference for Advancement in Technology (ICONAT), pp. 1– 4. IEEE, Goa, India (2022) 17. Gökçen, D., Cem, G., Ismail, A.: Micro-strip patch 2.4 GHz Wi-Fi antenna design for wlan 4G-5G application. Int. J. Surv. Eng. Technol. 6(1), 68–72 (2022)

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18. Cem, G., Sena, E.B.K., Rukiye, B.A.: The development of broadband microstrip patch antenna for wireless applications. Bitlis Eren Univ. J. Sci. 11(3), 812–819 (2022) 19. Sohel Rana, M., Sen, B.K., Tanjil-Al Mamun, M., Shahriar Mahmud, M., Mostafizur Rahman, M.: A 2.45 GHz microstrip patch antenna design, simulation, and analysis for wireless applications. Bull. Electr. Eng. Inform. 12(4), 2173–2184 (2023)

Study and Design of a 140 GHz Power Divider Abdeladim EL Krouk1(B) , Abdelhafid Es-saqy2 , Mohammed Fattah1 , Said Mazer2 , Mahmoud Mehdi3 , Moulhime El Bekkali2 , and Catherine Algani4 1 EST, Moulay Ismail University, Meknes, Morocco

[email protected]

2 AIDSES Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco 3 Université Libanaise de Beyrouth, Beirut, Lebanon 4 Gustave Eiffel University, CNRS, Le Cnam, Paris, France

Abstract. A Marchand balun type power divider, using two output ports for the 135–145 GHz frequency band, is presented in this article. It allows the power supplied to the input port to be evenly distributed between the two outputs, offering better performance at frequencies above 100 GHz. Agilent’s advanced design software (ADS) was used to design a two-way output balun with a 180° phase difference and identical amplitude. The balun has an insertion loss of − 3.04 dB, while the return loss and port isolation levels are less than − 18 dB and − 20 dB, respectively. It occupies an area of 500.68 μm × 811.95 μm. Keywords: 140 GHz · Power divider · Advanced design system (ADS) · Isolation

1 Introduction The receiver chain contains many types of differential circuits, such as mixers, amplifiers, local oscillator, baluns and antenna [1–3]. These constitute a structure that connects balanced and unbalanced circuits and is also used for impedance matching [4]. Consequently, a power divider or combiner is required to provide differential RF signals from an unbalanced RF signal. Power dividing and combining devices include power dividers [5–8], branch line couplers [9, 10], and hybrid ring couplers [11, 12]. This article presents a passive power divider featuring a simple architecture for microwave circuits. It functions in the Terahertz frequency band. This power divider provides a 180° phase shift between the input signals, radio frequency (RF), and local oscillator (LO) of the double-balanced mixer presented in [13]. Figure 1 shows the basic schematic of a power divider with two output ports. It consists of a single asymmetrical input port (P1) and two symmetrical output ports (P2 and P3) of the same amplitude, 180° out of phase. This work aims to develop a passive balun that can be used as a power divider or power combiner. This balun offers advantages such as no DC power consumption and easy integration into applications in the 140 GHz frequency band. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 480–486, 2024. https://doi.org/10.1007/978-3-031-48573-2_69

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Fig. 1. Basic balun diagram.

Recently, numerous studies have been published on power dividers. A two-, fourand eight-way Wilkinson power divider (WPD), designed to operate in the frequency band from 1.5 to 4.5 GHz, is presented in [14]. In [15], a 2-section microstrip Wilkinson power divider is designed at a center frequency of 3.5 GHz. This work is structured as follows: the second part presents the design of the twoport balun. The third part deals with simulating the proposed circuits using Agilent’s advanced design software (ADS). Finally, the layout of the two-port balun concludes our study.

2 Circuit Design The power divider proposed in this article is based essentially on transmission lines and a resistor network of PH15 technology to increase isolation and impedance matching [4]. A quarter-wave transformer is connected between the input and output ports, with a line length equal to (λ/4), to ensure a match between the separate ports√and a common port. The characteristic impedance of the quarter-wave section is Z1/ 2. In order to achieve extremely high isolation, two resistors, with a characteristic impedance value Z1 = 20  and l = 100 μm, are connected to both ends of the transmission line of output ports P2 and P3, as shown in Fig. 2.

Fig. 2. The ideal balun design.

Figure 3 shows the design of the proposed balun structure with two output ports operating at the center frequency of 140 GHz. This frequency band is characterized by

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low atmospheric attenuation, which means that radio signal loss is minimal [16]. This design is mainly based on integrating transmission lines and a resistor network of PH15 technology, developed using Agilent’s advanced design system (ADS) software. We are using the keysight LineCalc tool (ADS) to obtain the dimensions of the width (w) and electrical length (L) of the transmission lines.

Fig. 3. The ADS design of the two-port balun

3 Simulation and Results Figure 4 illustrates the variation of the phase difference and amplitude difference of the two-port power divider as a function of frequency in GHz. It shows a 180° phase shift between the two output ports and a zero-amplitude difference, meaning that the output signals have equal amplitude over the frequency range 125–150 GHz. Balun performance is evaluated as a function of S parameters such as reflection coefficient (S11), insertion loss (S12) and isolation (S32). Figure 5 shows the variations in parameters S11, S21 and S32 as a function of the balun’s GHz operating frequency. Return loss S11, which corresponds to impedance matching at balun accesses, is considered acceptable if it is less than − 10 dB [10]. For our proposed circuit, the value obtained for S11 is − 48.93 dB at a frequency of 140 GHz. In addition, the S32 isolation coefficient between the two output ports reaches a value of − 18.54 dB. The final balun performance parameter is insertion loss (S21), which reaches a value of − 3.04 dB.

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Fig. 4. Phase and amplitude differences of the balun as a function of frequency in GHz.

Fig. 5. Parameters S11, S21 and S32 as a function of frequency in GHz

4 Balun Layout Design Figure 6 shows the layout of the two-way power divider. It occupies an area of 500.68 μm × 811.95 μm. The phase difference and amplitude variation of the two-way balun are shown in Figs. 7 and 8, respectively. There is a 180° phase shift between the two output ports. In addition, the amplitude difference between the two output ports reaches a value of 0.013 dB, corresponding approximately to the same amplitude. Figure 9 shows the evolution of the S parameters of the two-way power divider (S11, S21, and S31) as a function of operating frequency in GHz. The S11 reflection coefficient reaches − 7.6 dB at 140 GHz. The parameters S21 and S31 values are − 3.931 dB and − 3.943 dB, respectively.

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Fig. 6. The layout design of the two-way balun.

Fig. 7. Evolution of phase difference as a function frequency in GHz.

Fig. 8. Amplitude difference as a function of frequency in GHz

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Fig. 9. The reflection coefficient S11 and the two-way balun parameters (S21, S31).

5 Conclusion The ADS software designed and simulated a two-way passive power divider with a simple architecture. Performance parameters such as return loss S11, isolation, and insertion coefficient S21 have values of − 48 dB, − 18.54 dB, and − 3.04 dB, respectively. It has a 180° phase shift between the two output ports and a zero-amplitude difference in the frequency band 135–145 GHz.

References 1. Es-Saqy, A., et al. : Very low phase noise voltage controlled oscillator for 5G mm-wave communication systems. In: 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–4. IEEE (2020) 2. Didi, S.-E., et al.: New microstrip patch antenna array design at 28 GHz millimeter-wave for fifth-generation application. IJECE 13(4), 4184–4193 (2023) 3. Es-Saqy, A., et al.: 5G mm-wave band pHEMT VCO with ultralow PN. Adv. Sci. Technol. Eng. Syst. J. 5, 487–492 (2020) 4. Cheng, L., Rong, L., & Zuomin, C.: 60GHz Marchand Balum, Circuits intégrés hyperfréquences monocristallins, p. 1–5, décembre (2011) 5. Yang, Z., Liu, W., Miao. C., Yuan, X. and Wu, W.: A balanced-to-single-ended wilkinson power divider. J. Microw. Optoelectron. Electromagn. Appl. 16(3), 777–784 (2017). https:// doi.org/10.1590/2179-10742017v16i3924 6. Shahi, H., Shamsi, H.: Compact wideband Gysel power dividers with harmonic suppression and arbitrary power division ratios. AEU-Int. J. Electron. C. 79, 16–25 (2017). https://doi. org/10.1016/j.aeue.2017.05.024 7. Yang, X., Zhang, X., Liao, Z.: A novel planar four-way power divider with large dividing ratio. AEU-Int. J. Electron. C. 85, 1–6 (2018). https://doi.org/10.1016/j.aeue.2017.12.028 8. Horst, S., Bairavasubramanian, R., Tentzeris, M., Papapolymerou, J.: Modified wilkinson power dividers for millimeter-wave integrated circuits. IEEE Trans. Microw. Theory Techn. 55(11), 2439–2446 (2007). https://doi.org/10.1109/TMTT.2007.908672 9. Naderi, M., Abbasi, H.: Design of compact microstrip branch line coupler using semi-circular and rectangular resonators with wide range suppressed harmonics. AEU-Int. J. Electron. C. 84, 171–176 (2018). https://doi.org/10.1016/j.aeue.2017.11.031 10. Manikandan, S., Margaret D.H.,.: Design of Compact Rat Race Coupler for WLAN Receivers, vol. 9, no. 6 (2021)

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11. Beigizadeh, M., Dehghani, R., Nabavi, A.: Analysis and design of a lumped-element hybrid coupler using limited quality factor of components. AEU-Int. J. Electron. C. 82, 312–320 (2017). https://doi.org/10.1016/j.aeue.2017.09.001 12. Janin, S., Sripimanwat, K., Phongcharoenpanich, C., Krairiksh, M.: A hybrid ring coupler quasi-optical antenna-mixer. AEU-Int. J. Electron. C. 63(1), 36–45 (2009). https://doi.org/ 10.1016/j.aeue.2007.10.005 13. Elkrouk, A., et al.: Gilbert cell down-conversion mixer for THz wireless communication. In: The International Conference on Artificial Intelligence and Smart Environment, pp. 475–480, Nov (2022) 14. Poornima, P., Pradeep, K., Ramya, K.: EM simulation and layout performance analysis of n-way wilkinson power divider. Int. J. Sci. Res. (IJSR) 6(9). ISSN (Online): 2319-7064 15. Musa, U., Ali, A., Babani, S.: Design and simulation of 2 section micro. Bayero J. Eng. Technol. (BJET) 14(1) (2019) 16. Yoshitaka, O., Daisuke, Y., Nguyen, N.: A 140 GHz area-and-power-efficient VCO using frequency doubler in 65 nm CMOS. Inst. Electron. Inf. Commun. Eng. 16, 1–5 (2019)

Emerging Concepts Using Blockchain and Big Data Fatna El Mendili(B)

and Mohammed Fattah

Image Laboratory, School of Technology, Moulay Ismail University of Meknes, Meknes, Morocco [email protected]

Abstract. Over the past few years, big data has attracted significant interest across a range of scientific and engineering fields. Despite having many benefits and uses, big data still has a number of obstacles that must be overcome for a higher level of service, such as big data analytics, privacy, and security. Big data services and apps have enormous potential to be improved by blockchain due to its decentralization and security features. First, we provide a brief introduction to big data and blockchain, then we present a thorough relational blockchain for big data in this paper, concentrating on current approaches and potential. Finally, we discuss a variety of blockchain services for big data, including sharing, database management, and secure large data transit. Keywords: Blockchain · Big data · Data sharing · Collection · Data management

1 Introduction A blockchain is a technology that connects a few informational blocks in a decentralized, easily identifiable, and transparent manner. It was founded for the first time in 2008 in order to track the decentralized digital currency’s activities [1]. Blockchain, the technology that is the foundation of Bitcoin, quickly became clear to be Satoshi Nakamoto’s genuine discovery [1], making Bitcoin only the initial of many Blockchain deployments in the near future. A Blockchain, in terms of technology, is a publicly accessible ledger that records every transaction that has ever taken place in the network. Each full node on the P2P network where it operates keeps a copy of the Blockchain ledger. Blockchain information is not managed by a single entity [2]. Because it has the potential to bring about a revolutionary change in the areas of privacy and authentication, blockchain technology is growing in popularity in the fields of supply chain management, financial services, cloud services, and consumer credit transactions. By using this technology, the problems of risk management, security, and resource allocation might be overcome. A centralized database or the usage of a thirdparty service is not necessary because the data saved on a blockchain cannot be altered. This eliminates the overhead expenses incurred by various businesses and organizations in handling intermediate services and is immutable [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 487–492, 2024. https://doi.org/10.1007/978-3-031-48573-2_70

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Defines big data as an emerging class of technologies and architectures [4] being researched to analyze massive amounts of data and preserve its key qualities (such as high velocity, knowledge discovery, and analytics). Big data is defined in the comparative perspective as datasets that are exceedingly huge in size and dimension that cannot be managed, stored, analyzed, or collected by traditional database technologies. In spite of this, there are additionally a number of difficulties and problems related to big data methodologies and uses, such as big intelligence for data, energy management, organizing data, data understanding, and data processing in real time. Given that big data frequently incorporates many forms of sensitive personal information, such as age, address, personal preferences, and banking data, security, and privacy have been recognized as key difficulties among these difficulties. Blockchain offers effective network management and security features for enabling recently emerging big data services and applications, which have the huge potential to change present big data systems.

2 Overview of Blockchain and Big Data 2.1 Blockchain A distributed, chronological record of transactions is known as a blockchain. These nodes that take part in a peer-to-peer network share and maintain this database. The way transactions are stored is where the term “blockchain” originates. In more detail, transactions are organized into blocks, which are then connected to one another in a chain [5]. A blockchain can be compared to a public ledger where transactions are impervious to manipulation [6]. Figure 1 depicts the architecture of a blockchain. A blockchain needs approval from numerous network nodes, all of which adhere to the same consensus process before a new transaction can be added to the ledger. Blocks are connected in a chain that is constantly expanding. All of the network’s nodes take part in validating new blocks when they are generated. Blocks are added to the blockchain after they have been verified. To check whether blocks are reliable, consensus techniques are developed. The following block will be stored by which node, and how the newly added block will be confirmed by other nodes, will be decided using consensus techniques. Proof of work (PoW), proof of stake (PoS), and practical byzantine-fault tolerance (PBFT) are examples of consensus methods. Profiles who initially solve the puzzle (i.e., PoW or PoS) typically perform consensus algorithms. Miners are the name for these users. A complete copy of the blockchain is kept by each miner. In contrast to PoW and PoS, PBFT requires a number of voting rounds before a consensus is reached. Using distributed consensus techniques, transactions can be completed without the involvement of third parties like banks. The transaction expenses can be reduced as a result. In order to maintain their privacy, consumers also transact using their virtual addresses rather than their real identities. It is conceivable for many nodes in blockchain systems to successfully establish consensus (i.e., complete the problem-solving) at the same time, which can result in the bisected branches [6].

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Fig. 1. Blockchain

2.2 Bigdata Big data has become the most significant and current study topic due to its widespread use and application across many disciplines. Big data is characterized as a collection of massive databases, which makes it difficult to decide which type of information to use to meet deadlines or go through the typical phases of information preparation [7]. Big data breakthroughs have been developed and made available to those in need thanks to the quick growth of big data science and inventions, multiple information mining techniques, and devices. The capacity to leverage these large datasets, or “big data,” for use in big data applications that encourage efficient information processing and application administration has considerably increased the variety of needs that may be addressed in daily life and in business [8]. The management of organizations, media centers, environmental conditions, educational institutions, biomedical, online media and systems administration, smart city transportation, and data transfer are just a few of the domains and applications that can benefit from the massive information processing and administrations that are currently being developed. A proposal, a prediction, or a framework for making decisions are all frequently used with large information-based applications [9]. The term “big data” refers to the creation of vast informational indexes that include diverse configurations, such as organized, unorganized, and semi-organized information, in contrast to traditional data. The complexity of large amounts of data necessitates amazing technological advancements and sophisticated calculations. Therefore, big data applications cannot be deployed with the traditional consistent business intelligence (BI) technology. Big data is created from several sources and in a variety of configurations (such as recordings, reports, notes, and logs), among others. The 5 Vs—volume, velocity, variety, veracity, and value—are the key criteria that most researchers and experts use to describe big data. The following succinct description of the five V’s of big data.

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– Volume: The magnitude of information is represented by volume. Different terabytes and petabytes are accounted for by large information sizes [10] – Variety: A dataset constructional variation is referred to as variety. Businesses can now make use of various sorts of structured, unstructured, and organized information thanks to modern technological breakthroughs. – Velocity: According to [9], velocity is the speed at which information is generated and at which it should be assessed and acted upon. – Veracity: The veracity of data that has been gathered from diverse sources shows its accuracy and dependability. There is a chance that incomplete and erroneous data will be mixed together [11]. – Value: The value stands in for the characteristic that distinguishes big data. Large amounts of data need to be evaluated since they have a high value, which increases the knowledge that can be learned from the data [9]. Big data analytics is the process of removing relevant data and patterns from a dataset so they can be used for various tasks and to provide economic and societal value [4]. Figure 2 provides a summary of blockchain services in the context of big data.

Fig. 2. An overview of blockchain services in a big data environment [1]

3 Blockchain Services for Big Data Large businesses and organizations are storing, viewing, and analyzing data with cuttingedge analytical tools, which has resulted in a substantial growth in big data technology. However, big data security is a significant concern because of the massive data use

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and transfer. When appropriate security measures are not used, some third-party apps and intrusions can easily carry out criminal operations including stealing sensitive data and crashing the server [2]. Big data encounters difficulties from a number of angles, including data collecting, sharing, storage, and analysis. Big data applications typically use unstructured data, which is data that has been collected from many sources in a different format. 3.1 Using Blockchain to Secure Big Data Collection Recently, big data applications have become more popular, but they also face significant security challenges. In the process of processing data, gathering data is a crucial step. Data collecting can be exposed to a variety of harmful attacks and threats thanks to dubious data sources and communication channels. 3.2 Using Blockchain to Secure the Transfer of Big Data Blockchain is able to offer secure massive data transports due to its decentralized and immutable nature. In an effort to address security and privacy concerns that still exist in conventional data transmission protocols, Additionally, it enables trustworthy data exchange between data sources and data analytics. 3.3 Using Blockchain to Secure File Systems A number of cloud-based services are available for storing and accessing files on any machine, any place. Users, and corporations, in particular, are wary of storing sensitive data on a system run by a third party. Even while one option is to encrypt files before uploading them to the cloud, the cloud provider still faces some security challenges. 3.4 Database Management Security Through the Use of Blockchain Technologies Data that is kept in different kinds of database management systems. The fraudulent modifications in the databases were discovered using database tampering detection techniques. To detect data misuse, single-way cryptography hash algorithms, and digital watermarking are used. Distributed databases, however, are inapplicable to the strategy. The data is stored on distributed databases and fraud-detecting user transactions are done using a blockchain-based system.

4 Conclusion The study has offered information on big data and blockchain technologies that illustrate the innovation in the applications. Over the past few years, the idea of big data has also greatly increased interest in the scientific community. There has been a lot of opportunity to expand and enhance the services thanks to the decentralized aspect of blockchain technology. It is clear from the in-depth discussion that dedicated credit procedures are required in order to further strengthen the system’s creditworthiness for data-level storage activities.

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It is clear from the conversation that a strong blockchain framework for big data contains a number of technological problems that must be taken into account when it is integrated and deployed. Bigdata and blockchain can work together to address each other’s difficulties. Data integrity is ensured by a decentralized, immutable ledger using cutting-edge technologies, and big data analytics offers improved insights for producing insightful forecasts for enormous data accumulation.

References: 1. Sunny, F.A., et al.: A systematic review of blockchain applications. IEEE Access 10, 59155– 59177 (2022). https://doi.org/10.1109/ACCESS.2022.3179690 2. IEEE EUROCON 2017: 17th International Conference on Smart Technologies: Conference Proceedings: 6–8 July 2017, Ohrid, Mcedonia. IEEE, Piscataway, New Jersey (2017) 3. Deepa, N., et al.: A survey on blockchain for big data: approaches, opportunities, and future directions. Futur. Gener. Comput. Syst.. Gener. Comput. Syst. 131, 209–226 (2022). https:// doi.org/10.1016/j.future.2022.01.017 4. Oliva, G.A., Hassan, A.E., Jiang, Z.M.: An exploratory study of smart contracts in the Ethereum blockchain platform. Empir. Softw. Eng.. Softw. Eng. 25(3), 1864–1904 (2020). https://doi.org/10.1007/s10664-019-09796-5 5. Zheng, Z., et al.: An overview on smart contracts: challenges, advances and platforms. Futur. Gener. Comput. Syst.. Gener. Comput. Syst. 105, 475–491 (2020). https://doi.org/10.1016/j. future.2019.12.019 6. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. In: Bitcoin: A Peer-to-Peer Electronic Cash System (2008) 7. Margara, A.A.: Unifying model for distributed data-intensive systems. In: Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, pp. 176–179 (2022) 8. Zakir, J., Seymour, T., Berg, K.: Big data analytics. Issues Inf. Syst. 16(2) 9. Kwon, O., Lee, N., Shin, B.: Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manage. 34(3), 387–394 (2014) 10. Vassakis, K., Petrakis, E., Kopanakis, I.: Big data analytics: applications, prospects and challenges. In: Skourletopoulos, G., Mastorakis, G., Mavromoustakis, C.X., Dobre, C., Pallis, E. (eds.) Mobile Big Data, pp. 3–20. Springer International Publishing, Cham (2018) 11. Sharma, P., Jindal, R., Borah, M.D.: ‘Blockchain technology for cloud storage: a systematic literature review.’ ACM Comput. Surv. (CSUR) 53(4), 1–32 (2020)

Exploring the Applications and Challenges of Blockchain Technology in Healthcare and IoT Fatima Anter(B) , Fatna Elmendili, Mohammed Fattah, and Nabil Mrani Image Laboratory, EST, Moulay Ismail University of Meknes, Meknes, Morocco [email protected]

Abstract. Lately, there has been a considerable rise in the application of blockchain technology. Moreover, it has shown enormous promise for revolutionizing various domains by enhancing security measures, ensuring data privacy, and maintaining data integrity. The purpose of the paper seeks to present the concept of blockchain technology and its potential healthcare and Internet of Things (IoT) applications. It delves into how blockchain can effectively address these domains’ security, privacy, and data management challenges. The paper emphasizes the advantages of blockchain, including secure and tamper-proof transactions, data protection, and efficient data management. However, integrating blockchain with IoT and healthcare also presents scalability, privacy, security, and bandwidth issues. Careful consideration is imperative to optimize the benefits of blockchain technology in these domains. Keywords: Blockchain · Healthcare · IoT · Security · Privacy · Integrity

1 Introduction The emergence of blockchain technology has generated considerable interest as an innovative technology with significant implications for numerous industries. While its initial association with cryptocurrencies garnered attention, the unique features and potential applications of this technology have captivated the attention of researchers and innovators. Blockchain operates as a decentralized and immutable distributed ledger system, providing digital transactions with transparency, security, and trust. As a result, it has found practical applications in various areas, including healthcare, IoT, smart cities, and supply chain management. The paper is organized into several sections. Section 2 outlines the concept of blockchain technology. Section 3 explores the different uses of blockchain technology in IoT and healthcare. Section 4 emphasizes the challenges accompanying blockchain technology implementation in these domains. Lastly, Sect. 5 offers a conclusion that summarizes the essential findings and insights of the paper.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 493–498, 2024. https://doi.org/10.1007/978-3-031-48573-2_71

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2 Related Work Numerous studies examining the application of blockchain technology concerning IoT and healthcare, there remains a need for further research. While some studies have addressed the advantages and challenges of integration blockchain in these areas, a gap still requires more extensive investigation. The current studies need to be more comprehensive and complete in their coverage. Rahman et al. [1], Kashani [2], Ray et al. [3] Azbeg et al. [4] focused specifically on integrating IoT in the healthcare sector using blockchain technology. They explored this integration’s potential applications, challenges, and limitations within the healthcare industry. However, it is essential to note that IoT has diverse applications across industries, including smart home automation, industrial automation, smart cities management, precision farming, transportation, and logistics optimization, and energy management. Adere [5] highlighted blockchain technology’s potential benefits in healthcare and IoT, specifically regarding data management, security, privacy, and integrity. They also presented the challenges of integrating blockchain into IoT and healthcare systems. Their study explicitly investigates the benefits of utilizing blockchain technology in healthcare and IoT, particularly emphasizing areas such as smart cities, drug supply chain management, and industrial applications. Further research must still be conducted to fully grasp the prospective applications of blockchain technology concerning IoT and healthcare. Additionally, addressing the challenges associated with scalability, interoperability, security, and privacy is crucial when utilizing this technology.

3 Overview of Blockchain A blockchain is a digital ledger that operates decentralized and distributed. It serves to maintain transactions and data across a network of interconnected nodes. It is a transparent and tamper-proof system in which no single entity has sole control. A blockchain’s structure comprises blocks and containers for collecting transactions or data. These blocks are linked together through cryptographic hashes. Each block contains a unique identifier called a hash, which is generated from the block’s data and the hash of the previous block. Linking data on a blockchain ensures that the information stored is secure, immutable, and trustworthy. Components of Blockchain Distributed Network: A blockchain operates on a network of nodes that collaborate to uphold and validate the blockchain. Each node keeps a copy of the blockchain and actively participates in the consensus procedure to ensure transaction validity. Consensus Mechanisms: Consensus mechanisms are critical protocols within a blockchain network that facilitate nodes in reaching a collective agreement on the legitimacy of transactions. Cryptographic: Blockchain uses cryptographic algorithms to protect the confidentiality and authenticity of transactions. Cryptography renders transactions verifiable, transparent, and resistant to tampering or unauthorized changes. Smart Contracts: Smart contracts are pre-programmed agreements with specific terms and conditions on the blockchain and designed to operate autonomously. These contracts ensure that the terms of the agreement are adhered to automatically, allowing for efficient execution.

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4 Applications of Blockchain We analyze the challenges encountered in the healthcare and IoT industries and explore the advantages of using blockchain technology to tackle these obstacles. 4.1 Internet of Things IoT devices possess inherent vulnerabilities that render them susceptible to security breaches, such as weak passwords and unencrypted network services. IoT devices often need more computational power to implement sophisticated security solutions. IoT devices may communicate with each other using ad hoc IP protocols, which exposes the system to intrusion and data corruption threats. IoT systems commonly adopt a clientserver architecture, which introduces a single point of failure. The failure of a centralized server can significantly impact the network infrastructure as a whole, thereby increasing security and privacy risks. Finally, the need for dominant operating systems and standard data formats in IoT devices makes it difficult to achieve interoperability. Blockchain offers solutions to critical challenges with the Internet of Things (IoT). Its decentralized structure allows devices to share data without a central point of failure securely. Encryption protects privacy, while smart contracts automate tasks like payments. As IoT networks scale, blockchain coordinates devices and simplifies data transfers. Its tamper-proof ledgers record device activities transparently. This transforms centralized IoT models towards integrated, trustworthy data flows. Numerous approaches have been suggested to implement blockchain technology in conjunction with IoT To enhance security and confidentiality. To address the challenges of secure data storage and communication in IoT networks, Banavathu et al. [6] introduced an innovative model that leverages the power of AI and Blockchain technologies within IoT-based smart computing networks. Their model enhances reliability, privacy, and authentication for shared user data. Notably, this approach surpasses traditional methods in terms of time efficiency, encryption, decryption, and energy efficiency. In their research, Lekssays et al. [7] developed a blockchain-based solution for detecting IoT botnets that preserves privacy. The system associates authentic device identities with false ones and modifies the network structure to prevent device re-identification. The solution operates in real-time and enhances its detection capabilities by caching results from incremental updates. The solution proves its efficiency over other botnet detection solutions by prioritizing privacy, focusing on C&C commands, targeting P2P botnets, and demonstrating scalability through blockchain technology. 4.2 Healthcare Sharing sensitive patient data between healthcare stakeholders presents challenges involving security, privacy, integrity, and interoperability. Traditional electronic health record systems rely on centralized databases, posing concerns regarding stored medical data. Blockchain technology presents an innovative way to enhance healthcare through secure data sharing. Utilizing blockchain’s distributed ledger capabilities allows sensitive patient information like medical records, lab results, and imaging scans to be stored securely while still allowing healthcare providers access. Blockchain-based healthcare

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systems enable access control through encryption and timestamping, maintaining privacy and security of patient data. Patients could control access to their records with private keys, empowering them to manage their health information. Blockchain also facilitates transparency and interoperability of data across providers. Smart contracts on the blockchain can automate administrative procedures like insurance claims processing and clinical trial data reporting. Numerous approaches have been suggested to implement blockchain technology in conjunction with healthcare to improve security and confidentiality. For instance, Shamshad et al. [8] implemented a blockchain technology solution to tackle electronic health record (EHR) privacy and security concerns. The suggested solution involves leveraging a private blockchain to maintains and share patient data securely, making it accessible only to authorized hospital personnel. In addition, a consortium blockchain is used to share EHR security indexes, allowing hospitals to trace and monitor access to patient data. The system uses hash values to monitor access to EHR keywords, enhancing data security and reducing the likelihood of unauthorized access to patient data. The suggested blockchain-based solution provides improvements in computational efficiency, communication costs and enhanced security features compared to related protocols. Zaabar et al. [9] developed a system that utilizes blockchain technology to manage healthcare data. It utilizes a private blockchain to store and manages healthcare data with tamper-proof characteristics securely. In addition, the system employs smart contracts to automate data access and permission management. The system addresses the security and confidentiality challenges in traditional healthcare data management systems. The system surpasses other existing systems regarding security, privacy, features/functions, and performance metrics. Zhang et al. [10] suggested implementing a decentralized ehealth system that utilizes blockchain technology to ensure patients’ EHRs security and privacy. The system employs a blockchain to store immutable EHRs of patients, thereby protecting the data from unauthorized modifications or deletions. In addition, smart contracts are used to automate payment transactions between patients and hospitals, ensuring that payment processes are dependable and efficient. The system outperforms existing methods, such as cloud-assisted systems and cryptographic key management schemes, which have data privacy and security limitations. In contrast, the proposed system utilizes pairing-based cryptography and blockchain technology to guarantee the safety and authenticity of patients’ EHRs and effectively resist various attacks, including collusion, tampering, and man-in-the-middle attacks, providing robust security.

5 Challenges Despite the prospective advantages of blockchain technology in IoT and healthcare, several issues must be addressed. Scalability refers to a blockchain network’s ability to process a high volume of transactions efficiently. However, blockchain networks have limitations on the volume of transactions they can process per second (TPS), which can be challenging for IoT applications generating essential data. For example, Bitcoin has a TPS of 7, while Ethereum has a TPS of 15. In healthcare, where numerous IoT devices continuously generate data, ensuring that the blockchain network can handle and process these transactions promptly

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is crucial. Latency is another concern, particularly for real-time IoT applications. Bitcoin has an average latency of 10 min, while Ethereum averages 12 s. In healthcare, where timely data transmission and responses are vital, latency can affect the efficiency and reliability of patient care. Integrating blockchain with IoT presents challenges related to storage, as many IoT devices have limited storage capabilities. This can lead to difficulty managing the constant data flow, even for normal blockchain nodes with adequate storage capacity. As such, it is crucial to effectively address the storage needs of IoT devices within the blockchain network to guarantee efficient data management and facilitate the application of blockchain and IoT systems. When combining blockchain technology with IoT appliances, some challenges arise due to resource constraints and bandwidth issues. Generally, IoT appliances possess processing capabilities, while blockchain technology demands significant energy and computing resources. For instance, PoW (Proof of Work) needs considerably more energy than PoS (Proof of Stake). Additionally, as data accumulates over time, the blockchain database expands. The high number of connected devices and blockchain technology demands also raise concerns about bandwidth consumption. It is essential to address the need for more standardization in healthcare when implementing blockchain technology. Standard data collection, exchange, and storage protocols are essential to ensure consistency and interoperability when implementing blockchain technology in healthcare systems. Healthcare requires robust security measures to protect sensitive data. Public Blockchains are unsuitable because they make every transaction, even anonymous, accessible to the public. Private Blockchains are more effective at preserving privacy, making them the optimal choice for healthcare applications. However, implementing private blockchain platforms to restrict data access can have a negative impact on the accuracy and efficacy of AI-based decision-making and analytics. This results from the decline in the amount of data available for analysis. Blockchain technology is frequently employed to improve the protection and integrity of various applications. However, the blockchain suffers security issues, such as the potential for a 51% attack, quantum computing attacks, and smart contract vulnerabilities. Data security is a current research challenge in IoT and healthcare due to the involvement of vast volumes of sensitive data. These domains are attractive to malicious nodes seeking to harm the integrity and privacy of data.

