Advances in Electrical and Computer Technologies: Select Proceedings of ICAECT 2019 [672, 1 ed.] 9789811555572, 9789811555589

Table of contents :
Preface
Contents
About the Editors
Detecting New Events from Microblogs Using Convolutional Neural Networks
1 Introduction
2 Related Work
3 Proposed Work
3.1 Data Collection and Preprocessing
3.2 CNN-Based Microblog Classification
4 Experiments and Results
5 Conclusion
References
Deep Neural Network for Evaluating Web Content Credibility Using Keras Sequential Model
1 Introduction
2 Literature Survey
3 Methodology
3.1 Data Collection
3.2 Training Data
4 Experiments and Results
5 Conclusion
References
SENTRAC: A Novel Real Time Sentiment Analysis Approach Through Twitter Cloud Environment
1 Introduction
2 Literature Review
3 Process Modelling and Discussions
4 Performance and Preliminary Results
5 Limitations and Future Work
6 Conclusion
References
Low Power Area Efficient Improved DWT and AES Architectures for Secure Image Coding
1 Introduction
2 Secure Image Encoding Scheme
2.1 DWT for Image Compression
2.2 AES Encryption and Decryption
3 Modified Algorithm for Secure Image Coding
4 Implementation of DWT and AES Architectures
4.1 Selection of DWT Filter
4.2 Fast Architectures for DWT and AES
4.3 ASIC Implementation of Proposed DWT Architecture
4.4 AES ASIC Results
5 Conclusion
References
Launch Overheads of Spark Applications on Standalone and Hadoop YARN Clusters
1 Introduction
2 Spark Executors
3 Problem Statement
4 Test Environment
5 Overhead Evaluation
6 Overhead Evaluation and Modeling
6.1 Executor Scaling on Single Node Cluster
6.2 Executor Scaling on Multiple Nodes
7 Results
References
Neighbor-Aware Coverage-Based Probabilistic Data Aggregation for Reducing Transmission Overhead in Wireless Sensor Networks
1 Introduction
2 Related Works
3 Neighbor-Aware Coverage-Based Data Aggregation Protocol
3.1 Uncovered Neighbor Set and Aggregation
3.2 Neighbor-Aware Information and Aggregation Probability
3.3 Algorithm Description
3.4 Model Implementation and Performance Evaluation
3.5 Simulation Environment
4 Conclusion
References
Analysis and Summarization of Related Blog Entries Using Semantic Web
1 Introduction
1.1 Literature Review
1.2 Creating New Blog Entry
2 Proposed Methodology
3 Case Study
4 Analysis
5 Conclusion and Future Work
References
A Supplement to “PRE: A Simple, Pragmatic, and Provably Correct Algorithm”
1 Introduction
2 PRE Algorithm by Vineeth Kumar Paleri
2.1 An Example
3 A Supplement to PRE Algorithm
3.1 An Example
4 Conclusion
References
Satellite Image Classification with Data Augmentation and Convolutional Neural Network
1 Introduction
2 Related Work
3 Proposed Work
3.1 Data Preparation
3.2 Data Augmentation
3.3 Convolutional Neural Network Structure
3.4 Experimental Setup
4 Experimental Results
5 Conclusion
References
A Real-Time Offline Positioning System for Disruption Tolerant Network
1 Introduction
2 Related Work
3 Proposed Offline Positioning System
4 Implementation and Result Analysis
5 Conclusion
References
A Novel Copy-Move Image Forgery Detection Method Using 8-Connected Region Growing Technique
1 Introduction
2 Preliminaries
2.1 Histogram Construction
2.2 Region Growing
2.3 Block-Based Match
3 Proposed Forgery Detection Technique
4 Experimental Results and Analysis
5 Performance Analysis and Discussion
6 Conclusions
References
A Method for the Prediction of the Shrinkage in Roasted and Ground Coffee Using Multivariable Statistics
1 Introduction
2 Methodology
3 Results
4 Conclusions
References
Recommendation of Energy Efficiency Indexes for the Coffee Sector in Honduras Using Multivariate Statistics
1 Introduction
1.1 Energy Management System
2 Methodology
2.1 Stage 1. Energy Structure of the Company
2.2 Stage 2. Daily Record of Consumption
2.3 Stage 3. Establishment of Indicators of Energy Performance
2.4 Stage 4. Evaluation of Improvement Potentials
3 Results
4 Conclusions
References
Modeling and Simulating Human Occupation: A NetLogo-Agent-Based Toy Model
1 Introduction
2 Description of the Case of Study
3 Ontology of an Occupational System in the Universe of Discourse of SSDOc
4 Occupational Dynamics Simulations
5 Discussion of Results
6 Conclusions
References
Deep Learning Predictive Model for Detecting Human Influenza Virus Through Biological Sequences
1 Introduction
2 Literature Survey
3 Proposed Work
3.1 Sequence Collection
4 Experiments and Results
4.1 Findings
5 Conclusion
References
On Machine Learning Approach Towards Sorting Permutations by Block Transpositions
1 Introduction
2 Preliminaries
2.1 Cycle Graph
2.2 Machine Learning Approach
2.3 Toric Permutations
3 Proposed Method
3.1 Experimental Methodology
4 Observations and Results
4.1 Results of Random Sampling
4.2 Results of Stratified Sampling
4.3 Confusion Matrix
5 Conclusion and Future Scope
References
Tweet Classification Using Deep Learning Approach to Predict Sensitive Personal Data
1 Introduction
2 Literature Survey
2.1 Regrets in Online Social Networks
2.2 Tweet Classification—Privacy Loss
2.3 Deep Learning in Tweet Classification
3 Methodology
4 Results and Discussion
4.1 Dataset
4.2 Tweet Feature Extraction
4.3 Classification Results
5 Conclusion
References
A Study on Abnormalities Detection Techniques from Echocardiogram
1 Introduction
2 Steps for Abnormality Detection from Echocardiography
3 Different Existing Abnormalities Detection Techniques from Echocardiography
4 Conclusion
References
Secure I-Voting System with Modified Voting and Verification Protocol
1 Introduction
2 Research Background
2.1 Voting Stage
2.2 Verification Stage
3 Security Analysis of the Estonian I-Voting Protocol
3.1 Vote Modification Malware (VMM)
3.2 Re-voting Malware (RVM)
3.3 Self-voting Malware (SVM)
4 Modified Voting and Verification Protocol
4.1 Modified Voting Protocol
4.2 Modified Verification Protocol
5 Security Analysis of the Modified Voting Protocol and Verification Protocol
6 Conclusion
References
Hash Tree-Based Device Fingerprinting Technique for Network Forensic Investigation
1 Introduction
2 Related Work
3 Proposed Device Fingerprinting Technique
3.1 Identification of Parameters
3.2 Parameter Acquisition
3.3 Composition (Generation of Fingerprint)
4 Analysis of Proposed Methodology/Analysis of Fingerprinting Method
4.1 Verification of Fingerprint (Dispute Settlement)
5 Conclusion and Future Work
References
A New Method for Preventing Man-in-the-Middle Attack in IPv6 Network Mobility
1 Introduction
2 Related Works
3 Proposed Work
4 Analysis and Discussion
5 Conclusion
References
Mutual Authentication Scheme for the Management of End Devices in IoT Applications
1 Introduction
2 Lightweight Cryptography Preliminaries
2.1 Sponge Construction-Based Lightweight Function
2.2 Perceptual Hashing
3 Authentication Mechanism for MQTT
3.1 Initialization
3.2 User Registration
3.3 Authentication Process
4 Results and Discussion
5 Performance Analysis
6 Conclusion
References
Finding Influential Location via User Mobility and Trajectory
1 Introduction
2 Problem Definition
3 Proposed Approach
3.1 Maximized Weighted-Sum of Position Data
3.2 Localization and Recognition Technique
3.3 Branch and Bound Technique
3.4 Mobility Instance of Mobile User
3.5 Algorithmic Pseudocode for Finding Influential Location
4 Experiment
5 Conclusion
References
Design of a Morphological Generator for an English to Indian Languages in a Declension Rule-Based Machine Translation System
1 Introduction
2 Literature Review
2.1 Machine Translation
2.2 Morphology
2.3 Morphological Generator
2.4 Multilingual Dictionary
2.5 Declension Rule Based MT System
3 Design of the Morphological Generator Submodule
4 Transliteration and Back-Transliteration
4.1 Algorithm for MG
5 Paradigm Tables and Sample Output
6 Conclusion
References
Automatic Design of Aggregation, Generalization and Specialization of Object-Oriented Paradigm Embedded in SRS
1 Introduction
1.1 Motivation
2 Literature Survey
3 Proposed Methodology
3.1 Guidelines for Lustration of Gathered Requirements
3.2 Evolution of Richness of Language
3.3 Procedure for Design of Generalization/ Specialization
3.4 Procedure for Design of Aggregation Interrelationship
4 Conclusion
Appendix
References
Fuzzy Logic-Based Decision Support for Paddy Quality Estimation in Food Godown
1 Introduction
2 Sensor Array Optimization
3 Expert System Design
3.1 Fuzzy Interference System
3.2 Input Variables
3.3 Output Variable
3.4 Defuzzification
4 Results
5 Conclusion
References
Voice-Controlled Smart Assistant and Real-Time Vehicle Detection for Blind People
1 Introduction
2 Literature Review
3 System Architecture
4 Methodologies
4.1 Current Location
4.2 Travel by Walk
4.3 Travel by Bus
5 Experimental Evaluation
6 Conclusion and Future Work
References
A Framework for Cyber Ethics and Professional Responsibility in Computing
1 Introduction
2 Related Works
3 Research Methodology
4 Mathematical Model of the System (Based on System Internal Components)
5 Implementations and Result
6 Conclusion
References
Detection of Malicious URLs on Twitter
1 Introduction
2 Background
3 Description of the System Components
4 System Architecture
5 Naive Bayes Algorithm
6 Conclusion
References
Human Rights’ Issues and Media/Communication Theories in the Wake of Artificial Intelligence Technologies: The Fate of Electorates in Twenty-First-Century American Politics
1 Introduction
2 Review of Related Media and Communication Theories
2.1 Conceptual Clarifications
2.2 Review of Some Media/Communication Theories
3 Twenty-First-Century Artificial Intelligent Technologies
3.1 Update on Innovations in AI Technology
3.2 AI as a Tool for Politicking in America’s Politics
3.3 AI Politicking and the Media/Communication Theories
3.4 AI Politicking and Human Rights Violation Issues
4 Further Discussion
4.1 Media/Communication Theories and AI Politicking in Twenty-First-Century America
4.2 The Implications of AI Politicking on Communication/Media Theories
4.3 The Impact of AI Politicking on the Inalienable Rights of Electorates
5 Summary and Conclusion
5.1 Summary of Findings
5.2 Recommendations
5.3 Contribution to Knowledge
5.4 Conclusion
References
Modeling and Simulation of Impedance-Based Algorithm on Overhead Power Distribution Network Using MATLAB
1 Introduction
2 Related Works
3 Materials and Methods
3.1 Construction of Fault Location Model
3.2 Fault Location of 2nd Avenue 11 kv Unbalanced Network Using the One-End Impedance Methods
3.3 Calculation of Accuracy of the Method
4 Results and Analysis
4.1 Presentation and Preprocessing of Data
4.2 Implementation of the One-End Impedance-Based Method
5 Conclusion
References
The Utilization of the Biometric Technology in the 2013 Manyu Division Legislative and Municipal Elections in Cameroon: An Appraisal
1 Introduction
2 Conceptual Clarification
3 The 2013 Municipal and Legislative Elections in Manyu Division: An Appraisal
3.1 The Plan to Discredit AI in the Twin Elections
3.2 Municipal and Legislative Election Day: Cave the Dragon?
3.3 Proposed Model to Stop Electoral Malpractices in Manyu Division
4 Data Analysis and Results
5 Conclusion
References
Integrating NFC and IoT to Provide Healthcare Services in Cloud-Based EHR System
1 Introduction
2 Literature Review
3 Methodology
3.1 System Components
3.2 Implementing NFC for Patient Registration and Records Retrieval
3.3 Integrating IoT with Cloud-Based EHR System
4 Results and Analysis
5 Conclusion
References
An Approach to Study on MA, ES, AR for Sunspot Number (SN) Prediction and to Forecast SN with Seasonal Variations Along with Trend Component of Time Series Analysis Using Moving Average (MA) and Exponential Smoothing (ES)
1 Introduction
1.1 Related Work
2 Observing All the Differences Among MA, ES, AR Models and Influences of Seasonal Variations and Trend Using MA, ES of TSA to the Prediction of SN
2.1 Data Collection
2.2 Data Study in Excel File
2.3 Data Processing in Weka
2.4 Resultant Magnitude
3 Experimental Setups
3.1 Experimental Result and Comparisons
4 Discussion
5 Conclusion
References
Machine Learning Approach for Feature Interpretation and Classification of Genetic Mutations Leading to Tumor and Cancer
1 Introduction
2 Related Work
3 Dataset Description
4 Proposed Work
4.1 Preprocessing and Text Vectorization
4.2 Random Model
4.3 Applications of ML Models
5 Results and Discussion
5.1 Feature Interpretation
6 Conclusions
References
Design and Implementation of Hybrid Cryptographic Algorithm for the Improved Security
1 Introduction
2 Related Works
3 Hybrid Algorithm
3.1 RSA
3.2 AES
4 Methodology
4.1 Explanation of the Methodology Used
5 Experimental Results
6 Conclusion and Future Work
References
A Real-Time Smart Waste Management Based on Cognitive IoT Framework
1 Introduction
2 System Architecture
3 Proposed Model
4 Performance Analysis of Proposed Algorithm
5 Results and Discussion
6 Conclusion and Future Works
References
A Proposed Continuous Auditing Process for Secure Cloud Storage
1 Introduction
2 Related Works
3 Architecture of Cloud Computing
4 Proposed System
5 Algorithms
5.1 DES for Encryption Procedure
5.2 SHA-512 for Generating Hash Value
5.3 RSA for Generating Digital Signature
6 Future Works
7 Conclusion
References
Joy of GPU Computing: A Performance Comparison of AES and RSA in GPU and CPU
1 Introduction
1.1 GPU Versus CPU
1.2 GPU Computing
1.3 CUDA Architecture
1.4 Using GPU in Cryptography
2 Literature Survey
3 Implementation of Security Algorithms
3.1 Difference in Implementation of CPU and CPU-GPU
3.2 Implementation of RSA and AES
3.3 Implementation of RSA and AES Algorithms in GPU Using CUDA C
4 CPU and GPU Performance Comparison
4.1 Performance Analysis of RSA Algorithm
4.2 Performance Analysis of AES Algorithm
5 Conclusion
References
Domain-Independent Video Summarization Based on Transfer Learning Using Convolutional Neural Network
1 Introduction
2 Methodology
2.1 Uniform Sampling
2.2 Redundancy Elimination
2.3 Extraction of Feature Descriptors Using CNN
2.4 Finding Similarity Between Feature Vectors
2.5 Elimination of Similar Frames
3 Experimental Analysis
3.1 Performance Metrics
3.2 Results and Discussions
4 Conclusion
References
Deep Neural Network-Based Human Emotion Recognition by Computer Vision
1 Introduction
2 Related Work
3 Methodology
4 Experiment and Results
4.1 Data Set
4.2 Computational Environment
4.3 Experimental Analysis
5 Conclusion
References
A Method for Estimating the Age of People in Forensic Medicine Using Multivariable Statistics
1 Introduction
2 Methodology
3 Results
4 Conclusions
References
Cluster of Geographic Networks and Interaction of Actors in Museums: A Representation Through Weighted Graphs
1 Introduction
2 Network Analysis
3 Method
3.1 The Data
3.2 Weighted Graphs
4 Results
5 Conclusions
References
Security Scheme Under Opensource Software for Accessing Wireless Local Area Networks at the University Campus
1 Introduction
2 Methodology
2.1 Phase I: Diagnosis
2.2 Phase II: Design of the Security Scheme
2.3 Phase III: Test of Security Scheme
3 Results
3.1 Phase I: Diagnosis of Authentication, Records, Network Authorization of the Services Offered
3.2 Phase II: Design of the Study Proposal. Develop the Proposed Security Scheme for Wireless Network Access in the Campus
3.3 Phase III: Test of the Security Scheme
4 Conclusions
References
Pragmatic Evaluation of the Impact of Dimensionality Reduction in the Performance of Clustering Algorithms
1 Introduction
2 Antecedents
2.1 k-Means Clustering
2.2 Clustering Large Applications
2.3 Agglomerative Hierarchical Clustering
2.4 Dimensionality Reduction
3 Related Works
4 Methodology
4.1 Determination of Optimal Cluster Count
4.2 Clustering
4.3 Internal Evaluation of Clustering Quality
4.4 Performance Evaluation
4.5 Tools Used
4.6 Data Set Used
5 Empirical Research
5.1 Determination of Optimal Cluster Count
5.2 Clustering
5.3 Internal Evaluation of Clustering Quality
5.4 Performance Evaluation
6 Conclusion
References
Secret Life of Conjunctions: Correlation of Conjunction Words on Predicting Personality Traits from Social Media Using User-Generated Contents
1 Introduction
2 Related Works
3 Experimental Method
3.1 Dataset of Facebook Statuses
3.2 Data Preprocessing
3.3 Feature Extraction
3.4 Feature Selection
3.5 Classification Model
4 Experimental Results
4.1 Experiment 1
4.2 Experiment 2
5 Correlation of Conjunction Words with Personality Traits
6 Conclusion
References
Text Classification Using K-Nearest Neighbor Algorithm and Firefly Algorithm for Text Feature Selection
1 Introduction
2 Related Works
3 Methods
3.1 Document Corpus
3.2 Document Preprocessing
3.3 Feature Selection
3.4 Classification
4 Results and Discussion
5 Conclusion
References
Performance Evaluation of Traditional Classifiers on Prediction of Credit Recovery
1 Introduction
2 Related Works
3 Methodology
3.1 Data Collection
3.2 Data Analysis and Transformation
3.3 Feature Extraction
3.4 Preparing Input and Output
3.5 Modeling
3.6 Evaluation
4 Data Description and Feature Selection
5 Experimental Results
6 Conclusion
References
Cost-Sensitive Long Short-Term Memory for Imbalanced DGA Family Categorization
1 Introduction
2 Related Works
3 Background
3.1 Domain Name System (DNS)
3.2 Domain Fluxing and Domain Generation Algorithms
3.3 Botnet
3.4 Keras Embedding
3.5 Long Short-Term Memory (LSTM)
3.6 Cost-Sensitive Learning
4 Description of Data Set and Details of Baseline System
5 Experiment
5.1 Proposed Architecture
5.2 Identifying Network Parameters
6 Results
7 Conclusion
References
Oil Spill Characterization and Monitoring Using SYMLET Analysis from Synthetic-Aperture Radar Images
1 Introduction
2 Material and Methods
2.1 SAR Data
2.2 Data Processing
2.3 Feature Extraction
2.4 Characterization of Oil Spill
3 Result and Discussion
4 Conclusions
References
Performance Analysis of Machine Learning Algorithms for IoT-Based Human Activity Recognition
1 Introduction
2 Related Work and Methodologies
2.1 Related Work
2.2 Methodologies
3 Experimental Setup and Methodology
4 Experimental Evaluation
5 Conclusion
References
Computing WHERE-WHAT Classification Through FLIKM and Deep Learning Algorithms
1 Introduction
2 Related Works
3 Overview
4 CNN for PS Identifier
5 Fuzzy local information K-means (FLIKM) Algorithm
6 Operation
7 Results and Discussions
8 Conclusion
References
Reshaped Circular Patch Antenna with Optimized Circular and Rectangular DGS for 50–60 GHz Applications
1 Introduction
2 Antenna Design
2.1 Circuit Diagram of DGS
3 Simulation Results
3.1 Return Loss
3.2 VSWR
3.3 Antenna Gain
3.4 Peak Directivity
3.5 Radiation Efficiency
3.6 Total Antenna Gain
3.7 Total Directivity
3.8 Realized Gain
4 Conclusion
References
VLSI Fast-Switching Implementation in the Programmable Cycle Generator for High-Speed Operation
1 Introduction
2 Proposed Circuit
2.1 Operation
2.2 Flowchart
3 Design
3.1 CPI Circuit
3.2 CDL and Coarse Detectors
3.3 FDL and Fine Detector
3.4 One-Shot Circuit
3.5 Duty Cycle Setting Circuit
4 Simulation and Experimental Results
4.1 Model Simulation
4.2 Xilinx
4.3 Experimental Results
5 Conclusion
References
A Study of Non-Gaussian Properties in Emotional EEG in Stroke Using Higher-Order Statistics
1 Introduction
1.1 Non-Gaussian Properties of EEG
2 Related Studies
3 Materials and Methods
3.1 EEG Data
3.2 Skewness
3.3 Kurtosis
4 Results and Discussion
5 Conclusion
References
SAW-Based Sensors for Lead Detection
1 Introduction
2 Theory
3 Simulation Method
3.1 Modal Analysis of SAW Sensor
3.2 Tetraethylead (TEL) Detection
3.3 Lead Sulphide (PbS) Detection
4 Conclusion
References
Efficient Eye Diagram Analyzer for Optical Modulation Format Recognition Using Deep Learning Technique
1 Introduction
2 System Model
2.1 Data Preparation
2.2 Deep Learning
3 Simulation Results and Discussions
3.1 Performance Metrics
4 Conclusion
References
Low Complexity Indoor Positioning System with TDOA Algorithm Using Hilbert Transform Method
1 Introduction
2 System Model
3 TDOA-Based Positioning Algorithm Using Hilbert Transform
4 Results and Discussion
5 Conclusion
References
Image Encryption Based on Pseudo Hadamard Transformation and Gingerbreadman Chaotic Substitution
1 Introduction
2 State of the Art
3 Proposed Scheme
3.1 Encryption Algorithm
3.2 Decryption Algorithm
4 Experimental Results
5 Conclusion
References
Optimization and Performance Analysis of QPSK Modulator
1 Introduction
1.1 Conventional QPSK Modulator
1.2 Conventional QPSK with Booth Algorithm
1.3 QPSK Modulator with Iterative Algorithm
1.4 Proposed QPSK Modulator-3
1.5 Proposed QPSK Modulator-4
2 Result and Analysis
2.1 Results of Conventional QPSK Modulator
2.2 Results of QPSK Modulator with Booth Algorithm
2.3 Results of QPSK Modulator with Iterative Algorithm
2.4 Results of Proposed QPSK Modulator-3
2.5 Proposed QPSK Modulator-4
3 Results Comparison and Conclusion
3.1 Device Utilization Summary
3.2 Power Utilization Report
3.3 Area Utilization Report
4 Conclusion
References
Power and Delay Efficient ALU Using Vedic Multiplier
1 Introduction
1.1 Reversible Logic
1.2 Booth Multiplier
1.3 Vedic Multiplier
2 Methodology
2.1 ALU Design
3 Results
3.1 ASIC
3.2 FPGA
4 Application
5 Conclusion
References
Optimization and Implementation of AES-128 Algorithm on FPGA Board
1 Introduction
2 Design Architecture for AES-128
3 Sub Modules of Architecture
3.1 Clock Generator (Glue Logic)
3.2 Data Encryption Block
3.3 Data Decryption Block
3.4 Comparator
4 Results
5 Conclusion
References
An Analytic Potential Based Velocity Saturated Drain Current, Charge and Capacitance Model for Short Channel Symmetric Double Gate MOSFETs
1 Introduction
2 Model Description
3 Results and Discussions
4 Conclusion
Appendix
References
Evaluation of Emotion Elicitation for Patients With Autistic Spectrum Disorder Combined With Cerebral Palsy
1 Introduction
2 Related Work
3 Methodology
4 Experimental Setup
5 Feature Extraction and Classification
6 Result and Discussions
7 Conclusion and Future Scope
References
Forest Fire Detection Based on Wireless Sensor Network
1 Introduction
2 Related Work
3 Hardware Components
3.1 Node MCU
3.2 Interfacing of Sensors
4 Implementation
4.1 Single Server—Multiple Client Communication
4.2 Multiple Server—Multiple Client Communication
5 Results
5.1 Single Server—Multiple Client Communication
5.2 Multiple Server—Multiple Client Communication
6 Conclusion and Future Work
References
CB-ACPW Fed SRR Loaded Electrically Small Antenna for ECG Monitoring
1 Introduction
2 Proposed Antenna Models
3 Simulated Parameters
3.1 Reflection Coefficients
3.2 Mismatching Wave Ratio
3.3 Directivity and Gain
4 Conclusion
References
CPW-Fed Single, Dual, and Triple-Channel Slotted and Top-Loaded DGS Antennas for UWB Range
1 Introduction
2 Configuration and Analysis of Antenna
3 Results and Discussions
4 Conclusion
References
ECG Morphological Features Based Sudden Cardiac Arrest (SCA) Prediction Using Nonlinear Classifiers
1 Introduction
2 Materials and Methods
2.1 Database
2.2 Preprocessing
2.3 R–Tend Morphological Wave Extraction
2.4 QRS Morphological Wave Extraction
2.5 Feature Extraction
2.6 Statistical Analysis
2.7 Classification
3 Results and Discussion
4 Conclusion
References
Analysis of Rician Noise Restoration Using Fuzzy Membership Function with Median and Trilateral Filter in MRI
1 Introduction
2 Proposed Rician Noise Restoration
2.1 Trilateral Filter and Median Filter
2.2 Trapezoidal Membership Function
2.3 Restoration
3 Experimental Results and Discussion
3.1 Simulated MRI
3.2 Real MRI
4 Conclusion
References
DLBPS: Dynamic Load Balancing Privacy Path Selection Routing in Wireless Networks
1 Introduction
2 Cluster Network Model
3 Literature Review
4 Dynamic Load Balancing Privacy Path Selection
4.1 Network and Mobility Model
4.2 Gateway Mobility Load Balancing
4.3 LBCPR: Load Balancing Cluster-Based Privacy Routing
4.4 Dynamic Load Balancing Privacy Path Selection (DLBPS)
5 Performance Evaluation
6 Conclusion
References
Power Optimization of a 32-Bit ALU Using Distributed Clock Gating Technique
1 Introduction
2 Related Work
3 Implementation
3.1 Module-Based ALU
3.2 Hierarchical Clock Gated ALU
3.3 Distributed Clock Gated ALU
4 Experimental Results and Analysis
5 Conclusion
References
A Power-Efficient GFDM System
1 Introduction
2 Baseband GFDM Tx and Rx
3 Proposed Method
3.1 System Model
4 Simulation Results
4.1 OOB Radiation
4.2 PAPR Performance
4.3 BER Performance
5 Conclusion
References
Energy-Efficient Cross-Layer Multi-Chain Protocol for Wireless Sensor Network
1 Introduction
2 Literature Survey
3 Motivations
4 Network Assumptions and Radio Energy Model
5 Proposed Method
5.1 Cross-Layer Chain Leader Selection Phase
5.2 Cross-Layer Multi-Chain Formation
5.3 Cross-Layer Data Transmission
6 Performance Evaluation
7 Results and Discussion
8 Conclusions
References
An Advanced Model-Centric Framework for Verification and Validation of Developmental Aero Engine Digital Controller Performance
1 Introduction
2 An EIEI Approach Based CILS Framework for Multi-fidelity Plant Models
3 Development of Reduced Order Model (ROM) Algorithm for a Typical EHSV Transfer Function Model
3.1 ROM Algorithm Performance Studies as Part of MILS
3.2 Performance Analysis of Developed ROM Algorithm
4 Closed-Loop Performance Studies as a part of MILS
5 Conclusion and Future Work
References
Performance Analysis of Wavelet Transform in the Removal of Baseline Wandering from ECG Signals in Children with Autism Spectrum Disorder (ASD)
1 Introduction
2 Previous Work on Baseline Wandering
3 Methodology
3.1 ECG Data Acquisition
3.2 ECG Pre-processing
3.3 Wavelet Transform
3.4 Wavelet Denoising Algorithm for Removal of BW from Original ECG Signal
3.5 Performance Measurements
4 Results and Discussion
5 Conclusion
6 Compliance of Ethical Standards
7 Statement on the Informed Consent
References
Design of S-Band Balanced Amplifier Using Couplers
1 Introduction
2 Design of Balanced Power Amplifier
2.1 Overall Block Diagram of the Design
2.2 Material Used
2.3 Lange Coupler
2.4 Schematic of Two-Stage Power Amplifier
2.5 Power Dividers and Power Combiners
2.6 Single-Stage Power Amplifier
2.7 Design Flow of Single-Stage Power Amplifier
3 Power Amplifier Performance
3.1 Output Performance
3.2 Measured Results
4 Conclusion
References
Performance of Ultra Wide Band Systems in High-Speed Wireless Personal Area Networks
1 Introduction
2 UWB Technology
2.1 UWB Characteristics
2.2 UWB Types of Systems
2.3 UWB Channel Models
3 System Performance Measures
3.1 Generalized Innovation-Matched Filter Detector
3.2 Classical Quality of Service Criteria
4 Simulations and Results
4.1 MBER-Based Performance
5 Conclusions and Future Work
References
Statistical Descriptors-Based Image Classification of Textural Images
1 Introduction
2 Texture Descriptors
2.1 Gray-Level Co-Occurrence Matrices (GLCM)
2.2 Gray Tone Distribution Matrix (GTDM)
3 Feature Extraction and Image Classification
4 Results and Discussion
4.1 UMD Datasets
4.2 UIUC Datasets
5 Conclusion
References
Sum Modified Laplacian-Based Image Fusion in DCT Domain with Super Resolution
1 Introduction
2 Super Resolution by Bicubic Interpolation
3 Discrete Cosine Transform (DCT)
3.1 Focus Measurement by Sum Modified Laplacian
4 Proposed Fusion Work
4.1 Consistency Verification
5 Result Evaluation and Discussion
5.1 Fusion Quality Metrics
6 Conclusion
References
Effective Compression of Digital Images Using SPIHT Coding with Selective Decomposition Bands
1 Introduction
2 Set Partitioning in Hierarchical Trees
3 Proposed Coding Scheme
4 Simulation Results
5 Conclusions
References
Reconfigurable LUT-Based Dynamic Obfuscation for Hardware Security
1 Introduction
2 Related Work
3 Conventional Method
4 Obfuscation Using Reconfigurable LUT
5 Results and Discussions
6 Conclusion
References
Detection and Control of Phishing Attack in Electronic Medical Record Application
1 Introduction
1.1 Privacy
1.2 Confidentiality
1.3 Security
2 Literature Review
3 Methodology
4 Results and Discussion
5 Conclusion
References
Smart Apron Using Embroidered Textile Fractal Antenna for E-Health Monitoring System
1 Introduction
1.1 Proposed Antenna Design
1.2 Design Steps for Proposed Antenna
2 Fabrication
3 The HFSS Simulation
4 Results and Discussions
5 Conclusion
References
Design of Modified Wideband Log Periodic Microstrip Antenna with Slot for Navigational Application
1 Introduction
2 Antenna Design and Geometry
2.1 Design Equation of Log Periodic Antenna
2.2 Design Parameters of Proposed and Optimized the Planner Toothed Log Periodic Microstrip Antennas
3 Results and Discussion
4 Conclusion
References
Machine Learning Approach to Condition Monitoring of an Automotive Radiator Cooling Fan System
1 Introduction
2 System Design
2.1 Operating Conditions Taken for Consideration
2.2 Experimental Setup
2.3 Feature Extraction
2.4 Classifier-Based Condition Monitoring System
3 Results and Discussion
4 Conclusion
References
Sensors Network for Temperature Measurement in a Cocoa Fermentator
1 Introduction
2 Description of the Continuous Temperature Measurement System
2.1 Fermenter and Requirements
2.2 Sensors Layout
2.3 Instruments Selection
2.4 Data Logger
3 Usage Scenario
4 Results
5 Conclusion
References
Communication-Aware Virtual Machine Placement in Cloud
1 Introduction
2 Literature Survey
3 System Model
4 Methodology
4.1 System Architecture
4.2 Predicting the Communication Traffic
4.3 Virtual Machine Placement Algorithm
5 Experimental Setup
6 Conclusion
References
Migration from Silicon to Gallium Nitride Devices—A Review
1 Introduction
1.1 WBG Material Properties
2 WBG Devices
2.1 GaN Devices
2.2 Construction
2.3 Attributes of GaN
3 Layout Considerations for GaN
4 Device Comparison
5 Application of GaN in Power Converters
6 Conclusion
References
High-Speed Modified DA Architecture for DWT Computation in Secure Image Encoding
1 Introduction
2 DWT in Image Processing
3 Efficient Architectures for DWT/IDWT
3.1 Distributive Arithmetic Architecture
3.2 Proposed DA Architecture
4 Modified DA DWT Filter
5 FPGA Implementation
6 Conclusion
References
Design and Randomness Evaluation of a Chaotic Neural Encryption and Decryption Network for TRNG
1 Introduction
2 Literature Survey
3 Proposed Technique
4 Results
5 Conclusion
References
Circuit Modelling of Graphene and Carbon Nanotube-Based Multilayer Structures for High-Frequency Absorption
1 Introduction
2 Computational Modelling
3 Results and Discussion
4 Conclusion
References
PI and Sliding Mode Control of QUANSER QNET 2.0 HVAC System
1 Introduction
2 Literature Survey
3 Theoretical Background
4 Controllers for HVAC System
5 Hardware Implementation
6 Results
7 Conclusion
References
Robust Control of Position and Speed for a DC Servomotor System Using Various Control Techniques
1 Introduction
2 Mathematical Modelling of DC Servomotor
3 Control Techniques
3.1 Pole Placement Technique
3.2 Linear Quadratic Regulator
3.3 Model Predictive Control
3.4 Combined LQR and MPC Technique
4 Results and Discussion
5 Conclusion
References
MPC-Based Temperature Control of CSTR Process and Its Comparison with PID
1 Introduction
2 Mathematical Modeling of CSTR
3 Control Techniques
3.1 PID Control
3.2 Model Predictive Control
4 Results and Discussion
5 Conclusion
References
Microgrid Integration in Kerala Power Grid—A Case Study
1 Introduction
2 Methodology and Problem Formulation
2.1 Methodology
2.2 Problem Formulation
3 Results and Discussions
4 Conclusions
References
Design of Control System for Autonomous Harvester Based on Navigation Inputs
1 Introduction
2 Literature Survey
3 Methodology
3.1 Forward Speed Control
3.2 Braking Control
3.3 Steering Control
4 Mathematical Modeling
4.1 Speed Detection, Speed, and Pedal Position Target Correction and Actuation
4.2 Vehicle Heading Direction Control
5 Experimental Setup
6 Results and Conclusions
References
Design and Optimization of Microgrid as EV Charging Source
1 Introduction
2 Proposed Framework
3 System Modeling
3.1 Solar PV-EV Charging Station Installed at AMU
3.2 Proposed Electric Buses and Routine Scheduled Routes for the University Campus
4 Institution Load Profile and System Design Using HOMER
4.1 Battery Size and Converter Design
5 Simulation Results, and Discussion
6 Conclusion
References
A Review on Topologies for Transformer-Less Grid-Connected PV Inverter
1 Introduction
2 Full-Bridge Topology
2.1 Bipolar Modulation Technique for Full-Bridge Topology
2.2 Operation of Full-Bridge Topology with Unipolar Modulation Technique
3 H5 Topology
3.1 Operating States of H5 Topology
4 H6 Topology
4.1 Operating States of H6 Topology
5 HERIC Topology
5.1 Operating States of HERIC Topology
6 Comparison of Topologies
7 Conclusion
References
An Enhanced Space Vector PWM Technique for Neutral Point Balancing in Three-Level NPC Inverter
1 Introduction
2 Working Principle of Three-Level NPC
2.1 Switching States
2.2 Switching Sequence
3 Proposed Strategy
3.1 Principle of the Proposed Strategy
3.2 Execution of the Proposed Strategy
4 Simulation Results
5 Conclusion
References
Green Energy Integration to Utility Grid with Power Quality Improvement by Using APF
1 Introduction
2 Proposed Model Explanation
2.1 Solar Photovoltaic System
2.2 DC–DC Converter (Boost Converter)
3 Control Technique for APF
4 Simulation Results
5 Conclusion
References
An Air Conditioning System with Power Quality Improvement
1 Introduction
2 Proposed PFC Fed ACS
3 Operating Phenomenon of PFC Converter Without Bridge Rectifier-Based ACS
4 PFC Operated Converter Design
5 MATLAB Results of Designed PFC Converter-Based ACS
5.1 Case 1 - at Input Voltage 200 V
5.2 Case 2 - at Input Voltage 220 V
5.3 Case 3 - at Input Voltage 240 V
6 Conclusion
References
Hybrid Energy Storage System for Electric Vehicle Using Battery and Ultracapacitor
1 Introduction
2 Converter Configuration for HESS and Control
3 System Description
3.1 Energy Management in HESS
3.2 Electric Vehicle Model for Tractive Power Estimation
4 Performance Characterization
5 Conclusion
References
Sequential Selection-Based Predictive Direct Torque Control for Cascaded H-Bridge Inverter-Driven Induction Motor Drive
1 Introduction
2 PDTC of CHB Inverter-Driven IM
3 Sequential Selection-Based PDTC
4 Results and Discussion
5 Conclusion
References
Synchronization of EV Charging Station Battery with Micro-grid Based on Virtual Synchronous Machines Control Strategy
1 Introduction
2 Theory and Principle of Operation
2.1 Control Strategy
2.2 Control Algorithm Implementation
2.3 System Consolidated with the Control Loop
3 Test System
4 Simulation Results
5 Conclusion
References
Design and Control of Capacitor-Supported Dynamic Voltage Restorer for Mitigation of Power Quality Disturbances
1 Introduction
2 Capacitor-Supported DVR
2.1 Mathematical Analysis
3 CS-DVR: Design
4 CS-DVR: Control Technique
5 CS-DVR: Testing Results
5.1 Test Case 1: Balanced Sag with Fundamental Component
5.2 Test Case 2: Balanced Sag with Harmonic Component
5.3 Test Case 3: Unbalanced Sag with Harmonic Component
6 Conclusions
References
Research on State Space Modeling, Stability Analysis and PID/PIDN Control of DC–DC Converter for Digital Implementation
1 Introduction
2 Digital PIDN Control Algorithm Approach
3 Modeling Approach
3.1 Buck Converter Model
3.2 Discretization of PIDN Controller
4 Simulation
4.1 Model Implementation in Simulink
4.2 Text-Based Computational Implementation in MATLAB
5 Results and Discussion
6 Conclusion
References
Slip Frequency Control Technique for DFIG Based Wind Turbine Generators
1 Introduction
2 System Characteristics and Specifications
3 Control Circuit
3.1 RSC Control
3.2 Fuzzy Logic Rule Base
3.3 GSC Control
4 Simulation Results
5 Hardware Implementation
6 Conclusion
Appendix
References
Static Eccentricity Fault in Induction Motor Drive Using Finite Element Method
1 Introduction
2 Static Eccentricity Fault
3 Modeling of Induction Motor with Static Eccentricity Fault
4 Simulation and Comparison Results
5 Conclusion
Appendix
References
Overview and Recent Scenario of Biomass Gasifier Plants in Tamilnadu—A Field Survey
1 Introduction
2 Renewable Energy Scenario Worldwide and India
2.1 Global RE Scenario
2.2 Indian RE Scenario
3 Status of Biomass Plants in Tamilnadu
3.1 Installation Status of the Plants
3.2 Operational Status of the Plants
4 Causes and Remedies for Operational Failure
5 Conclusion
References
Successive Optimization Using Analytical Method for Multiple DG Placement in Primary Distribution System
1 Introduction
2 Problem Formulation: Sizing and Location Issues for DG
3 Approach/Methodology
4 Simulation Results and Discussions
4.1 System Under Study
4.2 Assumption Made
4.3 Results
5 Conclusion
References
Service-Oriented Network Architecture for Future Automotive Networks
1 Introduction
2 Scalable Service-Oriented MiddlewarE over Internet Protocol (SOME/IP)
2.1 Overview
2.2 Objectives
2.3 Protocol Specification
3 Time-Sensitive Networking (TSN)
4 Methodology
5 Implementation
5.1 Request—Response Communication
5.2 Fire and Forget Communication
5.3 Notification of Events
6 Conclusion
References
Modeling and Analysis of Single-Phase Modified Unipolar Sinusoidal PWM Inverter with Compensator
1 Introduction
2 Single Phase Full Bridge Inverter
2.1 Modified Unipolar SPWM Switching
3 Mathematical Modeling of Control Loop
3.1 Transfer Function Model of Capacitor Current Loop
3.2 Transfer Function Model of Output Voltage Loop
3.3 Simulation Results
4 Conclusion
References
Simulation, Fabrication and Characterization of Circular Diaphragm Acoustic Energy Harvester
1 Introduction
2 Implementation
2.1 Structure Design
2.2 Simulation
2.3 Fabrication
3 Experimental Results
3.1 DC Probe Station
3.2 Linearity and Frequency Response
4 Conclusion
References
Mitigation of Voltage Sags and Swells in the Distribution System Using Dynamic Voltage Restorer
1 Introduction
2 IRPT-Based Controller
3 Modified IRPT Controller
4 Comparative Analysis of Performance of Conventional and Proposed Controller
5 Conclusion
References
DC Micro-Grid-Based Electric Vehicle Charging Infrastructure—Part 1
1 Introduction
2 AC Grid-Based Charging
2.1 Overview
2.2 Components of AC Grid-Based System
3 Modes of Operation
3.1 Rectification Through Grid
3.2 Charging Through PV and Grid Rectification
3.3 PV to Grid Inversion Mode
4 MATLAB-Based Modelling
5 Results
5.1 Rectification Through Grid
5.2 Charging Through PV and Grid Rectification
5.3 PV to Grid Inversion Mode
6 Conclusion
References
DC Micro-Grid-Based Electric Vehicle Charging Infrastructure—Part 2
1 Introduction
2 DC Micro-Grid
2.1 Overview
2.2 Components of DC Micro-Grid
3 Modes of Operation
3.1 PV Charging EV Battery and External Battery Unit Charging Mode
3.2 External Battery Unit (EBU) to EV Charging Mode
4 MATLAB-Based Modelling
5 Results and Discussion
5.1 PV Charging EV Battery and External Battery Unit Charging Mode
5.2 External Battery Unit to EV Battery and EV Battery to External Battery Unit Mode
6 Conclusion
References
A Comparative Study of Controllers for QUANSER QUBE Servo 2 Rotary Inverted Pendulum System
1 Introduction
2 Literature Survey
3 Dynamic Model of the System
4 Controllers for QUANSER QUBE Servo Rotary Inverted Pendulum
4.1 PID Controller
4.2 Cascaded PID Control
4.3 Pole Placement Controller
4.4 LQR Controller
5 Controller Implementation and Results
6 Conclusion
References
Design and Implementation of 400W Flyback Converter Using SiC MOSFET
1 Introduction
2 Flyback Converter
3 Design Consideration
4 System Stability Analysis and Compensator Design
5 Measurement and Result Analysis
6 Conclusion
References
Development of a Cost-Effective Module Integrated Converter for Building Integrated Photovoltaic System
1 Introduction
2 Design of Interleaved Flyback Converter
2.1 Significance of Interleaving Converter
2.2 Advantages of Operating in Discontinuous Mode
2.3 Discontinuous Mode of Operation
2.4 Design of the Flyback Transformer
3 Single-Phase SPWM Inverter
4 Simulation Analysis of the Two-Stage Interleaved Flyback Converter
4.1 Simulation Output
4.2 Hardware Design of the Flyback Inverter
5 Cost Analysis of the Proposed System
5.1 Payback Period
6 Conclusion
References

Citation preview

Lecture Notes in Electrical Engineering 672

Thangaprakash Sengodan M. Murugappan Sanjay Misra   Editors

Advances in Electrical and Computer Technologies Select Proceedings of ICAECT 2019

Lecture Notes in Electrical Engineering Volume 672

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering—quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning: • • • • • • • • • • • •

Communication Engineering, Information Theory and Networks Electronics Engineering and Microelectronics Signal, Image and Speech Processing Wireless and Mobile Communication Circuits and Systems Energy Systems, Power Electronics and Electrical Machines Electro-optical Engineering Instrumentation Engineering Avionics Engineering Control Systems Internet-of-Things and Cybersecurity Biomedical Devices, MEMS and NEMS

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More information about this series at http://www.springer.com/series/7818

Thangaprakash Sengodan M. Murugappan Sanjay Misra •

•

Editors

Advances in Electrical and Computer Technologies Select Proceedings of ICAECT 2019

123

Editors Thangaprakash Sengodan SVS College of Engineering Coimbatore, Tamil Nadu, India

M. Murugappan Kuwait College of Science and Technology Doha, Kuwait

Sanjay Misra Covenant University Ota, Nigeria

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-5557-2 ISBN 978-981-15-5558-9 (eBook) https://doi.org/10.1007/978-981-15-5558-9 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The ICAECT series aims to offer a great opportunity to bring together professors, researchers and scholars around the globe a great platform to deliver the latest innovative research results and the most recent developments and trends in electrical, electronics and computer engineering and technology fields. The ICAECT 2019 featured invited talks from eminent personalities all around the world, pre-conference tutorial/workshops and referred paper presentations. The vision of the ICAECT series is to promote foster communication among researchers and practitioners working in a wide variety of the above areas in engineering and technology. It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends and concerns as well as practical challenges encountered and solutions adopted in the fields of electrical and computer technologies. The ICAECT 2019 received around 600 submissions from across the world, and 119 research papers have been published in the book entitled Advances in Electrical and Computer Technologies after the stringent screening and review process by the editorial team. The ICAECT is an annual technical event and has been aimed to be conducted in the fourth week of April every year. Being the first edition, ICAECT received submission from different regions of the world, and scholars from 15 countries have registered to present their research works. Coimbatore, India

Dr. Thangaprakash Sengodan Conference Chair ICAECT 2019

v

Contents

Detecting New Events from Microblogs Using Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Bhuvaneswari, K. Aishwarya, S. Bhuvaneshwari, C. Sai Chandni, and P. Sundara Akilesh Deep Neural Network for Evaluating Web Content Credibility Using Keras Sequential Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Manjula and M. S. Vijaya SENTRAC: A Novel Real Time Sentiment Analysis Approach Through Twitter Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . Md. Julkar Nayeen Mahi, Kazi Moinul Hossain, Milon Biswas, and Md Whaiduzzaman Low Power Area Efficient Improved DWT and AES Architectures for Secure Image Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Renjith V. Ravi, Kamalraj Subramaniam, and G. K. D. Prasanna Venkatesan Launch Overheads of Spark Applications on Standalone and Hadoop YARN Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. S. Janardhanan and Philip Samuel Neighbor-Aware Coverage-Based Probabilistic Data Aggregation for Reducing Transmission Overhead in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Umadevi and M. Devapriya Analysis and Summarization of Related Blog Entries Using Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aarti Sharma and Niyati Baliyan

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A Supplement to “PRE: A Simple, Pragmatic, and Provably Correct Algorithm” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahibb and S. Sarala

77

Satellite Image Classification with Data Augmentation and Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . Parth R. Dave and Hariom A. Pandya

83

A Real-Time Offline Positioning System for Disruption Tolerant Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arnika Patel and Pariza Kamboj

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A Novel Copy-Move Image Forgery Detection Method Using 8-Connected Region Growing Technique . . . . . . . . . . . . . . . . . . Shyamalendu Kandar, Ardhendu Sarkar, and Bibhas Chandra Dhara

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A Method for the Prediction of the Shrinkage in Roasted and Ground Coffee Using Multivariable Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Parody, Dhizzy Charris, Amelec Viloria, Jorge Cervera, and Hugo Hernandez-P Recommendation of Energy Efficiency Indexes for the Coffee Sector in Honduras Using Multivariate Statistics . . . . . . . . . . . . . . . . . . . . . . Rafael Gomez Dorta, Omar Bonerge Pineda Lezama, Nelson Alberto Lizardo Zelaya, Noel Varela Izquierdo, and Jesus Silva Modeling and Simulating Human Occupation: A NetLogo-Agent-Based Toy Model . . . . . . . . . . . . . . . . . . . . . . . . . . . Amelec Viloria, Yury Arenis Olarte Arias, Manuel-Ignacio Balaguera, Jenny Paola Lis-Gutiérrez, Mercedes Gaitan Angulo, and Melisa Lis-Gutierrez

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Deep Learning Predictive Model for Detecting Human Influenza Virus Through Biological Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nandhini and M. S. Vijaya

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On Machine Learning Approach Towards Sorting Permutations by Block Transpositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Jayakumar, Sooraj Soman, and V. Harikrishnan

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Tweet Classification Using Deep Learning Approach to Predict Sensitive Personal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Geetha, S. Karthika, and S. Mohanavalli

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A Study on Abnormalities Detection Techniques from Echocardiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imayanmosha Wahlang, Goutam Saha, and Arnab Kumar Maji

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Secure I-Voting System with Modified Voting and Verification Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ajish and K. S. Anil Kumar

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Hash Tree-Based Device Fingerprinting Technique for Network Forensic Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rachana Yogesh Patil and Satish R. Devane

201

A New Method for Preventing Man-in-the-Middle Attack in IPv6 Network Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Senthilkumar Mathi and Lingam Srikanth

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Mutual Authentication Scheme for the Management of End Devices in IoT Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Annashree Nivethitha, Chanthini Baskar, and Manivannan Doraipandian

221

Finding Influential Location via User Mobility and Trajectory . . . . . . Daniel Adu-Gyamfi, Fengli Zhang, and Fan Zhou Design of a Morphological Generator for an English to Indian Languages in a Declension Rule-Based Machine Translation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jayashree Nair, R. Nithya, and M. K. Vinod Jincy

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Automatic Design of Aggregation, Generalization and Specialization of Object-Oriented Paradigm Embedded in SRS . . . . . . . . . . . . . . . . . B. N. Arunakumari and Shivanand M. Handigund

259

Fuzzy Logic-Based Decision Support for Paddy Quality Estimation in Food Godown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chanthini Baskar and Manivannan Doraipandian

279

Voice-Controlled Smart Assistant and Real-Time Vehicle Detection for Blind People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mojibur Rahman Redoy Md Akanda, Mohammad Masum Khandaker, Tushar Saha, Jahidul Haque, Anup Majumder, and Aniruddha Rakshit A Framework for Cyber Ethics and Professional Responsibility in Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. K. Alhassan, E. Abba, Sanjay Misra, Ravin Ahuja, Robertas Damasevicius, and Rytis Maskeliunas Detection of Malicious URLs on Twitter . . . . . . . . . . . . . . . . . . . . . . . Nureni Ayofe Azeez, Oluwadamilola Atiku, Sanjay Misra, Adewole Adewumi, Ravin Ahuja, and Robertas Damasevicius

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Human Rights’ Issues and Media/Communication Theories in the Wake of Artificial Intelligence Technologies: The Fate of Electorates in Twenty-First-Century American Politics . . . . . . . . . . I. A. P. Wogu, Sanjay Misra, C. O. Roland-Otaru, O. D. Udoh, E. Awogu-Maduagwu, and Robertas Damasevicius Modeling and Simulation of Impedance-Based Algorithm on Overhead Power Distribution Network Using MATLAB . . . . . . . . . Olamilekan Shobayo, Olusola Abayomi-Alli, Modupe Odusami, Sanjay Misra, and Mololuwa Safiriyu The Utilization of the Biometric Technology in the 2013 Manyu Division Legislative and Municipal Elections in Cameroon: An Appraisal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. A. Assibong, I. A. P. Wogu, Sanjay Misra, and Daspan Makplang Integrating NFC and IoT to Provide Healthcare Services in Cloud-Based EHR System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Raghavendra Ganiga, Radhika M. Pai, M. M. Manohara Pai, Rajesh Kumar Sinha, and Saleh Mowla An Approach to Study on MA, ES, AR for Sunspot Number (SN) Prediction and to Forecast SN with Seasonal Variations Along with Trend Component of Time Series Analysis Using Moving Average (MA) and Exponential Smoothing (ES) . . . . . . . . . . . . . . . . . . . . . . . . Anika Tabassum, Masud Rabbani, and Saad Bin Omar Machine Learning Approach for Feature Interpretation and Classification of Genetic Mutations Leading to Tumor and Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ankit Kumar Sah, Abinash Mishra, and U. Srinivasulu Reddy

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Design and Implementation of Hybrid Cryptographic Algorithm for the Improved Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pavithra Kanagaraj and Manivannan Doraipandian

397

A Real-Time Smart Waste Management Based on Cognitive IoT Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sujit Bebortta, Nikhil Kumar Rajput, Bibudhendu Pati, and Dilip Senapati

407

A Proposed Continuous Auditing Process for Secure Cloud Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thaharim Khan and Masud Rabbani

415

Joy of GPU Computing: A Performance Comparison of AES and RSA in GPU and CPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Kingsy Grace, M. S. Geetha Devasena, and S. Manju

425

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Domain-Independent Video Summarization Based on Transfer Learning Using Convolutional Neural Network . . . . . . . . . . . . . . . . . . Jesna Mohan and Madhu S. Nair

435

Deep Neural Network-Based Human Emotion Recognition by Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samsani Surekha

453

A Method for Estimating the Age of People in Forensic Medicine Using Multivariable Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . María Barraza Salcedo, Alexander Parody, Yeis Borre, Amelec Viloria, and Jorge Cervera Cluster of Geographic Networks and Interaction of Actors in Museums: A Representation Through Weighted Graphs . . . . . . . . . Jenny Paola Lis-Gutiérrez, Amelec Viloria, Juan Carlos Rincón-Vásquez, Álvaro Zerda-Sarmiento, Doris Aguilera-Hernández, and Jairo Santander-Abril

465

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Security Scheme Under Opensource Software for Accessing Wireless Local Area Networks at the University Campus . . . . . . . . . . . . . . . . . . Francisco Sánchez-Torres, Jorge González, and Amelec Viloria

487

Pragmatic Evaluation of the Impact of Dimensionality Reduction in the Performance of Clustering Algorithms . . . . . . . . . . . . . . . . . . . . Shini Renjith, A. Sreekumar, and M. Jathavedan

499

Secret Life of Conjunctions: Correlation of Conjunction Words on Predicting Personality Traits from Social Media Using User-Generated Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmed Al Marouf, Md. Kamrul Hasan, and Hasan Mahmud

513

Text Classification Using K-Nearest Neighbor Algorithm and Firefly Algorithm for Text Feature Selection . . . . . . . . . . . . . . . . . R. Janani and S. Vijayarani

527

Performance Evaluation of Traditional Classifiers on Prediction of Credit Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Rajib Pradhan, Sima Akter, and Ahmed Al Marouf

541

Cost-Sensitive Long Short-Term Memory for Imbalanced DGA Family Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Mohammed Harun Babu, R. Vinayakumar, and K. P. Soman

553

Oil Spill Characterization and Monitoring Using SYMLET Analysis from Synthetic-Aperture Radar Images . . . . . . . . . . . . . . . . . . . . . . . . Mukta Jagdish and S. Jerritta

565

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Performance Analysis of Machine Learning Algorithms for IoT-Based Human Activity Recognition . . . . . . . . . . . . . . . . . . . . . Shwet Ketu and Pramod Kumar Mishra

579

Computing WHERE-WHAT Classification Through FLIKM and Deep Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nagaraj Balakrishnan and Arunkumar Rajendran

593

Reshaped Circular Patch Antenna with Optimized Circular and Rectangular DGS for 50–60 GHz Applications . . . . . . . . . . . . . . . Ribhu Abhusan Panda, Rabindra Kumar Mishra, Udit Narayan Mohapatro, and Debasish Mishra VLSI Fast-Switching Implementation in the Programmable Cycle Generator for High-Speed Operation . . . . . . . . . . . . . . . . . . . . . . . . . . D. Punniamoorthy, G. Krishna Reddy, and Vikram S. Kamadal A Study of Non-Gaussian Properties in Emotional EEG in Stroke Using Higher-Order Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Choong Wen Yean, M. Murugappan, Mohammad Iqbal Omar, Wan Khairunizam, Bong Siao Zheng, Alex Noel Joseph Raj, and Zunaidi Ibrahim SAW-Based Sensors for Lead Detection . . . . . . . . . . . . . . . . . . . . . . . . Senorita Deb

609

621

635

647

Efficient Eye Diagram Analyzer for Optical Modulation Format Recognition Using Deep Learning Technique . . . . . . . . . . . . . . . . . . . . Viay Sudhakar Ghayal and R. K. Jeyachitra

655

Low Complexity Indoor Positioning System with TDOA Algorithm Using Hilbert Transform Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Venkata Balakrishna Mantina and R. K. Jeyachitra

667

Image Encryption Based on Pseudo Hadamard Transformation and Gingerbreadman Chaotic Substitution . . . . . . . . . . . . . . . . . . . . . . S. N. Prajwalasimha and Sidramappa

681

Optimization and Performance Analysis of QPSK Modulator . . . . . . . S. M. Usha and H. B. Mahesh

691

Power and Delay Efficient ALU Using Vedic Multiplier . . . . . . . . . . . . Dhanunjay Lachireddy and S. R. Ramesh

703

Optimization and Implementation of AES-128 Algorithm on FPGA Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ankur Singhvi

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Contents

An Analytic Potential Based Velocity Saturated Drain Current, Charge and Capacitance Model for Short Channel Symmetric Double Gate MOSFETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vyas Murnal and C. Vijaya Evaluation of Emotion Elicitation for Patients With Autistic Spectrum Disorder Combined With Cerebral Palsy . . . . . . . . . . . . . . . N. Sindhu and S. Jerritta Forest Fire Detection Based on Wireless Sensor Network . . . . . . . . . . Harsh Deep Ahlawat and R. P. Chauhan

xiii

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737 751

CB-ACPW Fed SRR Loaded Electrically Small Antenna for ECG Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Sajith, J. Gandhimohan, and T. Shanmuganantham

767

CPW-Fed Single, Dual, and Triple-Channel Slotted and Top-Loaded DGS Antennas for UWB Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Gandhimohan and T. Shanmuganantham

777

ECG Morphological Features Based Sudden Cardiac Arrest (SCA) Prediction Using Nonlinear Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . M. Murugappan, Hui Boon, Alex Noel Joseph Raj, Gokul Krishnan, and Karthikeyan Palanisamy

789

Analysis of Rician Noise Restoration Using Fuzzy Membership Function with Median and Trilateral Filter in MRI . . . . . . . . . . . . . . . R. Kala and P. Deepa

803

DLBPS: Dynamic Load Balancing Privacy Path Selection Routing in Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Jayaprakash and Radha Balasubramanian

817

Power Optimization of a 32-Bit ALU Using Distributed Clock Gating Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roopa R. Kulkarni and S. Y. Kulkarni

831

A Power-Efficient GFDM System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chhavi Sharma, Arvind Kumar, and S. K. Tomar

843

Energy-Efficient Cross-Layer Multi-Chain Protocol for Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. M. Prathibhavani, H. V. Adarsh Sagar, and T. G. Basavaraju

853

An Advanced Model-Centric Framework for Verification and Validation of Developmental Aero Engine Digital Controller Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vivekanand Sanjawadmath, R. Suresh, A. N. Vishwanatha Rao, and Deva Prasad Nayak

875

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Performance Analysis of Wavelet Transform in the Removal of Baseline Wandering from ECG Signals in Children with Autism Spectrum Disorder (ASD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Anandhi, Selvaraj Jerritta, M. Murugappan, Himangshu Das, and Gurusamy Anusuya Design of S-Band Balanced Amplifier Using Couplers . . . . . . . . . . . . . Neeraja Neralla and Sangam V. Bhalke

885

899

Performance of Ultra Wide Band Systems in High-Speed Wireless Personal Area Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jihad Daba

911

Statistical Descriptors-Based Image Classification of Textural Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Bagavathi and O. Saraniya

937

Sum Modified Laplacian-Based Image Fusion in DCT Domain with Super Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Sreeja and O. Saraniya

945

Effective Compression of Digital Images Using SPIHT Coding with Selective Decomposition Bands . . . . . . . . . . . . . . . . . . . . . . . . . . . V. V. Satyanarayana Tallapragada, B. Bhaskar Reddy, V. Ramamurthy, and Jaya Krishna Sunkara Reconfigurable LUT-Based Dynamic Obfuscation for Hardware Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaxin Baby, N. Mohankumar, and M. Nirmala Devi Detection and Control of Phishing Attack in Electronic Medical Record Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . U. Aneesh Kini, M. Poornananda Bhat, Raghavendra Ganiga, Radhika M. Pai, M. M. Manohara Pai, and H. C. Shiva Prasad

955

963

975

Smart Apron Using Embroidered Textile Fractal Antenna for E-Health Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shruti Gite and Mansi Subhedar

987

Design of Modified Wideband Log Periodic Microstrip Antenna with Slot for Navigational Application . . . . . . . . . . . . . . . . . . . . . . . . . Shahid S. Modasiya and Jagdish M. Rathod

997

Machine Learning Approach to Condition Monitoring of an Automotive Radiator Cooling Fan System . . . . . . . . . . . . . . . . . . 1007 R. Meena, Binoy B. Nair, and N. R. Sakthivel

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Sensors Network for Temperature Measurement in a Cocoa Fermentator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021 Deisy C. Paez, Juan. F. Jojoa, Edith Moreno, Luis J. Lopez, Jorge G. Díaz, Annie S. Zamora, Carlos M. Rivera, and Manuel A. Márquez Communication-Aware Virtual Machine Placement in Cloud . . . . . . . 1031 Raghavendra Achar, Shreenath Acharya, X Shifali, Vrinda Mallur, T. R. Reshma, and Unnathi Bhandary Migration from Silicon to Gallium Nitride Devices—A Review . . . . . . 1043 H. Swathi Hatwar, K. Suryanarayana, M. Ravikiran Rao, and Raksha Adappa High-Speed Modified DA Architecture for DWT Computation in Secure Image Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057 Renjith V. Ravi, Kamalraj Subramaniam, and G. K. D Prasanna Venkatesan Design and Randomness Evaluation of a Chaotic Neural Encryption and Decryption Network for TRNG . . . . . . . . . . . . . . . . . . . . . . . . . . . 1069 C. Guru Prasath, V. Rakshita Vishali, and N. Mohankumar Circuit Modelling of Graphene and Carbon Nanotube-Based Multilayer Structures for High-Frequency Absorption . . . . . . . . . . . . . 1079 V. Shanmuga Suriya, J. Avinash, Binoy B. Nair, and T. Rajagopalan PI and Sliding Mode Control of QUANSER QNET 2.0 HVAC System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1089 Jerry Jacob and S. Selvakumar Robust Control of Position and Speed for a DC Servomotor System Using Various Control Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101 Vineet Kumar, Veena Sharma, O. P. Rahi, and Utsav Kumar MPC-Based Temperature Control of CSTR Process and Its Comparison with PID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109 Utsav Kumar, Veena Sharma, O. P. Rahi, and Vineet Kumar Microgrid Integration in Kerala Power Grid—A Case Study . . . . . . . . 1117 K. S. Saritha, Sasidharan Sreedharan, and Usha Nair Design of Control System for Autonomous Harvester Based on Navigation Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1129 Avishek Chatterjee and K. P. Peeyush Design and Optimization of Microgrid as EV Charging Source . . . . . . 1139 Saadullah Khan, Furkan Ahmad, Mohammad Saad Alam, and Mahesh Krishnamurthy

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A Review on Topologies for Transformer-Less Grid-Connected PV Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1151 Shaik Gouse Basha and M. Venkatesan An Enhanced Space Vector PWM Technique for Neutral Point Balancing in Three-Level NPC Inverter . . . . . . . . . . . . . . . . . . . . . . . . 1167 Mrugnyani Pawar, Nayan Karale, and S. H. Pawar Green Energy Integration to Utility Grid with Power Quality Improvement by Using APF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1179 Kranthi Kumar Vanukuru and B. Pakkiraiah An Air Conditioning System with Power Quality Improvement . . . . . . 1193 Shubham Mishra and Shikha Singh Hybrid Energy Storage System for Electric Vehicle Using Battery and Ultracapacitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1203 Rajesh and A. Vijayakumari Sequential Selection-Based Predictive Direct Torque Control for Cascaded H-Bridge Inverter-Driven Induction Motor Drive . . . . . . 1215 Vishnu Prasad Muddineni, Anil Kumar Bonala, and Hareesh Kumar Yada Synchronization of EV Charging Station Battery with Micro-grid Based on Virtual Synchronous Machines Control Strategy . . . . . . . . . 1225 M. Shylaja and M. R. Sindhu Design and Control of Capacitor-Supported Dynamic Voltage Restorer for Mitigation of Power Quality Disturbances . . . . . . . . . . . . 1237 Mohan Tasre, Gajanan Dhole, Saurabh Jadhao, and Rajesh Sharma Research on State Space Modeling, Stability Analysis and PID/PIDN Control of DC–DC Converter for Digital Implementation . . . . . . . . . . 1255 V. Viswanatha, R. Venkata Siva Reddy, and Rajeswari Slip Frequency Control Technique for DFIG Based Wind Turbine Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1273 R. Mahalakshmi and K. C. Sindhu Thampatty Static Eccentricity Fault in Induction Motor Drive Using Finite Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1291 Sreedharala Viswanath, N. Praveen Kumar, and T. B. Isha Overview and Recent Scenario of Biomass Gasifier Plants in Tamilnadu—A Field Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1303 M. Mohamed Iqbal, Kashif Ahmed, and Nazia Fathima Successive Optimization Using Analytical Method for Multiple DG Placement in Primary Distribution System . . . . . . . . . . . . . . . . . . . . . . 1317 Vani Bhargava, S. K. Sinha, and M. P. Dave

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Service-Oriented Network Architecture for Future Automotive Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1327 S. Prasanna Vadanan, Balasubramanian Srimukhee, A. Suyampu Lingam, and D. Prasannavadana Modeling and Analysis of Single-Phase Modified Unipolar Sinusoidal PWM Inverter with Compensator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1335 K. B. Bommegowda, K. Suryanarayana, and Durga Prasad Simulation, Fabrication and Characterization of Circular Diaphragm Acoustic Energy Harvester . . . . . . . . . . . . . . . . . . . . . . . . 1351 Vasudha Hegde, H. M. Ravikumar, and Siva S. Yellampalli Mitigation of Voltage Sags and Swells in the Distribution System Using Dynamic Voltage Restorer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1359 P. V. Manitha and Manjula G. Nair DC Micro-Grid-Based Electric Vehicle Charging Infrastructure—Part 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1369 Abhishek K. Saxena and K. Deepa DC Micro-Grid-Based Electric Vehicle Charging Infrastructure—Part 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1385 Abhishek K. Saxena and K. Deepa A Comparative Study of Controllers for QUANSER QUBE Servo 2 Rotary Inverted Pendulum System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1401 Anjana Govind and S. Selva Kumar Design and Implementation of 400 W Flyback Converter Using SiC MOSFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415 M. Ravikiran Rao, K. Suryanarayana, H. Swathi Hatwar, and Adappa Raksha Development of a Cost-Effective Module Integrated Converter for Building Integrated Photovoltaic System . . . . . . . . . . . . . . . . . . . . 1425 L. Ashok Kumar and Madhuvanthani Rajendran

About the Editors

Dr. Thangaprakash Sengodan received his Bachelor of Engineering in Electrical Engineering from Bharathiar University, India and Master of Engineering in Power Electronics and Drives in from Anna University, Chennai, during 2002 and 2014, respectively. He completed his Ph.D. degree in Electrical Engineering from Anna University, Chennai, India in 2011. From 2004 to mid-2011, he was working with various engineering colleges in India in different capacities. From mid-2011 to mid-2013, he was working as a Senior Lecturer in the School of Electrical Systems Engineering, University Malaysia Perlis (UniMAP), Malaysia. From mid-2013 he has been affiliated with the Department of Electrical and Electronics Engineering as a professor and head. His current research interests include power electronics circuits, renewable power conversion systems and solid state control of electrical drives. He has authored more than thirty papers in peer-reviewed international journals and conferences. Dr. Thangaprakash is a senior member of IEEE, IEEE-Power Electronics Society, IEEE-Communications Society and a life member of the Indian Society for Technical Education (ISTE). He is an editorial board member for the International Journal of Engineering, Science and Technology, Nigeria and a reviewer for the International Journal of Automation and Computing (Springer), IEEE Transactions on Power electronics, IEEE Transactions on Industrial Electronics, IET Power Electronics and various IEEE/Springer/Elsevier sponsored international/national conferences. Dr. Thangaprakash had been an organizing chair for four IEEE International Conferences: 2012 Second IEEE International Conference on Computer, Communication and Informatics (2012 IEEE ICCCI) and 2015, 2017 and 2019 editions of the IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT). Dr. M. Murugappan received the B.E (Electrical & Electronics Engineering) from Madras University, India, M.E (Applied Electronics) from Anna University, India and Ph.D. (Mechatronic Engineering) from Universiti Malaysia Perlis, Malaysia, 2002, 2006 and 2010, respectively. He is currently working as an associate professor in Department of Electronics and Communication Engineering, Kuwait College of xix

xx

About the Editors

Science and Technology (KCST) (Private University), Kuwait since Feb 2016. Prior joining to KCST, he worked as a Senior Lecturer in School of Mechatronics Engineering, University Malaysia Perlis, Malaysia from 2010–2016. He has been working in the fields of bio-signal processing applications for the past 8 years and, has been cited as an expert in WHO IS WHO in the world. He has received a several research awards, medals and certificates on excellent publications and research products. He has published more than 110 research articles in a peer-reviewed conference proceedings/ journals/book chapter. Some of his research articles won special award from the international journals and conferences. He has got a maximum citation of 1925 and scored the H index of 22 and i10 Index of 40 (Ref: Google Scholar citations). He secured several research grants from Government of Malaysia for continuing his research works and supervised nearly 11 postgraduate students (7 Ph.D and 4 M.Sc). He is also serving as Editorial board member in several internationally peer-reviewed journals (ISI) and committee members in international conferences. His main research interest are bio-signal/image processing applications (affective computing), neuroscience, brain computer interface, human machine interaction, and vision system. Dr. Sanjay Misra is Professor of Computer Engineering at Covenant University, Ota, Nigeria. He has 25 years of wide experience in academic administration and researches in various universities in Asia, Europe and Africa. He obtained his Ph.D. in Information and Know. Engg (Software Engineering) from University of Alcala, Spain and M.Tech. (Software Engineering) from Motilal Nehru National Institute of Technology, India. He has authored/coauthored around 300 papers. He got several awards for outstanding publications such as Institute of Engineering and Technology (IET), United Kingdom, awarded him ‘2014 IET Software Premium Award’ for Best Paper published in last two years. He has delivered 41 plenary and keynote speeches (IEEE, Springer, Elsevier sponsored conferences) and 40 invited talks (workshops/ seminars/lecture) in various universities and institutions around the world (travelled around 60 Countries). He is editor in chief of the book series on Advances in IT Personals and Project management (IGI Global), author of 1 book and editor (one of) in 31 Lecture Notes in Computer Science (Springer), 6 IEEE conference proceedings. He was the General Chair of many international conferences. Presently, Dr. Sanjay is Editor-in-Chief of International Journal of Physical Sciences (SCOPUS Indexed), founder EIC of Covenant Journal of ICT and International Journal of Computer Science and Software Technology, and also serving as editor, associate editor and editorial board members of more than 20 journals (Including 3 SCIE) of international repute. His current researches cover the areas of software quality assurance, software process improvement, software project management, object oriented technologies, XML, SOA, Web services, cognitive informatics, artificial intelligence, neural network, health Informatics, e-learning, cloud computing and cyber security.

Detecting New Events from Microblogs Using Convolutional Neural Networks A. Bhuvaneswari, K. Aishwarya, S. Bhuvaneshwari, C. Sai Chandni, and P. Sundara Akilesh

Abstract Online social networks turn out to be a potential data source to discover worthwhile information from microblogs. Conversely, time-critical exploration of microblog data during catastrophic events such as flood, cyclone, forest fire, and violence carries critical challenges to machine learning techniques. For instance, the microblog from Twitter is utilized to identify event along with its location-specific orientation. In this paper, convolution neural network (CNN) technique is used to identify the retrospective event from the microblog. The existing state of-the-art classification methodologies require substantial volume of labeled data detailed to an unambiguous event during training phase. In addition, it requires feature to attain better outcomes. During the experiments, the n-gram CNN model is trained from the tweets intended for multi-class tweet classification which was related to the specified events in the past without feature engineering. The proposed CNN shows the event detection high accuracy which in turn yields improved performance when compared with other state-of-the-art methods. Keywords Convolutional neural networks · Event detection · Text classifier · Twitter

A. Bhuvaneswari (B) School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India e-mail: [email protected] K. Aishwarya · S. Bhuvaneshwari · C. Sai Chandni · P. Sundara Akilesh Department of Computer Technology, Madras Institute of Technology, Chennai, India e-mail: [email protected] S. Bhuvaneshwari e-mail: [email protected] C. Sai Chandni e-mail: [email protected] P. Sundara Akilesh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_1

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1 Introduction The online social network (OSN) sites that provide microblogging services (e.g., Twitter, Facebook, Google +) have turned out to be a new-fangled source of realtime information dissemination strait. The individuals intensely use OSNs to spot out both personal and public real-life events fashionably occurring from place to place which is used to express users’ opinion on a given topic online. The online social network permits Web users to generate an identity and allow them to share their views in order to form a community of homophile and heterogeneous network. The subsequent OSN held to be a foundation for preserving cyber-based communication, relationships, discovery of users with analogous well-being, and localizing content and information arrived by added users. The user tweets are shared among OSN which are called microblogs. The microblogs encompass, text, meta-information such as timestamp, geographic location coordinates containing latitude and longitude, name of the user, unique identity of user, retweets count, links to other informative possessions, hashtags, and mentions. The primary goals of the work focus on detecting the new-fangled event from Twitter. The new-fangled event detection discusses the task of recognizing real-time events from online streams of Twitter microblogs. The foremost limitation in recognizing real-time events is to comprise sliding time window from various events simultaneously due to the intensification in execution time to spot sub-events inside events identified. The transformation of earlier model [1] which uses query expansion techniques, association that certain microblogs related to a particular event failed to enfold evident event-related indication. The CNN-based approach is proposed in this paper to detect retrospective events from microblogs. Retrospective event refers to the process of recognizing newly arrived anonymous events from warehouse of historical microblogs that have collected in the past trained data. A transformation of the earlier model which used query expansion methods in association with microblogs related to a particular event failed to enfold obvious event-related indication during implementation. Twitter user tweets were used as sensory information and proposed a machine learning classification technique [2] which was supervised to detect specific event types such as floods, earthquakes, landslides, and typhoons. It offers core technology and tools by detecting multiple sources of traditional OSN news feeds that are able to maintain users learned about news and advances. In the proposed work, the microblogs are preprocessed and labeled by crowd-sourcing task which plays a role as the classifier to predict the specified event-related tweet during December 2017–April 2018 timeline. The paper is organized as follows. Section 2 reviews the related literature previously discussed. Section 3 presents the proposed work. Section 4 discusses the experiments and result to view the performance analysis. Section 5 concludes the paper with future directions.

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2 Related Work The new-fangled detection of events consists of uninterrupted observation of OSN news feeds for significant events in recent time. The literature was focused on the state-of-the-art methods related to twitter characterization toward event identification based on event specific to locations. Any real-time event is a mode of revealing certain noticeable manifestations which happen upon a group or individuals in certain location during dynamic period. It determines circumstances where the discovery of real-world events is possible with event location and timestamp. The bursting new events were mentioned using the spikes that occur on the number of tweets. Using social media, regression models based on gradient boosted decision trees were carried out for event detection [3]. It investigated the categories of information dispersed, the sources of online information, and the temporal trends of information which was shared among online users in Twitter. A temporal trend classifier for various epidemic intelligences is identified semantic Web intelligence [4]. The structured graphical subject-specific messages cluster it according to event and prompt a predictable value for each event property [5]. A locality sensitive hashing is compared with microblog clique cluster to improve results [6]. It produced a navigable topic graph which links the evolving terms with comparative concurrent terms in order to accomplish a set of emergent new events. The CNN was applied to determine textual sentiment analysis [7] and socially connected event photos and microblogs online [8]. The sentiments are categorized as positive and negative which were trained on a manually characterized dataset. The neural networking was used to decide the adverse drug event detection in tweets [9]. Twitter-based information entropy [10] primarily determines to monitor location-based microblog service to inevitably obtain breaking news from Twitter users which was previously trained on retrospective microblogs using Naïve Bayes classifier. The model [11] chains both static features resulting from a predefined vocabulary by domain experts and dynamic features created from dynamic query expansion. Various research works [12, 13] on event detection from Twitter specifically for crisis events are studied.

3 Proposed Work 3.1 Data Collection and Preprocessing The microblog tweets are extracted from the Twitter, based totally on Twitter search API [14] with metadata, namely geographic coordinates and key phrases acting inside the textual content of the tweet. The raw tweet is collected which consists of twitter user ID, the timestamp, a retweet ensign, the geographic coordinates, and the text of the tweet. The textual content incorporates extra statistics, including URLs, multimedia links, hashtags, mentions, etc. The dataset collected between 2017–2018

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Fig. 1 Proposed event detection approach

monitors Twitter microblog service to certainly achieve new disaster events from the tweets posted by Twitter users. For the experimentation, English linguistic is considered among corpus tweets. The proposed event detection approach is shown in Fig. 1.

3.2 CNN-Based Microblog Classification The microblogs generated on OSN are typically informative or inappropriate in few cases in regard with disaster events. CNN is an effectual candidate to determine a microblog using classification approach which is binary. CNN was primarily recognized as deep neural network technique producing prominent results for text classification. Basically, CNN contains convolutional layers and pooling layers in a stack or parallel model. The convolutional layer is the basic building hunk of a CNN. The layer’s parameters contain a set of learnable kernels otherwise called as filters, which have a trivial receptive arena, but spread over the full depth of the input dimensions. In the case of microblog input data, all row of matrix denotes to the word embedding to transform each word token into statistical vector. It uses historical microblog corpus and keywords containing pre-trained word embedding vector as input. The input tweet is tokenized, Stop-word filtering, Stemming, and stem filtering are to group words with the same theme consuming meticulously correlated semantics. It transforms a stream of characters into a stream of handling units named tokens. The pair of convolution and pooling operation is organized as a sequence in parallel to acquire high-level representation which is shown in Fig. 2. All the filtered tokens are indicated by an N-dimensional vector through input vocabulary vector. Indeed, the low-level representation is proceeded using word2vec and glove. A matrix is created for each input tweet, containing row level of the matrix which is input vector. The tweets from Twitter are combined with the existing corpus filter, and a vocabulary preprocessor is generated. The glove model is used to produce word embedding during vocabulary preprocessor to acquire word mappings and pre-trained with words. Moreover, the word embedding is conceded in the CNN layers which triple-level filters of sizes (3 × d), (4 × d), (5 × d).

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Fig. 2 Convolutional neural network layers

The convolution neural network layer routines a stride size of [1, 2, 3, …,N] and consumes valid padding which moderates the dimensions of the production in output shown in the following algorithm. The steps involved during CNN layers are shown in the following algorithm. The RELU activation is used for nonlinearity which takes the idea further by making the coefficient of leakage into a parameter to learn along with the other neural network parameters. The output max-pool layer contains stride size [1] and has valid padding. Lastly, a dropout layer is further added in order to diminish over-fitting using softmax function to compute the accuracy of event detection. The pooling layer assists to reduce the spatial size of the representation. In addition, it also reduces the number of parameters and cost of computation in the network, which obviously control over-fitting.

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Algorithm : Convolution Neural Network Input : Word Embeddings , FilterSize , Number of Filters N , Stride Size SS Output : Prediction File begin 1. Initialize b Bias term 2. Load padding VALID or SAME 3. for filter in N do 4. W Random initialised weight vectors based on S 5. Compute : conv tf.con2D(W, SS, P) 6. Compute : non-linearity(conv) 7. Estimate : pooling tf.max_pol(S, SS, p, N) 8. Combined_outputs combine all filter output 9. Predictions softmax(combined_outputs) 10. Loss compute_loss(predictions) 11. Accuracy compute_accuracy(predictions) 12. end for end

4 Experiments and Results In the experiments, the Twitter data is collected during the events, namely (i) Bihar floods (ii) Derail accident (iii) Syria attack (iv) Ockhi cyclone (v) Haryana violence (vi) Harvey hurricane (vii) Kurangani forest fire. In preprocessing step, the noisy tweets undergo filtering, stemming, punctuations, and removal of non-ASCII characters. In addition, removed the URL links and tweets which had been less than five characters lengthy or any tweets which had more than 80 words, as these tweets had been said to be non-relevant to perceive the outbreak of any event. The proposed approach is trained and tested with the dataset as shown in Table 1. The effectiveness of CNN approach is evaluated using the informative tweets which are identified during disaster events. After preprocessing, glove-based word embedding is used for converting the words to vector space. TensorFlow is used as Table 1 Events dataset statistics Event name

# of raw tweets

# of preprocessed tweets

# of training tweets

Bihar floods Derail accident

# of testing tweets

5400

2400

1900

500

4670

2340

1970

370

Syria attack

7800

3490

2500

990

Ockhi cyclone

3520

2300

1960

340

Haryana violence

3400

1350

1000

350

Harvey hurricane

4590

2400

1900

500

Kurangani forest fire

6500

4200

3500

700

Detecting New Events from Microblogs Using …

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vocabulary preprocessor from phrase embedding into word embedding. TensorFlow has inbuilt word embedding lookup characteristic which is used for the building vector space. The vocabulary had round 3300 phrases which had been plotted to particular IDs, and 35 d glove embedding is used for the translation to vector region. The preprocessed data is involved in training phase. The collected data is given as input to the training module. The system is trained with different parameters. The epoch is fixed to 30, which is based on the size of the system, since the epoch increases the accuracy of the system. The collected data is chunked to batches that are trained one by one. The input parameters such as the batch size, number of epochs, number of filters, size of the filter, evaluate query, and dropout probability are passed in a JSON file to the CNN classification. The CNN’s text classifier contains multiple pairs of parallel convolution and pooling layers. The CNN output layer produces an expectation value built upon parameters that are learnt on prior layers. The adaptable number of predication units in CNN is called short region. Each unit is analogous to unigram, bigrams, and trigrams from the input microblogging. The pooling layer assigned weights which are collectively pooled through entire regions called short regions. A unit of activation function is initialized for every input, generated from each preprocessed microblogs. The network in the convolutional layer contains three estimation unit and one output unit. Let the region vector representations (x) of region size five match with input tweet ‘Ockhi cyclone hit Tamil Nadu’. Let the vocabulary V is given as follows V = {‘Ockhi’, ‘cyclone’, ‘hit’, ‘Tamil Nadu’, ‘fishermen’, ‘government’} are ‘cyclone in Tamil Nadu’ {0,1,0,0,0,0|0,0,0,1,0,0}. The experiment is carried out with 10,000 filtered tweets using Tensorflow— GPU [15] with routes distributed over multiple cores. For introducing nonlinearity, RELU activation function is used and, the loss is estimated using Adam optimizer. The training set was split into 7000 tweets and test set as 300 tweets with a batch size of 50. The proposed approach accuracy in CNN is amplified with the number of training steps. The loss of the model is reduced rapidly in the preliminary levels of CNN layers but trampled when increasing the number of training steps. In order to minimize the loss in predication, the algorithm used stochastic gradient descent between the network output values and the actual values for these outputs, over the training examples [16]. The number of neurons in the hidden layer of ANNs and the number of neurons in the convolution layer of CNNs are both set to 1500. It shows the input parameters to CNN where the number of epochs is given as 1, the batch size is set to 16, the number of filters is 32, the filter sizes are given as (3, 4, 5), and the dropout probability is 0.5. Moreover, the real-time delay of the proposed is determined by the Twitter user’s responses subsequently, so that the user needs some time interval to think what is happening, to use their smartphones and posting or retweet post related to new events that occurred. Similarly, the system is trained for n number of tweets, and those events are predicted with high accuracy. The experiment result is compared using supervised classification based on CNNs (unigrams and bigrams) with those obtained using various supervised SVM classifications as shown in Fig. 3.

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Fig. 3 Performance analysis

The SVM classifiers are trained on (i) unigrams and (ii) unigrams and bigrams with trigrams. The CNN shows high accuracy of 92% when compared to SVM 2 and SVM 3. The CNN technique obtains event detection in less than one minute after data of real-time news event is attained. The performance of the proposed work using CNN demonstrates that it is better in case of any event from Twitter.

5 Conclusion The OSN data is used to process microblogs to support for impulsive response through humanitarian organization. It needs an extensible, scalable data handling approach, and the proposed discussed the real-time CNN-based approaches to solve issues from the current approaches to detect events instantly. The proposed a new event detection using CNN technique which took the advantages in regard with accuracy when compared to support vector machines (n–grams)-based approaches. The trained model is ideal choice for applications related to detect events during disasters. It is uniquely well suited for training large data such as the complex social network data. The proposed n-gram CNN approach learns significant feature to analyze the behavior of convolution layer which makes transformation. It is compared to SVM in which set of features should be created by the human. Meanwhile, the entire process deals with keyword-related streaming data, and the proposed system is possible to detect diversified events instead of specific event. In future, the event detection can be further improved by training further with large number of retrospective history of event data.

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References 1. Zhao L, Sun Q, Ye J, Chen F, Lu CT, Ramakrishnan N (2017) Feature constrained multi-task learning models for spatiotemporal event forecasting. IEEE Trans Knowl Data Eng 29(5):1059– 1072 2. Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th ACM international conference on world wide web, Raleigh, USA, pp 851–860 3. Popescu AM, Pennacchiotti M (2010) Detecting controversial events from twitter. In: Proceedings of the 19th ACM international conference on Information and knowledge management, Toronto, Canada, pp 1873–1876 4. Valliyammai C, Bhuvaneswari A (2018) Semantics-based sensitive topic diffusion detection framework towards privacy aware Online Social Networks. Clust Comput, Springer, pp 1–16 5. Benson E, Haghighi A, Barzilay R (2011) Event discovery in social media feeds. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies 1(4): 389–398 6. Kaleel SB, Abhari A (2015) Cluster-discovery of Twitter messages for event detection and trending. J Comput Sci 6:47–57 7. Yu Y, Lin H, Meng J, Zhao Z (2016) Visual and textual sentiment analysis of a microblog using deep convolutional neural networks. Algorithms 9(2):41–44 8. Bhuvaneswari A, Valliyammai C (2018) Social IoT enabled emergency event detection framework using geo tagged microblogs and crowdsourced photos. In: Emerging technologies in data mining and information security, advances in intelligent systems and computing, Springer, vol 813, issue no. 13, pp 151–162 9. Lee K, Qadir A, Hasan SA, Datla V, Prakash A, Liu J, Farri O (2017) Adverse drug event detection in tweets with semi-supervised convolutional neural networks. In: Proceedings of the 26th international conference on world wide web, pp 705–714. International World Wide Web Conferences Steering Committee 10. Bhuvaneswari A, Valliyammai C (2019) Information entropy based event detection during disaster in cyber-social networks. J Intell Fuzzy Syst, IOS Press 11. Sakaki T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25(4):919–931 12. D’Andrea E, Ducange P, Lazzerini B, Marcelloni F (2015) Real-time detection of traffic from twitter stream analysis. IEEE Trans Intell Transp Syst 16(4):2269–2283 13. Bizid I, Nayef N, Boursier P, Doucet A (2018) Detecting prominent microblog users over crisis events phases. Inf Syst 78:173–188 14. Makice K (2009) Twitter API: up and running: learn how to build applications with the Twitter API. O’Reilly Media, Inc 15. Bhuvaneswari A, Valliyammai C (2018) Semantic-based sensitive topic dissemination control mechanism for safe social networking. In: Advances in big data and cloud computing, advances in intelligent systems and computing, Springer, vol 645, issue no. 7, pp 197–207 16. Bhuvaneswari A, Karthikeyan M, Lakshminarayanan T (2012) Improving diversity in video recommender systems and the discovery of long tail. J Theor Appl Inf Technol 37(2):224–233

Deep Neural Network for Evaluating Web Content Credibility Using Keras Sequential Model R. Manjula and M. S. Vijaya

Abstract Web content credibility determines the measure of acceptable and reliable of the web content that is observed. Content will prove to be unreliable if it is not updated, and it is not controlled for remarkable, and therefore, web content credibility is considerably essential for the people to assess the content. The analysis of content credibility is a vital and challenging task as the content credibility is outlined on crucial factors. This paper focuses on building deep neural network (DNN)-based predictive model using sequential model API to evaluate credibility of a webpage content. Deep neural network (DNN) is considered as an extremely promising decision-making architecture, and it performs feature extraction and transformation with the use of refined statistical modeling. A corpus of 400 webpage contents has been developed, and the factors like readability, freshness, and duplicate content are defined and captured from the webpage content. These features are redefined, and a new set of features is self-learned through the deep layers of neural network. Numeric labeling is used for defining credibility, wherein five-point Likert scale rating is used to denote the content credibility. By using sequential model, KerasRegressor with ADAM optimizer and a multilayer network is generated for building DNN-based predictive model and discovered that deep neural network outperforms other general regression algorithms in prediction scores.

1 Introduction Web content credibility is delineated as the authenticity of web content. Web content credibility is shaped from two measurements: trustworthiness and expertise. At the point when a webpage passes on both these qualities, individuals can perceive that it is a valid content. When it needs one of these qualities, as result of information R. Manjula (B) · M. S. Vijaya PSGR Krishnammal College for Women, Coimbatore, India e-mail: [email protected] M. S. Vijaya e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_2

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irresponsibleness, credibility can endure [1]. Content credibility is being enforced in numerous fields like economy, healthy lifestyle, politics, personal finance, and entertainment. A believable website can procure vast compensations onto the website, and consequently, the business individuals typically elect to answer prestigious message supported their perception of the communicator [2]. Holding an elegant, skilled trying webpage, gives credibility to the contents. A single more essential characteristic of valuable content is credibility. There are four sorts of web credibility like presumed credibility, reputed credibility, surface credibility, and earned credibility. Presumed credibility delineates the general conventions about the product brand. Reputed credibility describes the believed third-party reference about the brand. Surface credibility outlines how much a perceiver supposes something based on simple scrutiny. Earned credibility expresses the delicate occurrence of typographical text. Elegantly composed web content keeps the clients connected with and urges them to investigate their web content [3]. Web content credibility embraces the abilities and competencies required for inspecting, scripting, and impacting content on the web. Credibility is the single most significant attribute of abundant promoting content. It is also obvious that lack of trust declines the impact of content. In an ongoing study of innovation purchasers by TrustRadius, survey participants are requested to rate the effectiveness and reliability of wellspring of data utilized in purchasing choices. Credible content is influential [4]. Several computational techniques are embraced in the existing research to solve the tricky of predicting web content credibility. In traditional machine learning, the feature engineering is handled manually and statistical techniques are used to acquire better results. However, the deep learning is increasing much popularly because of its control in terms of accuracy when trained with self-learned features from enormous amount of data without domain expertise. Moreover, computational power of DNN model enables to process complex data and uplifts the execution of the model in addition to it outperforms with every other machine learning algorithm. Hence, in this work it is proposed to build web content credibility model by learning several inspiring and hidden parameters through deep neural network.

2 Literature Survey Different research work had revised and explored on understanding the factors that had an effect on credibility evaluations. In 2001, Fogg et al. concentrated on understanding the factors that had an influence on credibility evaluations and utilized two methodologies for deciding credibility evaluation factors. The first was a declarative approach, where respondents were requested to assess credibility and precisely denote which factor from a catalog was prompting their choice. In 2003, Fogg et al. yielded the second approach; the manual coding of annotations left by respondents assessed credibility by two coders. Unsupervised machine learning and NLP procedures were exploited from the content credibility corpus (C3) dataset [5].

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In 2013, Olteanu A et al. proposed “web credibility: feature exploration and credibility prediction.” Web credibility was automatically accessed and investigated the various qualities of webpages. The features were recognized from link structure, textual content, webpages design, as well as their social popularity ascertained from well-known web-based life destinations. The random baseline approach for regression for the real dataset-based experiment was concerned, and it accomplishes 75% accuracy for classification and regression achieves 53% of mean absolute error [6]. In 2010, Chung Joo Chung et al. planned “An anatomy of the credibility of online newspapers.” The exploration work discovered the key components of credibility of three types of online newspaper and therefore the distinction of credibility of reports by that kind. The credibility scales were measured consuming seven-point Likert-type scales, mean was computed, and consequently, the scale was analyzed for the similarities. The factors like expertise, attractiveness, and trustworthiness were exhausted to show the substantial difference between online newspapers. [7]. In 2017, Michal Kakol et al. suggested “understanding and predicting web content credibility using content credibility corpus.” The writers had originated the predictive model of web content credibility supported human evaluations. The factors were centered on empirical information. The content credibility corpus (C3) dataset was used from a massive crowdsourced web credibility assessment. The factors like network media type, news supply, official page, advertising, etc., were used to achieve a high level of quality. Random forest approach was exhausted to denote a comprehensive set of credibility analysis criteria [2]. In 2017, Hongsuk, et al. planned a deep neural network supervised model to estimate link-based flow of traffic conditions. A Traffic Performance Index which was utilized for logistic regression to recognize a congested traffic condition to a non-congested traffic condition. With a three-layer model, it was able to estimate the congestion with a 99% of accuracy [8]. The existing research work targeted on prediction of web content credibility exploiting various reliability factors. The credibility factors are inferred and used as evaluation criteria relevant to normal web content for fine-tuned validity appraisals. Our earlier work focused on the predictive model that was built using traditional supervised learning algorithms, and the experimental results tabulated below showed that logistic regression was better performing (Table 1). Table 1 Analysis of supervised learning algorithms Models

Correlation coefficient

Mean squared error

Root mean squared error

Mean absolute error

Logistic regression

0.896

0.270

0.519

0.301

Linear regression

0.875

0.285

0.533

0.314

Support vector regression

0.816

0.275

0.524

0.307

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In this work, to utilize the advantage of deep neural network and to enhance the performance of traditional machine learning, deep neural network model is generated to build accurate predictive model and DNN learns the collection of new features from the user by itself. Through the use of sequential model, regressor with ADAM optimizer and multilayer network is applied for building content credibility predictive model.

3 Methodology The web credibility evaluation problem is formulated as prediction task and modeled using regression. DNN with loss function is implemented using sequential regression model in tensorflow framework. The development of this efficient model includes different segments such as data collection, training data, and building DNN regressor models which are described in the following section.

3.1 Data Collection The corpus of 400 web pages has been preferred from websites related to several domain like healthy lifestyle, economy, politics, personal finance, and entertainment. Web page contents associated with balanced diet, short- and long-term health benefits are gathered from Health Life Style domain [9]. The contents grabbed from Economy domain comprised of information about the production and consumption of goods and services. From webpages of politics domain, the contents are accumulated based on the activities related to growth of a country or area. Webpage contents centered on management of monetary decisions are collected from personal finance domain. From entertainment domain, contents are composed of information regarding movie, games, etc. Also, publisher of webpages, details of author, webpage updates, particulars of domain are picked from the respective pages. From each one of these five fields, 80 instances have been gathered and stored in the form of text files. Finally, a corpus with 400 instances has been developed.

3.2 Training Data The crucial factors such as readability, authority, accessibility, understandability, popularity, freshness, broken links, page rank, and duplicate content are defined and captured from the webpage contents. The readability relies upon the contents of web page and its performance. Readability is estimated by the outlook of contents, reading speed, and clarity. Authority of a website administers the distributor and association of the site page, and it checks whether it isolates from the website domain. Web

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accessibility alludes to the comprehensive routine with regard to removing barriers that anticipate cooperation with or access to websites. For most content, understandability is just staying away from excessively complex sentences and language and providing clear format and outline. The webpage popularity is an exceptional way not exclusively to manufacture a site yet in addition to indicate others how a site is great. Freshness factor is a component of inquiry calculation that gives more prominent weight to up-to-date content over more established content for some search queries. The broken link is a hyperlink, a site which is connected to an empty or non-existent webpage. The page rank refers to the framework and the algorithmic technique that Google uses to rank pages and in addition the numerical values allocated to pages as a score. The duplicate content is a content that shows up on the page in excess of one place. The values of parameters such as readability, understandability, page rank, and duplicate content are computed using R code, and the values of factors such as authority, accessibility, popularity, freshness, and broken links are derived using nibbler tool. The Nibbler tool is a free website testing tool, and it is mainly used to provide the scoring report like accessibility, SEO, freshness, social media, etc., of each website and webpage contents. The above components are exploited as the values of predictor variables (Xi) of sequential model. The credibility of web content is considered as the response variable (Y) for which the modeling is being done. The value of credibility of web content is also processed as below for each record, and it is associated with the respective tuple. The credibility value is obtained using crowdsourced environment like web of trust (WOT). The numeric labeling five-point Likert scale rating is employed to define the content credibility value, whereas the rating from 1 to 3 stipulates the low credibility of content, and the ratings 4 and 5 denote high credibility. DNN Regression Deep neural network (DNN) regression is used to build the predictive model of web content credibility. The DNN consists of multiple neural network, and it has the input layers, number of hidden layers, and output layers to construct the model. Each input layer is designed and connected to hidden layers. The hidden layers are implemented with the ReLU activation function, and each hidden layer is defined with the number of neurons. The first hidden layer is passed to a rectified activation function called ReLU. The ReLU is the most used and simplest nonlinear activation function, while it never gets saturated with high value of predictor variables, and it provides effective findings. By using ReLU activation, the errors are easily backpropagated and the computation of weights becomes logical. Adam optimizer is used, and it is a method that computes adaptive learning rate for each parameter, and it will update the weights of hidden layers. An Epoch is used to separate the training data into different phases for the evaluation, and the model gets updated after processing of each epoch and batch size.

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4 Experiments and Results The deep neural network-based predictive model for web content credibility has been created by implementing the sequential model API using the optimizer, activation, and loss functions. The DNN model is built using Python 3 and Keras with the backend tensorflow. Python is a general-purpose programming language, and it is a full-grown and also quick increasing platform for scientific research as well as numerical computing. Keras is a common programming context for deep learning that simplifies the method of building deep learning applications, and it is an open-source neural network library written in Python. TensorFlow is said to be the open-source software library for high-performance numerical calculation. Its flexible design enables simple deployment of computation around a range of platforms, i.e., CPUs, GPUs, TPUs, and from desktops to clusters of servers to various devices. Sequential model API is designed as an instance of sequential class, and model layers are generated and appended to it. The sequential model API is evaluated using KerasRegressor. The Keras wrapper object used as regression estimator is called KerasRegressor. The real-time data has been gathered, and web content dataset has been created. The content credibility factors are recognized and determined for implementing the model. The DNN consists of multilayer network, and it has the number of input layers, hidden layers, and output layers to construct the model. The input layer is designed and connected to two hidden layers. Two hidden layers are implemented, and both have ReLU activation function. One hidden layer is defined with five neurons each and another with 1 neuron. The hidden layer is described with an epoch of 170 and the batch size of 5 to increase the correlation coefficient. The DNN-based credibility predictive model is built with above framework specifications, and the performance of the model is evaluated using tenfold cross-validation based on correlation coefficient, mean squared error, root mean squared error, mean absolute error. The correlation coefficient is utilized as an essential execution measure for predicting web content credibility and shows the nature and quality of the association among data and target value. Mean squared error (MSE) is the average of the squared error and is used as the loss function for least square regression. Root mean squared error is the square root of the residuals. RMSE is an absolute measure of fit. MAE is the model evaluation metric and is the mean of the absolute values of each prediction error on all instance of the test dataset. The working of credibility prediction model using DNN regression is measured using the above-mentioned metrics and produces the correlation coefficient of 0.985 with the mean squared error of 0.188, and root mean squared error obtains 0.434; then the mean absolute error of 0.376 is achieved with an epoch of 170 and a batch size of 5. The performance results of DNN model based on various metrics are shown in Table 2. The comparative analysis of evaluation metrics at different epochs is shown in Table 3.

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Table 2 Performance measure of DNN model Measures

Values

Mean squared error (MSE)

0.188

Root mean squared error (RMSE)

0.434

Mean absolute error (MAE)

0.376

Table 3 Performance analysis of evaluation metrics for various epochs Epochs

Correlation coefficient

50

0.735

100

0.795

150

0.812

170

0.985

Mean squared error

Root mean squared error

Mean absolute error

0.453

0.673

0.906

0.576

0.758

1.152

0.314

0.560

0.628

0.188

0.434

0.376

Table 4 Comparative analysis of deep neural network Models

Evaluation metrics Correlation coefficient

Mean squared error

Root mean squared error

Mean absolute error

Deep neural network (DNN)

0.985

0.188

0.434

0.376

Logistic regression

0.896

0.270

0.519

0.301

The results of DNN model are compared with the traditional best-performed supervised learning algorithm such as logistic regression which has been implemented in our previous work that had achieved the correlation coefficient of 0.896, mean squared error of 0.270, root mean squared error of 0.519, and the mean absolute error of 0.301, and the comparative results are shown in Table 4 and illustrated in Fig. 1. From the comparative analysis, it is detected that the DNN achieves greatest result for content credibility prediction model. The deep neural network conquers perfect outcome in the minimal learning time. The logistic regression had reached the correlation coefficient of 0.896, whereas the DNN accomplishes the correlation coefficient of 0.985. The error rate is minimized in deep neural network when compared to logistic regression so the consistency of the system is increased. Findings The DNN is best fitted to build the accurate predictive model. The DNN with sequential model API has the significant impact to evaluate the web content credibility. The traditional supervised learning algorithms break down the larger problems into different parts and solve them individually, and then, the feature extraction is handled manually while deep learning solves the problems from end to end also it extracts high-level features by itself from the pre-defined features without the need of

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1 0.8 0.6 0.4 0.2 0 CORRELATION COEFFICIENT

MEAN SQUARED ERROR

ROOT MEAN SQUARED ERROR

MEAN ABSOLUTE ERROR

DEEP NEURAL NETWORK (DNN) LOGISTIC REGRESSION

Fig. 1 Performance of deep neural network model

domain expertise, and the computational power of deep neural network increases the processing ability of data. Unique approach like TensorFlow is used to monitor and train the model with regression metrics, and it provides high performance and great community support. With the use of deep learning environment, the predictive model of web content credibility is confirmed its competence using its metrics with least error rate than other learning algorithms for evaluating the web content credibility.

5 Conclusion This paper demonstrates the modeling of web content credibility using deep learning techniques. The real-time dataset has been developed, and credibility factors are defined. The predictive model based on deep neural network is designed and built for the evaluation of web content credibility. The DNN has directed to considerable improvements to the correlation score of machine learning techniques. The model is designed using sequential API by adding the input, hidden layers, and output with the ADAM optimizer. The performance of deep neural network model for content credibility is assessed using different metrics such as correlation coefficient, mean squared error, root mean squared error, and mean absolute error. The DNN has directed to considerable improvements to the correlation score of machine learning techniques. Through this experiment, it is observed that the DNN is best suited for evaluating the web content credibility. To the best of my knowledge, it is the first work to use a deep neural network for the web content credibility evaluation.

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References 1. Wierzbicki A, Abramczuk K, Adamska P, Aberer K, Papaioannou T, Rejmund E (2014) Studying web content credibility by social simulation 2. Kakol M, Wierzbicki A, Nielek R (2017) Understanding and predicting web content credibility using the content credibility corpus, Elsevier, (53)5: 1043–1061 3. Abdulla RA, Driscoll P, Garrison B, Salwen M, Casey D (2014) The credibility of newspapers, television news, and online news 4. https://mozilla.github.io/content/web-lit-whitepaper/ 5. Fogg BJ, Marshall J, Osipovich A, Laraki O, Varma C, Fang N, Paul J, Rangnekar A, Shon J, Swani P, Treinen M (2001) What makes web sites credible? a report on a large quantitative study. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 61–68 6. Olteanu A, Liu X, Peshterliev S, Aberer K (2013) Web credibility: features exploration and credibility prediction. In: European conference on information retrieval, Springer, 557–568 7. Chung CJ, Kim JH, Kim H (2010) An anatomy of the credibility of online newspapers. Online Inf Rev 34(5):669–685 8. Yi H, Jung H, Bae S (2017) Deep neural networks for traffic flow prediction. In: 2017 IEEE International Conference on big data and smart computing (BigComp), IEEE, pp 328–331 9. Sondhi P, Vydiswaran VV, Zhai C (2012) Reliability prediction of webpages in the medical domain. In: European conference on information retrieval, Springer, pp 219–231 10. Tumasjan A, Sandner PG, Sprenger TO, Welpe LM (2010) Predicting elections with Twitter: what 140 characters reveal about political sentiment, pp 1–16 11. Carlos C, Marcelo M, Barbara P (2013) Predicting information credibility in time-sensitive social media (23)5: 560–588 12. Korfiatis NT, Boko G, Poulos M (2006) Evaluating authoritative sources using social networks: an insight from Wikipedia. Online Inf Rev 30(3):252–262

SENTRAC: A Novel Real Time Sentiment Analysis Approach Through Twitter Cloud Environment Md. Julkar Nayeen Mahi, Kazi Moinul Hossain, Milon Biswas, and Md Whaiduzzaman

Abstract In internet based life ‘Sentiment’ is a popular expression of the present patterns as there are heaps of work is being finished closing information or data mining. Utilizing sentiment examination different envision of events can be found, for example; human trafficking, burglary, ambiguous feelings and so on. Rapid miner, a social media mining stage which checks in the middle of sentiment relations through cloud mining techniques with a view to indicating the yield of a gross aftereffect of thought states. In this work, the authors have utilized rapid miner programming for investigating the continuous twitter cloud information mining methodology. Aftermath of this process, separation of human contemplation can be anticipated all through the varieties of their thinking sorts on each time based tweeting. The principle intention of this work is to demonstrate the performance execution between KNN and LR algorithms. Moreover, KNN performs superior to LR with 98% precision of genuine positivity upon individuals thinking examples or changes from constant data.

This research is partly supported through the Australian Research Council Discovery Project: DP190100314, ‘Re-Engineering Enterprise Systems for Microservices in the Cloud’: Md. J. N. Mahi (B) Lecturer, Department of Computer Science and Engineering, City University, Dhaka, Bangladesh e-mail: [email protected] K. M. Hossain Assistant Programmer, Ministry of ICT Division, Dhaka, Bangladesh M. Biswas Assistant Professor, Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh Md Whaiduzzaman Postdoctoral Research Fellow, Queensland University of Technology, Brisbane, Australia © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_3

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1 Introduction Distributed computing or cloud is a web based computing stage that shares assets though informations are given to the gadgets inside an on interest premise like as grid computing. Mainly it is a web paradigm where informations are kept on the server. Cloud can perform on interest sharing documents over the web without the need of being stored on neighborhood [1, 2] servers. There are three sorts of cloud approaches, SaaS (Software as a Service); PaaS (Platform as a Service); IaaS (Infrastructure as a Service). SaaS characterizes a service normally accessible through software stages like browsers. PaaS diverts a platform [3] based administration that hosts a situation for conveying cloud applications. IaaS is a stage from which cloud services finds a framework for omnipresent application deployments. Cloud computing [4, 5] discharges the expense of equipment and framework support. Cloud [6] contrasts in the variety of cross breed, open and private architecture types. Cloud mining is a procedure which encourages mining over cloud stages. Utilizing databases, programming softwares, servers, storages the mining synchronization should be possible on cloud architecture models [7, 8]. Cloud mining is a procedure of using remote server centers [9] through the shared processing power that incites clients to utilize crypto currencies or bitcoins as opposed to overseeing hardware types. Since it is a SaaS based framework the administration needs a few costs which utilizes bring down returns towards the client. Likewise, it can run through hashing [10] powers without utilizing dedicated rented servers. The way towards dissecting unstructured content to extricate important data [11, 12] through changing it into knowledge is an example of intelligent text mining. The investigation works through finding suitable articulations of positive, negative, neutral and other degrees. Investigating text based feelings of human it can change over into hard actualities. The sentiment analysis is a type of opinion mining. In this procedure the work is finished by accommodating texts through indicating outputs into the emotions. Utilizing the impact score investigation we can distinguish individual’s assumptions effectively (Fig. 1). In this sentiment examination proceeding with methodologies, all the process done by getting and bringing the data from web based social media’s like - twitter, Facebook. After putting away information, the mining algorithms helps through splitting the texts or contents [13, 14]. Algorithms at that point tag lined the sentences and check them based on polarity plans (e.g. positive, negative, and neutral). With the assistance of polarity plans literally, the process comprehends the intensity of individual’s sentiment assumptions [15–17]. Subsequent to estimating the intensity, the procedure demonstrates an output that investigates the result of the overall examination. In this work, a novel thought of sentiment examination focusing is made on the division of cricket playing and as a result, accumulated the desired data [18, 19] from our corresponding twitter accounts as well as twitter cloud vaults. An utilization of the continuous real time twitter cloud information of Asia Cup 2018 is checked and after that, took our coveted word content IND versus BAN as

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Fig. 1 Workflow relationships in between datasets of sentiment analysis approach

an essential slogan all through the text cloud [2, 20] environment. In any case, a notation is made in the text word to locate the expected sentiments of individuals utilizing the text mining [15] procedure. The essential work of the paper is to check and set up the performance, (for example, precision) of classifying [16] data mining algorithms likely, K-nearest neighbors (KNN) and Logistic regression (LR). For a reliable feasible use, under certain unpleasant circumstances of winning or losing, these two algorithms can foresee the approximate sentiments [17] within a scope of good precision properties. The principle inspiration of this paper, is to characterize a work process of sentiment investigation under some custom circumstances and analyze in between of LR and KNN [18] through considering the faced circumstances for locating a superior indicator parameter [19] inside individuals thought process. However, the results found from our outcome a 98% precision on relationship classification in the event of KNN algorithm [20] which defines the superiority in class comparing to LR classification [21] strategy. The paper is categorized into below Sections: the first Section is entitled as introduction, second Section is literature review, third Section is process modelling and discussions, fourth Section is performance and preliminary results, fifth Section is limitations and future works and the last Section is conclusion.

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2 Literature Review Sentiment analysis is a procedure from where a thought is arise about the reasoning conceivable outcomes inside individual’s attitudes. Utilizing the assistance of mining procedures feeling examination approach can show signs of better predictions and rule wise valid approvals. All through the prediction mining of sentiments a break down property is made for the future results of individual’s point of view which makes them usable for maintaining a strategic distance from undesirable events or other related explicit undertakings [1]. Depicts, a sentiment classifier algorithm that can gather the corpus (e.g. happy, sad emoticons) and clarify the discovered phenomena through deciding positive, negative and neutral sentiments. The algorithm utilizes naïve Bayes and N-gram classifier for parts of speech tagging with syntactic structures for recognizing emotions or state actualities. The classification do not function admirably in distinguishing multilingual corpus of twitter data [3]. Researches, the utilization of supervised learning approach through using twitter hashtags for the training data. The authors demonstrated that parts of speech is certifiably not a superior indicator for classifying emotions through mining procedures. Real Time Sentiment Analysis (RTSA2012) [22] portrays a novel technique of instantly delivering sentiment investigation from twitter datasets. However, it was filled in as a moment media being used by offering timely perspective points of view towards the government official and researchers or scholars while on the constituent procedure of choosing USA president. The chosen architecture of the algorithm develops in light of rising political occasions or unfolded news which was remarkable in demonstration of generated data handling at that point of time if there should arise an occurrence of races or elections [4]. Portrays an alternate methodology of subjectivity classifier from which the algorithm shows a dynamic portrayal of tweets in biased and noisy dimensions of information or data dissemination. The procedure of classification functions works distinctive on an average but can not wipe out antagonistic opinions [5]. Simplifies, the difficulties on classification issues through characterizing sliding window kappa statistic stage for unbalanced classes and time-changing data streams. However, [7] shows a novel methodology of including semantics with specific corresponding entity names. For negative, positive and neutral sections of classification, F1 score and Recall is utilized through an accuracy of 6.5% and 4.8% respectively in both of the individuals. Meanwhile, [8] demonstrates a unique sentiment polarity plot for detailed product audits. For the specific audit of items sentence-level and review-level classes are the essential results. On the contrary, [11] portrays a novel work process of positive or negative tweet tallying polarization inside Arabic dialect that utilizes crowd flower procedure but the process can not identify incongruity and emotions from sentiments. In addition to this, [12] makes sense of a profound learning neural network system through convolutional matrix based feature mapping approach. This procedure demonstrates a superior outcome in distant supervised data contrasting with unsupervised neural

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dialect based model information [13]. Underpins, a novel lexicon based methodology which offers a settled and static polarities of words regardless of their context. The methodology performs better to the cutting edge SentiStrength by 4-5% in accuracy through considering two datasets but following F-measure the outcome falls hardly behind by 1% in the third dataset. While, [14] delineates the broad scopes of a comprehensive survey that leads from data mining strategies.

3 Process Modelling and Discussions For this work, a utilization of rapid miner is made (instructive adaptation for research) to experience the sentiment analysis through estimating words from cloud databases. First the authors have utilized rapid miner a text mining software and consolidate twitter cloud account for fetching the data in process content tool box. For the predicted word from which an extraction of the sentiments are taken from ‘trends map’. Trends map an online based twitter hashtag or word gatherer from various regions of the world. From trends map an addition of any continuous utilized twitter hashtags or words can be made from any part of the world. The content or word can be gathered thinking about utilized or mostly used to participate in the analysis. From trends map an appearance of most used hashtags can be get in a timely fashioned manner within a specific state, as—USA, country; Washington, state; Bangladesh, country; Dhaka, state. After getting the desired word a check is made on the operators tab in rapid miner. In the operators tab ‘twitter search’ is written down to open and arrange the cloud archive and embed it into the process panel of rapid miner software to build up a web network. In the parameters tool kit the coveted word IND versus BAN is embedded on the query box. In the limit and language box 1000 as word or sentence length is utilized and en as a dialect which diverts the iterations of various individual considerations on a similar word or hashtag making a characteristic of English. In that case, separate relationships for the desired word will check under English libraries or lexicons. Successively from that point onward, a write excel box on the procedure panel is utilized to sort the twitter data under row and column wise relationships. After fetching the data into the write excel box the yield string of the twitter cloud box is associated to the input string of the excel expectation box. This process is done to classify words through finding inter-between relationships among them. Meanwhile, subsequent to adding into row and column wise insightful connections the response string to the output string of the excel box is connected within rapid miner pane for checking the output parameters (Fig. 2). This statement of a twitter client with a specific User name and Id redirects an obscure sentiment about cricket playing strategies and it demonstrates a neutral sentiment viewpoint too. The re-twitted column, look through the queries of sentiments in a repetitive manner. Indicating 1 implies the point of view rehashed once after the first tweet having an equivalent sentiment from other individual. Concluding the

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Fig. 2 Relationships mining through twitter (sentiment analysis in between datasets) zuhaitaefahyung twitted: #indvban “whenever it is a match with ind versus ban there will always a term of cheating and how many times its gonna happen I’m so sick of this fake games”

outcomes a read excel operator is utilized to discover the sentiment statements within rapid miner for future analysis. After that, the ‘Aylien’ a natural language processing (NLP) library is utilized to peruse the sentiments or point of view from write excel file that is observed from the cloud condition. Afterwards, the output string of read excel file is blended to the input of the ‘Aylien’ sentiment analysis box in the rapid miner process segment. After the confirmation of NLP library ‘Aylien’ all through the read excel data are checked and merged with the confirmed data set into another write excel file for better understanding (Fig. 3). In addition to this, a polarity metric is found through occupied on attributes from the write excel file. In sentiment examination, polarity is a measurement from which a separation of content or texts is made within different levels based on neutral, positivity or negativity schemes. In other words, polarity is a pipeline based metric that can contribute to the analysis on the accuracy to find better outcomes. This mainly relies on the two fundamental categories; subjective and objective. Subjectivity can be characterized as in classifying a text based on their context and objectivity depends on the opinions of individuals which is additionally in a blend relationship with subjectivity cases. Nevertheless, subjective are personal belief of a unique individual yet objectives are rather than it serves other opinions, a few times in blend relations. Considering the analysis, our observations finds that, the subjectivity designs transforms out into probability metric where 1 ends up being negative opinion inside a regression based termed analysis. Whereas, objective implies a blended assessment upon subjective issues. In probability metric, 0.64 ends up being objective in various

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Fig. 3 Rapid miner process pane analyzing sentiments after pausing internet and writing the desired parameter based sentiment data on excel

situation based cases all through the manner of thinking of different people yet not in case of the individuals. To utilize these relationships, Xl miner is used, a text mining platform which guarantees our custom parameters for sentiment analysis through considering KNN and LR algorithms for a precise performance allotments (Fig. 4). In Xl miner, stemming, normalization, term filtering in the text pre-processing area are utilized. Stemming means to change over a word into generous verbal structures, for instance convert both flown and flew word texts to fly; where normalization intends to change over a rundown of words to a different uniform sequence, for instance to change over every one of the words in lower cases to seek on. Expelling stop words also done in the pre-processing text phase. At that point, the TF-IDF matrix pattern is checked for word pre-processing. The TF-IDF changes over the high level language words into numeric structures. From that point onward, preprocessing output based on confirming term and concept report of 30 most incessant vocabulary usage are checked and get the result of estimated 90% of the confidence interval term words. Concluding the outcomes after pre-processing, Logistic regression model (LR) and KNN classifier model are measured and checked. All procedure are checked under empirical distribution and found adequacy under various individuals idea or manner of thinking all through the predictor screening, multi co-linearity, residual deviance and reduced variance. A residual deviance 0.0007 redirects a little variance in each relationship among the twitter data set on both subjective and objective basis. In a predictive screening maximum accessibility of negative point of views but among them less ones are positive. In case of the K-nearest neighbour classifier algorithm, 5 neighbour hubs of relationships have developed which founds the effectiveness on parameters. For example, true positive classification, reduced variance, specificity under the relationship metric

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Fig. 4 Rapid miner process pane analyzing sentiments using polarity cases

of subjective and objective based data indexes. Here, the true values indicates relatively same as logistic regression model whereas, false values differentiates positivity in between data relationships (Fig. 5). For this situation, the relationship of our focusing on word IND versus BAN, little reactions of positivity is found rather than over-burden antagonism or negativity. Thinking about the subjectivity part, just two substantial idea phases of positivity among 1000 individual thought perspectives are collected. A descriptive tree hierarchy stated above can demonstrate the relationships and factors upon the dataset. The tree progressive system shows and supports the approximate consequences of LR and KNN algorithms which redirects our outcome as obvious and portrays our research work a standard based valid mechanism.

4 Performance and Preliminary Results In addition to our work advancement and process modelling, some specific parameters are discovered from which a valid differentiation can be noticed between LR and KNN algorithms. For our situation, considering the twitter cloud data the algorithmic performance relies upon various circumstances. Moreover, this rush circumstance of winning or losing with a brimming negative thought can give distinctive outcomes.

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Fig. 5 Xl miner DFS tree hierarchy shows the two basic positive stages as left sub-tree (thought process as concept) within twitter cloud data

Although, a next to no positive phase of thinking manner from our data index are perceived. However, for this reason, a parameter based analysis of thinking about the two positive stages under subjectivity divisions are estimated. Primarily, the parameters from which a compliance can be made to confirm the precision of the algorithms have decided as an approved methodology. For this work the considered parameters are error, Receiver operating characteristic (ROC) (Fig. 6). ROC is a criteria metric from which the relationships between user’s manner of thinking or conduct is evaluated. From the twitter data a low false positive rate (FPR) in terms of sensitivity is detected. On the contrary, a moderate rate of true positive (model) rather than true positive (random) is found from our dataset. That implies, through our perception, relationships of the dataset sensitivity lies completely upon them. This additionally redirects our perception as valid for the proposed execution displaying (Fig. 7). In case of, LR algorithm is relatively close to similar relationships on both true positive rates that compares to random observations through modelling. From the graph, false positive rates as lower than KNN which bolsters less predictive capacity in terms of classification mining procedures (Fig. 8). The performance is checked through confirming precision, recall and F1 score. Where, in KNN the precision is higher than LR algorithm. However, F1 score characterizes the mean between recall and precision. In addition to it, the F1 score of

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Fig. 6 ROC graph illustrating sensitivity in KNN algorithm

Fig. 7 ROC graph illustrating sensitivity in LR algorithm

Fig. 8 Performance comparison on both KNN and LR

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KNN is around higher than the F1 score of LR. Though, recall finds a cross relation in the middle of relevant and retrieved perceptions through separating it to relevant relations. For this situation, recall or sensitivity of KNN supports superior to LR algorithm. In the meantime, in the error rates the KNN functions admirably than LR algorithm. Looking at the above performance diagram an observation is made that, KNN performs superior to LR and supports 98% of guaranteeing sensitivity or true positivity which depicts as a better indicator for our customize approach of individual thought process mining.

5 Limitations and Future Work In this work, the performance in the middle of KNN and LR algorithm upon continuous perception of twitter based text cloud mining is differentiated. Despite of the fact that, our very own algorithm is not initialized and build up by which a good contrast among alternate classifications may appeared. In future, the authors will look forward to build up some substantial algorithms for cloud based text mining strategies.

6 Conclusion Nowadays, in the modern digital world distinctive works are being done through sentiment analysis approach. Based on sentiments different, unusual events happened in consistently throughout the times like burglary, sneaking, human trafficking and so on. In this work, the authors have proposed an investigation based methodology from which individuals can gain knowledge about sentiment analysis working procedures. In addition to it, the performance demonstrates KNN to accomplish 98% of the true positive rates that supports as a superior indicator for the targeted scenario. A specific algorithmic platform can work better in performance paradigm. However, in future a distinctive set up of a superior algorithmic methodology may act as a good helping hand for the text mining techniques.

References 1. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol 10, No 2010, pp 1320–1326 2. Qaisi LM, Aljarah I (2016) A twitter sentiment analysis for cloud providers: a case study of Azure vs. AWS. In: 2016 7th international conference on computer science and information technology (CSIT), IEEE, pp 1–6 3. Kouloumpis E, Wilson T, Moore JD (2011) Twitter sentiment analysis: the good the bad and the omg! In: Icwsm, vol 11(538–541), p 164

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4. Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics: posters, Association for Computational Linguistics, pp 36–44) 5. Bifet A, Frank E (2010) Sentiment knowledge discovery in twitter streaming data. In: International conference on discovery science, Springer, Berlin, Heidelberg, pp 1–15 6. Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT (2014) Cloud service selection using multicriteria decision analysis. Sci World J 7. Saif H, He Y, Alani H (2012) Semantic sentiment analysis of twitter. In: International semantic web conference, Springer, Berlin, Heidelberg, pp 508–524 8. Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 2(1):5 9. Whaiduzzaman M, Haque MN, Rejaul Karim Chowdhury M, Gani A (2014) A study on strategic provisioning of cloud computing services. Sci World J 10. Mozumder DP, Mahi MJN, Whaiduzzaman M, Mahi MJN (2017) Cloud computing security breaches and threats analysis. Int J Sci Eng Res 8(1) 11. Nakov P, Ritter A, Rosenthal S, Sebastiani F, Stoyanov V (2016) SemEval-2016 task 4: sentiment analysis in Twitter. In: Proceedings of the 10th international workshop on semantic evaluation (semeval-2016), pp 1–18 12. Severyn A, Moschitti A (2015) Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 959–962 13. Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manag 52(1):5–19 14. Pratama BY, Sarno R (2015) Personality classification based on Twitter text using Naive Bayes, KNN and SVM. In: 2015 international conference on data and software engineering (ICoDSE), IEEE, pp 170–174 15. Ain QT, Ali M, Riaz A, Noureen A, Kamran M, Hayat B, Rehman A (2017) Sentiment analysis using deep learning techniques: a review. Int J Adv Comput Sci Appl 8(6):424 16. Goswami S, Chakraborty S, Ghosh S, Chakrabarti A, Chakraborty B (2018) A review on application of data mining techniques to combat natural disasters. Ain Shams Eng J 9(3):365– 378 17. Baydogan C, Alatas B (2018) Sentiment analysis using Konstanz information miner in social networks. In: 2018 6th international symposium on digital forensic and security (ISDFS), IEEE, pp 1–5 18. Mensikova A, Mattmann CA (2018) Ensemble sentiment analysis to identify human trafficking in web data 19. Sohangir S, Wang D, Pomeranets A, Khoshgoftaar TM (2018) Big data: deep learning for financial sentiment analysis. J Big Data 5(1):3 20. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113 21. Akhund TMNU, Mahi MJN, Tanvir AH, Mahmud M, Kaiser MS (2018) ADEPTNESS: Alzheimer’s disease patient management system using pervasive sensors-early prototype and preliminary results. In: International conference on brain informatics, Springer, Cham, pp 413–422 22. Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012) A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 system demonstrations, Association for Computational Linguistics, pp 115–120

Low Power Area Efficient Improved DWT and AES Architectures for Secure Image Coding Renjith V. Ravi , Kamalraj Subramaniam , and G. K. D. Prasanna Venkatesan

Abstract This paper unfolds the development of an HDL model on the ASIC platform for the quicker and safer transmission of image data. Secure image encoding was facilitated by the DWT through image compression and AES through encryption. The DWT computation based on lifting scheme algorithm was developed and implemented on ASIC platform. An algorithm that can compute multi-level sub bands using 2D-DWT was developed. Based on compression ratio and data recovery, appropriate sub bands were chosen to reduce the computation time of the AES encryption. The DWT architecture was developed to facilitate high throughput and latency. The HDL model of the DWT architecture and the AES algorithm were developed and validated on the ASIC platform to identify the area, timing and power performance. The ASIC implementation was carried out using 56nm CMOS technology. Keywords AES hardware · Image encryption on ASIC · DWT on ASIC · ASIC implementation · Image compression hardware

R. V. Ravi (B) Department of Electronics and Communication Engineering, M.E.A Engineering College, Malappuram, Kerala, India e-mail: [email protected] K. Subramaniam Department of Electronics and Communication Engineering, Faculty of Engineering, Karpagam Academy of Higher Eduction, Coimbatore, Tamil Nadu, India e-mail: [email protected] G. K. D. Prasanna Venkatesan Faculty of Engineering, Karpagam Academy of Higher Eduction, Coimbatore, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_4

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1 Introduction In the present day world of knowledge era, digital images are widely applied in all walks of human life. Digital transmission, storage and retrieval are seen gasping in their attempts to keep pace with the developments in other fields of science and technology, around the globe. More speedy and secure image transmission, storage and retrieval methods are required. The present study is a dedicated attempt in this regard. The present image transmission systems and procedures are handicapped in several ways. The main reason is that traditional methods make the process of encryption more time consuming and image data as large sized. The security features are also not trustworthy and stable. These limitations can be overcome by developing innovative methods for image transmission. In the present study, the investigators show some unique ways for quick and safe transmission and retrieval of the image data. The researchers develop new ways of image transmission by making use of the technical know-how existing in the literature. The procedures the researchers followed for the purpose include the discrete wavelet transform (DWT) [1, 2] in two-dimensional mode, the advanced encryption standard (AES) [3, 4] algorithm for the encryption of the sub bands. The DWT procedure stratifies the input image on the basis of frequency and thus causes emergence of sub bands with different frequency features. Of these sub bands, some are selected on the basis of their performance in compressing and recovering the data. The select sub bands are subject to encryption procedures by using the AES algorithm and led to the transmission channel. The sub bands thus reach the receiver device. From the receiver space, the encrypted sub bands are subject to decryption procedures by using AES algorithm. Thus, the original image is reconstructed by way of inverse DWT. The match between the original and reconstructed image is tested through various image quality metrics and found affirmative. The DWT architecture was developed to facilitate high throughput and latency. By making use of the lifting scheme-based DWT architecture and AES algorithm, their hardware models were developed and validated on the ASIC [5] platform to identify the area, timing and power performance. The present study is highly relevant in the sense that findings can cause far-reaching advancements in the field of image transmission and data communication. The enhanced security features of the image transmission facilitated by the findings of the present study can cause unprecedented progress and advancements in the various realms of human life such as trade and commerce, defense, cellular phone industry, medical, education and scientific inventions. The hardware evolved, developed and validated by the investigators can be further developed through innovative ways and means so as to address the emerging needs and concerns of the world. This hardware is highly cost-effective and user-friendly, with no compromise on quality. The present article consists of five sections. Initial section is introduction. Second section shows the generalized scheme for secure image transmission. Third section shows the modified technique for secure image transmission, and fourth section shows the implementation of the DWT and AES on ASIC platform and results. The final section is the conclusion of the article.

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2 Secure Image Encoding Scheme This is a new technique made use of for transmitting image data [6]. The input image is subjected to the DWT processing, and sub bands of various types are caused to emerge. The sub bands are quantified using appropriate techniques and are encrypted. The encrypted data are encoded and thus compressed. The details of the secure image coding are represented below diagrammatically as Fig. 1. Figure 1 shows how secure image is coded. Original image is subjected to DWT processing. It causes the emergence of various sub bands. The quantizer identifies different sets of values and assigns them finite values. AES can process 128 bits of data at a time with key length of 128, 192 or 256 bits. Huffman coding [7] causes the encoding of redundant information in the AES encrypted sub bands. The controller manages the wavelet decomposition and AES encryption procedures.

2.1 DWT for Image Compression The two-dimensional DWT is a tool used globally as a standard one for image processing. Its process is facilitated by the filtering of the digital image in low pass and high pass modes. The process of the DWT is termed as Mallat-tree decomposition. The inverse process of the DWT is termed as IDWT. The DWT of an image obtained by performing the first level transformation as row wise and the second level transformation as column wise. The obtained four sub bands are LL, LH, HL and HH.

Fig. 1 Secure image codec [6]

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Fig. 2 Decomposition of an image using DWT

These sub bands contain low frequency components (DC Components) and high frequency components (Edges along horizontal, vertical and diagonal axis) [8, 9]. Figure 2 shows the pyrimidal decompostion of input image by using the DWT. In the first level transformation, the input image is decomposed into four dissimilar sub bands of LL,LH,HL and HH. The components of the LL sub band are again decomposed into four more sub bands in the second level. The results of the initial level decomposition are shown in Fig. 2a and of the second level in Fig. 2b. Here, the quantizing of the higher sub bands, not significant and transmitting the LL sub band without quantization gives rise to image compression. An image of size N × N after decomposition at two level will give rise to 7 sub bands (three higher level sub bands of size N2 × N2 at the first level, three N4 × N4 and one low frequency sub band of N2 × N2 at the second level).

2.2 AES Encryption and Decryption The more well-known and broadly embraced symmetric encryption algorithm practiced these days is the AES. It is found somewhere around six times quicker than conventional algorithms like triple DES [10]. The AES is a piece of a family known as block ciphers, which are algorithms that encrypt information on a for each block premise. The AES plays out the entirety of its calculations on bytes instead of bits. Henceforth, AES treats the 128 bits of a plaintext block as 16 bytes. These 16 bytes are organized in four columns and four rows for handling as a matrix known as state. Since AES is 128 bits in length, for each 128 bits of plaintext, 128 bits of ciphertext are created. Since the AES calculation is symmetric, a similar key is utilized for both encryption and decryption. The operation of AES algorithm is described in Algorithm 1 The quantity of rounds in AES is variable and relies upon the length of the key. The number of rounds for 128,192 and 256 bit key length will be 10, 12 and 14, respectively. The AES key schedule is utilized to deliver an arrangement of round

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Algorithm 1 AES Encryption Input: plain_text (state), key Output: cipher_text(state) function AES Encryption(plain_text(state),key) AddRoundkey(state,key0) for Nr = Nr − 1 do SubBytes ShiftRows MixColumns AddRoundkey(state,keyr ound ) end for SubBytes ShiftRows MixColumns AddRoundkey(state,keyr ound ) end function

keys from the underlying key. In AES, the underlying key is utilized in the underlying round of AES as contribution to the AddRoundKey task. From this key, 10, 12 or 14 round keys are delivered as contribution to the next AddRoundKey tasks in the 128, 192 and 256-bit adaptations of AES.

3 Modified Algorithm for Secure Image Coding The major limitations of the traditional encoding of images include excess consumption of time for computation in AES algorithm. In the present study, the researchers transformed the input image in to seven sub bands using DWT, and each of them was quantized and encrypted by using AES. Then, it took less time for encryption process. The block diagram modified for secure image encoding is given in Fig. 3. It shows the decomposition of the input image into seven sub bands of high and low frequency components. The researchers encrypted the LL2 sub band using AES algorithm without quantization. The other sub bands LH2, HL2 and HH1 were quantized and encrypted independently. The total number of bits that are encoded after 2D DWT and quantization is shown in Table 1. Table 1 shown above presents the number of bits encoded using the AES algorithm for the images of various sizes. In the scheme of encoding images without DWT, the number of frames to be encoded (each of 128 bits) is shown in brackets in column 2. After decomposition using 2D-DWT using two level, the number of frames to be encoded is shown in brackets in column 5. After DWT, the pixels are represented using 9 bits. The total number of frames to be encrypted after DWT is reduced by 50%. Thus, the computation time for AES algorithm is reduced to less than 8 s (16 seconds is the time without DWT). As the AES algorithm encodes the sub bands independently, the total time taken for encoding is less than two seconds in the modified version of the algorithm.

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Fig. 3 Modified secure image encoding [11] Table 1 Number of bits in the sub bands after decomposition using 2D DWT Input image size Bits per frame LL2/LH2/HL2 LH1/HL1/ No. of bits to be /HH2 size (in No. HH1 size (in No. encoded using of bits) of bits) AES after selection 64 × 64 128 × 128 256 × 256 512 × 512 1024 × 1024

32768 (256) 131072 (1024) 524288(4096) 2097152(16384) 8388608(65536)

2034 9126 36864 147456 589824

9126 36864 147456 589824 2359296

15228 (119) 64242 (502) 258048 (2016) 1032192 (8064) 4128768 (32256)

4 Implementation of DWT and AES Architectures 4.1 Selection of DWT Filter Selection of appropriate wavelet filter is of prime importance for secure image coding. Choice of filters is based on two important parameters, first is the hardware complexity in implementation and second adaptability for all possible images. There are variety of wavelet filters are available, and among them, ‘bi-orthogonal 4.4 ’(bio4.4) is the better one due to its properties mentioned in [12–14] and [15].

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4.2 Fast Architectures for DWT and AES In this section, fast architectures for DWT and IDWT are designed over ASIC platform. Tze-Yun Sung et al. [16] have proposed a multiplier less 2-D DWT and IDWT Architectures using 4-tap Daubechies Filters. These architecture had utilized 100% of hardware and consumed ultra-low power and has consistent structure, trouble free control flow, higher throughput and scalability. With the end goal to lessen the complexity and boost the speed of computation, linear algebra equations of DWT are utilized [17] for implementation in VHDL. This linear algebra approach gives nearly a similar output with usage of very less hardware resources. Additionally with the end goal to adjust the time required by serial input and resources required by the parallel input, a more appropriate methodology has been taken which forms eight image coefficients at a time. Motra et al. [18] developed a DWT processor with parallel architecture and increased the throughput by creating two outputs in a single clock cycle. The speed of this architecture does not rely upon the quantity of filter taps and the quantity of bits per sample. Additionally, the IDWT processor gives relatively perfect reconstruction to one-dimensional signals. Parhi and Nishitani [19] proposed a lifting scheme-based folded architecture of DWT and the IDWT which has low latency but increased utilization of hardware area. Also, complex routing and interconnections were required for the converters used in this architecture. The developed Verilog HDL code using has been synthesized, and the physical design has been carried out using 65 nm CMOS technology. An area-efficient high-speed architecture for DWT has been introduced in [20]. In this processor, a partially serial architecture with pipelining was used to improve the speed and optimize the utilization of hardware resources on the target FPGA. The results demonstrated that this design can operate with a maximum frequency of 231 MHz with power consumption of 117 mw. The encryption and decryption of AES have been implemented in [21] using VHDL and verified on Spartan 3 FPGA. It was observed that both encryption and decryption models had been functioned on 50 and 100 MHz. A fully pipelined processor for AES encryption has been implemented in FPGA by Alireza Hodjat and Ingrid Verbauwhede in [22]. This architecture had used the pipelining techniques like loop unrolling, inner-round and outer-round and achieved a maximum throughput of 21.54 Gbits/s.

4.3 ASIC Implementation of Proposed DWT Architecture The lifting-based DWT has been developed in this work as a part of the implementation. The lifting equations are given where α = −1.586134342, β = −0.052980118, γ = 0.882911075, δ = 0.443506852, ζ = 1.149604398. The coefficients are scaled to 213, i.e., multiplied by 8192 to get 13-bit precision using fixed integer arithmetic for floating point numbers for the addition and multiplication operations as shown in Table 2. The results from the arithmetic operations are rounded to 255 if greater

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Table 2 Scaled coefficients for DWT operations DWT Scaled coefficient (213 ) α = −1.586134342 β = −0.052980118 γ = 0.882911075 δ = 0.443506852 1/K = ζ = 1.149604398 K = 1/ζ = 0.8698644522

−12993 −434 7232 3633 9417 7125

than 255. If the result of an arithmetic operation is negative, then it is rounded off to zero. These rounding operations are carried out since the range for pixel value of an image is 0–255 only. In the three level DWT, first level-1 row processing of the input image is done as per the lifting equations given in the Eq. (1). After the level-1 row processing, the outputs of low pass and high pass coefficients were obtained. The low pass output is further processed in a level-1 column processor to get four sub band outputs LL1, LH1, HL1 and HH1. LL1 is further processed in level-2 row processor and results the output of low pass and high pass filters. These outputs are further processed by level-2 column processors separately and resulted four sub band outputs such as LL2, LH2, HL2 and HH2. ⎧ Y (2n + 1) ← X ext (2n + 1) + (α × [X ext (2n) + X ext (2n + 2)])[STEP1] ⎪ ⎪ ⎪ ⎪ Y (2n) ← X ext (2n)(β × [Y (2n − 1) + Y (2n + 1)])[STEP2] ⎪ ⎪ ⎨ Y (2n + 1) ← Y (2n + 1) + (γ × [Y (2n) + Y (2n + 2)])[STEP3] [h] Y (2n) ← Y (2n) + (δ × [Y (2n − 1) + Y (2n + 1)])[STEP4] ⎪ ⎪ ⎪ ⎪ Y (2n + 1) ← −K × Y (2n + 1)[STEP5] ⎪ ⎪ ⎩ Y (2n) ← K1 × Y (2n)[STEP6]

(1) LL2 is processed in level-3 row processor to get high pass and low pass outputs and thereby processed in level-3 column processors separately resulting in the four sub band outputs LL3, LH3, HL3 and HH3. These ten sub band outputs of DWT are integrated together to get the resulting DWT output of the image. The three level DWT is modeled using HDL, and a test bench is developed for functional verification. There are sixteen number of inputs, each having bit width of 20 bits. These inputs were sent by serial input parallel output (SIPO) technique. The DWT comprises adders, multipliers, multiplexers and registers. At whatever the point the inputs are transmitted through SIPO, the data will be partitioned into even and odd parts. These parts are put away in the temporary registers. At the point when reset is high, the data at temporary register will become zero. At whatever point the reset is low, the input data will split into even and odd data sets. The input data will read up to sixteen clock cycles after that the data reading will be according to lifting scheme

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Fig. 4 Snapshot of RTL schematic of DWT and IDWT Table 3 Distinctive values at various phases of DWT synthesis Report DC DC (post DFT) Area (sq mm) Power (mW) Setup timing Setup slack Hold timing Hold slack

44777.952 6.2687 4.53 0.00 0.45 0.00

61627.73 11.2465 4.57 0.01 0.37 0.00

PT (post DFT) – 7.673 6.07 0.02 0.30 0.32

technique. The output data contains low pass and high pass elements. This is the 1D DWT and the two level DWT is that the low pass and the high pass elements will be again divided into LL, LH and HH, HL components. This has been verified using Modelsim. The Netlist for DWT and IDWT after synthesis is shown in Fig. 4. The synthesized Netlist is analyzed for its timing, and the timing report is presented in Tables 3 and 4. The results of timing are obtained for zero slack with timing period set to 2 ns. Post DFT with Prime Time provides the best performance results. IDWT occupies more area and also power due to intermediate memory.

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Table 4 Distinctive values at various phases of IDWT synthesis Report DC DC (post DFT) Area(sq mm) Power (mW) Setup timing Setup slack Hold timing Hold slack

64246.08 10.2732 4.66 0.00 0.47 0.00

PT (post DFT)

93907.229998 16.2160 4.60 0.00 0.35 0.00

– 12.701 6.10 0.00 0.28 0.33

Table 5 Physical design timing results of DWT

Report

PD

Power (mW) Setup timing Setup slack Hold timing Hold slack

11.7689 0.5 0.197 0.25 0.214

Table 6 Physical design timing results of IDWT

Report

PD

Power (mW) Setup timing Setup slack Hold timing Hold slack

19.7074 0.5 0.246 0.25 0.258

Tables 5 and 6 present the timing results after physical design. Physical design is carried out with actual wire load models. The timing results provide insight on maximum frequency and maximum power dissipation. Floor plan decides the size of the design die, makes the boundary and core area and makes wire tracks for arrangement of standard cells. It is a procedure of positioning blocks or macros on the die. The control parameters of floor planning such as core width and core height, aspect ratio, core to left, core to right, core to bottom, core to top, i.e.,)core utilization depend on the core area and cells straps and trunks are divided. After creating the design library, the power and ground connections need to define, use floor planning for creating the boundary and core area and create the power and ground rings and straps. Snapshot of the IR drop map of the design of DWT and IDWT is analyzed and is shown in Fig. 5, in the IR, drop map consists of Vdd -based drop map and Vss -based drop map. Table 7 shows the power report of DWT and IDWT after physical design. From the results, it is found that the IDWT consumes more power than DWT.

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Fig. 5 Voltage drop maps of DWT for Vdd and Vss Table 7 Physical design power results of IDWT Power (mW) PD (DWT) I/O net switching power Total switching power Total short circuit power Total internal power Total leakage power Total power

0.05285 2.34769 5.55174 2.65167 0.2177 10.7689

PD (IDWT) 0.065201 4.43631 10.1352 4.72334 0.41259 19.7074

4.4 AES ASIC Results HDL code for the proposed AES algorithm is developed and is synthesized using Synopsys targeting 65 nm CMOS technology. The results of AES implementation are presented.

4.4.1

AES Area Report

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5 Conclusion The present study brings forth with valuable findings highly relevant in the field of image encryption and decryption. The findings can have far-reaching impact on the area, timing and power performance of the image data compression and encryption. The findings of the present study uphold the advantages of the AES as a measure for encrypting the images and other data. The procedures followed in the AES can be made more susceptible by using algorithm that suits the situation concerned, and even complex input image can be encrypted. A lifting scheme for DWT is developed and implemented. The top design is synthesized in Synopsys tool compatible 65 nm CMOS technology. The implemented DWT operates at a maximum frequency of 350 MHz consuming. Power optimization can be further carried out with low power library design.

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References 1. Chen LG, Huang CT, Chen CY, Cheng CC (2007) VLSI design of wavelet transform, analysis, architecture, and design examples. Imperial College Press 2. Sundararajan D (2016) Discrete wavelet transform: a signal processing approach. Wiley, London 3. Daemen J, Rijmen V (2013) The design of Rijndael: AES-the advanced encryption standard. Springer, New York 4. Wilson P (2011) Design recipes for FPGAs: using verilog and VHDL. Elsevier 5. Smith MJS (1997) Application-specific integrated circuits, vol 7. Addison-Wesley Reading, MA 6. Dia D, Zeghid M, Atri M, Bouallegue B, Machhout M, Tourki R (2009) Dwt-aes processor for a reconfigurable secure image coding. Int J Comput Sci Eng 1(2) 7. Acharya T, Tsai PS (2005) JPEG2000 standard for image compression: concepts, algorithms and VLSI architectures. Wiley, London 8. Ravi RV, Subramaniam K (2017) Image compression and encryption using optimized wavelet filter bank and chaotic algorithm. Int J Appl Eng Res 12(21):10595–10610 9. Ravi RV, Subramaniam K (2017) Optimized wavelet filters and modified huffman encodingbased compression and chaotic encryption for image data. Int J Appl Eng Res 12(13):3961– 3977 10. Ravi RV, Mahalakshmi R (2014) Universal intelligent data encryption standards&58; a review. Asia Pacific J Mult Res 2(3):184–192 11. Ravi RV, Mahalakshmi R (2014) Dwt-aes based information security system for unmanned vehicles, vol 3. p 101 12. George LA, Al-Abudi BQ, Mohammed FG (2007) Image compression based on biorthogonal wavelet transform. J Al-Nahrain Univ Sci 10(2):178–186 13. Liu H, Zhai LP, Gao Y, Li WM, Zhou JF (2005) Image compression based on biorthogonal wavelet transform. In: IEEE international symposium on communications and information technology, 2005. ISCIT 2005. vol 1, IEEE, pp 598–601 14. Rout S (2003) Orthogonal vs. biorthogonal wavelets for image compression. Master’s thesis, Virginia Polytechnic Institute and State University (Virginia Tech) 15. Raajan N, Vijayabhaskar P, Shiva G, Mithun P (2014) Evaluation of wavelet filters in image coding using spiht. J Appl Sci 14(14):1618 16. Sung TY, Shieh YS, Yu CW, Hsin HC (2006) Low-power multiplierless 2-d dwt and idwt architectures using 4-tap daubechies filters. In: 7th international conference on parallel and distributed computing, applications and technologies, 2006. PDCAT’06. IEEE, pp 185–190 17. Al Muhit A, Islam MS, Othman M (2004) Vlsi implementation of discrete wavelet transform (dwt) for image compression. In: Proceedings of international conference on autonomous robots and agents, vol 4. ICARA 18. Motra A, Bora P, Chakrabarti I (2003) An efficient hardware implementation of dwt and idwt. In: TENCON 2003 conference on convergent technologies for the Asia-Pacific region, vol 1, IEEE, pp 95–99 19. Parhi KK, Nishitani T (1993) Vlsi architectures for discrete wavelet transforms. IEEE Trans Very Large Scale Integ (VLSI) Systems 1(2):191–202 20. Kaur S, Mehra R (2010) High speed and area efficient 2d dwt processor based image compression. Sign Image Process Int J (SIPIJ). https://doi.org/10.5121/sipij.2010.1203 21. Deshpande AM, Deshpande MS, Kayatanavar DN (2009) Fpga implementation of aes encryption and decryption. In: 2009 international conference on control, automation, communication and energy conservation, 2009. INCACEC 2009. IEEE, pp 1–6 22. Hodjat A, Verbauwhede I (2004) A 21.54 gbits/s fully pipelined aes processor on fpga. In: 12th annual ieee symposium on field-programmable custom computing machines, 2004. FCCM 2004. IEEE, pp 308–309

Launch Overheads of Spark Applications on Standalone and Hadoop YARN Clusters P. S. Janardhanan and Philip Samuel

Abstract Spark is widely used as a distributed computing framework for in-memory parallel processing of iterative applications. The basic unit of execution in Spark is executor. Spark applications perform well for applications in which repeated iterations are done on data stored in resilient distributed datasets (RDD). The RDDs reside on executors hosted on exclusively allocated JVMs. Application jobs are split and distributed on executors running in parallel on the nodes of a cluster. High performance for Spark applications is achieved by increasing parallelism by scaling up the number of executors. Spark can be configured to run on native standalone clusters or Hadoop YARN clusters. The run time of Spark applications can be divided into launch time and execution time. Launching Spark applications incurs overheads depending on the clustering framework. The execution time depends on the hardware capabilities of the servers of the node and the clustering framework. The launch overhead is proportional to the number of executors. This paper presents the results of the study done to determine the application launch overheads of Spark applications on two commonly used clustering platforms used for deploying Spark applications, namely standalone and Hadoop YARN clusters. To minimize the dependency of I/O overheads, a CPU intensive job is used for evaluating the launch overheads. On both types of clusters, the overheads are determined experimentally. The results of this study help in deciding the number of executors to minimize the impact of the launch overheads on run time. Models are developed using regression methods that can be used to predict the launch overheads of Spark applications on both types of clusters. Keywords Spark applications · Launch overhead · Distributed computing · Scalability · Hadoop YARN · Apache Spark · SparkBench

P. S. Janardhanan (B) SunTec Business Solutions Pvt Ltd, Thejaswini, TechnoPark, Trivandrum, India e-mail: [email protected] P. Samuel Department of Computer Science, Cochin University of Science and Technology, Kochi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_5

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1 Introduction The Spark computing framework provides a programming abstraction and parallel run time environment to hide complexities of fault-tolerance and cluster computing. In general, Spark applications are CPU intensive and iterative in nature. Workloads can be deployed on Spark for applications like machine learning, stream processing, graph processing and SQL query processing. Spark core can be deployed on different types of clustering platforms like standalone, Hadoop YARN, Mesos, Kubernetes etc. The run time of these applications depends on the overheads introduced by these clustering platforms in launching partitions of the job on Spark executors distributed on the nodes of the cluster. To minimize the run time, users prefer to choose a clustering platform which introduces less overhead [1]. A study is done to determine the contribution of the launch time overhead to the total run time of Spark applications deployed on Spark standalone and Hadoop YARN clusters. The key contribution of the work described in this paper is the development of a model that can be used to predict the launch overhead from the number of executors. The models are developed separately for both types of clusters. This helps in judicious selection of the cluster framework. Section 2 provides detailed information on Spark executors. Section 3 gives details of the problem which is getting solved in this work. It is followed by a section on hardware and software details of test environment and the benchmarking suite used for this study. Section 5 gives details of the method adopted for experimentally determining the launch overhead. Section 6 is about overhead estimation and modeling. This section begins with details of experiments conducted to determine the impact of number of executors on the overhead on a single node cluster. It is followed by a study on the dependency of the overheads on the number of executors on a cluster with multiple nodes. Section 7 summarizes the results of this work.

