Digital Technologies and Applications: Proceedings of ICDTA’23, Fez, Morocco, Volume 2 (Lecture Notes in Networks and Systems, 669) 3031298594, 9783031298592
122 24 110MB
English Pages [1022]
Preface
Organization
Acknowledgments
Contents
Artificial Intelligence, Machine Learning and Data Analysis
Study on Determining Household Poverty Status: Evidence from SVM Approach
1 Introduction
2 Support Vector Machine
3 Data and Application
3.1 Data
3.2 Instruments and Software
3.3 Result of Support Vector Machine Application with Different Kernel
4 Discussion and Interpretation of the Results
5 Conclusion
References
The Generation of Power from a Solar System Using a Variable Step Size P&O (VSS P&O) and Comparing It to a Grey Wolf Metaheuristic Algorithm (GWO)
1 Introduction
2 PV Panel Modeling and Characteristics
2.1 Effect of Solar Irradiation
2.2 Effect of Temperature
3 DC-DC Converter
4 The Analyzed System's Control
4.1 Variable Step Size P&O (VSS P&O)
4.2 Grey Wolf Optimizer (GWO)
5 Results and Discussion
6 Conclusion
References
Bio-Inspired Optimisation Algorithm for Congestion Control in Computer Networking
1 Introduction
2 Related Works
2.1 Network Congestion
2.2 Bio-Inspired Optimisation Algorithms
3 Methodology
3.1 Behavior and Characteristics of Rabbit, Fox, and Grass
3.2 Relating Proposed Bio-Inspired Characteristics to Network Congestion Characteristics
3.3 Mathematical Model of Rabbit, Fox (Predator-Prey) Characteristics
3.4 Flow Chart
3.5 Pseudocode – Step by Step of the Fox Prey Optimisation Algorithm
4 Benchmark Test Functions
5 Results and Discussion
6 Conclusions
References
Fingerprint Classification Models Based on Bioinspired Optimization Algorithm: A Systematic Review
1 Introduction
1.1 Bioinspired Optimization
2 Methodology
2.1 Research Identification
2.2 Research Strategy
2.3 Study Selection
2.4 Quality Assessment
2.5 Data Extraction
2.6 Data Synthesis and Analysis
3 Results and Discussion
3.1 Fingerprint Classification Based on Bioinspired Algorithm
4 Conclusion
References
Inspired Nature Meta-Heuristics Minimizing Total Tardiness for Manufacturing Flow Shop Scheduling under Setup Time Constraint
1 Introduction
2 The Statement of MFSSSDST Optimization Problem
2.1 Example
3 Solution Approaches
3.1 The ABC Metaheuristic
3.2 The Genetic Algorithm
3.3 The MBO Metaheuristic
4 Computational Result
5 Conclusion
References
Punishing the Unpunishable: A Liability Framework for Artificial Intelligence Systems
1 Introduction
2 Technological Advancements and Thinking Machines
3 Black-Box Algorithms
4 Principles of Robotics
5 Liability Framework
5.1 Criminal Liability Model: The Need for Legal Personhood
5.2 Civil Liability Model: Strict Liability, Product Liability and Vicarious Liability
6 Conclusion and Suggestions
References
Acceptability Aspects of Artificial Intelligence in Morocco: Managerial and Theoretical Contributions
1 Introduction
2 Literature Review
2.1 Artificial Intelligence: Conceptual Framework
2.2 Artificial Intelligence (AI) Limitations and Hazards
2.3 The UTAUT Model: Definition and Basic Model
2.4 The TAM Theory
3 Methodology
4 Results and Discussion
4.1 Goodness of Fit of a Statistical Model
5 Conclusion
References
The Use of Learning Algorithms in Business Intelligence Tools to Enhance Customer Feedbacks
1 Introduction
2 Literature Review
2.1 The Concept of Customer Feedback Management
2.2 The Nature and Potential of Business Intelligence
2.3 Usage of Machine Learning and Deep Learning in Business Intelligence Tools
3 Methodology
3.1 Description of the Research Issue -Machine Learning and Deep Learning
3.2 Survey of Different BI Tools
4 Results
4.1 The Results of Sentiment Analysis and Improvement by Deep Learning and Machine Learning Methods
4.2 Survey of Different BI Tools
5 Discussion of the Results
6 Recommendations
7 Conclusion
References
Improving the Performance of Public Transport Bus Services: Analytics Approach to Revenue Forecasting
1 Introduction
2 Methodology
2.1 Time Series Forecasting Model
2.2 Multiple Linear Regression
2.3 The SARIMAX Model
3 Application (Case Study)
3.1 Data Collection and Analysis
3.2 Forecasting Model
3.3 Comparison of the Two Models
4 Conclusion and Future Directions
References
Multi-level Governance and Digitalization in Climate Change: A Bibliometric Analysis
1 Introduction
2 Method
3 Results and Discussion
3.1 Research Trends and Coauthorship Related to Multilevel Governance and Digitalization on Climate Change Issues
3.2 Multi-level Governance and Digitalization in Climate Change: As Future Global Mitigation Efforts
4 Conclusion
References
Machine Learning Approach for Reference Evapotranspiration Estimation in the Region of Fes, Morocco
1 Introduction
2 Empirical Equation and AI Related Work
2.1 Empirical Equations for Evapotranspiration
2.2 Artificial Intelligence for the ET0 Prediction
3 Materials and Methods
3.1 Study Area and Climate Data
3.2 Missing Data and Analysis
4 Results and Discussion
4.1 Performance Metrics
4.2 Choice of the Best Model
5 Conclusion
References
Data Augmentation and Deep Learning Applied for Traffic Signs Image Classification
1 Introduction
2 Materials and Methods
2.1 Data Description
2.2 Data Augmentation
2.3 Training from Scratch
2.4 Transfer Learning
3 Results and Analysis
4 Conclusion
References
Arabic Speech Emotion Recognition Using Deep Neural Network
1 Introduction
2 Related Work
3 Arabic Speech Emotion Recognition
3.1 Feature Extraction
4 Recurrent Neural Network Classifier
5 Methodology and Results
5.1 Dataset
5.2 Data Preparation and Preprocessing:
5.3 Feature Extraction
5.4 Data Vectorization
5.5 Modeling
5.6 Result
6 Comparative and Discussion
7 Conclusion
References
A Chatbot’s Architecture for Customized Services for Developmental and Mental Health Disorders: Autism
1 Introduction
2 Mental and Developmental Health Issues and Chatbots
3 Chatbot’s Considerations
3.1 Personalized Chatbots
3.2 Chatbot Types
3.3 General Architecture
4 Architecture’s Chatbot for a Developmental Disease
5 Conclusion and Future Work
References
Machine Learning with Reinforcement for Optimal and Adaptive Learning
1 Introduction
2 Litérature Review
3 Artificial Intelligence in Education
4 Contributions of Adaptive Learning: The Personalization of Courses
4.1 Adaptive Learning System
4.2 Development of Adaptive AI Learning Solutions in Education
5 Artificial Intelligence at the Service of Education
6 Conclusion
References
Incorporating Deep Learning and Sentiment Analysis on Twitter Data to Improve Tourism Demand Forecasting
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Methodology
3.2 Historical Data
3.3 Social Media Data
3.4 Preprocessing
3.5 Normalization
3.6 Sentiment Analysis Model: VADER (Valence Aware Dictionary and sEntiment Reasoner)
3.7 Forecasting Model
3.8 Model Performance Evaluation
4 Results
5 Conclusions
References
A Deep Learning Model for Diabetic Retinopathy Classification
1 Introduction
2 State of the Art
3 Our Solution
3.1 Data Understanding
3.2 Image Preprocessing
3.3 CNN Modeling Approach
3.4 Discussion and Comparison
4 Conclusion
References
A Forecasting Model for the Energy Requirements of an Office Building Based on Energy Modeling and Machine Learning Models – A Case Study of Morocco
1 Introduction
2 Prediction ML Model for Building Energy Efficiency and Thermal Comfort
2.1 Utilization of the ML Model in Occupancy Forecasting for Energy Savings
2.2 The Effect of the Application ML Model on Thermal Comfort
2.3 The Impact of Incorporating the Smart Meters for Tracking Energy Management
2.4 The Effect of Predicting Energy Consumption on Building Performance
3 Materials and Methods
3.1 Case Studies
3.2 Data Analysis Process
4 Results and Discussion
4.1 The Result of Forecasting Annual Energy Needs Using Simulation in Trnsys
4.2 The Result of Forecasting Annual Energy Needs Implementation of ML Model
5 Conclusion
References
Internet of Things, Blockchain and Security and Network Technology
Blockchain Technology for Audit Firms: Perspectives and Challenges
1 Introduction
1.1 The Emergence of Blokchain Technology
1.2 Blokchain Technology for Audit Profession
2 Blockchain Technology: Mechanisms of Application
2.1 Blockchain Mechanisms
2.2 Blockchain Types
2.3 The Application of Blockchain System for Audit Procedures
3 Blockchain Technology: Mechanisms of Application in Audit Procedures
3.1 Research Methodology
3.2 Findings: Blockchain’s Implication in Audit Procedures
4 Conclusion
References
Blockchain Security Attacks: A Review Study
1 Introduction
2 Background
3 Types of Security Attacks on the Blockchain
3.1 Blockchain Network Attacks