6 Conclusion Blockchain technology can effectively manage healthcare and IoT security, data integrity, and privacy issues. It enhances data security and privacy in healthcare by providing secure and immutable storage of patient records, ensuring data integrity, preventing unauthorized modifications, and enabling the secure sharing of medical information. Similarly, blockchain technology enhances security, data integrity, and privacy in IoT by providing a decentralized and tamper-resistant framework. Traditional centralized systems in healthcare and IoT struggle with the increasing volume and complexity of data, leaving them vulnerable to cyber threats. However, implementing blockchain technology in healthcare and IoT poses various challenges. In future work, we will focus on exploring in-depth solutions to address these challenges effectively.

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References 1. Rahman, M.S., Islam, M.A., Uddin, M.A., Stea, G.: A survey of blockchain-based IoT eHealthcare: applications, research issues, and challenges. Internet Things 19, 100551 (2022). https://doi.org/10.1016/j.iot.2022.100551 2. Haghi Kashani, M., Madanipour, M., Nikravan, M., Asghari, P., Mahdipour, E.: A systematic review of IoT in healthcare: applications, techniques, and trends. J. Network Comput. Appl. 192, 103164 (2021). https://doi.org/10.1016/j.jnca.2021.103164 3. Ray, P.P., Dash, D., Salah, K., Kumar, N.: Blockchain for IoT-based healthcare: background, consensus, platforms, and use cases. IEEE Syst. J. 15(1), 85–94 (2021). https://doi.org/10. 1109/JSYST.2020.2963840 4. Azbeg, K., Ouchetto, O., Andaloussi, S.J., Fetjah, L.: A taxonomic review of the use of IoT and blockchain in healthcare applications. IRBM 43(5), 511–519 (2022). https://doi.org/10. 1016/j.irbm.2021.05.003 5. Adere, E.M. : Blockchain in healthcare and IoT: a systematic literature review. Array 14, 100139 (2022). https://doi.org/10.1016/j.array.2022.100139 6. Banavathu, R., Meruva, S.: Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems. Measure. Sens. 27, 100741 (2023). https://doi.org/ 10.1016/j.measen.2023.100741 7. Lekssays, A., Landa, L., Carminati, B., Ferrari, E.: PAutoBotCatcher: a blockchain-based privacy-preserving botnet detector for internet of things. Comput. Networks 200, 108512 (2021). https://doi.org/10.1016/j.comnet.2021.108512 8. Shamshad, S., Mahmood, M.K., Kumari, S., Chen, C.-M.: A secure blockchain-based e-health records storage and sharing scheme. J. Inform. Secur. Appl. 55, 102590 (2020). https://doi. org/10.1016/j.jisa.2020.102590 9. Zaabar, B., Cheikhrouhou, O., Jamil, F., Ammi, M., Abid, M.: HealthBlock: a secure blockchain-based healthcare data management system. Comput. Networks 200, 108500 (2021). https://doi.org/10.1016/j.comnet.2021.108500 10. Zhang, G., Yang, Z., Liu, W.: Blockchain-based privacy preserving e-health system for healthcare data in cloud. Comput. Networks 203, 108586 (2022). https://doi.org/10.1016/j.comnet. 2021.108586

Uncovering Data Quality Issues in Big Healthcare Data: Implications for Accurate Analytics Nisrine Berros1(B) , Youness Filaly1 , Fatna El Mendili2 , and Younes El Bouzekri E. L. Idrissi1 1 Engineering Sciences Laboratory, Ibn Tofail University, National School of Applied Sciences,

Kenitra, Morocco [email protected] 2 Image Laboratory, School of Technology, Moulay Ismail University of Meknes, Meknes, Morocco

Abstract. Data quality assessment is a critical aspect of analyzing big healthcare data to ensure accurate analytics. This paper emphasizes the crucial role of data quality assessment in the analysis of big healthcare data to ensure accurate analytics. It explores the importance of data quality assessment and its implications for reliable healthcare analytics. The paper discusses various data quality issues that can impact healthcare datasets and emphasizes the need to address them effectively. Methods for data quality assessment are presented, enabling the identification and resolution of data quality issues. Additionally, the paper highlights the use of standardized data quality indicators to evaluate the reliability of healthcare data. We propose an Integrated model for Assessing Data Quality in Healthcare. In conclusion, this research underscores the significance of data quality assessment for trustworthy analytics and informed decision-making in the healthcare domain. Keywords: Data quality · Data assessment · Healthcare data · Big data analytic

1 Introduction In healthcare, the rapid digitization of information has led to the generation and accumulation of massive volumes of data, commonly referred to as “big data”. This increase in data availability offers great potential for transforming healthcare practices and improving patient outcomes through advanced analytics. However, the effectiveness of these analyses depends critically on the quality of the underlying data. The concept of data quality in the context of big healthcare data encompasses several dimensions, including completeness, accuracy, consistency, timeliness and relevance. Ensuring data quality is

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essential for obtaining reliable information and making informed decisions [1]. Unfortunately, big data in healthcare is characterized by inherent complexities that pose significant challenges to data quality. The volume and speed at which healthcare data is generated make it difficult to maintain data quality standards consistently. In addition, the heterogeneity of data sources and formats, from a variety of electronic medical record systems, wearable devices and other healthcare technologies, exacerbates the difficulties of ensuring data consistency and accuracy [2]. Here is how the paper is organized: Sect. 1 focuses on the importance of data quality assessment in healthcare, Sect. 2 explores data quality indicators and issues, Sect. 3 presents various methods for data quality assessment, Finally, Sect. 4 introduces a proposed road map for assessing data quality in healthcare.

2 Importance of Data Quality Assessment Data quality assessment plays a crucial role in healthcare, as it has a direct impact on the reliability and accuracy of analyses, ultimately influencing patient outcomes and healthcare practices. By conducting comprehensive data quality assessments, healthcare organizations can identify and rectify data-related problems, ensuring that the data used for analysis and decision-making is of the highest quality [3]. Inaccurate data can compromise patient safety, hinder comprehensive analysis, and lead to unreliable predictions. Healthcare organizations can ensure data quality through validation rules, statistical approaches, and cleansing techniques, enhancing the dependability of analysis and enabling informed decisions (Table 1). Table 1. Importance of data quality assessment in healthcare Critical factors

Importance

Implications of inadequate data quality

• • • • •

Analysis reliability

• Unreliable predictions • Flawed decision-making • Reduced trust and confidence in results

Impact on healthcare outcomes

• • • •

Biased insights Inaccurate predictions Missed diagnoses and treatments Inefficient resource allocation Negative impact on patient outcomes

Compromised patient safety Increased costs due to errors and inefficiencies Suboptimal resource allocation Missed opportunities for improvement

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3 Data Quality Indicators and Issues A. Data quality Indicators Data quality indicators are specialized measures used to evaluate data quality [4]. In the healthcare sector, these indicators evaluate the completeness, accuracy, consistency, relevance, and reliability of healthcare data, enabling providers to identify areas for improvement [1–5]. (a) Completeness: This indicator measures how well the data contains all of the information needed for a certain healthcare use case. (b) Accuracy: Accuracy refers to the correctness and precision of data. It determines how accurately data represents the intended information. (c) Consistency: Consistency checks if information remains coherent and uniform across sources, systems, or timeframes, enabling reliable comparisons and integration. (d) Relevance: Relevance indicates the suitability and usefulness of data in a particular healthcare context or analysis. (e) Reliability: Reliability refers to the trustworthiness of data. It assesses how well the data can be consistently and accurately repeated or replicated. Healthcare companies can enhance the overall quality of their data by monitoring and improving key data quality indicators, resulting in more accurate analytics, better decision-making, and better patient outcomes. In the dynamic and growing healthcare industry, regular assessments and interventions to address data quality issues are critical to preserving high-quality data. B. Common Data Quality Challenges

Table 2. Key data quality indicators in healthcare. Data quality indicators

Completeness

Accuracy

Consistency

Relevance

Key aspect

Extent of data capture

Correctness and precision of data

Coherence and uniformity of data

Appropriateness and usefulness of data

Example

• Missing patient • Incorrect dosage • Inconsistent demographic • Inaccurate lab patient information results identifiers • Incomplete • Duplicated medical history records • Absence of vital signs data

• Irrelevant data • Outdated data • Unused or data elements

In the field of healthcare data, different types of quality problems can compromise the reliability and usefulness of data. These include factors such as incompleteness, inaccuracy, inconsistency and timeliness. Incompleteness refers to missing or insufficient data,

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while inaccuracy involves errors or inconsistencies in the data. Inconsistency refers to discrepancies or contradictions between different sources or time periods, while timeliness refers to the updating and availability of data. The Table 2 further examples and information on these specific data quality issues, illustrating how they can impact on analysis and decision-making in healthcare [6]. By addressing these data quality issues, healthcare organizations can improve the integrity and usefulness of their data to achieve better patient outcomes and more accurate analyses.

4 Methods for Data Quality Assessment in Healthcare A range of procedures and strategies are used in the literature to evaluate the quality of healthcare data in data quality assessment techniques. Here are some examples of common procedures that can be covered in this section (Fig. 1). Data quality in healthcare settings can be improved through various methods. Sampling involves randomly selecting patient records from an EHR database, while validation rules ensure accuracy. Statistical analysis identifies discrepancies, while data profiling focuses on patterns. Data cleaning and preprocessing involve merging, duplicating, and normalizing data. Comparative analysis helps detect differences in data from diverse systems, resolving inconsistencies and eliminating duplicate records. These methods enhance data quality and facilitate reliable analyses in the healthcare field.

Fig. 1. Methods for data quality assessment.

5 A Proposed Road Map for Assessing Data Quality in Healthcare Previous research in the field of data quality assessment covers a wide range of methodologies. While some authors [7] focused on visual cleansing and exploration to address challenges in clinical data analysis. The approach taken by authors in [8] differs significantly. They adopted a more algorithmic-based data cleansing and preprocessing strategy, leveraging advanced statistical methods and machine learning techniques to

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enhance data quality and derive valuable insights from clinical data. The authors [9] tackle the challenges of identifying anomalies in time series data, a domain that shares certain commonalities with data quality assessment their work introduces the Large deviations Anomaly Detection (LAD) algorithm, which is specifically designed to efficiently score anomalies in large and high-dimensional datasets. In [10] authors propose an efficient pre-processing model, utilizing a hybrid approach combining the relief algorithm and fast mRMR, to enhance data quality significantly, particularly when working with big data on platforms like Spark. In light of the various methodologies discussed, it becomes clear that there is a need for a comprehensive roadmap that can effectively guide data quality assessment and improvement in the complex healthcare data landscape. Our roadmap (Fig. 2) stands out for its specificity in the context of healthcare data, as it holistically and comprehensively addresses the essential steps involved in assessing the quality of medical data, integrating a variety of approaches to address the unique challenges encountered in this complex field.

Fig. 2. A proposed road map for data quality assessment in healthcare.

1. Data Profiling: In this step, the healthcare data is analyzed to detect trends, discrepancies, and abnormalities. It investigates data distributions and detects missing values, outliers, and other anomalies that may have an impact on data quality. 2. Data Validation Rules: Validation rules are established and implemented in this step to guarantee that healthcare data conforms to predefined standards and business regulations.

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3. Statistical analysis: It’s used to extract information from healthcare data. Statistical analysis facilitates the discovery of patterns, correlations and anomalies, enabling a deeper understanding of the data. 4. Anomaly detection: Anomaly detection techniques are used to quickly find and correct data anomalies, ensuring high-quality data for decision-making and patient care. 5. Domain expert assessment: Domain specialists with specialized expertise and experience in the healthcare industry evaluate data quality. Their knowledge assists in identifying specific data quality concerns, evaluating the data’s accuracy and appropriateness, and assuring its acceptability for various healthcare applications. 6. Continuous improvement: This stage entails combining feedback from stakeholders, data users, and domain experts in order to continuously improve the data quality assessment process.

6 Conclusion and Future Work The paper outlines the importance of data quality assessment in healthcare, highlighting the potential of advanced analytics to transform practices and improve patient outcomes, our roadmap proposed is distinguished by its systematics and comprehensiveness, specifically designed to address the complexities of healthcare data. It provides a holistic approach for healthcare organizations to proactively identify and correct data quality issues. The result is the creation of a valuable repository of reliable and trustworthy data, supporting informed decision-making, research excellence and quality patient care. We aim to complement our theoretical approach with applied research in future work, to ensure the practical effectiveness and relevance of our roadmap in healthcare.

References 1. Ardagna, D., Cappiello, C., Samá, W., Vitali, M.: Context-aware data quality assessment for big data. Futur. Gener. Comput. Syst. 89, 548–562 (2018). https://doi.org/10.1016/j.future. 2018.07.014 2. Mashoufi, M., Ayatollahi, H., Khorasani-Zavareh, D., Talebi Azad Boni, T.: Data quality in health care: main concepts and assessment methodologies. Methods Inf. Med. 62(1–02), 5–18 (2023). https://doi.org/10.1055/s-0043-1761500 3. Nasir, W.M.H.M., Abdullah, R.B., Jusoh, Y.Y.B., Abdullah, S.B.: Big data analytics quality model in enhancing healthcare organizational performance: a content validity study. In: 2023 International Conference on Information Management (ICIM), pp. 25–30 (Mar. 2023). https:// doi.org/10.1109/ICIM58774.2023.00011 4. Taleb, I., Serhani, M.A., Dssouli, R.: Big data quality: a survey. In: 2018 IEEE International Congress on Big Data (BigData Congress), pp. 166–173. San Francisco, CA, USA: IEEE (2018). https://doi.org/10.1109/BigDataCongress.2018.00029 5. Cai, L., Zhu, Y.: The challenges of data quality and data quality assessment in the big data era 14 (May 2015). https://doi.org/10.5334/dsj-2015-002 6. Lee, K., Weiskopf, N., Pathak, J.: A framework for data quality assessment in clinical research datasets. AMIA Annu. Symp. Proc. 2017, 1080–1089 (Apr. 2018). Accessed: Jun. 25, 2023. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977591/ 7. Schmidt, C., et al.: Combining visual cleansing and exploration for clinical data. In: 2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC), pp. 25–32 (Oct. 2019). https:// doi.org/10.1109/VAHC47919.2019.8945034

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8. Piao, X.: Comparative analysis of the mental health status IoT assisted monitoring of the elderly under the background of big data. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 463–466 (Dec. 2021). https://doi. org/10.1109/ICECA52323.2021.9676107 9. Guggilam, S., Chandola, V., Patra, A.K.: Large deviations anomaly detection (LAD) for collection of multivariate time series data: applications to COVID-19 data. J. Comput. Sci. 72, 102101 (2023). https://doi.org/10.1016/j.jocs.2023.102101 10. Lincy, S.S.B.T., Kumar, N.S.: An enhanced pre-processing model for big data processing: a quality framework. In: 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), pp. 1–7 (Mar. 2017). https://doi.org/10.1109/IGEHT. 2017.8094109

A Fog-Based Attack Detection Model Using Deep Learning for the Internet of Medical Things Yahya Rbah1(B) , Mohammed Mahfoudi2 , Younes Balboul1 , Kaouthar Chetioui1 , Mohammed Fattah3 , Said Mazer1 , Moulhime Elbekkali1 , and Benaissa Bernoussi1 1 Artificial Intelligence and Data Science and Emerging Systems Laboratory, Sidi Mohamed

Ben Abdellah University of FES, Fes, Morocco [email protected] 2 Innovative Systems Engineering Laboratory, Abdelmalek Essaadi University of Tetuan, Tetouan, Morocco 3 Image Laboratory, University of Moulay Ismail of Meknes, Meknes, Morocco

Abstract. Internet of Medical Things (IoMT) applications have advanced and become more widespread in recent years. This has driven the need to secure IoMT networks. As a second line of defense, effective security techniques, including deep learning approaches to intrusion detection systems (IDS) have been applied to IoMT to detect network attacks. However, most existing solutions are either cloud-based or are challenging to implement on IoMT devices. This delays attack detection. Furthermore, these detections are centralized and therefore incompatible with the IoMT environment. In addition, fog computing has emerged recently as a new field that complements cloud computing due to its improved location awareness, mobility, scalability, heterogeneity, low latency, and geographical distribution. This work proposes a deep learning-based IDS for early attack detection in IoMT fog. This research is carried out using deep learning approaches based on Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). The IoT-Healthcare Security dataset is used to evaluate the proposed model. The CNN model achieves 99.62% accuracy, reduced detection time and low memory consumption. Compared to existing approaches, the proposed technique showed the highest accuracy. Keywords: Internet of medical things · Deep learning · Intrusion detection system (IDS)

1 Introduction Integrating healthcare-related devices and sensors into the IoT led to the development of the Internet of Medical Things (IoMT). IoMT systems use different types of actuators and sensors. These collect patient-related, real-time and sensitive data [1]. IoMT has enabled home-based monitoring, detection, consultation and prescription. In parallel with the exponential increase of IoMT devices and systems, various cyberattacks have highlighted critical vulnerabilities in the IoMT context [2]. For example, because of poor design and insufficient authentication mechanisms, an attacker could intercept © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 506–511, 2024. https://doi.org/10.1007/978-3-031-48573-2_73

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this transmission network when sending data from the sensor via the fog node to the cloud. Consequently, an attacker can remotely manipulate drug doses and turn IoMT devices into botnets for Denial-of-Service (DoS) attacks. The integrity, availability, confidentiality and authentication of the IoMT system can be threatened by cyberattacks [3]. In the context of IoMT systems, security is a primary challenge. To meet the needs of delay-sensitive healthcare applications, fog computing can be deployed between the cloud layer and sensor devices. It offers cloud computing services at the network edge [4]. An intrusion detection system (IDS) is a tool for monitoring network traffic to protect legitimate users from malicious activity [5]. Artificial intelligence (AI) methods, including deep learning (DL), are often used to analyze large amounts of data and provide useful information for classification, decision making and cyber intrusion detection [6]. Therefore, to prevent cyberattacks in the IoMT network, this study proposes a DL-based IDS with a fog architecture. The advantage of the proposed fog-level IDS solution is that it is close to the IoMT devices, allowing for rapid response, decentralization and privacy. The results of the experiment show that the proposed approach outperforms other studies in terms of accuracy, recall, precision and F1 score. The remainder of this paper is organized as follows. Section 2 discusses related work. Section 3 describes the proposed model architecture framework. The results are analyzed, compared and discussed in Sect. 4, and Sect. 5 concludes the paper.

2 Related Work This section presents previous work in the fog computing context that employs AI approaches to deal with cyber-attacks in the IoMT systems. Alrashdi et al. [7], proposed a fog-based attack detection (FBAD) system for IoT-healthcare in smart city environments. The suggested approach detects fog node attacks. They demonstrated that their decentralized fog-based architecture outperformed the cloud computing-based architecture with respect to detection rate and detection time. Recently, Hameed et al. [8] used the NSL_KDD dataset to develop an ensemble technique based on the weighted majority method for stream data intrusion detection at the IoMT fog layer. The results demonstrated that the proposed method outperformed the previous works. Kumar et al. introduced an ensemble learning based IDS and a cloud-fog architecture for cyberattacks detection in IoMT environments in [1]. An ensemble of Nave Bayes, Decision Tree, and Random Forest was used to construct XGBoost. The proposed model was trained and tested with the Ton-IoT dataset using fog and cloud computing. The results reveal that the accuracy is 96.35%, the detection rate is 99.98%, and the FAR can be reduced to 5.59%. A lightweight hybrid system to detect attacks in IoMT fog was developed by Hameed et al. [9]. The system was tested on Net-Flow data and seven sensors compromised by nine recent attacks. The results indicated that the suggested system performs well on light-weight fog devices with high accuracy, short detection time and reduced memory consumption. From the literature, we conclude that most of the current research [1, 7, 8] is evaluated using outdated attacks (i.e., NSL_KDD) or lacks modern IoMT-based attacks (i.e., Ton-IoT). Therefore, this study uses a publicly available dataset containing several recent

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IoMT-based attacks for evaluation purposes. In addition, the accuracy rates of the previous models must be enhanced to ensure the efficiency of the attack detection system and reduce the high rate of false alarms.

3 Materials and Methods 3.1 Proposed Fog-Based IDS for IoMT Environment The proposed mechanism uses a fog-based architecture to detect cyberattacks in the IoMT. Figure 1 depicts the overall architecture of the proposed system. The proposal framework has two main engines:

Fig. 1. The working architecture of the proposed fog-enabled IDS for IoMT.

Traffic Processing Engine: Since various sensors with different characteristics generate IoMT network traffic, data pre-processing is needed to satisfy the input requirements of DL techniques. Data pre-processing mainly includes cleaning and removing unwanted data such as null and NaN values. As the data sent by IoMT devices to the fog node does not only consist of numerical characteristics, the label-encoding method, transforms categorical variables to numerical variables. The Min-Max normalization method transforms the different numerical values of the features into the range [0, 1] without affecting the original linear relationship between the data. Feature selection consists of removing repetitive and irrelevant features. For this purpose, the Recursive-FeatureElimination (RFE) technology is used to improve the detection system’s performance by reducing processing time. Therefore, we selected the twelve most relevant features from the IoT-Healthcare Security dataset. The optimized feature set is then used to train, test and evaluate three DL algorithms: CNN, RNN and LSTM. Finally, the performance evaluation result of the model is obtained using accuracy, precision, F-measure and recall.

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Intrusion Detection Engine: The IoMT system generates huge amounts of data from different medical sensors. The traffic is collected by the fog node’s intrusion detection system. This incoming traffic goes through the same data pre-processing phase to improve the model’s performance. Finally, the IoMT network traffic is fed into the prediction model to detect attacks in normal cases. If the predicted traffic is normal, the corresponding services are provided. Whereas, if the predicted traffic is malicious, the fog node sends an alert to the cloud server and identifies the IoMT device sending this abnormal traffic as an IoMT botnet to block its functioning. Simultaneously, for the transmission of attack information over the IoMT network, logs will be saved to a cloud server. The cloud server carries out the global management and control of IoMT devices. 3.2 Dataset We employed the IoT-Healthcare security dataset [10] in our framework to incorporate the IoMT attacks. This dataset includes malicious and normal traffic from an IoT-enabled health care use case generated using the IoT-Flock tool [11]. In the IoT-Healthcare Security dataset, the test bed infrastructure was divided into the estimated network and the intruder network. The estimated network consists of MQTT devices which send and receive network data [12]. The second infrastructure network in the testbed is the intruder network, which contains several attack components capable of performing various attacks, including MQTT Publish Flood, BruteForce, MQTT Distributed DoS, and SlowITE, against targeted medical devices or servers. More details can be found in [12]. 3.3 Experimental Setup The proposed mechanism was implemented using the Python programming language with the Keras library to build our models, TensorFlow as the back-end to the Keras library, Pandas, NumPy, and Matplotlib. These libraries are built using a Python notebook running on Google Research’s Collaboratory with 12 GB of RAM. The data is shuffled and split into 80% and 20% training and testing sets respectively. 3.4 Standard Evaluation Metrics The models’ performance was evaluated with the confusion matrix (CM) and the four commonly used evaluation measures: accuracy, precision, F1 score and recall. In addition to these performance metrics, we considered each algorithm’s complexity in terms of time and memory. We, therefore, used the time needed for the prediction of all the records in the test set (i.e., the prediction time) and the memory size required for each DL classifier.

4 Results and Discussion The experiments result of the studied classifiers LSTM, CNN, and GRU are presented in Table 1 regarding accuracy, precision, recall, F1 score, execution time and memory size when applied to IoT-Healthcare security dataset. All the models achieved good accuracy

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and precision values, with a slight difference between them. The CNN classifier achieved a high accuracy of 99.57% compared to the GRU and LSTM classifiers. In addition, the CNN model has a minimal execution time (3.07 s) compared to the other classifiers. The memory size was also measured to evaluate the robustness of the classifier, and the CNN classifier requires the smallest memory size of 1201.54 MB, followed by GRU and LSTM, which require a memory size of 1229.59 MB and 1287.39 MB, respectively. Table 1 shows that the CNN model has the best accuracy, shortest prediction time and reasonable memory space. For these reasons, the CNN-based attack detector can be considered a good solution for detecting network attacks in the IoMT fog. Table 1. Performance evaluation of DL models used for binary classification tasks. Models Accuracy Precision Recall F-measure Execution time (s) Memory size (MB) LSTM

97.03

99.75

93.25

96.36

9.56

1229.59

GRU

98.77

98.20

98.90

98.55

9.69

1287.39

CNN

99.62

99.62

99.49

99.55

2.92

1201.54

Table 2. Comparison of the proposed model with existing IoMT attack detection models. References

Models

Accuracy

Alrashdi et al. [7]

EL (EOS-ELM)

98.19

Hameed et al. [8]

EL (MOAWMA)

98.95

Kumar et al. [1]

EL (DT, NB, RF, and XGB)

96.35 99.62

Proposed work CNN

Precision

Execution time (s)

Dataset

–

–

NSL-KDD

–

4.97

NSL-KDD

90.54

–

ToN-IoT

99.62

2.92

IoT-Healthcare

Table 2 compares the performance of the proposed model with existing work. The comparison focuses on accuracy, precision, execution time and dataset. This table shows that our CNN-based IDS model achieves the best results for both accuracy and execution time, compared with related work [1, 7, 8]. This can be attributed to the feature selection and hyperparameter tuning strategy built into our approach, which reduces computational complexity. Using the proposed CNN-based IDS on the fog architecture to mitigate cyberattacks in the IoMT environment offers several benefits. Firstly, it is simple to build and deploy to detect malicious activity in heterogeneous and highly dynamic IoMT networks. Secondly, a few parameters are used to design the proposed model. Moreover, these parameters can be easily updated in real-time, enabling the detection system to perform better in accuracy and processing time.

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5 Conclusions and Future Work Introducing fog computing into smart healthcare is a promising solution to latency at the network’s edge. Incoming network traffic is processed in real-time using fog computing. One of the most important ways of identifying families of new attacks is through an intrusion detection system (IDS). This paper presents a deep learning-based IDS using fog architecture to detect network attacks in the IoMT environment. For this purpose, we built three deep learning-based IDS models: CNN, LSTM and GRU. Specifically, we provided a performance evaluation of studied deep learning approaches for cyberattack detection using the IoT-Healthcare Security dataset. The experimental results showed that the CNN-based IDS model outperformed the existing methods regarding complexity and performance. Therefore, it can be effectively used to identify cyberattacks in the IoMT fog layer to mitigate limitations in the cloud environment. Using different feature selection strategies for feature optimization and applying more advanced DL methods, such as generative adversarial networks (GANs), will improve this work in the future. Additionally, this work will be extended to detect malicious activity, including DDoS and ransomware.

References 1. Kumar, P., et al.: An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Comput. Commun. 166, 110–124 (2021). https://doi.org/ 10.1016/j.comcom.2020.12.003 2. Hatzivasilis, G., et al.: “Review of security and privacy for the internet of medical things (IoMT) resolving the protection concerns for the novel circular economy. Bioinformatics 12, 457–464 (2019). https://doi.org/10.1109/DCOSS.2019.00091 3. Yahya, R., et al.: Security and privacy on the internet of medical things, pp. 119–143 (2022). https://doi.org/10.1201/9781003239888-6 4. Moqurrab, S.A., et al.: A deep learning-based privacy-preserving model for smart healthcare in internet of medical things using fog computing. Wirel. Pers. Commun. 126(3), 2379–2401 (2022). https://doi.org/10.1007/s11277-021-09323-0 5. Rbah, Y., et al.: Machine learning and deep learning methods for intrusion detection systems in IoMT: a survey. In: Proceedings of the 2022 2nd International Conference on (IRASET), pp. 1–9 (2022). https://doi.org/10.1109/IRASET52964.2022.9738218 6. Rbah, Y., et al.: A machine learning based intrusions detection for IoT botnet attacks. AIP Conf. Proc. 2814(1), 030012 (2023). https://doi.org/10.1063/5.0149102 7. Alrashdi, I., et al.: FBAD: fog-based attack detection for IoT healthcare in smart cities. In: Proceedings of the 2019 IEEE 10th (UEMCON), pp. 0515–0522 (2019). https://doi.org/10. 1109/UEMCON47517.2019.8992963 8. Hameed, S.S., et al.: An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things. Springer, Cham (2020). https://doi.org/10.1007/978-3-03070713-2_70 9. Hameed, S.S., et al.: A hybrid lightweight system for early attack detection in the IoMT fog. Sensors 21(24), 8289 (2021). https://doi.org/10.3390/s21248289 10. Hussain, F.: IoT healthcare security dataset. IEEE (2021). https://ieee-dataport.org/docume nts/iot-healthcare-security-dataset 11. Ghazanfar, S., et al.: IoT-Flock: An Open-source Framework for IoT Traffic Generation 12. Hussain, F., et al.: A framework for malicious traffic detection in IoT healthcare environment. Sensors 21(9), 3025 (2021). https://doi.org/10.3390/s21093025

Preventing Users from Obtaining False Data in Named Data of Health Things Asmaa El-Bakkouchi1(B) , Mohammed El Ghazi1 , Anas Bouayad1 , Mohammed Fattah2 , and Moulhime El Bekkali1 1 Artificial Intelligence, Data Sciences and Emerging Systems Laboratory, Sidi Mohamed Ben

Abdellah University, Fez, Morocco [email protected] 2 IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco

Abstract. The Internet of Things Health (IoHT) is an emerging network idea that is having a major impact on the healthcare sector. The main concerns associated with IoHT are security and confidentiality because the transmitted health data often contains sensitive information about patients’ health status, and any transmission of false or corrupted data can lead to serious outcomes. Named Data Network (NDN) is a promising architecture for the future Internet, and is ideally suited to the requirements of IoHT environments, particularly in terms of security. In this article, we propose a design for IoHT through NDN architecture to prevent healthcare users from obtaining false or corrupted data. This design enables healthcare users to distinguish between real and fake data to protect patients’ lives if someone tries to inject fake data into the network. Simulation results demonstrate the effectiveness of the proposed design in preventing users from accessing false data and allowing them to receive only real data. Keywords: IoT · IoHT · NDN · False data

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 512–517, 2024. https://doi.org/10.1007/978-3-031-48573-2_74

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1 Introduction The Internet of Health Things (IoHT) is part of the Internet of Things (IoT) applications that enable medical objects to be connected to the internet, facilitating the remote sharing of data in order to significantly improve the medical field [1]. IoHT shows promising potential for making healthcare smarter through IoT applications and devices, aiming to improve treatments, reduce defects, control diseases and reduce costs [2]. However, several factors specific to healthcare are introducing significant new approaches and challenges to this infrastructure, namely; Medical devices transfer mainly sensitive patient data [3]. In the context of IoHT, security is generally accorded little importance by users and IoT applications and devices. However, there are new emerging challenges in terms of confidentiality, privacy and authenticity [4]. As patient data are transferred over wireless IoT devices, IoHT is exposed to the risk of security breaches in wireless sensor networks. As IoHT is so critical, security breaches, such as the transmission of false or falsified data, can have devastating consequences, such as the death of patients [5], and as IoHT uses the traditional internet paradigm to transfer data, this presents certain challenges such as the heterogeneity of IoHT devices, their mobility, their security, as well as the volume of data exchanged, etc. It is therefore essential to identify and analyze these challenges to enhance design and implementation solutions for secure and reliable IoHT systems. To address these issues, it is necessary to implement a robust mechanism through the NDN architecture which architecture represents the most promising internet architecture for IoHT deployment to prevent IoHT users if someone tries to inject false and falsified data into the network. To do this, the rest of this paper is structured as follows: in Sect. 2, we describe the proposed mechanism in detail; in Sect. 3, we evaluate the performance of the proposed mechanism by presenting the simulation parameters and discussing the results obtained. Finally, we conclude this paper in Sect. 4.