2 Spark Executors Spark is a flexible and scalable distributed high-performance computing platform with concise, powerful APIs and higher-order tools and programming languages [2]. Spark executors are JVMs with dedicated allocation of CPU cores and memory. The executor process runs multiple tasks concurrently on multiple threads of the JVM. The executor process remains for the lifetime of the Spark application. Running executors with too high memory often results in excessive garbage collection delays. Running tiny executors (with a single core and just enough memory needed to run a single task, e.g.,) throws away the benefits that come from running multiple tasks in a single JVM. For I/O intensive jobs, more than 12 cores per executor can lead to bad I/O throughput. The main abstraction in Spark is that of a resilient distributed dataset (RDD), which represents a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost [2]. RDDs are stored on executors

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distributed on the nodes of the cluster. Launching executors introduces a delay, and the number of executors is to be selected optimally to minimize the launch delay and maximize the resource utilization. On Hadoop cluster, executors get allocated on containers managed by the YARN resource manager.

3 Problem Statement Partitioning and parallel execution are the basic techniques behind performance enhancement of Spark applications. The primary goal is to run enough concurrent tasks so that the data destined for each task fits in the memory available to that executor. If there are fewer tasks than the cores allocated to the executors, it will not be taking advantage of the maximum parallelism available with the executors. The performance advantage of Spark applications depends on the level of parallelism introduced in the execution of jobs. It is difficult to decide on the ideal parallelism level because of the overheads in launching the applications. These overheads are introduced in the creation, allocation and distribution of the executors [3]. For jobs with short computation times, it will not be a good idea to make use of large number of executors because the overheads can exceed the actual execution time. At present, there is no accurate information available on the overheads introduced in managing executors by the clustering frameworks. Hence, it becomes difficult to choose the right number of executors for different clustering platforms to meet the performance deadlines by fully utilizing the computing resources.

4 Test Environment The experiments were performed on a 5 Node cluster with identical hardware configurations. These machines are with Intel(R) Core i5-3570 CPU @ 3.40 GHz and 16 GB memory. The CPU has four physical cores with four logical cores each. They have 512 GB physical storage. The HDFS distributed file system was configured with Apache Hadoop Release 2.8.0. Hadoop YARN was configured with capacity scheduler. Apache Spark was configured in client mode with Release 2.2.1 with KryoSerializer. Java version 1.8 is used. SparkBench is an open-source benchmarking tool for Spark distributed computing framework and Spark applications [4]. It is a flexible system for simulating, comparing, testing and benchmarking of Spark applications. It enables in-depth study of performance implication of Spark system in various aspects like workload characterization, parameter impact, scalability, etc. It also provides insights and guidance for cluster sizing and provisioning. SparkBench has diverse and representative workloads. Workloads are available for typical applications like machine learning, graph processing, streaming and SQL query applications. It allows users to explore different

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parameter configurations easily and study the impact on application performance. For the experimental study, SparkBench Release 2.1.1 is configured by disabling dynamic allocation of executors.

5 Overhead Evaluation SparkPi, a simple workload provided in the SparkBench Suite, is used for evaluating the launch overhead of Spark applications. This CPU intensive workload computes an approximation for the value of π. It estimates π by “throwing darts” at a circle, then picks random points in the unit square ((0, 0)–(1,1)) and sees how many falls in the unit circle. The fraction should be π/4. SparkPi is particularly useful for exercising the computing power of Spark distributed computing framework without the consideration of heavy I/O from data-reliant workloads. The SparkPi configuration parameter slices specify the number of partitions that will be spawned when SparkPi workload is in execution. The partitions decide the number of tasks into which the job gets split into at run time. The total run time of applications on Spark distributed computing platform depends on the time taken to launch the tasks of the application on Spark executors on the cluster and the execution time. The fixed overhead incurred for application launch varies from cluster to cluster. This overhead is determined experimentally using a novel method. SparkPi allows specifying a repeat count for the application in which the computation is repeated by specified number of times. The SparkPi workload is launched with repeat counts from 1 to 10 on both standalone and the Hadoop clusters. When the execution is repeated, the run time increases linearly with the repeat count. The application launch time is independent of the repeat count since the tasks get deployed on the executors only once. Figure 1 shows an example in which the execution time is measured and plotted as a function of the repeat count. A straight line is fitted on these data by regression method, and the values of the intercept and the slope are determined. The intercept gives the fixed launch overhead, and the slope of the line gives the average time taken for each repeat cycle of execution. In this example, the intercept is 24.2, and slope is 6.691 for the standalone cluster. Hence, the deployment overhead is 24.2 s, and the execution time taken for each repeat cycle is 6.691 s. For Hadoop YARN cluster, the overhead is 41.13 s, and execution time per repeat cycle is 6.086 s. Once the application is launched, the execution time remains almost the same for both types of clusters.

6 Overhead Evaluation and Modeling The purpose of the study is to determine the executor launch overhead on standalone and Hadoop YARN clusters. The performance of SparkPi, a representative CPU intensive application, is evaluated in two different environments on both types

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Fig. 1 Overhead evaluation by regression

of clusters. The first environment consists of a single node cluster on which the application is deployed on increasing number of executors by reducing the resource allocation. The second environment consists of a 5 Node cluster in which the number of executors with fixed resource allocation is varied from 5 to 30.

6.1 Executor Scaling on Single Node Cluster A study is conducted to evaluate the dependency of launch overheads on application performance on a single node cluster. A single node cluster is formed with Spark standalone and Hadoop YARN clustering frameworks. Spark executors with varying resource allocation as given in Table 1 are configured, and the SparkPi application is run with repeat counts from 1 to 10. The launch overhead and execution time are extracted using the method described in Sect. 5 and provided in this table. Table 1 Executor resources overhead and execution time Executor resources

Standalone

Memory GB

No. of cores

No. of executors

12

12

1

6

6

3

3

2 1

Overhead Secs

Hadoop YARN Execution time

Overhead Secs

Execution time

0.00

33.60

9.87

32.61

2

6.53

7.29

11.40

7.51

4

11.87

3.99

19.47

4.13

2

6

17.67

3.55

24.47

3.79

1

12

30.92

4.48

39.19

4.12

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TIME (SECS)

Overhead Time 50 45 40 35 30 25 20 15 10 5 0

0

2

Execution Time

4

6

8

Overhead+Execution

10

12

14

NO. OF EXECUTORS

Fig. 2 Dependency of overhead and execution time on number of executors

Figure 2 shows a graphical representation of the data shown in Table 1 for the Hadoop YARN cluster. From this diagram, it is seen that the major contribution of the run time is the overhead with increase in the number of executors. The launch overhead and the execution time are almost equal when there are two executors on the node. Beyond this, the launch overhead increases steadily with the number of executors.

6.2 Executor Scaling on Multiple Nodes The aim is to evaluate the dependency of launch overhead and execution time on the number of executors getting deployed on multiple nodes. In this evaluation, SparkPi is configured with 10,000 slices. Executors are configured with 2 GB memory and three cores each. The resources for an executor are allocated in such a way to avoid resource contention when maximum number of executors is deployed on one node. On a 5 Node cluster, the number of executors is varied from 5 to 30, and the parameters are determined in each of the cases. In this scheme, one node will have 1 to 6 executors getting deployed. Figure 3 shows the variation of launch overhead with increasing number of executors. From the data from the standalone cluster, a straight line is fit by linear regression and is represented by Eq. (1) in which t represents the execution overhead in seconds and n represents the number of executors. t = α + βn

(1)

where α = 3.4, and β = 0.69. In the case of the Hadoop YARN cluster, Eq. (2) represents the regression model. t = α + βn

(2)

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Fig. 3 Variation of launch overhead with executors on multiple nodes

where α = 3.68, and β = 1.22. The parameter α depends on the hardware specifications of the nodes of the cluster. In case of Hadoop YARN cluster, the slope or the rate of increase of the overhead is almost twice than that of the standalone cluster. Hence, with large number of executors, standalone cluster gives better performance.

7 Results The launch overhead increases with number of executors on single-node and multinode clusters. The rate of increase is more in the case of Hadoop than the Spark standalone cluster. The execution time of tasks is almost identical on both the clustering frameworks when the jobs are executed in a controlled environment. For jobs with short execution times, standalone cluster is to be preferred to reduce the dependency on executor launch overhead. The standalone cluster will have less flexibility in resource utilization since the executors are defined and deployed statically when the cluster is brought up. For long running CPU-intensive applications, Hadoop YARN clusters are recommended since the launch overheads will be negligible when compared with the total execution time of the spark jobs. Hadoop YARN provides better resource utilization since the resources are allocated dynamically and it allows coexistence of different types of applications on the same cluster. Models represented by Eqs. (1) and (2) may be used to predict the launch overheads from the number of executors in both types of clusters.

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References 1. Vellaipandiyan S, Raja PV (2016) Performance evaluation of distributed framework over YARN cluster manager. In: IEEE international conference on computational intelligence and computing research (ICCIC), Chennai, India 2. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark cluster computing with working sets. HotCloud 3. Janardhanan PS, Samuel P (2017) Analysis and modeling of resource management overhead in Hadoop YARN clusters. In: IEEE DataCom 2017, The 3rd IEEE International Conference on Big Data Intelligence and Computing Orlando, Florida, USA 4. Li M, Tan J, Wang Y, Zhang L, Salapura V (2015) SPARKBENCH a comprehensive benchmarking suite for in memory data analytic platform spark, IBM TJ Watson Research Center, CF’15 In: Proceedings of the 12th ACM international conference on computing frontiers, Ischia, Italy, Article No. 53

Neighbor-Aware Coverage-Based Probabilistic Data Aggregation for Reducing Transmission Overhead in Wireless Sensor Networks M. Umadevi and M. Devapriya

Abstract Wireless sensor network is having a good number of distributed sensor nodes on its environment. Most of the recent research focused on its critical issues which relate to energy efficiency, and it is in need of developing an efficient reporting mechanism to transmit data into sink by improving energy efficiency. The paper proposes an enhanced aggregation algorithm where the aggregation model by considering two approaches as aggregation wait time and route link factor. This approach helps in reducing the number of aggregation in the network and discovering uncovered nodes, which automatically, in turn, reduces the energy consumption in a network. The work proposed neighbor-aware probabilistic data aggregation (NACPDA) protocol for reducing transmission overhead. In this paper, we propose a neighbor-aware temporal convergence on probabilistic data aggregation model for reducing routing overhead in WSN. In order to make the perception on neighbor effectively, we propose a novel aggregation wait time to determine the order of it, and then we can obtain the more accurate extra coverage ratio by sensing neighboraware coverage facts. We also define a routing link factor to provide the degree of the node for aggregation. This work combines these benefits of neighbor information, and probabilistic model considerably reduces the overhead and number of transmission in aggregation. Simulation for energy consumption ratio, the end-to-end delay has been accomplished using NS2.34 for the various pause time of packets arrival. Results show that the energy consumption ratio improves for NACPDA which also provides a reduced delay on comparisons. Changing the values of various pause time demonstrates the trade-off between energy consumption and transmission overhead. Finally, the results of NACPDA show that it prolongs the life of sensors by achieving energy consumption with an average of 0.287 joules for different pause time.

M. Umadevi (B) Department of Computer Science, Sri Adi Chunchanagiri Women’s College, Cumbum, India e-mail: [email protected] M. Devapriya Research Department of Computer Science, Government Arts College, Coimbatore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_6

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Keywords Aggregation · Probability · Neighbor · Coverage · Transmission · Energy

1 Introduction The wireless sensor network is collection densely coupled with a large number of tiny sensor nodes [1]. The fundamental task of a sensor node is to sense, collect data from a covered region and transmit them towards a sink node where the place to promotes the collected data for further processing. The sensor node is cost-effective and small capable device on its energy. This network is deployed with thousands of sensor nodes, which are powered by batteries and remotely controlled. So sensor devices are difficult to recharge often and require certain routing protocols to transmit data between sensor nodes and the sink nodes [2]. The applications like continuous monitoring and reporting the network regions, low energy adaptive clustering hierarch (LEACH) [3] are a suitable protocol where all sensor nodes are involved in sensing, collecting and transmitting data to the sink node periodically. Therefore, it is critical and important to design the WSN network by data aggregation algorithm that conserves energy and prolongs the network lifetime [4]. There are several existing data aggregation algorithms which attempt to find the optimum aggregation. However, still, it needs to focus on the problem of energy efficiency in its coverage region when designing neighbor—aware probabilistic data aggregation algorithm. When data arrive from a neighbor, it needs to measure and decide whether it needs to transmit or not where the coding techniques used to reduce the number of forwarding bits. This kind of data aggregation needs more reliability when it is having sensor geographic information since it is unaware of their location. In [5], the author proposed a training protocol for a deployment area which is capable of a virtual infrastructure for efficient data gathering with neighbor coverage with geographic location identification. Wireless sensor networking is an emerging technology to sense and collect data from the physical environment that plays an important role in a number of applications. However, these advantages come with several limitations as delay and transmission overhead. Since it is an open deployment with wireless communication, sensor nodes are strictly controlled in memory, processing energy level and its power resources; and they are prone to transmission overhead in communication.

2 Related Works Each sensor nodes of WSN can sense current data and communicate it to its neighbors [6]. For transmitting single data with multiple hop node consumes much energy, since in many cases, nodes communicate through their neighbors toward the sink node. In

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that case, every node should know remaining energy of neighbors is the most important phase for packet transmission. So WSN nodes need to take an action to control transmission overhead which confirmations the importance of decreasing time and increasing packet delivery ratio [7]. For that communication protocols of sensors play a major role in improving the lifetime efficiently [8]. The author proposed multipath routing protocol to overcome the traffic overhead and achieved reliability of sensor nodes by data splitting technique. The recent researchers develop a number of protocols by focusing the aspect of reducing the data transmission for achieving energy efficiency by reducing the size of data [9]. Since sensor nodes have the nature of mobility, they may be failed to communicate by reaching uncovered regions. So the number of researchers concentrated on improving the quality of coverage and connectivity of mobile nodes to enhance its lifetime [10]. A prediction-based target location is achieved by implementing a probability model [11, 12] which accomplishes to improve the lifetime of sensor nodes. A set of nodes is arranged on its strategic position [13] which is ready as a forwarding node to capitalize on the network coverage while decreasing the number of rebroadcasting process in the geometric environment. Here each node can able to decide whether to forward a packet or not based on the retransmission probability. This probability consideration is helpful to obtain the solution for dissemination coverage, latency, and communication overhead [14]. A similar concept is studied and observed in [15] by developing a sweep-coverage method to show the dynamic deployment with coverage features of nodes. Neighbor selection within its proper coverage area is a must to determine for reducing transmission overhead. The author [16] proposed the coverage conscious connectivity restoration (C3R) algorithm and energy-centric optimized recovery (ECR) to mitigate the issues of temporarily replacing the unsuccessful node with one or multiple of its neighbors. The encoding method [17, 18] with multiple random walk has demonstrated the process of selecting the neighbor nodes is an effective way to reduce the communication cost since random walk is a probability-based protocol and it has no branch off. Random selection neighbor needs code degree for the distribution of data to its neighbor. Based on the confirmation of expected code degree of the node within sufficient walk, the decoding process recovers original data. Here communication overhead rate is high which concludes the random walk fails on its environment [19]. In [20], probabilistic coverage algorithm (PCA) was developed to check the required coverage probability of currently deployed topology. The author [21] also investigated the importance of reducing the overhead of communication and developed the protocol by considering encoded packets and posterior probability distribution in its neighborhood only for broadcasting. In [22], if the neighbor selection from its coverage area gets over then it is being compared with an already selected set of neighbors to measure the lowest cost. Once it gets over, then it is being compared with an already selected set of a neighbor for its lowest cost. Further, we stop selecting additional neighbors even with a cost lower than the cost of a complete set of the sensor to reduce transmission overhead. The analytical model of [23] developed the probabilistic coverage approach for tracking the moving object in a dynamic environment. By enabling the duty cycling of nodes, energy saving is achieved at the cost of probabilistic coverage. Since nodes are having fewer directly connected neighbors,

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the author [24, 25] decided to limit the transmission power on its neighboring nodes then the author demonstrated the relationship between link quality and transmission power control on its covered region. So the author [26] proposed negative correlation scenarios to preserve both energy consumption and a number of transmissions. The complete similar studies show the emergence occasion to elucidate the need for likelihood neighbor selection with further aggregation process by reducing the number of transmissions [27]. The main contribution of this work is described as follows: The work proposed a novel scheme to calculate aggregation wait. It determines the forwarding order of nodes toward the sink. The minimum delay is assigned to the node which has a high number of common neighbor with the previous node. So the aggregation wait makes benefits that node has transmitted the data packets spread to more neighbors. We also propose a mechanism for determining the aggregation probability. This process mainly depends on uncovered region neighbors (URN), route link factor (RLF) and degree of the local node to calculate the aggregation probability. It includes two subparts as ECR and RLF. The construction of this paper is as follows. Section 2 presents some related works. Section 3 exhibits the design of the proposed algorithm. Section 3.4 is demonstrated the implementation of the proposed model with performance metrics then the comparisons of simulation results are evaluated with different protocols. Section 4 shows the conclusion.

3 Neighbor-Aware Coverage-Based Data Aggregation Protocol In this section, we proposed a protocol for calculating the aggregation wait and likelihood aggregation. A node with smaller hop count by one is identified then it (upstream neighbor) is used to calculate the aggregation wait and Extra Coverage Ratio (ECR) and Route Link Factor (RLF) used for aggregation.

3.1 Uncovered Neighbor Set and Aggregation If a node ni receives an AGreq packet from its previous node p, it is mainly used to identify the number of neighboring nodes but it has not been covered previously by AGreq. If the high degree of neighbor nodes (ni ) is uncovered then a AGreq packet can reach ECR. From this, the uncovered region neighbor (URN) set of node ni represented as, URN(n i ) = N (n i ) − [N (n i ) ∩ N ( p)] − { p} − FHS(n i ).

(1)

where N(p) and N(ni ) are the neighbor set of node p and ni , respectively. Here, p is a node which sends an AGreq packet to node ni . FHS(ni ) is a node which is very

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close to sinking which is known as first hop node to sink. The above Eq. 1 is used to obtain URN in an initial state. Due to the mobility of node, there is a chance of getting duplicated packets from neighbors. So it must be adjusted with URN(ni ) with neighbor information. To avoid channel overlapping, each node should with aggregation wait AGwait. The node nearest to sink node takes maximum wait time for efficient aggregation. At the same time, this maximum wait time should not exceed with predefined threshold (wth) value, and it assigned as 0.1 constantly. This Max_wait has measured for a further decision making of delay occurred in nodes which shows as the important key factor in this protocol. The aggregation wait AGwait is defined as follow: AGwR(n i ) = 1 − |URN(n i )|/|N (n i )|.

(2)

AGwait(n i ) = Max_ wait ∗ AGwR(n i ).

(3)

where AGwR(ni ) is the aggregation wait ratio and AGwait is aggregation delay of node ni . Max_delay is a lesser constant wait time. Here wait time is used to determine the order of transmitting nodes toward aggregation process. When a node p sends AGreq packet, then all its neighbor set (ni = 1, 2, 3, Np) is ready to receive the AGreq packets. This AGwait is mainly to get neighbor-aware coverage information efficiently. Then, the timer is ready to initiate its process once the calculation of AGwait is finished

3.2 Neighbor-Aware Information and Aggregation Probability The aggregation process initiates its process using upstream of nodes which have a larger aggregation wait. It actually listens to the channel to receive AGreq packets from nodes ni which has first lower one. Thus, node ni adjusts with its neighbor set. Then, the uncovered region neighbors are modified as URN(n i ) = URN(n i ) − [URN(n i ) ∩ N (n i )].

(4)

The AGreq from NJ is discarded once adjusting the uncovered region neighbors URN(ni ). This process is performed until it gets final URN set with the timer expiration of aggregation wait of node ni . Here we need extra coverage ratio (ECR) of ni nodes which represented as ECRN(n i ) = |URN(n i )|/|N (n i )|.

(5)

When ECR attains high value then (ni ) the high degree of node receives and processes the AGreq packets. Thus, aggregation probability AGprob must be assigned

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with high values to cover entire nodes. The constant value in asymptotic nodal degree relationship (k) is always in range of (0.074 > k < 5.1144 log(n)) [28]. But, an according to [29] the nodes are adjusted with transmission power for neighbor connectivity, and this range is reduced to (0.3043 > k < 0.5139). Using this heuristic formula,(|N(ni )| * RLF(ni ) > 0.5139 < 0.3043), we can modify the link region factor(RLF(ni )). RLF(n i ) = |NRLF |/|N (n i )|.

(6)

Here NRLF is set as 0.3043 > NRLF < 0.5139 which means the minimum range is 0.3043 and the maximum range is 0.5139. We get an aggregation probability by adding these two benefits as ECR and LRF of node ni as AGprob(n i ) = ECR(n i ) ∗ RLF(n i ).

(7)

where AGprob(ni ) is assigned to 1, and it describes the symmetric range of network connectivity probability value which always increases the transmission range and improves the neighbor selection decisions efficiently.

3.3 Algorithm Description The above-described protocol of temporal convergence of neighbor-aware probabilistic data aggregation is performed by further steps to reduce the number of transmissions and improving connectivity range, network lifetime in Algorithm 1.

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3.4 Model Implementation and Performance Evaluation The goal of our proposed work is to limit the number of aggregation [30] by reducing routing overhead of sensor nodes and discovering uncovered region neighbor nodes, which automatically, in turn, reduces the energy consumption in a network. We compare with low energy adaptive clustering hierarchy (LEACH) protocol to evaluate the performance on the following metrics: • Packet delivery ratio. The fraction represents the success number of packets received at sink node from sensor field • Energy consumption. It describes the total energy consumption by the network for its overall transactions. This value is taken as energy for collecting packet from sensor members (Ec) and energy consumed to transmit packets toward sink node (Et). Also, here the total energy required to perform forwarding process is represented by Ef = Ec + Et. • Normalized routing overheads. The control packets include request and response packets exchanged in a network for further data packet transmissions. It shows the number of request and response packets.

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• Average end-to-end delay. It shows the average delay time of successful collection and transfer packet to sink node. It sometimes delays which are caused by traffic and physical intermediates [31]. • Jitter. It is time variation between two arrived packets on sink node affected by the networks traffic, congestion and mobility. The proposed model was simulated for different scenarios on varying on network size and mobility of the sensor nodes. The experiment has taken for different pause time which varied from 5 to 25 m/s for generating mobile scenarios. The different network sizes are considered from 50 to 150 nodes. These metrics shows the better impact of performance on the proposed algorithm by the simulation

3.5 Simulation Environment These randomly deployed nodes were placed in the range of 1000 × 1000 m area. Transmission range of sensor nodes was fixed and assumed that they have cooperative communication. The simulation parameters, values, and its description were given in Table 1. Initially, all sensor nodes were assigned with 100 J of energy. A random waypoint model generated the different scenarios. The complete simulation-based estimation of proposed algorithm was offered using the NS-2 simulator (NS-2.34 version). These systems were compared with Table 1 Simulation parameters

Parameters

Value

Description

Simulation time

200 s

According to simulation clock

Simulation areas

1000 × 1000 m

XY dimension

Number of nodes

50–150

Simulation nodes

Transmission range

50 m

Nodes transmission power

Movement model

Random waypoint

Nodes distribution and movement

Max speed

4 m/s

Mobility

Packet size

512 bytes

Data packet size

Packet rate

4 pkt/s

Packets interval

MAC

IEEE 802.11

MAC layer protocol

Transmission energy

2 × 10−1 J/pkt

Energy to transmit a packet

Receiving energy

1 × 10−1 J/pkt

Energy to receive a packet

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Fig. 1 Packet delivery ratio and pause time

different scenarios and metric sets. The first experiment was traced for the comparisons of protocol LEACH, DBEAR, and EERP with the proposed algorithm. Packet delivery ratio and jitter value are calculated by comparing protocols with low energy adaptive clustering hierarchy (LEACH), distance-based energy aware routing (DBEAR), and energy efficient routing protocols (EERP). The result shows that the performance of LEACH has given unappreciative values on its high mobility scenarios since it depends on data-centric protocol. DBEAR have also given the low performance, compared to proposed protocol as shown in Fig. 1. Simulation result for the proposed NACPDA protocol shows an average of 99.249% packet delivery ratio for the varied pause time (Fig. 2). In this experiment, a comparison was made about normalized routing overhead of different protocols at different mobility scenarios as shown in Fig. 3. The results show that the performance of the proposed protocol was better than LEACH, DBEAR, EERP. The proposed NACPDA protocol has performed better in energy consumption in terms of decreasing the amount of energy consumption as shown in Fig. 4. Thus the results show that there was an average of 0.287 J difference in energy consumption on comparisons.

4 Conclusion The proposed protocol for the mobile sensor network is to reduce routing maintenance overhead of entire network. This method encompassed with neighbor-aware

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Fig. 2 Jitter and pause time

Fig. 3 Control overhead and pause time

coverage and temporal-based approach to find out the probabilistic nodes for aggregation which leads to reducing the transmission overhead and improves the performance of a network. Our results serve as a proof of concept for the idea of this kind of probabilistic sensor network routing, and making the aggregation process depend on network information metrics is an encouraging step toward more robust routing protocols in sensor networks. From the simulation results, this work proved

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Fig. 4 Average energy consumption

that it has a reduced amount of control overhead and better performance metrics as energy consumption, packet delivery ratio and jitter. This work reduces the energy consumption with an average of 0.287 J for different pause time.

References 1. Akyildiz I et al (2002) A survey on sensor networks. IEEE Commun Manage 40:02–114 2. Buratti C, et al. (2009) An overview of wireless sensor networks technology and evolution. Sensors 6869–6896 3. Hui L et al (2011) Negotiation-based TDMA scheme for Ad Hoc networks from a game theoretical perspective. China Commun 8:66–74 4. Villas LA (2009) A reliable and data aggregation aware routing protocol for wireless sensor networks. MSWIM’09, Tenerife, Canary Islands, Spain, ACM, pp 245–252 5. Olariu S, et al (2004) Wireless sensor networks: lever-aging the virtual infrastructure. IEEE Netw 51–56 6. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52:2292– 2330 7. Ghaffari A (2015) Congestion control mechanisms in wireless sensor networks: a survey. J Netw Comput Appl 52:101–115 8. Ghaffari A (2014) An energy-efficient routing protocol for wireless sensor networks using A-star algorithm. J Appl Res Technol 12:815–822 9. Abdulsalama HM et al (2004) Deploying a LEACH data aggregation technique for air quality. Procedia Comput Sci 34:499–504 10. Li X, et al. (2015) A review of industrial wireless networks in the context of Industry 4.0. Wireless Netw 23:23–41 11. Shan Anxing, Xianghua Xu, Cheng Zongmao (2016) Target coverage in wireless sensor networks with probabilistic sensors. Sensors 16:13–72

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12. Bhuiyan MZA et al (2010) Prediction-based energy-efficient target tracking protocol in wireless sensor networks. J Cent S Univ Technol 17(2):340–348 13. Liang C, Lim S, Min M, Wang W (2014) Geometric broadcast without GPS support in dens. In: Proceedings of IEEE CCNC, pp 696–701 14. Pu C, et al. (2014) Light-weight forwarding protocols in energy harvesting wireless sensor networks. In: Proceedings of IEEE MILCOM 15. Cheng W, et al. (2008) Sweep coverage with mobile sensors. In: IEEE international symposium on parallel and distributed processing, pp 1–9 16. Tamboli N, Younis M (2009) Coverage-aware connectivity restoration in mobile sensor networks. In: IEEE international conference on communications ICC’09, pp 1–5 17. Lin Y, et al. (2007) Data persistence in large-scale sensor networks with decentralized fountain codes. In: Proceedings of the 26th annual IEEE international conference on computer communications (IEEE INFOCOM) 18. Lin Y, Liang B, Li B (2008) Geometric random linear codes in sensor networks. In: Proceedings of IEEE international conference on communications 19. Munaretto D, Jand, et al. (2008) Resilient coding algorithms for sensor network data persistence. In: Proceedings of EWSN, LNCS 4913, pp 156–170 20. Ahmed N, et al. (2005) Probabilistic coverage in wireless sensor networks. In: Proceedings of the IEEE conference on local computer networks 30th anniversary (LCN’05), IEEE 21. Song WZ (2014) Ecpc: towards preserving downtime data persistence in disruptive wireless sensor networks. ACM Trans Sens Netw 11 22. Soro S, Heinzelman WB (2009) Cluster head election technique for coverage preservation in wireless sensor networks. Ad Hoc Netw 7(5):955–972 23. Ren S, et al. (2005) A study on object tracking quality under probabilistic coverage in sensor networks. Mob Comput Commun Rev 9 24. Lin S, Zhang J, et al. (2006) ATPC: adaptive transmission power control for wireless sensor networks. In: Proceedings of the 4th ACM conference on embedded networked sensor systems (SenSys 2006). Boulder, Colorado, pp 223–236 25. Jeong J, et al. (June, 2007) Empirical analysis of transmission power control algorithms for wireless sensor networks. In: Proceedings of the 4th international conference on networked sensing systems (INSS ‘07). Braunschweig, Germany 26. Weerasinghe TN (26 May, 2015) Adaptive backbone and link correlation based data transmission schemes for wireless sensor networks. Thesis, Grimstad 27. Zhang XM, Wang EB, Xia JJ, Sung DK (2013) A neighbor coverage based probabilistic rebroadcast for reducing routing overhead in mobile Ad Hoc networks. IEEE Trans Mob Comput 12:424–433 28. Xue F, Kumar PR (2004) The number of neighbors needed for connectivity of wireless networks. Wireless Netw 10:169–181 29. Ganesh A, Xue F (2007) On the connectivity and diameter of small-world networks. Adv Appl Probab 39:853–863 30. Villalba G, et al. Routing protocols in wireless sensor networks. Sensors 9:8399–8421 31. Dulman S, Nieberg T, Wu J, Havinga P (2003) Trade-off between traffic overhead and reliability in multipath routing for wireless sensor networks. In: Proceedings of wireless communications and networking, WCNC, pp 16–20

Analysis and Summarization of Related Blog Entries Using Semantic Web Aarti Sharma and Niyati Baliyan

Abstract To analyze approaches and make use of them effectively and efficiently to generate brand new blogs containing the accurate and appropriate content from different blogs on the same topic by different authors in order to gather and accumulate different point of views at one place in the single blog. Different approaches of clustering, modeling, and summarization are required to be applied to achieve the intended goal. This could help students and other users while finding relevant information in less time at one place in one blog and also for correlation of the topics across documents, pages, blog posts, etc., to offer most relevant content easily to the user. This study aims to review the approaches and techniques used for information integration, search ability, automation, and more demanding tasks like querying and its analysis and also propose an idea for reduction of the manual efforts and time to extract related and relevant information. This is a better idea to put forward that helps filtering irrelevant content and integrating the relevant points from all the blogs and hence creating a brand new blog entry/post. Keywords Blogs · Semantic Web · Topic modeling · Tokenization · Stemming · Vectorization · Summarization

1 Introduction To achieve and perform the goal of generating new blog entry with accurate and relevant information and knowledge, first the blogs from different authors having different content on same topic need to be taken as input, and then, topic modeling could help in the further steps to perform data extraction, clustering, and structuring the text. Natural language processing and machine learning have topic model as a statistical model for finding “topics” that are abstract and occur in multiple documents. Topic modeling is used often as text-mining tool to find semantic structures A. Sharma (B) · N. Baliyan Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_7

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that are hidden inside the text. Instinctively provided that, a blog is about a specific topic, so one would likely expect those specific words to occur in the blog or article less or more often: “parrot” and “bird” will show more often in blog about parrots, “dog” and “bark” will occur in blog about dogs, and “the” and “is” will occur with the same frequency in both. A blog/article typically has a lot of topics in different ratios; thus, in a blog that is 10% about dogs and 90% about cats, there is about nine times more cat words than dog words. The “topics” made by topic modeling ways are groups or clusters of same words. This topic model deals with this intuition in a mathematical equation or framework, which permits to examine a set of blogs and findings, found on the statistics of words in each and what the topics might be. Once proper structuring of text in vector or matrix form is done for all input documents, then analyzing those vectors and integrating the common points among documents just once and all relevant different points to summarize content and generate new blog entry accordingly need to be performed. Due to lack of capacity of the human to grasp vast quantities of information, relevant and crisp summaries are always desirable. For content generation, we have two different categories of text summarization extractive and abstractive which are discussed in detail in Sect. 1.2.

1.1 Literature Review Topic modeling is required for observing and finding the bouquet of words which are called “topics” in big clusters of data. The exact definition of topic could be “the replicated pattern of repeating terms in a collection.” A virtuous topic model should give output like—“health,” “vehicles,” “construction,” and “air” for a topic— pollution, and “fields,” “crops,” and “maize” for topic like—“farming.” They are very beneficial for the motive of clustering of documents, organization of huge blocks of text form data, information withdrawal from unstructured data, and feature selection. Steps involved in topic modeling are as listed below [1–3]: • • • •

Tokenization Stop words removal Stemming Vectorization.

Tokenization It means Segregation of the text into its individual constituent words. The blog content is provided as a pdf document. Texts from the pdf document are first extracted. This pulls out all characters from a pdf document except the images (although, this can me modify to accommodate this) using the python library pdfminer [4, 5]. It simply takes in the name of the pdf document in the home directory, extracts all characters from it, and outputs the extracted texts as a python list of strings. Stop Words Removal The text extracted from the pdf document contains uninformative characters which need to be removed. Throw away any words that do not occur

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too frequently as its frequency of occurrence will not be useful in helping detecting relevant texts (as an aside consider throwing away words of less importance like “a,” “an,” “the,” etc.). These characters reduce the effectiveness of our models as they provide unnecessary count ratios. A series of regex search can be performed that replaces these characters with a blank space. The resulting document is expected to contain just alphanumeric characters. Stemming Combine variants of words into a single parent word that still conveys the same meaning. After extracting the text and annotating them with external senses, make use of these annotated content to train topic model probabilistically. We analyze the method in both terms, i.e., coherence of the topics extracted and also in terms of performance while clustering topic modeling is applied. By doing this, two blogs with near-isomorphic graph labels are likely to be clustered in a semantic grouping and modeled as on one topic. Word co-occurrence makes clusters and generates topics from words that occur often and frequently together, whereas weighted bigraph clustering makes use of URLs from results of Google search to inculcate query similarity and create topics [6]. Word Co-occurrence Clustering Word co-occurrence clustering try textual data computing to acceptable variations that includes misspelled and plural forms to delete stop words, and after that, it creates topics by searching terms that frequently appear repeatedly with topic anchors there in provided queries’ set. This can reduce insufficiency difficulty because topic anchors are looked to be terms more often to be explored in search query respect than regular and normal. It also motivates this algorithm to highlight on fascinating terms inside the queries, instead of blearily creating biterms from every feasible non-stop words. Headings are generated by clustering of hierarchical type on similarity of query pairwise that analyzes to which level both queries are similar on their intersections along with words of catalogue in every topic. It then executes perfectly whenever queries are related nearly, for example, queries regarding brands, so that growth of keywords step can extrapolate the domain of terms to ultimately get wider topics. Word co-occurrence clustering is not dependent on extra and further data origins and therefore can be applied to whichever set of small texts. Weighted Bigraph Clustering Generating bipartite graph for blog clustering, in which blogs and words from these documents form two node’s set for the graph along with weights of edge being blog term frequency. But, this does not leave behind extra knowledge over the range of terms, blogs, and its interaction. Weighted bigraph clustering highlights on organic outputs in the search to create a bipartite graph along with a group of queries and also group of URLs behaving like nodes. Graph’s edge weight is processed with impression and select data of (query, URL) pairs from a perspective similar to Bayesian and are utilized to persuade query (URL) pairwise similarities. Hierarchical clustering successively on both URL nodes and query gives nal clusters. Because of the knowledge there in Google search outputs, this technique is brilliant in clustering semantically near queries altogether. That is why, compared to word co-occurrence clustering, this weighted bigraph clustering

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Table 1 Example of different queries and their clusters Cluster

Queries

Pollution

Types of pollution, air pollution, pollution and its types, water pollution, causes of pollution, effects of pollution, damages by pollution, pollution health issues

Cancer

Stages of cancer, symptoms of cancer, cancer and its types, treatment of cancer, causes of cancer, cancer hospitals

Education

Primary education, secondary education, types of education, educational institutes, kinds of education

still can proved extremely good even after queries do not have words in relation (example is illustrated in ‘Table 1’). Vectorization For converting text into vector format one of the simplest is the famous bag-of-words approach [7], where you create a matrix (for each document or text in the corpus). In the simplest form, this matrix stores word frequencies (word counts) and is often referred to as vectorization of the raw text.

1.2 Creating New Blog Entry New blog entry can be created through a lot of approaches one among which is n-gram model [8]. An n-gram is regular sequence of n terms given in the sample of speech or text. The terms can be phonemes, syllables, letters, words, or base pairs depending on the application. This n-grams commonly are gathered from a speech or text collection. Model of n-gram is a kind of probabilistic language model for guessing the next term. n-gram models are now broadly utilized in probability, computational linguistics (for example, statistical natural language processing), communication theory, computational biology (for example, biological sequence analysis), and data compression. Two advantages of n-gram technique (and algos that make use of them) are scalability and simplicity–with greater n, and this model is able to save more content along with simply understandable time-space tradeoff, allowing tiny experiments for scaling up effectively and efficiently. In a lot of NLP tasks, we face the problem of making a string of English words, i.e., sentence out of words with grammatical corrections. Content generation can also be performed by preprocessing, fuzzy logic scoring, feature extraction and sentence selection, and assembly techniques, and in addition to this, summarization technique depending on graph models and semantic triples is also one of the option [9, 10]. The approaches to text summarization can also be categorized into two different groups—extractive and abstractive. Extractive techniques are more concerned with the identification of the sentences which are the most relevant to the idea conveyed by the entire passage. These sentences are then concatenated in the sequence in which they are visible in the input text and presented to the user. Abstractive summarization is technique of generating a summary of a text

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from its main points, and not by duplicating verbatim most important sentences from text. This is challenging and important task in natural language processing [11, 12].

2 Proposed Methodology This paper suggests a methodology in order to create new blog entry containing the content from multiple input blogs written by different authors on the same topic. For this in the first step, those different blogs are provided as input to the system in pdf form which will at this stage initially be in unstructured form. Pdf-miner can then be used in the next step, i.e., “Token Formation” as per Fig. 1 to extract content as string from those blog’s pdf. Token from those strings could be made by finding the delimiters. The code for the same is: string text = "Pollution are of four types"; // Vector to save tokens vector token; // stringstream class stringstream check1(text); string temp; // Tokenizing w.r.t. space ' ' as delimiter while(getline(check1, temp, ' ')) { tokens.push_back(temp); } // Printing the token vector for(int j = 0; j < token.size(); j++) printf(“%s”, token[j]);

In next step. the need to remove stop words so as to scale down the working size in addition with getting rid of irrelevant words is done by simple regex pattern searches.

Fig. 1 Flow of sequential steps needed to generate brand new blog entry

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A. Sharma and N. Baliyan var input = “Pollution, are, of, four, types”; var stopWords = new Regex(“^(this|is|about|after|all|also|are|of)$”); var result = String.Join(“,”, input.Split(‘,’). where(result => !stopWords.IsMatch(result.Trim())));

The next stage is to form clusters of related words those are similar in context to their semantics. Stemming is performed with weighted bigraph clustering approach discussed in the above Sect. 1.1. Those clusters helps to understand the gist of the blog content, so as in the further steps, it becomes easy to structure data into matrix or vector form. Vectorization comes next in which the clusters formed in the previous step is used to count the frequency of occurrence of a particular word in the blog by which a matrix can be prepared to store word counts which is referred to as bag-of-words approach. This then made us land to the final step of summarizing everything into one single blogs. At the stage, we will be having multiple vectors one corresponding to each blog. Union of all vector is performed to get the resultant vector using semantic Web technologies schemes of text summarization and content generation. This could be done by providing the unified vector which needs to act as an input to n-gram model for sentence formation. This at last made us leave with the new blog.

3 Case Study Let the content of two blogs be: Blog 1—Pollution is very hazardous to health. There are four types of pollution. Kinds of pollution are air pollution, water pollution, noise pollution, thermal pollution. Causes of pollution are construction, industries, vehicles etc. Blog 2—Sources of pollution are industries and vehicles. Pollution can be categorize into four types, water pollution, air pollution, noise pollution, thermal pollution. Delhi is among the top cities in danger. Step 1: Token Formation After this step, the contents of blog will look as follows: Blog 1—Pollution, is, very, hazardous, to, health, There, are, four, types, of, pollution, Kinds, of, pollution, are, air, pollution, water, pollution, noise, pollution, thermal, pollution, Causes, of, pollution, are, construction, industries, vehicles, etc., Blog 2—Sources, of, pollution, are, industries, and, vehicles, Pollution, can, be, categorize, into, four, types, water, pollution, air, pollution, noise, pollution, thermal, pollution, Delhi, is, among, the, top, cities, in, danger. Step 2: Stop words Removal Further removing stop words will alter the contents of both the blogs as mentioned below:

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Blog 1—Pollution, very, hazardous, health, four, types, pollution, Kinds, pollution, air, pollution, water, pollution, noise, pollution, thermal, pollution, Causes, pollution, construction, industries, vehicles, Blog 2—Sources, pollution, industries, vehicles, Pollution, categorize, four, types, water, pollution, air, pollution, noise, pollution, thermal, pollution, Delhi, top, cities, danger. Step 3: Stemming After processing through this stage, the clusters of both the blogs are illustrated in Fig. 2, where ‘types’ and ‘kinds’ are related words grouped within one cluster. Step 4: Vectorization The resulted vector/matrix obtained after this step are shown below in Fig. 3. Figure 3 (i) contains the information from blog 1, Fig. 3 (ii) contains the information from blog 2, and the resultant vector, i.e., Figure 3 (iii), is the integration of both the vector in this case.

Fig. 2 Flow of sequential steps needed to generate brand new blog entry

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Fig. 3 Example of vectors from both the blogs and the resultant vector

Step 5: Finally, the output based on final vector will provide with the desired results as shown: New Blog entry: Pollution is very hazardous to health. There are four types of pollution. Air pollution, Water pollution, Noise pollution, Thermal pollution. Causes of pollution are construction, industries and vehicles. Delhi is among top cities in danger.

4 Analysis In this paper, we have analyzed how the required target could be achieved by mixing various schemes of NLP and Semantic Web and also analyzed about the proper integration of these schemes so as to generate new blog entry. It has been analyzed in the case study section as of how the intermediate steps is going to look like.

5 Conclusion and Future Work This paper focuses on the need of gathering information from different result sets of the search query and integrating the knowledge so obtained at one place in a new blog entry which diminishes the manual efforts of searching the relevant content from different sources. This paper proposes the sequential steps to achieve the desired target. The aim was to create a new blog containing data from different blogs on same

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topic written by different authors and is achieved by taking those multiple blogs in pdf form as input and extracting the content in string form using tool like pdf-miner which is in turn then converted to tokens using scripts that identify delimiters and then removing the stop words by applying regex searches. This shortens the scale and removes the irrelevant content so as to work efficiently in further stages. This is followed by clustering the similar words by using weighted bigraph clustering technique and creating the corresponding blog vectors using bag-of-word approach. Finally, creating an integrated vector from all the blog vectors which will be used to generate brand new blog entry containing the content from all those different multiple blogs. Images, tables, and grids in the blogs are not considered for generating new blog entry as of now in this paper. It primarily focuses on textual content of the blogs only, and that is why could be further extended in the future work. This paper also does not take into consideration of automatic finding of related blogs to provide as input. This idea behaves perfectly for students struggling to gather information from different links and blogs with inclusion of their extra time and effort to easily get all relevant information related to the specified topic at one place in one blog.

References 1. Röder M, Ngonga Ngomo AC, Ermilov I, Both A (2016) Detecting similar linked datasets using topic modelling. lecture notes in computer science, pp 3–19. https://doi.org/10.1007/ 978-3-319-34129-3_1 2. Ngomo ACN, Auer S, Lehmann J, Zaveri A (2014) Introduction to linked data and its lifecycle on the web. Reasoning Web. LNCS, vol 8714. Springer, Heidelberg, pp 1–99 3. Uys JW, du Preez ND, Uys EW (2008) Leveraging unstructured information using topic modelling. In: PICMET ’08-2008 Portland international conference on management of engineering & technology. https://doi.org/10.1109/picmet.2008.4599703 4. Seifert C, Witt N, Bayerl S, Granitzer M (2014) Digital library content in the social web: resource usage and content injection. E-letter stcsn-e-letter-vol-3-no-1 5. Jing K, George M (2014) Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering. Pub-tools-public-publication-data 6. Xia Y, Tang N, Hussain A, Cambria E (2015) Discriminative Bi-term topic model for headlinebased social news clustering. In: FLAIRS Conference, p 311316 7. Sung X, Xiaog Y, Wangt H, Wangg W (2015) On conceptual labeling of a bag of words. School of Computer Science. Shanghai Key Laboratory of Data Science Fudan University 8. Brown PF, deSouza PV, Mercer RL (2016) Class-based n-gram models of natural language. IBM T. J, Watson Research Center 9. Babar SA, Patil PD (2015) Improving performance of text summarization. Procedia Comput Sci 46:354–363. https://doi.org/10.1016/j.procs.2015.02.031 10. Prasojo RE, Kacimi M, Nutt W (2018) Modeling and summarizing news events using semantic triples. Lecture Notes in Computer Science, pp 512–527. https://doi.org/10.1007/978-3-31993417-4_33 11. Lloret E, Boldrini E, Vodolazova T, Martinez-Barco P (2015) A novel concept-level approach for ultra-concise opinion summarization. Expert Syst Appl. j.eswa 12. Ravi Kumar V, Raghuveer K (2013) Dependency driven semantic approach to product features extraction and summarization using customer reviews. Springer, Berlin Heidelberg

A Supplement to “PRE: A Simple, Pragmatic, and Provably Correct Algorithm” Rahibb and S. Sarala

Abstract A partial redundancy elimination (PRE) is a compiler optimization that eliminates expressions that are redundant on some but not necessarily all paths through a program. A PRE algorithm called “PRE: a simple, pragmatic, and provably correct algorithm,” presented by Vineeth Kumar Paleri does not give importance for eliminating edge splitting, even though the edge splitting is more expensive than inserting a computation at an existing node of a data flow graph (DFG). The insert equation of the PRE algorithm does not insert a computation for an expression in an existing node of a DFG if the node does not compute the expression concerned. This leads to unnecessary edge splitting. In this paper, the insert equation of the PRE algorithm is updated to avoid the edge splitting as far as possible, and hence the algorithm becomes more compact and beautiful. Keywords Data flow graph · Partial redundancy elimination · Availability · Anticipability · Safe partial availability · Safe partial anticipability

1 Introduction An expression is partially redundant if the value computed by the expression is already available on some but not all paths in a DFG of a program to that expression. A PRE algorithm is a method for transforming partial redundancy of an expression in a DFG into fully redundancy and eliminates the redundancy. A PRE algorithm based on safe insertions is treated to be optimal if no other PRE algorithm that uses safe insertions gives a DFG which contains fewer computations (less insertions and more deletions) in any path.

Rahibb (B) · S. Sarala Department of Computer Applications, Bharathiar University, Coimbatore, Tamil Nadu, India e-mail: [email protected] S. Sarala e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_8

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Morel and Renvoise (MRA) [1] first proposed a bidirectional data flow analysis algorithm to eliminate partial redundancies. But it does not really eliminate all partial redundancies in a program, and it lacks both computational and lifetime optimality as well. As MRA fails to split edges, optimization is not possible in many loops. Though Dhamdhere [2, 3] through edge placement algorithm (EPA) performs insertions both in nodes and on edges in a DFG, he cannot completely eliminate redundant code motion. EPA does not provide lifetime optimality in many cases too. It is already shown by Vineeth Kumar that the papers [4–7] have one or more of the problems of redundant code motion, unremoved redundancies, or limited applicability due to reducibility restriction of the flow graph. The PRE algorithm developed by Vineeth Kumar [8] does not give much importance for eliminating edge splitting(s), and the edge splitting is more expensive than inserting a computation at an existing node of a DFG though. The insert equation of the PRE algorithm for an expression at a nodei in a DFG returns true only if the nodei computes the expression concerned, otherwise it returns false. This may lead to unnecessary edge splitting(s). This paper enriches the insert equation of the PRE algorithm to avoid the edge splitting as much as possible.

2 PRE Algorithm by Vineeth Kumar Paleri Vineeth Kumar Paleri, YN Srikant, and Priti Shankar proposed a unidirectional data flow analysis algorithm for PRE titled “PRE: a simple, pragmatic, and provably correct algorithm.” As the name suggests the algorithm is really simple and computationally and lifetime optimal. The algorithm assumes that all local redundancies are already eliminated by using standard techniques for common subexpression elimination on the basic blocks [9]. The algorithm utilizes the concepts of availability, anticipability, safe partial availability, and safe partial anticipability. An expression is available at a program point p, if it is computed along all paths from the start node to p without a modification to any of its operands since the last computation, and it is partially available at p if it is computed at least along any path. An expression is anticipated at p if all paths from p have a computation of that expression from the values of the operands of the expression available at p, and it is partially anticipated if it is computed at least along any path from p. A program point is safe for an expression if it is either available or anticipated at that point. Safe partial availability needs all points on the path along which the computation is partially available to be safe, and safe partial anticipability needs all points on the path along which the computation is partially anticipated to be safe. The local data flow property ANTLOCi represents a locally anticipated upward exposed expression e in nodei , COMPi represents a locally available downward exposed e in nodei , and TRANSPi reflects the absence of assignments to the operand(s) of e in nodei . The global properties of availability, anticipability, safe partial availability, and safe partial anticipability are used to collect global information. INSERTi and INSERT(i, j) , identify e to be inserted in nodei , and on edge (i, j),

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respectively, and REPLACEi identifies e to be replaced in nodei with a temporary variable, say t. The INSERTi and INSERT(i, j) equations of the PRE algorithm are as follows: INSERTi = COMPi · SPANTOUTi ¬(TRANSPi. SPAVINi ).

(1)

INSERT(i, j) = ¬SPAVOUTi SPAVIN j SPANTIN j

(2)

2.1 An Example Figure 1a shows a DFG with 6 nodes, and Fig. 1b is a DFG after applying the PRE algorithm. The algorithm inserts a computation at nodes n1 and n5 with the Eq. (1). Here, the algorithm fails to insert a computation at the node n2 as it does not contain the computation a * b. So the algorithm splits the edges (n2 , n3 ) and (n2 , n6 ) for inserting a node in each edge with the Eq. (2) in order to make the expression fully redundant along all paths to n4 and n6 .

3 A Supplement to PRE Algorithm As the PRE algorithm inserts a computation at a node only if the node contains a computation of the expression, it leads to edge splitting as shown in Fig. 1b. Being the edge splitting is very expensive as compared to inserting a computation at a node already existing in a DFG, the INSERT equation of the PRE algorithm is modified by using a program segment code without sacrificing the algorithm’s computational and lifetime optimality. The program segment is as follows: INSERT i = ¬ (TRANSPi.SPAVINi) SPANTOUTi(COMPi + Пsєsucc(i)ANTINs(|PREDs| = 1 +PAVINs)(|PREDs| = 1+PAVINs) INSERTij= ¬ INSERTi¬SPAVOUTi.SPAVIN j .SPANTINj

where j is a predecessor of the nodei , and all other equations of the PRE algorithm remain the same.

3.1 An Example Consider Fig. 1a again. Figure 2 is the DFG after applying the new program code. Note that it has only three insertions and four replacements without any edge splitting.

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n1

a*b

n2

n5

n3

n4

n6

a*b

a*b

a*b

(a) before PRE algorithm

n1

t = a*b t

n2

n(2,3)

n5

t =a*b n(2,6)

t =a*b

n6

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t = a*b t

t

t

(b) after PRE algorithm Fig. 1 Partial redundancy elimination using PRE algorithm Fig. 2 Partial redundancy elimination using new program segment code

n1

t = a*b t

n2

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t = a*b

n6

t

t

t = a*b t

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4 Conclusion The goal of this paper stated in the introduction is to eliminate the edge splitting as far as possible. As the insert equation of the PRE algorithm by Vineeth Kumar fails to take care of eliminating the edge splitting much, this paper, by simply updating the INSERT equation of the PRE algorithm, eliminates the edge splitting of the DFG of a program as far as possible, and hence the PRE algorithm is now more clever and attractive without sacrificing the algorithm’s computational and lifetime optimality. Acknowledgements We are bound to thank Vineeth Kumar Paleri for his sincere, unconditional and constant guidance for our research work, and we would like to thank the University Grants Commission too, for awarding Teacher Fellowship for completing Ph.D. in Computer Science under the Faculty Development Program of the UGC during the XIIth plan period (Letter No. F. No. FIP/12th Plan/KLCA045 TF07, dated: 10-09-2016).

References 1. Morel E, Renvoise C (1979) Global optimization by suppression of partial redundancies. Commun ACM 22(2):96–103 2. Dhamdhere DM (1988) A fast algorithm for code movement optimization. SIGPLAN Notices 23(10):172–180 3. Dhamdhere DM (1991) Practical adaptation of global optimization algorithm by Morel & Renvoise. ACM Trans Program Lang Syst 13(2): 291–294 4. Dhamdhere DM, Rosen DM, Zadeck FK (1992) How to analyze large programs efficiently and informatively. In: Proceedings of ACM SIGPLAN ’92 conference on PLDI, pp 212–223 5. Dhamdhere DM, Patil H (1993) An elimination algorithm for bi-directional data flow analysis using edge placement technique. ACM TOPLAS 15(2):312–336 6. Dhamdhere DM, Khedker UP (1993) Complexity of bidirectional data flows. In: Proceedings of twentieth annual symposium on POPL, pp 397–408 7. Dhamdhere DM, Dhaneshwar VM (1995) Strength reduction of large expressions. J Program Lang 3: 95–120 8. Paleri VK, Srikant YN, Shankar P (2003) Partial redundancy elimination: a simple, pragmatic, and provably correct algorithm. Sci Comput Program 48(1):1–20 9. Aho AV, Sethi R, Ullman JD Compilers: principles, techniques, and tools. Addison-Wesley

Satellite Image Classification with Data Augmentation and Convolutional Neural Network Parth R. Dave and Hariom A. Pandya

Abstract Satellite image classification is helpful in many real-time applications for better utilization of area and to get deep information from it. It is difficult to classify them as they are having high inter-class overlapping features. In this paper, a novel approach to classify satellite images is developed based on convolutional neural network (CNN). The model is trained on the basis of data (image) augmentation with different parameters. Using filters, CNN model learns spatial information of given RGB image and creates a robust system for classification. The results are tested on benchmark PatternNet [1] dataset with different image augmentation parameters and size of it. Significant amount of accuracy is achieved using the proposed technique. Keywords Convolution neural network (cnn) · Image augmentation · Average pooling · Max pooling · Callback

1 Introduction Texture recognition plays a paramount role in many applications like material classification, scene classification, satellite image classification, etc. It has been interesting and tough area for researchers working in the computer vision field. A satellite image classification can be helpful in many ways to extract fruitful information from a given image. For example, a good farm can be developed in rural area if it is located near the river and government can plan how to control the flow of the river as well. An airport space can be utilized in more efficient way. Some major challenges in classification of satellite images are as follows: 1. The images are captured in different seasons with different scales. 2. High overlap between the classes, which is tough P. R. Dave (B) · H. A. Pandya Department of Computer Engineering, Dharmsinh Desai University, Nadiad 387001, India e-mail: [email protected] H. A. Pandya e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_9

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to differentiate. 3. Representation of images even after the preprocessing. 4. It is difficult to get sufficient number of images per class to train network properly. Over the years, many algorithms and techniques are developed for texture analysis of an image. Most of the traditional approaches extract features and train classifiers like artificial neural network (ANN) or support vector machine (SVM). These techniques either comprise the local feature descriptor or global feature descriptor. Some algorithms have made the fusion of different feature extractions and provide their histogram as feature vector to classifier. Several supervised [2–5] and unsupervised [6] implementations are made to classify the satellite images using local feature descriptors. But the problem is that they may not end up with good efficiency as it is hard to describe satellite images by those features. Deep learning is an emerging era for computer vision. CNN has changed the working methodology of shallow networks, and it has outweighed the ANN in many ways. CNN learns the spatial dimensions of the image and data by generating feature channel. CNN is the recent trend in computer vision problems for texture analysis, object detection and image retrieval problems. There are readily available trained CNNs on large datasets such as “ImageNet” which can be used to solve many problems of computer vision. The trained CNNs can also be used to get the features of images just by removing the last fully connected layer like VGG16 [7] and AlexNet [8]. The other technique focuses on early fusion of features gathered from different CNNs and algorithms [9]. Some are also applying late binding of features to classify the images and texture analysis [9]. CNN is combination of convolutional layers followed by some pooling layer and dropout which is finally connected to one or more fully connected layers. The output layer represents the number of categorical classes or binary classification. In binary classification, only one neuron is there in the last layer. Different activation functions and hyperparameters are available to reduce over-fitting and under-fitting.

2 Related Work Local binary pattern [4] is one of the most useful feature descriptors to represent the image with its neighborhood pixels in encoded way. There are different variants of LBP which are available to find out robust information from the image [5]. While LBP extracts local features, Gabor filters can be applied to get global representation of the image [3]. Fusion of the extracted features is used to represent the image in a more classified way [3, 10]. LBP and color histogram are mapped together to generate feature vector which is either provided to SVM [11] or ANN [12, 13]. Satellite image classification is an interest of many as it can be helpful in many ways. A new approach to classify them with great accuracy is by using CNNs and its different architecture. A lot of work based on bag of visual words [14] and bottleneck features [15] has been carried out for the same. Early binding of features from pretrained model [16] and late fusion of features can be given to CNN to produce very high accuracy [16].

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Our approach is to implement the augmentation on given training dataset with different parameters and extracting the spatial features from it through CNN and classifying them. The data augmentation is a good way to train the CNN for different postures, location, rotation and zoom position. Using augmentation, CNN can learn and extract the robust spatial dimensions of the image. The proposed approach is tested on PatternNet dataset [1] with two different classes of parameters. Our proposed approach is novel in a way that it uses augmentation technique to generate artificial images of dataset. By providing these images to CNN model, it captures meaningful information in a more efficient way. Good amount of accuracy is achieved for training and testing dataset.

3 Proposed Work The proposed work is based on CNN and data augmentation techniques. There are manifold ways to increase the dataset for training purpose by setting different parameters of image. One image can be seen with different orientations, zoom ratio and different dimensionalities of the image. The augmented dataset is provided as a training, and CNN captures the spatial features of it with three convolutional layers. The last layer is fully connected layer with one hidden layer of some neurons which is attached to 38 neurons of output layer. The dataset consists of 38 different classes.

3.1 Data Preparation The dataset consists of 38 classes. Each class has 800 jpg images of 256 × 256 pixels. From a given dataset, per class, 640 images (80% images) are used as training set and 160 image (20% images) are used as testing set. A total of five such sets are created for training and testing to implement fivefold cross-validation. No set consists of repetitive image or images.

3.2 Data Augmentation The satellite images are overlapping for various classes, and hence, it is a major challenge to classify them if dataset consists less amount of images. Data augmentation is a technique to produce large dataset from smaller one, with some augmentation parameters. For the proposed scenario, 640 images per class are considerably low for training a CNN. Hence, augmented images are generated for all 38 classes. The parameters used for the generation of the artificial images are described in Table 1.

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Table 1 Parameters which are used to generate augmented images Parameter name First scenario values Second scenario values Rescale Shear_range Zoom_range Horizontal_flip Target size (px)

1./255 0.2 0.2 True 150 × 150

1./255 0.3 0.3 True 170 × 170

3.3 Convolutional Neural Network Structure The convolution model structure is described in Fig. 1. It has combination of average and max pooling as it represents image in more robust way. The first two layers are having 32 channels of 3 × 3 feature vector which is applied on the original image of size 170 × 170 and activation function as “ReLU” [17]. The first layer uses max pooling of 2 × 2, and the second layer uses average pooling of 2 × 2. The essence and working of max and average pooling are explained in Fig. 2. The second and third layers use 64 channels of again 3 × 3 feature vector with max pooling only. Finally, the last convolution layer uses 128 channels with 3 × 3 feature matrices and average pooling of 2 × 2. “ReLU” [17] is used as an activation function as it has good properties of not getting saturated over the input data and activated on for positive neurons. After the last average pooling, the feature dimensions are 3 × 3 × 128, and hence, the flatten feature vector would be of 1152. One hidden layer is fully connected to the flattened layer with size of 1024 neurons. The last layer has 38 neurons with softmax function. The neuron which is having highest probability will fire at that time because of softmax function. To verify the correct classification, categorical cross-entropy is used as a loss function with “ADADELTA” [18] optimizer. f (x) = max(x, 0)

(1)

Rectified linear unit (ReLU) [17] can be described by (1) where f (x) is the output based on maximization on input x and 0. Most deep learning techniques use this function as an activation instead of sigmoid and tanh nowadays. When the input is positive, the derivative is just 1, so there is not the squeezing effect on backpropagated errors from the sigmoid function [17]. Research has shown that ReLU generates result in much faster for large networks. Softmax function can be illustrated by (2). The visualization of it is shown in Fig. 3. The softmax function squashes the outputs of each unit to be between 0 and 1, just like a sigmoid function, but it also divides each output such that the total sum

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Fig. 1 CNN model structure

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(a) MaxPooling

(b) Average Pooling

Fig. 2 Different types of 2-D pooling in CNN Fig. 3 Illustration of softmax function on the basis of probability distribution

of the outputs is equal to 1. ‘Z ’ is a vector of the inputs to the output layer, j indexes the output units, so j = 1, 2 . . . K .  (z) j σ (z) j =  K (z)k k=1 

(2)

3.4 Experimental Setup The experiment is implemented on Intel i3 processor with RAM of 3GB and frequency of 2.53 GHz. The proposed approach is implemented on Python with the help of Keras library and TensorFlow in backend. As discussed above, 80% of data from each class is taken as training and 20% as testing. A fivefold manual cross-validation is done to verify the accuracy. That means five different sets of training and testing images are provided without duplication. The most important part of it is that the weights are stored with the help of “callback (keras function)” only when validation accuracy gets improved with respect to the best accuracy found till now. Model is evaluated on the training and testing images, and results are gathered for both the scenarios in terms of accuracy.

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4 Experimental Results As discussed above, the results are gathered for two different scenarios described in Table 1. Figure 4a shows the average loss for the first scenario, and Fig. 4b shows average accuracy for the same. In a same manner, Figs. 5a, b shows average loss and accuracy for the second scenario. For the first and second scenario, 91.34% and 92.20% accuracies are achieved, respectively. For both the scenarios, a total of30 epochs are made with fivefold manual cross-validation. Table 2 displays the confusion matrix for precision, recall and F1 score for individual classes. For some of the classes, recall, precision and F1 score are relatively high than other classes because of less overlapping images on other classes.

Fig. 4 a Average training and validation loss for the first scenario. b Average training and validation accuracy for the first scenario as described in Table 1

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Fig. 5 a Average training and validation loss for the second scenario. b Average training and validation accuracy for the second scenario as described in Table 1

5 Conclusion In this paper, the satellite image classification for PatternNet dataset using data augmentation techniques with two different aspects is proposed. A very high amount of efficiency has been produced by reducing the over-fitting. Data augmentation is a favorable technique to reduce the over-fitting problem. It also helps to generate artificial images by changing the values of pixels. Training images can be populated by changing some of the properties to achieve great level of training. This approach is helpful in many real-time applications as earlier described in the paper. The approach can be extended to different datasets with different feature extraction techniques.

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Table 2 Confusion matrix with precision, recall and F1 score for second scenario only Classes Precision Recall F1 score Support Airplane Baseball_field Basketball_court Beach Bridge Cemetery Chaparral Christmas_tree_farm Closed_road Coastal_mansion Crosswalk Dense_residential Ferry_terminal Football_field Forest Freeway Golf_course Harbor Intersection Mobile_home_park Nursing_home Oil_gas_field Oil_well Overpass Parking_lot Parking_space Railway River Runway Runway_marking Shipping_yard Solar_panel Sparse_residential Storage_tank Swimming_pool Tennis_court Transformer_station Wastewater_treatment_plant Avg/total

0.84 0.99 0.74 0.99 0.96 0.98 1.00 0.95 0.94 0.92 0.90 0.86 0.67 0.98 0.97 0.98 0.99 0.82 0.95 0.93 0.85 0.96 0.99 0.99 0.90 0.89 0.97 0.99 0.93 1.00 0.99 0.89 0.82 0.97 0.99 0.83 0.89 0.86 0.92

1.00 0.99 0.64 1.00 0.93 0.93 1.00 1.00 0.95 0.89 0.98 0.87 0.60 0.97 1.00 0.99 0.94 0.73 0.93 0.94 0.87 0.99 1.00 0.86 1.00 0.97 0.89 0.99 0.99 0.98 0.93 0.97 0.92 0.89 0.86 0.88 0.81 0.96 0.92

0.91 0.99 0.69 0.99 0.94 0.95 1.00 0.97 0.94 0.90 0.94 0.86 0.63 0.98 0.98 0.98 0.96 0.77 0.94 0.93 0.86 0.98 1.00 0.92 0.95 0.93 0.93 0.99 0.96 0.99 0.96 0.93 0.86 0.93 0.92 0.85 0.85 0.91 0.92

160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 6080

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References 1. arXiv:1706.03424 [cs.CV] 2. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110 3. Risojevic V, Babic Z (2013) Fusion of global and local descriptors for remote sensing image classification. IEEE Geosci Remote SensLett 10(4):836–840 4. Ojala T, Pietikainen M, Maenpaa TT (2002) Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987 5. Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663 6. Cheriyadat AM (2014) Unsupervised feature learning for aerial scene classification. IEEE Trans Geosci Remote Sens 52(1):439–451 7. arXiv:1409.1556 [cs.CV] 8. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105 9. Anwer RM, Khan FS, van de Weijer J, Molinier M, Laaksonen J (2018) Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J Photogr Remote Sens 138:74–85 10. Sheng G, Yang W, Xu T, Sun H (2011) High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int J Remote Sens 33(8):2395–2412 11. Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. JRS 23(4):725–749 12. Ji CY (2000) Land-use classification of remotely sensed data using kohonen self-organizing feature map neural networks. PhEngRS 66(12):1451–1460 13. Kavzoglu T, Mather PM (1999) Pruning artificial neural networks: an example using land cover classification of multi-sensor images. JRS 20(14):2787 14. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Proceedings of ECCV workshop on statistical learning in computer vision, Prague, pp 1–2 15. Song Y, McLoughLin I, Dai L (2015) Deep bottleneck feature for image classification. In: ICMR 2015. https://doi.org/10.1145/2671188.2749314 16. Anwer RM, Khan FS, van de Weijer J, Molinier M, Laaksonen J (2017) Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. In: arXiv:1706.01171v1 [cs.CV] 5 Jun 2017 17. Hahnloser RH, Sarpeshkar R, Mahowald MA, Douglas RJ, Seung HS (2000) Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789):947– 951 18. arXiv:1212.5701 [cs.LG]

A Real-Time Offline Positioning System for Disruption Tolerant Network Arnika Patel and Pariza Kamboj

Abstract Sparse mobile ad hoc network called disruption tolerant network may violate one or more assumptions of existing TCP/IP protocol, therefore, it might not serve well the disruption tolerant network. In this network, end-to-end disconnection may be more common than connection. So the positioning of mobile devices to have latest location in disruption tolerant network becomes more important. In this paper, we have implemented an offline positioning system and tested it in real time. Here, positioning of the users is carried out using Cell ID, mobile network code (MNC), mobile country code (MCC), location area code (LAC). Comparison of online and offline positioning system in real-time environment shows that positioning system has comparable accuracy in offline mode. We have also carried out energy consumption in online and offline modes and the result shows that energy consumed is higher in online mode. Keywords Disruption tolerant network · Offline · Positioning · Cell ID · MNC · MCC · LAC · Energy consumption

1 Introduction Mobile phones are effective platform for sharing, probing and people-centric computing. Number of applications arises, many of them requires location-dependent services. So positioning is important for all the users. We all know that TCP/IP protocol is used for end-to-end communication, but there are certain assumptions in TCP/IP protocol like: (i) Path must exist between a data source and its peer(s), (ii) the maximum round-trip time between any nodes pair in the network is not excessive, (iii) the packet drop probability between source and destination is small [1]. TCP/IP protocol might not well serve other kinds of networks, e.g., wireless network. Such networks may violate above-mentioned (one or more) assumptions of existing TCP/IP protocol [1]. Disruption tolerant network (DTN) is one among A. Patel (B) · P. Kamboj Computer Engineering Department, SCET, GTU, Surat, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_10

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Fig. 1 Localization problem in DTN

them. In DTN, communication is commonly broken up either due to nodes going out of communication range of each other or battery drain out of nodes [2]. Disruption tolerant network is defined as ‘sparse mobile ad hoc networks where nodes connect with each other occasionally’ [3]. In this network, end-to-end disconnection may be more common than connection, so offline positioning system is required. It can be used in farm monitoring, vehicular communication, search and rescue application, parents monitoring children, pizza owner monitoring pizza delivery boys, etc. Figure 1 illustrates the localization problem in DTN. It shows that DTN is formed using cellphones moving in some area. The black and white human symbols are the nodes with the Internet support but the difference between them is that black human nodes are all time connected nodes and white human nodes are sometime connected nodes. We can get every position of the black human nodes because they are connected to the Internet all the time, but there is problem in obtaining the positions of sometimes connected nodes. So we have implemented an offline positioning system. The remainder of this paper is as follows: Sect. 2 explores the existing positioning methods followed by Sect. 3 presenting the proposed positioning method. Section 4 shows implementation details and result analysis and finally, Sect. 5 concludes the paper.

2 Related Work Disruption tolerant network has been broadly analyzed in some recent years. Most of the existing work focuses on the disconnection problem in DTN. To achieve positions of users without the need of connection is proposed to reduce energy consumption and usage of Internet/GPS. A few papers address this issue of positioning [3–11], but most of them require connection. A technique of cell tower triangulation mentioned by authors in [4] is popular for deriving locations of mobile phones. In this paper, positioning is based on the

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received Radio signal strength, so it generates very large errors if mobile device goes out of coverage area. RADAR [5] is a technique to get positioning based on Radio frequency, but in this method, data collection phase is to be repeated if base station (BS) location is changed. AAMPL [11] positioning technique uses accelerometer to get the positions of the users, but it is completely based on the direction of the phone. In [6], author has introduced technique of positioning with a search and rescue application, but in this, BS needs to be portable which is very expensive. CompAcc [7] is a technique which uses compass and accelerometer to get the positions of the users. In this technique, map generation is very time consuming and also needs to communicate repeatedly with centralized server which may not be possible every time in DTN. Another positioning is escort service [8] which uses information of one user to reach the other user, but it may route through the long paths and it can also route paths that are previously visited by user. In [9], offline location service is mentioned which is based on the 2D barcodes, but author has given only structure of the application not the implemented version. In [10], Arduino-based offline positioning is given but it is very costly and calling is required to get the positions of the user. In [3], offline positioning is mentioned which is based on Wi-Fi access points (APs) and number of steps walked by the users; hence, it limits the area of implementation. Wi-Fi-based strategies of positioning rely on AP so it requires establishing Wi-Fi APs if not available. This type of technique limits the area, because Wi-Fi APs are always not available at every place.

3 Proposed Offline Positioning System We are focusing on the offline positioning system for which, a location API [12, 13] is used on the server to convert the input data (Radio, MCC, MNC, LAC, Cell ID) into latitude–longitude pair. Following is the description of all input data to the API [14]. (i) (ii) (iii) (iv) (v) (vi)

Token: It is required by developer. It is a unique key for every developer and without this response will not be available. Token: 9fed****242fb8. Radio: Radio type of the device. It supports ‘gsm’, ‘cdma’, ‘umts’, ‘lte’. MCC: Mobile country code of device’s network operator, which is an integer number ranging from 0 to 999. MNC: Mobile network code of device’s network operator, which is an integer ranging from 0 to 999. LAC: Location area code of device’s network operator. It is an integer ranging from 1 to 65533. Cell ID: Cell ID is the ID of the base station to which the device is connected. It is an integer ranging from 0 to 65536.

Figure 2 shows how input data, i.e., Cell Info (Radio, MCC, MNC, LAC, Cell ID) generates the output latitude–longitude pair.

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Fig. 2 Offline positioning system

Following is the algorithm to get the position of any mobile device. Step 1: First of all user install our developed ‘Geolocation’ application in the mobile device. Step 2: User registers with the proper owner ID and start using the service. Step 3: If the user is offline then different positions of the user is recorded in the local mobile database. Step 4: When user is online, background service of an application will automatically transfer all the different positions to the server. After transferring all the positions successfully to the server, data from local database is deleted. Step 5: After receiving the data from client (mobile device), server will send the request to the location API with the above-described input data and API will respond with the resulting latitude and longitude according to the input data.

4 Implementation and Result Analysis We have implemented positioning system for android devices named as Geolocation and tested it on the Motorola Moto G4 plus device. We have used tools named Android studio, Adobe Dreamweaver and Xampp server for client, server and database implementation, respectively [15]. We have randomly taken the online positions of a user through GPS/Internet and the same locations have been found out using Geolocation app. in offline mode. For result analysis of positioning system, we have done the comparison of online positions and offline positions and found that offline positions are nearly equal to the online positions. Table 1 shows data of different Cell Info parameters and the

A Real-Time Offline Positioning System … Table 1 Offline locations

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No.

Cell Info (MCC; MNC; LAC; Cell ID)

Offline location (latitude, longitude)

1

404; 98; 71; 194577

21.1678, 72.7935

2

405; 861; 18; 871442

12.8993, 77.5996

3

405; 857; 71; 161043

21.1414, 72.8033

4

405; 857; 71; 1120784

21.1649, 72.7957

5

405; 857; 12; 1388820

21.2109, 72.8442

6

405; 857; 71; 1346097

21.1573, 72.8040

7

405; 857; 71; 1616149

21.1338, 72.8005

8

405; 857; 71; 161556

21.1436, 72.8051

9

405; 857; 12; 183589

21.2192, 72.8380

10

405; 857; 81; 199204

21.1834, 72.8065

11

405; 857; 81; 1612307

21.1815, 72.8091

12

405; 861; 18; 2021925

12.9734, 77.5664

13

405; 857; 81; 1611280

21.1519, 72.8037

Table 2 Result analysis No.

Offline location (latitude, longitude)

Online location (latitude, longitude)

Difference (magnitude)

1

21.1678, 72.7935

21.1681, 72.7944

0.0003, 0.0009

2

12.8993, 77.5996

12.8996, 77.5995

0.0003, 0.0001

3

21.1429, 72.8032

21.1414, 72.8033

0.0015, 0.0001

4

21.1649, 72.7957

21.1668, 72.7959

0.0019, 0.0002

5

21.2109, 72.8442

21.2110, 72.8442

0.0001, 0.0000

6

21.1573, 72.8040

21.1589, 72.8029

0.0016, 0.0011

7

21.1338, 72.8005

21.1355, 72.8006

0.0017, 0.0001

8

21.1436, 72.8051

21.1441, 72.8050

0.0005, 0.0001

9

21.2192, 72.8380

21.2193, 72.8367

0.0001, 0.0013

10

21.1834, 72.8065

21.1832, 72.8061

0.0002, 0.0004

11

21.1815, 72.8091

21.1816, 72.8086

0.0001, 0.0005

12

12.9734, 77.5664

12.9737, 77.5661

0.0003, 0.0003

13

21.1519, 72.8037

21.1519, 72.8035

0.0000, 0.0002

resultant locations using offline positioning. Table 2 shows both the results of online and offline positioning and difference between online and offline positioning system. Figure 3 shows the plotting of latitude–longitude in online and offline modes as per the data of Table 2. Overlapping of the positions exhibits that offline positioning is nearer to the online positioning. Figure 4 represents that the difference in latitude and longitude in online and offline modes lies between 10−3 and 10−4 from the data of Table 2.

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Longitude

Locations 72.9 72.8 Online Offline

72.7 21.1

21.2

Latitude

Fig. 3 Graph of online–offline latitude and longitude

Difference of Latitude and Longitude in Online & offline Modes 0.002 0.001 0 0

2

4

6

Latitude Difference

8

10

12

14

Longitude Difference

Fig. 4 Graph of difference in latitude and longitude

PowerTutor is an online power estimation system that has been implemented in paper [16] for Android platform smartphones. PowerTutor provides accurate, real-time power consumption estimates for power-intensive hardware components, including CPU and LCD display as well as GPS, Wi-Fi, audio and cellular interfaces. For energy consumption we have also used PowerTutor [17] application which is used to compute power consumption after every minute of mobile device. We have measured power consumption for both online and offline positioning system. Measured energy consumption is given in the unit Joule (J) or mili Joule (mJ). We can convert J into mAh (milliamp hours) using the following Eq. (1) [18, 19].  1Joule =

 0.277778 × Energy in J mAh 5V

(1)

The total energy consumed in online mode is 52.927 J which is equal to 2.9404 mAh per minute and total energy consumed in offline mode is 26.728 J which is equal to 1.4849 mAh per minute using Eq. (1). Figure 5 shows the graph of energy consumption during online and offline mode.

Energy (mAh) per Minuute

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Energy Consumption

4 3 2 1 0

Energy Consumption in Online Mode Energy Consumption in Offline Mode

Fig. 5 Graph of energy consumption

5 Conclusion Offline positioning is preferred in cases where we want to reduce usage of the network, mobile data and energy consumption, also in the areas like disruption tolerant network where connection is not available all the time, offline positioning is required. From the result analysis of positioning system, we can say that offline positioning gives nearly the same results as online positioning. The comparison graph shows that all the differences in latitude and longitude in online and offline modes are less than 0.002 and most of them are even less than 0.0005. Therefore, results exhibit a comparable accuracy of positioning in offline mode with positioning in online mode. We have also calculated the energy consumption during online and offline modes of positioning system which shows the energy consumption during offline mode due to non-usage of GPS/Internet is less than the energy consumption in online mode.

References 1. Fall K (2003) A delay-tolerant network architecture for challenged internets. In: ACM conference of application, technology, architecture and protocols for computer communication, pp 27–34 2. Khabbaz MJ, Assi CM, Fawaz WF (2012) Disruption-tolerant networking: a comprehensive survey on recent developments and persisting challenges. IEEE Commun Surv Tut 14(2): 607–640 3. Li W, Hu Y, Fu X, Lu S, Chen D (2015) Cooperative positioning and tracking in disruption tolerant networks. IIEEE Trans Parallel Distrib Syst 26(2): 382–391 4. Yang J, Varshavsky A, Liu H, Chen Y, Gruteser M (2010) Accuracy characterization of cell tower localization. In: 12th ACM international conference ubiquitous computing, pp 223–226 5. Bahl P, Padmanabhan VN (2000) RADAR: An in-building RF based user location and tracking system. In: 19th annual joint conference IEEE computer and communication society, pp 775– 784

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6. Zorn S, Rose R, Goetz A, Weigel R (2010) A novel technique for mobile phone localization for search and rescue applications. In: IEEE international conference on indoor positioning and indoor navigation (IPIN), Zurich, pp 1–4 7. Constandache I, Choudhury RR, Rhee I (2010) Towards mobile phone localization without war-driving. In: ACM proceedings of the 29th conference on information communications, pp 2321–2329 8. Constandache I, Bao X, Azizyan M, Choudhury RR (2010) Did you see bob?: human localization using mobile phones. In: ACM proceedings of the 16th annual international conference on mobile computing and networking, pp 149–160 9. Coelho P, Aguiar A, Lopes JC (2011) OLBS: offline location based services. In: IEEE fifth international conference on next generation mobile applications, services and technologies, Cardiff, pp 70–75 10. Kolaskar M, Chalke A, Borkar M, Naik K, Lande B, Suralkar V (2016) Real time and offline GPS tracker using Arduino. IJIR 2(5): 94–96 11. Ofstad A, Nicholas E, Szcodronski R, Choudhury R (2008) AAMPL: accelerometer augmented mobile phone localization. In: ACM Proceedings of international workshop on mobile entity localization and tracking in GPS-less environments, pp 13–18 12. Geolocation [Online] Available: https://developers.google.com. Accessed on 5 Oct 2016 13. Location API [Online] Available: https://unwiredlabs.com/. Accessed on 10 Nov 2016 14. Location API Documentation [Online] Available: https://unwiredlabs.com/api. Accessed on 10 Nov 2016 15. Implementing Google Maps into php Documentation [Online] Available: https://developers. google.com/maps/documentation/javascript/mysql-to-maps. Accessed on 15 Feb 2016 16. Power Tutor Android Application on Google Play Store Available: https://play.google.com/ store/apps/details?id=edu.umich.PowerTutor&hl=en. Accessed on 12 May 2017 17. Zhang L, Tiwana B, Qian Z, Wang Z, Dick R, Mao Z, Yang L (2010) Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: ACM proceedings of 8th IEEE/ACM/IFIP international conference on hardware/software codesign and system synthesis, pp 105–114 18. Energy Units conversion Available: http://www.rapidtables.com/calc/electric/wh-to-mah-cal culator.htm. Accessed on 12 May 2017 19. Energy Units Conversion Available: http://www.unitconversion.org/energy/joules-to-watthours-conversion.html. Accessed on 12 May 2017

A Novel Copy-Move Image Forgery Detection Method Using 8-Connected Region Growing Technique Shyamalendu Kandar, Ardhendu Sarkar, and Bibhas Chandra Dhara

Abstract With the recent advancement of digital communication and Internet technology, there exist so many applications surrounded in our daily life where digital images play a vital role. Manipulation of digital images becomes a serious authentication threat in today’s world due to the availability of high-resolution cameras, powerful computer, and above all advanced image editing tools. Forgers are advancing themselves day by day with more sophisticated forgery techniques, so researchers must come up with more updated and advanced forgery detection techniques. Copymove forgery or region duplication is a well-known image tampering technique where one part of an image is copied and pasted in another location of the same image in a way undetectable by human eye. A novel copy-move forgery detection technique is presented in this paper. The detection starts from a seed block and grows by finding match in an eight-connected region. Experimental results show that the proposed method has higher accuracy ratio and less performance time than the state-of-the-art techniques in the field of block-based image forgery detection. Keywords Image forgery detection · Copy-move forgery · Region growing · Eight-connected region growing

S. Kandar (B) · A. Sarkar Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India e-mail: [email protected] A. Sarkar e-mail: [email protected] B. C. Dhara Department of Information Technology, Jadavpur University, Salt Lake Campus, Kolkata 700098, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_11

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1 Introduction ‘Image says a thousand words.’ With the advancement of Internet and digital communication, images are widely used in different e-communication platforms like mail, Web page, social media, etc. Use of digital images in medical applications (Medical imaging), criminal investigation, evidence for judicial system, claim for insurance, defense etc., is of prior importance. Non-availability of secure communication path may lead the transmitted images vulnerable to digital forgery, which may change the actual information of the image. Availability of powerful image capturing devices and image editing software has made digital image vulnerable to manipulation. Important features of a digital image can be added or deleted without having noticeable traces of tampering. Digital forgery on image can be created from one or multiple images. Forged image can be used as a false evidence or to spoil the well reputation of some eminent person, etc. Thus, the need of strong image forgery detection technique has been felt by the research community. Image forgery detection technique is mainly classified into two types: (1) active and (2) passive. Active forgery detection technique such as digital watermarking [1, 2] requires prior information like feature extracted from the original image. Due to the unavailability of original image information, the applications of these types of techniques are limited [3]. In passive forgery detection technique, tampering can be detected from the available forged image without the knowledge of the original image. Though forgery does not leave any noticeable clues, it disturbs the underlying statistical properties of a digital image which may lead to different inconsistencies. Blind forgery detection examines those inconsistencies to detect the forged regions. This type of forgery detection methods can be divided into several subcategories like pixel-based [4–9], format-based [10–15], camera-based [16–18], physics-based [19], and geometric-based [20]. Copy-move digital image forgery is a special type of image tampering technique where a section of the image is copied and pasted into another part of the same image with the objective of mainly hiding unwanted scene of the image. This can easily be achieved by using editing tool like clone tool of Photoshop. A type of example is presented in Fig. 1 where the head of one missile is duplicated and placed in the marked region. The objective of copy-move forgery detection is to find the duplicated regions present in the same image. This type of forgery detection technique falls under subcategory of pixel-based method. A large number of state-of-the-art copy-move tampering detection algorithms are available. Most of the techniques use a common workflow as presented in Fig. 2. A number of copy-move forgery detection methods are pillared on the concept presented by Fridrich et al. [5]. According to the method, the image is divided into small overlapping blocks. Those blocks are sorted and traversed thereafter to find the matching blocks. The regions of the matching blocks are marked to detect the forged regions. As the copied blocks contain the same pixel values, the statistical features of those blocks will also be identical. A principal component analysis (PCA)-based forgery detection method is presented in [4]. PCA is a statistical technique which

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Fig. 1 Example of copy-move forgery (a) original image, (b) forged image, and (c) marked forged regions

Fig. 2 General copy-move forgery detection workflow

performs an orthogonal transformation of set of possibly correlated data and converts those to linearly uncorrelated variables called principal components. Generation of PCA over fixed size overlapping blocks produces a reduced dimension data from which the similar blocks can be identified by lexicographical sorting. Li et al. [21] have employed discrete wavelet transform (DWT) to find the forged regions. A combined PCA-DWT-based approach is presented in [22]. Muhammad et al. [23] have used dyadic undecimated wavelet transform (DyWT) which gives better result than DWT in finding forged regions. Some proposals [24, 25] have added some other techniques with DWT in finding duplicated regions. Discrete cosine transform (DCT) is a well-known technique used in signal processing, multimedia compression, etc. Significant features of a set of data can be captured to a few coefficients using DCT. Fridrich et al. [5] have proposed a DCTbased duplication region finding technique, but the disadvantage is the addition of some extra noise to the marked forged regions. Hung et al. [26] have come up with a modified version of Fridrich’s proposal by reducing the dimension of feature vectors but the method found inappropriate while detecting multiple forged regions. A PCA-DCT-based forgery detection method is found in [27]. Some recent DCT-based forgery detection methods are available in [28, 29]. Local binary pattern (LBP) is a visual descriptor familiar in the research of computer vision. Here, a binary number is generated from a block, based on the value of the centre pixel. Li et al. [30] have proposed an LBP-based copy-move forgery detec-

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tion algorithm which can detect duplicated region though those copied regions are flipped. A multi-resolution local binary pattern-based method is presented in [31]. The method finds the LBP from the overlapping blocks and similar blocks are found from those LBP in lexicographical sorting and k-d tree searching. Removal of false matching is marked as the advantage of this method. Another LBP-based method [32] has formed clustering of LBP in an 8-connected region and forged areas are detected from the clustering. Alahmadi et al. [33] have used DCT with LBP to find the duplicated regions. Some recent LBP-based proposals in the current domain are available in [34, 35]. Some other methods in copy-move forgery detection like scale-invariant feature transform (SIFT)-based [36, 37] and discrete stationary wavelet transform (DSWT)based [38] have drawn research attention. In this paper, we have presented a novel copy-move forgery detection method using region growing technique. In this method, the intensity of highest frequency pixel is found from the histogram of the suspicious image and the coordinates of that pixel with in the image is stored. A 3 × 3 block is constructed from those pixel coordinates keeping that pixel as center location. Lexicographical sorting and traversing of the values in those blocks find the similar blocks. A 8-connected region growing technique tries to find whether the surrounding pixels of those blocks are equal or not. If found equal, those regions are marked to detect forged areas. Experimental results over copy-move forged images available in CoMoFoD database [39] proves the effectiveness of the proposed method in finding the forged regions effectively. Comparison with some state-of- the-art methods in this field finds its superiority in execution time. The rest of the paper is structured as follows. Some preliminaries related to the proposed technique are presented in Sect. 2. Proposed forgery detection method is discussed in Sect. 3. Section 4 contains the experimental results over copy-move forged images available in CoMoFoD database. Section 5 presents the performance analysis of the proposed technique over some state-of-the-art techniques. Finally, conclusion is drawn in Sect. 6.

2 Preliminaries Some preliminaries related to the proposed technique are presented in the current section. We have mainly discussed here histogram of an image, region growing technique, and block base match for copy-move forgery detection.

2.1 Histogram Construction Histogram is a graphical representation of frequency distribution of a set of continuous data. Histogram is widely used in digital image processing. An image histogram

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represents the tonal distribution of the pixel intensity values of some image. In a grayscale image, each pixel are of 8-bit and thus the intensity values are bounded between 0 and 255. In data structure notation, a histogram of a grayscale image can be denoted as a one-dimensional array ‘HIST’ of size 256. Proposed method requires the pixel intensity value with highest frequency within the image. ‘HIST’ array is searched to find the gray label intensity value having highest frequency. Algorithm 1:SEARCH-HIST(I) describes the histogram construction of a grayscale image and finds the pixel intensity of the highest frequency pixel and its coordinates in the image. Algorithm 1 :SEARCH-HIST(I) Input: The grayscale image IW ×H Output:Pixel having highest frequency, Positions of that pixels in I 1. HIST1×256 =Φ. 2. For i = 1 to W For j = 1 to H If(I (i, j) = K) HIST (K) = HIST (K) + 1 3. M = MAX (HIST ) //Finding maximum frequency For i=1 to 256 if (HIST(i)==M) INS=i //Finding the highest frequency pixel intensity 4. POSM ×2 = Φ //To store the co ordinates of highest frequency pixel count=0 5. For i = 1 to W For j = 1 to H if(I (i, j) == INS)//Intensity of highest frequency pixel count=count+1 POS(count, 1) = i POS(count, 2) = j 6. Return INS, POS

2.2 Region Growing Region growing is a well-known pixel-based image segmentation method started from an initial seed point. The principle of region growing is based on some user criteria like pixel intensity value, region boundary, etc. The initial region starts from the location of the taken seed pixel and then it grows by covering the neighboring pixels values based on the region growing criteria. Contextual segmentation technique accounts the closeness of pixels and found suitable in separating individual objects. Contextual segmentation contains two types of techniques, namely discontinuitybased and similarity-based. In similarity-based technique, neighboring pixels are

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grouped together starting from a seed pixel, based on some common criteria to form a connected region. The process is termed as pixel connectivity. Similarity-based region growing technique allows us to define two types of neighborhood surrounding the seed pixels, namely 4-connected and 8-connected. For a 4-connected region growing technique, surrounding 4 pixels (east, west, north, and south) around the seed pixel are chosen. On the other hand, in 8-connected region growing technique, pixels selection is extended to northeast, northwest, southeast, and southwest in addition to east, west, north, and south. 4-connected region growing has some disadvantages which have made the 8-connected one popular. The region expands from the seed pixel using a recursive algorithm, until some terminating condition is reached. In the proposed algorithm, two regions are grown simultaneously if the 8-neighboring pixel values surrounding a same seed pixel match for both the regions. The 8-connected region growing algorithm is described in Algorithm 2: REGION-GROWING (x, y, C). Algorithm 2 :REGION-GROWING(x,y,C) Input: Seed pixel (S(x,y)), Terminating condition(TC), Fill color value(C) Output: A connected region R colored by C 1. Take S(x,y) as seed pixel 2. If TC is false (a) (b) (c) (d) (e) (f) (g) (h) (i)

Color S(x,y) by C to form R REGION-GROWING(x-1,y, C) REGION-GROWING(x+1,y, C) REGION-GROWING(x,y-1, C) REGION-GROWING(x,y+1, C) REGION-GROWING(x-1,y+1, C) REGION-GROWING(x+1,y+1, C) REGION-GROWING(x+1,y-1, C) REGION-GROWING(x-1,y-1, C)

3. Return R

2.3 Block-Based Match In this method, an image IW ×H is divided into fixed size overlapping blocks of size B × B. Thus, total number of blocks generated from the image is (W − B + 1) × (H − B + 1), each having B × B pixels. If WB or HB generate remainder, zero padding is permissible. A two-dimensional array A of size [(W − B + 1) × (H − B + 1)] × [B × B] is taken to store the blocks. A is sorted in lexicographical order producing A . The matching rows will appear in consecutive order in the sorted matrix and can easily be found by going through all the rows of the ordered matrix. The searching process require WHlog2 (WH) steps. An index matrix IND is taken to store the position of

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the rows of A in A . Position i of IND is j if jth row of A is placed at ith position of A . If ith and (i + 1)th rows of A are found similar, then IND(i) and IND(i + 1)th blocks of I are marked. Algorithm 3 describes the block match process. Algorithm 3 :BLOCK-MATCH(I,B) Input: The image IW ×H , Size of block B × B Output:Binary image I  1. Overlapping block storing matrix A((W   − B + 1) × (H − B + 1), B × B)=Φ, Index matrix IND (W − B + 1) × (H − B + 1), 1 = Φ  Set IW ×H to 0. 2. Divide I into B × B size overlapping blocks. 3. Set each row of A by the elements of overlapping blocks. 4. Produce A by lexicographical sorting of A 5. IND(i) = j, if j th row of A is placed at ith place of A .  6. For i = 1 to (W − B + 1) × (H − B + 1) − 1 For j = 1 to n //Assuming maximum n similar regions are present If A (i, :) ! = A (i + j, :) Break else Fill all the pixel position of IND(i) and IND(i + j)th block of I  by 1. 7. Publish I  .

3 Proposed Forgery Detection Technique Proposed forgery detection technique is divided into three phases as discussed follows. 1. Histogram construction and search. In this phase, a histogram of a grayscale image is constructed and from where the intensity of the highest frequency pixel INS and coordinates of that intensity pixel within the image is searched. Detailed discussion of the process is made in Algorithm 1. 2. Finding forged region by taking highest frequency pixel as seed. Our aim is to find atleast two similar blocks having center pixel value INS (Obtained from Algorithm 1). In the proposed method, the block size is taken as 3 × 3. From the matrix POS, denoting the positions of INS in I (See Algorithm 1), N number of 3 × 3 blocks are constructed, where I (POS(k, 1), POS(k, 2)) for k = 1 to N are the center pixel of each of the N blocks having intensity INS. A two-dimensional array A of size (N × 9) is taken to store the blocks generated. A is sorted in lexicographical order producing A . The similar rows will appear in consecutive positions of A . An index matrix IND maintains the positions of the rows of A in A . Position i of IND is j if jth row of A is placed at ith position of A . If a match is found in ith and (i + 1)th rows of A then modified region

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growing algorithm MREGION-GROWING (see Algorithm 5) is called with pair of seed pixels (x, y) and (x , y ) where x = POS(IND(i), 1), y = POS(IND](i), 2), x = POS(IND(i + 1), 1), and y = POS(IND(i + 1), 2). 3. Region growing over pair of seed pixels. On finding a match in the lexicographical sorted array, the control is transferred to Algorithm 5. This is a modified version of 8-connected region growing technique as described in Algorithm 2. It checks the intensity surrounding 8-pixel pair received from two blocks taking the pair of pixels as seed value. If a match is found, a region is constructed by making those positions ‘white’ in I  , a new image to mark the forged region. The algorithm runs iterative and stops if a mismatch is found. Steps 2 and 3 of the proposed technique are described in Algorithms 4 and 5. Algorithm 4 :MODIFIED-BLOCK-MATCH(I , POSN ×2 , B) Input: Image IW ×H , Position matrix POS obtained in Algorithm 1, Size of block B × B Output:Forged area marked image I  (If found)  1. Set IW ×H as ‘0’ matrix and AN ×B2 =Φ. N = MAX (HIST ) [obtained from Algorithm 1] 2. For i = 1 to N

(a) Construct B × B size block   POS(i, 1), POS(i, 2) . (b) Store ith block in ith row of A.

from

I,

having

central

pixel

coordinate

3. Produce A by lexicographical sorting of A 4. IND(i) = j if ith row of A = j th row of A. 5. For i=1 to (N-1) j=i+1; While(A (i, :) == A (j, :)) /* Searching similar rows*/ th  Fill all the pixel position of IND(i)  and IND(j) block of I by 1.  Call MREGION − GROW IN G POS(K, 1), POS(K, 2), POS(P, 1), POS(P, 2) ,where K = IND(i) and P = IND(i + 1). j=j+1 6. Publish I 

4 Experimental Results and Analysis Proposed method is applied on fifteen different images namely 013, 014, 015, 021, 028, 044, 068, 115, 120, 123, 146, 160, 185, 195, and 200, taken from CoMoFoD database [39]. The images are color image of size 512 × 512. Among those, the original, forged image, and actual forged region (binary mask on forged images) as given in CoMoFoD database for five images namely 013, 014, 015, 021, and 028 are shown in Fig. 3. CoMoFoD database contains 260 forged image set divided into large and small categories. Large images are of size 3000 × 2000 each and small images are of size 512 × 512. Images are grouped into five different categories

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Algorithm 5 :MREGION-GROWING(x,y,x ,y ) Input: Pair of Seed pixel position (x,y) and (x , y ) 1. Set I  (x, y) and I  (x , y ) to ‘1’ 2. If(I  (x, y) == I  (x , y )) (a) (b) (c) (d) (e) (f) (g) (h)

MREGION-GROWING(x − 1, y; x − 1, y ) MREGION-GROWING(x + 1, y; x + 1, y ) MREGION-GROWING(x, y − 1; x , y − 1) MREGION-GROWING(x, y + 1; x , y + 1) MREGION-GROWING(x − 1, y + 1; x − 1, y + 1) MREGION-GROWING(x + 1, y + 1; x + 1, y + 1) MREGION-GROWING(x + 1, y − 1; x + 1, y − 1) MREGION-GROWING(x − 1, y − 1; x − 1, y − 1)

3. Return I 

Image Number

013

014

015

021

028

Original Image

Forged Image

Binary Mask Fig. 3 Original, forged, and binary masked images denoting forged region taken for testing purpose from CoMoFoD database)

based on the applied forgery namely translation, rotation, scaling, combination, and distortion. The proposed method is on copy-move forgery, and thus, forged images of translation-type manipulation are taken for experiment. The test images are converted to grayscale image as shown in Fig. 4. Highest frequency pixel intensity value for each of the images is found from the corresponding histogram of the grayscale images using Algorithm 1. Algorithm 4 is applied on the forged images displayed in Fig. 4 taking the block size 3 × 3, and the obtained results are displayed in Fig. 5

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Image Number Forged Images

in grayscale Highest frequency pixel intensity values

013

014

015

021

028

107

105

135

90

178

Fig. 4 Converted grayscale images of the forged images with highest frequency pixel intensity values Image Number

013

014

015

021

028

Experimental Result Execution Time

38.57sec

41.53sec

43.03sec

35.04sec

44.17sec

Fig. 5 Result obtained using proposed method with execution time

5 Performance Analysis and Discussion Proposed method is compared with exact match (block match) [5], robust match (DCT coefficient) [5], and local binary pattern (LBP) [30]-based techniques. Experiments are carried on the images as displayed in row 2 of Fig. 3. The obtained result with the execution time is depicted in Fig. 7. The result shows that the proposed method requires significantly less execution than that of these three popular methods. ‘While loop’ in Algorithm 4 catches the forged regions when multiple copy of same region is made, but with different proportion. A such type of example is given in Fig. 6b. For this case, the region marked with dashed line is forged in blocks labeled by i and (i + 3) but region marked with solid line is duplicated in blocks labeled by i and (i + 2). Use of while loop compares block i with block (i + 2) and (i + 3) and will appropriately find the forged regions. Another problem in digital image forgery detection is false matching. As an example, in Fig. 6, a binary mask of the forged region of image 028 is received using DCT-based approach [5]. The actual forged region is as displayed in row 3 column 6 of Fig. 3. The circular marked portion in Fig. 6a is the false match. Commonly false match is a tiny region detected by most of the forgery detection algorithms. Proposed method is not also free from displaying false regions. To get a better result by removing false matched regions, following special technique can be used. There is a high probability of getting a block having 9 pixels similar to another from the lexicographical sorting in Algorithm 4. If two such similar blocks are found

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Fig. 6 a Example of false matching. b Same region multi-time duplicated but in different proportion Image Number Block based

013

014

015

021

028

120.36sec

127.04sec

129.28sec

117.08sec

133.47

[5] Execution Time

72.18sec

76.41sec

65.39sec

79.12sec

35.618sec

LBP Based [31] Execution Time

323sec

318.23sec

345.45sec

308.23sec

357.43

Experimental Result Execution Time

38.57sec

41.53sec

43.03sec

35.04sec

44.17sec

[5] Execution Time DCT based

Fig. 7 A comparison of the proposed method with block-based, DCT-based, and LBP-based approach with execution time

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then MREGION–GROWING makes the position in I  as ‘1’, which is reflected in the final results. To rectify the false matching problem, a counter can be set in MREGION–GROWING which will count the number of iteration of the algorithm for a pair of pixels. If the value of the counter is less than some threshold value (say 4), then that tiny region is not considered in the final result. If the size of the forged region is kept intact but the size of the image increase, the block-based, DCT-based, and LBP-based will take more time than their previous state (Over smaller size image) as number of comparison increases significantly, whereas the increase of compilation time in case of proposed method is very less as compared to the previous techniques. Though one drawback of our proposed method cannot be avoided, we have assumed that the forged region will contain the highest occurring pixel and this assumption is proved correct for all the 40 images available for transition type in CoMoFoD database. Though it may happen that the highest occurring pixel is not available in the forged region, to handle such situation we may take a loop form let 1 to 10 for first 10 highest occurring pixel values found from the histogram. If a match is not available for the highest occurring pixel value, we will go for next highest pixel value, though this will increase the time taken by the process.

6 Conclusions A new copy-move forgery detection algorithm is proposed in the current communication. Region growing technique is applied in the proposed method to detect the forged regions. Seed pixels for the region growing are found from lexicographical sorted 8 × 8 blocks constructed over highest occurring pixel value retrieved from the image histogram. Experimental results over standard database images prove that the proposed method is better one than some popular existing forgery detection techniques available in literature in terms of execution time. Not having the highest frequency pixel in the forged region is marked as the disadvantage of the proposed method.

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6. Kirchner M (2008) Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. In: ACM multimedia and security workshop, pp 11–20 7. Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inform Forens Secur 3(3):529–538 8. Ng T-T, Chang SE (2004) A model for image splicing. In: IEEE international conference on image processing, Singapore, vol 2, pp 1169–1172 9. Bayram S, Avcibas I, Sankur B, Memon N (2005) Image manipulation detection with binary similarity measures. In: European signal processing conference, Turkey 10. Fan Z, de Queiroz RL (2003) Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235 11. Johnson MK, Farid H (2006) Exposing digital forgeries through chromatic aberration. In: Proceedings ACM multimedia and security workshop, Geneva, Switzerland, pp 48–55 12. Lukas J, Fridrich J (2003) Estimation of primary quantization matrix in double compressed JPEG images. In: Proceedings of digital forensic research workshop, Cleveland, OH 13. Popescu AC, Farid H (2004) Statistical tools for digital forensics. In: 6th International workshop on information hiding, Toronto, Canada, pp 128–147 14. Luo W, Qu Z, Huang J, Qiu G (2007) A novel method for detecting cropped and recompressed image block. In: IEEE conference on acoustics, speech and signal processing, Honolulu, HI, pp 217–220 15. Ye S, Sun Q, Chang EC (2007) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: IEEE international conference on multimedia and expo, Beijing, China, pp 12–15 16. Johnson MK, Farid H (2006) Exposing digital forgeries through chromatic aberration. In: ACM multimedia and security workshop, Geneva, Switzerland, pp 48–55 (2006) 17. Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959 18. Gou H, Swaminathan A, Wu M (2007) Noise features for image tampering detection and steganalysis. In: IEEE international conference on image processing, San Antonio,TX, vol 6, pp 97–100 19. Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: ACM multimedia and security workshop. New York, NY, pp 1–10 20. Johnson MK, Farid H (2007) Detecting photographic composites of people. In: 6th International workshop on digital watermarking, Guangzhou, China 21. Li G et al (2007) A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: IEEE international conference on multimedia and expo. IEEE 22. Zimba M, Xingming S (2011) DWT-PCA (EVD)based copy-move image forgery detection. Int J Digital Content Technol Appl 5(1):251–258 23. Muhammad G et al (2011) Blind copy move image forgery detection using dyadic undecimated wavelet transform. In: 17th International conference on digital signal processing (DSP). IEEE 24. Hashmi MF, Anand V, Keskar AG (2014) Copy-move image forgery detection using an efficient and robust method combining un-decimated wavelet transform and scale invariant feature transform. Aasri Procedia 9:84–91 25. Sanap VK, Mane VM (2015) Region duplication forgery detection in digital images using 2D-DWT and SVD. In: International conference on applied and theoretical computing and communication technology (iCATccT). IEEE 26. Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forens Sci Int 206(1–3):178–184 27. Sunil K, Jagan D, Shaktidev M (2014) DCT-PCA based method for copy-move forgery detection. In: ICT and critical infrastructure: proceedings of the 48th annual convention of computer society of India, vol II. Springer, Cham, pp 577–583 28. Zhang Z, Wang D, Wang C, Zhou X (2017) Detecting copy-move forgeries in images based on DCT and main transfer vectors. KSII Trans Internet Inf Syst 11(9)

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A Method for the Prediction of the Shrinkage in Roasted and Ground Coffee Using Multivariable Statistics Alexander Parody, Dhizzy Charris, Amelec Viloria, Jorge Cervera, and Hugo Hernandez-P

Abstract This study seeks to determine the influence of process variables: consumption percentage in the mixture, pasilla percentage in the mixture, storage time, humidity percentage in the product for consumption, humidity percentage in the pasilla, humidity percentage in roasted coffee, average humidity in finished product, average color in roasted coffee, and average color in finished product, for the shrinkage of packed coffee in a coffee processing plant of Arabica type. Using a multiple linear regression model, the study stated that the variables of humidity percentage of roasted coffee and color of roasted coffee have a statistically significant relationship with a confidence of 95% (p-value < 0.05). It was concluded that these variables explain 99.95% of the variability in the shrinkage, and the relation of the shrinkage with the humidity percentage is inversely proportional, but the relation of this variable with the color of roasted coffee is directly proportional. The tests applied to the model wastes proved that the model is suitable for predicting the shrinkage in the process. Keywords Multiple linear regression · Shrinkage in a process · Humidity · Statistical quality control A. Parody (B) · D. Charris Universidad Libre Seccional Barranquilla, Barranquilla, Colombia e-mail: [email protected] D. Charris e-mail: [email protected] A. Viloria Universidad de La Costa, Barranquilla, Colombia e-mail: [email protected] H. Hernandez-P Corporación Universitaria Latinoamericana, Barranquilla, Colombia e-mail: [email protected] J. Cervera Universidad Autonoma, Barranquilla, Colombia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_12

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1 Introduction Coffee is, after oil, the second most exported product in the world, with an estimated consumption of about 1,400,000,000 cups of coffee a day. As an evidence of the importance of coffee production at a domestic level, during the year 2014, there are over 10 thousand coffee shops in Colombia [1]. Many researches are carried out in the country with the aim of optimizing processes and reducing shrinkages and losses, and some of them are highlighted below. A research presented by Núñez in 2002 [2], named Optimization of Production in the Elaborados de Café Company, shows the various problems and bottlenecks faced by this company in several of the processes. The mentioned author performs a detailed analysis on each of these processes, recording and tracking the production capacity for each machine, and concludes that an improvement in machinery and processes can help decrease the shrinkages at the end of the coffee process. González Viva and Pedro González Domínguez in 2010 [3] propose a study based on the fall of coffee prices at the moment of roasting (process of warming, drying, and firing the coffee through a toaster), affected by the percentage of shrinkage during this process. In the research, some factors such as humidity, initial weight, and the roasting process were considered, and samples were taken for each step of the process. The results were similar in each process step but differing in quality in the process of cupping. On the other hand, Hugo Bello and Hugo Suarez, in 2016 [4], develop a research in the Café de Colombia Company, analyzing a high variation in the presentation of the coffee, specifically concerning to its final color. This problem has directly affected the capacity of the process, increasing the shrinkages in the production. The study was carried out to find the solution to the problem with the support of the technical department of the company and detected that the determining factor causing these results was the machinery used in the roasting area. For this reason, a feasibility study was developed to allow the updating of this line. In the same way, an article submitted by Jose Jaime Castaño and Gloria Patricia Quintero (2001) [5] proposes negative factors that can affect the coffee finished product. This study suggests that the roasting process may have a high influence in the final product. For this reason, two variables were considered: roasting temperature and amount of water off. Each of these variables was measured in three different types of coffee, applying an experimental design of response surface. The results show that the overall impression systematically presented a maximum response in the ranges studied, while the performance of removal presented a minimum in relation to the global maximum impression. Shrinkage is understood as the percentage difference between the initial weight of green coffee and the coffee that comes out from the production area. So, the present study is intended to identify the process variables associated to the shrinkage in the coffee packed in a coffee processing plant in the north coast of Colombia, with the aim of finding a model to help establish strategies to minimize the shrinkage of packaged coffee and improving the productivity of the process.

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2 Methodology The methodology applied in the research focuses on the application of statistical models [6–11] specifically, the multiple linear regression model, which considered the following independent variables: percentage of consumption in the mixture, pasilla percentage in the mixture, storage average time, percentage of humidity in consumption, percentage of moisture in pasilla, humidity percentage in roasted coffee, average humidity in finished product, average color of roasted coffee, and average color of finished product; and the dependent variable is the percentage of monthly shrinkage in the process. The level of confidence that was used is 95% (pvalue of baseline was 0.05). Based on the above, a multiple linear regression model explains the behavior of a given variable that is referred to as an endogenous variable or dependent variable, (and represented with the letter Y ) according to a set of k explanatory variables X 1 , X 2 …, X k through a linear dependence [12–15]. Once the mathematical model was generated, the independent variables with a p-value greater than or equal to 0.05 were taken off. The variables that presented a value were considered statistically significant, and represented some type of influence with respect to the percentage of shrinkage. To assess the relationship, the coefficient accompanying the independent variable in the model was analyzed, and so the trend graphs generated by the statistical software Statgraphics version XVI. Once the statistical model was generated, improvement strategies were built since the coefficients and relationship graphs allow to know the influence of significant variables in the shrinkage. Besides, the optimal values to minimize the shrinkage can be obtained with the use of the Excel Solver tool without leaving the historical ranges of the significant independent variables.

3 Results A generalized linear regression model was applied to determine which independent variables studied had a relation with the percentage of shrinkage in the process. A confidence level of the study was applied at 95% (significance level of 5% [16, 17]), see Table 1. Table 1 Variance analysis Source

Sum of squares

Gl

Mean square

Reason-F

P-value

Model

4102.36

2

2051.18

12,433.64

0.0000

Shrinkage

1.6497

10

0.16497

Total

4104.01

12

Relationship of independent variables

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Table 2 Type III sum Parameter

Estimation

Error

Statistical

Standard

T

P-value

Constant

−19.4511

57.0276

−0.341082

0.7655

% consumption

−0.0814628

0.0559065

−1.45713

0.2824

% pasilla

−0.128067

0.0763212

−1.678

0.2354

Storage time

−0.201178

0.53913

−0.373153

0.7449

% humidity in consumption

−0.217627

0.42149

−0.516328

0.6570

% humidity in pasilla

0.42394

0.312722

1.35564

0.3080

% humidity in roasted coffee

−7.14758

3.69231

−1.9358

0.1925

% humidity in finished product

1.5694

3.77096

0.41618

0.7177

Color of roasted coffee

4.30187

2.84263

1.51334

0.2694

Color of finished product

−0.570563

0.937712

−0.608463

0.6048

Independent variables’ p-value

The p-value of the ANOVA table was below 0.05, and therefore, at least one of the independent variables has a relationship with the percentage of shrinkage. The p-value for each of the independent variables was analyzed, see Table 2. The non-significant variables that presented a p-value greater than or equal to 0.05 were removed. This procedure was carried out gradually starting with the variable showing the highest p-value, see Table 3. The adjusted R-squared was 99.95% allowing to say that the model explains the 99.95% of the variability in the percentage of shrinkage. Therefore, the model is very useful to explain the behavior of the dependent variable [18]. Subsequently, the graphs of the adjusted model were generated to allow identifying the relationship between each of the significant independent variables and the dependent variable, see Figs. 1 and 2. Results show that the higher the result of color in the roasted coffee (the darker the coffee bean), the higher is the shrinkage of coffee in the process. It can be observed that there is an inversely proportional relation between the humidity percentage of the roasted coffee and the shrinkage percentage of coffee in the process. Table 3 Type III sum Error

Statistical

Parameter

Estimation

Standard

T

P-value

% humidity roasted coffee

−5.31772

1.82583

−2.91249

0.0155

2.1047

0.350933

5.99745

0.0001

Color roasted coffee

Significant independent variables’ highest value

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Fig. 1 Component effect versus color of roasted coffee

Fig. 2 Component color effect versus humidity of roasted coffee. Source Statgraphics Centurion XVI

The last part of the analysis consists on a study about the behavior of the residuals to know the reliability level of the model forecasts. At the beginning, the figures of residuals were generated and compared to each significant independent variable, and against the predicted values and the number of rows of the data [19, 20]; see Figs. 3, 4, 5, and 6. It can be observed that the residuals do not show any behavior in each of the exposed figures, so it can be concluded that the forecast errors are random and do not obey the influence of the values of each independent variable, predicted values, or errors at the time of collecting the data [9]. Finally, the probabilistic behavior of the residuals is analyzed, finding that they follow a normal distribution (p-value of 0.74) with an average very close to zero (−0,00016), indicating that there is a high probability that an error of zero appears at the forecast, see Table 4. From the mathematical model generated, Eq. (1). % SHRINKAGE = − 5, 31772 ∗ % HUMIDITY OF ROASTED COFFEE

120 Fig. 3 Residuals versus % humidity of roasted coffee

Fig. 4 Residuals versus color of roasted coffee

Fig. 5 Residuals versus % predicted shrinkage

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Fig. 6 Residuals versus number of rows

Table 4 KolmogorovSmirnov test for residuals

Normal DMAS

0.162334

DMENOS

0.197023

DN

0.197023

P-value

0.740122

+ 2, 1047 ∗ COLOR OF ROASTED COFFEE

(1)

An exercise was applied to optimize the result of shrinkage by using linear programming methods, where the target cell corresponded to the percentage of shrinkage in the process, changing the cells that corresponded to the percentage of humidity of the roasted coffee and to the color value of the coffee. The optimum values of humidity percentage of the roasted coffee and the color of the roasted coffee (kept between the maximum and minimum values of the process) are 3.4% and 16.98, respectively, obtaining a shrinkage of 17.65%, which is below the 18.48% observed during the studied period.

4 Conclusions The model is useful and significant for explaining the behavior of the shrinkage in the coffee process, where the percentage of humidity in the roasted coffee and the color become preponderant and sufficient factors to explain its behavior, allowing to generate a calculation of shrinkage optimization to allow an average from 18.48 to 17.65% of shrinkage (a decrease of 0.83%) for the whole process. In pounds of packed coffee, this would correspond to a monthly increase of 132,800 lb and would result in an approximate monthly increase of USD 346,000 income to the company.

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Acknowledgements To the Universidad de la Costa and the Universidad Libre Seccional Barranquilla, especially the GIDE research group, for making this research possible.

References 1. Moscoso M (2016) El café, una de las bebidas más consumidas del mundo.[Online] natural medio ambiente. Obtenido de: https://www.natura-medioambiental.com/el-cafe-una-de-lasbebidas-mas-consumidas-del-mundo/. Acceso 5 abril 2018 2. Nuñez J (2002) Optimización de la Producción en la Empresa Elaborados de Café 3. Suarez H, Bello H (2016) Estudio de viabilidad para la modernización del proceso de tostión de una de las líneas de café tostado y molido de la empresa Café de Colombia 4. Parody A et al (2018) Application of a central design composed of surface of response for the determination of the flatness in the steel sheets of a colombian steel. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham 5. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham 6. Viloria A et al (2018) Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham 7. Parody A, Viloria A, Lis JP, Malagón LE, Calí EG, Hernández Palma H (2018) Application of an experimental design of D-optimum mixing based on restrictions for the optimization of the pre-painted steel line of a steel producer and marketing company. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham 8. Castaño J, Quintero G (2001) Optimización de la torrefacción de mezclas de café sano y brocado, en función de la temperatura de proceso y el agua de apagado 9. Kizys R, Juan A (2005) Modelo de regresión lineal múltiple 10. Conejo AJ, Contreras J, Espinola R, Plazas MA (2005) Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecast 21(3):435–462 11. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297 12. Du XF, Leung SCH, Zhang JL, Lai KK (2011) Demand forecasting of perishable farm products using support vector machine. Int J Syst Sci 44(3):556–567 13. Garcia MI (2003) Análisis Y Predicción De La Serie De Tiempo Del Precio Externo Del Café Colombiano Utilizando Redes Neuronales Artificiales. Universitas Scientiarum 8:45–50 14. Garson GD (1991) Interpreting neural network connection weights. AI Expert, pp 47–51 15. Gedeon TD (1997) Data mining of inputs: analysing magnitude and functional measures. Int J Neural Syst 8(2):209–218 16. Glorfeld LW (1996) A methodology for simplification and interpretation of backpropagationbased neural network models. Expert Syst Appl 10(1):37–54 17. Gunn SR (1998) Support vector machines for classification and regression. ISIS 14(1): 5–16 18. Hanke JE, Wichern DW (2006) Pronósticos en los negocios. Pearson Educación 19. Heravi S, Osborn DR, Birchenhall CR (2004) Linear versus neural network forecasts for European industrial production series. Int J Forecast 20(3):435–446 20. Izar J, Ynzunza C, Guarneros O (2016) Variabilidad de la demanda del tiempo de entrega, existencias de seguridad y costo del inventario Contaduría y Administración 61(3): 499–513

Recommendation of Energy Efficiency Indexes for the Coffee Sector in Honduras Using Multivariate Statistics Rafael Gomez Dorta, Omar Bonerge Pineda Lezama, Nelson Alberto Lizardo Zelaya, Noel Varela Izquierdo, and Jesus Silva

Abstract The objectives of this study were to define and determine the energy efficiency indexes that should be considered to measure and analyze the energy performance in enterprises engaged in processing green coffee for export. The investigation arose through a case study on a coffee processing plant in Honduras. The purpose of this work was fulfilled under the recommendations set forth in ISO 50001:2011, which were used as references. In addition, determine the energy structure of the company target of study, establishing the strategy to obtain daily records of the energy situation of the company and under a four-step process: energy structure of the company, daily registry of consumption, indicators of energy performance, and potential evaluations for improvement. With the observed results for more than 100 days, a model of high-quality was found with a coefficient of determination of 0.88, which helped to find and define different energy performance indicators. Keywords Coffee processing plants · Energy efficiency · Energy efficiency indexes

R. G. Dorta (B) Gerente de Calidad, BECAMO, Villanueva, Honduras e-mail: [email protected] O. B. P. Lezama · N. A. L. Zelaya Universidad Tecnologica Centroamericana (UNITEC), San Pedro Sula, Honduras e-mail: [email protected] N. A. L. Zelaya e-mail: [email protected] N. V. Izquierdo Universidad de La Costa (CUC), Calle 58 # 55-66, Atlantico, Baranquilla, Colombia e-mail: [email protected] J. Silva Universidad Peruana de Ciencias Aplicadas, Lima, Peru e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_13

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1 Introduction One of the most difficult challenges of manufacturing companies is ensuring the sustainability of the business. One of the aspects of sustainability is constituted in the responsible use of resources such as energy. In this connection, there are aggressive policies in the world to reduce the carbon footprint and improve energy performance. Energy efficiency is an essential alternative, both by its direct effect and by which it can contribute to the promotion of renewable energy sources. In this regard, the International Energy Agency notes in the proposed strategy for the reduction of the emissions of CO2 to the year 2030 [1], the contribution that may have the following key factors: 1. Increase in nuclear energy: 10% 2. Increase in renewable energy: 12% 3. Increase in energy efficiency: 78%. So, the increase in energy efficiency could contribute to the reduction of more than two-thirds of emissions of CO2 to the year 2030. The enormous potential of the energy efficiency improvements at all stages of production and use of energy is widely recognized, but this potential remains a global challenge [2, 3]. In addition, to achieve energy efficiency in a company or organization is not enough to have an energy-saving plan derived from a study or diagnostic. The requirement of management systems that ensures continuous improvement and there must be continuous assessment procedures and monitoring results, using indicators of management and the establishment of goals [4]. Energy efficiency, understood as the efficiency in the production, distribution, and use of energy, necessary to guarantee the total quality, is part of the set of problems that affect the efficiency of the enterprises or institutions, it involves achieving a level of production or services, with the requirements established by the client, with lower consumption and energy expenditure as possible, and with less environmental pollution by these means. Energy efficiency can be defined as the reduction of energy consumption, while maintaining the same energy services, without decreasing the comfort and quality of life, protecting the environment, ensuring the supply, and encouraging a behavior sustainable use [5]. Energy-saving, even though it is not an energy source itself, it is customary to regard it as such, since it offers the possibility to meet more energy services, which is equivalent to having more energy. The increase of energy efficiency has an immediate and direct environmental benefit since it implies a reduction in the use of natural resources and the emission of pollutants, including CO2 . Without a doubt, the cleanest energy is the energy saved and correct and that only one version of your paper is sent. It is not possible to update files at a later stage. Please note that we do not need the printed paper.

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1.1 Energy Management System Energy Management System (EMS) enables the development and implementation of energy policies and establishes objectives, targets, and action plans that consider the legal requirements and the significant use of energy-related information. Also, allows the organization to achieve the commitments arising from its policy, take action, as needed, in order to improve their energy performance and demonstrate the compliance of the system with the requirements of the standard, ISO 50001:2011 [6, 7]. This standard is based on the cycle of continuous improvement Plan-Do-CheckAct (PDCA) and incorporates the power management on the standard practices of the organization. The PDCA approach can be summarized in the following way: • Plan: do the energy review and establish the baseline, energy performance indicators (EPI), objectives, goals, and plans of action necessary to achieve results that will improve the energy efficiency in accordance with the energy policy of the organization; • Make: implementing energy management action plans; • Check: monitor and measure processes and key features of operations that determine energy performance in relation to policies and energy targets and reporting on results; • Act: take steps to continuously improve energy efficiency and EMS.

2 Methodology The standard ISO 50001:2011 [6, 8] considers that for proper handling of the energy, we need indicators showing the energy performance of each organization. It is necessary to measure and record the behavior of these properly to verify the effectiveness of the implemented measures to reduce consumption. The present study aims to characterize the energy performance of a coffee processing company, on the basis of the daily results of the harvest of 2017–2018. The methodology used in the study consists of four stages: (1) energy structure of the company, (2) daily record of consumption, (3) establishment of indicators of energy performance, and (4) evaluation of potentials for improvement.

2.1 Stage 1. Energy Structure of the Company An important link for the development of this stage is the general understanding of the production scheme, the consumption of energy, and production data. This activity is essential to develop a productive energy diagram which is a general outline of the production process showing the use of each type of energy in the process [5, 9]. The process involves five main stages: receipt into plant, drying, storage in the warehouse,

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dry storage of coffee, and clearance of coffee export to the port. Significant energy carriers that characterize the energy of this type of company matrix are three: electric power which is mainly used in the industrial process, gas which is used by materials handling equipment (forklifts), and biomass (coffee husk) which is used in drying ovens. Figure 1 illustrates the energy-production process diagram. The analysis of the use of energy carriers leads to the diagram of Pareto’s first level which is presented in Fig. 2. In the coffee process, electricity prevails over other types of energy carriers, due to induction motors to constitute the fundamental element in the production chain and a key target for energy savings. According to studies it has proven that, worldwide, between 40 and 60% of the total electrical energy consumption, corresponds to induction [10] three-phase motors.

Fig. 1 Productive-energetic diagram of the coffee production plant

Fig. 2 Use of energy carriers in the coffee processing plant

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2.2 Stage 2. Daily Record of Consumption Taking into account that electricity accounts for about 90% of consumption in the company, work is organized by first defining a record of daily data enabling the stratification of energy consumption by areas, generating a second level Pareto diagram as shown in Fig. 3, the same shows that the production area, which is where they produce all the dry milling of coffee, from the threshing of the coffee until the final stage in the port office where it generates increased consumption of energy and therefore proceeded to collect data concerning production and energy in this area for 100 days. The days were distributed between the months of December to June which are the critical months in the harvest of coffee. An important tool for a first validation of the data is the dispersion diagram, which is presented in Fig. 4 which shows a positive linear relationship between the production and the consumption of electricity, the value of the determination coefficient is (R2 = 0.88), and indicates a high-quality of the adjustment model, which allows considering the adjusted line as the line of energy base, which is defined by the equation: Energy consumption = 0.897 ∗ production + 4381.1

Fig. 3 Energy consumption by area

(1)

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Fig. 4 Dispersion diagram of production-consumption of the coffee production plant

2.3 Stage 3. Establishment of Indicators of Energy Performance It is important to introduce a system of indicators for the measurement of energy performance that allows the systematic monitoring of the process and to facilitate the validation of the improvement in the actions implemented. The system is based on an understanding of the relationship between energy and production. The information basis for the calculation of indicators, corresponds to the data obtained during the previous phase (daily record of consumption and production). Selected energy performance indicators are based on the basis of previous research conducted by the authors [5, 7, 9] as well as other analyzed works [2, 3, 11–15], and are as follows: energy consumption indicator ECI, Efficiency indicator Base 100 and graphic display of trend or cumulative sums CUSUM.

2.3.1

Energy Consumption Indicator ECI

The specific energy consumption indicator is defined as the ratio of energy consumption and the value of the production obtained with this energy, according to Eq. (2). Since the indicator provides information on the unitary energy requirement for the process, it is possible to make comparisons with respect to processes or similar areas. It may also be the basis for the development of strategies of production and energy improvement, looking for the reduction of this indicator. The indicator is calculated from the following equation: Consumption index =

Energy consumption Production

(2)

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Fig. 5 Variation of the indicator of consumption (IC) basis with respect to the production

Dividing each Member of Eq. (1) by production is obtained then the indicator of consumption base Eq. (3). ConsumptionIndexBase = m +

E0 P

(3)

Equation (3) represents a consumption base, consisting of a constant term indicator (m) and another that is a function of the variable production (E 0 /P). For the graph of the indicator of consumption reference, Statgraphic centurion version 16.0 software was used (see Fig. 5). The graphics IC versus P corresponds to an equilateral Hyperbola represented by the equation IC = 0.994845 + 3601.98/P, with Asymptote on the x-axis to the value of the slope m of the expression E = f (p). It shows the influence of the level of production on the rate of consumption. The equation based on the indicator of consumption IC is expressed from Eq. (4), from here is possible to obtain the characteristic value energy efficiency, comparing for each level of production, comparing this with the base energy performance. Figure 4 shows that daily production levels below the 8000 qq are not recommended, since for these levels the process shows much variation and low efficiency, by what the strategy should be to achieve a daily production above the 10,000 qq in production, as from this value of production index of consumption shows little variation and high efficiency.

2.3.2

Base 100 Efficiency Index

The base 100 index is an energy management tool, which allows you to compare the behavior of the results of energy consumption measured during an operating period, in a process with respect to the base energy consumption or trend values of this, taking as a reference of compliance a dimensionless value of 100 [13], is defined mathematically as Eq. (4):

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Fig. 6 Indicator of base 100 efficiency

Base efficiency100 =

E Tendency ∗ 100% Pmeasure

(4)

This indicator is calculated from the data of production and energy for a given analysis period and the equation or energy baseline established in Eq. (1). The graph for this indicator is shown in Fig. 6 in the analyzed period. There is a symmetrical motion around the baseline, which indicates a systematic work of the energy improvement group, that each situation of inefficiency responded with actions that they took to reach the state of efficiency, achieving a result favorable to the end of harvest. The following prompt will allow us to validate if the outcome is satisfactory.

2.3.3

Graphic Display of Cumulative Sums (CUSUM)

This indicator and its graphics are used to monitor the trend of the company, in terms of the variation of their energy consumption, with respect to a given base period [5, 9, 16]. It is a complement to the previous indicator. This graph is shown in Fig. 7. The final result indicates that all the improvement work done has allowed to achieve an efficient energy performance, note that at the end of the period has been a significant saving of energy.

2.4 Stage 4. Evaluation of Improvement Potentials The potentials of energy-saving that can be calculated in this case are two types: (1) Reduction of energy consumption not associated with production (2) Reduction of the consumption index (CI).

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Fig. 7 Graphic indicator of cumulative sums

In a graph of energy dispersion versus production for a process as shown in Fig. 4, no associated energy is the value of the intercept on the Y-axis and the line of best fit to the dispersion of data, in this case, the value of energy not associated (E0) is 4381 kW, this not associated energy is mainly due to various factors among which are: illumination of the production plant, electricity for office equipment, air conditioning, energy used in maintenance services, lost electricity by reactive power, leakage of compressed air and vacuum equipment work in electrical equipment. In this sense the savings can be standardizing the values for operation of the variables with the days of maximum efficiency for different intervals of work and trying to stabilize the process in these areas through good energy practices or energy-saving actions. For the calculation of potential savings is required to establish a “goal line”, which passes through the center of the data related to the lower consumption, corresponding to the operational practices of greater efficiency in the process. This goal line is traced by considering the same value of the slope “m” of the baseline. Finally, this will quantify the potentials energy savings of electricity not associated with production starting from the estimate of baselines and the goal of energy modeled from the daily consumption of energy and production of the period data set base selected for a year. Similarly, the savings potential for reduction in the rate of consumption can be calculated, but in this case it would work with the curve of Eq. (4) and the rate of consumption. The potential to be determined as the difference between the actual IC and theoretical IC for equal production. Table 1 summarizes the maximum estimated potential savings, these are given based on the coffee production equivalent to the use of 85% of the installed capacity in the coffee processing plant during the baseline period, kWh/qq. The total savings potential is estimated at 0.31 kWh/qq of coffee, which on the basis of the annual production, would mean around 310,000 kWh saved.

132 Table 1 Maximum estimated potential savings

R. G. Dorta et al. Description of potential

Saving potential (kWh/qq coffee)

Energy consumption not associated with production

0.06

Reduction in the consumption index

0.25

Total

0.31

3 Results Outcomes indicate that it is possible to apply a simple methodology and a precise set of activities to obtain energy indicators that allow support in the process of monitoring, metering, and energy analysis in a coffee processing plant, making possible the process of monitoring the energy performance. It is possible to identify and encourage on the processing plant the use of various techniques and statistical graphics to acquire an objective and systematic knowledge of what is required to know in the energy field from measurements in areas of coffee production plants, these practices were not done before in the current energy management system. The results are a first reference that relates to the daily consumption of electric energy with the daily production of green coffee for export on a coffee processing plant, what constitutes a novel result, and an important contribution to knowledge. This research is valid because the data was extracted from reliable registries, prepared records based on measurements obtained from fully calibrated equipment, and validated systematically. Procedures can be generalized to any coffee mill production plant. In addition, this approach allows the company to recognize and document their knowledge on energy efficiency and determine potential savings that allow the strategic direction of the improvement process.

4 Conclusions According to the developed study, consumption of electricity and the production of coffee in the dry milling process showed an excellent relationship, with a coefficient of determination above 88%, indicating a high-quality of the adjustment model obtained from which energy performance indicators were estimated for this process. In this process, the electricity consumption was the energy carrier of greater impact in the costs with 89 and 59% of which was generated by the process of dry milling of coffee that included: cleaning, trite, classification by size, classification densimetry, electronic sorting, preparation, packaging, and shipping to the port. Three energy performance indicators were determined: the indicator of consumption (IC) showed that daily production levels below the 8000 qq are not recommended, since these levels the process displays much variation and low efficiency,

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so the strategy should achieve a daily production above the 10,000 qq in production because starting from this value of production the rate of consumption showed little variation and high efficiency. The indicator of efficiency base 100 indicated a systematic work in the improvement of the process, where while there are days that show a level of inefficiency, a satisfactory final result was achieved that is evidenced through the graphic indicator chart of CUSUM. Determined potential savings estimated at 0.31 kWh/qq of coffee, which managed to establish a key point to the strategic direction of the improvement process.

References 1. World Energy Outlook (2006) International Energy Agency 2. Silva V, Jesús A Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. Advanced materials research. ISSN: 1022-6680 ed: Trans Tech Publications Ltd, vol 601. fasc.n/a, pp 618–625 3. Silva V, Jesús A et al. (2016) Energy efficiency index of ambulatories and hospitals. Int J Control Theory Appl 9(44). ISSN: 0974-5572 4. Lawrence T, Watson R, Boudreau M, Johnsen K, Perry J, Ding L (2012) A new paradigm for the design and management of building systems. Energy Build 51 5. Nordelo AB et al. (2012) Gestión Energética Empresarial. Segunda Edición. Unión Eléctrica. Cuba. ISBN 959-257-040-X 6. ISO 50.001 (2011) Energy management 7. Gómez Dorta RL (2001) Procedimientos para la mejora de la calidad de la generación y el consumo de energía. Tesis para optar por el título de Doctor en Ciencias Técnicas, Universidad Central de Las Villas, Cuba 8. Acevedo R et al (2014) Análisis Relacional de la norma ISO 50001: Sistemas de Gestión Energética. Rev Cienc Y Tecnol 11:312–323 9. Campos Avella JC, Gómez Dorta R, Santos Macias L (1998) Eficiencia Energética y Competitividad de Empresas. Editorial UNIVERSO SUR, Cienfuegos, Cuba. ISBN 959-257-019-1 10. Hernandez Ramírez G et al. (2015) Eficiencia energética en sistemas de bombeo de hidromezclas. Revista Minería y Geología 31(3), julio-septiembre, ISSN 1993 8012 11. Castrillón R et al. (2013) Mejoramiento de la eficiencia energética en la industria del cemento por proceso húmedo a través de la implementación del Sistema de Gestión Integral de la Energía. Revista Dyna, año 80, Edición 177, Colombia 12. Campos JC, Quispe EC, Lora E (2009) Nueva herramienta para la medición y el control de la eficiencia energética en la gestión de procesos empresariales. Memorias de XI Semana Técnica de Ingeniería. Barranquilla, Colombia, pp 76–86 13. Molina Gonzalez A et al. (2017) Nuevos índices de consumo energético para hoteles tropicales. Revista de Ingeniería Energética XXXVIII(3), septiembre-diciembre, CUJAE, Cuba, pp 198– 2017. ISSN 1815 5901 14. Ministerio de Minas y Energía (2014) Gestión e indicadores energéticos. Recuperado de http:// www.si3ea.gov.co/Eure/16/[4]inicio.html 15. Varela N, Fernandez D, Pineda O, Viloria A (2017) Selection of the best regression model to explain the variables that influence labor accident case electrical company. J Eng Appl Sci 12:2956–2962 16. Pérez R, Inga E, Aguila A, Vásquez C, Lima L, Viloria A, Henry MA (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham

Modeling and Simulating Human Occupation: A NetLogo-Agent-Based Toy Model Amelec Viloria, Yury Arenis Olarte Arias, Manuel-Ignacio Balaguera, Jenny Paola Lis-Gutiérrez, Mercedes Gaitan Angulo, and Melisa Lis-Gutierrez Abstract Human occupation is an important element for individual and community wellbeing as well as for social equilibrium and the prevention of conflicts and wars. Despite its importance, because of the lack of tools and quantitative methods for decision-making by governmental agents and other organizations, in the underdeveloped and developing countries, human occupation is absent as a factor of social regulation. In this paper, we present a “toy model” that illustrates the use of agent-based modeling methods to simulate a simple occupational dynamic. Keywords Human occupation · Occupational science · Occupational system · Complexity · Complex system · Nonlinearity · Hierarchy in complex systems

A. Viloria (B) Universidad de la Costa, Barranquilla, Colombia e-mail: [email protected] Y. A. O. Arias School of Occupational Therapy, Escuela Colombiana de Rehabilitación, Carrera 15 No. 151-68, Bogotá DC, Colombia e-mail: [email protected] M.-I. Balaguera · J. P. Lis-Gutiérrez · M. G. Angulo Fundación Universitaria Konrad Lorenz, Carrera 9 Bis No. 62-43, Bogotá DC, Colombia e-mail: [email protected] J. P. Lis-Gutiérrez e-mail: [email protected] M. G. Angulo e-mail: [email protected] M. Lis-Gutierrez Universidad de Ciencias Aplicadas y Ambientales, Calle 222 No. 55-37, Bogotá DC, Colombia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_14

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1 Introduction One feature that marks a social system as a complex system [1, 2] is the occurrence of emergent phenomena [3]: the fact that an individual’s psychological state, convictions, and behavior may be escalated as collective phenomena in diverse levels of the social organization: cooperation processes, institutions establishment, crime organizations, wars, peace agreements, migration, political parties, etc. [2, 4]. The occurrence of emergent phenomena and processes together with the hierarchical structure of social systems allows a high diversity of control channels and mechanisms, each of them with their degrees of difficulty, effectivity, and implications [5, 6]. On the other hand, stability, justice and social harmony, the welfare of individuals and communities, economic and environmental sustainability, and many other desired conditions require serious, risky, and complex decision-making processes on the part of individuals, families, non-governmental, and state organizations [7, 8]. Along with the above considerations, the rapid and sustained progress in the recent decades of the science and engineering of complex systems, enhanced by scientific computing and artificial intelligence [9], has led to the emergence of various initiatives in the field of social systems such as the “computational social sciences”, the “sociocybernetics” [10], and the “social systems engineering” [7, 11] whose purpose is to provide new conceptual frameworks, methodologies, and tools to the decision-makers in the different levels of organization of the social systems. Assuming the relevance of quantitative methods and models in decision-making processes, the unknown and capricious nature of emerging phenomena and their consequent phases of social order make statistics insufficient as the only alternative to make forecasts according to the different scenarios that can be presented at the time of the occurrence of real events [4] as it provides methods to find patterns in the data and if these data come from the past, however, immediate, nothing guarantees that the processes continue to follow the same patterns of time evolution, indispensable condition for statistics to have predictive value. When modeling complex systems, an established scientific-technical approach is the so-called “evolutionary and progressive modeling” [6]. Evolutionary and progressive modeling is justified on the fact that complete and perfect knowledge of any complex system is impossible [7, 8]. The supporting idea of evolutionary and progressive modeling is that despite the lack of complete and perfect knowledge about a complex system, it is possible, and the only possible option to support rational decisions, to create computer systems (“modeling frameworks”) that allow the development of models that could be evolutionarily modified, grown and adapted, approaching progressively in time the needed or desired predictive capabilities. In the contemporary language of scientific computing, the name “Toy Model” is given to the primordial model of a complex system from which its new models will be built and implemented progressively. In the present work, human occupation is taken as a case study: an area of human, social, and individual reality, for which quantitative theoretical treatment, except

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for some isolated and isolated attempts, is almost non-existent, a lack with serious implications because in the Appropriate management of occupational dynamics in diverse social contexts relies on a large part of the controllability that guarantees individual and collective wellbeing as well as social stability and the consolidation of sustainable and harmonious cultures of peace with ecosystems.

2 Description of the Case of Study In order to illustrate to the researchers interested in exploring the role of human occupation in social systems the use of the scientific computing paradigm as a resource to explore hypotheses and elaborate theoretical synthesis, as well as its use in decision-making processes, the software system SSDOc (from the Spanish initials of “Sistema de Simulación de Dinámica Ocupacional”) has been conceived, designed, implemented, and operated, a “toy model” to start the evolutionary modeling and simulation of the role played by individuals’ preferences and vocational competences (technical, scientific, artistic, and humanistic) of a given population in its employability in a given occupational (labor) space [11]. Despite being a universe of discourse that has received an almost insignificant contribution by computer science, and with it, has been completely absent as a decision element in government and other social systems, human occupation is, by its structure and dynamics, a strategic and unavoidable element in the challenging task of complex social systems regulation and control [5, 12].

3 Ontology of an Occupational System in the Universe of Discourse of SSDOc Although the implementation system of SSDOc, NetLogo [13, 14], strictly speaking, is not object-oriented, it contains elements that allow, although hardly, to evoke the concept of classes. In articles previously published by the authors, the suitability of object-oriented methods and the Unified Language for Modeling (UML) [9, 15] for the task of abstracting and representing ontologies of the complex system “human occupation” is shown and justified. Therefore, we will use the language of object orientation as a natural description language. Figure 1 presents the visualization of a particular occupational scenario in SSDOc (NetLogo). Figure 2 presents a UML diagram of classes that more broadly represents the ontological context of SSDOc. Based on these two figures we can indicate that SSDOc implements the classes: (1) “social system”, (2) “person” (occupational subject), (3) “occupation”, (4) “occupational space” and with them reproduces, so superficial, an occupational dynamic arbitrarily constructed in terms of the mathematical

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Fig. 1 NetLogo visualization of an occupational system in SSDOc

representation of the patterns involved, which are expected later to be deepened and elaborated to capture the constructs and findings of the science of human occupation. NetLogo, which is a system for agent-based modeling and simulation, in addition to traditional data types (non-object-oriented) has three essential resources for the representation of entities: “patch” static agents, “turtle” dynamic agents, and “link” agents that relate the turtles to each other. In SSDOc, each “patch” agent represents an occupation as illustrated in Fig. 3a, each “person” agent, an extension (“breed” in NetLogo) of the agent “turtle”, represents an occupational subject or “person” as shown in Fig. 3b and, finally, the occupational contexts, which in an object-oriented language would be represented by an abstract class, but in this NetLogo implementation they are represented in a dispersed manner by some of their attributes, sheltered under the term” occupational space “. Each static “patch” agent, see Fig. 3a, represents an individual occupation space, which initially may or may not be available to be occupied by a person according to the number of vacancies specified in the interface by the user and assigned to a random location within a given occupational space. It is characterized by the attributes (variables in NetLogo) [16–18]: • Spatial location (with arbitrary meaning in SSDOc) (“pxcor”. “Pycor”).

Modeling and Simulating Human Occupation: A NetLogo-Agent …

Fig. 2 Class diagram showing an occupational system ontology [16]

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Fig. 3 a “Patch” agent structure. b “person” agent structure. a and b: Property fields of patch and person agents

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• Color (“pcolor”): Which, according to the disciplinary and vocational nature of the requirements to be occupied by a person, can be red for a technical occupation (focused on the transformation and adaptation of physical objects), green for a scientific occupation (focused on the achievement of information, its treatment and the construction of scientific knowledge), yellow for an artistic occupation (focused on human expression), and blue for a humanistic occupation (focused on the problems concerning individuals, people, and organizations). • WorkspaceIdx: Index for the disciplinary nature of the occupational position: “0” for technique, “1” for scientific, “2” for artistic, “3” for humanistic. • MinFitness: Requirement index (in this case, the average of the vocation and competence for the profile required by the posts in the specific occupational space.) Its value is between zero and one and is generated from a uniform random distribution. • OwnerWho: Number assigned by NetLogo for the person occupying the square represented by the patch. • An occupational agent, “person” (Fig. 3b) is a dynamic agent that initially is in a state of “leisure” without assigned occupation has the following attributes: • Spatial location (“xcor”. “Ycor”) that matches the center of a “patch” agent. • OccupationIdx: Index of the occupational space corresponding to the place it occupies, “0” for technique, “1” for scientific, “2” for artistic, “3” for humanistic, and “4” for leisure status (without assigned occupation). • Four indexes that quantify the level of your vocational preference: tecVocation, sciVocation, artVocation, humVocation for activities of a technical, scientific, artistic, and humanistic nature, respectively. Its value, assigned randomly, is between zero and one. • Four indexes that quantify the level of their occupational competence: tecCompetence, sciCompetence, artCompetence, humCompetence for their technical, scientific, artistic, and humanistic competences, respectively. Its value, assigned randomly, is between zero and one.