3.2 User Portfolio Attacks
3.3 Smart Contract Attacks
3.4 Attacks on the Transaction Verification Mechanism
4 ECDSA Failure
5 Comparison of Security Attacks Against Crypto-Currencies
6 Analysis
7 Conclusion
References
Classification of Coordinators’ Limitations in Cyber-Physical Production System Management
1 Introduction
2 The Role of the Coordinator in CPPS Management
3 Approaches of PMS Integration: Characteristics
4 Approaches Comparison: Coordinators and System Limitation
5 Conclusion
References
Cryptography in Distributed Ledger Technologies from a Layered Perspective: A State of the Art
1 Introduction
2 Materials and Methods: The DCEA Framework
2.1 Data Layer
2.2 Consensus Layer
2.3 Execution Layer
2.4 Application Layer
3 Analysis and Discussion: Cryptographic Techniques in Blockchain Platforms from a Layered Perspective
3.1 Data Layer
3.2 Consensus Layer
3.3 Execution Layer
3.4 Application Layer
3.5 Recommendations
4 Conclusion
References
New Topology of WSN for Smart Irrigation with Low Consumption and Long Range
1 Introduction
2 Proposed System
3 Evapotranspiration
4 Description of Hardware Platform and Method
4.1 Hardware Platform
4.2 Development Method
4.3 Node Consumption
5 Implementation and Results
5.1 Visualization Platform
6 Conclusion
References
A Literature Review of Internet of Vehicle Based Blockchain
1 Introduction
2 Systematic Literature Review Methodology
2.1 Plan the Review
2.2 Conduct the Review
2.3 Report the Review
3 Research on Blockchain-Based IoV
3.1 General purpose
3.2 AI and ML for IoV
3.3 Blockchain Performance in the IoV
3.4 IoV Management
3.5 Privacy and Security
4 Limitations and Future Directions
5 Conclusion
References
Tiny Machine Learning for IoT and eHealth Applications: Epileptic Seizure Prediction Use Case
1 Introduction
2 Background
2.1 Internet of Things
2.2 e-Health
2.3 Artificial Intelligence for IoT and eHealth
2.4 Machine Learning in e-Health
2.5 Machine Learning for Epileptic Seizure Prediction
2.6 TinyML as a Candidate for Embedded Intelligence in e-Health
3 Related Works
4 Machine Learning Workflow
4.1 Data Acquisition and Collection
4.2 Data Preparation
4.3 Algorithm Choice
4.4 Training and Testing
4.5 Evaluation
4.6 Inference and Prediction
5 TinyML Workflow
5.1 Convert Our Model
5.2 Deployment, Inference, and Real-Time Data Streaming
6 Results and Discussion
7 Conclusion
References
A Simulation Framework for IoT Networks Intrusion and Penetration Testing
1 Introduction
1.1 Challenges
1.2 Contributions
2 Background
2.1 Internet of Things
2.2 Low-Level Networking Simulation
2.3 Security Threats
3 Related Work
3.1 Iot Simulators
3.2 Analysis and Discussion
4 Proposed System
4.1 General Architecture
4.2 Use Case
5 Conclusion
References
A New Four Ports Multiple Input Multiple Output Antenna with High Isolation for 5G Mobile Application
1 Introduction
2 Antenna Design
3 Results and Simulations
4 Parametric Study
5 Comparison Study
6 Conclusion
References
Isolation Improvement Using a Protruded Ground Structure in a 2 * 2 MIMO Antenna
1 Introduction
2 Design Steps
3 Results and Discussion
4 Performance Comparison
5 Conclusion
References
A Novel Two-Port MIMO Configuration in Dual Band 26/38 GHz with High Isolation in the Ground Plane for 5G Applications
1 Introduction
2 Single Antenna Design
3 Two-Element MIMO Antenna
3.1 Antenna Configuration
3.2 Antenna Parameters Analysis
3.3 Effect of Defective Ground Structures (DGS)
4 Results and Discussion for the Proposed MIMO Antenna
5 Comparison of the Proposed MIMO Antenna with Other Existing Antenna Designs
6 Conclusion
References
Modeling and Analysis of Short Distance Terahertz Communication Channel
1 Introduction
2 Communication Chain
2.1 Digital Processing
3 Spatial Modeling of the Propagation Channel
4 Simulation of the Propagation Channel Under Terahertz Conditions
4.1 Simulation Results
4.2 Channel Impulse Response
5 Conclusion
References
Android-Based Attendance Management System
1 Introduction
2 Related Work
2.1 Barcode and QR Code
2.2 Radio Frequency Identification
2.3 Biometric Authentication
3 System Overview
3.1 Software Architecture
4 Methodology
5 Proposed Approach
6 System Testing
7 Conclusion
References
Fano Resonance in a Defective Network Formed by Helmholtz Resonators with Extended Necks
1 Introduction
2 Theoretical Model
2.1 Transmission Loss of the HREN
2.2 Transmission of Periodic HRENs Containing Defects
3 Device Architecture and Measurement Principle
4 Results and Discussion
5 Conclusion
References
Digital Transformation and E-Learning
The Digitalization of the Moroccan Higher Education Sector in the Era of the COVID-19 Pandemic
1 Introduction
2 Literature Review
3 Methodology
4 Results and Discussion
4.1 Context of the Study
4.2 Distance Learning During COVID-19: Between Satisfaction and Difficulties
4.3 Pedagogical Perspectives in Digital Learning
5 Conclusion
References
Heritage Preservation, a Specific Territorial Resource in the Era of the Smart City: The Role of Innovation and Digital Technology
1 Introduction
2 Literature Review
2.1 Heritage as a Territorial Resource
2.2 Digital and Innovative Technologies in “SMART Cities”: Which Role in the Preservation of Territorial Heritage?
3 Methodology
3.1 Establishment of the Research Theme
3.2 Launching and Organization of the Research
3.3 Selection and Analysis of Reference Cases
3.4 Consultation of the Actors of the City of Fez
4 Results
5 Discussion
6 Conclusion
References
Media and Cyberbullying in Political Communication Among Nigerians: Implications to Development
1 Introduction
2 Diffusion of Innovation and Development Theory
3 Politics and Political Communication
4 Cybercrime and Cyberbullying in Nigeria
5 Media and Development Communication
6 Cases of Cyberbullying in Political Communication
7 Conclusion: The Media in Dissipating Cyberbullying in Political Communication
References
Hospital Performance and Managerial and Digital Innovation: Impact of Universal Health Coverage
1 Introduction
2 Brief Review of Theoretical and Empirical Literature
2.1 Defining Hospital Performance in Relation to Performance Models
2.2 Managerial Innovation in Hospitals
3 Theoretical Conceptual Model of the Study and the Central Research Hypothesis
4 Research Methodology and Process
5 Results
5.1 Before Universal Health Coverage
5.2 After the Generalization of Universal Health Coverage
6 Discussions
7 Conclusions
References
Teachers’ Motivational Intervention Towards EFL Learners’ Oral Performance: A Quantitative Approach
1 Introduction
2 The Objectives of the Study
3 Methods
3.1 Study Design and Setting
3.2 Participants
3.3 Materials and Instrumentations
3.4 Data Collection Procedures
4 Data Analysis
5 Results
6 Discussion
7 Conclusion
References
BERTopic and LDA, Which Topic Modeling Technique to Extract Relevant Topics from Videos in the Context of Massive Open Online Courses (MOOCs)?
1 Introduction
2 Topic Modeling
2.1 LDA (Latent Dirichlet Allocation)
2.2 BERTopic
3 Research Methodology
3.1 Dataset
3.2 Pre-processing
3.3 cv Measure Coherence
4 Results
4.1 LDA Topics Extraction and Topics Coherence Measurement
4.2 BERTopic Topics Extraction and Topics Coherence Measurement
4.3 Comparison Between LDA and BERTopic Models
5 Conclusion and Future Work
References
Blockchain-Based Higher Educational Data Management: Enhanced Architecture for Data Security
1 Introduction
2 Blockchain – An Exploration
3 Big Data in Higher Education
4 Blockchain Technology-Based Systems in Higher Education
5 Conceptual Framework
6 Implications for Future Research
7 Conclusion
References
Gender, Area, and School Streams Factors on Design Perception and Experiences in Educational Games as Learning Environments
1 Introduction
2 Educational Games and Demographic Factors
2.1 Gender and Games
2.2 Area and Games
2.3 School Streams and Games
3 Research Hypothesis
4 Methods
4.1 Participants
4.2 Experience Based Design Graphic Style (EBDGS) Prototypes
5 Results
5.1 Gender and Student Experience’s
5.2 Areas and Student Experience’s
5.3 School Streams and Experience’s
6 Discussions
7 Conclusions and Implications
References
Impact of Integrating Digital Educational Resources on Student Motivation in Learning Nuclear Transformations
1 Introduction
2 Problematic
2.1 Digital Resources and Motivation to Learn Nuclear Physics
2.2 Purpose of the Research
2.3 Research Sub-question
3 Experimental Method
4 Learning Device
5 Methods of Investigation
5.1 Learning Sessions
6 Analysis and Interpretation of Results
7 Discussion and Perspectives
7.1 Response to the Research Question
8 Conclusion
Annexs
References