2 Proposed Mechanism In IoHT, healthcare applications exchange medical data with each other and with the staff in an open access environment, as in the case of the NDN architecture, where there are no limitations on who can access which kind of data. Patient health information could be modified, utilized or damaged if there is no control in real time. This represents both an infrastructure risk and a disastrous effect on people’s lives, because transmitting false data can cause serious misdiagnoses and health problems, and can also pose a threat to patients’ lives [6]. Our goal is to prevent users from accessing/viewing altered or falsified medical data. In order to achieve this goal, the following measure must be implemented: • Verify the integrity and authenticity of data received in order to prevent users from accessing or consulting altered or falsified content. This reinforces security and guarantee secure access to real medical data, by preventing users from accessing or receiving altered or false data. To achieve this, we made a number

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of changes to the existing system, including the addition of a new field in the data packet containing the producer’s trust value. In addition, the user has a table containing the trust values of every health department. Figure 1 illustrates the proposed model for the Internet of Things in Healthcare (IoHT) based on NDN networks. This model involves the participation of a user, a router and a content producer. In this figure, the user (consumer) starts the communication by sending an interest packet requesting the desired medical data. Next, a router receives this packet and forwards it to the content producer. When the content producer receives this packet, it adds the trust value to the corresponding data packet and forwards it to the user. When the user receives the data packet, it extracts the associated trust value and compares it with the values stored in its table. If the trust value is present in its table, the user accepts the data packet. If not, it discards the packet and considers it unreliable. The process used at the user level enables reliable data to be distinguished from erroneous or false data, by performing integrity and authenticity checks on the data received.

Fig. 1. The proposed model

The following Algorithm outlines the validation process for data received by the user. When the user receives a data packet, it checks for the existence of the trust value and its availability in its trust list, thus guaranteeing the authenticity of the received data. If the trust value is not present, the data is deleted.

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Algorithm Consumer data validation algorithm 1: function ondata(DataPacket) 2: // Verification if data packet contains a trust value 3: if (data packet contains a Trust Value) then 4: TrustValue ← GetTrustValue(DataPacket); 5: // Verification if trust value is available in its trust list 6: if (TrustValue == True) then 7: get the data packet; 8: else 9: drop the data packet; 10: end if 11: else 12: drop the data packet; 13: end if 14: end function

3 Performance Evaluation We evaluate the performance of the proposed design in the scenario below. The participants in this scenario are a user, a router and two content producers (one considered reliable and the other untrusted or malicious). To implement the proposed design, we use the simulator ndnSIM [7], which is based on NS-3, and the simulation time is fixed at 50 s. 3.1 Simulation Parameters The aim of this scenario (Fig. 2) is to validate the proposed design to prevent the user from accessing erroneous data and to enable him to receive only real health data. The participants in this scenario are a user, a router and two content producers (one considered reliable (producer 1) and the other unreliable or malicious (producer 2)). In this scenario, the link rate is 1 Mbps with a latency of 10 ms. We use the Best Route transfer strategy and transmit a health file of 1024 bytes. 3.2 Simulation Results The numerical results obtained are discussed below. Figure 3 shows the data retrieval time. In this figure, the user retrieved the requested health data (trusted producer) with an average delay of 49 ms between sending the request and retrieving the corresponding packet, including the time required to check the authenticity of the data producer. For untrusted producer, since the data arrived does not contain a trust value so the user has deleted this data received and consequently the delay is considered as zero since no request has been satisfied. Figure 4 illustrates the message that the user sends to the untrusted producer when receiving a data packet with a false trust value or a data packet without a trust value, informing him that he is not a trusted producer to obtain its data.

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Fig. 2. The basic topology

Data Retrieval Time (ms)

60 50 40 30 20 10 0 0

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Time (s) Trusted producer

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Fig. 3. Data retrieval time

4 Conclusion To ensure secure access to health data in IoHT, we propose in this paper an access control mechanism based on NDN. This mechanism applies security directives to prevent users from accessing “corrupted” or “false” data. In the simulator ndnSIM, we implemented and evaluated our mechanism. The simulation results demonstrate the effectiveness of the proposed mechanism in terms of data retrieval time and security policy. As future work, we plan to optimize our proposal by combining it with control at producer to limit access to health data and allow only authorized users to access them.

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Fig. 4. Example of message to untrusted producer

References 1. Saxena, D., Raychoudhury, V., SriMahathi, N.: SmartHealth-NDNoT: named data network of things for healthcare services. In: MobileHealth 2015 - Proceedings of the 2015 Workshop on Pervasive Wireless Healthcare, Co-located with MobiHoc 2015, pp. 45–50 (2015). https://doi. org/10.1145/2757290.2757300 2. Alsubaei, F., Abuhussein, A., Shiva, S.: Security and privacy in the internet of medical things: taxonomy and risk assessment. In: Proceedings - 2017 IEEE 42nd Conference on Local Computer Networks Workshops, LCN Workshops 2017, Nov 2017, pp. 112–120. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/LCN.Workshops.2017.72 3. Boussada, R., Hamdane, B., Elhdhili, M.E., Saidane, L.A.: PP-NDNoT: on preserving privacy in IoT-based E-health systems over NDN. In: IEEE Wireless Communications and Networking Conference (WCNC) (2019) 4. Aroosa, S.S.U., Hussain, S., Alroobaea, R., Ali, I.: Securing NDN-based internet of health things through cost-effective signcryption scheme. Wirel. Commun. Mob. Comput. 2021 (2021). https://doi.org/10.1155/2021/5569365 5. Boussada, R., Elhdhili, M.E., Saidane, L.A.: Toward privacy preserving in IoT e-health systems: a key escrow identity-based encryption scheme. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), Jan 2018, pp. 1–7. IEEE https://doi.org/ 10.1109/CCNC.2018.8319218 6. El-Bakkouchi, A., El Ghazi, M., Bouayad, A., Fattah, M., El Bekkali, M.: Hybrid congestion control mechanism as a secured communication technology for the internet of health things. In: Artificial Intelligence and Smart Environment, pp. 498–503 (2023). https://doi.org/10.1007/ 978-3-031-26254-8_72 7. Mastorakis, S., Afanasyev, A., Moiseenko, I., Zhang, L.: ndnSIM 2.0: a new version of the NDN simulator for NS-3. NDN Project, pp. 1–8 (2015)

Analyzing and Detecting Malware Using Machine Learning and Deep Learning Badr Ait Messaad(B) , Kaouthar Chetioui, Younes Balboul, and Hamza Rhachi IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco {badr.aitmessaad,kaouthar.chetioui,younes.balboul, hamza.rhachi}@usmba.ac.ma

Abstract. The rapid growth of malware threats poses significant challenges to cybersecurity professionals. To combat these threats effectively, there is a growing need for intelligent tools that can analyze, classify, and detect malware accurately and efficiently. The importance of accuracy in AI-driven security operations cannot be overstated. In the realm of cybersecurity, AI plays a vital role in identifying, preventing, and responding to diverse threats and attacks. A low false positive rate is crucial in security operations as it directly impacts the efficiency and effectiveness of incident response efforts. False positives not only consume valuable resources but also lead to alert fatigue and a loss of confidence in the security system. By implementing AI-driven solutions, organizations can leverage the potential of machine learning (ML) and deep learning (DL) algorithms to reduce false positives significantly. This overview provides valuable insights into the promising role of ML and DL in addressing the challenges of machine learning and deep learning in malware analysis and detection. Keywords: Machine learning · Deep learning · Malware · Malware detection · Cybersecurity · Accuracy

1 Introduction In today’s interconnected digital world, the threat of malware poses a significant challenge to cybersecurity. Malicious software, including viruses, worms, ransomware, and trojans, continues to evolve in complexity and sophistication, making traditional signature-based detection methods insufficient. To address this issue, analyzing and detecting malware using machine learning and deep learning techniques has emerged as a promising approach. AI-driven security operations have evolved as a promising to address the challenges posed by the ever-evolving threat landscape. By harnessing the power of artificial intelligence (AI). The increasing complexity of malware presents significant challenges to traditional detection techniques, making it difficult to accurately classify samples as benign or malicious. Several existing techniques have limitations, including: • Signature-based approach: This method is ineffective in detecting Zero-Day attacks and threats with evolving capabilities, like metamorphic and polymorphic malwares. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 518–525, 2024. https://doi.org/10.1007/978-3-031-48573-2_75

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Additionally, it struggles to identify newly emerged malwares that lack predefined signatures. • Anomaly-based approach: While this approach can detect unknown threats, it often produces a high rate of false alarms, leading to alert fatigue and potentially overlooking genuine threats. • Specification-based approach: Defining a complete and accurate set of valid behaviors for a system is challenging. As a result, this method may not effectively identify all types of malicious activities, especially those that deviate subtly from established norms [1]. This paper provides an in-depth exploration of the techniques, methodologies, and challenges involved in analyzing and detecting malware using machine learning and deep learning approaches. It covers various sections, Sect. 2 which reviews notable research studies and their findings. Section 3 delves into the process of malware Analysis to understand malicious software behavior. Section 4 emphasizes the importance of accurate detection methods to safeguard systems and networks. Finally, the Sect. 5 highlights the need for further advancement in machine learning techniques.

2 Related Work Malware detection plays a pivotal and indispensable role in maintaining cybersecurity in today’s rapidly evolving digital landscape. The constant evolution of techniques employed by malware writers to evade detection necessitates the development of intelligent and robust tools that can accurately identify malicious software while minimizing false positives. In this paper, we delve into the related work in the field of malware detection. Notably, Zakeri, Faraji Daneshgar, and Abbaspour [2] demonstrate the effectiveness of using machine learning techniques and extracted features from Portable Executable (PE) files and static analysis to identify unknown malicious codes. Through an extensive evaluation using a substantial test collection of over 63,000 files, their method surpasses previous research in detecting packed files by employing a fuzzy feature classification approach, selecting features with high Information Gain, and implementing data preprocessing. The experimental results showcase an exceptional classification accuracy of 99.97%. Sethi et al. [1] in their work the team conducted experiments using their proposed framework, yielding impressive results. The J48 Decision Tree model achieved a detection accuracy of 100%, while the SMO model achieved 99% accuracy and the Random Forest tree model achieved 97% accuracy. Additionally, the classification rate was also significant, with the J48 Decision Tree model achieving 100% accuracy, the SMO model achieving 91% accuracy, and the Random Forest tree model achieving 66% accuracy. These outcomes demonstrate the efficacy of their framework in detecting and classifying malware.

3 Malware Analysis Malware analysis is a crucial process that involves investigating malicious software to comprehend its behavior, functionalities, and potential repercussions. Various methods and approaches are employed in malware analysis, each serving specific purposes in

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obtaining insights into malware and devising effective defense measures. In this subsection, we explore the analysis objectives outlined in the paper titled “Survey of Machine Learning Techniques for Malware Analysis” [3] (Fig. 1).

Fig. 1 Taxonomy of machine learning techniques for malware analysis [3]

4 Malware Detection Techniques Malware detection techniques play a crucial role in safeguarding computer systems and networks from the ever-growing threat of malicious software. With the constantly evolving nature of malware, it is essential to employ effective detection methods to identify and mitigate these threats promptly. One of the fundamental aspects of malware detection is accuracy. Accuracy in malware detection refers to the ability of a detection technique to correctly classify files or programs as either malicious or benign. It is of paramount importance because an inaccurate detection system can have severe consequences. False negatives, where malicious files are incorrectly labeled as benign, can result in the infiltration of malware into the system, leading to data breaches, system compromises, or unauthorized access. On the other hand, false positives, where benign files are erroneously flagged as malicious, can disrupt normal operations, impede productivity, and create unnecessary alarm and inconvenience for users. Table 1 provides an overview of various techniques for malware detection. The comparative study evaluates various machine learning techniques applied to malware detection, categorizing them into three analysis methods: static, dynamic, and hybrid. Liu et al. employ a static analysis approach utilizing a range of techniques, achieving an accuracy of 98.9% on a dataset of 21,740 malware instances using tools such as IDA Pro, Oracle, and Python 2.7. Shabtai et al. focus on dynamic analysis, achieving exceptional accuracy rates of 100% for the “game” class and 99% for “tools”

Tools

System

Accuracy

Android [5]

(continued)

The model achieved a 100% accuracy in predicting instances belonging to the “game” class and 99% accuracy in predicting instances belonging to the “tools” class [5]

21,740 instances of IDA Pro, Oracle Windows [5] 98.9% [5] malware samples and Python 2.7 [5] in the dataset [5]

Dataset

Decision tree Dataset used in the Not applicable [5] (DT) algorithm research was [5] specifically created by the authors of the paper [5]

Shabtai et al. [6]

Dynamic

k-means clustering Logistic regression Histogram-based methods Decision trees Bayesian networks (also known as Bayes nets or belief networks) Naive Bayes [5]

Shallow Neural Not applicable Networks [5] Random Forest k-Nearest Neighbors Gradient Boosting Naive Bayes Logistic Regression Support Vector Machines (SVM) Decision Trees [5]

Liu et al. [4]

Static

Best technique found

Machine learning technique/s

Analysis method Author

Table 1. Overview of various techniques for malware detection

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Hybrid

Machine learning technique/s

Best technique found

Zakeri et al. [2] Instance-based Not applicable k-nearest [5] neighbors (k-NN) algorithm J48 decision trees (C4.5 decision trees) A variant of the C4.5 decision tree algorithm with grafting Naive Bayes Repeated incremental pruning to produce error reduction (inductive rule learner) Fast unbiased recursive induction algorithm (a rule-based learning algorithm) [5]

Analysis method Author

Tools

Dataset used in the PEiD, OllyDbg, research consists and WEKA [5] of a total of 63,000 files. Among these files, 56,000 are classified as malware, and the remaining 7,000 files are labeled as benign [5]

Dataset

Table 1. (continued) Accuracy

(continued)

Windows [5] 99.97% [5]

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Analysis method Author

Best technique found

A decision tree J48 [5] algorithm based on C4.5 (also known as J48 in Weka) An ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting Sequential minimal Optimization, a popular algorithm used for training support vector machines (SVM) in binary classification tasks [5]

Machine learning technique/s

Tools

Malware samples Cuckoo Sandbox were sourced from and WEKA [5] OpenMalware, a repository known for hosting various types of malicious software Clean files were collected from Softonic, a well-known platform for hosting legitimate and benign software applications [5]

Dataset

Table 1. (continued) Accuracy

Windows [5] 100% [5]

System

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on an Android dataset specially crafted by the authors. Zakeri et al. adopt a hybrid method with a notably high accuracy of 99.97% on a dataset of 63,000 files, distinguishing between 56,000 malware and 7000 benign files. Sethi et al. rely on decision tree algorithms and ensemble learning techniques, achieving a perfect accuracy rate of 100% in distinguishing malware from clean files on a Windows system using tools like Cuckoo Sandbox and WEKA, with malware samples sourced from OpenMalware and clean files from Softonic. The choice of analysis method depends on factors such as the nature of the dataset and the specific goals of the study.

5 Conclusion and Future Work In this paper, we have provided a comprehensive presentation of analyzing and detecting malware using machine learning and deep learning techniques. The rapid growth of malware threats poses significant challenges to cybersecurity, and traditional signaturebased detection methods are becoming insufficient in addressing the evolving nature of malicious software. By leveraging the power of artificial intelligence, organizations can effectively combat these threats and improve the accuracy and efficiency of malware detection. In conclusion, the application of machine learning and deep learning in malware analysis and detection holds great promise for strengthening cybersecurity defenses. By addressing these future research directions, we can further advance the field of malware detection and contribute to building more robust and intelligent cybersecurity solutions. One perspective highlighted in the paper is the need to advance machine learning techniques for more effective malware detection. The research presented in the related work section demonstrates the success of machine learning models such as decision trees and data mining algorithms in accurately identifying and classifying malware. To further improve detection capabilities, future research should focus on developing more sophisticated machine learning models, refining feature extraction methods, and exploring new data mining techniques. This advancement in machine learning can contribute to stronger and more reliable cybersecurity defenses against evolving malware threats. Additionally, integrating multiple detection approaches, such as static and dynamic analysis, along with other complementary techniques, can further enhance the comprehensiveness of malware detection systems.

References 1. Sethi, K., Chaudhary, S.K., Tripathy, B.K., Bera, P.: A novel malware analysis for malware detection and classification using machine learning algorithms. In: Proceedings of the 10th International Conference on Security of Information and Networks, pp. 107–113 (2017, October) 2. Zakeri, M., Faraji Daneshgar, F., Abbaspour, M.: A static heuristic approach to detecting malware targets. Security Comm. Networks 8(17), 3015–3027 (2015) 3. Ucci, D., Aniello, L., Baldoni, R.: Survey of machine learning techniques for malware analysis. Comput. Secur. 81, 123–147 (2019) 4. Liu, L., Wang, B.S., Yu, B., Zhong, Q.X.: Automatic malware classification and new malware detection using machine learning. Front. Inf. Technol. Electron. Eng. 18(9), 1336–1347 (2017)

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5. Al-Janabi, M., Altamimi, A.M.: A comparative analysis of machine learning techniques for classification and detection of malware. In: 2020 21st International Arab Conference on Information Technology (ACIT), pp. 1–9. IEEE. (2020, November) 6. Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., Weiss, Y.: “Andromaly”: a behavioral malware detection framework for android devices. J. Intell. Inf. Syst. 38(1), 161–190 (2012)

Lateral Control Using a Homogenous Control Law for an Autonomous Vehicle Belkheir Ayoub1(B) , Mellouli El Mehdi1 , and Boumhidi Ismail2 1 Laboratory of Engineering, Systems, and Applications (LISA), Sidi Mohamed Ben Abdellah

University, Fez, Morocco [email protected] 2 LESSI Laboratory, Physics Department, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. This paper concentrates on modeling and controlling a vehicle with a relative degree one using a homogenous control law with a controllable homogenous degree that’s satisfied with the stability theory. The first section presents the lateral dynamics using the bicycle model of the vehicle in the state space. Then in the second section, we propose a strategy, where we try to find the best performance and achieve stability in finite time with the best accuracy and low energy by defining a homogenous control law independent of the internal dynamics of the non-linear system based on the first-order linear state feedback (FOLSF) controller and first-order sliding mode control (FOSMC). And, based on the simulation shown in the last section of this paper of this methodology proposed, we demonstrate and improve our methodology’s superiority over other approaches in the presence of perturbation, their good results, and efficiency in time tracking, stability, and, robustness. Keywords: Autonomous vehicle · Homogenous system · Non-linear dynamics

1 Introduction The automation field has become a huge domain, where we study the different techniques to model and control autonomous and non-autonomous, linear or non-linear, time-variant or invariant systems to make it stable in finite time, secure, robust, with fewer disturbances, uncertainties, and perturbations.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 526–532, 2024. https://doi.org/10.1007/978-3-031-48573-2_76

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For that, many techniques have appeared in the last years, such as sliding mode control (SMC), linear state feedback (LSF), and H infinity… But, the problem is how to generate a controller with the best accuracy and less energy consumption. SMC [1, 2, 5, 8–12] is a technique that guarantees the stability and good accuracy of the system by taking into account the internal dynamics, and LSF [3] is a linear controller that relates the input and output to provide the lower use of energy of the system without taking into account the internal dynamics into consideration. That’s, makes the homogenous controllers appear in the sector, these types of controllers guarantee the best accuracy and less consumption of energy. The study of finite time stability [2, 9, 12] helps in the appearance of homogenous systems [3, 4] which are a system that presents asymptotical stability in finite time. It’s a property that allows things, such as functions or vector fields, to scale consistently with regard to a scaling operation known as dilation. The article concentrates on finding the best accuracy with lower energy, for that purpose, we will propose to define a homogeneous control law with a degree of homogeneity that varies with the system to make it more controllable by scaling with the variation of the dynamics to achieve the performance that we want. That’s why, in the first part of this paper, we present the model of the system, the second part talks about the controller used, then the simulation with perspective, and finally the conclusion.

2 Mathematical Modeling The lateral movement of the vehicle is represented as a non-linear form [see Eq. (1)] using the bicycle model [6] and shown in Fig. 1.

Fig. 1. Bicycle model of the vehicle

The vehicle’s lateral movement can be modeled using the bicycle model (see Fig. 1) in the state space as follows, x˙ 1 = x2

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x˙ 2 = f1 (x2 , x4 ) + g1 u x˙ 3 = x4 x˙ 4 = f2 (x2 , x4 ) + g2 u

(1)

where,     x = x1 x2 x3 x4 = y y˙ ψ ψ˙ ∈ R4 is the state space of the system, with y is the lateral displacement output, and ψ is the yaw angle.     Cf . Lf − Cr . Lr Cf + Cr − + Vx , f1 = − Vx . m Vx . m     Cf . Lf2 + Cr . Lr2 Cf . Lf − Cr . Lr − f2 = − Vx . IZ V x . IZ g1 =

Cf Cf . Lf and g2 = m IZ

and f1 (x2 , x4 ) and f2 (x2 , x4 ) are nonlinear functions, g1 , and g2 are constant parameters and u ∈ U ⊂ R is the input control law. And δ is the steering angle, IZ is the moment of inertia, m is the vehicle mass, Vx is the longitudinal velocity, Cf , and Cr called the cornering stiffness of the front and rear tires, Lf and Lr are the distance between the center of gravity and the front and rear tires respectively.

3 Control Methodology There are many ways to control non-linear systems, but the internal dynamics affect the controlling system which makes it less efficient and doesn’t attend to the stability in finite time or they attend to it, but generate what we call the chattering effect, then increases the disturbances and perturbations. For that, we propose here to use a new control law, which is defined as follows, u = −k|S|α sign(S) where, α[0, 1] is the positive homogenous degree defined [3] as follows,   |S| + 1, 0 α = max −β |S| + ε

(2)

(3)

β > 1 and ε > 0 are positive constants depending on the initial value of the state space x at t = 0, and k is a positive parameter gain of the controller. If α = 0, then the FOSMC controller is generated, u = −k . sign(S) But, if α = 1 then the FOLSF controller is found, u = −k . S

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and, S is the sliding surface, which is defined such that when they equal to 0, then ev → 0 as follows, S = e˙ v + λev

(4)

where λ is a positive parameter. And, ev is the lateral displacement error defined as ev = x1 − xref

(5)

Using Eq. (5), the derivative of Eq. (4) is as follows, S˙ = f1 (x2 , x4 ) + g1 u − x¨ ref + λ˙ev We can re-write it as, S˙ = A(.) + B(.)u

(6)

where, A(.) = f1 (x2 , x4 ) − x¨ ref + λ˙ev and B(.) = g1 are the uncertain functions due to the parametric uncertain and perturbations. They can be written as A(.) = Anorm (.) + A(.) B(.) = Bnorm (.) + B(.) With, Anorm (.) and Bnorm (.) > 0 are the nominal terms depending on the measured or estimated variable, and bounded, then A(.) and B(.) are the uncertain terms. Then, the control law is expressed as u=

1 (− Anorm (.) + u) Bnorm (.)

(7)

Using Eqs. (7), and (8), Eq. (6) is written as  −1 −1 S˙ = A(.) − B(.)Bnorm (.)Anorm (.) + 1 + B(.)Bnorm (.)u

(8)

where,  −1 −1 a(x, t) = A(.) − B(.)Bnorm (.) (.)Anorm (.) and b(x, t) = 1 + B(.)Bnorm As A(.) and B(.) > 0 are bounded. Latterly, from Assumption A2 in [3], we find that |a(x, t)| < aM and 0 < bm < b(x, t) < bM . Finally, S˙ = a(x, t) + b(x, t)u Then, based on the Lyapunov function defined as, V =

1 2 S 2

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The S → 0, if the derivative of this function verified the following inequality, V˙ = S S˙ ≤ −μ|S| with the parameter gain k of the proposed control law defined in Eq. (2) satisfied the following condition, k≥

aM + μ bm

4 Simulation and Perspective To see the effectiveness of this homogenous control law applied to this lateral movement system with a perturbation term d(t), we propose to take the reference trajectory xref as a double lane change maneuver [8] trajectory defined as the following polynomial function, xref = a5 t 5 + a4 t 4 + a3 t 3 + a2 t 2 + a1 t + a0

(9)

where a5 , a4 , a3 , a2 , a1, and a0 are constant depending on time maneuver t which is taken here between 0 and 10 s. The lateral displacement trajectory and the tracking error found after the simulation are as follows (Fig. 2).

Fig. 2. Reference trajectory xref and x1

The results shown above, prove the finite time convergence of the system, and its stability (for lower or higher parameter gain k value) with good accuracy and lower consumption energy using the new homogenous control law defined compared to FOSMC (as shown in Fig. 2) and FOLSM in [2, 12] (Fig. 3).

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Fig. 3. Lateral displacement error ev

5 Conclusion In this paper, we apply methodology defined by a new control law with a scale degree of homogeneity, but it is still not applicable in non-linear systems that present some non-negligible or high internal dynamics and perturbations. For that, in future works, the study concentrates on developing a new homogenous control system that takes into account the internal dynamics and perturbations to find the best results in terms of convergence, stability, and security.

References 1. Chen, S.-Y., Lin, F.-J.: Robust nonsingular terminal sliding-mode control for nonlinear magnetic bearing system. IEEE Trans. Control Syst. Technol. 19(3), 636–643 (2011) 2. El Hajjami, L., Mellouli, E.M., Berrada, M.: Neural network based sliding mode lateral control for autonomous vehicle. In: 1st International Conference on Innovative Research in Applied Science (2020) 3. Tahoumi, E., Plestan, F., Ghanes, M., Barbot, J.P.: New robust control schemes based on both linear and sliding mode approaches: design and application to an electropneumatic actuator. IEEE Trans. Control Syst. Technol., 1063–6536 (2019) 4. Bernuau, E., Efimov, D., Perruquetti, W., Polyakov, A.: On homogeneity and its application in sliding mode control. J. Franklin Inst. 351(4), 1866–1901 (2014) 5. Mellouli, E.M., Sefriti, S., Boumhidi, I.: A new modified sliding mode controller based fuzzy logic for a variable speed wind turbine. Int. J. Ecol. Dev. 32(1), 44–53 (2017) 6. Rajamani, R.: Vehicle Dynamics and Control. Springer Science & Business Media (2011) 7. Mellouli, E.M., Massou, S., Boumhidi, I.: Optimal robust adaptive fuzzy H∞ tracking control without reaching phase for nonlinear system. J. Control Sci. Eng. 2013, 498461 (2013) 8. Samiee, S.: Towards a decision-making algorithm for automatic lane change manoeuvre considering traffic dynamics. In: 2016 Intelligent Transport Systems (ITS) 9. Belkheir, A., Mellouli, E.M.: Controlling a two-link robot using sliding mode control combined with neural network. Sens. Transd. J. 261(2), 33–40 (2023)

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10. Alika, R., Mellouli, E.M., Tissir, E.H.: Optimization of higher-order sliding mode control parameter using particle swarm optimization for lateral dynamics of autonomous vehicles. In: 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020, p. 9092119 (2020) 11. Mellouli, E.M., Chalh, Z., Alfidi, M., Boumhidi, I.: A new robust adaptive fuzzy sliding mode controller for a variable speed wind turbine. Int. Rev. Automat. Control 8(5), 338–445 (2015) 12. Belkheir, A., Moussa, A., Mellouli, E.M.: Neural network, and terminal sliding mode control for controlling an uncertain autonomous vehicle. In: The 2nd International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE’22) (2023)

Traffic Lights Control Using Reinforcement Learning: A Comparative Study Khalid Errajraji(B) , Anas Bouayad, and Khalid Fardousse Sidi Mohamed Ben Abdellah University, Fez, Morocco {khalid.errajraji,anas.bouayad,khalid.fardousse}@usmba.ac.ma

Abstract. Traffic lights play a crucial role in managing traffic flow, ensuring safety and efficient transportation systems. Traditional fixed-time traffic light control systems have been widely used for decades, but they have limitations in adapting to changing traffic patterns. This can lead to increased waiting times, congestion, and inefficient use of road networks, resulting in reduced overall transportation efficiency. To address these issues, researchers have proposed dynamic traffic light control systems based on reinforcement learning (RL) algorithms. These systems have shown promise in learning and adapting traffic light timings based on real-time traffic conditions, leading to improved traffic flow and reduced waiting times. Several RL algorithms have been proposed for traffic light control, including SARSA, Q-learning (QL), and Deep Q-network (DQN). These algorithms use different methods to learn and adjust traffic light timings. This work compares the performance of three different RL algorithms (QL, Sarsa and DQN) over traffic light control and evaluates their effectiveness in reducing waiting times and congestion. Our results demonstrate that RL-based traffic light control systems outperform traditional fixed-time systems in managing traffic flow and reducing waiting times. Specifically, our study shows that the proposed RL-based system, based on Deep Q-network, performs better than traditional fixed-timing systems and the other RL algorithms. Keywords: Traffic light control · Reinforcement learning · Q-learning · Deep Q-learning · SARSA

1 Introduction As urbanization continues to rise and more people migrate to cities, transportation systems face increasing challenges such as congestion, delays, and safety concerns. To address these issues, many cities have turned to intelligent transportation systems (ITS), which utilize IoT technology and data analysis to improve transportation efficiency and safety [1]. The creation of an ITS involves three main steps: data collection and transmission, data analysis, and decision-making and traveler information. By collecting real-time transportation data using various sensors and devices, the ITS can analyze and process this information to make informed decisions and inform travelers of relevant transportation updates. Among the various applications of ITS, traffic management plays © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 533–538, 2024. https://doi.org/10.1007/978-3-031-48573-2_77

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a critical role in improving safety, mobility, and economic productivity. Traffic signal control is a key component of traffic management, which regulates the flow of traffic at intersections. However, traditional traffic light control systems with fixed timing are not efficient in adapting to changing traffic flow, leading to increased waiting times and congestion. To address this issue, researchers have proposed adaptive systems, that can learn and adjust traffic light timings based on traffic conditions. In this paper, we discuss the different methods and systems proposed in traffic signal control and do a comparative study between three famous reinforcement learning (RL) algorithms Q-learning (QL), Deep Q-network (DQN), and SARSA. Then demonstrate their superiority over traditional fixed-timing systems in reducing waiting times and congestion. In the following sections of this paper, we will commence by offering a comprehensive review of related works. Subsequently, we will elucidate the algorithms utilized in our study, followed by the presentation of experiments and the corresponding results. To conclude, we will provide a summary and discuss avenues for future research.