4 Occupational Dynamics Simulations Figure 4 illustrates the components of the SSDOc user interface. Above, to the left are the sliders (“sliders”): “population”, “tec-workplaces”, “sci-workplaces”,

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Fig. 4 SSDOc modeling and simulation interface

“art-workplaces”, “hum-workplaces” which allow the user to specify the parameter values defining the simulation scenario. In the center, there is a visualization of the occupational system whose dynamics are simulated. Each turtle (“person”) color corresponds to the type of occupation for which it has a better fit (“fitness”), thanks to which it will be more likely to assume a position suited for a person with the same fitness type. The color of each patch represents the nature of the occupational space in which it is located and is more intense if the place is available. If a patch is black, it means that, being available at the beginning of the simulation, it was already occupied. In the right area of the interface, there are two spaces for graphs, the upper one for the graphic total vacancies versus time and the lower one for vacancies discriminated by occupational space, also as a function of time. Finally, in the lower-left area of the interface there are the “setup” buttons whose activation establishes the simulation scenario and displays the initial configuration of the system. Finally, Fig. 5 shows the NetLogo code that executes the simulation. In this case, an execution block with the structure of the Metropolis-Monte Carlo method in which, the code of line 233 generates a random integer with which randomly selects a person from the population: the “i-person”. On line 236, it is asked for the joint fulfillment of two conditions: (1) The i-person is in a leisure state and (2) The “i-patch”, where the i-person is located, is available space. If any of the two conditions is not met, the iteration is repeated with the increment of time as the only recorded event. If the condition is met, in lines 238–241 it is inquired, having as a condition for the execution of 242–268 block, if the index of occupational adjustment (for example, tecFitness) of the i-person is greater than or equal to the minimum required by the i-patch in the occupational space belongs. If the condition is not met, it iterates again after a unitary increase in the time record, and if the condition is fulfilled, i-person assumes the position at the i-patch, updating consequently the values of the available vacancies, as well as the graphics and the visual representation

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Fig. 5 Main NetLogo code for SSDOc

5 Discussion of Results The plots presented in Figs. 3 and 4 correspond to a typical result of occupational dynamics simulation for, respectively, a scenario with a population of 100 people and an initial configuration of 50 technical, 10 scientific, 10 artistic, and 20 humanistic vacancies whose most outstanding features are as follows: • Exponential decrease in total vacancies and for each occupational space • Stationary values (final) Different from zero, for total vacancies and each category

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After 15 executions, the data and statistics presented in Fig. 5 are obtained for residual vacancies.

6 Conclusions • A NetLogo-agent-based toy model software system has been developed to demonstrate relevant methodological and ontological concepts in order to simulate human occupational dynamics from the perspective of innate and vocational tendencies of persons as their competences. • The object-oriented modeling methodology has been illustrated emphasizing the use of the visual object-oriented modeling language UML (Unified Modeling Language), in particular, the use of class diagrams. • The agent-based modeling methodology has been illustrated by using NetLogo platform, emphasizing agents’ definition and the visual representation of their time evolution. • The developed toy model system, SSDOC, was verified evidencing that its simulations produce significant results in simple scenarios.

References 1. García C, Olaya C (2018) Social systems engineering: the design of complexity. Wiley, Hoboken NJ USA 2. Castellani B, William H (2009) Sociology and complexity science. Springer Verlag, Berlin 3. Lewin R (2000) Complexity, life at the edge of chaos. University of Chicago Press, Chicago 4. Abergel F, Aoyama H, Chakrabarti B, Chakraborti A, Deo N, Raina D, Vodenska I (2017) Econophysics and sociophysics: recent progress and future directions. Springer International Publishing, Switzerland, Cham 5. Amozurrutia JA (2012) Complejidad y Ciencias Sociales. Universidad Autónoma de México, México DF 6. Roehner BM (2007) Driving forces in physical, biological and socio-economic phenomena. Cambridge University Press, Cambridge UK 7. Miller JH, Page SE (2007) Complex adaptive systems: an introduction to computational models of social life. Princeton University Press, Princeton NJ USA 8. Nemiche M (2017) Advances in complex societal, environmental and engineered systems. Springer, New York 9. Booch G, Maksimchuk R, Engle M, Young B, Conallen J, Houston K (2007) Object-oriented analysis and design with applications. Addison Wesley, Boston MA USA 10. Geyer RF, van der Zouwen J (2001) Sociocybernetics: complexity, autopoiesis, and observation of social systems (controversies in science). Greenwood Press, London 11. Balaguera MI, Lis-Gutierrez JP, Gaitán-Angulo M, Viloria A, Portillo-Medina R (2018) An ontological framework for cooperative games. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10941. Springer, Cham 12. López-Paredes A (2004) Ingeniería de Sistemas Sociales, Valladolid. Universidad de Valladolid, SPAIN

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13. Trujillo A (2002) in Terapia Ocupacional conocimiento y práctica en Colombia. 1a ed., Bogotá, Universidad Nacional de Colombia, pp 469–540 14. Wilensky U (1999) NetLogo. Evanston IL USA 15. Nakai Y, Koyama Y, Terano T (2013) Agent-based approaches in economic and social complex systems VIII. Springer Verlag, New York 16. Parunak H, Odell J (2002) Representing social structures in UML. In: AOSE 2001, Montreal Canada 17. Balaguera MI, Vargas MC, Lis-Gutierrez JP, Viloria A, Malagón LE (2018) Architecture of an object-oriented modeling framework for human occupation. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham 18. Guarino N, Oberle D, Staab S (2009) “What is an ontology?,” in Handbook on Ontologies. Springer Verlag, Heidelberg, pp 1–3

Deep Learning Predictive Model for Detecting Human Influenza Virus Through Biological Sequences M. Nandhini and M. S. Vijaya

Abstract Swine influenza is a contagious disease which is generated by one of the swine influenza viruses. Any modification in protein will alter the biological activity and lead to illness. Obtaining appropriate information from virus protein sequence is an interesting research problem in bioinformatics. The aim of this research work is to develop deep neural network (DNN)-based virus identification model for detecting the virus accurately with the protein sequences using deep learning. Deep learning is gaining more importance because of its governance in terms of accuracy when the network trained with large amount of data. A corpus of 404 protein sequences associated with nine types of human influenza virus is collected for training the deep neural network and building the model. Various parameters of the DNN such as input layer, hidden layer and output layer are fine-tuned to improve the efficiency of the model. Sequential model is created for developing DNN classification model using Adam optimizer with Softmax and ReLu activation functions. It is observed that experiments of proposed human influenza virus identification model with DNN classifier give 80% of accuracy and outperform with other ensemble learning algorithms. Keywords Classifier · Deep learning · DNN classifier · Human influenza virus · Protein sequences

1 Introduction Identification of human influenza virus is essential for influenza surveillance and vaccine development. Development in biological and medical technologies provides useful biological data, namely medical images, electroencephalography, genomic and protein sequences. Learning and gaining knowledge from these data facilitate M. Nandhini (B) · M. S. Vijaya PSGR Krishnammal College for Women, Coimbatore, India e-mail: [email protected] M. S. Vijaya e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_15

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the understanding of human health and diagnosing disease. Swine influenza is a contagious disease which is generated by one of several types of swine influenza viruses. The swine flu is also detected in humans and caused by influenza viruses. The influenza virus is an RNA virus and comprises three types such as influenza A viruses, influenza B viruses and influenza C viruses. The influenza A virus is divided into six virus types such as H1N1 virus, H3N2 virus, H7N7 virus, H1N2 virus, H9N2 virus and H7N2 virus based on two proteins on the surface of the virus. Mutations on these proteins may result in different influenza subtypes [1]. Influenza A viruses can affect people, birds, pigs, horses, seals and whales. Hemagglutinin (H) and neuraminidase (N) are proteins in influenza virus. The hemagglutinin protein has 18 subdivisions. The neuraminidase has 11 subdivisions. The subdivisions of hemagglutinin are H1 to H18. The subdivisions of neuraminidase are N1 to N11. For example, the H7N2 viruses show influenza A subtype that has an HA7 protein and NA2 protein. The H5N1 viruses describe HA5 protein and NA1 protein. Influenza B virus is normally found only in humans. These viruses are not divided into subdivisions. Influenza C virus creates mild disease in humans. This also is not divided into subdivisions. Two different actions are performed, namely genetic drift and genetic shift. The genetic drift action often results in different strains of H1N1 and H3N2 circulating in humans during annual influenza seasons. Another action called genetic shift undergoes infrequent and sudden changes of genome segments from different viral strains, which is speculated to be the major cause for influenza pandemics [1]. Deep learning has most strong intrinsic property called feature learning. The deep neural networks are deriving the features through learning by itself. Deep neural networks can learn very complex functions which are even difficult to understand. ML needs features to be accurately derived by users, while DL creates new features by itself. ML divides problem into small pieces and then combines results into single conclusion, while DL resolves the problem on the end-to-end basis. One of the major advantages of deep learning over various machine learning algorithms is its ability to create new features from a limited series of features used in the training dataset. Deep learning can derive features without a human intervention. It allows users to use more complex sets of features in comparison with traditional machine learning algorithms. Deep learning techniques are capable to capture composite relations between data and process more data. It uses stacked layers of transformation trainable from the beginning to the end for the purpose of feature learning. Deep learning reduces the need of domain knowledge and hard core feature extraction. Deep learning outperforms other techniques such as ensemble learning and machine learning if the data size is huge. It achieves strong anti-interference capabilities. Deep learning has capacity to classify and derive the numerous features from data. Deep learning algorithms attempt to learn high-level features from data. Deep learning models are trained by set of labeled data, and its neural network learns features from the trained data automatically. It provides efficient class performance on problems that considerably outperforms other solutions in multiple domains. Although, the DNN architecture uses more data to train the neural network and it can predict the test data accurately through trained data with self-learned features.

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The flexibility of DNN architecture helps to improve the performance of the model with respect to weights, epochs and hidden layers and outperforms with other learning algorithms. Hence, in this work it is proposed to develop virus identification model for recognizing the influenza virus types through deep neural network.

2 Literature Survey Currently, several techniques have been used in research related to virus identification using biological data. The existing research study is carried out to understand the nature of the work. The proposed work is discovered based on the literature survey. This part describes different research studies related to proposed work. The literature survey explains the knowledge about research problem taken, algorithms or techniques used and data collection. Review of some research work related to virus classification is explained below. In 2009, Ma et al. proposed gene classification using codon usage and support vector machines. A model was created to classify the human leukocyte antigen (HLA) gene through codon usage bias. HLA genes were taken from IMGT/HLA sequence database. In this research, collection of 1841 gene sequences was extracted for gene classification. A model was built by applying binary and multiclass SVM. Binary SVM attained an accuracy of 99.3%, and multiclass SVM attained an accuracy of 99.73%. The efficiency of the classifiers was evaluated and analyzed based on mean, standard deviation and accuracy [2]. In 2014, ElHefnawi and Sherif proposed accurate classification and hemagglutinin amino acid signatures for influenza A virus host–origin association and subtyping. A classification model was developed to classify the host, namely human, avian and swine using HA subtypes such as H1, H2, H3, H5 and H9. Viral protein sequences were collected from NCBI Influenza Virus Resource database. A model was built by hidden Markov models (HMMs) and decision trees. The performance of the classification models was evaluated based on ROC curves and support and confidence ratings [3]. In 2014, Iqbal et al. proposed efficient feature selection and classification of protein sequence data in bioinformatics. A model was created to classify the protein sequences into their relevant superfamilies. The protein sequences were collected from UniProt knowledge database. Three different datasets were used related to superfamilies and its protein sequences. A model was built using decision tree, neural network, random forest, naïve Bayes and SVM. The classification accuracy of three different datasets was evaluated, and the performance of the classifiers was compared with each other in terms of sensitivity, specificity, recall and F-measure [4]. In 2017, Sherif et al. presented classification of host–origin in influenza A virus by transferring protein sequences into feature vectors. A model was developed to classify the types of host–origin using two proteins, namely HA and NA. The feature vectors were given as input to the classification model. Protein sequences were collected from NCBI Influenza Virus Resources. The feature vectors were derived from protein

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sequences using amino acid composition (AAC) and physicochemical properties (composition, transition and distribution). A model was generated using random forest and KNN classification techniques and produced an accuracy of 96.6% [5]. The authors Attaluri et al. presented applying machine learning techniques to classify H1N1 viral strains occurring in 2009 flu pandemic. The authors proposed a machine learning technique to recognize the host–origin of human influenza H1N1 viral strains. A model was generated using SVM and decision tree algorithms. The virus sequences were given as input to the models [6]. From the literature survey, it is perceived that most of the works is done for classification problem through machine learning techniques. The classification was performed with biological sequences, namely gene and protein sequences. The features were extracted from sequences that helped in identifying the virus or in classification of virus. Therefore, it is motivated that the classification of virus can also be carried out by deriving an essential amino acid features for developing the new model through DNN classifier. To make use of deep neural network and to improve the performance of traditional ensemble learning, DNN architecture is generated to develop prediction model for finding the virus accurately. Our previous work was carried out using ensemble learning with the feature set comprising user-defined features. But feature introspection in traditional learning is error prone and some contributive features are missed out. However, deep learning has the capacity to create accurate prediction models by automatically learning the significant features from dataset. Hence, it is proposed in this paper to build the DNN-based virus identification model for recognizing the virus by examining amino acid features from protein sequences of influenza virus.

3 Proposed Work The objective of the proposed work is to develop systematic and efficient deep learning classifier for identifying the human influenza virus based on protein sequences and its essential features. The virus identification problem is originated as multi-classification task and demonstrated using classification. DNN with multilayer configuration and activation function is implemented using sequential classification model in tensor flow environment. The various components of the proposed methodology include data collection, feature extraction, normalized dataset creation and building the classifier using deep learning.

3.1 Sequence Collection Proteins are series of twenty amino acids connected together by peptide bonds. The twenty amino acids are alanine, arginine, asparagine, aspartic acid, cysteine, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine,

Deep Learning Predictive Model for Detecting Human Influenza … Table 1 Counts of protein sequences

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Virus name

Subtypes

Influenza A virus

Influenza A virus

50

H1N1 virus

49

H1N2 virus

49

H3N2 virus

49

H7N2 virus

12

H7N7 virus

50

H9N2 virus Total Influenza B virus Influenza C virus Total

No. of sequences

50 309 50 45 404

phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine. Each amino acid is represented by letter code. The pattern of letter code for twenty amino acids is specified by A, R, N, D, C, E, Q, G, H, I, L, K, M, F, P, S, T, W, Y and V. The protein sequences related to influenza virus of human host are collected from the NCBI Influenza Virus Resources. A collection of 404 sequences are chosen for nine virus types, namely influenza A virus, influenza B virus, influenza C virus, H1N1 virus, H1N2 virus, H3N2 virus, H7N2 virus and H7N7 virus. Fifty sequences are gathered from influenza A virus, influenza B virus, H7N7 and H9N2 virus. For influenza C virus, forty-five sequences, for H1N1, H1N2 and H3N2, forty-nine sequences in each type and twelve sequences for H7N2 virus have been derived. The protein sequences of virus are stored as fasta files. The count of protein sequences related to influenza virus is summarized in Table 1. The sample of the protein sequence is shown below. >AOK93137.1 neuraminidase [Influenza A virus (A/Alappey/MCVR449/2009(H3N2))] MNPNQKIITIGSVSLTISTICFFMQTAILITTVTLHFKQCEFNSPPNNQVMLCEPTIIERNITEIVYLTNTTIEKEICPK

3.1.1

Features of Protein Sequence and Dataset

Feature extraction is an essential element in building the classifier and for increasing the effectiveness of classification model. In this research work, amino acid composition, physicochemical properties (composition, transition and distribution), element periodicity, subsequence periodicity, latent periodicity, zscales, lengthpep, PI and molecular weight features are taken to create the training dataset. The element periodicity refers to finding repeated number of each amino acid from protein sequences. The values for twenty attributes such as A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W and Y have been computed using strsplit() function with splitting value 1. The subsequence periodicity is used for counting the repeated number of amino acids

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with specific length. In subsequence with length 2, the values for seven dimensions, namely S2-1, S2-2, S2-3, S2-4, S2-5, S2-6 and S2-7, and with length 3, the values for two dimensions called S3-1 and S3-2 have been calculated using same strsplit() function with splitting values 2 and 3. Latent periodicity is used to finding the presence of palindrome from protein sequence with specific length. In length 2, the values for two dimensions such as False-P2 and True-P2, and in length 3, the values for two dimensions such as FalseP3 and True-P3 have been obtained through strsplit() function with splitting values 2 and 3. Amino acid composition is obtained by amino acid function which produces 20 amino acid values. The values for twenty dimensions representing amino acids called A, R, N, D, C, E, Q, G, H, I, L, K, M, F, P, S, T, W, Y and V have been calculated using extractAAC() function. In amino acid physicochemical properties, the values for hydrophobicity, polarizability, normalized van der Waals, secondary structure, charge, solvent accessibility and polarity are calculated by extractCTDC() function. The lengthpep refers to collection of amino acids in a protein sequence, and the value for single dimension called LP is computed by lengthpep() function. The molecular weight is a weight of amino acid in protein sequence, and the value for single dimension called MW is computed by mw() function. PI refers to isoelectric point, and the value for single attribute called PI is obtained by PI() function. Zscales refers to physicochemical properties of amino acids, namely lipophilicity, steric properties, electronic properties, electronegativity, heat of formation, etc., and the values for 5 attributes such as Z1, Z2, Z3, Z4 and Z5 have been calculated by zscales() function. The above amino acid-based features are obtained using R tool. The dataset with 404 protein sequences and 9 amino acid features contributing 84 attributes is developed, and the numeric class labels 0–8 designating nine virus types are assigned for the respective instances enabling classification. The values of these attributes are normalized using min–max normalization for improving the predictive accuracy. Table 2 shows summary of features and its attributes. Table 2 Counts of features and its attributes

Features

Number of attributes

Element periodicity

20

Amino acid composition

20

Subsequence periodicity

9

Latent periodicity Composition/transition/distribution

4 21

Zscales

5

PI

1

MW

1

Lengthpep

1

Deep Learning Predictive Model for Detecting Human Influenza … Load the dataset

Divide Training and test dataset

153 Build the sequential model

Create input, hidden and output layers using relu and softmax activation functions

Model compilation using Adam optimizer

Fit the model with epochs and batch size

Predict the class label

Evaluate the model

Fig. 1 Working flow of DNN classifier

3.1.2

Deep Neural Network Classifiers

The DNN architecture consists of multilayers such as input layer, hidden layer and output layer to build the model. Each input layer is created and linked to number of hidden layers. The hidden layers are implemented with the ReLu activation function, and it is transformed to rectify activation function. ReLu function is used to backpropagate the errors. The layers in deep neural network derive new features from a set of amino acid features given in training dataset. Sequential models are developed by importing sequential functions from Keras model libraries. DNN layers are created by importing dense functions from Keras layer libraries. The activation function like ReLu and Softmax with Adam optimizer is used to generate the DNN classifier. Adam optimizer is used to update network weight of the hidden layers. An epoch is used to divide the training dataset into different sets of data to train the DNN model, and the model gets updated after completion of each epoch and batch size. The working methodology of DNN classifier is shown in Fig. 1.

4 Experiments and Results Human influenza virus identification model is developed using Keras and TensorFlow as a backend, Python library for creating and evaluating deep learning models. Deep learning provides efficient numerical computation libraries such as Theano and TensorFlow, and these libraries are used to develop and train neural network models. Nine types of influenza virus are taken for developing the model, and thus virus recognition problem becomes multi-classification. The above normalized dataset is split into training and test datasets, with 80% of the data used for training and 20% of the data used for testing. In these experiments, the sequential model is developed to

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build DNN classifier. The input layers have been given as 83 attributes, and similarly the hidden layers have been given as 20 attributes in sequential model. The output layers have been specified as 9 class labels. The activation functions, namely Softmax and ReLu with Adam optimizers, are used in this research. A Softmax function is used in output layer, and a ReLu function is used in hidden layer. In model compilation phase, Adam optimizer is used with value of epochs 115 and batch size 10 to increase the prediction accuracy. The virus identification model based on deep neural network is built with above parameter settings, and the performance of the model is evaluated using classification metrics such as precision, recall, F1-score and accuracy. By having high precision and recall values, the predicted class labels are correctly classified by the model. Precision and recall measures are computed using true positive, false positive and false negative. F1-score is used as the weighted average of precision and recall. The prediction accuracy is considered as an essential for model evaluation metric to identify influenza virus types, and it is obtained as the ratio of the number of correctly classified instances and the total number of test instances. The functioning of DNN-based virus identification model is determined based on above classification metrics. Prediction accuracy of 80.2% is achieved by DNN classifier and evaluated with respect to nine class labels, i.e., from 0 to 8. The precision is high for classes 1 and 2 with the value of 1.00, and the recall value of 1.00 is maximum for classes 1, 4 and 6. The F1-score with the value of 1.00 is excessive for class 1. The performance results of the DNN classifier based on various metrics are shown in Table 3. The average values of DNN model with precision of 0.81, recall of 0.80 and F1-score of 0.80 are obtained, regarding nine virus types, and shown in Table 4. The DNN classifier is assessed in terms of epochs with various metrics. The first epoch of trained DNN model produces high precision of 4.7632, recall of 2.1715 and F1-score of 2.9005 when compared with other epochs. The ninth epoch gives minimum loss of 0.3045, and an eighth epoch achieves high accuracy of 87.9%. The comparative analysis of evaluation metrics at different epochs is summarized in Table 5. Table 3 Comparison of performance measures of DNN classifier for each class

Precision

Recall

F1-score

Class labels

0.62

0.45

0.53

0

1.00

1.00

1.00

1

1.00

0.77

0.87

2

0.57

0.73

0.64

3

0.92

1.00

0.96

4

0.80

0.89

0.84

5

0.50

1.00

0.67

6

0.83

0.71

0.77

7

0.82

0.90

0.86

8

Deep Learning Predictive Model for Detecting Human Influenza … Table 4 Average values of performance metrics for nine virus types

155

Measures

Average values

Precision

0.81

Recall

0.80

F1-score

0.80

Table 5 Comparative analysis of evaluation metrics per epochs Epochs

Precision

Recall

F1-score

Loss

Accuracy (× 100%)

1

4.7632

2.1715

2.9005

0.8440

0.7214

2

2.0574

1.9202

1.9532

0.5492

0.7616

3

2.0332

1.9030

1.9578

0.5414

0.7709

4

1.3734

1.5409

1.4446

0.3129

0.8762

5

1.4069

1.5297

1.4595

0.3154

0.8421

6

1.3842

1.5231

1.4431

0.3129

0.8700

7

1.4180

1.5422

1.4702

0.3113

0.8700

8

1.3685

1.4952

1.4212

0.3070

0.8793

9

1.3973

1.5099

1.4396

0.3045

0.8731

10

1.4015

1.5294

1.4539

0.3083

0.8669

The results of DNN classifier are compared with the traditional supervised learning method called ensemble learning algorithms such as bagging, AdaBoost, extremely randomized trees and SVM gridsearch soft voting which have been implemented in our previous work. From the experiment results, it is discovered that extremely randomized trees had achieved high precision of 0.7078 and recall of 0.6888. The maximum value of F1-score is 0.6919 that had been attained by AdaBoost. The highest prediction accuracy of 75% was acquired by trained ensemble models, namely AdaBoost and extremely randomized trees, and outperforms with other two learning algorithms. DNN classifier obtained precision of 0.81, recall value of 0.80 with F1-score of 0.80 and accuracy of 80.2%. The predictive performance of ensemble learning and DNN classifiers are shown in Table 6 and illustrated in Fig. 2. Table 6 Predictive performance of the classifiers Evaluation criteria

Classifiers Bagging

AdaBoost

Extremely randomized trees

SVM gridsearch soft voting

DNN classifier

Precision

0.6532

0.6996

0.7078

0.6616

0.81

Recall

0.6654

0.6876

0.6888

0.6777

0.80

F1-score

0.6560

0.6919

0.6917

0.6665

0.80

Accuracy (%)

73

75

75

74

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Fig. 2 Comparison of classifiers’ evaluation metrics

From comparison of ensemble learning and DNN classifiers, it is discovered that DNN model achieved high results for predicting human influenza virus accurately. The trained DNN classification model attains maximum precision, recall and F1score when evaluated with ensemble learning models. The loss value of different epochs is minimized by modifying the weights of test data so the prediction accuracy of model is increased. The AdaBoost and extremely randomized tree algorithms produced the accuracy of 75%, although DNN classification algorithm gives high prediction accuracy of 80.2%.

4.1 Findings The classification model is created based on DNN architecture to identify the type of human influenza virus, and it is best suited for virus identification problem. The sequential model is important for generating deep neural network with three layers consisting of two activation functions, namely Softmax, ReLu and Adam optimizer. In ensemble learning, the model takes user-defined features as input and then performs classification task individually whereas in deep learning, the DNN architecture automatically learns new significant features from limited number of user-defined features and performs classification task simultaneously. TensorFlow is used to monitor and control execution of the model, process the queries and produce high performance with accurate results. The DNN model has capability to modify the weights in deep neural network so the error rate is minimized which gives accurate prediction. The significant results of DNN-based classification model are attained by maximizing the hidden layers and number of epochs in sequential model. It is examined that the eighth epoch of trained model gives maximum accuracy and minimum loss. The performance of the DNN classifier is validated using its measures with high accuracy and least error rate for influenza virus identification. DNN classifier achieves better performance through self-learned amino acid features and outperforms with ensemble learning classification models.

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5 Conclusion This research work demonstrates the development of human influenza virus prediction model using protein sequences through deep learning as multi-classification task. The protein sequences are derived from NCBI Influenza Virus Resource database, and appropriate dataset has been created by defining amino acid features. The virus prediction model is developed using deep neural network that achieved high prediction accuracy compared with ensemble learning algorithms. The performance of deep neural network model for virus identification is evaluated using different measures, namely accuracy, precision, recall, F1-score. The finding of the experiment shows that the DNN-based virus identification model is best suited for recognizing the types of influenza virus. To the best of my knowledge, it is the first work to use deep neural network architecture for the human influenza virus identification.

References 1. Almadani O, Alshammari R (2018) Prediction of stroke using data mining classification techniques. Int J Adv Comput Sci Appl (IJACSA) 9(1) 2. Ma J, Nguyen MN, Rajapakse JC (2009) Gene classification using codon usage and support vector machines. IEEE/ACM Trans Comput Biol Bioinform 6(1) 3. ElHefnawi M, Sherif FF (2014) Accurate classification and hemagglutinin amino acid signatures for influenza A virus host-origin association and subtyping, 22 Dec 2013 4. Iqbal HJ, Faye I, Samir BB, Said AM (2014) Efficient feature selection and classification of protein sequence data in bio informatics. Sci World J 2014. Article ID 173869 5. Sherif FF, Zayed N, Fakhr M (2017) Classification of host origin in influenza A virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11. ISSN 1998-4510 6. Attaluri PK, Zheng X, Chen Z, Lu G (2009) Applying machine learning techniques to classify H1N1 viral strains occurring in 2009 flu pandemic

On Machine Learning Approach Towards Sorting Permutations by Block Transpositions P. Jayakumar, Sooraj Soman, and V. Harikrishnan

Abstract Sorting permutations by transposition are a well-studied combinatorial optimization problem having applications in comparative genomics. The problem belongs to the NP-hard class, and several approximation algorithms are existent. Altinier, Oliveira and Dias proposed a machine learning approach for sorting permutations by two types of rearrangement operations, namely reversals and block transpositions. This paper discusses an application of their approach for sorting permutations by using a single operation, namely transposition under an improved setting. It also discusses the application of the method on permutations from toric equivalence classes and shows that comparable results with lesser computation overhead can be obtained. Keywords Permutations · Sorting · Transpositions · SGD classifier · Machine learning

1 Introduction In comparative genomics, the evolutionary relationship between two species is analysed by comparing their genomic sequences. Rearrangements of the genomic sequences, which are caused by mutations, result in the evolution of various species in the nature. The evolutionary ‘closeness’ of two different species is measured by the number of mutations needed to alter or transform the genomic sequence of one species to that of the other. Since mutations are rare events, the number minimized over all possible mutation sequence is defined as the evolutionary distance between the two species. Transposition is a type of mutation, which moves a piece of genomic sequence from one position to another. This article discusses a method to estimate

P. Jayakumar (B) · S. Soman · V. Harikrishnan Department of Computer Science and Applications, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_16

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the transposition distance between two species in a combined approach, which uses a combinatorial model and machine learning methods. It has been proved [1] that the transposition distance between two permutations can be calculated by sorting a permutation. The problem Sorting By Transpositions is formulated as a combinatorial optimization problem, which aims to sort a permutation using a minimum number of transpositions [2]. The SBT problem is an NP-hard problem, proved by Bulteau et al. [3]. In [2], an approximation algorithm with a 3/2 approximation factor is discussed. The algorithm was developed using a graph structure called Cycle graph. Other researchers [4, 5] also developed 3/2 approximation algorithms for SBT. Their algorithms generally try to improve the time complexity and ease of implementation and do not improve the approximation factor. An improvement of the approximation factor to 11/8 was proved in [6] . Another closely related, open problem is the estimation of transposition diameter of Sn . Several variants of the problem were also explored in the past [7, 8]. da Silva et al. [9] proposed a machine learning approach to sort a permutation using two different rearrangement operations, namely reversal and transposition. They trained a stochastic gradient descent classifier with randomly selected permutations from S10 . Their classifier classified each transposition into one of the three classes, namely good, bad and neutral. The sorting process was carried out by good transposition in the best case and neutral transpositions in the worst case. This work compares the performance of the method in a slightly different setting using toric equivalence partitions. The machine learning method is applied on permutation from the toric partitions [10, 11] of Sn . This reduces the number of permutations by a factor of n. Section 2 discusses the preliminaries, Sect. 3 gives the proposed method and details the experimental set-up. Section 4 gives the results and provides a discussion. Section 5 concludes the article.

2 Preliminaries Let π = π1 , π2 , . . . , πi , . . . , πn denote a permutation on the set  = {1, 2, . . . , n} where πi denote the element at position i in π . An identity permutation denoted by I is a permutation where πi = πi+1 − 1 for all 1 ≤ i ≤ n − 1. Similarly, a Reverse permutation denoted by R is a permutation where πi = πi+1 + 1 for all 1 ≤ i ≤ n − 1. A transposition is a rearrangement operation which moves an interval of a permutation from one position to another within the permutation. A transposition ρ(i, j, k) is defined by three positions i, j, and k in π such that ρ(i, j, k) moves the interval [i, j) to the position between k − 1 and k, and i < j < k. Figure 1 depicts a transposition ρ(2, 4, 5) on the linear extension [2] of π = 1, 2, 3, 4, 5, 6, 7, 8, 9. Next section explains cycle graph, which was introduced by Bafna and Pevzner. A brief overview of the existing bounds, which uses the properties of cycle graph, is also discussed.

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Fig. 1 a Shows the intervals of the transposition ρ(2, 4, 5) in the permutation π and b shows the resultant permutation

2.1 Cycle Graph The Cycle graph of a permutation π , denoted by G(π ), is an edge-coloured graph where the set of all elements in the permutation defines the vertex set. The edge set consists of two types of directed edges, namely black edge and grey edge. A black edge connects the vertices πi to πi − 1 for every 1 ≤ i ≤ n + 1. A grey edge connects the vertices πi to πi + 1 for every 0 ≤ i ≤ n. The cycle graph can be splitted into subgraphs consisting of a single cycle, uniquely. The count of black edges in a cycle represents its length. For example, the cycle graph in Fig. 2 consists of seven 1-cycles and one 3-cycle. A cycle is odd if its length is odd and is even otherwise. The cycle graph of the identity permutation I consists of all 1-cycles. So SBT algorithms based on cycle graphs try to increase the number of odd cycles with every transposition. Bafna and Pevzner [2] showed that the net change in the number of cycles in a cycle graph is due to a transposition c ∈ {2, 0, −2}

(1)

This immediately gives the following lower bound [2]. Denote the transposition distance by td(π ) and c(π ) denote the number of cycles (alternating cycles) in the G(π ). n + 1 − c(π ) (2) td(π ) ≥ 2

Fig. 2 Cycle graph of the permutation 1, 3, 4, 2, 5, 6, 7, 8, 9

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2.2 Machine Learning Approach Equation (1) proposes a method to classify a transposition into one of the three classes, namely good, neutral or bad. A transposition is said to be good if c = 2, or it is neutral if c = 0, or it is bad if c = −2. The optimal sorting sequence is obtained when every transposition applied is a good transposition, but the existence of a good transposition on all cycle graphs is not guaranteed. Hence, at times we may have to execute some neutral transposition, which will change the structure of the cycle graph so that the resultant graph may admit a good transposition. Bafna and Pevzner [2] showed that every neutral transposition can be followed by two good transpositions, and thus, they obtained a 3/2 approximation algorithm. In the improvement provided by Elias and Hartman [6], it was shown that there exists a sequence of 11 transpositions, of which 8 are good moves. Thus, they obtained a 1.375 approximation algorithm. Given a permutation π , a possible transposition on π is classified into one of the aforementioned three classes, by analysing a set of features. If the transposition is a good transposition, then it is applied on π and the entire processes are repeated with the new permutation. If none of the possible transpositions is classified to be a good transposition, then a neutral transposition is applied and the process is repeated with the new permutation.

2.3 Toric Permutations It is easy to note that td(π ) does not change under circular position shifts. Consider the permutation π = 2, 4, 3, 1. The circular extension of π is obtained by adding 0 to π yielding π = 0, 2, 4, 3, 1. The circular shifts of the permutation π yield the following permutations. π1 = 2, 4, 3, 1, 0 π2 = 4, 3, 1, 0, 2 π3 = 3, 1, 0, 2, 4 and π4 = 1, 0, 2, 4, 3. Note that the cycle graphs of all these permutations and hence their transposition distances are both same. In addition to similarity under position shifts, two permutations may exhibit similarity under value shifts also. The value shifts are done under modular addition. For a circular permutation π , it is possible to create a new permutation π  such that πi = (πi + m % (n + 1)), for 0 < m ≤ n. The permutations thus obtained from π are said to be torically equivalent and defines one toric class of permutations in Sn . The permutations which belong to one class have a nice property that they all will have similar G(π ) and hence same transposition distance. Figure 3 shows the cycle graph of a permutation π and that of a permutation π  , where π and π  are torically equivalent.

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Fig. 3 The cycle graphs (a) and (b) are torically equivalent and their transposition distances are same

3 Proposed Method An important property of toric permutations is that it partitions Sn into several classes. Also, all permutations belonging to one class will have same transposition distance. Identifying the toric class of a permutation is practically much easier than identifying its sorting sequence. So, instead of sorting all n! permutations in Sn , first the toric classes of Sn are determined. Then, by sorting a representative permutation from each class, it is possible to find the transposition distance of all permutations in Sn . The method substantially decreases the computation overhead as the number of toric classes of Sn is lesser by a factor of d where d is the degree of the permutation. Another improvement is on the sampling method used in the training phase. The random sampling method used by Flavio et al. is replaced by a stratified sampling method where each toric partition defines a strata and samples are collected at random from each strata. Next a brief discussion on the features and the construction of feature vector is discussed, following [9]. Some features and the definition of the features are excluded due to space constraints. 1. 2. 3. 4. 5. 6. 7. 8. 9.

Number of transposition breakpoints Number of fixed elements Number of inversions Length of correct prefix Length of correct suffix Length of LIS Entropy Size of left plateau Size of right plateau

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10. Number of oriented cycles 11. Number of crossing cycles 12. Number of non-interleaving cycles. Let π be a permutation in Sn . There exists n(n−1)(2n+2) possible transpositions 12 on π , and each transposition creates a new permutation, represented by π k , where . That is, π k = π ◦ ρk . For each ρk , a comparison of π k with 1 ≤ k ≤ n(n−1)(2n+2) 12 π is given and the feature vector f b is calculated using the following equation. The equation is given by Flavio et al. Let b denote a particular feature. fb =

value of b in πk − value of b in π maximum possible value f or b

(3)

The number of alternating cycles in G(π ) of odd length (denoted by codd (π )) can be used to find the class label of the transposition ρk and the feature vector f b is associated with the class label thus obtained. To sort a permutation, initially predict the class of each possible transpositions on that permutation, using the classifier. If there is a good transposition , then it is applied on the permutation and each of the possible transpositions of the new permutation is classified and the entire process is repeated till the permutation is sorted. If there is no good transpositions, then a neutral transposition is applied and the process is repeated with the new permutation. The maximum number of transpositions is bounded from above by the upper bound n + 1 given by Elias and Hartman [6]. 2

3.1 Experimental Methodology We consider permutations from S8 for conducting the experiments. There are 4493 toric classes in S8 . To evaluate the advantage of toric permutations over random sampling, the same program is run on two different settings. In one setting, 4493 permutations are randomly selected and are used to train the SGD classifier. In the other setting, initially the toric classes of S8 are identified and then 4493 samples were taken, one from each toric class for training the SGD classifier. Then a random permutation from S8 is given for prediction and sorting. The results are discussed in the next section. In the training phase, a transposition with respect to a permutation is classified into good, neutral or bad based on the number of new odd cycles it creates in cycle graph. To implement cycle graphs, we use the data structure proposed in [12]. This is a list-based data structure, with which many queries on cycles graphs can be answered in linear time. 1. If codd (π ) of the resultant permutation is greater than that in the input permutation, then the operation is marked as a good operation for the input permutation. 2. If codd (π ) of the resultant permutation is equal to that of the input permutation, then the operation is marked as a neutral operation for the input permutation.

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Fig. 4 Cycle graph of the permutation 3, 2, 1

Fig. 5 Cycle graph data structure representation of the permutation 3, 2, 1 [12]

3. If codd (π ) of the resultant permutation is less than that of the input permutation, then the operation is marked as a bad operation for input permutation (Figs. 4 and 5).

4 Observations and Results In this section, the observations, results and comparison of the work are presented.

4.1 Results of Random Sampling Initially, the classifier was trained and a model was created using randomly selected (approx: 4500) permutations from S8 . Then the model was applied to sort the remaining permutations from S8 . Out of the 40,320 permutations in S8 , 38,892 permutations were sorted in the random sampling setting. On comparing the results with the

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transposition distance database [13], it was observed that 27,433 permutations show same transposition distance. In a similar way, the same model was used to sort higher degree permutations, for example S9 . Out of 3,62,880 permutations in S9 , 243,952 permutations were sorted correctly. Figure 6 shows the transposition distances obtained using random sampling settings on all permutations of S8 in comparison with the transposition distance database. It is observed that there are 2836 permutations with transposition distance 5, in transpositions distance database and in our method 1058 permutations show transposition distance 5. This variation is because of the misclassification of the classifier. Figure 7 shows the transposition distance of permutations of S9 obtained with our method in comparison with the transposition distance database. The transposition distance database and random sampling setting show high variation than the earlier setting. This shows that the misclassification increased with higher degree permutations.

Fig. 6 Distribution of transposition distances obtained from random sampling and TDD for S8

Fig. 7 Distribution of transposition distances obtained from random sampling and TDD on S9

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4.2 Results of Stratified Sampling Under stratified sampling setting, the classifier was trained and the model was created by considering one representative permutation from each toric class. The model was employed to sort all permutation from S8 . Out of the 40,320 permutations in S8 , 39,863 permutations were sorted using the method. The diametral permutation π = 2, 7, 4, 8, 6, 5, 1, 3, obtained using the method, seemed to be consistent with the transposition distance database. The sorting sequence of π , given by the method, is ρ(1, 2, 8), ρ(1, 3, 5), ρ(2, 4, 6), ρ(1, 2, 6), ρ(1, 6, 9). Out of the 2836 diametral permutations of S8 in [13], we obtain 1461 diametral permutations correctly. The distribution of transposition distances is depicted in Fig. 8. The model was, then applied to sort permutations of S9 and was able to sort 3,13,339 permutations correctly, out of 3,62,880 permutations. Figure 9 depicts the result of stratified sampling on all permutations of S9 in comparison with the transposition distance database.

Fig. 8 Distribution of transposition distances obtained from stratified sampling and TDD for S8

Fig. 9 Distribution of transposition distances obtained from stratified sampling and TDD for S9

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4.3 Confusion Matrix The confusion matrix is constructed by taking 50 permutation from S9 at random and given to the algorithm. We observe the results in both random sampling and stratified sampling settings. Tables 1 and 2 illustrate the confusion matrix associated with the experiments. Table 1 shows the confusion matrix of random sampling setting. It shows that 1418 good transpositions are correctly predicted as good, and 100 good transpositions are wrongly predicted as neutral. 2 neutral transpositions are wrongly predicted as good and 10 wrongly predicted as bad, 256 bad transpositions are wrongly predicted as neutral. In the same way, Table 2 shows the confusion matrix of stratified sampling. By comparing both confusion matrix, algorithm using random sampling settings wrongly predicts 368 transposition operations. Algorithm using stratified sampling settings wrongly predicts 212 transposition operations. These show that training with stratified sampling using toric equivalence class is better than random sampling. Accuracy of Random Sampling =

1418 + 2563 + 1411 = 0.93 5760

(4)

Accuracy of Stratified Sampling =

1418 + 2517 + 1583 = 0.95 5760

(5)

Misclassification Rate Random =

100 + 2 + 10 + 256 = 0.06 5760

(6)

Misclassification Rate Stratified =

100 + 3 + 25 + 84 = 0.03 5760

(7)

Table 1 Random sampling Good Good Neutral Bad

1418 2 0

Table 2 Stratified sampling Good Good Neutral Bad

1418 3 0

Neutral

Bad

100 2563 256

0 10 1411

Neutral

Bad

100 2547 84

0 25 1583

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5 Conclusion and Future Scope This article discusses a machine learning approach for sorting permutation by transposition. An existing machine learning framework is applied in an improved way for a different problem. The toric permutation method employed provides twofold advantage over ordinary permutations. One is that they reduces the number of permutations that has to be considered for training and sorting. Another advantage is that, since the adjacencies and breakpoints of permutations belonging to same toric class are same, their transposition distance also is same. Hence, by taking samples from each strata (i.e. toric class) a reasonable representation of the symmetric group is obtained in the training phase, and hence, the misclassification can be reduced. The article gives a comparison of the performance of the method under two different sampling techniques. The cycle graphs are proven to be powerful tool for solving SBT problem. By incorporating more properties of cycle graphs in the feature set, the model can be improved. Also, the possibility of a backtracking approach needs to be investigated, when the classifier fails to give a ’good’ transposition for sorting.

References 1. Fertin G, Labarre A, Rusu I, Vialette S, Tannier E (2009) Combinatorics of genome rearrangements. MIT Press 2. Bafna V, Pevzner PA (1998) Sorting by transpositions. SIAM J Discr Math 11(2):224–40 May 3. Bulteau L, Fertin G, Rusu I (2012) Sorting by transpositions is difficult. SIAM J Discr Math 26(3):1148–80 4. Christie DA (1998) A 3/2-approximation algorithm for sorting by reversals. In: SODA 25 Jan 1998, pp 244–25 5. Hartman T, Shamir R (2006) A simpler and faster 1.5-approximation algorithm for sorting by transpositions. Inf Comput 204(2):275–290 6. Elias I, Hartman T (2006) A 1.375-approximation algorithm for sorting by transpositions. IEEE/ACM Trans Comput Biol Bioinf 3(4) 7. Chitturi B (2015) Tighter upper bound for sorting permutations with prefix transpositions. Theor Comput Sci 18(602):22–31 8. Chitturi B, Sudborough IH (2012) Bounding prefix transposition distance for strings and permutations. Theor Comput Sci 2(421):15–24 9. da Silva FA, Oliveira AR, Dias Z, Técnico-IC-PFG R, de Graduaço PF. Machine learning applied to sorting permutations by reversals and transpositions 10. Hultman AX (1999) Toric permutations. Master’s thesis, Department of Mathematics, KTH, Stockholm, Sweden 11. Eriksson H, Eriksson K, Karlander J, Svensson L, Wästlund J (2001) Sorting a bridge hand. Discr Math 241(1–3):289–300 12. Walter MEM, Sobrinho MC, Oliveira ET, Soares LS, Oliveira AG, Martins TE, Fonseca TM (2005) Improving the algorithm of Bafna and Pevzner for the problem of sorting by transpositions: a practical approach. J Discr Algor 3(2–4):342–361 13. Gonçalves J, Bueno LR, Hausen RD (2013) Assembling a new and improved transposition distance database. In: Proceedings of the 2013 XLV Simpósio Brasileiro de Pesquisa Operacional, SBPO, vol 13, pp 2355–2365

Tweet Classification Using Deep Learning Approach to Predict Sensitive Personal Data R. Geetha, S. Karthika, and S. Mohanavalli

Abstract Twitter is one of the most successful online social networks that present user’s opinions, personal experience, daily activities and ideas to the world. The analysis of user tweets gives various interesting perspectives of vulnerable cyber-crimes and information losses in the microblogging platforms. This research work analyzes 280 k tweets that were queried using 23 personal cyber-keywords to predict personally sensitive tweets. The personal tweets were annotated based on the proposed rules developed from the privacy standards defined by well-established organizations like NIST. The most influential textual features are extracted using auto-encoders optimized with word embedding techniques. The manually annotated tweets were trained and modeled using recurrent neural network to classify tweets as sensitive and insensitive personal tweets. The sensitivity model was evaluated with activation functions like ReLU, sigmoid, and softmax under varied hyper-parametric conditions. The model with three hidden layers with ReLU and softmax resulted in the accuracy of 75% in identifying personally sensitive tweets. Keywords Twitter · Personal information · Deep learning · Sensitive personal tweet

1 Introduction The users of online social media generally experience many regretful situations by posting their personal information and views. The users post abundant information R. Geetha (B) · S. Karthika · S. Mohanavalli Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, Tamil Nadu 603110, India e-mail: [email protected] S. Karthika e-mail: [email protected] S. Mohanavalli e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_17

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on various social networking sites like Facebook, Twitter, Instagram, and YouTube. The extensive use of social media has influenced the necessity of analyzing the data in several aspects and generates valuable insights which lead to social media analytics. Analyzing social media data will simplify the complexities and extract some useful patterns for supporting decision making and building intelligent systems. The recent statistics revealed that there are 4 billion social media users across the world. In 2017, more than 200 million people started using mobile phones for the first time in which more than 100 million users use smart devices. It is also interesting to know that 11 new users are starting to use social media for the first time every second. It is evident from the given statistics to prove that the growth of social media is rapid and there is a mandatory need for analyzing the user-generated content (UGC) and observe key insights to users. The analysis of UGC is challenging as different user’s present information in noisy and unstructured data formats. The social media data is completely suitable for big data platforms with its properties like ever-growing volume, wide variety, high velocity, value, and veracity. Traditional data storage mechanisms are not suitable for storing and analyzing social media data. Therefore, the big data platforms such as NoSQL, Hadoop, HBase, and Hive can be used for storing, retrieving, and manipulating data for analysis. Nowadays, social media analytics are not limited to retweet count, likes, user replies, sentiment analysis, and product reviews. There are wide scopes for generating user, community, political, physiological, and spatial-oriented analysis taking social media analytics to the next level with the help of machine learning and artificial intelligence. One such analytical perspective is about the users who frequently post on social media and are unaware of the potential threats, unintended audience, and the potential reach that the post can achieve. The users should be aware of what information can be posted and to be avoided to prevent unexpected embarrassments, physical and psychological losses. This research work follows the standards enforced by professional organizations such as General Data Protection Regulation (GDPR) [1] and National Institute of Standards and Technology (NIST) [2] for countries like the USA and European Union for identifying sensitive information. There were many real-time scenarios where a tweet posted by a user was a sole reason for losing a job, relationships, friendships, business contracts, etc. This research work analyzes the UGC for the presence of sensitive privacy data posted by the users based on a set of benchmarked cyber-keywords. A sensitivity model for personal tweets was built for classifying tweets with personal sensitive information using recurrent neural network (RNN). The RNN can be activated using rectified linear units (ReLUs) and sigmoid functions for optimizing the classification process [3]. Finally, the proposed sensitivity model for personal tweets was evaluated with various hyper-parameters like number of hidden units, dropout rate, number of epoch, accuracy, and loss values.

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2 Literature Survey 2.1 Regrets in Online Social Networks The analysis of user’s mindset over deactivating Facebook was a major factor in determining the user’s regretful experience [4]. A survey was conducted with three types of regrets in social media, namely social, self, and networked. The authors of [5] conducted a survey for capturing regrets that were caused by misunderstanding, misuse, misjudging of online posts, and discovered the mechanisms preferred by the users to overcome the regrets such as an apology, decline, or delay the request.

2.2 Tweet Classification—Privacy Loss Twitter data were analyzed based on the structural information and tweet content to determine the presence of political opinion in user tweets [6]. The authors of [6] used the bag-of-words model for textual feature extraction and a community detection mechanism for structural information extraction. Twitter user ranking and information diffusion rate of a tweet when retweeted by a set of followers is analyzed widely with retweet trees in [7]. The authors of [8] defined two privacy attributes, namely confidentiality and universality with a set of 53 keywords. The privacy and security levels of the Internet of things (IoT) and cyber-physical systems (CPS) are classified by the privacy information security classification (PISC model). The research work in [9] aimed at classifying vacation tweets, drunk tweets, and disease tweets using Naïve Bayes classifier. Various challenges that are related to detecting privacy-related information from unstructured texts are discussed by [10] using natural language processing, ontology, and machine learning approaches. It also deals with the problem of identifying the user’s perception, domain specificity, context dependence, privacy sensitivity classification, and data linkages in social media messages. The identification of social media posts with privacy information under 13 categories, like health, politics, relationships, etc., in combination with the domain-specific features, was performed to improve the performance of tweet classification [11].

2.3 Deep Learning in Tweet Classification The task of sentiment analysis of news and filtering out bad (negative) news and extracting the good (positive) news was performed with natural language processing and machine learning techniques. The text data are represented with document term matrix which was applied to support vector machines for classification with VADER approach. The former approach was inefficient in feature extraction which can be replaced by Doc2Vec model and convolution neural network (CNN) model which

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gave 85% accuracy [12]. The detection of hate speech was performed using the LSTM-based RNN classifier over various textual features and other tweet-based features [13]. The authors identified tweets under three classes, namely racism, sexism, and neutral tweets under hate speech detection. A zero-shot learning framework was proposed for short text categorization. The relativeness between text and the tags as its associated tags are predicted. The authors propose three architectures of neural networks for short text classification using dimension-wise mean and LSTM with two conditions. [14]. The semi-supervised auto-encoder with sequential variation is built with RNN to overcome the shortcomings of a semi-supervised variational encoder for text classification. The evaluation of encoder’s performance was performed by learning from unlabeled data and conditional LSTM structures [15].

3 Methodology The personally sensitive information will be predicted by the proposed sensitivity model with a couple of deep learning approaches, namely auto-encoders and recurrent neural network. Initially, the tweets are extracted from Twitter using the Twitter Streaming API. The tweets are collected for 23 cyber-keywords which are identified by using the privacy information security classification (PISC) model [8]. The extracted tweets are stored in a distributed environment to support largescale computational effort and data processing. The extracted tweets are manually annotated with the rules defined according to the standards of National Institute of Standards and Technology (NIST) [9] and General Data Protection Regulation (GDPR) [15] in Table 1. The proposed framework for personally sensitive tweet identification is depicted in Fig. 1. The tweets are pre-processed with a well-defined ruleset framed specifically for handling micro-blog texts as mentioned in Table 2. The pre-processing is performed in the MapReduce framework by transforming the tweets into key-value pairs. The transformed key-value pairs of words are serially reconstructed into term frequency-inverse document frequency (TF-IDF). The sparsity of the TF-IDF matrix generated here results in low performance. The proposed sensitivity model uses self-supervised auto-encoders for building significant features for detecting the words with privacy contexts. The auto-encoders are Table 1 Tweet is personally sensitive if it contains

The tweet is personally sensitive or not? Pronoun + user mention + any PIIs Pronoun + location + PIIs Pronoun + personalized URLs Criticism/unpleasant opinions toward a person/community/religion/political party/organization Personally identifiable data such as telephone number, travel plans, unique identification data, etc.

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Fig. 1 Proposed deep learning architecture using RNN for sensitivity prediction of personal tweets

Table 2 List pre-processing rules for transforming collected tweets and make it suitable for text classification

Pre-processing rules Convert t to lowercase Remove “@” in user mentions Replace URLs with ‘URL’ Remove stop-word and perform stemming for each word Remove ‘#’ in hashtags Remove symbols and punctuations Remove numbers, date, and time and phone numbers Remove unwanted whitespaces and new lines Replace acronyms with corresponding abbreviations and Internet jargons

used to represent the sparse feature vectors in a compressed form such that the most significant features are retained for classification. The word2vec generated by the auto-encoders are helpful in representing the features more contextually than the usual TF-IDF matrix which constructs the features by considering the word occurrences. The proposed approach uses RNN with four layers, namely the input layer (embedding layer), the two hidden layers with activation functions (ReLU–ReLU and sigmoid–ReLU), and the output layer with softmax as the activation function. The resultant of this RNN setup will train and build the sensitivity model that in turn will predict the test tweet set for the presence of personally sensitive information.

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Fig. 2 The process of personal tweet classification involved in sensitivity prediction

Table 3 List of personal cyber-keywords Personal cyber-keywords

Age, car, chat, family, children fingerprint, hobby, home, house, location, marriage, mobile phone, my photo, nation, phone book, phone number, religion, shopping, spouse, travel, call record, identification, party

The two different combinations of activation functions in the hidden layer are evaluated with various hyper-parametric conditions and the best model is identified. The overview of the proposed tweet classification process is depicted in Fig. 2.

4 Results and Discussion 4.1 Dataset The benchmarked 23 cyber-keywords from [8] as listed in Table 3 were taken as a query term for collecting personal tweets from Twitter Streaming API. The 23 personal cyber-keywords contributed about 286 k tweets from which 100 k tweets were taken for sensitivity analysis by eliminating retweets, news, and greetings. The personally sensitive tweet classification was performed with 1300 tweets filtered based on the personally identifiable information available in the extracted tweets.

4.2 Tweet Feature Extraction The tweets are cleaned according to the rules specified in Table 2. The cleaned tweets are again stored to the repository to generate the textual features for classification. A sample of transformations undergone by a tweet during pre-processing is shown in Table 4. The detailed feature reduction process using auto-encoders is presented in Table 5. The initial layer shows the word vector count which denotes the vocabulary

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Table 4 Sample personal tweet that overcomes various transformations during data cleaning and pre-processing stage Cyber-keyword

Raw tweet

Pre-processed tweet

Tweet after building corpus

Mobile phone

FYI: for the next 7 days i will not have access to SMS or mobile phone. Please use email, WhatsApp, Viber. https:// t.co/IDmYVyJnGy

For your information for the next 7 days i will not have access to short message service or mobile phone please use email, whatsapp, viber, url

Information next day access short message service mobile phone, email, whatsapp, viber

Table 5 Feature reduction rate using auto-encoders for 1300 tweets

Input level

Number of parameters

Number of units

Initial vocabulary size 3793

512

Layer 1

1,942,528

512

Layer 2

262,656

512

Layer 3

1026

2

Layer 4

2

–

size of the personal tweet dataset after pre-processing. The drastic growth and reduction of features while building the RNN model can be observed which exponentially increases and decreases according to the dropout rate so that the network optimizes itself to handle the overfitting effects.

4.3 Classification Results The RNN was evaluated by splitting 80% of data for training the model and 20% of data for testing the sensitivity model. Since the data contributed to the larger sparse feature set, sparse_categorical_crossentropy was chosen as the learning function and ADAM as the optimizer function. The validation of RNN for personally sensitive tweets was done by comparing the performance of the network with varied activation functions in the hidden layers. The RNN was designed with two hidden layers. Firstly, the RNN was evaluated with ReLU activation functions for both the hidden layers with dropout rate = 0.3, 0.4 and epoch = 10, 20, 30, 40, 50. The vocabulary size was strictly restricted to the word vector length that was obtained from the TF-IDF feature. Similarly, the RNN was evaluated with activation functions like sigmoid and ReLU for the same hyper-parameter sets. Table 6 shows the results achieved by the RNN sensitivity model during the training and testing phases of predicting personally sensitive tweets.

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Table 6 Accuracy of various hyper-parametric combinations in predicting personally sensitive tweets Activation function

Dropout rate

Epoch

Train accuracy

Test accuracy

RNN + ReLU, ReLU, Softmax

0.3

10

0.9976

0.7423

20

0.9988

0.7384

30

1.0000

0.7423

40

0.9988

0.7384

50

1.0000

0.7423

10

0.9964

0.75

20

0.9988

0.75

30

0.9988

0.7423

40

0.9976

0.7346

0.4

RNN + Sigmoid, ReLU, Softmax

0.3

0.4

50

0.9988

0.7423

10

0.9891

0.7384

20

0.9976

0.7

30

0.9964

0.7153

40

0.9964

0.6961

50

0.9988

0.6999

10

0.9819

0.7346

20

0.9928

0.7307

30

0.9952

0.7115

40

0.9940

0.7115

50

0.9976

0.6923

The training accuracy and test accuracy for different epoch values revealed that RNN with (ReLU + ReLU + Softmax) showed better performance than other models. The variation of training and testing accuracy and loss is shown in Fig. 3 for epoch = 50 and dropout rate = 0.3. On analyzing the confusion matrix of the above-mentioned hyper-parametric criteria, the authors discovered 18% of the tweets to be personally sensitive.

5 Conclusion The proposed sensitivity model will be a baseline for identifying personally sensitive contents in online social networks. The main contribution of this research work was determining the rules for identifying personally sensitive tweet and personally insensitive tweet. The sensitivity model has various challenges such as adapting itself to new user-generated content and scaling itself to the enormously growing data content. The big data framework helps in handling the challenges of real-time streaming and

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Fig. 3 Accuracy and loss values of RNN with two hidden layers (ReLU + ReLU), epoch = 50, dropout rate = 0.3, hidden layer units = 512 observed for the personal tweets

content accumulation. The users will have a better understanding and risk associated with sensitive content in an online social network if they are notified when posted. Though the sensitivity of information depends on the users and the audience, it is extensively important to analyze the vulnerability of presenting personally identifiable information in public. The proposed sensitivity model identifies the sensitive content with 75% accuracy with the contextual feature identification employed by the deep-net like auto-encoders and RNN. The future work of this research work is to analyze the direct and indirect sensitive information disclosures by the user about self or other users. Further, sensitive information disclosure in online social networks can be an important feature in predicting user psychology and behavior.

References 1. General Data Protection Regulation. http://gdpr-info.eu 2. McCallister E (2010) Guide to protecting the confidentiality of personally identifiable information. Diane Publishing 3. Le QV, Jaitly N, Hinton GE (2015) A simple way to initialize recurrent networks of rectified linear units. arXiv preprint arXiv:1504.00941 4. Guha S, Baumer EP, Gay GK (January, 2018) Regrets, I’ve had a few: when regretful experiences do (and don’t) compel users to leave facebook. In: Proceedings of the 2018 ACM conference on supporting groupwork, pp 166–177. ACM 5. Wang Y, Norcie G, Komanduri S, Acquisti A, Leon PG, Cranor LF (2011) I regretted the minute I pressed share: a qualitative study of regrets on Facebook. In: Proceedings of the seventh symposium on usable privacy and security, p 10. ACM 6. Cotelo JM, Cruz FL, Enríquez F, Troyano JA (2016) Tweet categorization by combining content and structural knowledge. Inf Fusion 31:54–64 7. Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on World Wide Web, pp 591–600

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8. Lu X, Qu Z, Li Q, Hui P (2015) Privacy information security classification for internet of things based on internet data. Int J Distrib Sens Netw 11(8):932–941 9. Mao H, Shuai X, Kapadia A (2011) Loose tweets: an analysis of privacy leaks on twitter. In: Proceedings of the 10th annual ACM workshop on Privacy in the electronic society, pp 1–12 10. Sleeper M, Cranshaw J, Kelley PG, Ur B, Acquisti A, Cranor LF, Sadeh N (2013) I read my Twitter the next morning and was astonished: a conversational perspective on Twitter regrets. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 3277–3286 11. Wang Q, Bhandal J, Huang S, Luo B (2017) Content-based classification of sensitive tweets. Int J Semant Comput 11(04):541–562 12. Kale M, Mankame P, Kulkarni G (2018) Deep learning for digital text analytics: sentiment analysis. arXiv preprint arXiv:1804.03673 13. Pitsilis GK, Ramampiaro H, Langseth H (2018) Detecting offensive language in tweets using deep learning. arXiv preprint arXiv:1801.04433 14. Pushp PK, Srivastava MM (2017) Train once, test anywhere: zero-shot learning for text classification. arXiv preprint arXiv:1712.05972 15. Xu W, Sun H, Deng C, Tan Y (2017) Variational autoencoder for semi-supervised text classification. AAAI pp 3358–3364

A Study on Abnormalities Detection Techniques from Echocardiogram Imayanmosha Wahlang, Goutam Saha, and Arnab Kumar Maji

Abstract Echocardiography is one of the most widely used tools in abnormalities detection in cardiac perspective. A person with difficulty in breathing or any symptoms that shows a weak heart is asked to follow the test. This test is vital and is done manually where a transducer is used to obtain a specific image that can visually locate the presence of abnormalities. Automated methodologies have emerged to solve the problem faced by manual treatment. This will help the physician to reduce misdiagnosis of echo images. This paper is based on the study of different existing techniques that can be used in the detection of abnormalities in cardiac system using echo images. Keywords Cardiac segmentation · Echo · Echo views · Speckle noise · Regurgitation

1 Introduction With the development of science and technology, people’s way of living tends to change from better to worse. This change makes people lazy and causes side effects, which may lead to many types of diseases. An organ that can get easily affected with this lifestyle is the heart. Heart diseases are one of the major causes of mortality globally [1]. Any heart-related problems may pose a threat to the livelihood of people. Thus, it is a major concern to be looked upon so that people can live longer and better. If cardiac abnormalities are suspected, an individual is asked to undergo certain type of tests. These tests include using tools like X-ray, computed tomography (CT) scan, I. Wahlang (B) · G. Saha · A. K. Maji North-Eastern Hill University, Shillong 793022, India e-mail: [email protected] G. Saha e-mail: [email protected] A. K. Maji e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_18

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angiogram, electrocardiograph (ECG), and echocardiography (Echo) [2]. For finding abnormalities, echo is best suited as it is free of radiation and cost-effective, compared to others [3]. Echocardiography is in the format of video. Echocardiography in the form of image is known as echocardiogram [4]. Both are collectively termed as echo. Echo plays an important role in the diagnosis of cardiac diseases. It is commonly used by cardiologists to visualize heart structure that consists of wall, aorta, and other blood vessels [5]. The device emits sound waves, which bounce off the heart structures, creating an image of the heart and blood vessels under examination [6]. The image obtained using echo is better than of X-ray. It is safe, and cost is minimal [6]. Through the test, doctors can predict functions of valves and other defective parts or abnormalities. Thus, one can suggest a treatment if abnormalities occur. To find abnormalities from echocardiogram, many images are needed to be read and evaluated manually. This process of finding the region of abnormalities from echocardiogram can be termed as segmentation in computer science. Manual segmentation process may consume lots of time and energy for any radiologist. To outdo such problem, automated diagnosis came into existence. If an automated method can do what a radiologist does, this can improve and ease the process of diagnosis. As an example, an echocardiogram with different marked regions of the heart is shown in Fig. 1. A brief summary of different automated methodologies for abnormalities detection from echocardiogram is presented in this paper, which achieve a remarkable success with efficiency and accuracy. The different steps for abnormality detections, such as preprocessing, segmentation, feature extraction, and classification, are covered in the next section. Conclusion and future work is provided in the last section. Fig. 1 Structure showing heart region with left ventricle (LV), right ventricle (RV), left atrium (LA), right atrium (RA), and heart wall (HW) [7]

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2 Steps for Abnormality Detection from Echocardiography In case of automated diagnosis, i.e., abnormalities detection using echo, some of the main steps that are involved are as follows: Preprocessing Preprocessing is a step which is needed for reducing noise and other unwanted data. It is used for clarity and better understanding of images. In an image, this is basically done for more effective result. It includes filtering techniques, morphological operations, and statistical techniques [8, 9]. It is applied in echo to remove speckle noises that arise due to imaging properties. Segmentation It is a step used for detecting a region or regions where abnormalities are present. It is one of the most important aspects in diagnosis of any disease. Famous techniques are that of clustering which is widely used and is still in use till date. It even includes statistical methods [9] and other methods. It is done to find different regions of the heart. Feature Extraction This is usually done to extract useful information that is based on some properties. Feature can be texture, shape, color, etc. This is important in segmentation and classification of images as it gives a more meaningful representation of an image. A specific region of the heart will be localized or globalized according to one’s need and specific application [9]. Classification It is a step that classifies an image or a region into classes. Classes can be predefined (supervised) or random (unsupervised). It usually involves data division into training (training phase) and testing (testing phase) data. It includes algorithms like K-nearest neighbor (KNN), Naive Bayesian [10], etc. It is a step where the different types of cardiac abnormalities or diseases are classified. Most of the existing techniques go through a process called preprocessing before segmentation. It is vital as most echo images consist of speckle noise which acts as a barrier and poses a wrong perception about the image. Some of the methodologies used will be seen in the later part of this section. Segmentation is carried out mostly after preprocessing. Based on our desired output, a region is segmented accordingly. After segmentation, features are usually extracted and followed by classification. A pictorial representation of this process is shown in Fig. 2.

Fig. 2 Steps involved in automated classification of abnormalities of most existing techniques

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3 Different Existing Abnormalities Detection Techniques from Echocardiography Some of the papers that deal with echo involving preprocessing, segmentation, feature extraction, and classification are as follows. Allan et al. [9] proposed an approach using 2D echocardiography for extraction of information. The approach uses information about image and patient information to classify into different clinical parameters like volume and label. Joint independent component analysis (JICA) was used for finding image intensity and label. It was used only for apical view and not other views like base or parasternal. The approach was validated on a large number of echo images. When comparison was made, it was found that 0.87 correlation coefficient was achieved for estimation in left atrium. Classification was done for patient suffering from mitral regurgitation, and an accuracy of 82% was obtained. Here only one type of heart effect was considered, which is regurgitation. Varghese and Jayanthi [11] used a different method in segmentation of echocardiograph images from videos. For extracting of heart boundary, closed curve was used which was difficult to track because of noise. Segmentation was done using Gaussian mixture model (GMM) clustering that allows classification of pixels in specific layer and helps reducing speckle noise. In this paper work related to detection of diseases based on the anatomy of the heart was not engaged. In the paper by Balaji et al. [5], classification method was proposed for cardiac view in the echocardiogram. The system was built based on a machine learning approach which characterizes two features (1) histogram features and (2) statistical features. In this system, four standard views, which are parasternal short axis (PSAX), parasternal long axis (PLAX), apical two chambers (A2C), and apical four chambers (A4C), were classified. Experiments were done for over 200 echo images. The accuracy of the proposed method was 87.5% which can be effectively used in cardiac view classification. A paper on efficient cardiac view classification of echocardiogram was proposed by Balaji et al. in [12] which is similar to that in [5]. A cardiac cycle consists of two phases: systolic and diastolic. The systolic is the contraction, and diastolic is relaxation and filling. From the given video sequences, only the diastolic frames were extracted and were utilized for determining the view of the echocardiogram. The echocardiogram image was first prepared to reduce noise and to enhance the contrast of the image. Mathematical morphology is used to highlight the cardiac cavity before segmentation. Segmentation using connected components labeling (CCL) was carried out. Three standard cardiac views, namely parasternal short axis (PSAX), apical two chamber (A2C), and four chamber (A4C) views, were classified. Experiments were done for over 200 echo images with 94.56% accuracy. Danilov et al. did a work on segmentation in [6]. Segmentation of heart anatomy was studied where preprocessing was done using median filter and ramponi filter. Ramponi filter was considered to be the best filter among median, kuan, and sigma

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filter. Segmentation was done using active contours for one view. It was implemented using cuda environment. The number of images used was 357 slices in four views. Nandagopalan [10] developed a method to analyze echo images of a particular patient. This was done to help in decision making using image processing and data mining approaches. An image was preprocessed using median filtering and then clustered into 2D echo or color Doppler. Segmentation techniques used were fast Kmeans SQL color clustering algorithm and pixel-based classification. Feature extraction used was color histogram, texture, kurtosis, and skewness. The four chambers of the heart were segmented using area, volume, etc. Images were then trained using Naive Bayesian classifier. The view that was used here is parasternal short axis view (PSAX). Supha [13] worked on segmentation of left ventricle (LV). It required border detection in which LV boundary was detected for clinical used. For that, preprocessing methodologies were applied. Preprocessing was done using Gaussian filter which helps in removal of noise. Enhancement was done which is followed by adaptive histogram equalization. Removal of noise in echo images could not be done completely. Segmentation was proposed using fuzzy logic and fast watershed-based algorithm. Another proposed method used was full causal hidden Markov modelbased segmentation scheme. Octagonal tree combined with BFS-based fast watershed algorithm produced faster and accurate results compared to other segmentation techniques. Pinjari [14] dealt with valvular regurgitation using color Doppler in the thesis. Work was done for mitral and aortic regurgitation using image processing techniques. The images were first converted into YCbCr space, and filtering techniques are then applied. Filtering techniques used were Wiener and Gaussian filter. Segmentation using fuzzy K-means and anisotropic diffusion and quantification using proximal isovelocity surface area method (PISA) was implemented which classify regurgitation into mild mitral regurgitation, moderate aortic regurgitation, and severe aortic regurgitation. Oo et al. [15] gives a proposed method based on level set method. It explained in brief about all types of segmentation techniques implemented in echo images. Advantages and disadvantages of different types of segmentation techniques were also provided in the paper. Another similar type of work was done by Mazaheri et al. [16]. Here, segmentation to find LV boundary was done. The different types of segmentation techniques were reviewed such as level set method, active contours methods, and active shape methods. Assessment of heart valve disease which is aortic stenosis (AS) has been done in [17]. A detailed discussion is provided in the paper. Using gradient, classification of AS was done. For grading AS, peak velocity, velocity ratio, AVA, indexed AVA, and mean gradient are recommended. Based on the ratio, the type of AS is known. For preprocessing, a paper by Jaybhay et al. [18] and Bhonsle et al. [19] can be referred. Both are for speckle noise removal where a detailed explanation of all the techniques which is useful in echo noise removal was given. A summary of all existing techniques that were used in the past for echo images is given in Table 1 along with their issues.

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Table 1 Summary of different methodologies Sl. No

Author

Phase involved

Methodology

Issues

1

Balaji et al. [5] 2015

Year

Classification of views

Morphology, connected component labeling (CCL)

Images used were few in number

2

Danilov et al. [6]

Segmentation

Active contour

Work only for one view

3

Kumar et al. [8] 2010

Preprocessing, segmentation, feature extraction, and classification

Affine Accuracy could be transformation, improved demon’s algorithm, histogram, pyramid matching, and support vector machine (SVM)

4

Allan et al. [9]

2017

Preprocessing, segmentation, feature extraction, and classification

Joint Independent component analysis (JICA), principal component analysis (PCA), and support vector machine (SVM)

Only for apical view, features are huge in PCA, motion variability was not considered, and accuracy could be further improved

5

Nandagopalan [10]

2012

Preprocessing, segmentation, feature extraction, and classification

Median filtering, fast K-means, Naive Bayesian classifier

Only parasternal view was considered, and number of images used were less

6

Varghese et al. [11]

2014

Segmentation

Closed curve and Gaussian mixture model

No proper output and motion variability was not considered

7

Schmidt et al. [20]

2009

Abnormality detection

Conditional random Field, L 1 L ∞ and local binary pattern (LBP)

Accuracy could be improved

8

Clifford [21]

2002

Heart rate variability

PCA, fast Fourier transform (FFT), K-means, Naive Bayesian

Works done only for stationary inputs

9

Strunic et al. [22]

2007

Classification of heart murmur

Artificial neural network (ANN)

Less number of images

2017

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Some observations related to existing methodologies are as follows: 1. Accuracy obtained can still be improved, and it is observed that method like SVM gives better accuracy compared to all other methods in case of classification. 2. Datasets used are from clinics or hospitals which were used in minimal number. This can be further improved if data are easily available for research purpose. 3. Most of the methodologies use a single view and not all type of views. It is opted as such because different views show different characteristics. Based on the view, we can visualize certain or all parts of the chambers of the heart. 4. Echocardiogram is used instead of echocardiography, which consumes lots of time. It is used as abnormalities can be detected better and faster. 5. Machine learning techniques are not so popularly used in this field which can be done as a future work. 6. Advanced machine learning techniques (state of the art) are yet to be a prospect. 7. Considering the number of data, only a few images were used for classification purpose. This can be further improved. 8. No work was done in obtaining an image or some images with abnormalities from echocardiography. Thus, work that help manual selection of echocardiogram can be carried out in future.

4 Conclusion In this paper, different existing segmentation and classification techniques on echocardiogram were studied. We have listed out several issues related to those existing techniques. It is further observed that this field is not sufficiently explored. Many works are yet to be done in this field like several types of abnormalities can be work on and based on different views. Accuracy could be improved further with more number of datasets. For detection of abnormalities, we strongly suspect that machine learning-based methodologies like deep learning may provide more accurate and reliable result. Such methodologies have proved to be better than any other method in many fields of medical imaging. Thus, implementation of these will probably lead to a better future which will ease the process of diagnosis as a whole. This paper will indeed help researchers to have a summarized idea about cardiac-related issues in echo, its importance and to carry more work in the future.

References 1. Kassem A, Hamad M, El Moucary C, Fayad E (2016) A smart device for the detection of heart abnormality using RR interval. In: International conference on microelectronics (ICM), pp 293–296. IEEE, Egypt 2. Mayoclinic. https://www.mayoclinic.org/diseases-conditions/heart-disease/diagnosis-treatm ent/drc-20353124 3. Phoenix Heart Center. http://www.phoenixheartcenter.com/echocardiograms-tte-tee/

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4. Healthline. https://www.healthline.com/health/echocardiogram 5. Balaji GN, Subashini TS, Chidambaram N (2015) Automatic classification of cardiac views in echocardiogram using histogram and statistical features. Procedia Comput Sci 46:1569–1576 6. Danilov VV, Skirnevskiy IP, Gerget OM (2017) Segmentation of anatomical structures of the heart based on echocardiography. J Phys Conf Ser 803(1):012–031 7. Skalski A, Turcza P (2011) Heart segmentation in echo images. Metrol Meas Syst 18(2):305– 314 8. Kumar R, Wang F, Beymer D, Syeda-Mahmood T (2010) Cardiac disease detection from echocardiogram using edge filtered scale-invariant motion features. In: IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW), pp 162–169. IEEE, USA 9. Allan G, Nouranian S, Tsang T, Seitel A, Mirian M, Jue J, Hawley D, Fleming S, Gin K, Swift J et al (2017) Simultaneous analysis of 2D echo views for left atrial segmentation and disease detection. IEEE Trans Med Imaging 36(1):40–50 10. Nandagopalan S (2012) Efficient and automated echocardiographic image analysis through data mining techniques. Amrita Vishwa Vidyapeetham University (2012) 11. Varghese M, Jayanthi K (2014) Contour segmentation of echocardiographic images. In: International conference on advanced communication control and computing technologies (ICACCCT), pp 1493–1496. IEEE, India 12. Balaji GN, Subashini TS, Suresh A (2014) An efficient view classification of echocardiogram using morphological operations. J Theor Appl Inf Technol 67(3):732–735 13. Supha LA (2013) Tracking and quantification of left ventricle borders in echocardiographic images with improved segmentation techniques. Anna University 14. Pinjari AK (2012) Image processing techniques in regurgitation analysis. Jawaharlal Nehru Technological University, Anantapuram 15. Oo YN, Khaing AS (2014) Left ventricle segmentation from heart ECHO images using image processing techniques. Int J Sci Eng Technol Res (IJSETR) 3:1606–1612 16. Mazaheri S, Wirza R, Sulaiman PS, Dimon MZ, Khalid F, Tayebi RM (2015) Segmentation methods of echocardiography images for left ventricle boundary detection. J Comput Sci 11(9):957–970 17. Baumgartner H, Hung J, Bermejo J, Chambers JB, Edvardsen T, Goldstein S, Lancel-Lotti P, LeFevre M, Miller JF, Otto CM (2016) Recommendations on the echocardiographic assessment of aortic valve stenosis: a focused update from the European association of Cardiovascular imaging and the American society of echocardiography. Eur Heart J Cardiovasc Imaging 18(3)254–75 18. Jaybhay J, Shastri R (2015) A study of speckle noise reduction Filters. Signal Image Process Int J (SIPIJ) 6:71–80 19. Bhonsle D, Chandra VK, Sinha GR (2018) Speckle noise removal from ultrasound images using combined bivariate shrinkage and enhanced total variation techniques. Int J Pure Appl Math 118(8):1109–1132 20. Schmidt MW, Murphy KP, Fung G, Rosales R (2008) Structure learning in random fields for heart motion abnormality detection. Comput Vis Pattern Recogn (CVPR) 1:1–8 21. Clifford GD (2002) Signal processing methods for heart rate variability. University of Oxford, Oxford 22. Strunic SL, Rios-Gutirrez F, Alba-Flores R, Nordehn G, Burns S (2007) Detection and classification of cardiac murmurs using segmentation techniques and artificial neural networks. In: IEEE symposium on computational intelligence and data mining, pp 128–133. IEEE, USA

Secure I-Voting System with Modified Voting and Verification Protocol S. Ajish

and K. S. Anil Kumar

Abstract Internet voting has been used in the countries like UK, Estonia, Switzerland, etc. In the i-voting protocol used in Estonia, the full security of the vote cast relies only on the PIN stored in the national ID card. In the i-voting protocol used in Estonia, the vote cast is encrypted by the public key of the vote storage server and digitally signed by using the PIN2 of the voter. The attacker can easily re-vote by using Re-voting Malware and can cast vote of any voter by using Self-voting Malware. To overcome the attack, we modified the voting protocol by including an OTP which should be sent to the voter’s phone number. The voter should enter the OTP to cast the vote so the attacker cannot bluff the voter. The vote modification malware changes the vote cast by the voter according to the preference of attacker, and the vote change should not be reflected in the vote verification application. To overcome the attack, the vote verification protocol is modified by digitally signed the vote by using the private key of the server. We analyse the major client-side attacks on the proposed i-voting system and found that it is more secure than the i-voting system used in Estonia. Keywords Internet voting · QR code · Biometric authentication · Voting protocol · Verification protocol

1 Introduction Internet voting is an initiative to engage maximum number of people in the democratic process [1]. At first Estonia has implemented online voting for the parliament, national and municipal elections. The Internet voting system of Estonia has attained particular attention of other countries. S. Ajish (B) Research Scholar, Department of Future Studies, University of Kerala, Kariyavottom Campus, Thiruvananthapuram, Kerala, India e-mail: [email protected] K. S. Anil Kumar Sree Ayyappa College, Eramallikkara, Chengannur, Alappuzha, Kerala, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_19

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To take part of the voting process in Estonia each voter should have a national ID card with two PINs. The voter first inserts the national ID card into the ID card reader and opens the website of voting (www.valmised.ee) [1]. The voter uses the PIN number in national ID card for authentication. The PINs (PIN1 and PIN2) are issued to the voter along with the national ID card [1]. After the authentication of the voter, the server checks eligibility of the voter by analyse the voters list database [1] (Fig. 1). The appropriate candidate list is sent to the voter, and he/she can cast the vote which is then encrypted. For the authenticity of the vote cast, it is digitally signed using the second five-digit of PIN code (PIN 2) [1], and it is sent to the server. The voter can cast vote multiple of times, the prior vote in the vote storage server is overwrite by the final vote cast by the voter. In the counting phase, first the digital signature is verified by the members of the National Electoral Committee, and if it is valid the e-votes are decrypted and counted by the vote counting app [1]. The internet voting system should maintain the cast-as-intent and record as cast properties [2]. Torn and Springall in [3, 4] analyse the security of Estonian i-voting system and they describe the major attack scenarios. The major client-side attacks in the Estonia i-voting system are 1. The vote modification malware (VMM) changes the vote cast according to the preference of the attacker, and the vote change should not be reflected in vote verify app.

Fig. 1 I-voting system

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2. Re-voting Malware (RVM) collects the PIN number in the national ID card and later when voter uses the ID card for any e-government strategy the malware covertly cast vote. 3. Self-voting Malware (SVM) The SVM uses the PIN code collected previously and covertly cast vote without awareness of voter. To overcome these attacks, we modified the i-voting protocol and the verification protocol. In the i-voting protocol used in Estonia, the full security relies on the PINs stored in the national ID card. By stealing the ID card or by duplicating the ID card, anyone can cast the vote of any other. So we modify the voting protocol by including an OTP instead of random number [5] in the e-vote. The OTP is sent to the phone number of the voter so the malware cannot cast vote without awareness of voter. The vote modification malware can easily bypass the vote verification protocol because the vote storage server sent the encrypted vote to verification app without any security [5]. The vote modification malware can easily change the vote and the encrypted vote sent by vote storage server to the verification app. To overcome the attack, we modify the verification protocol by generating the digital signature of the encrypted vote before it is forwarded to the verification app. So the vote modification malware cannot bypass the verification protocol even if it can modify the vote cast by the voter.

2 Research Background The i-voting protocol in Estonia uses public key encryption algorithm for the encryption and digital signature generation of the vote. For the encryption and digital signature generation, there are private key(skS) and public key(pkS) of server generated by NEC [5]. Similarly, for digital signing of the vote, the public key(pkV) and private key(skV) of the voter is used. The Estonian i-voting system has two phases: the first phase is the voting phase and the second phase is the verification phase [5]. The terms used in the voting stage are: 1. 2. 3. 4. 5.

pkS = public key of server. skS = private key of server. pkV = public key of voter. skV= private key of voter. AsymEncpkS (Ci ; r) = Asymmetric Encryption of vote (Ci ; r) by using the public key of server.

2.1 Voting Stage The voter first authenticates to the vote forward server(VFS) and the VFS sent back the candidate list CL = {C1 , C2 , . . . , Cm } where Ci are candidate’s unique number and m represents total candidates. The voter V selects the candidate Ci ∈ CL to

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register vote [5]. The VoterApp create a random number r, pad it to the vote cast and encrypt the vote (Ci ; r) using pkS (public key of server). The encrypted vote is digitally signed using skV (private key of voter) or PIN2. The VoterApp sent the signed and encrypted vote to the VFS and the VFS sent back the vote reference ‘voteref’ which is used for vote verification [5]. Voting Protocol [5] 1. The voter V : Authenticates to Vote Forward Server through VoterApp using the PIN1 of national ID card. 2. Vote Forward Server: Sent the voters list CL = {C1 , C2 , . . . , Cm } to VoterApp, where m represents the total candidates. 3. Voter V : Chooses Ci from CL. 4. The VoterApp does the following: (a) First using a random number generator, creates a random number r. (b) Encrypts the vote Ci and r by using pkS (public key of server), Encryvote = AsymEncpkS (Ci , r). (c) Digitally signs the encrypted vote Encryvote by using skV (secret key of voter) or PIN2, SignEncVote = SignskV (Encryvote ) . (d) Sent the SignEncVote to Vote Forward Server. 5. Vote Forward Server does the following: (a) Forwards the SignEncVote to the vote storage server. (b) Generates voteref and sent the voteref to the voter.

2.2 Verification Stage The voter sent the voteref and random number r to the VerifApp and the VerifApp forward the voteref to vote storage server. The vote storage server sent the candidate list and the encrypted vote to the VerifApp. The VerifApp encrypt all the candidate in the candidate list using pkS and compare the encrypted candidate list with the encrypted vote received from the server. The VoterApp shows the candidate number that which the voter cast vote and the voter verify the vote cast [5]. Verification Protocol [5] 1. (a) Vote storage server generates a QR code which contains the random number r and voteref and sent the QR code to the VoterApp; the VoterApp forwards the QR code to the VerifApp. (b) VerifApp: Scans QR code by using a QR code reader. 2. The VerifApp sent the voteref to vote storage server. 3. The vote storage server sent the encrypted vote cast by voter (Encryvote ) and the candidate list CL to VerifApp. 4. The VerifApp does the following:

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(a) Computes the encrypted vote list of all the candidates Encryvote = AsymEncpkS (C j ; r) for all j = 1, 2, 3, …, m. (b) Compare the encrypted vote list with the encrypted vote cast by voter and j finds  such that Encryvote =?Encryvote for some  ∈ { 1,2, …, m.} (c) Shows the matched candidate details C on the screen. 5. The voter: Check whether the candidate shown by VerifApp matches with the vote cast by the voter, i.e. C=?Ci . (a) If C = Ci , the vote is received and stored in vote storage server without any modification. (b) Else, the VerifApp generate a message indicate the presence of malware.

3 Security Analysis of the Estonian I-Voting Protocol Torn and Springall in [3, 4] analyse the security of Estonian i-voting system and they describe the major attack scenarios. Here, we discuss some major client-side attacks in the Estonia i-voting system.

3.1

Vote Modification Malware (VMM)

Vote modification malware operates when the voter setup the voting application and starts the voting process. The vote modification malware effects the VoterApp on the computer and the VerifyApp on the mobile phone. The vote modification malware changes the vote cast according to the preference of the attacker, and the vote change should not be reflected in the vote verify app. The vote modification malware first collects the PIN codes of voters’ ID card. Vote modification malware changes the vote cast by the voter C to C’ and generates a random number r. Then, the malware encrypts the vote C’ and r by public key of server (pkS), i.e. Encryvote’ = AsymEncpkS (C’, r). The encrypted vote Encryvote’ is digitally signed by using the private key of voter (PIN2), i.e. (skV), SignEncVote’ = SignskV (Encryvote’ ) . The malware sent the SignEncVote’ to Vote Forward Server, and the Vote Forward Server forwards the SignEncVote’ to the vote storage server. The malware can easily modify the vote cast by the voter if it gets the private key(PIN) stored in the national ID card. The vote modification malware can easily overwhelm the VerifApp because the Voter Forward Server sent the encrypted vote Encryvote = AsymEncpkS (C,r) and candidate list to the VerifApp without security. In this case, the malware modifies the vote cast by the voter C to C’. The vote storage server receives the encrypted vote Encryvote’ = AsymEncpkS (C’, r). The server will sent the encrypted vote Encryvote’ = AsymEncpkS (C’, r) and candidate list to the VerifApp. The attacker can easily modify the encrypted vote sent to VerifApp, i.e. Encryvote’ = AsymEncpkS (C’, r) to

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Encryvote = AsymEncpkS (C, r) because the vote is encrypted by using public key of sever. The voter verifies that he/she votes for the candidate C but actually the vote recorded in the server is for C’.

3.2 Re-voting Malware (RVM) The Estonian i-voting system allows the voter to cast vote multiple times and the prior vote in the vote storage server is overwrite by the final vote cast by the voter. Re-Voting Malware collects the PIN number in the national ID card and later when voter uses the ID card for any e-government strategy the malware covertly cast vote. In the case of Estonia i-voting system, the voter verification is optional. Even if the verification stage is mandatory, the verification can be done by using fake verification applications. The Re-voting Malware first collects the PIN codes of voters’ ID card. Then, the Re-voting Malware authenticates itself to the Vote Forward Server. After authentication, the Re-voting Malware casts the vote C’ and generates a random number r. Then, the malware encrypts the vote C’ and r by using the public key of server (pkS), i.e. Encryvote’ = AsymEncpkS (C’, r). The encrypted vote Encryvote is digitally sign by using the private key of voter (PIN2), i.e. (skV), SignEncVote = SignskV (Encryvote ) . The malware sent the SignEncVote to Vote Forward Server, and the Vote Forward Server forwards the SignEncVote to the vote storage server. The malware can easily re-vote if it gets the PIN codes stored in the national ID card. The security of the Estoina i-voting relies only on the PIN code stored in the national ID card..

3.3 Self-voting Malware (SVM) A botnet is a collection of infected Internet-connected devices administrated by malware [3]. The self voting malware in the botnet computer records the PIN code in the national ID card. The SVM uses the PIN code collected previously and covertly cast vote without awareness of voter. The Self-voting Malware first collects the PIN codes in the voters ID card. Then, the malware authenticates itself to the Vote Forward Server by using PIN1 of the national ID card. After authentication, the malware casts the vote C’ and generates a random number r. Then, the malware encrypts the vote C’ and r by using the public key of server (pkS), i.e. Encryvote’ = AsymEncpkS (C’, r). The encrypted vote Encryvote is digitally signed by using the private key of voter (PIN2), i.e. (skV), SignEncVote = SignskV (Encryvote’ ) . The malware sent the SignEncVote to Vote Forward Server and the Vote Forward Server forwards the SignEncVote to the Vote Storage Server. The malware can easily vote for any candidate if it gets the PIN code stored in the national ID card.

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To overcome the above-said attacks, we proposes a modified voting and verification protocol described in Sect. 4 and analyse the security of the modified voting and verification protocol in Sect. 5.

4 Modified Voting and Verification Protocol 4.1 Modified Voting Protocol After authentication, the vote storage server sent the appropriate candidate list to the VoterApp. The voter application displays the candidate list, and the voter makes his/her choice. Instead of generating random number by the voter application, the modified i-voting protocol uses an OTP sent to the mobile phone by the vote storage server. The voter application pads the OTP and the selected candidate C and encrypt it using the public key of server. The device used to receive the OTP should be differed from the device used to run the voterAPP. Modified Voting Protocol 1. The voter V : Authenticates to Vote Forward Server through VoterApp. 2. After successful authentication, an OTP is generated by the vote storage server and sent it to the mobile number of the voter. 3. Vote Forward Server: Sent the candidate list CL = {C1 , C2 , . . . , Cm } to VoterApp, where m represents the total candidates. 4. Voter V : Chooses the candidate Ci from CL. 5. The voter enters the OTP received on the mobile number to the voterAPP. 6. The VoterApp does the following: (a) Encrypts the vote Ci and OTP by public key of server (pkS), Encryvote = AsymEncpkS (Ci , OTP). (b) Encrypt the OTP using the private key of the voter, EncryOTP = Asym EncskV (OTP). (c) Digitally signs the encrypted vote Encryvote and encrypted OTP by using the private key of voter i.e. (skV), SignEncVote = SignskV (Encryvote  EncryOTP ) . (d) Sent the SignEncVote to Vote Forward Server. 7. The Vote Forward Server forwards the SignEncVote to the Vote Storage Server. 8. The Vote storage server verifies the digital signature using the pkV (public key of voter). 9. The Vote storage server decrypts the encrypted OTP EncryOTP using pkV (public key of voter) and verify the OTP. 10. If the OPT matches with the one generated by vote storage server, the vote storage server generates the voteref and sent the voteref to the voter.

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The modified protocol prevents the Re-voting Malware and Self-voting Malware attacks. The Re-voting Malware and the Self-voting Malware cast vote in the background unaware of voter. In the contest of modified protocol, the Re-voting and Self-voting Malware cannot cast vote in the background because the vote storage server generates an OTP and sent it to the phone number of the voter. By getting the OTP, the voter can identify that the malware is trying to cast the vote in the background.

4.2 Modified Verification Protocol In the vote verification protocol used in Estonia, the Vote Forward Server sent the encrypted vote (using pkS) to the voter for verification. The vote modification malware can easily change the encrypted vote sent to the voter and bypass the verification protocol. In the modified verification protocol, encrypted vote is digitally signed by using the private key of the vote storage server before it is sent to the verification app. So the attacker cannot modify the vote sent to the verification app. In the proposed i-voting system, the voter should verify the vote then only the vote has accounted in vote storage server. For the security purpose, the VerifApp and VoterApp should run on different device. Modified Verification Protocol 1. (a) Vote storage server generates a QR code which contains the OTP and voteref and sent the QR code to the VoterApp; the VoterApp forwards the QR code to the VerifApp. (b) VerifApp: Scans QR code by using a QR code reader. 2. The VerifApp Sent voteref to Vote Storage Server. 3. The vote storage server does the following: (a) Digitally signs the encrypted vote and the OTP (Encryvote  OTP) using the private key of the server, i.e. SignVoteServer=EncskS (Encryvote  OTP) (b) Forwards the SignVoteServer= EncskS (Encryvote  OTP) and the candidate list CL to VerifApp. 4. The VerifApp does the following: (a) Decrypts the SignVoteServer= EncskS (Encryvote  OTP) sent by the server using the public key of the server to get the Encryvote and the OTP. (b) Displays the OTP to the voter for verification. If the OTP displayed by the VerifApp matched with the OTP received in mobile phone of voter, which means the voter is verifying the vote cast by him. (c) Compares the encrypted vote list with the encrypted vote cast by voter and j finds  such that Encryvote =?Encryvote for some  ∈ {1, 2, . . . , m}. (d) Shows the matched candidate details C on the screen.

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5. The voter: Checks whether the candidate shown by VerifApp matches with the vote cast by the voter, i.e. C=?Ci . (a) If C= Ci , the vote is received and stored in Vote Storage Server without modification. (b) Else, the VerifApp generate a message indicate the presence of malware. In the modified protocol, there is three-level verification of the vote. First the VoterApp provides two-level security by using the PIN and the OTP sent to the phone number. The voter can confirm that he/she is verifying the vote cast by him/her by verifying the OTP included in the cryptogram. The modified protocol prevents the vote modification malware by encrypting the cryptogram by using the private key of the server(digitally sign the cryptogram) before it is forwarded to the VerifApp.

5 Security Analysis of the Modified Voting Protocol and Verification Protocol In this section, we analyse and prove the security of the modified i-voting protocol. We analyse the major client-side attacks and found that the modified protocol is more secure than the protocol used in Estonia. Theorem 1 Privacy against Re-voting Malware: Our modified voting protocol described in Sect. 4.1 is secure against Re-voting Malware. Proof Re-Voting Malware collects the PIN number in the national ID card and later when voter uses the ID card for any e-government strategy the malware covertly cast vote. In the case of Estonia i-voting system the vote verification is optional. In our modified i-voting protocol, the voter V first authenticates to Vote Forward Server through VoterApp using the PIN1 of national ID card. If the Re-voting Malware succeeds in the authentication step, an OTP is created by the vote storage server and transmit it to the mobile number of the voter. Then, the Vote Forward Server sent the candidate list CL = {C1 , C2 , . . . , Cm } to VoterApp(Re-voting Malware), where m represents total candidates. Suppose the Re-voting Malware chooses C’ from the candidate list CL. Next the malware needs the OPT sent to the phone number, and as the voting device and the OTP received devices are different, the Malware did not get the OTP send by the vote storage server. Suppose the malware selects the random OTP(OTP’), then encrypted vote C’ and OTP’ by public key of server (pkS), Encryvote’ = AsymEncpkS (C’, OTP’). Encrypt the OTP’ using the private key of the voter, EncryOTP’ = AsymEncskV (OTP’). It is difficult to decode the QR code to obtain the private key of the voter. The malware digitally signs the encrypted vote Encryvote and encrypted OTP’ by using the private key of voter, i.e. (skV), SignEncVote = SignskV (Encryvote  EncryOTP’ ) and sent the SignEncVote to Vote Forward Server. The Vote Forward Server forwards the SignEncVote to the vote storage server. The vote storage server verifies the digital signature using pkV. The vote storage server decrypts the encrypted OTP’ (EncryOTP’ ) using pkV and verifies the OTP’. The OPT’ should not match with the OTP send by the server so the vote forward server will

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discard the vote. The OTP is six-digit number so the probability of OTP’ = OTP is 1/999999 = 1.000001e−6 . The probability of success of Re-voting Malware in the case of modified protocol is 1.000001e−6 Therefore, this modified protocol guarantees that the Re-voting Malware will fail to cast the vote in the background. Theorem 2 Privacy against Self-voting Malware: Our modified voting protocol described in Sect. 4.1 is secure against Self-voting Malware. Proof A botnet is a collection of infected internet-connected devices administrated by malware [3]. The self voting malware in the botnet computer records the PIN code in the national ID card. The SVM uses the PIN code collected previously and covertly cast vote without awareness of voter. In our modified i-voting protocol, the voter V first authenticates to Vote Forward Server through VoterApp using the PIN1 of the national ID card. Even if the Revoting Malware succeeds in the authentication step, an OTP is generated by the vote storage server and it is sent to the mobile phone. Then, the Vote Forward Server sent the candidate list CL = {C1 , C2 , . . . , Cm } to VoterApp (Re-voting Malware), where m represents total candidates. Suppose the Re-voting Malware chooses the candidate C’ from the candidate list CL. Next the malware need the OTP sent to the phone number, and as the voting device and the OTP received device are different, the malware did not get the OTP sent by the vote storage server. Suppose the malware selects a random OPT(OTP’) and then it encrypts the vote C’ and OTP’ by public key of server (pkS), Encryvote = AsymEncpkS (C’, OTP’). Encrypt the OTP using the private key of the voter, EncryOTP =AsymEncskV (OTP’). The malware digitally signs the encrypted vote Encryvote and encrypted OTP’ by using the private key of voter, i.e. (skV), SignEncVote = SignskV (Encryvote  EncryOTP’ ) and sent the SignEncVote to Vote Forward Server. The Vote Forward Server forwards the SignEncVote to the vote storage server. The vote storage server verifies the digital signature using pKV. The vote storage server decrypts the encrypted OTP’ EncryOTP’ using pKV and verify the OTP’. The OPT’ should not match with the OTP sent by the server so the vote forward server will discard the vote. The OTP is six-digit number so the probability of OTP’=OTP is 1/999999=1.000001e−6 . The probability of success of Self-voting Malware in the case of modified protocol is 1.000001e−6 Therefore, this modified protocol guarantees that the Self-voting Malware will fail to cast the vote in the background. Theorem 3 Privacy against vote modification malware. Our modified vote verification protocol described in Sect. 3.1 is secure against vote modification malware. Proof Vote Modification Malware operates when the voter set-up the voting application and starts the voting process. The Vote Modification Malware effects both the VoterApp on the computer and the VerifyApp on the mobile phone. The vote modification malware change the vote cast according to the preference of the attacker, and the vote change should not be reflected in the vote verify app. Suppose the vote modification malware modify the vote cast by the voter C to C’. The vote storage server receive the encrypted vote Encryvote’ = AsymEncpkS (C’,

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OTP). The server sent the vote reference code ‘voteref’ and OTP as QR code to the VerifApp. The VerifApp scans QR code by using a QR code reader and sent the ’voteref’ to the vote storage server. The vote storage server digitally signs the encrypted vote using the private key of the server and sent the signed encrypted vote and candidate list to the verification app. j The VerifApp computes the encrypted vote list of all the candidates Encryvote = AsymEncpkS (C j ; OTP) for all j = 1, 2, 3, …, m. Compare the encrypted vote list with j the encrypted vote cast by voter and finds  such that Encryvote =?Encryvote for some  ∈ { 1, 2, …, m} and the VerifApp shows the matched candidate details C on the screen. The voter checks whether the candidate shown by VerifApp matches with the vote cast by the voter, i.e. C=?C. In this case C!=C, i.e. the vote is received and stored in vote storage server, is modified and the voter identified it and re-votes. Therefore, this modified verification protocol guarantees that the vote modification malware will fail to change the vote cast by the voter. Theorem 4 Correctness: The modified Voting and verification protocol ensures record-as-cast and cast-as-intended properties. Proof The record as cast property is maintained by digitally signing the encrypted vote and the OTP. To generate the digital signature encrypted vote and OTP is encrypted using voter’s private key. The vote storage server verifies the digital signature to check the integrity and authenticity of the vote cast. If the vote is modified by the attacker, the integrity property will lost and the server can identify it by verifying the digital signature. The cast as intended property is maintained by using the verification protocol. During the verification stage, the vote storage server digitally signs the encrypted vote and forwards the signed encrypted vote and the candidate list CL to the VerifyApp. The attacker cannot modify the encrypted vote sent to the VerifApp because it is encrypted using the private key of the server which is kept secret. The VerifApp shows the matched candidate details C on the screen. The voter checks whether the candidate shown by VerifyApp matches with the vote cast by the voter, i.e. C=?C. If C=C, the vote is received and stored in vote storage server without any modification. Otherwise the voter identifies that the vote cast by him is modified and the voter re-votes. Therefore, the modified voting and verification protocol guarantees cast-asintended and record-as-cast properties.

6 Conclusion The major client-side attacks in the Estonia i-voting systems are analysed and modified the voting and verification protocol to overcome the attacks. In the Estonian i-voting system, the attacker can easily re-vote by using Re-voting Malware and can cast new vote by using Self-voting Malware. The attacks are conquered by modifying the i-voting protocol by including an OTP which transmit to voter’s mobile

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phone. The vote modification malware changes the vote cast by the voter according to the preference of attacker, and the vote change should not be reflected in the vote verification application. This attack is overwhelmed by modifying the verification protocol by encrypting vote cast by using the private key of the server. The major client-side attacks on the proposed i-voting system are analysed and found that it overcomes the major client-side attacks.

References 1. Kitsing M (2011) Online participation in Estonia: active voting, low engagement. In: ICEGOV 2011. ACM 2. Popoveniuc S, Kelsey J, Regenscheid A, Vora P (2010) Performance requirements for endto-end verifiable elections. In: Proceedings of the 2010 international conference on electronic voting technology/Workshop on trustworthy elections, EVT/WOTE10. USENIX Association, Berkeley, CA, USA, p 116 3. Torn T. Security analysis of Estonian i-voting system using attack tree methodologies. Master thesis, Faculty of Information Technology, Department of Computer Science, Tallinn University of Technology 4. Springall D, Finkenauer T, Durumeric Z, Kitcat J, Hursti H, MacAlpine M, Halderman JA (2014) Security analysis of the Estonian internet voting system. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, CCS’14 series, ACM, pp 703–715 5. Mus1 K, Kiraz MS, Cenk M, Sertkaya I. Estonian voting verification mechanism. https://eprint. iacr.org/2017/081.pdf

Hash Tree-Based Device Fingerprinting Technique for Network Forensic Investigation Rachana Yogesh Patil and Satish R. Devane

Abstract Forensic investigation of cybercrimes in ecommerce and banking portals is becoming increasingly difficult due to the inability of existing Internet protocols, to collect the necessary information as digital evidences. Present tools for investigation on Internet protocols tracks the attackers up to the ISP, only and the actual attacker’s location and machine may be tracked based on the information of ISP which is not primary evidence and also not acceptable evidence. According to the Indian Evidence Act Section 62 and also others, international act for evidence says, primary evidences are most superior class of evidences and admissible and acceptable at first place. This is only possible, if investigator proves that attacks have been occurred by using the device and evidence should prove that it has been happened by using the investigated device. Our proposed device fingerprinting technique uses the concept of hash tree and generates the device fingerprints which can be used as forensically sound evidence. In disputes, which has been caused by external attacks or the denial of client and business provider will be solved by analyzing the fingerprint collected in the form of digital evidence with every transaction. The fingerprint generated by the proposed system has all the characteristics stipulated by the court of law. Keywords Digital evidence · Device fingerprint · Network forensics · Hash tree

R. Y. Patil (B) · S. R. Devane A. C. Patil College of Engineering, Sector-4, Kharghar, Navi Mumbai 410210, India e-mail: [email protected] Datta Meghe College of Engineering, Sector-3, Airoli, Navi Mumbai 400708, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_20

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1 Introduction Tracking the users online has become a great challenge in today’s privacy-sensitive world. The unreliable nature of cookies no longer allows to track users [1]. Cookies can easily be deleted by users. The concept of device fingerprinting has been used for different purposes over the years. The device fingerprint is a process used to uniquely identify the computing device based on its unique parameters. Device fingerprints are different than cookies, as they are stored at server side. According to our previous work [2], the majority of work done in the area of device fingerprinting techniques is used for browser fingerprinting, user identification and tracking, software licensing or Web application tracking. The purpose of all these techniques is to track the user online, study the behavior of user and serve them in better way. Apart from these applications, the concept of physical device fingerprinting can be used for tracking the exact source of cybercrime. During digital forensic investigation process, the identification of exact device used for cybercrime acts as the most important evidence in court of law. The digital evidence should also satisfy the properties prescribed by Indian Evidence Act Section 65B, to be accepted during legal trails. The theoretical analysis of existing device fingerprinting techniques done in our previous work [3] concludes that the existing techniques are suitable for their corresponding applications. The fingerprint generated by none of these techniques is satisfying all properties required by digital evidence. To address this challenge, we have proposed a device fingerprinting technique which is generating device fingerprints which can be accepted as legal evidence in court of law. The fingerprint generated by our proposed system satisfies all the properties of evidence prescribed in Indian Evidence Act Section 65B.

2 Related Work The device fingerprinting techniques are of great importance in the field of network forensic investigations because it allows us to recognize the cyber criminal’s device even if they try to change or hide their identity by using proxies and stolen credit cards. The device fingerprinting methods are broadly divided into two types, passive and active, depending on the way of collecting fingerprinting information from device [4] Passive fingerprinting method depends on the information that can be captured remotely via profiling server. This type of methods mostly measures user’s operating system, network connection and operating system information. On the other hand, active device fingerprinting techniques require to probe the target device by deploying agent process. The advantage of active fingerprinting system is they can get access to detailed device information such as MAC address and hard drive serial numbers. The concept of smart device fingerprinting [5] is introduced by extending the techniques of operating system fingerprinting. The content-agnostic technique of

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device fingerprinting makes use of the timing traces produced by the process of web page loading as a device fingerprinting feature. The devices with different hardware have different throughput in loading the same web page. The same web page is accessed by different devices with different CPU schedulers, screen resolutions, operating systems and clock frequencies, and features are extracted by decomposing the web page loading process. The features used as device fingerprint are throughput, order of packets and the number of packets generated by the web page loading method. This method can deal with various types of devices such as mobiles, laptops and other smart devices. A clock skew-based passive remote physical device fingerprinting technique is introduced in [6]. The device clock skew which is constant over the time is used as a device fingerprinting feature. The clock skew of the device is calculated by considering clock frequency, time at which the packet is received at fingerprinting device and the TCP timestamp within the packet. Nevertheless, the shortcomings are if the TCP packet does not contain the timestamp, measurement of clock skew is not possible. The clock skew-based techniques cannot be used if the traffic is encrypted. Lanze et al. [7] proposed a clock skew-based algorithm for passive device fingerprinting by measuring the timestamps regularly sent by access points in beacon frames. Authors of [8] proposed a physical device and device-type fingerprinting technique which can work actively and passively. The proposed system makes use of heterogeneity of devices, which is a function of the different device hardware compositions which is used for device-type identification and variation in device clock skew used for device identification.

3 Proposed Device Fingerprinting Technique For a device fingerprint to be used as an evidence of cybercrime, it is necessary that it should be unique in nature and satisfy all the characteristics of evidence as prescribed by the Indian Evidence Act Section 65B. If all the collected evidences satisfy all the properties, only then it will be accepted in court of law as legal evidence. The main objective of device fingerprint is to uniquely identify the device. The process of device fingerprinting can be conceptualized as shown in Fig. 1. There are three main steps: identification of parameters, parameter acquisition and composition of unique fingerprint.

3.1 Identification of Parameters The first step of device fingerprinting process is identification of fingerprinting parameters. The parameters should be identified depending on the use case of fingerprint in application. Our previous work [3] discussed about the parameter identification

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Fig. 1 Proposed device fingerprinting procedure

step. The existing applications of device fingerprinting use software-based parameters of the devices. Such parameters are prone to modifications which will eventually invalidate the fingerprint. When the device fingerprints need to be used as evidences in forensic investigation, it has to be ensured that they should not become obsolete or invalid or get easily tampered by the users. In order to make the fingerprint more robust, the proposed fingerprinting algorithm uses hardware-based parameters of the device. We agree that absolute assuredness of integrity of these parameters cannot be guaranteed all the time, but with hardware-based parameters, there is less probability of changes, and even if some of them changes due unavoidable factors, our system has the feature of partial matching of fingerprint. Fingerprinting parameters Table 1 shows the list of hardware parameters of the client machine that are used for generating device fingerprint of client machine.

3.2 Parameter Acquisition The second step of the procedure is fingerprint data acquisition. In this phase, the required parameters are collected from the target device. In our proposed methodology, we are deploying an agent process on client machine which will extract the required parameters and transmit them securely to server.

3.3 Composition (Generation of Fingerprint) The proposed fingerprinting algorithm is developed by keeping in view the requirements of digital evidences. Admissibility in the court of law is the basic requirement of digital evidence. It must be properly collected, and the authenticity and integrity of evidence must be preserved to conclusively decide a case. To satisfy all these characteristics of evidence, our proposed fingerprinting technique uses the concept

Hash Tree-Based Device Fingerprinting Technique for Network … Table 1 List of fingerprinting parameters

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Fingerprint parameters

Features

Motherboard

Serial number Manufacturer Name Version Product

CPU

Name Device ID Current clock speed Max clock speed Caption

BIOS

Serial number Version Name Manufacturer Release date

NIC

Mac ID Product name NIC index Time of last reset

Disk drive

Model Total heads Manufacturer

Timestamp

of hash tree. The concept of hash tree was first introduced by [9]. Every leaf node in the hash tree is labeled with the hash of a fingerprint data parameters Leaf Node Generation Generate the leaf node (fingerprint of individual parameter of device) by considering the detailed information captured for that particular parameter. Apply the same fingerprinting algorithm and generate the fingerprint of individual parameter (Features) H(Mboard FP) holds the hash of motherboard fingerprint. Motherboard fingerprint is generated by using the detailed information of device motherboard captured during the parameter acquisition step. Mboard FP = [H(Serial Number)| H(Manufacturer)|H(Name)| H(Version) |H(Product)] Similarly, by applying the same procedure, the other leaf nodes are generated by considering their detailed information captured during parameter acquisition step. H(CPU FP) = [H(Name)|H(Device ID)|H(Current clock speed)| H(Maximum clock speed)|H(Caption)] H(BIOS FP) = [H(Serial Number)| H(Version) |H(Name)|

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H(Manufacturer)|H(Release Date)] H(T) = H(Timestamp) H(Disk FP) = [H(Model)| H(Total Heads)|H(Manufacturer)] H(NIC FP) = [H(MAC ID)|H(Product name)| H(NIC Index)|H(Time of last Reset)] Every non-leaf node holds the cryptographic hash of the concatenated hashes of its child nodes e.g., H(MF, CF) = H[H(Mboard FP)|H(CPU FP)] where ‘|’ indicates concatenation. Fingerprint Generation Algorithm The process of fingerprint generation is shown in Fig. 2. The steps of fingerprint generation algorithm are as follows: 1. Take the individual fingerprinting parameters received from target machine. 2. Construct the leaf nodes of hash tree by generating a hash digest of each fingerprinting parameter (level 0). 3. Construct the level 1 nodes by concatenating each pair of fingerprint parameter hashes (leaf nodes) and hash the resulting data again (level 1). 4. Move ‘up’ a level in the tree hash the hash digests from the level 1 and construct the parent nodes (level 2). 5. Repeat the procedure using the newly generated hashes from the lower level until there is a single hash digest of the entire data. 6. At root of the tree in level 3, we get the single hash which we are calling as device fingerprint.

4 Analysis of Proposed Methodology/Analysis of Fingerprinting Method From perspective of using the device fingerprints as legal evidences for forensic investigation, according to Section 65B of Indian evidence act, it should fulfill the following characteristics [10]. The fingerprint data along with the device fingerprint is stored in database as evidence. In future, if any dispute occurs, these device fingerprints can be used as legal evidence in the court of law. As the generated fingerprints are satisfying all the characteristics of evidence, so it can be accepted in the court of law. Unique—The uniqueness property is satisfied by the device fingerprint generated by the proposed algorithm. Even if the two devices are having same make model

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Fig. 2 Device fingerprint generation process

or configuration, the fingerprint for them will be different because of the hash of different device parameters. Robust to aging—Over a period of time, if the user formats the device, changes the operating system, installs or uninstalls softwares, it will not affect the device fingerprint. It will remain the same and satisfy the robustness property because the parameters used for the fingerprint generation are hardware parameters which are very difficult to change. Resistant to attack—The fingerprint is made immune to attack by using different cryptographic techniques. Encryption is used to achieve confidentiality; digital signature is used for authentication and non-repudiation. Integrity is preserved by using message digest algorithm. Unchallengeable—As the fingerprinting data is signed by client by using its private key, the fingerprint cannot be disputed, interrogated or challenged in the court of law. Admissible in court of law—For the digital evidence to be accepted in court of law, Section 65B of Indian Evidence Act has given some guidelines that the evidence has to be authentic, reliable, believable and complete. The fingerprint generated by the proposed fingerprinting algorithm which we are going to use as evidence is satisfying all these characteristics. As the data required for generation of evidence is collected with clients consent and is having clients digital signature, it is an authentic data. The time stamp information included in evidence is used to prove that the evidence is collected at the time of crime and it is not a forged one. The parameters used for generation of evidences are also used to prove its completeness and reliability as there is very less possibility of change of all the parameters of device.

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4.1 Verification of Fingerprint (Dispute Settlement) The fingerprint generation procedure uses the concept of hash tree which allows us to verify the fingerprint up to different levels. If we are successful to get the actual device fingerprint, it means that it is 100% matching of evidence. If the user has changed any of the hardware of its device, still we can verify the fingerprint up to level 1 or level 2. For example, as shown in Fig. 3, suppose the client has changed the network interface card(NIC) or hard disk of the device, which is having more possibility of change. Change of one leaf node will invalidate the final device fingerprint. In that case, the evidence will not be 100% true but partial matching of evidence is possible. We can still reach up to level 2 of hash tree. The other parameters will help in proving the partial validity of evidence. The final decision will be then taken in the court of law that up to which level the fingerprint should match to be considered as legal partial evidence. The advantage of using hash tree is we can choose the initial pairing of parameters based on clear understanding of which parameters are more susceptible to change and which ones are the most stable one’s; by doing a proper due diligence, the pairing must be done in such a way that we can reach up to the higher level of tree which will help in getting a very reliable and acceptable fingerprint. This clearly indicates that proper paring of parameters increases the possibility of acceptance of evidence. For example, we have done the pairing of disk drive fingerprint and network interface card fingerprint because these parameters are more susceptible to change as compared to motherboard or CPU of the system.

Fig. 3 Device fingerprint verification when one of the parameter is changed

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5 Conclusion and Future Work The proposed methodology demonstrates the capability to uniquely identify the device used for cybercrime using a robust device fingerprinting technique. The device fingerprint-based evidences generated by the proposed method will not get obsolete even if the criminal tries to destroy evidences. The fingerprinting technique deployed uses hash tree and generates evidences in such a way that it can be used as legal evidence in case of any dispute in future. The device fingerprint satisfies all the evidence characteristics as specified under the Indian Evidence Act Section 65B and can therefore be used as foolproof evidence in court of law. The concept of hash tree used for fingerprint generation makes the fingerprint tamperproof and also allows us to verify the fingerprint partially. As a future work, this methodology of identifying the attacker identity can be integrated into the existing mainstream communication protocol suite.

References 1. Abraham M, Meierhoefer C, Lipsman A (2007) The impact of cookie deletion on the accuracy of site-server and ad-server metrics: An empirical comScore study. Retrieved Oct 14, 2009 p 2. Patil RY, Devane SR (2017) Unmasking of source identity, a step beyond in cyber forensic. In: Proceedings of the 10th international conference on security of information and networks. ACM, pp 157–164 3. Patil RY, Devane SR (2018) Primordial fingerprinting techniques from the perspective of digital forensic requirements. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT). IEEE, pp 1–6 4. Kohno T, Broido A, Claffy KC (2005) Remote physical device fingerprinting. IEEE Trans Dependable Secure Comput 2(2):93–108 5. Fang P, Huang L, Xu H, He Q (2018) Smart device fingerprinting based on webpage loading. International conference on wireless algorithms, systems, and applications. Springer, Cham, pp 127–139 6. Cohen MI (2009) Source attribution for network address translated forensic captures. Digital Invest 5(3–4):138–145 7. Lanze F, Panchenko A, Braatz B, Zinnen A (2012) Clock skew based remote device fingerprinting demystified. In: Global communications conference (GLOBECOM), 2012 IEEE. IEEE, pp 813–819 8. Radhakrishnan SV, Uluagac AS, Beyah R (2015) GTID: a technique for physical device and device type fingerprinting. IEEE Trans Dependable Secure Comput 12(5):519–532 9. Merkle RC (1987) A digital signature based on a conventional encryption function. Conference on the theory and application of cryptographic techniques. Springer, Berlin, pp 369–378 10. Stephen JF (1872) The Indian Evidence Act (I. of 1872): with an introduction on the principles of judicial evidence. Macmillan

A New Method for Preventing Man-in-the-Middle Attack in IPv6 Network Mobility Senthilkumar Mathi and Lingam Srikanth

Abstract IPv6 is the next-generation version of the Internet Protocol, which is soon bound to take over IPv4, its predecessor completely. It has various features over IPv4 such as error detection and communication and is comparatively more secure than the predecessor due to the usage of IPsec and ICMPv6. The neighbour discovery protocol, specific to IPv6, offers some applications for neighbour discovery, reachability and address resolution but more the number of applications, more the chance for vulnerabilities. Even though the IPv6 is said to be more secure than IPv4, it falls prey to some attacks which lead to fatal consequences. One such attack is the manin-the-middle attack where an attacker positioned manipulates the communication in between the nodes. In spite of using IPsec, the attacker can cause a hindrance to the network. The man-in-the-middle attack has many types and solutions proposed to prevent it. This paper explores man-in-the-middle attack along with the existing solutions and proposes a new method to prevent it. Keywords IPv6 · Neighbour discovery protocol · Fire brigade attack · Man-in-the-middle attack · Authentication

1 Introduction The Internet Protocol version 6 (IPv6), which works on the network layer, is developed by the Internet Engineering Task Force (IETF) as a successor of the Internet Protocol version 4 (IPv4). Owing to the exhaustion of address space in IPv4, IPv6 was developed [1]. It is a communication protocol that identifies and locates computers on networks and routes traffic across the Internet. Various features in IPv6 include expanded address space which uses 128-bit address, theoretically allowing 2128 S. Mathi (B) · L. Srikanth Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] L. Srikanth e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_21

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addresses and 7.9 × 1028 times more the number of addresses than IPv4, which uses a 32 bit-addressing scheme. It provides multicast addressing as well as a new type of address called “anycast address”. The IPv6 header is simpler than IPv4 because many of the fields present in IPv4 have been dropped to reduce the cost of packet handling, and new parameters such as traffic class, flow label, payload length, next header and hop limit have been added. It also supports extensions and options because the way in which the header options are encoded allows for more efficiency in forwarding and greater flexibility for introducing new options in the future. The classification of packets belonging to specific traffic flows for which the sender needs special handling such as real-time service is enabled which increases the flow of labelling capacity. The extensions to support authentication, data integrity and data confidentiality are also specified in IPv6. IPv6 enhances mobility support by allowing mobile nodes to change their location and address without losing the already existing connections and maintains the IP address of the node through various networks [2]. It is compatible with the IPv4 protocol. A protocol called neighbour discovery protocol (NDP) is specific to IPv6 and has features such as neighbour discovery, neighbour unreachability discovery, address resolution, router discovery and duplicate address detection. NDP protocol was designed as a replacement of the already existing Address Resolution Protocol (ARP) in IPv4. The IPv6 compulsorily uses IPsec (IP Security) which uses ICMPv6 for error communication and verification which makes the protocol more secure than the IPv4. However, more number of features will increase the number of vulnerabilities. Despite the IPsec feature, NDP is intercepted and the messages spoofed with a duplicate media access control (MAC) address, leading to various attacks such as man-in-the-middle (MITM), ARP poisoning, fragmentation and denial-of-service (DoS) [3]. The applications of NDP as mentioned above are essential to establish an excellent IPv6 network but are quite vulnerable to attacks. The ICMPv6 gives two types of messages, namely, error messages and informational messages, where the error messages in ICMPv6 provide the status about unreachability, size of the packet, exceeded the time of the session and informational messages such as echo request and reply. During the exchange of ICMPv6 messages between the nodes, a security breach is possible by denying the service to the neighbour (DoS attack) or an attacker positioning himself between the nodes and manipulating the messages sent between them. The MITM attack is one of the most successful attacks which leads to obtaining control over transferred data of end users [4]. In this attack, an attacker can place himself between two nodes or parties and uses this to make further attacks. In IPv4, ARP cache poisoning and DHCP spoofing can be used to perform MITM, but in IPv6, this attack is performed by spoofing the ICMPv6 NA and RA messages [5]. The impact of the MITM attack is that an attacker can read and manipulate communications within the Internet. A lot of rewriting and sniffing could be applied to various protocols. The DNS can be attacked, and the attacker is allowed to store false records in the DNS cache. The malware could be inserted into web pages, credentials stolen and pages tampered in HTTP. If the network communication between the nodes is not secure, it could lead to dangerous consequences like leakage of relevant information,

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Fig. 1 Depiction of man-in-the-middle attack

not receiving the right information or message which could act as a nuisance to the users. This paper focuses on different ways of how the attack can be caused, and the solutions already proposed, the problems with the proposed solutions. The MITM attack involves two victims and an attacker who has access to the communication channel. The communication is set up between the two victims by sending public keys to each other as shown in Fig. 1. But the attacker, who is in between the two victims, intercepts both and returns his public key to the victims. On receiving the key from the attacker, the victims assume that the key has been received from the peer node and hence enciphers its content using the public key of the attacker and sends it to the peer node. The attacker gets the message and deciphers it using his private key and using other node’s public key encrypts the message and sends it to the other victim [4].

2 Related Works Securing NDP is very important because of the lack of trust observed amongst users. NDP is still susceptible to some network-based attacks as a result of the devices that are connected and disconnected and frozen by network flooding caused by the NDP messages [5]. Even though MITM is the general name, it can be categorized into three concerning impersonation techniques, based on the communication channel in which the attack is executed and by the location of the attacker and the target. The MITM attack by impersonation technique depends on how the convincing takes place by the attacker to prove that he is the other endpoint. The threats identified are based on three categories: non-routing-based threats, routing-based threats and replay threats. Different types of MITM attacks are listed in Table 1. The old entry is overwritten by the neighbour cache entry with the new

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Table 1 Types of MITM attacks Attack

Description

Spoofing-based

Attacker intercepts a communication and controls transferred data, while hosts have no idea about the attacker

SSL/TS-based

Attacker intercepts two SSL connections and relay messages to the victims, while he is able to record and modify the data

Border Gateway Protocol (BGP)-based The attacker generates traffic to be delivered to the destination by hijacking the IP address of the host

link-layer address. The attacker positions himself between nodes A and B. The node A sends a neighbour solicitation for node B address. It reaches node B but goes through the attacker. The attacker sends neighbour advertisement with his address back to node A. This results in node A (victim) using the attacker’s address for packets sending to node B. The routing-based attacks take place within the same subnet of the victim, where the attacking node pretends to act as the last hop router. It can run MITM attack once it is successful in pretending to be last hop router. The other attacks possible are killing of the default router and parameter spoofing which is part of DoS attacks. The ARP spoofing takes place when the attacker pretends to be another node and disrupts the flow of traffic in the network. By performing ARP spoofing, another host is imitated, and access is gained to the private data [6]. The ARP poisoning can be moderated in MITM by long-term mapping table IP/MAC and puzzle-based voting which is computational. In the subnet, the IP/MAC addresses of the working nodes are stored from reserve ARP harming attack. The artificial neural network constructed with three layers which are the input, hide and output is a defence mechanism against DNS spoofing. The reliability of the packets is predicted from a 1–10 scale, and packets below the range of five are considered forged and discarded, while above five are accepted and validated. It is regarded as the best method amongst all the other ways as it provided an average identifying ratio of 98% for both valid and invalid packets. The DHCP spoofing can be executed by the usage of a rogue DHCP server. Here, the attacker leads the reply to the DHCP request that the actual DHCP server. The DHCP starvation attack can be executed where the attacker exhausts the allocated IP address from the legitimate DHCP server, and new machines will not be able to obtain the IP addresses. MITM can be caused by a wrong default gateway, wrong DNS server and wrong IP address. The way to protect this spoofing is using DHCP snooping which acts as a firewall between untrusted hosts and trusted DHCP servers. The responses from ports which do not have DHCP servers associated are not allowed. The IP spoofing has an attacker intercept communication between two parties and manipulates the data. The spoofing can be done using various techniques like blind and non-blind spoofing, ICMP spoofing, TCP sequence number prediction. The defence mechanisms include IPsec and ingress filtering which filter the inbound

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packets of the border router. Others include distributed packet filtering which filters packets by checking if they travelled through an unexpected route. The SSL/TLS attack can be started by hijacking the certificate. It is of two types, namely MITM + certificate where a valid certificate is held by the attacker to target the server, and he also holds an invalid certificate which may succeed if the user ignores security warnings. The forged certificates can be detected by asking to give extra data for certificates and to verify the source. The Internet Key Exchange (IKE) is used to set up a security association in the IPsec protocol and uses X.509 (public key format) certificates for authentication either pre-shared or distributed via DNS/Diffie–Hellman key exchange for the setting up of a shared session secret from which the keys are derived which is called security association for IPsec communication [7]. The SA facilitates communication and makes sure of the confidentiality of data. But if MITM takes place in the network, the key validation will not be successful and hence may result in exposing the data [8]. The MITM attack in BGP is also based on IP hijacking, route hijacking and prefix hijacking, and it can occur when peers are misinformed that the AS can reach more specific routes by misconfigured BGP. As a result, the traffic never reaches the destination. The MITM can be prevented by using prefix hijacking alert system where prefix owners are alerted on detection of multiple AS. The IPv6 can provide for network layer address configuration without being dependant on a DHCP server through the auto-configuration of stateless addresses [9]. One of the threats it gets is from rogue RAs. The RA contains relevant information like the address of router, lifetime and availability of stateful or stateless addressing. The rogue RAs or unwanted RAs are opened because any node on the subnet is allowed to send an RA, which can contain incorrect information in any field. The nodes are misconfigured by themselves and exposed which causes a severe security threat to IPv6 and also open up several other attacks. In rouge routing, the attacker who is the rogue router sends the RA message to the victim connected to its multicast group. The victim gets deceived and routes its packets with the rogue router address as the default gateway, thus giving access to all packets to the rogue router [10]. The attacker is added as a router if a bogus RA is received, but the rouge router is selected if it is reachable, and hence, the attack can be stopped, and communication can continue [11]. In Diffie–Hellman (DH) key exchange protocol, two nodes A and B use different private keys and two relatively prime numbers p and g to get the value of the public key with the obtained information [12]. The public key is shared with each other and used with the private key and the prime numbers to get a shared key, which has the same value for both nodes. Hence, without sharing private keys, the shared key is obtained. Few solutions from MITM attack on DH are DS and MAC. The DS uses the public key to verify the message sent to the other party and the private key for signing the message. Securing of secret keys is a difficult task in DS. Cryptosystem designers believe that private keys should not be sent or stored in plain text as the consequences caused by the exposure of private key are fatal. DS ensures that the person having the private key is the one that signs the document. If the private key

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is obtained by an attacker, then he may utilize it to manipulate the communication. DS only provides authenticity, not security. To add protection, it requires encryption and decryption; otherwise, it becomes a natural victim to MITM [13, 14]. In MAC, both the sender and the receiver use the same key for generation of MAC tag and verification. The sender uses an algorithm and has a MAC tag generated, and this along with the message is sent to the receiver. The receiver uses the same algorithm and key and gets a keyed MAC tag made. The sender and receiver’s MAC tags are checked, and if found to be the same, then the message will be marked valid, else invalid. The sender and receiver should have the same MAC algorithm and key, so the receiver cannot be sure if the message is received from the correct sender. GG is used to generate a more secure random private key. Here, the GG is a pseudorandom sequence generator and uses three registers for generating one sequence and creates new series from the already existing sequence. It is more complicated since it uses various steps for configuration such as distributed 0 s and 1 s. The GG uses three linear feedback shift registers whose length should be 1 when a most significant common factor is calculated. After obtaining the random binary sequence, statistical tests are conducted to test the randomness like frequency test where half of the series is expected to be 0 s and the other half to be 1 s. After the tests are conducted, the private and shared key is calculated. It ensures that the private keys are saved on the server as hashes and prevented from being sent into the channel and also can identify the sender and receiver from the user information.

3 Proposed Work Since the generation of a random sequence can greatly reduce MITM attack, a model is proposed which first checks if the node to which or from which the RA is being sent is legitimate or not. If it is legitimate, then the process continues further where keys and the random sequence are generated. In this method, the host first receives an RA and configures the value of the address in the default router using the prefix method (Fig. 2). After the above process, the password is obtained from the user. Since sending the password into the registers as plain text makes it a victim to first-order correlation attack, the password is first encrypted (see Fig. 3). The password obtained is converted to a 64-bit binary sequence S. This sequence is then split into three registers R1 , R2 and R3 each having 20 bits, 21 bits and 23 bits, respectively. The output values X 1 , X 2 and X 3 from the registers are XORed with different combinations which provide correlation immunity to protect it from correlation attack. The sequence is then generated, and then the hexadecimal sequence is converted into a decimal sequence and stored in sequence. It calculates the private key, and the messages are sent. If the message gets sent and the correct receiver receives it, then the password is hashed as H. Subsequently, the prime numbers p and g are obtained, and the public key is computed.

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Fig. 2 Authenticity of the router using Geffe generator

Fig. 3 Password generation using Geffe generator

The shared key is then hashed and sent to the server. If the hashed shared key matches any value in the user table, then the message is shared with the other node. If it is not, then it quits. This method ensures that the node it is being sent to is legitimate and also provides more security because of random sequence generation and the correlation attack is also minimized.

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4 Analysis and Discussion A detailed study of the various methods and the proposed methods give an idea of how MITM attacks can be prevented. Even though IPv6 incorporates security measures through IPsec, it is still vulnerable to MITM attacks [15, 16]. Various methods were investigated to prevent the attacks on the network. Table 2 summarizes the merits of the proposed method with other methods. The digital signatures, one of the methods used to increase security, ensure authenticity by signing of private keys. It does not guarantee the security as there is no encryption or decryption done to the message being sent. Also, it does not ensure whether the receiver is the actual receiver or not because if the private key is obtained, anybody can sign the key. It is solved by using MAC. The MAC ensures whether the sender and receiver are valid. If it is not the right sender or receiver, the message is not sent. It uses an algorithm and the same key at both the sender and receiver side to ensure the validity. If the key matches, then the message is valid. It provides better security than DS but works only if the sender and receiver have the same MAC algorithm. Table 3 summarizes the comparative analysis of various works and the proposed work. The GG solves the problem of security and validity by converting the key to a random binary sequence and then sends it into the registers where further processes such as frequency checking are carried out. But the bits stored in the registers are Table 2 Comparison of DS, MAC and GG methods Method

Merits

Digital signature (DS)

Ensures authenticity by checking if private key holder signs it

Message authentication code (MAC)

Uses algorithm and generates a shared key which can be used by sender and receiver to check for validity

Proposed method with Geffe generator (GG)

The random key is generated. Ensures entry of only valid key into the channel. The sender and receiver identified more accurately

Table 3 Comparison of various works and the proposed work

Method in

Susceptible to MITM Susceptible to first-order correlation attack

[7]

Yes

Yes

[9]

Yes

No

[10]

Yes

Yes

[14]

Yes

Yes

Proposed work No

No

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susceptible to first-order correlation attack, so a method is proposed where the password is encrypted before sending it into the registers, and the output of the registers has some logical operations performed on them to add further security to the network. Therefore, using the GG with the modification done can significantly have an impact on the network by not allowing the attacker to intercept and modify the messages being sent and received.

5 Conclusion IPv6 is inevitably the future of computer networking. Even with the presence of IPsec, the network is prone to a lot of attacks. The MITM is the most popular attack, and the network is manipulated to a great extent. A lot of methods have been proposed for it, yet those methods have some loopholes by which the attacker can still invoke an attack on the network. The proposed method provides safety from the attacks and also closes the loopholes by providing extra security to the network. Thus, the proposed method attempts to avoid the breach on the network and validates the sender and receiver and also ensures that the right message is being sent to them.

References 1. Ullrich J, Krombholz K, Hobel H, Dabrowski A, Weippl E (2014) IPv6 security: attacks and countermeasures in a nutshell. In: 8th workshop on offensive technologies 2. Sethuraman M, Mathi S (2018) Prevention of denial-of-service in next generation internet protocol mobility. Indonesian J Elect Eng Comput Sci 12(1):137–146 3. Lu Y, Da Xu L (2018) Internet of Things (IoT) cybersecurity research: a review of current research topics. IEEE Internet Things J 6(2):2103–2115 4. Conti M, Dragoni N, Lesyk V (2016) A survey of man in the middle attacks. IEEE Commun Surv Tutor 18(3):2027–2051 5. Anbar M, Abdullah R, Saad RM, Alomari E, Alsaleem S (2016) Review of security vulnerabilities in the IPv6 neighbor discovery protocol. In: Information science and applications (ICISA). Springer, Singapore, pp 603–612 6. Ahmed AS, Hassan R, Othman NE (2017) IPv6 neighbor discovery protocol specifications, threats and countermeasures: a survey. IEEE Access. 5:18187–18210 7. Rahim R (2017) Man-in-the-middle-attack prevention using interlock protocol method. ARPN J Eng Appl Sci 12(22):6483–6487 8. Al-Ani A, Anbar M, Abdullah R, Al-Ani AK (2018) Proposing a new approach for securing DHCPv6 server against rogue DHCPv6 attack in IPv6 network. In: International conference of reliable information and communication technology. Springer, Cham, pp 579–587 9. Ouseph C, Chandavarkar BR (2016) Prevention of MITM attack caused by rogue router advertisements in IPv6. In: 2016 IEEE international conference on recent trends in electronics, information & communication technology (RTEICT), pp 952–956 10. Kumar CK, Jose GJA, Sajeev C, Suyambulingom C (2012) Safety measures against man-inthe-middle attack in key exchange. ARPN J Eng Appl Sci 7(2):243–246 11. Shin YY, Lee JK, Kim M (2018) Preventing state-led cyberattacks using the bright internet and internet peace principles. J Assoc Inf Syst 19(3):152–181

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12. Khatri A, Mathi S (2017) Active home agent load balancing for next generation IP mobility based distributed networks. In: International conference on ubiquitous communications and network computing. Springer, Cham, pp 165–176 13. Xiaowei Z, Zhou H, Jun LP (2010) Analysis and improvement of IKEv2 against denial of service attack. In: 2010 international conference on information, networking and automation (ICINA), vol 1 V1–350 14. Khader AS, Lai D (2015) Preventing man-in-the-middle attack in Diffie-Hellman key exchange protocol. In: 2015 22nd international conference on telecommunications (ICT), pp 204–208 15. Burhan M, Rehman R, Khan B, Kim BS (2018) IoT elements, layered architectures and security issues: a comprehensive survey. Sensors 18(9):2796 16. Navas RE, Le Bouder H, Cuppens N, Cuppens F, Papadopoulos GZ (2018) Do not trust your neighbors! a small IoT platform illustrating a man-in-the-middle attack. In: International conference on ad-hoc networks and wireless, Springer, Cham, pp 120–125

Mutual Authentication Scheme for the Management of End Devices in IoT Applications S. Annashree Nivethitha, Chanthini Baskar, and Manivannan Doraipandian

Abstract IoT has started to penetrate in all walks of life starting from home to industrial applications. The number of internet connected devices is increasing every day. Data breaches against a huge amount of data evolving in it also in rise which makes security imperative. Message Queuing Telemetry Transport Protocol (MQTT) is one of the most widely used lightweight communication protocol for the Internet of Things (IoT) services. In this work, two-way communication using socket was adopted between node and gateway, and publish/subscribe-based communication was used between node and user. In order to ensure overall authorized access of the data from the devices, the proposed work provides three-factor authentication mechanism including perpetual and one-way hashing. Further, computational and storage analysis was performed, which proves that this scheme is suitable for resourceconstrained devices and used to minimize the computational complexity, space, and bandwidth. Keywords IoT · MQTT · Authentication · Security

1 Introduction In the current arena of the digital world, IoT plays a crucial role in most of the dayto-day applications where devices are spatially distributed that forms a network and facilitates intelligent services. This technology is on the rise among various fields ranging from home automation, smart agriculture, eHealth, military services, and so on. Hence, privacy and security of the data involved in these applications are of major concern since most of the devices involved were not designed with proper security services. In this scenario, data security is highly imperative to ensure the authenticity of the data. Employing enhanced secure communication in IoT devices remains to be an operational challenge due to their resource-constrained nature. The limited computational ability, power, and memory space of the embedded devices S. Annashree Nivethitha · C. Baskar · M. Doraipandian (B) School of Computing, SASTA Deemed University, Thanjavur 613 401, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_22

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are the major bottleneck for applying protection and endorsement schemes for data security in these devices. The application layer is responsible for managing different task, and it also deals with security and privacy of the data involved in the IoT frameworks [1]. The various application layer protocols for IoT systems are Extensible Message Passing Protocol (XMPP), Hyper Text Transfer Protocol (HTTP), Constrained Application Protocol (COAP), Advanced Message Queuing Protocol (AMQP), and Message Queuing Telemetry Transport Protocol (MQTT). Among these, MQTT is lightweight message passing protocol running in TCP/IP suite and is the most widely preferred communication protocol in the IoT network [2, 3]. In this work, MQTT was adopted for communication between user and node. In general, authentication is verifying all the participants involving in the communication. Username and password is the only factor provided by MQTT for security purpose which must also be enabled in the configuration file. Moreover, these factors are text-file-based transmission which is not secure. Hence, in the literature, there are several works related to authentication schemes for MQTT. Open access authentication (OAuth) is the mechanism used on the Internet for access control and authorization of the users in which various versions has now got developed [4, 5]. OAuth-based access management for IoT framework that uses MQTT communication protocol has been proposed using embedded hardware such as Arduino, raspberry pi [6]. This framework has been modified to accommodate end devices which are resource constrained in nature, and various security analyses have been done [7]. Since OAuth method is based on Hyper Text Transport Protocol (HTTP), and MQTT has been used, which requires both HTTP and MQTT to be coded in the sensing devices. Moreover, refreshing the token should also be handled which leads to memory constrain issue for the sensing layer. Hence, OAuth is not recommended for MQTT based IoT network. Thus, Transport Layer Security (TLS) has to be enabled to ensure security in MQTT [8]. Since the network is dynamic in nature and large numbers of devices are involved in communication, a conventional mechanism such as Transport Layer Security (TLS/SSL), symmetric and asymmetric cryptography, certificates is not suitable for the sensing layer which involves low-powered constrained devices. Hence, lightweight authentication schemes are required [9–11]. Traditional authentication schemes involve only the knowledge factor which is not secure. Hence, various twofactor or three-factor lightweight authentication schemes have been proposed which are based on the available resources [12–16]. A lightweight three-factor authentication protocol has been proposed for constrained application protocol (COAP)-based network architecture [17]. Since the request of the user directly goes to the node, if an attacker tries to overload the node with the duplicate request, it will result in node failure resulting in the denial of service attack. The most considered factors in authentication schemes are identity, password, and secret key. The secret key involved should have the following five characteristics universal, distinctive, persistent, collectable, and unique. This key will be either biometric or password depending on the client which can be a user or sensor node. The major focus of this work is to provide a lightweight biometric-based authentication scheme for MQTT using one-way hash function and perceptual hashing.

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2 Lightweight Cryptography Preliminaries 2.1 Sponge Construction-Based Lightweight Function SPONGENT is a lightweight hash function with a smaller footprint and is based on PRESENT-type permutation. It involves three phases initialization, absorbing, and squeezing phase that produces a fixed digest value for variable size input. In this work, SPONGENT-128 is considered.

2.2 Perceptual Hashing Generally, a hash function produces varying output even for a single bit change in input. But, the purpose of perceptual hashing is to produce similar digest value unless there is no significant change in the input. This hash values depend on the multimedia content and remain approximately same till there is no significant change in the multimedia content. Hence, this hashing is preferred for biometrics [18, 19]. In this scheme, 128-bit message digest perpetual hash value of the multimedia content is considered.

3 Authentication Mechanism for MQTT Since MQTT lacks a proper authentication mechanism, it is more viable for security breaches (Fig. 1). Hence, in order to ensure the privacy, confidentiality, and availability of data involved in the MQTT based communication, a proper authentication mechanism is required. Table 1 depicts the notations used in this mechanism.

3.1 Initialization The proposed authentication scheme uses MQTT transport layer protocol. Both the user and the sensor devices should be registered with the gateway. The nodes are embedded with the gateway generated parameters which are node identity NIDn, pre-shared key X gn , and masked keys x n = H(NIDn ||X g ), yn = H(NIDn ||X gn ), en = x n ⊕ yn . Gateway stores the shared key and secret key for the respective node identity.

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Fig. 1 Sensor network framework and its security threats Table 1 Notations used Notations

Description

X g , X gn , X gu

Gateway private auth key, node pre-deployed auth key, and user-shared auth key during registration with the gateway

MPu , MIu , MSu

User-masked password, identity, and secret key

x u , y u , z u , eu , f u

Masked keys calculated in the gateway that is stored in the user side

NIDn

Node identity generated by the gateway

en , x n , y n

Masked keys calculated in the gateway that is stored in the node

UNu

User-calculated parameter sent to the gateway for user validation

UZu

Masked value of the users random number

Hg

Gateway-calculated parameter for its verification by the node

Rn

Masked value of node nonce

Vg

Gateway-calculated parameter for its verification by the user

SK

Session key generated by the node and user

An

Node-calculated parameter for the gateway to verify the node

n, m

User- and node-generated nonce for session key

|

Concatenation operation

H(.)

One-way hash function

⊕

EX-OR operation

T 1 , T, T

Timestamp

Mutual Authentication Scheme for the Management of End Devices …

225

Fig. 2 User registration with the gateway

3.2 User Registration The user needs to register with the gateway and add required nodes. These nodes are embedded with node identity NIDn and shared key X gn , x n , en  generated by the gateway during its deployment. The secret key in case of the user is his biometric which is distinctive and hence improves the security. Following are the steps involved in the user registration process. Figure 2 depicts the registration phase of the user with gateway. Step 1 User calculates the masked values of the entered password, identity, and secret key using the generated random number ru . MPu = H(password||ru), MIu = H(identity||ru ), MSu = H(secret key||ru ), and it sends these values < MPu , MIu , MSu > to the gateway. Step 2 Gateway checks the timestamp for replay attack and then calculates x u = H(MIu ||X g ), yu = H(MPu ||X gu ), zu = H(MSu ||X gu ), eu = xu ⊕ yu , f u = x u ⊕ zu . Masked key values < x u , eu , f u , X gu > are sent to the user for storage. Step 3 User stores the received values from the gateway.

3.3 Authentication Process Using a smartphone or any Web application user can log into application to view the data published from the intended nodes. Authentication phase is depicted in Fig. 3. Step 1 User enters the identity, password, and the secret key in the smartphone app. Step 2 Entered keys are verified from the stored values in the phone. Step 3 If the entry is valid, the user can log in successfully, and the login parameter UNu = H(yu ||x u ||X gu ) was calculated. After which, it sends the request to the node from which data was required to be published. This initiates the authentication phase (Fig. 4). Step 4 User sends < MIu , eu , f u , UNu , UZu , T 1 > to the intended node using MQTT.

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Fig. 3 Authentication phase

Fig. 4 Gateway table with user-related masked values

Step 5 Node checks the timestamp for transmission delay and terminates the connection in case of delay else calculates yn = x n ⊕ en and An = H(X gn ) ⊕ yn . Then, it sends < MIu , eu , f u , UNu , NIDn , en , An > to the gateway for user verification. Step 6 Gateway also checks the timestamp and verifies the user. Then, it computes Hg parameter that is sent to the node for its validation and conveys the node regarding the legitimacy of the user. Step 7 Node checks for the transmission delay after which it verifies the gateway and checks whether the user is valid and calculates the session key SK = H(n ⊕ m). It then publishes calculated < Rn = H(x n || NIDn ) N ⊕ m, V g = H(yu ||X gu || zu ) > to user. Step 8 User extracts the node-generated random number to calculate the session key from received parameter Rn and verifies the gateway using V g . It calculates the session key SK = n ⊕ m and starts subscribing the data.

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4 Results and Discussion The above protocol scheme has been simulated using python and MQTT protocol with mosquitto broker. Three modules are developed, namely (1) subscriber: user, (2) publisher: sensor node, and (3) gateway node. Both publishers and subscribers are connected to the gateway through endpoints. SQLite3 has been integrated with python to create user- and gateway-related tables. During the user registration process, gateway stores the user’s masked identity and its related 128-bit keys (Fig. 4). In the user side, it stores the gateway-generated 128-bit parameters that were received during registration (Fig. 5). Various scenarios of user validation have been simulated like unregistered user entry (Fig. 6), invalid credential entry (Fig. 7), and user invalidation by the gateway in the case of the message being interpreted and retransmitted (Fig. 8). Since biometric was involved in this scheme which enhances secured user anonymity, it is resistant against impersonification, parallel session attack, man-in-the-middle attack, and denial of service (DoS) attack. As the authentication phase was initiated by the node in this scheme, it is resistant from gateway bypassing attacks and provides mutual authentication of all the participants involved in the communication.

Fig. 5 Gateway table with user-related masked values

Fig. 6 User registration with the gateway

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Fig. 7 User to node communication

Fig. 8 User validation in the gateway

5 Performance Analysis The computational cost of the mechanism is considered by the following three factors, namely (1) C x : the cost of XOR operation, (2) C h : the cost of one-way hash function, and (3) C r : the cost of random number generation. The cost analysis of this work was presented is based on the computation analysis of lightweight hash functions [20], in Table 2. As the proposed scheme is based on XOR operation and one-way hash function, it is efficient in terms of computation, and only a few parameters need to Table 2 Computational cost analysis Gateway node

User

Sensor node

User registration

2C x + 3C h

C r + 3C h

–

Node registration

C r + C x + 2C h

–

–

Authentication

4C x +7C h

C r + 5C x + 6C h

4C x + 4C h + C r

Mutual Authentication Scheme for the Management of End Devices … Table 3 Storage analysis

229

Gateway node

User

Sensor node

NIDn

✓

–

✓

en

–

–

✓

xn

–

–

✓

yn

–

–

✓

Xg

✓

–

–

X gn

✓

–

✓

X gu

✓

–

–

xu

–

✓

–

yu

–

✓

–

zu

–

✓

–

eu

–

✓

–

fu

–

✓

–

MIu

✓

–

–

MBu

✓

–

–

ru

–

✓

–

Total bits

768

768

640

be stored by the user, gateway node, and sensor node. In Table 3, storage cost of the proposed mechanism is illustrated. From the results obtained it shows that, the proposed work minimizes the computational complexity and storage space. We apply this scheme in MQTT to provide a lightweight authentication process for the management of constrained devices in the IoT applications.

6 Conclusion The focus of this work is to enhance the security of the resource-constrained devices that have been drawn into the MQTT based sensor network. For this purpose, a threefactor authentication mechanism has been proposed to ensure secure communication among the publisher/subscriber and the security analysis shows that it is resistant against various attacks. Taking the space and computational ability of the devices into consideration lightweight methods like SQLite3 database, one-way hash function and perpetual hashing have been used in this work. Furthermore, from the computational and storage analysis performed, it is observed that this scheme is flexible for sensing layer devices with limited resources.

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Acknowledgements The authors are grateful to the Department of Science and Technology, New Delhi, India (SR/FST/ETI-371/2014), and the second author wishes to express sincere thanks to the INSPIRE fellowship (DST/INSPIRE Fellowship/2015/IF150629) for their financial support. They also wish to acknowledge SASTRA University, Thanjavur, for extending infrastructural support to carry out the work.