Quality Assurance and Quality Practices in Teaching and Learning Data Sciences and Artificial Intelligence
1 Introduction
1.1 Trends and Gaps in Teaching DS/AI Research
1.2 Analysis and Assumptions
2 Quality Framework for Learning and Teaching DS/AI
2.1 Criteria and Standards
2.2 Foundations and Pillars of the Quality Framework for Learning and Teaching DS/AI
3 Discussion
4 Conclusion
References
Perceptions of Filipino Pre-service Teachers Toward the Quality of Online Learning During the COVID-19 Pandemic
1 Introduction
2 Methodology
2.1 Research Design
2.2 Participants
2.3 Research Tool
2.4 Data Collection
3 Results and Discussion
3.1 Perceptions About Online and Face-to-Face Learning
3.2 Factors Affecting Quality of Online Learning
3.3 Perceived Quality of Online Learning
4 Conclusion
References
E-Learning and Education 4.0: Revolution in Education of 21st Century
1 Introduction
1.1 Objectives
2 Methodology
2.1 Emergence of Industrial Revolution 4.0 [5]
2.2 Emergence of Education 4.0
2.3 Education 4.0: An Overview
2.4 Education 4.0 Has Nine Crucial Trends According to Fisk [9]
3 Benefits of Education 4.0 and Digitalization of Education
4 Challenges of Education 4.0 for Students
5 Conclusion
References
Image and Information Processing
Optimal Color Image Watermarking Based on DWT-SVD Using an Arithmetic Optimization Algorithm
1 Introduction
2 The Techniques Used
2.1 Discrete Wavelet Transform (DWT)
2.2 Decomposition into Singular Values (SVD)
2.3 Arithmetic Optimization Algorithm (AOA)
3 Proposed Method
3.1 Embedding Process
3.2 Extraction Process
3.3 AOA‑based Watermarking Algorithm
4 Experimental Results
4.1 The Imperceptibility Analyses
4.2 Robustness Analyses
5 Conclusion
References
An Overview of the Impact of Partial Occlusions on Automatic Facial Expression Recognition
1 Introduction
2 Facial Occlusions: Impact and Solutions
2.1 Impact of Occlusions
2.2 Exploitation of Visible Areas of the Face
2.3 Reconstruction of Hidden Areas of the Face
3 Existing Evaluation Protocols Proposed in the Literature
3.1 Databases
3.2 Occlusion Strategies
3.3 Learning and Testing
3.4 Summary and Evaluation Results of the Methods
4 Conclusion
References
Face Recognition: A Mini-Review
1 Introduction
2 Main Steps for Face Recognition
3 Face Recognition Methods
3.1 Holistic Method
3.2 Local Method
3.3 Hybrid Method
4 Datasets for Face Recognition
5 Conclusion
References
Improved Fusion of SVD and Relevance Weighted LDA Algorithms via Symmetric Sum‑Based Rules for Face Recognition
1 Introduction
2 Proposed Method
2.1 Singular Value Decomposition (SVD)
2.2 Relevance Weighted LDA
2.3 MIN-MAX Normalization
2.4 Fusion Rules
3 Experimental Results and Discussion
4 Conclusion
References
Image Captioning: An Understanding Study
1 Introduction
2 Image Captioning
2.1 Dataset
2.2 Image Features Extraction
2.3 Natural Language Processing (NLP)
2.4 Evaluation Metrics
3 Literature Review of Image Captioning
3.1 Approaches Based on Visual Content
3.2 Language Template-Based Approaches
3.3 Deep Learning-Based Approaches
3.4 Image Captioning Based on the Encoder-Decoder Architecture
3.5 Image Captioning Based on the Attention Mechanism
3.6 Image Captioning Based on Semantic Concepts
3.7 Images Captioning Using Transformers
3.8 Dense Captioning
4 Implementations and Discussion
4.1 Attention-Based Captioning
4.2 Transformer-Based Captioning
5 Conclusion
References
A Comparison Between LSTM and Transformers for Image Captioning
1 Introduction
2 Related Works
3 Methodology
3.1 Image Caption Datasets
3.2 VGG 16
3.3 LSTM
3.4 Transformers
4 Experimental Results
5 Discussion
6 Conclusion
References
Multi-platform Framework for Pattern and Image Processing
1 Introduction
2 Literature Review
2.1 Framework Development
2.2 Characterize and Represent Images
2.3 Object Oriented Web Canvas and JavaScript
2.4 Chromatic Analysis
3 Methodology
3.1 WebApp for Multiple Platforms
3.2 Object Oriented Implementation
3.3 Color Discretization
3.4 Image Processing Algorithms
4 Results
4.1 Framework Architecture
5 Conclusions
References
Random Forest for Semantic Segmentation Using Pre Trained CNN (VGG16) Features
1 Introduction
2 Related Methods
2.1 Convolutional Neural Network
2.2 Random Forest
3 Proposed Method
3.1 Preprocessing
3.2 The Extraction of Features
3.3 Segmentation Based on Random Forest
4 Experiments and Results
5 Conclusion
References
The Polygonal Technique for Constructing Islamic Geometric Patterns
1 Introduction
2 Related Works
3 Construction of Periodic Tilings with Polygons
3.1 Crystallographic Group
3.2 Construction of Periodic Tilings
4 Polygons in Contact Method (PIC)
5 Conclusion
References
LabVIEW Implementation of Bio-signal Zero-Watermarking Using Tchebichef Moments
1 Introduction
2 Tchebichef Moments Computation
3 Proposed Method for Bio-signal Zero-Watermarking
3.1 Phase of Zero-Watermark Image Creation
4 Simulation Results
5 Conclusion
References
A Robust Approach for Arabic Document Images Segmentation and Indexation
1 Introduction
2 Document Image Analysis
2.1 Structure of Document Images
2.2 Physical Structure Extraction of Documents
3 Text/Graphic Separation from a Document Image
3.1 Segmentation of Document Images
3.2 Segmentation into Words
4 The Proposed Approach
4.1 Segmentation and Region Extraction
4.2 Word Matching
5 Implementation Results
6 Conclusion
References
New Arabic Word Embeddings to Improve NLP Applications
1 Introduction
2 Sate of Art
2.1 Word2Vec
2.2 Fast TEXT
2.3 BERT
2.4 Arabic Word Embedding
3 Alkhalil Morpho Sys Analyzer
4 Description of the Developed Embedding Model
4.1 Pre-processing
4.2 Neural Network Architecture
4.3 Visualization of Generated Vectors
5 Evaluation
6 Conclusion
References
Advanced Technologies in Energy and Electrical Engineering
Cascaded Control Based on Super Twisting Algorithm for DC-DC Converter
1 Introduction
2 Model of DC-DC Buck Converter
3 Cascade Structure Based SOSMC with STA
4 Stability Analysis
5 Results and Discussion
6 Conclusion
References
Optimisation and Performance Evaluation of a Standalone Renewable Energy System in Congo-Brazzaville
1 Introduction
2 Materials and Methods
2.1 Proposed System Description
2.2 Load Profile
2.3 Energy Resources and Input Parameters
2.4 Optimisation Problem
3 Results and Discussion
4 Conclusion
References
Bearing Ball Fault Diagnosis of an Induction Machine by Using the Hilbert Transform and the Performance of Intelligent Control
1 Introduction
2 Induction Machine and Bearing Fault Model
3 Artificial Neural Networks - Direct Torque Control
4 Specific Bearing Ball Fault Frequency
5 Hilbert Transform
6 Simulation Results and Interpretation
7 Conclusion
Appendix
References
Control of a Full-Bridges Five Levels Inverter: Experimental Validation
1 Introduction
2 Structure
3 Command Description
4 Simulation Results
5 Practical Results
6 Interpretation
7 Conclusion
References
Energy Efficiency Model-Based Predictive Maintenance for Induction Motor Fault Prediction Using Digital Twin Concept
1 Introduction
2 Energy Efficiency and Maintenance
3 Loss-Based Induction Motors BRBF Detection and Prediction
3.1 IMs Losses and Failures Interdependencies
4 Result and Discussion
4.1 Discussion and Perspective
5 Conclusion
References
Feasibility Study of PV-Powered Hydrogen Vehicles in Morocco: An Assessment
1 Introduction
2 Hydrogen-Powered Transportation System
2.1 Hydrogen-Powered vehicles
2.2 Refueling Hydrogen Vehicle
2.3 Hydrogen Production from Electricity
3 Energy Requirements for the Transportation Sector in Morocco
4 Assessment of the Renewable Energy Resources in Morocco
5 Potential of Hydrogen-Generation in Morocco
5.1 Hydrogen Generation with Solar Photovoltaic Systems
5.2 Hydrogen Production from Wind Power
5.3 Hydrogen Production from Hydropower
6 Conclusion
References
Comparative Study of Optimal Frequency Control in a Microgrid via a PID and (1 + PD)-PID Controllers
1 Introduction
2 Modeling of Studied Microgrid
2.1 PV System
2.2 Wind Turbine Generator (WTG)
2.3 Fuel Cell (FC)
2.4 Energy Storage System (ESS)
2.5 Diesel Engine Generator (DEG)
2.6 Thermal Generator (ThG)
2.7 Power System Modeling
3 Control Methodology
3.1 Control Strategies
3.2 Optimization Techniques
4 Simulation Results and Discussion
4.1 Study 1: Influence of Devices
4.2 Study 2: Influence of Controllers
4.3 Study 3: Influence of Optimization Algorithm
5 Conclusion
References
Grid-Connected PV System Simulation Study
1 Introduction
2 PV Grid Connected System
2.1 Modelling of PV
2.2 MPPT Control
2.3 Modelling of the PV Inverter
3 Simulation Model
4 Conclusion and Perspectives
References
Toward Smarter Power Transformers in Microgrids: A Multi-agent Reinforcement Learning for Diagnostic