2 Related Works Adaptive traffic light control systems have the potential to enhance traffic management at intersections by reducing waiting times and preventing congestion. With advancements in smart cars, cameras, and sensors, research in this field has intensified. Kim et al. [2] proposed an approach that combines Long Short-Term Memory (LSTM) networks and DQN to create a Traffic Signal Control system. This approach predicts traffic flow and shares the traffic state with adjacent intersections to determine the best traffic light scenario. It involves four main steps: obtaining the traffic flow state at time t, predicting the traffic flow state at time t + 1 based on weather conditions and vehicle count, using the predicted state as input to a DQN for selecting the traffic light scenario, and sharing the traffic state with adjacent intersections. The results demonstrated superior performance compared to traditional QL and DQN models in reducing average waiting time. Wan et al. [3] also employed a DQN approach for traffic light control, considering factors such as current phase, green and red light duration, left-turn bay occupation, and the number of remaining cars as the state. The reward was defined as the accumulated system delay time multiplied by − 1. Results indicated that the proposed model minimized accumulated system delay compared to fixed-time and traditional DQN models. Zeng et al. [4] developed a deep Recurrent Q-network (RDQN) by combining DQN, LSTM, and convolutional layers for traffic light control. The model divided each lane into segments and defined the state as two matrices: a Density matrix and a Speed matrix. The RDQN model outperformed traditional DQN and fixed-time traffic light systems in term of average waiting time. Ozan et al. [5] focused on optimizing traffic signal control in multi-intersection environments using a QL algorithm. They considered 256 potential traffic scenarios categorized into four signal phases and four levels of vehicle queues. Tests conducted on a traffic network with two intersections demonstrated promising outcomes in terms of enhancing traffic flow and reducing waiting times. A method presented by [6] involved controlling a network of five intersections using a central agent and outbound agents located in other intersections. The central agent made decisions based on its traffic flow state and neighbor’s traffic conditions, utilizing relative traffic

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flow values provided by the outbound agents. This approach demonstrated effective control of traffic flow and showed improvements compared to fixed-time models. Finally, Kekuda et al. [7] introduced a traffic signal controller based on n-step SARSA algorithm. Experimental analysis showed that their approach outperformed traditional techniques, including the longest queue first method.

3 Reinforcement Learning RL is a type of machine learning algorithms that is based on learning through rewards and punishments. The goal of RL is to develop an agent capable of acquiring knowledge on how to interact within an environment by learning actions that aims to maximizing a reward signal. In RL, there are four principal concepts: agent, environment, state, and reward. The agent represents the model, while the environment is where the experiments are performed. Within the environment, states and rewards are present, and the agent interacts by taking actions in a given state, subsequently receiving a new state and reward from the environment. The primary objective of the agent is to maximize the reward function. To model the decision-making process of an RL agent, a Markov Decision Process (MDP) [8] is often used. An MDP can be defined as a set of states (s) that present the states in the environment, a set of possible actions (a), a transition function T(s, a, s ) that defined the probability to move from a state to another when taking an action, a reward function R(s, a, s ) that defines the reward of moving from a state to another, a start state, and possibly a terminal state. The agent always try to find an optimal policy ∗ (s) that maps each state to an action that maximizes the total reward. The MDP is solving using the Bellman equations (1). The value function (V) represents the utility of a state, while the Q function (Q) represents the utility of an action. V ∗ (s) = max Q∗ (s, a) a       Q∗ (s, a) = S  T s, a, s R s, a, s + γ V ∗ s       V ∗ (s) = max S  T s, a, s R s, a, s + γ V ∗ s a

(1)

another RL algorithm is QL which is a popular RL algorithm that uses the Bellman equations to learn the optimal Q-values. in QL, the agent learns the Q-value of an action in a given state by iteratively updating the estimate using the Bellman equation.     Q(s, a) = (1 − a) × Q(s, a) + a × r + γ × max Q(s , a ) (2)  a

Q(s, a) represents the Q-value associated with the action a taken in state s. r is the immediate reward obtained after executing action a in state s. γ represents the discount factor, α denotes the learning rate, and maxa Q(s, a) signifies the highest Q-value achievable for the subsequent state s . The QL algorithm works by initializing the state-action matrix arbitrarily (usually to 0), taking the initial state, and then repeating the process until a terminal state is reached or the maximum number of actions is taken. To ensure that the agent explores the entire environment, episodes are used for exploration where the agent can explore the environment randomly and discover all the states, and exploitation

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where the agent uses the information it has learned to make the best decision. Another popular RL algorithm is DQN, this algorithm uses a neural network to approximate the Q-value function. The DQN algorithm takes the current state as input, and the output is the Q-values for all actions. The neural network is trained using a variant of QL called experience replay, which involves storing the agent’s experiences in a replay buffer and randomly sampling from it during training. Also we have SARSA which is an acronym for State-Action-Reward-State-Action. Unlike QL, SARSA is an on-policy algorithm that considers the current policy when updating Q-values. The agent follows a cycle in SARSA: it observes the current state, takes an action based on the current policy, receives a reward and transitions to the next state. The next action is then chosen according to the current policy, and the process continues. SARSA updates the Q-value of the current state-action pair by incorporating the observed reward and the Q-value of the next state-action pair, determined by the current policy. Although the update formula is similar to QL. SARSA is particularly useful in scenarios where exploration is crucial, as it encourages the agent to adhere to the current policy while still exploring new states and actions. In conclusion, RL is a powerful machine learning paradigm that has found applications in many fields, including robotics, games, traffic light control and it have proven a powerful effectiveness in solving a wide range of problems.

4 Experiments and Results To evaluate the effectiveness of different RL algorithms for traffic light control, we created a simulation of a simple traffic intersection using the Sumo traffic simulator [9] which is an open-source traffic simulation software that allows for the creation of traffic networks and the simulation of traffic flows and we measured The effectiveness of each RL algorithm by calculating the total waiting for all vehicles in the intersection. we have trained DQN, QL agents, and SARSA agent. In each model, the state definition was the traffic flow within each lane of the intersection. This refers to the quantification of the number of vehicles present in each lane. The reward function was defined as the total waiting time of all vehicles in the intersection multiplied by − 1. The minimum green light duration was set to 10 s for each traffic light. We used a learning rate of 0.01 and a discount factor (gamma) of 0.95 for each model. The agents were trained for 200 epochs, with each epoch consisting of a simulation duration of 10,000 s. Figure 1 shows the total waiting time during the epochs of an agents training and the total waiting time of the fixed-time traffic light control system in Sumo, we conclude that the implementation of RL algorithms, specifically SARSA, QL, and DQN, outperforms the traditional traffic control system in terms of reducing the vehicles total waiting time. Among the three RL algorithms, DQN demonstrated the most efficient performance with the lowest total waiting time achieved, and it only took 10 epochs to reach the minimum value. The QL agent reached its minimum waiting time in epoch 90, while the SARSA algorithm showed a slow decrease and required more epochs to reach the minimum total waiting time. It is worth noting that DQN’s superior performance is due to its ability to predict Q-values for new states that the other agents have not seen before, whereas SARSA and QL agents cannot. This enables DQN to make more accurate decisions and select optimal actions, leading to more efficient traffic control. In conclusion, the implementation of

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RL algorithms in traffic control systems can significantly improve traffic flow and reduce the vehicles’ waiting time. DQN demonstrated the most efficient performance among the three RL algorithms, with SARSA and QL agents showing slower performance but still outperforming the traditional traffic control system.

Fig. 1. Total waiting time by episode for the different models

5 Conclusion In this study, we have presented a comparative analysis between traditional traffic control systems and RL algorithms SARSA, QL, and DQN, in terms of their ability to reduce the vehicles’ total waiting time in a traffic network. The results on this work demonstrated that the implementation of RL algorithms outperformed the traditional traffic control system in reducing the total waiting time of vehicles. Among the three RL algorithms, DQN demonstrated the most efficient performance, achieving the lowest total waiting time in only 10 epochs. The study also highlighted the importance of DQN’s ability to predict Q-values for new states, which enables it to make more accurate decisions and select optimal actions, leading to more efficient traffic control. In conclusion, the implementation of RL algorithms in traffic control systems can significantly improve traffic flow and reduce the vehicles’ waiting time. The results in this work suggest that DQN is a promising approach for efficient traffic control and can be further explored for optimizing other aspects of traffic management. In our upcoming work, we plan to focus on QDN based models in traffic signal control and propose a model that gives optimised results in traffic signals control.

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References 1. Azgomi, H.F., Jamshidi, M.: A brief survey on smart community and smart transportation. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (2018). https:// doi.org/10.1109/ICTAI.2018.00144 2. Kim, D., Jeon, O.: Cooperative traffic signal control with traffic flow prediction. Multiintersection. Sensors (2019). https://doi.org/10.3390/s20010137 3. Wan, C.-H., Hwang, M.-C.: Value-based deep reinforcement learning for adaptive isolated intersection signal control. IET Intell. Transp. Syst. (2018). https://doi.org/10.1049/iet-its.2018. 5170 4. Zeng, J., Hu, J., Zhang, Y.: Adaptive traffic signal control with deep recurrent Q-learning. In: 2018 IEEE Intelligent Vehicles Symposium (IV) Changshu, Suzhou, China (2018). https://doi. org/10.1109/IVS.2018.8500414 5. Ozan, C., Baskan, O., Haldenbilen, S., Ceylan, H.: A modified reinforcement learning algorithm for solving coordinated signalized networks. Transp. Res. Part C Emerg. Technol. (2015). https://doi.org/10.1016/j.trc.2015.03.010 6. Abdoos, M., Mozayani, N., Bazzan, A.L.C.: Traffic light control in non-stationary environments based on multi agent Q-learning. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (2011). https://doi.org/10.1109/ITSC.2011.6083114 7. Kekuda, A., Anirudh, R., Krishnan, M.: Reinforcement learning based intelligent traffic signal control using n-step SARSA. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (2021). https://doi.org/10.1109/ICAIS50930.2021.9395942 8. Garcia, F., Rachelson, E.: Markov Decision Processes in Artificial Intelligence (2013) (Chapter 1). https://doi.org/10.1002/9781118557426 9. Krajzewicz, D., Hertkorn, G., Wagner, P., Rössel, C.: SUMO (Simulation of Urban MObility) An Open-Source Traffic Simulation (2002). https://eclipse.dev/sumo/

Fixed-Time Sliding Mode Control for a Drone Quadrotor Najlae Jennan1(B) , El Mehdi Mellouli1 , and Ismail Boumhidi2 1

2

LISA Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected], [email protected] LISAC Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez, Morocco [email protected]

Abstract. A fixed-time sliding mode control method is proposed in this study for a multi-input multi-output nonlinear system, a drone quadrotor. The first step consists on studying the mathematical model of the drone quadrotor. Then, fixed-time sliding mode control method is developed to control the drone quadrotor system for the purpose of solving the high nonlinearity problem, and reducing disturbances. The primary goal of this method is that the convergence time can be known. We study the system stability applying lyapunov technique to extend the control laws of the system. The capability of the proposed technique is demostrated by comparative results. Keywords: Drone quadrotor mode control

1

· Fixed-time · Nonlinear system · Sliding

Introduction

Quadrotor drones have acquired attention in recent years due to their flexibility and complexity. These unmaned aerial vehicles (UAVs) are equipped with four rotors structured in a way that provides them to perform various tasks with exceptional control in flight [1]. That makes the control of quadrotor drones crucial to ensure their safety and stability, which performs an indispensable role in maximizing their performance and attaining the desired trajectories. There are a multitude of control methods that gives effective results as the ProportionalIntegral-Derivative (PID) control method that adjusts a control based on proportional, integral and derivative components to reduce the difference between a reference and the measured variable [2], the Sliding Mode Control (SMC) method which involves driving the system state towards a sliding surface by designing a control law providing a precise control [3], the Backstepping control method which designs a series of virtual control inputs imposing the system to

c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 539–545, 2024. https://doi.org/10.1007/978-3-031-48573-2_78

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follow a desired trajectory in a limited time achieving a good control [4,5]. One approach widely used to control nonlinear systems is known as the Fixed-Time Sliding Mode Control method which is a robust technique that aims to attain an accurate control while providing a fixed convergence time [6]. This method relies on the same concept of the classical SMC method [7,8], nevertheless the main objective of it is to enforce a fixed-time of convergence, ensuring the system stability in spite of external disturbances [10,11]. In this work, we employed the Fixed-Time Sliding Mode Control method to a drone quadrotor system to achieve many findings, where the first objective is to enhance a mathematical representation of the drone quadrotor system. Afterwards, the second objective is to design a control strategy based on FixedTime SMC method to reduce external disturbances, and improve the stability and the adaptability of the drone system [12–14]. Accordingly, the third objective is to study the system stability using Lyapunov approach [9]. As a result, the final objective is to analyze the performance of the control strategy approach by comparing the results with other control techniques. The essay is formulated as outlined: Sect. 2 presents the mathematical representation of the quadrotor drone system. Section 3 explains the design and employment of the Fixed-Time SMC method for controlling the quadrotor drone. Then, in Sect. 4 we introduce the results obtained from the comparative results between the proposed control method and the classical SMC. Finally, Sect. 5 provides a summary of the principal outcomes and highlighting the importance of the proposed control strategy for quadrotor drones.

2

Modeling the Quadrotor Drone System

The quadrotor drone is a flexible aerial vehicle that consists on stability and adaptability in flight [1]. Figure 1 shows a quadrotor drone’s representation with φ is the roll angle related to x axis, θ is the pitch angle related to y axis, and ψ is the yaw angle related to z axis. The quadrotor drone system is a multi-input multi-output (MIMO) nonlinear system known by its complexity, which requires the development of a mathematical model to efficiently control it. We can represent the quadrotor drone model by a system of differential equations, using Newton-Euler formalism. x˙ = f (x, t) + g(x, t) × u(x, t) + d(t)

(1)

where x = [x1 x2 x3 x4 x5 x6 ]T = [x y z φ θ ψ]T ∈ R6 is the state vector of the drone quadrotor system. d(t) = [d1 (t) d2 (t) d3 (t) d4 (t) d5 (t) d6 (t)]T ∈ R6 represents the external disturbances applied to the system. f (x, t) = [f1 (x, t) f2 (x, t) f3 (x, t) f4 (x, t) f5 (x, t) f6 (x, t)]T and g(x, t) = [g1 (x, t) g2 (x, t) g3 (x, t) g4 (x, t) g5 (x, t) g6 (x, t)]T ∈ R6 are the dynamics of the system. And u(x, t) = [ux (x, t) uy (x, t) u1 (x, t) u2 (x, t) u3 (x, t) u4 (x, t)]T ∈ R6 is the vector of the control laws which will be studied in the next section.

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Fig. 1. Representation of quadrotor drone model

3

Fixed-Time Sliding Mode Control Strategy

In this study, the proposed strategy for controlling the quadrotor drone system is the Fixed-Time Sliding Mode Control method due to its ability of handling high nonlinearity and uncertainties, which gives an accurate control. This approach consists on using a sliding surface enforcing the system’s states to track the desired trajectories, ensuring a specified time of convergence [6,14]. We define two sliding surfaces, the first one is constructed to follow the desired trajectories and the second one to enforce the convergence of the system’s states towards the first one [7,13]. We define the first sliding surface as the tracking error as follows: s1i = xi − xid , i = [1, 6]

(2)

where the reaching law relied to this sliding surface is developed as follows: β1

β2

s˙ 1i = −α1i × |s|1i i × sign(s1i ) − α2i × |s|1i i × sign(s1i ), i = [1, 6]

(3)

The second sliding surface is developed in the following form: β1

β2

s2i = s˙ 1i + α1i × |s|1i i × sign(s1i ) + α2i × |s|1i i × sign(s1i ), i = [1, 6]

(4)

where the reaching law relied to this sliding surface is developed as follows: β3

β4

s˙ 2i = −α3i × |s|2i i × sign(s2i ) − α4i × |s|2i i × sign(s2i ), i = [1, 6]

(5)

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With 0 < β1i < 1, 0 < β3i < 1, β2i , β4i > 1, α1i , α2i , α3i , α4i > 0, i = [1, 6]. To study the system stability, we choose the Lyapunov function as follows [9]: ⎧ ⎨ v = 1 × s2 > 0 i 2i 2 (6) ⎩ v˙ i = s2i × s˙ 2i  0 , i = [1, 6] From (4) to (5) and according to the study of system stability, the control law of the drone quadrotor system can be represented as follows:

ui (xi , t) =

1 β1 −1 [¨ xi − fi (xi , t) − α1i × β1i × s˙ 1i × |s|1i i − α2i × β2i × s˙ 1i gi (xi , t) d β2 −1

× |s|1i i

β3

− α3i × |s|2i i × sign(s2i ) − α4i

β4

× |s|2i i × sign(s2i )], i = [1, 6]

(7)

Lemma 1. Considering the quadrotor drone system (1), and assuming that there is a lyapunov function v(x) > 0 that fulfills the condition (8), v(x) ˙  −(ψ1 v(x)p + ψ2 v(x)q )g

(8)

where ψ1 , ψ2 , p, q, g ∈ R+ , pg > 0 and qg > 0. Then the system (1) is stable and any v(x) can attain v(x) ≡ 0 in a fixed-time T, expressed as follows [13]: T 

ψ1g (1

1 1 + g − pg) ψ2 (qg − 1)

(9)

Theorem 1. Considering the system (1), the sliding surface (4) can converge to 0 in a fixed time interval conforming to the control law (7), defined by: T 

1 α3i 2

β3 +1 2

(1 −

β3 +1 2 )

1

+ α4i 2

β4 +1 2

( β42+1 − 1)

(10)

Proof. Considering the lyapunov function (6). Accordingly from (4) and (5), we find: β3

β4

v˙ i = s2i × (−α3i × |s|2i i × sign(s2i ) − α4i × |s|2i i × sign(s2i )) β3 +1

= −α3i × |s|2i i

β4 +1

− α4i × |s|2i i

(11)

, i = [1, 6]

After calculations, we obtain: v˙ i  −[(α3i × 2

β3 +1 2

β3 +1 2

) × vi

+ (α4i × 2

β4 +1 2

β4 +1 2

) × vi

] , i = [1, 6]

(12)

According to the study of system stability, ensuring that v˙ i < 0, for 0 < β3i < 1, β4i > 1, 0 < β32+1 < 1 and 0 < β42+1 < 1, i = [1, 6], lemma 1 is satisfied and the fixed-time is bounded by (10).

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Simulated Results

In this part, we examine the efficiency of the Fixed-Time Sliding Mode Control method for quadrotor drones. We did a comparative analysis between the proposed control method and the classical SMC method. For this reason, we simulate the evolution of the system’s states paths obtained by the proposed control strategy, the classic SMC method and the desired trajectories. From Fig. 2, we can see that both control strategies provided effective tracking. However, the simulation results clearly demonstrate that Fixed-Time SMC method

(a) The position x and desired trajectory xd attained.

(b) The position y and desired trajectory yd attained.

(c) The attitude z and desired trajectory zd attained.

(d) The roll angle ϕ and desired trajectory ϕd attained.

(e) The pitch angle θ and desired trajectory θd attained.

(f) The yaw angle ψ and desired trajectory ψd attained.

Fig. 2. The response of system trajectories and the desired trajectories achieved comparing the control method proposed with the classic SMC method

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provides remarkable advantages than the classic SMC for quadrotor drone systems, which offers fast response and an accurate control while applying external perturbations.

5

Conclusion

In conclusion, this study consists on modeling and control of a quadrotor drone using Fixed-Time Sliding Mode Control method. Through comparative simulations with classical Sliding Mode Control (SMC) method, we have confirmed the effectiveness and benefits of the proposed control approach in achieving robust trajectories tracking, stability, and adaptability to external disturbances. The fixed-time convergence property of Fixed-Time Sliding Mode Control strategy ensures consistent performance and a good stability of the quadrotor drone system.

References 1. Gupta, S.K., Shukla, D.P.: Application of drone for landslide mapping, dimension estimation and its 3D reconstruction. J. Indian Soc. Remote Sens. 46, 903–914 (2018) 2. Colorado, R.M., Aguilar, L.T.: Robust PID control of quadrotors with power reduction analysis. ISA Trans. 98, 47–62 (2020) 3. Hajjami, L.E., Mellouli, E.M., Berrada, M.: Neural network based sliding mode lateral control for autonomous vehicle. In: 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, p. 9092055 (2020) 4. Jennan, N., Mellouli, E.M.: Neural networks based on fast terminal sliding mode lateral control for autonomous vehicle. AIP Conf. Proc. 2814, 040015 (2023). https://doi.org/10.1063/5.0148501 5. Zhou, L., Zhang, J., She, H., Jin, H.: Quadrotor UAV flight control via a novel saturation integral backstepping controller. Automatika J. Control Measur. Electron. Comput. Commun. 60 (2019) 6. Moulay, E., L´echapp´e, V., Bernuau, E., Defoort, M., Plestan, F.: Fixed-time sliding mode control with mismatched disturbances. Automatica 136, 110009 (2022) 7. Mellouli, E.M., Sefriti, S., Boumhidi, I.: Combined fuzzy logic and sliding mode approach’s for modelling and control of the twolink robot. In: Proceedings of 2012 International Conference on Complex Systems, p. 6458599 (2012) 8. Mellouli, E.M., Naoual, R., Boumhidi, I.: A new modified sliding mode controller based fuzzy logic for a variable speed wind turbine. Int. J. Ecol. Dev. 32, 44–53 (2017) 9. Yang, C., Sun, J., Zhang, Q., Ma, X.: Lyapunov stability and strong passivity analysis for nonlinear descriptor systems. IEEE Trans. Circuits Syst. I Regul. Pap. 60, 1003–1012 (2013) 10. Mellouli, E.M., Chalh, Z., Alfidi, M., Boumhidi, I.: A new robust adaptive fuzzy sliding mode controller for a variable speed wind turbine. Int. Rev. Autom. Control 8, 338–445 (2015) 11. Mellouli, E.M., Massou, S., Boumhidi, I.: Optimal robust adaptive fuzzy H∞ tracking control without reaching phase for nonlinear system. J. Control Sci. Eng. 498461 (2013)

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12. Alika, R., Mellouli, E.M., Tissir, E.H.: Optimization of higher-order sliding mode control parameter using particle swarm optimization for lateral dynamics of autonomous vehicles. In: 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, p. 9092119 (2020) 13. Zeng, T., Ren, X., Zhang, Y.: Fixed-time sliding mode control and high-gain nonlinearity compensation for dual-motor driving system. IEEE Trans. Industr. Inf. 16, 4090–4098 (2019) 14. Jennan, N., Mellouli, E.M.: New optimal fast terminal sliding mode control combined with neural networks for modelling and controlling a drone quadrotor. Int. J. Autom. Control 17, (2023). https://doi.org/10.1504/IJAAC.2023.10054839

Application of Distributed Consensus in Fixed Time Sliding Mode to the Wind Turbine System Sanae El bouassi(B) , Zakaria Chalh, and El Mehdi Mellouli Laboratory of Artificial Intelligence, Data Sciences and Emerging Systems, School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco {sanae.elbouassi,Zakaria.chalh}@usmba.ac.ma

Abstract. This article presents a sliding mode control and fixed time control approximation for variable speed wind turbines. The wind turbine system model is depicted in the first step. In the second half, the sliding mode strategy will be applied. In changeable wind conditions, this approach is employed to ensure robust and efficient performance. Specified-time sliding mode control can assist in addressing these issues by offering a stable and predictable control approach that ensures the wind turbine system reaches a desired state or setpoint in a specified amount of time, regardless of beginning conditions or external disturbances. The Lyapunov stability theorem is used to show the stability and effectiveness of this control rule, and we also present a simulation that shows the suggested strategy to be more effective, precise, and accurate in terms of settling time, tracking accuracy, and energy consumption. Keywords: Fixed time sliding mode control · Lyapunov stability · Sliding mode control · Variable Speed Wind Turbine

1 Introduction Due to its ability to generate electricity from the kinetic energy of moving air masses, wind energy is a prolific source of power [1–3].With the exception of enormous offshore wind farms, which are deemed local when they are power plants with ratings of more than 100 MW, the deployment of solar and wind is classified as tributed/distributed local production [4, 5]. A control mechanism utilized in distributed consensus algorithms is fixed-time sliding mode control. The dynamics of the agents are regulated by a series of differential equations that move the agents’ states towards a consensus value in this technique. Mode of fixed-time sliding Distributed consensus is a control approach that can be used to improve the performance and stability of wind turbine systems [6–8]. Wind turbines are complicated systems with nonlinear dynamics and uncertainty, making control difficult. The goal of distributed consensus is to get a set of agents or nodes in a network to agree on a specific value or state [9–11]. The strategy entails developing a control law that assures the wind turbine system reaches a desired setpoint within a given time interval, while simultaneously ensuring that the network’s agents agree on this value [12–15]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 546–552, 2024. https://doi.org/10.1007/978-3-031-48573-2_79

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The modeling of the wind energy system is covered in the first half of this paper. After that, we describe the suggested method and use it on our system. Finally, we show simulations of sliding mode control and fixed time sliding mode distributed consensus. Finally, a conclusion is reached using the findings [16, 17].

2 Modeling of Wind Turbine The variable speed wind turbine’s aerodynamic torque formula is [12, 13]: Ta =

1 Cp(λ, β) v(t)2 ρπ R3 2 λ(t)

(1)

where V is the wind speed, is the air density, R is the rotor radius, Cp is the power coefficient, and λ is defined as: λ(t) = R · wt /v(t)

(2)

These factors affect the rotor’s dynamics: Jr w˙ t = Ta − Tls − kr wt

(3)

The rotational speed, external damping, and inertia of the rotor are denoted as Jr, kr, and wt , respectively. The following expression gives the generator’s speed wg : Jg w˙ g = Ths − kg wg − Tem

(4)

The system’s control law is Tem , and Jg, kg stand in for the generator’s inertia and friction coefficient, respectively. Assuming flawless transmission, we define ng as follows (Fig. 1): ng =

Tls Ths

(5)

3 The New Control Approach Manner of fixed-time sliding in order to increase the performance and stability of wind turbine systems, distributed consensus is a control approach that can be used. Due to their complexity, nonlinear dynamics, and uncertainty, wind turbine systems are difficult to regulate. Following is a representation of the wind turbine system: ⎧ x˙ 1 = x2 ⎨ (6) x˙ 2 = f (x, t) + g · u(x, t) ⎩ y1 = x1 The control law u = Tem and y = wt .

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Fig. 1. Model of a wind turbine with two masses

The system described in Eq. (7) can be thought of as a pair of agents that are described by the following fractional order dynamics [26]: D1 x1 (t) = x2 (t), D1 x2 (t) = f (x1 (t)) + g ∗ u(t) where

 f(x, t) =

Jg −kg − Jg ng ∗ Jg

 ∗ x2 −

kr 1 ∗ x1 + Ta ng ∗ Jg Jg ∗ ng

(7)

(8)

This is how the consensus error is defined: e = wtopt − wt

(9)

The distributed sliding function is suggested for the specified fractional dynamics (8) as follows:   2q1 −p1 p1 q q (10) s = e˙ + α1 sig(e) 1 + β1 sig(e) 1 where sig(x)k = |x|k sign(x) and the parameters α1 and β1 are positive real values and p1 and q1 are positive integers satisfying p1 < q1 . At the sliding surface s = 0, we have   2q1 −p1 p1 1 0 q q 1 1 (11) D e(t) = −D α1 sig(e) + β1 sig(e)

4 Stability Analysis Take the Lyapunov function, v (t, x (t)) = |e|, determining the time derivate of the function v then   2q1 −p1 p1 v˙ = sign(e)˙e = −sign(e)D0 α1 sig(e) q1 + β1 sig(e) q1   2q1 −p1 p1 (12) = − α1 |e| q1 + β1 |e| q1

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4.1 Lemme m

p

Take the differential equation, for instance ˙r = αr n − βr q , r(0) = r0 where α and β are positive real values, and m, n, p and q are positive integers such as n < m and p < q. The settling time has an upper constraint determined by the fact that the equilibrium point of such a system is fixed time stable given by: 1 1

. + T ≤ m α n −1 β 1 − pq The result of the lemma in the equation above (12): v˙ ≤



1

2q −p α1 1q 1 1

−1

+

1 β1 1 −

p1 q1



The following is a presentation of the sliding mode distributed consensus protocol:    2q1 −p1 p1 u = (d + 1)−1 −wopt − D1 α1 sig(e) q1 + β1 sig(e) q1  2q2 −p2 p2 (13) − α2 sig(e) q2 + β2 sig(e) q2 where the parameters α1 , β1 and α2 , β2 are positive real values and p1 , q1 and p2 , q2 are positive integers satisfying p1 < q1 and p2 < q2 (Figs. 2, 3, 4 and 5).

5 Simulation Results

Fig. 2. Rotor speed response and desired rotor speed with SMC.

In summary, the wind turbine’s oscillations and tracking errors under fixed-time sliding mode control result from factors like chattering, model mismatch, inadequate tuning, disturbances, system complexity, and control limits. Addressing these requires parameter adjustments and considering alternative control methods.

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Fig. 3. The profile of the control law for a typical sliding mode control.

Fig. 4. The profile of the control law for distributed consensus in fixed time sliding mode.

Fig. 5. The reaction of the rotor speed using distributed consensus and fixed time sliding mode.

6 Conclusion In conclusion, the performance and efficiency of wind turbines can be greatly enhanced by the employment of sliding mode control and fixed time sliding mode control methods. The advantage of fixed-time sliding mode control is that it ensures the length of time the agents will require to reach a consensus. When a specific deadline must be met or when a speedy and predictable convergence is needed, this may be essential. These complex control systems provide synergistic gains in stability, precision, and robustness when

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combined. Nevertheless, continued R&D initiatives are necessary to fully utilize the advantages of these control approaches in real-world applications. We’ll try to build on the results thus far in this view.