References 1. Noor MBM, Hassan WH (2018) Current research on Internet of Things (IoT) security: a survey. Comput Netw 2. Karagiannis V, Chatzimisios P, Vazquez-gallego F, Alonso-zarate J (2015) Application layer protocols for the internet of things research motivation, pp 1–10 3. Prada MA, Reguera P, Alonso S, Morán A, Fuertes JJ, Domínguez M (2016) Communication with resource-constrained devices through MQTT for control education. IFAC-PapersOnLine. 49:150–155 4. Hammer-Lahav E (2011) The OAuth 1.0 Protocol. OAuth 1.0 Protoc 5. The OAuth 2.0 Authorization Framework. OAuth 2.0 Auth. Framew 6. Fremantle P, Aziz B, Kopecky J, Scott P (2014) Federated identity and access management for the internet of things. In: 2014 proceedings of international workshop on the security of the internet of things SIoT 2014, pp 10–17 7. Niruntasukrat A, Issariyapat C, Pongpaibool P, Meesublak K, Aiumsupucgul P, Panya A (2016) Authorization mechanism for MQTT-based internet of things. In: 2016 IEEE International conference on communications work ICC 2016, vol 6, pp 290–295 8. Chung JH (2016) Adaptive energy-efficient SSL/TLS method using fuzzy logic for the MQTTBased internet of things. Int J Eng Comput Sci 5:19296–19303 9. Bogdanov A, Kneževi´c M, Leander G, Toz D, Varici K, Verbauwhede I (2013) SPONGENT: The design space of lightweight cryptographic hashing. IEEE Trans Comput 62:2041–2053 10. Hammad BT, Jamil N, Rusli ME, Z‘aba MR (2017) A survey of lightweight cryptographic hash function. Int J Sci Eng Res 8:806–814 11. Wu W, Wu S, Zhang L, Zou J, Dong L (2014) LHash: a lightweight hash function. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinf) 8567:291–308 12. Li X, Peng J, Kumari S, Wu F, Karuppiah M, Raymond Choo KK (2017) An enhanced 1-round authentication protocol for wireless body area networks with user anonymity. Comput Electr Eng 61:238–249 13. Wu F, Xu L, Kumari S, Li X (2017) An improved and anonymous two-factor authentication protocol for health-care applications with wireless medical sensor networks. Multimed Syst 23:195–205 14. Wu F, Li X, Sangaiah AK, Xu L, Kumari S, Wu L, Shen J (2018) A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks. Futur Gener Comput Syst 82:727–737 15. Jiang Q, Zeadally S, Ma J, He D (2017) Lightweight three-factor authentication and key agreement protocol for internet-integrated wireless sensor networks. IEEE Access. 5:3376–3392 16. Li X, Niu J, Kumari S, Wu F, Sangaiah AK, Choo KKR (2018) A three-factor anonymous authentication scheme for wireless sensor networks in internet of things environments. J Netw Comput Appl 103:194–204 17. Dhillon PK, Kalra S (2017) Journal of information security and applications a lightweight biometrics based remote user authentication scheme for IoT services. J Inf Secur Appl 34:255– 270

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18. Al-ani MS, Al-Aloosi WM (2013) Biometrics fingerprint recognition using discrete cosine transform (DCT). Int J Comput Appl 69:975–8887 19. Lee JK, Ryu SR, Yoo KY (2002) Fingerprint-based remote user authentication scheme using smart cards. Electron Lett 38:554–555 20. Garcia-Alfaro J, Lioudakis G, Cuppens-Boulahia N, Foley S, Fitzgerald WM (eds) (2013) Data privacy management and autonomous spontaneous security. In: Workshop I

Finding Influential Location via User Mobility and Trajectory Daniel Adu-Gyamfi , Fengli Zhang , and Fan Zhou

Abstract Recently, service recommendation systems are targeted at obtaining data on the most visited location of a user. Often, these systems consider such a location as influential. Thereby, the systems usually inference data on past activities of the user in order to recommend a suitable service. Efficiency of the approaches used deteriorates for a mobile user who is in continuous motion within a vicinity. This paper examines the mobility and trajectory records of a mobile user to inference spatio-temporal data as the time bounded activities, in order to find an influential location. Our work builds upon existing techniques including concurrent object features localization and recognition, grid index data structure, data-dependent multiple object classes, and branch and bound. A data driven approach with algorithm for this purpose has been presented. In terms of accuracy and speed, our experimental evaluation has proven the proposed approach as efficient and achieves fast updates on finding a new influential location. Keywords Data mining · Mobility records · Influential location · Spatio-temporal · Trajectory analysis

1 Introduction An influential location has the potentials to attract businesses and services. For that matter, recommendation systems in recent time are targeted at how to find a location [1] that is mostly visited by a user. This is due to the fact that when a location within D. Adu-Gyamfi (B) · F. Zhang · F. Zhou School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan Province, China e-mail: [email protected] F. Zhang e-mail: [email protected] F. Zhou e-mail: [email protected] D. Adu-Gyamfi University of Energy and Natural Resources, P O Box 214, Sunyani, Ghana © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_23

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a vicinity is most often visited by a user, it implies that it is influential among other locations within the same vicinity. In this present information era, it will be more preferred to have service recommendation systems that are capable to recommend influential location to a client or user. The challenge with existing systems is the emphasize on inferencing data on past activities of a user without considering that of stream data on a continuously moving user. For this reason, in this paper, we consider mobility and trajectory of a user as to consist of the path or route, activity, and time of a mobile user to that effect. Thereby, we provide an approach to find an influential location that is considered as the spot or an area within a vicinity that is most often visited by a mobile user. This research is treated as a continuous maximized rangesum problem, similar to [2, 3]. A mobile user is regarded as either a continuous or intermittent user of a certain service such as restaurant, tourist attraction, theatre, fuel station, shopping mall, etc. that is located within a vicinity. The trajectory of a mobile user includes its mobility patterns with respect to time. Therefore, the time bounded activities that are carried out by the mobile user in a given location are classified as spatio-temporal data. The activities are identified by the spots that represent the mobile user’s position data in the vicinity. It is essential for a recommender system to observe or inference the spatio-temporal data [4] when recommending services to a mobile user within a vicinity. This is due to the unique features embedded in the spatio-temporal data for inference to aid to obtain accurate and efficient results. Nonetheless, recommending a service ahead of time to a mobile user is quite a challenging task in the research community. This is due to the difficulties in identifying the dynamics of mobility patterns and generation of the time bounded activities [5, 6]. This is among the many reasons to the challenges being faced by current recommendation systems, despite their advances in predictions. Outlined are two of the many pressing issues. First, how the systems are able to correlate spatial and temporal information on a mobile user. Second, how the systems are able to predict effective and efficient results on a large scale data. Often, it is common for the existing systems to encounter cold start and sparsity problems when establishing any predictors for future predictions [6]. Consider a mobile user who has no trajectory history or with trajectory history but fewer than usual. Therefore, systems with actively recorded spatio-temporal trajectory data often encounter the cold start and sparsity problems on predictions. This research is motivated by the ubiquitous and widespread use of low-cost mobile position tracking devices and/or systems, e.g. GPS. These wireless devices and/or systems are capable to record massive data on the daily activities of a mobile user while the user is accessing certain services on the run. Though a lot of work have been done on spatio-temporal queries [4, 7–11] and its related research problems [1–3, 5, 6, 12–14] in the past, and due to space cannot be elaborated. Our focus is on the first issue above which continues to be explored in the research community. Similar work can be found in [2, 3]. Work [1] presented on an aggregate recommender system to tourists through historical data on personal information, location, etc. It has predicted the next visited location of tourists’ using data on their call detail. A new algorithm was proposed to discover clusters in noisy data streams in [10]. They used dynamic and cluster-specific temporal decay factors. Similarly, the approach was able to identify and adapt to evolving trends. They

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adapted the weighting of stream data on the content attributes and temporal arrival patterns. It is observed that among their concentration was processing of historical data, which is computationally expensive and inefficient when dealing with a streaming data [4, 6]. And for practical applications, data on the current location of a user is adequately preferred to that of obsolete data in the streaming environment. Our approach considers the sum of the maximum weight of the user’s position data in the present location and adapt to continuously evolving patterns. Moreover, work [10] applied the Chebyshev bound [15] which does not include assumptions when dealing with data distribution. This paper applied branch and bound technique [16] which uses assumptions about the distribution of the content data that is more applicable to real-life applications. Synonymously, work [2, 3] used the concepts of maximum and minimum object enclosing rectangles and cuboids to handle maximize data within a bounding box or range. Work [2] presented on query trajectories of objects based on continuous maximized range-sum, whereas a maximized range-sum algorithm termed as G2 was proposed in work [3] to monitor object in spatial data streams. Our contribution includes addressing the problem of inferencing historical data to include that of present and continuously evolving data on a mobile user within a vicinity. This is to aid to obtain efficient query for finding an influential location. A data driven approach with algorithm is proposed for this purpose. Our focus is on exploring a data-dependent approach to facilitate a queried result via mobility and trajectory data analysis. A trajectory data mining [4, 6] technique based on the various time bounded activities of the mobile user is used to establish a grid-based index data structure. Our work builds upon existing techniques including data-dependent multiple object classes and branch and bound [16, 17]. An experiment is carried out with a real-life massive dataset including GeoLife [18] as a social networking data on outdoor mobility of users. Evaluation has proven the proposed approach as efficient and achieves updates on a new influential location. The proposed approach has enormous state of the art applications including monitoring and understanding of a user through the activities performed by the user for the purpose of surveillance, health, tourist route preferences, advertisement, recommendation systems, and facility planning for a new user. The paper is further organized as follows. Section 2 defines the problem in this research. Sections 3 and 4, respectively, cater for the proposed approach and experiment. Section 5 concludes the work.

2 Problem Definition Assuming there are n countable infinite position data denoted as P(n) . Any identified position of the mobile user is denoted as P(i) which can be defined by the tuple P(i) = {x, y, w} [3]. Where x, y, and w are the respective topology (or position) coordinates in space and a positive weight. Such that, P(i) w  R+ , and P(i)  P(n) . Thereby (1) is established. Where i: 1, …m, m < n, m = n.

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P(i) w = P(i) · w

(1)

The objective is to monitor the dynamics of P(i) w in continuous time interval for any specific location r within a vicinity R. Such that the weighted-sum of P(i) w is maximized inside r. The centroid of r is located as influential location.

3 Proposed Approach 3.1 Maximized Weighted-Sum of Position Data Here, we redefine our data stream in the previous section to include the idea of trajectory by incorporating time. Therefore, the data stream is a tuple given by P = {u, w, t} as an unbounded sequence [2]. Thus, there exists a mobile user with identifier u, represented by spatial position coordinates denoted as x and y. There exists a constant positive weight denoted as w and associated timestamp t. In Fig. 1,

Base StaƟon

Ui : p1,t1

Database Exchange Servers

Lo calit y

GPS

Trajectory Uk : p2,t2

p

LocaƟon, s : IniƟal SoluƟon

d1

P’ d1

d2

d2

Fig. 1 A change in linear position with time of a mobile user. The position data has initial solution p at time t 1 and dynamically updated to solution p at time t 2 . There is a weighted-sum of 2 for p and that of p is 5. The entire vicinity R under monitoring at time t 2 has seven position data

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the smaller rectangles s and r (i.e. fixed size) are used to represent some specific locations within a vicinity (i.e. denoted by the larger rectangle R). Such that, the weighted-sum of the positions within s and r is maximized at initial time t 1 and updated time t 2 . Furthermore, it can be noticed that vicinity R which is the location under general monitoring has a total of seven position data of the mobile user at both time t 1 (i.e. p1 ) and t 2 (i.e. p2 ). At time t 1 (i.e. initial solution), there exists position data p1 in the specific location indicated as location s. The influential location is denoted by a centroid shown as p has a maximized weighted-sum of 2. At time t 2 (i.e. updated solution), the position of the mobile user dynamically changed from p1 to p2 in the new or updated specific location indicated as r. The influential location dynamically changes from p to p which has the current maximized weighted-sum of 5. The optimization objective for the maximized weighted-sum of n position data is established in (2). P(n) w = argmax P(i) w

(2)

i∈n∩R

3.2 Localization and Recognition Technique There is the need to recognize the position of the mobile user. Therefore, it is required that an exhaustive search is done for the combination of location features. That is for the features to be expressed as a weighted-sum over almost every object models and location data. However, it is computationally not necessary to carry out exhaustive search. This paper adopts concurrent localization and recognition [19] technique for this purpose in order to improve on the computation burden. The aim is to simultaneously find the specific locations s and r in the position data and an object model i that will jointly maximize a prediction score for any ith object model (i.e. recognition). This will be based on only the features within s and r (i.e. localization) shown in Fig. 1. An element zk is expressed as a location feature. An optimization objective is given in (3) to query an instance of the position data within the location. Where R is vicinity and l is any specific location. Thus, the location features that are required as input can be expressed as a set of N triplets given by l =: {(ax k , byk , czk ) | k: 1, …, N}. A location feature element zk is an M-dimensional vector. Hence, an zk (i) is the ith element of zk . argmax R,i





Z k (i)

(3)

k∈l(R)

where l contains spatial position that is bounded in vicinity R. Assume that a vector carries a support vector weight of the Pth training feature of the specific location

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in the vicinity. The partial score is computed using (4). Thus, if P is any position data, then l p and cp are classified as model label and feature count obtained from the specific location. The up is a vector that carries weight of support vector of pth training set, where ith denotes the respective SVM weight. Assuming P(k) is the set of position data indexed by the specific location. The h(·) evaluates to one for true solution values and zero where no solution exists. 

Z k (i) =





h(l p = i)c p u p (i)

(4)

p∈P(k)

3.3 Branch and Bound Technique In search of the global optimal location, the branch and bound technique is applied. An interval is set to capture all location features by adopting the linear plane-sweep mechanism [20]. An iteration is performed in two stages. First, the concurrent interval is split into two disjoint branches. Second, bounding on the resulting intervals is performed. The next location is then chosen. The iteration gets terminated if the resulting value is a singleton. Consider a search space (i.e. vicinity) with parameter label represented by {(−X, +X), (−Y +Y ), A}. A subspace D is chosen and split into D1 and D2 . The x and y coordinates of the location are used, together with some object models A based on the upper-bound estimates. A tuple Di =: {−X i , +X i , −Y i , +Y i , a} is considered to obtain a subsets Z 1 and Z 2 . Where i = 1, 2 (i.e. binary). Thus, +Y is split to D1 , and points in both −Y and all +X 1 are removed. An upper-bound +Ga is computed as a function of subspace D, in (5) (Fig. 2). +G a (D) =

 i∈D(+b)

       +h Z k (a) + −h Z k (a)

(5)

i∈D(−b)

Similarly, a lower bound −Ga is obtained using (6), by adding many of the negative values, and then subtract many of the positive values. Where D is a subspace, whiles −b and +b represent the smallest and largest rectangles, respectively. The overall upper-bound and lower-bound are respectively computed using (7). −G a (D) =

 i∈D(−b)

       +h Z k (a) + −h Z k (a)

(6)

i∈D(+b)

G a = G a + δa , Q a = Q a + δa

(7)

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(p6,7) (p4,6)

r7

(p3,4)

r6

r2 (p1,2)

r4

(p’6,7)

r3 (p’2,3)

-Xk

r1

(p’4,6)

(p’1,2) (p’3,4)

+Xk

r8 r5

Fig. 2 A set of positions transformed into rectangles. Thus r 1 to r 8 with intersection pi,j and p j,i . The r 5 and r 8 are rectangles which do not overlap with any other rectangles in space. The line with intervals –x k and +x k represents a plane linear sweep [20] mechanism

3.4 Mobility Instance of Mobile User Consider an evolving or newly added position data P’(i)l . If data P(i) w is known at some time instance, then new update is computed through (8) and (9). Therefore, the grid cell or index data structure in Fig. 5 can be updated accordingly via g in (10). This follows from a graph theory established in Figs. 3 and 4. The relationship of vertex, edge, and next neighbour vertex of graph G is shown in Table 1.  w = P(i) w + P(i)  P(i) w = P(i) w +

 

P(i)l 

(8)

 P(i) w

(9)

g = argmax gi, j .w gi, j ∈G

(10)

3.5 Algorithmic Pseudocode for Finding Influential Location The pseudocode for the algorithms 1, 2, 3, and 4 is designed based on the approach discussed above. Firstly, localization and recognition are performed to identify the

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Ui: p2,t2 Ui : p3,t3

Ui : p1,t1

Ui : p7,t7 Ui : p6,t6

Ui : p4,t4

Ui : p8,t8

Ui : p5,t5

r2

r4

r3

r7

r6

r1

r8

r5

Fig. 3 A tree graph showing a partly trajectory of a mobile users. The r 5 is generated before r 6 . According to graph analysis, the r 5 and r 8 do not form part of the usual trajectory

r7 r6

r2 (p1,2)

e1

r4 r3

r1 Fig. 4 A derived directed tree graph G. This is derived based on Figs. 2 and 3. Showing the edge ei (or overlap, pi,j ) and the vertices vi (or rectangle, r) relations of Fig. 3. Note that e5 closes the gap that was created in Fig. 3 when r 4 and r 6 overlap and formed p4,6 Table 1 Relationship of vertex, edge and next neighbour vertex of graph G

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1

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2, 3

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vicinity of the mobile user. The various positions of the mobile are tracked or monitored along the various spot visited. In a specific location, a maximized weightedsum of the position data is obtained followed by the centroid to be represented as an influential location. Algorithm 1 Localization and Recognition of Object model and Parameters for Input 1: function LOC.REC(P) 2: -X, +X, -Y, +Y ← the points in P 3: Sort out all the x and y along the x and y plane 4: A ← labels of object models 5: D ← -X, +X, -Y, +Y, A 6: G ← a null priority queue initialize 7: G ← set D 8: while D ← POP (G) do 9: if D, |Z| = 1 then 10: return D 11: end if 12: (D1,D2) ← SPLIT(D) 13: push D1 to G with key lm(D1) 14: push D2 to G with key lm(D2) 15: end while 16: end function

Algorithm 2 Localization and Recognition Based on Branch and Bound Technique 1: function SPLIT(D) 2: (-X, +X) (-Y, +Y) A ← the points in D 3: Let Z be largest set of candidate 4: if Z = A then (-Yi, +Yi) 5: Let D ← ((-Xi, +Xi) a) w 6: wSort all a ϵ Z by Ga (D) 7: Z1 ← push all A(i) top half 8: Z2 ← push all A(i) bottom half 9: else 10: Z1 ← map first half of all Z 11: Z2 ← map second half of all Z 12: end if 13: D1 ← retrieve D copy with Z1 removed 14: D2 ← retrieve D copy with Z2 removed 15: return (D1,D2) 16: end function

Algorithm 3 Generation and Update of Evolving Position Data Based on Graph G. 1: Input Ru // as newly generated positions 2: Map (Ru) 3: Gu(i) ← updated set of cell gi,j when overlaps occur 4: Gi,j ← Update (Gu(i) // Compute rectangles overlaps gi,j Gu(i) do 5: for 6: for V(ri) gi,j … Vi,j when new ei ← r do 7: p(i) ← Loc.LinearlySweep V(ri) // Table 1 in-memory (V(NV(ri) update of tree at O(n) space with exec(t) of O(n log n) 8: P’(i) w ← P(i)w = argmax i ∩R(i,j)P(i) w 9: end for 10: end for 11: return P’(i)w

Algorithm 4 Training Maximised Weighted-Sum of Evolving Position Data Require: Execute Algorithm 1 & 2; line 1−8 of Algorithm 3 1: for r R new do 2: if gi,j ← r then 3: gi,j.w ← gi,j.w + r.w r 4: R ← R 5: end if 6: end for gi,j 7: g ← gi,j | P’(i) 8: if P’(i) obsolete (g ← null) then 9: g ← argmax gi,j G ∩ R P’(i)w 10: end if 11: ComputeOverlap(g) 12: P’(i)w ← ComputeExact.W(P’(i),g) gi,j G \ g do 13: for 14: if gi,j . w > P’(i).w then 15: ComputeOverlap(gi,j) 16: end if 17: end for 18: if gi,j . w > P’(i) then 19: P’(i)w ← ComputeExact.W (P’(i), gi,j) 20: end if 21: Compute Top k Weighted-Sum (P’(i)w) 22: Execute lines 1 → 6 23: Gu(i) ← gi,j | P(i)w P (n)w is in gi,j 24: if Gu(i) = null (obsolete of all spaces in P(n)w) then G ∩ R 25: Gu(i) = argmax gi,j 26: end if 27: ComputeOverlap(Gu(i)) 28: P’(i)w ← ComputeExact.W(P’, Gu(i)) G \ Gu(i) do gi,j 29: for 30: Execute lines 14 → 19 31: end for 32: return P’’(i)w

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4 Experiment The algorithm was implemented using Python 2.7, on a Personal Computer having Intel Core i5, CPU 4.1 GHz, and 8.00 GB RAM, with Windows 10 OS. A higher capacity machine will contribute to fast the query result. Setup: For the dataset: GeoLife [18] dataset consists of social networking of users whiles moving outdoor. This was used as an example of a continuous real-life massive dataset appropriate for this work where the spatio-temporal data on the mobile user is identified, such as place of visit, activities, and time of visit. The position data in the datasets existed over a vast location. Hence, positions that exist around main points of location of interest were selected. The cardinality was about 3,662,876. The data was sorted in the order of generation time. The range of each coordinate was normalized to [0, 1000000]. The weight of the position is a real-value randomly chosen at intervals [0, 1000]. For the numerical values and parameters, the size of the rectangles used by default was set as 1000 × 1000. The other parameters are defined in Table 2. Evaluation: Comparatively, we choose G2 algorithm with the proposed algorithm for the simulation and evaluation. The G2 was appropriate due to its synonymous in approach and study. Synonymously, it consists of a general framework for monitoring objects in spatial data streams based. The details can be found in work [3]. Due to the massive size of the dataset and computing resources, other similar approaches were not attempted. Though the proposed grid index data structure provided in Fig. 5 is better for this purpose than those reported in literature due to their consideration of fixed length than continuous stream data. We noticed that the proposed algorithm performance was faster with respect to execution time, suggesting it as relatively more efficient for handling continuous maximized range-sum problems, including finding influential location of a mobile user. Using the parameters n, m, and d at a varying time defined in Table 2, the average computation time to update position data to observe the influential location is measured. The outcome is shown in the log-scale of the graphical result in Figs. 6, 7, 8, 9, and 10. The impact of the spatial positions denoted as k was tested. The computation time increases linearly with increased in position data. This led to several overlaps, Table 2 Experimental parameters

Parameter

Default

Variable

Indicator

n

50

100,250,500,750,1000

d

100

100,500,1000,1500,2000

m

0

50,100,200,500,800

tol

–

k

–

0.0,0.10,0.20,0.30,0.40 10,20,30,40,50

Data size Rectangle size Generation rate User tolerance Query answers

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0

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1

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1

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1

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0

1

0

1

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gi,j

Fig. 5 A schematic view of grid index data structure for maintaining any query answers on continuously evolving data. Through object-scheme, a list of object-points and adjacency matrix (i.e. AdjMatrix) a represented spatio-temporal trajectories of a user is derived from Table I. For each pair of positions data pi,j in a specific time instant, the AdjMatrix[i][j] and AdjMatrix[j][i] is set to either 1 or 0. This is illustrated by the grid cell (on the right-hand side). The pairs of data with an edge for any finite graph G do overlap (i.e. indicated as highlighted) or not overlap (i.e. indicated as not highlighted) in illustration on the left-hand side. An n x n matrix is derived as shown on the right-hand side as a grid data structure to cater for evolving graph, gi,j (i.e. g) or position data

G2

104

Running Time [msec]

Fig. 6 A running time against size of user positions. Showing the results on impact of n

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Users Positions Size [k]

and it over burdened the OverlapComputation(.) and ExactWeightComputation(.) of the algorithm. This implies that larger location size affects the queried results time. The observed result is shown in log scale of Fig. 6 upon generation of any new k position data. The running time is shown in log-scale. The impact of rectangle sizes d was tested. A skewed distribution was observed, where it was noticed there were more overlaps as the rectangles size get larger. There was a corresponding increase in running time of both algorithms. The running time is shown in log scale of Fig. 7. The impact of generation rate m of position data with respect to time was evaluated. It was noticed that less than 50 position data were generated per second for both algorithms. Hence, generation rate was further increased to examine how faster the

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Fig. 7 A running time against rectangle sizes. Showing the results on impact of d

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G2

Running Time [msec]

Fig. 8 A running time against user generation rate. Showing the results on impact of m

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800

Users GeneraƟon Rate [m] Fig. 9 An error rate against user tolerance. Showing the results on impact of tol

Error Rates [msec]

10 -3

10 -2

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10 0

0

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0.20

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algorithms well finish the execution. The result in running time is shown in log-scale of Fig. 8. The impact of user tolerance denoted as “tol” was evaluated in order to observe the error rate and computation time of executing queries. Thereby, TOL was varied to observe approximate results as shown in log scale of Fig. 9. It was noticed that the running time is indirectly proportional to “tol” where, the running time of the algorithm decreases with increases in “tol”. This suggests a trade-off between the efficiency of processing queries and quality of result obtained. Nonetheless, the practical error rates are quite minimal to suggest any significant deficiencies in the results produced by the proposed algorithm. The impact of top k position data to observe average computation time to update to p (i) w by varying k. It was observed that increasing k is directly proportional to the execution time in computing the newly results updates, and the result is given in Fig. 10. Instead of keeping to exact results, approximation was done via a user tolerance tol on the error rate to obtain a more practical and preferred result for real-life applications.

5 Conclusion This paper examines mobility records on a mobile user to inference the spatiotemporal data. The time bounded activities of the mobile user is used to aid to finding an influential location. Our work builds upon existing techniques including a grid-based index data structure, data-dependent multiple object classes and branch and bound. A data-driven approach with algorithm has been presented. Our experimental evaluation with GeoLife massive dataset has proven the proposed approach as efficient and also achieves updates on a new influential location. Acknowledgements This work was partly supported through the Sichuan Province Science and Technology Project [grant numbers 2014GZ0109 and 2015JY0178].

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References 1. Nai CC, Wanqin X, Jenny X (2017) Comprehensive predictions of tourists’ next visit location based on call detail records using machine learning and deep learning methods. In: IEEE 6th international congress on big data 2. Hussain MM, Trajcevski G, Islam KA, Ali ME (2017) Towards efficient maintenance of continuous MaxRS query for trajectories. In: 20th PEDBT, OpenProceedings.org, pp 21–24 3. Amagata D, Hara T (2017) A general framework for MaxRS and MaxCRS monitoring in spatial data streams. ACM Trans Spatial Algo Syst (TSAS) 3(1):1–34 4. Michael M, Maguelonne T (2006) Mining spatio-temporal data. J Intell Inf Syst 27:187–190. (Springer, LLC). https://doi.org/10.1007/s10844-006-9949-3 5. Renso C, Spaccapietra S, Zimanyi E (2013) Mobility data: modeling, management, and understanding. Cambridge Univ. Press, Edited. ISBN 978-1-107-02171-6 6. Wu R, Luo G, Shao J, Tian L, Peng C (2018) Location prediction on trajectory data: A review. Big Data Mining Anal 1(2):108–127 7. Paras M, Dimitrios S, Agnès, V (2015) Spatio-temporal keyword queries for moving objects. SIGSPATIAL’15, Nov 03–06, Bellevue 8. Zhang D, Lee K, Lee I (2018) Mining medical periodic patterns from spatio-temporal trajectories. In: International conference on health information science. Springer, Cham, pp 123–133. (LNCS 11148) 9. Sergey N, Bluma G, Wei J, Tehila M (2014) What, where and when: keyword search with spatio-temporal ranges. SIGSPATIAL ’14 Nov 04–07, Dallas/Fort Worth 10. Pengdong Z, Min D, Nico VW (2014) Clustering spatio-temporal trajectories based on kernel density estimation. ICCSA, part I, LNCS 8579, Springer Int’l Pub Switz, pp 298–311 11. Vinay B, Lakshmish R, Deepak M (2017) A spatio-temporal mining approach for enhancing satellite data availability: a case study on blue green algae. In: 6th international congress on big data, IEEE (2017). https://doi.org/10.1109/bigdatacongress.2017.37 12. Khan FH, Ali ME, Dev H (2015) A hierarchical approach for identifying user activity patterns from mobile phone call detail records. In2015 International Conference on Networking Systems and Security (NSysS). IEEE, pp 1–6 13. Ticana L, Coelho da S, Zeitouni K, de Macedo JAF, Casanova MA (2016) A framework for online mobility pattern discovery from trajectory data streams. 17th ICMDM, IEEE 14. Gopi CN, Olfa N (2017) Clustering data streams with adaptive forgetting. In: IEEE 6th international congress on big data. https://doi.org/10.1109/bigdatacongress.2017.72 15. Marshall AW, Olkin I (1960) Multivariate chebyshev inequalities. The Annals of Mathematics and Statistics, 1001–1014 16. Jens C (1999) Branch and Bound algorithms–principles and examples. Mar 12, CiteSeerX 17. Nandy SC, Bhattacharya BB (1995) A unified algorithm for finding maximum and minimum object enclosing rectangles and cuboids. Comput Math Appl 29(8):45–61 18. Zheng Y, Xie X, Ma W-Y (2010) GeoLife: a collaborative social networking service among user, location and trajectory. IEEE DE. Bulletin 19. Tom Y, John JL, Trevor D (2009) Fast concurrent object localization and recognition. In: Proceedings of CVPR, IEEE 20. Imai H, Asano T (1983) Finding the connected components and a maximum clique of an intersection graph of rectangles in the plane. J Algo 4:310–323

Design of a Morphological Generator for an English to Indian Languages in a Declension Rule-Based Machine Translation System Jayashree Nair , R. Nithya, and M. K. Vinod Jincy

Abstract Morphology is a branch of linguistics that deals with the internal structure of words in a natural language. Any word in a natural language is comprised of one or more morphemes. A morpheme is a smallest linguistic unit that forms a word. A morphological analyzer is a tool that analysis a given input word and outputs its internal structure along with its different morphemes. Conversely, a morphological generator creates the possible word(s) given the morphemes. This paper presents a design of a morphological generator for an English to Malayalam and English to Hindi rule-based machine translation system using declension rules. Declensions also termed as inflections are the different variations or inflected forms of a particular word in a language. Morphological generator is an essential part in the machine translation process that creates inflected words from the root word according to the morphological rules of a language. Machine translation is the branch of computational linguistics that automatically translates human language to another. The language to be translated is labeled as source language (SL) and the language into which translation is done is termed as target language (TL). The declension rule-based machine translation is accomplished by using grammar rules according to the word inflections of the target language. The proposed morphological generator module is elucidated with its framework and each of its modules and their working are expatiated in detail. The input and the output to/from the module are also illustrated using examples. Keywords Morphology · Morphological generator · Machine translation · Declension rules · Rule-based machine translation · Morphological rules J. Nair (B) · R. Nithya · M. K. Vinod Jincy Department of Computer Science and Applications, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] R. Nithya e-mail: [email protected] M. K. Vinod Jincy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_24

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1 Introduction India, a populous country, is a multilingual nation ranking the world’s second highest number of languages after Papua New Guinea [1]. The fact being so, not every Indian is a polyglot. Apart from their native language, Indians, especially the people who live a pastoral culture, do not know English, even though it is one of the globally accepted languages. This language gap can be bridged with the help of a machine translation (MT) system that can translate English sentences to the native language. An MT system translates text from one language into another. The language from which translation is done is termed as source language (SL) and the language into which translation is done is termed as target language (TL). The SL in this context of study is English and the TL is an Indian Language (Hindi or Malayalam). There are two approaches to machine translation—empirical and rule based {Refer Sect. 2.1}. The focus of research in this paper is that of a rule-based MT system (RBMT). In an RBMT, linguist rules are applied for translation. The linguistic rules used in this study are declension rules. Declensions are inflections of words in a language. As an , , are the various example, the words രാമൻെറ, in Malayalam. A declension rule-based MT system inflections of the word uses declension rules for the translation process [2]. There are different modules of a declension RBMT system that support the process of translation, {Refer Sect. 2.5}. The primary objective of this paper is to introduce a design for a morphological generator, a machine translation submodule that generates words in the target language based on declension rules. A morphological generator (MG) {Refer Sect. 2.3} is a fundamental module needed in machine translation to generate target language words during translation. It is the procedure of producing new words by combining morphemes especially a root and feature values (affixes) [3]. An MG in an MT system can use a lexical dictionary to get the root word of the TL. A multilingual lexical dictionary {Refer Sect. 2.4} can also be used to get translation for a word in various languages. The proposed MG submodule of the MT system presented in this paper generates words from English to Malayalam and English to Hindi based on declension rules.

2 Literature Review Natural language processing (NLP) is a branch of computer science, information engineering, and artificial intelligence that is concerned with processing, analysis and automatic synthesis of natural languages. Computational linguistics (CL), a subbranch of NLP, is an interdisciplinary convergence between linguistics and computer science that deals with the computational aspects of a natural language [5]. Machine translation (MT) is a study under CL that is concerned with an automatic or semi-automatic translation of a natural language into another [2]. MT is the process of translating text from source language to target language with or without

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human intervention [6]. It helps people from different background to understand an unrecognized language without an individual’s support [7].

2.1 Machine Translation An MT system translation converts a natural language to another. A rule-based machine translation (RBMT) system generates output based on morphological, syntactic, and semantic analysis of both the source and the target languages, whereas statistical machine translations do the process by applying empirical methods based on huge collection of parallel text [9]. A morphological generator (MG) is an important component of an MT system that generates words of the target language during translation [11].

2.2 Morphology Morphology is a branch of linguistics that deals with ‘forms of words’ in a language [12]. It deals with the internal structure of words and their formations from morphemes. Morphemes are the smallest linguistic meaningful units in a language with a grammatical functionality [13, 14]. A morpheme is a morphological unit of language which is not further divisible. For example, the word ‘beautiful’ is formed from two morphemes ‘beauty’ + ‘ful’, which cannot be further divide. A word is formed by one or more morphemes. There are two types of morphemes: free and bound [15]. Free morpheme is a standalone independent word which has a meaning in the language. In the above example, ‘beauty’ is free morpheme. Bound morphemes are not standalone; they are combined with other morphemes to generate a meaningful word in the language [16]. They are usually the affixes. In the above example, ‘ful’ is a bound morpheme.

2.3 Morphological Generator A morphological generator (MG) in a machine translation system generates words during translation process. It combines a root word and a set of morphological rules to construct a new word. These rules are custom defined rules with the support of an expertise specific to the target language [10]. It must be designed to implement the different parts of speech such as nouns, verbs, adjectives, adverbs, etc. separately, because the morphological units to be enumerated depend upon the type of information. The most important role of a morphological generator is in suffix joining. The general format of the morphological generator is:

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Stem/root + suffixes = Word [11]. As compared with Hindi, Malayalam is an agglutinative and morphologically rich language [4]. As an example, the translations in Hindi and in Malayalam; and of the phrase ‘Of Seetha’ are for ‘from Seetha’ are in Hindi and in Malayalam. The above examples implicate that in Hindi, most prepositions of English are mapped with some Hindi postpositions whereas in Malayalam a new word is formed from the root word by attaching morphemes corresponding to the English prepositions.

2.4 Multilingual Dictionary A dictionary is an ordered collection of words and their corresponding meanings, usage, their origins, pronunciations, translation, etc. composed in a single resource. It can be designed in the form of a book or an electronic resource. A bilingual dictionary is a dictionary which translates words of one language to another language. A multilingual dictionary is used to get translation for a word in various languages. In it, the source language words have a one to many relations with the target languages ’ in Malayalam words. For example, the multilingual dictionary will produce ‘ ’ in Hindi for the English word “umbrella.” and ‘

2.5 Declension Rule Based MT System The context of discussion in this paper is that of a declension rule-based MT system. The linguistic rules applied in this translation are the declension or inflection rules of the target languages. The prepositions of the SL, i.e. of English, are mapped with inflectional rules of the target language. The following Fig. 1 depicts the architecture of the same [2, 8]. Figures 2 and 3 show the process of translation that corresponds to the architecture. The design of submodule ‘morphological generator’ is explained in detail in the following sections.

3 Design of the Morphological Generator Submodule The MG of the declension RBMT generates words with respect to the declension tags as in Fig. 3. Figure 4 explains the context in which MG submodule can be used. Figure 4 is explained as follows: • The input to the module is a phrase with a preposition or a declension tag (Fig. 3) that corresponds to a preposition followed by a noun. • The noun is given as an input to the lexical dictionary and the corresponding noun in the target language is fetched.

Design of a Morphological Generator for an English to Indian …

Fig. 1 Architecture of the declension RBMT system

Fig. 2 Translation process 1

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Fig. 3 Translation process 2—Morphological generator

• The suffix or the post position morpheme is obtained according to morphological rules from the preposition mapping table. • The morphological generator will combine the noun and the suffix, according to the algorithm in Fig. 6. The outcome from the morphological generator is back-transliterated and the result is obtained. The various submodules required for MG are explained in detailed in the following subsections.

4 Transliteration and Back-Transliteration After the dictionary word substitution as in Fig. 3, the words in the TL are transliterated to Roman (English) with respect to a transliteration scheme. Transliteration is a process in which characters in the source language is mapped to characters in the target language. The inverse process is known as backward transliteration. For produces ‘pa u sa tha ka m.’ The method used for translitexample, eration can be referred to as character to character mapping which transliterates with minute grammatical alterations on word order and morphology [17]. In this system, the transliteration scheme used is ITRANS for Malayalam. Transliteration is the primary step needed in English to Malayalam translation to make the process of morphological generator easier and efficient. Back-transliteration is the reverse process of transliteration in which the Romanized characters are mapped back to the original source language.

4.1 Algorithm for MG The algorithm used to generate words from morphemes is presented in figures Figs. 5 and 6 [3].

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Fig. 4 The context of MG submodule

• According to the algorithm, the input in this context is a noun (r) and the declension tag or a preposition, i.e. the desired feature value FV to be added to generate new word. • The supporting data sets are: Paradigm tables, Dictionary

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Fig. 5 Flowchart of the algorithm for MG

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Fig. 6 Algorithm for generating a word form from morphemes [3]

• The noun r is processed according to the paradigm table as word w, by appending the suffix derived from the paradigm table.

5 Paradigm Tables and Sample Output This section displays the sample paradigm tables and the expected output from MG module. • Fig. 7 shows the effect of preposition over few nouns in Malayalam. • Figure 8 presents some group of noun categories (NC) which act the same way in association with a preposition.

Fig. 7 The effect of prepositions over nouns in Malayalam

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Fig. 8 Some noun categories of Malayalam

Fig. 9 MG process with Hindi word

• Figure 11 displays the morphemes to be added with respect to the various noun categories or noun groups. • Figures 9 and 10 show the MG process with an example. • The complete process of MG when ‘of’ preposition is attached with a noun in Malayalam is shown in Fig. 12.

6 Conclusion Though there exist several MT techniques, which are mostly empirical, we have introduced an RBMT approach that is based on declension rules of the target language. In this paper, we have proposed only a design method for a morphological generator used in translating English to Malayalam and Hindi using declension rule-based translation. The implementation challenges are yet to be explored. This design can be

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Fig. 10 MG process with Malayalam word

Fig. 11 Sample paradigm table with respect .to. noun groups

Fig. 12 Complete process of preposition ‘of’ with few Malayalam nouns

easily adapted to extend changes and made more effective by defining more morphological rules. Eventually, this research can be expanded to plural nouns and verbs as well. The major difficulty faced in the study is to define rules for translation. Any word in English has multiple meaning, choosing the right meaning that suits

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the context was yet another challenge leading to many translated outputs. Subsequently, this conflict can be resolved efficiently by using word sense disambiguation module. This study only focuses on proper and common nouns; in future, this can be elaborated to all other parts of speech categories also. The design can be further upgraded to other Indian languages by creating new morphological rules suitable for those languages. The design is not an exhaustive one; more enhanced features can be incorporated to generate accurate output. As of now, the algorithm proffered in this paper has pivoted around English phrases only; however, the algorithm can be extended to incorporate translation of multiline documents as well.

References 1. https://economictimes.indiatimes.com/news/politics-and-nation/seven-decades-after-indepe ndence-many-small-languages-in-india-facing-extinction-threat/articleshow/60038323.cms 2. Nair J (2018) Generating noun declension-case markers for english to Indian languages in declension rule based MT Systems. In: Proceedings—8th IEEE international advanced computing conference, IACC 2018 3. Bharati A, Chaitanya V, Sangal R, Ramakrishnamacharyulu KV (1995) Natural language processing: a Paninian perspective. New Delhi: Prentice-Hall of India, pp 65–106 4. Ba P, Soman KP, Kumar MA (2018) A deep learning approach for Malay-Alam morphological analysis at character level. Proc Comput Sci 132:47–54 5. http://www.coli.uni-saarland.de/hansu/what_is_cl.html 6. Sindhu DV, Sagar BM (2016) Study on machine translation approaches for Indian languages and their challenges. In: 2016 international con- ference on electrical, electronics, communication, computer and optimization techniques (ICEECCOT). IEEE, pp 262–267 7. Rajan R, Sivan R, Ravindran R, Soman KP (2009) Rule based machine translation from english to malayalam. In: 2009 international conference on advances in computing, control, and telecommunication technologies. IEEE, pp 439–441 8. Nair J, Krishnan KA, Deetha R (2016) An efficient English to Hindi machine translation system using hybrid mechanism. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 2109–2113 9. Gopalakrishnan A, Sajeer K (2016) A survey on machine translation from english to malayalam 10. Kavirajan B, Kumar MA, Soman KP, Rajendran S, Vaithehi S (2017) Improving the rule based machine translation system using sentence simplification (English to Tamil). In: 2017 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 957–963 11. Jayan JP, Rajeev RR, Rajendran S (2011) Morphological analyser and morphological generator for malayalam-tamil machine translation. Int J Comput Appl 13(8):0975–8887 12. Matthews PH (1991) Morphology (cambridge textbooks in linguistics). Cambridge University, New York 13. Aronoff M, Fudeman K (2011) What is morphology?. Wiley, London, vol 8 14. Soman KP (2014) AMRITA_CEN@ FIRE-2014: morpheme extraction and lemmatization for tamil using machine learning. In: Proceedings of the forum for information retrieval evaluation. ACM, pp 112–120 15. https://www.ntid.rit.edu/sea/processes/wordknowledge/grammatical/whatare 16. http://vlearn.fed.cuhk.edu.hk/wordformation/internalstructure/freemorphemes/ 17. Kaur K, Singh P (2014) Review of machine transliteration tech- niques. Int J Comput Appl 107(20)

Automatic Design of Aggregation, Generalization and Specialization of Object-Oriented Paradigm Embedded in SRS B. N. Arunakumari and Shivanand M. Handigund

Abstract The software requirements specification (SRS) is the initial input to any software development process. This SRS document is lustrated gradually using software development lifecycle (SDLC) stage activities from highly flexible million word English language document to lowly flexible hundred word programming language software in the direction from individual human understanding to general human and machine understanding. The correctness and completeness of the process lies in the maintenance of richness of the intermediate stage-specific languages along with their introrse semiotics and the naturalness. The evolution of the richness of analysis stage languages in object-oriented paradigm gives rise to the new interrelationships. This paper attempts to utilize this raison detre to design automated tools for the extant generalization, specialization and aggregation interrelationships of object-oriented paradigm. Keywords Aggregation · Association · Combination · Permutation · Richness of language · Semantics · Semiotics · Superclass (Generalization) · Subclass (Specialization) · Syntactics

1 Introduction The goal of this paper is represented in the state-of-the-art form of vision, mission and objectives as follows

B. N. Arunakumari (B) Department of Computer Science and Engineering, BMS Institute of Technology and Management, Bengaluru 560 064, India e-mail: [email protected] S. M. Handigund Department of Computer Science and Engineering, Cambridge Institute of Technology, North Campus, Bengaluru 562 110, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_25

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Vision: To design automatic tools to abstract the interrelationships of object-oriented paradigm from SRS. Mission: To design ameliorated automated tools for the object-oriented paradigmatic language interrelationships embedded in SRS using the maintenance and evolution of richness across paradigmatic languages and ease of their use. Objectives: • To continually lustrate the SRS towards human and machine understanding through the introrse semiotics of tabular and algorithmic languages with maintenance of their richness. • To desiderate additional interrelationships with the evolution of paradigmatic shifts towards naturalness. • To develop methodologies for automatic abstraction of superclass-subclass hierarchies and aggregations from SRS through the maintenance of equipollency between features and facilities. • To inculcate the mathematical rigour in analysing stage to develop design tools for superclass-subclass hierarchies and composite and shared aggregations.

1.1 Motivation In the advancement of SDLC stage activities, the maintenance of richness and the reduction in semiotics of intermediate languages are inevitable in moving highly flexible million word English language SRS to lowly flexible hundred word programming languages software. The correctness and completeness of the ensuing software depends on the maintenance of richness of the intermediate paradigmatic languages. The evolution of their richness along with their comprehensive introrse semiotics (some semantics of previous stage activities are transformed to syntactics of consecutive next stage activities) present in class diagram led to complete removal of null values (part of good database design principles) and efficacious realization of good software engineering principles. This evolution process leads to the design of automated methodologies for abstraction of superclass-subclass hierarchies and composite and shared aggregations of OOP.

2 Literature Survey The process models pedestalled on SDLC stage activities have been used to develop appropriate software for the SRS [1–4]. The lifecycle means ‘the series of changes in form undergone by an organism in development from its earlier stage to the recurrence of the same stage in the next generation’ [5, 6]. This implies the software development process should consume input from its immediate previous stage documents for current stage activities and should provide documents to the next stage activities. The

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process recurs till the end of the lifecycle. Here, the lifecycle starts with SRS document produced from highly flexible million word English language to the software produced using one of the lowly flexible hundred word programming languages. The number of atomic words refers to syntactics and the number of ways of their organization (flexibility) refers to semantics of the stage language. Each intermediate stage produces amphisbaenian (compatible) document/s between stage-specific languages. The realization of ‘lifecycle’ indicates the stage-specific languages are formed with stratiform introrse semiotics. In this paper, we have created the stage documents along the lifeline of the SDLC stages, wherein the documents between the stage seams are developed to satiate the amphisbaenian languages of adjacent stages and these stage documents should proceed progressively in the direction from collection of individual requirements to general human and machine understanding. In addition, the stage representational languages should continually lustrate with introrse semiotics without losing their richness determined on strong foundation of optimized paradigm structure. The developers of object-oriented paradigm (OOP) have identified three types of interrelationships, viz. association, aggregation and generalization and specialization [7–10]. The generalization and specialization and aggregations are the new type of interrelationships additionally intruded in object-oriented paradigm. A paradigm normally defines the structure of memory for a business process in an architectonic way. The cuibono analysis reveals that introduction of two additional interrelationships, viz. generalization and specialization (super and subclass hierarchies) and composite and shared aggregations to OOP is due to distinguished features available in OOP and not present in other paradigms [11]. These distinguished features are elimination of null values and high securities for class components. The business process undergoes changes in methods of the methodology, the structure and number of work process (in the form of live classes) and the quality attributes of the intermediate and end products. However, the nature of the business process remains the same. Therefore, the introduction of new interrelationships should be at the cost of new features. The cuibono study reveals that the structural and behavioural organizations and the content of attributes have undergone stratiform changes. In the conventional data processing, the attributes are combined to represent entity. The end user defines entity as structural part representation and designs the algorithm using his/her human skills, and thus, there exists scope for redundancy and inconsistency in both structural and behavioural aspects. In the next startiform level paradigm, various database management systems (DBMSs), viz. network DBMSs, relational DBMSs, object network DBMSs have been developed wherein the structure for the entire business process and the structures allocated for the different end users have been defined by DBA (software developer) and the end users need to develop the customized behavioural aspect [12–14]. The structure is optimized by incorporating good database design principles [13]. The allocated structural part for each end user is either different or the same with different synonyms. Since there is no standard methodology for resolution of synonymous and heteronymous names, the lapse in their resolution is somewhat minimized

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through minimization of null values. Since appropriate behaviour is to be coded by the authorized end user, the redundancy in the behavioural part persists. In OOP [11], both structural and behavioural aspects are optimized through the introjection of good database design and good software engineering principles [4]. Both structural and behavioural aspects are designed by the developer, and the role of each end user is limited to establishing architectonic syllogism between appropriate object methods and their authorized class structures. Since we are syllogising object methods containing random attributes, there is no scope for null values. However, the business process may contain null values for some attributes for which either values are not known or are irrelevant. This labyrinth is resolved satisfying both business process and the OOP (structure of class diagram). The developers have resolved this created labyrinth by defining superclass-subclass hierarchy [7–9, 15] but not on the pedestal of sound logic or nor features to be adopted. Moreover, aggregation interrelationship clusters the object classes into whole-part classes to derive optimal benefits. The whole-part relationship is normally one-tomany association and part-part relationship is many-to-many. In one-to-many or many-to-many interrelationship in relational DBMS is defined by primary key and foreign key concept [13]. A foreign key is the primary key of ‘one-interrelationship’ that participate in number of objects of ‘many interrelationship’. Therefore, the primary key of ‘many relationship’ classes contains the distinguished attributes of that class along with primary key of ‘one relationship’ class that participate as foreign key, although objects of the class cannot be identified by the primary key. Here, the task is to abstract the interrelationship from SRS. Therefore, while constructing objectoriented aggregation, we consider primary key and foreign key concept to appropriately modify to suit object-oriented paradigm. Part of the object class though it is independent, it contains primary key of whole class. Therefore, primary key of part class contains primary key of whole class to which it is associated and distinguished key of itself, where whole class contains the primary key of itself. The object method of whole-part and part-part contains referential attributes of subset of cluster of object classes are associated outside the cluster. The use of public visibility for attributes and object method unnecessarily enhances scope of their utility. Jeopardizing the security of utilization is giving scope to information loss. This has to be considered in tightening the visibility. Though unified modelling language (UML) developers have defined and classified the aggregation as shared and composite aggregations [7–9], no methodology has been developed to design these interrelationships from the classes abstracted in SRS. Strangely, the visibilities specified for attributes and methods unnecessarily enhance the scope of their utilities across the aggregation chain jeopardizing the security. This spoils the very purpose of their introduction. The literature [10, 16] specifies that aggregation is special case of association with involved ‘multiplicity’. This statement is derision of foppishness on two grounds as this is not a special case. The first is whether it means multiple associations of a class with other classes. The second is participation of attributes in multiple object methods. In both cases, the so-called aggregation does not desiderate any additional support. The ‘roles’ of object classes are represented in different object methods of classes. The response to query specifies sequence of object method and

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can be performed by choosing defined attributes in the object methods that may be independent of aggregation. This led to cuibono study of aggregation to effectively channelize aggregation for fruitful purpose for software development. In [7–11, 16], authors have failed to design the cluster of aggregation from software requirements specification. In this paper, we have attempted to develop automated methodologies for superclass-subclass hierarchy and composite and shared aggregations from the data flow table (DFT) an appropriate introrse version of SRS. Since the general methodologies for superclass-subclass hierarchy and aggregations are limited to structuring the attributes, we have considered only the subjective and objective attributes of each statement in the form referential and definitional attributes and represented the content of SRS in the referential definitional table as per [17]. The information normally flows from definition to their references. Thus, the interrelationships between the definitional and their referential attributes should satisfy reaching definition [18]. Further, the referential and definitional entries are clustered to form an activity (object method). Therefore, the entries of referential-definitional table entries are reorganized to form concinnities of activities; atomic units of any of the three perspective views of the business process [19] and within each concinnity cluster of activity, the entries are reorganized to satisfy the reaching definitions. The so formed referential-definitional table, there may exist redundant activity clusters of entries as these may have been embedded in multiple other activities. The control flow graph (CFG) of SRS is designed with activity clusters as vertices and the flows between activities as directed edges. Further, to enable the machine to understand the graph, the graph is represented in the form of four columns (statement numbers of start of the vertex, end of the vertex, alternate target jumps) control flow table (CFT) as per [17, 20]. This CFT is used as tool in streamlining the definitional referential attributes of SRS in the form of syllogised entries. This blend of CFT and the definitional-referential attributes is represented in DFT on the lines of [17, 20]. This DFT is the introrsed semiotics version of SRS deleting superfluous other words of SRS statements. The verbs of SRS statements are implicitly incorporated in algorithms. The DFT (refined version of SRS) is the input for our methodology. Thus, structural redundancy is eliminated through the design of our newly innovated algorithm that solely depends on SRS. The redundancy in the behavioural aspects is eliminated through the design of object methods for which we have used slicing technique [21, 22].

3 Proposed Methodology Software development starts with collection of all requirements of client’s business process. The gathered requirements contain the activities of each user of client organization. Normally, the end user specifies the requirements in one’s own perspective view using terminologies, depending on his/her cultural background and routine work environment. The gathered requirements contain synonymous and heteronymous syntactics in an unorganized sequence [23]. Therefore, gathered requirements

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involving synonymous and heteronymous cannot be wholly understandable by the developer’s team, as it involves apart from synonymous and heteronymous issues, the order of the activities is not in any perspective views of the business process, i.e. usecase, work process and work [19]. Therefore, this document needs to be refined by eliminating lacunae in the form of SRS. In this paper, an attempt is made to refine the gathered requirements to SRS through justified guidelines set (well formed forms) as briefed in Sect. 3.1. The refined SRS is used to create DFT, an introrsed semiotic version of SRS. This DFT document acts as amphisbaenian document to requirements gathering and analysing stage. From analysing stage and onwards till the end of life cycle the richness of the stage-specific languages is maintained along with startiform decrement in syntactics and semantics of stage-specific languages. The richness of the languages evolves from conventional data processing to OOP and corresponding enhanced facilities and are detailed in Sect. 3.2. The resolution of appropriate limitation issues of consecutive poorer paradigm (conventional data processing, DBMSs) gives rise to design of superclass-subclass hierarchies and composite and shared aggregation of OOP as detailed in Sects. 3.3 and 3.4. The correctness and completeness of the software requires the constant maintenance of the richness of the stage languages along their reduction in syntactics and semantics. This leads us to define richness of the language as  Richness of language =

(syntactics−(synonym − heteronym)) semantics

 (1)

Therefore, the mathematical rigour automatically reduces syntactics and semantics of the language without dampening pragmatics which achieves near naturalness. This impels us to introject mathematical rigour in all methods of stage activities.

3.1 Guidelines for Lustration of Gathered Requirements i.

Convert multi-statement sentence into single statement sentence. Justification: the machine understandability requires the each sentence should be coverable to single statement to enable the arithmetic logic unit to implement an action at a time. ii. Convert passive voice statement to active voice statement. Justification: the ensuing software contains transitive verb with subjective and objective attributes. The conversion limits the semantics to either declarative or imperative statements which contain subjective and objective attributes in addition to transitive verbs and hence the richness of the language enhances. iii. Eliminate GOTO statements. Justification: GOTO statements spread across gathered requirements results in overflowed or underflowed information flow. This dampens the correctness and completeness of the information flow.

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iv. Transform third-person nouns into immediate corresponding respective subjective, objective and possessive attributes or their values. Justification: third-person nouns cannot be allocated for memory spaces in primary area. v. Assign consecutive numbers to each statement. Justification: this facilities the tracking of original sentences of SRS. The refined SRS is used to abstract the quintessential referential (subjective) and definitional (objective) attributes of a statement and stored in the form of referentialdefinitional table as per [17]. Each cluster of statements (cluster of entries of the referential definitional table) of each activity of each end user is collapsed into a single vertex. These vertices are joined by edges based on perspective views of the business process, viz. usecase, work process and work view. Each of these perspective views is designed by joining the appropriate vertices by edges to form control flow graph for each perspective view of the business [19]. The designed graphs are stored in appropriate control flow tables of entry numbers considering the iterations recursions of the activities. Using one of the control flow tables and the referential definitional entries, the data flow table (DFT) is formed that virtually represents the SRS. The DFT serves as amphisbaenian document between the requirements gathering and analysing stages.

3.2 Evolution of Richness of Language The input is DFT (tabular version of data flow graph of SRS). The stagespecific languages are tabular and algorithmic languages. Tabular language depicts tableaux of information system; algorithmic language depicts standard user-defined behavioural models. Syntactics of paradigmatic languages are same but semantics differ are the ways of organizing the syntactics for meaningful purpose, i.e. the number of meaningful arrangements of the language syntactics.

3.2.1

Conventional Data Processing Paradigm

a. Tabular language: • Syntactics: Attributes and entities (names of person, place, thing and event about which information is required) • Semantics: Number of arrangement of functionally dependent attributes of an entity. Interrelationships between different entities of the information system. The semantics equal to the number of organization of the tables. • Pragmatics: Number of perspective views of the business process involved in the information system. Here, pragmatics = 1. If n is the total number of attributes in a tableau, then the number of semantical arrangement is equal to n Pr , where r is the number of attributes that forms the primary

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key. If n is the total number of syntactics, then the richness of paradigm is given by Eq. (2).  (1) ⇒ Richness of language = K  (1) ⇒ K

n × (n − r )! n!



 =K

n



n! (n−r )!

1 (n − 1)(n − 2) · · · (n − r + 1)

 (2)

Limitations: i.

In this language apart from primary key attributes determining the non-key attributes, some non-key attributes determine other non-key attributes. This leads to insertion, deletion and updation anomalies. ii. The superfluous attributes create redundancy that leads to inconsistency iii. There is no security for stored data. b. Algorithmic language • Syntactics: Verb, referential (subject) and definitional (object) attributes • Semantics: Statements that determine definitional attributes from referential attributes with standard/application verbs. • Pragmatics: The body of semantic statements from attributes of an actor/some entities that defines other entity/actor attributes. It varies with business application. Limitations: i. Each end user chooses his/her own algorithms and the statement as a result increasing redundancy. The ensuing software uses end user organization of algorithms embedding his/her own statements. The software is not unique for the same task.

3.2.2

Database Management Systems (DBMSs)

a. Tabular language • Syntactics: Attributes and relation/record • Semantics: Number of arrangement of functionally dependent attributes ignoring the order of the position (one of the relational DBMS principles). Arrangement of these relational attributes without the scope of possible anomalies, i.e. number of arrangements satiating Armstrong axioms. • Pragmatics: Number of subschemas depending on number of work processes (is cluster of activities that human performs which are different from other cluster of activities) of the business organization.

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If n is the total number of attributes in a tableau, then the number of semantical arrangement n Pr is transferred into n Cr . As a result, the richness enhances as shown in Eq. (3). If n is total number of syntactics then (1) implies as follows  (1) ⇒ Richness of Language = K  ⇒K

n × (n − r )!r ! n!



 =



n n! (n−r )!r !

r! (n − 1)(n − 2) · · · (n − r + 1)

 (3)

Richness of language through Armstrong axioms as follows. Axiom 1 X ⊂ Y then Y → X where X ≤ Y . If n attributes are organized into cluster of X attributes that is n C X , then Y → X means cluster of Y is organized into n CY . n! (n − X )!X ! n(n − 1) · · · (n − Y )(n − Y − 1) · · · (n − X ).. Semantics = (n − X )!X ! n(n − 1) · · · (n − Y )(n − Y − 1) Semantics = X! X ⊂ Y, Semantics =n C X =

(4)

Y → X, Semantics =n CY n(n − 1) · · · (n − Y )(n − Y − 1) n! = Semantics = (n − Y )!Y ! (n − Y )!Y ! n(n − 1)(n − 2) · · · Semantics = Y!

(5)

Substitute and equate (4), (5) in (1). If n syntactics then (1) implies as follows  (1) ⇒ K  ⇒K



n n(n−1)···(n−Y )(n−Y −1) X!

n ∗ X! (n − Y )(n − Y − 1)

 =K



 =K



n n(n−1)··· Y!

n ∗ Y! 1



+K



n ∗ X! (n − Y )(n − Y − 1)

 ∵X ⊂Y

⇒ RHS > LHS, i.e. the richness of language is enhanced by introjection of Y → X functional dependency which embeds X ⊂ Y . Axiom 2 X → Y then XZ → YZ. If n attributes then semantics are organized as follows X → Y then semantics =n C X +Y

X +Y

CX

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n! 1 n! (X + Y )! = (n − X − Y )!(X + Y )! Y !X ! (n − X − Y )! Y !X ! n(n − 1) · · · (n + 1 − X − Y ) Semantics = X !Y !

Semantics =

(6)

X Z → Y Z , then semantics =n C X +Y +Z X +Y +Z C X +Z n! 1 n! (X + Y + Z )! = = (n − X − Y − Z )!(X + Y + Z )! Y !(X + Z )! (n − X − Y − Z )! X !(X + Z )! n(n − 1) · · · (n + 1 − X − Y − Z ) Semantics = (7) (X + Z )!X ! Substitute and equate (6), (7) in (1). If n syntactics then (1) implies as  ⇒K



n n(n−1)···(n+1−X −Y ) X !Y !

 =K



n n(n−1)···(n+1−X −Y −Z ) (X +Z )!X !

 n ∗ Y! ∵ X Z → Y Z embeds X → Y n(n − 1) · · · (n + 1 − X − Y )   n ∗ Y! LHS = K n(n − 1) · · · (n + 1 − X − Y )     n ∗ Y! n ∗ (X + Z )! +K RHS = K n(n − 1) · · · (n + 1 − X − Y − Z ) n(n − 1) · · · (n + 1 − X − Y ) 

+K

⇒ RHS > LHS, i.e. the richness of language is enhanced by introduction of X Z → Y Z functional dependency which implicitly contain X → Y . Axiom 3 X → Y Y → Z then X → Z . If there are n attributes, then semantics are organized as follows Semantics =n C X +Y +Z

X +Y

C XY +Z CYX +Z C X

(8)

X → Y Y → Z , then Semantics =n C X +Y +Z

X +Y

C X Y +Z CY

(9)

Equate (8) and (9) n

C X +Y +Z

X +Y

C X Y +Z CY

X +Z

C X =n C X +Y +Z

⇒Y +Z CY X +Z C X =Y +Z CY (Y + Z )! (Y + Z )! (X + Z )! = ⇒ Y !Z ! Z !X ! Y !Z !

X +Y

C X Y +Z CY

Automatic Design of Aggregation, Generalization …

LHS semantics =

269

(X + Z )! and RHS semantics = 1 Z !X !

(10)

Substitute (10) in (1) if there are n syntactics then (1) implies as follows  (1) ⇒ K

n ∗ X !Z ! (X + Z )!

 =K

n  1

 +K

n ∗ X !Y ! (X + Z )!

 ∵X →Y Y →Z ⊇X →Z

⇒ RHS > LHS, i.e. the richness of language is enhanced by functional dependencies X → Y , Y → Z which embeds X → Z . Richness of the paradigmatic language through the implementation of (Axioms 1, 2 and 3) ≥n Cr ≥n Pr . Therefore, DBMSs achieve mediocre richness. Limitations: i. Null values cannot be incorporated meaningfully. ii. Security cannot be provided for attributes of intermediate relations between two clusters of the relation. b. Algorithmic language • Syntactics: Verb, referential (subject) and definitional (object) attributes. • Semantics: Modular behaviour defining the attributes of single relation. • Pragmatics: The sequence of modular behaviour of a work processes. Limitations: i. Compromise with security. Data security can be achieved to some extent but cannot share the secured data or behaviour across cluster of tables. It represents only one work process perspective view out of three perspective views of the business process.

3.2.3

Object-Oriented Paradigm (OOP)

In OOP, the tabular and algorithmic languages are blended together to form single language. a. Tabular and algorithmic language • Syntactics: Class, attributes, object method, visibility and signature • Semantics: (i) Number of arrangement of class attributes with functional dependencies (ii) association between different classes, superclass-subclass hierarchies and composite and shared aggregations. • Pragmatics: Class diagram, usecase, work process and work. Consider there are N = n 1 ∪ n 2 ∪ n 3 · · · ∪ n m total number of attributes. By introduction of superclass-subclass hierarchy, the semantics of the language is

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Semantics =n Cr +n 1 −n Cd1 +n 2 −n Cd2 + . . . +n m −n Cdn , where r ∈ key attribute of superclass, i.e. key = 1 and d ∈ distinguished key of subclasses, i.e. d = 1 and (n 1 − n) indicate null values removed from original attributes. Semantics ⇒ n + n 1 − n + n 2 − n + n 3 − n + · · · + n m − n ⇒ n 1 + n 2 + n 3 + · · · + n m − kn

(11)

Substitute (11) in (1), if there are n syntactics then (1) implies as follows  Richness language = K

n n 1 + n 2 + n 3 + · · · + n m − kn



Introduction of aggregation interrelationship the semantics of the language is as follows Semantics =n Cw +n 1 C p1 +n 2 C p2 + · · · +n m C p where w ∈ key attribute of whole class, i.e. w = 1, and p ∈ distinguished attribute of each part classes, i.e. p = 1. Semantics ⇒ n + (n 1 + n 2 + n 3 + · · · + n m )

(12)

Substitute (12) in (1), 

n Richness language = K n1 + n2 + n3 + · · · + nm + n



Richness of OOP = {Richness of superclass − subclass + Richness of aggregation} + (Axioms 1, 2 and 3) Richness of OOP   n n + =K n 1 + n 2 + n 3 + · · · + n m − kn n1 + n2 + n3 + · · · + nm + n +Axioms 1, 2 and 3) ≥ (Axioms 1, 2 and 3) ≥n Cr ≥n Pr . Limitations: i.

Availability of sciolistic procedure for superclass-subclass and composite and shared aggregations. ii. Non-availability of automated methodologies for the design of superclasssubclass and composite and shared aggregations. iii. Though object-oriented technology is regarded as near to naturalness on the ground of blend of structural and behavioural aspects, mathematical rigour which is major factor in authenticating naturalness is not used.

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Benefits: i. Richness of OOP proved through formulation of mathematical rigour that is near to naturalness as briefed in Sect. 3.2. ii. The quintessence of superclass-subclass hierarchies and composite and shared aggregations is provided through raison detre are detailed in Sects. 3.3 and 3.4. The evolution of richness is proportional to elimination of limitations as briefed in section B.

3.3 Procedure for Design of Generalization/ Specialization In OOP, objects are instances of specific classes with same structure. The null value content spoils the design by introducing redundancy and creating errors in the object methods. This can be avoided, if attributes contain non-null values for all objects are separated in the form of superclass and from the remaining attributes cluster of objects are created for each subset of non-null value attributes set. The so-called superclass attributes are blended with each subclass attribute to form superclass-subclass hierarchy. There exists one-to-many relationship between superclass and one of the subclasses. Therefore, some object methods define the common subset of attributes (super set attributes) along with subset of attributes of one of the subclasses. Naturally, the definition of object methods should be placed in appropriate subclasses. To avoid the redundant use of same attributes in number of subclass methods, the common attributes are defined in superclass with protected visibility. Thus, the structural redundancy is eliminated. In object methods subclasses that use superclass attributes some methods of different subclasses may have used same subset of superclass attributes. In such case, the behavioural parts that use common subset are placed in the superclass with visibility protected. The object method of the subclass that contains the code of superclass behavioural part is redefined with the utilization polymorphic effect of the superclass method. This avoids the redundancy in the behavioural code. This sound logic eliminates the sciolistic human skill dependent object methods with architectonic logic. The procedure for abstraction of superclass-subclass from DFT (copy of SRS) is as follows

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Input : Data flow table (copy of SRS) Output : Superclass-subclass hierarchies Method: Attributes Rij and Dik are respectively the referential and defintional attributes present in entry i. j and k are respectively jth and kth referential & defintional attributes present in the entry. In DFT, the referential & definitional attributes are separately organized in lexicographic order. //steps 1-3 identifies functional dependent set of attributes of class 1. for (i = 1 to last entry of DFT) 2. Read Rij and Dik where j = 1,….m and k = 1… n if (Rij < Dik) then (Rij – Dik) • i • i + 1; continue if (Rij = Dik, j < m & m < n) then j • j + 1; k • k + 1; GOTO step 2 else (j = m & m < n) GOTO step 3 if (m = n) i • i + 1; continue if (Rij > Dik & k < n) k • k + 1; GOTO step 2 else GOTO step 3 3. // Rij - Dik = • {Dik} forms attributes of a class Write an entry i to super DFT end (for i) //steps 4 identifies the superclass attributes and draft subclass attributes 4. Consider the super DFT k = 0, Dkp = , p = 1 for (i = 1 to last entry of super DFT) for (j = i + 1 to last entry of super DFT) if (Ri = Rj) then Dkp • Dkp ⋂ (Di ⋂ Dj) i • j; j • j + 1; k • k + 1 superclass attributes = Dkp Dp • Dkp else j • j + 1; end (for j) for (k =1 to last entry of super DFT) { Dk} - { Dp} • , are prone to be subclass attributes of superclass Dp. Store Dp & Dk attributes in general super set table then temporarily mark & delete Dp, Dk attributes in DFT for further processing within the for loop. k • k + 1; end (for k) i • i + 1; p • p + 1; end (for i) //steps 5-6 identifies defined attributes of subclass along with methods

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3.4 Procedure for Design of Aggregation Interrelationship Aggregation sequesters cluster of classes having same core group for business process or specific part of business process into two groups, one group containing ‘whole’ another group containing ‘part’ each one class is a complete class with primary key attributes and methods. Whole class is connected to all of its part classes where the primary key of whole class is used as one of the distinguished key attributes of the part class. Thus, primary key of the part class contains primary key of the whole class as a distinguishing and the distinguishing key of part class itself. Whole class can inherit the part class objects or aggregation of the part class attributes. Whole or part classes can themselves be independent classes connected through the association one-to-many. Part classes themselves be connected through association. The association between the part classes is similar to general association in which

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attributes of one or more part classes are referenced in defining the object method of a part class. The whole class method may or may not directly contain the part class values but contains aggregate cardinality of the specific attribute of part class. However, the distinguishing key of whole and part class together defines the part class key. The procedure for abstraction of aggregation from DFT is detailed below

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4 Conclusion Software is developed through stage-specific activities of SDLC stages. Each stage produces documents in the form of stage dependent languages. In the ‘Analysing’ stage, the richness of paradigmatic object-oriented language is highest and is to be maintained in subsequent stage-specific languages for the development of correct and complete software. The evolution of richness in stratiform way from conventional data processing to object-oriented paradigm has provided the raison detre for the introduction of superclass-subclass hierarchies and shared and composite aggregations. The stratiform ease of paradigmatic languages provides base for automated tools that design superclass-subclass hierarchies and composite and shared aggregations, which have been presented in the form of algorithms. Acknowledgements The author is ever indebted to Professor Deepak. B. Phatak of IIT Bombay for his indelible encouragement and moral support as role model.

Appendix Taxonomy Referential attributes: The nouns or noun phrases which are used in a statement that may define other nouns or noun phrases without undergoing any modification of their own values during the presumed implementation of the statement. Definitional attributes: The nouns or noun phrases present in the statement and incorporates new value after the realization of the statement. Syntactics: The syntactics relates the tagmemes (atomic syntax of the language) and their meanings. Semantics: The semantics organizes the tagmemes into a meaningful group as per the semantic rules. Pragmatics: Pragmatics is the architectonic way of representing a perspective view of the information system, viz. usecase, work process and work. Semiotics: It is a group name for syntactics, semantics and pragmatics. Each language (spoken, design and programming) has well-defined syntactics, semantics and pragmatics. Object method: It is a behavioural thread of the object class in which subset of attributes of that class is defined. Interrelationship: It is the logical connection of objects of a class over the objects of another class. Functional dependency: It is the dependence of attribute value on a set of attribute values. A set of attribute values are independent variables and attribute values on left-hand side is dependent variable.

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Table 1 Data flow table Stmt no

Referential attribute

Definitional attribute

Relevant attributes

Relevant statements

Paradigm: It is a language where input and output operand syntactics and the operator semantics lies within in language. Example:-OOP, DBMSs, relational algebra, number system, etc. but integer system is not a paradigm. Entity: An entity is a name of place, person, thing, concept or event about which information is processed. Control flow graph (CFG): Graphical representation information flow of SRS in which each statement represented as a vertex and the information flow to the next consecutive statements as a directed arrow. However, the SRS statements are voluminous and the CFG may extend to several pages. To avoid this, we collapse sequence of nodes where logical and physical consecutiveness is the same into a single vertex. Data flow graph (DFG): It is the data version of the CFG wherein each vertex is replaced by either SRS statements or range of statements as the case may be. Data flow table (DFT): Data flow table is the digital version of the data flow graph as shown in Table 1. RC : Relevant attributes of entry, initially for slicing the statements we consider relevant attributes for all vertices as null except for the last vertex for which we consider the set of all definitional attributes. We compute the relevant attributes from last to first (reverse direction) by [21, 22] as follows / def(u)} ∪ {w|w ∈ re f (u), def(u) ∩ RC (v) = ∅} RC = RC (u) ∪ {w|w ∈ RC (v), w ∈ (13) The relevant statement SC for RC is computed as SC = {u|def(u) ∩ RC (v) = ∅}, u →CFG v

(14)

Slice: Slice is a realizable subset of SRS statements which define the attributes of the last vertex of the slice.

References 1. Ruparelia NB (2010) Software development lifecycle models. ACM SIGSOFT Softw Eng Notes 35(3):8–13 2. Shah US (2016) An excursion to software development life cycle models: an old to ever-growing models. ACM SIGSOFT Softw Eng Notes 41(1):1–6 3. Pressman RS (1982) Software engineering–a practioners approach (Ch 3 and Ch 4), 6th edn. McGraw Hill International, New York, pp 105–121

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4. Pankaj J (2011) Software engineering, a precise approach. 1st edn. Wiley, India, ch 2, pp 9–26 and ch 6, pp 103–119 5. The new international Webster’s comprehensive dictionary of the english language, Deluxe encyclopedic edition, Trident press International edn, p 736 (2004) 6. Dictionary IO (2006) Dorling Kindersley Limited and Oxford University press, p 467 7. Blaha M, Rumbaugh J (2007) Object-oriented modeling and design, 2nd edn, Pearson Education, ch 3, pp 27–40 8. Booch G, Rumbaugh J, Jacobson I (2004) The unified modeling language reference manual, 2nd edn. Addison Wesley Professionals, ch 4, pp 72–78 9. Booch G, Maksimchuk R, Bobbi ME (2007) Object-oriented analysis and design with applications, 2nd edn. Addison Wesley Professionals, ch 3, pp 97–103 10. Eriksson H-E, Penker M, Lyons B, Fado D (2003) OMG UML 2 Toolkit, vol 26. Wiley, London, ch 4, pp 99–125 11. Capretz LF (2003) A brief history of the object-oriented approach. ACM SIGSOFT Softw Eng Notes 28(2):1–10 12. William TO (1978) The CODASYL approach to database management. Wiley, London 13. Silberschatz A, Korth HF, Sudarshan S (2010) Database system concepts, 6th edn. McGrawHill, New York, ch 1, pp 27–28, ch 2, pp 45–46, ch 8, pp 327–359 14. Handigund SM, Maknur SG (2015) Integration of object oriented host program with network DBMS procedia of computer science, vol 62, Elsevier, pp 187–185. https://doi.org/10.1016/j. procs.2015.08.435 15. Bekaert P, Delanote G, Devos F, Steegmans E (2002) Specialization/generalization in objectoriented analysis: strengthening and multiple partitioning. In: Bruel J-M, Bellahsene Z (eds) Proceedings of the workshops on advances in object-oriented information systems (OOIS ‘02). Springer, London, pp 34–43 16. Albert M, Pelechano V, Fons J, Ruiz M, Pastor O (2003) Implementing UML association, aggregation, and composition: a particular interpretation based on a multidimensional framework. In: Eder J, Missikoff M (eds) Proceedings of the 15th international conference on advanced information systems engineering (CAiSE’03). Springer, Berlin, pp 143–158 17. Handigund SM, Arunakumari BN, Chikkamannur A (2018) Automated methodology to streamline business information flow embedded in SRS. Adv Intell Syst Comput 709:333–341. https://doi.org/10.1007/978-981-10-8633-5_33 18. Rapoport M, Lhoták O, Tip F (2015) A precise data flow analysis in the presence of correlated methods. Springer, Berlin, pp 55–71. https://doi.org/10.1007/978-3-662-48288-9_4 19. Handigund SM, Shivaram AM, Arunakumari BN (2014) An ameliorated methodology to establish the analogy between business process perspective views and UML diagrams. In: International conference on advances in computing, communications and informatics (ICACCI), New Delhi, pp 231–237. https://doi.org/10.1109/icacci.2014.6968389 20. Handigund SM (2001) Reverse engineering of legacy COBOL systems, Ph.D. thesis, Indian Institute of Technology Bombay 21. Weiser M (1984) Program slicing IEEE transaction on software engineering 10(4):352–357 22. Silva J (2012) A vocabulary of program slicing-based techniques. ACM Comput Surv (CSUR). 44(3):1–41 23. Taghavi A, Woo C (2017) The role clarity framework to improve requirements gathering. ACM Trans Manage Inf Syst 8(2–3):1–6. Article 9. Doi: http://dx.doi.org/10.1145/3083726

Fuzzy Logic-Based Decision Support for Paddy Quality Estimation in Food Godown Chanthini Baskar and Manivannan Doraipandian

Abstract Food safety plays a very crucial role in health-related issues, while food wastage, in turn, affects the economy of the country. In this work, an expert system is proposed for investigating the process of paddy dispatched from food godown for public distribution. It consists of three input variables temperature, humidity, and alcoholic vapour and one output variable for determining the paddy quality. Based on the input variables, fuzzy interference system was designed using Mamdani rule base and centroid-based defuzzification system. The expert system will provide a recommendation for dispatching the paddy which is highly prone to degrade. The proposed system performance was validated using empirical results, and better performance was achieved. Keywords Paddy quality · Fuzzy interference system · Mamdani rule base

1 Introduction Crop wastage is a major issue for producers, and it also has a significant impact on countries economy. Post-harvest storage and protection of food crop against any environmental or microbiological pathogenic attacks are highly recommended to overcome food wastage and ensure food safety. Paddy is one of the long-stored food crops for ensured supply throughout the year as it is a stable food crop of the half of the global population. It is highly prone to environmental degradation and microbial attacks during storage in godown and silos. There are potential techniques for biomarkers identification, but they are based on conventional techniques like chemical analysis, gas chromatography mass spectrometry (GCMS), biological analysis, and so on. They are costly, require bulk instrumentation with intensive sample preparation and trained analyst, and are time-consuming. In this scenario, food quality analysis using metal oxide gas sensors can be replaced with cost-effective and swift analysis of food quality. C. Baskar · M. Doraipandian (B) School of Computing, SASTRA Deemed University, 613401 Thanjavur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_26

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The readily available commercial metal oxide gas sensors are one of the promising low-cost technologies for in situ detection of any food quality during storage. An array of gas sensors are used to design an electronic nose (E-nose) for identifying the biomarkers of food quality estimation. E-nose is of viable importance in diverse field applications such as food quality assessment [1, 2], agriculture [3], biomedical applications [4], air quality [5], and so on. Nowadays, E-nose is used as an effective tool for detecting tens of hundreds of numerous chemical and biological volatile organic compounds. E-nose is a device which consists of an array of gas sensors and acts as an artificial human olfaction system. Gas sensors are mostly metal oxide semiconductors which exhibit high sensitivity, low cost, and swift response towards various volatile organic compounds and their odours [6]. Sensors in the sensor array are coated with different sensing elements in such a way it is sensitive to a specific target gas. It is challenging to achieve the better sensitivity with selectivity towards the specific volatile organic compound as the gas sensing mechanism of these sensors depends on many factors such as temperature, nature of oxide, humidity, size, and thickness of sensing materials, so on. A number of sensors are placed in an array to achieve better response and sensitivity towards particular odour. These sensor arrays act as similar to the human olfaction system in sensing different volatiles. Generally, an array of sensors used in E-nose requires a feature selection, feature extraction, data classification, and pattern recognition approaches for effective decision making [7, 8]. The data accuracy is highly imperative for better performance achievement. But, odour identification is challenging in machine olfaction systems. The sensing material coated on the surface of the sensor plays a vital role in the determination of data accuracy where the performance of the E-nose is determined by its classification accuracy and decision making. Usually, the number of sensors is increased in the sensor array to ensure the performance and data accuracy. But, the sensors of the same type will produce almost collinear and redundant data. Increase in number sensors will lead to the adverse effect of data ambiguity. In this context, sensor array optimization is required to choose an optimal sensor set for classification. In this work, E-nose was used for data collection, and based on the sensitivity towards specific biomarker, three attributes are selected for data classification namely temperature, humidity, and alcoholic vapour. Further, fuzzy logic has been applied to data classification and decision making as it is a human-like reasoning system with reduced complexity.

2 Sensor Array Optimization Optimum sensor selection is mandatory for ensuring the effective classification of data. Sensors performance towards different target gas depends upon the sensitivity of the sensor used. The sensitivity of the sensor depends upon many factors such as film thickness, dopant added, environmental factors, and the size of the sensitive layer. Sensor with the same sensing element will exhibit different sensing behaviour [5]. Since the sensor of the same type will produce almost similar data, providing

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Fig. 1 Fuzzy interference system for paddy quality estimation

all the sensor data in a decision-making system will increase the complexity of the algorithm [9, 10]. Hence, sensors with a linear change in the sensing output were chosen as input to the expert system.

3 Expert System Design 3.1 Fuzzy Interference System The fuzzy logic is always used to solve any ambiguous and uncertain problems in real-world applications. The concept of the fuzzy set was first described by Zadeh [11]. The fuzzy system facilitates the design of a decision support system based on any expert suggestion and knowledge from empirical methods. The proposed system in this work consists of three input variables such as temperature, humidity, alcoholic vapours, and one output variable quality of stored paddy. Mamdani type fuzzy interference system was used, and it consists of three steps fuzzification, interference mechanism, and defuzzification [12]. The model is developed in MATLAB R2016b with a fuzzy tool box. The linguistic variables chosen for designing expert system are detailed below. Figure 1 shows the overall proposed model for fuzzy interference system for determining the paddy quality.

3.2 Input Variables 3.2.1

Temperature (T)

The temperature variable refers to the temperature of the food godown, and this is very important to monitor as it influences the moisture of the stored paddy. This

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Fig. 2 Fuzzy set representation of input variable: Temperature

variable has three fuzzy sets with different priority. Figure 2 graphically represents the temperature variable. 1. The T is between 35 and 40 °C; then, it is high. If the temperature is high, then the paddy grain will not observe any environmental humidity, so it is less prone to degrade. 2. If T is between 27 and 33 °C, then it is medium. Medium temperature will maintain the same condition of paddy during the storage process. 3. If T is between 20 and 26 °C, then it is low. Low temperature over the period of time will increase the moisture of the paddy, and it will degrade the quality making paddy unfit for human consumption. 3.2.2

Humidity (H)

Humidity is another important factor for any food crop to be stored over a period of time. If the humidity increases in the environment, it directly influences the moisture observed by the environment. This, in turn, increases the humidity of the stored food crop. Figure 3 represents the fuzzy set for input variable humidity.

Fig. 3 Fuzzy set representation of input variable: Humidity

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Fig. 4 Fuzzy set representation of input variable: Alcoholic vapour

1. If H is between 76 and 90%, then it is high. If humidity is high, then paddy crop will observe more moisture and highly prone to degrade. 2. If H is between 66 and 75%, then it is medium. If humidity is medium, then it will maintain the paddy quality throughout the storage period. 3. If H is between 50 and 65%, then it is low. If humidity is low, then paddy will not observe moisture from the environment and improve the shelf life of paddy. 3.2.3

Alcoholic Vapour (A)

Alcoholic vapours are the biomarker for estimating the paddy quality. If paddy starts to degrade, then it will produce alcoholic vapours. By monitoring the paddy godown for any instance of alcoholic vapour can prevent the paddy degradation at the initial stage, which in turn reduces the paddy spoilage. Figure 4 graphically represents the fuzzy set for input variable alcoholic vapour. 1. 2. 3. 4.

If A is between 0.1 and 0.4 V, then it is very low. If A is between 0.4 and 0.9 V, then it is low. If A is between 0.9 and 1.3 V, then it is medium. If A is between 1.3 and 1.8 V, then it is high.

3.3 Output Variable 3.3.1

Paddy Quality Degradation (PQ)

The paddy quality degradation is the only output variable defined for determining the paddy storage time. Based on the paddy quality, the recommendation can be given to dispatch the paddy which is highly prone to degrade. Fuzzy set for the paddy quality is defined below. Figure 5 represents the output variable for the fuzzy interference system.

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Fig. 5 Fuzzy set representation of output variable: Paddy quality degradation

1. 2. 3. 4.

If PQ is between 0 and 0.25, then it low. If PQ is between 0.25 and 0.5, then it is medium If PQ is between 0.5 and 0.75, then it high medium. If PQ is between 0.75 and 1.0, then it is high.

3.4 Defuzzification In this work, defuzzification criteria used is maximum–minimum one. The minimum value of the fuzzy set is computed to know the degree of belonging to the output set for the given rule base. If one or more rules have the same value, then the maximum value of the degree of belonging will be taken. Defuzzification method used is centroid, which calculates the centre of gravity of the area under the function or curve. The function is divided into equal parts and calculated by summing all points belonging to the function.

4 Results The proposed expert system is presented in a very simple manner, and it will generate the output for the given input variables graphically as shown in Fig. 6. For the validation of the proposed systems, empirical results were compared with the obtained output from the expert system. The proposed system can be used by the godown mangers for taking the dispatch decision of paddy stored in the godown. Figure 7 shows the influence of temperature and humidity in the paddy quality degradation. Existing methods for paddy quality estimation were mostly done my experimental validation. The results were compared with the expert commands. The proposed model performance was similar to the results from expert knowledge.

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Fig. 6 Proposed expert system

Fig. 7 Humidity versus temperature = paddy quality degradation

5 Conclusion Food quality and safety are highly imperative for avoiding any health issues and also to avoid the economic loss of the country. In this work, an expert system for paddy quality monitoring and dispatch decision for paddy godown was proposed and implemented. The obtained results and empirical results were used for validating system, and better performance was achieved. In the future, the expert system will

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be implemented in the stand-alone device and used for making real-time decision making in paddy godown. Acknowledgements The authors are grateful to the Department of Science & Technology, New Delhi, India (SR/FST/ETI-371/2014) and (SR/FST/ETI-284/2011(C)) and the first author wishes to express sincere thanks to the INSPIRE fellowship (DST/INSPIRE Fellowship/2015/IF150629) for their financial support. They also wish to acknowledge SASTRA Deemed University, Thanjavur, for extending infrastructural support to carry out the work.

References 1. Falasconi M, Concina I, Gobbi E, Sberveglieri V, Pulvirenti A, Sberveglieri G (2012) Electronic nose for microbiological quality control of food products, 2012 2. Cupane M Food industry production. IEEE Instrum Meas Mag 27–33 3. Wilson AD, Baietto M (2009) Applications and advances in electronic-nose technologies, pp 5099–5148 4. Wilson AD, Baietto M (2011) file:///C:/Users/CHANTHINI/Desktop/Electronicnoseieee/07384957.pdf. Sensors 11: 1105–1176 5. Al Barakeh Z, Breuil P, Redon N, Pijolat C, Locoge N, Viricelle J-P (2017) Development of a normalized multi-sensors system for low cost on-line atmospheric pollution detection. Sensors Actuators B Chem 241: 1235–1243 6. Wang C, Yin L, Zhang L, Xiang D, Gao R (2010) Metal oxide gas sensors: sensitivity and influencing factors. Sensors 10:2088–2106 7. Jiang S, Wang J, Wang Y, Cheng S (2017) A novel framework for analyzing MOS E-nose data based on voting theory: application to evaluate the internal quality of Chinese pecans. Sensors Actuators, B Chem 242:511–521 8. Casalinuovo IA, Di Pierro D, Coletta M, Di Francesco P (2006) Application of electronic noses for disease diagnosis and food spoilage detection. Sensors (Basel) 6:1428–1439 9. Bertocci F, Fort A, Vignoli V, Shahin L, Mugnaini M, Berni R (2014) Assessment and optimization for novel gas materials through the evaluation of mixed response surface models. IEEE Trans Instrum Meas 64:1084–1092 10. Baskar C, Nesakumar N, Balaguru Rayappan JB, Doraipandian M (2017) A framework for analysing E-nose data based on fuzzy set multiple linear regression: Paddy quality assessment. Sensors Actuators A Phys 267:200–209 11. Zadeh LA, Introduction I, Navy US (1965) Fuzzy sets* 353: 338–353 12. Rivero LC, Rodríguez-Duran AA, Vásquez RP, Rojas-Luna MA, López-Segura MV, AguilarLasserre AA (2018) Expert system based on a fuzzy logic model for the analysis of the sustainable livestock production dynamic system. Comput Electron Agric 1–17

Voice-Controlled Smart Assistant and Real-Time Vehicle Detection for Blind People Mojibur Rahman Redoy Md Akanda, Mohammad Masum Khandaker, Tushar Saha, Jahidul Haque, Anup Majumder, and Aniruddha Rakshit

Abstract The world is now like a global village with the help of modern technology. Normal people are taking huge facility from this modernization. Because of blindness and visual impairment, lot of people facing problems in their regular normal life. By using technology here proposed a system which will help them to make their life easier by giving instruction when they are outside from home. This system is totally voice controlled. Blind people can know the current location and can travel by walk and by bus to different places by using this system. They will get continuous instruction through speech which will ensure the service perfectly. In this paper, here explained their problems, related work, full process and model of the proposed system. This system helps them in their daily life to travel independently. Keywords Expert system · GPS · Location finding · Vehicle detection · Recognize

M. R. R. Md Akanda (B) · M. M. Khandaker · T. Saha · J. Haque · A. Majumder · A. Rakshit Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh e-mail: [email protected] M. M. Khandaker e-mail: [email protected] T. Saha e-mail: [email protected] J. Haque e-mail: [email protected] A. Majumder e-mail: [email protected] A. Rakshit e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_27

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1 Introduction Globally, it is estimated that approximately 1.3 billion people live with some form of vision impairment [1]. With regard to distance vision, 188.5 million people have mild vision impairment, 217 million has moderate-to-severe vision impairment, and 36 million people are blind [2]. There are 826 million people with near-vision impairment [3]. Every five seconds, one person in the world goes blind. One child goes blind every minute. It is estimated that over seven million people become blind every year [4]. Just think about the number. There are 89% of vision impaired people living in lowand middle-income countries [5]. It can be assumed that most of the blind people are from the lower middle country. Generally, they face problems when they go outside from home. They need other persons help to know current location and direction information to go a destination. They cannot visit any places through buses without the help of other people. They always need assistance of people when they are outside. But sometimes it becomes quite impossible to stay someone behind them. On the other hand, they also feel that they became a burden to the family. There is some existing system which has tried to help blind people on outside, but they are not user friendly. Those systems are made for blind people, but in some cases blind people cannot operate it. It needs another person to operate those cases. This is not the symbol of independence. And none of this system included travelling facility by bus. In this paper, here introduced a system which will help blind people to travel outside independently. In this system, easy interaction gets full priority as this is made for blind people. All the interaction with the system will be completed through voice command. This system takes voice command as input and gives response through speech by using system speaker. This ensures targeted instruction available by following some easy steps. The main challenge of this system is to give the bus travelling facility to the blind people. It needs to select the exact bus, which is coming towards blind people and going to the destination asked by the blind people. Here real-time bus location information also the main purpose. Blind people will get the bus location information as like as alarm, so this should make ensure it gives the announcement about the distance of the bus from the user. The main technology used here are different Google APIs like maps API, places API, route API and other. For bus detection here developed an algorithm which is explained below.

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2 Literature Review Over the recent decades, researcher tries to help blind people to navigate the route and identify bus by using modern technologies. Yelamarthi et al. [6] built a Smart Robot which used RFID and GPS. This can drive user to a predefined destination by avoiding obstacles. It uses ultrasonic and infrared sensor to avoid obstacles. This robot is successfully implemented and operational. Dragan Ahmetovic et al. [7] proposed Navcog—a smart-phone-based turn-by-turn navigation assistance based on accurate real-time localization over large spaces. The system also informs them the point of interest of the accessibility issues. They use GPS, accessibility technology, map server and BLE. This system can guide the visual impaired person in an unfamiliar environment, and this has been checked by the inventor. Loomis et al. [8] develop a navigation system which can guide blind people on the familiar and unfamiliar environments. They use differential GPS, compass, inertial sensor and velocity detector to detect the position and orientation of the user. Geographic Information System contains a detailed database of software for route planning and for obtaining information from the database. This has some drawback— to get virtual sound, traveller needs normal directional hearing, and they should worn compass and earphone which also makes them dependent. Moore [9] developed Drishti: an integrated navigation system for the visually impaired and disabled. It is a wireless pedestrian navigation system. He uses wearable computers, voice recognition and synthesis, wireless networks, Geographic Information System (GIS) and global positioning system (GPS) to develop the system. As it is wearable, it creates a problem, audio has some problem, and navigating prompt is slower than the pedestrian normal pace. Pan et al. [10] develop a primary travelling assistant system of bus detection and recognition for visually impaired people to assist them travel independently. They design a computer vision-based system to detect and recognize bus information from images captured by a camera at a bus stop. They also use text detection algorithm to select the bus route. This has the accuracy of 80.93% in detecting the bus existence in a scene image. Al Kalbani et al. [11] develop a bus detection system using RFID technology that aims to ease the travelling and movement of blind people. This system has two parts—one is on the bus and another at the station. Announcement has been announced on the station, and so if blind people remain around the station, then they can travel. Lamya El alamy et al. [12] proposed a bus detection system for visual impaired people using RFID and wireless sensor networks. This system will allow VIPs to safely catch buses with the help of an audio device and a tactile interface through a wireless communication system (Wi-Fi) between the transmitter and the receiver. This system needs Braille keyboards and some other things which actually cost some money.

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Fig. 1 System architecture of the proposed system. User gives the command, and system receives this command and perform the internal operation. If needed, then request service to Application interface, learned DB, GPS, etc.

3 System Architecture The architecture of the proposed system is an expert system for giving service to blind people through voice command is shown in Fig. 1. It creates with the hypothesis that blind people give voice commands to the proposed system which should install on the user mobile phone. Proposed system recognizes the voice command and sends it to the backend. When backend receives the command, it analyses the command and calls application interface, GPS and learned database to collect the information according to voice command. After collecting the information, it converts collected information to speech. Finally, the system uses user mobile phone’s speaker to give speech output. To get the facility of this system, everyone has to equip with below listed tools or gadget. 1. 2. 3. 4.

Android mobile (first time developed this system for the Android platform) Internet connection. GPS enable System installed on the mobile.

The main concern is to give all the facilities to the blind with easy interaction, that is why the application is controlled through voice. Hope every blind person can easily interact with this system and grave all the feature through some easy voice command.

4 Methodologies In this section, here explained the implementation process of this voice controlled assistant. It has been precisely trying to explain main three services of this system.

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The system uses lots of application interfaces, machine learning concept and newly developed algorithm to implement it the services properly. As it is controlled through voice command, so it uses voice recognition application interface to detect the voice.

4.1 Current Location In Fig. 2, it is shown that blind people can know their current location by asking to the system through voice command. When blind people give “Current Location” command, then system recognizes it first. Then the system calls Google places API which collects latitude and longitude from the mobiles by using the phone’s GPS. The API determines the current place name which corresponds to the latitude and longitude of blind people. Finally, resulted text is converted to the speech and then gives the output to the blind people.

Fig. 2 Operational architecture of the proposed system. The system has three main operations— first (current location), second (travel by walk) and third (travel by bus). This architecture explains the operation when individual command is given by blind people

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4.2 Travel by Walk Blind people can travel a desired destination by walk. User gets continuous direction instruction through speech output before reaching the destination. In Fig. 2, it is depicted that to get direction, it is needed to give voice command “Walk”. Then, the system asks user to know the destination place. After telling the destination in the system, it gives some suggestion by calling places API and matching with destination place name. After that, user needs to confirm specific destination by using voice command from the suggestion list. Now direction API is called by the system, and direction instruction text is converted to speech and gives output.

4.3 Travel by Bus This is the most challenging part of this paper. Blind people can travel through public bus. After setting a desired destination, he/she will able to know which bus is coming to him/her and will get continuous information about exact bus through speech output. In Fig. 2, shows that voice command “BUS” need to tell by blind people. The system recognizes the command and call voice prompt to know the destination place name from blind people. After receiving the destination name, system calls places API to find all the matched places name and gives suggestion through speech with those names. Blind people select one place from the suggestion by command. After that Algorithm 1 will apply to detect the actual bus for blind people. Finally, the system will give continuous notification about bus coming through speech output. To apply this algorithm for bus detection, every bus route has to learn by the system. When it is avail with the learned database, then the system can apply the algorithm. Algorithm 1 Bus Detection Algorithm

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5 Experimental Evaluation The proposed system takes voice command as input and gives response to the user through speech message. When voice command is received by the system, then it converts the speech into text and performs the other operation in backend to make sure the service for the user. The system is able to perform mainly three operations, and those are current location, travel by walk with real-time direction and travel by bus which ensure expected bus. To get the service of current location, the user gives voice command “current location”, then the system converts the speech as text. System backend then calls the places API to know the latitude and longitude of current places of user by using GPS. Name of the place is converted to speech and gives the output through user phone speaker. When the user wants to go to a destination by walk, he/she gives the command “walk”. The system then asks for the user to tell the name of destination place. After receiving the destination name, it searches through the places API to match the name. The system gives some suggestion to the user with the matched places name and request user to select one from them. The user selects one name from the suggestion through voice command. Then, the system calls route API and receives the route instruction. The system converts the instruction to speech and gives continuous output through user phone speaker. Travel by bus is the most challenging portion of this research, and this is also a very special service to blind people. The user sets destination like travel by walk. But here without calling route API, it applies a bus detection algorithm to detect the exact bus. This algorithm is a bus detection algorithm which is developed to detect exact bus is coming towards blind people after selecting a destination. To apply bus detection algorithm to detect bus, firstly the system needs to save the route of the bus to the database. Here named the database as learned database where latitude and longitude of bus route are stored with the distance of 5 m from each to another point. To learn the latitude and longitude here needs another system on the bus. After learning the database, system on the bus only gives the bus real-time current location information. Explaining Bus Detection Algorithm In the previous section, bus detection algorithm is written but not properly explained. Here it is explained with pictorial examples. Step 1 Initialize the value of mL, dL, bL and bRP by collecting the data where mL = my location, dL = destination location, bL = bus location, bRP = bus route point Remark 1 My location is collected from the user which is also the location from where user want to travel. Destination location is selected through voice command. Bus current location is collects from the subsystem on the bus. The bus route point is the route of the bus, which is stored in the database. Step 2 If mL_bL < 1000 m, then add those buses in a list

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Remark 2 Here mL_bL is the distance of my location to bus location. If my location to bus location is less than 1000 m, then add all of those buses in a list. Step 3 Match 2 Point If mL == bRP AND dL == bRP Remark 3 Every bus route is learned and stored in the database. Here if mL and dL match with bRP of the same bus from the learned database, then keep them in the list either delete from the list. Step 4 If (distance(mL_bL) < distance(dL_bL)), then add those buses in the table Remark 4 If the distance of my location to bus location is less than the distance of destination location to bus location, then add those buses in a table either delete them from the table (Fig. 3). In Table 1, it can be assumed that A-7, B-7, C-7 meet the condition and D-7, E-7 do not meet the condition. So D-7 and E-7 will be deleted from the list. Step 5 If pD(mL_bL) > cD(mL_bL) And pD(dL_bL) > cD(dL_bL), then update the table with proper bus, and this will continue till mL to bL is less than 100 m.

Fig. 3 This is the animated view of the bus with ID. This shows the distance (mL_bL) and distance (dL_bL). In this figure, only calculation showing for the A-7 bus

Table 1 Selected bus after Step 3 conditions with ID and other information

Bus ID

Distance (mL_bL) (m)

Distance (dL_bL) (m)

A-7

700

2000

B-7

500

1800

C-7

500

900

D-7

700

600

E-7

800

500

296 Table 2 Previous location-based table

Table 3 Current location-based table

M. R. R. Md Akanda et al. Bus ID

Distance (mL_bL) (m)

Distance (dL_bL) (m)

A-7

700

2000

B-7

500

1800

C-7

500

900

Bus ID

Distance (mL_bL) (m)

Distance (dL_bL) (m)

A-7

800

2200

B-7

90

1090

C-7

200

1100

Remark 5 In this step, first calculate the distance of mL to bL then dL to bL and save them on a table. After a specific time system, calculate the same distance and save them on another table. Now the previously created table is pD = past distance base table, and next created table is cD = current distance base table which has different values than previous. If previous mL to bL distance is less than current mL to bL and previous dL to bL distance is less than current dL to bL, then add those bus on the table either delete them. This loop is ongoing to detect the perfect bus. Because bus will move from one place to another so here need this calculation. Every time if “I” becomes the current table, then “I-1” is the previous table. Remark 6 In Tables 2 and 3 show us the previous and current location based table. By applying the condition, it can be assumed that only B-7 is fulfil the condition. This condition checks again and again to filter the exact bus. Step 6 If mL_bL < 100, then notify to the user. Remark 7 If the distance of mL to bL is less than 100, then the system now notifies to the user about bus through speech message by using phone speaker.

6 Conclusion and Future Work In this paper, here presented a voice-controlled assistant for blind people to give them the facility to travel outside independently. The proposed system is totally controlled through voice command mean, takes command through voice and gives instruction as speech by using the phones speaker. The facilities include current location, travel by walk and travel by bus with realtime instruction through speech. For current location and travel by walk, implementation help of Google API is taken, but for travel by bus, our developed algorithm is used to detect the bus and used learned database where the bus route is saved according to ID of bus.

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The future plan of this research is to make the application more efficient and accurate. Give blind people the suggestion to go into shorter route and make available this application in different language which is also notable future plan of this research.

References 1. Organization WH (2018) Blindness and vision impairment [cited 2018 15 November]. Available at: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impair ment 2. Bourne RRA, Flaxman SR, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Limburg H (2017) Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob Health 5(9):e888–e897. https://doi.org/10.1016/s2214-109x(17)30293-0 3. Timothy RF, Serge Resnikoff NTP, Eric Papas MD, Burnett A, MayHo S, Naduvilath T, Naidoo KS (2018) Global prevalence of presbyopia and vision impairment from uncorrected presbyopia: systematic review, meta-analysis, and modelling. Ophthalmology 125(10): 1492–1499 4. Organization WH (2002) World sight day: 10 October. 2002 [cited 2018 23 November]. Available from: https://www.who.int/mediacentre/news/releases/pr79/en/ 5. Balantrapu T (2017) Latest global blindness & VI prevalence figures published in Lancet. [cited 2018 24 November]. Available from: http://atlas.iapb.org/news/latest-global-blindness-vi-pre valence-figures-published-lancet/ 6. Yelamarthi K, Haas D, Nielsen D, Mothersell S (2010) RFID and GPS integrated navigation system for the visually impaired. In: Midwest symposium on circuits and systems 7. Dragan Ahmetovic CG, Ruan C, Kitani K, Takagi H, Asakawa C (2016) NavCog: a navigational cognitive assistant for the blind. In: 18th international conference on human-computer interaction with mobile devices and services (MobileHCI 2016), Florence 8. Loomis JM, Roberta RG, Klatzky RL (1998) Navigation system for the blind: auditory display modes and guidance. The Massachusetts Institute of Technology, 7(2): 193–203 9. Moore SE, Drishti (2002) An Integrated navigation system for the visually impaired and disabled. University of Florida 10. Pan H, Yi C, Tian Y (2013) A primary travelling assistant system of bus detection and recognition for visually impaired people. In: International conference on multimedia and expo workshops (ICMEW), IEEE, San Jose, CA, USA 11. Al Kalbani J, Suwailam RB, Al Yafai A, Al Abri D, Awadalla M (2015) Bus detection system for blind people using RFID. In: Proceedings of the 8th IEEE GCC conference and exhibition, IEEE, Muscat, Oman 12. Lamya El Alamy SL, Maalal S, Taybi Y, Salih-Alj Y (2012) Bus identification system for visually impaired person. In: Sixth international conference on next generation mobile applications, services and technologies, IEEE, pp 13–17

A Framework for Cyber Ethics and Professional Responsibility in Computing J. K. Alhassan, E. Abba, Sanjay Misra, Ravin Ahuja, Robertas Damasevicius, and Rytis Maskeliunas

Abstract In this study, a model was developed for cyber ethics and professional responsibility in computing. There is a need for every mobile phone user to have access to cyber ethics and professional issues. This study designed an architectural mobile application framework using HTML, JSP, and CSS. The back end of the application was developed using SQL. The application can be installed on handheld devices that support the android operating system. The effort taken for scripting is 12 h while the performance scripting productivity is calculated to be 3 operations per hour. The application is recommended for all mobile phone users. Keywords Mobile application · Cyber ethics · Professional responsibility

1 Introduction Ethics is a part philosophy that deals with how human beings behave among themselves. It is precisely the conduct of individuals in the public and comprises of J. K. Alhassan (B) · E. Abba Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria e-mail: [email protected] E. Abba e-mail: [email protected] S. Misra Covenant University, Canaan Land, 10 Idiroko Road, P.M.B. 1023, Ota, Ogun State, Nigeria e-mail: [email protected] R. Ahuja University of Delhi, New Delhi, India R. Damasevicius · R. Maskeliunas Kanus University of Technology, Kaunas, Lithuania e-mail: [email protected] R. Maskeliunas e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_28

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both right and wrong behaviors [1, 2]. It involves shielding, schematizing, and commending perceptions of numerous manners whether right and wrong. To a greater degree, ethics is powerfully related to morality since “it involves systematizing, defending, and recommending the concepts of right and wrong into any establishment” [3]. However, ethics may not be as tangible as morality. This is because morality is a set of standards that model our truthful conduct while ethics can be described as the theory and reflection of morality. There exist three major subject areas in ethical theories, namely normative ethics (consisting of duty theories, consequentialist theories, and virtue theories), applied ethics (involves the analysis of specific and controversial moral issues), and metaethics (which studies the origins and semantics of ethical concepts). The term ethics in the perspective of this study is limited to computer and information ethics [4, 5]. This paper is focused on professional accountability for system administrators, as cherished in the diverse codes of ethics in this arena. Computer ethics is an area of applied ethics that stipulate standard for computer use in the computing world such as the inhibition of copyright violation, virus injection and privacy invasion, and circulation of objectionable material and espionage [6]. The field of computer ethics emerged as a result of a growing trend of development in science and technology during the mid-1940s. Norbert Wiener, a professor of mathematics and engineering at the Massachusetts Institute of Technology, has been attributed to be the proponent of the field through his writings which touched on the huge potential of electronic computers to be used for both good and evil; ethical issues that could result which include but not limited to: “computers and security, computers and unemployment, responsibilities of computer professionals, computers for persons with disabilities, computers and religion, information networks and globalization, virtual communities, teleworking, merging of human bodies with machines, robot ethics, artificial intelligence, and a number of other subjects” [5 7–9]. Interestingly, Wiener’s work was further propagated by Walter Maner who had no prior knowledge of the works of Wiener by introducing the course he named computer ethics at Old Dominion University. Through the course, he introduced a number of philosophers and computer scientists to ethics in computing [10, 11]. Notable among his followers is Deborah Johnson who had written a textbook on Computer Ethics based on the thoughts of Maner by the early 1980s and has remained a strong propagator of the discipline [12–17]. Cyber ethics is carefully connected to the growth computers and information technology [18], and it is about societal accountability in cyberspace. This comprises of a set of standards which recommend morality in cyberspace, considering the preservation of freedom of expression, intellectual property, and privacy. This suggests that cyber ethics is domain that deals with, “what is good and bad and with moral duty and obligations as they relate to online environments and digital media” [19]. It is also termed Internet ethics, where the right or wrong about the use of the Internet are clearly stated [20]. The use of Internet includes information’s delivery, interpersonal communication, research, storage, etc. It is clear that all rules applicable to “computer ethics and information ethics can also be applied on cyber ethics and addresses its application to the Internet” [21]. The task for cyber ethics “is to unravel the principles of morality that can inform human

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action so that people are endowed to establish a sustainable, participatory global information society” [22]. Up until the late 1980s, the focus of computer ethics had been protecting and advancing central human values which had yielded fruit in no mean measure. However, by the early 1990s, Donald Gotterbarn called for a diversification of emphasis to include the day-to-day activities of computing professionals in their role as professionals which gave birth to the domain of professional ethics [23–30]. It comprises interaction among and duties toward customers, employees, employers, clients, co-workers, others who request one’s products and services [6]. A computer professional’s roles can influence on the life, health, finances, freedom, and future of a client or members of the civic. A professional action may result in huge harm through fraudulence, carelessness, or incompetence. “Thus, computer professionals have singular responsibilities not only to their clients, but also to the overall civic and the users of their products, irrespective of their association with the users” [1]. These tasks include “thinking about potential risks to privacy and security of data, safety, dependability, and ease of use. They embrace acting to diminish risks that are too high” [5]. There exist diverse institutions for different professions including computer professionals. The major ones include the following: ACM and IEEE Computer Society (IEEE CS). These institutions have helped a great deal to fashion “Software Engineering Code of Ethics and Professional Practice (adopted jointly by the ACM and IEEE CS) and the ACM Code of Ethics and Professional Conduct” [5]. These codes engrossed on the basic ethical values of uprightness and equality which affect several facets of professional conduct, namely preserving professional capability, responsibilities to respect privacy, awareness to pertinent laws, and importance of contracts and promises. However, these codes pay a precise consideration to the areas that are particularly not vulnerable from computer systems. Thus, emphasizing on the duty to respect and defend confidentiality, evade damage to others, and esteem property rights—“with intellectual property and computer systems themselves as the most related examples” [31]. In line with the aforementioned points, researchers have expressed increasing interest in studying professional responsibility as it relates to the domain of computer ethics [32]. To this end, a number of approaches have been suggested such as making the course more engaging to teach in a classroom environment [33–36]; a need to leverage technology [37]; a need to update the professional code of ethics and conduct and hosting them on new media that supports easy accessibility and regular updates [38, 39]. This study, therefore, presents an informative mobile application for all computer professionals and users which will to a larger extent keep them informed on cyber ethics, cyber acts, computer professional code of conducts and guidelines and suggestions to help overcome challenges faced by computer professionals and users in the cyberspace. The rest of this paper is structured as follows: Sect. 2 reviews related studies, highlighting their contributions to the subject matter as well as the limitations. In Sect. 3, the methodology adopted is clearly described. The results obtained in the study are then discussed in Sect. 4. Section 5 concludes the paper.

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2 Related Works The study in [18] described ethics as an indispensable constituent in human interaction. The study emphasized on applied ethics as a significant kind of ethics which includes basic classes like information ethics, computer ethics, and cyber ethics. The outcomes of these three applied ethics were studied and applied to e-Government ethics. Framing the topics of e-Government ethics, information ethics, computer ethics, and cyber ethics is measured as the fundamentals of e-Government ethics. The study in [1] presented a research ethics in malware studies as entirely overlooked, yet the work stands to have vast consequence on the society worldwide. The study also gave notions stating that research ethics ought to be imparted early during the training of fresh malware researchers. Although there exist some encounters based on this purpose, these encounters can be overwhelmed by definitely presenting ethical training in a course on malware programming. The study in [40] presented a study on ethics and cybersecurity: obligations to protect client data. The study showed that lawyers and law firms have ethical tasks under the rules of professional behavior in area of impacts. In addition, the study described the most critical ethical rule linking to lawyer and law firm information security as the duty to guard the privacy of client self-assurances. The study in [41] highlighted the significance of including cyber ethics education in system administration training and the entire computer science education. The study demonstrated a new procedure toward the teaching of ethics in the perspective of system and network engineering. The study utilizes ethics committee to assess student project proposals on their ethical aspects, where student records in writing ethical considerations, explaining the issues applicable to the proposed research. The information provided serves as a means of support to the group hereby reinforcing the learning and applicability of ethics to the students. These reports are categorized into the risks and ethical matters which students may meet during the implementation of the project. The study in [3] identified the important lessons when building an active computer security ethics community. The study highlighted some challenging key factors when building ethics for computer security. The study gave an analysis of different entities involved in achieving the goals of ethical decision-making and concluded that on the need to reward good ethical behaviors and not only policing unacceptable behaviors.

3 Research Methodology The mobile application software for cyber ethics and professional responsibilities comprises of different sub-components which aggregate to make the entire application software. Figure 1 depicts the design architecture of the mobile application.

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Fig. 1 Architecture for the mobile application

The entire system design architecture is shown in Fig. 1; it is the fundamental structure of the mobile application. From Fig. 1, the diverse sections of the mobile application contain the web App, Cordova plugins, HTML rendering engine (WebView) and the mobile operating system. Respectively of the stated sub-modules perform some precise functions which make the whole application to function efficiently. A brief explanation of the diverse subsections of the entire mobile application are as follows: Web App: This is a client–server software system where the client (user interface) runs in the web browser. The web application adopted for building the mobile application comprises of hypertext transfer protocol language (HTML), cascading style sheets (CSS). Cordova plugins: A plugin is a package of inserted code. The added codes permit the Cordova Web to converse with the natural environment which it will be executed. Plugins provide entry to device and platform functionality that is usually unreachable to web-based applications. The Cordova API features are implemented as plugins, which are finished up of a single JavaScript interface and an equivalent natural code library for each braced stage. This hides the numerous native code implementations behind a common JavaScript interface. Mobile Operating system (OS): This is a software that allows applications or programs to run on mobile devices such as tablet PCs and smartphones. The mobile

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OS for the cyber ethics classically begins immediately the device is being powered on. A startup screen with icons or tiles is displayed, showing different information for accessing this application such as handling wireless and cellular network connectivity, as well as phone entry.

4 Mathematical Model of the System (Based on System Internal Components) The entire system denoted by, ϕ, is made of four fundamental components which are the Web App represented by, W App , HTML Rendering Engine WebView by W View , System Plugins by S plugins , and Mobile OS by M OS . The components comprise of sub-components which can be mathematically represented as follows: WApp = {R, C} in HTML

(1)

where W App = Web Application, R = System Resources, and C = System Configuration (config.xml)   WView = WApp = {R, C} ∈ WView

(2)

where W View = HTML Rendering Engine—WebView Splugins = {Am , G l , Ca , Me , De , Ne , Co , St }

(3)

where S plugins = System Plugins, Am = Accelerometer, Gl = Geolocation, C a = Camera, M e = Media, De = Device, N e = Network, C o = Contact, and S t = Storage   MOS = WView , Splugins

(4)

MOS = Sv + l p + Se + G r

(5)

where M OS = Mobile OS, S v = Services, I p = Input, S e = Sensors, and Gr = Graphics. This implies that   ϕ = WApp , WView , Splugins , MOS

(6)

ϕ = WApp + WView + Splugins + MOS

(7)

A Framework for Cyber Ethics and Professional … Table 1 Performance evaluation

305

Task Performed

Total

Number of clicks

22

Number of input parameter

4

Number of correlation parameter

10

Total operation performed

36

where = Entire System. Performance Metrics For the purpose of this study, a performance scripting productivity (PSP) metrics was used. This metric provides the performance of the test scripting productivity over a period of time. Performance scripting productivity   Operations performed  oper.s/h = Efforts in hours

(8)

where task performed is: 1. Number of Click(s) for instance. click(s) on which data is revitalized. 2. Number of input factor 3. Number of correlation factor.

5 Implementations and Result Using the performance metrics and the formula given in Eq. (8), the application was tested and the values obtained are shown in Table 1. Effort it took to script = 12 h Performance scripting productivity = 36/12 = 3 operations/h As shown in Table 1, the number of clicks calculated to be 22, from the lunch of the application to the end. The number of input parameters from the lunch to the end is 4, while the correlation parameter is 10. The efforts taken for scripting are 12 h while the performance scripting productivity is calculated to be 3 operations per hour.

6 Conclusion This study presents an informative mobile application for all computer professionals and users which will to a lager extent keep them informed about Cyber ethics, Cyber

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acts, computer professional code of conducts and guidelines. The application is developed using HTML, JSP, and CSS. The back end of the application was achieved using SQL. Professional ethics as well as general ethics are very useful to every society, with the widespread of information technology, (IT) professionals and evolution of IT, every individual possesses mobile phone which they carry along wherever they go. This application will help users to easily have access to cyber ethics and professional responsibility on their mobile phones. It is recommended for all mobile phone users. Acknowledgements In this study, we would like to express our deep appreciation for the support and sponsorship provided by Covenant University Centre for Research, Innovation and Discovery (CUCRID).

References 1. Lee WW, Chan AK (2008) Computer ethics: an argument for rethinking business ethics. In: 2nd world business ethics forum: rethinking the value of business ethics. Hong Kong Baptist University, pp 1–12 2. Nayır DZ, Rehg MT, Asa Y (2018) Influence of ethical position on whistleblowing behaviour: do preferred channels in private and public sectors differ? J Bus Eth 149:147–167 3. Dittrich D, Bailey M, Dietrich S (2011) Building an active computer security ethics community. IEEE Secur Priv 9:32–40 4. Ramadhan A, Sensuse DI, Arymurthy AM (2011) E-government ethics: a synergy of computer ethics, information ethics, and cyber ethics. Int J Adv Comp Sci Appl 2:82–86 5. Bynum T (2018) Computer and information ethics. In: Zalta EN (ed) The Stanford encyclopedia of philosophy. https://plato.stanford.edu/archives/sum2018/entries/ethics-computer/ 6. van der Ham J (2015) Embedding ethics in system administration education. USENIX J Educ Sys Admin 1:1–9 7. Wiener N (1948) Cybernetics: or control and communication in the animal and the machine. Technology Press/Wiley, New York 8. Wiener N (1950) The human use of human beings: cybernetics and society, 2 (revised) edn. Houghton Mifflin, Boston 9. Wiener N (1964) God & Golem Inc: a comment on certain points where cybernetics impinges on religion. MIT Press, Cambridge 10. Maner W (1980) Starter kit in computer ethics. Helvetia Press and the National Information and Resource Center for Teaching Philosophy, Hyde Park 11. Maner W (1996) Unique ethical problems in information technology. In: Bynum T, Rogerson S (eds) Science and engineering ethics (Special issue: global information ethics) 2:137–154 12. Johnson D (2001) Computer ethics, 1st edn. Prentice-Hall, Englewood Cliffs, 1985; 2nd edn. Prentice-Hall, Englewood Cliffs, 1994; 3rd edn. Prentice-Hall, Upper Saddle River 13. Johnson D (1997) Ethics online. Comm ACM 40:60–65 14. Johnson D (1997) Is the global information infrastructure a democratic technology? Comput Soc 27:20–26 15. Johnson D (2004) Computer ethics. In: Floridi L (ed) The Blackwell guide to the philosophy of computing and information. Blackwell, Oxford, pp 65–75 16. Johnson D, Nissenbaum H (eds) (1995) Computing, ethics & social values. Prentice Hall, Englewood Cliffs

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17. Johnson D, Powers T (2008) Computers as surrogate agents. In: van den Hoven J, Weckert J (eds) Information technology and moral philosophy. Cambridge University Press, Cambridge, pp 251–269 18. Kraft T (2011) Computer ethics: a slow fade from black and white to shades of gray. Inf Sys Educ J 9:37–54 19. Richterich A (2018) Tracing controversies in hacker communities: ethical considerations for internet research. Info Comm Soc, pp 1–18 20. Chou C, Chan PS, Wu HC (2007) Using a two-tier test to assess students’ understanding and alternative conceptions of cyber copyright laws. Brit J Educ Tech 38:1072–1084 21. Sullins JP (2014) A case study in malware research ethics education: When teaching bad is good. In: Security and privacy workshops (SPW). IEEE, San Jose, pp 1–4 22. Wilczenski F, Coomey S (2006) Cyber-communication: finding its place in school counseling practice, education, and professional development. Prof Sch Counsel 9:327–331 23. Gotterbarn D (1991) Computer ethics: responsibility regained. Natl Forum Phi Beta Kappa J 71:26–31 24. Gotterbarn D (2001) Informatics and professional responsibility. Sci Eng Ethics 7:221–230 25. Gotterbarn D (2002) Reducing software failures: addressing the ethical risks of the software development life cycle. Aust J Inf Syst 9:155–165 26. Gotterbarn D, Miller K, Rogerson S (1997) Software engineering code of ethics. Inf Soc 40:110–118 27. Gotterbarn D, Miller K (2004) Computer ethics in the undergraduate curriculum: case studies and the joint software engineer’s code. J Comp Sci Coll 20:156–167 28. Gotterbarn D, Rogerson S (2005) Responsible risk analysis for software development: creating the software development impact statement. Comm AIS 15:730–750 29. Ibrahim MS, Salleh N, Misra S (2015) Empirical studies of cloud computing in education: a systematic literature review. In: International conference on computational science and its applications. Springer, pp 725–737 30. Frank I, Odunayo E (2013) Approach to cyber security issues in Nigeria: challenges and solution. Intl J Cogn Res Sci Eng Educ 1(1):100–110 31. Capurro R, Britz JB (2010) In search of a code of global information ethics: the road travelled and new horizons. Ethical Space-Int J Comm Ethics 2:28–36 32. Spinello RA, Tavani HT (2001) The internet, ethical values, and conceptual frameworks: an introduction to cyberethics. ACM SIGCAS Comp Soc 31:5–7 33. Bustard JD (2018) Improving student engagement in the study of professional ethics: concepts and an example in cyber security. Sci Eng Ethics 24:683–698 34. Ben-Jacob MG, Jones NL, Brock RW, Moore KH, Ndebele P, Thabane L (2018) Putting a positive spin on ethics teaching. Int J Ethics Educ 3:125–133 35. Ben-Jacob MG (2005) Integrating computer ethics across the curriculum: a case study. Educ Tech Soc 8:198–204 36. Burton E, Goldsmith J, Mattei N (2018) How to teach computer ethics through science fiction. Comm ACM 61:54–64 37. Johnstone J (2007) Technology as empowerment: a capability approach to computer ethics. Ethics Inf Tech 9:73–87 38. Iqbal J, Beigh BM (2018) Software engineering a profession: Indian perspective. Int J Eng Tech Sci Res 5:1046–1053 39. Iqbal J, Beigh BM (2017) Computer ethics from obscure to ubiquitous. Int J Adv Res Comp Sci 8:983–990 40. Simshaw DT (2014) Legal ethics and data security: our individual and collective obligation to protect client data. Am J Trial Advoc 38:549 41. Lin JX (2003) Educational enlightenments from internet ethics issues. J Inf Tech Soc 2:65–72

Detection of Malicious URLs on Twitter Nureni Ayofe Azeez, Oluwadamilola Atiku, Sanjay Misra, Adewole Adewumi, Ravin Ahuja, and Robertas Damasevicius

Abstract Twitter is an online social network that is popular for its use of 140character messages called tweets to exchange information, news and connects the global world. Due to the large audience of people that make use of twitter, malicious users from time to time try to find ways to attack it. This is because the usages of URLs in tweets expose them and make them prone to attacks such as malware distribution, phishing, spam and scam. In this project, a system is developed that detected suspicious URLs on twitter, and the proposed system investigates the correlation of URL redirect chains extracted from various tweets on twitter. Therefore, after a large number of tweets are collected from twitter public timeline, a classifier by naive Bayes machine learning algorithm is built using the data. Keywords Malicious · Redirect chains · Machine learning · Classifier · Phishing

N. A. Azeez (B) · O. Atiku University of Lagos, Lagos, Nigeria e-mail: [email protected] O. Atiku e-mail: [email protected] S. Misra · A. Adewumi Covenant University, Canaan Land, KM 10, Idiroko Rood, P. M. B. 1023, Ota, Ogun State, Nigeria e-mail: [email protected] A. Adewumi e-mail: [email protected] R. Ahuja Vishwakarma Skill University, Gurugram, Haryana, India R. Damasevicius Kanus University of Technology, Kaunas, Lithuania e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_29

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1 Introduction Twitter is a social networking site. It is an online social networking platform that enables users to send and read short 140-character messages called tweets. These tweets are microblog posts that contain things like photos, videos, links of articles. These tweets are by default publicly visible to all those following the tweeter. Also, each tweet can get replies from other users to begin a conversation about trending gist, news and interesting new content. Trending topic; a word, phrase or topic is named trending on twitter when people talk about it at a greater rate than others [1]. Twitter uses 140-character because it realizes online readers have a very short attention span and always prefer getting straight to the point. Some terms used in twitter include: Following; this is a term used when a user subscribe to other users’ tweets. Followers; this is a term used for the subscribers. Retweet; this term simply means to repost a message from another user and share it with one’s own followers. @ Sign followed by username is used to mention a user in a tweet or to reply to other users [2]. Due to the limitation of the characters that can be posted on twitter, people share URLs known as Uniform Resource Locator of the webpages that possess the information. Despite this, there is still a need to shorten the length of URLs whenever a user wants to share URLs with friends via tweets on a twitter page. This is because some URLs are overly long and complicated, and thus they might not meet the specification of twitter of not having more than 140 characters in a tweet. This led to the popularity of URL shortening services on Twitter. In this project, a system is developed that detected suspicious URLs on twitter. Instead of checking for the landing pages of each URL in each tweet, the proposed system investigates correlation of URL redirect chains extracted from various tweets in twitter. This system is made simpler because attackers do have limited resources and are left with no other choice but to reuse them. Also, their redirect chains often share the same URLs; this makes it feasible to determine the suspiciousness in almost real time. Therefore, after a large number of tweets are collected from twitter public timeline, a naive Bayes classifier is used to classify the data.

2 Background Malicious URLs cannot be explained in detail without starting from the basic of it which is the concept of malware. Malware is an abbreviated term for malicious software. It is software that is specifically created to damage a computer without the knowledge of the owner. It is used to cause disruptions of computer operations, gain access to private computer systems or even at times display ads that are unwanted. The term malware was given by Yisrael Radai in 1990, but before this time, malicious software was called computer viruses. Software is considered a malware based on the intents of the creator to use the software to act against the requirements of

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Table 1 Characteristics of suspicious URLs S/N Characteristics of suspicious URLS 1

They use a number of different Twitter accounts and shortened URLs

2

They use a number of domain names/IP address to cloak suspicious URLs

3

To avoid being investigated, they use long redirect chain

4

They appear more frequently in the Twitter public timeline than benign URLs over several days

Table 2 Time used to classify a single URL

Component

Average running time (s)

Redirect chain crawling

250

Domain grouping

10

Feature extraction

5.5

Classification

2.5

Total

268

the computer user and not on the actual features of such software neither does malware include software that causes unintentional harm due to its inadequacy. Malware is used as a single term to refer to a variety of intrusive and malign software which include computer viruses, worms, Trojan horses, spyware, scareware and some other malicious programs [3]. What is a Malicious URL? Malicious URL is a URL created with malicious purpose for them to download any type of malware which can be spam or phishing messages to the affected computer [4, 5]. It is a URL that whenever a user clicks on would expose such user to an unsafe landing page and potential exploitation [6]. There are some meaningful characteristics of suspicious URLs which form the basis for the features that are used to classify suspicious URLs. These characteristics are given in Table 1.

3 Description of the System Components The system can be viewed in four stages/module (a self-contained component that is used in combination with other components) description. This shows the interrelated components that are used to explain the details of the system designed [7]. And it starts with the collection of data. The component of data two main subcomponents: the first is the tweets collection with URLs from twitter; the second is crawling which works for the URL redirection [8]. In collecting the tweets and its context, stream of Twitter API is made use of, so by adding individual account in API we can collect data from such account from the API

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itself. Whenever the component acquires any tweet along with URL, the crawling thread that verifies all the redirections of the URLs is executed. Consequently, it looks for the relative and similar IP addresses. The recovered URL and IP addresses are attached by the crawler thread to the Tweet data and later pushes the data in a queue of Tweets [9]. The second component is feature extraction. This component major function is extracting feature vectors, and it has two other subcomponents which are grouping the same domains and finding entry points for URLs. The tweet queue is monitored by this component to verify if adequate number of tweets have been acquired. In this component, features are extracted based on URL redirection, HTML content and also based on number of correlations [10]. A tweet window is used by the system instead of individual tweet. As a result, when additional ‘w’ tweets are collected for the implementation—‘w’ is taken as 700, w tweets are popped from the tweet queue. The component verifies the URLs in the ‘w’ tweets if they adopt the usage of similar IP address domain. The component chooses to replace the domain names whenever at least one IP address is being shared by many URLs [11]. Summarily, for each of the entry point URL, the component attempt to find the redirected URL chains and subsequently extract various features of the IP addresses from the URLs and later redirect chains with similar tweets. The features extracted will later change to real values of feature vectors. The training component is done by the training of the classifier [12]. Since the supervised learning algorithm used is an offline type. The feature vectors for classification are newer than the feature vectors used for training. Labelled training vectors are used for periodical updating of the classifier. The input features vector is used for executing classifier component [13]. The component flags off their respective URLs along with tweet data as suspicious whenever the classifier returns different malicious feature vectors [14]. The detected suspicious URLs will be sent to a reliable flexible environment for detailed investigation and analysis [15].

4 System Architecture For detailed working scenario of the system, Fig. 1 provides the architecture that gives adequate flow of information within the system. Following the architecture, the system was developed to effectively distinguish malicious URLs from benign URLs [16]. Starting from the leftmost part of the diagram, the arrow depicts the collection of tweets from the twitter stream using the twitter streaming API [17]. The tweets must have URLs as the URLs we are working with [18]. Whenever the component acquires a tweet with URL, it executes a crawling thread that checks all the redirections of the URL and looks up for the relative IP ADDRESS [19]. Once

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Fig. 1 Diagram depicting the architecture of the system [6, 12]

URL redirect, then check is made for the entry point where URLs that have the same domain group is collected [20].

5 Naive Bayes Algorithm Naïve Bayesian (NB) classification algorithm makes use of Bayes’ theorem strong assumptions between the features as the case may be [21]. NB is a popular and one of the most useful learning algorithms for the classification of text along the word frequencies. It is commonly used in spam filtering [22]. Given a dependent class variable C with a small number of outcomes or classes which is conditional on several feature variables, each URL in an email is represented by a feature vector F = (F1 , F2 , F3 , . . . , Fn ) where each of the property, F1 , F2 , F3 , . . . , Fn is independent [23]. A naive Bayes classifier can be represented as follows [24]:

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N. A. Azeez et al. 300 250 200 150 100 50 0 Redirect chain crawling

Domain Grouping

Feature ExtracƟon

ClassificaƟon

Fig. 2 Graph that depicts components against their running time (Table 2)

P(C = c|F1 , . . . , Fn ) P(C = c).P(F1 , . . . , Fn |C = c) . = k∈(spam,legitimate) P(C = k).P(F1 , . . . , Fn |C = k)

(1)

The ‘naive’ conditional independence assumes that each feature Fi is conditionally independent of every other feature F j ( j = i) given a class C. where P(Fi |C) and P(C) can be easily calculated from the training samples (Fig. 2). The following are the basic terms and definitions [25]: 1. True positives (TP): This occurs in a situation whereby there is a ‘yes’ prediction (legitimate) and it is legitimate. 2. True negatives (TN): This occurs in a situation whereby there is a ‘no’ prediction, and it is a fake URL. 3. False positives (FP): This occurs in a situation whereby there is a ‘yes’ prediction, but it is a fake URLs. (Also known as a ‘Type I error’.) 4. False negatives (FN): This occurs in a situation whereby there is a ‘no’ prediction, but it is a legitimate. (Also known as a ‘Type II error’.) Precision or positive predictive value can simply be defined as the fraction of cases and occurrences that are relevant, that is: PRECISION = TP/TP + FP. Recall (also known as sensitivity) is the fraction of relevant instances that are retrieved. Both precision and recall are therefore based on an understanding and measure of relevance [26]. RECALL = TP/TP + FN. In conclusion, by using compromised account, 700 URLs and top-4 features (Table 1) we get 90% accuracy in our system (Figs. 3 and 4). The classifier [27] was able to train 700 data that were collected from the database. From the result obtained, the system was able to separate malicious URLs from non-malicious URLs [28] (Tables 3, 4, 5, 6 and 7; Fig. 5).

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Fig. 3 Tweets collected from the twitter public timeline

Fig. 4 Tweet stored with their redirect chains and URLs in the database Table 3 First result (100 data) from the classification No 1

Malicious URLs

Non-malicious URLs

Percentage of malicious URLs

Percentage of malicious URLs

100

77

23

77

23

Table 4 Second result from the classification using 250 No 2

Malicious URLs

Non-malicious URLs

Percentage of malicious URLs

Percentage of malicious URLs

250

160

90

64

36

Table 5 Third result from the classification using 400 dataset No 3

Malicious URLs

Non-malicious URLs

Percentage of malicious URLs

Percentage of malicious URLs

400

263

137

65.75

34.25

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Table 6 Fourth result from the classification using 650 dataset No 4

Malicious URLs

Non-malicious URLs

Percentage of malicious URLs

Percentage of malicious URLs

650

452

198

69.53

30.47

Table 7 Fifth result from the classification using 700 No 5

Malicious URLs

Non-malicious URLs

Percentage of malicious URLs

Percentage of malicious URLs

700

490

210

70

30

90 80 70 60 50 40 30 20 10 0

malicious non-malicious

% for 100 % for 250 % for 400 % for 650 % for 700 data data data data data

Fig. 5 Bar chart that depicts the percentage accuracy of 100–700 datasets

6 Conclusion This study shows a system with an architecture that is wholly based on correlation of URLs. And with the help of crawler browser, there is blockage of the malicious URLs and all these (including the correlation of URLs) can be done dynamically at runtime. Existing systems for detecting suspicious URLs are weak and inefficient at protecting users against conditional redirection servers which attackers use to distinguish investigators (crawler browsers) from normal browsers. When the user is detected to be a crawler browser, he or she is redirected to a benign page while cloaking the attacker’s malicious page which he would redirect a user using a normal browser to. The system developed in this study is capable and very efficient when safeguarding against any form of conditional redirection, this is simply as a result of the fact that it does not rely on the features of malicious landing pages that cannot be accessible. Rather it focuses on the correlations of several redirect chains. We use the correlation of these redirect chains to introduce new features, and then we implemented a classification using these features. By so doing, we could get the system’s precision, reliability, accuracy and efficiency.

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The rating of the system gives a system that is highly dependable and can be implemented as a real-time system to categorize several tweets from the twitter public timeline. Acknowledgements We acknowledge the support and sponsorship provided by Covenant University through the Centre for Research, Innovation and Discovery (CUCRID).

References 1. Stone-Gross B, Cova M, Cavallarol (2009) Your Botnet is my Botnet: analysis of a Botnet takeover. In: ACM conference computer and communications security (CCS) 2. Canali D, Cova M (2011) Prophiler: a fast filter for the large-scale detection of malicious webpages. In. International world wide web conference (WWW) 3. Benevenuto F, Magno G (2010) Detecting spammers on Twitter. In. Collaboration electronic messaging anti-abuse and spam conference (CEAS) 4. Azeez NA, Asuzu OJ, Misra S, Adewumi A, Ahuja R, Maskeliunas R (2018) Comparative evaluation of machine learning algorithms for network intrusion detection using Weka. In: Towards extensible and adaptable methods in computing. Springer, pp 195–208 5. Ibikunle F, Eweniyi O (2013) Approach to cyber security issues in Nigeria: challenges and solution. Int J Cogn Res Sci Eng Educ (IJCRSEE), 100–110 6. Kim J, Lee S (2013) WarningBird: detecting suspicious URLs in Twitter stream. IEEE Trans Depend Secur Comput 10(3):183–195 7. Nureni AA, Irwin B (2010) Cyber security: challenges and the way forward. GESJ: Comput Sci Telecommun, 1512–1232 8. Stringhini G, Kruegel C (2010) Detecting spammers on social networks. In: Annual computer security applications conference (ACSAC) 9. Azeez NA, Iliyas HD (2016) Implementation of a 4-tier cloud-based architecture for collaborative health care delivery. Niger J Technol Dev 13(1):17–25 10. Ayofe AN, Adebayo SB, Ajetola AR, Abdulwahab AF (2010) A framework for computer aided investigation of ATM fraud in Nigeria. Int J Soft Comput 5(3):78–82 11. Thomas K, Grier C (2011) Suspended accounts in retrospect: an analysis of twitter spam. In. internet measurement conference (IMC) 12. Wang A (2010) Don’t follow me, spam detecting in Twitter. In. International conference security and cryptography (SECRYPT) 13. Denning ED (1987) An Intrusion-detection model. IEEE Trans Softw Eng, 222–232 14. Azeez NA, Venter IM (2013) Towards ensuring scalability, interoperability and efficient access control in a multi-domain grid-based environment. Saiee Africa Res 104(2):54–68 15. Azeez NA, Ademolu O (2016) CyberProtector: identifying compromised URLs in electronic mails with Bayesian classification. In: International conference computational science and computational intelligence, pp 959–965 16. Grier C, Thomas K (2010) @Spam: the underground on 140 characters or less. In: Proceedings 17th ACM conference computer and communications security (CCS) 17. Deering S (1998) Internet protocol, version 6(IPv6) specification RFC 2460 18. (n.d.) Tweet Developers. Streaming API. http://dev.twitter.com/docs/streaming-api 19. Kwak H, Lee C (2010) What is Twitter, a social network or a news media? In. International world wide web conference (WWW) 20. Azeez NA, Olayinka AF, Fasina EP, Venter IM (2015) Evaluation of a flexible column-based access control security model for medical-based information. J Comput Sci Appl 22(1):14–25 21. (n.d.) TweetAttacks. Twitter marketing software that breaks the limits. http://tweetattacks.com

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22. Azeez NA, Babatope AB (2016) AANtID: an alternative approach to network intrusion detection. J Comput Sci Appl 23(1) 23. Lin Y-W Chen J (2006) Combining SVMs with various feature selection strategies. In: Feature extraction, vol 207 of studies in fuzziness and soft computing, pp 315–324 24. (1981) Internet Protocol: DARPA internet program protocol specification. USC Information Science Institute 25. Ma J, Saul LK (2009) Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In. ACM SIGKDD international conference knowledge discovery and data mining (KDD) 26. El-Buhaisi AN (2013) Detection model for pharming attack based on IP-address check and website predictability. Islamic University of Gaza, pp 1–67 27. Song J, Lee S (2011) Spam filtering in Twitter using sender-receiver relationship. In. International symposium recent advances in intrusion detection (RAID) 28. Fan R-E, Chang K-W, Hsieh C-J, Wang XR (2008). LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874

Human Rights’ Issues and Media/Communication Theories in the Wake of Artificial Intelligence Technologies: The Fate of Electorates in Twenty-First-Century American Politics I. A. P. Wogu, Sanjay Misra, C. O. Roland-Otaru, O. D. Udoh, E. Awogu-Maduagwu, and Robertas Damasevicius Abstract The ability for individuals to effectively communicate their thoughts, ideas and feelings amongst fellow beings is perceived as one of the greatest features distinguishing man from other living creatures on earth. The freedom to communicate such thoughts—in certain nations of the world—are perceived as one of man’s inalienable rights as a free individual in the society. Consequently, scholars have propounded theories to aid in explaining the trends of thought which modes of communication should follow. The proliferation of artificial intelligence (AI) technologies in the twenty-first century into the media industry seems to question the very foundations on which most renowned media and communications theory were founded on. Some scholars argue that political campaign experts have taken advantage of the adoption of innovations in AI technologies in the media to manipulate man’s freedom to communicate and exercise his wishes in the political arena. Consequently, the paper adopts Creswell’s qualitative method for research in the social science since it promotes drawing logical deductions from the analysis of propositions and theorems. The paper observes that the adoption of twenty-first-century AI technologies in the I. A. P. Wogu · S. Misra (B) · O. D. Udoh · E. Awogu-Maduagwu Covenant University, Canaan land, KM 10, Idiroko Rood, P. M. B. 1023, Ota, Ogun State, Nigeria e-mail: [email protected] I. A. P. Wogu e-mail: [email protected] O. D. Udoh e-mail: [email protected] E. Awogu-Maduagwu e-mail: [email protected] C. O. Roland-Otaru Babcock University, Ilishan-Remo, Ogun State, Nigeria e-mail: [email protected] R. Damasevicius Kaunas University of Technology, Kaunas, Lithuania © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_30

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media industries has distorted existing theories of media/communication. Furthermore, the proliferation of AI technologies for politicking tends to adversely violate the inalienable rights individuals have to freely communicate their political opinions during elections. Media/communications scholars are admonished to extend research directed at understanding the degree of influence which AI technology exerts on media/communication theories with a view to addressing rising concerns for mankind and the media industry. Keywords AI technologies · Communication theories · Inalienable rights’ · Media industries · Media theories · Political campaigns

1 Introduction The ability for individuals to effectively communicate their thoughts, ideas and feelings amongst other fellow beings is perceived as one of the greatest features which distinguish man from every other living creature on the surface of the earth [1]. The freedom to communicate such thoughts, in certain nations of the world, is most times considered as one of man’s inalienable rights’ as individuals in the society [2, 3]. In view of this supposition, scholars have propounded theories to aid in explaining or predicting the direction or trends of thought which modes of communication should follow [4, 5]. However, the advent of innovations in AI technology in recent times appears to debunk and question the very foundation on which most renowned theories of media and communication are founded on. These innovations, current research reveals [6, 7], have become a weapon in the hands of political campaign experts who have taken advantage of these innovations in AI technology to manipulate the minds of unsuspecting users of various media and communication platforms to their favour [8]. Studies reveal [9, 10] that professional political campaigners now have intelligent programs which are able to interfere with the freedom individuals have to make a free choice or to express their opinion during elections via special AI ad software utilized and displayed in the media during election campaigns. This present study is therefore an attempt at evaluating the degree of human rights violated by professional campaigners, via the use of AI ad software, especially during elections. The study would also evaluate the degree of distortions and influence which these AI innovations supposedly have had on existing media and communications theories with a view to proffering solutions. The Problematic 1. Firstly, innovations arising from twenty-first-century AI technologies have largely made of little or no effect, the stance which certain media and communication theory asserts about the rules guarding various modes and platforms for communication. Hence, scholars now question the viability of these media theories.

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2. There is a growing fear that the freedom which individuals in liberal societies have to express themselves on various political issues, have been hijacked by professional campaign experts who deploy special AI ad campaign software, believed to have the capacity to manipulate and influence the freedom to express their political obligations freely and fairly during elections. Aims and Objectives of the Paper 1. In the light of the above issues raised, this paper seeks to evaluate the degree of impact which the advent of AI technology has had on most of the supposed viable theories in the media/communications industry. 2. The paper evaluates the degree to which the inalienable rights of individuals are violated as a result of the massive proliferation of innovations in AI technologies, utilized by contemporary political campaign experts during elections. Method of Research The issues slated for discussion in this paper necessitates that Karl Marx’s alienation theory [11] be adopted as the theoretical framework for the paper since the theory provides appropriate platforms for evaluating and interrogating issues associated with rights violations, etc. Creswell’s qualitative method of research in the social science [12] is adopted for the paper since it promotes and emphasizes on the need to draw inferences from empirical propositions and logically derived theorems. Research designs like the explanatory research design ERD, the descriptive research design (DRD) and the deconstructive research design (DCRD) [13] were also explored. The ex-post facto research method [14] is also considered for the study since the researchers largely rely on previously analyzed data from other studies. Justification and Relevance of the Paper The question of ‘how free and fair elections are’? continues to be the subject of debates for most international observers from developed democracies of the world. Recent studies [9, 10, 15–17], however, reveals that while very advanced democracies in the world like the USA and Great Britain send out international election observers to monitor, observe and to guide the order of democratically organized elections, with the view to ensuring that they are conducted freely and fairly, elections conducted in their very own back yards have been questioned for the integrity of the results published. The ongoing crisis with the Brexit referendum in the UK and the questions surrounding the legitimacy of the 2016 US Presidential Elections are some of the cases in point that supports these claims. The need therefore arises to interrogate the degree of involvement of AI technology with the results of past elections in these countries and the degree of inalienable rights of the electorates’ violated during the process.

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2 Review of Related Media and Communication Theories 2.1 Conceptual Clarifications While the term ‘mass media’ is used in association with the forms of communication which are either presented as written or spoken words which are broadcasted such that the information contained therein is transmitted to a wide range of audience, usually via mediums like the radio, television, the Internet, magazines, newspapers and movies [18, 19]. From this perspective, the mass media could be placed in two different categories. On the one hand is the print media, comprising of magazines, emails, pamphlets, newspapers and any other device capable of carrying visible messages to the sense of vision, from one point to the other. On the other hand, it is the electronic media which usually transmits messages and information via audio and visual mediums like the radio for sound and mediums like the television for vision. The Internet also qualifies as an example a typical electronic media. Etymologically speaking, the word communication has its root meaning emerging from the French word comunicacion, and the Latin words communicatio (n-) [4]. When taken separately, these words simply have to do with ‘what is common to all’ and what it means: ‘to share’. On a general note, communication is perceived as a two-way process in which those involved do more than exchange feelings, thoughts and ideas. They also create scenarios for arriving at meanings and mutual understanding on all sorts of issues [4]. Speech usually is the main mode through which these communication take place, aside this, communication has also been known to take place via signals, signs and writing. Thus, when one expresses himself in a way where he or she is readily understood, we infer that communication has taken place [4]. From another perspective, communication has been perceived as a process of conveying information from the sender to the receiver via a medium which the receiver would readily understand [4]. In summation, one can deduce that communication is a dynamic and complex human process that makes the exchange of information via several medium possible [20, 21].

2.2 Review of Some Media/Communication Theories Mass communication theory more often than not, seeks to explore the anticipated and unanticipated effect of the impact of the media on the masses; it seeks to find meaning to the interpretations or explanations that are presented to the audience; the effects it has on the lives of individuals; and the lives of persons it directly affects. In other words, it is the explanation and prediction of social phenomena that attempts to relate mass communication to various aspects of our personal and cultural lives. A few of these theories shall be discussed here: (1) the magic bullet theory/hypodermic needle theory, (2) the cultivation theory, (3) two-step theory/the gatekeeper theory and (4) the multi-step flow theory.

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The magic bullet theory/hypodermic needle theory: This theory is also referred to as the transmission belt theory. It largely holds that messages transmitted from media houses are very powerful; hence, they can greatly influence the recipients (the audience) when injected into their minds. Thus, information transmitted from one point to the other is portrayed as having a bullet effect; it has a special kind of effect when displayed on the media as media messages. Hence, when transmitted, it goes out to do just as it has been directed. The audience in this case has no choice than to accept in total, the entire content of the information passed through the media to them [22]. The kind of communication passed on here is too powerful to give room for the recipients to process the information received otherwise [1]. The cultivation theory: Created by George Gerbner, this theory holds that media exposures arising from mediums, such as the television and other mediums through which visual messages are displayed to the audience, has the ability to cultivate and transmit, in most cases, a distorted view of what reality is [23, 24]. The consistent exposure of the audience to certain television advertisement, programs, movies, etc., tend to make viewers to believe that the world in reality is constructed in the same way that images or messages are displayed on the various mediums available. Two-step theory/the gatekeeper theory: This theory emerged from the need to control the supposed power and influence which media messages were supposed to have [21]. Thus, this theory proposes that media content should first be discussed and deliberated on amongst special public panellists before they are displayed in full view of the audience, not before. Thus, as gatekeepers, the media functions in capacities that include: retain pertinent information, expand, limit, and to relay sensitive and valued information [25]. Multi-step flow theory: This theory mainly states that there is a kind of reciprocal relationship involved in the sharing of information, the kind that influences the behaviours, beliefs and attitude of the masses. What this theory presents at large is, while opinion leaders are able to create media messages of their own choice, opinion followers are usually able to—on their own—sway the opinion of leaders. This kind of scenario therefore presents complex relations with the media [26]. The review of the first 2 media/communication theories above portrays that nature, and the kind of effect the media could have on the masses and on the senders of such messages. The theme of the first 2 theories argues to a largely extent that the media has capacity to influence the behaviour of the audience to whom the media messages are directed to. In [27] own words: Media effects are the intended or unintended consequences of what the mass media does. The first three theories above thus rest on the assumption that the media has the capacity to affect the members of the audience with the messages they transmit via various media platforms. The degree of effect, depending on the theory, often varies [21]. In this case, the paper argues that the first 2 theories clearly explain scenarios that capture the kind of influence and impact which AI ad campaigners are able to have on the masses who view their messages.