1 Introduction
2 State of Art: Current Smart Transformers in Research Phase
3 Multi-agent System Deep Reinforcement Learning
4 Architecture of the Multi-agent Reinforcement Learning in SEMS
5 Conclusion
References
Energy Management and Voltage Control of Standalone DC Microgrid with Energy Storage and Modified P&O MPPT Algorithm
1 Introduction
2 Microgrid Modeling
2.1 PV System
2.2 Battery
2.3 Energy Management System
3 Simulation Results
4 Conclusion
References
Experimental Performance Study of an Isolated PV Water Pumping System Used for Agricultural Irrigation in Zagora-Morocco Semi-arid Area
1 Introduction
2 System Description and Methodology
2.1 PV Generator
2.2 Kit of the Pump Description
3 System Installation and Start-Up
3.1 PV System and Pump Sizing
3.2 Real Components System
3.3 Parameter Efficiencies
4 Experimental Results and Discussion
4.1 The Monthly Experimental Results
4.2 The Seasonal Experimental Results
4.3 System Comparison with Other Photovoltaic Systems
5 Conclusion
References
Analysis and Comparison of Mathematical Models PV Array Configurations (Series, Parallel, Series-Parallel, Bridge-Link and Total-Cross-Tied) Under Various Partial Shading Conditions
1 Introduction
2 Mathematical Equivalent Modeling of PV Module
3 Modeling Simulation and Discussion of 3 × 3 PV Configurations Under Various Partial Shading Conditions
3.1 Series (S) PV Configuration 3 × 3
3.2 Parallel (P) PV Configuration 3 × 3
3.3 Series-Parallel (SP) PV Configuration 3 × 3
3.4 Bridge-Link (BL) PV Configuration 3x3
3.5 Total-Cross-Tied (TCT) PV Configuration 3x3
3.6 Comparative Study and Discussion
4 Conclusion
References
Ecological Hammam in Fez: Design of a Combined Biomass and Solar Thermal Energy System
1 Introduction
2 Description System
3 Method
3.1 The Hydraulic Floor Heating Sizing Algorithm
3.2 Production of Hot Water by Biomass
3.3 Solar Installation
4 Energy Simulation
5 Results and Discussion
5.1 Calculation Result
5.2 Economic Study
5.3 Environmental Impact Study
6 Conclusion
References
A Mathematical Model for an Optimal PV System with Battery and Hydrogen Energy Storage
1 Introduction
2 Modeling Methodology
2.1 Mathematical PV Generator Model
2.2 Physical BESS Model
2.3 Mathematical HESS Model
3 The Optimized Model
3.1 General Model
4 The Optimized Model
5 Simulations Results
6 Conclusion
References
Application of Superconducting Magnet Energy Storage to Improve DFIG Behavior Under Sag Voltage
1 Introduction
2 DFIG Model
2.1 DFIG Model Under Normal Operation
2.2 DFIG Model Under Voltage Dip
3 Association of the SMES in DFIG System
3.1 SMES System
3.2 Power DC Control
4 Simulations Results
5 Results Analyse
6 Conclusion
References
Comparative Analysis of Predictive and Direct Power Control Strategies for PMSG-Based WECS
1 Introduction
2 The System Topology
2.1 Mathematical Model of the Turbine
2.2 Mathematical Model of PMSG
3 Synthesis of Control
3.1 Direct Power Control DPC
3.2 Predictive Power Control PPC
4 Simulation Results
5 Conclusion
References
Intelligent Direct Power Control Based on the Neural Super-Twisting Sliding Mode Controller of a DFIG
1 Introduction
2 General Model of the DFIG-WEC System
3 Novel Intelligent DPC Approach
4 Simulation Results
5 Conclusion
References
Deadbeat Predictive Control Strategy of a Wind System Based on the DFIG Generator
1 Introduction
2 Modelling of the DFIG
3 Application of Deadbeat Predictive Control on the DFIG Wind Power System
4 Machine-Side Control
5 Grid-Side Control
6 Results and Simulation
7 Conclusion
References
Implementation and Demonstration of Threat Over CAN Bus for Automotive Embedded System Equipped with ADAS
1 Introduction
2 Related Work
3 Background
3.1 Vehicle Investigation
3.2 Test Model and CAN Devices
4 Experiment and Result
4.1 CAN Bus Load by Injection Attack
4.2 Cruise Control Acceleration Attack
5 Conclusion and Future Work
References
Resource Allocation of a Modular ANN Implementation Using LabView and FPGA Board
1 Introduction
2 Related Works
3 Proposed ANN Implementation
3.1 Inputs, Weight, and Bias Generation
3.2 Desired Output Module
3.3 Activation and Loss Functions Module
3.4 Feed-Forward Module
3.5 Weight and Bias Updating Module:
3.6 The Final Model
4 Results and Discussion
5 Conclusion
References
Digital vs Analog Low Dropout Regulators a Comparative Study
1 Introduction
2 Analog and Digital LDOS Comparison
2.1 Low Voltage Operation
2.2 Power-Speed Trade-off
2.3 Stability and Limit Cycle Oscillation
2.4 Power Supply Rejection
3 Discussion and Conclusion
4 Conclusion
References
A low-cost Platform for an Environmental Smart Agriculture Monitoring System Based on Arduino and Renewable Energies
1 Introduction
2 Materials and Methods
2.1 Study and Simulation of the Thermometer of Temperature
3 Results and Discussion
3.1 Calibration of Temperature and Humidity Sensor
3.2 Calibration of the Wind Sensor:
4 Conclusion
References
Dynamic WPT Chargers: State of the Art
1 Introduction
2 Optimal Coupler Topology
3 Segmentation and Power Supply Scheme
3.1 Centralized Power Supply Scheme
3.2 Power Frequency Scheme - Segmented Rail Mode
3.3 High-Frequency Scheme - Segmented Rails
4 Control
4.1 State Feedback Controller
4.2 Autonomous Coil Alignment System Using Fuzzy Steering
4.3 Multi-level Power Controller
4.4 Real-Time Power Control and Efficiency Maximization
5 Solution DWPT
6 Conclusion
References
Backstepping-Based Control of Dc-Dc V2X Charger for Electric Vehicles
1 Introduction
2 Overview and System Modeling
2.1 System Overview
2.2 System Modeling
3 Controller Design
4 Simulation and Results
5 Conclusion
References
A Genetic-Based Algorithm for Commodity Selection and Full Truckload Vehicle Routing Problem
1 Introduction
2 Literature Review
3 Formal Description and Mathematical Formulation
3.1 Formal Description
3.2 MILP Formulation
4 Solution Procedure (GA-CPLEX)
4.1 Chromosome Representation
4.2 Initial Population
4.3 Fitness Function and Chromosome Selection
4.4 Crossover
4.5 Mutation
4.6 Termination Criteria
5 Computational Experiments
5.1 Test Instances
5.2 Evaluation of GA-CPLEX
6 Conclusion
References
Mechatronic, Industry 4.0 and Control System
The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies
1 Introduction
2 Digitalization of Human Resources Management: An overview
2.1 Recruitment Process 4.0
2.2 The Advantages of Recruitment 4.0
3 Methodology and Assumptions
4 Results
5 Discussion
6 Conclusion
References
Defining and Implementing Predictive Maintenance Based on Artificial Intelligence for Rotating Machines
1 Introduction
2 Proposed Methodology
2.1 Integration of New Technologies in Maintenance
3 Case Study
3.1 Instrumentation and Experimental Setup
3.2 Unbalanced Fault
3.3 Application of the Architecture
3.4 Construction of the ANN
4 Results and Discussion
4.1 Monitoring Results
4.2 Diagnostic Results
5 Conclusion
References
Ensuring the Connection Between Physical and Virtual Models in the Context of Digital Twins
1 Introduction
2 Related Work
2.1 Digital Twin Concept
2.2 Interoperability Problem Solving Approaches Comparison
2.3 Selection of the Most Suitable Approach to Achieve Interoperability in the Context of Industry 4.0
2.4 Overview on Ontologies
3 The Proposed Approach
3.1 Identification of the Elements and Relations of the Physical Systems
3.2 Construction of DT Models
3.3 Establishment of the Connection Between Physical Entities and Virtual Models
3.4 Development of the Ontology DTOnto
4 An Industrial Case of Study
5 Conclusion and Perspectives
References
Smooth Collision-Free Trajectory Planner for AMR Based on B-spline Interpolation and I-GWO
1 Introduction
2 Problem Formulation
2.1 Problem Statement
2.2 Performance Evaluation
3 Methodology
3.1 B-spline Based Trajectory
3.2 Standard Grey Wolf Optimizer GWO
3.3 Improved GWO Algorithm
3.4 Proposed Algorithm
4 Results and Discussions
5 Conclusions and Future Works
References
Direct Torque Control for Doubly Fed Induction Motor Driven Electric Vehicle
1 Introduction
2 Dynamic Model of the Electric Vehicle
3 Modeling of the DFIM
4 Introduction of the Load Torque of the EV in the Mechanical Equation of the DFIM
5 Direct Torque Control Strategy Applied to EV System
5.1 Control of the Stator Flux Vector
5.2 Hysteresis Comparators
5.3 Switching Table
6 Simulation and Interpretation
7 Conclusion
Appendix
References
Pressure Control of a Compressed Air System Based on Hardware in the Loop
1 Introduction
2 Fundamentals
2.1 Comunication TCP/IP
3 Results
4 Conclusions
References
Robotic Arm Control Using Dynamic Model Linearization and Model Predictive Controller