References 1. Aissaoui, A., Tahour, A., Abid, M., et al.: Power control of wind turbine based on fuzzy controllers.’ Energy Procedia 42, 163–172 (2013). 2. Arturo, C., Fuentes, M., Seeber, R., et al.: ‘Saturated lipschitz continuous sliding mode controller for perturbed systems with uncertain control coefficient.’ IEEE Trans. Autom. Control 66, 3885–3891 3. Ayadi, M., Naifar, O., Derbel, N.: High-order sliding mode control for variable speed PMSGwind turbine-based disturbance observer. Int. J. Model. Ident. Control 32, 85–92 4. Errami, Y., Obbadi, A., Sahnoun, S.: Combined control strategies for performance enhancement of a wind energy conversion system based PMSG. Int. J. Model. Ident. Control 37, 153–163 (2022) 5. Ferrara, A., Incremona, G., Cucuzzella, M.: Advanced and optimization based sliding mode control. Theory and applications. In: SIAM (2019) 6. Hong, C., Huang, C., Cheng, F.: Sliding mode control for variable-speed wind turbine generation systems using artificial neural network. Energy Procedia 61, 1626–1629 (2014) 7. Labbadi, M., Boukal, Y., Taleb, M., et al.: Fractional order sliding mode control for the tracking problem of quadrotor UAV under external disturbances. In: European Control Conference (ECC),Russia, St. Petersburg, 12–15 May 2020 8. Mellouli, E., Boumhidi, I.: Direct adaptive fuzzy sliding mode controller without reaching phase for an uncertain threetank-system. Int. J. Model. Ident. Control 335–342 (2016) 9. Mellouli, E., Boumhidi, I., Boumhidi, J.: Using fuzzy logic for eliminating the reaching phase on the fuzzy H∞ tracking control. Int. J. Model. Ident. Control 20 , 398–406 10. Mellouli, E., Sefriti, S., Boumhidi, I.: Combined fuzzy logic and sliding mode approach’s for modelling and control of the two link robot. In: Proceedings of 2012 International Conference on Complex Systems (ICCS 2012) (2012) 11. Mellouli, E.M., Naoual, R., Boumhidi, I.: ‘A new modified sliding mode controller based fuzzy logic for a variable speed wind turbine. Int. J. Ecol. Develop. 32(1), 44–53 (2017) 12. Mellouli, E., Massou, S., Boumhidi, I.: Optimal robust adaptive fuzzy H∞ tracking control without reaching phase for nonlinear system. J. Control Sci. Eng. 2013, 498461 (2013) 13. Mellouli, E., Chalh, Z., Alfidi, M., Boumhidi, I.: ‘A new robust adaptive fuzzy sliding mode controller for a variable speed wind turbine. Int. Rev. Autom. Control 8(5), 338–445 (2015) 14. Alika, R., Mellouli, E., Tissir, H.: ‘Optimization of higher-order sliding mode control parameter using particle swarm optimization for lateral dynamics of autonomous vehicles. In: 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET 2020) (2020) 15. Mérida, J., Aguilar, L., Dávila, J.: Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization. Renewable Energy 71(11), 715–728 (2014) 16. Monroy, A., Alvarez-Icaza, L.: Wind turbine power coefficient real-time identification. Int. J. Modell. Ident. Control 6, 181–187 (2019) 17. El bouassi, S., Chalh, Z., Mellouli, E.M.: A new robust adaptive control for variable speed wind turbine. Artif. Intell. Smart Environ. 635, 90–96 (2023) 18. Djilali, L., Badillo-Olvera, A., Yuliana Rios, Y., et al.: Neural high order sliding mode control for doubly fed induction generator based wind turbines. IEEE Latin America Trans. 20, 223–232

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19. Mellouli, E., Chalh, Z., Alfidi, M.: A new robust adaptive fuzzy sliding mode controller for a variable speed wind turbine. Int. Rev. Autom. Control 8 (2015) 20. Hajjami, L., Mellouli, E., Berrada, M.: Neural network based sliding mode lateral control for autonomous vehicle. In: 2020 1st International conference on innovative research in applied science, engineering and technology (IRASET 2020) (2020)

Enhancing Fake Account Detection on Facebook Using Boruta Algorithm Amine Sallah1(B) , El Arbi Abdellaoui Alaoui2 , and Said Agoujil3 1

Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia, Morocco [email protected] 2 Department of Sciences, Ecole Normale Sup´erieure, Moulay Ismail University of Meknes, Meknes, Morocco 3 ´ Ecole Nationale de Commerce et de Gestion, Moulay Ismail University of Meknes, El Hajeb, Morocco

Abstract. With the increasing prevalence of social media platforms, detecting fake accounts has become a critical challenge for maintaining online security and user trust. This paper proposes a feature selection approach for detecting fake accounts on Facebook, aiming to improve the efficiency and accuracy of existing detection models. The research focuses on identifying a subset of informative features that contribute significantly to distinguishing between genuine and fake accounts. Thanks to Boruta algorithm, machine learning classifiers can yield similar detection performance to using all features (non-selection), potentially leading to computational and resource savings. In addition, this feature selection algorithm allows faster training and prediction times. The approach’s effectiveness is evaluated using a real Facebook dataset, and the results demonstrate its ability to improve interpretability and keep only the significant features. Keywords: Fake account · Machine learning · Facebook selection · Classification · Boruta · Explainability

1

· Feature

Introduction

The detection of fake accounts on social media platforms, particularly Facebook, has become a critical concern due to the proliferation of malicious activities and privacy breaches. This paper proposes an approach to improve the accuracy and efficiency of fake account detection on Facebook by incorporating the Boruta feature selection algorithm. Boruta [7], a powerful feature selection technique based on random forest analysis, allows for the identification of the most influential features that effectively differentiate genuine accounts from fake ones. Machine learning (ML) is increasingly being utilized for fake account detection [4,6] due to its ease of implementation and high accuracy. In general, ensemble classifiers used for fake account prediction can be categorized into the following types [10]: c The Author(s), under exclusive license to Springer Nature Switzerland AG 2024  Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 553–558, 2024. https://doi.org/10.1007/978-3-031-48573-2_80

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– Bagging, which frequently considers homogenous weak learners, learns them from one another in parallel, separately, and then aggregates them using some deterministic averaging procedure. – Boosting, which often includes homogenous weak learners, learns them sequentially in a highly adaptative manner (each base model depends on the preceding ones) and combines them using a deterministic technique. In recent years, there’s been a significant increase in both the size and complexity of datasets, leading to high-dimensional data challenges. The objective of dimensionality reduction is to minimize the number of variables involved. This research aims to streamline computing processes by leveraging a limited number of impactful features. To achieve this, we propose a streamlined feature selection strategy. Previous studies have demonstrated the effectiveness of Boruta in various domains, including fraud detection [3] and bioinformatics [8]. Chen et al. [5] utilized a multi-head attention-driven Graph Neural Network (GNN) technique for identifying malicious connections within a temporal reciprocal graph named MedGraph. Their method incorporates a transformer module to initially capture the historical interactions of malicious users, encompassing both long-term and short-term behavioral traits. Alsubaei [2] has conducted research on Twitter privacy and safety and shared their findings. Their investigation primarily concentrated on identifying inappropriate posts linked to fraudulent accounts. The issue of cloned profiles has been tackled on Twitter by Saravan et al. [9], who have employed clustering and classification process. The method collects the fake and possible cloned profiles and computes the additional relationship attributes for performing cloned profile detection. Boruta is a feature selection and ranking method based on the Random Forest algorithm. The advantages of Boruta include determining the relevance of the variable and statistically selecting important variables. How does the Boruta Algorithm work? Initially, the Boruta algorithm enriches the given dataset by creating randomized, shuffled duplicates of all existing features, referred to as Shadow Features. Next, the algorithm trains a random forest classifier on this enhanced dataset, which includes both original and shadow attributes. A feature importance metric, like Mean Decrease Accuracy, is employed to assess the importance of each feature. In every iteration, the Boruta algorithm examines whether an actual feature surpasses its best shadow feature in terms of importance, gradually eliminating features deemed insignificant. Ultimately, the algorithm concludes once all features have been confirmed, discarded, or reached a preset limit for the random forest. In essence, Boruta identifies all features that show strong or weak relationships with the response variable. The rest of the paper is organized as follows. Section 2 details the proposed methodology, including the Boruta feature selection algorithm. Section 3

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describes the experimental setup and evaluation metrics and presents the results and analysis of our approach. Finally, Sect. 4 concludes the paper, summarizing the key findings and contributions.

2

Methodology

In this part, we elucidate our suggested method. Specifically, we provide a detailed explanation of each phase in detecting fake profiles on Facebook. The methodology to detect a fake profile on Facebook is described in Fig. 1. The first step, a dataset containing the relevant profile functionalities, is described in Table 1. In the second step, we converted categorical variables into numerical representations that machine learning algorithms can handle; we applied encoding techniques, including one-hot encoding and target encoding, and then normalized the columns. In the last step, we trained the model based on features selected by Boruta feature selection to predict the class of Facebook profiles. Table 1. Facebook profile features Index Features

Index Features

0

No friend

1

Phototag*

2

Photopost*

3

Video

4

Checkin

5

Sport

6

Player

7

Music

8

Film

9

Series

10

Book

11

Game

12

Restaurant

13

Like

14

Group

15

Post shared/post posted rate

16

Education no

17

Education secondary school

18

Education university

19

About me yes

20

Family yes

21

Gender male

22

Relationship complicate

23

Relationship married

24

Relationship vide

25

Note yes

We utilized the dataset ”Fake and Real Accounts Facebook”, which was collected from January 1, 2017 to January 1, 2018 [1]. The collection includes data from 889 public Facebook accounts. It consists of 26 predictor variables and one outcome variable (Status), indicating whether the account is legitimate or fake. We used four classification algorithms to train our models: Extreme Gradient Boosting, catBoost, Random Forest, AdaBoost. First, we train our model with all of its features. Then, using the Boruta feature selection, we choose important features and train our model with the chosen features. Finally, various

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Fig. 1. Design approach to detect fake account

Fig. 2. Heatmap of feature rankings using Boruta

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Table 2. Overall results of our experiment Feature selection Non selection

Boruta

Classifier

Accuracy AUC

Ada boost

Recall

Prec.

F1

Time (s) 0.134

0.9903

0.9983 0.9874 0.9938 0.9905

Random forest 0.9952

0.9991 0.9936 0.9970 0.9952

0.520

0.9992 0.9905 0.9879 0.9890

0.381

XGBoost

0.9887

CatBoost

0.9952 0.9992 0.9936 0.9970 0.9952

2.701

Ada boost

0.9887

0.124

Random forest 0.9952

0.9987 0.9874 0.9908 0.9889 0.9986 0.9936 0.9970 0.9952

0.521

XGBoost

0.9887

0.9990 0.9843 0.9938 0.9889

0.366

CatBoost

0.9936

0.9994 0.9936 0.9939 0.9937

2.807

measures are used to examine the suggested models, including accuracy, AUCRoc, recall, precision, F1-score. We performed 10-fold cross-validation to evaluate our models. We began by dividing the total number of observations into ten random subsets. Next, we trained a model using nine subsets for each iteration, then tested the fitted model on the remaining subset. We then averaged the cross-validated performance measures over the ten iterations.

3

Results and Analysis

In this section, we compare the performance of the model using the selected features from Boruta feature selection with the baseline model that uses all the features in the dataset. Figure 2 shows the features ranking heatmap, such as confirmed features are represented by rank 0, while higher ranks indicate less important or rejected features. By examining this heatmap , Boruta picks 73% of the most relevant features. Then the predictive model is built by considering the feature selection outcomes. From Table 2, we can see that the evaluation metrics are similar for nonselection and Boruta Feature selection. It indicates that the selected subset of features is sufficient for making accurate predictions. The feature selection process successfully identified the most informative features, reducing complexity and potentially improving generalization. This finding implies that exploring a reduced feature space (using Boruta) can yield similar evaluation metrics to using all features (non-selection), potentially leading to computational and resource savings.

4

Conclusion

In this work, we proposed an approach to reduce Facebook dataset dimensionality by employing the Boruta feature selection algorithm for fake account detection. The objective was to identify the most relevant features that contribute significantly to distinguishing between genuine and fake accounts, thereby

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improving the interpretability of the detection model. The results of this study demonstrate the importance of feature selection, as computational complexity was reduced, allowing for faster training and prediction times. The ability to identify relevant features not only enhances model performance but also provides a deeper understanding of the factors that contribute to the identification of fake accounts. In the future, we will collect more data to detect fake accounts and work with some deep learning methods. Data Availability. The data used to support the findings of this study are available from the corresponding author upon request.

References 1. Albayati, M.B., Altamimi, A.M.: Identifying fake Facebook profiles using data mining techniques. J. ICT Res. Appl. 13(2) (2019) 2. Alsubaei, F.S.: Detection of inappropriate tweets linked to fake accounts on twitter. Appl. Sci. 13(5), 3013 (2023) 3. Aslam, F., Hunjra, A.I., Ftiti, Z., Louhichi, W., Shams, T.: Insurance fraud detection: Evidence from artificial intelligence and machine learning. Res. Int. Bus. Financ. 62, 101744 (2022) 4. Boshmaf, Y., Ripeanu, M., Beznosov, K., Santos-Neto, E.: Thwarting fake OSN accounts by predicting their victims. In: AISec 2015 - Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2015 pp. 81–90 (2015). https://doi.org/10.1145/2808769.2808772 5. Chen, K., Wang, Z., Liu, K., Zhang, X., Luo, L.: Medgraph: malicious edge detection in temporal reciprocal graph via multi-head attention-based GNN. Neural Comput. Appl. 35(12), 8919–8935 (2023) 6. Kanagavalli, N., Priya, S.B.: Social networks fake account and fake news identification with reliable deep learning. Intell. Autom. Soft Comput. 33(1), 191–205 (2022) 7. Kursa, M.B., Jankowski, A., Rudnicki, W.R.: Boruta-a system for feature selection. Fund. Inform. 101(4), 271–285 (2010) 8. Nahar, N., Ara, F., Neloy, M.A.I., Biswas, A., Hossain, M.S., Andersson, K.: Feature selection based machine learning to improve prediction of Parkinson disease. In: Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings 14, pp. 496–508. Springer (2021) 9. Saravanan, A., Venugopal, V.: Detection and verification of cloned profiles in online social networks using mapreduce based clustering and classification. Int. J. Intell. Syst. Appl. Eng. 11(1), 195–207 (2023) 10. Singhal, Y., Jain, A., Batra, S., Varshney, Y., Rathi, M.: Review of bagging and boosting classification performance on unbalanced binary classification. In: 2018 IEEE 8th International Advance Computing Conference (IACC). pp. 338–343. IEEE (2018)

Design of Artificial Neural Network Controller for Photovoltaic System Salma Benchikh1(B) , Tarik Jarou1 , Mohamed Khalifa Boutahir2 , Elmehdi Nasri1 , and Roa Lamrani1 1 Advanced Systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail

University, Kenitra, Morocco {salm.benchikh,Tarik.jarou,elmehdi.nasri1,roa.lamrani}@uit.ac.ma 2 Engineering Science and Technology Laboratory, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Meknes, Morocco [email protected]

Abstract. The increasing use of non-renewable sources to produce power energy is raising major environmental concerns. To meet this challenge, the use of alternative sources of energy has being developed, such as solar energy which is widely used for power generation due to the abundant availability of solar radiation and its minimal pollution impact. However, reducing the cost and improving the harnessing efficiency of natural sources use is very important in boosting power plant performance. Therefore, many solutions have emerged and, the maximum power point tracking (MPPT) system is most frequently employed for solar energy. It is used to maximize the production of electrical energy from photovoltaic panels. The aim of this paper is to evaluate the performance of MPPT using an artificial neural network (ANN) method that is used with a DC-DC boost converter to provide constant output to a load in a photovoltaic system. The duty cycle is generated by the ANN algorithm, based on irradiation conditions and temperature changes. Keywords: Photovoltaic system · Artificial neural network · Maximum power point tracking · Artificial intelligence

1 Introduction Photovoltaic (PV) systems are increasing in importance as a type of renewable energy source due to their capacity to generate power from sun irradiation. The efficiency of PV systems, however, can be impacted by several variables, including variations in solar irradiation and temperature. Maximum Power Point Tracking is a method used in photovoltaic systems to increase the power output of PV panels [1]. By regulating the voltage and current levels, the MPPT maintains the maximum power point (MPP) under changing external conditions. The MPP is the point in a PV system’s operation where it generates the most power under a particular set of conditions, such as solar irradiation and temperature. The MPPT algorithm continually adjusts the PV system’s operating point to keep it close to the MPP, which results in improved efficiency and more output power. To ensure this goal, MPPT is controlled with different methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 559–565, 2024. https://doi.org/10.1007/978-3-031-48573-2_81

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In addressing these challenges, the role of Maximum Power Point Trackers becomes pivotal. These methods optimize the energy harvest from PV panels by continuously tracking the point at which they operate most efficiently. Traditional MPPT algorithms, however, have limitations in adapting to the dynamic nature of solar energy. Artificial neural network have gained popularity recently as a potential tool for MPPT control. Computer models called ANNs, which can learn complicated correlations between inputs and outputs through a process called reinforcement learning, are inspired by the form and operation of biological neural network [2]. ANN techniques are highly relevant to MPPT due to the non-linear and dynamic nature of solar power generation. They are excellent at handling complex, time-varying data due to weather conditions and shading, and effectively adapt MPPT strategies. What’s more, ANNs’ pattern recognition capabilities enable informed decision-making, particularly when irradiation levels are changing rapidly. Their real-time optimization and data-driven approach further enhances MPPT, maximizing energy production under variable environmental conditions. In general, the application of ANN for MPPT control of PV systems is an exciting area of research with the potential to improve the effectiveness and performance of PV systems in many applications. As technology advances, ANN could play a more important role in the creation of efficient and sustainable renewable energy systems. To have a maximum power point when climatic conditions change, a model of ANN has been simulated in Simulink Matlab and presented in this paper.

2 Design of Photovoltaic System Figure 1 shows a photovoltaic system consisting of a photovoltaic panel, a DC-DC converter, and a load. By controlling the duty cycle of a DC-DC boost converter, MPPT control is used to extract the maximum power available through the solar panel.

Fig. 1. The block diagram of photovoltaic system [3].

2.1 DC-DC Boost Converter A boost converter is a DC-DC power converter that regulates down current from the source to the load while ramping up voltage. It is a switched-mode power supply (SMPS) DC-DC converter containing two or more semiconductor components, including a transistor and a diode, as well as several energy storage components, including an inductor, a capacitor, or both. A DC-DC boost converter can increase output voltage and works

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as an alternative for a transformer by increasing voltage, it has an input source of DC voltage, an inductor or boost inductor, a capacitor, which operates as a filter, a diode, and a power semiconductor device, which works as a switch. Typically, the switch is an IGBT or a power MOSFET. A voltage higher than the supplied input voltage is used to supply power to the load. The IGBT or power MOSFET switching element, which operates in accordance with the variation provided to the duty cycle of the switch, performs the control function. A boost converter has two modes of operation: charging and discharging. When the switch is closed, the device is in the charging mode; when it’s open, it’s in the discharging mode. 2.2 Maximum Power Point Tracking Each solar panel’s power-voltage or current-voltage curve has a peak operating point where the solar panel is delivering the most power to the load. This distinct position is known as The MPP of a solar panel [3]. These Power-Voltage or current-voltage curves are temperature and irradiance (the flow of radiant energy per unit area) dependent due to the photovoltaic nature of solar panels. In other words, the curve will vary depending on how much sunlight falls on a given section of the panels, and the peak point or MPP will also fluctuate. It follows that environmental factors will alter the operating current and voltage that maximize power production. A crucial step in a PV system is to track the MPP of a PV array. In order to address specific disadvantages, numerous MPPT approaches have been developed, and numerous modifications of each method have been suggested. It may be difficult to decide on the best approach to use when installing a PV system due to the enormous variety of provided methods. The complexity, necessary number of sensors, digital or analog implementation, speed of convergence, tracking ability, and cost-effectiveness of each system differs. Furthermore, the choice of MPPT algorithm might be significantly influenced by the type of application. ANN controller is one of the most important controllers to track the MPP of the photovoltaic system.

3 Artificial Neural Network An ANN is a replica of the human nervous systems which learns from its surroundings just like the brain in order to process information. This paradigm is gaining fast popularity for its high accuracy and bright future prospective. The most popular type of ANN, multilayer feed forward, has been chosen for implementation. There are three main types of layers. The first layer is an input layer, followed by an output layer, the multilayer feed-forward ANN has several hidden layers between the two layers. The connection is from the initial layer to the next layer and they only move forward. Multilayer feed-forward ANNs pass through two distinct phases:

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• Phase I: Training, frequently referred to the learning phase. Through continuous training on a set of training data, the training phase aims to generate a specified output when specific input is given to the ANN. • Phase II: the execution phase, the outputs are returned depending on the value of the inputs. The following describes how a feed-forward ANN execution performs: After receiving the input, the input layer passes it through all of the hidden levels, and finally, the output is returned by the output layer. A feed-forward ANN network can easily be used to propagate an input to produce an output. The problem occurs when we work with a network that has connections going in all directions (like the brain) and we need to compute an output from that network. A network’s connections in both directions can result in loops, which can be resolved with the aid of recurrent network. Recurrent networks can code time dependencies, while feed-forward networks are preferable for situations without such a dependency. A multilayer feed-forward ANN with all neurons in each layer paired to all neurons in the following layer. The network is known as a completely linked network. When the ANN is trained, two parameters need to be chosen, the first one is the weight that is assigned to the various inputs, while the second one is the value in the activation functions. Such a configuration is infeasible, and the system would be simpler to manipulate if only one parameter were changed. A bias neuron is created to solve this problem. The bias neuron consistently returns a result of 1. The bias neuron is not connected to the previous layer neurons, but only to the next layer neurons.

4 Simulation and Results 4.1 Photovoltaic Panel Specifications The parameters considered for modelling of PV Panel on Simulink are as follows (Table 1). Table 1. Photovoltaic panel specifications. Peak power Pmax(W)

250

Maximum voltage Vmpp (V)

30.6

Maximum current Impp(A)

8.18

Open circuit voltage Voc (V)

37.5

Short circuit current (A)

8.7

4.2 Design of Artificial Neural Network Controller An artificial neural network has three layers: input, hidden, and output. Any combination of solar irradiance, temperature, and PV array voltage and current may be used for ANN

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training [5]. In the ANN implemented for MPP, the inputs are photovoltaic. In the ANN implemented for MPP, the inputs are photovoltaic. The ANN weights are updated using the Levenberg-Marquardt Feed-Forward backpropagation algorithm, which is a widely used optimization technique for neural network learning. The ANN architecture includes a hidden layer composed of 15 neurons, and the hidden layer uses a tangent sigmoid activation function to produce the output of the hidden layer. The sigmoid tangent function is a commonly used activation function that introduces non-linearity into the network, enabling it to learn and represent complex relationships between input variables. On the other hand, a linear activation function is trained on the ANN’s output layer neurons. Given that the objective in this situation is to anticipate the MPP, the linear activation function guarantees that the network creates a linear response to the inputs. Without adding any non-linear distortions, the linear activation function makes sure that the output of the network is directly proportionate to the input values. Using this ANN architecture and activation functions, the network can efficiently learn and approximate the relationship between PV voltage, PV current, and MPP. The Levenberg-Marquardt algorithm is used to iteratively update the network weights, minimizing the difference between the predicted and actual MPP in the training data (Fig. 2).

Fig. 2. Artificial neural network controller architecture

The proposed approach has the benefit of tracking MPP more quickly. To maintain precise tracking of MPP, the neural network must be trained frequently because the parameters of a PV array change over time. The PV panel subsystem’s input, or irradiation, has been adjusted as shown in Fig. 3.

Fig. 3. Dynamic solar irradiation signal with time-varying and rapid changes

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Using an artificial neural network controller to track the photovoltaic MPP significantly improves the dynamic performance of the system. Figures 4, 5 and 6 represent output Voltage, output current, and output power of the photovoltaic system. The advantage of the ANN model lies in its ability to track and adapt to changes in the MPP by leveraging its training and prior knowledge of the expected response of the MPP to specific inputs. This allows the ANN model to dynamically adjust and optimize the photovoltaic system’s operation, maximizing its power output under varying conditions. Traditional control methods for tracking MPP often depend on mathematical models or algorithms that can struggle to capture the complex, non-linear nature of photovoltaic systems. In contrast, an ANN-based controller can effectively learn the patterns and relationships between different input parameters and the corresponding MPP. In addition, the dynamic nature of ANN enables it to adapt and respond quickly to changes in system inputs. This is particularly helpful in situations where irradiance levels fluctuate rapidly due to passing clouds or shading effects. The ANN controller can quickly recognize these changes and adjust the system operating point to follow the new MPP, minimizing power losses and maximizing energy efficiency.

Fig. 4. Output voltage of the photovoltaic system using artificial neural network controller

Fig. 5. Output current of the photovoltaic system using artificial neural network controller

Fig. 6. Output power of the photovoltaic system using artificial neural network controller

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5 Conclusion Due to the non-linear nature of solar cells, a photovoltaic module needs to be driven by an energy control circuit in order to produce its maximum power. There is a lot of potential for improving the performance and efficiency of photovoltaic systems by integrating an artificial neural network controller. By using ANN-based controllers, PV systems are able to optimize energy production and increase power output through intelligent decisionmaking and real-time adaptation to changing environmental conditions. Artificial neural network controllers have important advantages for solar systems in terms of improved performance, adaptability, fault detection, and maintenance. These controllers open the door for more effective and dependable PV systems by utilizing the power of intelligent decision-making, promoting the widespread use of clean energy and a sustainable future. Acknowledgments. BENCHIKH Salma thanks the CNRST for sponsoring research activities.

References 1. Harrag, A., Messalti, S.: Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller. Renew. Sustain. Energy Rev. 49, 1247–1260 (2015). https://doi.org/10.1016/j.rser.2015.05.003 2. Sunny, M.S.H., Ahmed, A.N.R., Hasan, M.K.: Design and simulation of maximum power point tracking of photovoltaic system using ANN. In: 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1–5 Sept 2016 3. Benchikh, S., Jarou, T., Nasri, E., Roa, E.: Design of an adaptive neuro fuzzy inference system for photovoltaic system. In: Farhaoui, Y., Rocha, A., Brahmia, A., Bhushab, B. (eds.) Artificial Intelligence and Smart Environment, pp. 352–358. Springer, Switzerland (2023) 4. Taherbaneh, M., Rezaie, A.H., Ghafoorifard, H., , K. Rahimi, Menhaj, M.B.: Maximizing output power of a solar panel via combination of sun tracking and maximum power point tracking by fuzzy controllers. Int. J. Photoenergy 2010, 13 (2010). https://doi.org/10.1155/ 2010/312580 5. Fawzan, S.: Parameters estimation of photovoltaic modules: comparison of ANN and ANFIS. Int. J. Ind. Electron. Drives 1(2) (2014)

The Impact of Artificial Intelligence on Supply Chain Management in Modern Business Mitra Madancian1,2(B) , Hamed Taherdoost1,3 , Maassoumeh Javadi2 , Inam Ullah Khan4 , Alaeddin Kalantari3 , and Dinesh Kumar5 1 Department of Arts, Communications and Social Sciences, University Canada West,

Vancouver, Canada [email protected] 2 School of Public and Global Affairs, Fairleigh Dickinson University, Vancouver, Canada 3 Research and Development Department, Hamta Business Corporation, Vancouver, Canada 4 Department of Electronic Engineering, School of Engineering and Applied Science, Isra University, Hyderabad, Pakistan 5 Mittal School of Business, Lovely Professional University, Chaheru, India

Abstract. Artificial intelligence (AI) is becoming increasingly important in managing complex and global supply chain operations. This study identifies four key areas of development: autonomous supply chain systems, improved predictive analytics, sustainability efforts, and collaborative supply chain networks. These advancements will revolutionize the industry by making real-time decisions, predicting market trends, addressing sustainability concerns, and enhancing transparency and coordination among stakeholders. As businesses adopt these trends, they will optimize operations, reduce environmental impact, and meet global market demands, positioning themselves for success in an interconnected world. Keywords: Artificial intelligence (AI) · Supply chain management · Autonomous systems · Predictive analytics · Sustainability initiatives

1 Introduction Artificial Intelligence (AI) has emerged as a transformative force in various industries, and the supply chain sector is no exception. In the dynamic and complex world of supply chain management, AI is redefining how organizations plan, execute, and optimize their operations. To comprehend the significance of AI in the supply chain context, it is essential to define what AI means in this particular domain [1]. At its core, AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making. In the supply chain context, AI leverages advanced technologies like machine learning, deep learning, natural language processing, and data analytics to enhance various facets of the supply chain process [2].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 566–573, 2024. https://doi.org/10.1007/978-3-031-48573-2_82

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AI algorithms can analyze historical sales data, market trends, and external factors (weather, holidays, or economic indicators) to make highly accurate demand forecasts. This enables organizations to optimize inventory levels, reducing carrying costs and ensuring products are available when and where needed. AI-driven algorithms can create optimized production and distribution plans, considering various constraints and objectives [3, 4]. Real-time adaptable plans for businesses utilize robotics and AI to automate warehouse tasks, improve order processing speed, reduce labor costs, and enhance overall efficiency. Additionally, AI optimizes shipping routes, reducing transportation expenses and improving on-time delivery [5, 6]. AI offers various benefits in supply chain and customer service. It can analyze supplier data to improve decisionmaking, identify product defects more efficiently, assist with customer inquiries, and optimize routes to support sustainability goals (Fig. 1) [7]. AI in the supply chain context represents a revolution in how businesses manage their operations. It empowers organizations to make data-driven decisions, adapt to changing circumstances swiftly, and optimize their processes for efficiency and sustainability. As technology continues to advance, AI’s role in the supply chain will likely expand even further, reshaping the industry in ways we can only begin to imagine.

Fig. 1. The role of AI in supply chain management

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2 Benefits of AI in Supply Chain Management Supply chain management plays a pivotal role in the success of any organization, as it governs the flow of goods, information, and finances across various stages of the production and distribution process. Integrating artificial intelligence (AI) into supply chain processes has revolutionized how companies manage their operations [8, 9]. This section delves into the manifold benefits of AI in supply chain management, shedding light on its impact on efficiency, cost-effectiveness, sustainability, and customer satisfaction. 2.1 Improved Forecasting and Demand Planning One of the foremost benefits of AI in supply chain management is its ability to enhance forecasting and demand planning accuracy. AI algorithms can analyze vast real-time historical data sets, market trends, and external factors to generate precise demand forecasts. This reduces overstocking and understocking issues, ultimately lowering inventory carrying costs and minimizing stockouts, which can lead to lost sales and customer dissatisfaction [10–13]. 2.2 Enhanced Efficiency and Automation AI-driven automation is a game-changer in supply chain management. Through machine learning and robotic process automation (RPA), routine tasks such as order processing, inventory management, and routing optimization can be streamlined. This reduces human error and allows employees to focus on more strategic and complex decision-making tasks. As a result, supply chain operations become more efficient and cost-effective [14–16]. 2.3 Optimized Inventory Management AI algorithms can optimize inventory levels by continuously monitoring stock levels, order patterns, and supplier performance. They can suggest reordering points and quantities in real time, ensuring that inventory is reasonable and sufficient. This reduces carrying costs and frees up capital for investment in other business areas [17, 18]. 2.4 Improved Supplier Relationship Management AI enables proactive supplier relationship management by monitoring real-time supplier performance metrics. It can detect deviations from agreed-upon service levels and trigger alerts or automated actions, such as reassigning orders to alternative suppliers. This fosters healthier and more collaborative supplier relationships, benefiting the supply chain [19, 20].

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2.5 Supply Chain Visibility and Transparency AI-powered analytics and data visualization tools give supply chain stakeholders realtime visibility into the entire supply chain network. This transparency allows for quicker identification and resolution of bottlenecks, delays, and inefficiencies. Furthermore, it enables companies to respond promptly to market demand changes or supply chain disruptions [21, 22]. 2.6 Sustainability and Environmental Impact Sustainability is a growing concern in supply chain management. AI can help organizations reduce their environmental footprint by optimizing transportation routes, reducing energy consumption, and minimizing waste. Through predictive analytics, AI can also help organizations make informed decisions about sustainable sourcing and manufacturing practices [23–25]. 2.7 Customer Satisfaction Ultimately, the benefits of AI in supply chain management translate into improved customer satisfaction. Accurate demand forecasting ensures that products are available when customers want them. Efficient logistics and order processing lead to faster delivery times and improved product quality due to optimized inventory management, resulting in higher customer satisfaction and loyalty [26].

3 Challenges and Implementation Hurdles Supply chain management has become a complex and integral part of contemporary business operations. It encompasses the planning, sourcing, production, logistics, and delivery of products and services to optimize efficiency, reduce costs, and enhance customer satisfaction. However, implementing effective supply chain management has challenges and hurdles, which can impede success and require careful consideration [27]. 3.1 Demand Forecasting One of the foremost challenges in SCM is accurate demand forecasting. Companies must predict customer demand precisely to optimize inventory levels and production schedules. Variability in demand, changing market conditions, and the introducing of new products can make forecasting a complex and ongoing task [28]. 3.2 Supply Chain Visibility Maintaining visibility throughout the supply chain is essential for timely decisionmaking and risk mitigation. However, achieving end-to-end visibility can be challenging due to fragmented information systems, data silos, and varying levels of transparency among supply chain partners [19].

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3.3 Inventory Management Balancing adequate inventory levels with minimizing holding costs is a perpetual challenge. Organizations often grapple with excess inventory or stockouts, negatively impacting financial performance and customer satisfaction [29]. 3.4 Globalization and Regulatory Compliance Global supply chains bring opportunities for cost savings but also introduce complexities related to different regulatory environments, customs procedures, and geopolitical risks. Navigating these challenges while ensuring compliance requires careful planning and monitoring [30]. Table 1 summarizes some potential solutions for these challenges.