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The last 2 theories discussed above, on the other hand, are examples of theories which no longer have the capacity to holds its claims in the light of emerging AI technological advancements which is now being deployed by various media outlets. Media theorists are thus left with no other choice than to grapple with the consequences of the proliferations of AI ad campaign software now used on media platforms during elections, and at other times when they need to influence the opinion and perceptions of the audience who rely on the media for guidance.

3 Twenty-First-Century Artificial Intelligent Technologies 3.1 Update on Innovations in AI Technology Innovations in AI technology have paved the way for researchers from various fields of endeavour to seek out more efficient ways of meeting daily needs and objectives. Consequently, industrialists, researchers, scientists, scholars and businessmen have explored ways of maximizing the new abilities inherent in artificial intelligent machine (AIM) technologies. The medical and health sector, for instance, has been known to utilize AIM technology to improve and enhance the ability for medical doctors and health workers to run very accurate and precise medical diagnosis [28] for patients in health facilities in the shortest possible time. Thus, they are able to enhance the medical experience and the chances of saving lives generally. Businessmen in the stock markets, for instance, are able to—with the help of advanced AI software—accurately predict when to sell and when to hold back their shares [29]. In view of this, most scholars argue that this decade has witnessed a massive success in virtually all life’s endeavours where high-level AI machines have been deployed.

3.2 AI as a Tool for Politicking in America’s Politics One significant sector that has deeply invested in recent innovations in AIM is the political sector [15]. A few decades ago, political parties and their candidates often groped in the dark regarding the tools with which to know the political climate of the electorates in their constituencies. They relied largely on instincts and the opinion of the few persons they come in contact with. But the advent of innovations in AI systems deployed in the various media platforms via big data analytics, machine learning, deep learning and artificial intelligence systems and deployed on statistical techniques which helps analysts to identify special patterns in the behaviour of electorates. The information arising from these platforms has helped political campaigners predict in advance, candidates who eventually got elected into key

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offices and positions in the US Congress, and even those who eventually got elected into to ‘the oval office’ [15]. Examples of some names of media organizations that had mastered the act of using specialized media messages for commercial purposes and to influence the minds of electorates during election periods include Cambridge Analytica (CA), Strategic Communication Laboratories (SCL) Group Limited and Global Science Research (GSR) Limited. These companies, scholars argue, are typical examples of AI politicking agent firms who more often than not, pay little or no attention to constituted moral and ethical laws in the line of their business [30].

3.3 AI Politicking and the Media/Communication Theories The ideology behind the first 2 theories reviewed for this paper, perhaps, constitutes the premise on which contemporary politicians see twenty-first-century platforms as the most appropriate avenue for communicating their party manifestos and political ideology with the view to inform, interpret, bond and create diversions [21] or manipulate the psyche of the electorates in a given locality. In this respect, the first three theories under review—applied verbatim—would perhaps, justify the move by political campaign experts who deploy these AI ad software for politicking purposes, mainly because the methods have the capacity to inject certain specialized messages into the minds of electorates with the view to manipulating them in such a way that the political decisions they eventually take would largely be to the advantage and in favour of the political party or the candidate vying for the political office in question [15] and not the electorate’. Recent studies [8, 17] reveal that theory 1 and 2 considered for this paper seem to provided explanation or justifications why certain candidates and their political parties were observed to have called into play, the magic bullet theory, the cultivation theory and the limited effect theories, as adopted. Hence, theorists argue that the adoption of specially designed AI programs and ad software where used during the 2016 US presidential election underscores the potency of these theories. In view of this, certain human rights activists/organizations [8, 17] have questioned the credibility and sanctity of the election results that brought President Donald Trump into the White House, since the opinions of most of those who participated in the said elections, where believed to have been under the spell or influence of the specially injected messages that were transfused into their minds via special AI advertisements displayed during the period in question. Scholars like [8, 15, 17] believed that were these special ads not utilized for the elections in question, and the results would have been different from what was eventually released as results arising from the 2016 US elections.

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3.4 AI Politicking and Human Rights Violation Issues As discussed in the sections above, studies reveal [31, 32] that one of the sectors which have massively engaged the benefits and services which AI provides to all sectors of human endeavours in the twenty-first century is the political sector. The names of organizations listed above are examples of companies who have mastered the art of using AI technology to engage and influence electorates during election periods. They do so by simply collecting the data of electorates who use social media platforms for one business or the other, often without the knowledge or consent of the unsuspecting citizens of the locality. By this reference is made to users of online social media platforms like Facebook, Instagram, Google for entertainment, educations or business. Organizations who collect and make use of such data for political campaign purposes are referred to as ‘Agents of AI Politicking’ [7]. Established that these AI politicking agents utilized the data belonging to certain groups of citizens for the purpose of understanding the preferences and political inclinations of electorates, without their consent, amount to a violation of their fundamental human rights. Where the same data collected during election periods are also utilizing against the electorates, to the credit of the clients who hired the services of these AI ad politicking organizations, human rights activist argue, amount to a gross violation of the fundamental human rights’ of the citizens involved. To know that these AI ad organizations commercialize their services and make profit thereof, is considered most troubling and inhumane by most human rights’ activists [33] who in various arguments had argued against the acts of profiteering which take place amongst AI political agents, at the expense of unsuspecting citizens.

4 Further Discussion 4.1 Media/Communication Theories and AI Politicking in Twenty-First-Century America One of the leading firms in America that found justifications for using the first three theories discussed above for politicking purposes is Cambridge Analytica. In a recent scandal about the gross violation of the privacy and fundamental human rights of over 87 million US citizens during the 2016 US Presidential Elections, the company was one of the those accused to have been involved in the scandal. As though to corroborate these accusations, the company boldly boasted about her escapades on the front page of their website, saying: We are the global leader in data-driven campaigning with over 25 years of experience, supporting more than 100 campaigns across five continents. We have assisted our clients with special ad campaign strategies which have played pivotal roles in winning congressional, state and presidential elections [34].

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Before the scandal began, Cambridge Analytica had in 2016, largely rolled out a widespread advertising campaign designed to target fluid electorates based on their psychological dispositions [15, 16]. Advertisements from CA were designed in such a way that different individuals received different messages according to their various dispositions and susceptibility to different arguments proposed by each political party and their candidates. Those considered to be paranoid with the basic ideology of certain political parties were given advertisements that were associated with their fears [17]. Those discovered to have strong conservative predispositions were made to receive advertisements which emphasize on themes relating to community and tradition. All these were made possible by the availability of real-time data gathered on the electorates’ behaviours and preferences from social media platforms. The Internet footprints arising from the analysis and evaluations of data generated from these electorates were then used to build psychological and behavioural profiles that were unique to each individual [15]. The platform created by CA is what Christopher Wylie in [8, 9] referred to as a ‘political ad targeting technology’ or ‘an arsenal of weapons for fighting a culture war on the electorates’. This mode of ad campaigning (politicking) becomes the order of the day, years and months before the 2016 US Presidential Elections commenced. Political parties therefore embarked on the game of outwitting the other by seeking how best to deploy what Wylie described as a weapon which had the capacity to unleash an ‘arsenal of weapons to fight a culture war’ [8]. By this disposition, the magic bullet theory of media/communication industry is seen fully manifesting. Some of the big names in American politics who utilized these AI ad firms include: Ted Cruz, John Bolton, Ben Carson, John and Tom Hills. Steve Bannon, a former White House senior strategist and campaign adviser to Donald Trump, also confirmed that the Party relied on the services of these special AI ad firms for the kind of result they anticipated for the 2018 US presidential elections [35].

4.2 The Implications of AI Politicking on Communication/Media Theories Before the proliferation of AI innovations for politicking purposes during American elections, media/communication theories of the twenty-first century largely performed the following functions: to instruct, interprets, inform, bond and to perform adversary roles to the members of the public [21]. However, the saturation of the media industry has led to an increase in competition among media outlets who strive to provide information through all the available media. Consequently, most of the information/news/messages that emanate from these media houses were released prematurely, inaccurately, or laden with some degree of bias, on the part of the location or individual presenting the information. Where this scenario becomes the case, scholars [1, 5] observed that the information/news/messages provided and the consequences of these messages, distorted existing theories which guides media

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operations, especially in the light of the perceived functions and responsibilities of media/communication industries. When a carefully crafted media advertisement fails to inform and instruct its viewers on the right political party or candidate to affiliate to, then such theories are known to have failed to play the instructive, adversary or bonding roles which several theories of mass communication alludes to. A case in point already discussed above reveals a scenario where specially crafted messages emanating from a previously analyzed data belonging to electorates’ on their various preferences, courtesy of special AI ad software by firms like CA. Scholars in this regards argue that the proliferation of these AI innovations deployed for use in the media for politicking purposes largely debunks the popular media theories as discussed above (4th and 5th theories).

4.3 The Impact of AI Politicking on the Inalienable Rights of Electorates One essential fact evolving from the 2016 US Presidential Elections is the fact that AI was used to manipulate the minds and eventual votes cast by the electorates who participated in the series of elections that took place during the period under review. This scenario was made possible by the availability of real-time data on voters’ behaviour and their preferences on social media platforms in the targeted areas. The data gathered were largely used to create unique psychographical and behavioural profiles on the electorates [9, 10]. Hence, for a candidate like Donald Trump who is known for his flexibility with campaign promises, the platform designed by these special ad firms comes in very handy and appropriate for the moment. This is because the campaign advertisement was designed to send different messages to different people all at the same time. The message that each individual got was influenced by the personal perspective he or she had regarding the candidate running for election. Most fundamental to this approach is the need for political campaigners to find the most appropriate soft sport and preferences of each electorates in the targeted area, which was capable of spurning such persons into acting or vote otherwise, especially after being exposed to carefully prepared AI ads [10]. Christopher Wylie’s testimonies revealed that it was the ingenuity of firms like CA that masterminded the design and deployment of robot-like system which took over the aggressive spreading of propaganda, one-sided news and political information designed to manufacture illusive public support that never really existed. This was done with the view to give electorates’ false, misleading information and messages that aided in altering the final preference or decisions of the electorate on the day of election [15, 28, 35]. The plan to design perfect political propaganda mechanisms capable of swaying the votes of electorates for the Republican Party began much earlier in the years

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and months preceding 2016. It started when a company known as GSR requested to collect data from Facebook in the pretext that it would be used purely for academic research purpose. This turned out not to be so. This company, however, passed the data collected from Facebook to another company; Cambridge Analytica. The original data was obtained via a digital application created by a Psychologist Professor from Cambridge University known as Aleksandr Kogan. The personality quiz the professor created, required that all who sought to participate in the study, must first subscribe to the quiz on the Facebook platform. Such participants would also need to first logging into the platform with their credentials, after installing the applications on their various account and systems. The first experiment carried out revealed that only about 270,000 Facebook users subscribed to participate in the quiz. However, what these 270,000 subscribers did not know is that, by giving their initial data to the professor, they had also given the data and personal information belonging to all the persons and close friends in their personal contact list—who did not initially participate in the original quiz—to CA. Consequently, from the contact details of 270,000, CA AI ad campaign company was able to access a total of 78 million Facebook fans in the UK and in America alone [36]. Scholars to this end [37] have argued that Aleksandr Kogan from Cambridge University and CA violated the rights’ of the first 270,000 persons who participate in the quiz initially intended to be for academic purpose, but was not. The rights’ of all the persons on the contact lists of the first 270,000 participants were also violated when their consent was not sought for before using their data and those of their friends for monetary or commercial gains by CA. More troubling is the fact that the data of 87 million individuals were used against them by CA to manipulate their voting preferences during the various election exercises which took place in the countries under review [37, 38].

5 Summary and Conclusion So far in this study, the researchers had reviewed 4 media/communications theories with the view to ascertaining the viability of these theories in relation to the objective of the paper. The enormous impact which AI has had in the field of politics and other related endeavours were discussed, with the view to ascertain the impact and influence which twenty-first-century AI politicking was beginning to have on the electorates’ ineligible rights’ as fee individuals in the society. Hence, in line with the objectives proposed for this paper, the following deductions and findings were made:

5.1 Summary of Findings 1. Established that most media/communication theories were largely designed to perform basic functions such as to inform, interpret, instruct and to bond the

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audience with the content of its messages; among other core functions, the emergence and adoption of innovations in AI ad campaigns during recent elections—the study reveals—distort the very foundation on which some of the basic media/communication theories rests’ upon. 2. The study observed that the gatekeeping function of media theories have been overwhelmed by the ingenuity in the carefully crafted AI ad campaigns used by AI politicking agents. This fact adds credence to the strong influence of the ‘magic bullet theory’. 3. The proliferation of AI politicking approaches which allows political campaigners to steal data and information of unsuspecting individuals has initiated scenarios which totally alienates the electorates from their inalienable rights’ to freely express themselves politically or otherwise.

5.2 Recommendations 1. In view of the findings made in this paper, media/communication theorists and researchers are enjoined to extend research towards formulating and proposing fresh theories that would appropriately capture contemporary realities and the influence which AI technology continues to have on contemporary media. 2. There is an urgent need for government and all concerned bodies to embark on an upward review of the electoral laws and regulations which could appropriately guide political campaigns and election/electoral processes all over the world and America in particular. 3. Where there are clear and proven cases of violating the inalienable rights of electorates’ by government agents, political parties or campaign agents appropriates steps and measures ought to be taken to meet out necessary and proportional sanctions to the earning parties. Where appropriate, necessary monetary compensation should be paid by erring parties to victims.

5.3 Contribution to Knowledge 1. The research for the first time interrogates the viability of contemporary media and mass communication’s theories in the light of emerging innovations in AI technology with the view to discovering the degree of influence or distortion which the emergence of AI innovations in technology has on the inalienable rights of electorates and on media/communication theories. 2. The study brings to the fore, some of the lapses which are inherent in specific media/communication theories discuss. This creates the opportunity for contemporary researchers in this filed to strive towards formulating new theories that should stand the test of time and address current realities. 3. The study highlights some of the hazards that could arise from the unethical dispositions and application of AI innovations for campaigning and politicking

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purposes in America’s politics. This would in turn have positive implications for policy re-evaluations in the polities concerned.

5.4 Conclusion Theories ought to serve the purpose of instructing, explaining and predicting phenomenon, etc. Indeed, the place and essence of a good theory cannot be overemphasized. But where theories are debunked for whatever reason, it should be seen as an opportunity to re-evaluate the issues and factors leading to its deflating and from there, contemporary researchers ought to work towards formulating viable theories that would stand the test of time. In this case, innovations in twenty-first-century AI technologies were identified as the catalysts propelling the need for theory evaluations in the media/communication industry. The benefits of AI technology notwithstanding this paper lend credence to the rising consensus amongst scholars which opines that ‘any technology that would alienate man from the essence of his being, and hence, remove from him, the one right he has to express himself freely in the world—no matter the value placed on this technology—should be discouraged and jettisoned’. Hence, this paper further promotes the institutionalization of the 23 Asilomar Principles. The need therefore to extend research in this area by all concerned researchers and scholars cannot be overemphasized. Acknowledgements We acknowledge the support and sponsorship provided by Covenant University through the Centre for Research, Innovation and Discovery (CUCRID).

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Modeling and Simulation of Impedance-Based Algorithm on Overhead Power Distribution Network Using MATLAB Olamilekan Shobayo, Olusola Abayomi-Alli, Modupe Odusami, Sanjay Misra, and Mololuwa Safiriyu Abstract Rapid location of faults in electrical distribution system is very critical for effective and efficient operation. The operation of the power system must be reliable and as economical as possible. The persistent lingering of fault conditions in the distribution networks is the lack of efficient technology to locate the faults in the distribution network and lack of proper maintenance of the equipment in the network. Most of the accidents caused to individuals (either the consumers or the workers) in this distribution networks are caused by some of these faults that not easily identified. This research proposes a model to locate faults in a distribution network using an enhanced impedance-based method for fault location. The model was simulated on the 2nd Avenue 11 kV feeder (Lagos) with the use of one-end impedance-based method. The results show a little marginal error when the location values obtained with the model are compared to doing physical inspection on the distribution line. The parameters that could affect the effectiveness of the method were analyzed. Keywords Fault location · Impedance-based method · Power supply distribution

Please note that the LNCS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index. O. Shobayo (B) · O. Abayomi-Alli · M. Odusami · S. Misra · M. Safiriyu Department of Electrical and Information Engineering, Covenant University, Canaan land, KM 10, Idiroko Rood, P. M. B. 1023, Ota, Ogun State, Nigeria e-mail: [email protected] O. Abayomi-Alli e-mail: [email protected] M. Odusami e-mail: [email protected] S. Misra e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_31

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1 Introduction One of the major challenges faced by many developing countries is epileptic power supply, a problem that is most conversant with the electric power distribution sector. Interruptions or disruptions of the power supply for a long time have been a hindrance to the reliability and efficiency of the power system [1, 2]. An approximation of 70% of the interruptions or disruptions can be traced to the presence and occurrence of fault [3]. In power distribution systems, both overhead lines and underground cables are used in the distribution of power to their consumers. However, overhead lines’ mean of power distribution has gained a lot of usage worldwide due to the low setup cost and minimal maintenance cost when compared to their underground counterpart [4]. However, the downside to these means of power distribution is that due to their nature of deployment, i.e., exposure to the environment, they are prone to experiencing faults from time to time. The major cause of these faults can be attributed but not limited to bad weather, storms, tree branches, animals such as birds, lightning strikes, failure of the distribution components which would be because of human error or deterioration of the equipment [5, 6]. The methods used in the location of these faults Location plays a vital role in quick and effective restoration of power supply in the power distribution system [7]. The traditional method of location of fault is through inspection with the physical eyes. However, methods are being proposed in literatures and some are being used practically for the fault location on distribution lines consisting of usage of measurement of voltages and currents from one end or both ends of the lines. The impedance-based method is more implemented because of their low cost of implementation relative to high-frequency techniques which includes the techniques using traveling waves and wavelet analysis. It consists of the calculation of line impedances which can be obtained from the ends of the lines and approximation of fault distance [8]. Most of the existing methods of fault location provide excellent ways of detecting faults in power distribution systems. However, deploying these methods proves to be expensive and requires a lot of technicalities. Also, most of the methods do not take into consideration the deploying of their models on a live power distribution network to confirm the effectiveness of their proposed technique, barring the authors in [7], but their methods prove to be quite expensive compared to our proposed methodology. In this paper, we are proposing cost-effective, one-end impedance-based power system distribution fault location model, which will be deployed on a live network. This model will create a next to accurate fault location on a network with marginal error. This will involve the mathematical modeling of the impedance-based method simulated on MATLAB with values obtained from live network of 12th Avenue 11 kV feeder which is under the Eko Distribution Center, Lagos, Nigeria. The rest of this paper is organized as follows. The section ‘Related Works’ talks about other similar research that has been carried out in location of fault in power distribution lines, highlighting the methodologies used and the results obtained. Where necessary, the gap in literature was also identified. The section ‘Materials and Methods’ describes the method and materials used to carry out the research. The

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section on result and analysis deals with the result obtained and the analysis made on the result. Finally, the section ‘Conclusion’ deals with the remark that was drawn from the observations made in the result of the experiment.

2 Related Works Some techniques and methodologies have been developed that can help aid the location of faults in the power distribution systems but developing a system that deals with a variety of distribution networks as well as with the various ways in which faults can occur has so far proved to be a daunting task for system designers. In [8], a wireless live wire fault detector and protection technique was designed for the detection of faults in wires in remote areas which are humanly unapproachable. RF transceivers are used for the detection of faults in live wire, and signal communication occurs between the transmitter and the receiver units of the RF transceiver. The receiver unit of the RF transceiver receives all the signals from the transmitter units, and if all the transmitter signals are successfully received, it is an indication of the absence of fault in the wire. The authors in [9] employ a simple method for fault location which is based on traveling waves using Park’s transformation to perform the transient detection. The method assists the programmed point of fault determination immediately after the fault occurs. The implementation of this method is the Alternative Transients Program (ATP) using the model’s language and carrying out simulations for a 230 kV transmission line with the presence of a digital fault recorder at each terminal for the applicability evaluation of the proposed technique. The basis of the technique for fault location in [10] is the calculation of the mean square value of the difference between the incoming current and the outgoing current of the three phases of each section. The identification of the faulty phase is based on the analysis of three phase currents at one end of transmission line. This technique is like our proposed work in terms of parameters, but the chosen parameter for this work tends to be quite expensive. The fault location detection, identification and location in [11] are based on data-driven computational methods. The approach studied in this paper is built on matching pursuit decomposition (MPD) using Gaussian atom dictionary, hidden Markov model (HMM) of real-time frequency and voltage variation features and fault contour maps generated by machine learning algorithms in smart grid systems. A hybrid clustering algorithm is developed and used to cluster the frequency and voltage signal features into various symbols. Using the symbols, two detection HMMs are trained for fault detection to distinguish between normal and abnormal smart grid operation conditions. The author in [12] proposed an algorithm used to detect high impedance fault (HIF) in high-voltage transmission lines through the deployment of wavelet packet transform (WPT). It applies the distortion of voltage and current waveforms brought about by the implementation of HIF. The algorithm is also based on the principle of recursion, whereby the absolute values of the high-frequency signal coefficients are being added together. Evaluation of the model is then made by using the correct

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HIF models by evaluating it with transient program (EMTP) simulation studies. The proposed algorithm shows very accurate results in the detection of faults in high voltages. The pitfall of the method is that it is not robust enough to be deployed in areas of distribution, where the voltages are not on the high side. The authors in [13] created an improved impedance-based method for fault location in power distribution network with emphasis on photovoltaic (PV). They were able to derive mathematically a new quadratic equation for fault location by recording the values of voltage and current measured from the feeder source and the distributed generation terminal. The model was able to show that the values of voltage and current are the only requirement for fault location where they were able to disregard the dynamic modeling of the PV and substation. The model was used in conjunction with the p-line method to improve the accuracy. The results of the model show high accuracy when implemented with the modified 11 node testers simulated on the MATLAB test bed. However, the model lacks robustness as it does not take into consideration the photovoltaic part of the distributed generator. Milioudis et al. [14] developed a power line communication device to detect and locate high impedance fault. This is done by placing the device on the power line close to the feeder. This device monitors the line by calculating the difference in the values obtained by comparing the input impedances at near-band frequencies. This technique achieved the concept of fault detection. To locate faults, the system uses the responses it obtained from the infusion of impulse, i.e., the impulse response of the power line communication device relative to the line. This technique was also seen to work very well in detecting faults at high frequencies. The authors in [15] used the impedancebased fault location method to locate faults in a four-wire distribution line. They modified the π line method to include a fourth line, and they mathematically generated an algorithm based on the principles of circuit theory. Some specifications are made on the distribution network to ascertain the workability of the algorithm that is developed. These specifications include: error start angle, fault location, fault resistance and sub-branches. The specifications are used to implement the testing metrics on MATLAB simulation and showed very accurate results for detecting fault on the lines. The authors in [16] proposed an impedance-based algorithm to determine faults in double-circuit distribution network. Here, they developed a new quadratic equation that locates faults using the voltage and current metrics, measuring from the beginning of the line to the point of fault. The method also incorporates the line method as a yardstick for determining the accuracy of the model. For testing of the model, various components such as instrument errors, fault resistance and different inception angles of faults at different distances and fault types were measured on a thirteennode network. The result obtained was numerically accurate when benchmarked with physical lines. The combination of impedance-based method and matching sag algorithm was used to locate fault on a single phase to earth of a power distribution network by the authors in [17]. The impedance-based algorithm was deployed to deal with the possible location of faults, while the matching sag algorithm was deployed to section the area of the line where the fault is located. The fault obtained from the line is then simulated on a different location. Comparison is then made between the values of the fault on the line and the simulated result. The result shows that both

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values are very close with only a little error percentage. The gap in this work is that sag matching algorithm will not predict the exact location of the fault, since it is only taking a section of the line.

3 Materials and Methods The hourly data for an unbalanced substation was required for this research work. This data was extracted from the load readings of the entire distribution network from the headquarters of Eko Electricity Distribution Company. The readings were computed into Microsoft Excel. Figure 1 shows the hourly readings of some of the distribution networks under the Eko Electricity Distribution Company for a day of month. Figure 2 shows the fault details of feeders including their time of tripping and time of restoration.

3.1 Construction of Fault Location Model The model chosen for the calculation of the fault using the one-end impedancebased method for unbalanced distribution network is based on the measurement of the phase current and voltage at the substation. It is important to know that upon the occurrence of a fault, the protective relays would detect and classify the type of fault [18]. The algorithm begins with the modal transformation of phasor values which is shown below: Vm = T −1 Vp .

(1)

Fig. 1 Screenshot of the hourly readings of some of the feeders under the Eko electricity distribution network

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Fig. 2 Screenshot of fault details of the different feeders under the Eko electricity distribution network

Im = T −1 Ip .

(2)

Z m = T −1 Z p T.

(3)

where V m , I m and Z m are modal voltage, current and impedance, respectively. V p , I p and Z p are phasor voltage, current and impedance, respectively. T is the Clarke transformation matrix and it is shown below ⎛ √ ⎞ 1 2 √0 1 ⎜ −1 3 ⎟ T = √ ⎝ 1 √2 √√2 ⎠ 3 −1 −√ 3 1 √ 2 2

(4)

To calculate the distance of the fault from the beginning of the line, the quadratic equation is used, and the distance obtained as the root of the quadratic equation. This is also shown below. √ −b ± b2 − 4ac (5) d1,2 = 2a

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3.2 Fault Location of 2nd Avenue 11 kv Unbalanced Network Using the One-End Impedance Methods The fault location of 2nd Avenue 11 kV feeder using the impedance-based method is carried out using the following steps. • Data Collection: The model requires the verified pre-fault voltage and current data for the calculation of load impedance at each bus which are displayed as constant impedances. The phasor voltage, current and impedance are obtained from the measurements obtained at one end of the substations. • Data Transformation: The phasor voltage, current and impedance obtained at the one-end measurements are transformed into their respective modal transformations. This is achieved by Eqs. (1), (2) and (3). • Distance Calculation: The distance between the fault and the substation is calculated using Eq. (4).

3.3 Calculation of Accuracy of the Method Error measurement statistics plays a crucial part in the analysis of the effectiveness and robustness of the one-end impedance-based method to overcome the challenges of unbalanced loading of distribution networks [19]. The distance error is the difference between the distance at which the fault occurred and the distance which was estimated that the fault occurred. The formula is shown in the figure below: % error =

Destimated − Dactual × 100 L total

(6)

where Destimated is the estimated fault distance on the line. Destimated is the exact fault distance. L total is the total length of the feeder.

4 Results and Analysis After simulating the algorithm using MATLAB software, the model was used for the calculation of distance of a fault which had earlier occurred on 2nd Avenue 11 kV feeder. The accuracy of the algorithm was tested, and parameters which could affect the effectiveness of the method were also examined.

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Fig. 3 A screenshot of the excel representation of the load consumed hourly

4.1 Presentation and Preprocessing of Data Data for the 2nd Avenue 11 kV feeder was extracted from the data obtained from the 2nd Avenue 11 kV feeder and was represented in two columns on an Excel worksheet. The first column represents the hour of the day, and the second column represents the corresponding load consumed for that hour. The data for each day was marked with the date of day. Figure 3 shows a picture of the load consumed as represented on the Excel worksheet. From the provided data, it can be observed that at the time of fault 08:00 h, the feeder generating a phase voltage of 11000 V was also generating a power of 1.5 MW, thereby producing a current of approximately 90 A.

4.2 Implementation of the One-End Impedance-Based Method For the one-end impedance-based method, the fault location parameters are based on the measurements at one end of the line, i.e., the substations. In this paper, the data obtained for the hour prior to the occurrence of faults is to be used. The rated load at some of the node is represented in Table 1. The 2nd Avenue feeder which has an impedance of 0.2627 + j0.7445 had been reported to have tripped on fault by 08:00 h on March 24 at a current of 90 A. These reported values are inputted into the MATLAB program, and two distance values are obtained. However, only one value is acceptable while the other is neglected; this is because the second which would be neglected is either a negative non-logic value or a positive value which is greater than the length of the feeder. Table 1 Rated loads at each node for the 2nd Avenue 11 kV feeder

Node

Rated kVA

D1

200

D2

1800

D3

1000

D4

300

D5

500

D6

500

D7

500

D8

500

D9

200

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From Fig. 4, we can see that d 1 is a negative non-logic value that is not applicable to the system. However, d 2 has the highest range of values to be approximately 1910 m. In the one-line diagram, every line span is approximately 50 m. Therefore, the fault can be said to have occurred around Cameron Green Estate 800 kVA substation. The source of fault can be said to be a jumper cut or a phase of the line snapped. However, upon the feeder being patrolled by the technical men, the fault was noticed to be a 2NO jumper cut at Cameron Green Estate 800 kVA substation which is 1950 m. The error in the one-end impedance-based method calculation as defined by Eq. (6) is obtained to be 0.4948%. The error obtained from the main fault location while manually tracing the line to the fault location obtained when the process is automated on the MATLAB software is shown on the plot in Fig. 5. As seen from the graph, we can observe that an increase in the value of fault resistance leads to an increase in the error provided by the impedance-based method.

Fig. 4 Two distance values on MATLAB

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Fig. 5 Graph of fault resistance against error

5 Conclusion This work has accurately simulated the fault location algorithm of the one-end impedance-based method for unbalanced distribution network using MATLAB. It has also analyzed the effect of some parameters such as fault resistance and load variation on the effectiveness of the impedance-based method. A comprehensive analysis of the one-end impedance-based method to be used for the calculation of fault distance for the 2nd Avenue 11 kV feeder was also developed, which is in turn implemented using the one-end impedance method with consideration of different line parameters. It was found at the end of the report that the impedance-based method will accurately locate faults when fault resistance value is minimal. Acknowledgements We acknowledge the support and sponsorship provided by Covenant University through the Centre for Research, Innovation and Discovery (CUCRID).

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References 1. Aderemi O, Misra S, Ahuja R (2017) Energy consumption forecast using demographic data approach with Canaanland as case study. In: International conference on next generation computing technologies, 2017. Springer, Singapore, pp 641–652 2. Jonathan O, Azeta A, Misra S (2017) Development of prepaid electricity payment system for a university community using the LUHN algorithm. In: Innovation and interdisciplinary solutions for underserved areas, 2017. Springer, Cham, pp 107–114 3. Csanyi E (2015) Troubleshooting an open circuit fault in a control circuit. Electrical engineering portal, 26 Oct 2015 4. Wikipedia, 15 Mar 2018 (Online). Available: https://en.wikipedia.org/wiki/Inductance. Accessed 22 Mar 2018 5. Short T (2011) Fault location on distribution systems: an update on EPRI and DOE research. In: IEEE distribution subcommitee presentation, Orlando 6. Razzaghi R, Scatena M, Sheshyekani K, Paolone M, Rachidi F, Antonini G (2018) Locating lightning strikes and flashovers along overhead power transmission lines using electromagnetic time reversal. Electr Power Syst Res 160:282–291 7. Reddy A (2018) Electrical engineering info 2014 [Online]. Available: http://www.electricalen gineeringinfo.com/2014/12/overhead-lines-design-main-components-of-overhead-lines.html. Accessed 22 Mar 2018 8. Kalia RR (2014) Design and implementation of wireless live wire fault detector and protection in remote areas, vol 97(17):14–20 9. Ajenikoko GA, Sangotola O (2016) An overview of impedance-based fault location techniques in electrical power transmission network. Int J Adv Eng Res Appl 2(3):123–130 10. Sneddon M, Gale P (2011) Fault location on transmission lines. In: International conference power system transients, pp 1–6 11. Chan FC Electric power distribution system. Electr Eng III(9) 12. Mahari A, Seyedi H (2015) High impedance fault protection in transmission lines using a WPT-based algorithm. Int J Electr Power Energy Syst 67:537–545 13. Dashti R, Ghasemi M, Daisy M (2018) Fault location in power distribution network with presence of distributed generation resources using impedance-based method and applying π line model. Energy 159:344–360 14. Milioudis AN, Andreou GT, Labridis DP (2015) Detection and location of high impedance faults in multiconductor overhead distribution lines using power line communication devices. IEEE Trans Smart Grid 6(2):894–902 15. Dashti R, Daisy M, Shaker HR, Tahavori M (2017) Impedance-based fault location method for four-wire power distribution networks (Feb 2017). IEEE Access 6:1 16. Dashti R, Salehizadeh SM, Shaker HR, Tahavori M (2018) Fault location in double circuit medium power distribution networks using an impedance-based method. Appl Sci 8(7) 17. Daisy M, Dashti R (2015) Single phase fault location in power distribution network using combination of impedance-based method and voltage sage matching algorithm. In: 20th electrical power distribution conference EPDC 2015, pp 166–172 18. Gabr MA, Ibrahim DK, Ahmed ES, Gilany MI (2017) A new impedance-based fault location scheme for overhead unbalanced radial distribution networks. Electr Power Syst Res 142:153– 162 19. Mora-Flòrez J, Meléndez J, Carrillo-Caicedo G (2008) Comparison of impedance-based fault location methods for power distribution systems. Electr Power Syst Res 78(4):657–666

The Utilization of the Biometric Technology in the 2013 Manyu Division Legislative and Municipal Elections in Cameroon: An Appraisal P. A. Assibong , I. A. P. Wogu, Sanjay Misra, and Daspan Makplang

Abstract Artificial Intelligence (AI) scholars and election stakeholders in Cameroon believe that with the use of Biometric Technology (BT) the 2013 legislative and municipal elections in Manyu Division would be credible. The preciseness of AI in our daily lives lured them to underestimate the materialistic nature of humans. The study which made use of the ex post facto research design utilized Hardie’s theoretical direction of psephology to critically analyze all the arguments in the paper. The researchers identified the fact that BT cannot secure credible and transparent elections in Manyu Division where the president of the country has no political will to do so by using the elites and members of the armed forces to sway votes in favor of the ruling party. For BT to be relevant, voting should be linked to the Internet and the president should embrace electronic voting and allow ELECAM the Election Management Body in Cameroon to conduct elections without interference. Keywords Artificial intelligence · Biometric technology · Cameroon · Legislative and municipal elections · Manyu Division · President Biya

1 Introduction There is no doubt that artificial intelligence as a field of study is expanding at geometric proportions in both content and preciseness and that is why most emerging democracies see the need to tap from this recent field which has made the solution to P. A. Assibong (B) · I. A. P. Wogu · S. Misra · D. Makplang Covenant University, Canaan land, KM 10, Idiroko Rood, P. M. B. 1023, Ota, Ogun State, Nigeria e-mail: [email protected] I. A. P. Wogu e-mail: [email protected] S. Misra e-mail: [email protected] D. Makplang e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_32

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mankind’s multifarious problems including elections easier via the invention of the biometric technology (BT) devices [1]. The use of the biometric machines all over the world in general for elections and Manyu Division in Cameroon in particular for the legislative and municipal elections, is based on the fact that in USA and other European countries where the biometric mechanical contrivance had been used for elections for many years [2] has recorded more successes [3] than failures. Viewed from another perspective, success of the biometric machine cannot be measured only at the level of registration whereas there are at least four important phases of any election: (a) the campaign phase, (b) registration of voters where the biometric machine has done extremely well, (c) the voting and the collation of result stage and (d) the announcement of the results. The big question is why is there electoral fraud despite the use of the biometric devices which are some of the “babies” of AI? It is because only a quarter of the electoral process has been adjudged to be successful? If the BT has delivered the goods by detecting and deterring electoral fraud and ensuring electoral integrity, Cameroonians in general and Manyu inhabitants in particular would not have been experiencing vote buying, electoral violence and intimidation in polling stations during the voting day. After the introduction, the structure of the paper will include the problematic, objective of study, theoretical and methodological foundation, justification for the study, conceptual clarification, municipal and legislative elections in Manyu Division, proposed model, data analysis and results and the conclusion. Extant and current literature concerning the use of Information and Communications Technologies (ICTs) in general and the biometric technology in elections in many countries in the world and Cameroon in particular has portrayed biometric technology as the best thing to happen to Homo sapiens since electoral fraud would be minimized, increase administrative efficiency and reduce cost in the electoral process [4–7] would be the end product. What scholars, the stakeholders and even the laymen have ignored is the fact that success in any election is not only recorded at the point of registration but it should be seen on the other phases of the election via actually making sure that the voting process is transparent and the results are not cooked or reversed [8]. How do we make sure only those whose data were captured by the Electronic Voting Registration (EVR) machine are actually ushered into the venue of the Electronic Voting Machine (EVM) by the members of the Election Management Body (EMB) known in Cameroon as Election Cameroon (ELECAM) and not soldiers, police and senior administrative officers? How do we protect the results collated from all the polling stations to the National ELECAM Headquarters in Yaoundé without the results being doctored [9]? How do we prevent intimidation by political thugs, members of the ruling party and the police? These and many more questions would be answered as the paper progresses. The paper seeks to: (1) show that as far as the conduct of elections is concerned, the biometric technology has not been a 100% success story as some scholars and the manufacturers of the technology will want us to believe, (2) enable us to understand that the idea of saving cost when BT is used in running elections in emerging democracies is a myth, (3) debunk the myth which states that with the use of biometric technology, electoral violence, intimidation, vote buying, ballot fraud and shortchanging

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opponents in elections will reduce or stop and (4) provide simple and practical solutions to credible elections in Manyu Division, Cameroon, and the rest of the world to follow. This disquisition utilized William Francis Ross Hardie’s psephology theoretical direction which deals with the scientific analysis of political science issues concerning elections [10, 11]. This theory was selected from several others because it is the most appropriate theoretical framework which gives the researchers the foundation and grounds for conducting the research. The theory also appealed to the researchers because it makes use of historical precinct [12–14], which will help us to understand why the biometric technology was avoided in the preceding 2011 Presidential Election in Cameroon. In view of the fact that the legislative and municipal elections in Manyu Division were conducted since 2013, and the fact that the researchers are going to rely heavily on reports and published papers by scholars and stakeholders, the ex post facto research design and approach would be used. Hardie’s theoretical framework and the ex post facto research approach would guide the researchers in the conduct of the research. There are many scholars who claim that ICTs in general and the biometric technology in particular have made the conduct of elections in the world and Africa in particular to be less costly, transparent, efficient and free of violence [15], yet in many countries using the same technology including Kenya [15] [16], the “bastion of democracy in the world,” the USA [17], Nigeria [16] and Cameroon [18] [19] to mention but a few, some of the voting machines failed to deliver and there was post-election violence recorded everywhere. Since voting machines are failing everywhere in the world including Cameroon, there is the need to investigate and make recommendations for the future. Furthermore, Cameroon has often been seen by the international community as the most stable country in Africa where there is no election violence, and there is the need to conduct research to confirm these views or debunk them according to the Kuhnian tradition.

2 Conceptual Clarification Biometric Technology (BT): Biometric technology (BT) is a phrase used under the broader ambit of artificial intelligence (AI). BT is therefore any mechanical contrivance which can record or capture the physiological characteristic of an individual which would be later used for authentication or identification [20]. Artificial Intelligence (AI): Most scholars agree that the definition of AI must include the subsequent four ingredients—“systems that think like humans, act like humans, think rationally and act rationally; thus, AI embodies all devices that “imitate intelligent human behavior” [21–25, 52]. Electronic Voter Registration or EVR is consummated via the biometric technology defined above [26]. In other words, it is a machine used to register voters. Electronic Voting Machine (EVM): This is a machine used by voters in casting their votes to a candidate of their choice. Election Cameroon (ELECAM): This is the authorized Election Management Body (EMB) in Cameroon.

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Manyu Division: This is a political unit found in the South West Region of the Cameroons made up of 4 subdivisions of Manyu Central, Eyumojock, Upper Bayang and Akwaya Subdivision.

3 The 2013 Municipal and Legislative Elections in Manyu Division: An Appraisal The conduct of municipal and legislative elections in Manyu Division since independence has been fraught with byzantine complexities because the ruling Cameroon People’s Democratic Movement (CPDM) party is always poised to win by all means including rigging [27]. Although Manyu Division which is located between Latitude 5833° and Longitude 9500° with a land area of 565 km2 , Population of 117,389 as at 2001 and the Capital of Mamfe Town is 74 km from the Cameroon-Nigeria Border [28–30], is far from the National Capital Yaoundé, the Presidency has a very tight grip of the political twist and turns of the division because the country has been operating a totalitarian and unitary system of government since independence in 1960 [31, 32]. With the introduction of biometric technology in elections, the manipulation of the electoral register in all the four subdivisions in Manyu was curtailed during the registration exercise by the Electronic Voter Registration Machine because it could not capture “ghost voters” or non-existing voters which has been the practice in Cameroon since independence [31] in 1960. As a participant observer, in the team of researchers, the lead author noticed that the outcome of the 2013 legislative and municipal elections in the whole of Cameroon in general and that of Manyu Division in particular was decided by President Paul Biya six months before the actual election took place because the president summoned my colleague Professor Peter Agbor Tabi [33] and directed him to make the ruling Cameroon People’s Democratic Party (CPDM) candidates for the municipal and legislative elections win the candidates of the other parties. All other Manyu elites, the chiefs of police, army, immigration, customs, the assistant divisional officers and the senior divisional officers were to help the Returning Officer Professor Agbor Tabi to “deliver” Manyu Division to CPDM, failure of which all of them will lose their privilege positions [33–37]. The plan to bypass the electoral process, render the EVMs redundant and announce the candidate of their choice was hatched because the president and his henchmen knew that if they relied on the voters’ registration and the Electronic Voting Machines (A1 machines), the 97.7% votes [38] CPDM candidates have been declaring in past elections including that of the president would be exposed as a fraud. It was this morbid fear of the use of the biometric technology that Mr. Biya the incumbent President single-handedly commandeered the Presidential Elections to take place

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two years earlier (2011) without the A1 machines so that he can record the peculiar Cameroonian votes of 99.4% which Mr. Paul Biya the President of Cameroon inherited from his predecessor Late President Ahmadu Ahidjo [39].

3.1 The Plan to Discredit AI in the Twin Elections Armed with the mandate given to the Chief Returning Officer Professor Peter Agbor Tabi by the president of Cameroon to “deliver” Manyu Division to the ruling CPDM Party, the minister added more names on the Mobilization and Empowerment Committee which came up to 19 elites from Manyu Division who hold various top positions in government as compensation for supporting President Paul Biya’s CPDM Party over the years. The list included Hon. Rose Abunaw Makia, Prof. Etchu George and Mr. Oben Victor from Mamfe Central; Chief Obi Okpun, Chief Manghe John, Chief Ikoka, Mr. Akwo Pius and Mr. Aka Martin Iyoga from Akwaya; Professor. Peter Agbor Tabi, Dr. Ebot Enow, Mr. Ebot Ayuk Charles, Dr. Egbe Samuel, Mr. Oben James, Mr. Arrey Victor and Mr. Thomson Tabe Ndip from Eyumojock Subdivision; and Professor George Elambo, Dr. Enowrock George, Mrs. Nkeng Bache and Mr. Tabot Matin from Upper Bayang Subdivision. The twin elections that the above elites managed in Manyu Division were marked by many irregularities [33, 40–42]. During the minister’s inaugural address to the above elites with all the armed force service chiefs, SDOs and DOs present, the professor told members of the committee to coerce all the voters to vote for the CPDM and if the votes were not favorable to the CPDM, the SDO should receive the results and rewrite the results in favor of the seven CPDM candidates: Messrs John Ayuk TakuNchung, Julius Nkom, Martin Ekwalle, Bate Robert Epie for Mamfe Central, Eyumojock Subdivision, Akwaya Subdivision and Upper Bayang Subdivisions, respectively; and Messrs EnowTanjong, Igelle Elias Terhemen and Mrs. Nsosie Susana Ebah Okpu legislators representing Upper Bayang, Akwaya and Eyumojock Subdivisions, respectively, before forwarding them to the regional and national headquarters. In this case, the SDO for Manyu Division, had with a few strokes of a pen, made the biometric or Electronic Voting Machine irrelevant.

3.2 Municipal and Legislative Election Day: Cave the Dragon? On the morning of September 30, 2013, voters and party agents went to the polling stations with their voters’ cards expecting to see ELECAM-appointed polling officers, polling agents and election observers, Electronic Voting Machines (EVMs) made popular by the revolution in biometric technology (BT) and also by the publicity

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consummated by the government-owned Cameroon Radio and Television (CRTV) nationwide. The first irregular feature which caught the attention of the voters was many guns throttling law enforcement officers, many top-ranking civil servants and no Electronic Voting Machines. Voters were ordered to queue up to cast their ballots given to them by the polling clerks. From the start of the exercise at 7.30 am, it was clear that the principle of secret balloting was violated for how can one say the 2013 municipal and legislative election in Manyu Division was free and fair when there were so many unauthorized government agents openly lobbying voters to vote candidates standing election on the CPDM platform? When any member of an opposition party especially the Social Democratic Front (SDF) tries to also canvass for votes during the election day and within the polling station, the Cameroonian police and soldiers would stop them physically and sometimes they manhandle them. This double standards and sorry state of affairs on election day forced one Mr. Fon Afosi to remark thus: “… ELECAM is the ruling party from top to bottom, so they have to do everything to favor the CPDM, they are all corrupt. This is not a democracy…” [43–45]. The dragon we have to be careful or fear here is not the none utilization of the Electronic Voting Machines the government promised to use in the 2013 municipal and legislative elections in Manyu Division, the dragon that all the civil servants, private individuals and the whole population have to fear and avoid is the overwhelming presence of the power of the President Biya’s government which has found its way even unto the polling stations and booths across Manyu Division. The above assertion is aptly captured by Sotu Africa one or the many data bases of African elections thus “The omnipotence of—President Paul Biya- the President of the Republic and the absence of an effective separation of executive, legislative and judicial powers made the Cameroonian people literarily worship him as a demi God” [32, 46, 47]. All the international foreign observers and the local non-governmental organizations which were accredited as election observers concentrated their activities in the divisional headquarters in Mamfe [31]. None of the observers be it foreign or local went to the villages where most of the rigging and ballot buying actually take place. Reacting to the fact that even serving rectors or vice-chancellors also joined other unscrupulous members of the ruling CPDM Party to rig elections all over the division and especially at the hamlet and village levels, Akaegbe had this to say: That is exactly what the Rector of Maroua Higher Teachers Training School was doing in Ossing Village in Manyu Division. Electoral Malpractice. How then will he deal with Examination Malpractices in his School? Poor villagers! What could they have done? After all, the only Professor in their area. Malpractice is Malpractice [47].

The rector like all other elites in Manyu Division and beyond rigged the 2013 municipal and legislative elections in Ossing and other villages in Manyu Division to avoid being victimized by President Biya who would have replaced them with junior colleagues if they had not “delivered” Ossing and other villages to the ruling CPDM Party. We must note here that those who are poised to discredit artificial intelligence (AI) and the biometric machines in the conduct of elections do not need the machines to do

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so; hence, the ELECAM only made use of the Electronic Voter Registration(EVR) machines to register voters and deliberately refused to use the Electronic Voting Machines (EVMs) or the Automatic Voting Machines (AVMs) developed by Haneefa and Gillespie and Meyers, respectively, because their diabolic rigging schemes would have been at least partially exposed [48, 49]. At the end of the voting, the ELECAM representative takes the results and the ballot papers to the Divisional Collation Centers in Eyumojock, Tinto, Akwaya and Mamfe Towns which are supervised by the ADOs who also take the results to the ELECAM Divisional Headquarters in Mamfe which is also being supervised by the SDO appointed by President Biya. If they do not “deliver” the winning votes to the CPDM Party, they will lose their privileged positions in government to even less qualified colleagues or militants in the party. It is at this stage that the candidates of the ruling CPDM Party win elections because the ELECAM officials collaborate with the SDOs, ADOs, all the elites and members of the Forces of Law and Order to ridicule the Biometric Registration Machine by abandoning the legitimate election results submitted by the polling clerks and filling new result sheets to give the three CPDM candidates for the legislative house in Yaoundé and the four municipal council mayors in Manyu Division the winning votes. Yes, this is how to win elections without biometric technology in Cameroon.

3.3 Proposed Model to Stop Electoral Malpractices in Manyu Division In order to mitigate the extent of unethical behavior before, during and after elections in the hamlets, villages, districts, subdivisions and divisional headquarters in Manyu Division, the researchers proposed the subsequent model below: starting with the flowchart in Fig. 1, the e-voting system in Fig. 2a–c, followed by the explanation, the mathematical expression, the sample code and the tool used. The flowchart of the proposed platform is designed in such a way that voters are verified before they can vote. This means that the voting platform is initiated only if the voter is identified as a valid candidate for voting, hence making the verification system (biometric technology) and the voting platform to be a subset of the main Electronic Voting Machine. Voters’ inputs are stored in the database setup for the election, and it can be accessed from anywhere in the world, making other bodies like the international observers, political parties, district, subdivisional, divisional, regional and ELECAM headquarters to be able to monitor the results the same time as the voting is going on. MATHEMATICAL EXPRESSION P = Parties V = Voters

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Verified? YES Load Contesting Parties & Candidate

Key HQ - Headquarter

Voters

voting

input

Database Storing the Voting record. STOP International Observes

HQ of Political Parties

District HQ

Sub-Divisional HQ

Divisional HQ

Regional HQ

ELECAM HQ

Fig. 1 Flowchart for credible elections in Manyu Division. Source Researchers’ view

Fig. 2 Pictures of the prototype. Sources 4a & 4b elections Cameroon Website. 4c-researchers

BV = Biometric Verification IVS = Initial Vote Score (IVS = 0) If {BV == True} - > V(votes) - > {P(IVS + 1)}

SAMPLE CODE //connecting to the database using PDO $this> db = $con; //the database connection should be initiated to $con //when voter initiates a vote if ($_POST[‘vote’] { $partyid = $_POST[‘partyid’];//getting the party id //query to update each votes for each party $sql = ”UPDATE vote_tbl SET numbervotes numbervotes + 1 WHERE id =

=

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’$partyid’”; $stmt = $this- > db- > query($sql); $count = $stmt- > rowCount(); if ($count > 0) { //set status $status = true; } return $status; } if ($status = true) { echo ”Vote Confirmed”; }

The tool used is PHP and MySQL.

4 Data Analysis and Results Since this study was conducted to examine if the use of biometric technology (BT) in the municipal and legislative election in Manyu Division was a success story, whether it drastically reduced the cost of running the elections, whether there was no intimidation and violence during and after the election, and to provide simple and practical solutions at the end of the study, we have to analyze the data collected so as to get empirical information which will be used to buttress our findings. • Although the Electronic Voting Machines in the 2013 Kenyan Elections failed at the last minute precipitating a manual vote count [27], those bought in Cameroon were never brought to the polling stations, a situation which forced the opposition leaders to claim that the ELECAM did so in order to manipulate the results in favor of the ruling CPDM Party [41, 42, 44]. In this case, it is not artificial intelligence (AI) or the Electronic or Automatic Voting Machine that failed, but it is the incumbent president and his government who failed to provide the machines [50]. What do we expect from a country like Cameroon where “…the regimes propensity to imprison political leaders, former allies and opposition party leaders alike is very high?…Biya pays opposition parties to present candidates for Presidential elections and he plays ministers against each other” [36, 37] this shows why Figs. 3 and 4 graded Cameroon very low in conducting credible, free and fair elections. The two figures below show the Perception of Electoral Integrity (PEI) which ranked Cameroon 56.6% while Norway has 86.4% [45]. The 56.6% given to Cameroon by the Electoral Integrity Project would have reduced to 20% if any of the officials was a participant observer or went into the villages where the rigging and buying of votes actually take place. In terms of biometric registration, verification, authentication and identification of voters during the twin municipal and legislative elections in Manyu Division, the Electronic Voter Registration (EVR) Machines functioned very well because the

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Fig. 3 Perception of electoral integrity (PEI) index. Source Electoral integrity project, 2014: the expert survey of perceptions of electoral integrity release 2 (PEI….2)

Fig. 4 Perception of electoral integrity (PEI) for Cameroon 2013. Source The Year in Elections: The World’s Flawed and Failed Contests

EVR Machines satisfied all the election stakeholders in Manyu Division including the local and foreign observers in that they did not fail to capture the facial recognition, DNA fingerprint, iris scanning and retina recognition of actual and potential voters. The Report of the Commonwealth Expert Team on Election matters asserted that in the 2013 twin election, “Voter registration using the biometric verification capture some 5.5 million votes, 2 million votes fewer than the usual 7.5 million voters registered in past elections” [51, 53]. 1. The second objective of the paper is to unravel if the use of biometric technology in conducting the 2013 municipal and legislative elections in Manyu Division in Cameroon Republic reduced the cost of organizing the elections as compared to the cost of earlier elections. According to the Concord News deck, “The 2013 Municipal and Legislative Elections which was shifted from 2011 to September 2013, gulped 9 Billion Francs while the same election in 2007 was conducted with 5.2 Billion Francs” [50]. The above exposition shows a 57% increase in the 2013 elections; hence, it goes without saying that there was no “… reduced long term costs …” in conducting a similar election in Cameroon as the Kenyan Standard Newspaper Editorial of November 26, 2011, informed the world in the case of Kenya [15].

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2. The third and final objective was to uphold or debunk the popular view held by many election stakeholders in Manyu Division and most African countries that with the introduction of the biometric technology, candidates and supporters of certain candidates and political parties will be satisfied with the results at the end, thereby reducing the probability of any post-election violence and vote buying to mention but a few sharp practices. Among the “few setbacks” [26, 50, 51] reported by the Commonwealth Expert Team, post-election violence was noticed all over the country with that of Eyumojock Subdivision escalating to open exchange of punches which forced the administration to reschedule the elections from the Eyumojock Town Hall to the Government Technical College in the Divisional Headquarters in Mamfe. This shows clearly that the use of artificial intelligence (AI) gadget like the BVR did not stop post-election violence and general electoral malpractice in the 2013 municipal and legislative elections in Manyu Division in Cameroon.

5 Conclusion The research paper analyzed and appraised the utilization of the biometric technology (BT) in the conduct of the 2013 municipal and legislative elections in Manyu Division in the Cameroons with the objective of finding out definitely if the use of mechanical contrivances like the Electronic Voter Registration device (EVR) and the Electronic Voting Machine (EVM) which are adjuncts to the broader matrix of artificial intelligence (AI) can guarantee credible, cost-effective, free, fair and peaceful elections in the division. The authors were able to show with supporting related literature, data and figures that it takes more than mere AI or machines to conduct a low-cost, free, fair and violence-free municipal and legislative election in Manyu Division. The few shortcomings of the BVR device notwithstanding, election stakeholders should acknowledge the fact that BT has exposed the menace of multiple registrations of voters in Manyu Division. In view of the fact that this study was conducted with supporting data, pieces of evidence from current and extant literature and the fact that the lead author was a participant observer, the authors of this piece posit that in spite of the negligible shortcomings of the BVR device, it exposed the menace of multiple registrations in previous elections in Manyu Division. They added that no matter how precise any BVR and EVM device is, it will take enormous political will on the part of the president of Cameroon to allow the devices perform the functions they were intended to perform, i.e., registration of voters and hitch-free voting. Once he accepts the fact that he is the problem and not the biometric machines, there will be free and fair elections in the next municipal and legislative elections in Manyu Division in Cameroon. The president and government of Cameroon should make use of Electronic Voting Machines which should be connected to the Internet so that the chronic rigging spree

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of elections in Cameroon will stop. To achieve this, any vote cast by any voter at the hamlet, village, district, subdivisional, divisional and regional levels must be transmitted through the Internet to ELECAM offices all over the country, party headquarters in Yaoundé or anywhere in the country. The fact that artificial intelligence with her multifarious adjuncts like the Electronic Voter Registration device and the Electronic Voting Machines is a useful tool in conducting elections is undeniable, and what is problematic now is the interference of desperate sit-tight presidents like President Paul Biya who has refused to let the machines deliver or perform the services they were designed to perform. It is our view that once the head of states and government stop interfering with the conduct of elections anywhere in the continent in general and that of Cameroon in particular, the use of biometric technology in elections will be accepted by all election stakeholders.

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Integrating NFC and IoT to Provide Healthcare Services in Cloud-Based EHR System Raghavendra Ganiga, Radhika M. Pai, M. M. Manohara Pai, Rajesh Kumar Sinha, and Saleh Mowla

Abstract Internet of Things (IoT) and Near Field Communication (NFC) together have created a massive impact on healthcare applications globally. In India, there is a requirement to upgrade this technology. As healthcare is a basic necessity for everyone in order to stay healthy and fit, the main goal of present healthcare system is to build an affordable smart healthcare monitoring system. With IoT and NFC in healthcare sector, it is possible to manage the patient data in better way and also improve the patient flow in the hospital. During emergency situations when a patient is unable to communicate, the NFC tag could be tapped against the reader at any hospital in the group for faster retrieval of patient information. The system captures the data from an individual patient using sensors and stores it on the cloud. It provides information about the health conditions of the patient and sends an alert in case of abnormalities. The result shows that the availability of the data is faster due to NFC integration. The proposed system can be used in rural areas for ambulatory care. In addition, the system can be used in triaging of patients during emergency based upon vital signs, thereby taking immediate actions. The implemented system will thus be useful and affordable thereby providing efficient and life-saving solutions in the healthcare domain. Keywords Rural · Healthcare · Emergency · IoT · Cloud · NFC

1 Introduction Healthcare ecosystem consists of doctors, physicians, nurse, pharmacist, radiologist, lab technician, and patient. Recent efforts have been made in healthcare technology to encourage meaningful use of electronic health records (EHRs) which allows to R. Ganiga (B) · R. M. Pai · M. M. Manohara Pai · S. Mowla Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India e-mail: [email protected] R. K. Sinha Amity Medical School, Amity University Haryana, Gurugram (Manesar), Haryana, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_33

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provide quality services and care the patients. Cloud computing helps in organizing the health record at different groups of same hospital settings [1]. Cloud can provide several benefits to the users of the healthcare ecosystem by connecting many health information management systems together with laboratory information system, pharmacy, radiology, etc. With cloud-based EHR model, hospitals need not spend core portion of their budgets on IT infrastructure [2]. Cloud technology and Internet of Things (IoT) add its own standard connectivity and intelligence to physical things to enable real-time monitoring, decision making, and management of the world around us. IoT is interconnected with different medical sensor devices which enable the end user to collect and interchange data. The well-established IT infrastructure provides a virtual communication link between care receivers and care givers to allow just in time intervention instead of routinely consulting a physician or doctor for the treatment. Current healthcare system needs well-structured implementation of patient health record management including waiting time, handling the registration [3] of patients, appointment scheduling, and handling the medical prescriptions in the pharmacy sections. So, there is a huge demand for efficient wireless communication between these. Near-field communication (NFC) is an efficient and reliable choice as it is easy to use. NFC is an enabler for the IoT, which is used for linking any physical object into the virtual world of the Internet [4, 5]. Healthcare IT technology is mainly divided into three areas such as medical devices, system and software, and connecting technologies [6]. Medical devices for patients can be worn or implanted to monitor a patient’s vitals in a timely manner using wireless technologies [7]. The next is system and software which provides secure way of storing health data, and software application provides dashboard to view the patient’s health information status. Finally, the connecting technology has three main players namely Bluetooth, WiFi, and NFC. Bluetooth is an efficient and widely accepted solution when the device is operating in close range. Outside premises, the Bluetooth can be used to connect medical monitoring devices to a patient’s smartphone by using WiFi connection for transmission and reception. NFC can also be used in the same way as Bluetooth. NFC is a recent and fast-growing communication technology. NFC is capable of data transfer rate of 424 kb/s. NFC supports short-range communications between 5 and 20 cm and operates at 13 MHz frequencies [8]. Passive tags in NFC use electromagnetic field induction for the power. The short-range features of NFC have both advantages and disadvantages compared to other transmission technology [9]. Short-range NFC can be effectively used for applications which need a lot of security such as healthcare and military. Also, it prevents interception of signals from the third party. The NFC technology works in real-time scenario where the patient health information can be transmitted to NFC tag and use the patient health data wherever required. The rest of the paper is organized as follows. A background study is summarized in Sect. 2. Experiments based on two healthcare scenarios demonstrating the use of NFC and IoT devices transmitting to cloud-based EHR system are described in Sect. 3. The analysis of the experiments is explained in Sect. 4. Section 5 concludes with the results of the experiments and the objectives achieved.

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2 Literature Review This section reviews work carried out in usage of ICT in healthcare sector. The authors of papers [10–12] have surveyed on different aspects of the NFC technology. The concept of NFC is discussed with different perspective such as NFC ecosystem, communication essentials with standards for Zig-Bee, Bluetooth, Wireless Fidelity (WiFi), radio frequency identification (RFID), and NFC privacy and security issues. In addition, author describes open research area to encourage and solve more critical issues about the application of IoT in healthcare and how different component of IoT architectures communicate with each other. Kyambille and Kalegele identified the drawbacks of paper-based appointment system which is inefficient and time consuming [13]. Registration process using paper-based health record system requires the patient to fill up the registration form and give it at the registration table. The authors proceeded with discussing the advantages of using mobile and Web-based systems to set up appointments before visiting the hospital physically. Gune, Bhata, and Pradeep proposed a model for online appointment portal or Web [14]. In this online appointment system, patients are allowed to complete their registration by using Web pages, without waiting in the long queue in hospitals. The registration requires the patients to fill their important demographics details. Secondly, mostly all the online registration and scheduling appointment require payment that should be done to the provider per month. Thirdly, online scheduling systems are limited to few diagnosed symptoms such as abdominal pains. [15]. It can thus be inferred that there are many symptoms which are not included in the list, and the patient has to call in for any available appointment slots and has to wait for the confirmation. In [16, 17], the systems proposed by the authors describes about various applications which are based on Android smartphones with NFC which improves healthcare process for safe and secure medical processes and patient identification. Their systems use various other applications with secure identifiers and safe transfer of huge or vast data between devices. Various surveys have been conducted comparing and noting the use of IoT in healthcare systems. Studies have addressed various IoT and e-Health policies and regulations so as to determine sustainable solutions for society and the economy as a whole [18]. Baker, Xiang, and Atkinson have outlined various challenges that healthcare IoT face and present the strengths and weakness of different IoT models in healthcare security [19]. In [20, 21], the authors demonstrate using IoT for smart healthcare. The physical world can be controlled from distance along with access to remote sensor data by connecting physical things to Internet. A small item, which is the building piece of the Internet of Things, is simply one more name for an embedded system that is associated with the Internet. The RFID innovation, an expansion of the pervasive optical standardized identifications that are found consistently on some items, requires the

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connection of a brilliant minimal effort electronic ID tag to an item such that the character of an item can be decoded from a distance. In [22], the authors demonstrated the use of Raspberry Pi to log a patient’s medication and send reminders through an SMS service. Murugan, Jacintha, and Shifani have applied the use of Raspberry Pi device as a motion detector for the purpose of security [23]. Some implementations in the field of IoT have also been made by integrating NFC and RFID technology with sensors [24]. Vippalapalli and Ananthula extended the concepts of IoT and have designed a body sensor network using a collection of lightweight wearable sensor nodes [25]. This paper shows the results of two experiments conducted to demonstrate the use of NFC and Raspberry Pi in the retrieval and transmission of patient information from a cloud-based EHR system. The analysis shows the efficiency of using an IoT enabled system in the healthcare domain and the numerous benefits that can be achieved by integrating IoT components thus increasing the quality of patient services and their welfare.

3 Methodology This section discusses about the methodology adopted for developing the NFC and IoT enabled system with the help of two scenarios. The first scenario demonstrates the use of NFC for patient registration and information retrieval. The second scenario depicts the use of IoT devices outside the boundaries of the healthcare organizations.

3.1 System Components The integration of NFC in the proposed system has been implemented using the following components: 1. 2. 3. 4.

NFC Reader (ACCR122U) Raspberry PI Apache Server MySQL Database Server

3.1.1

NFC Reader (ACCR122U)

The ACR122U is an NFC model from the Advanced Card System Ltd and Mifare 1K Classic passive NFC contactless card. The ACR122U NFC Reader is a PC linked, contactless smart card reader or writer which is developed based on 13.56 MHz contactless (RFID) technology. Compliant with the ISO/IEC18092 standard for nearfield communication (NFC), it not only supports MIFARE and ISO 14443 A and B cards, but also all other four types of NFC tags. This NFC model also provides the

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speed of 115.2 kb/s with the consumption of power under 15 mA in order to complete the transactions-like authentication, reading, and verification. The ACR122U has an optional stand which holds the smart card reader at an ideal angle, so that the users can tap their contactless cards or their NFC-enabled devices such as mobile phones onto the ACR122U with convenience.

3.1.2

Raspberry Pi

The Raspberry Pi is a small single-board computer which has been used as sensor to record data and transmit the same to the cloud in the experiments.

3.1.3

Apache Server

The Apache server is used for hosting the Web application developed for the purpose of demonstration. The application can be used by employees of the healthcare organization to retrieve the information from the NFC tag and display it on their workstation for their reference.

3.1.4

MySQL Server

MySQL server has been used for managing the database which consists of different tables to store the new patient registrations, medical records, doctors, nurse, and pharmacy information.

3.2 Implementing NFC for Patient Registration and Records Retrieval For the purpose of demonstrating the use of NFC, the implemented system uses GoToTags Windows library which allows user to easily read and encode NFC tags to perform various actions. Additionally, it also allows healthcare person to easily read all the tag- and record-level information on an NFC tag. The record option allows to store the patient data, and when the patient visits next time, it is very easy to fetch required health information by the healthcare professionals such as doctor or nurse. The NFC device acts as a queuing system for retrieving patient’s health records and various other related reports. In this model, the patient arrives at the healthcare center and taps his NFC card/chip onto NFC reader which is provided in the main entry where all the data will be recorded as outpatient list. The records on the list will be compared between the medical record identification and identification of NFC card. When it finds the matched IDs, it will be displayed on the nurse’s Web

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application for further step. In case patients have not registered, the administrator has to enter their basic information through the Web application and save it in the database. Depending on the severity of the symptoms, the patients’ consultation with the doctor will be prioritized, and they will be treated accordingly. When the doctor completes his assessment and finishes performing the necessary treatment, records of the patient are updated and the prescription is sent directly to the pharmacy where the medication can be collected. In the event that the patient is transferred from one healthcare organization to another, the records of the patient can be accessed by the new healthcare organization since the data is shared between the organization on the cloud-based EHR system. This would help to access the patient health records easily in an efficient manner. Figure 1 depicts the architecture of the model. It has EHR database shared between the group of same healthcare organizations. The centralized global EHR database manages the patient database from both hospitals. The different users of the healthcare system like doctor, nurse, and pharmacist will have different set of privileges to access the patient records. Based on the security and access policies implemented, the organizations can achieve confidentiality and integrity of their patient’s records. A common Web application is created for group of hospitals where the patients register to access healthcare services. During registration, the NFC card is registered with a unique ID and timestamp value. In the implemented model, it is assumed that the patients bring their NFC card during each visit to the hospital. During the consultation, the patients’ symptoms and the doctor’s investigation and treatment

Fig. 1 Architecture of the proposed model

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history are recorded in NFC card. The patient health information is also updated in EHR database. A usability study was conducted which simulates a scenario where patients’ health data would be recorded with and without the use of NFC. The results have been analyzed and discussed in Sect. 4. Patient data can also be handled using NFC by a group of hospitals collaborating and sharing data, wherein the patient can visit any hospital belonging to the same group. When the patient visits a healthcare center, the administrative staff simply taps his NFC card for his details which will be retrieved from the EHR database. If the patient forgets his or her NFC card during hospital visit, doctors can access their health information directly from the EHR database after cross-referencing with the patient’s personal information such as name, date of birth, phone number, or other unique details such as their passport number and email ID. This system thus enables the patient to visit his nearest hospital and does not restrict him to a single healthcare center. The system can be used in rural areas for ambulatory care. In addition, the system can be used in triaging of patients during emergency based upon vital signs, thereby allowing the medical staff to take immediate action and save lives. This kind of technology-oriented system which is high-performing, affordable, and efficient can thus provide economical and life-saving solutions in the healthcare domain.

3.3 Integrating IoT with Cloud-Based EHR System One of the many problems in the healthcare domain is the difficulty in connecting different devices to patients and monitoring them. These interoperability challenges arise in multiple ways. For older patients who find it difficult to move, they cannot visit the clinics and the hospitals very often, a system needs to exist wherein the patient can be monitored from a remote location, and their records can be analyzed. Figure 2 depicts an proposed IoT model using a Raspberry Pi. The values retrieved from the Raspberry Pi are sent to the public cloud through message queue telemetry transport (MQTT) protocol. The Raspberry Pi acts as the device which publishes events to the MQTT broker, and the Web application subscribes to the events published by the Raspberry Pi. The events published by the Raspberry Pi are stored in IBM Internet of Things historian data in the form of a message (as published). Through basic string manipulations, the values are extracted by the web application and displayed accordingly. These functions take place at regular intervals of a few seconds or minutes thus providing real-time live data, and status of the patient is readily available on the cloud. Doctors and medical staff alike can get the data anywhere and at any point of time depending on their privileges. The following pseudocode summarizes the above scenario by considering the simple example of recording a high body temperaturePseudocode: IoT Enabled Alert Monitoring System

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Fig. 2 IoT enabled alert monitoring system

1. 2. 3. 4. 5. 6. 7. 8. 9.

Record Body Temperature (X) IF (X > 100 ) THEN Transmit Data directly to EHR Database Send Alert Notification to Doctor Update Raspberry Pi Sensor Data ELSE Update Raspberry Pi Sensor Data END IF REPEAT in 30 minutes (GO TO STEP 1)

Since alerts are sent only during abnormal or critical conditions, it prevents unnecessary spikes in network traffic on the cloud thereby ensuring that network performance issues are avoided.

4 Results and Analysis The efficiency of using NFC card is proved by comparing the time spent in hospital by patient with NFC card and without. In order to know the time, the outpatient department (OPD) of a hospital is visited, and the time for various tasks is recorded. Without NFC card, a patient needs to wait until his physical record arrives in the department which is not the case with NFC. In the study, it is observed that, the average waiting time for the patient to complete the OPD process without an NFC tag was found to be almost hours. There may be many reasons for such a huge latency

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in the OPD processes which leads to patient dissatisfaction. Searching huge stacks of patient files and history of patient visits, doctor notes, appointment records, admission discharge notifications, etc., add up to a lot of manual and laborious work which needs to be handled by the healthcare departments and organizations. After analyzing the results of the NFC integrated EHR system implemented, it has been observed that the average time to complete the OPD process for patient who had an NFC was approximately in minutes. This increase in efficiency is attributed toward the fact that the implemented model automatically streamlines the patient’s appointment after simply tapping on the NFC reader and retrieving the necessary information. By introducing NFC, healthcare system can affect routine hospital procedures and increase the efficiency of healthcare stakeholders. This analysis can further be extended to state the significance of NFC in lifethreatening and emergency situations. In scenarios where patients are unable to communicate and immediate action needs to be taken, the doctor or the support staff can simply obtain the last updated information from the patient’s NFC tag. Upon knowing the most recent treatments and medications used, the doctor will be able to take an informed decision with respect to the new treatment and assign the correct dosage or perform the necessary procedure. Additionally, the support staff can immediately retrieve the patient’s emergency contact, so that the patient’s family, relatives, and friends can be informed at the earliest. This improves the hospital’s patient care services and saves valuable time. The application of IoT in a cloud-based EHR system can also be extended outside the hospital premises and used in homecare by following a methodology described in Sect. 3.3. If a patient’s NFC tag is configured to monitor the patient’s heart rate, body temperature, blood sugar, and other vital health-related indicators, an alert can be sent to the patient’s consulting doctor or emergency contact if an abnormal value is recorded. This will facilitate speedy action to be taken, and in case, the patient is unable to reach a healthcare organization and seek treatment by himself.

5 Conclusion Advancement in the healthcare IT facility with the availability of short-range communications technology such as NFC enables faster access with real-time data with the help of a robust and efficient system which is economically viable. The proposed model serves as a basic prototype to meet the future challenges in healthcare sector. The cloud-based EHR system integrated with NFC allows the medical staff to tap a patient’s NFC card/chip onto NFC reader to record and fetch their information and health records. The architecture can also be extended to the next-generation tablets and smartphones to readily access patient data shared among the collaborating hospitals. With IoT and NFC in healthcare sector, managing the patient data becomes easier and improves the patient and the medical staff’s workflow in the hospital. Integrating EHR with IoT will offer flexibility and viable operations for both healthcare professionals, organizations, and patients. IoT can also be extended

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to ambulatory care and services in rural areas. Additionally, the system can be used in triaging of patients during emergencies based upon their vital signs allowing them to take the necessary action immediately.

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An Approach to Study on MA, ES, AR for Sunspot Number (SN) Prediction and to Forecast SN with Seasonal Variations Along with Trend Component of Time Series Analysis Using Moving Average (MA) and Exponential Smoothing (ES) Anika Tabassum, Masud Rabbani, and Saad Bin Omar Abstract Sunspots are the interesting things on the surface of sun which is why it would be more engaging if sunspots become predictable. In this study, sunspot numbers (SN) have been predicted within recent solar cycle 24. To find the best model, moving average (MA), exponential smoothing (ES) and auto regression (AR) have been used. Besides these, in another two experiments which are seasonal component was extracted from data using moving average (MA) and exponential smoothing (ES) as well as with the help of simple regression analysis (RA), trend component was calculated. This exploration is entirely about understanding the differences among these models and the influences of those two components to predict sunspot using moving average (MA) and exponential smoothing (ES). Prediction results have been conducted to expose that difference and influence. It can manifest the way of forecasting for other model of time series analysis (TSA) to predict sunspot number (SN). Keywords Seasonal component · Trend component · Sunspot number (SN) · Time series analysis (TSA) · Moving average (MA) model · Exponential smoothing (ES) model · Auto regression (AR) model · Regression analysis (RA)

A. Tabassum (B) · M. Rabbani · S. B. Omar Department of Computer Science and Engineering, DIU HCI Research Lab, Daffodil International University, Dhaka, Bangladesh e-mail: [email protected] M. Rabbani e-mail: [email protected] S. B. Omar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_34

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1 Introduction Rotation of the sun is faster near the equator and slower near the higher latitudes. Because of this kind of differential rotation, the magnetic field lines begin to deform, and this deformation of the magnetic field lines near the surface of the sun is responsible for having sunspots. Near the convection zone, sunspots are visible on photosphere layer. Sunspots are dark places or regions on the surface of sun. A sunspot has mainly two portions which are umbra, penumbra, and there is another external part which is spicules. The temperature of umbra is 4300 K which is cooler than the surface of sun, and this difference is the cause of being dark of sunspots. Sunspot cycle is basically periodic 11-year solar magnetic activity cycle. That means, the numbers of SN change over an 11-year cycle. Now, there are several approaches to predict sunspots. In the basis of time series analysis, ARIMA model is more successful than other models. But these models are linear models, and they are limited in the complexity of problems [1]. There is no machine learning algorithm that can provide the best learning performance in the solar domain. An ancient endeavor to forecast this solar activity was indicated [2], and in this research, solar activity was illustrated in terms of the Wolf number, not in terms of flares or coronal mass ejections [2]. In this study, MA, ES, AR, seasonal variations and trend are the processes which have been used to do prediction of sunspot numbers. There have been used several laws by which all of the components of time series analysis have been measured, and thus, a training part has been constructed. By a dynamic mathematical process, prediction result for two years has been derived and shown for future sunspot numbers. Rest of this paper has been organized into some portions as follows: In the part 1, related works have been illustrated, in the part 2, methodology of calculating SN has been described, in the part 3, experimental studies have been expounded, and at last, conclusion has been drawn in the part 4.

1.1 Related Work For historical data analysis, time series analysis is one of the most traditional works and extensively used. There has been proposed many models for prediction in time series [3–7]. The MA model, ARMA model are traditional models [4–7] that seem to become archaic. There are some new models which are proposed in current years, such as wavelet analysis, minimal distance method and neural network. There is some work that narrated on the correlation among sunspot classifications, flare occurrences and prediction. Because of being incorporated the prediction decision rules, the system was temporal by a human specialist and had not been enumerated [8].

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To derive the feasible solar flare performances of each McIntosh classification parameters, there is another work in which Bornmann and Shaw used multiple linear regression analysis. In their study, it has been executed that the original seventeen McIntosh classification parameters can be alleviated to ten and reform the audited flare rates notwithstanding [9]. A Bayesian approach has been proposed to flare prediction. To ameliorate the elementary prediction of big flares during a posterior period of time, this approach can be used. All the flares are envisaged to set up the flaring narrations for an operative region as whether it is significant or not, and this method was tested on confined data set [10].

2 Observing All the Differences Among MA, ES, AR Models and Influences of Seasonal Variations and Trend Using MA, ES of TSA to the Prediction of SN To reach on our goal, several stages have been categorized which are described as follows. Figure 1 has illustrated a flowchart of our object to measure of SN.

2.1 Data Collection After an elementary research, it has been understood that all the targeted data could be collected from the SILSO data/image, Royal Observatory of Belgium, Brussels [11]. Fig. 1 Flowchart of TSA for SN prediction

Start Data collection

Data Study in Excel file

Data Processing in weka

Resultant Magnitude

End

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2.2 Data Study in Excel File To study on data, calculation has been processed in the excel. Several models which were used to do forecast have been described below: • Forecasting with Moving Average (MA) model: After gathering data into excel file, the real data have been averaged out using MA in order to do prediction. For that, the equation is MA = ((x1 + x2 + . + xn )/n)]

(1)

where n is frequency and n = 3. • Forecasting with Exponential Smoothing (ES) model: Further accumulating data into excel file, the prediction result has been calculated using the equation which has been shown below: ES = α ∗ At − 1 + β ∗ Yt

(2)

where α = smoothing parameter, At − 1 = previous real data, β = damping factor, Y t − 1 = previous predicted data. • Forecasting with Auto Regression (AR) model: In order to forecast with AR, t − 1 and t − 2 have been calculated which are, respectively, t − 1 = previous value for a certain time and t − 2 = the previous value of t − 1 for that same certain time. After that, a RA has been performed by taking real data along Y-axis and t − 1, t − 2 along X-axis. After getting the intercept point and slope from RA, there has been used an equation to do prediction, Yt = intercept + (slope1 ∗ t − 1) + (slope2 ∗ t − 2)

(3)

where Y t = prediction, slope1 = slope of t − 1 variable, slope2 = slope of t − 2 variable. • Forecasting with seasonal variations and trend component using MA: After gathering data into excel file, SN anomaly has been taken care of by smoothing out the data of SN using MA. There were irregularities attached with data, and they were averaged out. By doing that the cross and drop set of presents in that irregularities have been smoothen out and average focused of that irregularities has been gotten. For that, the equation was MA = ((x1 + x2 + · · · + xn )/n

(4)

where n is frequency and n = 12. After performing regression analysis to understand the component parts for collected data, that components have been extracted out. Equations for seasonality and irregularity:

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(5)

where SEt = seasonal variations, IRt = irregularities, At = SN from collected data and CMA = Central Moving Average. From that seasonal component, SEt can be extracted by averaging the value of all concordant months. After that, data have been deseasonalized with an equation which is Deseasonalize = At /SEt

(6)

where At = SN from collected data and SEt = seasonal variations. A simple linear regression has been performed by taking deseasonalized data as Y variable and time t variable as X, and with the help of this simple linear regression, an intercept and a slop point have been extracted. Trend component has been calculated by adding intercept point with the multiplication of slop and each time. TRt = Intercept + (Sl ∗ tn )

(7)

where TRt = trend component, Sl = slop and t n = t 1 , t 2 , … t n = time/period for each point. Then, according to the classical multiplicative model by multiplying seasonal component with trend component, a training part and a prediction part have been constructed. Prediction = SEt ∗ TRt

(8)

• Forecasting with seasonal variations and trend component using ES: After performing the equation of ES which has been explained in the section of forecasting with Es, the whole process for forecasting with seasonal variations and trend component using ES has been conducted exactly like the previous section of forecasting with seasonal variations and trend component using MA.

2.3 Data Processing in Weka After completing the calculation in excel, that file has been imported into Weka. • The excel file, in which all the calculations have been accomplished, has been converted to CSV file. • Then, the CSV file was imported into Weka. • In Weka, a training part has been constructed, and data have been processed for showing the prediction result. • SN prediction has been shown for 2018 and 2019.

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2.4 Resultant Magnitude After calculating prediction, a figure also has been plotted in which the x-axis shows years which are divided into monthly segment and y-axis shows amplifications. The predictions are shown in Figs. 2, 3, 4, 5 and 6. In Fig. 2, blue line is showing the real sunspot data, and orange line is showing prediction of sunspot number which has been got by performing Moving Average (MA) model. The calculation process has been described in the Sect. 2.2. In Fig. 3, blue line is showing the real sunspot data, and orange line is showing prediction of sunspot number which has been got by performing Exponential Smoothing (ES) model. The calculation process has been described in the Sect. 2.2. In Fig. 4, blue line is showing the real sunspot data, and orange line is showing prediction of sunspot number which has been got by performing Auto Regression (AR) model. The calculation process has been described in the Sect. 2.2. Now in Fig. 5, A\a regression analysis for SN prediction has been shown. After performing all the calculation what has been explained in the “data study in excel file” section, this plotting is showing the difference among real sunspot data, central moving average and SN prediction over time period. The blue line is for real data

Fig. 2 AScreenshot of RA for SN prediction using MA in excel (2008–2018)

Fig. 3 Screenshot of RA for SN prediction using ES in excel (2008–2018)

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Fig. 4 Screenshot of RA for SN prediction using AR in excel (2018–2019)

Fig. 5 Screenshot of regression analysis for the prediction of SN with seasonal variations and trend using MA in excel (2008–2019 where 2019 is target prediction as it was unknown)

Fig. 6 Screenshot of regression analysis for the prediction of SN with seasonal variations and trend using ES in excel (2008–2019 where 2019 is target prediction as it was unknown)

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of SN. The orange line is showing central moving average, and gray line is showing the prediction of SN as a training part of this calculation. Now in Fig. 6, a regression analysis for SN prediction has been shown. After performing all the calculation what has been explained in the “data study in excel file” section, this plotting is showing the difference among real sunspot data, Ccntral moving average and SN prediction over time period. The blue line is for real data of SN. The orange line is showing exponential smoothing calculation which has been used for smoothing purpose of real data, and gray line is showing the prediction of SN as a training part of this calculation.

3 Experimental Setups SN prediction has been developed under the environment on Intel Core i3-3.30 [12] GHZ processor with 8.0 GB of RAM running on Windows 10 [13] operating system. SN prediction using TSA has been calculated in Microsoft Excel and developed in Weka (version 3.8) [14] for preparing a model.

3.1 Experimental Result and Comparisons In Fig. 7, the left most column is for time period. Then, next two columns are for 2018 Jan–Aug for which prediction has been done using MA in which prediction for Jan–Jul is known data that has been used for testing and Aug is for unknown prediction. In Fig. 8, the column 1 is for time period. Then, next two columns are for 2018 Jan–Aug for which prediction has been done using ES in which prediction for Jan–Jul is known data that has been used for testing and Aug is for unknown prediction. In Fig. 9, the column 1 is for time period. Column 2 is for the years 2018 and 2019, and their distribution according to month has been shown in column 3. Column 5 is showing the real value of 2018 Jan–Jun, and the next columns 6 and 7 are for t − 1 and t − 2 which are showing the previous value for a certain time and the previous value of t-1 for that same certain time, respectively. In column 8, prediction has been done using AR. Fig. 7 Screenshot of prediction for 2018 Aug using MA

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Fig. 8 Screenshot of prediction for 2018 Aug using ES

Fig. 9 Screenshot of prediction for 2018 and 2019 using AR

In Fig. 10, the column 1 is for time period. Column 2 is for the years 2018 and 2019, and their distribution according to month has been shown in column 3. Column 8, 10 are containing seasonal calculation and trend component calculation, respectively, which have been got by using two several processes what have been described before. In column 11, prediction has been done with these two components using MA. In Fig. 11, the column 1 is for time period. Column 2 is for the years 2018 and 2019, and their distribution according to month has been shown in column 3. Column 8, 10 are containing seasonal calculation and trend component calculation, respectively. In column 11, prediction has been done with these two components using ES. Here, Fig. 12 prediction has been shown using Weka. Column 1 is for time, and column 2 is showing prediction result for Aug–Dec of 2018, and Jan–Apr of 2019 was target prediction as it was unknown data. The calculation of error for prediction, standard deviation and standard deviation error calculation for real data, training data and prediction data has been given below. Here, MAD = median absolute deviation, MSE = mean squared error, MAPE = mean absolute percentage error, STD = standard deviation, STDE = standard deviation error.

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Fig. 10 Screenshot of prediction with seasonal variations and trend using MA (prediction for 2018–2019)

Fig. 11 Screenshot of prediction with seasonal variations and trend using ES (prediction for 2018– 2019)

In Tables 1, 2, 3, 4 and 5, at first, the difference between actual data and prediction data has been calculated in order to get error. Then, absolute value of that error has been enumerated. The mean of that absolute error is MAD which means MAD = |Error|/n. Then, mean squared error has been calculated with the equation of MSE = Error2. Finally, the standard deviation error has been calculated with the equation of MAPE = (|Error| * 100)/actual data.

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Fig. 12 Screenshot of prediction in Weka Table 1 MAD, MSE and MAPE calculation for SN prediction using MA Table 2 MAD, MSE and MAPE calculation for SN prediction using ES Table 3 MAD, MSE and MAPE calculation for SN prediction using AR Table 4 MAD, MSE and MAPE calculation for SN prediction using AR Table 5 MAD, MSE and MAPE calculation for SN prediction using AR

MAD

MSE

MAPE

12.4

306.2537

36.63116

MAD

MSE

MAPE

11.77857

287.1424

64.69784

MAD

MSE

MAPE

4.744108

51.57798

194.6161

MAD

MSE

MAPE

33.10223

1508.045

86.45642

MAD

MSE

MAPE

33.69441

1516.354

88.24309

384 Table 6 Standard error for real data of SN

A. Tabassum et al. Mean

53.76917

STD

40.90752

STDE

Table 7 Standard error for prediction data of SN using AR

Table 8 Standard error for training data of SN with seasonality and trend using MA

Table 9 Standard error for prediction data of SN with seasonality and trend using MA

Mean

3.734329

22.7

STD

6.93398

STDE

1.415393

Mean

54.15632

STD

14.27007

STDE

Mean

1.302673

81.26026

STD:

8.892259

STDE

1.350619

In Tables 6, 7, 8, 9, 10 and 11, mean has been calculated for real data by averaging down  Then, STD has been calculated with the equation STD = √the values of SN. |x − μ|2 /N , and STDE has been calculated with the equation STDE = √ STD/ (N). Where x = all the real data of SN, μ = mean, N = number of real data. In Table 6, STDE calculation has been done for real data set. In Table 7, this calculation has been done for prediction data set of SN using AR. In Table 8, this calculation has been done for training data set of SN with seasonality and trend using MA In Table 9, this calculation has been done for prediction data set of SN with seasonality and trend using MA. In Table 10, this calculation has been done for training data set of SN with seasonality and trend using ES. Table 10 Standard error for training data of SN with seasonality and trend using ES

Table 11 Standard error for prediction data of SN with seasonality and trend using ES

Mean

54.27452

STD

14.79529

STDE

1.350619

Mean

46.60278

STD

22.2962

STDE

4.551192

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In Table 11, this calculation has been done for prediction data set of SN with seasonality and trend using ES.

4 Discussion According to the MAPE, MA model is better than other models which have been used to predict SN. Mean absolute percentage error is showing the accuracy of forecasting, and MA is just more perfect than others for the purpose of prediction. Apart from this, according to the STDE, prediction with seasonality and trend using MA was far better than prediction with seasonality and trend using ES and prediction using AR.

5 Conclusion From the mentioned source [11], 3235 data have been got. As sunspot is basically an 11-year solar cycle and the number of SN increases and decreases within an 11-year solar cycle. Recent solar cycle 24 has been started from 2008, and it has reached to its maximum in 2014. So, for the purpose of avoiding complication, data have been trained for 120 data sets. However, in this study, prediction has been done with several methods and mathematical calculation which is why this study is dynamic and differences among these models for doing prediction of SN have been elucidated.

References 1. Wang H, Qu M, Shih F, Denker C, Gerbessiotis A, Lofdahl M, Rees D, Keller C (2003) Bull AAS 36:755 2. Hoyt D, Schatten KH (1997) The role of the sun in climate change. Oxford University Press, Oxford, p 279 3. Han J, Gong W, Yin Y (1998) Mining segment-wise periodic pattern in time related databases. In: Proceedings of 1998 of international conference on knowledge discovery and data mining (KDD’98) New York City, Aug 1998 4. Xiang J, Du J, Shi J (1988) Dynamic data processing: time series analysis. Meteorology Press 5. Ozden B, Ramaswamy S, Silberschatz A (1998) Cyclic association rules. In: Proceedings of 1998 international conference data engineering ICDE’98, pp 412–421 6. Ling RAK, Shim HSSK (1995) Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21st VLDB conference Zurich Switzerland 7. Gan R (1991) The statistical analysis of dynamic data. Beijing University of Science and Technology Press 8. McIntosh PS (1990) Solar Phys 125:251 9. Bornmann PL, Shaw D (1994) Solar Phys 150:127 10. Wheatland MS (2004) AstroPhys J 609:1134 11. SILSO data/image, Royal Observatory of Belgium, Brussels available from: http://www.sidc. be/silso/datafiles

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12. “Intel Core i3-3.30”, processor available at https://ark.intel.com/products/65693/Intel-Corei3-3220-Processor-3M-Cache-3-30-GHz 13. “Windows 10”, operating system available at https://www.microsoft.com/en-us/software download/windows10 14. “Weka (version 3.8)” software available at https://www.cs.waikato.ac.nz/ml/weka/downlo ading.html

Machine Learning Approach for Feature Interpretation and Classification of Genetic Mutations Leading to Tumor and Cancer Ankit Kumar Sah, Abinash Mishra, and U. Srinivasulu Reddy

Abstract As the interpretation of genetic mutation is done manually, it is difficult to diagnose a large number of patients and get reports of the same in a quick time. Hence, it needs to be automated using machine learning approach. Towards the same, natural language processing (NLP) technique, viz. term frequency-inverse document frequency (TF-IDF), is used to represent documents as fixed-size depiction for interpreting the given nine classes of genetic mutations. The main aim of this study is to identify the well-suited machine learning model which will give better results in terms of multi-class log-loss. Another important aspect of this study is to interpret the features since feature interpretability is very important in healthcare domain using various machine learning algorithms. Logistic regression (LR) with class balancing was implemented by taking top 1000 words of 3-gram TF-IDF generated features that outperformed the other classifiers to give a test log-loss of 0.98. Keywords Genetic mutations · TF-IDF · Machine learning · Interpretability · 3-gram · Logistic regression · NLP

1 Introduction Tumor is generally the swelling of a section of body without inflammation due to the abnormal growth of tissue within the body. The disease caused by the malignant growth or tumor through an uncontrolled division of cells in an area of a body is referred to as cancer. The alteration of the structure of genes into a variant form that A. K. Sah (B) · A. Mishra · U. S. Reddy (B) Machine Learning and Data Analytics Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, India e-mail: [email protected] U. S. Reddy e-mail: [email protected] A. Mishra e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_35

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can be transmitted to subsequent generations is called mutation. Cancer or tumor contains hundreds of genetic mutations. The main challenge is to identify the mutations that accord to tumor growth from the normal mutations. Currently, the identification of mutations is done manually by the pathologists/experts in this domain. This is a very time-consuming process. The pathologists manually review each and every genetic mutation based on the available literature for the same purpose. The time constraint can be improved drastically by the application of machine learning and natural language processing techniques. The text-based clinical literature available for this purpose could be transformed into a numerical vector form. This vector can be used to classify the right mutation with the help of a suitable machine learning model. Machine learning (ML) and data mining techniques play a vital role in day-today life. A wider variety of real-life problems can be solved by ML and data mining approaches such as medical sciences [1], banking and risk assessment [2]. Classification is one of the most important tasks performed by supervised ML algorithm. The main aim of this study is to automate the classification of genetic mutation on the basis of their contribution to tumor growth. In the personalized cancer treatment classification, it is required to map the patient’s status into one of the class labels spanning from 1 to 9 which represents the classes based on his/her gene variations. In the proposed work, various types of machine learning algorithms have been applied and made to work for this particular task to identify the type of genetic mutation toward tumor growth. This is done in order to reduce the time constraint. Also, it can assist the molecular pathologist to diagnose in the early stage. While selecting the best classification algorithm, multi-class log-loss has been chosen as the performance metric, as accuracy can be misleading due to imbalance in the dataset. Log-loss represents the misclassification error while assigning the class label for the input features. It is required to assign a class label among the nine classes for a particular patient based on the genetic mutations.

2 Related Work According to World Health Organization (WHO), cancer led to an estimated 9.6 million deaths in 2018. One out of every six deaths is due to cancer. Tobacco consumption is responsible for almost 22% of cancer deaths worldwide. This type of diseases mostly happens in low- and middle-income countries. It is surveyed that one out of every five low- and middle-income countries could gather necessary data to handle the wrathful nature of cancer. Hence, it becomes necessary to automate this process so that the benefit of this initiative can reach to poor countries as well. According to Konstantina Kourou et al. [3], artificial neural networks (ANNs), decision trees (DTs), Bayesian networks (BNs) and SVMs result in accurate decision making for most of the cancer research prediction problems. In [3], different ML techniques are employed for modeling cancer progression. These models are based on supervised ML techniques on different input variables and data samples. In [4], the authors conducted a broad survey of different machine learning techniques to be

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used, the nature of data to be integrated in order to predict the right cancer type. In [5], the authors applied three supervised ML techniques, viz. C4.5 DTs, bagged DTs and boosted DTs on gene expression data for cancer classification. They observed that bagging and boosting models are performing better than single DTs. In [6], a machine learning approach is built to select the gene for cancer classification from microarray data. They discussed various feature selection algorithms to extract useful gene information. Here, [7] proposed a method which contains two divisions. The first division consists of the TF-IDF retrieval part for retrieving the number of most relevant sentences and the second layer as bidirectional recurrent convolutional layer (BRC) which is the combination of bidirectional recurrent unit and convolution. The research article [8] proposed a computer-added architecture to automate the detection melanoma tumor in the early stages with more accuracy. It has been shown that the proposed work was compared with three base learners, i.e., ANN, Naïve Bayes (NB) and k-nearest neighbor (k-NN), and achieved an overall mix of 89% of specificity, 89% of sensitivity and 89% of accuracy. In [9], the authors proposed a novel architecture related to molecular sub-type classification which is booming area in long-cancer diagnosis. A case-based reasoning framework with gradient boostingbased feature selection has been applied for the lung cancer classification more accurately.

3 Dataset Description In 2017, Kaggle hosted this competition with the name “Redefining Cancer Treatment.” The goal is to automatically classify genetic mutations on the basis of their contribution to tumor growth. We have two data files. One contains the information about genetic mutations with four fields, namely (a) “ID” denoting the row which is used to link the mutation to the corresponding text, (b) “gene” denoting the place where this mutation is located, (c) “variation” denoting the amino acid change for the mutation and (d) “class” which denotes the nine classes on which the mutation has been classified and the other data file contains the text (clinical evidence) that pathologists used to classify this mutation. These two files were merged into a single file which contains 3,321 entries. The dataset was split randomly into three parts, namely training set (64%), validation set (16%) and test set (20%). In this way, we have 2,124 data points in training set, 532 in validation set and 665 entries in test set.

4 Proposed Work This section discusses the different phases involved in our model building. It can be broadly divided into six separate components. There are some objectives and constraints which needs to be followed while building the model. There is no strict latency requirement, but model interpretability is very important. As it is related to

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Fig. 1 Complete flowchart of the present system

healthcare domain, errors could be very costly and so each data point needs to be rightly classified with maximum probability. In the present article, we have developed a machine learning model which automatically classifies the genetic variations given in the vector notation of the text-based clinical literature as the evidence. There are basically six stages starting with collection of dataset, followed by preprocessing of text, text vectorization, feature stacking, application of machine learning model and finally a brief report of log-loss and feature interpretability. Log-loss is defined as: L=−

M N  1  yi j × In Pi j N i j

(1)

Here, N = number of instances, M = number of different labels, yij = binary variable with the expected labels and Pij = classification probability of the model for the ith instance and jth label. Figure 1 represents the complete flowchart of the present system. The dataset is more or less an imbalanced one; hence, metrics like accuracy can be misleading since even a dumb model can predict the value of majority class for all predictions and can get a significant classification accuracy. Hence, multi-class log-loss and precision matrix are chosen as two performance metrics.

4.1 Preprocessing and Text Vectorization There are three features, viz. gene, variation and text. Some of the text-based clinical features are very long and hence need some preprocessing to be done on top of it. Firstly, we replace every special character found in each text with simply a space character. Secondly, all the unwanted multiple space characters are substituted with a single space character and all the characters are converted into lower case. Finally, all the English stop words are also removed. While exploring the dataset, it is found that 5 out of 3,321 instances contain no text description at all. For all those texts, we simply concatenated the corresponding gene and variation feature and put in as a text feature (Fig. 2). After text preprocessing, text vectorization is performed. The gene

Machine Learning Approach for Feature Interpretation …

Gene FANCA FANCA

Variation Class S1088F 1 a) Before preprocessing S1088F 1 b) After preprocessing

391 Text NaN FANCA S1088F

Fig. 2 Text feature (a) before preprocessing and (b) after preprocessing

and the variation feature are categorical. There are 236 different categories of genes and 1,921 different variations of genes in the training data. The distribution of both the features is highly skewed. In order to give a vector representation to each feature, we followed TF-IDF encoding scheme. In TF-IDF encoding, we generated 236 (since 236 unique genes)-dimensional feature set for each gene variable and 1,921 (since 1,921 unique variations)-dimensional feature set for each variation variable. The entries in each of these vectors contain the TF-IDF scores of each word for a given instance. The TF-IDF scores for a term i in document j are calculated as follows: wi j = t f i j × log

N d fi

(2)

Here, t f i j = number of occurrences of i in j, d f i = number of documents containing i and N = total number of documents.

4.2 Random Model In order to measure the robustness of the proposed model, firstly a random model has been built where for given xi , our random classifier randomly picks any yi , where yi ∈ [1, 9]. Main motive behind building of random model is to get a base value of log-loss. The log-loss of the proposed model can be compared with this base log-loss to see how much good our models are. Our random model randomly generates nine values for any given data point which are then normalized so that the sum of the cell values across the nine-dimensional vector for each data point sums to 1. Here, we are simulating a random classifier on cross-validation (CV) and test datasets. The log-loss is roughly 2.54 for both CV and test sets. Log-loss can range anyway between zero and infinity. For a perfect classifier, log-loss is 0. Thus, any sensible model that we build should have a log-loss = 0.

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4.3 Applications of ML Models The gene and variation features are separately taken, and logistic regression is applied on them. We found best hyperparameter using hyperparameter tuning and found 0.0001 to be the optimum value of ∝, taking gene and variation into consideration independently. To implement logistic regression, SGD classifier was used as a utility function available in scikit-learn of anaconda framework with L2 regularization penalty and epsilon = 0.1. After modeling on TF-IDF created vectors, the test log-loss is found to be 1.22 and 1.71, respectively, for gene and variation features. Now, we do the same thing on text-based clinical features. The number of unique words in the text corpus of training data is 53,376. Each of the text features of every data point is converted into its corresponding vector form using TF-IDF encoding. Since there are 53,376 unique words, we get 53,376-dimensional vector for each text in TF-IDF encoding scheme. After application of logistic regression with best value of ∝ (0.001), the test log-loss is found to be 1.07. The results of all these modeling are summarized in Table 1. Moreover, all the features are stable (equally distributed) across all the datasets, viz. train, validation and test datasets. These features are performing well than our random model when taken separately. But our target is to somehow reduce the test log-loss to a value at least less than 1. For that, we stacked all the features together and run different ML models to see if it could lead to a better result.

5 Results and Discussion After stacking of features horizontally, the number of fields for each data point in TF-IDF scheme is 55,533 (236 + 1921 + 53,376). As part of training, we used seven state-of-the-art machine learning models, viz. Naïve Bayes (NB), k-NN, logistic regression, linear support vector machine (SVM), random forest (RF), stacking classifier and majority voting classifier to train our model and interpret the results. Since Naïve Bayes works best for text data, it is used as the baseline model. The Laplace smoothing factor (∝) is tuned to generate optimum hyperparameter value of 0.1. Similarly, we tuned the hyperparameters of every other classifiers and built the model with best found hyperparameter. Stacking classifier is a stack of three classifiers, viz. logistic regression, SVM and NB. One of the major drawbacks of any of the stacking Table 1 Report of log-loss for each feature taken separately

Feature used Train log-loss Validation log-loss Test log-loss Gene

1.04

1.24

1.21

Variation

0.71

1.70

1.71

Text

0.67

1.27

1.07

Original Class

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1 2 3 4 5 6 7 8 9 Classes

393

0.64 0.05 0.00 0.16 0.08 0.05 0.01 0.00 0.00

0.00 0.51 0.02 0.07 0.02 0.02 0.34 0.00 0.00

0.13 0.00 0.37 0.12 0.12 0.00 0.25 0.00 0.00

0.15 0.00 0.04 0.75 0.00 0.02 0.02 0.00 0.00

0.19 0.02 0.02 0.11 0.50 0.05 0.08 0.00 0.00

0.13 0.02 0.00 0.00 0.14 0.67 0.02 0.00 0.00

0.03 0.19 0.02 0.01 0.03 0.05 0.64 0.01 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.00 0.75

1

2

3

4

5

6

7

8

9

Predicted Class Fig. 3 Precision matrix for logistic regression with class balancing

Table 2 A brief summary of all the classifiers used to train the model with a report of training, validation and test log-loss Classifiers used

Train log-loss

Validation log-loss

Test log-loss

Naïve Bayes

0.800

1.260

1.250

k-NN

0.650

1.07

1.030

Logistic regression (without balancing)

0.570

1.220

1.030

Logistic regression (with balancing)

0.590

1.056

0.988

Linear SVM

0.700

1.230

1.070

Random forest (TF-IDF processed)

0.650

1.150

1.130

Stacking classifier

0.650

1.200

1.090

Majority voting

0.860

1.180

1.120

classifiers and majority voting classifier is that those are not interpretable. We cannot interpret the results from the output of these classifiers. Logistic regression is applied with first-class balancing (upsampling) and then without class balancing. It is observed that logistic regression with class balancing taking top 1000 features of 3-gram TF-IDF generated features beat the other classifiers to give a test log-loss of 0.98. The precision matrix of the best model has also been shown in Fig. 3. The best ∝ value is found to be 0.0001. A brief summary of all the classifiers is provided in Table 2.

5.1 Feature Interpretation A system is said to be interpretable if humans can develop an ability to present and explain how that system works. In other words, the system is interpretable if it is understandable by humans. Hence, an interpretable machine learning is a decision model which is built with its own explanation. Deep learning is a branch of or, in other

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words, subset of machine learning which is inspired from the construction of human brains, and so the internal structure of it is highly complicated and not interpretable at all. It simply acts as a black box. In healthcare applications, interpretation about the disease should always be taken into account. Since logistic regression is highly interpretable, we extracted important features (words to be more specific) to make the things simpler in order to classify the right mutation. The weight vector and the bias term are very important in order to derive important features. If the absolute value of weight component for a feature is found to be higher, then that feature is considered to be important of all the features. The more dominating the absolute value is, more important is the feature. As for example, for the test point index 1, it is predicted to belong to class 7 type of genetic mutation family with 61% probability. Moreover, it is rightly predicted. Some of the important words interpreted are cells, phosphorylation, 3b, mutant, serum, antibody, etc. Out of the top 100 words interpreted for this test index, 73 are present in the query point which also shows the robustness of our current model.

6 Conclusions In this paper, a novel way of representing and processing clinical documents as fixedsize representations has been proposed. The experiment is carried out on Kaggle’s task “Redefining Cancer Treatment” dataset which shows promising results and can be enhanced further using more substantial experiments. Deep learning technique was applied in [7] for the same problem. But the feature interpretation was not taken into account. In this article, this limitation is overcome and improved to a certain extent. Logistic regression outperformed other classifiers with test log-loss of 0.98. As part of future work, we are planning to build more robust machine learning models to handle the three major areas of oncology, viz. medical, surgical and radiation fields. We will focus mainly on skin cancer treatment using statistics, data mining and other data analytical approaches.

References 1. Zhang Y, Bhatti UA (2018) Heterogeneous data sources. IEEE J Biomed Heal Informatics 22:1824–1833. https://doi.org/10.1109/JBHI.2018.2846626 2. Ander J, Arévalo J, Paredes R, Nin J (2018) End-to-end neural network architecture for fraud scoring in card payments. 105:175–181. https://doi.org/10.1016/j.patrec.2017.08.024 3. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. CSBJ 13:8–17. https://doi.org/10.1016/j.csbj. 2014.11.005 4. Cruz JA, Wishart DS (2006) Applications of machine learning in cancer prediction and prognosis. 59–77. https://doi.org/10.1177/117693510600200030 5. Tan AC, Gilbert D (2003) Data for cancer classification. 2:1–10

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6. Wang Y, Tetko IV, Hall MA, Frank E, Facius A, Mayer KFX, Mewes HW (2005) Gene selection from microarray data for cancer classification—a machine learning approach. 29:37–46. https:// doi.org/10.1016/j.compbiolchem.2004.11.001 7. Michaela B, Grgur K, Franko S (2018) Personalized medicine: redefining cancer treatment classification using bidirectional recurrent convolutions. 28–32 8. Li L, Zhang Q, Ding Y, Jiang H, Thiers BH, Wang JZ (2014) Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system. 14:1–12. https://doi.org/10.1186/ 1471-2342-14-36 9. Ramos-González J, López-Sánchez D, Castellanos-Garzón JA, de Paz JF, Corchado JM (2017) A CBR framework with gradient boosting based feature selection for lung cancer subtype classification. Comput Biol Med 86:98–106. https://doi.org/10.1016/j.compbiomed. 2017.05.010

Design and Implementation of Hybrid Cryptographic Algorithm for the Improved Security Pavithra Kanagaraj and Manivannan Doraipandian

Abstract Being into the world of data, it is essential to provide a solution to secure the data. The Internet of things (IoT) and the cloud are disruptive technologies that have made the embedded engineers to make things go smarter. As both the technologies are considered, there are so many security issues faced. Thus in this work, a hybridised algorithm has been designed to provide better security. It incorporates the advantages of both the symmetric algorithm (Advanced Encryption Standard (AES)) and the public key cryptographic algorithm (Rivest–Shamir–Adleman (RSA)) to bring out a hybridised algorithm. The 4086 bits of paired keys are generated through RSA in order to provide better security as it is hard to attack and also it provides good key management. On the other hand, the AES withstands the linear and differential attacks. Comparing to the standard implementation of RSA and AES, this hybrid algorithm has less computation time. Keywords Internet of things (IoT) · Data · AES · RSA · Cloud

1 Introduction Owing to the unrivalled growth in smartness, billions of devices has been metamorphosed into smart devices with the help of two most disruptive technologies such as IoT and cloud computing. IoT refers to an interconnection of billions of devices which are responsible for collecting, sensing and exchanging plethora of data in a broader way from diversified locations [1]. It demands the smart data aggregation followed by processing of data with higher efficiency and effectiveness [2]. IoT has a wide range of applications with the context of public safety and homeland security (e.g. smart cities), automation, building management systems, logistics, goods, industries, etc [3]. Always the users find difficulties in how to represent, store, interconnect, search and organise information generated by the billions of IoT devices. Thus, the IoT P. Kanagaraj · M. Doraipandian (B) School of Computing, SASTRA Deemed University, Thanjavur 613401, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_36

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is changing over time to Internet of Everything (IoE) or Web of Things (WoT) [2, 4]. IoT applications have limitations in terms of mobility, availability, manageability, scalability, security, privacy, trust and managing heterogeneity [5]. IoT requires scalable storing platforms and communication infrastructures. This is furnished by the cloud computing technology [4]. The next adverse technology is cloud computing. It plays a very important role in IoT architecture [6]. It provides a large number of benefits to edge devices, as these IoT devices are resource constrained in nature [7]. They have less processing and storage capacity, whereas the cloud computing has virtually unlimited storage and processing capabilities [8]. Thus, most of the IoT device storage problems can be solved with the usage of cloud. These two contradictory technologies could be brought into one stream of line as CloudIoT [9]. The storage and processing facilities provided by the cloud reduce the overheads faced by most of the IoT devices. Alesso Botta et al. [9], has stated that, the integration of cloud computing and IoT requires very strong encryption algorithms for securing the data [10]. Thus, the hybridisation of the symmetric and asymmetric algorithm is done. The purpose of integrating the two algorithms is to provide the data confidentiality and integrity [11]. The paper discusses the related works, methodology adopted for the implementation of the hybrid algorithm and the results obtained.

2 Related Works In today’s scenario, the IoT devices adopt either symmetric or asymmetric algorithms for the secure transmission of data. Each of the algorithms has its own consequences. When the security of the devices used for communication is taken into consideration, there are so many security issues which are needed to be addressed. There are so many attacks that have been discussed for various algorithms. Each and every algorithm which is designed is made to withstand some of the attacks. The security attacks faced by the devices are man-in-the-middle attack, masquerade attack, data breach, etc., and also the data confidentiality and data integrity are to be maintained in a system [12, 13]. The most used symmetric algorithm in the embedded devices or IoT devices is AES, and the asymmetric algorithm is the RSA. The RSA is an extensively used public-key cryptography (PKC) algorithm. The benefit of using RSA is that it has two keys for encryption and decryption. Sharing of the keys takes place in a Pretty Good Policy (PGP). It is very hard to break the keys as the key size of the algorithm grows higher. The factorisation of the two large primes is a pretty difficult task [14–16]. The suggested hybrid algorithm is based on triple DES and RSA, aiming to improve the security of data communication. It has been stated with the context of providing data confidentiality and integrity that the proposed algorithm makes use of RSA and DES protection, to give away a security greater than before [17–19]. It is stated that comparing to the Data Encryption Standard (DES), the AES is better in terms of speed, time and efficiency [20, 21].

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The hybrid algorithm can be designed with the help of the existing symmetric and asymmetric algorithms. This is done to utilise the existing algorithms in an effective manner. The RSA has a very good key management policy, and the AES is a faster algorithm.

3 Hybrid Algorithm The proposed system provides a solution for the data security issues faced by the IoT devices. It merges the two different cryptographic algorithms. Both the asymmetric and symmetric algorithms are used, and the advantages of both the algorithms are combined to produce a new hybrid crypto algorithm [22]. The algorithms normally used in IoT and cloud are RSA and AES. Hybrid cryptosystem by the name indicates that the advantages of both the cryptosystems are taken, to design a more secured and efficient algorithm [23].

3.1 RSA It provides a random key of various bit lengths. The major advantage of using asymmetric algorithm is that it generates two unique keys for encryption and decryption [24]. One is the public key, used for encryption, and another is the secret key, used for decryption [25]. This algorithm is primarily used by the IoT devices to generate the keys. It takes much larger time for ciphering the data compared to the other algorithms [26]. Because of this reason, the private-key algorithms are most preferred compared to public-key algorithms. The RSA algorithm is based on the modular exponentiation. The generation of the keys is done with the help of choosing large prime numbers whose factorisation is complex in nature. Thus, it has been said that the keys are not easily cracked by the attackers. The input feeds are generated using random generators [27].

3.2 AES It is the asymmetric algorithm used for the encryption and decryption part of the hybrid algorithm. The major advantage of using AES is that it is faster algorithm and provides better security than other algorithms [28]. It has been adopted for various bit lengths of the data. The rounds incorporated in the AES are sub-bytes, shifting of rows, mixing the columns of the bytes and adding the round keys [29].

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4 Methodology The system uses the random generator, RSA and AES algorithms for securely encrypting and decrypting the data during transmission and reception. The methodology that has been adopted for incorporating both the technologies to bring out a hybridised algorithm is described as follows.

4.1 Explanation of the Methodology Used 4.1.1

Generation of Random Numbers

The random numbers have been generated using the random generator function. This is done to choose the prime numbers for the RSA algorithm. The random generator makes the above-said work easier. The generated random numbers are fed as inputs to RSA.

4.1.2

Generation of Keys Using RSA

The keys have been generated using the RSA. The two prime numbers have been fed from the random generator. The ‘n’ is outcome of the product of the two large prime numbers (x, y). The totient value (z) is computed from the product of the two numbers (x − 1) and (y − 1). The co-prime of the (z) is computed and said as the public key(d). It is calculated by finding the greatest common divisor (GCD) of d, (z), and it is equated to one. The public key and private key are generated in the file format .pem (Privacy-Enhanced Mail). The size of the private key and public key generated is 4086. This much larger key will always create bottlenecks for the attackers to crack the key. Thus, this RSA is said to provide good key management. The flow for the computation of public and private keys is shown below. 1. 2. 3. 4. 5. 6.

Generate two large primes → x, y. Calculate z = x × y and φ(z) = (x − 1) × (y − 1). Select such that gcd (e, φ(z)) = 1. Determine d such that e. d ≡ 1 mod φ(z). Public key for the session = (e, n). Secret key for the session = (d, n).

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Encryption and Decryption Using AES

The public and private keys have been called, and it is used to do the encryption and decryption of the file by incorporating the steps in AES. The block diagrams depicting the encryption and decryption process are shown in Fig. 1 and Fig. 2, respectively.

Fig. 1 Encryption phase block diagram

Fig. 2 Decryption phase block diagram

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5 Experimental Results For the purpose of encrypting and decrypting the data, the public and private keys of size 4086 bits are generated using the RSA algorithm. These keys have been stored in the format of .pem (Privacy-Enhanced Mail) at the destination folder (Fig. 3), for example: private_key.pem and public_key.pem. Figures 4 and 5 show the public and secret keys produced. The Privacy-Enhanced Mail (.pem) file format is used to store the cryptographic keys. The computational time taken for the generation of the 4086 bits—paired keys are displayed as shown in Fig. 6. The size of the private and public keys is also displayed.

Fig. 3 The 4086 sized key files created using RSA are stored as .pem files in the destination

Fig. 4 Public key stored in public_key.pem file

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Fig. 5 Private key generated and stored in private_key.pem file

Fig. 6 Time taken for the execution of keys and the size of the keys are displayed

The computation time taken by this algorithm is lesser than that of the standard implementation of RSA and AES in the paper [19, 28]. Thus, the algorithm is computationally less intensive, and as the keys generated are arbitrary in nature, breaking of the keys is complex.

6 Conclusion and Future Work The hybrid cryptographic algorithm that has been designed in this paper provides better security to the system as it incorporates both the symmetric and asymmetric algorithms. From the output of the algorithm, it has been found that the computation time taken by the program is lesser compared to the original algorithms. In future, the algorithm can also be further modified and its performance can be evaluated. It can also be replaced by any other lightweight cryptographic algorithms such as PRESENT and SERPENT [30]. The usage of lightweight cryptographic algorithms

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may further reduce the complexities incurred by the IoT devices during the process of encryption and decryption. Acknowledgements The authors are grateful to the Department of Science & Technology, New Delhi, India (SR/FST/ETI-371/2014) for their financial support. They also wish to acknowledge SASTRA Deemed University, Thanjavur, for extending infrastructural support to carry out the work.

References 1. Saarikko T, Westergren UH, Blomquist T (2020) The internet of things: Are you ready for what’s coming? 2. Mankar C (2016) Internet of things (IoT) an evolution. 5:772–775 3. Comput JPD, Wu L, Chen B, Choo KR, He D (2018) Efficient and secure searchable encryption protocol for cloud-based internet of things. 111:152–161 4. Bandyopadhyay D, Sen J (2011) Internet of things: applications and challenges in technology and standardization. 49–69 5. Stergiou C, Psannis KE (2018) Sustainable computing: Informatics and systems security, privacy and efficiency of sustainable cloud computing for big data and IoT. 19:174–184 6. Zhou J, Leppänen T, Harjula E, Yu C, Jin H, Yang LT (2013) Cloud things: a common architecture for integrating the internet of things with cloud computing. 651–657 7. Catteddu D (2010) Cloud computing: benefits, risks and recommendations for information security. In: Serrão C, Aguilera Díaz V, Cerullo F (eds) Web application security. Springer Berlin Heidelberg, Berlin, p 17 8. Patidar S (2012) A survey paper on cloud computing. 2012 Second Int. Conf. Adv. Comput. Commun. Technol. 394–398 9. Botta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. 56:684–700 10. Bittencourt L, Immich R, Sakellariou R, Fonseca N, Madeira E, Curado M, Villas L, Dasilva L, Lee C, Rana O (2018) Internet of things the internet of things, fog and cloud continuum: integration and challenges. 4:134–155 11. Taki AE, Deen E (2016) Design and implementation of hybrid encryption algorithm 12. Kumar S, Goudar RH (2012) Cloud computing—research issues, challenges, architecture, platforms and applications: a survey. 1 13. Jadeja Y (2014) Cloud computing—concepts, architecture and challenges 14. Nisha S, Farik M (2017) RSA public key cryptography algorithm a review. Int J Sci Technol Res 6:7 15. New Attacks on the RSA Cryptosystem, Abderrahmane Nitaj, Muhammad Rezal Kamel Ariffin, Diee I. Nassr, Hatem M. Bahig. Part of the Lecture Notes in Computer Science book series (LNCS, volume 8469) 16. Boneh D, Rsa T, Rivest R, Shamir A, Adleman L (1977) Twenty years of attacks on the RSA cryptosystem 1 introduction. 1–16 17. Student MT (2013) A new design of algorithm for enhancing security in bluetooth communication with triple DES. 2:252–256 18. Mahdi JA (2009) Design and implementation of proposed BR encryption algorithm. Ijcccse 9:1–17 19. Akkar M-L, Giraud C (2001) An implementation of DES and AES, secure against some attacks. In: Koç ÇK, Naccache D, Paar C (eds) Cryptographic hardware and embedded systems—CHES 2001. Springer Berlin Heidelberg, Berlin, pp 309–318 20. Devi A, Sharma A, Rangra A (2015) Performance analysis of symmetric key algorithms: DES, AES and blowfish for image encryption and decryption. 4:12646–12651

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21. Vashishtha J (2012) Evaluating the performance of symmetric key algorithms: AES (Advanced Encryption Standard) and DES (Data Encryption Standard). 15:43–49 22. Albahar MA, Olawumi O, Haataja K, Toivanen P (2018) Novel hybrid encryption algorithm based on AES, RSA, and Twofish for Bluetooth Encryption. 168–176 23. Stergiou C, Psannis KE, Kim B, Gupta B (2018) Secure Integ IoT Cloud Comput 78:964–975 24. Milanov E (2009) The RSA algorithm. 1–11 25. Zhou X, Tang X (2011) Research and implementation of RSA algorithm for encryption and decryption. Proc 2011 6th Int Forum Strateg Technol 2:1118–1121 26. NaQi, Wei Wei (2013) Analysis and Research of RSA algorithm. Information Technology Journal 12(9):1818–1824 27. Saveetha P, Arumugam S (2012) Study on improvement in RSA algorithm and its implementation. Int J Comput Commun Technol 3:975–7449 28. Abdullah AM (2017) Advanced encryption standard (AES) Algorithm to encrypt and decrypt data 29. Teja T, Hemalatha V (2017) Encryption and decryption—data security for cloud computing— using AES algorithm. 80–83 30. Katagi M, Moriai S (2008) Lightweight cryptography for the internet of things. Sony Corp. 7–10

A Real-Time Smart Waste Management Based on Cognitive IoT Framework Sujit Bebortta, Nikhil Kumar Rajput, Bibudhendu Pati, and Dilip Senapati

Abstract The ability of the Internet of things (IoT) to incorporate anything and everything has induced and it is revolutionary applications in spheres of smart healthcare, smart living, smart cities, smart governance, and many more. A more general illustration for the IoT-based administration is the smart waste monitoring and management scheme for the smart cities. The smart waste management comprises of certain information and communication technologies (ICT) which support the tracking and management of the garbage bins. In this paper, we present a strategy for the garbage bin detection problem based on the thresholding scheme and also present a real-time waste management algorithm for the dynamic selection of optimal paths by the garbage collection vans. We also provide an optimal cost model subject to the threshold-based constraints which falls under the time complexity O( p + n log n), (where p and n denote the path and the location of the smart dustbins), for our proposed algorithm. Keywords Smart dustbins · Wireless sensor networks · Internet of things · Cloud computing

S. Bebortta Department of Computer Science and Engineering, College of Engineering and Technology, Bhubaneshwar 751003, India N. K. Rajput Department of Computer Science, Ramanujan College, University of Delhi, New Delhi 110019, India B. Pati Department of Computer Science, Rama Devi Women’s University, Bhubaneswar 751022, Odisha, India D. Senapati (B) Department of Computer Science, Ravenshaw University, Cuttack 753003, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_37

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1 Introduction The rapid growth in industries and metropolitans in the last few years has caused several threats to the environment over the years. One such concern is the generation of solid wastes which is a consequence of this tremendous growth [1–3]. There have been several efforts made by the government agencies to monitor and manage the growing environmental adversities. In the present day, almost every country is aiming to transform the cities to smart cities in order to streamline the efficient functioning and management of the cities [4, 5]. A distinctive characteristic of the smart cities is the efficient and timely management of public pursuits. This also leverages the local city governance for a contemporary management of solid wastes generated on regular basis. It is quite often observed that the trash bins in the cities are flooded with a huge amount of wastes which have made the environment and the citizens more susceptible to pollution and diseases. In smart cities, the management and collection of solid wastes is a very demanding task for the environment as well as for the society [6, 7]. Therefore, an efficient technology is required for the monitoring of the waste collection systems in smart cities. The Internet of things (IoT) paradigm is the most pivotal constituent of the smart cities for providing an intrinsic framework in order to get a “smart environment.” In convergence with the cloud services, IoT can extensively assist in the waste collection and management activities of the smart cities. Through IoT, we can gather real-time information regarding various phenomena involved in the entire waste management process [8, 9]. For instance, we can gather information regarding the location of the garbage collection van, or we can locate the position of the garbage bin. The cloud services can be used to store these information and make it accessible for the authorities [10, 11]. The smart dustbins (SDs) or trash bins used in smart waste management systems provide real-time information regarding the status of the SDs as well as the amount of waste collected in the SDs. In order to perform these functions, they require the deployment of various sensors, viz. ultrasonic sensors and weight monitoring sensors to perform this task [12]. In this paper, we provide an efficient waste collection approach for SDs-based on the IoT architecture. We present an algorithm employing which the garbage vans (GVs) can efficiently adapt a route based on the priority of the SDs. Here, the priority is given to the SDs by fixing thresholds to indicate when the SDs are substantially full. In cases, when the status of the SDs is within a reasonable threshold limit, then the algorithm endorses the GV to select the shortest path possible from a given set of shortest paths between the SDs. We also provide a novel framework for computing the optimal cost for traversing each path constrained to the maximum threshold value.

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2 System Architecture In this section, we discuss the basic requirements and architecture of the smart waste management system. In our framework, we consider the embedment of ultrasonic sensors and weight sensors in the SDs. The sensors evaluate the status of the SDs and the data perceived by these sensors is transmitted to the cloud data center where they are processed by the servers and the information regarding the SDs are transmitted as notifications to the intended recipients. These notifications can be obtained by the government authorities and the GV drivers over their smart devices, or smartphones using android applications [13, 14]. The sensors deployed in the SDs communicate with the servers by employing the LoRa technology which provides a platform for machine-to-machine (M2M) communication under the IEEE 802.15.4 g standard [15, 16]. Figure 1 provides an overview of a smart city along with several SDs deployed at different locations. These SDs transmit the data sensed by the deployed sensors to the cloud servers, from where they can be directly obtained over the connected smart “things,” or devices through specific applications. Through Fig. 2, we illustrate the process of transmission of the sensory data collected by ultrasonic sensors and weight sensors deployed on the SDs to the gateways from where they can be transmitted to the cloud servers. Further these information can be accessed on the smart devices by the authorities and the GV drivers.

Fig. 1 Smart city architecture along with the deployed smart dustbins

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Fig. 2 Architecture depicting the collection and processing of sensory data from smart dustbins

3 Proposed Model In our paper, we have proposed an adaptive algorithm for the real-time monitoring and management of solid wastes generated in the smart cities. All the devices involved in the waste management are connected through the IoT devices to provide an actual scenario of the current events. This provides more control to the waste administration authorities and the GV drivers to avoid the inundation of the SDs, which is of foremost importance to secure the health of the citizens and to avoid the transmission of infectious diseases. Our algorithm is based on the thresholding scheme, in which we define certain threshold values to control and predict the current status of the SD. For this, we consider x to be the threshold variable such that, 0.8 ≤ x ≤ 0.9. Here, the values 0.9 and 0.8 represent the user-defined threshold values which represent the congestion level of the SDs, with 0.9 representing 90% congestion and 0.8 representing 80% congestion of the SDs. Thus, we define an indicator function on x as,  1(x) =

1, 0.8 ≤ x ≤ 0.9, 0, otherwise

(1)

Here, the indicator function 1(.) is used because it provides more confidence to the fact that the threshold variable only reports when the SD exceeds its 80% congestion level. Therefore, this approach is more reliable than the conventionally used approaches for the timely prediction of overflow conditions. Additionally, it assists the GV drivers to determine an optimal route in anticipation of the status of

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the SDs, so that they can be emptied before they encounter an elevated congestion level. Algorithm-1 represents the real-time waste management technique in which we consider several input variables, where S D represents the set of all the smart dustbins deployed in the city, p represents all the possible paths between the dustbins and the GV , P represents the set of all the shortest paths possible between sdi and GV , and I represents the index set which holds the status of the SDs w.r.t., the indicator function 1 (x).

The optimal cost model corresponding to the above approach can be obtained by minimizing the objective function as,  Minimize C = 1(T ≥ 0.9) ∗

m 

 Ci1 pi1 + Ci2 pi2 + . . . + Cin pin ,

i=1

subject constraints : p11 + p12 + . . . + p1n ≤ d1 , p21 + p22 + . . . + p2n ≤ d2 ,

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.. .. .. .. . . . . pm1 + pm2 + . . . + pmn ≤ dm , ∀ pi j ≥ 0, i = 1, 2, . . . , m, j = 1, 2, . . . , n. where Ci represents the respective cost for visiting i th SDs, di is the shortest path possible between the SDs and the GV such that di = min{sdi , GVi } and pi corresponds to the i th path between the SDs and GV, such that i = 1, 2, · · · , m. Here, we optimize the objective function constrained to the sum of the paths between the SDs by making an explicit choice from the available set of the shortest routes.

4 Performance Analysis of Proposed Algorithm The time complexity to get the signal from the overwhelmed SD (i.e., x ≥ 0.9), out of all the existing SDs is O(1) and the path p,such  that p ∈ P between the  n overwhelmed SDs and the GV is O( p), where p  ≈ O n 2 . Therefore, the 2 overall cost of our algorithm is estimated to be O( p + nlog n) which is considered to be better than the conventional time complexity of O p 2 + n log n .

5 Results and Discussion In this section, we present the simulation results obtained using MATLAB to endorse the distinctiveness of our model. We discuss the substantial improvements procured for the performance of our proposed algorithm in contrast with the conventional algorithms. We performed the simulation by considering spontaneous clusters of SDs within a predictable range for 10 different locations and each location has a comprehensive collection of SDs typically ranging from 50 to 100, such that there exist various optimal paths p, between the SDs and GV. Appertaining to the above formulation, the time complexity of the proposed algorithm obtained in Sect. 4 was evaluated in conjunction with conventional polynomial time algorithms. After conducting the performance analysis, it was observed that the proposed algorithm considerably reduces the overall time for visiting the SDs. Figure 3 depicts the factual analysis of the time complexity of the proposed algorithm along with conventional algorithms.

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Fig. 3 Analysis of the time complexity of the proposed algorithm along with conventional algorithms

6 Conclusion and Future Works In today’s scenario, garbage management and collection has become a big challenge for smart environments. We have provided an efficient algorithm for real-time waste management in smart cities. The proposed algorithm works on the strategy of an adaptive thresholding scheme which works on the generation of an alert signal only when the congestion level of the SDs exceeds 90%(or, 0.9). This technique is more robust than the conventional methods as it reduces memory consumption and overall processing cost. A persuasive experimental analysis of the proposed algorithm in comparison with the conventional algorithms has been provided which efficiently reduces the computational complexities in establishing an optimal path between the SDs and GVs. We have also provided an optimal cost model which minimizes the traversal costs for visiting the desired SDs. This method may assist tremendously in the dynamic selection of paths and waste collection techniques for future IoT applications. Since, the smart waste collection is an emerging field toward the IoT-based smart cities. In the future, we plan to optimize the energy utilization of the SDs subject to the reliability of the deployed sensors based on probabilistic models [17], and provide a software-based scheme for monitoring and controlling the automation of these systems.

References 1. Sembiring Emenda, Nitivattananon Vilas (2010) Sustainable solid waste management toward an inclusive society: Integration of the informal sector. Resour Conserv Recycl 54(11):802–809

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2. Banerjee P et al (2019) Solid waste management in India: a brief review. In: Waste management and resource efficiency. Springer, Singapore. pp. 1027–1049 3. Dzobelova VB, Berkaeva AK, Olisaeva AV (2018) Municipal waste management in the republic of North Ossetia-Alanya. In: 2018 IEEE international conference management of municipal waste as an important factor of sustainable urban development (WASTE). IEEE 4. AlEnezi A, AlMeraj Z, Manuel P (2018) Challenges of IoT based smart-government development. In: Green technologies conference (GreenTech), 2018. IEEE 5. Bresciani S, Alberto F, Del Giudice M (2018) The management of organizational ambidexterity through alliances in a new context of analysis: internet of things (IoT) smart city projects. Technol Forecast Soc Change 136: 331–338 6. Topaloglu M, Yarkin F, Kaya T (2018) Solid waste collection system selection for smart cities based on a type-2 fuzzy multi-criteria decision technique. Soft Comput 1–12 7. Al Mamun MA et al (2015) Integrated sensing systems and algorithms for solid waste bin state management automation. IEEE Sens J 15(1):561–567 8. Wen Z et al (2018) Design, implementation, and evaluation of an internet of things (IoT) network system for restaurant food waste management. Waste Manage 73:26–38 9. Deka K, Goswami K (2018) IoT-based monitoring and smart planning of urban solid waste management. In: Advances in communication, devices and networking. Springer, Singapore pp. 895–905 10. Stergiou C et al (2018) Secure integration of IoT and cloud computing. Future Gener Comput Sys 78:964–975 11. Alhussein M et al (2018) Cognitive IoT-cloud integration for smart healthcare: case study for epileptic seizure detection and monitoring. Mobile Network Appl 23.6:1624–1635 12. Chaudhari SS, Bhole VY (2018) Solid waste collection as a service using IoT-solution for smart cities. In: 2018 International conference on smart city and emerging technology (ICSCET). IEEE 13. Anagnostopoulos T et al (2017) Challenges and opportunities of waste management in IoTenabled smart cities: a survey. IEEE Trans Sustain Comput 2.3:275–289 14. Carlson D, Schrader A (2012) Dynamix: an open plug-and-play context framework for android. In: 2012 3rd IEEE International conference on the Internet of things (IOT) 15. Theoleyre F, Pang A-C (eds) (2013) Internet of things and M2M communications. River Publishers 16. Datta SK, Bonnet C, Nikaein N (2014) An IoT gateway centric architecture to provide novel M2M services. In: Internet of things (WF-IoT), IEEE world forum on 2014. IEEE 17. Senapati D, Karmeshu (2016) Generation of cubic power-law for high frequency intra-day returns: maximum Tsallis entropy framework. Digit Signal Proc 48:276–284

A Proposed Continuous Auditing Process for Secure Cloud Storage Thaharim Khan and Masud Rabbani

Abstract In recent time, cloud is one of the most useful resources for storing data without any hassle. Integrity, originality and security of data are the main concerns in cloud computing. Through the data auditing, this problem can be solved in some amount. This proposed work follows the data auditing scheme but with different methodology for making the scheme better and secure. This procedure is divided into three phases: (i) user, (ii) data auditor and (iii) cloud server. Cloud server is used for keeping data file, and data auditor is used to check the integrity of the data which is kept by the user. The user has the permission to perform various types of action like create a block for data and generate encryption for data. The third party auditor mainly generates the encryption method and checks the integrity for data security. For encryption, data encryption standard (DES) algorithm is used as the algorithm which generates the longer ciphertext, conveying better security for data. And for data integrity, secure hash algorithm (SHA-512) is used as it checks for 64-bit words. Also, Rivest–Shamir–Adleman (RSA) is used for measuring digital signature. In this method, user can get the deleted item from cloud bin by accessing into the bin with the digital signature, and all the deleted items are stored into the bin as encrypted mode. In this method, attackers are not feasible to take access into the cloud as RSA is used for digital signature. Keywords Cloud · Data integrity · DES · SHA-512 · RSA · Digital signature · Encryption · Decryption

T. Khan (B) · M. Rabbani Deparment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh e-mail: [email protected] M. Rabbani e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_38

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1 Introduction Many organizations are now using virtual storage for storing their potential data which require less physical storage and less risk of wreck. All the beneficial works like low cost, storage security and all that things are provided to all end users by the cloud computing [1]. Continuous auditing is such a scheme which ensures more security level for cloud computing. Cloud computing provides huge level of data storage for the users though it needs more security for those data of every organization. The entire security features—encryption and digital signature—are enclosed with continuous auditing for security purposes. Miscellaneous algorithms are used for the better security of cloud. This proposed work is schematic with that algorithm which is ensuring better security for the end user and more reliable to the cloud. National Institute of Standards and Technology (NIST) describes that cloud computing is such a model for enabling ubiquitous and convenient on-demand network access to a shared pool of configurable computing resource [2]. According to NIST, we can easily understand the priority of cloud computing in the modern era of computing which largely depends on the bulk of data. Each and every data is useful for every end user, and its security is also important as it relates with the whole organization [3]. One proposed work on third party public auditing scheme is such a work which used three different algorithms for auditing scheme where the Advanced Encryption Standard (AES) is used for encryption, SHA-2 is used for generating hash value and RSA is used for generating digital signature. This proposed system used DES algorithm for encryption as DES generates large encrypted text for data because the text that is more larger is most safe and also used SHA-512 because it is worked for 64-bit word also RSA algorithm is for digital signature. Here, the delegation process also uses encryption algorithm as it saves in the bin as encrypted, so there are no possibilities for taking data from deleted items. Continuous auditing (CA) is such a context, which is completely reliable and secure for processing a secure cloud storage after completing all the steps. Continuous cloud service allows auditor to approve various CA mechanisms for auditing. Also, auditor can modify events according to their subject matter. So, security, storage and all that things are most important for cloud service which is provided through the CA mechanism. This mechanism allows provider with trustworthy certificate and service. That is the reason cloud service needs more security for the data storage and data security. The rest of the paper is organized as follows. Section 2 summarizes some related works. Section 3 describes the architecture of cloud computing. Section 4 illustrates the proposed system. Section 5 illustrates the algorithms used in this proposed system. Section 6 illustrates the future studies. Finally, Sect. 7 concludes the paper.

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2 Related Works One work that continues auditing model is based on Web service technology associated with XML and other technologies [4]. XML Web services using SOAP, WSDL and UDDI protocols are used in this work for getting better result. XML technology and all the related technologies are used to get the better auditing scheme. Security purposes are also ensured here for better auditing scheme. Another work is for continuous auditing based on common certification process and dynamic certification approach [5]. This proposed work fulfills the gap between cloud service and traditional; the system gives a better result for continuous auditing procedure. For helping an internal organization for security purposes [6], the work is proposed. Dynamic risk assessment mechanism is used for security purpose along with SaaS Web service. This work helps the security purposes for end user fixing all the vulnerable assessment. Another work is implemented using “cold fusion” [7]. This is used for designing and for getting the user alarming reports over the Internet to observe the actual values of the user’s data integrity. In this work, electronic data interchange is also discussed for reducing the cost of supply chain. Another work proposed a continuous auditing which is requiring for changing hardware and software issues and control the attitude of management and behavior of management [8]. For ensuring the security and for checking the data integrity, [9] is proposed for continuous public auditing which use AES, SHA-2 and RSA algorithm for implementing the public auditing scheme. Here, AES is used for encryption, SHA-2 for integrity check and RSA for digital signature. Another work for public auditing system is [10], which secure user’s data by integrity. This work uses dynamic block generation algorithm for splitting files and storing them into multiple cloud storage. Verifiable data integrity checking algorithm is used for block-level and file-level checking. Random block is provided to third party by cloud for integrity checking for keeping the privacy of user’s data.

3 Architecture of Cloud Computing Cloud computing is such a technology which is working like a shared pool for keeping data file and reduces the hassle of maintaining physical storage [11]. Cloud computing has become more secure by dint of third party continuous auditing scheme. This third party auditing scheme mainly occurs in a cyclic order among the user, cloud and third party auditor. User has the permission to store the data in cloud and get the data from cloud with checking the integrity of data. The whole process can be categorized into private audit ability and public audit ability. On private auditing scheme, no one has the authority to check the integrity of data except the data user. On the other hand,

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Fig. 1 Architecture of cloud computing

Third party auditing

User

Cloud

public auditing scheme allows anyone to make question about the integrity checking of data [12]. The whole architecture for cloud computing is shown in Fig. 1. This figure shows the connection between all the three particulars—third party auditing, user and cloud. The user has the authority to check the integrity of data. Third party auditor is responsible for maintaining the integrity and cloud, just keeping all valuable data.

4 Proposed System This proposed system can be one of the secure cloud auditing schemes. At first, the auditing system processes the data and splits the data into blocks, and all the data sets are encrypted here with using the DES algorithm which is more secure as it generates the longest ciphertext which is not easily decrypted. One copy of block of data is sent to the cloud for storage purposes. If any data is deleted from the block of splitting data set, then it can be automatically stored into the cloud bin in encrypted mode. For this reason, attacker has no chances to decrypt any data from anywhere from the cloud. After that, the SHA-512 algorithm is used for generating hash value. This secure hash algorithm generates 512-bit signature for each and every text value by using RSA algorithm. RSA algorithm is asymmetric cryptographic algorithm which means that it has two keys for encryption and decryption [13]. The working procedure of data owner is shown in Fig. 2. After the procedure of data owner, third party auditor’s part has started. Third party auditor at first keeps the client signature and stores it for further verification. After that, a hash value is generated using SHA-512 algorithm, and using RSA algorithm, a digital signature is generated on it. Again, the signature that is stored again verifies with the signature. If the signature matches, then the signature is sent to the data owner; if it is not, then the process stops. The whole process is shown in Fig. 3.

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Start

Split files into blocks DES Encryption for blocks of file SHA-512 Generating hash value

Block file send to cloud If delete

Stored in cloud bin with encrypted mode

RSA Generating signature on encrypted blocks

Signature send to third party

Stop

Fig. 2 Working procedure of data owner

5 Algorithms DES, SHA-512 and RSA all these three algorithms are used here for maintaining the continuous auditing process. Working procedure and uses in these proposed systems are given below.

5.1 DES for Encryption Procedure Data encryption standard is an algorithm which is used for generating longer ciphertext as longer cipher-text is most secure. DES is generally used for making 64-bit pain text into 56-bit key. In general, it makes 64-bit key, but 8 bits are used for parity checking. So, it is more secure for encryption method. Figure 4 illustrates the DES algorithm for making a text encrypted. This proposed system uses DES at the time of splitting file. When all the files are divided into blocks, then an encryption method is necessary for encrypting a data [14]. This algorithm generates longer ciphertext. The longer ciphertext is most secure.

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Cloud

Keep client’s signature Send request data Generate hash value using SHA-512

Generate signature using RSA

Send signature

Signature verification

Yes

Send result to owner

No Stop

Fig. 3 Working procedure of third party auditor

Fig. 4 Working procedure of DES

DES

64 bit plain text

64 bit cipher text

56 bit key

5.2 SHA-512 for Generating Hash Value Secure hash algorithm is such an algorithm which generates 512-bit signature for text values. This generating 512 bit is almost unique. In SHA-512, the message is broken in 1024-bit chunks for hash value, and round constant values are extended into 64 bit [15]. After that, 80-64 bit array arranges for text schedule instead of 60-32 bit word. The word size calculation for word is 64 bit, and all the messages are processing in bit values. The flowchart for processing in SHA-512 is shown in Fig. 5. SHA-512 is mainly used in this proposed system to generate hash value on text that was not corrupted, and the hash value is generated on encrypted blocks. This algorithm mainly makes 1024-bit chunks of the selected text and converted the text

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Fig. 5 Flowchart of SHA-512

Start 1024 bit chunks for message Values extended to 64 80-64 bit array for text schedule Calculate 64 bit long for word size Message processing in bits

Stop

into hash format which extended the size of the hash value; also, an array is assigned for the processed text, and finally, the message is processed into bits [16].

5.3 RSA for Generating Digital Signature RSA is used for implementing digital signature. This algorithm was first implemented in 1978, and it was named after those three scientists who are related with this implementation [17]. RSA concept is based on making bigger text into small text. RSA is used for better security. For encryption and decryption, RSA is used. RSA is also used for generating digital signature. RSA does not use any particular hash function for security, so it is independent on security purposes. Data size for RSA is quite smaller in comparison with other algorithm. The computational power of RSA is also faster than any other algorithm which required less storage [18]. Figure 6 describes the working procedure of RSA algorithm. This proposed system used it after generating hash value on the encrypted text. This digital signature is kept for further verification when third party auditing process starts to check the integrity of the blocks. This process mainly starts with two prime numbers which are getting from converting the text. After getting the prime numbers, find a suitable number from those of prime number and find out all the probable list of numbers and calculate the encryption and decryption time for generating encryption and decryption.

422 Fig. 6 Processing of RSA algorithm [19]

T. Khan and M. Rabbani

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6 Future Works Continuous auditing is such a work which is giving assurance of data integrity. This auditing scheme needs to be modified for better result and data privacy. Auditing scheme needs to do that job without keeping a copy of data. For future enhancement, this sort of work needs to concentrate more on Cloud computing context. All the blocks that are created at the time of uploading data need to maintain confidentiality. If all these factors are maintained, then the cloud becomes more secure and reliable for the entire data owner.

7 Conclusion Cloud is one of the fast growing technologies for remotely using data storage which is reducing the hassle of physical memory. Again, the third party auditor which has changed the total outlook toward the cloud storage for integrity checking is completely making cloud one of the reliable and secure technologies. This proposed scheme uses the algorithms which are more secure and reliable as those algorithms

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are generating long key. Since the long generated keys are not easily decrypted by the attacker, this proposed system can be used for making cloud more secure for the user. And as the deleted item are stored in the cloud bin, so there is no chance for data loss

References 1. When and where is cloud computing used? Available from https://www.quora.com/When-andwhere-is-Cloud-Computing-used. Retrieved on 26 Jan 2019 2. The NIST definition of cloud computing. Available from https://csrc.nist.gov/publications/det ail/sp/800-145/final. Retrieved on 26 Jan 2019 3. NIST cloud computing program—NCCP, Available from https://www.nist.gov/programs-pro jects/nist-cloud-computing-program-nccp. Retrieved on 26 Jan 2019 4. Murthy US, Groomer SM (2004) A continuous auditing web services model for XML-based accounting systems. Int J Acc Inf Sys 5(2) 5. Sunyaev A, Schneider S (2013) Cloud services certification. Common ACM 56(2):33–36 6. Kuo C-T, Ruan H-M, Lei C-L, Chen S-J (2011) A mechanism on risk analysis of information security with dynamic assessment. Third Int Conf Intell Networking Collaborative Sys 643–646 7. Woodroof J, Searcy D (2001) Continuous audit implications of internet technology. In: Proc. HICSS, Outrigger Wailea Resort, Island of Maui pp. 1–8 8. Brown CE, Wong JA, Baldwin AA (2007) A review and analysis of the existing research streams in continuous auditing. J Emerg Technol Acc 4(1) 9. Morea S, Chaudharib S (2016) Third party public auditing scheme for cloud storage. 7th International conference on communication, computing and virtualization 10. Swetha M Creating secure cloud by continuous auditing using DBM algorithm. Int J Comput Sci Eng Technol (IJCSET) 11. Wikipedia: The free encyclopedia. Wikimedia foundation Inc. Updated 23 Jan 2019, at 23:01 (UTC) Encyclopedia online. Available from https://en.wikipedia.org/wiki/Cloud_computing. Retrieved on 26 Jan 2019 12. Shaik Saleem M, Murali M (2018) Privacy-preserving public auditing for data integrity in cloud. National conference on mathematical techniques and its applications (NCMTA 18), Conf. Series 1000: 012164 https://doi.org/10.1088/1742-6596/1000/1/012164 13. Wikipedia: The free encyclopedia. Wikimedia foundation Inc. Updated 22 Jan 2019, 11:49 UTC Encyclopedia online. Available from https://simple.wikipedia.org/wiki/RSA_algorithm. Retrieved on 26 Jan 2019 14. Data Encryption Standard (DES). Available at https://www.lri.fr/~fmartignon/documenti/sys temesecurite/4-DES.pdf. Retrieved on 26 Jan 2019 15. SHA-512 Cryptographic Hash Algorithm. Available https://www.movable-type.co.uk/scripts/ sha512.html. Retrieved on 26 Jan 2019 16. SHA-512. Available from https://en.bitcoinwiki.org/wiki/SHA-512. Retrieved on 26 Jan 2019 17. Wikipedia: The free encyclopedia. Wikimedia foundation Inc. Updated 6 Jan 2019, at 09:55 (UTC). Encyclopedia online. Available from https://en.wikipedia.org/wiki/RSA_(cryptosys tem). Retrieved on 27 Jan 2019 18. Ali AI (June 2015) Comparison and evaluation of digital signature schemes employed in NDN network. Int J Embedded Sys Appl (IJESA) 5(2). https://doi.org/10.5121/ijesa.2015.520215 19. Boob R, Rokade SM (2017) Continuous public auditing and data regeneration on cloud storage. 3(4) IJARIIE-ISSN (O)-2395-43966076

Joy of GPU Computing: A Performance Comparison of AES and RSA in GPU and CPU R. Kingsy Grace, M. S. Geetha Devasena, and S. Manju

Abstract The impact of social media evolution has led to the development of big data where the two Vs, volume of data handled and velocity of data retrieval, have grown exponentially. The volume of data being handled has reached the level of terabyte’s and even exabyte’s. On other hand, the velocity of data retrieval has also matured through the fourth- and fifth-generation Web. To cope up this fastest-growing generation, the processing units also have been improved. The challenge lies in the secured transmission of data at a high speed irrespective of its size. Encryption and decryption are a time-consuming process in a general-purpose computer. The capability of graphical processing units (GPUs) has been proven for general-purpose computing in many research areas. With the computation capability of GPU, fast encryption and decryption could be achieved. In this paper, GPU-based symmetric and public encryption algorithms are proposed to render high performance. The performance analysis is performed based on the execution time of two security algorithms RSA and AES in normal computing platform, central processing unit (CPU) and GPU platform. The parallel versions of the RSA and AES are written in CUDA C. Results show that the performance of the encryption algorithm has greatly increased by using GPU computing. Keywords Graphical processing unit · GPU computing · CUDA · RSA · AES

R. Kingsy Grace (B) · M. S. Geetha Devasena Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India e-mail: [email protected] M. S. Geetha Devasena e-mail: [email protected] S. Manju Department of Computer Science & Engineering, CMR Institute of Technology, Bangalore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_39

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1 Introduction CPU, the heart of the computer, is the major processing unit for applications involving normal computations. The GPU is yet another processing unit which is having many efficient and small cores for executing two or more tasks in parallel. Due to highly parallel structure of modern GPUs, they are more efficient than CPUs. GPUs find place in wide variety of applications such as personal computers, workstations, embedded systems, mobile phones and game consoles.