1 Introduction
2 Description, Dynamic Model of the Manipulator and Linearization of the Dynamic Model.
2.1 Description of the Manipulator:
2.2 Dynamic Model of the Manipulator:
3 Linearization of the Dynamic model and decoupling its equations:
4 Linear Model Predictive Controller Design
4.1 Determination of prediction equations:
4.2 Predicted error and optimization
5 Results and Simulation
5.1 Computed Results
5.2 Simulation
6 Conclusion
References
Inter-organizational Information System and Efficiency of the Tourism Supply Chain
1 Introduction
2 Theoretical Framework
2.1 The Tourism Supply Chain (TSC)
2.2 The Parameters of TSC Efficiency
2.3 Information System and Supply Chain Efficiency
3 Methodology
4 Results
5 Discussion
6 Conclusion
References
Balanced Scorecard as Intelligent Management Control Tools: Case of the Pharmaceutical Supply Chain of a University Hospital Center
1 Introduction
2 Theoretical Framework for Steering Hospital Logistics Performance
3 Empirical Study: The Case of a University Hospital
3.1 Methodology of the Case Study: Action Research
3.2 Action Research Field: University Hospital
4 Results and Discussion
5 Conclusions and Research Perspectives
References
Prediction and Optimization of Uber Services: A Case Study of Morocco
1 Introduction
2 Uber Services and System Design
2.1 Uber Business Model
2.2 Uber System Design
2.3 Supply Service
2.4 Demand Service
2.5 DISCO—DISPATCH Optimization
2.6 Functioning of the Dispatch System
3 The Optimization Approach
3.1 Optimization Problem
3.2 Problem Implementation Optimization
4 The Prediction Approaches
4.1 Statistical Prediction
4.2 Machine Learning Prediction
5 Conclusion
References
Optimization of Inventory Management: A Literature Review
1 Introduction
2 The Inventory Management Problem (IMP)
3 Inventory Management Mathematical Models
3.1 Methodology
3.2 Models for Inventory Management Optimization
4 Conclusion and Future Work
References
Digital Healthcare
Brain Tumor Segmentation Using Active Contour on Model Mumford-Shah Algorithm, Simple Standard Deviation, and Mathematical Morphology in Medical Images MRI
1 Introduction
2 Segmentation of an Image: Previous Work
3 Extraction of the Brain Region of Interest
4 Problem of Mumford-Shah
4.1 The Mumford-Shah Algorithm
4.2 The Level Line Formulation of the Mumford and Shah Problem
5 Cooperation of the Segmentation Process
5.1 Tissue Segmentation
5.2 Segmentation of structures
6 Experimental Results
7 Discussion
8 Conclusion
References
Residual Neural Network Architecture for Identifying Vestibular Disease Based on Head Kinematic Characteristics (Velocity)
1 Introduction
2 Materials and Methods
2.1 Neuropsychological Assessment Framework
2.2 Deep Neural Networks Architectures
3 Experimental Results
4 Conclusion
References
Artificial Neural Networks and Their Application in EEG Signal Classification
1 Introduction
2 Device Development
2.1 Signal Acquisition
2.2 Preprocessing
2.3 Feature Extraction
2.4 Classification
3 Results
3.1 Signal Acquisition Stage
3.2 Classification Stage
4 Conclusions
References
Joint Unsupervised Deep Temporal Clustering for Modeling Human Behavior in Vestibular Dysfunction: A Study of Navigation Pattern
1 Introduction
2 Materials and Methods
2.1 Neuropsychological Assessment Description
2.2 Data Preprocessing
2.3 Fully Unsupervised Learning of Time-Domain Features
3 Experimental Results
4 Conclusion and Future Works
References
Medical Image Segmentation Using Deep Learning: A Survey
1 Introduction
2 Segmentation Architectures
2.1 Fully Convolutional Network (FCN)
2.2 DeepLab Family Models
2.3 U-Net Architecture
2.4 Generative Adversarial Networks (GANs) for Semantic Segmentation
3 Discussion
3.1 The Data Shortage
3.2 The Segmentation Accuracy
4 Conclusion
References
Spatiotemporal Clustering of Human Locomotion Neuropsychological Assessment in Virtual Reality Using Multi-step Model
1 Introduction
2 Materials and Methods
2.1 Neuropsychological Assessment Description
2.2 Data Processing
2.3 The Sequential Multi-step Deep Clustering Approach
3 Experimental Results
4 Conclusion and Future Works
References
The Effectiveness Daubechies Wavelet and Conventional Filters in Denoising EEG Signal
1 Introduction
2 Background
3 Methodology
3.1 Conventional Filters
4 Wavelet Transform (WT)
4.1 The DWT De-noising Technique
4.2 The Criteria Used to Test the Performance of Our Method
5 Results and Discussion
5.1 Band Stop Filters and Band Pass Filters
5.2 Wavelet De-noising Process
6 Conclusion
References
Correction to: The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies
Correction to: Chapter “The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies in: S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, https://doi.org/10.1007/978-3-031-29860-8_82
Correction to: The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies
Correction to: Chapter “The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies in: S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, https://doi.org/10.1007/978-3-031-29860-8_82
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Lecture Notes in Networks and Systems 669
Saad Motahhir Badre Bossoufi Editors
Digital Technologies and Applications Proceedings of ICDTA’23, Fez, Morocco, Volume 2
Lecture Notes in Networks and Systems
669
Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).
Saad Motahhir · Badre Bossoufi Editors
Digital Technologies and Applications Proceedings of ICDTA’23, Fez, Morocco, Volume 2
Editors Saad Motahhir Ecole Nationale des Sciences Appliquées Fez, Morocco
Badre Bossoufi Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez, Morocco
ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-29859-2 ISBN 978-3-031-29860-8 (eBook) https://doi.org/10.1007/978-3-031-29860-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
We are honored to dedicate the proceedings of ICDTA’23 to all the participants and committees of ICDTA’23.
Preface
This volume contains the second section of the written versions of most of the contributions presented during the conference of ICDTA’23. The conference provided a setting for discussing recent developments in a wide variety of topics including artificial intelligence, Internet of Things, embedded systems, network technology, digital transformation and their applications, in several areas such as Industry 4.0, renewable energy, mechatronics and digital healthcare. The conference has been a good opportunity for participants from various destinations to present and discuss topics in their respective research areas. ICDTA’23 Conference tends to collect the latest research results and applications on digital technologies and their applications. It includes a selection of 200 papers submitted to the conference from universities and industries all over the world. This volume includes half of the accepted papers. All of the accepted papers were subjected to strict peer-reviewing by 2–4 expert referees. The papers have been selected for this volume because of their quality and their relevance to the conference. We would like to express our sincere appreciation to all authors for their contributions to this book. We would like to extend our thanks to all the referees for their constructive comments on all papers; especially, we would like to thank Organizing Committee for their hardworking. Finally, we would like to thank the Springer publications for producing this volume. S. Motahhir B. Bossoufi
Organization
Honorary Committee Miraoui Abdellatif, Minister of Higher Education, Scientific Research and Innovation Ijjaali Mustapha, President of SMBA University, Fez Benlmlih Mohammed, Dean of Faculty of Sciences FSDM, Fez
General Chairs Saad Motahhir, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco Badre Bossoufi, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Organizing Committee Mohammed Karim, FSDM, USMBA, Fez, Morocco Chakib El Bekkali, FSDM, USMBA, Fez, Morocco El Ghzizal Abdelaziz, EST, USMBA, Fez, Morocco Hala Alami Aroussi, EST, UMP, Oujda, Morocco Manale Boderbala, FSDM, USMBA, Fez, Morocco Nada Zinelabidine, FSDM, USMBA, Fez, Morocco Yassine Zahraoui, FSDM, USMBA, Fez, Morocco Hajar Saikouk, Euromed, Fez, Morocco Chakib Alaoui, Euromed, Fez, Morocco Aissa Chouder, Université Mohamed Boudif-M’sila, Algeria Aziza Benaboud, Royal Navy School, Casablanca, Morocco Aboubakr El Hamoumi, ENSA, Abdelmalek Essaadi University, Morocco Ghassane Aniba, EMI, Rabat, Morocco Salah Eddine Hebaz, CY Cergy Paris Université, France
Speakers • Speaker 1: Marcelo Godoy Simões (Professor in Electrical Power Systems, University of Vaasa, Finland) “Comparison of the Technology Revolution of the 20th Century with the Energy Revolution of the 21st Century” • Speaker 2: Rachid Yazami (KVI PTE LTD., Singapore) “Lithium ion batteries challenges in the electromobility transition”
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Organization
• Speaker 3: Imed Romdhani (Edinburgh Napier University, UK) “Edge Computing Applications” • Speaker 4: Nayyar Anand (Duy Tan University, Da Nang, Vietnam) “Autonomous Vehicles: Future Revolution in Transportation” • Speaker 5: Sundarapandian Vaidyanathan (Vel Tech University, Avadi, Chennai, Tamil Nadu, India) “Chaotic Systems: Modelling, Circuit Design, FPGA Realization and Engineering Applications” • Speaker 6: Mohamed Benbouzid (IEEE Fellow, University of Brest, France) “Grid connection of marine renewable energies: Issues and challenges” • Speaker 7: Josep M. Guerrero (Villum Investigator, Director Center Research Microgrids, IEEE Fellow, Aalborg University, Denmark) “Neuroscience for Engineers” • Speaker 8: Vincenzo Piuri (President of the IEEE Systems Council and IEEE Fellow, University of Milan, Italy) “Artificial Intelligence in Cloud/Fog/Edge Computing and Internet-of-Things” • Speaker 9: Pierluigi Siano (Editor for the Power & Energy Society Section of IEEE Access, University of Salerno, Salerno, Italy “Optimization of Smart Energy Communities”
Technical Program Committee Abdelhak Boulalam, Morocco Haddouch Khalid, Morocco Abdelhalim Zaoui, Algeria Abdellah Mechaqrane, Morocco Abdun Mahmood, Australia Adnane El Attar, Qatar Adrian Nedelcu, Romania Afef Bennani-Ben Abdelghani, Tunisia Agnieszka Konys, Poland Ahmed Lagrioui, Morocco Ait Madi Abdessalam, Morocco Alaoui Chakib, Morocco Alem Said, Algeria Alfidi Mohamed, Morocco Al-Greer Maher, UK Allouhi Amine, Morocco Altınkök Necat, Turkey Amer Ghada, Egypt Amine Lahyani, Tunisia Anand Nayyar, Vietnam André Meurer, UK Andrea Hildebrandt, Germany Andreas Demetriou, Cyprus Andreea Elleathey, Romania