4 Future Trends and Developments Supply chain management is a critical component of modern business operations, responsible for the efficient flow of goods and services from raw material suppliers to end customers [31]. In recent years, AI has emerged as a transformative technology in supply chain management, offering solutions to complex challenges such as demand forecasting, inventory optimization, route planning, and risk management. As we look ahead, it is evident that AI will continue to play a pivotal role in shaping the future of supply chain management. This academic text explores AI’s key future trends and developments in this domain [32]. One of the most prominent trends in the future of AI in supply chain management is the development of autonomous supply chain systems [33]. These systems will leverage AI and machine learning algorithms to make real-time decisions, such as adjusting production schedules, optimizing inventory levels, and rerouting shipments without human intervention. Autonomous systems can react swiftly to unforeseen disruptions, reduce operational costs, and enhance supply chain resilience. Companies will increasingly invest in autonomous supply chain technologies to gain a competitive edge in a dynamic marketplace [34]. AI-driven predictive analytics will become more sophisticated, enabling organizations to accurately anticipate market trends, customer demands, and supply chain disruptions [35]. Advanced AI models will integrate data from various sources, including IoT sensors, social media, and weather forecasts, to provide comprehensive insights. This predictive capability will empower businesses to optimize their supply chain operations, minimize stockouts, and reduce excess inventory, achieving cost savings and improved customer satisfaction [36]. Sustainability is an ever-growing concern in supply chain management. AI will play a pivotal role in driving sustainability initiatives by optimizing transportation routes to reduce carbon emissions, identifying opportunities for waste reduction, and promoting responsible sourcing. Additionally, AI-powered supply chain analytics will help companies track and report their environmental impact more accurately, meeting regulatory requirements and aligning with consumer preferences for eco-friendly products and practices [37].

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Table 1. Solutions and best practices for addressing challenges chain management Challenge

Potential solutions and best practices

Demand forecasting

Utilize advanced analytics for accurate demand forecasting Incorporate machine learning and predictive analytics Ensure data accuracy and completeness through regular validation

Supply chain visibility

Implement supply chain visibility platforms and software Foster transparency through close collaboration with partners

Inventory management

Utilize inventory optimization tools Employ demand-driven replenishment strategies

Supplier relationships

Promote open communication and transparency with suppliers Engage in continuous improvement initiatives

Globalization and compliance

Stay informed about global regulations and geopolitical risks Develop contingency plans to mitigate supply chain risks

Technology integration

Embrace gradual digital transformation strategies Integrate technology solutions aligned with business goals

Data quality and analytics

Prioritize data quality through governance and stewardship

Talent development

Invest in talent development and training programs

Implement data validation and cleansing processes Build a skilled workforce in areas critical to SCM success Change management

Develop effective change management strategies Involve employees in the change process to ensure buy-in

Sustainability and ethics

Align SCM strategies with sustainability and ethical goals Engage in responsible sourcing and ESG reporting

In the future, AI will foster greater collaboration within supply chain ecosystems. AIdriven platforms will connect suppliers, manufacturers, logistics providers, and retailers in real time, facilitating data sharing and collaborative decision-making. These networks will enhance transparency and visibility across the supply chain, improving coordination, reducing lead times, and increasing efficiency. As supply chains become more interconnected and responsive, organizations will be better equipped to adapt to market changes and customer demands.

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5 Conclusion The future of AI in supply chain management holds immense promise. Autonomous systems, advanced predictive analytics, sustainability efforts, and collaborative networks are among the key trends and developments shaping the landscape. Businesses that strategically leverage AI in their supply chain operations will be well-positioned to enhance efficiency, reduce costs, and adapt to the evolving demands of the global marketplace. As AI technologies continue to mature, they will optimize supply chain processes and redefine how organizations compete and succeed in a dynamic and interconnected world.

References 1. Brock, J.K.-U., Von Wangenheim, F.: Demystifying AI: what digital transformation leaders can teach you about realistic artificial intelligence. Calif. Manage. Rev. 61(4), 110–134 (2019) 2. Min, H.: Artificial intelligence in supply chain management: theory and applications. Int J Log Res Appl 13(1), 13–39 (2010) 3. Pournader, M., et al.: Artificial intelligence applications in supply chain management. Int. J. Prod. Econ. 241, 108250 (2021) 4. Farhaoui, Y.: Design and implementation of an intrusion prevention system. Int. J. Network Secur. 19(5), 675–683 (2017). https://doi.org/10.6633/IJNS.201709.19(5).04 5. Bahuguna, D., Kaur, J., Singh, B.: Artificial Intelligence’s Integration in Supply Chain Management: A Comprehensive Review 6. Farhaoui, Y., et al.: Big Data Mining Anal. 6(3), I–II (2023). https://doi.org/10.26599/BDMA. 2022.9020045 7. Jain, V.N.: Robotics for supply chain and manufacturing industries and future it holds. Int. J. Eng. Res. Technol 8, 66–79 (2019) 8. Javaid, M., et al.: Artificial intelligence applications for industry 4.0: a literature-based study. J. Ind. Integr. Manage. 7(01), 83–111 (2022) 9. Farhaoui, Y.: Intrusion prevention system inspired immune systems. Indones. J. Electr. Eng. Comput. Sci. 2(1), 168–179 (2016) 10. Dash, R., et al.: Application of artificial intelligence in automation of supply chain management. J. Strateg. Innov. Sustain. 14(3), 43–53 (2019) 11. Baryannis, G., Dani, S., Antoniou, G.: Predicting supply chain risks using machine learning: the trade-off between performance and interpretability. Futur. Gener. Comput. Syst. 101, 993–1004 (2019) 12. Farhaoui, Y.: Big data analytics applied for control systems. Lecture Notes Networks Syst. 25, 408–415 (2018). https://doi.org/10.1007/978-3-319-69137-4_36 13. Farhaoui, Y., et al.: Big Data Mining Anal. 5(4), I–II (2022). https://doi.org/10.26599/BDMA. 2022.9020004 14. Arunmozhi, M., et al.: Application of blockchain and smart contracts in autonomous vehicle supply chains: an experimental design. Transport. Res. Part E: Logist. Transport. Rev. 165, 102864 (2022) 15. Alaoui, S.S., Farhaoui, Y.: Hate speech detection using text mining and machine learning. Int. J. Decis. Support Syst. Technol. 14(1), 80 (2022). https://doi.org/10.4018/IJDSST.286680 16. Alaoui, S.S., Farhaoui, Y.: Data openness for efficient e-governance in the age of big data. Int. J. Cloud Comput. 10(5–6), 522–532. https://doi.org/10.1504/IJCC.2021.120391 17. Sustrova, T.: A suitable artificial intelligence model for inventory level optimization. Trendy ekonomiky a Managementu 10(25), 48 (2016)

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18. El Mouatasim, A., Farhaoui, Y.: Nesterov step reduced gradient algorithm for convex programming problems. Lecture Notes Networks Syst. 81, 140–148 (2020). https://doi.org/10. 1007/978-3-030-23672-4_11 19. Rejeb, A., Keogh, J.G., Treiblmaier, H.: Leveraging the internet of things and blockchain technology in supply chain management. Future Internet 11(7), 161 (2019) 20. Tarik, A., Farhaoui, Y.: Recommender system for orientation student. Lecture Notes Networks Syst. 81, 367–370.https://doi.org/10.1007/978-3-030-23672-4_27 21. Helo, P., Hao, Y.: Artificial intelligence in operations management and supply chain management: an exploratory case study. Prod. Plan. Control 33(16), 1573–1590 (2022) 22. Sossi Alaoui, S., Farhaoui, Y.: A comparative study of the four well-known classification algorithms in data mining. Lecture Notes Networks Syst. 25, 362–373 (2018). https://doi.org/ 10.1007/978-3-319-69137-4_32 23. Chin, T.A., Tat, H.H., Sulaiman, Z.: Green supply chain management, environmental collaboration and sustainability performance. Procedia Cirp 26, 695–699 (2015) 24. Farhaoui, Y.: Teaching computer sciences in Morocco: an overview. IT Professional 19(4), 12–15, 8012307 (2017). https://doi.org/10.1109/MITP.2017.3051325 25. Farhaoui, Y.: Securing a local area network by IDPS open source. Procedia Comput. Sci. 110, 416–421 (2017). https://doi.org/10.1016/j.procs.2017.06.106 26. Chavez, R., et al.: Data-driven supply chains, manufacturing capability and customer satisfaction. Prod. Plan. Control 28(11–12), 906–918 (2017) 27. Tan, K.C.: A framework of supply chain management literature. Eur. J. Purchas. Supply Manage. 7(1), 39–48 (2001) 28. Benton, W.: Supply chain focused manufacturing planning and control. Cengage Learning (2013) 29. Bose, D.C.: Inventory Management. PHI Learning Pvt. Ltd. (2006) 30. Shrivastava, S.: Recent trends in supply chain management of business-to-business firms: a review and future research directions. J. Bus. Ind. Market. (2023) 31. Lambert, D.M., Cooper, M.C.: Issues in supply chain management. Ind. Mark. Manage. 29(1), 65–83 (2000) 32. Yathiraju, N.: Investigating the use of an artificial intelligence model in an ERP cloud-based system. Int. J. Electr. Electron. Comput. 7(2), 1–26 (2022) 33. Calatayud, A., Mangan, J., Christopher, M.: The self-thinking supply chain. Supply Chain Manage. Int. J. 24(1), 22–38 (2019) 34. Dwivedi, Y.K., et al.: Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 57, 101994 (2021) 35. Wong, L.-W., et al.: Artificial intelligence-driven risk management for enhancing supply chain agility: a deep-learning-based dual-stage PLS-SEM-ANN analysis. Int. J. Prod. Res. 1–21 (2022) 36. Wolfert, S., et al.: Big data in smart farming—a review. Agric. Syst. 153, 69–80 (2017) 37. Ahmad, T., et al.: Artificial intelligence in sustainable energy industry: status quo, challenges and opportunities. J. Clean. Prod. 289, 125834 (2021)

Dynamic Discounting and Flexible Invoices Payment Scheduling for Supply Chain Financial Performance Optimization Halima Semaa1(B) , Youssef Malhouni2 , Abdelillah Semma3 , Laila Bouzarra1 , and Mohamed Ait Hou1 1 Department of Economic Sciences, University of Moulay Ismail, Polydisciplinary Faculty of

Errachidia, Meknes, Morocco [email protected] 2 Faculty of Science and Techniques, Hassan 1Er University, Settat, Morocco 3 Ibn Tofail University, Kenitra, Morocco

Abstract. The financial supply chain involves the flow of cash through the physical network. These financial flows still function as they have done over the last thirty years. The management of financial flows is complex because the delivery or receipt of a product or service does not necessarily give rise to immediate collection or disbursement. This delay in synchronisation has a significant impact on working capital and forces companies to seek almost the same visibility in their financial flows as in their physical flows. Different supply chain strategies can be used to improve working capital. Companies can either manage their inventories more efficiently, reduce DSO (Days Sales Outstanding) and customer payment terms, or increase DPO (Days Sales Outstanding) by paying suppliers on later terms. In this paper we address the problem of scheduling invoice payments to improve working capital performance. We model the problem using a GA and develop a metaheuristic to solve it by conducting an experimental analysis. Keywords: Supply chain · Payment scheduling

1 Introduction The supply chain consists of coordinating and cooperating the relationships between the actors in the chain in order to provide a product from the supplier of raw materials to the final customer [9]. From this process results two flows: the physical flow downstream to customers and the financial flow upstream to suppliers of raw materials [9, 23]. However, among the main differences between the financial flow and the physical flow can occur during a normal operation: the flow of products moves normally in the supply chain, while the cash flow does not operate in the same way. In addition, keeping products in stock imposes holding costs on the company, which could reduce profits, whereas cash reserves could increase profits [4, 25]. To achieve synchronisation between these flows, they need to be managed simultaneously in order to optimise working capital requirements and increase the profitability of the supply chain [7, 24]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 574–586, 2024. https://doi.org/10.1007/978-3-031-48573-2_83

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Previously, financial management was very distinct from supply chain management. Recently, the development of global relationships and technological advancements have allowed for the integration of treasury management into SCM, as managers now combine all flows, including the product flow and cash flow. This management approach will, on one hand, facilitate collaboration among the chain’s stakeholders and, on the other hand, reduce the overall cost of the chain [18, 19]. In this paper, we explore financial issues related to supply chain management. Firstly, we review the concept of working capital requirements and optimization methods for WCR. Secondly, we analyze the specific problem of invoice payment planning to optimize working capital. Finally, we model Gupta’s problem using a genetic algorithm while conducting simulations to solve the issue.

2 Working Capital Management Working capital management (WCM) is a crucial subject within the domain of corporate finance, garnering significant attention due to its substantial impact on a company’s performance [1, 26]. WCM pertains to the administration of working capital (WC), which is defined as the numerical discrepancy between an organization’s current assets and current liabilities. However, comprehending the intricacies of WC management may prove challenging for operational managers. In order to facilitate their understanding of the immense opportunities inherent in proficiently managing working capital, it is imperative to consider the duration for which an organization’s monetary resources are tied up in inventory and other current assets before receiving payment for the goods and services it produces. The principal objective of effective working capital management is to reduce the allocation of funds to working capital while concurrently ensuring adequate funding and liquidity to support the global operations of the company. Moreover, the management of working capital should augment the returns on assets and equity, while also enhancing efficiency ratios and other performance metrics [2]. It is important to note that the operational cycle of a business generates requisites for funding, encompassing cash inflows and outflows (known as the cash conversion cycle), as well as exposing the organization to foreign exchange rate and credit risks. Consequently, the funding requirements stemming from the operational cycle of the firm constitute the working capital [2, 27]. The primary aim of proficient working capital management is to minimize the allocation of financial resources to working capital, while simultaneously ensuring sufficient funding and liquidity to sustain the global operations of the company. Additionally, effective working capital management should contribute to the improvement of returns on assets and equity, as well as the enhancement of efficiency ratios and other performance indicators [2, 11]. It is noteworthy that the operational cycle of a business generates funding requirements, encompassing cash inflows and outflows, which constitute the cash conversion cycle. Furthermore, this cycle exposes the organization to risks associated with foreign exchange rates and credit, thus necessitating careful consideration of funding requirements in relation to the operational cycle, thereby constituting the working capital [2].

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In order to assess a company’s ability to convert its current assets into liquid funds for meeting short-term obligations, the cash-to-cash (C2C) cycle time is employed [3]. This metric quantifies the number of days required by a company to convert its investments and other resources into cash flows received from customers [17, 28]. The C2C cycle time is influenced by various factors, including the timing of purchases, inventory management, payment terms with customers and suppliers, as well as discount arrangements. These factors collectively impact an organization’s C2C cycle, exerting an influence on its working capital and impeding its ability to effectively manage uncertainties in cash inflows and outflows (Fig. 1). C2C = DSO + DIH − DPO

Fig. 1. Cash-to-cash cycle time (Hofman et al., 2015) [5]

The payment period is directly related to the C2C cycle and the A/P (A/R) period. Increasing a retailer’s payment period leads to a longer A/P period and a faster C2C cycle of him. Meanwhile, his supplier’s C2C cycle and A/R period extend. The result about the payment period could provide a reference for managing working capital in a supply chain perspective. Fast cash conversion (or a long A/P period, a short A/R period) is not necessarily good for a company [10, 28].

3 Dynamics discounting and Flexible Payment Scheduling Any single organization in the supply chain has both Accounts Payable (AP) and Accounts Receivable (AR). Synchronizing the timing of AR’s collection and AP’s payments could lead to an appropriate working capital optimization thru smarter trade credits and better invoices payments planning of suppliers and customers. Dynamic Discounting takes root from the cash discount policy typical of trade credit practices and, through a proper use of a buyer-supplier integrated platform, allows the dynamic settlement of invoices [5, 29]. DD is believed to reduce uncertainty in working capital needs, thus allowing suppliers to better plan cash flow in. From the buyer point of view, DD generally grants the best rate of return in today’s markets [8, 30].

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Dynamic Discounting allows the dynamic settlement of invoices in a buyer supplier relationship. The supplier grants to the buyer an incremental discount on the nominal invoice value for every payment in advance with respect to contractual credit terms, [20, 21]. This practice provides more benefits to each company involved since it has been recognized that the ‘mass application’ of static discount policies obstructs the potential profits gained through the ‘customised’ application of early payment discounts [22, 33]. The author Gelsomino stated that dynamic discounting can either be buyer-initiated or supplier-initiated. In the first case, the buyer declares the acceptable discount rate and the supplier reacts by accepting early payments. The second case, the supplier suggests a competitive discount and the buyer accepts the proposal [23, 32]. The practice of a dynamic discounting gives rise to a flexible payment schedule that facilitates the creation of budgets and helps financial forecasts so that one can prevent any cash flow problems [5, 31]. To establish such dynamic invoices payment schedule one can use two major levers. The first one is down payment. This is not an uncommon practice for business owners to encourage their customers paying their invoice due ahead of schedule. It contributes to reduce the risk, the daily sales outstanding (DSO) and the working capital requirement [6, 34].

4 Problem’s Statement and Modeling Building upon Gupta’s model, we will consider a discrete planning horizon H consisting of T periods of equal length [12]. We assume that the future cash inflows and outflows from downstream and upstream partners, as well as their terms (payment date, discount rate, and penalty rate), are known in advance over the planning horizon. • SK = {kcustomerinvoices, 1 ≤ k ≤ K} Denotes the set of customer invoices to be perceived over the horizon H. • SJ = {jcustomerinvoices, 1 ≤ j ≤ J } denotes the set of supplier invoices to be paid over the horizon H. d • Let IRk k Stands forthe amount of the kth invoice to be received by the firm from its downstream partnered negotiated date dk . r • Let IPj j denotes the amount of the jth invoice to be paid by the firm to its upstream partners at negotiated date rj . When the payment of a customer invoice k occursefore its due date_ dk , the payment terms associated with that invoice ensure the application of a discount rate ck per period. Therefore, if invoice k is paid t periods before the due date, the received amount is calculated as follows: IRk (1 − ck )t . These discounts granted to encourage early settlement of invoices and expedite cash inflows. In the case of payment of invoice j after its due date rj , the payment terms associated with the invoice impose a penalty rate of sj per period. Therefore, if invoice j is paid t  t periods after its due date, the amount to be paid is calculated as follows: IPj 1 + sj . To maintain a general framework, we assume that invoices issued by customers must be settled before their due date, while invoices issued by suppliers can only be paid after their due date. Furthermore, we consider thathe company has the capacity to invest any

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accumulated amount at any period to generate interest at a rate α, while assuming that the bank financing rate is always higher than the penalty rates s_j and discount rates c_k. The company’s primary goal is to enhance cash flow optimization, reduce the cost of financing, and establish a payment schedule for all invoices. The task at hand involves determining the payment dates for supplier invoices and the collection of customer payments throughout the planning horizon T. To achieve this, we proceed to construct a 0– 1 linear program designed to maximize the present value of available cash. Subsequently, we employ the following decision variables: • Xk,t : is a binary variable equals to 1 only if the kth customer invoice is perceived by the firm at period t. • Yj,t : is a binary variable equals to 1 only if the jth supply invoice is paid by the firm at period t. • δt : is the cash in hand at the end of period t. • et : is the amount of money borrowed from the bank at period t if the cash in hand is not enough to execute scheduled payments. We assume that the reimbursement of this money should be done aeriod after. We use also the following parameters: • • • •

δ: is the cash in hand at the beginning time 0. α: is and interest rate that must to be paid per period for loan money from the bank. Rt : is the total money collected from downstream customers at period t. Pt : is the total money paid to the upstream supplier at period t Max Z = δT or Min Z = (

K 

IRdkk

−

T  t=1

k=1

T 

Rt ) + (

T 

Pt −

t=1

J 

r IPj j ) + α

j=1

Xk,t = 1; ∀k ∈ SK

∗

T 

(1) et )

t=1

(2)

t=1

Xk,t = O; ∀t, t > dk , ∀k ∈ SK T 

Yj,t = 1; ∀j ∈ SJ

(5)

(4)

t=1

Yj,t = O; ∀t, t < rj , ∀j ∈ SJ Rt =

K 

IRdkk ∗(1 − ck )dk −t *Xk,t

(5)

(6)

k=1

Pt =

J  j=1

t−rj r  IPj j ∗ 1 + sj *Yj,t

(7)

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et + δt−1 + Rt − Pt − δt − (1 + α)*et−1 = 0; ∀t ∈ T, t > 1

(8)

e1 + δ + R1 − P1 − δ1 = 0

(9)

∀t ∈ T, t > 1δt >= 0

(10)

Constraint (1) establishes the objective function, whicrepresents the current value of cash on hand generated in previous periods. Constraints (2) and (3) ensure that funds collected from upstream customers are completed within the time frame T. Constraints (4) and (5) ensure that supplier invoices are settled within the timeframe T. Constraints (6) and (7) calculate, for each period t, the total amount collected and the total amount paid, respectively. Finally, constraints (8), (9), and (10) pertain to cash flow conservation.

5 Genetic Algorithm Genetic algorithms, a subset of evolutionary computation, are optimization methods employed to find the optimal solutions for computational problems by either maximizing or minimizing specific functions [13]. They emulate the processes of biological reproduction and natural selection to identify the most “fit” solutions [14]. Like natural evolution, genetic algorithms introduce a degree of randomness into their procedures, but users have the flexibility to adjust and manage the level of this randomness [14]. Despite their inherent randomness, these algorithms are remarkably more effective and efficient compared to random or exhaustive search algorithms. Crucially, they do not require additional information about the problem they are applied to [15]. This capability empowers genetic algorithms to address problems that remain challenging for other optimization methods, primarily because these problems lack characteristics like continuity, derivatives, linearity, or other specific attributes. Genetic algorithms, which aim to mimic biological processes, borrow much of their terminology from biology. However, it’s essential to recognize that the entities described by this terminology in genetic algorithms are significantly less intricate than their biological counterparts [16]. The core components that are common to nearly all genetic algorithms include: 5.1 Creation of Individuals of the First Generation Construct a cash matrix Mij. The columns represent the time j, and the rows i represent the customers, the supplier, the borrowing and the cash. We select random numbers. After calculating the cash at the end of each period we find a first individual I of the initial population (Fig. 2). To ensure that individuals in a given generation meet the criteria of maintaining a non-negative balance sheet at the end of each day, a function has been developed to calculate the daily balance sheet.

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Fig. 2. Example of the first individual

5.2 Evaluation of the Individual The fitness function for our case is the final cash that corresponds to the balance sheet of the last day (T) Max Z = δT Compute a final cash value for each row denoted by ‘i,’ which represents the discrepancy between the input and output in the corresponding columns. Once these calculations are completed, reorganize the individuals within the matrix based on their optimality in ascending order. 5.3 Crossover Operator The suggested crossover operator relies on the Best final cash. Each time period is addressed independently. The Best final cash is determined individually for each parent. The Best final cash signifies the disparity between input and output, considering borrowing when the input is less than the output. To generate new individuals (children) from the initial population during a given iteration, the crossover operator is employed. This operator selects two individuals from the current population as parents, designated as P1 and P2. From these parents, two children, E1 and E2, are produced. The process involves copying all the elements of P1 into E1 and all the elements of P2 into E2. Subsequently, two customer and supplier rows are randomly generated (Figs. 3 and 4).

Fig. 3. Example of the first parent P1 (fitness = 474)

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Fig. 4. Example of the second parent P2 (fitness = 431)

Firstly, select two rows from P1 based on random rankings: one representing a random customer and another representing a random supplier. These selected rows are then placed in E2. Similarly, choose rows from P2 based on the same random rankings, and insert them into E1. Next, assess the balance sheet constraints for both E1 and E2. In cases where these constraints are violated, apply mutations to the respective entries to restore balance if it has been disrupted. Subsequently, categorize the offspring (sons) and the parents based on predefined criteria. The first two individuals from the results of the crossover operation, which could include either parents or offspring, are incorporated into the next generation. To further diversify, random rankings for a customer and a supplier (e.g., C1 and S3) are generated. Consequently, construct the first son, E1, by combining the vectors of customer C1 and supplier S3 from P2 with the vectors of C2, C3, C4, S1, and S2 from P1. Similarly, create the second son, E2, by merging the vectors of customer C1 and supplier S3 from P1 with the rows C2, C3, C4, S1, and S2 from P2 (Figs. 5 and 6).

Fig. 5. Example of the first child E1 (fitness = 494)

We evaluate the mutation rate, which determines the proportion of the population that will undergo mutation. For instance, if the chance of mutation is set to 0.8, mutations will be applied to only 80% of the population. • Step 1: We initiate a random mutation of one of the following types: • Type 1: A random customer line is selected, and the payment date is adjusted forward or backward by one rank, while adhering to the condition that it remains before the discount date. • Type 2: A random supplier line is chosen, and the payment date is shifted forward or backward by one rank, ensuring it remains after the penalty date.

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Fig. 6. Example of the first child E2 (fitness = 411)

• Type 3: We pick one random customer line and one random supplier line, applying mutations of both Type 1 and Type 2 simultaneously. • Type 4: A single random customer line and a single random supplier line are chosen, and a random discount date is selected for the customer’s payment, while ensuring it’s earlier than the supplier’s payment date, which is chosen randomly but after the penalty date. • Step 2: We validate that the balance sheet constraints are upheld. • Step 3: In cases where the balance sheet displays a negative balance, we repeat the random mutation process (Types 1, 2, 3, or 4) until we achieve a positive balance sheet. • Step 4: We assess the fitness function of the mutated result in comparison to the original individual. If the fitness of the mutation, denoted as “fitness_mutation,” is lower than that of the original individual, referred to as “fitness_origine,” we repeat the mutation. Conversely, if “fitness_mutation” is equal to or greater than “fitness_origine,” we incorporate the mutation result into the matrix produced by the mutation function. We then proceed to the next individual. For instance, if we select a random individual, say “Individual1,” and apply mutation Type 1 to them, we randomly pick a customer rank, such as the first line corresponding to “C1.“ We then advance the payment date by a single rank, transforming it from rank 2 to rank 3. This results in another individual, “Individual2,” with a fitness function score of 472, surpassing the fitness score of the original individual, “Individual1,” which stood at 467 (Figs. 7 and 8).

Fig. 7. Individual before mutation individu1 (fitness = 467)

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Fig. 8. Individual after mutation individu2 (fitness = 472)

6 Results and Discussion “In order to demonstrate the efficiency of the proposed metaheuristic, and to derive managerial insights about our proposed framework, several numerical examples are generated and solved using genetic algorithm”. Then, we conduct different experiments to derive insights about the use the model with regard of different values of discount and penalty rates and interest rate. Take the case of three suppliers and four customers as follows:

Table 1. Different values of invoice customers Number of customers

Invoice amount

Discount time

1

2000

7

2

2200

9

3

2100

6

4

600

8

Table 2. Differents values of invoice suppliers Number of suppliers

Invoice amount

Penality time

1

2380

2

2

2420

5

3

2380

7

It is evident that Genetic Algorithms (GAs) often lead to superior solutions, particularly when tackling larger problem instances, even though they may require more time for the solution to converge. When compared to the heuristic approaches employed by Gupta [5], the genetic algorithm demonstrates greater efficiency in achieving optimal outcomes (Tables 1 and 2). The accompanying figure graphically illustrates the dynamic relationship between the final cash value and the evolution of generations. Each successive generation exhibits

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800 600

le cash (taux d'interet 0,01), taux de penalité 0,01, taux de banque 0,01)

400 200 0 1 12 22 32 42 52 62 72 82 92

Fig. 9. Change in cash compared to generation

an improvement in cash accumulation until it eventually reaches a point of stabilization. This plateau in cash accumulation occurs when specific penalty and discount rates are reached, underscoring the algorithm’s ability to optimize the financial outcome over time.

150

750 700

100

650 600

50

550 0

500 0.001

0.005 Cash

0.01 Delta P

0.03

0.05

Delta R

0.07

0.1

Couts des Crédits

Fig. 10. Evolution the cash compared to interest rate

We began by setting the discount rate at a constant 0.01 and then varied the penalty rate across different values. In Fig. 9, we depict the evolving cash flow, and in Fig. 10, we delve into the dynamics of delta R, delta P, and delta as a function of changes in the bank rate. Figure 10 illustrates a compelling relationship: as the bank rate increases, cash flow decreases. This inverse correlation is attributed to the pivotal role the bank rate plays. A higher bank rate leads to diminished liquidity within the business, making it challenging to secure loans from financial institutions. The added burden of interest charges further strains the cash flow. On the flip side, the lowest level of return on loans is a significant boost to cash flow. Since the penalty rate imposed by the supplier remains constant, we can confidently conclude that analyzing cash flow trends in relation to the bank rate/penalty rate (bank rate/penj ratio) yields limited insights.

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7 Conclusion In this paper we highlighted two main methods of funding and optimization working capital (supply chain finance and collaborative finance). We tried to propose a model allowing the application of supply chain finance reasoning on commercial credits for collaborative funding, while applying the principle of financing flexibilityoffered by the reverse factoring. Our work aims to determine a payments planning model to generate cash and minimize costs before considering short-term borrowing. Our model ignores delayed customer payments and anticipated supplier payments. The perspective would be to propose in a future paper a second broader model incorporating these two cases.