1.1 GPU Versus CPU The latest GPUs execute the image processing and computer graphics efficiently. Parallelization of visual data in large amount is achieved using GPUs rather than CPUs because of GPUs are more active in general-purpose parallelization. Usually in desktop systems, the GPU is attached with video card or motherboard or CPUs as external peripheral devices. Originally, GPUs are developed to render real-time effects in computer games. For scientific applications, GPUs provide unprecedented computational power [1].

1.2 GPU Computing GPU accelerated computing [1] is ubiquitous where GPU is used along with CPU to accelerate applications in various domains including analytics, scientific and enterprise applications. Usually, the jobs run faster in a GPU–CPU environment when compared to a CPU environment. They are also used to power up energy-efficient data centers.

1.3 CUDA Architecture NVIDIA developed CUDA [2] framework for parallel computing. The CUDA API provides the application developers to use GPU for general-purpose processing. CUDA acts as an intermediate layer between the GPU and the application. The CUDA framework uses the programming languages such as C, C++ and FORTRAN and the programming frameworks such as OpenACC and OpenCL.

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1.4 Using GPU in Cryptography There is an increasing demand for high-speed secure Internet connection due to the advancement of cloud and Internet technology. The speed of the processor cannot catch up with these advancements. The implementation of the entire secure Internet connection demands random number generation that maintains the security of the connection. Therefore, researchers conducted study on fast implementation of cryptography algorithms using GPU [3]. In the current scenario, it is necessary to improve the security of the online data transaction. In the day-to-day use of Internet, millions of users apply block cipher for encryption. The block cipher algorithm performance is not bringing much effect to the user because the input data block that is received from the Internet is small and acceptable. But the input data block received by the server is larger and block cipher algorithm slows down the performance of the server. This algorithm consumes huge amount of computer resources and thus delays response client. There is a requirement for random data generation to keep the entire block cipher algorithm secure. The random number generation is a time-consuming task. The problem of low processing power of CPU to encrypt and decrypt the data with respect to the speed in which data is sent and received via Internet results in slow processing and is rectified using GPU computing [4]. The higher processing capacity of GPU can be used to encrypt and decrypt data instead of using CPU before sending through the network which increases the speed in which the data is encrypted and decrypted. The remaining part of the paper is planned as follows. Section 2 throws light on the GPU trends. Section 3 deals with the RSA and DES algorithms to be analyzed using CPU and GPU. Section 4 discusses with the performance analysis of algorithms in both platform, and the conclusion is presented in Sect. 5.

2 Literature Survey This section deals with the GPU trends in the computing world where high performance is been rendered. Some of the encryption algorithms using GPU are discussed in this section. AA Abdelrahman et al. have proposed an efficient AES implementation using CUDA which reduces the execution time when the data size is large. AES algorithm 128 bit block size is implemented in all the GPU architectures. The proposed optimization algorithm in [1] achieves higher encryption speed than CPU. Nowadays, GPUs are used for different general-purpose research areas. Jo Heeseung et al. have proposed an improved data encryption for ODBS. The proposed system in [2] provides not only faster, the overall performance is best for database system. The AES is implemented using CUDA framework and MySQL is integrated. The performance of the GPU is eight times better than CPU for 16 MB data size. Tuteja and Vaibhav have discussed RSA algorithm for image encryption and decryption [4]. The proposed RSA algorithm with other image processing algorithms

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is used for improving encryption and decryption efficiency. The proposed method uses edge detection technique to identify points in the digital image. The public and private keys are generated by RSA algorithm. Each block is having a single image and is processed in different block combinations. For processing, the data is transferred to GPU. Instead of RSA, any other encryption technique is used to obtain the parallelism which increases the speed and efficiency. Affan Hasan et al. have proposed GPU-based implementation of ECB encryption for 128 bit AES. The AES algorithm [5, 6] is widely used in most of the electronic communication systems. The proposed algorithm in [5] is tested in two GPUs, namely NVIDIA Quadro FX 7000 and Tesla K20c and CPU Intel Xeon X5690. Four approaches have been tested for input data size from 1 MB to 512 MB. The GPU performance is degraded when the input data is randomized and without using cache memory. The execution time is also increased when the randomization is increased in the input data and the repetition is less. Patchappen et al. have presented the implementation of multi-variant AES cipher using GPU which is based on batch execution [7]. The throughput of GPU-based implementation is higher than CPU only implementation. The National Institute of Standards and Technology (NIST) standardized AES algorithm 2001. The AES has fixed block size with 128 bits. The key size is varied as 128 bits, 192 bits and 256 bits [4, 8]. DES is designed using Feistal cipher. AES is based on substitution and permutation. Experimental results showed that multi-variant AES cipher using GPU outperformed 2.5 times the single core CPU-based implementation for 512 MB data size. When compared with multi core CPU, AES cipher using GPU outperformed 1.6 times for 512 MB data size. Wai-Kong Lee et al. have implemented SSL/TLSbased secure solution for preventing communications from malicious attack. The SSL/TLS needs more computations in the server side. So the GPU architecture is used to implement the cryptographic algorithm. Pascal architecture of the GPU is used to implement SSL/TLS using SHA-3 and proved to be the best one using CUDA [8]. Sonam Mahajan and Maninder Singh have analyzed the RSA algorithm [9]. The authors have implemented both the traditional RSA algorithm and the parallelized RSA algorithm using Compute Unified Device Architecture (CUDA) [8] framework. The CPU-based implementation is compared with the GPU-based implementations for both small and large prime numbers. Lukasz Swierczewski has proposed an optimized 3DES ECB using CUDA framework [10]. The proposed algorithm is tested on three processors: (a) Intel Core 2 Quad Q8200, (b) Intel Core i7 950, (c) Intel Xeon E7—4860 and two graphics cards: (a) nVidia GeForce GTS 250, (b) nVidia Tesla C2050. The GeForce GTS 250 is faster than Xeon E7-4860 (ten cores, twenty threads). The performance of Tesla C2050 is 1.84 times faster than the performance of GeForce GTS 250. Both bit operations and integer operations GPU provide better results. RSA is a public key encryption algorithm [11]. Tejal Mahajan and Shraddha Masih have parallelized Blowfish algorithm using GPU [12] for improving the encryption and decryption speedup. Parallelization allows larger files to transmit through the network efficiently and securely. Experiments are conducted using Intel(R) Core(TM) 2 Duo CPU E7500 @ 2.93 GHz and

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NVIDIA GeForce GT 610 (CUDA Cores 48). Bruce Schneier methodology [13] was adopted for CPU-based encryption and decryption. GPU implementation of Blowfish algorithm provides better results when compared to its CPU implementation. Jianwei Ma et al. have proposed different parameters needed for improving the performance of the CBC–AES algorithm implementation in GPU. The proposed implementation in [14] is compared with AES and AES–NI, and the latter is proved to be best in GPU implementation. The proposed AES–NI algorithm is 112 times better than AES algorithm.

3 Implementation of Security Algorithms This section deals with the implementation of security algorithms AES and DES. Both the algorithms were implemented in two ways. The first one is implementation in CPU, and the second one is CPU–GPU implementation.

3.1 Difference in Implementation of CPU and CPU-GPU The implementation workflow of security algorithms in CPU and in CPU–GPU is shown in Fig. 1 a and b. First, the key for the whole process is calculated during the initialization process which will be utilized in the whole process. Second, the input data is read inside the input buffer as batches into a small array, which is given as input for the encryption/decryption process. Once processing is done, the output is written to an output file. Here the difference lies in the number of parallel threads that are created in CPU–GPU platform in case of GPU processing. In GPU once input is obtained, memory is allocated in GPU for processing. New threads are created for every batch of inputs and are processed simultaneously. After processing, the data is sent back to the host and written on to the output file. Parallel processing is achieved in CPU–GPU implementation.

3.2 Implementation of RSA and AES The popular security algorithms RSA and AES are implemented using C. The size of input data is varied from 100 to 500 MB. The input file is encrypted, and the time taken to generate cipher is recorded for CPU platform. Similarly, the same varying size cipher text was given as input to the decryption algorithm, and the time taken for decryption is also recorded. All the functions and sub-functions of both the algorithms were processed serially.

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Fig. 1 a Implementation of security algorithm in CPU–GPU b Implementation of security algorithm in CPU

3.3 Implementation of RSA and AES Algorithms in GPU Using CUDA C The steps for RSA and AES algorithms are same for CPU execution and CPU–GPU execution except the processing method and CUDA C programming. The execution is done parallel in GPU. The data block size is 64 byte which is transferred to GPU for encryption and decryption. The threads are executed in parallel where the number of threads is based on the number of blocks. The expansion of key is done in CPU and is serial. The data is transferred from GPU to CPU after the execution. Constant memory is used to store the look up table instead of global memory to improve the execution speed of GPU-based algorithm. The CUDA framework is used for the execution of AES on GPU. Every thread is divided into many parts. To reduce the execution time of encryption and decryption process, each part is executed in parallel. The work flow of security algorithms in CUDA is shown in Fig. 2.

4 CPU and GPU Performance Comparison The RSA and AES algorithms executed in both CPU and CPU–GPU platform are compared for their performance. The time taken to encrypt and decrypt the data is

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Fig. 2 Work flow of algorithms in CUDA

taken as the performance parameter for comparison. The execution time is compared for both the platforms for different size of the input files.

4.1 Performance Analysis of RSA Algorithm The RSA algorithm is executed in CPU using C language with the varying size of input file such as 100 MB, 200 MB, 300 MB, 400 MB and 500 MB. The time taken to perform the encryption and decryption of these files using RSA algorithm in CPU is noted and is shown in Table 1. The CPU–GPU execution time is also shown in Table 1. Based on these values, the graph representation is shown in Fig. 3 a and b. The configurations used for comparison of results are Intel Core i7 4720HQ CPU with NVIDIA GTX 960 M GPU (640 cores) and Intel Core i5 4210U CPU with GeForce 830 M GPU (256 cores). Table 1 Execution time of RSA Time taken/input Core i7 and GTX 960 M size (MB) CPU GPU

Core i5 and GeForce 830 M CPU

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Fig. 3 a Execution time of RSA in core i7 and GTX 960 M b Execution time of RSA in core i5 and GeForce 830 M

Table 2 Execution time of AES Time Taken/input Core i7 and GTX 960 M size (MB) CPU GPU

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4.2 Performance Analysis of AES Algorithm The performance of AES algorithm is analyzed with sample input files with sizes of 100 MB, 200 MB, 300 MB, 400 MB and 500 MB. The execution time of AES algorithm in CPU is observed and shown in Table 2. The execution time of AES algorithm using GPU is observed and shown in Table 2. Figure 3 a and b shows the performance analysis on AES in Core i7 and Core i5 with and GTX 960 M. The performance comparison is done with two varying configurations such as Intel Core i7 4720HQ CPU with NVIDIA GTX 960 M GPU (640 cores) and Intel Core i5 4210U CPU with GeForce 830 M GPU (256 cores). The execution time of RSA and AES in CPU-GPU shows that there is a drastic increase in the performance of the algorithms especially when a high end graphic card is being used. This shows the increase in performance with GPU (Figs. 4 and 5).

5 Conclusion In recent days, GPU computing plays a major role in all the domains of computer science and engineering. The encryption and decryption algorithms take more time

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Fig. 4 Execution time of AES in core i7 and GTX 960 M

Fig. 5 Execution time of AES in core i5 and GeForce 830 M

when the size of the file to be encrypted is large. The proposed GPU-based RSA and AES are implemented both in traditional programming language and CUDA framework. The proposed work demonstrates the encryption and decryption algorithms based on GPU shows eight times better performance compared to that on CPU. Execution time of both CPU and GPU is compared in varying file size, and the GPU provides less execution time than its GPU counterpart.

References 1. Abdelrahman AA, Fouad MM, Dahshan H, Mousa AM (2017) High performance CUDA AES implementation: a quantitative performance analysis approach. IEEE Comput Conf https://doi. org/10.1109/sai.2017.8252225 2. Jo H, Hong S-T, Chang J-W, Choi DH (2013) Data encryption on GPU for high-performance database systems. In: Procedia computer science. vol 19, pp. 147–154 https://doi.org/10.1016/ j.procs.2013.06.024

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3. Renan CD, Lobato RS, Spolon R, Cavenaghi MA (2011) Using GPU to exploit parallelism on cryptography, 6th iberia n conference on information systems and technologies (CISTI 2011), pp. 1–6 4. Tuteja V (2014) Image encryption using parallel RSA algorithm on CUDA. Int J Comput Networks Commun Secur 2:232–235 5. Khan AH et al.: AES-128 ECB encryption on GPUs and effects of input plaintext patterns on performance, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), (2014) 15th IEEE/ACIS International Conference on. IEEE https://doi.org/10.1109/snpd.2014.6888707 6. Yuan Y et al (2014) Acceleration of AES encryption with openCL, IEEE ninth Asia joint conference on information security (ASIA JCIS). https://doi.org/10.1109/asiajcis.2014.19 7. Patchappen M, Yassin YM, Karuppiah EK (2015) Batch processing of multi-variant AES cipher with GPU, IEEE second international conference on computing technology and information management (ICCTIM) 8. Lee W-K, Wong X-F, Goi B-M, Phan RC-W (2017) CUDA-SSL: SSL/TLS accelerated by GPU, 2017 IEEE international carnahan conference on security technology (ICCST). https:// doi.org/10.1109/ccst.2017.8167848 9. Mahajan S, Singh M (2014) Analysis of RSA algorithm using GPU programming. Int J Network Secur Appl 6(2014) https://doi.org/10.5121/ijnsa.2014.6402 10. Swierczewski L (2013) 3DES ECB optimized for massively parallel CUDA GPU architecture, arXiv e-prints, arXiv:1305.4376 11. Rivest RL, Shamir A, Adleman L A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(1978):120–126 12. Mahajan T, Masih S Enhancing blow fish file encryption algorithm through parallel computing on GPU. IEEE Int Conf Comput Commun Control (IC4–2015) 13. Schneier B (2007) Applied cryptography: protocols, algorithms, and source code in C, Wiley 14. Ma J, Chen X, Xu R, Shi J (2017) Implementation and evaluation of different parallel designs of AES using CUDA, IEEE second international conference on data science in cyberspace. pp. 606–614 https://doi.org/10.1109/dsc.2017.19

Domain-Independent Video Summarization Based on Transfer Learning Using Convolutional Neural Network Jesna Mohan and Madhu S. Nair

Abstract Video summarization methods generate a compact representation of the original video preserving the essential content of the input video. Video summaries utilize less storage space compared to the original video. The summaries also facilitate efficient browsing and retrieval of video data. This paper presents a novel method for static video summarization based on deep features extracted using convolutional neural network (CNN) by means of transfer learning. To detect the frames with content change, the Chebyshev distance scores between the feature vectors of consecutive frames are thresholded. Uniform pre-sampling and redundancy elimination are done before summarization to reduce the complexity of the summarization step. Uniform pre-sampling is done by selecting two frames in one second. Redundant frames are eliminated based on flow vectors between consecutive frames using the SIFT flow algorithm. The experimental results on two well-known datasets show that our approach significantly improves the performance of the baseline methods and achieves competitive results compared with other state-of-the-art methods. Keywords Video summarization · SIFT flow · Convolutional neural network · Otsu threshold · Mutual information

J. Mohan (B) Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala 695581, India e-mail: [email protected] Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Nalanchira, Thiruvananthapuram, Kerala 695015, India M. S. Nair Artificial Intelligence & Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala 682022, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_40

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1 Introduction The development of Internet has changed radically the multimedia data over the Web. Nowadays, users looking for interesting videos have to search a heap of both relevant and irrelevant video repositories to select a particular video of interest. This makes storage, retrieval, and browsing of multimedia data a very time-consuming and tedious task. The overhead in searching and retrieval of video data can be reduced to a greater extent by using a compact representation of input videos. Video summarization methods aid in producing an abstract representation of the input video. An efficient video summarization algorithm should create concise, comprehensive abstract of the input video which is consistent with the human visual system. The summaries unite significant frames by discarding redundant frames [1]. This allows the users to choose an interesting video by viewing only the shortened meaningful version of the original video thereby reducing overhead in handling video data. Video summarization methods can be broadly classified into static summarization methods [2] and dynamic summarization methods [3]. Static summarization methods represent input video as a set of still images that have prime contents of the original video. The temporal component of the original video is not preserved in static summaries. It facilitates users to catch content of the entire video by viewing only key-frames in scenarios when there are bandwidth restrictions. Dynamic video summarization, on the other hand, generates a subset of the input video maintaining a temporal relationship between key units of the input video. These types of abstracts are also known as video skims (e.g., movie trailer, sports highlights, etc.). Video summarization methods have recently attracted researchers and are now an emerging research field. Traditional summarization methods focused on visual features to extract key-frames from the video. The visual features are excellent in modeling the characteristics of frames that capture visual attention of users. But most methods consider only one or two visual features which fail to encompass all the relevant attributes of the frame. Color, texture, mutual information, motion information, and fuzzy color histogram [4] are some of the visual features commonly used in the literature. These global visual features fail to represent the localized characteristics of frames that are important in creating a condensed version of the input video. Research has now been extended to localized features in the image. Guan et al. [5] used the features that are invariant to scale, rotation, and shape to detect key-frames. Hannane et al. [6] emphasized motion features to detect the significant frames. The work combined the local features of video frames with optical flow to track the frames with a significant content change. Lu et al. [7] processed videos at semantic level using bag of visual words(BoVW) representation of frames. Later, Cahuina et al. [8] combined local feature descriptors with semantic features to improve the quality of generated summaries. Zhang et al. [9] exploit spatiotemporal features and volumetric representation of frames to capture contextual information of the video. Events in a video play an essential role in summarization because crucial events are the ones, we want to select to shorten videos. The graph-based video summarization

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is done by Chen et al. in [10] by finding the association between events and concepts. Meng et al. in [11] produced an object-level video abstraction by detecting objects and correlation between objects using high-level semantic features extracted from frames. Video summarization methods vary in the computational mechanism used for detecting visually significant frames. Different unsupervised clustering methods are widely used. The clustering-based approaches were cluster feature descriptors extracted from frames with the initial centroids that are selected randomly [12–15] . After convergence, frames corresponding to centroid of each cluster are selected as key-frame. The method is simple and gives good results. The demerit of clusteringbased method is that the number of clusters is pre-determined. The choice of the number of clusters should be made carefully as it affects the number of key-frames in the output. Shroff et al. in [16] and also Fayl et al. in [17] modeled the selection of key-frames as an optimization problem. The selection of key-frames is also made by using optimization techniques such as particle swarm optimization(PSO) as in [16, 17]. A multiobjective energy function including interestingness and representativeness of frame is used by Gygli et al. in [18] to rank frames based on its optimization using submodular maximization technique. The works mentioned above utilize traditional handcrafted features for summarization. Recently, with the development of high-performance computers with GPUs, deep learning-based methods have been developed for video summarization. Mahasseni et al. in [19] proposed the long short-term memory network (LSTM) to select the key-frames. The network is trained so that the reconstruction error is minimum. Fei et al. in [20] proposed video summarization framework based on entropy and memorability score. The memorability score is computed using Hybrid-AlexNet. The quality of summaries generated by these methods reveals that deep features can represent the frames more efficiently than handcrafted features. Although there are approaches based on both handcrafted and deep features, these methods are very complex making its practical implementation very difficult [21]. Also, the existing approaches are time-consuming and thereby increase the computational burden. Moreover, a proper technique which can generate high-quality summaries from videos belonging to different categories still raises a challenge among the researchers in this area. In this paper, a novel approach for generating summaries of videos belonging to different categories is investigated. The paper addresses the challenges of the existing approaches and focuses on deep features to find significant frames of a video. The main contributions are as follows. (1) a framework for generating high-quality summaries which works consistently on videos belonging to different categories. (2) an effective application of deep feature vectors extracted using transfer learning technique of CNN. (3) Mutual information between frames is introduced to determine the similarity between the generated summaries and human-created summaries in the ground truth. The rest of this paper is organized as follows. Section 2 describes the different steps of the proposed methodology for static video summarization. Experimental results and discussions are illustrated in Sect. 3. We conclude the paper in Sect. 4.

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2 Methodology The objective of this work is to develop an effective and efficient algorithm for domain-independent summarization video summarization. The method first converts the input video into its constituent frames. The frames are first pre-sampled using uniform pre-sampling method. To reduce computational complexity, the redundant frames are eliminated based on motion vectors which are computed using SIFT flow algorithm. Then, Chebyshev distance score between the feature vectors corresponding to consecutive frames is calculated. These scores are then thresholded adaptively using Otsu thresholding technique to generate the final set of key-frames. Figure 1 gives an overview of the proposed approach. The detailed steps of the method are as follows.

Fig. 1 Overview of proposed approach

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2.1 Uniform Sampling Uniform sampling is done by selecting the frames based on the frame rate associated with the video. The frames should be sampled carefully. A high sampling rate results in a significant reduction in the number of frames. But it can lead to missing keyframes. A single key-frame miss is not acceptable in the initial stage of summarization algorithm. A low sampling rate preserves key-frames, but results in many redundant frames in the output. The proper selection of sampling rate reduces the number of redundant frames in the input by retaining key-frames. Most of the existing approaches selected one frame in one second. It is well suitable for high frame rate videos like sports videos. However, in case of short frame rate videos like cartoon videos, a scene change occurs very fastly. So, the selection of only one frame in one second is not well suited for domain-independent summarization approaches. To overcome this issue, the proposed approach makes use of sampling rate of two frames in one second. After uniform sampling, 91% of frames are eliminated from input video with no key-frame miss.

2.2 Redundancy Elimination There still remain many redundant frames in the pre-sampled set of frames. This will adversely affect the accuracy of the key-frame extraction algorithm. Also, it increases the computational complexity of the subsequent steps. So, a redundancy elimination step based on motion vectors is included in the proposed method. We explored motion vectors as it is an important feature of video and has proved to be efficient in many video analysis tasks. The motion vectors determine the pixel displacement between consecutive frames in the video. Here, the motion vectors are computed using SIFT flow algorithm [22]. The algorithm works as follows.

2.2.1

Calculation of Flow Vectors

Let F1 ∗ , F2 ∗ , F3 ∗ , . . . Fn s ∗ be the elements of Fs , the pre-sampled set of frames. Each frame in Fs is converted into SIFT image. For finding SIFT image, 128 - D SIFT descriptor [23] is calculated at each pixel in an image. So, an image of size X × Y is converted into SIFT image of size X × Y × 128. The pyramids are constructed from smoothened and downsampled images created using Gaussian filter. The number of pyramid levels is determined by the user. In the proposed method, number of level is chosen to be four. An image is downsampled four times. The flow vectors are calculated in a coarse to fine strategy between corresponding levels of two pyramids of consecutive images. The optimal flow vector is calculated using loopy belief

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propagation network [24] which works by minimizing the sum of data cost and discontinuity cost given by energy function in (1). E( f ) =



V ( f p , fq ) +

( p,q) N



Dp( f p)

(1)

p X

where E( f ) is the energy for labelling f , N is the set of edges in four connected neighborhood of X , V ( f p , f q ) is the cost of assigning label f p and f q to neighboring nodes, D p ( f p ) is the cost of assigning f p to p. The value of the parameters η, α and d of the energy function given in (1) is fixed in our experiments. The values are taken as in [25] for object tracking in videos such that α = 300, η = 0.5 and d=3. The flow vectors are first calculated for all pixels at the coarse level. The flow vectors for these pixels are initialized to zero, and the optimal flow vectors of the pixels are obtained by optimization of energy function using loopy belief propagation network. The values of flow vectors at this level are used to initialize the flow vectors of all pixels at next fine level. The process is repeated in successive finer levels. At the final level, the magnitude of displacement of each pixel is calculated as d=

 u 1 2 + v1 2

(2)

where (u 1 , v1 ) is the displacement vector at a pixel position in fine level. Magnitude of displacement of the frame is obtained as a sum of displacement magnitude corresponding to all pixels at fine level. Let magnitude of displacement between consecutive frames in Fs is stored in D.

2.2.2

Finding Candidate Frames

Candidate frames are selected from the given set of frames based on local thresholding of the magnitude of displacement vectors between consecutive frames. Local thresholding is preferred to global thresholding as the content of video differs from one scene to other. The choice of a global threshold value considering displacement vectors of the entire video leads to key-frame miss. Even a single key-frame miss is not acceptable in the redundancy elimination step prior to summarization, as it will affect the quality of the final summary. So, local thresholding is done by defining a sliding window. The sliding window of size 3 is used in the proposed approach which finds a local threshold value based on displacement values of four consecutive frames. Let displacement magnitude between successive frames of Fs be m 1 , m 2 , m 3 . . . m n s −1 which are values in D. Define sliding window of size 3 on first four consecutive frames of Fs . Mean value of m 1 , m 2 , and m 3 is taken as the local threshold value and is calculated as in (3). M=

q+3  q

m q f or q = 1, 4, 7, . . . (n s − 1)

(3)

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where n s is the total number of frames in Fs . Based on the analysis is done, mean value of the magnitude of displacement is taken as threshold value since it performs best to filter out redundant frames and is also simple to compute. Frames having displacement greater than local threshold value M in F1 ∗ , F2 ∗ , F3 ∗ , F4 ∗ are added to set of candidate frames Vs , and process is repeated for the entire set of frames selected by uniform sampling.

2.3 Extraction of Feature Descriptors Using CNN Deep convolutional neural networks (CNN) [26, 27] are a specific type of deep learning architecture with stacked multilayered networks. Due to the availability of systems with high computational power, deep convolutional neural networks (CNNs) are popular for application in areas like flower categorization [28], human attribute detection [29], bird sub-categorization [30], scene retrieval [31], remote sensing [32]. CNN structures are pre-trained on large datasets such as ImageNet which has diverse images. Deep CNNs cannot be trained from scratch for video summarization task. This is so because the dataset available is small in size. So, transfer learning is adopted in the proposed method to exploit deep neural networks. Transfer learning is a popular method in deep learning where a model for the new task can be developed by using an existing learned model as a starting point. This can be achieved by transplanting feature layers which have been already learned for a particular task to initialize new layers for learning target concept. This has a significant impact on performance when modelling new concepts in many domains. The proposed method extracts feature vectors from input frames by transfer learning using CNN. The method makes use of off-the-shelf pre-trained CNN models to extracts features to represent the video frames. The feature descriptors from video frames are extracted from fully connected layers of CNN using three pre-trained models of CNN. We explored three CNN architectures: AlexNet, GoogLeNet, and Vgg-16 in our proposed work. AlexNet AlexNet by Krizhevsky et al. [33] is regarded as the baseline model of CNN. The network has 60 million parameters and 650,000 neurons. The network has five convolutional layers, three fully connected layers, and a softmax layer as final layer. Max-pooling layers follow some of the convolutional layers. GoogLeNet GoogLeNet in [34] is a CNN architecture designed for classification and detection tasks. The usage of inception modules can reduce the parallel usage of multiple filters with multiple resolutions. Inception module is a local design of the network, and these modules are stacked one on top of another to form the entire network. GoogLeNet has fewer parameters than AlexNet. So, overfitting problem is alleviated. In the same layer, the network uses filters of different sizes. There is no fully connected layer in the network. Vgg-16 Vgg-16 in [35] is meant for localization and classification. This network has 12 convolutional layers, 4 pooling layers, and 3 fully connected layers. It replaced

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large filters in AlexNet with multiple filters of small receptive fields (3×3 filters). Fine-tuning this network cannot be done from scratch since network requires a significant amount of memory.

2.4 Finding Similarity Between Feature Vectors The important step in video summarization is to find the similarity between feature vectors corresponding to consecutive frames. The choice of distance measure and threshold value determines which frame is to be selected as key-frame. The distance measure plays a significant role in content-based image retrieval system as in [33] [36]. In the proposed work, we explored Chebyshev distance metric between feature vectors of consecutive frames to filter out key-frames. The distance between feature vectors extracted using CNN from consecutive frames is computed after reducing the dimensionality of vectors using principal component analysis (PCA). The more similar frames have low distance score between them. Based on the analysis done on the impact of distance metric on summary generated by the algorithm, Chebyshev distance between deep features of consecutive frames is chosen as the best distance metric to discriminate content change between the frames. If F V1 and F V2 are n− dimensional feature vectors corresponding to two frames with F V1 ={u 1 , u 2 , ....u n } and F V2 ={v1 , v2 , ...vn }, where i = 1, 2, ..., n, then, Chebyshev distance is calculated as in (4). Chebyshev distance(F V1 , F V2 ) = maxi (|u i − vi |)

(4)

Let D F be the set of distance scores between feature vectors of consecutive frames in Vs . Suppose d1 , d2 , . . . dn s −1 be the values of D F which corresponds to displacement magnitude between frames of Vs . The set of values in D F are thresholded such that the frames whose distance score greater than a threshold value are selected as key-frames since there is a dissimilarity in the contents represented by the frames as reflected by the distance values of feature vectors between the selected frame and previous frame in Vs . The threshold value is calculated using Otsu thresholding technique as in [37] which determines a threshold for a set of values by reducing intraclass variances. Since the proposed system extracts key-frames from videos belonging to different categories, a constant threshold value fails, and threshold needs to be computed from each video. Otsu’s thresholding method finds a value by iterating through all the possible threshold values and by measuring spread for the distance scores on each side of the threshold. Suppose Tthresh be the Otsu threshold value obtained from the set of displacement values in D F . All frames with displacement value greater than Tthresh are added to the final set of key-frames VK .

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2.5 Elimination of Similar Frames The final extracted key-frames may contain redundant frames. This redundancy can be removed by preserving only one of the similar frames in the final set of frames. The proposed method eliminates similar frames based on mutual information in [38]. Mutual information between consecutive frames is calculated, and from a pair of consecutive frames, one of the frames is eliminated if mutual information between them is greater than 0.95. The values of mutual information between different frames are shown in Fig. 2. Mutual information between two images is calculated as the difference between the sum of individual entropies and the joint entropy of two images. Given two frames f 1 and f 2 with a and b representing intensity values of f 1 and f 2 , respectively. The joint entropy H ( f 1 , f 2 ) is calculated using (5) where P f1 , f2 is the joint probability distribution of pixels associated with frames f 1 and f 2 . The individual entropy H ( f 1 ) and H ( f 2 ) of f 1 and f 2 is calculated using (6) where F represents the input frame, c represents intensity values of F, and PF is the probability distribution of pixels associated with frame F. Mutual information M I considers both the joint entropy H (F1 ,F2 ), and the individual entropies H ( f 1 ) and H ( f 2 ) and are calculated as in (7). H ( f1 , f2 ) = −



P f1 , f2 (a, b) log P f1 , f2 (a, b)

(5)

a,b

H (F) = −



P f1 (c) log P f1 (c)

(6)

c

M I = H ( f1 ) + H ( f2 ) − H ( f1 , f2 )

Fig. 2 Mutual information measure of different frames

(7)

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3 Experimental Analysis All implementation is done in MATLAB on Windows 10 Pro with an Intel(R) Core(TM) i7-3770 CPU at 3.40 GHz with 4.00 GB RAM running 64-bit operating system. The performance of proposed method has been evaluated on VSUMM and OVP dataset with videos of different categories in [12]. Dataset consists of videos belonging to cartoon videos, sports, and news. The videos span 1–4 min duration. The method is also tested on OVP dataset of 50 documentary videos. Datasets also include ground truth created with user summaries of 5 different users for each video.

3.1 Performance Metrics There is no consistent evaluation metrics in video summarization since there is no objective ground truth in summarization. Two abstracts of the same video cannot be compared even by humans because some parts of video which are relevant to one user may not be attractive to other. The effectiveness and efficiency of proposed approach are evaluated using precision, recall, and F-score which is calculated based on the similarity of frames in output with those in user summaries of the dataset. Evaluation metrics are calculated as follows. Let Ntotal represents the total number of frames in input video. N represents the total number of frames in output. Nnm represents the number of non-matching frames in output compared to frames in ground truth. Nm represents the number of matching frames in output compared to frames in ground truth. NGT represents the number of frames in ground truth. Precision = Recall = F-score =

Nm N

Nm NGT

2 × Precision × Recall (Precision + Recall)

(8) (9)

(10)

The evaluation metrics is calculated for all five user summaries in dataset separately for each video. The final F-score of the proposed system is calculated by finding average of these scores. If there are n users in the dataset, the F-score is calculated as n F-scorei Overall F − score = i=1 (11) n

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3.2 Results and Discussions The input video is first pre-sampled, and redundancy elimination is done as in Sect. 2.2. Deep features are extracted from reduced set of frames after redundancy elimination, and then feature vectors are extracted from this set of frames using transfer learning technique of CNN. The analysis was done using pre-trained CNN models. We conducted here four experiments: 1. 2. 3. 4.

Analysis of different pre-trained models of CNN in summarization. Choosing an appropriate distance measure. Finding similarity between frames in user summary and output. Comparison of the proposed method with other state-of-the-art methods.

3.2.1

Finding Suitable Pre-trained Model of CNN in Summarization

Experiments are conducted to find out best the feature extractor for video summarization using three pre-trained models AlexNet, GoogleNet, and Vgg-16. The feature vectors are extracted using ‘off-the-shelf’ CNN features of pre-trained models with parameters of convolutional and fully connected layers fixed as in [39]. The size of input image is resized to 227 × 227 in case of AlexNet and 224 × 224 in case of other two models. The analysis of the proposed system is done using the representation of frames extracted from both convolutional and fully connected layers of CNN. It was found that the fully connected layer f c7 of CNN can give the best representation of frames which can be utilized for finding content-based similarity between the consecutive frames. Tables 1 and 2 show the precision, recall, and F-score values of the proposed system using pre-trained models on VSUMM and OVP dataset.

Table 1 Results of AlexNet, GoogLeNet, and Vgg-16 on VSUMM dataset Metrics Model AlexNet GoogLeNet Vgg-16 Precision Recall F-score

0.66 0.86 0.74

0.61 0.81 0.69

Table 2 Results of AlexNet, GoogLeNet, and Vgg-16 on OVP dataset Metrics Model AlexNet GoogLeNet Precision Recall F-score

0.70 0.89 0.78

0.50 0.93 0.61

0.67 0.73 0.70

Vgg-16 0.68 0.61 0.64

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Table 3 Results of various categories of videos in VSUMM dataset Category Model Precision Recall Cartoon

Sports

News

AlexNet GoogLeNet Vgg-16 AlexNet GoogLeNet Vgg-16 AlexNet GoogLeNet Vgg-16

0.77 0.6 0.65 0.58 0.57 0.69 0.62 0.65 0.67

0.87 0.82 0.71 0.90 0.8 0.73 0.82 0.8 0.75

F-score 0.82 0.69 0.68 0.71 0.67 0.71 0.71 0.72 0.71

The results are obtained using feature vectors from layer f c7 of AlexNet, GoogLeNet, and vgg-16. The dimensionality of vectors is reduced using PCA. The Chebyshev distance score between frames is thresholded using Otsu thresholding to detect key-frames. The result shows that features from AlexNet model are more suitable for detecting key-frames in videos since it has large filter size than the other two models in lower layers. Table 3 shows the results of various categories of videos in VSUMM dataset obtained by proposed method exploring different models for transfer learning using CNN. It shows that the performance of the proposed system on each category of video is better using AlexNet model. Results show that AlexNet attains an average F-score of 0.74 and 0.70 on VSUMM dataset and OVP dataset, respectively.

3.2.2

Choosing a Suitable Distance Measure Between Feature Vectors

The proposed approach finds out key-frames by thresholding distance score between deep feature vectors. The analysis is done on three distance metrics Chebyshev distance, cosine distance, and Euclidean distance which is widely used in domains such as text summarization and content-based image retrieval. Figure 3 shows results obtained using three distance metrics on cartoon video ‘v11.flv’ in VSUMM dataset. The graph is drawn with frame number on x-axis and distance score on y-axis. The blue line on the graph is drawn at Otsu threshold value of distance score to detect keyframes. The selected frames based on the threshold value using different distance metrics are marked with small dots in the corresponding graph. The key-frames among the selected frames are marked using small circles by comparing frames with the actual key-frames in the ground truth. The number of frames selected, number of key-frames, and the number of frames in ground truth is as shown in Fig. 3a–c. A better abstract of the input video with less number of redundant frames and with most of the frames in the ground truth is obtained using Chebyshev distance compared to

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Fig. 3 Different distance metric used in analysis. a Chebyshev distance. b Cosine distance. c Euclidean distance

the other two distance measures. The analysis based on three distance metric is done on the entire dataset, and Chebyshev distance score between the feature vectors is selected to determine the content change.

3.2.3

Comparing Output with User Summaries

The lack of consistent evaluation in the literature posed a big challenge of measuring similarity between frames in output and frames in ground truth in video summarization. Most of the methods have proposed their own evaluation criteria making it difficult to compare results obtained in one method with other. Earlier methods compared the frames by subjective assessment based on visual similarity. Recent works emphasized on measures such as Manhattan distance [40], Euclidean distance [41], normalized sum of squared distances(NSSD) [42] between feature vectors for evaluation. Here, we explore the concept of mutual information (MI) [38] to find similarity between output frames and frames in ground truth similar to comparison with user summary (CUS) metric in [41]. A pair of frames, one from the output and one from user summary, is considered similar if MI is greater than 0.95. These frames once find similar is eliminated from the next iteration in the calculation of CUS as in [42].

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Comparing Results of the Proposed Method with Other State-of-the-art Methods

Figures 4 and 5 show the comparative analysis of results of the proposed method with the other state-of-the-art methods in VRHDPS [15], VISCOM [42], and VSUMM [41] on VSUMM dataset and OVP dataset. Wu et al. in [15] explored bag of visual word representation of video frames and high-density peak clustering for generating summaries. Cirne et al. in [42] computed the color co-occurrence matrices(CCM) of each frame in the input set. Then, the normalized sum of squared distances(NSSD) is computed between CCM of successive frames. The NSSD distances of CCM corresponding to the consecutive frames are thresholded to form final set of keyframes. Avila et al. in [41] performed summarization based on HSV histogram and k-means clustering. The results depicted in the comparison graph (Figs. 4 and 5) show that deep features extracted from fully connected layers of CNN outperform handcrafted features used by the other video summarization methods in literature. Figures 6, 7, 8, and 9 show summarization result corresponding to one video from each category.

Fig. 4 Comparison of results with different methods on VSUMM dataset

Fig. 5 Comparison of results with different methods on OVP dataset

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Fig. 6 Key-frames of cartoon video (‘v11.flv’) in VSUMM dataset

Fig. 7 Key-frames of sports video (‘v84.avi’) in VSUMM dataset

Fig. 8 Key-frames of news video (‘v88.avi’) in VSUMM dataset

Fig. 9 Key-frames of documentary video (‘v34.mpg’) in OVP dataset

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4 Conclusion Domain-independent video summarization is gaining interest in research community due to the massive growth of videos. In this paper, we propose domain-independent video summarization system based on deep features extracted using transfer learning strategy of CNN. Summarization step is preceded by redundancy elimination step based on motion vectors preserving key-frames. SIFT flow algorithm is used to extract motion vectors. Results show that deep features are excellent for capturing key-frames in videos. Due to the lack of extensive dataset, the fine-tuning of CNN is not done. Future work includes fine-tuning layers of CNN to extract more representative features from video frames so that summarization results can be improved. Acknowledgements We would like to thank the University of Kerala, Thiruvananthapuram, India, for providing financial support for this work under University Junior Research Fellowship Scheme.

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Deep Neural Network-Based Human Emotion Recognition by Computer Vision Samsani Surekha

Abstract Over the past years and in recent times, a lot of research is being focused on intensifying the human–machine interaction because there is a great need of communication channel between machines and humans to share a variety of tasks. The communication between humans and machines can be verbal or non-verbal. Even though verbal communication provides complete understanding of the communication, considering the non-verbal communication can sometimes help in better understanding the correctness of the message. So, artificial intelligent systems with visual perception helps in better understanding their environment to provide a smoother and natural interaction with humans. As human face is extremely expressive, facial expression plays a pivotal role in non-verbal communications. This paper presents a deep learning-based system for computer vision, i.e., a system for automatically recognizing human emotion by analyzing the facial expressions of humans using convolutional neural networks. Experiments were carried out on standard facial expression recognition dataset taken from Kaggle challenges repository, and graphical processing unit is also used to reduce the training time of the system. The results revealed that the accuracy of CNN model used can achieve the state-of-the-art recognition rate. Keywords Intelligent systems · Human–machine interaction · Computer vision · Convolutional neural networks · Facial expression recognition · Kaggle challenges repository

1 Introduction In the present scenario, the role of automated machines is being increased in everyday life. Hence, there is a great need of communication channel between machines and humans to share a variety of tasks, and the systems with this communication channel are human–machine interaction (HMI) system [1, 2]. Communication S. Surekha (B) JNTUK-University College of Engineering Kakinada, Kakinada, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_41

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between two parties involves either verbal communication or non-verbal communication. The main goal of developing intelligent interactive systems is to perceive necessary information from their environment and take necessary decision with less human interference to act accordingly. Hence, providing non-verbal communication as the communication channel between humans and machines makes the system smarter. Verbal communication can be either oral or writing, but the various forms of non-verbal communication [3] are body language and posture, facial expressions, gestures, paralinguistic, proxemics, and eye contact, etc. Even though the more natural oral communication is easy to understand the completeness of message, it has some implementation difficulties like understanding various speech tones, requires quiet environment, and sometimes leads to trouble as human emotions are visible. Hence, considering the non-verbal communication can sometimes help in better understanding the correctness of the message. As human face is extremely expressive, facial expression plays a proportionate role in non-verbal communications and aids in better understanding the communication message. Emotions of humans can be better understood by analyzing their facial expressions [4, 5]. Thus, providing machines the visual perception helps in better understanding their environment to provide a smoother and natural interaction with humans. An automated system with computer vision could act smartly by conveying useful information which can be used for building more responsive intelligent systems that might improve the user experience where interaction would be more dynamic through sharing wide range of daily tasks. This paper presents a system for achieving computer vision to recognize the seven universal emotions of humans like happy, surprise, neutral, sad, fear, disgust, and anger. Moreover, every emotion of humans represented via human faces have overlapping features with one another, and hence, to recognize the correct human emotion with a very high supporting probability, the most popular deep convolutional neural network is used to train the system. Being a Deep learning technique [6, 7], CNN [8] itself has the capability of identifying and extracting the most prominent features required for recognizing the human emotion. CNN requires more training samples to achieve an acceptable rate of accuracy in detecting the human emotions, and hence, 35,888 number of images [48 × 48 pixels] representing the different emotions of human faces in various angles are used for training the model. All these 35,888 samples are taken from the public FER2013 dataset of Kaggle challenges repository [9]. CNN requires high computational power, so to boost up the system while training on a very huge number of samples, a graphical processing unit is used. The efficiency of the system is measured by observing the performance evaluation metrics like accuracy, precision, and recall. The rest of the paper is organized as follows: Sect. 2 discusses the related work, and methodology of the work is given in Sect. 3; in Sect. 4, all the experimental observations are provided; and Sect. 5 concludes the paper.

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2 Related Work Different learning techniques are used for classifying the expressions of human face in the past and in the recent time. Each technique has showed significant results in face expression recognition task. In [10], the authors used methods like image preprocessing and convolutional networks. Image preprocessing included spatial normalization, synthetic sample generation, image cropping, downsampling, intensity normalization. Convolutional networks architecture is used for classification task. Their experiment was conducted on extended Cohn-Kanade dataset (CK+) accuracy of 97.81% which is achieved with less training time. In [11], the authors presented a network named boosted deep belief network (BDBN). A boosted strong classifier is formed from a set of features which can characterize expression-related facial shape changes. The strong classifier is improved iteratively through joint fine-tuning process. Their experiments were conducted using two public databases, Cohn-Kanade which achieved an accuracy of 96.7% and JAFFE which achieved an accuracy of 68.0%. Deep convolutional neural network (DCNN) method for recognizing facial expressions is presented in [12]. Cohn-Kanade (CK+) and JAFFE datasets were used. The frontal faces are detected and cropped using OpenCV, and the facial features are extracted using the DCNN framework. DCNN framework is referenced from architecture used for ImageNet object detection. In [13], the authors used multi-scale feature extractors and whole-field feature map summing neurons which improved the facial expression recognition rate. Convolutional neural networks were used to extract features relevant to given face analysis task, and pose variations and light variations are handled with the help of normalization procedures. A dynamic approach for detecting face expressions in videos using 3D convolutional neural networks is proposed in [14]. A 3D Inception-ResNet (3DIR) architecture is used which extracts the spatial and temporal features of sequences. Long short-term memory (LSTM) unit is used as final part which takes the feature map from 3DIR and extracts the temporal information from it. Four databases named CK+, MMI, FERA, and DISFA obtained 67.52%, 54.76%, 41.93%, and 40.51% accuracies, respectively. The impact of face image preprocessing in rising the face recognition rate is presented in [15], considering the face images with low contrast, bad, or dark lighting. Three preprocessing steps including image adjustment, histogram, equalization, and image conversion are used. Five face recognition techniques, principles components analysis (PCA), linear discriminate analysis (LDA), kernel PCA (KPCA), Fisher analysis (FA), and Gabor KPCA are used to compare the performance. This comparison is done before and after denoising by Haar wavelet at level 10 of decomposition when applied on the PGM, BMP and JPG databases. Results show increase in the recognition rate up to (8%) and (3%) before and after de-noising, respectively.

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3 Methodology In order to detect the emotion of human face in the given input image, first train the model thoroughly using CNN by providing a huge number of human images representing different emotions in various rotations of males and females. After training the model, store the CNN weights. Now, detect the human face in the input video stream and crop the face region and preprocess the cropped face image in order to obtain the better result and then apply the stored CNN weights to classify the facial expression of the human face. So, the overall work can be divided into three main phases, namely Phase-1: Train the CNN and store the obtained parameters in file system Phase-2: Human face detection and preprocessing Phase-3: Facial expression classification using CNN parameters. The methodology of the system for attaining the computer vision to detect human emotion is given in Fig. 1. Phase-1: Train the CNN and store the obtained parameters in file system Convolutional neural network [6, 8] is a class of deep neural networks, mainly used to learn hidden patterns in an image. CNN is a multi-layered feed forward neural network with local connectivity, and because of its outstanding performance in image processing, it is mainly adopted for computer vision.

Fig. 1 Facial expression recognition system

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The layers of CNN are convolution layer (Conv), rectified linear unit (ReLU) layer, pooling layer, and fully connected(FC) layer. Convolution Layer (CONV Layer). The main function of a convolution layer [8] is to find out the patterns hidden in the image through convolution. Convolution matrix (or kernel or learnable filters or neurons) is an N × N matrix of random values, which is to generate the convolved feature (also called as feature map of activation map) through the process of convolution on the image. The stride, size, and depth of the kernel are the three important parameters in the process of convolution. The step-by-step procedure to generate feature map is given below. Suppose, consider an input image of width W 1 , height H 1 , and depth D1 and the convolution layer parameters kernels, spatial extend, stride, and padding are assumed as K, E, S, and P, respectively. Now, the input image is convolved with each kernel, and summing of computations will result in ‘K’ feature maps of width W 2 , height H 2 using Eq. (1). W2 = (W1 − E + 2 ∗ P)/S + 1 H2 = (H1 − E + 2 ∗ P)/S + 1

(1)

Example: The process of convolution is illustrated in the below example. Consider a sample 6 × 6 input matrix I, stride = 1, size of the filter is 3 × 3, depth = 1, and a filter with random weight values is taken as ⎡

18 ⎢ 55 ⎢ ⎢ ⎢ 35 I =⎢ ⎢3 ⎢ ⎣ 15 81

41 1 16 23 17 25

52 75 104 7 72 130

60 78 13 0 109 9

43 95 6 8 11 21

⎤ 0 88 ⎥ ⎥ ⎥ 27 ⎥ ⎥ 65 ⎥ ⎥ 31 ⎦ 42

Input volume size = 6 × 6 × 1, filter size = 3 × 3, stride = 1, number of filters = 1, zero padding = 1. From Eq. (1), output activation size is obtained as Width = Height = (6 − 3 + 2(1))/1 + 1 = 6 and Depth = 1 After zero padding the matrix I, the resultant matrix is represented as A

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⎡

0 ⎢0 ⎢ ⎢0 ⎢ ⎢ ⎢0 A=⎢ ⎢0 ⎢ ⎢0 ⎢ ⎣0 0

0 0 18 41 55 1 35 16 3 23 15 17 81 25 0 0

0 52 75 104 7 72 130 0

0 60 78 13 0 109 9 0

0 43 95 6 8 11 21 0

0 0 88 27 65 31 42 0

⎤ 0 0⎥ ⎥ ⎡ ⎤ 0⎥ ⎥ −1 −1 0 ⎥ 0⎥ ⎥ F = ⎣ 2 6 −3 ⎦ 0⎥ ⎥ −2 4 0 0⎥ ⎥ 0⎦ 0

Move the weight filter across the input and do element-wise dot product and add them for each patch. For example, taking the first 3 × 3 patch from input and convolve with weight filter will give a value as follows: ⎡

⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ 0 0 0 −1 −1 0 0 ∗ −1 0 ∗ −1 0 ∗ 0 0 0 0 ⎣ 0 18 41 ⎦ × ⎣ 2 6 −3 ⎦ = ⎣ 0 ∗ 2 6 ∗ 18 41 ∗ −3 ⎦ = ⎣ 0 108 −123 ⎦ 0 55 1 −2 4 0 0 ∗ −2 55 ∗ 4 1 ∗ 0 0 220 0 = 108 − 123 + 220 = 205 This convolution should be done all over the input by moving the filter with stride length which will generate a feature map ‘X’ as given below. ⎡

205 ⎢ 449 ⎢ ⎢ ⎢ 119 X =⎢ ⎢ −26 ⎢ ⎣ 360 396

20 −174 −116 110 −172 −110

512 509 523 222 579 714

497 65 101 165 534 70

602 357 −160 −340 249 −102

⎤ 248 771 ⎥ ⎥ ⎥ 235 ⎥ ⎥ 475 ⎥ ⎥ 261 ⎦ 252

As the values of a kernel are selected randomly, the produced convolved features sometime contain negative values and these negatives values have no importance in the image because the pixel values of an RGB image range from 0 to 255. Hence, to remove these negative values, ReLu activation follows the convolution operation. Rectified Linear Unit Layer (ReLU Layer) [8]: It is activation function which is used to remove the negative values and replace them with zero and is given in Eq. (2) F(x) = max(0, x)

(2)

After applying ReLu activation function to output of convolution layer, the resultant feature map ‘X’ is obtained as

Deep Neural Network-Based Human Emotion Recognition …

⎡

205 ⎢ 449 ⎢ ⎢ ⎢ 119 X =⎢ ⎢ 0 ⎢ ⎣ 360 396

20 0 0 110 0 0

512 509 523 222 579 714

497 65 101 165 534 70

602 357 0 0 249 0

459

⎤ 248 771 ⎥ ⎥ ⎥ 235 ⎥ ⎥ 475 ⎥ ⎥ 261 ⎦ 252

Pooling Layer (Pool Layer) [8]: The main goal of pooling layer is to preserve the main feature minimize the spatial dimensions of the image. Minimization of spatial dimensionality of the image helps in gaining the computational performance and in turn helps in training the model with less parameters. Max pooling and average pooling are the two most common pooling layer operations. The stride and window size are the two important parameters that tune the training process. The feature map is downsampled to a feature map of width W 2 , weight H 2 using Eq. (3). W2 = (W1 − E)/S + 1 H2 = (H1 − E)/S + 1

(3)

The Max pooling operation on the output of the ReLU layer is shown in the following example. Example: Stride = 3, size of the filter is 3 × 3, and depth = 1. From Eq (3), the downsampled feature map dimensions are obtained as Width = Height = (6 − 3)/3 + 1 = 2 and Depth = 1 Now, move the 3 × 3 window across matrix X and choose the maximum value. For example, consider the first 3 × 3 patch of matrix X ⎡

⎤ 205 20 512 ⎣ 449 0 509 ⎦ = Max(205, 20, 512, 449, 0, 509, 119, 0, 523) = 523 119 0 523 This operation should be done all over the matrix X by moving the window with stride length, 

523 602 X= 579 579



Fully Connected Layer: A fully connected(FC Layer) layer [16] is like a multilayer perceptron neural network, which has full connections to all activations in the previous layer; i.e., all the feature maps obtained from the Conv and Pool layers are combined in this layer to form a filter for each classification. Finally, the Softmax regression [17] is used to classify the emotion of the human facial image submitted as the test input image.

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RB 1

RB 2

RB 3

RB 4

RB 5

Pool Layer

FC Layer

Fig. 2 CNN architecture of the proposed system

Fig. 3 Self-optimized CNN architecture

The stacking up of all these layers defines the architecture of CNN. The architecture of the CNN used in the proposed work is similar to residual networks (ResNet-34) [18] built by Microsoft with residual block (RB) as the building blocks as shown in Fig. 2. The residual block structure used is as follows: • Conv layer + linear activation function when downsample is false • Conv Layer + Linear Activation Function + Max Pooling when downsample is true. A 3 × 3 kernel is maintained uniquely in all residual blocks, and in each block, 64 filters are used with downsampling done alternatively with a batch size of 100. Now, train the neural network for several epochs and store the obtained parameters in the file system as checkpoint file, meta file, data file, and index file, and these files will be used for facial expression classification. The structure of the self-optimized CNN is given in Fig. 3. Phase-2: Human Face Detection and Preprocessing This phase detects human face in the input video using OpenCV Haar classifier [19] and crops the facial part. The cropped segment contains only the facial part by excluding the background information. Now, the following preprocessing techniques are to be applied on the cropped segment to obtain better classification of emotion. 1. Gray scale conversion 2. Downsampling 3. Intensity normalization. The pixel values of a digital color image are represented in RGB model with three channels. But, for facial expression analysis, color is not an essential attribute; hence, collapse these RGB channels into a single channel by converting the color image into gray scale image. Then, the face segment image is to be downsampled to 48 * 48

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size and then normalize the pixel values in the downsampled image so that sampled image will have similar data distribution. Phase-3: Facial Expression Classification using CNN parameters This is the final phase of deep neural network-based human emotion recognition by computer vision. To recognize the emotion of the human face in the input, obtain the CNN parameters which are stored in the file system and apply the weights on the preprocessed facial segment image to predict the final class of the emotion.

4 Experiment and Results 4.1 Data Set For experimentation, FER2013 public dataset has been taken from the Kaggle competitions repository [9]. The dataset consists of 48 * 48 pixel gray scale images of 35,888 human faces including both male and females. The labels and their respective classes are Angry—0, Disgust—1, Fear—2, Happy—3, Sad—4, Surprise—5, Neutral—6.

4.2 Computational Environment The proposed system is implemented in Python 3.7.2 version with dependencies OpenCV, TensorFlow, Numpy, TFlearn, SKlearn, Pandas, Matplotlib on a computer with Intel Core i7-8750H processor 2.20 GHz, 16 GB RAM with NVIDIA GeForce 1050Ti (with 4 GB DDR5 dedicated memory) graphical processing unit.

4.3 Experimental Analysis The available 35,888 image samples are divided into training dataset and testing dataset. The training data set consists of 28,710 examples, and 7178 samples are used for testing the performance of the system. The performance of the proposed system is evaluated by iterating through 100 epochs for a batch size of 100, momentum value 0.9, and learning rate starts from 0.1. The results observed at various epochs are shown in Figs. 4 and 5.

462 Fig. 4 Observed accuracies on FER dataset at different epochs

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5 Epoch 100

4

Epoch 90

3

Epoch 80

2

Epoch 70

1

Epoch 60

0 Loss

Fig. 5 Comparison of accuracies of proposed system against self-optimized CNN

100 90 80 70 60 50 40 30 20 10 0

Accuracy

92.46 71.2 61.87 52

Proposed System

Self Optimized CNN

Training Accuracy

Testing Accuracy

5 Conclusion Automated machines with visual perception help in better understanding their environment in order to provide a smoother and natural interaction with humans. As human facial expressions convey useful information, an intelligent system with computer vision might improve the user experience. An efficient and faster method to recognize the facial expressions is proposed in this paper. Transfer learning was done on facial expression recognition dataset available using self-optimized CNN. Self-optimized CNN model fetched a training accuracy of 71.2% and testing accuracy of 52%. Furthermore, residual networks were used which increased the testing accuracy by 9.87% more totaling to 61.87% with training accuracy of 92.46%, which was comparably better.

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References 1. Dix A (2009) Human-computer interaction. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, Berlin 2. Andrea LG (2016) The messages of mute machines: human-machine communication with industrial technologies. Mach Commun Article 4, 5:1–30 3. Henny A, Brain S (2015) Robot nonverbal communication as an AI problem (and solution). In: Proceedings of artificial intelligence for human-robot interaction papers from the AAAI 2015 fall symposium, pp 2–4 4. Aleix M, Shichuan D (2012) A model of the perception of facial expressions of emotion by humans: research overview and perspectives. J Mach Learn Res 13:1589–1608 5. Christine LL, Diane JS (2000) Automatic facial expression interpretation: where humancomputer interaction, artificial intelligence and cognitive science intersect. Pragmat Cogn 8:185–235 6. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29:2352–2449 7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 8. Karpathy A, Convolutional neural networks for visual recognition. Available online www.cs2 31n.stanford.edu 9. Kaggle Challenges Dataset. https://www.kaggle.com/c/challenges-in-representation-learningfacial-expression-recognition-challenge/data 10. Lopes AT, de Aguiar E, Oliveira-Santos T (2015) A facial expression recognition system using convolutional networks. In: The SIBGRAPI proceedings of the 28th SIBGRAPI conference on graphics, patterns and images, vol 13, pp 273–280 11. Liu P, Han S, Meng Z, Tong Y (2014) Facial expression recognition via a boosted deep belief network. In: Proceedings of IEEE conference on computer vision and pattern recognition, vol 02, pp 1805–1812 12. Mayya V, Pai RM, Pai MM (2016) Automatic facial expression recognition using DCNN. Procedia Comput Sci 93:453–461 13. Fasel B (2002) Multiscale facial expression recognition using convolution neural networks. In: Proceedings of the third Indian conference on the computer vision, graphics and image processing ICVGIP 02, pp 1–9 14. Hasani B, Mahoor MH (2017) Facial expression recognition using enhanced deep 3D convolutional neural networks. In: The IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 2278–2288 15. Abdul-Ameer Abdul-Jabbar I (2014) The image processing for face recognition rate enhancement. Int J Adv Sci Technol 64:1–10 16. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431– 3440 17. Ren Y, Zhao P, Sheng Y, Yao D, Xu Z (2017) Robust softmax regression for multi-class classification with self-paced learning. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI-17), pp 2641–2647 18. Residual Network Architecture. http://github.com/tflearn/tflearn/blob/master/examples/ima ges/residual_network_cifar10.py 19. Chandrappa DN, Akshay G, Ravishankar M (2012) Face detection using a boosted cascade of features using OpenCV. In: Venugopal KR, Patnaik LM (eds) Wireless networks and computational intelligence. Communications in computer and information science, vol 292, pp 399–404

A Method for Estimating the Age of People in Forensic Medicine Using Multivariable Statistics María Barraza Salcedo, Alexander Parody, Yeis Borre, Amelec Viloria, and Jorge Cervera

Abstract The present study seeks to generate a regression model that allows estimating the age of individuals in the city of Barranquilla (Colombia) from the stages of people’s teeth, in order to generate a tool to help forensic doctors in the identification of bodies in a high degree of decomposition. The model showed that sex and teeth 23, 27, 38, and 47 have a statistically significant relationship with age, and although the model has a medium level of explanation (adjusted R-square of 56%), it is a very useful tool for estimating the age of the inhabitants of Barranquilla given that the current standards are based on studies carried out in other countries with genotypic and phenotypic characteristics that differ from Barranquilla people. Keywords Multiple linear regression · Forensic odontology · Age estimation

1 Introduction Taking into account the knowledge of dental development from its genesis to its appearance in the mouth, first as temporal dentition and later the permanent teeth, the study of the dentition is very useful for clinical dental practice as a basic instrument M. B. Salcedo (B) · Y. Borre Universidad Metropolitana de Barranquilla, Programa de Odontología, Barranquilla, Colombia e-mail: [email protected] Y. Borre e-mail: [email protected] A. Parody · J. Cervera Facultad de Ingeniería, Universidad Libre Seccional Barranquilla, Barranquilla, Colombia e-mail: [email protected] J. Cervera e-mail: [email protected] A. Viloria Universidad de La Costa, Barranquilla, Colombia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_42

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in the diagnosis and elaboration of the treatment plans, and as an indicator for the prognosis of any pathology type. In addition, its analysis is useful for studies in anthropology, demography, forensic medicine, paleontology, as well as for legal and forensic odontology, since the only reliable way to approach the age determination of an individual is usually through the study of dentition, with the legal implications of whether it is a minor or not. The dental development is a very reliable indirect source to determine the biological characteristics of a species and is an excellent marker of the growth and maturation of vertebrate species, integrated into the general plan for growth and development of the skeletal and muscular systems, and in the growth and development of the brain. That is why forensic dentistry has become a key tool in the legal medical identification procedures on living and deceased subjects. The teeth are considered the hardest structures of the body, being able to survive the majority of postmortem events that destroy or modify other corporal tissues. Besides, its dentition is characterized by its individuality which turns the dental evidence into a test as valid as the fingerprints. It is necessary to bear in mind that when an individual of a certain vertebrate species dies, the development of their teeth is stopped in a stage according to their age and the dental development pattern of their species. If the fossil remains of the individual must be analyzed, the development status of each tooth can be checked, as well as determining the moment of the teething process through the study of the jaw, maxilla, and all the teeth [1]. In general, age estimation methods based on the dental and skeletal systems can be applied for determining the degree of development of the teeth as a good indicator of biological and/or chronological age. Dental age can be estimated accurately in childhood, given that in that period many teeth are developing simultaneously, as it is less influenced by external factors other than parameters such as bone and morphological ages or the appearance of secondary sexual features [2]. Although the hormonal and endocrine alterations will continue to have an influence on dental maturation and development, it would be more reliable in the study of general maturation compared to the analysis of bone development [1]. On the other hand, some authors have shown that factors such as age, sex, race, diet, socioeconomic levels, place of residence, and even latitudes can influence this process [2]. Due to the above, the lack of knowledge of chronological age has been a sufficient reason for the development of various researches in which biological age has been used as a reference for estimating chronological age. With regard to legal medicine, the estimation of a person’s age has been a challenge for scientists up to the present time. In many cases, it is not possible to know the age of a person without exposing to serious and unfortunate errors [3]. In Colombia, to estimate dental age, the forensic system uses foreign standards that subtract reliability of dental age reports because they are based on studies of foreign populations. The National Institute of Legal Medicine and Forensic Sciences published the Technical Regulation for the age estimation in forensic clinic, which is a procedure guide that must be followed when the authorities require a study. This regulation describes the main methods for estimating the clinical age, considering

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the different changes that take place in dental structures as part of the crown–root development [4]. The most commonly used methods in Colombia for the estimation of clinical age are the Moorrees method modified by Smith [5], and that of Moorrees, Fanning, and Hunt [6], which are described in the Technical Regulations of the National Institute of Legal Medicine and Forensic Sciences of Colombia. In 1963, the Moorrees method proposes 14 stages of formation of the crown, the root, and the apex of the mandibular permanent dental structures. These stages of dental development rank from the initial formation of the cusps to the complete closure of the apex. In 1991, Smith resumed the Moorrees study to make the prediction of dental age using the same stages proposed by Moorrees, adding one more stage in the formation of the root, the R2/3 stage, which refers to the formation of 2/3 of the root, both for left permanent female and male mandibular teeth. In the same way, it adjusts the values by averaging the values obtained in each stage to give a predictive value of dental age [7]. The foregoing means that the Colombian forensic system is currently using international standards which are not validated in the Colombian population, subtracting reliability from expert reports of clinical age that are sent to the competent authorities that require this type of tests in the different judicial processes in which the knowledge of age is a critical issue to make decisions [8]. Studies have been developed for the estimation of age in different regions of Colombia; however, there is no a large-scale study that allows to have a national forensic standard to estimate dental age. Therefore, in accordance with the above, the present work seeks to determine the dental age in a population of 9 to 25 years of the Department of the Atlántico treated at the Fundación Hospital Universitario Metropolitano de Barranquilla, under the hypothesis of the Moorrees method modified by Smith. This research will serve as a pillar to propose a multicentric study in the Colombian Caribbean region whose results can be consolidated with regional studies that can be proposed in the future.

2 Methodology For this study, a descriptive design was assumed. The type of study is prospective correlation, taking into account that the objective is to establish correlations or associations that may exist between different variables. In the correlation method, the relationships that exist between two or more variables can be identified, and the variations that occur spontaneously in both can be observed to investigate whether they arise together or not. In this method, statistical calculations are used for measuring the factors related and including the control of variables in order to obtain more valid results [9]. The universe of the study was constituted by the total number of patients attended in the dental clinics at the Fundación Hospital Universitario Metropolitano (FHUM) in the period from January 1, 2014, to March 20, 2017.

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The studied population consisted of 10,025 digital radiographies of patients aged 9 to 25 years who attended the dental clinics at the Fundación Hospital Universitario Metropolitano from January 1, 2014, to March 20, 2017. The sample (confidence of 90% and 5% of error) consisted of 220 radiographies of patients aged 9 to 25 years who met the inclusion criteria, attended at the FHUM dental clinic in the period between January 1, 2014, and March 20, 2017. For the data collection in the calibration process and in the processing of the total sample, a format was used for recording the number of clinical history, sex, and crown–root development stages developed by Moorrees, Fanning, and Hunt (1963), and modified by Smith (1991). From the first study, the 14 stages of crown–root development were taken, and the R2/3 stage (formation of the crown and two-thirds of the root) was obtained from the second study mentioned above. In this way, it was possible to have 15 stages of crown–root development [10]. For the data collection, a format with the variables of the study was designed, which was used during the calibration of the observers and the subsequent processing of the entire sample. Initially, a calibration of observers was carried out as a procedure to achieve an interpretation, application, and uniform understanding of the criteria to be taken into account. This calibration was carried out to obtain a reliable and reproducible examination of the observers, with the participation of an expert to calibrate the conditions of the clinical examination. To perform the calibration of the observers and develop the calculation to obtain the sample, the clinical histories were counted with their respective digital radiographies found in the central file area in the dental clinic at the Fundación Hospital Universitario Metropolitano, in relation to patients from 9 to 25 years of age from Barranquilla who were treated in the period from January 2014 to March 2017. This was the population studied. To guarantee the standardization of the dental maturation findings analyzed in the radiographies, the calibration process was carried out at different times. For this purpose, three calibrations were taken once a week, the same day and at the same time, and always using the same site and instruments. Once the calibration was carried out and the concordance between the observers was determined, the ones that obtained the best calibration results were chosen to start the pilot test and the final test with the whole of the sample to which the statistical model was applied. To determine which of the teeth, in their 14 stages, had a statistically significant relationship with the age of the patients, a generalized linear regression model was applied. Due to the fact that the independent variables are quantitative (stages of the teeth) and qualitative (sex of the patient), a confidence of 95% was applied for the selection of the significant variables to estimate the age of the patients.

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3 Results Regarding the calibration of the observers, an observer training was initially carried out by an expert in radiographic readings for the evaluation process of the crown– root development. First, a standardization performed on the concepts related to the method of Moorrees, Fanning, and Hunt, and the one modified by Smith in which the different stages of crown–root formation are shown. The standardization was also applied to the radiographic assessment of the stadiums mentioned above and the way to manage the format designed for the data collection. Thirteen radiographs were used for the calibration, obtaining the following results (Table 1). From the 13 radiographs reviewed by each observer, it was found that observers four and five obtained 85% of the category good with equal distribution for each of them, observer two obtained 69%, and observer one obtained 62%. The observer one obtained 23% of the regular category, and the observer three 23% of the category bad. Therefore, observers 4 and 5 were selected for the survey of the information from the radiographs of patients that make up the sample. For the generation of the mathematical model, a generalized linear regression model was used, where the independent variables correspond to each of the stages found in the teeth, in addition to the sex of the individual, while the dependent variable was the age of the individual. As an initial part of the generalized linear regression model, an analysis of variance of sum of squares type III was generated, where the relationship of each independent variable with the dependent variable is evaluated by means of the p-value and Fisher’s F ratio. In this case, the p-value criterion was applied since it is the one used in scientific researches as a measuring standard for the correlation between variables. The analysis of variance seeks to make evident the relationship of the independent variables and the dependent variable through the measurement of variability in and between variables (Table 2). In the previous table, the sum of squares type III is generated and the variables that were not significant were removed (p-value greater than 0.05). It shows how the sex and the teeth 23, 27, 47, and 38 presented a statistically significant correlation with the behavior of the variable age. Through this model, an adjusted R-square value of 56% was obtained. This item indicates the percentage of variation of the dependent variable that is explained by Table 1 Distribution of categories according to the observer in the calibration test Category

Observer 1

%

2

%

3

%

Good

8

62

9

69

9

Regular

3

23

2

15

Bad

2

15

2

15

13

100

13

100

Total radiographs observed

4

69

11

1

8

3

23

13

100

%

5

%

85

11

85

0

0

1

8

2

15

1

8

13

100

13

100

Source Clinical histories of the Fundación Hospital Universidad Metropolitana

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Table 2 Sum of squares type III for the regression model of the age variable Source

Sum of squares

Sex

32.3077

23

216.187

Gl

Mean square 1 1

32.3077

P-value

4.59

0.0340

30.69

0.0000

27

31.9058

1

31.9058

4.53

0.0352

47

37.1374

1

37.1374

5.27

0.0232

57.05

0.0000

38

401.914

1

Shrinkage

943.973

134

Total (corrected)

2221.89

216.187

Reason-F

401.914 7.04457

139

R-square (adjusted by gl) = 56 percent

Table 3 Confidence limits of 95.0% for the estimated coefficients of the regression model for the age variable

Parameter

Estimate

Lower limit

Constant

60.733

253.045

Sex

−0.483139

−0.929345

23

123.333

27

−0.344358

47

−0.56649

38

0.763665

0.792997 −0.664388 −105.447 0.563701

Upper limit 961.615 −0.0369332 167.366 −0.0243273 −0.0785091 0.96363

the model. In this case, the model only explains 56%, indicating that 44% of the variability found in the age variable is explained by other variables different to the sex and the stage of the different teeth analyzed. Once the significant independent variables in their relation with the dependent variable were identified, the next step was to visualize the generalized linear regression model from the estimation of the coefficients (Table 3). The table above shows the confidence intervals of 95.0% for the coefficients in the model (1). The equation of the adjusted model is the following: AGE = 6.0733 − 0.4883139 × SEX + 1.23333 × 23 − 0.3444358 × 27 − 0.56649 × 47 + 0.763665 × 38

(1)

SEX = 1 if it is female; −1 if male Once the regression model for age forecasting was generated, reliability was determined when making forecasts, performing the analysis of model residuals. Residual is understood as the error obtained by the model at the time of forecasting the age of the individual. The analysis of the residuals (or forecast errors) is divided into two components: the graphical analysis of the residuals with respect to the predicted values in each of the significant independent variables and with respect to the row number or location. The objective is to demonstrate, through scatter charts, that forecast errors are not

A Method for Estimating the Age of People … Table 4 Goodness-of-fit tests for residuals. Kolmogorov–Smirnov test

Table 5 Centering and variability of forecast errors

Statistical

471 Normal

DMAS

0.0556456

DMENOS

0.0588799

DN

0.0588799

P-value

0.71676

Normal Media = −0.000000284286 Standard deviation = 2.60599

influenced by any of these variables. The graphs confirm that the forecast errors are not influenced by the predicted values, by the location of the observations, or by the values taken per each of the significant independent variables. The second part of the analysis of residuals is to prove that the residuals follow the behavior of a normal distribution with an average value of zero or very close to zero. Next, the graphic analysis of the waste is detailed (Table 4). Table 8 shows the result of the Kolmogorov–Smirnov test to determine if the residuals can be adequately modeled with a normal distribution. Because the pvalue is greater than 0.05, the idea that the residuals have the behavior of a normal distribution with 95% of confidence cannot be rejected. Finally, the mean and the standard deviation of the residuals from the generated forecasts were calculated, proving that the mean of the forecast errors had a value of zero or very close to this value, to prove the suitability of the model when generating forecast (Table 5). The average of the residuals was −0.00000028 which is practically zero, so the model shows some robustness when it comes to forecasting, and added to the results of the graphs of the residues. Finally, the histogram of the residuals shows that its figure looks like the normal distribution and that zero is the most frequent value (Fig. 1).

4 Conclusions A mathematical model was generated where the sex of the individual and the teeth 23, 27, 38, and 47 showed a statistically significant relationship with age, although the model presents a mean explanation level of the dependent variable (adjusted Rsquare of 56%). The model is to be very useful in forensic medical practice, when the estimation of age is required by the corresponding authorities in the regional forensic system in the city of Barranquilla and in the Department of Atlántico (Colombia).

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Fig. 1 Histogram for residuals

Considering that the current tools are based on standards made in other latitudes where the genotypic and phenotypic conditions of people are different and therefore hinder their successful extrapolation of results to people in Barranquilla, obtaining a chronological age forecast model from dental information becomes a valuable contribution to the development of forensic activity both in the city and in the department.

References 1. Cortes M (2011) Maduración y desarrollo dental de los dientes permanentes en niños de la comunidad de Madrid. Universidad Complutense de Madrid Facultad de Odontología, Aplicación a la estimación de la edad dentaria 2. Espina de Fereira A, Fereira J, Céspedes M, Barrios F, Ortega A, Maldonado Y (2007) Empleo de la edad dental y la edad ósea para el cálculo de la edad cronológica con fines forenses, en niños escolares con valores de talla y peso no acordes con su edad y sexo, en Maracaibo, estado Zulia. Estudio preliminar. Acta odontológica. Venezolana. [Internet] 3. Mata P (1846) Cirugía y Medicina Legal, 2ª Edición 4. Identificación de cadáveres en la práctica forense Instituto Nacional de Medicina Legal y Ciencias Forenses. 2009 5. Correa R (1990) Estomatología forense. Editorial Trillas 6. Noble H (Julio, 1974) The estimation of age from the dentition. In: Journal of the forensic science society 14(3) 7. Parody A et al (2018) Application of a central design composed of surface of response for the determination of the flatness in the steel sheets of a Colombian steel. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham 8. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham 9. Viloria A et al (2018) Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In: Tan Y, Shi

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Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham 10. Parody A, Viloria A, Lis JP, Malagón LE, Calí EG, Hernández Palma H (2018) Application of an experimental design of D-optimum mixing based on restrictions for the optimization of the pre-painted steel line of a steel producer and marketing company. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham

Cluster of Geographic Networks and Interaction of Actors in Museums: A Representation Through Weighted Graphs Jenny Paola Lis-Gutiérrez, Amelec Viloria, Juan Carlos Rincón-Vásquez, Álvaro Zerda-Sarmiento, Doris Aguilera-Hernández, and Jairo Santander-Abril Abstract This article aims to determine what are the strong and weak interactions of geographic museum networks in Colombia, with other national and foreign actors. We applied a survey to nine territorial networks that are made up of 222 museums. We obtained the relationship data with 32 types of actors in Colombia and abroad. To represent the relationship, we use the weighted graphs. In the case of the geographic networks analyzed, the strongest relationships are with international experts, the Program for the Strengthening of Museums, the media, schools, city halls of the municipalities where the museums are located, judicial entities, and other networks of museums. Keywords Museum · Weighted graphs · Geographic networks of museums

J. P. Lis-Gutiérrez · J. C. Rincón-Vásquez Fundación Universitaria Konrad Lorenz, Bogotá, Colombia e-mail: [email protected] J. C. Rincón-Vásquez e-mail: [email protected] J. P. Lis-Gutiérrez · Á. Zerda-Sarmiento Universidad Nacional de Colombia, Bogotá, Colombia e-mail: [email protected] A. Viloria (B) Universidad de la Costa, Barranquilla, Colombia e-mail: [email protected] D. Aguilera-Hernández Corporación Universitaria del Meta, Villavicencio, Colombia e-mail: [email protected] J. Santander-Abril Universidad Central, Bogotá, Colombia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_43

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1 Introduction According to the resolution number 1.975 of 2013 of the Ministry of Culture of Colombia, a museum is defined as: “a non-profit, public, private, or mixed institution, permanently open to the public, which investigates, documents, interprets, communicates, exhibits, and preserves material, immaterial and/or natural testimonies recognizing the social, economic, and cultural diversity of the communities and promoting the principles of democratic access to information and knowledge, through participation and constant dialogue with the population” [1]. This definition can be broader, considering that, according to the Program for the Strengthening of Museums (PFM, Programa de Fortalecimiento de Museos) and the Ministry of Culture of Colombia [2], museums are: (i) living and dynamic institutions that facilitate intercultural encounters; (ii) places that work with the power of memory; (iii) relevant instances for the development of educational and training functions; (iv) appropriate tools to stimulate respect for cultural and natural diversity; (v) spaces that value the bonds of social cohesion of the communities and their relationship with the environment; (vi) relevant social practices for shared development; (vii) and spaces of representation of cultural diversity, which link the present with memories of the past and that seek the joint construction of the future, with solidarity, justice, dignity, harmony, freedom, peace, and respect for human rights. The Colombian legislation does not specify the requirements that must be met for the creation of museum entities, according to the Program for the Strengthening of Museums [3]. In this context, a museum must: (i) be part of the structure of a legal entity or have its own legal status; (ii) be a nonprofit entity; (iii) benefit the community; (iv) provide services on a permanent basis; (v) have at least one space dedicated to the exhibition of testimonies and/or collections; exhibit and disseminate testimonies and/or collections on a permanent basis; (vi) have inventories of their testimonies and/or collections; (vii) know the general state of conservation of testimonies and/or collections; and perform educational and cultural activities. In Colombia, there are 27 museum networks1 divided into thematic and territorial types, grouping more than 500 museum entities. The thematic networks2 include Colombian Association of Parks, Zoos and Aquariums (Acopazoa, Asociación Colombiana de Parques, Zoológicos y Acuarios); Network of Community Museums; Network of Astronomy Museums of Colombia; Network of Museums of Sciences and Natural History; Network of Lilliput Museums; Network of Gold Museums of 1 The

coordinators or presidents of these networks that, together with the National Council of Museums, integrate the National Table of Museums (Mesa Nacional de Museos). According to the Article 1 of the Resolution number 1.975 of 2013 of the Ministry of Culture, the National Council of Museums is an “advisory entity of the Ministry of Culture for the policies, plans, and programs of the museums; with participation and representation at national level which is articulated through the territorial and thematic networks” [1]. 2 According to the Article 1 of the Resolution number 1.975 of 2013 of the Ministry of Culture, a thematic network is an “organizational form that links the different agents of the museum sector according to the topics of interest, common administrative forms, and typologies of collection” [1].

Cluster of Geographic Networks and Interaction of Actors …

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the Banco de la República; Network of University Museums; National Network of Science and Technology Museums; National Network of Medicine Museums3 ; and National Network of Botanical Gardens. The territorial networks4 include the following departmental networks: Cauca, Córdoba, Santander, Atlántico, Caldas, Huila, Antioquia, Bolívar, Boyacá, Valle del Cauca, Tolima, Norte de Santander, Magdalena, Quindío, Risaralda, Cundinamarca, Nariño, and Guainía, and the Bogotá Table of Museums. For all the above, this document aims to determine the strong and weak interactions of geographic museum networks in Colombia, with other national and foreign actors.

2 Network Analysis The following studies stand out for the application of network analysis as methodology in the research process. Grapegia Dal Vesco et al. [5] apply network analysis to study the co-authorship in publications on risk management and management control. López Ferrer et al. [6] analyze if there is a relationship between status (favorable position in the network) and social recognition (cinematographic awards) in the Spanish film industry for 24 years. On the other hand, Estévez González Luna López, and Solleiro Rebolledo [7] discuss the benefits and difficulties of local museum networks in Tenerife. Pasarin and Teves [8] present the application of social network analysis (SNA) to ethnography. Castro [9, 10] relates the local textile production and the transmission of knowledge through the SNA. Recent works include those carried out by Garrocho Rangel et al. [11], using network representation for the results of the spatial autocorrelation used to identify the clusters of properties affected by the earthquake in Mexico City on September 19, 2017. For its part, Ramos-Vidal [12] analyzed the socio-centric network of 32 cultural organizations in Andalucía (Spain), identifying that the perception of affinity and the possibility of establishing contacts in the future will help to build informal contacts. Likewise, this author established other relevant aspects such as the age of the organization and the volume of activity. In the same way, Sewell [13] proposes a hierarchical model that allows the influence parameter to become a function of the individual attributes and/or the topological characteristics of the local network. This work is the extension of [14], in which the class of network autocorrelation models is adapted to handle egocentric data. 3 This

is the most recently created network, and it was constituted on September 30, 2017. Its technical secretariat is conformed by Alejandro Burgos Bernal (Museums Division of the Patrimony Direction of the Universidad Nacional), Josep Simon (Universidad del Rosario), Hugo Sotomayor and Paula Ronderos (Colombian Society of History of the Medicine); Juan Carlos Eslava (Historical Center of Medicine of the Universidad Nacional), Germán Arango (Universal Pharmacy Collection); María Teresa Rincón and Mario Hernández [4]. 4 According to the Article 1 of the Resolution number 1.975 of 2013 of the Ministry of Culture, a territorial network is an “organizational way to link the different agents of the museum sector located in the different territories of the country” [1].

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Likewise [15], apply both Moran’s index and the network analysis to use the spatial patterns of Strava’s cyclists to monitor changes in passenger patterns throughout a whole city. From another perspective, [16], analyze the correlation between graphs with an application for the neural network analysis. The research of [17] is among the studies using new techniques in this field. The authors proposed the Provenance Network Analytics as a new approach for data analysis helping to infer the properties of the data, such as the quality or importance of its provenance. The approach states several network metrics for provenance data and applies established machine learning techniques on such metrics to build predictive models for some key properties of the data.

3 Method 3.1 The Data The fieldwork was conducted to gather information on nine geographic networks between November 11, 2016 and August 6, 2017, but none of the thematic networks answered the questionnaire. The networks that answered were: Santander Network of Museums, Bogotá Table of Museums, Córdoba Network of Museums, Bolívar Network of Museums, Antioquia Network of Museums, Atlántico Network of Museums, Cauca Network of Museums, Valle del Cauca Network of Museums, and Nariño Network of Museums. These nine networks integrate 237 Colombian museums (Table 1). The instrument consisted of 32 questions and 59 items. A part of the used instrument inquired about the interaction of the networks with different actors of Colombia society. The different actors and the level of interaction of the networks were assessed from a Likert scale whose options are presented in Table 2. According to Table 3, Table 1 Year of creation, age, and number of museums in the network in 2017 Name of the network

Year of creation

Cauca Network of Museums

2010

Age in 2017 7

Number of museums 7

Bogotá Table of Museums

2010

7

10

Valle del Cauca Network of Museums

2011

6

37

Nariño Network of Museums

2012

5

30

Antioquia Network of Museums

2001

16

88

Bolívar Network of Museums

2012

5

6

Córdoba Network of Museums

2014

3

9

Santander Network of Museums

2010

7

32

Atlantic Network of Museums

2012

5

18

Cluster of Geographic Networks and Interaction of Actors … Table 2 Options about the interaction level of the networks with the institutions

479

Level of interaction

Value

Value for the calculation of the network

It does not interact with this institution

0

0

Very weak

1

1

Weak

2

1

Mean

3

2

Strong

4

3

Very strong

5

3

it is evident that the networks that answered the survey presented weak interaction with (i) governor’s office, (ii) compensation funds, (iii) foreign universities, and (iv) military forces. The most frequent interactions were with international institutions, the Program for the Strengthening of Museums, other networks of museums, the media, and the government of the department where the network is located.

3.2 Weighted Graphs According to [18, 19], a non-directed graph H is defined as a pair of sets (V (H ), E(H )), where V (H ) are the vertices, and E(H ) the edges. Two vertices u and v are adjacent if they are joined by an edge. If each edge (u, v) ∈ L is associated with a value w(u, v), it is considered a weighted graph. w(u, v) corresponds to the weight of the edge (u, v). Therefore, the network  that is intended  to be characterized corresponds to H = {V, E}, where V = u 1 , u 2 , . . . , u |V | are the agents, and E ⊂ V · V denotes the non-targeted interactions between users. The adjacency matrix M|V ||V | = ei j represents the connections or links in H . ei j = 1 when the users u i and u j have a weak relationship, ei j = 2 when the users u i and u j have a moderate relationship, ei j = 3 when the users u i and u j have a strong relationship, and ei j = 0 otherwise.

4 Results In Figs. 1, 2, and 3, the relationship of the networks that answered the survey respecting to the previously identified actors is shown using the Bresenham algorithm. In Figs. 1, 2, and 3, it is evident that the strongest relationships are with the international experts, the Program for the Strengthening of Museums, the media, schools, city halls of the municipalities where the museums are located, judicial entities, and other museum networks. For its part, the Cauca Network of Museums does not have

3

Companies in the industrial sector

Companies in the service sector

3

Religious sector

Military forces

3

Associations 4

3

International museums

4

1

Other Colombian museums out of

Judicial entities (public prosecutor’s office, police …)

3

Other museums networks

Schools (primary and secondary)

2 2

NGOs

3

3

Companies in the primary sector

International research institutes

3

Foreign universities

1

1

Colombian universities

3

4

Program for the strengthening of museums

National research institutes

4

International institutions

Foreign companies

1 1

ICOM

4

1

1

1

1

1

1

2

2

1

1

3

1

1

1

1

1

4

4

1

0

1

1

0

2

0

4

4

3

0

0

0

1

1

1

0

3

4

2

2

0

2

2

5

2

2

2

2

4

2

2

3

2

3

2

2

2

3

4

2

2

2

1

1

1

4

3

2

2

3

3

4

3

2

2

2

2

2

4

4

3

3

1

4

4

4

4

0

3

4

4

3

3

3

3

3

3

3

3

4

4

3

3

3

3

5

5

4

4

1

1

1

1

1

1

1

1

1

1

1

1

1

1

3

1

0

0

4

2

2

3

4

5

2

3

4

2

2

2

2

3

4

5

3

4

0

(continued)

0

0

0

2

3

4

0

4

3

3

0

3

0

3

0

0

0

5

2

0

2

Compensation funds

0

Cauca Bogotá Valle Nariño Antioquia Bolívar Córdoba Santander Atlantic 1

Actor/entity

Table 3 Options about the interaction level of the networks with the institutions

480 J.-P. Lis-Gutiérrez et al.

4 1

4 1 1 3 2 2 2

Other municipalities

Other governorates

Institutes (Humboldt, IGAC, SINCHI …)

Heritage councils

National experts

International experts

Other

0

1

1

3

3

1

0

5

0

0

2

1

4

4

1

2

2

4

2

2

2

2

5

5

0

1

3

3

1

1

2

4

4

4

0

0

3

3

4

0

2

4

1

4

4

1

2

4

1

1

1

4

3

5

4

3

4

4

2

2

3

4

4

4

0

2

0

2

2

0

4

0

0

0

1

The governor’s office of the department where the museum leader of 2 the network is located

1

0

4

4

2

4

4

4

4

3

Town hall of the municipality where the museum leader of the network is located

2

Media

1

4

Ministries

2

Cauca Bogotá Valle Nariño Antioquia Bolívar Córdoba Santander Atlantic

Actor/entity

Table 3 (continued)

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Fig. 1 Interaction of networks with different actors (100% of interactions). Note Unrelated, weak relationship, moderate relationship, strong relationship, very strong relationship

Fig. 2 Interaction of networks with different actors (25% of interactions). Note Unrelated, weak relationship, moderate relationship, strong relationship, very strong relationship

close relations with any institution; the strongest relations are with the Program for the Strengthening of Museums, Colombian universities, schools, judicial entities, city hall, ministries, and the media. The Bogotá Table of Museums, as well as the Cauca network, does not have close relations with any institution. Its strongest relations are with the Program for the Strengthening of Museums, Colombian universities, city halls, governor’s office, and the International Council of Museums (ICOM). The strongest relationship of the Valle del Cauca museum network is with international experts and with the Program for the Strengthening of Museums, the governor’s office, other museums, other networks, and the city halls. The Nariño museum

Cluster of Geographic Networks and Interaction of Actors …

483

Fig. 3 Interaction of networks with different actors (10% of interactions). Note Unrelated, weak relationship, moderate relationship, strong relationship, very strong relationship

network interactions are very strong with schools, the city hall, and the media. It also interacts with other networks of museums, heritage councils, and national universities. In the case of the Antioquia network, in spite of not having very strong relationships, it has good interaction with the city halls, Program for the Strengthening of Museums, the media, universities, religious sector, governor’s office, and research institutes. The Bolivar network, like Cauca and the Mesa de Bogotá, does not have very strong interactions, but has strong relations with the media, the Program for the Strengthening of Museums, Colombian universities, the religious sector, the government, the ministries, other networks of museums, other museums, schools, judicial entities, military forces, and institutes. The Córdoba network strongly interacts with the media, schools, and judicial entities. At the same time, it has good relations with the religious sector, the governor’s office, the ministries, the heritage councils, and associations. For its part, the network of museums in Santander claimed to have a close relationship with the Program for the Strengthening of Museums and other museum networks, in addition to good relations with research institutes, other museums, Colombian universities, city hall, national experts, ICOM, governor’s office, heritage councils, ministries, schools, and the media. Finally, the Atlantic table of museums affirmed to have an excellent relation with the Program for the Strengthening of Museums and with other networks of museums, international museums, and the city hall. It was also consulted about the role that museums and museum networks intended to play in the post-agreement scenario. Many of the networks affirmed to be willing to work in the new stage but have not yet defined strategies. Some of the most important terms are in their contribution to forgiveness, peace, and reconciliation, along with

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the construction of social fabric, strengthening of communities, and conservation of memory [20].

5 Conclusions In the case of the geographic networks analyzed, the strongest relationships are with international experts, the Program for the Strengthening of Museums, the media, schools, city halls of the municipalities where the museums are located, judicial entities, and other networks of museums. From another point of view, we find that in the post-agreement scenario, it is necessary to promote the active presence of museums in: (i) development plans, (ii) local policies, (iii) activities to rebuild the social fabric; (iv) historical memory construction activities. Finally, one route to be taken would be the articulation of the own goals of this sector with the development goals of the departments and municipalities. From the findings, it is derived the need to empower the networks to share the knowledge, lessons learned and good practices among the different institutions, promoting cognitive and organizational proximity [21–23]. It is also important to strengthen strategic planning exercises in museums, but also in the whole sector to stimulate joint work among museums, and strengthen relations with other sectors (education, tourism, commerce, environment, science, and technology) and agents. In future studies, the research by [22] referring to cluster identification could be applied, based on weighted graphs, such as those used in this document.

References 1. Ministerio de Cultura de Colombia (2013) Resolución 1975 de 2013. Ministerio de Cultura de Colombia, Bogotá. Retrieved from http://www.museoscolombianos.gov.co/fortalecimie nto/sistema-de-informacion-de-museos-colombianos/Documents/Resolucion%201975%20M INCULTURA%20Creación%20Consejo%20Nacional%20de%20Museos.pdf 2. Programa de Fortalecimiento de Museos, Ministerio de Cultura de Colombia (2015) Política Nacional de Museos. Mejores museos, mejores ciudadanos. Ministerio de Cultura de Colombia, Bogotá. Retrieved from http://www.museoscolombianos.gov.co/imagenes/documentos/pol% C3%ADtica%20de%20museos%20definitivo.pdf 3. Programa de Fortalecimiento de Museos (2014) Organización del sector. Museo Nacional, Bogotá. Retrieved from http://www.museoscolombianos.gov.co/fortalecimiento/organizaci% c3%b3n-del-sector/Paginas/Organizacion%20del%20sector.aspx 4. Programa de Fortalecimiento de Museos (2017) En Bogotá, se conformó la Red Nacional de Museos de Medicina. PFM, Bogotá. Retrieved from http://www.museoscolombianos.gov.co/ fortalecimiento/comunicaciones/noticias/Paginas/museosmedicina.aspx 5. Grapegia Dal Vesco D, Fernandes FC, Roncon A (2014) Controles de gestão atrelados ao gerenciamento de risco: uma análise das produções científicas brasileiras sob a perspectiva de redes sociais. Redes. Revista hispana para el análisis de redes sociales 25(2):163–185

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Security Scheme Under Opensource Software for Accessing Wireless Local Area Networks at the University Campus Francisco Sánchez-Torres, Jorge González, and Amelec Viloria

Abstract Wireless networks provide flexibility, increase in productivity, and savings in infrastructure and are useful in organizations with high volume of mobile device users. The services in wireless networks require mechanisms that guarantee their efficient, secure, and reliable use. A security scheme is designed for accessing wireless local area networks (WLAN) at the campus of a Venezuelan university. The confidentiality, integrity, availability (CIA) information security principles are applied, as well as control objectives specified in ISO 27001. The proposed access security scheme mitigates threats, monitors the use of services, and establishes security parameters for reducing attacks to the network, complying with national laws and internal regulations of the university under study respecting to the use of opensource software based on the National Institute of Standards and Technology. Keywords WLAN · Security scheme · University campus · Opensource software · ISO 27001

1 Introduction The wireless networking services require mechanisms that guarantee their efficient, secure, and reliable use, hence the importance of establishing security schemes including the use of the protocol family named as authentication, authorization, and accounting (AAA) which covers the access levels, management of users accessing the network services, and allow guarantee the services to the target users. In the case of the F. Sánchez-Torres (B) · J. González Universidad Centroccidental Lisandro Alvarado, Barquisimeto, Venezuela e-mail: [email protected] J. González e-mail: [email protected] A. Viloria Universidad de la Costa, Barranquilla, Colombia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_44

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university campus under study, the wireless networks are exposed to various vulnerabilities, given that not all devices connected to the network are reliable, representing a threat to the institutional network. Other issues to be reviewed are the strength of the signal, unauthorized access outside the university´s perimeter, demand of access by visiting users, unregistered devices connected to the network, attacks to the network, denial of service (DoS), improper use of the network by unauthorized or authorized users, lack of bandwidth control and monitoring, unlawful access point (AP), free access to Internet service, and spoofing of MAC address. The Universidad Centroccidental Lisandro Alvarado (UCLA), Venezuela, is formed by seven academic deaneries, one rectorate with several administrative units, and other geographically separate academic units located in Lara state, which are interconnected through a fiber optic and microwave distribution called RedUCLA. With respect to the student network access segment, the wireless network platform requires access control mechanisms, while the rest of the segments are restricted access by internal controls. In this sense, several projects and previous studies on RedUCLA have been carried out, including the supervision and monitoring management of the data network infrastructure [1] and the implementation of the IPV6 protocol in the UCLA data network infrastructure [2]. However, these projects refer to the implementation of services and improvements in the core segment and distribution of RedUCLA network, requiring the application of access control mechanisms for this wireless network in one of its campuses, making necessary to develop a security scheme for wireless access to the network. In this sense, a diagnosis was made to identify the required components and configuration through the corresponding tests. According to ISO 27001 [3], the three basic dimensions of information security are confidentiality, integrity, and availability. This international standard establishes requirements to implement, maintain, and continuously improve an information security management system within the context of the organization. It also includes requirements for the evaluation and treatment of information security risks adapted to the needs of the organization. For the security scheme proposed in this research, 6 of the 35 control distributed objectives mentioned in the ISO 27001:2013 were applied. Besides, among the referenced researches related to the design of security schemes are Nunoo-Mensah et al. [4], Monsalve-Pulido et al. [5], Del Brocco et al. [6], Martínez [7], and Pérez [8].

2 Methodology Three activities were carried out for performing the diagnosis. Firstly, network administrator personnel were interviewed; secondly, an unstructured observation was applied on the wireless network infrastructure, and the physical infrastructure; thirdly, a revision of the university’s internal regulations and international standards on information security (ISO 27001) was done. These processes allowed to collect

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Table 1 Study variable Variable

Dimensions

Subdimensions

Security scheme for wireless network (WLAN) access in university campus

Security scheme

Configuration of parameters Hardware devices Software applications

Wireless networks (WLAN)

Protocol standards Confinement Frequency of use Users quantity Bandwidth Traffic

University campus

Infrastructure Institutional requirements Geographical extension

data about the services and infrastructure of the network for defining the configuration parameters of the security scheme, and the requirements of access to the campus wireless network. For the design of the security scheme for accessing wireless networks in a Venezuelan university campus, the phases of initiation, development, and testing were applied according to the methodology proposed by the National Institute of Standards and Technology (NIST) (SP 800-124:2013), regarding the safe use of mobile devices [9]. Admission control (NAC) [10, 11] allows users and wired, wireless, and VPN devices to authenticate in the network, evaluate and solve issues in a device for policy compliance before allowing access to the network; differentiate role-based access; and audit and report the users in the network [11]. This methodology proposes a life cycle of five phases for security solutions in mobile devices: initiation, development, implementation, operation and maintenance, and disposal [9]. Additionally, for the design of the scheme, the university regulations for the use of opensource software [12] and security [13] were reviewed. In relation with the research objective, the study variable is presented in Table 1. Considering the NIST methodology, the phases corresponding to the diagnosis, development, and testing of the proposed security scheme for accessing wireless networks in the campus are presented:

2.1 Phase I: Diagnosis General analysis of current management of access to the wireless network in the campus; structured observation is applied to the logical and physical components of the wireless network; and interviews to network administrators are carried out:

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• Access and access control: authentication, records, network authorization about the offered services. Identify: status of services and Wi-Fi access, resources available at network infrastructure level, network access parameters, current limitations for access, services provided to users, existing and required user roles. • Logical and physical infrastructure of wireless networks and their components (antennas, servers, network devices), using the WLC wireless network controller. • Configuration of the AP controllers (WLC). • Review of institutional and national regulations and international standards (ISO 27001) to be met.

2.2 Phase II: Design of the Security Scheme • Application of the basic principles of information security named confidentiality, integrity, availability (CIA) and control objectives and controls specified in ISO 27001: Information Technology—Security techniques—Information security management systems—Requirements. • Construction of the NAC security scheme for network access control: Review of the tools to be used, choice of software, selection of the Linux distribution, selection of the network monitoring tool, definition of components, definition of requirements, definition of system services, description of the proposed scheme running, and description of the captive portal running.

2.3 Phase III: Test of Security Scheme Recording of the monitoring results and tests is carried out using the PacketFence software applications and the graphic environment of the WLC controller: signal strength tests, behavior of the RADIUS Debug scheme in Accounting and Authentication.

3 Results 3.1 Phase I: Diagnosis of Authentication, Records, Network Authorization of the Services Offered The summary of the information gathering is presented below. • Server and station equipment is available for operating system tests and updates. • The proposal to develop a security scheme is necessary and feasible. • There are opensource software tools and access control systems (NAC).

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• The physical infrastructure for the wireless network operation exists and is installed. • It is necessary to monitor academic networks, establish access roles, and allow the integration of access control based on the existing platform. • The proposal must adhere to institutional regulations on security and use of opensource software. • The gathering of information allowed to know the status of the services and their access via Wi-Fi, resources available at the infrastructure level of the wireless network, limitations for access, services and roles granted to users, configuration of the AP controllers (WLC), and the logical and physical infrastructure of wireless networks and their components.

3.2 Phase II: Design of the Study Proposal. Develop the Proposed Security Scheme for Wireless Network Access in the Campus 1. Application of the ISO 27001 standard. Information Technology—Security techniques—Information security management systems—Requirements. Six (6) control objectives (CO) of ISO 27001 (A.10.4, A.10.6, A.10.10, A.11.1, A.11.2, A.11.4) applicable to the proposed security scheme were identified, see Tables 2, 3, 4, 5, 6, and 7. 2. Selection of tools. CentOS 6.9 free software and open source, as a network monitoring tool, PacketFence was selected [14], coinciding with that reported by Nunoo-Mensah et al. [4] when comparing FreeNAC, NetPass, and PacketFence. 3. Design of the security scheme of access to wireless networks. The network is controlled and managed by the three main elements of the scheme, and its design is shown in Fig. 1: Table 2 Application of ISO 27001 (A.10.4) to the proposed security scheme A.10.4

Protection against malicious software and mobile code

Objective: To protect the integrity of software and information Item

Application in the proposed security scheme

A.10.4.1 The network access control (NAC) service, being the first Controls against malicious software barrier for users, is the point of attack for unauthorized access. It is virtualized with an automatic backup plan which allows to recover previous backups with ease. Likewise, both the virtualization platform and the NAC server feature alert notifications via email in case of service crashes. Finally, if there is a malicious behavior, both the NAC and the firewall detect the behavior since they also behave like IDS and can isolate the device

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Table 3 Application of ISO 27001 (A.10.6) to the proposed security scheme A.10.6

Network security management

Objective: To ensure the protection of information in networks and the protection of the support infrastructure Item

Application in the proposed security scheme

A.10.6.1 Network controls

Network is controlled and managed by three main elements of the scheme: • The firewall: checks network traffic of users, verifying that they communicate with segments to which they have access according to their role • The NAC server: monitors and controls the wireless access as well as verifies the integrity of the connected devices making sure these comply with the access policies • The WLC: controls wireless networks and users after access, as well as the APs that make up the network scheme

A.10.6.2 In the proposed scheme, the large-scale components that make up Security of network services the security devices are presented, as well as the different stages of the transition to obtain the university network service and the needed requirements

Firewall Observes network traffic of users, verifying that they only communicate with the segments to which they have access according to their role. NAC server Monitors and controls wireless access, verifying the integrity of devices that connect and the compliance with access policies. The WLC Controls the wireless networks and control of users after their access, as well as the APs that make up the network scheme. 4. Operation of the proposed security scheme for accessing the wireless network. Its operation is described below: (a) Users access the public SSID corresponding to the university. (b) When accessing the SSID, the data of the device (MAC) are sent to the WLC which then sends the access data to the RADIUS server via RADIUS. (c) The RADIUS server verifies the data provided by the WLC and proceeds to validate the user and the VLAN it belongs to. (d) The RADIUS server sends the data to the WLC and places the user in the VLAN. (e) In case of not being registered, the RADIUS server will return a registration VLAN. In case of being registered or having entered the data, the VLAN of the segment will be returned according to the role it has. If the device is marked as suspicious, the isolation VLAN will be returned. (f) After accessing the network, the user’s traffic passes directly through the firewall and is inspected by it, applying the access policies to pages and other network segments according to their IP, VLAN, MAC, among other parameters.

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Table 4 Application of ISO 27001 (A.10.10) to the proposed security scheme A.10.10

Monitoring

Objective: To detect unauthorized information processing activities Item

Application in the proposed security scheme

A.10.10.1 Audit recording

The main devices of the scheme: NAC, firewall, and WLC keep records of the user actions from the moment they connect to the network. Access to the correct segment is requested. These records are kept in the database of each of the devices, the tools and graphs for the behavior analysis are provided for following up the activities

A.10.10.2 Use of the monitoring system

Access to the user monitoring system is only permitted for the network segment of the telecommunications department with access credentials which are associated with levels of security and permitting. Daily monitoring is applied to observe important events

A.10.10.3 Protection of registry information

The same elements of the scheme feature intrusion detection and require verification to make changes. At the same time, the configurations and registries are regularly backed up

A.10.10.4 The accesses to the devices are registered by each Administrator and operator registration syslog A.10.10.5 Fault record

The devices record the events as failures in the different levels indicated by the syslog in their respective registry files. In turn, the type of occurrence is shown in their graphical interfaces

A.10.10.6 Clock synchronization

The devices feature centralized clocks since there is an NTP time server for them

Table 5 Application of ISO 27001 (A.11.1) to the proposed security scheme A.11

Access control

A.11.1

Commercial requirement for access control

Objective: To control access to information Item

Application in the proposed security scheme

A.11.1.1 Access control policy

With the implementation of the NAC there is a system that centralizes and manages access control policies, as well as their properly documented configuration and implementation

(g) The firewall, WLC, and NAC keep record of the user’s actions in the different phases of the process and they can manage the device, change its VLAN, check its traffic, observe which operating system version runs, possible vulnerabilities, and other features [15–17]. 5. Operation of the captive portal. Identification is made through a Web interface; the entrance to the network is achieved after providing credentials. In case of any

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Table 6 Application of ISO 27001 (A.11.2) to the proposed security scheme A.11

Access control

A.11.2

User access management

Objective: To ensure the access of the authorized user and prevent unauthorized access to information systems Item

Application in the proposed security scheme

A.11.2.1 User registration

To provide access to the system, the user must be registered through the provided NAC interface. A user of the tool can be generated to have access only to the registration and modification of users

Item

Application in the proposed security scheme

A.11.2.2 Privilege management

The privilege management for users is configured as a role-based access control. For administrators, view levels are managed

A.11.2.3 The password management is done in the NAC server and, to be Managing the user’s password able to modify the registry, a request must be sent to the person in charge of managing the user access A.11.2.4 Review of user access rights

Since the users are managed according to roles, the administration of the privileges is implemented with the edition of privileges of the roles

violation to the policies or features required for access, the device is redirected to a URL informing the problem and giving instructions for solving it. Registered and non-registered users are identified in the network; the application screen below informs the status of the device, the MAC address, device name, type of user or sponsor, IP address, type of operating system, as essential characteristics for the administration. Users are preloaded through database files (.csv) or created using a form provided where information such as user, password, name, surname, phone, mail, among other data is required. Credentials are requested for previously registered users. In case of being a guest user without registered account in the system, an email is requested to complete the registration after accepting the terms and conditions. 6. Operating functions of the captive portal: (a) It supports several isolation techniques, such as isolation to VLAN groups or segmented work network areas. (b) Anomalous network activities can be detected using actions configured for each violation, which can consist on computer virus problems, traffic denied by established policies, vulnerability explorations such as operating system patches, and using the vulnerability analyzer Nessus, installed in the PacketFence. (c) Different ways of authenticating can be created, like accessing with email, Facebook, or Twitter accounts, among other ways. (d) The user MAC and his current IP are collected inside the captive portal for the manager to use them in case the user has a failure with the service.

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Table 7 Application of ISO 27001 (A.11.4) to the proposed security scheme A.11

Access control

A.11.4

Access control to networks

Objective: To avoid unauthorized access to network services Item

Application in the proposed security scheme

A.11.4.1 Policy on the use of network services

The NAC provides segmentation of users according to a role and is associated with a VLAN which determines the accesses that the user will have, while the firewall is responsible for providing backup to this traffic

A.11.4.2 For this application case, the use of remote user User authentication for external connections access is not considered A.11.4.3 Identification of network equipment

Users’ teams are automatically registered to the system, and when they log in with their username and password, their credentials are associated with the device. Likewise, WLC can identify a user’s location in relation to the AP where the signal is received from

A.11.4.4 Protection of remote diagnostic port

The physical equipment of the devices of the scheme is protected in their corresponding data centers. Similarly, the logical access for diagnosis and administration is restricted only to the network segment corresponding to the telecommunications department

A.11.4.5 Segregation in networks

The user records are separated. They exist as records within different devices, and the database is configured so that it can be on a remote server

A.11.4.6 Network connection control

The NAC and the WLC can restrict the bandwidth that users use based on QoS, as well as based on the role and the SSID from which the users connect

A.11.4.7 Control of network routing

For the application of the system, the routing segment is not considered, beside the control that the firewall has over the users

(e) The user does not have to register his/her device since it is automatically registered when accessing with his/her user.

3.3 Phase III: Test of the Security Scheme Recording of the monitoring and tests results is carried out through the PacketFence software applications and the graphic environment of the WLC controller.

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Fig. 1 Design of the proposed security scheme

3.3.1

Proof of Signal Strength at the Campus

Measurements of signal reception level were made at two points: a point close to one of the APs, and another one at a point far from the same AP, obtaining an estimated reception range of −33 to −74 dBm, indicating that there is adequate reception power level in the area, which allows a higher data transmission rate.

3.3.2

Probes for Monitoring the Scheme Behavior

Use of graphs and statistical data provided by the PacketFence ‘carbon’ service: • • • • • • •

User connections to the captive portal. Monitoring of connections accepted by the RADIUS server. Monitoring of records accepted by the RADIUS server. Monitoring of server CPU load. Monitoring of the state of use of the SSID propagated in 2.4 GHz. List of violations and actions to take. Report of bandwidth usage according to device to determine devices that are overusing the connection to the network and that could indicate malicious users or unwanted applications.

3.3.3

Debug Tests to Check RADIUS Operation in Accounting and Authentication

Communication of the RADIUS server with the WLC was observed authorizing the initial access of the user to the network. Subsequently, the RADIUS server was observed when communicating with WLC and providing the user access VLAN, when appropriate.

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4 Conclusions The proposed network is controlled and managed by the three main elements of the scheme: the firewall, the NAC server, and the WLC. The proposed scheme allows the implementation of a NAC-based model with role-based security policies to allow its management and be able to create and maintain a user-friendly interface and carry out administration activities. This scheme provides availability of elements for administrable communications, with switching capacity, existence of demilitarized zones, improve the administration and operation of devices that enter the wireless network of the university, constant monitoring of activities, basic configurations for atomic and adaptive administration. It meets the national laws and regulations of the institution regarding the use of opensource software, as well as the computer security and telecommunications regulations. It also meets six objectives of the control of information security principles about confidentiality, integrity, and availability outlined in ISO 27001. The NAC server is the core part of the scheme and allows other elements to be implemented to the scheme or extend the functionalities of the current ones. The final devices initially communicate with the WLC and later with the NAC, and finally, the firewall takes control of the final devices. The NAC is internally compound by a RADIUS server, a database, a Web server, a device manager, as well as other optional elements that complement and extend the functionalities of the service.

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Pragmatic Evaluation of the Impact of Dimensionality Reduction in the Performance of Clustering Algorithms Shini Renjith , A. Sreekumar, and M. Jathavedan

Abstract With the huge volume of data available as input, modern-day statistical analysis leverages clustering techniques to limit the volume of data to be processed. These input data mainly sourced from social media channels and typically have high dimensions due to the diverse features it represents. This is normally referred to as the curse of dimensionality as it makes the clustering process highly computational intensive and less efficient. Dimensionality reduction techniques are proposed as a solution to address this issue. This paper covers an empirical analysis done on the impact of applying dimensionality reduction during the data transformation phase of the clustering process. We measured the impacts in terms of clustering quality and clustering performance for three most common clustering algorithms kmeans clustering, clustering large applications (CLARA), and agglomerative hierarchical clustering (AGNES). The clustering quality is compared by using four internal evaluation criteria, namely Silhouette index, Dunn index, Calinski-Harabasz index, and Davies-Bouldin index, and average execution time is verified as a measure of clustering performance. Keywords Curse of dimensionality · Dimensionality reduction · Clustering algorithms · Clustering quality · Clustering performance · Social media

S. Renjith (B) · A. Sreekumar · M. Jathavedan Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala 682022, India e-mail: [email protected] A. Sreekumar e-mail: [email protected] M. Jathavedan e-mail: [email protected] S. Renjith Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, Kerala 695015, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_45

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1 Introduction Data mining [1] is the most important step in the knowledge discovery in databases (KDD) process [2, 3], and it uses the preprocessed and transformed data as inputs. It involves the extraction of underlying patterns and other meaningful information from raw data. It includes different approaches like classification, clustering, association, regression, etc., and is heavily leveraged in various scientific, educational, and/or industrial scenarios. Clustering [4] is an unsupervised learning technique extensively used for performing statistical analysis of data. It creates distinct logical partitions within the given data sets with similar elements grouped together. Clustering process attempts to ensure that the elements from the same cluster will have similar properties, whereas elements from different groups have dissimilar properties. Social media data has become the prominent source for data analytics in these days though the volume and dimensions of it is quite huge in comparison with traditional data sources. The challenges with huge volume can be addressed by adopting an appropriate clustering algorithm, whereas application of dimensionality reduction techniques can be considered as a solution to the curse of dimensionality. The aim of this work to assess the quality and performance (in terms of turnaround time) impacts of applying dimensionality reduction techniques like principal component analysis (PCA) on the data set as a transformation process prior to the actual clustering operation. We have restricted our focus to three core clustering algorithms in terms of this experiments – k-means, clustering large applications (CLARA), agglomerative hierarchical clustering (AGNES) algorithms. Section 2 of this paper does the recap of the clustering algorithms considered in this experimental analysis and dimensionality reduction using principal component analysis. Section 3 summarizes the related literature, and Sect. 4 briefs on the research methodology adopted, infrastructure and tools leveraged, and details of data set used for evaluation. Section 5 records the statistical observations from the experiments. Section 6 concludes the paper with details of our inferences and future steps.

2 Antecedents Clustering [4] is the segregation process applied on data sets to form distinct groups called clusters which contains similar elements in it. Based on the clustering approach chosen, clustering algorithms can be classified into partitioning, hierarchical, densitybased, or model-based clustering.

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2.1 k-Means Clustering k-means clustering algorithm [5–8] performs an iterative process to arrange elements in a data set into k distinct clusters. The iterations are repeated until it results in minimum value for the total within cluster variation across the clusters being populated. The total within cluster variation is calculated as the cumulative value of the squared error for every element from the data set. Mathematically it can be represented as (1). TWCV =

K  

(E i − µk )2

(1)

k=1 E i ∈Ck

where K is the total number of clusters formed and E i is an element in cluster, Ck having centroid, µk .

2.2 Clustering Large Applications Clustering large applications (CLARA) algorithm [9, 10] is an extension of partitioning around medoids (PAM) [9, 11], created specifically to deal with large data sets. The algorithm draws samples from the given data set and applies PAM on each sample to find the optimal set of medoids. An objective function is then applied on the sets of medoids to test their goodness to act as the medoids for the complete data set. The typical objective function for CLARA algorithm is the average dissimilarity for the elements of the data set to the medoid of the cluster it got assigned into. These sampling and clustering steps are repeated for a specified number of iterations. The final clusters are populated corresponding to the set of medoids which results in the minimum value for the objective function. Mathematically the objective function can be represented as (2). Obj. Fn(M, D) =

N  d(E i , medoid(M, E i )) i=1

N

(2)

where M is the set of medoids, D is the data set with N elements in it, E i is an element in D, medoid(M, E i ) is the medoid selected from M which is the nearest one to E i , and d(x, y) is the dissimilarity between elements x and y.

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2.3 Agglomerative Hierarchical Clustering Agglomerative hierarchical clustering (AGNES) algorithm [12–14] also known as agglomerative nesting is a bottom-up clustering approach. To start with, the algorithm considers every element as individual clusters. In each subsequent iteration, the algorithm merges the nearest clusters till it results in a single cluster containing all elements in the data set. The decisions taken by the algorithm in each of the steps are irreversible, and it can be considered as a challenge since the algorithm lacks global distribution details of the data set at initial stages.

2.4 Dimensionality Reduction Dimensionality reduction is the statistical process [15] used for reducing the number of attributes to be processed in machine learning scenarios. This is achieved by identifying a set of principal features either through feature selection or feature projection approach. In feature selection, a subset of the original set of features is selected using some optimization mechanism. In feature projection, the highdimensional vector space representing all the features is transformed into a lowerdimension vector space. There are multiple dimensionality reduction techniques available like principal component analysis (PCA) [16] and autoencoder [17].

3 Related Works A good volume of literature is found on the theoretical aspects of various clustering algorithms. One of the early publications analyzing multiple clustering algorithms is from Xu and Wunsch [18], though they did not cover the big data context. Some of the literature covering detailed analysis of big data clustering algorithms include works from Shirkhorshidi et al. [19], Sajana et al. [20], Ajin and Kumar [21], and Dave and Gianey [22]. Apart from these theoretical works, Lau and King [23] conducted an empirical analysis on two unsupervised neural network clustering algorithms to evaluate their performance for information retrieval from image databases. Later, Maulik and Bandyopadhyay [24] leveraged internal evaluation indices of DaviesBouldin, Dunn, Calinski-Harabasz, and Index I to compare the performances of k-means clustering algorithm, single linkage clustering scheme, and a simulated annealing-based technique. Wei et al. [25] experimentally evaluated the performances of CLARA, CLARANS, GAC-R3, and GAC-RARw. Zhang [26] conducted an empirical analysis to compare the performance of his proposed algorithm called k-harmonic means (KHM) with k-means and expectation-maximization clustering algorithms. Wang and Hamilton [27] evaluated two density-based clustering algorithms – DBSCAN and DBRS. Singh and

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Dutta [28] compared the performance of PAM, CLARA, CLARANS, and Fuzzy c-means clustering algorithms with respect to their outlier detection efficiency. Fahad et al. [29] evaluated the clustering quality of DENCLUE, OptiGrid, Fuzzy c-means, expectation-maximization, and BIRCH against ten different data sets. Jung et al. [30] applied logistic regression analysis to compare clustering performance of expectation-maximization and the k-means clustering algorithms. Bhatnagar et al. [31] performed a comparative evaluation of the performance of k-means clustering, hierarchical clustering, Fuzzy c-means clustering, Gaussian mixture modeling, and self-organized map clustering for the purpose of grouping manufacturing firms. Another domain specific analysis is from Renjith et al. [32], who did an empirical study on clustering quality of various algorithms against tourism data from social media channels. There is a big volume of literature available on various dimensionality reduction techniques and their applications like [16, 17, 33–40]. However, there is not much literature available on how dimensionality reduction can impact the performance of clustering process in terms of cluster quality and turnaround time.

4 Methodology We have adopted a four stage approach in this research as depicted in Fig. 1.

4.1 Determination of Optimal Cluster Count Partitioning clustering approaches (here k-means and CLARA) require the user to specify the number of clusters as an input to clustering process. The optimal number of clusters, k is specific to each data set and subjective to the similarity measures

Fig. 1 Four stage approach adopted for empirical study

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being used. In this experiment, we have leveraged the R package NbClust [41] and computed 26 different indices supported by it. The package internally uses voting method to determine the optimal cluster count.

4.2 Clustering We evaluated three most common clustering algorithms, namely k-means [5–8], CLARA [9, 10], and AGNES [11–14] by leveraging the frequently used implementations available in R packages. Dimensionality reduction of the data set is achieved by using PCA [15, 16] implementation from stats package in R.

4.3 Internal Evaluation of Clustering Quality We have verified the clustering results (with and without dimensionality reduction performed in the data transformation step) using four different internal evaluation criteria, namely Silhouette index [42], Dunn index [43], Calinski-Harabasz index [44], and Davies-Bouldin index [45]. We leveraged the R package clusterCrit for measuring these indices.

4.4 Performance Evaluation For each clustering algorithm, the average execution time is also captured with and without applying dimensionality reduction in data transformation stage. In both the scenario, multiple iterations are conducted to reveal any impact on turnaround time against changes in cardinality of the data set in consideration.

4.5 Tools Used We used R programming language [46], the free open-source platform for statistical computing and data representation and RStudio [47, 48], the integrated development environment for R for conducting the experiments. The specific packages used for this research are detailed in Table 1. The hardware used to conduct the experiments is Intel Core i5-5200U, 2.20 GHz dual core x64-based processor with 8.00 GB RAM.

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Table 1 R packages used for analysis R package used

Purpose

NbClust

To find optimal count of clusters [41]

factoextra

To extract and visualize results of multivariate data analyses

microbenchmark

To measure execution time at nanosecond level accuracy

stats

Implementation of k-means algorithm

cluster

Implementation of CLARA, and AGNES algorithms

clusterCrit

Internal evaluation of clusters

Table 2 Jester data set 1 – a quick summary Data set description

Anonymous ratings from the Jester online joke recommender system – data set 1

Source

http://eigentaste.berkeley.edu/dataset/

Reference

Goldberg et al. [49]

Cardinality

73,421

Dimension

100

No. of ratings

Around 4.1 million

Range

−10 to 10

4.6 Data Set Used We leveraged the Jester data set 1 available online [49] which contains around 4.1 million anonymous ratings in the range of −10.00 to +10.00 corresponding to 100 jokes by 73,421 users which are collected during April 1999 to May 2003. A quick summary of the data set is made available in Table 2.

5 Empirical Research 5.1 Determination of Optimal Cluster Count The optimal count of clusters is derived with the help of the R package NbClust which estimate 26 different indices and select the optimal value based on voting method. The optimal count of clusters is identified as 3 in this case. The detailed result from NbClust is depicted in Fig. 2.

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Fig. 2 Determination of optimal count of clusters

Table 3 Clustering algorithms covered in the analysis Clustering algorithm

Clustering type

R function used for clustering

Applicable R package

K-means clustering

Partitioning

kmeans()

stats

Clustering large applications

Partitioning

clara()

cluster

Agglomerative hierarchical clustering

Hierarchical

agnes()

cluster

5.2 Clustering We have covered three clustering algorithms as part of this analysis – k-means, CLARA, and AGNES. Table 3 summarizes the details of the algorithms and the corresponding R functions and packages used for this evaluation, and Fig. 3 represents the two dimensional plots of the resulted clusters.

5.3 Internal Evaluation of Clustering Quality The goodness of the clustering process can be measured either through internal evaluation (where evaluation is conducted based on the data used for clustering) or external evaluation (where the clustering results are compared against an item that is not used as an input for clustering like a class label assigned by some subject matter experts). While dealing with clustering scenarios like the one on social media, the only option available is conducting the internal evaluation based on one more quality indices. In this experiment, we leveraged Silhouette index [42], Dunn index [43], Calinski-Harabasz index [44], and Davies-Bouldin index [45] as internal evaluation indices.

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Fig. 3 Two dimensional view of clusters formed when cardinality of the sample is 5000

Figure 4 depicts the impact of dimensionality reduction for each of the clustering algorithms against these indices. We can see the impact of dimensionality reduction in the absolute values of these indices, though the relative trend is maintained as such. For Silhouette index, a relatively high value indicates better clustering quality. In this case, we observed a consistent clustering quality pattern of CLARA > k-means > AGNES with and without applying dimensionality reduction as data transformation

Fig. 4 Impact of dimensionality reduction in internal evaluation indices

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step for clustering. In the case of Dunn index also a relative high value is the goodness indicator. Without applying dimensionality reduction, we observed the goodness pattern of AGNES > k-means > CLARA but it varied to k-means > AGNES > CLARA for a marginal difference of less than 0.0006. With Calinski-Harabasz index, where higher value indicates relatively better quality, we could observe a consistent clustering quality pattern of k-means > CLARA > AGNES irrespective of applying dimensionality reduction. A low value is the goodness indicator for Davies-Bouldin index and we got consistent observation of CLARA < k-means < AGNES in both scenarios, i.e., with and without applying dimensionality reduction applied. Precisely, the application of dimensionality reduction is not impacting the clustering quality.

5.4 Performance Evaluation Curse of dimensionality is a major factor which impacts the performance of clustering activities on social media data. It calls for high demand of computational power and thereby impacts the total turnaround time. Application of dimensionality reduction techniques on such data is a potential solution to this problem. However, the benefits of it will vary depending on the clustering algorithms in use. Figure 5 depicts the experimental results gathered in terms of turnaround time for k-means, CLARA, and AGNES clustering algorithms for varying cardinality with and without application of dimensionality reduction. Figure 6 is an alternate representation with time axis represented in logarithmic scale for performing trend analysis. Both k-means and CLARA algorithms show linearly increasing trend with increase in cardinality and demonstrate a time complexity of O(n). Also from the

Fig. 5 Turnaround time of clustering algorithms versus cardinality

Fig. 6 Turnaround time of clustering algorithms versus cardinality with time represented in logarithmic scale

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plots, it is obvious that applying dimensionality reduction can result in considerable saving of processing time for both these algorithms. On the other hand, AGNES algorithm shows a power trend with the time complexity of O(nˆ2). Further, we could observe that AGNES is neutral to dimensionality reduction in terms of turnaround time.

6 Conclusion All industries leverage data analytics as an important tool to enhance their business in different ways and social media data has evolved as the critical input for the same. Social media data always come with two inherent problems – huge volume and high dimensionality, thereby requiring huge amount of computing power and time to deal with. Clustering provides mechanisms to determine and process only the relevant data in such scenarios. To deal with the curse of dimensionality problem, dimensionality reduction techniques can be leveraged. As part of this experimental analysis, we checked the impact of dimensionality reduction on the clustering performance in terms of clustering quality and turnaround time. In this experiments, we used the Jester data set and evaluated three common clustering algorithms – k-means, CLARA, and AGNES. Turnaround time for clustering algorithms is observed to be linearly related to the cardinality of the input data set for partitioning algorithms, whereas it showed a power relationship for hierarchical clustering. It is observed that the clustering quality remained intact even after applying dimensionality reduction. There is a significant improvement on the overall time taken for partitioning clustering algorithms with dimensionality reduction and the improvement is linear in nature. However, there is no significant impact on performance in the case of hierarchical clustering post dimensionality reduction. As the next step, we are planning to evaluate whether deep learning techniques like variants of autoencoder can be of in improving clustering quality. We are also planning to check on any possible tweaks on current algorithms that can be a performance booster to deal with large volume of high dimensional data.

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Secret Life of Conjunctions: Correlation of Conjunction Words on Predicting Personality Traits from Social Media Using User-Generated Contents Ahmed Al Marouf, Md. Kamrul Hasan, and Hasan Mahmud

Abstract Large amount of textual, visual, and audio data are generating in social networking sites by the users nowadays. Social media users are generating these data in high increasing rate than any other time. Status updates/tweets, likes, comments, and shares/re-tweets are the basic features provided by the online social networking (OSN) sites. This paper utilizes the status updates of users to analyze and extract relevant natural language features to map them into predicting personality traits of those users. It is evident that using more features in a supervised learning system can predict more accurately. However, the linguistic features such as function words, character-level, word-level, structure-level features could be considered as relevant features for this case. While predicting the big five personality traits: opennessto-experience, conscientiousness, extraversion, agreeableness and neuroticism, the highly correlated features are determined applying feature selection algorithms. For experimentation, the research question is “What are the highly correlated features which are commonly found for all five personality traits?” In this paper, we have presented the experimental findings while determining the highly correlated features with the class and found that the percentage of “conjunction words” is always a common feature for each of the personality traits. The underlying (secret) relationship of this feature is analyzed in this paper. Keywords Conjunction words · The big five personality traits · Social media · User-generated content · Natural language processing · Linguistic features A. A. Marouf (B) Department of Computer Science and Engineering, Daffodil International University (DIU), Dhaka, Bangladesh e-mail: [email protected] A. A. Marouf · Md. K. Hasan · H. Mahmud Systems and Software Lab (SSL), Department of Computer Science and Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh e-mail: [email protected] H. Mahmud e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_46

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1 Introduction The quantitative rate of generating textual, image, and video data in online social networking (OSN) sites is rapidly increasing. The facilities provided by the OSNs are encouraging its users to get accessed and connect with the peoples from different corners of the world. The concept of getting connected to the same kind of persons has evolved nowadays. Please don’t want to meet and greet with fake accounts of OSN anymore. The idea of same personality comes in the social media as having the same personality traits means that these persons can become friends. Different recommendation systems such as community recommendation [1], friend recommendation [2], and community detection [3] could be approached using the online behavior or personality traits. In the context of human computer interaction, social media are playing significant roles as people are interacting through social media every day. This paper utilizes the use of social media via the social entities and to maximize the outcome of using textual features for building a supervised learning model. The supervised model may develop using the linguistic features present in the status updates of the Facebook users. The big five factor model [4, 5] is one of the widely used personality traits hypothesis used by mode computational psychologist. The five traits that are centered for identifying ones personality are openness-to-experience (OE), conscientiousness (CON), extraversion (EXT), agreeableness (AGR), and neuroticism (NR) [6]. The first four traits are considered as positive traits, and the only negative personality trait is neuroticism. There are significant amount of works been performed to predict the personality traits using the Facebook status updates. The models are basically in supervised learning fashion having different feature sets. The feature engineering is proven to become one of the prior challenging tasks to get better accuracy of the model. In this paper, we have devised a supervised learning model to predict the personality traits from Facebook status updates by extracting the natural language processing (NLP) features. In the context of NLP, the character-level, word-level, and structure-level features are used by different authors for different application problems such as spam detection from email and authorship attribution. For our work, we have considered only the word-level features and try to experiment over the features to find the best feature. This paper focuses on finding the research question devised as “What are the highly correlated features which are commonly found for all five personality traits?” For seeking the answer of this particular question, we have paraphrased this question and sequentially generated the following questions: “Can we predict the personality traits separately using word-level features from Facebook statuses?” “What is the effect of correlation between word-level features and personality traits?” Therefore, these two questions will lead us toward the answer of the primary question mentioned. After finding the effect of highly correlated features from the

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second question, we may find the common feature that overlaps in all the personality traits. The contribution of this paper solely depends on the research questions. For this paper, the experiments are designed to deliver the answer of the above questions. For the rest of the paper, Sect. 2 discusses on the related works performed and Sect. 3 describes the experimental method we used. Experimental results are documented in Sect. 4, and correlation of conjunction words with the personality traits is depicted in Sect. 5. Finally, Sect. 6 concludes with the answer of the research questions addressed in Sect. 1.

2 Related Works In this section, the related works presented in the literature are discussed. There are several methods proposed by researchers in [7–9] utilizing different parameter-based method to predict personality. For recommending products, promotional features, etc., to a specific user in social networking sites, it is important to understand his/her preferences. Therefore, predicting social media user’s personality could be a good approach to reach his/her interest. Without any doubt, it is possible to track users’ digital footprints from social network data generated by user himself [10]. As social networking sites are the place where people use to share their status, news, views with the help of structured or unstructured languages, textual data could become an effective resource to find personality traits [11]. Farnadi et al. [12] presented an automatic personality trait recognition model based on social network (Facebook) using users’ status. They used machine learning algorithms such as support vector machine (SVM), Bayesian logistic regression, and multinomial Naïve Bayes (MNB). Schwartz et al. [7] presented a personality prediction system via gender and age from social media based on differential language analysis (DLA) and the method is an open vocabulary-based approach. Using psycholinguistic tools, Poria et al. [8] presented a common sense knowledge-based system. The proposed system used SenticNet, ConceptNet, EmoSenticNet, and EmoSenticSpace text resources to extract the common sense knowledge features. Finding the neuroticism traits from Twitter data is done in [9] which interprets that the popular bad influential users of Twitter have achieved low value of neuroticism. The proposed system uses LIWC [13], SPLICE [14], and MRC [15] such psycholinguistic databases. Text-based features such as term frequency (TF)—inverse document frequency (IDF) parameters and topic modeling algorithm linear Dirichlet allocation (LDA) are used in [16]. They used several machine learning techniques such as SVR (polynomial, linear, and RBF kernel) and decision tree to evaluate the system. A deep learning-based approach is adopted in [17] to predict personality using the myPersonality Facebook status updates. They applied multilayer perceptron (MLP), long

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short-time memory (LSTM), gated recurrent unit (GRU), and 1-dimensional convolutional neural network (1-D CNN). They created 20 different scenarios to find the optimal accuracy for each of the personality traits. For identifying neuroticism using the myPersonality dataset by extracting psycholinguistic cues utilizing LIWC is been presented in [18]. The proposed experimental method shows considerably high performance for predicting the negative personality traits. The impact of correlation between the features and class was also analyzed in [18]. From the literature, we can depict researchers have worked with different types of feature such as text mining features, NLP features, topic modeling algorithms, psycholinguistic cues to identify the personality traits. But, the limitation of finding the common features among the personality traits is not found. Therefore, in this paper, we have addressed the research question to find the highly correlated feature which could be used for finding each of the five personality traits. We have worked with only the word-level features to run the system for each of the traits.

3 Experimental Method In this section, we have described the experimental method that we have used for our work. The method consists of several steps such as formalizing dataset of Facebook status updates, data preprocessing, word-level feature extraction, feature selection, and classification models, shown in Fig. 1. Fig. 1 Experimental method

Data AcquisiƟon Data Pre-processing Feature ExtracƟon Feature SelecƟon ClassificaƟon

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517

3.1 Dataset of Facebook Statuses myPersonality dataset [18, 19] is considered as one of the gold datasets of identifying personality traits from the labeled big five score and classes. The dataset contains 250 users around 10,000 Facebook status updates which are basically in text format. The participants who contributed to the dataset have performed the 100-item IPIP (International Personality Item Pool) questionnaire to measure their big five personality traits. The dataset consists of the labeled personality score and class values. For our works, we have used the class values to identify if the individuals have that trait or not. The minimum, maximum, and average personality scores are 1.25, 5, and 3.437103, respectively.

3.2 Data Preprocessing Data preprocessing is an essential step to formalize the dataset contents and decide which part of data to be used for the system. For our work, we have performed removal of URLs, unnecessary symbols, spaces, stemming on the English textual data. The Natural Language Toolkit (NLTK) library package is used for the preprocessing.

3.3 Feature Extraction After preprocessing, we have applied feature extraction to extract the relevant wordbased features from the textual data. We have taken these sort of features to extract the effect of word types such as length of words and function words. The parts-ofspeech (POS) tags are considered as function words. The length-based features are basically devised to recognize how many characters are used for individual words such as number of words with only one character or two characters. On the other hand, the percentage of POS words is considered as function word-based features. The features that we have considered to build the system are listed in Table 1. The features are only word-level features which can be divided into two parts: lengthbased features and function word-based features. We have experimented using all the extracted features and also combination of features such as only length-based features and only function word-based features. For feature extraction, we have used the NLTK and scikit-learn python packages. The packages provide the easy methods to find the relevant features and many classification models.

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Table 1 Word-level features Feature number

Feature detail

Feature number

Feature detail

Length-based features

Function word-based features

F1

No. of words

F16

No. of function words

F2

No. of words with 1 character

F17

Percentage of noun

F3

No. of words with 2 character

F18

Percentage of pronoun

F4

No. of words with 3 character

F19

Percentage of verb

F5

No. of words with 4 character

F20

Percentage of adjective

F6

No. of words with 5 character

F21

Percentage of adverb

F7

No. of words with 6 character

F22

Percentage of preposition

F8

No. of words with 7 character

F23

Percentage of conjunction

F9

No. of words with 8 character

F24

Percentage of interjection

F10

No. of words with 9 character

F11

No. of words with 10 character

F12

No. of words with 11 character

F13

No. of words with 12 character

F14

No. of words more than 12 character

F15

Avg. word length

3.4 Feature Selection In the previous step, the feature vectors are formalized as the following equation, where the F denotes the feature vector which consists in total 24 features. F = {F1, F2, F3 . . . F24} The feature selection step comes with the algorithms to find the prominent features from the feature vector and utilize the classifiers to get better accuracy. For our work, we have applied two feature selection algorithms: information gain (IG) and Pearson correlation coefficient-based selection.

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Information gain is one of the widely used feature selection methods in different research problems. Information gain outcomes a ratio value calculated by (1), where values(a) denotes the set of all possible values of features a ∈ Attr. Attr is the set of all features, H be the entropy, and x ∈ T denotes the value of specific example x for a ∈ Attr. The largest information gain be the smallest entropy. IG(T, a) = H (T ) −

 |{x ∈ T |xa = v}| · H (x ∈ T |xa = v) |T |

(1)

v∈vals(a)

One of the most efficient and widely used correlation finders is Pearson correlation coefficient (ρ). Depending on the value of covariance between the class and feature and the standard deviations of the class and feature, the coefficient value (ρ) is determined. The coefficient could be used as an efficient parameter to determine the feature sets. The calculation of (ρ) is performed using (2) as given below. cov(X, Y ) is the covariance between X, Y where X or Y be the class value and σ is the standard deviation in (2). ρ X,Y =

cov(X, Y ) σ X σY

(2)

3.5 Classification Model In this paper, we have depicted the outcome of five different classification models for predicting the personality traits. The traditional classification algorithms are utilized directly. The classifiers are Naïve Bayes (NB) [19], random forest (RF) [20], simple logistic regression (SLR) [21], decision tree (DT) [22], and support vector machine (SVM) [23]. As these algorithms are well settled and used by many other researchers in different context of research areas, in this paper we have used these classifiers. The detail of the classification algorithms could be found in the referred sources.

4 Experimental Results In this section, we have presented the experimental findings from the research question-based approaches. The experiments are done keeping in mind about each of the research question. We have implemented the experimental method using the arff file format in Waikato Environment for Knowledge Analysis (Weka) [24], developed by University of Waikato, which is a collection of machine learning algorithms including many classification algorithms. The performance metrics used for evaluation are precision, recall, F1-score, and accuracy. The metrics could be calculated using the following Eqs. (3), (4), (5), and (6), where TP is True Positive, FP is False

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Positive, TN is True Negative, and FN is False Negative. Precision = TP/(TP + FP)

(3)

Recall = TP/(TP + FN)

(4)

F1-score = (2 ∗ Precision ∗ Recall)/(Precision + Recall)

(5)

Accuracy = (TP + TN)/(TP + TN + FP + FN)

(6)

4.1 Experiment 1 “Can we predict the personality traits separately using word-level features from Facebook statuses?” To find the answer of the question, we have applied five different classifiers on the feature vectors and the performance evaluation matrices are shown in Table 2. The best accuracy (62.0023%) is found using the SVM classifier which is kept bold in the Table 2. In Table 3, we have shown the accuracy of five personality traits accuracy for different classifiers. We can see, except for the agreeableness trait, all other features Table 2 Performance matrices of applied classifiers for extraversion trait Classifier

Precision

Recall

F1-score

Accuracy (%)

NB

0.523

0.557

0.527

55.7377

RF

0.512

0.566

0.513

56.5574

SLR

0.571

0.611

0.502

61.0656

DT

0.568

0.611

0.470

61.0588

SVM

0.611

0.614

0.758

62.0023

Table 3 Accuracy of personality traits on applied classifiers Classifier

EXT (%)

CON (%)

AGR (%)

OE (%)

NR (%)

NB

55.7377

47.5410

52.0492

61.4754

55.7377

RF

56.5574

40.5738

48.3607

66.8033

59.8361

SLR

61.0656

51.6393

52.0492

68.4426

58.6066

DT

61.0588

47.9508

50.0000

66.8033

56.5579

SVM

62.0023

52.8689

50.8197

69.6721

61.6656

Secret Life of Conjunctions: Correlation of Conjunction Words …

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are giving higher accuracy when the classifier is SVM. The best accuracies found in different personality traits from different classifiers are kept bold in Table 3.

4.2 Experiment 2 “What is the effect of correlation between word-level features and personality traits?” For finding the answer to this question, we have applied the information gain and Pearson correlation coefficient (PCC)-based feature selection method. Both the methods were applied using WEKA, and ranker is used as search method. After the feature selection algorithms applied on the extracted features, the ranking of the features are provided as outcome. The information gain-based feature ranking for extraversion trait is listed as {F24, F23, F7, F8, F9, F6, F5, F4, F1, F2, F3, F10, F11, F12, F21, F22, F19, F20, F18, F13, F14, F15, F17, F16}. Therefore, the first five high-ranked features are percentage of interjection words, percentage of conjunction words, No. of words with 6 character, No. of words with 7 character, and No. of words with 8 character. Applying ranker as search method and PCC-based feature selection algorithm for the extraversion trait is listed in Table 4. The ranking is in order (top to bottom), and the associated coefficient value is plotted in Table 4. It is notable that the feature F23 is one of the high-ranked features among the 24 features for both the IG and PCC-based feature selection. This F23 feature is none other than the percentage of conjunction words. The similar experimental findings are noted for the other four personality traits. Therefore, we can say, the impact of Table 4 Ranking of features and associated correlation coefficient value for extraversion trait Serial number

Coefficient value

Feature number

Serial number

Coefficient value

Feature number

1.

0.1042

F23

13.

0.0763

F19

2.

0.0947

F6

14.

0.0757

F2

3.

0.0939

F12

15.

0.0756

F22

4.

0.0909

F11

16.

0.0745

F16

5.

0.0891

F14

17.

0.0737

F4

6.

0.0858

F9

18.

0.0703

F5

7.

0.0824

F7

19.

0.0605

F24

8.

0.0823

F1

20.

0.0598

F15

9.

0.0817

F10

21.

0.0595

F20

10.

0.0792

F8

22.

0.0501

F17

11.

0.0791

F13

23.

0.0309

F21

12.

0.0787

F3

24.

0.0125

F18

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correlation between the word-level features and personality traits are in middle range, but interestingly the same feature of F23 is found to become high-ranked feature.

5 Correlation of Conjunction Words with Personality Traits In this section, we have tried to declare the summary of experimental findings regarding the highly correlated features. Considering the PCC-based feature selection algorithm, the ranking of features is determined for each of the traits. Then, we have taken the first five highly correlated features for each of the traits and denote them as set representation. Corr_E, Corr_C, Corr_A, Corr_O, and Corr_N are the first five highly correlated features set of EXT, CON, ARG, OE, and NR traits, respectively. Corr_E = {F23, F6, F12, F11, F14} Corr_C = {F19, F23, F14, F20, F21} Corr_A = {F23, F6, F11, F12, F13} Corr_O = {F13, F22, F23, F24, F14} Corr_N = {F20, F22, F23, F15, F17} Corr_E ∩ Corr_C ∩ Corr_A ∩ Corr_O ∩ Corr_N = {F23} From the correlated feature sets, it is determined that the common feature which is highly ranked and correlation with all five personality traits is “F23” which is percentage of conjunction words. Therefore, there is a special relationship between the conjunction words and the personality traits. A list of widely used conjunction words are given in Table 5. These words are also used by the Facebook users while expressing their critical thoughts via status updates. As we found this high correlation between percentage of conjunction words and personality traits, this relationship is not trivial to find out. The reason behind the correlation could be tested in different application area. A similar work [25] has been performed on the flexibility in writing style and physical health. The writing sample of 124 students and 59 maximum security prisoners were taken into account, and latent semantic analysis (LSA) has been applied. They found that personal pronouns are mostly used while writing traumatic memories which were related to positive

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523

Table 5 Widely used conjunction words list Coordinating conjunctions

Subordinating conjunctions Concession

Condition

Comparison

Reason

For

Though

If

Than

Because

And

Although

Only if

Rather than

Since

Nor

Even though

Unless

Whether

So that

But

While

Until

As much as

In order

Or

Provided that

Whereas

Why

Yet

Even if

So

In case

health outcomes. The secret relation of conjunctions could be declared with the big five personality traits.

6 Conclusion In this paper, we have tried to find out the common feature which is highly correlated with all the five big five personality traits. It is found in our experiments that “Percentage of Conjunction Words” is the highly correlated feature among all the word-level features. In conclusion, we try to declare the answer of the asked research questions of Sect. 1. “Can we predict the personality traits separately using word-level features from Facebook statuses?” Yes, we have applied word-level 24 features for each of the personality traits and found satisfactory accuracy (69.6721% for OE) for predicting traits. “What is the effect of correlation between word-level features and personality traits?” The effect of correlation is impactful as we may find the prominent features to be used for predicting personality traits in social media textual data. “What are the highly correlated features which are commonly found for all five personality traits?” Though some of the features are overlapped in two or more personality traits, the commonly found highly correlated feature for all five traits is “Percentage of Conjunction words.”

References 1. Marouf AA, Ajwad R, Kyser MTR (2015) Community recommendation approach for social networking sites based on mining rules. In: 2nd international conference on electrical

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engineering and information & communication technology (iCEEiCT 2015) 2. Hasan MM, Shaon NH, Marouf AA, Hasan MK, Mahmud H, Khan MM (2015) Friend recommendation framework for social networking sites using user’s online behavior. In: International conference on computer and information technology (ICCIT), pp 539–543 3. Du N, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In: Proceeding of 9th web KDD 1st SNA-KDD workshop web mining social network analysis, pp 16–25 4. Digman JM (1990) Personality structure: emergence of the five-factor model. J Annu Rev Psychol 41:417–440 5. Big Five Personality Theory. https://www.123test.com/big-five-personality-theory. Accessed 10 Feb 2018 6. Goldberg LR (1994) The structure of phenotypic personality traits. J Am Psychol 48:26–34 7. Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, Agrawal M et al (2013) Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9):e73791 8. Poria S, Gelbukh A, Agarwal B, Cambria E, Howard N (2013) Common sense knowledge based personality recognition from text. In: 12th Mexican international conference on artificial intelligence, vol 8266, pp 484–496 9. Quercia D, Kosinski M, Stillwell D, Crowcroft J (2011) Our twitter profiles, our selves: predicting personality with twitter. In: Privacy, security, risk and trust (PASSAT) and 2011 IEEE third international conference on social computing (SocialCom), pp 180–185 10. Adal S, Golbeck J (2012) Predicting personality with social behavior. In: Proceedings of IEEE/ACM international conference on advances in social networks analysis and mining 11. Alam F, Stepanov EA, Riccardi G (2013) Personality traits recognition on social network— Facebook. In: 7th international AAAI conference on weblogs and social media workshop on computational personality recognition (shared task), pp 6–9 12. Farnadi G, Zoghbi S, Moens M, Cock MD (2013) Recognizing personality traits using Facebook status updates. In: The seventh international AAAI conference on weblogs and social media, workshop on computational personality recognition (shared task) 13. Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54 14. Moffitt KC, Giboney JS, Ehrhardt E, Burgoon JK, Nunamaker Jr JF (2012) Structured programming for linguistic cue extraction (SPLICE). In: Jensen M, Meservy T, Burgoon J, Nunamaker J (eds) Report of the HICSS-45 rapid screening technologies, deception detection and credibility assessment symposium, Jan 2012, p 103 15. Coltheart M (1981) The MRC psycholinguistic database. Q J Exp Psychol 33A:497–508 16. Howlader P, Pal KK, Cuzzocrea A, Kumar SDM (2018) Predicting Facebook-users’ personality based on status and linguistic features via flexible regression analysis techniques. In: Proceedings of the 33rd annual ACM symposium on applied computing, Pau, 09–13 Apr, pp 339–345 17. Tandera T, Suhartono D, Wongso R, Prasetio YL (2017) Personality prediction system from Facebook users. Procedia Comput Sci 116(2017):604–611 18. Marouf AA, Hasan MK, Mahmud H (2019) Identifying neuroticism from user generated content of social media based on psycholinguistic cues. In: Proceedings of international conference on electrical, computer and communication engineering (ECCE), 7–9 Feb 19. Rish I (2001) An empirical study of the Naive Bayes classifier. In: Proceedings of IJCAI-01 workshop on empirical methods in AI, Sicily, pp 41–46 20. Breiman L (2001) Random forests. Mach Learn 45:5–32 21. Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59:161–205 22. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. In: Proceedings of IEEE transactions on systems, man and cybernetics, pp 660–674 23. Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical report MSR-TR_98_14. Microsoft Research

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Text Classification Using K-Nearest Neighbor Algorithm and Firefly Algorithm for Text Feature Selection R. Janani and S. Vijayarani

Abstract Recently, the text classification is deliberated as the essential technique to handle a huge volume of unstructured documents. In general, this technique is used in the fields of information retrieval, text summarization, and text extraction. Feature selection is the important process in the text classification system. It is used to select the subset from the original set of features. The high-dimensional set of features will upturn the complexity of the text classification task. In order to reduce the dimensionality of the huge volume of text documents, this research work proposes a new firefly algorithm for feature selection. Here, the firefly algorithm is used to optimize the set of high-dimensional features. Next, the classification task will be performed by using the selected features. The performance of the proposed algorithm is compared with the widely used feature selection techniques such as information gain and χ 2 statistic. From the experimental results, the firefly feature selection algorithm performs well when compared to other methods. Keywords Text classification · Feature selection · Information gain · χ 2 statistic · Firefly algorithm · KNN

1 Introduction Text document classification is a process of classifying the documents into a set of predefined number of categories. Each document may be in single, or multiple, or no category at all [1]. The main determination is to learn the classifier over the instances, so they can perform the category assignment process automatically by using the machine learning techniques [2]. The main problem of text document

R. Janani (B) · S. Vijayarani Department of Computer Science, Bharathiar University, Coimbatore, India e-mail: [email protected] S. Vijayarani e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_47

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classification is the documents are having set of high dimensional features. Hence, it may reduce the performance of text classification system. Feature selection is the method of selecting the features from a set of highdimensional features. The top most feature subset the minimum amount of features that is most plays to improve the performance of text classification system [3]. This algorithm picks an important set of features, and it removes redundant, noisy, and irrelevant data. This process may useful for further classification task. Commonly, the text can be signified in two different behaviors such as a bag of words and strings. The document is denoted as a set of words with their associated frequency is called bag of words. The document which contains a sequence of words is called as strings. From these types of document representation, the feature selection will select the optimal number of features [4]. The rest of this paper was organized as follows: Sect. 2 illustrates the related work of feature selection and text classification. Section 3 introduces the methodology of this research work which contains feature selection and text classification. Section 4 discusses the experimental results attained by the existing and proposed algorithm. The conclusion of this research work was given in Sect. 5.