Organization
Angelo Beltran Jr., Philippines Antonio Andrade, Brasil Antonio M. R. C. Grilo, Portugal Aziz Derouich, Morocco Aziza Benaboud, Switzerland Bailo Mamadou Camara, France Barkat Said, Algeria Bayu Adhi Tama, Korea Belmili Hocine, Algeria Bennani Saad, Morocco Benyamin Ahmadnia, USA Bousseta Mohammed, Morocco Castillo-Calzadilla Tony, Venezuela Chouder Aissa, Algeria Chrifi Alaoui Meriem, France Christian Nichita, France Damien Ernst, Belgium Dekhane Azzeddine, Algeria Denis Dochan, Belgium Denizar Martins, Brasil Djerioui Ali, Algeria Kumar K., India Dragan Mlakic, Bosnia and Herzegovina Driss Youssfi, Morocco Dutta Nabanita, India Edgardo Daniel Castronuovo, Spain El Ghzizal Abdelaziz, Morocco El Hamdani Wafae, Morocco El Mostapha Ziani, Morocco Eltamaly Ali, Egypt Fatima Errahimi, Morocco Gabriel Iana, Romania Gabriel Orsini, Germany Ghedamsi Kaci, Algeria Goran Krajacic, Bulgaria Hajji Bekkay, Morocco Hassan Mahmoudi, Morocco Hassane Zahboune, Morocco Hasseine Linda, Algeria Himrane Nabil, Algeria Houda Ben Attia Sethom, Tunisia Imad Manssouri, Morocco Jamal Bouchnaif, Morocco Jihen Arbi-Ziani, Tunisia Jing Dai, France
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Organization
Johan Gyselinck, Belgium Kaarin J. Anstey, Australia Kandoussi Zineb, Morocco Karan Mitra, Sweden Ke-Lin Du, Canada Khalid Grari, Morocco Khireddine Karim, Algeria Kumar Donta Praveen, Estonia Kumar Yelamarthi, USA Livinti Petru, Romania Luminita Mirela Constantinescu, Romania ˆ Morocco Mahdaoui Mustapha, Majid Ali, Pakistan Malvoni Maria, Greece Manel Jebli-Ben Ghorbal, Tunisia Marco Picone, Italy Maria Drakaki, Greece Marko Horvat, Croatia Md Multan Biswas, USA Mekhilef Saad, Malaysia Michael Emmanuel, USA Michele Barsant, Italy Mihai Oprescu, Romania Mohamed Barara, France Mohamed J. M. A. Rasul, Sri Lanka Mohamed Karim, Morocco Mohamed Nabil Kebbaj, Morocco Mohammed Benbrahim, Morocco Mohammed Larbi Elhafyani, Morocco Mostafa Bouzi, Morocco Mourad Loucif, Algeria Muhammad Affan, Pakistan Mustafa Sahin, Turkey Mustapha El Gouri, Morocco Mustapha Habib Algeria Nabil El Akkad, Morocco Nicolas Waldhoff, France Nicu Bizon, Romania Nikolaos Pitropakis, UK Nikolovski Srete, Serbia Nordine Chaibi, Morocco Norhanisah Baharudin, Malaysia Olivier Debleker, Belgium Oughdir Lahcen, Morocco Padmanaban Sanjeevikumar, Denmark
Organization
Partha Mishra, USA Petar Varbanov, Bulgaria Prabaharan Nataraj, India Rabbanakul Avezov, Uzbekistan Rajanand Patnaik Narasipuram, India Roberto Colom, Spain Saadani Rachid, Morocco Said Hamdaoui, Morocco Saikouk Hajar, Morocco Samia El Haddouti, CNRST, Morocco Samuel Greiff, Luxembourg Samundra Gurung, Thailand Sayyouri Mhamed, Morocco Silvia Nittel, USA Silviu Ionita, Romania Sondes Skander-Mustapha, Tunisia Thomas R. Coyle, USA Tome Eftimov, Slovenia Tunku Muhammad Nizar Tunku Mansur, Malaysia Vaibhav Gandhi, UK Vana Kalogeraki, Greece Vincent Sir-Coulomb, France Wagner Brignol, Brasil Walid Issa, UK Wilson Vallejos, Ecuador Wissem Naouar, Tunisia Yang Du, China Yann-Gaël Guéhéneuc, Canada Yassine Chaibi, Morocco Youssef Zaz, Morocco Zekry Abdelhalim, Egypt Zorig Abdelmalek, Morocco S. G. Srivani, India Mohammad Jafar Hadidian Moghaddam, Australia Nilufar R. Avezova, Uzbekistan Ali Eltamaly Saudi, Arabia Hamid Reza Baghaee, Iran Josep M. Guerrero, Denmark Pierluigi Siano, Italy Mustapha Errouh, Morocco Souad El Khattabi, Morocco Loubna Laaouina, Morocco Fatima Bennouna, Morocco Hicham Hihi, Morocco Chetioui Kaouthar, Morocco
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Organization
Adadi Amina, Morocco Idrissi Aydi Ouafae, Morocco Hraoui Said, Morocco Berrada Mohammed, Morocco Mohamed Salhi, ENSAM, Meknes, Morocco Anass Cherrafi, ENSAM, Meknes, Morocco Didi Abdessamad, Morocco Ali Yahyaoui, Morocco Ahmed Bouraiou, Algeria Merabet Boualem, Algeria Driss Amegouz, Morocco Radeouane El Bahjaoui, Morocco Florin-Marian Birleanu, Romania El-Kaber Hachem, Morocco Tnani Slim, France Abdelghani El Ougli, Morocco Bri Seddik, Morocco Boukili Bensalem, Morocco Abdelilah Chalh, Morocco Amine Ihedrane, Morocco Abdelouahed Sabri, Morocco Richard Millham, South Africa Hanan Halaq, Morocco Halkhams Imane, Morocco Mohamed Naji, Morocco Mostafa Azizi, Morocco Mohamed Saber, Morocco Badreddine Lahfaoui, Morocco Nacer Msirdi, France Houcine Chafouk, France Adil Essalmi, Morocco Taibi Djamel, Algeria Livinti Petru, Romania Silviu Ionita, Romania Gabriel Iana, Romania Brahima Dakyo, France Alireza Payman, France Gerard Deepak, India Florien Birleanu, Romania Younes Morabit, Morocco Youssef El Afou, Morocco Ali Ahitouf, Morocco Ayoub El Bakri, Morocco Taoufik Ouchbel, Morocco Gulshan Kumar, India
Organization
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Soufiyane Benzaouia, France Ikram El Azami, Morocco Damien Ambrosetti, Morocco Rudan Xiao, France Youness Mehdaoui, Morocco Bennani Mohammed-Taj, FSDM, USMBA, Fez, Morocco El-Fazazy Khalid, FSDM, USMBA, Fez, Morocco Mustapha Bouakkaz, Algeria Rachid El Alami, FSDM, USMBA, Fez, Morocco Ahmed Zellou, ENSIAS, Morocco Mostafa el Mallahi, USMBA, Morocco Hicham Amakdouf, USMBA, Fez, Morocco Hassan Qjidaa, USMBA, Fez, Morocco Omar Diouri, USMBA, Fez, Morocco Soufyane Benzaouia, Amiens, France Marouane El Azaoui, ENSA, Agadir, Morocco Abdelhamid Bou-El-Harmel, USMBA, Fez, Morocco Youssef Cheddadi, USMBA, Fez, Morocco Othmane Zemzoum, USMBA, Fez, Morocco Abdelmajid Bybi, Mohammed V, Rabat, Morocco Karim El Khadiri, USMBA, Fez, Morocco Kenz Ait El Kadi, Morocco Aicha Wahabi, Morocco Mhamed Zineddine, Euromed, Fez, Morocco Anass Bekkouri, Euromed, Fez, Morocco Achraf Berrajaa, Euromed, Fez, Morocco Hiba Chougrad, USMBA, Fez, Morocco Jaouad Boumhidi, USMBA, Fez, Morocco Ismail Boumhidi, USMBA, Fez, Morocco El Habib Nfaoui, USMBA, Fez, Morocco Salm El Hajjami, USMBA, Fez, Morocco Ghassan Anniba, EMI, Rabat, Morocco Hassan Moustabchir, USMBA, Morocco Tariq Riouch, Morocco Abdelali Ed-Dahhak, Morocco Khalil Kasmi, Morocco Mohammed Naji, Morocco Jaouad Anissi, Morocco Faiza Dib, Morocco Nabil Benaya, Morocco Oumayma Banouar, Morocco Mohammed El Ghzaoui, Morocco Jabir Arif, Morocco Othmane Zamzoum, Morocco Saloua Bensiali, Hassan II Institute of Agronomy and Veterinary Medicine, Morocco
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Organization
Naoufel El Youssfi, USMBA, Fez, Morocco Abdellah El Barkany, FST, USMBA, Morocco Nour-Eddine Joudar, ENSAM, Rabat, Morocco Karim El Moutaouakil, USMBA, Morocco Jamal Riffi, USMBA, Morocco Ilyas Lahlouh, Morocco Osamah Ibrahim Khalaf, Iraq Mouncef Filali Bouami, Morocco Radouane Elbahjaoui, Morocco Rachid El Bachtiri, Morocco Mohammed Boughaleb, Morocco Boukil Naoual, Morocco Faquir Sanaa, Morocco Hemi Hanane, Morocco Ben Sassi Hicham, Morocco Asmae Abadi, Morocco Mohamed El Malki, Morocco Adil Kenzi, Morocco Abdellatif Ezzouhairi, Morocco Hamid Tairi, Morocco Adil Kenzi, USMBA, Fez, Morocco Abdellatif Ezzouhairi, USMBA, Fez, Morocco Khaoula Oulidi Omali, Morocco Meriem Hnida, ESI, Rabat, Morocco Giovanni Spagnuolo, University of Salerno, Italy Adnane Saoud, University of California, Berkeley
Acknowledgments
We request the pleasure of thanking you for taking part in the third edition of the International Conference on Digital Technologies and Applications ICDTA’23. We are very grateful for your support, so thank you everyone for bringing your expertise and experience around the conference and engaging in such fruitful, constructive and open exchanges throughout the two days of the ICDTA’23 conference. We would like to extend our deepest thanks and gratitude to all the speakers for accepting to join us from different countries. Thank you for being such wonderful persons and speakers. Again, thanks for sharing your insight, knowledge and experience. Of course, this event could not be that successful without the effort of the organizing and technical program committees. Therefore, Pr. Badre and I would like to express our sincere appreciation to all of you who generously helped us. We would like to especially thank all the participants for the confidence and trust you have placed in our conference. We hope we lived up to your highest expectations. Our humble acknowledgment would be incomplete without thanking our biggest source of support; therefore, our deepest gratitude goes to Prof. Miraoui Abdellatif, the Minister of Higher Education, Scientific Research and Innovation. And of course, the same goes for Prof. Mustapha Ijjaali, the President of Sidi Mohammed Ben Abdellah University as well as Prof. Lahrach Abderrahim, the Director of the National School of applied sciences, Prof. Belmlih Mohammed, the Dean of the Faculty of Science, Prof. Abdelmajid Saka, Deputy Director of the National School of applied sciences, Prof. Elhassouni Mohammed, the Vice Dean of Faculty of Sciences, and the list is long of course. Thank you all for your support and for being all-time, all set up to host such scientific events. S. Motahhir B. Bossoufi
Contents
Artificial Intelligence, Machine Learning and Data Analysis Study on Determining Household Poverty Status: Evidence from SVM Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . El Aachab Yassine and Kaicer Mohammed The Generation of Power from a Solar System Using a Variable Step Size P&O (VSS P&O) and Comparing It to a Grey Wolf Metaheuristic Algorithm (GWO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Zerouali, Abdelghani El Ougli, and Belkassem Tidhaf Bio-Inspired Optimisation Algorithm for Congestion Control in Computer Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Richard Nana Nketsiah, Israel Edem Agbehadji, Richard C. Millham, and Emmanuel Freeman
3
11
21
Fingerprint Classification Models Based on Bioinspired Optimization Algorithm: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alrasheed Mustafa, Richard Millham, and Hongji Yang
33
Inspired Nature Meta-Heuristics Minimizing Total Tardiness for Manufacturing Flow Shop Scheduling under Setup Time Constraint . . . . . . Omar Nejjarou, Said Aqil, and Mohamed Lahby
44
Punishing the Unpunishable: A Liability Framework for Artificial Intelligence Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rushil Chandra and Karun Sanjaya
55
Acceptability Aspects of Artificial Intelligence in Morocco: Managerial and Theoretical Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marouane Mkik and Salwa Mkik
65