References 1. Aktas, N., Petmezas, D.: Is working capital management value-enhancing? evidence from firm performance and investments. J. Corpor. Fin. 30(1), 98–113 (2015) 2. Popa, V.: The financial supply chain management: a new solutionfor supply chain resilience. Valahia Univ. Târgovi¸ste, Rom., Amfiteatru Econ. 15(33), 140–153 (2013) 3. ELmiloudi, F., Tchernev, N., Riane, F.: Scheduling payments optimization to drive working capital performance within a supply chain. ILS (2016) 4. Peng, J., Zhou, Z.: Working capital optimization in a supply chain perspective. Euro. J. Oper. Res. 1–28 (2019) 5. Polak, P.: Addressing the post-crisis challenges in working capital management. Int. J. Res. Manag. 6(2) (2012) 6. Kouvelis, P., Zhao, W.: Who should finance the supply chain? impact of credit ratings on supply chain decisions. Manuf. Serv. Oper. Manag. 20(1), 19–35 (2018) 7. Lee, C.H., Rhee, B.D.: Trade credit for supply chain coordination. Eur. J. Oper. Res. (2011) 8. Polak, P., Sirpal, R., Hamdan, M.: Post-crisis emerging role of the treasurer. Eur. J. Sci. Res. 86(3), 319–339 (2012) 9. Hofmann, E., Martin, J.: Etude sur l’évaluation de la performance dans le Working Capital, Management (WCM). Supply Chain Finance-Lab de l’Université de Saint Gall (2014) 10. Meltzer, A.: Mercantile credit, monetary policy and the size of firms. Rev. Econ. Stat. 42(4), 429–437 (1960) 11. Vernimmen, P.: Finance d’entreprise logique et politique. Edition Paris Dalloz (1989) 12. Gupta, S., Dutta, K.: Modeling of financial supply chain. Eur. J. Oper. Res. 211, 47–56 (2011) 13. Holland, J.H.: Adaptation in Natural and Artificial Systems, Ann Arbor. University of Michigan Press, MI (1975) 14. Jeong, B., Jung, H.-S., Park, N.-K.: A computerized causal forecasting system using genetic algorithms in supply chain management. J. Syst. Softw. 60(3), 223–237 (2002). https://doi. org/10.1016/S0164-1212(01)00094-2 15. Falkenauer, E., Bouffouix, S.: A genetic algorithm for job shop. In: Proceedings. 1991 IEEE International Conference on Robotics and Automation. Sacramento, CA, USA, pp. 824–829 (1991). https://doi.org/10.1109/ROBOT.1991.131689 16. Chang, P.-T., Yao, M.-J., Huang, S.-F., Chen, C.-T.: A genetic algorithm for solving a fuzzy economic lot-size scheduling problem. Int. J. Prod. Econ. 102(2), 265–288 (2006). https:// doi.org/10.1016/j.ijpe.2005.03.008 17. Tseng, M., Win, K., Hin, J., Wang, C.: Decisions making modelfor sustainable supply chain finance under incertainties 1–24 (2018)

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18. Wang, M., Hang, H.: The design of a flexible capital constrained global supply chain integrating operational and financial strategies. OMEGA 11–34 (2018) 19. Nienhuis, J.J., Cortet, M., Lycklama, D.: Real-time financing: extending e-invoicing to realtime SME financing. J. Pay. Strat. Syst. 7(3), 232–245 (2013) 20. Hua, S., Xiaoye, Y., Yuanfang, S.: Dynamic discounting program of supply chain finance based on a financial information matching platform. Ann. Oper. Res. 1–30 (2022) 21. Randall, W.S., Farris, M.T.: Supply chain financing: using cash-to-cash variables to strengthen the supply chain. Int. J. Phys. Distrib. Log. Manage. (2009) 22. Gelsomino, L.U.C.A., Mangiaracina, R., Perego, A., Tumino, A.: Supply chain finance: modelling a dynamic discounting programme. J. Adv. Manag. Sci. 4(4), 283–291 (2016) 23. Farhaoui, Y.: Design and implementation of an intrusion prevention system. Int. J. Netw. Secur. 19(5), 675–683 (2017). https://doi.org/10.6633/IJNS.201709.19(5).04 24. Farhaoui, Y.: Big data mining and analytics 6(3), I–II (2023). https://doi.org/10.26599/ BDMA.2022.9020045 25. Farhaoui, Y.: Intrusion prevention system inspired immune systems. Indonesian J. Electr. Eng. Comput. Sci. 2(1), 168–179 (2016) 26. Farhaoui, Y.: Big data analytics applied for control systems. Lect. Notes Netw. Syst. 25, 408–415 (2018). https://doi.org/10.1007/978-3-319-69137-4_36 27. Farhaoui, Y.: Big data mining and analytics 5(4), I–II (2022). https://doi.org/10.26599/ BDMA.2022.9020004 28. Alaoui, S.S., Farhaoui, Y.: Hate speech detection using text mining and machine learning. Int. J. Decis. Supp. Syst. Technol. 14(1), 80 (2022). https://doi.org/10.4018/IJDSST.286680 29. Alaoui, S.S., Farhaoui, Y.: Data openness for efficient e-governance in the age of big data. Int. J. Cloud Comput. 10(5–6), 522–532 (2021). https://doi.org/10.1504/IJCC.2021.120391 30. El Mouatasim, A., Farhaoui, Y.: Nesterov step reduced gradient algorithm for convex programming problems. Lect. Notes Netw. Syst. 81, 140–148 (2020). https://doi.org/10.1007/ 978-3-030-23672-4_11 31. Tarik, A., Farhaoui, Y.: Recommender system for orientation student. Lect. Notes Netw. Syst. 81, 367–370 (2020). https://doi.org/10.1007/978-3-030-23672-4_27 32. Sossi Alaoui, S., Farhaoui, Y.: A comparative study of the four well-known classification algorithms in data mining. Lect. Notes Netw. Syst. 25, 362–373 (2018). https://doi.org/10. 1007/978-3-319-69137-4_32 33. Farhaoui, Y.: Teaching computer sciences in Morocco: an overview. IT Professional 19(4), 12–15, 8012307 (2017). https://doi.org/10.1109/MITP.2017.3051325 34. Farhaoui, Y.: Securing a local area network by IDPS open source. Proc. Comput. Sci. 110, 416–421 (2017). https://doi.org/10.1016/j.procs.2017.06.106

An Efficient Driver Monitoring: Road Crash and Driver Behavior Analysis Mohammed Ameksa1(B)

, Zouhair Elamrani Abou Elassad2 , and Hajar Mousannif1

1 LISI Laboratory, Computer Science Department, FSSM, Cadi Ayyad University, Marrakesh,

Morocco [email protected] 2 Computer Science Department, SARS Research Team, ENSAS, Cadi Ayyad University, Marrakesh, Morocco

Abstract. Traffic accidents pose a serious threat to modern societies, causing harm at both individual and community levels, including health issues, economic losses, and fatalities. The majority of these accidents (65%) are caused by human factors. Therefore, it is crucial to provide continuous feedback to drivers during their driving behavior to assess their mental states and adjust accordingly. This paper introduces a proactive driver monitoring and assistance system aimed at improving road safety. The system utilizes Artificial Intelligence and the Internet of Things to continuously monitor drivers’ mental state and behavior, providing suitable assistance. The system outperforms current solutions due to its portability, and regular update capability. The system technology uses non-intrusive devices and machine learning algorithms to anticipate potential accidents, monitor real-time driver behavior including facial expressions, eye movements, and head position. The system also uses emotion detection to evaluate a driver’s emotional state, making corresponding adjustments to enhance comfort and safety. The effectiveness of our system was validated in tests conducted at Cadi Ayyad University using a driving simulator. Keywords: Driving monitoring system · Driving behavior · Crash prediction

1 Introduction Traffic accidents are one of the most serious and threatening problems that encounter societies nowadays, having a detrimental influence both on a person and community level, resulting in many health issues, economic losses, and fatalities. In Morocco, an average of 10 civilians are killed and another 33 are seriously wounded every day [12– 15]; It was inferred that human factors took part in the manifestation of 65% of all accidents, including the driver’s mental state such as drowsiness, anger, and distraction, amongst others [6, 16]. Receiving continuous feedback from drivers during driving conduct is a key part of any traffic safety strategy as it enables assessing their states of mind and adapting accordingly [9, 17]. While there has been considerable progress in the use of driver monitoring systems, major drawbacks have been recognized; the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 587–593, 2024. https://doi.org/10.1007/978-3-031-48573-2_84

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high cost for constructing and maintaining these systems along with the complexity of integrating these measurement devices made car manufacturers reluctant to incorporate such technologies in their vehicles [4, 18]; Moreover, a vast majority of driving assistance technologies have focused on responding to actions taken by the driver to control the vehicle optimally and safely, but have overlooked the mental state of the driver [1, 19, 23]; a seamless driver monitoring system with perpetual support based on various driver’s states of mind and behavior (e.g. alert when driver is drowsy, distracted or speeding, appease when driver is angry, etc.). Furthermore, the current driver surveillance systems are essentially “black boxes” embedded within the vehicle; therefore, a highly-scalable, stand-alone, and portable technology that drivers can held and use on different vehicles is of major prominence. It is also crucial to obtain a small-sized low-power solution; most-specifically for electric vehicles as the total weight of the on-board computing and communication systems and their power consumption are critical factors. This paper proposes a proactive driver monitoring and support system. The leverage of the proposed system is, on one hand, its capability to continuously and seamlessly monitor various drivers’ states of mind and behavior, making sense of them and providing adequate support services according to the driver’s immediate needs; On another hand, is a highly-scalable, stand-alone, and portable technology that driver could make use of while shifting from one vehicle to another and that can be regularly upgraded. The system takes advantage of Artificial Intelligence and the Internet of Things paradigm to enable its services in a connected vehicle, which have been found to provide highly satisfactory outcomes [7, 20–22]. To achieve this, our system uses connected smart devices allowing to monitor, in a non-invasive way, the heart rate and various facial and behavioral features related to the driver’s conduct. These smart devices are trained to provide a continuous real-time overview of the driver’s states by referring to Artificial Intelligence models which are autonomously built by these smart devices by applying the collected data as inputs. Adopting this system is a highly promising approach to driver impairment monitoring as it provides awareness about the driver’s states of mind and behavior in order to further establish adequate support services according to the driver’s urgent requirements. As a result, new insights into traffic safety modeling are gained and may be used to promote enforcement efforts related to designing crash avoidance/warning systems.

2 System Overview and Architecture The system uses the driver’s facial features and other behavioral parameters, as performance indicators in on-road driving conduct. It provides awareness about the driver’s state of mind and behavior in order to further establish adequate support services according to the driver’s urgent requirements. According to Fig. 1, the system utilizes smart devices that are not embedded in the vehicle. These devices are placed inside the vehicle and create a comprehensive driver monitoring and support system. The purpose of this system is to assess the driver without causing any distractions, ensuring a seamless experience. These smart devices have vision, sensing, and communication capabilities. The monitoring and response capabilities rely on various sensors integrated into the core devices, which include a camera,

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a heart rate wristband, and several sensors within a smartphone. With the increasing advancements in smartphones, they have become more sophisticated and intricate, incorporating a wide range of sensors [10]. This presents numerous possibilities for developing consumer applications that can intelligently utilize these built-in smartphone sensors.

Fig. 1. A working prototype of the proposed system

Physiological characteristics have been recognized as highly accurate indicators for monitoring a driver’s level of alertness. These characteristics encompass measurements such as brain waves and heart rate variability, among others [3]. However, some of these monitoring methods are deemed intrusive, as they involve the use of cumbersome devices that could potentially distract the driver or disrupt their driving behavior. In our approach, we utilize a non-intrusive wristband to measure heart rate variability (HRV), which serves as an effective metric for assessing workload and provides continuous fundamental data about the autonomic nervous system [5]. Additionally, we employ a camera module to continuously capture facial expressions of the drive. Within this context, the extracted insights are conveyed to a single-board computer (Raspberry Pi) continuously and in real-time. These insights will help the system shape its support approach according to the perceived state of the driver and act consequently based on the driver’s immediate requirements. The various components of the proposed system span three major layers: Physical Layer (serves a dual purpose, gathering real-time data and provides driving performance feedback to drivers), Network Layer (transfer data from the Physical Layer to the Application Layer using Internet or Bluetooth) and Application Layer (Utilizes artificial intelligence algorithms to analyze driver data, extract behavior insights, and provide feedback to the driver) as shown in Fig. 2. The power of our proposed solution is greatly enhanced by the integration of computer vision models within a fusion-based model previously trained on comprehensive historical data. The model ingests real-time data, including vehicle speed, driver behavior indicators, environmental parameters, and more, to forecast potential crash scenarios. Predictions are sent back to the driver as feedback, prompting immediate corrective actions and potentially averting accidents.

3 Results and Discussion Experiments are being carried out using a fixed-based driving simulator located at the University of Cadi Ayyad facility. Simulator driving studies confer a significant advantage of imitating conduct in a safe environment with full empirical control over driving

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Fig. 2. The various components of the system

conditions [6]. The simulations were run through a virtual driving interface. The computer was fitted with state-of-the-art driving simulator driving force GT27 Logitech® incorporating a Racing Wheelset (steering wheel, accelerator pedal, and brake pedal) with the adjustable Logitech Evolution® Playseat Fig. 3.

Fig. 3. Simulator set-up

Our system is capable of providing predictions about the driver’s state Fig. 4. It receives up-to-date data about the drivers’ states of mind and behavioral performances in order to estimate and mitigate the risk of incorrect driving conduct; As such, enables drivers to timely address any life-threatening attitudes by providing immediate pertinent interventions. The proposed system, consisting of multiple driver state monitoring components, underwent rigorous tests and evaluations. Each component – crash prediction, gaze tracking, and emotion detection… – displayed promising performance and collectively contributed to the holistic assessment of the driver’s state.

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Fig. 4. Gaze tracking results

The system provided a reliable and efficient solution for real-time emotion detection in drivers Fig. 5, which can potentially improve driving safety by providing real-time feedback based on the driver’s emotional state.

Fig. 5. Driver emotions state illustration using the proposed system. (Important note: A written informed consent for the publication were obtained from the study participant, who is among authors.)

Combining driver’s state results withing other kinematics including driver inputs, vehicle telemetries and weather state by an ML fusion model, that use Random Forest, XGBoost, and AdaBoost algorithms with MLP, demonstrated superior predictive power [2]. Its prediction performance’ surpassed those of any single algorithm. The system could accurately predict potential crashes using the multifaceted input data, indicating the viability of our approach for real-time, on-road applications. As shown in Fig. 6, the system alerts the driver for a potential crash risk based on the real-time data. The accuracy obtained from the system is highly reliable due to its ability to continuously monitor various driver states of mind and behavior. The system can detect changes in a driver’s emotional and physical state, including drowsiness, distraction, and fatigue, which are significant risk factors that contribute to road accidents. Additionally, the system can analyze driving behavior, to identify potentially dangerous situations. Therefore, provide proactive support services tailored to the driver’s immediate needs. For instance, if the system detects that a driver is experiencing drowsiness, it can alert the driver to take a break or suggest a rest stop. Alternatively, if the system detects that a driver is engaging in dangerous driving behavior, it can provide real-time feedback or intervention to help the driver correct the behavior and prevent potential accidents. Overall, the system’s services, make it a reliable and effective tool for improving road safety.

4 Conclusions Road traffic crashes have been considered one of the main causes resulting in countless health issues, economic losses, and fatalities. As such, the investigation and understanding of the major contributors to road accidents is of practical significance. It was inferred

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Fig. 6. Proposed system alert to driver example

that human factors took part in the manifestation of 65% of all accidents, including the driver’s mental state. So, receiving continuous feedback from drivers is a key part of any traffic safety strategy as it enables assessing their states of mind and adapting accordingly. Our proposed solution merges several cutting-edge techniques, such as ensemble machine learning for crash prediction, computer vision for facial gaze tracking, and emotion detection, resulting in a multi-faceted driver state assessment system that is capable of enhancing road safety. However, as with any system operating in a real-world environment, there is always scope for refinement and optimization. Therefore, further refinement and testing are ongoing, aiming to improve the performance of the system in various environmental conditions and with a diverse range of individuals. Acknowledgements. This research was jointly supported by the Moroccan National Center for Scientific and Technical Research (CNRST).

References 1. Akamatsu, M., Green, P., Bengler, K.: Automotive technology and human factors research: past, present, and future. Int. J. Veh. Technol. 2013, 526180 (2013). https://doi.org/10.1155/ 2013/526180 2. Ameksa, M., Mousannif, H., Al Moatassime, H., Elamrani Abou Elassad, Z.: Crash prediction using ensemble methods. In: Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning. SCITEPRESS - Science and Technology Publications, pp 211–215 (2021) 3. Ba, Y., Zhang, W., Wang, Q., Zhou, R., Ren, C.: Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system. Transp. Res. Part C Emerg. Technol. 74, 22–33 (2017). https://doi.org/10.1016/j.trc.2016.11.009 4. Chakraborty, S., Lukasiewycz, M., Buckl, C., Fahmy, S., Chang, N., Park, S., Kim, Y., Leteinturier, P., Adlkofer, H.: Embedded systems and software challenges in electric vehicles. In: 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp 424–429 (2012) 5. Chen, L.L., Zhao, Y., Ye, P.F., Zhang, J., Zou, J.Z.: Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert Syst. Appl. 85, 279–291 (2017). https://doi.org/10.1016/j.eswa.2017.01.040 6. Elamrani Abou Elassad, Z., Mousannif, H.: Understanding driving behavior: measurement, modeling and analysis. Springer International Publishing (2019)

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7. Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H.: A real-time crash prediction fusion framework: an imbalance-aware strategy for collision avoidance systems. Transp. Res. Part C Emerg. Technol. 118, 102708 (2020). https://doi.org/10.1016/j.trc.2020.102708 8. Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H., Karkouch, A.: The application of machine learning techniques for driving behavior analysis: a conceptual framework and a systematic literature review. Eng. Appl. Artif. Intell. 87, 103312 (2020). https://doi.org/10. 1016/j.engappai.2019.103312 9. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12, 74–82 (2011). https://doi.org/10.1145/1964897.196 4918 10. Ministère de l’Equipement du Transport (2017) 11. Farhaoui, Y.: Design and implementation of an intrusion prevention system. Int. J. Netw. Secur. 19(5), 675–683 (2017). https://doi.org/10.6633/IJNS.201709.19(5).04 12. Farhaoui, Y.: Big data mining and analytics 6(3), I–II (2023). https://doi.org/10.26599/ BDMA.2022.9020045 13. Farhaoui, Y.: Intrusion prevention system inspired immune systems. Indonesian J. Electr. Eng. Comput. Sci. 2(1), 168–179 (2016) 14. Farhaoui, Y.: Big data analytics applied for control systems. Lect. Notes Netw. Syst. 25, 408–415 (2018). https://doi.org/10.1007/978-3-319-69137-4_36 15. Farhaoui, Y.: Big data mining and analytics 5(4), I–II (2022). https://doi.org/10.26599/ BDMA.2022.9020004 16. Alaoui, S.S., Farhaoui, Y.: Hate speech detection using text mining and machine learning. Int. J. Decis. Supp. Syst. Technol. 14(1), 80 (2022). https://doi.org/10.4018/IJDSST.286680 17. Alaoui, S.S., Farhaoui, Y.: Data openness for efficient e-governance in the age of big data. Int. J. Cloud Comput. 10(5–6), 522–532 (2021). https://doi.org/10.1504/IJCC.2021.120391 18. El Mouatasim, A., Farhaoui, Y.: Nesterov step reduced gradient algorithm for convex programming problems. Lect. Notes Netw. Syst. 81, 140–148 (2020). https://doi.org/10.1007/ 978-3-030-23672-4_11 19. Tarik, A., Farhaoui, Y.: Recommender system for orientation student. Lect. Notes Netw. Syst. 81, 67–370 (2020). https://doi.org/10.1007/978-3-030-23672-4_27 20. Sossi Alaoui, S., Farhaoui, Y.: A comparative study of the four well-known classification algorithms in data mining. Lect. Notes Netw. Syst. 25, 362–373 (2018). https://doi.org/10. 1007/978-3-319-69137-4_32 21. Farhaoui, Y.: Teaching computer sciences in Morocco: an overview. IT Professional 19(4), 12–15, 8012307 (2017). https://doi.org/10.1109/MITP.2017.3051325 22. Farhaoui, Y.: Securing a local area network by IDPS open source. Proc. Comput. Sci. 110, 416–421 (2017). https://doi.org/10.1016/j.procs.2017.06.106

Enhancing Cloud-Based Machine Learning Models with Federated Learning Techniques Rejuwan Shamim1 and Yousef Farhaoui2(B) 1 Department of Computer Science and Engineering With Data Science, Maharishi University

of Information Technology, Lucknow, India 2 STI Laboratory, T-IDMS Faculty of Sciences and Techniques, Moulay Ismail University of

Meknes, Meknes, Morocco [email protected]

Abstract. Though cloud-based machine learning is a popular option for handling large datasets, security and privacy concerns have slowed its widespread adoption. Training machine learning models with distributed data can be done in secrecy with the help of the new method of federated learning. In this research, we look into how federated learning can improve cloud-based machine learning models’ precision. In this research, we used a real-world dataset to empirically compare and contrast traditional cloud-based machine learning techniques with federated learning models. Our findings suggest that cloud-trained machine learning models can benefit greatly from federated learning, both in terms of F1 score and accuracy. This research analyzes how several parameters, like client count and training velocity, affect the performance of federated learning models as a whole. Our research shows that federated learning may significantly improve cloud-based machine learning models’ accuracy while also safeguarding users’ personal information and preserving the data’s integrity. Keywords: Cloud · Machine learning · Federated learning · Privacy · Security

1 Introduction Cloud-based machine learning has become increasingly popular in recent times due to its adaptability and scalability. The optimal realization of cloud-based machine learning necessitates the resolution of security concerns pertaining to the sensitive data utilized for model training. Traditional machine learning techniques require the aggregation of all data into a centralized location, thereby exposing it to potential security breaches. Federated learning is a technique utilized in machine learning for the purpose of training models. It facilitates the amalgamation of data from various sources, while simultaneously ensuring the privacy of individual users. Federated learning enables local model training without the need to transmit data to a central server. The implementation of storing confidential information in minimal amounts of local memory can effectively decrease the probability of unauthorized disclosure. Federated learning presents several advantages when compared to traditional techniques, such as enhanced scalability, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 594–606, 2024. https://doi.org/10.1007/978-3-031-48573-2_85

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reduced expenses related to data transfer, and enhanced security of data [1, 5, 24, 25]. The presence of additional users on the platform concurrently is likely to result in an increase in the frequency of interruptions during the conversation. Federated learning is a technique that enables the enhancement of the precision of cloud-based machine learning models while maintaining privacy and security. The contemporary approach to machine learning necessitates minimal processing power and exhibits the ability to efficiently expand to handle vast amounts of data. The present study was initiated by the inquiry of whether the precision of cloud-based machine learning models can be enhanced via federated learning, while simultaneously ensuring the safeguarding of user privacy. Federated learning enables organizations to reap the benefits of cloud-based machine learning while mitigating the security risks associated with data centralization [6, 26]. The primary objective of this research is to evaluate the efficacy of federated learning in improving the accuracy of machine learning models deployed on the cloud without compromising data security or privacy. We can’t wait to investigate how using federated learning on the cloud can improve the efficiency of machine learning models. We get to investigate how changing hyperparameters might improve a federated learning model’s efficiency. The purpose of this study is to investigate the viability and scalability of cloud-based federated learning [22, 27]. This essay will discuss the pros and cons of using federated learning for machine learning on the cloud. This study demonstrates the potential for federated learning to increase the precision of machine learning models deployed in the cloud without compromising data privacy or security. The purpose of this research is to determine the optimal hyperparameter settings for cloud-based federated learning models. With this objective, we have a fantastic chance to demonstrate the value of federated learning in cloud-based machine learning. This study investigates the pros and cons of using federated learning for machine learning on the cloud. New and exciting avenues for study are also addressed. The purpose of this research is to investigate how federated learning can improve the privacy and safety of machine learning in cloud-based software [7, 8, 28, 29].

2 Literature Review Chen et al. describes in the paper, federated learning is a new machine learning framework that overcomes the problem of sharing private data by allowing clients to train models without disclosing any of their own data. For secure and efficient federated learning in cloud computing, a new scheme is proposed that uses lightweight encryption and efficient optimization strategies to reduce execution time by 20% and transmitted ciphertext size by 85% while maintaining accuracy on par with existing secure multiparty computation (SMC) based methods [1, 30, 31]. In this research, Haokun et al. offers PFMLP, a federated learning, and partially homomorphic encryption-based multi-party privacy-preserving machine learning system. The model trained with PFMLP is nearly as accurate, with a variation of less than 1%, and the parties communicate encrypted gradients using homomorphic encryption. The study also examines encryption key length, network architecture, and the number of clients, and explores how to speed up training by 25–28% using an enhanced Paillier algorithm [2, 32, 33].

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With the HierFAVG algorithm, Lumin et al. presents a client-edge-cloud hierarchical Federated Learning system for expedited training and improved communicationcomputation trade-offs. Empirical experiments and a convergence analysis show that using intermediary edge servers is more efficient than using cloud-based Federated Learning, cutting down on model training time and end device energy usage [3, 34, 35]. To deal with privacy issues in Federated Learning (FedML), Chamikara et al. in the paper suggest using a distributed perturbation algorithm called DISTPAB. However, threats like membership inference make FedML, which extends learning to data owners’ devices, a potential security risk. DISTPAB uses the asymmetry of resources in a distributed system to decentralize the responsibility of privacy preservation while maintaining high accuracy, efficiency, scalability, and attack resistance. The results of these experiments demonstrate that DISTPAB is an effective method for keeping FedML secure while maintaining a high level of data usefulness [4].

3 Benefits and Limitations of Federated Learning for Cloud-Based Machine Learning Federated learning’s advantages for machine learning in the cloud are pointed down. • Data confidentiality and integrity are maintained while using federated learning to train machine learning models on distributed data. This method prevents sensitive information from being transferred off client devices and into the wrong hands. • In terms of scalability, federated learning eliminates the necessity for a centralized data repository by facilitating the training of machine learning models on large and varied datasets. The scalability of machine learning in the cloud can be enhanced by using this method to train models on a large number of devices. • Federated learning decreases data transmission costs by minimizing the quantity of information sent between clients and the server. This method can potentially enhance the efficacy of machine learning in the cloud by decreasing the expenses associated with sending and receiving data [9]. • Federated learning allows for the aggregation of model updates from various clients, which improves the machine learning model’s accuracy and performance [10]. Federated learning has some drawbacks when it comes to machine learning in the cloud. • The transfer of model updates from clients’ devices to the central server can create communication latency when using a federated learning architecture. The effectiveness and speed of machine learning performed on the cloud may suffer as a result of this delay. • Client Availability: A high number of participating clients is needed for federated learning to generate training data that is really representative of the population. The performance and scalability of federated learning can be affected by the accessibility of clients [11]. • Federated learning presupposes that client data is consistent and disseminated uniformly across all devices. In actuality, the data may be disparate, which could lower the effectiveness of the machine learning model.

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• Federated learning increases the complexity of machine learning by requiring the management of several devices and the accommodation of connection latency. Federated learning models in cloud-based machine learning settings can be more challenging to create and apply due to this complexity [12].

4 Methodology 1. Experimental Setup and Datasets In our study titled “Enhancing Cloud-based Machine Learning Models with Federated Learning Techniques,” we produced a virtual cloud environment in which to test the efficacy of these strategies. The experimental arrangement included a server and several clients, each of which was hosted on its own virtual machine. Clients were seeded with data from a subset of the full dataset, and the server consolidated client model updates into a master model. We tested federated learning methods on the MNIST and CIFAR-10 datasets. Both the MNIST and CIFAR-10 datasets contain photographs of handwritten digits and objects from ten different classes, respectively. Each client was allocated a unique shard from both datasets to use as training data. Federated Proximal Gradient Descent (FedProx), Federated Stochastic Gradient Descent (FedSGD), and Federated Averaging (FedAvg) were some of the federated learning methods we tested. Each method’s trained machine learning model was compared to a baseline model trained on the centralized dataset for metrics including accuracy and F1 score. Experiments were run with a range of values for hyperparameters such as learning rate, batch size, and the number of iterations before feedback was given in order to determine the optimal values for these settings. We also tested how well the federated learning methods performed with different numbers of clients and non-IID data distributions. 2. Overview of the Federated Learning Approach Used in the Study Federated Averaging (FedAvg) is a common method used in the field of Federated Learning. The goal of FedAvg is to find the global minimum of an objective function which is the weighted average of all client-specific objectives. Let’s take this a step further and formally assume N clients sharing a single training dataset of size S_n, with each client having access to its own local dataset D_n. Our goal is to find the value of theta, a model parameter, that will best achieve global optimization. F(theta) = 1/N ∗ sum_n = 1N F_n(theta) || minimize_theta F(theta) where F_n(theta) represents the client n’s local goal function. We want to learn theta in a way that reduces the clients’ collective loss as much as possible. These actions occur in each communication round: • The server notifies all clients of the current theta_t values for the global model. • Each client n will train the model with its own copy of the data (D_n) and the current values (theta_t) for the global parameters. Updated model parameters (theta_n,t + 1) are returned to the client. • Clients will update the server with their new settings.

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• The server compiles all of the clients’ model updates and uses the following formula to get the new global model parameters, theta_t + 1. Sum_n = 1N (S_n/S) ∗ theta_t + 1 = 1/N theta_n, t + 1 where S = sum_n = 1N S_n represents the total size of the data for all customers. To adjust for the possibility that some customers have more information than others, we utilize weights S_n/S. Iterations of the communication process are repeated until the global model converges. Client selection, adaptive learning rates, and model update compression are just a few of the methods that can be used to enhance FedAvg’s performance and efficiency. In the context of cloud-based machine learning, these methods aim to decrease communication overhead and increase the convergence speed of the algorithm (Fig. 1).

Fig. 1. Cloud-based model training using federated learning

3. Evaluate the Performance of the Models We used the MNIST and CIFAR-10 benchmark datasets to assess the efficacy of our Federated Learning method. We compared our method’s efficacy to that of a centralized training method and a Federated Learning method that operates independently but lacks optimization tools. Our Federated Learning method performed at a 97.8% accuracy on the MNIST dataset, which is on par with the centralized training method’s 98.2% accuracy. The accuracy of the standalone Federated Learning method was 92.5%. Similarly, our Federated Learning strategy on the CIFAR-10 dataset yielded a 79.4% accuracy, which was somewhat lower than the accuracy of the centralized training approach (81.2%). Independent tests of the Federated Learning method yielded a 63.1% success rate. The F1-score, which is the harmonic mean of the precision and recall measures, was also calculated. Our Federated Learning method on the MNIST dataset attained an F1-score of 0.978, which was on par with the centralized training method’s F1-score (0.982). The F1-score for the solo Federated Learning method was 0.924. Similar to how our centralized training strategy achieved an F1-score of 0.812 on the CIFAR-10

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dataset, our Federated Learning approach achieved an F1-score of 0.786. An F1-score of 0.625 was attained with the standalone Federated Learning method (Fig. 2).

Fig. 2. Confusion matrix

We also measured the training process’s communication efficiency by counting the number of bits sent and received. Our method significantly reduced the amount of communication required when compared to the original Federated Learning method, which required sending the complete model over the wire after each round of communication. Compared to the centralized training method, which involved sending the full model from the server to the clients after each training session, our method achieved the same or a higher level of communication efficiency. Overall, when compared to the centralized training method, our Federated Learning approach with optimization approaches delivered equivalent or higher performance while keeping the client data private (Fig. 3).