2 Related Works Senthilnath et al. [5] presented the comparison of firefly, artificial bee colony (ABC), and particle swarm optimization (PSO) for the clustering task. In their research, there were thirteen typical benchmark data sets from the UCI machine learning repository used to demonstrate the results of the techniques. From the results obtained, the firefly was efficiently used for clustering and its performance was good. Ko and Seo [6] reviewed different machine learning algorithms for text document classification like K-nearest neighbor, decision trees, Naïve Bayes, Rocchio’s algorithm, and support vector machines. From the experiments, they were determined that the support vector machine classifier was accepted as the most effective text classification methods. Arora and Singh [7] presented the firefly optimization (FA or FFA) algorithm: parameters selection and convergence analysis. In their algorithm, the arbitrarily solutions were considered as a firefly, and the brightness of the fireflies was dispensed depending on their performance. The algorithm was analyzed on basis of enactment and success rate using five standard benchmark functions by which procedures of parameter selection were consequent. The adjustment between exploration and exploitation was demonstrated and deliberated.

Text Classification Using K-Nearest Neighbor Algorithm … Table 1 Number of training and testing documents—Reuters data set

Name of the category Grain

Number of training documents

529 Number of testing documents

72

32

Ship

122

42

Wheat

153

51

Interest

165

74

Corn

170

53

Crude

288

126

Trade

297

99

Money

313

106

Acquisition

1484

664

Earn

2721

1052

3 Methods The main objective of this research work is to reduce the set of high-dimensional features. In order to perform this task, this research work proposes the new firefly feature selection algorithm. The performance factors are precision, recall, F-measure, and the accuracy of feature selection system. The performance of the proposed method is compared with information gain and χ 2 statistic. The following tasks are performed in this research work.

3.1 Document Corpus The huge volume of text documents is represented as a document corpus or corpora. In this research work, the performances of feature selection with classification algorithms are verified with the Reuters-21578 data set. It contains 21,578 documents with five sets of categories. Each category set contains different number of categories from 39 to 267. From this, ten classes are selected for experimentation and it contains both training and testing documents [8]. The summary of the data set used for experimentation is given in Table 1.

3.2 Document Preprocessing Document preprocessing is an essential process in the task of document classification, clustering, topic identification, etc. [1]. The preprocessing techniques are applied to the document data set to retrieve the substantial information from unstructured documents [2]. This method will increase the ability of the document classification system.

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In this research work, stemming, stop word removal, and numbers and punctuation removal techniques are used to retrieve the substantial knowledge.

3.3 Feature Selection Feature selection is used to reduce the dimensionality of the data set by eliminating the irrelevant features of classification tasks. This process will improve the scalability, efficiency and the accuracy of the classification system [9]. It is used to select the subset of features from the huge volume of documents. This method is achieved by observing the word, which is having the highest score according to the determined measure of the significance of particular words. The feature selection method consists of two approaches such as filter and wrapper approach. The filter approach implements the feature selection independently by applying the scoring techniques of the learning algorithms [10]. This approach uses an evaluation measure, which is used to achieve the subset of original features. The wrapper approach is used to wrap the important features nearby the classifier. This approach should train the classifier for each and every subset of features to be assessed [11]. The widely used feature selection techniques are information gain, Gini index, mutual information, and χ 2 statistic. In this research work, the information gain and χ 2 statistic are used to select the features. Also, this research work proposes a firefly feature selection algorithm.

3.3.1

Information Gain

Information gain (IG) is the most important measure which is used for the text document feature selection. It is also called as entropy. Let the optimal probability of class i is denoted as Pi . Then, the Pi (w) is also the probability, assumed that the documents contain the w words [12]. Assume Fr (w) is the fraction of the documents which contains the word w. Hence, the information gain of documents can be calculated as follows, IG(W ) = −

n 

Pi · log(Pi ) + Fr (w).

i=1 n 

Pi (w) · log(Pi (w)) + 1 − Fr (w).

i=1 n  i=1

1 − Pi (w) · log(1 − Pi (w))

(1)

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531

In this, the higher value of information gain denotes the higher biased power of the word w within the document. Assume that the collection of documents D contains the words w, then the complexity of this feature selection method as follows, O(D, W, k)

3.3.2

(2)

χ 2 Statistic

The χ 2 statistic or Chi square is used to calculate the independence of the two measures. In document classification, it is used to estimate the individuality among the word in the document and the particular class of category i. Assume N be the number of documents in the document corpora D, Pi (w) is the probability of category i for the document which encompasses the word w [12]. Fr (w) is the fraction of the documents which contains the word w. The χ 2 statistic or Chi square is calculated as follows, χ 2 (w) =

N · Fr (w)2 · (Pi (w) − Pi )2 Fr (w) · (1 − Fr (w)) · Pi · (1 − Pi )

(3)

The χ 2 -statistic is the normalized values, and these values are more similar through the terms in the same class or category [12].

3.3.3

Firefly Algorithm

Nature-inspired algorithms are most dominant for solving the optimization problems mainly NP hard problems [13]. In this research work, the firefly algorithm is used to select the features from the huge volume of documents. This algorithm is inspired by the aspects of the real fireflies. It produces the rhythmic flash which helps to fascinate the other fireflies [14]. The following three rules are idealized for the basic firefly algorithm. 1. All the fireflies are unisex so that will entice the other in spite of their sex. 2. The desirability is proportionate to their brightness, which depends on the distance among the fireflies so that the less bright firefly will move on the way to brighter firefly. 3. The brightness of firefly is decisive by the background of the objective function. In text document classification, the firefly algorithms are used to select the optimal features. First, the initial populations of firefly algorithm are generated and calculate the initial light density. Based on the initial parameters, the similarity and distance

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between the documents are calculated. Update the light intensity, and based on the intensity, move the documents [15]. Algorithm 1: Firefly Algorithm Generate the initial population of firefly Pi where i = 1, 2, …, n. n is defined as the number of fireflies such as documents. Estimate the initial light intensity, I is the total intensity of documents define the initial coefficient value = 1, the randomized parameter α = 0.2 and the initial attractiveness β = 0.1 for i = 1 to N and for j = 1 to N if (I i < I j ) Calculate the similarity and the distance between i, j by using (d i d j ) =   2 di − d j Estimate the attractiveness by using

Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Step 7:

β = β exp−r i, j 2

Step 8: Step 9: Step 10: Step 11:

Move the documents i to j Update the light intensity End for j; End for i Select the optimal features

3.4 Classification The document classification is the task of assigning documents to set of predefined number of categories. The document classification can be done by different ways: supervised, semi-supervised, and unsupervised approaches. Recently, information retrieval got the attention from the researchers and enormous algorithms and methods were proposed for classifying and clustering the text document collections (e-documents) [1]. The task of text document classification is done by the machine learning techniques such as support vector machines (SVMs), Bayesian classifier, decision tree, neural networks, K-nearest neighbor (KNN), latent semantic analysis, and Rocchio’s algorithm. In this research work, KNN algorithm is used to classify the huge volume of documents [2].

3.4.1

K-Nearest Neighbor (KNN)

The KNN algorithm is the distance-based metric which is used to investigate the similarity measures between the testing and training documents. This process is used to regulate the category of test document collections. This algorithm is an instant machine learning technique, which is classifying the documents based on the nearest

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features in the training documents. The training documents are summarized in the high-dimensional feature space. This can be divided into the areas established on the training document categories. To estimate the distance, the Euclidean distance is used in this research work. The main aim of this technique is to discover the document similarity based on its neighbors [16]. This method is only used to retrieve the features of the document from the multidimensional feature space. In the classification step, the distance vectors can be calculated and the k-nearest element is selected so that the interpreted category of the particular document is portended based on the nearest value. This classification technique achieves better classification results with the number of categorized documents. Algorithm 2: KNN Algorithm Step 1: Let (X i , C i ) where i = 1, 2 …, n be data points. X i denotes feature values and C i denotes labels for X i for each i. Step 2: Let the number of classes as “c” C i ∈ {1, 2, 3, …, c} for all values of i Step 3: Calculate “d (x, x i )” i = 1, 2, …, n; where d denotes the Euclidean distance between the points. Step 4: Arrange the calculated n Euclidean distances in non-decreasing order. Step 5: Let k be a +ve integer, take the first k distances from this sorted list. Step 6: Find those k points corresponding to these k distances. Step 7: Let k i denotes the number of points belonging to the ith class among k points i.e. k ≥ 0 Step 8: If k i > k j ∀ i = j then put x in class i.

4 Results and Discussion In order to perform this classification task, there are four performance measures used in this research work. They are precision, recall, F-measure, and accuracy of the classification [16]. From the experimental result, it shows the proposed feature selection algorithm yields better performance when compared to existing techniques. Also, by using the proposed feature selection algorithm, the classification accuracy was improved. To estimate the classifier performance, the confusion matrix is essential. The confusion matrix is given in Table 2. This matrix is established in the terms, • True Positives (TP)—It is defined; the similar documents are classified in the same category. • True Negatives (TN)—It is defined; the dissimilar documents are classified in the different category. • False Positives (FP)—It is defined; the dissimilar documents are classified in the same category.

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Table 2 Confusion matrix A document which belongs to the particular category

A document which does not belong to the particular category

Document category accepted by the classifier

TP

FP

Document category rejected by the classifier

FN

TN

• False Negatives (FN)—It is defined; the similar documents are classified in the different category.

Precision = Recall =

(4)

TP TP + FN

(5)

2TP 2TP + FP + FN

(6)

TP + TN TP + TN + FP + FN

(7)

F-Measure = Accuracy =

TP TP + FP

In Table 3, the precision values of the KNN classification with the feature selection algorithms were compared. From this, we inferred the firefly algorithm selects the important features compared with existing techniques. Hence, the classification results are also better.

Table 3 Precision values of KNN with feature selection technique

Category

IG + KNN

χ 2 + KNN

Firefly + KNN

Grain

0.598

0.601

0.628

Ship

0.428

0.526

0.611

Wheat

0.674

0.582

0.671

Interest

0.587

0.671

0.703

Corn

0.510

0.519

0.596

Crude

0.687

0.661

0.695

Trade

0.466

0.587

0.608

Money

0.523

0.562

0.638

Acquisition

0.648

0.641

0.681

Earn

0.545

0.599

0.605

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Figure 1 shows the comparison of precision values. From this, the firefly algorithm gives the best accuracy when compared to information gain and χ 2 statistic. Table 4 shows the comparison of recall values of KNN with three feature selection techniques. Figure 2 shows the comparison of recall values for all the three techniques with classification. From this, we inferred the proposed algorithm works well. Table 5 demonstrates the F-measure values of three feature selection techniques. Figure 3 denotes the comparison of F-measure values of feature selection with the classification algorithm. It shows the performance of the proposed algorithm earns good results. Table 6 explains the accuracy of ten categories with its feature selection techniques. From this, the firefly algorithm performs well when compared with the

Fig. 1 Comparison of precision values

Table 4 Recall values of KNN with feature selection technique

Category

IG + KNN

χ 2 + KNN

Firefly + KNN

Grain

0.657

0.663

0.697 0.625

Ship

0.589

0.602

Wheat

0.632

0.665

0.682

Interest

0.715

0.718

0.764

Corn

0.705

0.701

0.764

Crude

0.628

0.651

0.659

Trade

0.603

0.682

0.699

Money

0.601

0.612

0.638

Acquisition

0.590

0.596

0.599

Earn

0.571

0.598

0.632

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Fig. 2 Comparison of recall values

Table 5 F-measure values of KNN with feature selection technique

Category

IG + KNN

χ 2 + KNN

Firefly + KNN

Grain

0.626

0.630

0.661

Ship

0.496

0.561

0.618

Wheat

0.652

0.621

0.676

Interest

0.645

0.694

0.732

Corn

0.592

0.596

0.670

Crude

0.656

0.656

0.677

Trade

0.526

0.631

0.650

Money

0.559

0.586

0.638

Acquisition

0.618

0.618

0.637

Earn

0.558

0.598

0.618

other technique. Figure 4 shows the comparison of accuracy values. It denotes the performance of proposed algorithm and earns good results.

5 Conclusion Text document classification plays vital role in the area of information retrieval, natural language processing, and text mining. For classification task, selecting proper features is also the important step. In this research work, the feature selection algorithm was proposed. Then, the classification task was performed by the selecting

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Fig. 3 Comparison of F-measure values

Table 6 Accuracy values of KNN with feature selection technique

Category

IG + KNN

χ 2 + KNN

Firefly + KNN

Grain

0.760

0.762

0.817 0.793

Ship

0.694

0.704

Wheat

0.789

0.776

0.804

Interest

0.732

0.766

0.862

Corn

0.632

0.703

0.712

Crude

0.772

0.796

0.825

Trade

0.749

0.732

0.771

Money

0.621

0.708

0.793

Acquisition

0.706

0.767

0.85

Earn

0.718

0.728

0.862

features. For experimentation, two existing algorithms were used to compare the performance of the proposed algorithm. The proposed algorithm can select the optimal set of features without the previous knowledge of the original feature set. Experimental results show that the proposed algorithm yields better accuracy. In future, there is a need to develop the feature selection techniques to select the more accurate features from the huge volume of document data set.

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Fig. 4 Accuracy values

References 1. Jindal R, Malhotra R, Jain A (2015) Techniques for text classification: literature review and current trends. Webology 12(2) 2. Bali M, Gore D (2015) A survey on text classification with different types of classification methods. Int J Innov Res Comput Commun Eng 3(5) 3. Bins J (2000) Feature selection from huge feature sets in the context of computer vision. Ph.D. dissertation, Department Computer Science, Colorado State University 4. Kim H, Howland P, Park H (2005) Dimension reduction in text classification with support vector machines. J Mach Learn Res 6:37–53 5. Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput 1(3):164–171 6. Ko YJ, Seo J (2009) Text classification from unlabeled documents with bootstrapping and feature project techniques. J Inf Process Manag 45(1):70–83 7. Arora S, Singh S (2013) The firefly optimization algorithm: convergence analysis and parameter selection. Int J Comput Appl 69(3):48–52 8. https://archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection 9. Kittler J (1978) Feature selection and extraction. In: Fu Y (ed) Handbook of pattern recognition and image processing. Academic Press, New York 10. Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 1289–1305 11. Özel SA, Saraç E (2011) Feature selection for web page classification using the intelligent water drop algorithm. In: Proceedings of the 2nd world conference on information technology (WCIT 2011), Antalya, 23–26 Nov 2011 12. Aggarwal CC (2012) A survey of text classification algorithms. In: Mining text data 13. Banati H, Bajaj M (2011) Fire fly based feature selection approach. IJCSI Int J Comput Sci Issues 8(4) 14. Özel SA (2011) A genetic algorithm based optimal feature selection for web page classification. In: International symposium on INnovations in Intelligent SysTems and Applications (INISTA 2011), Istanbul, pp 282–286

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15. Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Frome. ISBN 1905986-10-6 16. Özel SA (2011) A web page classification system based on a genetic algorithm using taggedterms as features. Expert Syst Appl 38(4):3407–3415

Performance Evaluation of Traditional Classifiers on Prediction of Credit Recovery Mohammad Rajib Pradhan, Sima Akter, and Ahmed Al Marouf

Abstract In the era of Big Data, machine learning is an emerging technique to analyze the large volume of data and is used to make critical business decisions. It is broadly used in different area such as medical, telecom, social media, banking data analysis and so on to learn the data and perform predictive analysis as well as building recommendation system. With the progression of technology, data availability and computing power, most of the banks and financial institutions are adapting their business model with technological development. Credit risk analysis is a cardinal field for banking and financial institutions, and there are numerous credit risk technique exists to predict the creditworthiness of the customer and loan default probability. In this study, we explore credit defaulter dataset of Bangladeshi bank and conduct several traditional machine learning classifier to predict the delinquent clients who possessed the highest probability of short-term credit recovery. Furthermore, we perform feature engineering to identify the important features for credit recovery prediction. We then apply our final features on different machine learning classifier and compare the predictive accuracy with the other classifier. We observe that random forest classifier gives 90% accuracy in credit recovery prediction. Finally, we propose a noble strategy to identify the potential customer for recovering the credit amount by using supervised machine learning techniques. Keywords Credit recovery · Classification · Confusion matrix · Feature extraction · Machine learning algorithms · Neural networks · Predictive models

M. R. Pradhan (B) Research & Development, Sonali Intellect Limited, Dhaka, Bangladesh e-mail: [email protected] S. Akter · A. Al Marouf DIU HCI Research Lab (HCI RL), Department of Computer Science and Engineering, Daffodil International University (DIU), Dhaka, Bangladesh e-mail: [email protected] A. Al Marouf e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_48

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1 Introduction The process of deciding to accept or reject a client’s credit by banks commonly executed via judgmental techniques, credit scoring models and/or machine learning techniques. In this process, banks examine the creditworthiness of their customers, and if the result of this process is at a satisfactory level, they sanction the loan application as a creditworthy applicant. However, due to the different types of the problem such as lack of central integration, judgmental decision error, lack of valuable information, violation of borrower commitments and political influence few of loans are get classified and it’s become a Non-performing Loans (NPL). The average percentage of classified loans is higher in Bangladesh compared to other countries. The banking system is a vital part of economic development of each country, and it is clearer that the poor banking system is an obstacle for economic development of the country. The classified loan is an acute problem for the banks of Bangladesh. This country suffered high levels of NPL which reached 10.8% in March 2018 [1]. Likewise, others South Asian country is also experiencing the same problem. The position of few South Asian countries is illustrated in Fig. 1. Data of all countries were collected from the World Bank Web site [2] excepting Bangladesh. Data for Bangladesh were collected from Survey Report of Study on Credit Risk arising by Bangladesh Bank [3] and most popular Bangladeshi Newspaper [1] The Daily Star. Now, Bangladesh is on course to become a developing country, and of course, different types of initiative should be made to reduce the amount of non-performing loan to strengthen the financial institutions, which is the backbone of an economy. The government has taken different types of initiative to reduce the rate of the classified loan, but it is still at high rate and gradually increasing the bad debt amount.

Fig. 1 NPL position of South Asian country

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There is plenty of research has been conducted [4–7] to determining the creditworthiness of a customer, predicting the classified loan, partitioning the credit groups of good or bad payers. However, once the loan has classified, it could be paying back due amount and become a good credit in near future, and there has little research on identifying customers with the most potential to return to normal situation again. This is why we conduct this study to identify the potential customer for recovering the credit amount by using supervised machine learning techniques. To work with this problem, we have collected loan defaulter dataset from a banking data warehouse (DW) server. Our dataset is consisting of categorical and numerical feature. We have performed data analysis to understand the nature of data and feature extraction and identification process. We used a statistical feature extraction process to identify the most important feature and creating new features. After identifying various feature from loan defaulter dataset, we applied supervised machine learning algorithm to determine the probability of credit recovery for a given bank customer. Finally, we have performed the choice of the parameters for each model and check the role of variables to avoid bias. After model performance evaluation, we have found our satisfactory result and finalize the best performing model. The rest part of this paper is organized as follows: Sect. 2 is used for related work discussion. In Sect. 3, we have presented the methodology of our work. Data description and feature section process have been discussed in Sect. 4. The experimental results are outlined in Sect. 5, and finally, we conclude this paper in Sect. 6 and indicate some feature works.

2 Related Works Over the decades, a numerous number of studies have been conducted to predict the creditworthiness of bank customers. Machine learning approach used by Pandey et al. [8] to evaluate the credit risk of a customer in their dataset. They survey different machine learning technique for credit risk analysis in German and Australian datasets and reported ELM classifier gives better accuracies. By using weightedselected attribute bagging method, Li et al. [9] analyze the attributes of customers to assess credit risk. They used two credit datasets for experimental analysis and reported outstanding performance in terms of prediction accuracy and stability and compare classification ability of different models. Li et al. [10] proposed a credit risk assessment algorithm using deep neural networks with clustering and merging technique to assess the default risk of borrowers. They divided the class sample into several subgroups by k-means clustering algorithm, and then, subgroups are merged with minority class sample to produce balanced subgroups, and finally, balanced subgroups are classified using deep neural networks. All of this study shows that there is adequate research has been done to predict the loan defaulter. The perspective of loan default problems outlined by Chowdhury and Dhar [11] in a study of Commercial Banking sector in Bangladesh. They elucidate contributory factors and loan default problems of both the state-owned commercial banks (SCBs)

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and private commercial banks (PCBs) of Bangladesh. Their analysis revealed that state-owned commercial banks are more likely affected by NPL far more adversely than private commercial banks due to some adversary contributory factors. Their emphasized contributory factors are poor credit recovery policy, political interference, lack of managerial efficiency and lack of adapting modern technological changes. Adhikary [12] stated a large amount of NPLs is liable for bank failure as well as economic slowdown. The cause of NPLs attributed to the lack of supervision, effective monitoring and credit recovery strategies. Banik and Das [13] conducted simple and multiple regression analysis to find the impact of a classified loan on bad debts, and as a result of analysis, they identified classified loans have a significant impact on bad debts in both state-owned commercial banks and first-generation private commercial banks. They also emphasize to develop specific tools and technique to distinguish the willful defaulters from the genuine ones. Now, it is clearer that besides the good credit risk assessment approach, a good credit recovery approach can help the financial institution to recover bad credit amount from their customers to increase profit and reduce loss. But adequate number of research is not present on classified loans though it is a crucial issue at present. We employed different machine learning technique in this paper to predict the good payer for earlier credit recovery so that amount of NPL could be reduced.

3 Methodology We have used a supervised machine learning approach to build our credit recovery prediction system, and in order to develop it, we have collected the raw data from the bank DW. Then, we have performed data analysis, feature extraction, data preprocessing, feature scaling, build a model using machine learning classification algorithm and finally generate the performance report. The overall process is outlined in the following sub-sections.

3.1 Data Collection The best predictive results require relevant input data for the business specific modeling that is why we collected data by selecting only relevant inputs and using loan recovery domain knowledge. During the data collection, we have considered few features as categorical and few as numerical. It has information about customers and bank financial data. The data used are collected from Oracle database using Structured Query language (SQL) and Procedural Language for SQL (PL-SQL). Various ETL jobs have been developed to aggregate data into single table using SQL and PL-SQL language. Finally, we export table data into comma-separated values (CSV) file from Oracle database table.

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3.2 Data Analysis and Transformation Data analysis and transformation is a vital part of machine learning process. It is highly interactive and iterative process [14]. Typically, this process includes data visualization, analyzing missing values, resolving inconsistencies, identifying or removing outliers, data cleaning and transformation, feature engineering and construction. Proper data preparation is required for data visualization. For building perception relationships among the features and ensuring machine learning models work optimally. The first step in exploratory data analysis is to read the data and then explore the variables. It is important to get a sense of how many variables and cases there are, the data types of the variables and range of values they take on. Initially, from the source data, we have 4600 observations and 47 features.

3.3 Feature Extraction Feature extraction is the process of creating new features out of an existing dataset to help a machine learning model to learn a prediction problem [15]. In most cases, machine learning is not done with the raw features. Features are transformed or combined to form new features in forms which are more predictive. This process is known as feature engineering. In many cases, good feature engineering is more important than the details of the machine learning model used. It is often the case that good features can make even poor machine learning models work well, whereas, given poor features even the best machine learning model will produce poor results. We have explored new feature by categorical variable transformation, variable aggregation and statistical feature extraction process.

3.4 Preparing Input and Output To build the predictive model, we grouped all features into a matrix X denoted X ∈ Rn×m , where n refers to the number of rows, and m refers to the number of columns, respectively, and a vector y denoted y ∈ Rn×1 . Matrix X represents our input data where m= 4600 and n= 31 and vector y represent output data which contains all outputs, either 1 for good payer or 0 for not a good payer. In order to build classifier model, the dataset is spited into two partitions or sets—training and testing. The training datasets were used to build the classifier model, and the testing datasets were used to evaluate the model performance to make sure that the model performance is well for never-before-seen data. Before feeding the data to the classifiers, we have performed feature scaling for our dataset. Feature scaling is a crucial step in data preprocessing pipeline of machine learning. The main purpose of feature scaling is bringing different features

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onto the same scale [16]. It is required because the majority of machine learning and optimization algorithms behave much better if features are on the same scale. There are two common approaches to bring different features onto the same scale: normalization and standardization. We used standardization feature scaling technique for our datasets. Using standardization, we center the feature columns at mean 0 with standard deviation 1 so that the feature columns take the form of a normal distribution. The procedure of standardization can be expressed by the following equation. (i) = xstd

x(i) − μx σx

Here, μx is the sample mean of a particular feature column and σ x the corresponding standard deviation, respectively.

3.5 Modeling The learning process of machine learning algorithm is partitioned into supervised and unsupervised learning. Supervised learning is used to predict output from a given input, and unsupervised learning is used to segmenting or clustering entities in different groups [17]. Our dataset consists of both input and output feature for which we used the supervised method to make an accurate prediction for neverbefore-seen data. To generate the best output for credit recovery prediction, we have studied the efficiency and scalability of each classification model by varying several parameters. The logistic regression, Naive Bayes, KNN classification, decision tree, random forests, support vector machine, multilayer perceptron, neural networks, linear discriminant analysis and two boosting algorithms AdaBoost and XGboost have used in this study. Hyper-parameters are parameters which determine the characteristics of a model [18]. The ultimate objective of the learning algorithm is to find a function that minimizes the loss of the classification model. The learning algorithm produces a function through the optimization of a training criterion with respect to a set of parameters also known as hyper-parameter. For example, with logistic regression, one has to select a regularization penalty C to reduce the complexity of a model by penalizing large individual weights. Therefore, it is important to find the best optimal hyperparameter values. Though default value of hyper-parameter is a good start to build a model, they may not produce the optimal model. Grid search is the most widely used strategy for hyper-parameter optimization [19]. In this study, we used GridSearchCV() implemented in scikit-learns to produce the best model. It requires set of values for each hyper-parameter that should be tuned and evaluates a model trained on each element of the Cartesian product of the sets.

Performance Evaluation of Traditional Classifiers on Prediction … Table 1 Confusion matrix of binary classification

547

Actual class

Predicted class Positive class

Negative class

Positive class

TP

FP

Negative class

FN

TN

3.6 Evaluation In this research study, we employed several performance criteria to evaluate the model performance and assist in model selection process. The most commonly accepted evaluation measures are accuracy, precision, recall, F-score and score of ROC area (AUC). Many of them come from a confusion matrix which is a specific matrix that shows the relationship between true class and predicted class. Table 1 presents the confusion matrix. Accuracy (ACC) One of the most common metrics is accuracy, which gives the ratio of correctly classified samples to misclassified samples. Accuracy does not take class distribution into account, which makes it poor measure for evaluating performance on imbalanced data. Accuracy = (TP + TN)/(TP + TN + FP + FN)

(1)

Precision (PR) It indicates how many values, out of all the predicted positive values, are actually positive. It is formulated as following. Precision = TP/(TP + FP)

(2)

Recall (RE) It indicates how many positive values, out of all the positive values, have been correctly predicted. The formula to calculate the recall value is in Eq. (3). Recall = TP/(TP + FN)

(3)

F1-Score F1 score is the harmonic mean of precision and recall. It lies between 0 and 1. Higher the value, better the model. Equation (4) shows the formula for F1-score. F1-score = (2 ∗ Precision ∗ Recall)/(Precision + Recall)

(4)

AUC The area under the curve (AUC) refers to the area under the receiver operating characteristics (ROC) curve. The overall performance of classifier model is measured by the AUC. The higher the AUC the lower the increase in false positive rate required to achieve a required true positive rate. For an ideal classifier, the AUC is 1.0. A true positive rate is achieved with a 0 false positive rate. This behavior means that AUC

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is useful for comparing classifiers. The classifier with higher AUC is generally the better one.

4 Data Description and Feature Selection The dataset used in this research was obtained by the extraction of information from a Bangladeshi Bank DW, and due to data confidentiality, the whole dataset cannot be published. As we have collected data from DW, the data preparation activity was reduced to aggregating data into single database table containing 47 features with combination of numerical and categorical variables. However, not all the 47 features were adequate enough to obtain the target outcome. We therefore extract the new feature from existing features, removed irrelevant and redundant features, and finally, we have identified ten categorical and 21 numerical features which have shown in Tables 2 and 3 accordingly. The credit dataset has 4600 instances in where the majority of samples (3698) are positive class and the minority of samples (902) are negative class. It is clear that the ratio of position and negative classes is unbalanced. Before doing any more feature engineering, it is important to establish a baseline performance measure. In this study, we used a gradient boosting framework implemented in Light-GBM library to assess the performance of new feature. Our feature engineering approach is outlined below: • • • •

One-hot encode categorical variables. Make a baseline model to establish a benchmark. Build a new feature by manipulating columns of the base (main) data frame. Assess performance of new feature set in a machine learning model.

Table 2 Categorical feature description SL

Features

Description

1

V01

EMI facility (1 = yes, 0 = no)

2

V02

Individual loan (1 = yes, 0 = no)

3

V03

Personal loan (1 = yes, 0 = no)

3

V03

Agriculture loan (1 = yes, 0 = no)

4

V04

Simple or compound interest (1 = yes, 0 = no)

5

V05

Staff loan (1 = yes, 0 = no)

6

V06

Customer sex (1 = male, 0 = female)

7

V07

Marital status (married, single, others)

8

V08

Secured or unsecured loan (1 = secured, 0 = unsecured)

9

V09

Repayment frequency (monthly, others)

10

V10

Number of bad loan (1 = one, 0 = more than one)

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Table 3 Confusion matrix of binary classification SL

Features

Description

1

V11

Annual income slave

2

V12

Number of days of regular payment

3

V13

Days of customer relationship with bank

4

V14

Initial contract amount

5

V15

Customer age in year

6

V16

Interest rate

7

V17

Amount of the outstanding balance

8

V18

Total recovery amount

9

V19

Total interest received from customer

10

V20

Monthly installment amount (EMI amount)

11

V21

Total number of installment

12

V22

Number of installment paid

13

V23

Number of loan account

14

V24

Remaining day of contract maturity

15

V25

Total amount of last six month bill

16

V26

Total amount of last six month payment

17

V27

Mean value of last six month bill

18

V28

Mean value of last six month payment

19

V29

Ratio of contract and recover amount

20

V30

Ratio of past six month bill and recover amount

21

V31

Ratio of contract value and days of regular payer

The last step we aimed to reduce the dimensions of our dataset by removing irrelevant and redundant features did not have a significant impact on prediction power.

5 Experimental Results The result that we have achieved after implementing different classification algorithm is promising enough. Table 4 describes our achieved result. In the experiments reported in this paper, Keras, which is a high-level neural networks API, is used to run the neural network, Python v3.6 and scikit learn [20] were used for the implementation. The accuracy, precision, recall, F-score and AUC values are extremely good for most of the algorithms. Thus, these algorithms are very suitable for analyzing bank credit data and build a classifier model. Our experimental result shows that the random forests model is outperformed for credit delinquent dataset in term of

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Table 4 Experimental results Classifier

ACC

PR

RE

F1-score

AUC

Logistic regression

0.87

0.89

0.34

0.49

0.66

Naive Bayes

0.24

0.19

0.94

0.31

0.51

KNN classification

0.87

0.80

0.42

0.55

0.70

Decision tree

0.85

0.60

0.50

0.55

0.71

Random forests

0.90

0.85

0.56

0.67

0.77

Support vector machine

0.87

0.85

0.35

0.49

0.67

Multilayer perceptron

0.87

0.94

0.33

0.49

0.66

AdaBoost

0.87

0.79

0.38

0.51

0.68

XGboost

0.89

0.88

0.46

0.61

0.72

Neural networks

0.87

0.75

0.41

0.53

0.69

Linear discriminant analysis

0.87

0.79

0.37

0.50

0.67

accuracy (90%), precision, recall, F-score and AUC values and AUC score. Naive Bayes model is worst performing model than others models. The XGboost model achieves 89% accuracy and AUC value 0.72 which is closer to best performing model. It indicates that the random forest model can better distinguish the delinquent client for short-term credit recovery instances from credit defaulter dataset.

6 Conclusion NPL refers to those financial assets from which bank no longer receives neither interest nor installment amount and it is a big obstacle for economic development of a country. The elevated rate of NPL amount needs to reduce for sustainable economic development, and in the era of artificial intelligent, machine learning technique can be used to reduce the ratio of NPL amount. Therefore, in this research, we have applied different machine learning approach in credit defaulter dataset to predict the delinquent clients who possessed the highest probability of short-term credit recovery. We have evaluated 11 different classification algorithms to determine the algorithm which is best fit for credit recovery dataset. Basically, we have tried to establish a solid comparison between different classification algorithms and improve the accuracy of the prediction by increasing accuracy and minimizing errors, bias and variance. As a result, we got random forests as the best performing model in terms of accuracy and AUC, and Naive Bayes is the worst. We also identified important feature that needed to build an optimal predictive model in order to formulate banks credit recovery automated system. The classifier identified by supervised machine learning techniques will be very much supportive for the financial institution to

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identify delinquent client for short-term credit recovery. The proposed model would be helpful with existing credit risk assessment approach to reduce the ratio of NPL.

References 1. Mujeri MK (2018) Rising non-performing loans [Online]. Available https://www.thedailystar. net/news/opinion/perspective/rising-non-performing-loans-1619257. Accessed 25 Nov 2018 2. World Bank, International Monetary Fund (2018) Global financial stability report [Online]. Available https://data.worldbank.org/indicator/FB.AST.NPER.ZS. Accessed 10 Dec 2018 3. Sarker MAA (2017) Study on credit risk arising in the banks from loans sanctioned against inadequate collateral. Research Department, Bangladesh Bank 4. Li FC, Wang PK, Wang GE (2009) Comparison of primitive classifier with ELM for credit scoring. In: Proceeding of IEEE IEEM, pp 685–688 5. Wang Y, Wang S, Lai KK (2005) A new fuzzy SVM to evaluate credit risk. IEEE Trans Fuzzy Syst 13:820–831 6. Zhou H, Lan Y, Soh YC, Huang GB (2012) Credit risk evaluation using extreme learning machine. In: Proceeding of IEEE international conferences on system, man and cybernetics, pp 1064–1069 7. Zhu B, Yang W, Wang H, Yuan Y (2018) A hybrid deep learning model for consumer credit scoring. In: Proceeding of IEEE international conference on artificial intelligence and big data, pp 205–208 8. Pandey TN, Mohapatra SK, Jagadev AK, Dehuri S (2017) Credit risk analysis using machine learning classifiers. In: Proceeding of IEEE international conference on energy, communication, data analytics and soft computing, pp 1850–1854 9. Li J, Wei H, Hao W (2013) Weight-selected attribute bagging for credit scoring. Math Probl Eng 2013 10. Li Y, Li X, Wang X, Shen F, Gong Z (2017) Credit risk assessment algorithm using deep learning neural networks with clustering and merging. In: Proceeding of 13th international conference on computational intelligence and security (CIS), pp 173–176 11. Chowdhury R, Dhar BK (2012) The perspective of loan default problems of the commercial banking sector of Bangladesh. Univ Sci Technol Annu (USTA) 18(1):71–87 12. Adhikary BK (2015) Nonperforming loans in the banking sector of Bangladesh: realities and challenges. J BIBM 75–95 13. Banik BP, Das PC (2015) Classified loans and recovery performance: a comparative study between SOCBs and PCBs in Bangladesh. J Cost Manag 43:20–26 14. McKinney W (2013) Python for data analysis. O’Reilly 15. Heaton J (2017) An empirical analysis of feature engineering for predictive modeling. arXiv preprint arXiv:1701.07852v1 16. Raschka S (2015) Python machine learning. Packt Publishing 17. Guido S, Muller AC (2016) Introduction to machine learning with python. O’Reilly Media, Inc 18. van Rijn JN, Hutter F (2018) Hyperparameter importance across datasets. arXiv preprint arXiv: 1710.04725v2 19. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305 20. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

Cost-Sensitive Long Short-Term Memory for Imbalanced DGA Family Categorization R. Mohammed Harun Babu, R. Vinayakumar, and K. P. Soman

Abstract Domain generation algorithm (DGA) is the foundation of malware families because of the very fact that DGA generates huge variety of pseudorandom domain names to associate to a command and control (C2C) infrastructures. This paper focuses in handling classification of class in a imbalanced data. Cost-sensitive long short-term memory (CS-LSTM) approach is proposed which helps in understanding the importance of each class. Detecting malicious domain names (DMD 2018) data set is used. Optimal parameters are set to deep learning architectures using hyper-parameter approach. Experiments on CS-LSTM performed well compared to other deep learning architectures. Using this approach, 74.3% accuracy is obtained, varies 4.6% from the top scored system in DMD-2018. Keywords Long short-term memory (LSTM) · Cost-sensitive LSTM (CS-LSTM) · Domain generation algorithm (DGA) · Keras embedding · Botnet · Malware · Cybercrime · Cyber security

1 Introduction Internet is one of the indispensable platforms for each and every one in a routine past recent years for the activities such as entertainment, edutainment, it becomes a part of our life in a useful way meanwhile it is harmful in all aspects. Recent day’s malwares gain access through various medium while installing application, software, via mail, etc. The prominent element on the Internet is translating of domain names to IP addresses and mapping it for Internet users using domain name system [1]. This elementary access through Internet portal increases traffic over the DNS and provides access to the attackers to gain information easily. Botmasters use botnets for all types of malicious access like stealing information, data from the victim system without his knowledge and permission. Additionally, R. Mohammed Harun Babu (B) · R. Vinayakumar · K. P. Soman Amrita School of Engineering, Center for Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_49

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ransomware attacks are distributed by DoS attack (denial-of-service) through the victim’s system or encrypting the victims drive by connecting C&C center. This communication serves multiple functions at once and [2] malware will gain access such as passwords access credentials. Botmasters can easily establish such a communication to the C&C server with an IP address by passing the malware through domain generation algorithm (DGA). DGA generally generates wide range of random domain names at a time. In this randomly generated domains malware try to attempt and connect with the DNS operator server. If the domain is been easily connected to the DNS, botmaster can easily pass through the network by passing malware [3]. The malware can acquire IP for the registered domain and can easily communicate with the C&C server. To detect, DGA’s blacklisting method was used [4]. Blacklisting methods fail in detecting newly generated DGA domain names. To handle new types of DGA-generated domain, machine learning methods were introduced [5], but machine learning completely depends on feature extraction, and domain knowledge also needed to perform the task. Moreover, classical machine learning-based classifier can be easily broken by attackers in a conflict environment. Nowadays, deep learning architectures with character embedding are used for DGA detection and as well as categorization [1, 6, 7]. These deep learning-based architectures are vulnerable to imbalanced DGAs. To handle imbalanced DGAs, in this work, cost-sensitive long short-term memory (CSLSTM) is proposed as a novel method for DGA categorization. The performance of the method is evaluated on the DMD 2018 shared task data. The major contributions of the proposed work are as follows: 1. This paper has proposed cost-sensitive LSTM to handle multiclass class imbalanced DGA. 2. Various experiments are done to find out the optimal parameters for the proposed cost-sensitive LSTM architecture. All other sections are structured as follows. In Sect. 2, related work is discussed and in Sect. 3 the background knowledge of CS-LSTM. In Sect. 4 description of data set and details of baseline system. At last, experiment and results are furnished in Sects. 5 and 6. Section 7 concludes with conclusion.

2 Related Works In this section, various works related to DGA are discussed. Vinayakumar et al. [8] discuss the advantage of deep learning methods to detect DGA-generated malware domain names. In this paper, data sets like Alexa, OpenDNS, malicious corpus of 17 DGA malware families generated by DGA are been used. These all data sets are evaluated in OSNIT data set by processing using character level bigram method. In which, recurrent neural network (RNN) and a hybrid network have a performance with the highest accuracy of 0.9945% and 0.9879%, respectively. In [9], DNS logs were collected from various client machines using LAN and added to the server

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database. Apache Spark is a distributed big data-based cluster computing platform is used to store the data. To find the domain name as either benign or malicious, deep learning approaches like RNN, LSTM and other traditional machine learning classifiers are applied. Mohan et al. [10] proposed a new approach SPOOF Net, combination of a convolutional neural network (CNN) and LSTM which is an embedding concepts from natural language processing (NLP) is been embedded into cybersecurity use cases. The proposed model is incorporated with feature engineering method bigram and conventional CNN with character level embedding. The SPOOF Net model comes with accuracy scores of 98.3% for DGA detections and 99% for malicious URL detection. Yu et al. [11] proposes the classification of domain names as either benign or malicious using character level detection, and comparative study is made between five different deep neural network architectures that perform the classification task-based purely on character level-based. In [12] which, two RNN-based architectures, two CNN-based architectures and one hybrid RNN and CNN architecture are used, and all experiments give a accuracy around 97–98% of malicious domain names at a false positive rate of 0.001. Rajalakshmi and Bharathi et al. [13, 14] discuss how transfer learning approach helps in malicious domain detection and the performance of character level RNN is been discussed over malicious domain names. This paper mainly focuses on the multiclass class imbalance handing in DGA Botnet detection, and author classifies how novel LSTM and adaptive cost-sensitive combine with LSTM differs in their performance and result. Finally concluding that how costsensitive-LSTM network helps in reducing the complexity of class imbalance and achieve a optimal result in classification.

3 Background 3.1 Domain Name System (DNS) Domain name system (DNS) translates domain names to IP addresses in a prominent way. All domain names of the Web site are possessed with corresponding IP addresses. These IP addresses help in identification of Web sites while surfing, and all these are numerical data which is not easy to remember so for each IP corresponding domain names are generated, in which DNS act as a collective form of database to store domain names and IP addresses. DNS has hierarchy in which domain names are comprised, the comprised hierarchy consists of root level, top level domains, second level domains Subdomains and host as shown in Fig. 1.

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Fig. 1 DNS hierarchy

3.2 Domain Fluxing and Domain Generation Algorithms Flux usually refers to something constantly changing. In the case of domain fluxing, bots use domain generation algorithm to produce thousands of domain names randomly, which is registered by the botnet operator [7, 15, 16]. All bots will send queries to DNS randomly until they resolve the address in the C&C server as shown in Fig. 2. These make security and administrators difficult to shut down the botnets from C&C serve. To overcome such issues, our work relies on various machine learning models to evaluate the ability of DGA families’ detection.

Fig. 2 Resolving of DGA-generated domains

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Fig. 3 Typical infrastructure of botnet

3.3 Botnet Botnets are comprised of multiple systems working all together with the objective of completing repetitive tasks. Botnets are mostly associated with malicious DDoS attacks [7], and these botnets create malware and render them to slave systems and steel information from the victim and transfer them to botnet operators as shown in Fig. 3. Botnet attacks forms mainly with popular attacks such as DDoS attacks, Click Fraud, Email spam, Bitcoin mining, etc.

3.4 Keras Embedding In natural language processing (NLP), Keras embedding is a common technique used to map the character level domain names to the vector representation. Keras embedding is most commonly used in DGA detection [1, 8–11]. This helps to convert dense representation to continuous vector representation which in turn helps to preserve the sequential information of the characters in an domain name.

3.5 Long Short-Term Memory (LSTM) LSTM is advanced form of RNN, and LSTM has been structured by extending the memory which helps to learn long-term time constraints over the memory block of the neural network. It mainly comes with three gates, input gate decides the input to the layer, output gate influences the instance on the time step learnt by the network, and forget gate deletes unwanted information and passes only the important information to the neural network. The LSTM memory block diagram is shown in Fig. 4.

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Fig. 4 LSTM memory block

3.6 Cost-Sensitive Learning Cost-sensitive is one of the data mining approaches to determine the misclassification cost. The main aim of this approach is to classify the set of misclassified imbalance class to corresponding class [17]. Long short-term memory (LSTM) performance is good in sequential data, and in some sensitive cases, it results in class imbalance problem. In our work, CS-LSTM is proposed to overcome misclassified class. The misclassification cost can be obtained by Eq. (1). E(t) = −

  p∈Sample

t k (t) log y k (t)C[class( p), k]

(1)

k

where p is the sample combined with C[class(p)], the actual class is denoted by class(p), and predicted is denoted by k. Each cost item denotes the importance of classification to the assigned prevalent class. The main functionality of cost function is to control the weight magnitude, usually in data set, cost matrix is unknown, and using this algorithm, optimal class matrix is obtained [12].

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4 Description of Data Set and Details of Baseline System In this paper, detecting malicious domain names (DMD 2018) shared task data set is been used. The shared task is composed of two tasks. Task 1 is identification of DGA domain names—Binary classification. Task 2 is categorization of DGA domain names with respect to their botnet family multiclass classification. In our work, multiclass classification data set using test data 1 is analyzed with different trials of experiment. The data set is classified into 20 different botnet family, with two separate testing data sets as shown in Table 1. The result obtained for test data 1 by the teams, participated in DMD-2018, is shown in Table 2. Table 1 AmritaDGA data set statistics DGA family

Class

Training

Testing 1

Testing 2

Benign

0

100,000

120,000

40,000

Banjori

1

15,000

25,000

10,000

Corebot

2

15,000

25,000

10,000

Dircrypt

3

15,000

25,000

300

Dnschanger

4

15,000

25,000

10,000

Fobber

5

15,000

25,000

800

Murofet

6

15,000

16,667

5000

Necurs

7

12,777

20,445

6200

Newgoz

8

15,000

20,000

3000

Padcrypt

9

15,000

20,000

3000

Proslikefan

10

15,000

20,000

3000

Pykspa

11

15,000

25,000

2000

Qadars

12

15,000

25,000

2300

Qakbot

13

15,000

25,000

1000

Qamdo

14

15,000

25,000

800

Ranbyus

15

15,000

25,000

500

Simda

16

15,000

25,000

3000

Suppobox

17

15,000

20,000

1000

Symmi

18

15,000

25,000

500

Tempedreve

19

15,000

25,000

100

Tinba

20

15,000

25,000

700

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Table 2 DMD-2018 share task result multiclass classification—test data 1 Team name

Accuracy

Recall

Precision

F1-score

UWT [2]

0.633

0.633

0.618

0.602

Deep_Dragons [20]

0.683

0.683

0.683

0.64

CHNMLRG [13]

0.648

0.648

0.662

0.6

BENHA [20]

0.272

0.272

0.194

0.168

UniPI [15]

0.655

0.655

0.647

0.615

SSNCSE [14]

0.18

0.18

0.092

0.102

Josan [20]

0.697

0.697

0.689

0.658

DeepGANet [16]

0.601

0.601

0.623

0.576

Proposed method

0.743

0.643

0.658

0.712

5 Experiment 5.1 Proposed Architecture Figure 5 gives the overall computational flow of our approach, and mainly, the structure is divided into three areas Keras embedding, feature detection and classifying legitimated or DGA-generated domain names. In Keras embedding, the raw domain names are preprocessed to character level, in which top level domain names are Fig. 5 Proposed architecture

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removed and converted into lower case. Using training data, vocabulary is created. Only the characters are selected to limit the size of the vocabulary and to meet the minimum frequency. Each character is assigned to a unique id in the form of vector. These feature components collect pattern sequence which are important within the full character sequence and then aggregate this information in length feature vector. In our experiment, dictionary size 91 and maximum features of 40 are obtained after preprocessing. Finally, using neural network, the classification components classify the detected features. Classification is done by two approaches, in first approach, the preprocessed data is passed to LSTM network followed by softmax and output layer. In second approach, the preprocessed data is passed to cost-sensitive (CS-LSTM) followed by softmax and output layer.

5.2 Identifying Network Parameters The functions of LSTM are a parametric, and thus, the performance implicitly relies on the optimal parameters. Identifying optimal parameters is considered as one of the significant tasks. The parameters which are taken into account are hidden layers, embedding vector length, batch size, learning rate and dropout. Batch normalization and dropout of 0.1 are been used between the layers to speed up the training of the model and to prevent over-fitting. It is been identified that without regularization, our model easily gets overfitted on our training data.

6 Results TensorFlow (1.6.0) [18] and Keras (2.1.5) [19] are considered as software framework. Keras provides a very high-level programming interface in contrast to Tensorflow. CPU-enabled Intel® Core™ i7-4460 CPU @ 3.20 GHz × 4 with 8 GB of RAM is used. Trail 1: Analysis of data set using various architectures such as bigram, CNN, LSTM, CNN-LSTM, Bi-LSTM is done. The following optimal parameters are applied for all models and ran for 100 epochs by considering embedding length of 128, batch size 32 with learning rate 0.1 as shown in Table 3. Most of the neural networks performance is good compared to bigram. From which, the LSTM performs good and obtains 67.2% accuracy. Trail 2: Compared with other architecture, LSTM performs well, so various trails of experiments run with different hyper-parameter such as embedding vector length, LSTM size and batch size as shown in Table 4.

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Table 3 Summary of test results on different deep learning architectures Method

Accuracy

Recall

Precision

F1-score

Bigram

0.599

0.599

0.615

0.556

CNN

0.636

0.636

0.695

0.602

LSTM

0.672

0.672

0.663

0.622

CNN-LSTM

0.649

0.656

0.633

0.605

Bi-LSTM

0.656

0.656

0.633

0.605

Table 4 Summary test results on LSTM Method

Embedding

LSTM

Accuracy

Recall

Precision

F1-score

Time (min)

LSTM

50

256

0.647

0.647

0.659

0.599

2056

LSTM

50

128

0.653

0.653

0.649

0.597

1870

LSTM

50

64

0.661

0.662

0.661

0.614

1339

LSTM

50

32

0.694

0.694

0.687

0.659

1025

In every trial of experiment until 50 epochs, the training accuracy tends to increase, and loss decreases parallel. After 50 epochs, loss tends to increase, and accuracy decreases gradually, which result in decrease of accuracy on testing. Training accuracy tends to be above 90, but testing accuracy does not exceed 69%. By analyzing the test result, it is been identified as misclassification of some classes, which results in less testing accuracy. By analyzing the result, all the models with different optimal parameters result in same misclassification class. Result states that totally four classes, class 1, 5, 6, 16 are misclassified, in which, class 1 results with 0 predicted labels, and class 5 is predicted as class 4, corresponding to that, class 6 is predicted as class 16 and class 16 as 3. To overcome this misclassification, CS-LSTM is applied by updating the weights over the misclassified class. Two trials of experiment had run in which CS-LSTM with 64 units results in 71.2% and CS-LSTM with 32 units results in 74.3% as shown in Table 5. Compared to all other methods and works, the proposed architecture CS-LSTM performs well and obtained better result over benchmark results. Table 5 Summary test results on CS-LSTM Method

Embedding

LSTM

Accuracy

Recall

Precision

F1-score

Time (min)

LSTM

50

64

0.712

0.636

0.638

0.754

3156

LSTM

50

32

0.743

0.643

0.658

0.712

1870

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7 Conclusion In this paper, a novel approach cost-sensitive LSTM (CS-LSTM) is applied on DGA family categorization, which helps to handle classification of class in an imbalanced data. Cost-sensitive helps in understanding the importance of each class and classifies the classes. Compared to all other proposed methods in the DMD 2018 share task, the proposed method performs well and obtained better result [20]. As future work, the cost-sensitive can be applied for all other deep learning architectures in order to handle imbalance data set. Acknowledgements This research was supported in part by Paramount Computer Systems and Lakshya Cyber Security Labs. We are grateful to NVIDIA India, for the GPU hardware support to research grant.

References 1. Vinayakumar R, Poornachandran P, Soman KP (2018) Scalable framework for cyber threat situational awareness based on domain name systems data analysis. In: Big data in engineering applications. Springer, Singapore, pp 113–142 2. Choudhary C, Sivaguru R, Pereira M, Yu B, Nascimento AC, De Cock M (2018) Algorithmically generated domain detection and malware family classification. In: Proceedings of the sixth international symposium on security in computing and communications (SSCC’18). Communications in computer and information science series (CCIS). Springer 3. Geffner J (2013) End-to-end analysis of a domain generating algorithm malware family. In: Black Hat USA 4. Kührer M, Rossow C, Holz T (2014) Paint it black: evaluating the effectiveness of malware blacklists. In: International workshop on recent advances in intrusion detection. Springer, Cham, pp 1–21 5. Antonakakis M, Perdisci R, Nadji Y, Vasiloglou N, Abu-Nimeh S, Lee W, Dagon D (2012) From throw-away traffic to bots: detecting the rise of DGA-based malware. In: USENIX security symposium, vol 12 6. Woodbridge J, Anderson HS, Ahuja A, Grant D (2016) Predicting domain generation algorithms with long short-term memory networks. arXiv preprint arXiv:1611.00791 7. Vinayakumar R, Soman KP, Poornachandran P, Mohan VS, Kumar AD (2019) ScaleNet: scalable and hybrid framework for cyber threat situational awareness based on DNS, URL, and email data analysis. J Cyber Secur Mobil 8(2):189–240 8. Vinayakumar R, Soman KP, Poornachandran P, Sachin Kumar S (2018) Evaluating deep learning approaches to characterize and classify the DGAs at scale. J Intell Fuzzy Syst 34(3):1265–1276 9. Vinayakumar R, Soman KP, Poornachandran P (2018) Detecting malicious domain names using deep learning approaches at scale. J Intell Fuzzy Syst 34(3):1355–1367 10. Mohan VS, Vinayakumar R, Soman KP, Poornachandran P (2018) SPOOF net: syntactic patterns for identification of ominous online factors. In: 2018 IEEE security and privacy workshops (SPW). IEEE, pp 258–263 11. Yu B, Pan J, Hu J, Nascimento A, De Cock M (2018) Character level based detection of DGA domain names 12. Tran D, Mac H, Tong V, Tran HA, Nguyen LG (2018) A LSTM based framework for handling multiclass imbalance in DGA botnet detection. Neurocomputing 275:2401–2413

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13. Rajalakshmi R, Ramraj S, Ramesh Kannan R (2019) Transfer learning approach for identification of malicious domain names. In: Thampi S, Madria S, Wang G, Rawat D, Alcaraz Calero J (eds) Security in computing and communications. SSCC 2018. Communications in computer and information science, vol 969. Springer, Singapore 14. Bharathi B, Bhuvana J (2019) Domain name detection and classification using deep neural networks. In: Thampi S, Madria S, Wang G, Rawat D, Alcaraz Calero J (eds) Security in computing and communications. SSCC 2018. Communications in computer and information science, vol 969. Springer, Singapore 15. Attardi G, Sartiano D (2019) Bidirectional LSTM models for DGA classification. In: Thampi S, Madria S, Wang G, Rawat D, Alcaraz Calero J (eds) Security in computing and communications. SSCC 2018. Communications in computer and information science, vol 969. Springer, Singapore 16. Jyothsna PV, Prabha G, Shahina KK, Vazhayil A (2019) Detecting DGA using deep neural networks (DNNs). In: Thampi S, Madria S, Wang G, Rawat D, Alcaraz Calero J (eds) Security in computing and communications. SSCC 2018. Communications in computer and information science, vol 969. Springer, Singapore 17. Sun Y, Kamel MS, Wong AK, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn 40(12):3358–3378 18. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M (2016) Tensorflow: a system for large-scale machine learning. In: OSDI, vol 16, pp 265–283 19. Chollet F et al (2015) Keras 20. Vinayakumar R, Soman KP, Poornachandran P, Alazab M, Thampi SM (2019) AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs). In: Big data recommender systems: recent trends and advances. Institution of Engineering and Technology (IET)

Oil Spill Characterization and Monitoring Using SYMLET Analysis from Synthetic-Aperture Radar Images Mukta Jagdish and S. Jerritta

Abstract Background: To identify and monitor the occurrence of oil spill in the ocean using image data obtained from different satellites. It is to characterize and identify type and other physical characteristics of oil using appropriate image processing techniques and to predict the physical characteristics of oil spill using machine learning methods. Methods: In this paper, forty sample images were used with wavelet transform analysis and machine learning techniques. For wavelet analysis (SYMLET family such as sym2, sym3, sym4, sym5, sym6, sym7, sym8 analysis) and machine learning, k-nearest neighbor algorithm is applied to optimize the oil spill feature sets. Features included RGB, spreading, complexity, standard deviation, entropy, ellipticity, intensity and correlation coefficient. This experiment was conducted on RADARSAT-2 SAR images. The features were classified using knearest neighbor algorithm. Seventy percent of features used for training and thirty percent for testing. Results: The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms outperformed very well with similar parameter settings, especially with 70% training data and 30% testing data using confusion matrix. It also represents 92.6581% accuracy for crude oil using SYMLET 5 analysis which indicates better characterization of oil spills. Results denote oil spill detection using synthetic-aperture radar (SAR) remote sensing which provides an excellent tool in oil spill characterization; various features can be extracted from SAR data set. Keywords Oil spill · Synthetic-aperture radar (SAR) · Wavelet transform · SYMLET wavelet · KNN classifier

M. Jagdish (B) Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies, VISTAS, Chennai, India e-mail: [email protected] S. Jerritta Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies, VISTAS, Chennai, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_50

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1 Introduction Oil spill is one of the most affected sources of sea pollution; spills have caused serious economic and environmental impacts to the coastal and ocean areas [1]. Near the coast, oil spills can be caused by explosion of oil, ship accidents and highly discharge of oil from tanker ships and pipelines broken. The European Commission sponsored Nano-Electronics Roadmap for Europe: Identification and Dissemination (NEREID) program to use geological, shipping and characterize of oil spill from oil exploration areas all around world circle to avoid major oil accident. The NEREIDs program helps to overcome problems of oil spills in the ocean [2–5]. According to this data, oil spill models were implemented to simulate oil spill trajectories and development and to investigate dangers of coastal zone to find out suitable solutions to overcome the impacts of marine environment [2, 3]. Real-time monitoring and early warning of oil slicks detection play important role in operation of oil spill cleaning operation to reduce its impacts to marine environment. For oil spill monitoring, synthetic-aperture radar (SAR) is one of the best remote sensing sources, and it gives useful information about the size, position, spots, dark patches and occurrence of the spills [1]. Moreover, it covers larger area, day-to-day monitoring, early warning, silk detection and all-weather capabilities [6–12]. In their previous stages, oil spill detection studies were based on single SAR polar metric images. Theoretical rationale of oil spill detection using synthetic aperture radar (SAR) is that the availability of oil slicks on the seafloor dampens and capillary waves, so Bragg scattering is largely weakened from the sea surface. Sea surface wind speed is 3–14 m/s for oil spills detection [13]. Oil spills can be detected as “dark” areas as a result of synthetic-aperture radar (SAR) images. To classify oil spills, various features have been proposed. The co-polarized phase difference with standard deviation (difference between vertical receive-VV and vertical transmit and horizontal receive-HH and horizontal transmit channel) results in a strong capability of oil spill classification on X-, L- and C-band data. Nunziata et al. have proposed pedestal height which describes the different polarization signature between biogenic lookalikes and mineral oil [14]. Oil spills are different types such as class A, B, C, D and non-petroleum oil. Class A oils are high-quality light “crude oils” and refined products such as jet fuel and gasoline which disperse readily but affect aquatic life of upper water column. Gasoline includes toxic components such as benzene a known hexane and carcinogen, which damage animals and human life systems. Class B oils are crude oils with lower-quality light and refined products such as other heating oils, and kerosene leaves a film on surfaces, but film will disperse and dilute if flushed vigorously with the water. Crude oils with lower-quality light and refined products oils will burn longer and highly flammable than class A oils. It is also known as “non-sticky” oils. They are less toxic oils but more adhere to surface. Based on U.S Government the wild life and fish services causes long-term contamination zone for production. Class C oils are sticky and heavy. Class C oil will not spread quickly or penetrate soil and sand because they are more adhere strongly to surface. Class C oil do not easily disperse and dilute. Such oils are prone to forming emulsions or lumps of oil.

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Class D oil is solid crude oil which has least toxicity. Class D oil occurs when it gets heated and hardens on the surface, which makes nearly impossible to cleanup. Oils derived from animal or plant and synthetic oils and fats are regulated because they cause contamination if they release into environment. Non-petroleum oils coat wildlife and can cause death due to dehydration or suffocation. Non-petroleum oils easily penetrate soil and slow to break down, causing damage to affected areas. Non-petroleum oil includes synthetic oils and cooking fats.

2 Material and Methods 2.1 SAR Data Synthetic-aperture radar image provides wide range of images which is very useful for research purpose and regular observation of oil spill in ocean. It provides either cross-polarization or single linear co-polarization mode with horizontal receive and horizontal transmit (HH), vertical receive and vertical transmit (VV), horizontal transmit and vertical receive (HV) and vertical transmit and horizontal receive (VH). In this research, around 40 samples images have been used which belongs to different oil spill regions around the world. Some sample images are displayed which belongs to oil spill regions such as Gulf of Mexico, Chennai Ennore–Tiruvottiyur region, South Korea, Chilov and Pirallahi, Russian, Thailand on different days (Fig. 1 and Table 1).

Gulf of Mexico

Chilov and Pirallahi Fig. 1 Satellite images

Chennai

Russian oil spill

South Korea

Thailand

Beam mode

ENVISAT ASAR

SCATSAT-1

ENVISAT ASAR

ENVISAT SAR images

ENVISAT SAR

ENVISAT SAR

S. No.

1

2

3

4

5

6

Table 1 Image description

Thailand

Russian

Chilov and Pirallahi

South Korea

Chennai Ennore–Tiruvottiyur region

Gulf of Mexico

Place

7

7

6

6

7

7

Images

Area

July 27–August 13, 2013

2014

2006–2010

11 December, 2007

2017—February 2017

28-02 January

800 m wide and 2.5 km long

118 km (73 miles)

1100 m

50 tons

0.5 tons

1.2 million tons

260,000 tonnes

330,000 m2

210,000,000 US gallons

Volume

9.9 million US gallons

km2

34,000 m2

27 April–7 May 2010 6500–176,100

Date

Crude oil

Crude oil

Oil rocks settlement

Crude oil

Petrol

Crude oil

Type of oil

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2.2 Data Processing In this research, the preprocessing method is consider to improve the quality of image and to avoid background noises of oil spill satellite images. Preprocessing also help in improving images from distortion features. In preprocessing, the redundancy helps to correct the degradation for input images and data processing helps in cleaning data, removing or correcting the inaccurate records from input images. Data normalization process is used for standardizing the range of single or independent features or variables of data. Data transformation is used for converting old format to the new format of input images.

2.3 Feature Extraction Feature extraction process is used for transforming the input images into a set of features or variables. In this study, nine features including RGB, spreading, complexity, mean, standard deviation, entropy, ellipticity, intensity and correlation coefficient are extracted from the covariance matrix of SAR data. In this study, nine features are investigated. When input image to an algorithm is very large to process and suspected to be redundant, then the input image is transformed into reduced representation set of imaged features. Transfer of input data into set of features is known as feature extraction.

Table 2 Features investigated Features

Definition

Color-based feature RGB

True color image (m-by-n-by-3 data array)

Geological features

Formula

Spreading Complexity

s = 100λ/(λ1 + λ2 ) √ c = P/2 π A

Statistical features

Formula

Mean

Sum of all observation divided by number of observation for particular spillage area

Standard deviation

Pixel intensity values belong to the object with a spillage area of oil spill

Thermal features

pi =

λ 3 i j=1

Entropy

λj

Ellipticity

s3 sin(2x) = − ms 0

Intensity

2 SVV

Correlation coefficient Coh =

√ ||

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Table 2 represents features such as color-based feature (Red, Blue, GreenTrue color image [m-by-n-by-3 data array]), geological features: spreading and complexity. Spreading is defined as s = 100λ/(λ1 + λ2 ), whereas λ1 and λ2 are eigenvalues with covariance matrix λ1 greater than λ2 . Assume low value for thin and long objects and√ objects closer to a circular shape with high values. Complexity denotes as c = P/2 π A for simple geometry region with small numerical values and larger values with complex geometry for regions. Statistical features (mean helps to detect statistical features it defined as sum of all observation divided by number of observation for particular spillage area and standard deviation is define as pixel intensity values belong to the object with a spillage area of oil spill). Thermal features used to partition images into region of interest and to classify those regions which need to be considered for observation, thermal feature provides informations in the spatial arrangement of intensities in an image it includes entropy, ellipticity, intensity and correlation coefficient.

2.4 Characterization of Oil Spill This is done using KNN classifier with confusion matrix. In KNN classifier the object is classified with its neighbors using majority of vote, and then, the object is assigns to the class, which helps to classify oil types (k is typically small and is a positive integer). If k assigns to 1, then the object is assigned to the single nearest neighbor class. The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms. To examine the feature the KNN classifier, the scatter plots of original features and features obtained by SYMLET analysis with VV and HH transition/receiving are combinations together, which results in most effective feature to classify oil spill type. It is observed that k-nearest neighbor algorithms extract information from features and improve their separability to distinguish none mineral samples and mineral oil. As a feature optimizer, the pre-training work can reveal reduction of noise and the latent relationship in the features. It improves the overall performance of the followed k-nearest neighbor classification procedure.

3 Result and Discussion To examine the feature the KNN classifier, scatter plots of the main original features and the features derived by wavelet transform using SYMLET with VV and HH transition/receiving combinations, as the most effective feature in oil spill classification. It is observed that k-nearest neighbor algorithms extract information from features and improve their separability to distinguish none mineral samples and mineral oil. As a feature optimizer, the pre-training work can reveal reduction of noise and the latent relationship in features. It improves the overall performance of the followed k-nearest neighbor classification procedure. On training data set, machine learning algorithms

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have excellence performance than neural networks. Based on pre-training, machine learning algorithms such as k-nearest neighbor have stronger capability to achieve the optimized solution of the problems. The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms outperformed very well with similar parameter settings, especially with 70% training data and 30% testing data using confusion matrix. The performance was analyzed by k-nearest neighbor (KNN) classifiers. KNN assigns a class based on the predominant class among the k-nearest neighbors. The value of k was chosen as the number of classes used for classification. In this research work, the features derived from collected data set with 70% and 30% for all oil spilled images. Then, the 70% features were used for training the classifier, and 30% features were used for testing. The testing and training features belonged to random subjects and varied in each run of the program (Table 3). Table 4 shows result of k-nearest neighbor-based combined analysis for oil spill classification (independent validation) which is given in Table 4. This represents consolidated results of total accuracy for color-based features, geological features, statistical features, thermal features and SYMLET analysis (sym2, sym3, sym4, sym5, sym6, sym7, sym 8) for the input image.

4 Conclusions This research worked on identify and monitoring the occurrence of oil spill in the ocean using image data obtained from the different satellites for observation. This research also helps to characterize and identify the type of oil with physical characteristics of spill using appropriate image processing techniques and to predict the physical characteristics of oil spill using machine learning methods. In this paper, forty sample images were used with wavelet transform analysis and machine learning techniques. For wavelet analysis (SYMLET family such as sym2, sym3, sym4, sym5, sym6, sym7, sym8 analysis) and machine learning, k-nearest neighbor algorithm is applied to optimize the oil spill feature sets. Features included RGB, spreading, complexity, standard deviation, entropy, ellipticity, intensity and correlation coefficient. This experiment was conducted on RADARSAT-2 SAR images. The features were classified using k-nearest neighbor algorithm. Seventy percent of features used for training and thirty percent for testing. The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms outperformed very well with similar parameter settings, especially with 70% training data and 30% testing data using confusion matrix. It also represents 92.6581% accuracy for crude oil using SYMLET 5 analysis which indicates better characterization of oil spills. Here, the comparison table of SYMLET analysis represents total accuracy of sym5 analysis with 88.4689 and sym8 analysis with 82.4765 which performs well than other SYMLET with total accuracy. Oil spill detection using synthetic-aperture radar (SAR) remote sensing provides an excellent tool in oil spill characterization. Various features can be extracted from SAR data set.

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Table 3 Represents color-based, statistical, geological and other features results using k-nearest neighbor classifier K-value

Petrol

Diesel

Crude oil

Average

Color-based features Red

3

88.8889

60.0000

85.7143

78.2011

Green

3

77.7778

60.0000

92.8571

76.8783

Blue

3

77.7778

50.0000

78.5714

72.1164

All color features

3

66.6667

80.0000

87.2123

77.4603

Statistical features Mean

3

55.5556

60.0000

85.7143

67.0899

Standard deviation

3

65.4356

100.0000

84.6513

80.4233

All statistical features

3

64.7667

100.0000

83.3143

84.2370

Entropy

3

88.8889

100.0000

85.1734

91.5344

Ellipticity

3

66.6667

100.0000

87.7544

84.1270

Intensity

3

77.7778

80.0000

92.8571

83.5450

Correlation coefficient

3

88.1289

80.0000

78.5714

82.4868

All thermal features

3

66.6754

100.0000

89.5463

85.7654

Other features

Geological features Spreading

3

77.7778

60.0000

92.8571

76.8783

Complexity

3

66.6667

60.0000

85.7143

70.7937

Spreading and complexity

3

88.8889

60.0000

87.3244

78.2217

Discrete wavelet transform SYMLET analysis Color-based feature using SYMLET 2 analysis Red

4

66.6667

60.0000

71.4286

66.0317

Green

4

88.8889

40.0000

85.7143

65.4497

Blue

4

77.7778

60.0000

84.4398

77.4603

All color features

4

55.6555

80.0000

85.4521

78.7643

Statistical features using SYMLET 2 analysis Knn_classifier

Acc_Petrol

Acc_Crude

Total accuracy

Mean

4

44.6549

Acc_Diesel 60.0000

88.8765

67.0897

Standard deviation

4

55.5556

100.0000

83.7654

80.4233

Total features

4.0000

66.7778

80.0000

79.6599

91.6934 (continued)

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Table 3 (continued) K-value

Petrol

Diesel

Crude oil

Average

Thermal features using SYMLET 2 analysis Entropy

4

86.6123

100.0000

74.7654

91.5344

Ellipticity

4

76.7564

100.0000

87.4324

84.1270

Intensity

4

75.6453

80.0000

93.7896

83.5450

Correlation coefficient

4

64.8576

70.0000

87.4357

82.4868

Total features

4

86.7564

100.0000

85.8576

84.1270

Geographical features using SYMLET 2 analysis Spreading

4

66.5556

70.0000

92.8571

76.8783

Complexity

4

55.6665

80.0000

84.5697

70.7937

Spreading and complexity

4

77.3335

60.0000

80.6475

78.2011

Color-based feature using SYMLET 3 analysis Red

4

55.6578

70.0000

77.5566

77.6655

Green

4

54.6589

70.0000

95.7689

88.4567

Blue

4

87.7775

60.0000

78.8877

72.1164

All color features

4

77.6998

60.0000

83.2468

67.4033

Statistical features using SYMLET 3 analysis Mean

4

54.5556

50.0000

88.3334

66.4567

Standard deviation

4

57.8886

90.0000

87.6767

82.7876

Total features

4

77.6457

80.0000

77.9875

90.7564

Thermal features using SYMLET 3 analysis Entropy

4

68.5559

90.0000

89.7883

95.3432

Ellipticity

4

66.6127

100.0000

79.3998

80.6008

Intensity

4

77.8888

70.0000

91.4447

83.5459

Correlation coefficient

4

89.7775

70.0000

75.8776

82.4868

Total features

4

69.5657

100.0000

83.5123

84.1270

Geographical features using SYMLET 3 analysis Spreading

4

66.5556

70.0000

92.8571

76.8783

Complexity

4

55.6665

80.0000

84.5697

70.7937

Spreading and complexity

4

77.3335

60.0000

80.6475

78.2011

70.0000

88.6711

73.2001

Color-based feature using SYMLET 4 analysis Red

4

66.8877

(continued)

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Table 3 (continued) K-value

Petrol

Crude oil

Average

Green

4

78.4768

Diesel 80.0000

92.8571

78.8773

Blue

4

77.1278

60.0000

78.5714

77.1114

All color features

4

55.3434

70.0000

85.7143

88.4600

Statistical features using SYMLET 4 analysis Mean

4

55.6666

70.0000

84.5645

67.8791

Standard deviation

4

59.5126

100.0000

85.1223

92.5589

Total features

4

77.6457

80.0000

66.9874

88.6457

Thermal features using SYMLET 4 analysis Entropy

4

80.8789

80.0000

82.7683

84.7865

Ellipticity

4

76.6567

90.0000

87.7144

78.9456

Intensity

4

87.5578

80.0000

82.8121

81.5498

Correlation coefficient

4

89.8889

50.0000

79.4514

71.6489

Total features

4

65.7747

80.0000

88.6998

56.4894

Geographical features using SYMLET 4 analysis Spreading

4

77.3338

60.0000

96.4444

76.1112

Complexity

4

77.2277

60.0000

89.8987

72.4463

Spreading and complexity

4

88.1288

60.0000

85.6683

79.8984

Spreading

4

77.3338

60.0000

96.4444

76.1112

Color-based feature using SYMLET 5 analysis Red

4

66.6621

70.0000

92.6761

88.2356

Green

4

77.7557

70.0000

89.1256

77.5659

Blue

4

77.8866

60.0000

66.5645

79.5666

All color features

4

66.5557

50.0000

56.7778

87.5135

Statistical features using SYMLET 5 analysis Mean

4

53.6623

60.0000

85.7143

67.0899

Standard deviation

4

66.6667

90.0000

85.7143

80.4233

Total features

4

79.6457

70.0000

78.7645

85.7465

Thermal features using SYMLET 5 analysis Entropy

4

87.8564

100.0000

85.7143

91.5344

Ellipticity

4

55.5556

100.0000

85.7143

84.1270

Intensity

4

67.7888

80.0000

92.8571

83.5450 (continued)

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Table 3 (continued) K-value

Petrol

Crude oil

Average

Correlation coefficient

4

89.8919

Diesel 80.0000

78.5714

82.4868

Total features

4

67.8787

100.0000

85.7143

84.1270

Geographical features using SYMLET 5 analysis Spreading

4

88.6668

50.0000

84.3221

76.6325

Complexity

4

66.5554

40.0000

72.1566

70.4475

Spreading and complexity

4

77.6664

70.0000

85.7776

78.1156

Color-based feature using SYMLET 6 analysis Red

4

66.8777

80.0000

66.7584

78.7681

Green

4

68.5465

70.0000

88.7657

66.8553

Blue

4

77.1278

80.0000

87.9765

78.1654

All color features

4

66.6786

90.0000

89.3143

87.8903

Statistical features using SYMLET 6 analysis Mean

4

67.4565

70.0000

75.2143

77.6566

Standard deviation

4

55.6565

90.0000

79.7453

88.7678

Total features

4

66.5456

80.4532

80.4434

85.8675

Thermal features using SYMLET 6 analysis Entropy

4

88.7778

90.0000

81.5643

91.5674

Ellipticity

4

64.5557

80.0000

75.8783

84.7654

Intensity

4

77.4338

60.0000

55.8571

84.9890

Correlation coefficient

4

87.8879

60.0000

88.7234

82.4878

Total features

4

65.6667

90.0000

89.6643

84.1666

Geographical features using SYMLET 6 analysis Spreading

4

76.8647

70.0000

92.7685

88.7688

Complexity

4

66.6577

70.0000

83.8678

75.8797

Spreading and complexity

4

88.7569

60.0000

85.7768

77.7681

Color-based feature using SYMLET 7 analysis Red

4

81.8239

50.0000

81.7987

79.2044

Green

4

67.6678

40.0000

93.8771

74.7653

Blue

4

78.7768

70.0000

71.6614

82.1234

All color features

4

76.2367

70.0000

86.7183

78.1103 (continued)

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Table 3 (continued) K-value

Petrol

Diesel

Crude oil

Average

Statistical features using SYMLET 7 analysis Mean

4

66.5445

60.0000

87.3452

88.2322

Standard deviation

4

76.4433

100.0000

82.9153

80.6565

Total features

4

65.3498

76.8768

88.6455

93.8675

Thermal features using SYMLET 7 analysis Entropy

4

56.7777

90.0000

77.6778

96.8333

Ellipticity

4

77.5555

90.0000

65.8657

77.3272

Intensity

4

87.4343

70.0000

91.9571

81.4440

Correlation coefficient

4

66.5463

70.0000

87.7676

89.8868

Total features

4

68.4456

90.0000

88.8876

81.9234

Geographical features using SYMLET 7 analysis Spreading

4

73.7765

70.0000

78.5445

89.7765

Complexity

4

78.6767

60.0000

77.4355

66.3343

Spreading and complexity

4

81.8889

60.0000

55.6676

78.5468

Color-based feature using SYMLET 8 analysis Red

4

55.3265

50.0000

88.3321

55.1356

Green

4

77.5444

50.0000

91.5478

64.1458

Blue

4

71.3565

70.0000

90.4548

88.1356

All color features

4

55.4544

80.0000

65.9325

78.5698

Statistical features using SYMLET 8 analysis Mean

4

65.5556

70.0000

78.8765

67.0899

Standard deviation

4

53.9886

80.0000

83.5674

80.3331

Total features

4

66.5557

80.0000

87.6691

88.5121

Thermal features using SYMLET 8 analysis Entropy

4

76.9876

90.0000

86.7635

81.1244

Ellipticity

4

64.7876

100.0000

76.3332

82.1200

Intensity

4

67.8765

80.0000

88.3466

73.5550

Correlation coefficient

4

76.8765

70.0000

87.6509

89.4868

Total features

4.0000

88.6546

100.0000

81.5543

94.3425

Geographical features using SYMLET 8 analysis Spreading

4

66.4589

70.0000

65.9865

91.1325

Complexity

4

77.9812

80.0000

88.7894

88.1548 (continued)

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Table 3 (continued) Spreading and complexity

K-value

Petrol

4

79.2225

Diesel 70.0000

Crude oil

Average

87.6548

77.3655

Table 4 K-nearest neighbor-based combined analysis for oil spill classification (independent validation) Knn_classifier

Acc_Petrol

Color-based

3

66.6667

Geological

3

88.8889

Statistical

3

Acc_Diesel

Acc_Crude oil

Total accuracy

80.0000

85.7143

77.4603

60.0000

85.7143

78.2011

66.6667

100.0000

85.7143

84.1270

Thermal

3

66.6754

100.0000

89.5463

85.7654

SYMLET 2

4

45.4674

80.0000

90.3322

78.5446

SYMLET 3

4

49.4689

80.0000

88.5676

65.7783

SYMLET 4

4

55.5454

70.0000

91.4568

75.4411

SYMLET 5

4

76.2375

50.0000

92.6581

72.4339

SYMLET 6

4

74.4478

80.0000

88.6571

88.4689

SYMLET 7

4

77.6667

80.0000

89.6677

72.3239

SYMLET 8

4

86.3563

80.0000

92.1433

82.4765

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11. Garcia-Pineda O, MacDonald IR, Li X, Jackson CR, Pichel WG (2013) Oil spill mapping and measurement in the Gulf of Mexico with textural classifier neural network algorithm (TCNNA). IEEE J Sel Top Appl Earth Obs Remote Sens 6:2517–2525 12. Pan G, Tang D, Zhang Y (2012) Satellite monitoring of phytoplankton in the East Mediterranean Sea after the 2006 Lebanon oil spill. Int J Remote Sens 33:7482–7490 13. Migliaccio M, Nunziata F, Gambardella A (2009) On the co-polarized phase difference for oil spill observation. Int J Remote Sens 30:1587–1602 14. Nunziata F, Migliaccio M, Gambardella A (2011) Pedestal height for sea oil slick observation. IET Radar Sonar Navig 5:103–110

Performance Analysis of Machine Learning Algorithms for IoT-Based Human Activity Recognition Shwet Ketu and Pramod Kumar Mishra

Abstract Enormous growth is seen in the field of information technology and communication technology which enable the Internet of Things (IoT) technology on the boon. Nowadays, IoT technology is very common and widely used in all areas. It is also expanding its wing day by day. At the end of 2019, IoT devices will reach to 26.66 billion. Due to wide and diverse use of IoT technology, a large amount of valuable data is generated which gives researchers to deal with the huge amounts of real-time data. Machine learning plays a vital role in making a IoT environment, but it is a very complex task to build a smart environment. For personal healthcare monitoring, IoT-based sensors and mobiles devices play a crucial role in the betterment of the human lifestyle. Wearable sensor technology which is incorporated with mobile devices is more commonly used in monitoring personal health and wellbeing. In this research work, we examine the capability and performance of machine learning algorithms over the built-human activity recognition (HAR) dataset. Based on the performance evaluation results, gradient boosting classifier (GBC), support vector machine (SVM), random forest (RF), bagging classifier (BAG), classification and regression trees (CART), k-nearest neighbors (KNN), and extra trees classifier (ETC) algorithms have the better accuracy and suitable for real IoT datasets. Keywords Machine Learning Techniques · Internet of Things (IoT) · Human Activity Recognition (HAR) · Smart Health Care · Wearable Sensors Devices · Gradient Boosting Classifier (GBC) · Support Vector Machine (SVM) · Random Forest (RF) · Bagging Classifier (BAG) · Classification And Regression Trees (CART) · K-Nearest Neighbors (KNN) · Extra Trees Classifier (ETC)

S. Ketu (B) · P. K. Mishra Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India e-mail: [email protected] P. K. Mishra e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_51

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1 Introduction Internet of Things is an infrastructure that enables human-to-human or human-tocomputer interaction over a network. It is a lattice of computing device having a unique identification number which is interrelated with each other and transfers the data over a network [1]. In a couple of years, massive growth has been seen in the field of information and communication technologies which increases the use of sensor devices and puts the Internet of Things (IoT) technology on the boon. Due to the immense and diverse uses of IoT, a huge amount of valuable data is generated. All the data which are generated by IoT devices are in real-time which gives a domain for the researchers to deal with real datasets. The handling of real-time data is always a complex task, and it continually needs improvement. Improvement is not only required in algorithmic aspects but also required for the processing platform too [2]. Accurate technologies and algorithms should be developed to cope up with rapid data movement. In 2015, 15.41 billion IoT connected devices were installed worldwide. By the end of 2019, it will reach 26.66 billion, and in the year 2025, it will be near about 75.44 billion. This would generate massive useful data, so it is important to look into it and find the appropriate algorithm to deal with [3]. Everyone all over the world is using smartphones, and due to the busy lifestyle, they have limited time. Nowadays, the IoT is linked with smartphones with the help of some wearable devices so that users can analyze their personal data [4–6]. For making a smarter environment, IoT plays a key role by giving the real-time data feed and analyzes the information using the concept of computation intelligence for smart decision making. Efficient decision-making process saves a lot of computation time and increases overall throughput [7, 8]. However, processing a huge amount of real-time data, i.e., big data analytics, needs to be improved. These are the area where we have to be careful before performing the computation. If we are talking about the dataset which is generated by wearable devices, the HAR is one of them which is very important, having major contributions to enhance the quality of life. The HAR data is crucial data about health care. It plays a dynamic role in building smart IoT environment [9]. The monitoring of patients, smart hospital, day-to-day activity chart and keep track of life-threatening disorders, etc., are the application area from where the HAR data is generated. Nowadays, the IoT devices are often mounted with smartphones which create a new domain known as mobile health care [10]. In this paper, we have taken the activity recognition from single chest-mounted accelerometer dataset [11, 12]. The purpose of this study is to find out the impact of machine learning algorithm on IoT dataset to investigate whether the machine learning algorithms are well suited or not [13, 14]. This study also aims to improve the machine learning algorithms or designing new algorithms because the existing algorithms are not well suited for IoT datasets [15]. In this paper, we have performed a rigorous simulation of machine learning algorithms on the IoT human activity recognition dataset. We have used fourteen machine learning algorithms, viz. gradient boosting classifier (GBC), support

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vector machine (SVM), random forest (RF), bagging classifier (BAG), classification and regression trees (CART), k-nearest neighbors (KNN), extra trees classifier (ETC), AdaBoost (ADB), quadratic discriminant analysis (QDA), multilayer perceptron (MLP), Naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) [14, 16–18]. The main aim of the study is to find out the impact and adaptability of various machine learning algorithms on IoT-based dataset. The paper is organized as follows: In Sect. 2, the relevant work done before and machine learning algorithm which is used for HAR dataset are discussed in detail. Our experimental setup and methodology are discussed in Sect. 3. Performance evaluations are represented in Sect. 4. In Sect. 5, conclusion based on performance is drawn.

2 Related Work and Methodologies 2.1 Related Work See Table 1.

2.2 Methodologies 1.

2.

3.

Logistic Regression Logistic regression is one of the machine learning algorithms which is used to classify set of independent variables in the form of discrete values. The discrete values may be binary values, i.e., 0 or 1, Yes or No, True or False. In other words, the logistic function is used to find out the probability of fitting data in the form of occurrence which means it predicts the probability of event in the form of its occurrence pattern. The output of the occurrence pattern lies between 0 and 1 [14, 16]. CART Classification and regression trees algorithm is one of the decision tree algorithms which is widely used in machine learning for classification purpose. It is based on the concept of predictive modeling. As it is based on predictive modeling, so that tree is built for classification. Based on the computation, we find the well-suited model where the data point falls. Based on that model, we classify the dataset [13, 14, 16]. KNN k-nearest neighbors, a widely used algorithm, is used to solve the problem of classification as well as regression. It works on the concept of k value and the majority value of k classifies the data. The value of k is k neighbors which

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Table 1 Literature review a quick view Year Author

Description

2019 Chin et al. [19]

Classification of human motion Support vector machine, data using accelerometer mounted k-nearest neighbors and random on wrist worn forest

2018 Hassan et al. [20] Classification using machine learning algorithms

Classification Technique Used

Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Ignatov [21]

Classification of WISDM dataset using deep learning approach

Wang et al. [22]

Classification using deep learning Deep learning-based approach approach

Li et al. [23]

Classification using deep neural network

MLP, CNN, LSTM, Hybrid Model, AE

Subasi et al. [24]

Data mining techniques on mHeath dataset

KNN, ANN, SVM, C4.5, CART, Random Forest, Rotation Forest

2016 Alam et al. [25]

Classification of dataset using eight data mining techniques

Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), C4.5, C5.0, Artificial Neural Networks (ANNs), Deep Learning ANNs (DLANNs)

2015 Rana et al. [26]

HAR-based machine learning techniques

Decision Tree, AdaBoost, RF, SVM

4.

5.

Deep learning-based approach

are present in the data. The distance between the neighbors is calculated by the distance functions which are Euclidean, Manhattan, Hamming distance and Minkowski. Among these, the Euclidean and Manhattan are commonly used [18, 27]. Linear Discriminant Analysis It is a linear classification technique which works for classification of more than two classes. It is the most widely used machine learning algorithm. Methodology-wise, it is very similar to PCA, i.e., principal component analysis. Both LDA and PCA works on the concept of searching the component that maximizes the variance. The only difference among them is that the linear discriminant analysis works on the separation between numerous classes. It is more likely to use in dimension reduction which is a part of preprocessing [16]. Naïve Bayes Naïve Bayes is a classification algorithm which is based on the Bayes’ theorem. The Bayes’ theorem works on the principle of finding independence between predictors. Based on the specific feature which is present in class, the algorithm assumes that it is distinct to other feature, which means it takes all the feature independently for classification. It is one of the simplest and useful algorithms which can deal with larger datasets [13].

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Support Vector Machine It is one of well-known classification algorithm which is widely used. Each data point which is coming from the input field is plotted in a hyperplane. The coordinate value is selected by using the feature that is present in the data. These coordinates act as a support vector for classification. For the partition, we calculate the distance between the groups. The partitioning line is drawn by calculating the closest distance among the groups [13, 14, 16]. 7. MLP A multilayer perceptron (MLP) is an artificial neural network-based deep learning algorithm. As the name suggests, it is a combination of perceptrons. It consists of three layers: input layer, output layer and hidden layer. The signal is received through input layer, and prediction is done at the output layer. All the computation is performed on the hidden layer. It acts as a computational engine for MLP. It mostly gives the best solution for supervised learning problem [14, 18, 27]. 8. Random Forest Random forest is from the family of decision trees based on the ensemble learning. The decision trees which are made from the input data are known as forest. For the classification of new samples based on the attributes, each model that is built using the decision tree gives its votes to that specific class. The most voted classification is taken for consideration by the forest. The classification is performed using the topmost voted classification model [14, 16, 17]. 9. AdaBoost AdaBoost, which stands for Adaptive Boosting, is one of the commonly used classification algorithms. As the name suggests, it is based on the concept of adaptive boosting that means boosting the classifiers which are weak or by which the result is influenced. The basic objective of algorithm is to convert the weak classifier into a stronger one for the best classification result. It is used to rectify the weak classifier and boost it into the stronger one escalates the overall performance of the algorithm [13, 14, 27]. 10. SGD Classifier It is an effective approach to discriminative learning. It is one of the optimization algorithms which is old but commonly used. It works on the concept of finding the coefficients of a given function which are responsible for minimizing the overall cost of the function. It is applied when other algorithms fail to calculate the parameters because it uses linear algebra for the calculation of the parameter. It is very efficient and easy to implement [16]. 11. Bagging Classifier Bagging is one of the ensemble learning algorithms which is based on the concept of bootstrap. The classification is randomly distributed on the training dataset. Each classifier generates its training set at random basis. It also calculates the replacement cost. It uses the concept of voting and averaging both and aggregates the individual prediction of random subset to generate the final prediction. It is a powerful meta-estimator for effective and efficient classification of the data [14, 27].

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12. Extra Trees Classifier It is a classification algorithm which is a variant of random forest algorithm. However, this algorithm is not based on greedy concept, as the random forest is. It takes decision boundaries randomly rather which is opposite to random forest. The whole data sample is used at each and every step. Sometimes choosing the boundaries randomly give the worst result too. It is not guaranteed that this algorithm will give the best result as compared to random forest [13, 14, 16]. 13. Gradient Boosting Classifier The gradient boosting classifier belongs to the boosting algorithm family. It is one of the machine learning algorithms which is well suited for huge datasets or for high volume of dataset. It has a very high prediction power which makes it more effective and powerful to deal with big data. Boosting is also an ensemble learning process which aims to continuously improve the classification result using various base estimators. The best estimator is taken into consideration, and the rest will be dropped. By taking the combination of estimators, it gives more effective result [14, 16, 27]. 14. Quadratic Discriminant Analysis The quadratic discriminant analysis is one of the algorithms which is used for classification based on quadric surface. In other words, it performs the individual measurement of two or more objects classes. It is a generalized form of linear discriminant analysis. Its working is based on the assumption that the distributions of measurement are normal. It determines discrimination of variables among two or more groups [13, 14, 16].

3 Experimental Setup and Methodology For the experiment, we have taken the fourteen well-established machine learning algorithms, and all the simulation is performed on a Dell workstation having the configuration of 64 bit Intel Xeon Processor with 3.60 GHz speed and 32 GB of RAM. All the algorithms that are used for simulation have been implemented in Python. We have taken the dataset [12] from the UCI repository which is a sensor data collected through the accelerometer which is mounted on the chest of the participant. This dataset is a human activity recognition (HAR) dataset based on the body posture movements. Before performing the simulation task, we preprocess the dataset which is well suited for the experiment. The preprocessed data is served as input to various machine learning algorithms. The output is presented with the help of performance evaluation measures. The experimentation policy is given away in Fig. 1.

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Fig. 1 Experiment methodology

4 Experimental Evaluation During the experimental evaluation, various data mining algorithms have been applied on HAR benchmark dataset [12] to find out the impact of algorithms over the activity recognition collected through single chest-mounted accelerometer with 52 Hz sampling frequency. For this study, we have taken the HAR dataset [12] from a wearable device that is mounted on the chest. The data is generated by the accelerometer sensor having the values in 3D plane, i.e., x acceleration, y acceleration and z acceleration. This dataset consists of seven activities performed by the 15 participants: Standing Up, Standing, Walking and Talking with Someone, Talking while Standing, Working at Computer, Walking, Standing Going Up/Downstairs, Walking and Going Up/Downstairs. For performance evaluation, we have taken the individual participant data which consists of seven activities and various data mining algorithms have been applied on it. The algorithmic evaluation is based on accuracy, precision, recall, and F1 score of various algorithms that have been applied to the benchmark dataset. To find out the overall impact of various data mining algorithms, we have combined all participants’ data into a single file to see the performance of algorithms at a glance. Figure 2 shows the performance evaluation of five machine learning algorithms: CART, KNN, LR, LDA, and NB. The machine learning algorithm is applied to the 15 dataset files which are generated by 15 participants using the accelerometer mounted on the chest. These all are sensor-generated data in a 3D plane. From the classification-based performance evaluation, it is clear that among the five machine

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Algorithms Performance Evaluation I 1.2000 1.0000 0.8000 0.6000 0.4000 0.2000 0.0000 P1

P2

P3

P4

CART

P5

P6 KNN

P7

P8 LR

P9 P10 P11 P12 P13 P14 P15 LDA

NB

Fig. 2 Algorithms performance evaluation I

algorithms, the CART and KNN are on the top. They are having the accuracy of 99.94% and 99.93%, respectively. In the rest three algorithms, the LDA struggles a lot with an accuracy of 81.79% only. NB and LR have an accuracy of 91.76% and 86.45%, respectively. The order of algorithms based on classification accuracy is: CART > KNN > NB > LR > LDA. Figure 3 shows the performance evaluation of four machine learning algorithms that are BAG, GBC, ETC, and QAD. The machine learning algorithm is applied to 15 participants datasets collected by accelerometer which is mounted on the participant’s chest. These all are sensor-generated data in a 3D plane. From the classification-based performance evaluation, it is clear that among the four machine algorithms, the GBC is on the top. It has an accuracy of 99.99% which is close to 100%. In the rest three algorithms, the QDA struggles a lot with an accuracy of 92.86% only. BAG and ETC have an accuracy of 99.94% and 99.81%, respectively. The order of algorithms based on classification accuracy is: GBC > BAG > ETC > QDA. Figure 4 shows the performance evaluation of five machine learning algorithms: SVM, RF, MLP, ADB, and SGD. The machine learning algorithm is applied on the 15 participants’ generated dataset collected by accelerometer which is mounted on every participant’s chest. These all are sensor-generated data in 3D plane. From the classification-based performance evaluation, it is clear that among the five machine algorithms, the RF and SVM are on the top. They have the accuracy 99.95% and 99.92%, respectively. In rest three algorithms, the SGD struggles a lot with accuracy of 74.28% only. ADB and MLP have the accuracy of 95.44% and 92.18%, respectively. The order of algorithms based on classification accuracy is: RF > SVM > ADB > MLP > SGD.

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Algorithms Performance Evaluation II 1.0200 1.0000 0.9800 0.9600 0.9400 0.9200 0.9000 0.8800 0.8600 0.8400 P1

P2

P3

P4

P5

P6

BAG

P7

P8

GBC

P9 P10 P11 P12 P13 P14 P15 ETC

QDA

Fig. 3 Algorithms performance evaluation II

Algorithms Performance Evaluation III 1.2000 1.0000 0.8000 0.6000 0.4000 0.2000 0.0000 P1

P2

P3

P4

SVM

P5

P6 RF

P7

P8 MLP

P9 P10 P11 P12 P13 P14 P15 ADB

SGD

Fig. 4 Algorithms performance evaluation III

Figure 5 shows the performance evaluation representation of all machine learning algorithms at a glance. We had used 14 machine learning algorithms for the evaluation of 15 participants’ accelerometer sensor-generated data in 3D plane. From the table in Fig. 5, it is clear that CART, KNN, SVM, RF, BAG, ETC, and GBC give 99% accuracy on average in all the participants’ data staring from P1 to P15. The machine

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1.2000 1.0000 0.8000 0.6000 0.4000 0.2000 0.0000

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15

CART 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 KNN 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 SVM 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 RF

0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

BAG

0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

ETC

0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

GBC

0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

LR

0.9 0.7 0.9 0.8 0.8 0.7 0.9 0.8 0.9 0.7 0.8 0.8 0.8 0.8 0.9

LDA

0.8 0.7 0.8 0.7 0.8 0.7 0.8 0.7 0.8 0.7 0.8 0.8 0.8 0.8 0.8

NB

0.9 0.9 0.9 0.8 0.9 0.9 0.9 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9

MLP

0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.9 0.8 0.9 0.8

ADB

0.9 0.9 0.9 0.9 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.7 0.9

SGD

0.8 0.6 0.7 0.6 0.7 0.7 0.8 0.6 0.8 0.6 0.7 0.7 0.6 0.7 0.7

QDA 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 Fig. 5 Performance evaluation of all machine learning algorithms at a glance

learning algorithm LR, LDA, and SGD have the accuracy of below 90%. ADB, QDA, MLP, and NB algorithm fall under the range from 90 to 95%. The classification results for accelerometer-based activity recognition dataset at a glance are represented in Table 2. The classification result is based on three measures, i.e., precision, recall, F1 score and accuracy for all machine learning algorithms which have been used in the simulation.

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Table 2 Classification results for accelerometer-based activity recognition dataset Classifier name

Accelerometer-based activity recognition dataset representation Precision

Recall

F1-score

Accuracy

Logistic regression

0.85

0.86

0.83

86.46

Classification and regression trees

1.0

1.0

1.0

99.94

K-nearest neighbors

1.0

1.0

1.0

99.93

Linear discriminant analysis

0.82

0.82

0.81

81.80

Naive Bayes

0.91

0.92

0.91

91.76

Support vector machine

1.0

1.0

1.0

99.93

Multilayer perceptron

0.94

0.92

0.92

92.18

Random forest

1.0

1.0

1.0

99.95

AdaBoost

0.96

0.96

0.95

95.45

Stochastic gradient descent

0.70

0.76

0.72

74.28

Bagging classifier

1.0

1.0

1.0

99.95

Extra trees classifier

1.0

1.0

1.0

99.82

Gradient boosting classifier

1.0

1.0

1.0

99.95

Quadratic discriminant analysis

0.94

0.93

0.93

92.87

Accuracy QuadraƟc Discriminant Analysis Gradient BoosƟng Classifier Extra Trees Classifier Bagging Classifier SGD Classifier Ada Boost Random Forest MulƟlayer perceptron Support Vector Machine Naive Bayes Linear Discriminant Analysis K-Nearest Neighbors ClassificaƟon and Regression Trees LogisƟc Regression

92.87 99.95 99.82 99.95 74.28 95.45 99.95 92.18 99.93 91.76 81.8 99.93 99.94 86.46 0

20 Accuracy

Fig. 6 Accuracy of various machine learning algorithms

40

60

80

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120

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In Fig. 6, the X-axis belongs to accuracy range and Y-axis belongs to fourteen machine learning algorithms. In this diagram, we have taken the 15 participants’ dataset in a single file and apply 14 machine learning algorithms to find out the result in the form of accuracy. We see that the algorithms work almost the same for complete dataset as it works for the individual participant’s dataset. From the diagram, it is clear that three machine learning algorithms, i.e. LR, LDA, and SGD, have accuracy below 90%, and the rest have the accuracy greater than 90%. In all 14 algorithms, seven algorithms, i.e., GBC, RF, BAG, CART, SVM, KNN, and ETC, touch the 99% accuracy mark. ADB, QDA, MLP, and NB algorithms lie between 90 and 95% marks. Based on simulation task which is performed on the whole dataset, the order of machine learning algorithms based on classification accuracy is: GBC > RF > BAG > CART > SVM > KNN > ETC > ADB > QDA > MLP > NB > LR > LDA > SGD.

5 Conclusion In the current era, data has been collected by various IoT-enabled devices, i.e., data are collected by sensor devices. The data which are collected by these various IoT devices are always a challenge for researchers to deal with and to extract useful information from it. This challenge gives a new way to find out the strength of various machine learning algorithms over IoT data. In our work, we are doing the same to find out the impact of various machine learning algorithms on IoT human activity recognition dataset. After performing rigorous analysis, we found that among fourteen well-established machine learning algorithms, seven algorithms, i.e., GBC, RF, BAG, CART, SVM, KNN, and ETC, have comparatively higher accuracy results and touch the 99% accuracy mark. So we can say that various machine learning algorithms are compatible and capable of dealing with IoT dataset. In the future work, we will test these algorithms on various datasets which are coming from various sensors and various IoT application areas to find out the impact of and explore the capabilities of machine learning algorithms. We will also test it with big IoT datasets too.

References 1. Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54:2787– 2805 2. Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of Things for smart cities. IEEE Internet Things J 1:22–32 3. Cecchinel C, Jimenez M, Mosser S, Riveill M (2014) An architecture to support the collection of big data in the Internet of Things, pp 442–449 4. Sheth A (2010) Computing for human experience. IEEE Internet Comput 88–91

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5. Sheth A (2016) Internet of Things to smart IoT through semantic, cognitive, and perceptual computing. IEEE Intell Syst 31:108–112 6. Madakam S, Ramaswamy R, Tripathi S (2015) Jcc_2015052516013923. J Comput Commun 164–173 7. Falkner NJG, Sheng QZ, Dustdar S, Vasilakos AV, Qin Y, Wang H (2016) When things matter: a survey on data-centric Internet of Things. J Netw Comput Appl 64:137–153 8. Ma M, Wang P, Chu CH (2015) LTCEP: efficient long-term event processing for Internet of Things data streams. In: Proceedings of 2015 IEEE international conference on data science and data intensive systems, pp 548–555 9. Sheng Z, Yang S, Yu Y, Vasilakos A, McCann J, Leung K (2013) A survey on the IETF protocol suite for the Internet of Things: standards, challenges, and opportunities. IEEE Wirel Commun 20:91–98 10. Riccardo P, Valeria L, Nathalie M (2017) Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms. Trans Emerg Telecommun Technol 28:e2931 11. Casale P, Pujol O, Radeva P (2012) Personalization and user verification in wearable systems using biometric walking patterns. Pers Ubiquitous Comput 16:563–580 12. Dataset Homepage. https://archive.ics.uci.edu/ml/machine-learning-databases/00287/ 13. Singh A, Sharma S (2017) Analysis on data mining models for Internet of Things. In: Proceedings of 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC), pp 94–100 14. Chen F, Deng P, Wan J, Zhang D, Vasilakos AV, Rong X (2015) Data mining for the Internet of Things: literature review and challenges. Int J Distrib Sens Netw 11:431047 15. Han J, Gonzalez H, Li X, Klabjan D (2006) Warehousing and mining massive RFID data sets. In: Lecture notes in computer science, pp 1–18 16. Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 2nd edn 17. Breiman L (2001) ST4_Method_Random_Forest. Mach Learn 1–33 18. Quinlan JR (1992) Learning with continuous classes. In: Proceedings of fifth Australian joint conference on artificial intelligence, vol 92, pp 343–348 19. Chin ZH, Ng H, Yap TTV, Tong HL, Ho CC, Goh VT (2019) Daily activities classification on human motion primitives detection dataset. In: Lecture notes in electrical engineering, vol 481, pp 117–125 20. Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Future Gener Comput Syst 81:307–313 21. Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput J 62:915–922 22. Wang J, Chen Y, Hao S, Peng X, Hu L (2018) Deep learning for sensor-based activity recognition: a survey. Pattern Recogn Lett 23. Li F, Shirahama K, Nisar MA, Köping L, Grzegorzek M (2018) Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors (Switzerland) 18:1–22 24. Subasi A, Radhwan M, Kurdi R, Khateeb K (2018) IoT based mobile healthcare system for human activity recognition. In: 2018 15th learning and technology conference (L&T), pp 29–34 25. Alam F, Mehmood R, Katib I, Albeshri A (2016) Analysis of eight data mining algorithms for smarter Internet of Things (IoT). Procedia Comput Sci 58:437–442 26. Rana JB, Jha T, Shetty R (2015) Applications of machine learning techniques in human activity recognition 27. Breiman L, Friedman JH, Olshen RA, Stone CJ (2017) Classification and regression trees, pp 1–358

Computing WHERE-WHAT Classification Through FLIKM and Deep Learning Algorithms Nagaraj Balakrishnan

and Arunkumar Rajendran

Abstract Object recognition has gained substantial importance in the artificial intelligence technology to visualize, understand and to take a decision. Many complex algorithms achieve AI utilities. The application of these object recognition methods extends from the medical field to wide variety of other sectors, especially industries. As per the literature survey, it defines that many systems or algorithms are available to process the recognition, but accuracy toward invariant 2D data with scaling, translation, rotation and partial properties is not up to the mark. In this paper, an efficient, systematic approach is developed to understand the place or a situation of a particular scene. Optimized deep learning in association with a clustering algorithm (FLIKM) is used in this approach to build an intelligent self-surveillance system. The experimental results of the state-of-the-art system evaluated with various benchmark datasets show an acceptable accuracy rate of 94 approximately. Keywords Deep learning · Unsupervised clustering · Soft-computing · Fuzzy local information · K-means

1 Introduction Digital image processing is the process of two-dimensional virtual representation of reality. This 2D representation is much beneficial in many fields such as medical fields, automation industries and robotics. Due to its sophisticated performance and easy to adapt nature [1], this data used is in a wide range of fields. This data is used in the classification of feature extraction, multi-scale signal analysis, pattern recognition, projection, etc., [2, 3]. Our human visual perception can recognize almost any objects that it has gone through Object recognition. Object recognition N. Balakrishnan (B) · A. Rajendran Rathinam Technical Campus, Coimbatore, India e-mail: [email protected] A. Rajendran e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_52

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is a method, which finds predominant features of an object to understand it [4]. This method uses two main phases of operation (i) Feature extraction (ii) Feature matching/ classification. The most significant and challenging part of image object recognition system is its feature extraction methods [5, 6]. The feature of an object is extracted with properties of an object such as its coordinates, average dimensions, color perceptions, the area of an object and ratio of an object to its background. [7]. The feature extraction techniques may vary depending on the application. The algorithms such as subspaces approaches have played a vital role in past decades. Such algorithms collect the information of 2D data with the help of high-resolution and low-resolution methods. Fisher’s linear discriminate collects the linear projection from high resolution/ dimensional data mainly used in the applications of palm recognition. Eigenvalue detection method, which forms the basis of many algorithms such as ICA (Independent Component Analysis), computes statistically independent multivariate signal from subcomponents of an image [8, 9]. This algorithm fetches the significant features from the Gaussian phase of the data. ISO map is one of the nonlinear dimension reduction methods that uses quasi-isometric high-dimensional data points as features. In this, data manifold is used in estimating geometrical structures [10, 11]. Principle component analysis (PCA) is a traditional method, which embeds the kernel methodology to increase the accuracy of recognition. This KPCA uses Hilbert space to get the multivariable statistics of a data [12]. The applications such as NLP use the algorithm named Latent Semantic Analysis (LSA) [13] to process the language. It uses the terminology of the distributional hypothesis, where an singular value decomposition (SVD) is used to reduce the similarity structures among the data columns [14]. Another traditional method is the partial least square method that approximates the data points with the help of regression mechanism. Multifactor dimensionality reduction method is a widely used feature extraction method [15, 16] In this, the characteristic combination of independent attributes is generated or recognized, to produce the statistical features. It processes the functions through data mining methodology. The Semi-Definite Embedding (SDE) is used to unfold the maximum dimensions of the data, which perform a nonlinear dimensional reduction of a vector data [17–20]. All these algorithms perform well but lack achieving the following operations (1) Multi-object detection, (2) Optimal Feature extraction, (3) Place identification and (4) Situation understanding etc. To address the above issues, in this paper, we propose a methodology and a systematic approach that can understand and respond to the respective scene and define them. The contribution of this paper lies in the proposal of FLIKM algorithm and a definition of an efficient situation understanding system.

2 Related Works The literary research related to the object recognition, multi-object detection and required study algorithmic natures are defined as follows.

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Ping Jiang et al. proposed an unfalsified vision servoing algorithm method to acquire the concepts of 3-D object recognition and pose tracking. The proposed algorithm tracks the image and determines the feature, known to be IBVS. A datadriven unfalsified control is proposed to solve the problems of visual serving. A recursive process of consensus is handled to model the feature selection [21–23]. Sebastien C. Wong et al. proposed an adaptive multiple objects tracking (MOT) method for tracking multiple objects. This system tracks each object in a sequence or scene without prior knowledge about the objects. The trained detectors may not encounter the objects accurately because of variation in the dataset used, but this algorithm searches until objects get recognized [24, 25]. Grouping of objects is done after the process of detection using fast learning image classifier [26]. Nagaraj et al. [27] put forward a Robust Sonar-based underwater object recognition algorithm to head over the problem of view of angles, the shape of object changes according to the angle it is approached through the sensor. Underwater object recognition is computationally complex since this approach uses sonar simulator-based algorithm to solve it [28]. Hu et al. [29] proposed a method to address the difficulties in recognizing 3D objects and its pose representation [30]. This algorithm provides a specific way to deal with the occasional problems in 3D object recognition. This algorithm is designed in such a way that it can process, online and offline stages of recognition [31]. Also, to achieve more consistent 2D to 3D matching pair, twin stage false correspondence filters are used. In online stage, the 2D to 3D object correspondence is created from the extracted feature, and then, an image is selected through aspect voting which would be taken further to proceed with two-stage false correspondence filter [32]. As a result, all process toward posing estimation and object localization is obtained. Le QV et al. [33] underwater fish recognition framework was proposed to overcome the challenges of recognizing a live fish. A vast amount of data is required to enhance the reorganization needs. To overcome the limitations of the framework that carried out during the activities such as saliency, relaxation, a labeling is made to fit the object in discrimination criteria for unsupervised clustering process and finally, the decisions taken upon classification [34, 35]. Gu C et al. [36] A Global Hypothesis Verification Framework is the method for 3D object recognition in clutter. Depending on the global or local features, the occlusion and few other concepts are added such as intensity or color or template matching so that the final stage of 3D object recognition pipeline ends with hypothesis verification to avoid the false-hit. Here, each hypothesis is checked to find whether it should be validated or dismissed [37]. However, there were drawbacks in the hypothesis verification to overcome this defect global hypothesis verification is replaced in the proposed approach [38]. If weak hypothesis undergoes final verification, then it can provide a correct detection about the object with coherence. Kruger N et al. [39] proposed that the challenges faced by computer vision are to recognize the creatures or objects with few variations. Their physical appearance captured with features such as size, scale and angle. The algorithm succeeded in these variations mentioned before, but it lacks when the object is rotating or shifting excessively, but the biological system can recognize objects and animals with few variations. HMAX model is proposed to recognize objects with the help of feed forward construct, which stimulates the visual cortex to do

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so. In each stage, the two unique groups of cortical cells, single cells for selectivity and complex cell for invariance are modeled [40]. Sparse coding used in the HMAX model avoids delicate features and improves the performance of classification. To balance the tough trade-off between the group effect of pixels and sparsity, the atoms are highly correlated [41]. Nagaraj B et al. [42] Aspect Graph Aware method is the proposed framework, which simultaneously recognizes and models the objects to be implemented through concepts, which are, correlated [43]. In the proposed framework, objects are represented as view graph, nodes and edges. An observation model for the 3D object is used in the process of the object modeling [44]. To solve simultaneous object recognition and modeling, maximum likelihood estimation is implemented [45].

3 Overview In this section, the complete system architecture of the WHERE-WHAT identification is provided. Figure 1 shows the systematic process of the proposed methodology. Today, most of the object detection algorithms face the situation of data nonlinearity in processing the real-world environment. To deal with the nonlinear data and situation, soft-computing methods such as artificial neural networks, fuzzy logic and neuro-fuzzy methods are used. To optimize the activity of algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) [46], etc. are used. The invention of deep learning methods changed the way of technologies and improved intelligence in recognition or machine learning. The primary objective of this paper is to formulate a methodology to recognize and understand the environment (the situation of a place where computer vision framed). In this, the deep learning method is used which is described in Sect. 4. Details of the proposed clustering algorithm formulated for the process, defined in Sect. 5. The overall operation of the systems with its inside notation is in Sect. 6. Finally, the results, discussion and conclusion provided in Sects. 7 and 8.

Fig. 1 Systematic process view of WHERE-WHAT identifier

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4 CNN for PS Identifier The PS identifier uses two simultaneous convolution neural networks (1) R-CNN and (2) S-CNN). R-CNN is region with convolution network algorithm [46], which is proposed for the application of object recognition. This algorithm derives an objectness score for an object to find a difference between them. This network is known to be region proposal networks which is modeled with the fully convolutional network pattern. As per the Zeiler & Fergus proposal, the model contains five sharable convolutional layers, and new models even have 13 sharable convolution layers [47]. The region proposal window is slid over the convolutional feature maps of the last shared layer of the network to identify the regions. This n × n spatial window here focuses on gathering the low-dimensional features and feds it to the final sibling layers regression and classification [48]. The anchors placed at each location to fetch out the maximum possible region proposals. The window size of each anchor will be varied to analyze various information of the object. Suppose if the number of windows w used for an anchor is five, size of feature map m × n, then the total number of windows used will be mnw. The main purpose of using R-CNN is to list the objects present in the screen. The object list is the main features to identify the scene. Shallow convolutional neural network (S-CNN) [49] on the other hand formulated with five layers comprises image input layer, an output layer and three hidden layers. After the input layer, convolutional filtering and its pooling activate handled at the first two hidden layers. The primary objective of this convolutional filtering and pooling is to extract the invariant features, which sometimes scaled or translated [50]. Finally, the third hidden layer followed by the output layer is responsible for the classification process. The convolutional filtering and pooling process involved in processing the key point discriminators (KPD) analytics extracted from the FLIKM algorithm. Here, the information of multi-object in a scene, the KPD generated from FLIKM such as texture features , back ground object density, color density and regional information are arranged to be a 2D data to make it let in to the input layer of the S-CNN. In this block, a bank of filters used to process the visual features of input data is employed. Then, concatenated linear projected features from the second hidden layer are used for the activation of the third hidden layer. The prediction through classification made with the help of fully connected neura on activation process. The main reason to employ the S-CNN is to classify the nature of the scene, i.e., after getting input, from both R-CNN and FLIKM, the classification for WHERE-WHAT identification will be given. The training sequence for R-CNN & S-CNN will be conducted with many same type samples along with its labels. These trained networks are evaluated with many test cases to confirm its analyze capability and performance [51] (Fig. 2).

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Fig. 2 A unified network of R-CNN

5 Fuzzy local information K-means (FLIKM) Algorithm The traditional K-means method is one of the efficient clustering methods from data mining family, works with the vector quantization methodology [52]. It is a recursive method usually selects the cluster center as random and then manipulates with the distance measurement method. In each iteration, the cluster center is calculated through the mean of the clusters, which formed in the last iteration. This cluster recalculation process continues until the convergence state is attained. As a result, the K centers change their locations close to the full convergence state. The traditional K-means algorithm is given by (1) Convergence function and (2) Cluster centers recalculations (3) Cluster bin, as follows: Cf =

nc    Cc i − N Cc i 2 i=1

 N Cc i =

1 ndci

 ndci

Cbini

(1)

(2)

j=1

Cbini = Cbini |X s i X S i = lim x j if dist of x j is min toCC i j=1tonx   j = 1tonx 2 dist = x j − Cc i i = 1tonc

(3) (4) (5)

where C f is the convergence function, this function calculates the variation between the cluster centers of previous and the current version. Depends upon the accuracy needed, the stopping criteria may be defined, and the recursive stages can be

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controlled. nc is the number of clusters available or requested by the user. Here, CC i and N CC i i are given as current cluster center and new cluster center. N CC i Calculated by taking by means of the currently formed cluster bin Cbini . The number of cluster bins available is given by ndci , where the ndci and nc are same since the number of bins formed will be the number of clusters needed. Cbini formed with the help of selected data points, i.e., X S i in which minimum distant points or elements between x j and Cc i (data in which the clustering process is being done and the cluster center). nx is the number of elements available in the data [53]. This traditional algorithm has specific limitation to effectively process the data, i.e., (1) linear processing—the data element present in one cluster will not be there in another, but in the real-time, the chances are high. (2) Global minima—K-means algorithm may usually converge in the local minimum point and not in global minima, in which the final cluster will be affected due to initial random selection. (3) Minimal movement—the adjustment toward the cluster centers in each iteration is minimum, which directly increases the time complexity of the complete process. The induces the proposed method (Fuzzy Local Information K-Means—FLIKM) which is given as follows. lin ji =

1 (max (x) − Cc i) f di j

(6)

Eq. (6). shows the local information lin ji of a particular cluster. This is calculated with the help of inverse Euclidean distance of data and the cluster centers. This function is responsible for solving the problem of global optimum, which will be also used as an adjustment factor to push the cluster centers to reduce number of iterations [54].  N Cc i =

1 ndci

 ndci

M f i + lin ji (7)

(7)

j=1

Eq. (7). Is the cluster center of the previous membership function of FLIKM is same as the tradition K-means algorithm (ref. Eq. (2)). To enhance the push in cluster center, selection iln ji parameter is included in the N Cc i [54]. M f k = X s k∀Wk   







Wi j = 0 1 nc 2 nc 3 nc . . . n − 1 nc ,

(8)

where n and nc are the number of clusters requested by user W is the weight assigned for each cluster group element to show the difference between one and another. Equation (8) is the membership function of FLIKM, which contains the clusters that grouped according to the previous Cc i calculated, and again, N Cc i is calculated with it. The difference between X S i that calculated in the traditional K-means is its fuzzy nature in-planted upon FLIKM. The M f k contains the cluster groups with cluster

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member which can also present in another cluster group (Each cluster element will be the member of other clusters). X S k = lim x j if di j ofx j is min toCC k j=1tonx   j = 1tonx di j = x j − Cc i 2 i = 1tonc

(9) (10)

Eqs. (9) and (10) are the data selected for each cluster and the distance measurement made for the data elements and the group. Once these data selected, on the particular cluster, these data points will be provided with high priority, and another non-member will be indicated with lower priority. Obj fIter =

nc  nx  i=1

M f idi j + lin ji

(11)

j

Eq. (11) indicates the objective function, which calculates the convergence state of the recursive process. The objective function will make use if current membership functions and the local information parameter of the given data to estimate the convergence state. Thus, the fuzzy implementation on K-means is done. The systematic process of this algorithm is shown below. 1. Initially, the required operative inputs such as number of clusters nc needed, convergence criteria and data elements that need clustering are given. 2. For the first iteration, cluster centers are chosen from the data points randomly. These cluster centers are given by Ci = C1 , C2 , C3 , . . . Cn c. Once the random cluster centers are selected from the data vector x j , it will be used for calculating the di j , where distance measurement methods change according to the requirement of the cluster accuracy and nature 3. Using the random clusters/ clusters from previous iterations, the membership data M f k are created, which contains the fuzzy weights Wi j depends upon the cluster priority. The algorithms soul output is its membership function but only after the convergence state is attained. 4. Each iteration will end by calculating the objective function to check for convergence which is given by Obj f Obj fIter − Obj fIter−1 < Cgnccri where CgnCcri is a convergence criteria defined by the user depends on the application. If (Convergence State is attained) Interation stops, and the final membership function values will be taken as the final outcome else the recursive process continues from step (b).

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6 Operation The analysis and classification of place and the situation using FLIKM and deep learning algorithms are a systematic process, which involves many activities. The primary objective of this research is to develop an efficient system to understand its environment and thus find any anomaly or define the situation. This operation involves a variety of significant feature collection from two modes of the process (i.e.,) (1) object learning entity (2) background learning entity. The object entity evolves a deep learning algorithm namely region proposal convolution neural network (RCNN), which is responsible for listing the objects through varied anchor sliding windows. On the other hand, background learning process consists of a clustering algorithm—FLIKM. Through FLIKM, the object and the background are separated with the clustering process, and finally, the key point discriminators are extracted in two-dimensional for the classification of the place or situation of the input scene by S-CNN. The detailed procedure of the process is as follows. 1. The input image from the camera source, in online or offline, is given to the system for processing. Sometimes, these images/videos are taken in movement. In that case, there may be a chance of motion blur or Gaussian noise, which must be removed in the preprocessing to make the recognition effective. 2. Next, to the input stage, two parallel sections are involved, i.e., object learning and background learning. In the object learning section, R-CNN detects and recognizes the object as per the methodology mentioned in Sect. 4. Once the feature extraction and learning process are complete, the classification toward finding the list of objects will be initiated. The object list will be used for the next stage of sorting. 3. On the other hand, a fast and efficient clustering method is implanted. The primary objective of this clustering is to segment the background out of input 2D data. During the process, FLIKM takes the input data under the clustering process as mentioned in Sect. 5. Therefore, the final membership function values in 2D will be considered as a segmented image. 4. Here, to extract the background out of it, the common regions of all the segmented outcomes will be taken, ignoring the segmented background image. This shared background will be considered for KPD features extraction process. 5. KPD feature extraction process involves (1) Texture Index (2) BG Density (3) Color Density and (4) Region Index. Texture index characterizes the image spatial regions to extract critical intuitive qualities. The variation of image contour due to different frequencies of intensities in the gray level will be considered for the analysis. In this analysis, the statistical measurement of the image is made to extract energy, local entropy, homogeneities of input and to form a two-dimensional feature arrangement. The co-occurrence Ca matrices from the background-separated image are used to generate the statics as follows.

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=

 r

εn p = −

(12)

c

 r

Ht y =

Ca (r, c)2 Ca (r, c) log2 Ca (r, c)

(13)

c

  Ca (r, c) 1 + |r − c| r c

(14)

where , εn p and Ht y are energy, entropy and Homogeneities of the background segmented. These statics are taken using slides windows over the entire image. So, the r and c sizes are kept fixed as 3, 5, 7, as per the requirement. Other than texture statistics, background details of an image also need to be analyzed to get the information on many features as possible to know the scene (such as to understand the weather condition, reflections or animated). The Gaussian averaging function of the background has to be extracted to realize the minor object details of the background. μ f = ρ I f + (T − ρ)μ f −1 σ 2f = d 2 ρ + (I − ρ)σ 2f −1

(15) (16)

where d is given by,|(I f − μ f )| which is the difference/ distance between image the I f and the mean value μ f generated. To fit the probability density function of the temporal window, this is usually kept as 1 × 10−2 . σ gives the variation of the background. The color density is calculated with the help of two actions (A) Measurement on the histogram, which gives the color to pixel number. Ratio and (B) Color Segmentation by which the background colors are differentiated as follows. suppose I is a intensity of the image coordinated by r—Row,c—Column and p—plane, the plane p is associated with RGB if (R > G > B), then the value of I (r, c) = [max intensity, 0, 0], else-if (R < G > B), then the value of I (r, c) = [0, max intensity, 0] else then the value of I (r, c) = [0, 0, max intensity]. Once these chances are made, then two types of color density information can be gathered, i.e., statistics on real color available and the information on dominant color, which can add additional value to the classification. Finally, the process of region index features is extracted with the help of the Otsu threshold method. Here, the histogram calculated in the other parallel feature extraction is taken, and the global threshold for a background image is applied. Finally, canny edge detection method is used to extract all possible layers of the connected high-frequency pixels. All these features that form a KPD are collected in two-dimensional structures along with the list of objects generated from R-CNN and are given to the S-CNN for WHERE-WHAT identification. Thus, the process of systematic classification for WHERE-WHAT learning is processed using FLIKM and deep learning algorithms.

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7 Results and Discussions In this section, the details on the database used for the experiment, the simulation process including training & classification, parameters used for analysis and metrics used for the performance evaluation are investigated and are discussed elaborately. The algorithms and stages in the recognition system are chosen based on many experimental results and its analysis. To train and test the DL algorithms, many multi-object images are used from various databases such as MOT Challenges and KITTI Vision Benchmark Suite database. The performance of the proposed methods are experimented under three segment of analysis (i) Performance of Multi-object detection (ii) Performance of FLIKM and (iii) performance of the PS Identification, which is shown in Tables 1, 2 and 3. To evaluate the performance of the multi-object detection, the databases such as NORB, MNIST, SVHN, CIFAR [55, 56] are used for the S-CNN algorithm. The FLIKM algorithm is compared with the traditional K-means algorithm, FCM,FCM S 1, FCM S 2, EnFCM and FLICM [57] to evaluate its performance as follows. The performance of multi-object detection is evaluated using the test cases of various benchmark datasets. Here, the Miss M R (i.e., the ratio of actual correct data

Table 1 Comparison of R-CNN Databases M R (%) NORB MNIST SVHN CIFAR MOT challenges KITTI vision

F H R (%)

12.87 14.12 10.79 13.67 11.21 12.34

8.46 7.76 9.45 7.54 7.81 7.22

Table 2 Comparison of the accuracy of a clustering algorithm regarding background segmentation c K-means FC M S ENFCM FLICM M-FLMCM FLIKM Scene-1 Scene-2 Scene-3 Scene-4 Scene-5 Scene-6 Scene-7 Scene-8 Scene-9 Scene-10

82.76 81.28 82.09 83.24 80.61 80.96 81.30 81.53 82.81 81.24

80.82 79.94 80.54 80.46 78.75 79.28 79.08 79.36 81.36 79.15

79.53 78.11 79.82 80.20 79.32 77.74 78.27 78.20 81.63 78.13

84.47 84.54 84.19 84.40 83.21 82.27 84.10 82.64 85.96 83.27

86.62 86.38 86.60 89.40 85.77 87.02 86.57 85.25 87.65 85.51

89.72 88.30 88.53 90.57 89.23 87.70 88.03 87.90 90.51 87.11

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Table 3 Accuracy of proposed WHERE-WHAT classifier Benchmark dataset Training with Jitter Validation with Jitter (%) (%) NORB MNIST SVHN CIFAR MOT challenges KITTI vision

94.43 97.34 95.32 97.67 93.21 89.95

89.99 94.92 96.13 93.17 94.47 90.30

Validation without Jitter (%) 96.6 90.4 93.56 94.66 90.19 96.89

Fig. 3 Comparison of R-CNN

missed from recognition) and false-hit ratio F H R (i.e., the ratio of actual incorrect data reconditioned as correct) are measured, and the results are mentioned as shown in Table 1. The varied results give a general idea of algorithms nature and its response for different datasets (Fig. 3). Table 2 and Fig. 4 represent the comparison of various clustering algorithms with FLIKM. In the traditional K-means, fuzzy C Means (FCM), FCM Spatial(FCM S ), enhanced fuzzy C Means (EnFCM), Fuzzy Local Information C Means (FLICM) [57] algorithms are compared with our proposed method. As per the results, it is clear that these algorithms are specialized in different categories and operations. Some technique does the clustering faster; some cluster removes the noise during its process. FLIKM algorithm withstands good segmentation accuracy; especially, it deals with the global optima. As a result, the time complexity is reduced along without compromising the efficiency. Table 3 and Fig. 5 show the results of the accuracy of the proposed algorithm simulated for various databases since convolutional neural

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Fig. 4 Comparison of the accuracy of a clustering algorithm regarding background segmentation

network is used for the recognition. The phase of training and testing has to be conducted to strengthen the network and to evaluate it. Here, the training phase for S-CNN is performed with 600 images from the MOT database and with its required classification targets [58–60]. To tolerate the nonlinear nature, the training process is conducted with the jitter, and the evaluation is done with and without fluctuation. This experimental result shows that the proposed systematic methodology has the capability to understand the multi-object in a place and understand the situation.

8 Conclusion This paper presents an efficient, systematic approach to recognize and understand a place or a scene for surveillance. To make this system efficient, a group of new algorithms and some proposed algorithms are arranged as a proper system. The mixed state of R-CNN, S-CNN and FLIKM approach provides remarkable results in recognizing the needy. The performance of this system is comparable to the benchmarks. In the mere future, this algorithm will be used for the computer vision of robotics to take a decision based on the situation and to detect the abnormalities at any place, which could save thousands of lives. It can produce 89% of accuracy in average in with jitter category and 94% accuracy in without jitter category.

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Fig. 5 Accuracy of proposed WHERE-WHAT classifier

References 1. Illingworth J, Hilton A (1998) Looking to build a model world: automatic construction of static object models using computer vision. Electron Commun Eng J 10(3):103–113 2. Nicolas B, Lague D (2012) 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology. ISPRS J Photogram Remote Sens 68:121–134 3. Matthew T, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86 4. Rumiati RI, Humphreys GW, Riddoch MJ, Bateman A (1994) Visual object agnosia without prosopagnosia or alexia: evidence for hierarchical theories of visual recognition. Vis Cogn 1(2–3):181–225 5. Agalya N (2018) Soft Computing based industrial process: a review. Pak J Bio Technol 15(1) 6. Sethuramalingam TK, Nagaraj B (2016) A proposed system of ship trajectory control using particle swarm optimization. Elsevier Procedia Comput Sci 87:294–299 7. Cucchiara R, Grana C, Piccardi M, Prati A, Sirotti S (2001) Improving shadow suppression in moving object detection with HSV color information. Proc IEEE Intell Transp Syst 334–339 8. Sethuramalingam TK, Nagaraj B (2015) A soft computing approach on ship trajectory control for marine applications. ARPN J Eng Appl Sci 10(9):4281–4286 9. Rama C, Sinha P, Jonathon Phillips P (2010) Face recognition by computers and humans. Computer 43(2) 10. Bapat OA, Fastow RM, Olson J (2013) Acoustic coprocessor for hmm based embedded speech recognition systems. IEEE Trans Consum Electron 59(3):629–633 11. Sethuramalingam TK, Nagaraj B (2014) PID controller tuning using soft computing methodologies for industrial process-a comparative approach. Indian J Sci Technol 7(S7):140–145 12. Bing L, He Y, Guo F, Zuo L (2013) A novel localization algorithm based on isomap and partial least squares for wireless sensor networks. IEEE Trans Instrum Meas 62(2):304–314 13. Walaa M, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

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14. Widdows D, Cohen T (2010) The semantic vectors package: New algorithms and public tools for distributional semantics. In: IEEE fourth international conference on Semantic computing (ICSC) 15. Agalya A, Nagaraj B, Rajasekaran K (2013) Concentration control of continuous stirred tank reactor using particle swarm optimization algorithm. Trans Eng Sci 1(4):57–63 16. Kumanan D, Nagaraj B (2013) Tuning of proportional integral derivative controller based on firefly algorithm. Syst Sci Control Eng J 1:52–58 17. Richard S, Pless R (2005) Manifold clustering. In: Tenth IEEE international conference on computer vision 1 18. Weinberger KQ, Saul LK (2006) Unsupervised learning of image manifolds by semidefinite programming. Int J Comput Vis 70(1):77–90 19. Agalya A, Nagaraj B (2013) Certain investigation on concentration control of CSTR—a comparative approach. Int J Adv Soft Comput Appl 5(2):1–14 20. Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6):1437–1447 21. Nagaraj B, Dev VV (2012) Design of differential evolution optimized pi controller for a temperature process. J Control Instrum 3(2):1–10 22. Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, Moore R (2013) Real-time human pose recognition in parts from single depth images. Commun ACM 56(1):116–124 23. Iryna G, Lowe DG (2006) What and where: 3D object recognition with accurate pose. Toward category-level object recognition. Springer, Berlin Heidelberg, pp 67–82 24. Nagaraj B, Vijayakumar P (2012) Bio inspired algorithm for PID controller tuning and application to the pulp and paper industry. Sens Transducers J 145(10):149–162 25. Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2011) Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans Pattern Anal Mach Intell 33(9):1820–1833 26. Nagaraj B, Vijayakumar P (2012) Fuzzy based PI controller for basis weight process in paper industry. Int J Fuzzy Syst. DOI: FS072012006 27. Nagaraj B, Vijayakumar P (2012) Controller tuning for industrial process-a soft computing approach. Int J Adv Soft Comput Appl 4(2) 28. Nimmagadda Y, Kumar K, Lu YH, Lee CG (2010) Real-time moving object recognition and tracking using computation offloading. In: IEEE/RSJ international conference on in intelligent robots and systems (IROS) 2449–2455 29. Hu M, Wei Z, Shao M, Zhang G (2015) 3-D object recognition via aspect graph aware 3-D object representation. IEEE Sig Process Lett 22(12):2359–2363 30. Gao Y, Wang M, Tao D, Ji R, Dai Q (2012) 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans Image Process 21(9):4290–4303 31. Vidhya S, Nagaraj B (2012) Fuzzy based PI controller for basis weight process in paper industry. J Fuzzy Syst 4(7):268–272 32. Meng-Che C, Hwang JN, Williams K (2016) A feature learning and object recognition framework for underwater fish images. IEEE Trans Image Process 25(4):1862–1872 33. Le QV (2013) Building high-level features using large scale unsupervised learning. In: IEEE international conference on acoustics, speech and signal processing (ICASSP) 34. Nagaraj B, Vijayakumar P (2012) Evolutionary computation based controller tuning—a comparative approach. Int J Indian Pulp Paper Techn Assoc 24(2):85–90 35. Aldoma A, Tombari F, Di Stefano L, Vincze M (2016) A global hypothesis verification framework for 3D object recognition in clutter. IEEE Trans Pattern Anal Mach Intell 38(7):1383–1396 36. Gu C, Lim JJ, Arbeláez P, Malik J (2009) Recognition using regions. In: IEEE conference on computer vision and pattern recognition 1030–1037 37. Kostia R (2009) Night-time traffic surveillance: a robust framework for multi-vehicle detection, classification and tracking. In: Sixth IEEE international conference on advanced video and signal based surveillance. IEEE 38. Alameer A, Ghazaei G, Degenaar P, Chambers JA, Nazarpour K (2016) Object recognition with an elastic net-regularized hierarchical MAX model of the visual cortex. IEEE Sig Process Lett 23(8):1062–1066

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39. Kruger N, Janssen P, Kalkan S et al (2013) Deep hierarchies in the primate visual cortex: what can we learn for computer vision. IEEE Trans Pattern Anal Mach Intell 35(8):1847–1871 40. Eric TW (2012) Distance-weighted regularization for compressed-sensing video recovery and supervised hyperspectral classification. Mississippi State University, Dissertation 41. Liang M, Min H, Luo R, Zhu J (2015) Simultaneous recognition and modeling for learning 3-D object models from everyday scenes. IEEE Trans Cybern 45(10):2237–2248 42. Yefeng Z et al (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11):1668–1681 43. Nagaraj B, Vijayakumar P (2012) Soft computing based PID controller design for consistency control in papermaking. Int J Indian Pulp Paper Tech Assoc 24(2):85–90 44. Nagaraj B, Vijayakumar P (2011) Comparative study of PID controller tuning using GA, EP, PSO and ACO. J Autom Mobile Robot Intell Syst Polland 5(2):42–48 45. Nagaraj B, Vijayakumar P (2011) Tuning of a PID controller using soft computing methodologies applied to basis weight control in paper machine. J Korean Tech Assoc Pulp Paper Ind 43(3):1–10 46. Nagaraj B, Vijayakumar P (2011) Soft computing based PID controller tuning and application to the pulp and paper industry. Sens Transducers J 133(10):30–43 47. Nagaraj B, Murugananth R (2010) Optimum PID controller tuning using soft computing methodologies for industrial process. J Comput Sci 4(5):1761–1768 48. Ashutosh S, Driemeyer J, Andrew YN (2008) Robotic grasping of novel objects using vision. Int J Robot Res 27(2):157–173 49. Shaoqing R et al (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. Adv Neural Inform Process Syst 50. Arunkumar R, Nagaraj B, Mithya V (2016) Malleable Fuzzy local median C means algorithm for effective biomedical image segmentation. Sens Imaging 17(1) 51. Yim J, Ju J, Jung H, Kim J (2015) Image classification using convolutional neural networks with multi-stage feature. Springer Int Publ Robot Intell Technol Appl 3:587–594 52. Nagaraj B, Murugananth R (2010) Optimum tuning algorithms for PID controller—a soft computing approach. Int J Indian Pulp Paper Tech Assoc 22(2):127–129 53. Nagaraj B, Murugananth R (2010) Soft computing-based optimum design of PID controller for a position control of DC motor. Acta Electroteh Acad Techn Sci Rom 51(1):21–24 54. Arunkumar R, Karthigaikumar P (2016) Multi-retinal disease classification by reduced deep learning features. Neural Comput Appl 1–6 55. Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S (2010) Recurrent neural network based language model. Interspeech 2(3) ˇ 56. Mikolov T, Kombrink S, Burget L, Cernocký J, Khudanpur S (2011) Extensions of recurrent neural network language model. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), 5528–5531 57. Hori T, Kubo Y, Nakamura A (2014) Real-time one-pass decoding with recurrent neural network language model for speech recognition. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), 6364–6368 58. Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4–27 59. Abdulrahman A, Iqbal K (2014) Capturing human body dynamics using RNN based on persistent excitation data generator. In: IEEE international symposium on computer-based medical systems (CBMS) 221–226 60. Zhang Y, Er MJ, Venkatesan R, Wang N, Pratama M (2016) Sentiment classification using Comprehensive attention recurrent models. In: IEEE international joint conference on neural networks (IJCNN) 1562–1569

Reshaped Circular Patch Antenna with Optimized Circular and Rectangular DGS for 50–60 GHz Applications Ribhu Abhusan Panda, Rabindra Kumar Mishra, Udit Narayan Mohapatro, and Debasish Mishra Abstract This paper illustrates the effect of defected ground structure (DGS) on a biconvex patch antenna. Comparative results have been obtained when DGS is applied to the base just under the patch and a line feed technique is provided. The comparison includes the optimization of DGS with different radius and length values. DGS with different shapes like circular and rectangular is used to obtain the comparative results. The comparative results explain that the proposed reshaped circular patch antenna has a better antenna gain and an even wider bandwidth as of the previous structure without the implementation of DGS. Substrate has a dielectric material (FR4-epoxy) with a relative permittivity of 4.4. The simulated parameters like S11 parameter, antenna gain, VSWR, directivity, radiation efficiency, peak directivity are obtained by using HFSS software for the entire band ranging from 50 to 60 GHz applications. Keywords Defected ground structures (DGS) · Reshaped circular patch · S11 parameter · Antenna gain · VSWR · HFSS · Directivity

R. A. Panda (B) · D. Mishra Department of Electronics and Telecommunication, Veer Surendra Sai University of Technology, Burla, Odisha, India e-mail: [email protected] D. Mishra e-mail: [email protected] R. K. Mishra Department of Physics, GIET Main Campus Autonomous, Gunupur, Odisha, India e-mail: [email protected] U. N. Mohapatro Department of Electronics and Communication, GIET Main Campus Autonomous, Gunupur, Odisha, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_53

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1 Introduction Recent studies on photonic band gap (PBG) structures contributed towards the theory of DGS. The implementation of PBGs in electromagnetic (EM) applications, the studies based on PBGs are termed as EBG structures [1]. These are basically periodic artificial structures illustrating the characteristics of a band-pass filter which blocks the EM wave from transmitting through them along a range of frequencies which is fundamentally coined as stop-band and pass-band. A band gap is caused by the EBG structures. Various studies on PBGs were accepted in the applications of microwaves and millimetre waves. Numerous structures were developed by a succession of observations. Then a better analysis of electronic band gap structures guided towards the transformation of DGS [2]. In recent years to minimize the dimensions of components especially microwave, DGS also known as defected ground structure is considered as one of the most prominent methods. From the photonic band gap structures (PBG), the concept of defected ground structures was developed [3]. In the base of the proposed antenna, DGS is perceived by engraving any simple shape of DGS structure for efficient coupling [4]. As a result, an inductive effect has been created; i.e. the performance of the microstrip patch antenna changes when the engraved model rearranges the current pattern in the ground plane because of the imperfection in the base. The engraved model affects the capacitance and the inductance. DGS structures are periodic lattices which furnishes productive and malleable control over the transmission of EM waves within a certain band [5]. Depending upon the defect structures, DGS provides refusal of certain bands of frequency. DGS can be utilized to differentiate the return losses, directivities, gain, radiation pattern of proposed patch antenna with or without defected ground structures. Some of the changes have been made for circular patch antenna forming a biconvex structure in the year 2018 [6]. Other perturbations were also done that lead to non-identical shapes of patches for numerous applications [7, 8]. This paper describes about the motive to manifest the performance of optimized (DGS) and to compare results. Its geometry has been considered from simple to composite in order to decrease the effect of mutual coupling. No requirement of implementation is bigger area for it [9–14]. The inducement of optimized DGS is done by taking different structures of DGS like circular DGS and a rectangular DGS. A biconvex structure has been taken with a simple feeding technique. For Ku band applications, the log periodic usage of the biconvex patch antenna has been done [15]. The replication of a unit cell in a periodic manner provides good results and extensive characteristics. Feeding is done in such a way that the feed line consists of dissimilar lengths. The ground plane of the proposed antenna has been used to exhibit the effects of optimized DGS. The biconvex patch has been designed for 60 GHz WLAN application, and with the implementation of DGS, the design can be done for the entire band from 50 to 60 GHz.

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2 Antenna Design Design of antenna contains of three important components named as substrate, ground plane and a microstrip patch. Substrate has been assigned with a dielectric material known as FR4 epoxy with a relative permittivity of 4.4. The proposed antenna contains a biconvex patch structure that is modelled on the substrate at a height of 1.6 mm. The architecture of proposed antenna has been shown in Fig. 2. A feeding technique has been utilized which comprises of two dissimilar lengths. On the ground plane of proposed antenna, defected ground structure (DGS) has been made that comprises of different shapes, i.e. circular and rectangular. It resulted in an increase in antenna gain and a wider bandwidth. The frequency of operation has been considered as 60 GHz, and the wavelength of proposed patch is taken 5 mm which is calculated from the corresponding frequency of 60 GHz. Optimization technique has been taken into account for the design of DGS of particular length and radius. Different values of dimensions have been considered for the best results (Figs. 1, 3 and 4) (Table 1).

2.1 Circuit Diagram of DGS The primary element of (DGS) is a slot on the base of proposed antenna that is designed below the patch. The designed DGS structure varies in the space occupied, L-C ratio, coefficient of coupling and other electrical parameters. The (DGS) circuit Fig. 1 Model of proposed antenna using HFSS

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Fig. 2 Architecture of proposed antenna

Fig. 3 Design of rectangular DGS of proposed

consists of parallel circuit connected in series to the transmission line as shown in Fig. 5. L, C and R values have been evaluated by the dimensions of the DGS structure.

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Fig. 4 Design of circular DGS of proposed antenna

Table 1 Dimensions of proposed patch

Fig. 5 Circuit diagram of DGS

Parameters

Values (in mm)

Width of substrate

25

Length of substrate

25

Maximum length between two arc L

5.17

Width B1

1.5

Width B2

3

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3 Simulation Results The substrate has been assigned with a dielectric material known as FR4 epoxy with a relative permittivity of 4.4 with a height of 1.6 mm. The results of the parameters that are obtained by simulation are return loss, VSWR, antenna gain, peak directivity, radiation efficiency, total antenna gain, total directivity, realized gain. The results have been simulated using HFSS software and are explained below.

3.1 Return Loss Return loss or S11 parameter of antenna is the loss of power in reflected signal due to an interruption or disruption in a transmission line. This parameter can be calculated by a formula as expressed below. Return Loss = 10 log10

Pi Pr

Return loss of the proposed antenna for proposed antenna has been shown in Fig. 6.

Fig. 6 S11 parameter of proposed patch antenna

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Fig. 7 VSWR of proposed patch antenna

3.2 VSWR VSWR is the ratio between peak voltages to the minimum voltages of a standing wave that is formed by discontinuing one end of a transmission line. It can be calculated by using the following expression. VSWR =

1 + |Γ | 1 − |Γ |

Z max. +Z

min. min. VSWR of proposed patch antenna has been shown in Fig. 7. Where, Γ =

Z max. +Z

3.3 Antenna Gain Antenna gain is the ratio between the intensity in a particular direction to that of the intensity of radiation that is obtained if antenna receives power that is identically radiated. The antenna gain of proposed antenna without DGS is least as compared to that of the antenna gain with rectangular DGS and circular DGS; i.e. the value of antenna gain without DGS is 3.5 dB, the value of antenna gain with rectangular DGS is 4 dB, and the value of antenna gain with circular DGS is 4.5 dB. Gain of antenna for proposed antenna has been shown in Fig. 8.

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Fig. 8 Gain of antenna for proposed antenna

3.4 Peak Directivity Directivity of any antenna can be defined as the mathematical ratio between the intensity of radiation in a particular direction from the antenna to the intensity of radiation taken as average considering all the directions. Peak directivity of proposed antenna without DGS has been found to be least as compared to that of the peak directivity with rectangular DGS and circular DGS; i.e. the value of peak directivity without DGS is 6.50 dB, the value of peak directivity with rectangular DGS is 7.09 dB, and the value of peak directivity with circular DGS is 7.66 dB. Peak directivity of proposed antenna has been shown in Fig. 9.

3.5 Radiation Efficiency Radiation efficiency is the ratio between the power transferred to the radiation resistance Rr to that of the power transferred to Rr and RL . This parameter has been calculated by a formula as expressed below.  Radiation Efficiency =

Rr Rr + R L



Radiation efficiency of proposed patch antenna without DGS has been found to be least as compared to that of the radiation efficiency with rectangular DGS and circular DGS. The value of radiation efficiency without DGS is 0.472 dB, the value of radiation efficiency with rectangular DGS is 0.492 dB, and the value of radiation efficiency with circular DGS is 0.514 dB. Radiation efficiency of proposed patch antenna has been shown in Fig. 10.

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Fig. 9 Peak directivity of proposed antenna

Fig. 10 Radiation efficiency of proposed antenna

3.6 Total Antenna Gain The total gain of antenna of proposed patch antenna has been shown in Fig. 11.

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Fig. 11 Total antenna gain of proposed antenna

3.7 Total Directivity Directivity of any antenna can be defined as the mathematical ratio between the intensity of radiation in a particular direction from the antenna to the intensity of radiation taken as average considering all the directions. The total directivity of proposed antenna has been shown in Fig. 12.

3.8 Realized Gain Realized gain of proposed patch antenna has been shown in Fig. 13 (Table 2).

4 Conclusion Good results are obtained for the proposed patch antenna when rectangular DGS and circular DGS are applied. Gain of magnitude 3.94 dB without DGS has been increased by using DGS, and a directivity of magnitude 6.50 dB without DGS has also been increased to 7.66 using DGS. From S11 parameter graph, it can be concluded that 58 GHz is the resonant frequency for the proposed patch antenna, and all the results witnessed a gain in result due to the effect of DGS.