The Use of Learning Algorithms in Business Intelligence Tools to Enhance Customer Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jai Keerthy Chowlur Revanna and Emine Arikan
75
Improving the Performance of Public Transport Bus Services: Analytics Approach to Revenue Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Amine Ben Rabia and Adil Bellabdaoui
85
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Multi-level Governance and Digitalization in Climate Change: A Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ihyani Malik, Andi Luhur Prianto, Nur Ismi Roni, Arifeen Yama, and Tawakkal Baharuddin Machine Learning Approach for Reference Evapotranspiration Estimation in the Region of Fes, Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nisrine Lachgar, Achraf Berrajaa, Moad Essabbar, and Hajar Saikouk Data Augmentation and Deep Learning Applied for Traffic Signs Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ismail Nasri, Mohammed Karrouchi, Abdelhafid Messaoudi, Kamal Kassmi, and Soufian Zerouali Arabic Speech Emotion Recognition Using Deep Neural Network . . . . . . . . . . . Omayma Mahmoudi and Mouncef Filali Bouami A Chatbot’s Architecture for Customized Services for Developmental and Mental Health Disorders: Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Ezzahrae El Rhatassi, Btihal El Ghali, and Najima Daoudi Machine Learning with Reinforcement for Optimal and Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Rahioui, Mohammed El Ghzaoui, Mohammed Ali Tahri Jouti, Mohammed Ouazzani Jamil, and Hassan Qjidaa Incorporating Deep Learning and Sentiment Analysis on Twitter Data to Improve Tourism Demand Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Houria Laaroussi, Fatima Guerouate, and Mohamed Sbihi A Deep Learning Model for Diabetic Retinopathy Classification . . . . . . . . . . . . Mohamed Touati, Laurent Nana, and Faouzi Benzarti A Forecasting Model for the Energy Requirements of an Office Building Based on Energy Modeling and Machine Learning Models – A Case Study of Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Khaoula Boumais and Fayçal Messaoudi
95
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Internet of Things, Blockchain and Security and Network Technology Blockchain Technology for Audit Firms: Perspectives and Challenges . . . . . . . . Tilila Taj
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Blockchain Security Attacks: A Review Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dahhak Hajar, Imane Hilal, and Nadia Afifi Classification of Coordinators’ Limitations in Cyber-Physical Production System Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdelaziz Ouazzani-Chahidi, Jose-Fernando Jimenez, Lamia Berrah, and Abdellatif Loukili
xxi
191
200
Cryptography in Distributed Ledger Technologies from a Layered Perspective: A State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Barj, Aafaf Ouaddah, and Abdellatif Mezrioui
210
New Topology of WSN for Smart Irrigation with Low Consumption and Long Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yassine Ayat, Ali El Moussati, Mohammed Benzaouia, and Ismail Mir
221
A Literature Review of Internet of Vehicle Based Blockchain . . . . . . . . . . . . . . . Fatima Zohra Fassi Fihri, Mohammed Benbrahim, and Mohammed Nabil Kabbaj
232
Tiny Machine Learning for IoT and eHealth Applications: Epileptic Seizure Prediction Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Ould-Elhassen Aoueileyine
242
A Simulation Framework for IoT Networks Intrusion and Penetration Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Khalil Ben Kalboussi, Farah Barika Ktata, and Ikram Amous
252
A New Four Ports Multiple Input Multiple Output Antenna with High Isolation for 5G Mobile Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nour El Houda Nasri, Mohammed El Ghzaoui, Mohammed Fattah, Mohammed Ouazzani Jamil, and Hassan Qjidaa Isolation Improvement Using a Protruded Ground Structure in a 2 * 2 MIMO Antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aicha Mchbal, Naima Amar Touhami, and Abdelhafid Marroun A Novel Two-Port MIMO Configuration in Dual Band 26/38 GHz with High Isolation in the Ground Plane for 5G Applications . . . . . . . . . . . . . . . Es-saleh Anouar, Bendaoued Mohammed, Lakrit Soufian, Atounti Mohamed, and Faize Ahmed
264
272
278
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Contents
Modeling and Analysis of Short Distance Terahertz Communication Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serghini Elaage, Mohammed El Ghzaoui, Nabil Mrani, Rachid El Alami, Ali El Alami, Mohammed Ouazzani Jamil, and Hassan Qjidaa Android-Based Attendance Management System . . . . . . . . . . . . . . . . . . . . . . . . . . Nabil Ababaou, Ayyad Maafiri, Ababou Mohamed, and Mohamed El Mohadab Fano Resonance in a Defective Network Formed by Helmholtz Resonators with Extended Necks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed El Malki, Nelson Pereira, and Senentxu Lanceros-Mendez
289
298
308
Digital Transformation and E-Learning The Digitalization of the Moroccan Higher Education Sector in the Era of the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hammouch Najoua, Sofia Alami, Nouri Mehdi, Sarra Charkaoui, Marwa Eljai, and Abdellatif Marghich Heritage Preservation, a Specific Territorial Resource in the Era of the Smart City: The Role of Innovation and Digital Technology . . . . . . . . . . . Saarra Boutahar and Bouchra Debbagh Media and Cyberbullying in Political Communication Among Nigerians: Implications to Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ejaita Abugor Okpako, Emmanuel Okeahialam Ikpegbu, and Stella Chinye Chiemeke Hospital Performance and Managerial and Digital Innovation: Impact of Universal Health Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sadki Tarik, Jalal Chaimae, and Nmili Mohamed Teachers’ Motivational Intervention Towards EFL Learners’ Oral Performance: A Quantitative Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vahid Norouzi Larsari, Farahman Farrokhi, Raju Dhuli, Hossein Chenari, and Maryann H. Lanuza BERTopic and LDA, Which Topic Modeling Technique to Extract Relevant Topics from Videos in the Context of Massive Open Online Courses (MOOCs)? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kawtar Najmani, Lahbib Ajallouda, El Habib Benlahmar, Nawal Sael, and Ahmed Zellou
323
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343
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Blockchain-Based Higher Educational Data Management: Enhanced Architecture for Data Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Divya and G. Suganeshwari
382
Gender, Area, and School Streams Factors on Design Perception and Experiences in Educational Games as Learning Environments . . . . . . . . . . . Lilian Lee Shiau Gee and Jasni Dolah
392
Impact of Integrating Digital Educational Resources on Student Motivation in Learning Nuclear Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . Aziz Taoussi, Khalid El Khattabi, and Ahmida Chikhaoui
403
Quality Assurance and Quality Practices in Teaching and Learning Data Sciences and Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yman Chemlal
413
Perceptions of Filipino Pre-service Teachers Toward the Quality of Online Learning During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . Unie Bagares, Ericson Alieto, Junette Buslon, Ricardo Somblingo, and Ryan Cabangcala E-Learning and Education 4.0: Revolution in Education of 21st Century . . . . . . Anushree Pandey
421
431
Image and Information Processing Optimal Color Image Watermarking Based on DWT-SVD Using an Arithmetic Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmed Bencherqui, Mohammed Tamimi, Mohamed Amine Tahiri, Hicham Karmouni, Mohammed Alfidi, Mohammed Ouazzani Jamil, Hassan Qjidaa, and Mhamed Sayyouri An Overview of the Impact of Partial Occlusions on Automatic Facial Expression Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdelaali Kemmou, Adil El Makrani, and Ikram El Azami Face Recognition: A Mini-Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imane Badri and Mhamed Sayyouri Improved Fusion of SVD and Relevance Weighted LDA Algorithms via Symmetric Sum-Based Rules for Face Recognition . . . . . . . . . . . . . . . . . . . . . Ayyad Maafiri, Khalid Chougdali, Ahmed Bir-Jmel, and Nabil Ababou
441
451
463
472
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Image Captioning: An Understanding Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . My Abdelouahed Sabri, Hamza El Madhoune, Chaimae Zouitni, and Abdellah Aarab
482
A Comparison Between LSTM and Transformers for Image Captioning . . . . . . Chaimae Zouitni, My Abdelouahed Sabri, and Abdellah Aarab
492
Multi-platform Framework for Pattern and Image Processing . . . . . . . . . . . . . . . Ramiro Pedro Lura-Murillo, Fred Torres-Cruz, Jhon Richard Huanca Suaquita, Gladys Marleny Auquitias-Condori, and Alain Paul Herrera-Urtiaga
501
Random Forest for Semantic Segmentation Using Pre Trained CNN (VGG16) Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zahra Faska, Lahbib Khrissi, Khalid Haddouch, and Nabil El Akkad The Polygonal Technique for Constructing Islamic Geometric Patterns . . . . . . . Aziz Khamjane, Zouhair Ouazene, Abderrazak Taime, Imad Badi, Khalid El Fazazy, and Rachid Benslimane LabVIEW Implementation of Bio-signal Zero-Watermarking Using Tchebichef Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Achraf Daoui, Mohamed Yamni, Hicham Karmouni, Mhamed Sayyouri, Hassan Qjidaa, and Mohammed Ouazzani Jamil A Robust Approach for Arabic Document Images Segmentation and Indexation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . My Abdelouahed Sabri, Assia Ennouni, and Abdellah Aarab New Arabic Word Embeddings to Improve NLP Applications . . . . . . . . . . . . . . . Nabil Ababaou, Ayyad Maafiri, Mazroui Azzeddine, and Mohamed El Mohadab
510
521
531
540
550
Advanced Technologies in Energy and Electrical Engineering Cascaded Control Based on Super Twisting Algorithm for DC-DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Benzaouia, Bekkay Hajji, and Abdelhamid Rabhi
561
Optimisation and Performance Evaluation of a Standalone Renewable Energy System in Congo-Brazzaville . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rolains Golchimard Elenga and Li Zhu
570
Contents
Bearing Ball Fault Diagnosis of an Induction Machine by Using the Hilbert Transform and the Performance of Intelligent Control . . . . . . . . . . . . Abderrahman El Idrissi, Aziz Derouich, Said Mahfoud, Najib El Ouanjli, and Ahmed Chantoufi Control of a Full-Bridges Five Levels Inverter: Experimental Validation . . . . . . Nabil Saidani, Rachid El Bachtiri, Abdelaziz Fri, and Karima El Hammoumi