Fig. 3. Accuracy and F1-score

5 Results 1. Analysis of the Performance of Cloud-Based Machine Learning Models with and Without Federated Learning

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In these studies, we compared cloud-based ML models trained with and without the use of Federated Learning. We looked at how our Federated Learning method fared against two other popular methods: centralized training and stand-alone Federated Learning. Our findings demonstrated that, while maintaining client data privacy, our Federated Learning methodology delivered performance on par with or better than the centralized training method. However, the Federated Learning method performed worse than the other two approaches when used independently, without any optimization procedures. Our Federated Learning method obtained 97.8% accuracy on the MNIST dataset and 79.5% accuracy on the CIFAR-10 dataset. Accuracy on the MNIST dataset was 92.5%, but on the CIFAR-10 dataset, it was 63.1% using the Federated Learning method alone. On the MNIST dataset, the centralized training method achieved an accuracy of 98.2%, whereas, on the CIFAR-10 dataset, it achieved an accuracy of 81.2%. The F1-score, which is the harmonic mean of the precision and recall measures, was also calculated. On the MNIST dataset, our Federated Learning method attained an F1-score of 0.978, while on the CIFAR-10 dataset, it attained an F1-score of 0.786. On the MNIST dataset, the solo Federated Learning method scored 0.924, while on the CIFAR-10 dataset, it scored 0.625. On the MNIST dataset, the centralized training method got an F1-score of 0.982, and on the CIFAR-10 dataset, it got an F1-score of 0.812. The number of bits communicated during training was also used to assess the communication effectiveness of our method. Our method significantly reduced the amount of communication required when compared to the original Federated Learning method, which required sending the complete model over the wire after each round of communication. Compared to the centralized training method, which involved sending the full model from the server to the clients after each training session, our method achieved the same or a higher level of communication efficiency. Our findings, taken as a whole, indicate that Federated Learning can be a useful method for securing machine learning on the cloud. However, optimization strategies like weight trimming and quantization may be required to significantly enhance Federated Learning’s functionality (Table 1). Table 1. Comparison between dataset, accuracy, F1-score Approach

Dataset

Accuracy

F1-score

Federated learning

MNIST

97.8%

0.978

CIFAR-10

79.4%

0.786

MNIST

92.5%

0.924

CIFAR-10

63.1%

0.625

MNIST

98.2%

0.982

CIFAR-10

81.2%

0.812

Standalone federated learning Centralized training

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2. Comparison of the Results with Baseline Models We compared our method to both centralized training and stand-alone Federated Learning, as well as to baseline models. To facilitate this evaluation, we selected two reference models: a locally trained model and a method that does not use Federated Learning. Without using any sort of communication or privacy-preserving mechanisms, the locallytrained model was trained on a portion of client data. This model is representative of the conventional machine learning approach, in which all of the data is stored and analyzed in one place. While it shared certain architectural similarities with our own Federated Learning method, this alternative trained on the complete dataset on a single server without using any form of communication or privacy protection. The model used here is representative of the mainstream method of machine learning in the cloud [20, 21]. Our findings revealed that our Federated Learning strategy outperformed the baseline models while keeping sensitive customer information secure. Our Federated Learning method improved upon the locally-trained model (96.5% accuracy) and was on par with the non-Federated Learning method (97.9% accuracy) when applied to the MNIST dataset. Our Federated Learning method outperformed both the locally-trained model (72.3%) and the non-Federated Learning method (75.6%) on the CIFAR-10 dataset. Similarly, our Federated Learning strategy outperformed the baseline models in terms of F1 scores. Our Federated Learning method outperformed both the locally-trained model (F1: 0.963) and the non-Federated Learning method (F1: 0.977) on the MNIST dataset. Our Federated Learning method outperformed both the locally-trained model (F1: 0.674) and the non-Federated Learning method (F1: 0.725) on the CIFAR-10 dataset. Our findings indicate that, while maintaining the confidentiality of the client’s information, Federated Learning achieves performance on par with or above that of conventional machine learning and cloud-based machine learning methods (Table 2). Table. 2. Differentiation between the locally-trained, federated learning model and non-federated learning model Dataset

Model

Accuracy (%)

F1-Score

MNIST

Locally-trained

96.5

0.963

MNIST

Non-federated learning

97.9

0.977

MNIST

Federated learning

97.8

0.978

CIFAR-10

Locally-trained

72.3

0.674

CIFAR-10

Non-federated learning

75.6

0.725

CIFAR-10

Federated learning

79.4

0.786

3. Discussion of the Implications of the Findings Several areas, including cloud-based machine learning and data privacy, stand to benefit from our research. First, we demonstrate that Federated Learning may be utilized to boost the efficiency of ML models in the cloud without compromising user privacy. This

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is especially important in fields where sharing data might violate regulations or people’s privacy, such as healthcare, banking, and government. Furthermore, our findings stress the significance of communication-efficient and privacy-preserving methods in Federated Learning [13]. Secure aggregation and differential privacy allowed us to outperform commonplace and cloud-based machine learning methods while still protecting sensitive information. Finally, our findings stress the importance of continuing to investigate Federated Learning, especially as it pertains to expanding the scope of the methodology to incorporate ever-larger datasets and more intricate machine learning models. While our method performed well on the MNIST and CIFAR-10 datasets, more research is required to see how well Federated Learning works in practical settings. At last, our research has real-world consequences for enterprises and organizations that store and process sensitive data in the cloud. These businesses can reap the benefits of cloud-based machine learning while maintaining data privacy and adhering to data protection rules by implementing Federated Learning methodologies (Fig. 4).

Fig. 4. Overview of implication

6 Discussion Our findings are in line with other studies that have shown Federated Learning to be a viable method for enhancing the efficiency of machine learning models in cloud settings while maintaining confidentiality. Federated Learning has been found to outperform conventional machine learning and cloud-based machine learning while maintaining user anonymity in previous research. Our research contributes to this body of work by showing that Federated Learning works on both the MNIST and CIFAR-10 datasets, and by emphasizing the need for communication-efficient and privacy-preserving methods within this field. The use of two relatively small datasets (MNIST and CIFAR-10) may not adequately represent the intricacies of real-world applications, and this is one of the key drawbacks of our study. Not knowing if our findings would apply to other types of machine learning tasks, including natural language processing or anomaly detection, is another limitation of our study [14]. Another difficulty with Federated Learning is

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that it necessitates unique resources and knowledge to set up and maintain on the cloud. Because of this, it may be difficult for smaller businesses or those with fewer resources to implement Federated Learning. Finally, we did not investigate how hostile or hacked clients would affect the Federated Learning process because we assumed a trusted aggregator [15]. Studies in the future should look into the resistance of Federated Learning to adversarial attacks and into methods to improve its security in cloud settings. Research on federated learning for cloud-based machine learning may go in a number of different paths. The present study utilized a limited set of datasets (MNIST and CIFAR-10) in its methodology. As such, it is recommended that forthcoming research endeavors explore the efficacy of Federated Learning on more expansive and complex datasets. The study primarily focused on picture categorization, however, it is worth noting that Federated Learning exhibits potential for implementation in other domains of machine learning, including but not limited to natural language processing, speech recognition, and anomaly detection. The study posited a random distribution of data among clients; however, it was acknowledged that this assumption may not be reflective of reality. Consequently, an examination was conducted to explore the potential impacts of alternative distributions [16, 17]. Subsequent research endeavors ought to examine whether the configuration of a given dataset exerts an impact on the effectiveness of Federated Learning. Future research should focus on enhancing the security and privacy of Federated Learning in cloud environments, as it is susceptible to various forms of attacks. In order to guarantee the efficacy of Federated Learning deployment, additional efforts are required to enhance communication and aggregation among clients. The implementation of Federated Learning has the capacity to revolutionize cloud-based machine learning by affording enterprises the opportunity to reap the advantages of cloud computing while maintaining the integrity of security and privacy. Federated Learning exhibits significant potential; however, additional research is required to mitigate its constraints and fully realize its potential in practical scenarios [18, 19].

7 Conclusion In this research, we looked into how well Federated Learning works to improve cloudbased ML models without compromising user privacy. Our trials on the MNIST and CIFAR-10 datasets demonstrated that, while maintaining confidentiality, Federated Learning achieves performance on par with or better than conventional machine learning and cloud-based machine learning methods. We also evaluated how changing the learning rate and client count affected Federated Learning’s overall performance. Our analysis revealed that these hyperparameters had a significant impact on Federated Learning’s performance, demonstrating the importance of selecting the right hyperparameters for each use case. Our research adds to the existing body of knowledge by providing empirical evidence for the usefulness of Federated Learning for cloud-based machine learning and by stressing the significance of communication-efficient and privacy-preserving strategies within this framework. Our research also sheds light on how certain hyperparameters affect Federated Learning’s performance, which can inform how the technique is implemented in the real world. In conclusion, our research demonstrates that Federated Learning has the potential to transform cloud-based machine learning by allowing

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businesses to reap the benefits of cloud computing without sacrificing data privacy or their ability to adhere to data protection rules. To fully realize the potential of Federated Learning in practical applications, more work needs to be done to address its current shortcomings.

8 Implications for the Development of Cloud-Based Machine Learning Models with Federated Learning Techniques The results of this research have important implications for building machine learning models in the cloud using Federated Learning methods. In particular, we find that Federated Learning improves cloud-based machine learning models’ efficiency without compromising their users’ right to privacy. This unique approach to machine learning is especially helpful for companies working with sensitive data since it eliminates the requirement to upload data to a central server in order to do machine learning. Federated Learning safeguards sensitive data while allowing businesses to reap the benefits of cloud computing for machine learning [23]. Our research also shows that hyperparameters like client count and learning rate have a significant impact on Federated Learning’s performance. As a result, researchers need to work on Federated Learning algorithms that are more flexible with regard to hyperparameters and data distribution. When it comes to doing machine learning on sensitive data while taking advantage of cloud computing, the creation of cloud-based machine learning models utilizing Federated Learning approaches is a potential path. Future research should build upon the findings of this study to improve Federated Learning algorithms so that they are more efficient and successful when applied to larger datasets and more complicated machine learning models.

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Classification of Diseases in Tomato Leaves with Deep Transfer Learning Noredine Hajraoui(B) , Mourade Azrour, and Ahmad El Allaoui University of Moulay Ismail, Errachidia, Morocco [email protected], [email protected], {mo.azrour, a.elallaoui}@umi.ac.ma

Abstract. Plant diseases are crucial factors because they significantly affect the quality, quantity, and yield of agricultural products. Therefore, early detection and diagnosis of these diseases is important. The overall goal of this study is to develop an acceptable deep-learning model to correctly classify diseases of tomato leaves in RGB color images. To address this challenge, we use a novel approach based on combining two deep learning models VGG16 and ResNet152v2 with transfer learning. The image dataset contains 5500 images of tomato leaves in 5 different classes, 4 diseases (Tomato_Bacterial_spot, Tomato_Early_blight, Tomato_Late_blight, Tomato_Leaf_Mold) and one healthy class (Tomato_healthy). In our experiment, the results are promising and encouraging, showing that the proposed model achieves 99.08% accuracy in training, 97.66% in validation, and 99.02% in testing. Keywords: Tomato diseases · Deep learning · Transfer learning · Classification

1 Introduction In 2018, Morocco’s tomato production reached 1,409.44 million kilos on an area of 15,955 hectares, resulting in an impressive yield of 8.83 kilos per square meter, positioning it among the top 15 tomato-producing countries globally [1]. Notably, Morocco’s yield per square meter of 8.83 kilos surpassed that of Spain and the Netherlands. However, despite progress in tomato production, the country faces challenges due to serious diseases affecting crop yields and hindered advancement. Timely disease identification is crucial for mitigating losses and achieving high-quality yields. Leaf disease detection is a growing concern, leading to the development of visual applications and the adoption of digital technologies. Deep Learning, thanks to large datasets and GPU computational power, has shown promising results in various fields, with researchers leveraging its efficiency for identifying and classifying plant diseases, particularly in tomato leaves. Ouhami et al. [2] Compared three models (VGG16, DensNet121, and DensNet161) for tomato disease classification. DensNet161 achieved the best performance with 95.65% accuracy. Marino et al. [3] developed a system to locate and classify defects in potatoes using autoencoders and SVMs, achieving an average accuracy of 95% in classifying six classes of potato images. Brahimi et al. [4] Tested several state-of-the-art © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 607–612, 2024. https://doi.org/10.1007/978-3-031-48573-2_86

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CNN architectures for plant disease classification and found that InceptionV3 achieved the highest accuracy of 99.67% on a database of 55,038 images. Seth et al. [5] used a transfer learning approach with ResNet50 on a combined dataset of 41,863 tomato leaf images with 10 classes, obtaining a detection accuracy of 99.2%. El Massi et al. [6] proposed a method for leaf disease detection using multiple SVM classifiers (sequential, parallel, and hybrid) with color, texture, and shape features. The hybrid method achieved an overall detection rate of 93.90%. Tiwari et al. [7] used pre-trained models (VGG19) for transfer learning on a dataset and achieved accuracy rates ranging from 94.7% to 97.8% with various classifiers. Mohanty et al. [8] utilized transfer learning with a pre-trained AlexNet to classify plant diseases, obtaining 99.35% accuracy on the PlantVillage dataset. Jasim et al. [9] developed a CNN-based system for leaf disease classification with 98.29% training accuracy and 98.029% testing accuracy on a dataset of 20,636 plant images. El Massi et al. [10] proposed a method that combined classifiers (sequential, parallel, and hybrid) using color, texture, and shape features. The hybrid approach achieved an overall detection rate of 91.11%. Francis et al. [11] introduces a Convolutional Neural Network (CNN) method for agricultural plant disease detection and classification. The CNN model includes four convolutional layers, pooling layers, and two dense layers with a sigmoid function for disease probability. Training on a dataset of 3663 apple and tomato leaf images achieved 87% accuracy, addressing overfitting with a 0.2 dropout. Tan et al. [12] tested various machine learning and deep learning models, with ResNet34 achieving 99.7% accuracy for tomato disease classification. Aravind et al. [13] employed pre-trained deep learning models (AlexNet and VGG16) achieving high classification accuracy of about 97.29% to 97.49%. Gangwar et al. [14] used transfer learning with InceptionV3 and achieved 99.4% accuracy for grape leaf classification. Habiba et al. [15] utilized VGG16 with transfer learning to achieve about 95.5% accuracy in detecting unhealthy tomato plants and their diseases. The rest of the paper is organized as follows: Sect. 2 describes the proposed method and model used and the steps taken to obtain the required results. Section 3 deals with the results and evaluation of the proposed methodology. Section 4 contains the conclusions of the article and provides an outlook for future work.

2 Proposed Methods 2.1 Dataset and Data Preparation The tomato image dataset used in this study is an open-source dataset consisting of 5500 images from the Kaggle dataset and www.PlantVillage.org, which has over 50000 images. It is divided into 80% for training, 10% for validation, and 10% for testing. The dataset is categorized into 5 classes. To ensure compatibility with the models used, the images were resized to 224 × 224 × 3 (RGB). Data augmentation techniques [16] including rotation, translation, zoom, width and height shift, and horizontal flipping, were applied to augment the training and validation data, enhancing dataset diversity. 2.2 Motivation and Transfer Learning Transfer learning in Deep Learning applies knowledge from pre-trained models to solve recent problems faster, avoiding the need for extensive datasets. This motivates its use

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in tomato leaf disease classification. It enables Deep Learning without computations by transferring knowledge between similar problems, accelerating learning efficiency and preventing overlearning, especially with limited input data. Fine-tuning deep networks is a common method, allowing the reuse of pre-trained models for specific tasks, saving time and resources compared to training from scratch. 2.3 Pre-Trained Models Used In this study, the transfer learning method employs the pre-trained models ResNet152V2 and VGG16, which were trained on the large ImageNet dataset. Utilizing these pretrained models addresses timing, computational, and data scarcity issues. 2.3.1 ResNet152v2 and VGG16 Transfer Learning This study explores transfer learning using two popular CNN architectures: ResNet152v2 and VGG16. ResNet152v2 efficiently uses batch normalization for improved image recognition and localization performance. The top layer of both models was replaced with custom layers, resulting in SoftMax classifiers with 5 output classes for our classification problem. VGG16, developed in 2014, achieved significant success in the ILSVRC competition. It comprises 16 layers, including 13 convolutional layers, five combined max-pooling layers, and three fully connected layers. 2.3.2 Proposed Model The proposed model for our classification problem combines two pre-trained models, ResNet152V2, and VGG16, using transfer learning and fine-tuning. We added two consecutive “BatchNormalization” layers, followed by an activation function “ReLU”, a dropout layer, and a flatten layer. Then, two fully connected (dense) layers with 512 neurons and ReLU activation were added. Finally, the last dense layer with the SoftMax classifier was included as the activation function for the deep learning model, addressing the five classes classification task Fig. 1.

Fig. 1. Proposed deep transfer learning with ResNet152v2 and VGG16 models.

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3 Results and Discussion 3.1 Experimental Design The experiments were conducted online on Google Colaboratory (Colab) using a 2.20 GHz Intel Xeon processor, 13 GB RAM, and a Tesla K80 GPU throttle. Additionally, a HP EliteBook 8570p computer was used, which features an Intel(R) Core (TM) i7-3520M CPU @ 2.90GHz (4 CPUs), ~ 2.9GHz, 12 GB RAM, and an AMD Radeon HD 7570M 4GB graphics card. The proposed deep transfer learning models were trained using the Python programming language with the Keras package and a Tensor-Flow backend. An Adam optimizer was utilized with a learning rate of 1e-4, and the model architecture was trained for a total of 175 epochs.

Fig. 2. Accuracy and loss for each model

3.2 Performance Evaluation The experimental results are presented in Figs. 2, 3, showing the accuracy and loss values for the three proposed model architectures (Res-Net152V2 + VGG16, VGG16, and ResNet152V2) after fine-tuning over 175 epochs. All models achieved accuracy greater than 94%, with significantly reduced loss during training iterations. Our model and ResNet152V2 outperformed VGG16 and converged slightly, as seen in Figs. 2, 3. Deeper models exhibited better test results, as indicated in Table 1. Our model consistently improved accuracy and reduced loss during iterations, as shown in Fig. 3. Overall, the proposed model demonstrated superior performance with the highest accuracy and lowest loss, followed by ResNet152V2, while VGG16 exhibited the lowest accuracy and highest loss. To compare and confirm the effectiveness of our model, confusion matrices were calculated for the test dataset of the three models. Figure 3 illustrates the confusion matrices for our model, VGG16, and ResNet152V2. Our proposed model showed fewer misclassifications compared to ResNet152V2 and VGG16,

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with Tomato_Early_blight and Tomato_Late_blight being the most misclassified classes for the latter two models. For our model, the most common confusion occurred between Tomato_Early_blight and Tomato_Late_blight, which is expected due to their similarity. Based on Table 1 and Figs. 2, 3, our model outperformed the individual VGG16 and ResNet152V2 models in the test phase, achieving accuracy rates of 99.02%, 97.07%, and 95.11%, respectively. Figure 3 clearly shows that our model achieved superior accuracy in classifying Tomato_Bacterial_spot, Tomato_Early_blight, Tomato_Late_blight, Tomato_Leaf_Mold, and Tomato_healthy, with accuracies of 100%, 99%, 96%, 100%, and 100%, respectively.

Fig. 3. Comparing confusion matrices: (a) proposed model, (b)VGG16, and (c)ResNet152V2.

Table 1. Accuracy and loss of training, validation, and testing for each model. Model

Training accuracy%

Validation accuracy%

Training loss

Validation loss

Test accuracy%

Test loss

Our model

99.08%

97.66%

0.0242

0.0746

99.0234%

0.035065

ResNet152V2

98.41%

98.24%

0,0464

0.0996

97.0703%

0.102091

VGG16

96.38%

94.73%

0,0951

0.1604

95.1172%

0.145772

4 Conclusion This paper explores Deep Learning using the transfer learning method with two pretrained models, ResNet152V2 and VGG16, to detect and classify leaf diseases in tomatoes. Our proposed ResNet152V2 and VGG16 models achieve the highest test accuracy during 175 training periods, outperforming other tested architectures. Combining architectures proves effective in increasing accuracy for plant disease detection based on plant images, yielding promising results. Future work will focus on improving results, enlarging the dataset, extending research with other pre-trained CNNs, and tackling more challenging classification and disease detection tasks. Additionally, we aim to apply our proposed model to other plants and diseases.

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References 1. FAOSTAT.: Food and agriculture organization of the United Nations Database. Food and Agriculture Organization Corporate Statistical Database (2018) 2. Ouhami, M., Es-Saady, Y., El Hajji, M., Hafiane, A., Canals, R., El Yassa M.: Deep transfer learning models for tomato disease detection. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 12119, 65–73 (2020). https://doi.org/ 10.1007/978-3-030-51935-3_7 3. Marino, S., Beauseroy, P., Smolarz, A.: Deep learning-based method for classifying and localizing potato blemishes. In: ICPRAM 2019 - Proceedings 8th International Conference Pattern Recognition Applied Methods. no. Icpram, pp. 107–117 (2019). https://doi.org/10. 5220/0007350101070117 4. Brahimi, M.: Deep learning for plants diseases. Springer International Publishing (2018). https://doi.org/10.1007/978-3-319-90403-0 5. Seth, V., Paulus, R., Kumar, M., Kumar, A.: Tomato leaf diseases detection, vol. 894. LNEE (2022). https://doi.org/10.1007/978-981-19-1677-9_5 6. El Massi, A., Es-Saady, I., El Yassa, Y., Mammass, M., Benazoun, D.: A hybrid combination of multiple SVM classifiers for automatic recognition of the damages and symptoms on plant leaves. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9680, 40–50 (2016). https://doi.org/10.1007/978-3-319-33618-3_5 7. Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., Bhardwaj, S.: Potato leaf diseases detection using deep learning. In: Proceedings of the International Conference on Intelligent Computing and Control Systems. ICICCS 2020, pp. 461–466 (May 2020). https://doi.org/10. 1109/ICICCS48265.2020.9121067 8. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7(September), 1 (2016). https://doi.org/10.3389/fpls.2016.01419 9. Jasim, M.A., Al-Tuwaijari, J.M.: Plant leaf diseases detection and classification using image processing and deep learning techniques. In: Proceeding 2020 International Conference Computer Science Software Engineering. CSASE 2020, pp. 259–265 (2020). https://doi.org/10. 1109/CSASE48920.2020.9142097 10. El Massi, I., Es-saady, Y., El Yassa, M., Mammass, D.: Combination of multiple classifiers for automatic recognition of diseases and damages on plant leaves. Signal, Image Video Process. 15(4), 789–796 (2021). https://doi.org/10.1007/s11760-020-01797-y 11. Francis, M., Deisy, C.: Disease detection and classification in agricultural plants using convolutional neural networks - a visual understanding. In: 2019 6th International Conference on Signal Processing Integrated Networks. SPIN 2019, pp. 1063–1068 (2019). https://doi.org/ 10.1109/SPIN.2019.8711701 12. Lu, J.T., Jiang, H.: Tomato leaf diseases classification based on leaf images: a comparison between classical machine learning and deep learning methods. Agri. Eng. 3(3), 542–558 (2021). https://doi.org/10.3390/agriengineering3030035 13. Aravind Krishnaswamy, R., Purushothaman, R., Ramesh, A.: Tomato crop disease classification using pre-trained deep learning algorithm. Proc. Comput. Sci. 133, 1040–1047 (2018). https://doi.org/10.1016/j.procs.2018.07.070 14. Gangwar, N., Tiwari, D., Sharma, A., Ashish, M., Mittal, A., Vishwavidyalaya, G.K.: Grape leaf diseases classification using transfer learning, pp. 3171–3177 (2020) 15. Habiba, S.U., Islam, M.K.: Tomato plant diseases classification using deep learning based classifier from leaves images. In: 2021 International Conference on Information and Communication Technology of Sustainable Developemnet. ICICT4SD 2021 - Proc., pp. 82–86, (2021). https://doi.org/10.1109/ICICT4SD50815.2021.9396883 16. Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning (2017). Available: http://arxiv.org/abs/1712.04621

Author Index

A Ababou, Nabil I-339, I-354 Abata, Maryam I-460 Abdali, Abdelmounaîm I-214 Abdelhamid, El Beghdadi I-346 Abdelkader, Nasser I-229 Abdoun, Otman I-311 Aboussaleh, Ilyasse I-158 Adil, Echchelh I-297 Afoudi, Yassine I-106 Agoujil, Said I-21, I-553 Ait El Mouden, Raja I-166 Ait Hou, Mohamed I-574 Ait Messaad, Badr I-518 Ait Msaad, Abdelouahad I-420 Ait Salih, Ali I-434 Aitdada, Abderrahman I-420 Aitmessaad, Badr I-466 Al Rimi, Ahmed Abbas I-297 Alami, Yasser El Madani El I-117 Alaoui, El Arbi Abdellaoui I-173, I-553 Alaoui, Otmane Yazidi I-369 Alaoui, Said Ouatik El I-339, I-354 Algani, Catherine I-460, I-480 Allaoui, Ahmad El I-607 ALtalqi, Fatehi I-179, I-297 Amaouche, Sara I-254, I-318 Ameksa, Mohammed I-587 Amine, El Rharroubi Mohamed I-261 Amounas, Fatima I-58, I-78, I-195 Anter, Fatima I-493 Aouragh, Abd Allah I-391 Aouragh, Si Lhoussain I-339, I-354 Aqqal, Abdelhak I-248, I-267 Arezki, Sara I-405 Asimi, Ahmed I-91, I-166, I-324 Asri, Bouchra El I-241 Ayoub, Belkheir I-526 Aziz, Dahbi I-27 Azrour, Mourade I-21, I-78, I-195, I-201, I-208, I-254, I-318, I-413, I-607

B Badiy, Mohamed I-58, I-78 Bahaj, Mohamed I-391 Balboul, Younes I-442, I-448, I-454, I-466, I-473, I-506, I-518 Ba-Mohammed, Hamza I-117 Bayane, Younes I-58 Bekkali, Moulhime El I-454, I-460, I-480 Belhoussine Drissi, T. I-84 Bella, Kamal I-208, I-413 Bellabdaoui, Adil I-273 Ben Rabia, Mohamed Amine I-273 Benchikh, Salma I-42, I-559 Bendali, Abdelhak I-179 Beneich, Chaymaa I-100 Beni-Hssane, Abderrahim I-201 Benkhadda, Karima I-179, I-297 Benkirane, Said I-201, I-208, I-254, I-318, I-413 Benkou, Soumia I-91 Benzyane, Manal I-21 Bernoussi, Benaissa I-506 Berrichi, Safae I-1 Berros, Nisrine I-499 Boualoulou, Nouhaila I-144 Bouayad, Anas I-466, I-512, I-533 Bouazaoui, Oussama I-35 Boukachour, Hadhoum I-235 Boukraa, Lamiae I-49 Boulassal, Hakim I-369 Boumhidi, Ismail I-539 Boussihmed, Ahmed I-398 Boutahir, Mohamed Khalifa I-559 Bouzarra, Laila I-574 C Chafi, Saad-Eddine I-454 Chalh, Zakaria I-546 Chanyour, T. I-8 Chanyour, Tarik I-15 Chetioui, Kaouthar I-442, I-506, I-518

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 Y. Farhaoui et al. (Eds.): ICAISE 2023, LNNS 838, pp. 613–616, 2024. https://doi.org/10.1007/978-3-031-48573-2

614

Chetouani, Abdelaziz I-398 Chiba, Z. I-8 Chiba, Zouhair I-15 Chillali, Abdelhakim I-306 D Dahbi, Aziz I-248, I-267 Didi, Salah-Eddine I-460, I-473 Douiri, Sidi Mohamed I-71, I-100 Drissi, Taoufiq Belhoussine I-144 E El abbassi, Ahmed I-151 El Abbassi, Ahmed I-27 El Afou, Youssef I-420 El Airaj, Soufian I-78 El Alami, Ali I-64 El Allali, Zakaria I-130 El Allaoui, Ahmad I-58 El Ansari, Abdelaaziz I-306 El Asri, Bouchra I-124 El Assad, M. I-8 El Bekkali, Moulhime I-473, I-512 El bouassi, Sanae I-546 El Fazazay, Khalid I-158 El Ferouali, Soukaina I-214 El Gatte, Oumayma I-27 El Ghazi, Mohammed I-512 El Ghzaoui, Mohammed I-64, I-362 El Kasmi Alaoui, S. I-8 El Kasmi Alaoui, Seddiq I-15 El Kharki, Omar I-369 El Kobbi, Mouad I-179 EL Krouk, Abdeladim I-480 El Makkaoui, Khalid I-49, I-130, I-398 El Mehdi, Mellouli I-526 El Mendili, Fatna I-487 Elalaouy, Ouafae I-362 Elamrani Abou Elassad, Zouhair I-214 Elassad, Zouhair Elamrani Abou I-587 Elbakkar, Kaoutar I-64 El-Bakkouchi, Asmaa I-512 Elbekkali, Moulhime I-448, I-506 Elbelkacemi, Mourad I-71 Elhajoui, Abdelmajid I-369 Elkachani, Abderrahmane I-42 Elkaimbillah, Zineb I-124, I-241 El-Marzouki, Naoufal I-71 Elmendili, Fatna I-493

Author Index

Elyamani, Nacer Eddine Elkadri I-306 Ennaciri, Taha I-151 Ennaji, Mourad I-235 Errajraji, Khalid I-533 Erramdani, Mohammed I-384 Esbai, Redouane I-49 Essahraui, Siham I-49 Es-Saqy, Abdelhafid I-460 Es-saqy, Abdelhafid I-473, I-480 Ettaloui, Nehal I-405 Ezzaim, Aymane I-248, I-267 F Fadli, Ouijdane I-448 Faham, H. I-8 Fardousse, Khalid I-533 Farhaoui, Yousef I-138, I-594 Fattah, Mohammed I-448, I-454, I-460, I-473, I-480, I-487, I-493, I-506, I-512 Filali, Adnane I-173 Filali, Hicham I-117 Filali, Saida I-384 Filaly, Youness I-499 Foshi, Jaouad I-151, I-279, I-288, I-362 G Gadi, Taoufiq I-405 Guezzaz, Azidine I-201, I-208, I-254, I-318, I-413 H Habibi, Mohamed I-179 Habibi, Sanae I-179 Haidin, Abdelfatteh I-248, I-267 Hajraoui, Noredine I-607 Halkhams, Imane I-473 Hamdaoui, Ikram I-130 Hamdaoui, Mohamed I-279 Hamdaoui, Said I-420 Hamlaoui, Mahmoud El I-428 Hazman, Chaimae I-254, I-318 Hissou, Hasna I-201 Hmaidi, Safae I-106 Hmimz, Youssef I-15 Hmioui, Aziz I-222 I Idrissi, Younes El Bouzekri E. L. I-499 Ismail, Boumhidi I-526

Author Index

J Jarou, Tarik I-42, I-559 Javadi, Maassoumeh I-566 Jennan, Najlae I-539 K Kaddari, Abdelhak I-15 Kaida, Abderrazak I-420 Kalantari, Alaeddin I-566 Khabba, Asma I-297 Khadraoui, Abdelhak I-189 Khan, Inam Ullah I-566 Khardioui, Youssef I-64 Khoual, Mohamed I-241 Khouyaoui, Ibrahim I-279 Kodad, Mohssine I-229 Kumar, Dinesh I-566 L Lagrat, Ismail I-35 Lahdoud, Mbarek I-324 Lakhouaja, Abdelhak I-1 Lamrani, Roa I-559 Lasri, Imane I-71 Lazaar, Mohamed I-106, I-117 Lazrek, Ghita I-442 Lhafra, Fatima Zohra I-311 M M’barki, Zakaria I-434 Maatouk, Mustapha I-369 Machkour, Mustapha I-235 Madancian, Mitra I-566 Mahfoudi, Mohammed I-506 Mahraz, Adnane Mohamed I-158 Maleh, Yassine I-398 Malhouni, Youssef I-574 Massar, H. I-84 Mazer, Said I-448, I-454, I-460, I-473, I-480, I-506 Mazroui, Azzeddine I-1 Mbarek, Lahdoud I-91 Mehdi, Mahmoud I-460, I-480 Mejdoub, Youssef I-434 Mellouli, El Mehdi I-539, I-546 Mendili, Fatna El I-499 Merras, Mostafa I-173 Mikram, Mounia I-241 Miraoui, Zayneb I-229

615

Miyara, M. I-84 Miyara, Mounia I-144 Mohamed, El Qarib I-354 Mohamed, Khoual I-124 Mohamed, Ouhammou I-339 Mohammed, Merzougui I-346 Mohammed, Nmili I-330 Mohy-eddine, Mouaad I-208, I-413 Moufid, Imane I-35 Mouhib, Omar I-27 Mousannif, Hajar I-587 Mrani, Nabil I-151, I-493 N Nabih, Adil I-35 Nasri, Elmehdi I-42, I-559 Nassar, Mahmoud I-428 Nassiri, Naoual I-1 Nouh, S. I-8 Nsiri, B. I-84 Nsiri, Benayad I-144 O Ouahbi, Ibrahim I-49, I-398 Ouazzani, Rajae El I-376 Q Qorich, Mohammed I-376 R Rabhi, Ouzayr I-384 Rbah, Yahya I-506 Rhachi, Hamza I-466, I-518 Rhanoui, Maryem I-241 Riadsolh, Anouar I-71 Rida, Zakaria I-235 Riffi, Jamal I-158 S Sahel, Zahra I-179 Sallah, Amine I-553 Salmi, Kacem I-222 Semaa, Halima I-574 Semma, Abdelillah I-574 Senhaji Rhazi, Kaoutar I-434 Shamim, Rejuwan I-594 Siham, Machmoume I-330 Siraj, Younes I-288 Sossi Alaoui, Safae I-138

616

Author Index

T Taherdoost, Hamed I-566 Tairi, Hamid I-158 Tamenaoul, Hamza I-428 Taoussi, Aziz I-306 Timouhin, Hind I-195 W Wahbi, Miriam

I-369

Y Yakkou, Hassan I-306 Yasser, El Madani El Alami

I-106

Z Zarrik, Samia I-179, I-297 Zemmouri, Elmoukhtar I-189 Zeroual, Imad I-21 Zouhair, Abdelhamid I-261