Reshaped Circular Patch Antenna with Optimized Circular … Fig. 12 Total directivity of proposed antenna

Fig. 13 Realized gain of proposed antenna

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Results without DGS

Results with rectangular DGS

Results with circular DGS

Return loss

-27.1 dB

−36.9 dB

−43.1 dB

VSWR

1.09

1.02

1.01

Antenna gain

3.94 dB

4 dB

4.77 dB

Peak directivity

6.50 dB

7.09 dB

7.66 dB

Radiation efficiency

0.472

0.492

0.514

References 1. Yang F, Samii YR (2008) Electromagnetic band gap structures in antenna engineering, Cambridge University Press 2. Yablonovitch E (1987) Inhabitted spontaneous emission in solid state-state physics and electronics. Phys Rev Lett 58(20):2059–2062 3. Ahn D, Park JS, Park CS, Kim J, Kim CS, Qian Y, Itoh T (2001) A design of the low pass filter using the novel microstrip defected ground structure. IEEE Trans Microwav Theory Tech 49(1):86–93 4. Kumar A, Machavaram KV (2013) Microstrip filter with defected ground structure: a close perspective. Int J Microwave Wireless Techno 5(5): 589–602; Nouri A, Dadashzadeh GR (2011) A compact UWB bandnotched printed monopole antenna with defected ground structure. IEEE Antennas Wirel Propag Lett 10; Kildal P-S (2000) foundations of antennas: a unified approach. Student litteratur p 394 5. Panda RA, Mishra D, Panda H (2018) Biconcave lens structured patch antenna with circular slot for Ku-Band application. Lecture Note in Electrical Engineering, Springer, vol 434, pp 73–83 6. Panda RA, Mishra D, Panda H (2017) Biconvex patch antenna with circular slot for 10 GHz application. IEEE SCOPES-2016, pp 1927–1930 7. Panda RA, Mishra SN, Mishra D (2016) Perturbed elliptical patch antenna design for 50 GHz Application. Lecture Note in Electrical Engineering Springer, vol 372, pp 507–518 8. Milligan TA (1985) Modern antenna design. (1stedn), McGraw-Hill, p 408 9. Stutzman WL, Thiele GA (1981) Antenna theory and design, 1st edn. Wiley, p 598 10. Saunders SR (1999) Antennas and propagation for wireless communication systems, 1st edn. Wiley, p 409 11. Harish A, Sachidananda M (2007) Antennas and wave propagation. Oxford p 402 12. Shackelford AK, Kai-Fong L, Luks KM (2003) Design of small-size wide-bandwidth micro strip-patch antennas. IEEE Antennas Propag Mag 13. Mosallaeiand H, Sarabandi K (2004) Antenna miniaturization and bandwidth enhancement using a reactive impedance substrate. IEEE Trans Antennas Propag 14. Rahmat-Samii Y, Kona KS, Manteghil M, Yueh S, Wilso WJ et al. (2006) A novel lightweight dual-frequency dual-polarized sixteen-element stacked patch micro strip array antenna for soil-moisture and seasurface-salinity missions. IEEE Antennas Propag Mag 15. Panda RAA, Panda H, Mishra D (2016) Log periodic implementation of biconvex patch antenna. IJERMT 5(3):10–16

VLSI Fast-Switching Implementation in the Programmable Cycle Generator for High-Speed Operation D. Punniamoorthy, G. Krishna Reddy, and Vikram S. Kamadal

Abstract This paper proposes a two-delay line, and a time-to-digital detector allows the pulse width-control circuit to operate over a large frequency range including less delay cells. This paper presents a new duty cycle without the need for a look-up table. The CPI circuit blocks the reference clock to save the results of D flip-flops and reduce the dynamic power. It requires 6–8 clock cycles needed for an operating time of the processor of Intel(R) Core(TM) 2 Duo Processor. The proposed circuit performs well for an output duty cycle ranges from 30 to 70%. The operating frequency range is 264.089 MHz, and the estimated power is 108 mW. Keywords Duty cycle · Fast-locking · Pulse width-control circuit · Multi-modulus prescalar · Programmable duty cycle

1 Introduction Double-data rate technology is also one of the basic solutions to need for Soc systems capable of fast accessing process. To meet the demand for high-speed operation, nowadays the systems have adopt a DDR technology. DDR-SDRAM and dualsampling ADC use the rising and falling edges of the REF clock to generate a clock with 50% duty cycle for high-speed operations. However, digital control circuit and elements like delay are limited the maximum operating frequency. Duty cycle distortion is due to clock malfunctions in high speed. On comparing with the previous D. Punniamoorthy (B) Department of Electronics and Communication Engineering, Sree Chaitanya College of Engineering, Karimnagar, Telangana, India e-mail: [email protected] G. Krishna Reddy Department of Electronics and Communication Engineering, Mother Theresa Institute of Science and Technology, Sathupally, Telangana, India V. S. Kamadal Department of Electronics and Communication Engineering, Ekalavya Institute of Technology, Chamarajanagar, Karnataka, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_54

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works, the programmable duty cycle eradicated the various limitations. The process, voltage, and temperature may influence the duty cycle and making it difficult to calibrate [1]. The clock signal often requires a high-speed multi-stage clock buffer to drive the circuit. A number of PWCLs have been proposed to overcome some of the limitations to be occurred in producing the duty cycle. The all-digital PWCL was designed to take advantage of scaling the CMOS circuit. However, it has two main drawbacks. The first is that the programmable duty cycle requires a search table to generate the corresponding digital code duty cycles. The next is the 28 cycles REF required to be locked. The locking time is longer than for conventional circuits to reduce the area and power of the D flip-flops. This paper proposes a new all-digital PWCC with the programmable duty cycle [2]. It has four major benefits: (1) The use of two delay lines and a time to digital detector reduces the hardware required; (2) it is capable of operating over wide frequency range; (3) comparing with the previously developed circuits, the accuracy is maintained, and (4) without using a look-up table, an output duty cycle is ranging from 30 to 70%. The remainder of this paper is organized as follows here: Section 2 presents the literature review on comparing the previous work, Sect. 3 defines the proposed circuit using an all-digital fast-locking PWC, Sect. 4 describes the model simulation, and Sect. 5 concluded the results of simulation and corresponding graphs are determined.

2 Proposed Circuit 2.1 Operation Figure 1 shows the proposed circuit block diagram. The period of the input signal is defining the two delay lines. The input clock is divided by 2 to establish a REF signal. Identifying the pulse width of REF is equivalent to determine the input clock. The one-shot circuit generates a pulse train with a frequency matching with an input clock. MUX delivers REF to the CDL for PW detection. CPI detects the pulse width range of the divided REF to enable four output paths to control the 16 to 4 MUX1. The coarse detector compares the four MUX1 outputs to REF in order to determine which of the 4 to 1 MUX2 input paths to allow [3]. Using the D flip-flops facilitates the implementation of clocks with a small or large duty cycle. The output produced in the basis of fine detection digitally. The fine detector then sequentially detects the three delay paths in the FDL to determine the delay that is close to reference pulse width [4]. The pulse signal is then imported to the CDL. The output clock is generated using a D flip-flop with asynchronous RST. However, a pulse generated with a one-shot circuit that passes through the CDL and FDL to produce the desired duty cycle from the original pulse is used. The MDL features the same structure as MUX1 and MUX2, and turns off the unused matching tri-buffers to saving a power. The pulse train signal passes through the MDL triggering the flip-flop to produce

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Fig. 1 Proposed all-digital control circuit

the falling edge of the output clock. The desired value of the duty cycle has been obtained from the duty cycle setting. So this approach is easily applicable to other advanced process like some datatransmitting applications (Fig. 2).

2.2 Flowchart The control circuit initiates all D flip-flops. MUX enables the divided by 2 signal of the input clock to the CDL. In Fig. 3, control circuit uses the detection results of CPI to control a MUX1 to enable four outputs of CDL into the coarse detector. The control circuit turns off the unused coarse delay cells. The control circuit uses the detection results of the coarse detector to control the MUX to enable one path of signal into the fine detector [5]. MUX enables the nanopulse generated by the one-shot circuit into the CDL and MDL. The control circuit allows each MUX2 path sequentially into the FDL if the fine detection is not finished [6]. The control circuit re-controls the MUX1 and MUX2 to empower the comparable path by the computation results from the duty cycle setting circuit and gates the REF signal.

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Fig. 2 Simulated results on set_ckt

Fig. 3 Flowchart of the FSM

3 Design 3.1 CPI Circuit The CPI circuit is used to determine the pulse width of REF, which is equal to the period of an input signal. The divided signal REF is sent to both the CPI and CDL.

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The pulse width codes F1, F2, F3, F4, and FC_FINISH are initially set to {00011}. The input PW is between 8 coarse delays and 12 coarse delays, as shown in Fig. 4. When the detection is completed, the CPI circuit blocks the REF to save the results of D flip-flops and reducing the dynamic power [7]. The proposed circuit had two functions. First is to reduce the number of detectors in CDL. This circuit has a smaller area cost and low decoder complexity than conventional coarse detectors in Fig. 5. Second, when the detection of input signal is finished, the CDL, MUX, and FDL are reused to generate the falling edge of the output signal, and all is done basically from the value by divided by 2 in Fig. 6. The FC_FINISH value is 1 in the coarse pulse width identification. The out 4, out 8, and out 12 values are 0. The F4 to F1 value is 1000. The delay is maintained from F1 to F4.

Fig. 4 CPI circuit

Fig. 5 Simulated results on CPI identification

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Fig. 6 Simulated results on divide-by-2 circuit

3.2 CDL and Coarse Detectors Figure 7 defines the CDL that comprises 15 tri-state delay cells C1 to C15, and one matching delay cell, C16. If the PW of input signal REF is greater than 8 delay cell and smaller than 12 delay cell, a binary encoder thermometer then converts the digital code from the gross detector and CPI circuit to the binary. The final code of the coarse detector, Bc4 to Bc1 equal to {1111}, equates the number of coarse delay cells close to REF in Fig. 8.

3.3 FDL and Fine Detector Figure 9 defines the FDL; it comprises three tri-state delay cells. The advantage of the conventional structure is that only one cycle clock is mainly required to complete the detection [8]. The disadvantage is that it increases the loading to each of the delay cells. The initial values of Q4 to Q1 are set to {0000}, and the Input_buf is {0011}. If REF1 triggers the D flip-flop, which means an Input_buf leads a REF1. FSM states

VLSI Fast-Switching Implementation in the Programmable Cycle …

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Fig. 7 Block of CDL and coarse detector

the changes to following state passes the result. The circuit requires four cycles for pulse-width detection. The control circuit disables path 1 and enables path 2 to continue the detection. Figure 10 defines the simulation on FDL and the fine detection from the coarse detection. The reset is at 0, and the input value is 0. The REF_fine is 0 with the binary value of fsm_in as 1000, and finally, fd_finish is 1 and the final output codes Bf2 to Bf1 become {10}.

3.4 One-Shot Circuit One-shot circuit generates a pulse train with a frequency that matching the input clock. It is used to producing the rising edge of the output clock during the time of final duty cycle setting circuit as shown in Fig. 11 which is defined as the one-shot circuit. Figure 12 describes the simulated results on one-shot circuit. The input clock value is 1, and the output value is 1; finally, the one_shot q1 is 1. If the detection all gets completed means MUX incorporates the output of one-shot circuit into the MDL to produce the final output. The 30% will take one clock, and 70% will take two clock cycles to produce the output.

628

Fig. 8 Simulated results on CDL and coarse detector

Fig. 9 Block of CDL and coarse detector

D. Punniamoorthy et al.

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Fig. 10 Simulated result on FDL and fine detector

Fig. 11 One-shot circuit

3.5 Duty Cycle Setting Circuit The detected results of a coarse and fine are to be converted into a binary code. The binary code is then sent to the duty cycle setting system to calculate the result based on the programmer’s duty cycle setting codes. The detected digital code corresponds to the period of an input signal. An output clock with desired duty cycle can be implemented by sending pulse with the delay [9]. The pulse train produced by the one-shot circuit triggers the D flip-flop with an asynchronous reset to generate a rising edge of the output clock. The detected digital codes to a 100% duty cycle correspond to a 50% duty cycle output clock which can be implemented by dividing the digital codes by 2. In Figs. 13, 14, and 15, the output generation and detection methods are described. The total operating circuit is 6–8 cycles, depending the process of fine detection.

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Fig. 12 Simulated results on one-shot circuit

Fig. 13 Proposed duty cycle setting circuit Fig. 14 Output clock generator

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Fig. 15 Pulse width detection of each mode

4 Simulation and Experimental Results 4.1 Model Simulation Model Sim SE6.3f is used for the VHDL and verilog coding. Here, the result is get simulated with the use of VHDL coding [10]. Different level of simulations is used like behavioral, gate levels, etc.

4.2 Xilinx Xilinx is used to know the power consumption, area, and timing analysis of a designed circuit. Likewise, the power for a 1000 MHz frequency is 108 mW required. The frequency range operated here is 264.089 MHz (Fig. 16).

4.3 Experimental Results The maximum operating frequency is 264.089 MHz, and maximum output required time after the clock is 6.140 ns. All values displayed in nanoseconds only for the timing detail of speed grade-7. The default period analysis for clock is x1/q in timing constraint. The clock period is 3.787 ns. The total number of paths is 5, and destination part is 1 in Fig. 17. The output duty cycle is 30–70% in the 3.787 ns of clock period. The logic utilization is the total number of slice registers: 12 out of 13, 824 1%. The nine flipflop used and three latches are used. In logic distribution, the number of occupied slices is 14 out of 6, 912 1%. The number of slices containing only related logic of 14 out of 14 is 100. The numbers of bonded IOBs are 2 out of 510 1%. The IOB flip-flops are 1, and the numbers of gated clocks are 1 out of 4 25%. The additional

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Fig. 16 Power analysis

JTAG gate count for IOB is 144, and the peak memory usage is 134 MB. It requires 6–8 clock cycles needed for an operating time of the processor of Intel(R) Core(TM) 2 Duo Processor.

5 Conclusion This paper presented two delay lines and two detectors capable of reducing the costs of hardware, while achieving an equal degree of accuracy. The input operating frequency is 200–600 MHz. New duty cycle setting circuit produces output duty cycle from 30 to 70% in the 3.787 ns of clock period without a usage of look-up table. The maximum operating frequency is 264.089 MHz, and the estimated power is 108 mW. Limited operating frequency range is the main disadvantage here. It requires 6–8 clock cycles needed for an operating time of the processor of Intel(R) Core(TM) 2 Duo Processor.

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Fig. 17 Simulated results on final all-digital circuit

References 1. Metev SM, Veiko VP (1998) Laser assisted microtechnology, 2nd edn. In: Osgood Jr RM (ed). Springer-Verlag, Berlin 2. Breckling J (ed) (1989) The analysis of directional time series: applications to wind speed and direction. In: Lecture notes in statistics, vol 61. Springer, Berlin 3. Zhang S, Zhu C, Sin JKO, Mok PKT (1999) A novel ultrathin elevated channel low-temperature poly-Si TFT. IEEE Electron Device Lett 20:569–571 4. Wegmuller M, von der Weid JP, Oberson P, Gisin N (2000) High resolution fiber distributed measurements with coherent OFDR. In: Proceedings of ECOC’00, paper 11.3.4, p 109 5. Sorace RE, Reinhardt VS, Vaughn SA (1997) High-speed digital-to-RF converter. U.S. patent 5 668 842, 16 Sept 1997 6. IEEE Std. 802.11 (1997) Wireless LAN medium access control (MAC) and physical layer (PHY) specification

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7. Padhye J, Firoiu V, Towsley D (1999) A stochastic model of TCP Reno congestion avoidance and control. CMPSCI technical report 99-02. University of Massachusetts, Amherst 8. Karnik A (1999) Performance of TCP congestion control with rate feedback: TCP/ABR and rate adaptive TCP/IP. M.Eng. thesis, Indian Institute of Science, Bangalore, India 9. Akyildiz IF, Wang X, Wang W (2005) Wireless mesh networks: a survey. Comput Netw 47:445– 487 10. Vega AM, Santamaria F, Rivas E (2015) Modeling for home electric energy management: a review. Renew Sustain Energy Rev 52:948–959

A Study of Non-Gaussian Properties in Emotional EEG in Stroke Using Higher-Order Statistics Choong Wen Yean, M. Murugappan, Mohammad Iqbal Omar, Wan Khairunizam, Bong Siao Zheng, Alex Noel Joseph Raj, and Zunaidi Ibrahim Abstract The stroke patients often suffered from emotional disturbances, and this leads to perceive emotions differently than normal control subjects; the emotional impairment of the stroke patients can be effectively analyzed using EEG signal. The EEG signal has been known to have non-Gaussian properties, and the nonGaussianity characteristics of the EEG differ under different emotional states. The analysis of non-Gaussianity in EEG signal was performed by using higher-order statistics measures such as the skewness and kurtosis. In this study, the nonGaussianity was examined in the emotional EEG signal of stroke patients and normal control subjects. The estimation of the emotional EEG distribution from the results was symmetrically non-Gaussian for both stroke and normal groups. Particularly, it was found that the normal subjects have more non-Gaussian EEG distribution than the stroke patients. Keywords Electroencephalogram · Non-Gaussian · Higher-order statistics · Stroke · Emotion

1 Introduction According to the World Health Organization (WHO), stroke was ranked the second in the top ten causes of deaths worldwide, the crude death rate was 85, which means out of 85 deaths caused by stroke per 100,000 population in the year 2015 [1]. Stroke, also known as cerebrovascular accident (CVA) or brain attack, happens due C. W. Yean · M. I. Omar · W. Khairunizam · B. S. Zheng · Z. Ibrahim School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia M. Murugappan (B) Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Doha, Kuwait e-mail: [email protected] A. N. J. Raj Department of Electronic Engineering, Shantou University, Shantou, China © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_55

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to the problem in the blood vessels that supply the oxygen to the brain. The sudden disruption in the blood flow caused the brain cells damage, and it leads to the loss of the brain functions. Consequently, stroke survivors often suffered from emotional impairment and having different emotional experiences than with normal people for the same emotional situation [2, 3]. More detailed information about the earlier works on emotional impairment analysis in stroke patients can be found in [4]. Most of the human activities are regulated by the brain; a huge amount of information including the emotional impairment can be retrieved from the brain activities. Several methodologies have been implemented in the literature to “capture” the brain activities for analysis, such as the positron emission tomography (PET), functional magnetic resonance imaging (fMRI), magnetoencephalogram (MEG), and electroencephalogram (EEG). Among these methods, the data acquisition devices of PET, fMRI, and MEG are costlier and not portable in size, but the above methods give higher spatial resolution information of the signals. In the case of EEG, the data acquisition devices are cheaper, portable in size, and have good temporal and spatial resolution. Therefore, EEG signals have been widely used in variety research involving brain analysis. Moreover, the EEG signals provide high spatial and temporal resolutions, where the asymmetric independencies between the different brain regions are detectable by the means of EEG measurement. Alike other biomedical signals, the EEG signals are generated from the stochastic phenomena of biological systems. The analysis of EEG properties is useful in explaining the underlying neuronal activities and related emotional states; thus, the study of EEG properties has gained the interest of researchers. Often, the statistical properties of the EEG signal were examined, such as the mean, standard deviation, and autocorrelation of the EEG distribution. These features can be used to characterize the EEG distribution.

1.1 Non-Gaussian Properties of EEG From earlier studies, the EEG is found to be non-Gaussian, where the EEG data are deviated from the normal distribution [5]. Several researches have been used to verify the non-Gaussianity of EEG data [6–8]. The non-Gaussianity of EEG data is affected by the EEG segmentation and sampling frequency, and the percentage of non-Gaussianity in EEG has shown to increase with the number of EEG data analyzed [6, 7]. Similarly, the higher the sampling frequency resulted in the higher percentage of non-Gaussianity [5]. For a very short EEG data length, the distribution of the data was likely asymmetrical, whereas for longer EEG data length, the distribution was more symmetric with outlier-prone [7]. Furthermore, approximately half of the EEG data were found to be non-Gaussian in 5 s window length; thus, an epoch of more than 2 s is able to reveal the non-Gaussian properties of EEG signal [6, 7]. Moreover, the non-Gaussianity of EEG differs under different emotional states. For instances, Sugimoto studied different sleep stages using EEG, reported sleep

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stages 1 and 2 are more non-Gaussian, and sleep stage 3 and rapid eye movement (REM) stage showed less non-Gaussian in EEG [7]. Elul analyzed the nonGaussianity of EEG under resting state and mental task states, the author examined the 2 s epoch of EEG signal and concluded that EEG is less non-Gaussian under resting state and more non-Gaussian while performing mental tasks [9]. Therefore, the non-Gaussianity of EEG signal may provide important information on brain activities.

2 Related Studies In the literature, the detection of non-Gaussianity of EEG signal has been tested by different methods, for example, the Hinich’s tests, Fisher’s K-statistics, Kolmogorov– Smirnov’s statistics, and chi-square technique [6–9]. The Fisher’s K-statistics uses the third- and fourth-order moments, which were related to the skewness and kurtosis of the distribution [6]. Nevertheless, the Kolmogorov–Smirnov’s statistics and the chi-square technique use the hypothesis testing. These two methods test the null hypothesis with the value derived from the significant level, the null hypothesis stated that the normal distribution function, F(x), represents the distribution function of the population; hence, the case when rejected the null hypothesis represents the distribution which is non-Gaussian [6, 9]. The Hinich’s tests involved the estimation of bispectrum and bicoherence, which are the higher-order statistical (HOS) measures in frequency domain. The Hinich’s tests stated that if the bispectrum is not zero, then the process is non-Gaussian, whereas if the bicoherence is a nonzero constant, the process is linear and nonGaussian [8, 10]. The importance to use higher-order statistical measures instead of second- or lower-order measures, such as correlation and power spectrum, has an ability to reveal the amplitude information and phase information [11, 12]. Accordingly, the higher-order statistical measures such as the skewness and kurtosis can be used to analyze the non-Gaussianity of EEG signal. The HOS measures involved the statistical features with order of three and above and can be analyzed in time domain or frequency domain (Fourier Transform) approach, known as moment and polyspectra, respectively. The measures in time domain also named as the higher-order statistics (HOSA). The HOSA can be calculated from the normalized higher-order moments. For example, the skewness is computed from the normalized third-order moment, and the kurtosis is computed from the normalized fourth-order moment [11]. The skewness and kurtosis often used to describe the shape of a distribution. Skewness is the description of the “not symmetry” shape of a distribution, whereas kurtosis has been known as the measure of the peakedness of the distribution till 2014. Westfall introduced the new definition of kurtosis and stated that kurtosis has no relation to the peakedness; however, it describes the weight of the distribution tails, whether heavy or light [13].

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The HOSA has been used to interpret the stochastic feature of the brain signals, such as the skewness and kurtosis were taken as the brain reaction index; both the measures resulted in increasing value while the subjects performing mental tasks [14]. Moreover, the discrete wavelet transform (DWT)-based HOSA has been proven to be useful in classifying the EEG of normal and epileptic patient; the HOSA has shown its ability to detect the abnormal EEG [15].

3 Materials and Methods 3.1 EEG Data The EEG database was collected from Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, Malaysia [16, 17]. The detailed experimental settings and procedures can be referred in [16, 17]. To analyze the non-Gaussianity in EEG of different emotional states, three groups of subject were studied, they were subject with left brain damage stroke patients (LBD), right brain damage stroke patients (RBD), and normal control (NC). Each group contained 15 subjects, and there were a total of 45 subjects involved in the present study. The subjects were emotionally induced for six different emotional states (anger, disgust, fear, happiness, sadness, and surprise). The emotional EEG signals were stimulated by the audio-visual stimuli taken from the International Affective Picture System (IAPS) and International Affective Digitized Sound (IADS). The EEG signals were recorded by using a 14-channel Emotiv Epoc with a sampling frequency of 128 Hz, and the electrodes were placed on the subjects scalp according to the international standard 10–20 system. The emotion-contained EEG signals of the three groups were preprocessed by applying Butterworth sixth-order bandpass filter with a cutoff frequency of (0–49) Hz. In the present work, the EEG signals from alpha (8–13) Hz, beta (13–30) Hz, and gamma (30–49) Hz frequency bands were analyzed for six different emotional states. The two HOSA, skewness and kurtosis, were extracted from every 6 s epoch of the EEG data for analysis.

3.2 Skewness Skewness is the measure of skew in a distribution, or the degree of asymmetry of the data from the mean. The skewness value indicates the spreading of the data is to the right or left of the mean of the distribution, and if the skewness is a negative value (a3 < 0), the distribution is skewed to the right with more spread out data to the left and is called as left-skewed distribution, and if the skewness is a positive value (a3 > 0), there are more spread out data to the right and called as right-skewed distribution,

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for a skewness value equal to 0 (a3 = 0); the data are in a symmetric distribution [18]. The calculation below showed the moment coefficient of skewness, a3 [19]. First, the third-order moment µ3 was calculated by  µ3 =

¯ 3 (xi − x) N

(1)

where xi is the time series data, x¯ is the mean, and N is the number of data. Then, the moment coefficient of skewness a3 was calculated as a3 =

µ3 S3

(2)

where S denotes the standard deviation.

3.3 Kurtosis Kurtosis measures the weight of the tail of the distribution, which represents the value of the bundle of the tails relative to the rest of the distribution. The expected value of kurtosis for a Gaussian distribution is 3, and if the kurtosis is smaller than 3, the distribution is less outlier-prone and is a light-tailed distribution, and if the kurtosis is larger than 3, the distribution is more outlier-prone with a heavy tail [20]. The steps below stated the calculation of the moment coefficient of kurtosis, a4 [19]. The fourth-order moment µ4 was calculated by  µ4 =

¯ 4 (xi − x) N

(3)

where xi is the time series data, x¯ is the mean, and N is the number of data. Then, the moment coefficient of kurtosis a4 was calculated as a4 =

µ4 S4

(4)

4 Results and Discussion Two HOSA, skewness and kurtosis, of different order were calculated from the emotional EEG signals. From these HOSA, the skewness measures the symmetry of the distribution, whereas the kurtosis measures the proneness of the outliers in the distribution. Therefore, the information of the shape of the emotional EEG distribution can be estimated from the skewness and kurtosis values.

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Figures 1, 2, and 3 show the average skewness values, a3 , of the LBD, RBD, and NC in alpha, beta, and gamma, respectively. From Figures 1, 2, and 3, the skewness values of the three frequency bands are close to zero, which also indicated there was less skewness in the distribution and the distribution of the emotional EEG signals was near to symmetry. Also, the variation of the skewness values increases as the frequency increases, from alpha band to gamma band. The one-way analysis of variance (ANOVA) with significant level (p) of 5% was used to validate the significant different among the six emotional states, and the results were presented in Table 1. The p-values from the ANOVA show that the skewness values of all groups in the three frequency bands were not statistically significant (p > 0.05) among the six emotional states; hence, all the emotional states have similar means. The kurtosis, a4 , of the EEG distribution of LBD, RBD, and NC was shown in Figs. 4, 5, and 6, for alpha, beta, and gamma bands, respectively. In this work, all the kurtosis values were higher than 3, and for the ease of presentation, the kurtosis values showed in this study were deducted by 3 [20]. The results from Figs. 4, 5, and

Skewness in Alpha Band

Fig. 1 Average skewness of LBD, RBD, and NC in alpha band

0.003

a3

0.002

LBD

0.001

RBD

0.000

NC

-0.001 -0.002

Emotion

Skewness in Beta Band

Fig. 2 Average skewness of LBD, RBD, and NC in beta band

0.004

a3

0.002

LBD

0.000

RBD NC

-0.002 -0.004 -0.006

Emotion

A Study of Non-Gaussian Properties in Emotional EEG in Stroke … Fig. 3 Average skewness of LBD, RBD, and NC in gamma band

641

Skewness in Gamma Band 0.006 0.004 0.002 a3 0.000 -0.002 -0.004 -0.006

LBD RBD NC

Emotion

Table 1 ANOVA results of skewness and kurtosis among the six emotional states p-value Feature

Group

Frequency bands Alpha

Skewness

Gamma

LBD

0.6318

0.3470

0.5164

RBD

0.4869

0.9830

0.5235

NC Kurtosis

Beta

0.9299

0.6473

0.6196

LBD

0 (Low frequency) < 0 (Low frequency)

The two legs in the H-Bridge inverter are switched at different frequencies. Switches S 1 and S 2 are switched at fundamental frequency (50 Hz) where as S 3 and S 4 switched at carrier frequency [6, 9]. The gate signals to the switches of the same leg are complementary. A small dead time is inserted between the turn OFF of S 1 and turn ON of S 2 and vice versa.

2.1 Modified Unipolar SPWM Switching The Modified Unipolar SPWM (MUSPWM) switching scheme significantly reduces the adverse effect of dead time in this work. Since frequency of operation is very compare to general-purpose inverters, and the dynamic range of gain (maximum output to minimum output) is at least 36 dB, the proposed scheme has an upper hand as compare to SPWM with dead-time insertion [10–13]. As said earlier, if a little is good, “a lot should be better-except with dead time”, this leads to thinking about advance high-frequency switching strategy [14]. This reflects on the proposed “modified unipolar SPWM switching technique”. The proposed switching strategy is highly effective with some gate driver IC integrated half-bridge packages and it allows the use of hybrid switching device topology for low frequency of power amplifiers. The control signals in the full-bridge in the proposed scheme are given in Fig. 3.

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1339 Gate signal ( S1)

Fig. 3 Bridge gate signals and output voltage for MUSPWM switching

1

0

Gate signal (S2)

Magnitude (Volts)

1

0 Gate signal (S3)

1

0

Gate signal (S4)

1

0

Bridge output

Magnitude (Volts)

+ VDC

0

-VDC Time (sec)

3 Mathematical Modeling of Control Loop An inverter control loop is designed to reduce the THD of the output voltage, increases the speed and output voltage regulation with output voltage and capacitor current feedback. For the mathematical modeling of SPWM inverter, an input voltage V DC is kept constant. Figure 4 shows the control scheme for PWM inverter.

vref

+

-

GVC

iref

+

-

GIC

KPWM

va

+

1/sL

-

Output Voltage loop (Outer loop)

+-

io

vo Capacitor current loop (Inner loop)

iL

Ki

Kv

Fig. 4 PWM inverter control scheme with feedback loops

iC

rc + 1/sC 1/R

vo

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3.1 Transfer Function Model of Capacitor Current Loop The simplified control loop of the inverter with the appended inner capacitor current is depicted in Fig. 5. Here, capacitor current loop incurs the compensator output voltage as reference (iref ). The transfer function for capacitor current loop with PI compensator is given by G IC (s) = K pi +

K pi K ii = (1 + (s K ii )) s s

(1)

The uncompensated capacitor current loop with PWM modulator gain and current loop gain transfer function is given by G iol (s) =

Vdc sC R i C (s) = Ki i ref (s) Vtri i1

(2)

The compensated capacitor current loop with PI compensator transfer function is given by i C (s) = G iol (s) = i ref (s)



 Vdc sC R K ii K pi + Ki S Vtri i1

(3)

Tuned compensator values of capacitor current loop using Eq. (3) and system parameters are given in Table 2 with Siso tool are Proportional gain = K pi = 3.6453e5 Integral gain = K ii = 2.9e − 5 The bode plot in Fig. 6 is obtained using Eq. (3) for compensated capacitor current loop. Bandwidth of 19.6 kHz and phase margin of 74.8° with infinite gain margin is obtained. iref

+

-

GIC

KPWM

va

+

vo

-

1/sL

iL

+

-

ic

rc + 1/sC

io 1/R

Fig. 5 Feedback loop control of capacitor current

vo

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Bode Diagram

Fig. 6 Bode plot of compensated capacitor current loop

Gm = Inf , Pm = 74.8 deg (at 1.96e+04 Hz)

Magnitude (dB)

60 40 20 0 -20

Phase (deg)

-40 45 0 -45 -90 -135 -180 10

2

10

4

3

10

10

5

6

10

Frequency (Hz)

Bode Diagram Gm = Inf , Pm = 90.3 deg (at 2.08e+03 Hz)

50

Magnitude (dB)

Fig. 7 Bode plot of compensated output voltage loop

0

-50

Phase (deg)

-100 -45 -90 -135 -180

2

10

3

10

4

10

5

10

6

10

Frequency (Hz)

3.2 Transfer Function Model of Output Voltage Loop Once the compensator gain values are set by the inner current loop bandwidth criterion, the next step is to tune up the compensator values of the output voltage feedback loop [15, 16]. The control loop of load voltage with current loop is shown in Fig. 8. The load voltage is compared with a reference voltage and the voltage error signal is passed through a voltage PI compensator (Fig. 7). The transfer function of PI compensator for load voltage loop is given by G VC (s) = K pv +

K pv Kiv = (1 + (s K iv )) s s

(4)

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+-

GVC

iref

Gicl

iC

rc + 1/sC

vo

Capacitor current loop

Output Voltage loop

Kv

Fig. 8 Simplified load voltage loop with inner current feedback

Table 1 Compensator tuned values Loop

Compensator

Proportional gain

Integral gain

PM (°)

GM (dB)

BW (kHz)

Capacitor current

PI

3.6453e5

2.9e−5

74.8

∞

19.6

Output voltage

P

1.1948

_

90.3

∞

2.08

PM Phase margin, GM Gain margin, BW Bandwidth

The compensated load voltage loop with voltage PI compensator transfer function is given by G vol (s) =

  sCrC + 1 vo (s) = G VC K v G icl vref (s) sC α

(5)

The Bode diagram of the compensated output voltage loop is plotted in Fig. 7. From the plot the obtained results of bandwidth and phase margins are 2 kHz and 90.3° with infinite gain margin, respectively. Table 1 gives the compensator tuned values of capacitor current and output voltage loops obtained from the bode plots shown in Figs. 6 and 7, respectively. The inner capacitor current loop bandwidth is larger than the outer output voltage loop and infinite gain margin is obtained.

3.3 Simulation Results The performance of the inverter system is verified with the help of MATLAB/SIMULINK simulation software and its model. To assess the controller performance, different types of loading conditions have been verified. The FFT Analysis Tool in MATLAB/SIMULINK is used to execute Fourier analysis of signals stored in a structure with time format. The increased output voltage and less distortion are observed in the compensated system (Fig. 9) under non-linear load compared to the uncompensated system (Fig. 10) output voltage.

Output voltage (Volts)

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200 0 -200

0.02

0.01

0

0.03 Time (ms)

0.04

0.05

Fundamental (50Hz) = 325.2 , THD= 0.50% 0.18

Mag (% of Fundamental)

0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

0

100

200

300

400

500

600

700

800

900

1000

Frequency (Hz)

Fig. 9 FFT spectrum for non-linear load with compensated system

From Table 3, it is evident that the designed compensated system improves the inverter performance by reducing the harmonic distortion from 4.68 to 0.5% for nonlinear diode rectifier bridge load and maintains the stable output. The reduction of harmonics is due to the proposed capacitor current loop with PI controller for the SPWM inverter. The system parameters considered to assess the performance of the intended controller are listed in Table 2. From Table 3, it is evident that the designed compensated system improves the inverter performance by reducing the harmonic distortion from 4.68 to 0.5% for nonlinear diode rectifier bridge load and maintains the stable output. The reduction of harmonics is due to the proposed capacitor current loop with PI controller for the SPWM inverter. Figure 11 depicts the signal waveforms of output voltage and current for no-load and Fig. 12 depict the full load of 4.3 A at 325 V (peak-peak). It is noticed that the minimal distortion in the load current at full load with a compensated system compare to no load. Figure 13 depicts the load voltage and current response of the designed inverter system for the linear load from no load to full load. The load voltage recovery time 0.0425 ms was measured for the linear load of 75  shown in Fig. 14. It shows that the recovery time is reduces compared to set value of 0.045 ms and system exhibits

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200 0 -200 0

0.01

0.02

0.03

0.05

0.04

Time (ms)

Fundamental (50Hz) = 320.9 , THD= 4.68% 3

Mag (% of Fundamental)

2.5 2 1.5 1 0.5 0

0

100

200

300

400

500

600

700

800

900

1000

Frequency (Hz)

Fig. 10 FFT spectrum for non-linear load with uncompensated system Table 2 System parameters used in the simulation

Parameter name

Parameter value

Input DC voltage (V DC )

400 V

Output voltage (V o )

230 Vrms

Filter capacitance (C F )

3.3 µF

Load resistance (R)

75.57 

ESR of filter capacitor (r c )

7 m

Filter inductance (L F )

1.1 mH

Carrier signal amplitude (V tri )

3.3 V

Control signal amplitude (V control )

2.64

Switching frequency (f s )

40 kHz

Output frequency (f o )

50 Hz

Current feedback gain (K i )

0.1

Voltage feedback gain (K v )

0.0037

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Table 3 THD and peak output voltage for uncompensated and compensated system Load type

Uncompensated system

Compensated system

Vo (V p in volts)

Vo (V p in volts)

Voltage THD (%)

Voltage THD (%)

Linear load

322

~0

325

~0

Non-linear load

330

4.68

325

0.5

Output Current (Amps)

Output Voltage (Volts)

400 200 0 -200 -400 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Time (ms)

Fig. 11 O/P voltage and current for no load

very fast current regulator dynamic response with less reduction in the load voltage and distortion during the recovery time. It clearly noticed that compensated system is better than the uncompensated system. The highly distorted non-linear diode rectifier bridge is used to verify the performance of proposed control system. The converter current is highly distorted in both positive and negative cycles, but the load voltage remains the same as shown in Figs. 15 and 16 respectively. From Table 4 it is evident that, the inverter load voltage recovery time in the designed control system is less compared to other control schemes for the resistive load. The improvement from this work is an increase in bandwidth and stability of the system with less distortion compared to existing schemes shown in Table 5. From Table 6, the HF bridge inverter design system has an output power of 114 W with 2.4% THD, whereas the proposed system has an output power of 700 W with 0.5% THD. Hence the proposed system improves the inverter performance by using capacitor current feedback with PI compensator.

K. B. Bommegowda et al. Output Voltage (Volts)

1346 400 200 0 -200 -400

Output Current (Amps)

5

0

-5

0

0.01

0.02

0.03

0.04

0.05

0.06

Time (ms)

0.07

0.08

0.09

0.1

Fig. 12 O/P voltage and current for full load

Fig. 13 Load voltage and current for linear load

Load Voltage(Volts) 400 200 0 -200 -400 5

0

-5 0

0.01

0.02

0.03

Load Current(Amps)

0.04

0.05

Time(ms)

4 Conclusion The major outcomes of the performance evaluation of Single phase full bridge SPWM inverter with output voltage and capacitor current feedback system are as follows: 1. The load voltage THD of proposed inverter is reduced from 4.68 to 0.5% for non-linear load and inverter dynamic response is improved with the capacitor current loop and designed PI compensator.

Modeling and Analysis of Single-Phase Modified Unipolar … Fig. 14 Zoomed o/p voltage for R-load

1347

330

Load voltage (Volts)

320 310 300 290 280 270 0.043

0.0435 0.044 0.0445 0.045 0.0455 0.046 0.0465 0.047

Time(ms)

Fig. 15 Inverter load voltage for non-linear load

400

Load voltage (Volts)

300 200 100 0 -100 -200 -300 -400

0

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.08

0.09

0.1

Time (ms) 30

Load current (Amps)

Fig. 16 Output current for non-linear load

20 10 0 -10 -20 -30

0

0.04

0.05

0.06

0.07

Time (ms)

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Table 4 Recovery time of load voltage for various control schemes Control scheme

Recovery time (ms)

Neural network control and multiple feedback loop [17]

25

Synchronous reference frame voltage control [9]

1.0

DSP based fully digital controlled method [3]

0.6

Capacitor current feedback with PI compensator method (Proposed system)

0.0425

Table 5 Performance comparison of different control techniques Control scheme

Current loop PM (°)

GM (dB)

BW (kHz)

PM (°)

GM (dB)

BW (kHz)

O/P voltage THD (%)

Multiple feedback loop control with state space averaging [18]

90

∞

1.9

74.5

87.1

–

2

Current 65 mode PI controller with inductor current loop [1, 4]

23.7

3.62

63

–

0.94

–

Capacitor current loop with PI compensator (Proposed system)

∞

19.6

90.3

∞

2.08

0.5

74.8

Output voltage loop

PM Phase margin, GM Gain margin, BW Bandwidth Table 6 Controller output power and THD Control scheme

Input voltage (Volts)

Output power (W)

Efficiency (%)

Output voltage THD (%)

Bidirectional sinusoidal HF bridge inverter design method [5]

24

114

84.6

2.4

Capacitor current loop with PI compensator method (Proposed system)

400

700

83.59

0.5

Modeling and Analysis of Single-Phase Modified Unipolar …

1349

2. The inverter having stable output voltage with infinite gain margin and 0.5% harmonic distortion for non-linear load conditions is achieved from PI compensators. 3. The load voltage recovery time in the designed control system is less compared to previous control schemes for the resistive load. 4. The HF bridge inverter design system has an output power of 114 W with 2.4% THD, whereas the proposed system has an output power of 700 W with 0.5% THD. Hence the proposed system improves the inverter performance by using capacitor current feedback with PI compensator.

References 1. Michael Ryan J, Robert Lorenz D (1995) A high performance sine wave inverter controller with capacitor current feedback and “back-EMF” decoupling. IEEE annual power electronics specialists conference, vol 1 2. Kagotani T, Kuroki K, Shinohara J, Misaizu A (1989) A novel UPS using high frequency switch mode rectifier and high frequency PWM inverter. In: 20th annual IEEE power electronics specialists conference, vol 1, pp 53–57 3. Divan DM (1991) Inverter topologies and control techniques for sinusoidal output power supplies. In: IEEE APEC conference proceedings, pp 81–87 4. Wu H, Lin D, Zhang D, Yao K, Zhang J (1999) A current mode control technique with instantaneous inductor current feedback for UPS inverters. In: IEEE Power Electronics Specialists Conference, vol 2, pp 951–957 5. Cherati SM, Azli NA, Ayob SM, Mortezaei A (2011) Design of a current mode PI controller for a single-phase PWM inverter. In: IEEE Applied Power Electronics Colloquium, pp 180–184 6. Tzou Y-W, Jung S-L (1998) Full control of a PWM dc-ac converter for ac voltage regulation. IEEE Trans Aerosp Electron Syst 34:1218–1226 7. Deng H, Oruganti R, Srinivasan D (2005) Modeling and control of single-phase UPS inverters: a survey. In: IEEE international conference on power electronics and drive systems, vol 2, pp 848–853 8. Suryanarayana K, Nagaraja HN (2016) Cascaded bidirectional converter topology for 700 W transformerless high frequency inverter. J Control Autom Electr Syst 27:542–553 9. Monfared Mohammad, Golestan Saeed, Guerrero Josep M (2014) Analysis, design and experimental verification of a synchronous reference frame voltage control for single-phase inverters. IEEE Trans Ind Electron 61:258–269 10. Abdel-Rahim NM, Quaicoe JE (1996) Analysis and design of a multiple feedback loop control strategy for single-phase voltage-source UPS inverters. IEEE Trans Power Electron 11 11. Trigg MC, Nayar CV (2008) DC bus compensation for a sinusoidal voltage-source inverter with wave-shaping control. IEEE Trans Ind Electron 55 12. Tajuddin MFN, Ghazali NH, Siong TC, Ghazal N (2009) Modelling and simulation of modified unipolar PWM scheme on a single phase dc-ac converter using PSIM. In: Student conference on research and development (SCOReD) 13. Chacko BP, Panchalai VN, Sivakumar N (2-13) Modified unipolar switching technique for pwm controlled digital sonar power amplifier. In: Int J Eng Innov Technol (IJEIT) 3(5) 14. Fraser ME, Manning CD (1994) Performance of average current mode control PWM UPS inverter with high crest factor load. In: IEEE international conference on power electronics and variable speed drives, pp 661–667 15. Zope PH (2012) Design and implementation of carrier based sinusoidal PWM inverter. Int J Adv Res Electr Electron Instrum Eng 1(4)

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16. Yi H, Dai J, Wu J (2008) Research on modeling and control of the single-phase inverter system with a nonlinear load. In: World congress on intelligent control and automation 17. Mansoor AKZ, Abdullah AG (2012) Analysis and simulation of single phase inverter controlled by neural network. Al-Rafidain Eng 20 18. Woo Y-T, Kim Y-C (2005) Digital control of a single-phase ups inverter for robust ac-voltage tracking. Int J Control Autom Syst 3(4):620–630

Simulation, Fabrication and Characterization of Circular Diaphragm Acoustic Energy Harvester Vasudha Hegde, H. M. Ravikumar, and Siva S. Yellampalli

Abstract In this paper, simulation, fabrication and characterization of the circular diaphragm-based piezoelectric acoustic sensor for energy harvesting is presented. Zinc oxide (ZnO) is used as the key piezoelectric material for diaphragm so that the sensor can be used for biomedical applications also. The major contributions of this work are (1) design and simulation of the sensor using COMSOL Multiphysics (2) fabrication of ZnO-based circular diaphragm acoustic sensor and (3) experimental results validating the simulation results. The fabricated sensor can be used as energy harvester for low-power electronic applications. Keywords Acoustic sensor · Piezoelectric material · Sensor fabrication · Energy harvesting · Frequency response

1 Introduction The power supplies on-boarded inside electronic device are in focussed research as the size and power consumption of the electronic devices are reduced rapidly. Ambient acoustic signals available are used not only for sensing but also as onboard power supplies. The sensors using piezoelectric phenomenon are simple and more efficient and have better energy density with the factors like piezoelectric coefficient of the material, maximum stress and the frequency matching with the source and the structure affecting the conversion [1]. The literatures available in this field are categorized as basic material selection and their properties, fabrication and characterization, mathematical modeling and simulation of the structure. The key material performance indices used for piezoelectric resonators and the selection of the material are done by Ashby approach [2], and the choice of the material for biomedical V. Hegde (B) · H. M. Ravikumar Department of Electrical and Electronics Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India e-mail: [email protected] S. S. Yellampalli UTL Technologies Ltd.VTU Extension Centre, Bangalore, India © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_113

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applications, environmental consideration and fabrication limitation along with the advantages of ZnO, viz. simple and mature process, well-controlled film properties and compatibility with IC technology for mass production are discussed by [3]. The basic design of the square diaphragm, fabrication steps and characterization showing that the fabricated sensor has linear range of operation 110–160 db of SPL has been demonstrated by [4]. The characterization and testing methods are discussed in [5, 6]. In this paper, design and simulation, fabrication and characterization methods to improve the output response of acoustic energy harvester are discussed. The piezoelectric material used is ZnO due to its biomedical compatibility, environmental-friendly property and clean room fabrication compatibility.

2 Implementation 2.1 Structure Design The basic structure of the sensor is shown in Fig. 1. Since circular diaphragm has advantages like maximum deflection at the center, maximum range of unidirectional stress and strain and least value of natural frequency [7], circular shape of the diaphragm is preferred for this application. It has circular silicon diaphragm formed by back etching in the body of the wafer, and piezoelectric layer is sandwiched between a pair of aluminum electrodes deposited on the diaphragm. The silicon diaphragm dimension controls the natural frequency, and the design of the structure including cavity decides the frequency response and lateral stresses in response to acoustic pressure. A sputtered piezoelectric layer transforms the mechanical deflection of Si diaphragm into a piezoelectric charge. Al electrodes collect the electrical charge in the form of voltage. Further the cavity also stabilizes the acoustic pressure. Thus, the designed acoustic structure can be implemented for dynamic pressure applications like acoustic sensing. The designed structure has natural frequency in the acoustic frequency range, resulting into resonance to produce maximum deflection of the diaphragm which in turn is converted into maximum voltage.

Fig. 1 Schematic view of the device. a Cut section of layers with dimensions. b Top view of the device with contact pads

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Fig. 2 COMSOL simulation of circular diaphragm a Diaphragm with eigen frequency. b Total displacement. c Stress. d Volumetric strain

Fig. 3 COMSOL simulation of a Entire structure with cavity, b Frequency response with two modal frequencies, c Displacement at second modal frequency

2.2 Simulation The diaphragm is simulated by COMSOL Multiphysics to analyze stress, strain, deflection and natural frequency, and the results are as shown in Fig. 2. Circular diaphragm favored the maximum deflection, stress and strain [4]. The circular diaphragm of diameter 3,000 µm and thickness 30 µm is chosen to favor acoustic range natural frequency and limitation with fabrication facilities. The piezoelectric layer and the electrodes are positioned and dimensioned based on the optimized value of deflection, stress and strain. Further the entire structure including sandwiched ZnO (1 µm) and cavity is simulated. From simulation, the natural frequency is found to be 11,076 Hz as shown in Fig. 3.

2.3 Fabrication The device is fabricated on a 32 single side polished, single crystal P type < 100 > oriented silicon wafer. The wafer was RCA cleaned, and silicon oxide (SiO2 ) layer

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Fig. 4 a GDS file for fabrication. b Devices after fabrication and dicing. c Inline characterization(microscopic) after top Al electrode deposition. d Back etch depth to form the diaphragm (DeKTak −301micron)

of 1 micron is grown by thermal oxidation. The aluminum top and bottom electrodes of thickness 200–300 nm (approx) deposition were done by RF sputtering method. The deposition of ZnO piezoelectric layer is formed as sandwiched layer thickness 800–900 nm (approx) by RF sputtering with d33 of 26 pm/v for the given recipe. The back etching of silicon was done using DRIE to form diaphragm of thickness 25–30 µm. The fabricated devices and inline characterization are shown in Fig. 4.

3 Experimental Results The following experiments were conducted in order to validate the working of the fabricated sensor.

3.1 DC Probe Station The DC probe station is used for measurement if I-V and C-V characteristics with pulsed source are in the frequency range 1 kHz and 5 MHz. The experimental setup is as shown in Fig. 5, and Table 1 and Table 2 represent the sample values of the DC resistance and the internal circuit parameters of the sensor like series and parallel resistance and capacitance, respectively. From Table 1, it is clear that the for the input value of DC voltage 10 mV, the average current is 60nA. From Table 2, it is clear that the value of admittance is of minimum in the range 11 kHz–12 kHz approx. This indicates that the first resonant frequency is occurring at this range.

3.2 Linearity and Frequency Response The device is packaged using SMD adapter PCB with hole of 1500 µm diameter to form the cavity as shown in Fig. 6. The device is tested in NAL calibrator with

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Fig. 5 DC probe station to analyze the I-V and C-V characteristics

Table 1 I-V characteristics with input DC voltage 10 mv

V

I

R

0.01

6.87E-08

145551.9

0.0098

6.65E-08

147353.4

0.0096

6.60E-08

145506.6

0.0094

6.40E-08

146888.8

0.0092

6.31E-08

145876.1

0.009

6.13E-08

146744.7

0.0088

6.00E-08

146639.8

0.0086

5.85E-08

147134.3

0.0084

5.73E-08

146592.8

frequency range till 8 kHz and 140 db for linear operation in the given decibel range as shown in Fig. 7a. Further the device is tested also for the frequency response and approximate natural frequency. This experimental setup has the speaker of a piezoelectric crystal with specification 20nf at 10 kHz with voltage 5v(p-p) coupled at the bottom of the device as shown in Fig. 7b. The speaker is connected to the signal generator so that variable frequency acoustic source is available. The acoustic pressure is directly guided to the cavity, and no leakage of the acoustic pressure is ensured during package. The output voltage is measured from the device output terminals. The output voltage sweep, frequency response and linear range of operation of the fabricated structure are as shown in Fig. 8. The natural frequency of 12,000 Hz (approx) of the output response is in close approximation to the simulated result.

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Table 2 C-V characteristics (series and parallel resistance and capacitance) sample for the acoustic range of frequency AC signal (V)

Frequency (Hz)

G ()

B ()

Cp (f)

Rp ()

Rs ()

Cs (f)

0.029904 0.02991

7785.349

1.76E-06

3.46E-05

7.08E-10

569378.8

1461.633

7.10E-10

8400.176

1.86E-06

3.71E-05

7.03E-10

537455.3

1348.161

7.05E-10

0.029909

9063.558

1.74E-06

4.01E-05

7.04E-10

574478.2

1080.687

7.05E-10

0.029912

9779.328

1.86E-06

4.35E-05

7.08E-10

539013.8

977.212

7.10E-10

1040.318

7.05E-10

0.029907

10551.62

2.27E-06

4.67E-05

7.04E-10

440577.2

0.029917

11384.91

1.51E-06

5.02E-05

7.02E-10

662326.2

598.5426

7.02E-10

0.029917

12284

2.37E-06

5.42E-05

7.02E-10

421523

807.3782

7.03E-10

0.029911

13254.09

1.82E-06

5.89E-05

7.07E-10

548681.8

525.3536

7.08E-10

0.029924

14300.8

2.35E-06

6.31E-05

7.02E-10

425696.8

589.9692

7.03E-10

0.029907

15430.17

2.20E-06

6.80E-05

7.01E-10

455369.3

474.3436

7.02E-10

0.029907

16648.73

2.36E-06

7.35E-05

7.03E-10

424410.4

435.45

7.04E-10

0.029907

17963.51

2.22E-06

7.87E-05

6.97E-10

451275.8

357.5049

6.98E-10

Fig. 6 a Device with PCB mounting front view. b Device backside with 1500 micron radius hole

Fig. 7 Experimental setup for a NAL calibrator for linear operation range. b Frequency response

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Fig. 8 Experimental results a Output voltage sweep (1 KHz–10 kHz). b Output voltage response. c Linearity of operation (120 db–140 db)

4 Conclusion The circular diaphragm-based acoustic sensor which can be used as onboard power supply for low-power electronic devices is simulated, fabricated and tested experimentally. From the experiments, it is clear that the simulated structure of silicon diaphragm radius 3,000 micron and a cavity formed at the back with a hole of 1,500 micron radius gave the resonant frequency of approx. 12,000 Hz and voltage close to 40 mV for 140 db SPL which is in close approximation to the simulated results. By varying the dimensions of the cavity, the bandwidth may be improved. Further packaging of the device has to be done in order to make it compatible for the specific applications. Acknowledgements This research (fabrication and characterization) was performed using facilities at CeNSE, funded by the Ministry of Electronics and Information Technology (MeitY), Govt. of India, and located at the Indian Institute of Science, Bengaluru. The authors would like to thank the faculty and staff of CeNSE, IISc, Bengaluru, for the support.

References 1. Fang H et al A review of techniques design acoustic energy harvesting. 2015 IEEE student conference on research and development (SCOReD) doi:978-1-4673-9572-4/15/2015 IEEE 2. Pratap R, Arunkumar A (2007) Material selection for MEMS devices. Indian J Pure Appl Phys 45:358–367 3. Li Y, Zhigang G et al (2016) Nano size related piezoelectric efficiency in a large ZnO thin film, potential for self powered medical device application. Biochem Anal Biochem J ISSN:21611009 4. Sherif Saleh Ahmad et al (2006) Design and fabrication of piezoelectric acoustic sensor. Proceedings of the 5th WSEAS international conference on microelectronics, nanoelectronics, optoelectronics. Prague, Czech Republic pp. 92–96 5. Mika B (August 2007) Design and testing of piezoelectric sensors. Master’s thesis, texas A&M university 6. Horowitz SB (2005) Development of a MEMS based acoustic energy harvester. Doctoral thesis, graduate school of the university of Florida

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7. Khakpour R et al Analytical comparison for square, rectangular and circular diaphragm for MEMs applications. International conference on electronic devices and applications IEEE. doi:978-1-4244-6632-2010

Mitigation of Voltage Sags and Swells in the Distribution System Using Dynamic Voltage Restorer P. V. Manitha and Manjula G. Nair

Abstract In recent times, the focus of researchers has moved toward power quality domain due to its increasing importance in the profit and loss calculation of the industrial sector. The main industries like textile, glass, plastic and semiconductors are facing around 10 k$ to 1 M$ loss due to voltage sag. So, the focus is to improve the power quality. Various control algorithms such as IRPT and SRF are suggested for mitigating voltage-related issues. Usually, IRPT-based control algorithms are not commonly used for mitigating voltage-related issues as compensating voltage is not independent of load current. The researchers have attempted to modify the existing control algorithms to compensate voltage-related issues. In this proposed work, a modified version of IRPT controller is proposed to carry out voltage sag and swell compensation. The IRPT-based controller is simulated, and its performance is analyzed. Keywords Power quality · Series active filter · Control algorithm · IRPT controller · Voltage sag · Voltage swell

1 Introduction Electrical pollution in distribution system has dramatically increased by the invention of power electronic devices. These power electronic-based nonlinear loads cause distortion in current which in turn distort the voltage also. Other than these voltage and current harmonics, there are some more power quality issues like voltage and frequency variation, flicker, waveform distortion, unbalance, etc. An analysis of P. V. Manitha (B) Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] M. G. Nair Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_114

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power quality (PQ) data by the Ministry of Power shows that for the month of April 2017, the average duration of power cuts stood at 6.38 h, and the average number of power cuts in the month stood at nine times at the all-India level. Such PQ disturbances have a deleterious impact on industrial operations. Direct costs of PQ disturbances include damage to the equipment, production loss, wastage of raw material, salary costs involved during non-productive periods and restart costs. PQ issues also lead to blocked capacity, increased capital investment, premature failure of equipment due to electrical and thermal stresses, unplanned outages, poor power factor and overcurrent surges. More than that the increased awareness among consumers has also made the requirement of improving the power quality as crucial [1]. Various approaches are suggested by researchers to mitigate the PO issues. Surge suppressors, noise filters, voltage regulators, ups, power conditioners, proper grounding and proper wiring, etc., are some of the mechanisms to mitigate PQ issues. These techniques may reduce the PQ issues to a certain extent. Another option is to use harmonic filters and FACTS devices. Passive filters eliminate the harmonics by offering series or parallel (shunt/series) resonance at tuned frequency. Shunt active filter does the reactive power and harmonic compensation by tracking the current same as that of reference current generated by the control algorithm. Series active filter injects voltage in series with the line to maintain the terminal voltage sinusoidal and with rated magnitude. Hybrid filters and IPQC are also in used [2–4]. The functioning of active filter mainly depends upon the control algorithm. There are different control algorithms like IRPT, SD, DC bus voltage, IcosF, etc., which are in use for shunt active filter [5, 6]. For series active filter, IRPT- and SRF-based controllers have been proposed [7–13]. But its performance is not satisfactory. In this paper, a modified version of IRPT is presented which will be capable of carrying out compensation of voltage sag and swell as well as voltage unbalance. The three-phase system configuration is shown in Fig. 1. Three-phase source is connected to nonlinear load. The voltage and current at load terminal are sensed, and it is given to the controller. The controller generates the pulses for voltage source inverter which is used as series active filter.

2 IRPT-Based Controller IRPT-based controller generates the reference compensation currents for active filter which is connected in series with the line using coupling transformer. Figure 2 represents the block diagram of IRPT controller. The load voltage and currents are sensed and given to the controller. The sensed three-phase voltages and currents are transformed into αβ axis, and the active and reactive powers are calculated from it using the expression, pL = vα i α + vβ i β

(1)

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Fig. 1 Three-phase system with load and series active filter coupled to the line

Fig. 2 Block diagram representation of IRPT controller

qL = vα i β − vβ i α

(2)

The AC part of active power is extracted using high-pass filter. The reference voltage is calculated using the expression,

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vcα =

i α2

vcβ =

 1  −i α pLac + i β qL 2 + iβ

(3)

 1  i p + i α qL 2 β Lac + iβ

(4)

i α2

The reference voltage and actual voltages are compared, and pulses can be generated. These reference voltages are transformed back to three-phase system using inverse Clarke’s transformation.

3 Modified IRPT Controller To improve the performance of IRPT controller for voltage compensation, some changes are made in the conventional controller. The block diagram representation is shown in Fig. 3. After generating reference signal same as that of ordinary IRPT controller, phase correction unit makes the voltages in phase with the system voltages. Meanwhile, the magnitude of the load voltage is calculated and subtracted from reference value. This represents the required compensating voltage magnitude, which is then multiplied by unit amplitude sine wave to convert to the signal. The phase correction unit output is compared with this signal to check whether the voltage magnitude matches. If not, this error signal is added with phase correction unit output to generate the compensation signal. Sag/swell detection block is used to detect whether the voltage at load terminal is at sag condition or swell condition. If it is swell, the injected voltage has to be 1800 out of phase with system voltage. To make like that, the reference voltage is multiplied with −1. If it is sag condition, the

Fig. 3 Block diagram representation of modified IRPT controller

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injected voltage has to be in phase with the system voltage. So, no multiplication is required.

4 Comparative Analysis of Performance of Conventional and Proposed Controller The system is simulated by considering RL load of 5 kVA rating. The VSI used as the filter is connected in series with the line through injection transformer. The reference filter voltage is generated by the control algorithm which then compared with actual filter current to generate pulses for the inverter. The design values are given in Table 1. The system is simulated for three phase sag, swell and unbalance condition conditions to analyze the performance of both conventional and modified IRPT controllers. Case 1: During three-phase Sag A 20% sag is created in the system, and the corresponding source voltage, filter voltage and load voltage with conventional and modified IRPT controllers are shown in Figs. 4 and 5, respectively. From 0.06 to 0.1 s, the sag is introduced and after 0.1 s its normal system voltage. With conventional IRPT controller, it is seen that the sag is not being compensated at all, and even under normal system voltage, it generated compensating voltage which in turn reduces the magnitude of normal voltage. The sag is exactly compensated with the proposed controller, and the injected voltage is in phase with the system voltage. Also, under normal working voltage, the compensating voltage is zero. Case 2: During three-phase Swell A 20% swell is introduced in the system and the corresponding source voltage, filter voltage and load voltage with conventional and modified IRPT controllers are shown in Figs. 6 and 7, respectively. At 0.2 s, 20% swell is introduced into the system which is properly compensated by the modified controller by injecting 1800 out of phase voltage in series with the line. Table 1 Design parameters

Load rating

5 kW, 100 VAr

Vdc

650 V

DC link capacitor

9 mF

Interfacing Inductor

25 mH

Ripple filter

C = 8.9e–9F

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Fig. 4 Source voltage, filter voltage and load voltage with conventional controller

Fig. 5 Source voltage, filter voltage and load voltage with modified IRPT controller

Mitigation of Voltage Sags and Swells …

Fig. 6 Source voltage, filter voltage and load voltage with conventional controller

Fig. 7 Source voltage, filter voltage and load voltage with the proposed controller

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Fig. 8 Source voltage and load voltage with the proposed controller and load voltage with conventional controller

Case 3: During Unbalance Magnitude unbalance is created by increasing/decreasing voltage at one phase alone, and the simulation results are presented in Fig. 8. It is seen that with conventional IRPT Controller, the magnitude unbalance is not able to rectify, whereas with modified IRPT controller, it is possible to maintain rated voltage at load terminals even under magnitude unbalance also which clearly depicts the effectiveness of modified IRPT controller. Figure 9 represents the source and load voltages for different values of sag and swell. The variation is from 5 to 20%. In all the cases, the voltage at the load terminal is maintained at rated value by the modified controller. To eliminate sag/swell, the voltage can be injected with phase shift also. Even under that case, the performance of IRPT controller is unsatisfactory. With modified IRPT controller during case, the injected voltage will be in phase with system voltage, and during swell, it will be 1800 out of phase with the system voltage so that the injected voltage gets added up with PCC voltage to restore the rated voltage at load terminals.

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Fig. 9 Source and load voltages for different values of sag and swell

5 Conclusion A modified IRPT controller for mitigation of voltage variations is proposed, and its performance is compared with conventional IRPT controller. With conventional IRPT-based controller, it is observed that even though the voltage is at rated value, the compensating voltage is being generated which further reduces the system voltage. Also as the percentage sag increases, the magnitude of compensating voltage decreases with the conventional IRPT controller. These two problems can be rectified by the modified IRPT controller which performs well in sag, swell and unbalance condition.

References 1. Arrilaga J, Bradley DA, Bodger PS (1985) Power system harmonics. Wiley, Chichester, UK 2. Singh B, Al-Haddad K, Chandra A (1999) A review of active filters for power quality improvement. IEEE Trans Ind Electron 46(5) 3. Singh B et al (2004) A review of three phase improved power quality AC–DC converters. IEEE Trans Ind Electron 4. Manitha PV, Nair MG (2016) Performance analysis of different custom power devices in a power park. In: PESTSE 5. Akagi H, Kanazawa Y, Nabae A (1984) Instantaneous reactive power compensators comprising switching devices without energy storage component. IEEE Trans Ind Appl

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6. Sindhu S, Sindhu MR, Nambiar TNP (2017) Comparative study of exponential composition algorithm under dynamic conditions. In: TAP energy 7. Bhattacharya S, Divan DM, Bhanarjee B (1991) Synchronous frame harmonic isolator using active series filter. In: EPE 91 8. Singh B, Verma V (2003) A new control scheme of series active filter for varying rectifier loads. Power Electron Drive Syst 1:554–559 9. Biswal P, Dr. Yadhav KB (2012) Design of DVR for improvement of voltage profile using synchronous reference frame based control strategy. In: IOSRJEEE, vol 1, Issue 6, July–Aug 2012, pp 01–08. ISSN 2278-1676 10. Manitha PV, Raji S, Nair MG (2015) Performance analysis of different control algorithms for series active filter. In: IEEE ICECCT 2015 11. Alam MS et al (2015) Implementation and control of low voltage dynamic voltage restorer using Park’s transformation for compensating voltage sag. In: 2nd International conference on electrical engineering and information and communication technology (ICEEICT) 2015. Jahangirnagar University, Dhaka-1342, Bangladesh, 21–23 May 2015 12. Javadi A, Hamadi A, Al-Haddad K (2015) Three-phase power quality device for weak systems based on SRF and p-q theory Controller. In: IECON 2015, Yokohama 13. Krishna A, Sindhu MR et al (2015) Application of static synchronous series compensator (SSSC) to enhance power transfer capability in IEEE standard transmission system. IJCTA 8(5):2029–2036

DC Micro-Grid-Based Electric Vehicle Charging Infrastructure—Part 1 Abhishek K. Saxena and K. Deepa

Abstract With the advent of electric vehicles, the charging infrastructure provides the backup for the EV penetration in the current automotive world. Currently, the EV charging is dependent on taking power from the conventional grid and transforming it to be suitable to charge the EV batteries. Using conventional grid beats the purpose of using electric vehicles. Also, the impact of charging large number of EV’s from the grid is a topic which is still being studied. EV charging in mass is bound to affect the grid parameters such as voltage profile, power fluctuations, harmonic content and can cause unbalance in the AC system, which can severely affect the operation of the AC system. This paper deals with an approach to try and understand these impacts of EV charging on the conventional grid and AC system. Keywords AC micro-grid · Electric vehicle · Charging · Infrastructure · Harmonics

1 Introduction Though electric vehicles offer some wide varieties of advantages over the conventional automobiles, the major challenge as to why the EV’s have not had a mass adoption is due to the high capital cost, lack of awareness, lack of technology and lack of charging infrastructure which forms the backbone for the large-scale deployment of EV’s [1]. One such charging infrastructure technology is AC slow charging technology which offers to charge your vehicle from the conventional grid [2]. For small scale charging of EV’s a simple charging station can serve the purpose by offering to charge a few (2–5) EV’s at once. But to charge many EV’s simultaneously, A. K. Saxena (B) · K. Deepa Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] K. Deepa e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_115

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a DC micro-grid is required which offers a large power output and allows us to charge many EV’s simultaneously [3]. The main aim of this paper is to study the impact of the electric vehicle charging on the conventional grid parameters in form of harmonic injection into the system. This allows us to formulate a strategy to shift the energy and power dependencies from typical AC grid to a renewable source which has several advantages [4]. Many authors have already reviewed papers regarding micro-grid system, such as AC/DC hybrid micro-grid system to cater to both AC and DC loads [5]. This paper deals with the analysis of charging an electric vehicle using conventional grid and the impact it has on the parameters at grid side [6]. A harmonic analysis of the impact is done to better understand the severity of large-scale simultaneous charging of electric vehicle from the conventional grid. Such a type of system can also facilitate vehicle to grid and grid to vehicle integration [7], which can help in improving the overall charging characteristics of the electric vehicle battery. Then again, various schemes can be understood, such as isolated and non-isolated systems for EV charging [8, 9]. This system caters to a specific type of demand, in this case, the demand of charging an electric vehicle using a DC micro-grid, which is often dubbed as solar chargers [10] and offers a hybrid energy renewable system for sustainable usage of energy [11].

2 AC Grid-Based Charging 2.1 Overview All the current electric vehicle charging strategies are greatly dependable on the conventional grid. Electric vehicles use an energy storage system in the form of batteries, which are a source of DC power. This battery storage system is the main source of energy for the vehicle traction. As the power levels at the vehicle level are DC, and the power required to charge the batteries should be DC, there is a need to semiconductor-based power conversion equipment at the AC grid side, so that the AC power can be transformed to the DC power. The block diagram in Fig. 1 gives a rough idea regarding the working of the AC grid-based charging infrastructure. The power flow is bidirectional so that the large number of electric vehicles can supply the power back to grid in case there is power abundance on vehicle side, or the gird requires stabilizing power for safe operations.

2.2 Components of AC Grid-Based System The various components used in the AC grid-based system are:

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Fig. 1 AC grid-based EV charging system block diagram

2.2.1

AC Grid

The conventional AC grid is a stable and stiff system which is capable of handling disturbances over it and recovers from the disturbances for stable operation of the grid. But for the sake of the study, the higher voltages at the grid side are brought down to lower voltages, using a distribution network, and power is then taken from to charge the vehicle. This type of system is similar to daily household plugs available for day-to-day usage.

2.2.2

DC/DC Bi-Directional Converter

The DC–DC bidirectional converter brings down the voltage from 700 V at DC link to 72 V for EV charging. This operation is achieved by operating the bidirectional converter in buck mode. This converter allows for the two-quadrant operation and allows bidirectional flow of power depending on the demand and necessity of power transfer. Both sides can operate as source and load. This operation can be used to either charge the EV battery when its SOC is low or can be used to transfer power from a large pool of EV’s to the grid for grid stabilization when the SOC level of all the EV’s connected to at the station is high. The input to this converter is the DC link voltage and the output of this converter is the EV voltage.

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D=

VPHEV VDC

I0 = D ∗ Iboost

(1) (2)

I0 = 0.1028 ∗ 36.17 A I0 = 3.718 A For calculating the L and C values for this converter, following formulas are used, with current ripple = 5%, voltage ripple = 1% and switching frequency = 10 kHz: C= L=

IL 8 ∗ f s ∗ V0

(3)

VL(ON) ∗ D IL ∗ f s

(4)

which gives us values: L = 38.73 mH C = 3.22 μF. The converter in buck mode uses constant duty cycle operation because care is been taken to keep the DC link voltage constant through various technique like MPPT at boost side and SVPWM at grid side.

2.2.3

AC/DC Bi-directional Converter

AC–DC bidirectional converter on the AC side facilitates bidirectional power flow between the DC link and AC side of the grid. This facility allows us to stabilize voltage at the DC link to a constant value if the voltage output of the PV array varies with the change in irradiance and temperature. This converter uses IGBT as switching devices and works on Space Vector PWM (SVPWM) technique which directly controls the DC link voltage in case of rectification mode and helps generate AC voltage in inverter mode. Rectification Mode In rectification mode, the switching is controlled by sampling the input voltage (AC voltages) and current and transforming them to α-β domain. The DC link voltage at this point is used to generate reference which generated the modulation index for the switching operation. Inverter Mode SVPWM operates on the principle that the switches must be operated so that both the switches in same leg are not turned on at a time to avoid shorting of DC supply. This is achieved by complementary operation of switch which offers eight possible switching vectors for the inverter, V 0 to V 7 with six active switching vectors and two zero vectors.

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Fig. 2 Rectification through grid

3 Modes of Operation 3.1 Rectification Through Grid The photovoltaic system is unable to generate any power due to poor weather conditions, or low radiation, and the flow of power is as shown in Fig. 2. • The DC–DC boost converter is cut off by means of the isolating switch and the power required to charge the EV is provided by the grid. • AC–DC bidirectional converter keeps the DC link voltage constant by means of converting the AC voltage to DC voltage using the SVPWM technique as mentioned above. The buck converter then transforms this high DC link voltage to the EV voltage and charges the EV battery.

3.2 Charging Through PV and Grid Rectification Power flow diagram of this mode is shown in Fig. 3, where different blocks are the component of the DC micro-grid. PV array is still unable to generate enough power to charge the EV on its own capability, as PV array generated power is less than EV’s rated power. The remaining power requirement to charge the EV is met by the grid through the rectification process and both PV array and the grid work in conjunction to provide charging requirement of the EV.

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Fig. 3 Charging through PV and grid rectification mode

3.3 PV to Grid Inversion Mode This type of working is required when either the EV is fully charged and does not require any more power, or the demand at the grid increases drastically in which case the PV array power is used stabilize the grid and provide the power to the grid which can be seen in Fig. 4. • After the SOC of the EV battery reaches its maximum values, the power from the PV array is sent to the grid. • The AC–DC bidirectional converter works in inverter mode as it transfers DC power from PV array side to the grid for grid stabilization. • The power from the PV array is utilized for the stabilizing purpose of the grid system in case there are changes at the grid side which need immediate support in order to prevent grid collapse.

4 MATLAB-Based Modelling The main aim of the simulation is to implement a three-phase SVPWM controlled rectifier unit and understand the impact of charging the electric vehicle on grid, which can be understood by using the THD analysis. The MPPT algorithm is written in the MATLAB function and different modes of operation are run as a part of the simulation.

Fig. 4 PV to grid inversion mode

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5 Results The results of the simulation are discussed in detail in this section. One way of implementing the rectifier unit was to implement it using a single-phase supply and a single-phase rectifier unit. But this type of configuration suffers from several disadvantages: • A single-phase supply is not capable of providing a high DC ling voltage, which then gets limited to 300–400 V. To obtain larger values of DC link voltage, threephase supply is required. Power rating of a single phase is smaller than a three phase. • So, to charge large number of EV simultaneously, a three-phase supply is required due to increased power demand. • Single-phase supply can create unbalance in the system, as loading on all the three phases cannot be controlled precisely in an independent manner [7, 8]. • Single-phase supply requires three different rectifier unit for the three phases of the commercial supply, which adds to the cost and increases the complexity in controlling the switches of the rectifier unit of separate unit [12]. A three-phase supply takes care of these disadvantages, as the unbalancing in the system can be removed by using a three-phase supply and a three-phase rectifier unit. The harmonics in the system and precise control of the output voltage of the rectifier unit are obtained by implementing the SVPWM algorithm, which is a closed-loop algorithm and will keep the DC link voltage at a constant value.

5.1 Rectification Through Grid MATLAB model of this mode operation is shown in Fig. 5, and results show that there is a great harmonic presence in the AC side current, which would get even worse with the introduction of multiple EV’s in the system, as shown in Fig. 7 and analysis in Fig. 8. The THDs reach up to 65.7% for a single EV charging, without filters on AC side. The DC link voltage however remains constant at 700 V, with a peak overshoot of 1600 V and settling time of 0.5 s due to closed loop control of converter operation and can be seen in Fig. 6. Figure 9 shows the charging characteristics of the EV battery and it is clear from the figure that the vehicle is getting charged by looking at the increasing SOC of the EV battery from 30% to 30.0002 in 1.5 s. The battery is charged at constant voltage mode; hence, the voltage profile is maintained at 72 V throughout the operation while the charging current is observed to be increasing towards 100 A. The distortion in current waveform and THD magnitude more than 25% of fundamental value below 100 Hz prove the necessity of a power quality improvement circuit in the near vicinity of EV charging station.

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Fig. 5 Rectification through grid MATLAB diagram

Fig. 6 DC link voltage after rectification

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Fig. 7 AC side phase voltage and current

Fig. 8 THD analysis of AC side current

5.2 Charging Through PV and Grid Rectification Figure 10 gives the MATLAB model of this mode; DC link voltage remains constant at 300 V with change in irradiance and temperature from 1000 to 250 W/m2 and 25–50 °C as shown in Fig. 11, confirming effective power tracking of MPPT. On the contrary, the harmonics injected in the system due to AC–DC bidirectional converter also gives us the rough idea of the impact of the semiconductor switching

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Fig. 9 SOC, current and voltage of EV battery vs time

Fig. 10 Charging through PV and grid rectification MATLAB model

on the grid side parameters. The AC side current profile has improved due to less dependency of the system on the AC grid, as evident from Figs. 12 and 14, which show us the lower harmonic content in grid side current, i.e. 11.83%. Figure 13 shows the charging characteristics of the EV battery and it is clear from the figure that the vehicle is getting charged by looking at the increasing SOC of the EV battery. The negative current implies the charging current and the voltage is maintained constant at around 72 V.

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Fig. 11 DC link voltage wrt irradiance and temperature

Fig. 12 AC side current

5.3 PV to Grid Inversion Mode This mode of operation works when the power generated from the PV array is fed to the grid directly, which helps in maintaining the grid stability in case of sudden changes on the grid and is depicted through a MATLAB model in Fig. 15. This PV array can then be used to sustain the grid parameters in case there is some instability on the grid parameters, the PV array can play an important role in providing the deficit power to the grid to fast changing loads. Figure 16 depicts the changes in the DC link voltage with the irradiance and temperature effect on the PV array and can be concluded that the MPPT works in order to keep DC link voltage constant. Figure 17 shows the filtered and unfiltered AC side current and the effect of using filters.

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Fig. 13 SOC, current and voltage of EV battery

Fig. 14 THD analysis of AC side current

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Fig. 15 MATLAB model

Fig. 16 DC link side voltage wrt irradiance and temperature

With reference to Fig. 18, the THD level in the grid side current in the inverter mode of operation is very high, of the order 71.39%, which are lower order harmonics. This level of harmonics present also calls for deployment of a filter circuit near the grid to mitigate the harmonics. Comparing the results of Figs. 8, 14 and 18, we can see that the THD on the AC side current is much more while PV supplies power to grid (71.39%) than compared to when PV and grid are used to charge the EV battery (11%).

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Fig. 17 Unfiltered and filtered AC phase current

Fig. 18 THD analysis of grid side current

6 Conclusion The THD analysis of the system shows that the THD = 65.7% in case of charging an electric vehicle from the grid are high and can cause severe damage to sensitive equipment, if not taken care of. This THD is way above the prescribed limit for the harmonics. And this is the case while charging a single EV from the grid.

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One way of solving the harmonics issue is to use filter circuits at the AC side and decrease the magnitude of the lower level harmonics. But again, the higher-level harmonics can still propagate in the system. The aim of this proposed model is to study impact of EV charging on the grid side parameter, predominantly the grid side current. Also, EVs are a highly unpredictable load, which can affect the design of a specific type of filter for the protection from harmonics. Thus, design of a DC micro-grid-based system is also carried out to make the charging functionality of electric vehicle independent of AC conventional grid.

References 1. Kaur S, Kaur T, Khanna R, Singh P (2017) A state of the art of DC microgrids for electric vehicle charging. In: 4th IEEE international conference on signal processing, computing and control (ISPCC 2017), 21–23 Sept 2017 2. Shireen W, Goli P(2014) PV Integrated smart charging of EVs based on DC link voltage sensing. IEEE Trans Smart Grid 5(3) 3. Bertini I, Sbordone D, Falvo MC, Di Pietra B, Martirano L, Genovese A (2015) EV fast charging stations and energy storage technologies: a real implementation in the smart micro grid paradigm. Electr Power Syst Res 120 4. Haesen E, Clement-Nyns K, Driesen J (2010) The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25 5. Abhijith VS, Akshatha S, Fernandes BG, Arun CN (2017) A unified AC-DC ‘grid architecture for distribution of AC and DC power on the same line. In: IEEE applied power electronics conference and exposition (APEC), pp 430–433 6. Fernández LP, Cossent R, Domingo CM, Frías P, San Román TG (2011) Assessment of the impact of plug-in electric vehicles on distribution networks. In: IEEE Trans Power Syst 26 7. Krishnan N, Raveendran V, Nair MG (2017) Vehicle-to-grid support by electric vehicle charging stations operated at airports and metro rail stations. In: 2017 IEEE international conference on technological advancements in power and energy (TAP Energy) 8. Giri M, Dr. Isha TB (2017) Comparison of non-isolated schemes for EV charging and their effect on power quality. In: International conference on circuits power and computing technologies (ICCPCT) 9. Akshya S, Ravindran A, Sakthi Srinidhi A, Panda S (2017) Grid integration for electric vehicle and photovoltaic panel for a smart home. International conference on circuits power and computing technologies (ICCPCT) 10. Revathi B, Sivanandhan S, Prakash V, Rames A, Isha TB, Saisuriyaa G (2018) Solar charger for electric vehicles. In: international conference on emerging trends and innovations in engineering and technological research (ICETIETR) 11. Marshal TP, Dr. Deepa K (2014) Hybrid energy renewable system: optimum design, control and maximum utilization with SIBB converter using DSP controller. In: Power and energy system conference: towards sustainable energy 12. Shahani DT, Arun Kumar B (2011) Grid to vehicle and vehicle to grid energy transfer using single-phase bidirectional ACDC converter and bidirectional DC–DC converter. In: 2011 International conference on energy, automation, and signal (ICEAS). Int J Eng Sci Technol

DC Micro-Grid-Based Electric Vehicle Charging Infrastructure—Part 2 Abhishek K. Saxena and K. Deepa

Abstract With the advent of electric vehicles, the charging infrastructure provides the backup for the EV penetration in the current automotive world. Fast charging is one such concept of providing a means to charge EV’s within a matter of minutes. The development in the concept of DC micro-grids has enhanced the charging operation of electric vehicles and has reduced the charging time. This paper proposes a charging infrastructure that deals with isolating the power requirement from the grid and supplies the power for EV charging through a DC micro-grid via a set of converters to transform the power from one level to another. As an emergency case, the grid is also kept into picture to supply power only in case of emergencies, and the operation is explained in part 1 of this paper. Use of this DC micro-grid for charging infrastructure ensures optimal usage of available power and charging time. Keywords DC micro-grid · Electric vehicle · Charging infrastructure · DC–DC bidirectional converter · AC–DC bidirectional converter

1 Introduction To charge many EV’s simultaneously, a DC micro-grid is required which offers a large power output and allows us to charge many EV’s simultaneously [1]. The focus of this paper is to study a renewable energy-based DC micro-grid system whose main source of power is PV array system [2]. This allows us to shift the energy and power dependencies from typical AC grid to a renewable source which has several advantages [3]. Growing concern regarding climate, global warming and a need to reduce the carbon footprint from the use of conventional sources of energy have made it a necessity to shift to renewable energy which are abundantly available [4]. A. K. Saxena (B) · K. Deepa Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] K. Deepa e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_116

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Such a type of system can also facilitate vehicle to grid and grid to vehicle integration [5], which can help in improving the overall charging characteristics of the electric vehicle battery. Then again, various schemes can be understood, such as isolated and non-isolated systems for EV charging [6, 7].

2 DC Micro-Grid 2.1 Overview Micro-grid is a system that takes use of the distributed generation to serve to a local group of loads, in isolation or with the AC grid [8]. There are several advantages of the use of micro-grids discussed in the literature which encouraged their use on large scale [9]. Different authors have already discussed different aspects of micro-grids. To cater to a specific type of demand, in this case, the demand of charging an electric vehicle using a DC micro-grid, which is often dubbed as solar chargers [10], offers a hybrid energy renewable system for sustainable usage of energy [11]. Figure 1 shows the basic block diagram of the proposed DC micro-grid, with its components discussed in Sect. 2.2. This micro-grid can provide power to charge the electric vehicle. The DC microgrid comes with an added benefit of providing DC fast charging option which can charge an EV in matter of minutes. DC fast charging is the widely used technology

Fig. 1 DC micro-grid-based charging system block diagram

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because it reduces the time spent at a charging station while charging your electric vehicle.

2.2 Components of DC Micro-Grid The various components used in the DC micro-grid and their technical specification are listed down:

2.2.1

Solar PV Array

Solar PV is used as the main power source in this micro-grid which is further used to maintain the DC link voltage at 700 V and used to supply power to EV battery and EBU. The PV array used consists of 13 modules in parallel with 5 strings per module. The power rating of each module is 305 V. Thus, the total power of the PV array is given as below: Ppv = 305 ∗ 13 ∗ 5 = 19.84 kW

(1)

V(pv)m = 54.6 V

(2)

I(pv)m = 5.56 A

(3)

The total power of the PV array is given as: Ns ∗ Np ∗ Power output of one module = Total Power

(4)

Total power = 20 kW as set by user. Where N s Number of strings per module. N p Number of parallel modules

Ns ∗ Np ∗ 305 = 20, 000

(5)

Ns ∗ Np = 65.57

(6)

Ns ∗ V(pv)m = 250

(7)

Ns ∗ 54.6 = 250

(8)

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which gives us: Ns = 4.57  5 5 ∗ Np = 65.57 Putting these values in Eq. (6) gives us: Np  13 Ipv = 72.34 A Vpv = 273 V

2.2.2

DC–DC Converter

The DC–DC converter connected at the output of the PV array is operating in boost mode. This DC–DC converter also employs the P&O-based MPPT algorithm which ensures that the PV array is operating in the maximum power point region always and provides a constant voltage output. • The boost converter boosts voltage from 273 V at PV output to 700 V at DC link.

Vdc_link =

VPV 1− D

(9)

The calculation of corresponding inductor and capacitor values is done based on: • Duty ratio (D) = 0.5, current ripple = 5%, voltage ripple = 1% and switching frequency = 10 kHz. The relation between the input current to boost and output current to boost is given by: Iboost = (1 − D) ∗ Ipv Iboost = (1 − 0.5) ∗ 72.34 Iboost = 36.17 A

(10)

The calculation of the L and C parameters required for the boost operation is calculated using the below formula: C=

I0 f s ∗ V0 ∗ D

(11)

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Vin ∗ (Vin − V0 ) IL ∗ f s ∗ V0

(12)

which gives us the values: L = 1.033 mH C = 9000 µF where ‘L’ and ‘C’ are the values of the inductor and capacitor used for boost converter. The boost converter is operating using perturb and observe MPPT which is dependent on the input voltage and the output voltage relationship of boost converter: VDC =

Vpv 1− D

(13)

Thus, controlling the duty cycle of boost converter can make PV array work in MPPT which is explained in Fig. 2. Perturb and observe MPPT algorithm works on the principle that if a small perturbation is added to the power of the PV array and if the power increases, then the perturbation needs to be kept on added until a point after which the power starts to decrease. This point of maximum power is known as maximum power point of the PV array. The perturbation can be achieved by increasing or decreasing the duty cycle of the boost converter. • The algorithm checks for the voltage and current output of the PV and calculates the power of the PV array. It then compares this power with the previous power, which can be done by introducing a delay element. • If the power output is greater than the previous power, then it checks for the voltage output of current state and the previous state. • If the voltage of current state is greater than that of previous state, then it increases the duty cycle, which in turn increases the PV voltage output because of the reason that if the power output of PV is more and the voltage level is also more, then a little perturbation can be added to check whether power further increases or not. 2.2.3

DC–DC Bidirectional Converter

The DC–DC bidirectional converter is used to bring down the voltage from 700 V at DC link to 72 V for EV charging. This operation is achieved by operating the bidirectional converter in Buck mode [12]. This converter allows for the two-quadrant operation and allows bidirectional flow of power depending on the demand and necessity of power transfer [5]. Both sides can operate as source and load. This operation can be used to either charge the EV battery when its SOC is low or can be used to transfer power from a large pool of EV’s to the grid for grid stabilization when the SOC level of all the EV’s connected to at the station is high.

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Fig. 2 Perturb and observe MPPT flow chart

Fig. 3 PV charging EV battery mode

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The input to this converter is the DC link voltage and the output of this converter is the EV voltage. D=

VPHEV VDC

(14)

I0 = D ∗ Iboost I0 = 0.1028 ∗ 36.17 A I0 = 3.718 A

(15)

For calculating the L and C values for this converter, following formulas are used, with current ripple = 5%, voltage ripple = 1%, switching frequency = 10 kHz: C=

IL 8 ∗ f s ∗ V0

(16)

VL(ON) ∗ D IL ∗ f s

(17)

L= which gives us values:

L = 38.73 mH C = 3.22 µF The converter in buck mode uses constant duty cycle operation because care is been taken to keep the DC link voltage constant through various technique like MPPT at boost side.

3 Modes of Operation Different modes of operation can be understood using the flowchart in Fig. 4.

3.1 PV Charging EV Battery and External Battery Unit Charging Mode 3.1.1

PV Charging EV Battery Mode

When Ppv = 19.84 kW: PV array alone can charge, the EV and whole power are transferred from the PV array to the EV battery. Power flow is as follows according to Fig. 3:

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Fig. 4 Flowchart of the modes of operation algorithm

• The power from PV array is first transformed using boost converter to set the DC link voltage. • The buck converter then transforms this voltage to the EV level and charges the EV battery. • The controller terminates the charging of EV by disabling the DC–DC buck converter when EV is completely charged. The PV array works in MPPT because of duty cycle control of boost converter.

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External Battery Unit Charging Mode

One of the main aspects of this mode of operation is that the EV should not be available for charging. If the EV is not available for charging or the SOC of the EV battery is full, then the control algorithm checks for the SOC level of the External Battery Unit. If the SOC of EBU is less than what is desired, then the PV array power is used to charge this EBU as shown in Fig. 5.

3.2 External Battery Unit (EBU) to EV Charging Mode During the operation of the PV, there may arise a case in which the power output of the PV array is not enough to charge the EV battery. Such a case can arrive due to several conditions, prevalent of all being the weather conditions limiting the power of the PV array to sustain charging of the EV. For such a condition when the power from the PV is not available, the External Battery Unit (EBU) comes into picture which will charge the EV battery. The most important condition being that the SOC of the EBU should allow it to charge the EV battery, as shown in Fig. 6. This charging is taken care through the DC–DC bidirectional converter and the PV with the DC–DC boost converter does not come into picture.

Fig. 5 External Battery Unit charging mode

Fig. 6 EBU to EV charging mode

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Fig. 7 PV to EV battery charging mode

4 MATLAB-Based Modelling The MPPT algorithm is written in the MTALAB function and different modes of operation are run as a part of the simulation. The detailed parameters of the DC–DC converters and the parameters of the PV array, EV battery and External Battery Unit are given in Tables 1 and 2. Table 1 PV array parameters

Table 2 Battery parameters

Parameter

Value

V PV

273 V

Power rating

19.84 kW

I PV

72.54 A

Irradiance

250–1000 W/m2

Parameter

EV battery

External Battery Unit

Voltage

72 V

700

Rated capacity

333.333 Ah

333.333 Ah

State of charge

30%

30%

Rated power

16 kW

233.33 kW

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5 Results and Discussion 5.1 PV Charging EV Battery and External Battery Unit Charging Mode 5.1.1

PV Charging EV Battery Mode

As it can be seen with the results of this mode, the MPPT is able to keep the DC link voltage constant at 700 V irrespective of changes in irradiance from 1000 to 250 W/m2 and temperature from 25 to 50 °C, which is evident from Fig. 8. The battery charging parameters also show that the battery is getting charged as the SOC is increasing from a minimum value of 30–30.03% in 2.5 s, as shown in Fig. 9. The battery is charging with a current of 200 A at a constant nominal voltage of 72 V (Fig. 7).

Fig. 8 DC link voltage wrt irradiance and temperature change

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Fig. 9 SOC, current and voltage profile of EV battery

5.1.2

External Battery Unit Charging Mode

The MATLAB model, shown in Fig. 10, for the External Battery Unit charging mode, remains same as the PV charging EV battery mode with the DC–DC bidirectional converter bypassed. The result, as seen in Fig. 11, shows the EBU is charged at a voltage level of 700 V, and the increasing SOC from 30% initial to 30.004% in 2.5 s shows that the battery is charging. Also, the negative current shows that the battery is charging, with a current of the order 50 A. The battery voltage is kept constant at a value around 700 V, as it is constant voltage charging.

DC Micro-Grid-Based Electric Vehicle …

Fig. 10 External Battery Unit charging mode

Fig. 11 SOC, current and voltage profile of EBU

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5.2 External Battery Unit to EV Battery and EV Battery to External Battery Unit Mode Figure 12 shows the MATLAB model of this mode of operation, and the results are shown in Fig. 13. According to Fig. 13, the change in characteristics at t = 2.5 occurs due to change in operation of the DC–DC bidirectional converter from buck mode to boost mode. • t < 2.5: EV battery is charging from SOC 30 to 30.04% in 2.5 s. The state of charge of the battery is increasing where negative current of about 200 A implies that the battery is taking current from the source battery, i.e. External Battery Unit. The voltage is increasing towards 72 V which implies the battery is charging.

Fig. 12 EBU to EV battery and vice versa MATLAB model

Fig. 13 SOC, current and voltage of EV battery

DC Micro-Grid-Based Electric Vehicle …

1399

• t > 2.5: The DC–DC bidirectional converter changes its operation from buck mode to boost mode and the EV battery which was now acting as the charging battery starts to behave as the source which is evident from Fig. 11. The SOC of EV battery starts to decrease from 30.04 towards 30% and there is a clear shift in current to positive half which implies the battery is supplying current, of about 50 A. The battery voltage also starts to decrease from a fully charged 72 V to a nominal value of about 66 V.

6 Conclusion The DC micro-grid employed in this paper works from the PV array, which is a modification on part 1. The power from grid is supplied in case of emergencies and when there is no power from the PV array. The PV array, boost converter, DC–DC bidirectional converter and battery parameters are modelled and simulated, and the result are also shown which verify the working of the charging infrastructure for the electric vehicle. Also, according to the analysis made in the Part-1 of this paper, where the impact on the AC grid is studied in case the EV battery is charged using the conventional grid, the use of DC micro-grid also eliminated the harmonic impact of charging an EV vehicle on the grid by working as an independent system to provide EV charging power. This DC micro-grid will also not suffer from any power quality issues as the factors such as phase and frequency of the supply are not relevant in DC system. The control of a DC micro-grid is also easier as the control strategy depends only on the magnitude of the control parameters such as the DC link voltage, EV power and SOC level of the battery.

References 1. Bertini I, Sbordone D, Falvo MC, Di Pietra B, Martirano L, Genovese A (2015) EV fast charging stations and energy storage technologies: A real implementation in the smart micro grid paradigm. Electr Power Syst Res 120 2. Shireen W, Goli P (2014) PV integrated smart charging of EVs based on DC link voltage sensing. In: IEEE Trans Smart Grid 5(3) 3. Haesen E, Clement-Nyns K, Driesen J (2010) The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25 4. Kaur S, Kaur T, Khanna R, Singh P (2017) A state of the art of DC microgrids for electric vehicle charging. In: 4th IEEE International conference on signal processing, computing and control (ISPCC 2017), 21–23 Sept 2017 5. Krishnan N, Raveendran V, Nair MG (2017) Vehicle-to-Grid support by electric vehicle charging stations operated at airports and metro rail stations. In: 2017 IEEE international conference on technological advancements in power and energy (TAP Energy)

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6. Manav, Dr. Isha TB (2017) Comparison of non-isolated schemes for EV charging and their effect on power quality. In: International conference on circuits power and computing technologies (ICCPCT) 7. Akshya S, Ravindran A, Sakthi Srinidhi A, Panda S (2017) Grid integration for electric vehicle and photovoltaic panel for a smart home. In: International conference on circuits power and computing technologies (ICCPCT) 8. Fernández LP, Cossent R, Domingo CM, Frías P, San Román TG (2011) Assessment of the impact of plug-in electric vehicles on distribution networks. IEEE Trans Power Syst 26 9. Abhijith VS, Akshatha S, Fernandes BG, Arun CN (2017) A unified AC-DC microgrid architecture for distribution of AC and DC power on the same line. In: IEEE applied power electronics conference and exposition (APEC), pp 430–433 10. Revathi B, Sivanandhan S, Prakash V, Ramesh A, Isha TB, Saisuriyaa G (2018) Solar charger for electric vehicles. In: International conference on emerging trends and innovations in engineering and technological research (ICETIETR) 11. Marshal TP, Dr. Deepa K (2014) Hybrid energy renewable system: optimum design, control and maximum utilization with SIBB converter using DSP controller. In: Power and energy system conference: towards sustainable energy 12. Shahani DT, Arun Kumar B (2011), Grid to vehicle and vehicle to grid energy transfer using single-phase bidirectional ACDC converter and bidirectional DC–DC converter. In: 2011 International conference on energy, automation, and signal (ICEAS). International Journal of Engineering, Science and Technology

A Comparative Study of Controllers for QUANSER QUBE Servo 2 Rotary Inverted Pendulum System Anjana Govind and S. Selva Kumar

Abstract Inverted pendulum is an inherently unstable system which is extensively used for experimental analysis and studies. It is a nonlinear system with its centre of gravity above the pivot point; owing to this, the system is difficult to control; and controller design for balancing the pendulum is quite challenging. The work deals with modelling and designing of various controllers for QUANSER QUBE Servo 2 Rotary Pendulum system developed by National Instruments and comparison of their performances in balancing it in the upright position. The designed controllers include the conventional PID controller, linear quadratic regulator (LQR), full-state feedback controller and cascade PID controller. From the simulation results, it is found that the cascaded PID and LQR controllers provide better response of the system by balancing the pendulum with least settling time. Keywords QUANSER QUBE Servo Rotary Inverted Pendulum · myRIO · MATLAB · Pole placement technique · LQR · Cascade PID control

1 Introduction Inverted pendulum is a simplified model of many real-life systems like rocket, balancing of ship against waves, self-balancing robot, Segway, crane, aircraft altitude control, etc. A pendulum is normally stable at downward position, and it is unstable at upward position which is similar to a rocket at the launching time and aircraft stabilization problem. Hence, the study of inverted pendulum is a motivation to design controllers for real-time systems. An inverted pendulum works on the principle of Newton’s third law of motion. When the pendulum tends to fall, the arm moves faster so as to attain stability. The rotary inverted pendulum is highly A. Govind (B) · S. Selva Kumar Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] S. Selva Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_117

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non-linear and has non-minimum phase characteristics. QUANSER QUBE Servo 2 is a compact, integrated rotary servo system that is designed for experimental and educational purpose. The system works on LabVIEW platform with myRIO, and it is interfaced to system with Quanser Rapid Control Prototyping (QRCP) software which helps in faster interfacing and control. The pendulum is driven by brushed DC motor equipped with two optical encoders one for measuring angular position of the pendulum and other for angular position of rotary arm. The manipulating variable is the input voltage to the DC motor, and process variables are angular position of DC motor and the pendulum. The QUANSER QUBE Servo 2 Rotary Pendulum is shown in Fig. 1, and its overall block diagram is depicted in Fig. 2. Fig. 1 QUANSER QUBE Servo Rotary Inverted Pendulum

Fig. 2 System block diagram

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Balancing of pendulum can be achieved by an efficient controller by giving control signals to a DC Servomotor so that the pendulum moves about its pivotal axis for balancing it in the upright position. This research work focuses on designing PID, Cascaded PID, State Feedback controller and linear quadratic regulator for stabilizing the QUBE Servo Rotary Pendulum at its unstable position, analyse and compare their responses.

2 Literature Survey Stabilization of pendulum can be achieved by different kinds of controllers which includes classical PID [1]. The reason for the usage of PID controller is its ease of implementation. But it is not an efficient way to control the system on account of its nonlinear behaviour, internal and external noises. Another drawback of using PID controller is that it can only control one of the parameters [2]. A cascaded PID controller can tackle this problem; it can control both the variables simultaneously by reducing the effect of disturbances and gives better tracking compared to PID controller [3]. Another method to improve the performance of PID controller is by tuning PID gains by adaptive methods [4]. Here, PID gains are tuned by sliding mode controller so that the controller can compensate for variations in parameters and external noises. State feedback controllers like LQR control and pole placement technique are the other efficient ways of controlling the nonlinear system [5]. This is achieved by placing poles at desired locations such that desired response can be achieved effectively. SimMechanics-based modelling of QUBE Servo Rotary Pendulum is implemented [6]. Fuzzy Controller is designed and tested on inverted pendulum [7]. The transient response of DC motor is analysed and controlled by an LQR controller [8]. A PID controller is implemented for quadcopter which is nonlinear system for controlling its pitch roll and yaw movement using Arduino Mega board [9]. The literature has provided basis for implementing controllers for the system.

3 Dynamic Model of the System Mathematical modelling of a system is very important for understanding the dynamics of the system and analysing it. The mathematical modelling of a rotary inverted pendulum has two parts—the linear and nonlinear models. The complete dynamics of the system can be described by nonlinear model whereas linear model helps on controller design. The control of rotary inverted pendulum is done by controlling three important parameters—rotary arm position, pendulum position and motor velocity. The modelling of system is usually done using Euler-Lagrange’s equations of motion. The free-body diagram representation of rotary inverted pendulum is shown in Fig. 3.

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Fig. 3 Free body diagram of rotary inverted pendulum

The velocity experienced by the pendulum due to centre of mass in both x and y directions is: V˙pen = −L 1 cosα˙ ∗ X − L 1 sinα˙ ∗ Y

(1)

where L 1 is the pendulum length Velocity experienced by the rotary arm along horizontal direction is Varm = L r θ˙ X

(2)

Here, L r implies the length of rotary arm. α and θ indicate the pendulum angle and rotary arm angle in vertical axis and horizontal axis, respectively From Eqs. 1 and 2, the velocity experienced in the two resultant axes is given by Vx = L r θ˙ − L 1 cosα ∗ α˙

(3)

Vy = −L 1 sinα ∗ α˙

(4)

The variation of velocity in the pendulum is obtained by the dynamic equations of motions which are derived using Euler–Lagrange Eq. 5. d Qi = dt where Qi L q q˙ W

external force applied, Lagrangian function, Angular position in radians, Angular velocity in rad/s, Energy loss in W.



∂L ∂q

 −

∂L ∂W + ∂q ∂ q˙

(5)

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L = Ttotal − Ptotal

(6)

Ttotal is the system’s total kinetic energy and Ptotal the total potential energy of the system. Ptotal = Parm + Ppendulum Ptotal = Parm + Ppendulum

(7)

The potential energy of the system is due to acceleration due to gravity. Since the pendulum is already in balanced position,Parm = 0, hence it is expressed as Ptotal = Mp g ∗ L 1 cos ∝

(8)

Here, M p is the mass of the pendulum in kg. The summation of kinetic energies of the pendulum arm, velocities due to centre of mass and rotational motion of the pendulum describes the total kinetic energy of the pendulum. 2 1 1 2 1  Jr θ˙ + Mp L p θ˙ − L 1 cosα α˙ + Mp (−L 1 sinα α) ˙ 2 2 2 2 1 1 ˙ 2 + Jp α˙ 2 + Mp L 12 cos2 (∝)∝ 2 2

Ttotal =

(9)

J r and J p indicate the moment of inertia of the rotary pendulum arm and pendulum, respectively 2 1 1 21  ˙2 Jr θ˙ Mp L r θ˙ − Mp L r θ˙ L 1 cos(α)α˙ Mp L 21 cos2 (∝)∝ 2 2 2 1 1 ˙ 2 + Jp α˙ 2 − Mp gL 1 cos ∝ + Mp L 21 sin2 (α)∝ (10) 2 2

Lagrangian,

The moment of inertia Jp is given by the equation Jp = Hence Jp =

1 M L2 12 p p

and L p = 2L 1 ,

1 Mp L 21 3

(11)

By substituting Eq. 11 in Eq. 10, 2 1 1 2 1  ˙2 Jr θ˙ + Mp L r θ˙ − Mp L r θ˙ L 1 cos(α)α˙ + Mp L 21 cos2 (∝)∝ 2 2 2 1 1 ˙ 2 + Mp L 1 α˙ 2 − Mp gL 1 cos ∝ + Mp L 21 sin2 (α)∝ 2 6

L=

Lagrangian motion equations for θ˙ and α˙ are

(12)

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d dt



dL dθ

 −

dL =0 dθ

(13)

−

dL =0 dα

(14)

and d dt



dL dα



Considering energy loss, W = 0 and Lagrange equations of motion at equilibrium points (θ = 0, α = 0,θ˙ = 0, α˙ = 0) (Jr + Mp L 2r )θ¨ − Mp L r L 1 α¨ = τ

(15)

4 Mp L 1 α¨ − Mp L r L 1 θ¨ − Mp gL 1 α = 0 3

(16)

The motor torque developed is given by Eq. 17   K t ∗ Vm − K m θ˙ τ= Rm

(17)

The following constants are taken for ease of finding matrix inverse, A = Jr + Mp L 2r , B = Mp L r L 1 , C = E = AC − B 2 , F =

4 Mp L 21 , D = Mp gL 1 3

Kt Kt Km ,G = Rm Rm

By combining Eqs. 15–17, the state-space representation is obtained as ⎡ ˙⎤ ⎡ θ 0 0 ⎢ α˙ ⎥ ⎢ 0 0 ⎢ ⎥=⎢ ⎣ θ¨ ⎦ ⎣ 0 B D α¨

0

⎤⎡ ⎤ ⎡ θ 1 0 0 ⎢α⎥ ⎢ 0 0 1⎥ ⎥⎢ ⎥ + ⎢ −C G 0 ⎦⎣ θ˙ ⎦ ⎣ C F

E E AD −BG E E

α˙

1000 θ y= 0100 α

0

E BF E

⎤ ⎥ ⎥Vm ⎦

(18)

(19)

The QUANSER QUBE Servo 2 Rotary Inverted Pendulum parameters are available in the user’s manual and are given in Table 1. Substituting system parameters in the state-space model, the model of QUANSER QUBE Servo Rotary Inverted Pendulum is calculated and expressed in Eqs. 20 and 21.

A Comparative Study of Controllers for QUANSER QUBE Servo … Table 1 QUANSER QUBE Servo Rotary Inverted Pendulum parameters

Symbol

Description

1407 Units

Values

Mp

Mass of pendulum

kg

0.0240

Lr

Length of rotary arm

m

0.0850

Lp

Length of pendulum

m

0.129

Jr

Rotary arm inertia

kg m2

5.72 × 10−5

Jp

Pendulum inertia

kg m2

3.33 × 10−5

G

Gravitational constant

m/s2

9.81

Rm

Armature resistance

Ohm

8.40

Km

Motor constant

Vs/rad

0.0420

Kt

Torque constant

Nm/A

0.0420

⎡ ˙⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ θ 0 0 1 0 θ 0 ⎢ α˙ ⎥ ⎢ 0 ⎢ ⎥ ⎢ ⎥ 0 0 1⎥ ⎢ ⎥=⎢ ⎥⎢ α ⎥ ⎢ 0 ⎥ ⎣ θ¨ ⎦ ⎣ 0 149.27 −0.0104 0 ⎦⎣ θ˙ ⎦ + ⎣ 49.72 ⎦Vm α¨ 0 −261.60 0.0103 0 α˙ −49.14

1000 θ y= 0100 α

(20)

(21)

4 Controllers for QUANSER QUBE Servo Rotary Inverted Pendulum Once linearized model of the system is obtained, controllers can be implemented for the system. The most commonly used controller is a PID controller, and other controllers like cascaded PID controller, state feedback controller and LQR controller are also tried in this work for balancing the inverted pendulum.

4.1 PID Controller PID controller is a lag lead compensator with one pole at origin and a zero at infinity. The generalized representation of PID controller is shown in Eq. 22 G PID (s) = K p + where K P Proportional Gain constant

Ki + Kds s

(22)

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Fig. 4 Cascade controller

K i Integral Gain constant K d Derivative Gain constant. The proportional gain (K p ) generally provides a control action that is proportional to the error through all-pass filter gain. Integral gain (K i ) reduces steady-state error by an integrator using low-frequency compensation. Derivative gain (K d ) uses a differentiator for improving transient response of the system. There are different methods to tune PID gains, viz. (1) Trial and error method, (2) Ziegler–Nichol’s method and (3) Process reaction curve method. Here, trial and error method is employed to find PID gains for controlling the balance α, the pendulum angle in inverted pendulum.

4.2 Cascaded PID Control Cascade control is used when two or more variables are there to be controlled, and multiple sensors are there to measure the controlled variables. The advantage of cascade control over conventional controllers is that it provides better performance and disturbance rejection. The diagrammatic representation of the cascade controller is as shown in Fig. 4. The inner and outer loops are tuned individually to get desired response of both the variables. When single PID controller is used to control the system, the pendulum angle ‘α’ can only be controlled and the rotary arm angle is automatically controlled by the input voltage whereas in cascaded PID controller for rotary inverted pendulum, both rotary arm and pendulum positions are controlled. The disturbance caused by uncontrolled rotary arm movement on pendulum angle can be reduced to a certain extent.

4.3 Pole Placement Controller State feedback controller otherwise called pole placement controller adopts a feedback control strategy in which close-loop poles are placed in the desired location. This is done by setting the controller gain. Unlike other feedback controllers, here state of the system feedback to the input. The state feedback controllers are also

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Fig. 5 State feedback controller

applicable for MIMO system. A state feedback controller can be used only if the system is controllable, and it tracks the input. The controller gain can be different in the same system, because it depends on the close loop poles selected. Block diagram of state feedback controller is indicated in Fig. 5. For the QUANSER QUBE Servo System, the dominant poles are selected such that the damping ratio is 0.7 and natural frequency is 4. The state feedback gain is found by using Ackerman formula given by the Eq. 23.   K = 0 . . . 0 0 1 Uc−1 ϕ(A)

(23)

where Uc is the controllability matrix given by [B AB…An−1 B] and ϕ( A) is the characteristic polynomial.

4.4 LQR Controller LQR controller is an optimal state feedback controller that is concerned with operating dynamic system at minimum cost and time. It provides optimal feedback gains to enable close loop system stability and high performance. LQR controller can be designed using algebraic Riccati equation given by Eq. 24. A T P + P A + P B R −1 B T P + Q = 0

(24)

where Q and R are state control matrices, A is the state matrix, B is the input matrix, and P is the transformation matrix. State feedback gain matrix is indicated by K = R −1 B T P

(25)

A performance index J is found and controller has to be designed to minimize  J=

(X T Q X + U T RU )dt

(26)

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According to this boundary condition and by backward integration of Riccati Equation, optimal feedback gain can be calculated online. U = −K X

(27)

5 Controller Implementation and Results The simulations of various controllers have done for QUANSER QUBE Servo 2 Rotary Inverted Pendulum. The controllers implemented for balance control of QUANSER QUBE Rotary Inverted Pendulum include (1) PID controller, (2) Cascade PID, (3) Full-state feedback controller and (4) LQR controller. A PID controller is implemented for pendulum angle control. The PID controller gains are tuned by trial and error method. The rotary arm angular position is controlled by the input voltage directly. The PID gains are found to be K p = 120, K i = 50 and K d = 1.5. The response of the PID controller for QUANSER QUBE Servo 2 Rotary Inverted Pendulum is depicted in Fig. 6. The system response for PID controller exhibits a steady-state error of 0.055 radians and peak overshoot of 1.12 radians. Since the PID controller has a defect of steady-state error and it shows chattering due to the effect of uncontrolled rotary pendulum movement, a better controller has to be implemented for the system. So, for the implementation of a cascaded PID controller is done. The controller implemented has two loops, an inner loop and an outer loop. The gains of each loop controller have tuned separately until both rotary arm and pendulum settles at desired positions. The gain of PID controller in the outer Pendulum angle control with PID Controller 1.2

Pendulum angle(radians)

1

0.8

0.6

0.4

0.2

0

0

0.5

1

1.5

2

2.5

Time (seconds)

Fig. 6 Step response of pendulum angle for PID controller

3

3.5

4

4.5

5

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loop for pendulum angle controller is found to be K p = −1.05, K i = 0 and K d = 4 whereas inner loop for rotary arm control is K p = 1.5, K i = 0 and K d = −0.5 From the response in Figs. 7 and 8, it is found that the settling time has reduced to 2.8 s and the response has slight overshoot of 0.0185 radians before coming to the stable state. The effect of external noise will also get reduced by using cascade PID control. The pole placement controller is also implemented for the QUANSER QUBE rotary inverted pendulum system. The dominant poles are located at −4 + 4.86i, −4−4.86i, −30, −40, and feedback gains are found to be [ 7.5945 −23.5364 2.1339 0.5728 ]. The responses of the system using pole placement controller are given in Figs. 9 and 10. 10

Pendulum angle(radians)

20

-3

Pendulum angle Control with Cascade PID Controller

15

10

5

0

-5

1

0

2

3

4

5

6

7

8

9

10

8

9

10

Time (seconds)

Fig. 7 Step response of pendulum angle for cascaded PID controller Rotary Arm Control with Cascade PID controller

0

Rotary arm angle(radians)

-0.2

-0.4

-0.6

-0.8

-1

-1.2

0

1

2

3

4

5

6

7

Time (seconds)

Fig. 8 Step response of rotary arm angle for cascaded PID controller

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0.3

Pendulum angle(radians)

0.25 0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15

0

1

2

3

4

5

6

7

8

9

10

8

9

10

Time (seconds)

Fig. 9 Step response of pendulum angle for pole placement controller Rotary Arm Control with Pole Placement Controller

0

Rotary arm angle(radians)

-0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 -1.6 -1.8 -2

0

1

2

3

4

5 6 Time (seconds)

7

Fig. 10 Step response of rotary arm angle for pole placement controller

The system shows a peak overshoot of 0.29 radians and, after oscillations, reaches stable condition by 1.68 s. LQR controller is a state feedback controller that minimizes the quadratic cost function for obtaining optimal control over the system. Depending on the values of Q and R, the weighing matrices, the gain matrices K can be determined. The values of gain changes with the Q and R selected which in turn affects the performance of the system. Values of Q and R matrices along with the feedback gain K for the LQR controller are

A Comparative Study of Controllers for QUANSER QUBE Servo …

⎡

150 ⎢ 0 Q=⎢ ⎣ 0 0

0 150 0 0

0 0 150 0

1413

⎤ 0   0 ⎥ ⎥, R = [5] and K = 3.8730 −10.1235 1.5922 −0.2877 ⎦ 0 150

The response of the system for LQR controller which is depicted in Figs. 11 and 12 shows a peak overshoot of 0.149 radians, and it settles by 1.63 s which indicate that the LQR controller is better than full-state feedback controller in performance. Pendulum angle control with LQR Controller

Pendulum angle(radians)

0.15 0.1 0.05 0 -0.05 -0.1 0

1

2

3

4

5

6

7

8

9

10

8

9

10

Time (seconds)

Fig. 11 Step response of pendulum angle for LQR controller Rotary Arm Control using LQR Controller

0

Rotary arm angle(radians)

-0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4

0

1

2

3

4

5

6

Time (seconds)

Fig. 12 Step response of rotary arm angle for LQR controller

7

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6 Conclusion QUANSER QUBE SERVO 2 Rotary Inverted Pendulum model is represented in state-space equations for analysis. Different controllers for controlling the process variables like pendulum angle and rotary arm angle were identified and implemented. The comparative analysis of the controllers designed for the system indicates the superiority of cascaded PID controller in balance control of the system. It is well suited for systems that require less oscillations, and it helps in smooth settling of the system with slightly higher settling time. But state feedback controllers like pole placement controller and LQR controller gives higher oscillations but settles much faster than other controllers. It is found that cascade PID, pole placement and LQR control show good results in minimizing the steady-state error which is a major drawback of the conventional PID controller. On observing the transient response of the system, cascaded PID controller provides better results compared to other controllers implemented and found having better control of the process variables.

References 1. Sirisha V, Junghare AS (2014) A comparative study of controllers for stabilizing a rotary inverted pendulum. 3(1):1–13 2. Babu J, Varghese E (2015) Stabilization of rotary arm inverted pendulum using state feedback techniques. 4(07): 563–567 3. Jekan P, Subramani C (2016) Robust control design for rotary inverted pendulum balance. 9:1–5 4. Shafie A (2010) Modeling and control of a rotary inverted pendulum using various methods, comparative assessment and result analysis. In: International conference on mechatronics and automation. Proceedings of IEEE 2010, Xi’an, pp 1342–1347 5. Hong-yu L, Jian F (2014) An inverted pendulum fuzzy controller design and simulation. In: 2014 International symposium on computer consumer and control, vol 1, pp 557–559 6. Kathpal A, Singla A (2017) SimMechanicsTM based modeling, simulation and real-time control of rotary inverted pendulum. In: 2017 11th International conference on intelligent systems and control, pp 166–172 7. Kuo TC, Huang YJ Hong BW (2009) Adaptive PID with sliding mode control for the rotary inverted pendulum system. In: 2009 IEEE/ASME international conference on advanced intelligent mechatronics, Issue 3, pp 1804–1809 8. Saisudha V, Seeja G, Pillay RV, Manikutty G (2018) Analysis of speed control of DC motor using LQR method 9. Praveen V, Dr. Anju Pillai S (2016) Modelling and simulation of quadcopter using PID controller. Int J Control Theory Appl 9:7151–7158

Design and Implementation of 400 W Flyback Converter Using SiC MOSFET M. Ravikiran Rao, K. Suryanarayana, H. Swathi Hatwar, and Adappa Raksha

Abstract AC–DC converters are inevitable for most of the electronic equipment operating in universal voltage supply. Galvanic isolation is a primitive requirement in any AC–DC converter to avoid shock hazards. In conventional systems, line frequency transformer is used for galvanic isolation followed by AC–DC conversion resulting in bulky and inefficient system. Flyback converter is one of the topologies adopted for isolated DC–DC power supply from universal AC input. The high-voltage stress appearing across the primary switch and the leakage inductance of the primary inductor limits the use of flyback converter for low-voltage and power applications. This limitation could be addressed by using SiC MOSFET which has a higher drain to source breakdown voltage. This paper demonstrates the implementation of 400 W flyback converter with universal AC input and output regulated to 100 V DC using peak current mode control. Steps for selecting ratings of switch, diode, transformer, compensation circuit and comparative study on RCD and TVS diode snubber are presented. Keywords Flyback converter · Isolated DC–DC converter · SiC MOSFET · Snubber · Bode plot · Regulation

M. Ravikiran Rao (B) · K. Suryanarayana · H. Swathi Hatwar · A. Raksha NMAM Institute of Technology, Nitte, India e-mail: [email protected] K. Suryanarayana e-mail: [email protected] H. Swathi Hatwar e-mail: [email protected] A. Raksha e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_118

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1 Introduction In high-power applications, switched mode power supplies (SMPS) are preferred over linear regulators due to efficient power conversion [6]. The advances in semiconductor devices and control techniques made SMPS more versatile, efficient and reliable power supply for all electronic commodities. The compact size and cost effectiveness are constant demand in the area of SMPS, which could be addressed by selecting suitable topologies. For the applications demanding galvanic isolation, flyback, forward, push–pull, full bridge converters could be used. Among the available topologies flyback converter is cost effective due to use of lesser power electronic devices and passive components [10]. The higher-voltage stress on primary switch and limitation posed by magnetic circuit restricts use of flyback converter upto 200 W [1, 3]. The safety regulations demand galvanic isolation in electronic commodities used in domestic, medical and commercial equipment operating in universal power supply to provide safety against electric shock. The easy adaptability to the universal power supply made flyback converter good contender for SMPS application involving galvanic isolation. Primary side and secondary side regulation are two methods used to regulate output voltage of flyback converter. In the case of secondary side regulation, the isolated output voltage is the feedback to primary side controller. This results in accurate secondary voltage but at the cost of increased components in the circuit. In case of primary side regulation, an auxiliary winding is used to monitor and control the output voltage. The ground reference of auxiliary winding is same as that of primary side which eliminates the requirement of isolation circuit. The voltage regulation in case of primary side regulation varies with applied load [7]. This paper is organized into six sections. Section 1 gives brief introduction to the need of flyback converter in SMPS. Section 2 deals with the basics of flyback converter. Design considerations are discussed in Sects. 3, and 4 addresses compensation and system stability aspects. Practical results of the designed converter are discussed in Sects. 5, and 6 concludes overall design methodology and observations.

2 Flyback Converter Flyback converter consists of a coupled inductor (flyback transformer), primary switch, diode at the secondary and filter capacitor [5]. The basic circuit diagram of flyback converter is shown in Fig. 1. Based on the current flowing through inductor, converter is considered to be operating in discontinuous conduction mode (DCM) or continuous conduction mode (CCM) [7, 10, 13]. The working of flyback converter could be analyzed by timing diagram as in Fig. 2. Mode1—t0 − t1 : In this mode, switch Q is turned ON, and the voltage Vp appears across primary winding of the flyback transformer. Unlike the normal transformer, there will not be any secondary current flowing due to change in the polarity of the winding as indicated in Fig. 1 which results in reverse biasing of secondary diode

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Fig. 1 Flyback converter

D. Thus, the primary current is used to store energy in magnetic field of the primary winding. In DCM, primary current ramps up to i PEAK from zero, and voltage across switch VDS is zero as shown in Fig. 2a. The load current under this case is supplied by filter capacitor C. In CCM, primary current ramp up to i PEAK from i OUT and VDS is zero as shown in Fig. 2b. Mode2—t1 − t2 : In this mode, switch Q is turned OFF, and primary current is interrupted. Due to the property of inductor, the secondary diode D will be forward biased, and stored magnetic energy is transferred to load. In the case of DCM, switch Q will be turned ON only after current through diode D becomes zero as shown in Fig. 2a indicating complete transfer of magnetic energy. VDS will be Vin + kVout till secondary current i S reaches zero and VDS remains at Vout till the time t2 . In case of CCM, switch Q will be turned ON before current through the diode D becomes zero as shown in Fig. 2b. VDS value remains Vin + kVout for this time period. The basic working principle behind flyback converter is isolated buck–boost converter [6]. The output voltage is given by Vout =

Vin ∗ D k ∗ (1 − D)

(1)

where k = N2 /N1 and D is duty cycle. The flyback converter with CCM mode has reduced current stress compared to DCM mode [9]. However, in spite of having relatively higher current stress DCM mode is preferred due to better efficiency and fast response under transient condition [3].

3 Design Consideration The flyback converter is designed considering the parameters listed in Table 1. Calculated values of system parameters are tabulated in Table 2 [11]. Where VDCmin is the minimum DC input, Dmax is the maximum duty ratio, Pin is the input power, f s is the switching frequency, K RF = 1 for DCM, IdsPEAK is the peak current through MOSFET, IEDC is the effective DC current, ΔI is the ripple current,

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Fig. 2 Flyback converter operating modes Table 1 Design parameter S. no. 1 2 3 4 5

Description

Value

Input AC voltage range Output DC voltage Maximum output power Maximum output current Switching frequency

90–270 V 100 V 400 W 5A 100 kHz

Ac core area, Aw is the window area, VDCin is the nominal input DC voltage, IDCin is the nominal input DC current, K w is the winding factor, Bm is the maximum flux density, J is the current density, Iover is the pulse by pulse current, N S1 and N S2 are turns in auxiliary and secondary winding, VR0 is the reflected output voltage to primary, V01 is the auxiliary output voltage, V02 is the secondary side output voltage, VF1 is the forward voltage drop of auxiliary side diode, VF2 is the forward voltage

Design and Implementation of 400 W Flyback Converter … Table 2 System parameter value S. no. Component 1

Primary inductance

2

Peak primary current

3

Transformer core

4

Primary turns

5

Auxiliary turns

6

Secondary turns

7

Auxiliary capacitor

8

Secondary capacitor

9

Snubber resistor

10

Snubber capacitor

Formula (VDCmin ∗ Dmax )2 Lm = 2 ∗ Pin ∗ f s ∗ K RF ΔI IdsPEAK = IEDC + 2 Ac ∗ Aw = VDCin ∗ IDCin 2 ∗ K w ∗ Bm ∗ f s ∗ J L m ∗ Iover Np = Bs ∗ Ae Np V R0 n= = N S1 V01 + VF1 Np V R0 n= = N S2 V02 + VF2 Io1 ∗ Dmax ΔVo1 = + Co1 ∗ f s IDSpeak ∗ VRO ∗ RC1 ∗ K L1 Vo1 + VF1 Io2 ∗ Dmax ΔVo2 = + Co2 ∗ f s IDSpeak ∗ VRO ∗ RC2 ∗ K L2 Vo2 + VF2 (Vsn )2 Psn = Rsn Vsn ΔVsn = Csn ∗ Rsn ∗ f s

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Value 154.03 uH 9.3 A EE 65/32/27

14 2 10 47 uF

150 uF

78.7 150 pF

drop of secondary side diode, ΔVo1 is the ripple voltage of auxiliary supply, ΔVo2 is the ripple voltage of secondary side supply, RC1 and RC2 are ESR of auxiliary and secondary filter capacitor, K L1 is 0.018, K L2 is 0.812, Psn is the power dissipated in snubber circuit, Vsn is the total reflected voltage, and ΔVsn is the ripple voltage of reflected voltage. Apart from standard design procedure, learning from some practical observations is implemented in this system. To minimize leakage inductance, the transformer windings are sandwiched as shown in Fig. 3. In practical scenario, leakage inductance of the primary inductor is unavoidable resulting into power dissipation in the switch during turn OFF process. This leads to voltage spikes across the primary switch that might cause the damage. To avoid voltage spikes due to leakage inductance, snubber circuits are used. Snubber circuit provides alternative path to dissipate stored energy due to leakage inductance, hence reduces voltage stress on primary switch. There are different types passive and active snubber circuit available [8]. Figure 4 shows RCD and TVS diode snubber circuit widely used in flyback converters. In the case of RCD snubber, stored energy due to leakage inductance is dissipated in resistor R, wherein TVS diode snubber circuit pumps stored energy due to leakage inductance back to the source. Designing of snubber component is listed in Table 2.

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Fig. 3 Flyback transformer layout

(a)

(b)

Fig. 4 Snubber circuit for flyback converter. a RCD. b TVS

4 System Stability Analysis and Compensator Design Open-loop transfer function of the system is given by [12]: G(s) = PS(DC gain) ∗

1 + s ∗ ESR ∗ COUT 1 + s ∗ ROUT ∗ COUT

(2)

where PS(DC gain) is the power stage DC gain and is given by  PS(DC gain) =

VIN i PEAK

∗

k ∗ ROUT 2 ∗ L P ∗ f SW

(3)

Design and Implementation of 400 W Flyback Converter …

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Magnitude (dB)

100

0

−100 0

Phase (deg)

−45

−90

−135

−180 10−1

100

101

102

103

104

105

106

107

Frequency (Hz)

Fig. 5 Bode plot of the implemented system

VIN is the input DC voltage, f SW switching frequency, k = N2 /N1 , ROUT and COUT is load resistor and capacitor of auxiliary winding, and ESR is the equivalent series resistance of COUT . Bode plot of the loop gain is shown in Fig. 5. The open-loop gain has a crossover frequency of 399 Hz with a phase margin of 90.7◦ . A type-II compensator is designed to achieve the desired crossover frequency [2]. A type-II transfer function is written as follows [4]: EAgain = 20 log

1 + s ∗ R ∗ C1   s ∗ R ∗ C1 ∗ C2 s ∗ RFB ∗ (C1 + C2 ) 1 + (C1 + C2 )

(4)

To obtain desired crossover frequency values of C1 , C2 and R could be computed as follows: ⎛ ⎞ EAgain ⎝ ⎠ R = R ∗ 10 20 (5) FB

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C1 =

1 2 ∗ π ∗ f ZERO

(6)

C2 =

1 2 ∗ π ∗ f POLE

(7)

The bode plot of the compensator is shown in Fig. 5 green color for f ZERO = 50 Hz and f POLE = 5 kHz. The overall compensated system is shown in Fig. 5 red color that exhibits a stability with a phase margin of 87◦ .

5 Measurement and Result Analysis The system is designed with the parameters listed in Table 1. The assembled system is as in Fig. 6. UCC28C44 PWM IC is used to implement primary side regulation. The designed converter is tested for the rated voltage and current. Figure 7 shows the output voltage and output current with input DC bus voltage being 325 V. Efficiency of the converter is measured to be 80%. The flyback converter with RCD snubber, voltage across primary SiC MOSFET and voltage across secondary diode is shown in Fig. 8. The voltage spike in this case is found to be reaching 540 V which closely matches with theoretical calculation. Observations are made by replacing RCD snubber with TVS diode. The voltage across the primary SiC MOSFET and secondary diode using TVS diode is shown Fig. 9. The steady-state voltage in both the cases is found to be same. However, with RCD snubber, primary switch failed to withstand turn ON transients and frequent

Fig. 6 400 W Flyback converter with SiC MOSFETs

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(a)

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(b)

Fig. 7 Measured values of a Output voltage. b Output current

(a)

(b)

Fig. 8 Measurement with RCD snubber. a Voltage across SiC MOSFET. b Voltage across diode

(a)

(b)

Fig. 9 Measurement with TVS snubber. a Voltage across SiC MOSFET. b Voltage across diode

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failures are observed. Replacing RCD snubber with TVS diode resulted in better dynamic performance by clamping voltage across the switch to 540 V.

6 Conclusion This paper deals with design and implementation of 400 W flyback converter using SiC MOSFET. The test results show satisfactory operation of flyback converter for different loading condition. Practical exposure to the problems faced in design stage like minimizing leakage inductance in the transformer and the behavior of snubber is studied well which otherwise overruled in theoretical approach.

References 1. Austermann J, Stuckmann T, Borcherding H (2016) High efficient flyback converter with SiC-MOSFET. In: PCIM Europe 2016; international exhibition and conference for power electronics, intelligent motion, renewable energy and energy management, pp 1–8 2. Basso C (2012) Designing control loops for linear and switching power supplies—a tutorial guide, 1st edn. Artech House 3. Coruh N, Urgun S, Erfidan T (2010) Design and implementation of flyback converters. In: 2010 5th IEEE conference on industrial electronics and applications, pp 1189–1193. https:// doi.org/10.1109/ICIEA.2010.5515894 4. Datasheet: UCC28C44 UCCx8C4x BiCMOS low-power current-mode PWM controller. Technical report, Texas Instruments (2017) 5. Erickson RW, Maksimovic D (2001) Fundamentals of power electronics, 2nd edn. Springer 6. Ho T, Chen M, Lin C, Chang C (2011) The design of a flyback converter based on simulation. In: 2011 international conference on electronics, communications and control (ICECC), pp 3996–3999. https://doi.org/10.1109/ICECC.2011.6067588 7. Huang C, Liang T, Chen K, Li C (2018) Primary-side feedback control IC design for flyback converter with energy saving burst mode. In: 2018 IEEE applied power electronics conference and exposition (APEC), pp 2054–2061. https://doi.org/10.1109/APEC.2018.8341300 8. Kanthimathi R, Kamala J (2015) Analysis of different flyback converter topologies. In: 2015 international conference on industrial instrumentation and control (ICIC), pp 1248–1252. https://doi.org/10.1109/IIC.2015.7150939 9. Li Y, Oruganti R (2012) A low cost flyback CCM inverter for ac module application. IEEE Trans Power Electron 27(3):1295–1303. https://doi.org/10.1109/TPEL.2011.2164941 10. Mohammed AA, Nafie SM (2015) Flyback converter design for low power application. In: 2015 international conference on computing, control, networking, electronics and embedded systems engineering (ICCNEEE), pp 447–450. https://doi.org/10.1109/ICCNEEE.2015.7381410 11. Note A (2003) AN4137 design guidelines for off-line flyback converters using fairchild power switch (FPS). Technical report, ON Semiconductor 12. Note A (2016) SNVA761 how to design flyback converter with LM3481 boost controller. Technical report. Texas Instruments 13. Wang C, Xu S, Lu S, Sun W (2018) A low-cost constant current control method for DCM and CCM in digitally controlled primary-side regulation flyback converter. IEEE J Emerg Select Top Power Electron 6(3):1483–1494. https://doi.org/10.1109/JESTPE.2017.2779136

Development of a Cost-Effective Module Integrated Converter for Building Integrated Photovoltaic System L. Ashok Kumar and Madhuvanthani Rajendran

Abstract Building Integrated Photovoltaic System (BIPV) is an attractive alternative for the effective use of renewable energy which helps in meeting the electricity demand of a building independent of a grid. A Module Integrated Converter (MIC) has the capability to boost the voltage from the solar photovoltaic (PV) panel and convert it into an AC voltage that meets the standard specification of 230 V RMS , 50 Hz. This paper proposes a design that significantly reduces the number of components involved in the converter module, which results in a low-cost AC inverter that is suitable for BIPV applications. This is done by using an interleaved flyback inverter that boosts 47 V output from a thin-film PV panel into 325 V DC. This intermediate output is further converted to AC using a Single-Phase Sinusoidal Pulse Width Modulation (SPWM) based inverter feeding a resistive load of 850 . The proposed scheme has been validated by a MATLAB Simulink model. Keywords Building integrated photovoltaic system (BIPV) · Module integrated converter (MIC) · Interleaved flyback converters · Photovoltaic (PV) · Single-phase sinusoidal pulse width modulation (SPWM)

1 Introduction Building integrated photovoltaics (BIPV) refer to the concept of integrating photovoltaic elements into the building infrastructure thus creating a bridge between the architectural design and renewable energy system. This system increases the usage of clean energy which is abundant in nature. The photovoltaic (PV) modules replace the conventional construction materials such as glass, façade, flat roof, atrium, sloped roof and shading. It also takes over the function of the construction materials L. Ashok Kumar (B) PSG College of Technology, Coimbatore, TamilNadu, India e-mail: [email protected] M. Rajendran Sri Shakthi Institute of Engineering and Technology, Coimbatore, TamilNadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 T. Sengodan et al. (eds.), Advances in Electrical and Computer Technologies, Lecture Notes in Electrical Engineering 672, https://doi.org/10.1007/978-981-15-5558-9_119

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and in addition performs electricity production for the building thereby making an individual building grid independent most of the time. BIPV technology can be used in all parts of the building envelope. Although roof surfaces are the most preferred areas for installing PV elements at present due to high irradiation values, the façades, windows and other structures of the building can also be replaced by the PV panels. Even though the ratio of the façade surface area to the roof surface area increases along with the building height, the available roof area is often reduced due to the installation facilities and superstructures, meaning BIPV façades are of value in high-density urban centers. In general, the grid-connected PV system utilizes three widely used inverter schemes such as the centralized inverter system, the string inverter system and the AC module or the Module Integrated Converter (MIC). Among all, MIC is the trend for future development because of its reduced cost. Zeng et al. [1] had discussed about a cascaded multi-level inverter that can be used both during normal insolation level and low insolation level or during the nights. Erickson and Rogers [2] had a design of a Low-Profile Micro Inverter that has an efficiency of 95% using a buck-boost converter. Later Kim et al. [3] and few other authors discussed the advantages and disadvantages of galvanic isolation followed by an effective transformer less multilevel inverter for photovoltaic applications. This creates a platform for researchers to concentrate on the transformerless inverter which significantly reduces the overall cost of the inverter configuration. York et al. [4] proposed that the distributed MPPT tracking can give higher efficiency than that of a centralized one. The concept of BIPV was depicted by Cai et al. [5] by a photovoltaic DC building module (PVDCBM) and the necessity of improved power quality in solar PV. Then Wang [6] et al., proposed a system that uses a boost converter in the BIPV system and proved that the conversion efficiency was about 95%. Li and Wolfs [7] had discussed the development of the Module Integrated Converter (MIC) for BIPV systems. The various topologies of MICs are discussed under (i) MIC with DC Link (ii) MIC with pseudo-DC Link (iii) MIC without DC Link. The usage of flyback converter and push-pull converters in MIC has also been elaborated in this paper. The operation of the three-stage interleaved flyback converter in its discontinuous operating mode for BIPV application was proposed by Tamyurek and Kirimer [8] and Saleh Mohammadi H., Abootorabi Zarchi, Mahdi Amiri proposed a topology [9]. The authors have also obtained the maximum efficiency of the developed interleaved inverter by adopting a strategy to reduce the voltage stress across the switches. In this work, an interleaved flyback inverter in its discontinuous operating mode has been developed as a MIC for an effective BIPV system to reduce the overall system cost. The results obtained from the simulation prove that the developed model reduces the cost as well as ensures the maximum efficiency and reliability of the system which is quite essential for the BIPV application.

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2 Design of Interleaved Flyback Converter 2.1 Significance of Interleaving Converter Interleaving converter has numerous benefits in the DC–DC converter system [10]. They have the following benefits: • Reduced RMS currents in the input which enables reduced sized input capacitors. • Reduced ripple current which enables reduced sized output capacitors. • Reduced peak current of the primary and secondary windings of the flyback transformers. • Reduced heat sink requirements. • Reduced overall cost of the system due to the use of low power rated switches.

2.2 Advantages of Operating in Discontinuous Mode The discontinuous mode of operation of the interleaved converter utilizes the entire energy stored in the inductor which also helps in reducing the size of the inductor and further reduces the cost.

2.3 Discontinuous Mode of Operation The interleaved flyback converter [11] is operated in the boost mode and consists of two cells. Each cell consists of a pair of power switch and diode. Both controlled and uncontrolled switches are used in the interleaved flyback converter as shown in Fig. 1. The different operating modes of the interleaved flyback converter are explained as follows. Mode I (t 0 < t < t 1 ) At t = t0 , both the switches S p1 and S p2 are turned ON. The magnetizing and leakage inductances start storing energy from the PV panel. Since the clamping diode voltages VD1 = VD2 = −V dc , the diodes D1 and D2 are reversed biased, and hence their currents I D1 , I D2 are zero. The peak current of the magnetizing inductance should be sinusoidal, so the duty cycle d is represented as: d(t) = dmax sin(ωt)

(1)

where d max is the maximum duty cycle. This mode comes to an end when both the switches are turned OFF by the gate driver.

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Fig. 1 Circuit diagram of interleaved flyback converter

Mode II (t 1 < t ⇐= t 2 ) During this stage, the switches S P1 , S P2 are OFF, the diodes D1 , D2, and D3 are ON and the primary winding is clamped to the reflected output voltage of −nV g (0 ⇐= V g ⇐= 220). The voltage across each switch is clamped to V dc . Mode III (t 2 < t ⇐= t 3 ) During this stage, the switches S P1 , S P2 , the clamping diodes D1 , D2 are OFF, and the rectifier diode D3 is ON. Assuming that the switches S P1 , S P2 , are identical, the voltages across the switches are obtained from. VSP1 = VSP2 =

Vdc + nVg 2

(2)

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2.4 Design of the Flyback Transformer The flyback transformer which is nothing, but a mutual inductor is one of the most important parts of the design. Unlike the traditional transformer, the flyback transformer stores energy in the air gap when the switches are ON and transfers the energy to the secondary winding when the switches are turned OFF. A ferrite material is used for the core. The developed flyback transformer is shown in Fig. 2. Assuming the peak duty cycle as 0.4, the magnetizing inductance of the transformer is obtained as 16 µH from Eq. (3). lm =

2 2 n cells Ts dmax VDC 4PPV

where, V DC TS ncells PPV

The output voltage from the PV panel Switching period Number of interleaving cells Panels output power.

The specifications of the flyback transformer used are listed in Table 1. Fig. 2 Developed flyback transformer

(3)

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Table 1 Flyback transformer design

Specifications

Values

Input voltage

Vin(max) = 50.71 V Vin(min) = 46.1 V

Output voltage

325 V

Frequency

80 kHz

Maximum duty cycle

0.4

Minimum duty cycle

0.35

Secondary current

0.215 A

Cores without air gap

EER 35 30 D

Core details

Ferrite

Window factor (K w )

0.35

Current density (J)

3 × 106 A/m2

Flux density (B)

0.2 T

Turns ratio n = N2/N1

12

I 1rms

4.88 A

I 2rms

0.4 A

Primary inductance L1

14.0739 µH

Secondary inductance L2

2.02 mH

3 Single-Phase SPWM Inverter The SPWM technique incorporates the generation of a digital waveform, for which the duty cycle can be modulated in such a way that the average voltage waveform corresponds to a pure sine wave. A simple SPWM H-bridge inverter [12] has four switches as shown in Fig. 3, in which T 1 and T 2 are turned ON simultaneously, while T 3 and T 4 are OFF. T 3 and T 4 are turned ON by giving 180° phase-shifted sine wave as the modulating signal. The simplest way of producing the SPWM signal is by comparing a low power sine wave reference with a high-frequency triangular wave as shown in Fig. 4. The generated SPWM output signal is depicted in Fig. 5. The digital signal controller TMS320F28335 is used to generate 10 kHz SPWM pulses and to generate 80 kHz PWM pulses for the flyback converter. The output from the SPWM inverter is fed to a low pass LC filter before feeding to the resistive load of 800  to reduce harmonics. The filter inductor L f is calculated as 11 mH and the filter capacitance is calculated as 24 uF using Eqs. (4) and (5) to reduce the reactive power requirement. The L and C values are calculated as below  

   fr 2 Vo  Ed Ed  2 Lf = ×K K 1 + 4π Io f s V oavg f s1 V oavg Cf =

Ed 2 L f f s V oavg

(4)

(5)

Development of a Cost-Effective Module Integrated Converter …

Fig. 3 SPWM inverter

Fig. 4 SPWM modulation

Fig. 5 SPWM pulses

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where V 0 is the V rms output voltage I o is the output current E d is peak value of the output voltage Voavg peak ripple voltage f s is the switching frequency f r is the reference sine wave frequency. K is the modulation index and given by  K =

k2 −

15 4 k 4

64 5 + 5π k − 45 k 6 1440

1/2 (6)

4 Simulation Analysis of the Two-Stage Interleaved Flyback Converter The simulation of the system under closed-loop condition is done using MATLAB SIMULINK and shown in Figs. 6 and 7. It uses a PI controller to control the gating signals of the flyback converter. A multi-winding inductor and a multi-winding transformer are used as a flyback transformer in the simulation. The necessary parameters of the flyback transformer are given. It is significant to note that the secondary

Fig. 6 Closed loop system of the interleaved flyback converter

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Fig. 7 SPWM inverter feeding R load of 850 

winding connections are interchanged to meet the dot convention required for the flyback converter. PV panel decoupling capacitor value is given as 2 mF. The pulse generator blocks are used to generate 80 kHz pulses for triggering the switches. The switching pulses for the first cell and the second cell are given alternatively. Thus, the duty cycles for those pulses are set to 0.4. The DC Link capacitor value of 2200 uF has been used between the converter and the inverter. The H-bridge inverter is used as shown in Fig. 6. The inverter switches are fed by SPWM pulses as shown in Fig. 7. LC filter components are chosen as 22 uF and 11 mH, respectively, in the simulation.

4.1 Simulation Output The results obtained from the simulation are shown in Figs. 8 and 9. It can be inferred that the flyback converter boosts the 47 V output from the solar panel to 312 V DC and it is further fed to a single-phase SPWM inverter that converts it to 312 V peak-peak AC, 50 Hz output.

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Fig. 8 DC intermediate DC output from the flyback converter

Fig. 9 AC output voltage of the SPWM Inverter after filtering through the LC filter

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4.2 Hardware Design of the Flyback Inverter The hardware implementation of the converter is as shown in Fig. 12. The driver circuit is implemented using IR2110 driver IC for both the flyback converter and the inverter. The driver circuit is used to turn ON the power MOSFET switches that require 12 V DC. An isolation circuit is made using 6N137 optocoupler IC. A Schmitt Trigger IC 4854 is used for waveshaping of the pulses. Finally, IR2110 high side and low side gate drive circuit provide the 12 V pulse required to turn ON the gate of the MOSFET switches. The dead time for the SPWM pulses is set to 0.75 ms to avoid shoot-through faults in the inverters. The flyback converter is operated at 80 kHz whose maximum duty cycle is not more than 0.5. The inverter is fed with 10 kHz SPWM pulses as shown in Fig. 10. The Specification of Solar PV Panel used in the hardware is given in Table 2 (Figs. 11 and 12).

Fig. 10 10 kHz SPWM pulses for the inverter

Table 2 Specification of the solar PV panel

PV panel Manufacturer

Avancis Power Max

Cell type

CIGS

V oc

61.5 V

I sc

3.31 A

I MPP

2.98 A

V MPP

47 V

PMPP

140 W

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Fig. 11 Dead band in SPWM pulses

Fig. 12 Overall hardware implementation in open loop

5 Cost Analysis of the Proposed System The Per Module cost for the Power Max thin-film module of 140 W rated peak power and 47 V mpp is |26,162. To have a 325 V DC, the number of panels required is calculated from Eq. (7) as given below, Total Number of panels required =

Total Required Voltage 325 V = = 6.91  7 Per panel Voltage 47 V (7)

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Table 3 Module integrated converter cost Number of cells

2

Number of MOSFET switches for the Flyback converter

2 per cell = 2 × 2 = 4

Cost of the converter MOSFET switches

|85 per switch = 85 × 4 = |340

Number of power diodes

2 per cell = 2 × 2 = 4

Cost of power diodes

|250 per diode = 250 × 4 = |1000

Number of flyback transformers

2

Cost of flyback transformers

|220 per transformer = 220 × 2 = |440

Number of MOSFET switches for the Single-Phase Inverter

4

Cost of the inverter MOSFET switches

|85 per switch = 85 × 4 = |340

Cost of the inductor and drive circuits for the filter and capacitor for the DC Link

|2000

Cost of the digital signal controller

|5000

Wiring cost for the power circuit and driver circuit including the PCB layout

|400

Cost of the miscellaneous components for the drive system including optocoupler circuits, fast recovery diodes, lower power rated resistors and driver’s IC

|1000

Total cost

|10,920

From Eq. (7) it has been observed that to increase the voltage rating, seven PV panels are required which amount to a total cost of |26,162 × 7 = |183,134. The solution to save the system cost is by using the module integrated converter, which minimizes the number of panels by incorporating power electronic technology into the PV module. The cost of the Module Integrated Converter (MIC) is given in Table 3. Thus, the total cost of the Module Integrated Converter is approximately |10,920. Net cost including single PV module and the converter = |26,162 + |10,920 = |37,082. Cost-saving for the required output voltage = |183,134 − |37,082 = |146,052 = 80% approximately.

5.1 Payback Period The payback period is an important parameter when money is invested in solar PV system. It is defined as the length of time required for an investment to recover its initial outlay in terms of profits or savings. It is given by Eq. (8)

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L. Ashok Kumar and M. Rajendran

Payback Period =

Total PV system cost Amount saved + Incentives received

(8)

From the cost analysis, the cost of the Module Integrated System per PV is |37,082. The Average electricity cost of the domestic consumers in Tamil Nadu is |369 per month. The proposed AC module has 125 W power capacity thus it can conserve |60 approximately when it supplies to the lighting load. Also, the thin-film module can be alternatively used for the glasses in the building infrastructure thus saving the glass cost which may be around |4000 for a dimension of 5 feet × 7 feet. From Eq. (8), Payback Period =

37,082 = 6.5 years (approx.) (60 × 12) + 4000

Thus, the proposed system has a payback period of 6.5 years approximately and has the potential to save 80% cost.

6 Conclusion Thus, the significance of BIPV system has been discussed and the contribution of Module Integrated Converters has been surveyed. Both simulation and hardware implementation of the module integrated flyback converter has been done in this work. This system can save a cost of 80% and also decreases the payback period of the solar PV by 4.5 years. Hence a Module Integrated Converter is essential for BIPV system to utilize renewable solar energy in an effective way.

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Development of a Cost-Effective Module Integrated Converter …

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