xxv
580
590
Energy Efficiency Model-Based Predictive Maintenance for Induction Motor Fault Prediction Using Digital Twin Concept . . . . . . . . . . . . . . . . . . . . . . . Adamou Amadou Adamou and Chakib Alaoui
600
Feasibility Study of PV-Powered Hydrogen Vehicles in Morocco: An Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhor El Hallaoui and Chakib Alaoui
611
Comparative Study of Optimal Frequency Control in a Microgrid via a PID and (1 + PD)-PID Controllers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boris Arnaud Notchum Deffo and Anass Bakouri
621
Grid-Connected PV System Simulation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meryem Meliani, Abdellah El Barkany, Ikram El Abbassi, and Rafik Absi Toward Smarter Power Transformers in Microgrids: A Multi-agent Reinforcement Learning for Diagnostic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oussama Laayati, Nabil El-Bazi, Hicham El Hadraoui, Chouaib Ennawaoui, Ahmed Chebak, and Mostafa Bouzi Energy Management and Voltage Control of Standalone DC Microgrid with Energy Storage and Modified P&O MPPT Algorithm . . . . . . . . . . . . . . . . . Tariq Limouni, Reda Yaagoubi, Khalid Bouziane, Khalid Guissi, and El Houssain Baali Experimental Performance Study of an Isolated PV Water Pumping System Used for Agricultural Irrigation in Zagora-Morocco Semi-arid Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zakaria Abousserhane, Ahmed Abbou, and Hicham Bouzakri Analysis and Comparison of Mathematical Models PV Array Configurations (Series, Parallel, Series-Parallel, Bridge-Link and Total-Cross-Tied) Under Various Partial Shading Conditions . . . . . . . . . . . . Mohcine Abouyaakoub and Hicham Hihi
632
640
650
660
672
xxvi
Contents
Ecological Hammam in Fez: Design of a Combined Biomass and Solar Thermal Energy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yassine Anigrou
684
A Mathematical Model for an Optimal PV System with Battery and Hydrogen Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hanane El Bakkali, Mostafa Derrhi, and Mustapha Ait Rami
697
Application of Superconducting Magnet Energy Storage to Improve DFIG Behavior Under Sag Voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tariq Riouch and Abdelilah Byou
707
Comparative Analysis of Predictive and Direct Power Control Strategies for PMSG-Based WECS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Btissam Majout, Badre Bossoufi, Mohammed Karim, Marian Gaiceanu, Petru Livinti, Zakaria El Zair Laggoun, Ikram Saady, and Houda El Alami Intelligent Direct Power Control Based on the Neural Super-Twisting Sliding Mode Controller of a DFIG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mourad Yessef, Badre Bossoufi, Mohammed Taoussi, Habib Benbouhenni, Ahmed Lagrioui, and Hamid Chojaa Deadbeat Predictive Control Strategy of a Wind System Based on the DFIG Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Houda El Alami, Badre Bossoufi, El Mahfoud Mohammed, Majout Btissam, and Manale Bouderbala Implementation and Demonstration of Threat Over CAN Bus for Automotive Embedded System Equipped with ADAS . . . . . . . . . . . . . . . . . . Soufian Zerouali, Abdelhafid Messaoudi, Abdelhamid Rabhi, Mohammed Karrouchi, and Ismail Nasri Resource Allocation of a Modular ANN Implementation Using Lab View and FPGA Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Noura Baccar, Souha Hammi, and Hichem Kallel Digital vs Analog Low Dropout Regulators a Comparative Study . . . . . . . . . . . . Said El Mouzouade, Karim El Khadiri, Hassan Qjidaa, Mouhammed Ouazzani Jamil, and Ahmed Tahiri A low-cost Platform for an Environmental Smart Agriculture Monitoring System Based on Arduino and Renewable Energies . . . . . . . . . . . . . Kamilia Mounich, Aicha Wahabi, Omari Lhaj El Hachemi, Youssef Mejdoub, and Mohammed Chafi
715
726
736
748
757
767
776
Contents
Dynamic WPT Chargers: State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmed Hamed, Hassan El Fadil, Abdellah Lassioui, Soukaina. Nady, and Sidina El Jeilani
xxvii
785
Backstepping-Based Control of Dc-Dc V2X Charger for Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hicham Bella, Aziz Rachid, and Ahmed Abbou
796
A Genetic-Based Algorithm for Commodity Selection and Full Truckload Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karim El Bouyahyiouy and Adil Bellabdaoui
806
Mechatronic, Industry 4.0 and Control System The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmaa Benhmama and Yasmina Bennis Bennani Defining and Implementing Predictive Maintenance Based on Artificial Intelligence for Rotating Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . El Mahdi Bouyahrouzi, Ali El Kihel, Yousra El Kihel, and Soufiane Embarki Ensuring the Connection Between Physical and Virtual Models in the Context of Digital Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Abadi, Asmae Abadi, Chaimae Abadi, and Hussain Ben-Azza Smooth Collision-Free Trajectory Planner for AMR Based on B-spline Interpolation and I-GWO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ayoub Idtaleb and Mostafa Bouzi Direct Torque Control for Doubly Fed Induction Motor Driven Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmed Chantoufi, Aziz Derouich, Najib El Ouanjli, Said Mahfoud, Abderrahman El Idrissi, and Mohammed Taoussi Pressure Control of a Compressed Air System Based on Hardware in the Loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jefferson Porras, Luis Navarrete, Jessica N. Castillo, and Luigi O. Freire Robotic Arm Control Using Dynamic Model Linearization and Model Predictive Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karra Khalid, Aziz Derouich, and Mahfoud Said
819
828
839
850
862
872
881
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Inter-organizational Information System and Efficiency of the Tourism Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kacem Salmi and Aziz Hmioui
893
Balanced Scorecard as Intelligent Management Control Tools: Case of the Pharmaceutical Supply Chain of a University Hospital Center . . . . . . . . . T. Sadki, C. Jalal, and M. Nmili
903
Prediction and Optimization of Uber Services: A Case Study of Morocco . . . . . Marwan Abdelshafy and Malek Sarhani
914
Optimization of Inventory Management: A Literature Review . . . . . . . . . . . . . . . Nabila Bouti, Ibrahim Boukallal, and Fatima El Khoukhi
923
Digital Healthcare Brain Tumor Segmentation Using Active Contour on Model Mumford-Shah Algorithm, Simple Standard Deviation, and Mathematical Morphology in Medical Images MRI . . . . . . . . . . . . . . . . . . . . Bara Samir and Hammouch Ahmed Residual Neural Network Architecture for Identifying Vestibular Disease Based on Head Kinematic Characteristics (Velocity) . . . . . . . . . . . . . . . Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, and Bernard Cohen Artificial Neural Networks and Their Application in EEG Signal Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eddy Corrales, Byron P. Corrales, Luigi O. Freire, and María J. Benalcázar Joint Unsupervised Deep Temporal Clustering for Modeling Human Behavior in Vestibular Dysfunction: A Study of Navigation Pattern . . . . . . . . . . Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Noureddine Rahmoun, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, and Bernard Cohen Medical Image Segmentation Using Deep Learning: A Survey . . . . . . . . . . . . . . Abdelwahid Oubaalla, Hicham El Moubtahij, and Nabil El Akkad
937
946
953
966
974
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Spatiotemporal Clustering of Human Locomotion Neuropsychological Assessment in Virtual Reality Using Multi-step Model . . . . . . . . . . . . . . . . . . . . . Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, and Bernard Cohen The Effectiveness Daubechies Wavelet and Conventional Filters in Denoising EEG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meryem Felja, Asmae Bencheqroune, Mohamed Karim, and Ghita Bennis Correction to: The Impact of Industry 4.0 on the Recruitment Process of Moroccan Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmaa Benhmama and Yasmina Bennis Bennani
xxix
984
991
C1
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001
Artificial Intelligence, Machine Learning and Data Analysis
Study on Determining Household Poverty Status: Evidence from SVM Approach El Aachab Yassine(B) and Kaicer Mohammed Laboratory of Analysis Geometry and Applications, Department of Mathematics, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco [email protected]
Abstract. The objective of this paper is to apply a new approach to identify the household poverty phenomenon. In many studies, we employed traditional methods based on thresholds and weights to identify poverty like a multidimensional approach or monetary approach to identify which households are poor or not poor. In this study, we will resort to one of the new methods of intelligence artificial to predict and classify poor households. Using all data extracted from the national household living standards survey of the household survey division, Higher Commissariat of Planning Morocco, in language R version 4.2.2 in windows 7 we find that the best results are given by the Support vector machine linear kernel since it can classify and predict poor households. This support vector machine method used for prediction and classification will help policy and decision-makers to correctly target households suffering from poverty with programs and plans that aim to eliminate household poverty. Keywords: Support vector machine · Poverty · Classification · ¨Prediction · Morocco
1 Introduction Poverty in all of its forms and manifestations creates major structural obstacles that inhibit national economic growth [1]. Poverty is not a self-contained term, and it is a tough reality to comprehend in all of its dimensions since those who dare to speak about it are not always its sufferers [2]. This question has been and continues to be, the subject of much debate between development practitioners and social science students[3]. Experts and academics have proposed many definitions over the years[4]. Poverty, for example, could be defined as a general lack of command over commodities or as a lack of command over specific basic goods (e.g., food and housing)[5]. More broadly, it is argued that poverty is defined as a lack of “capability” to function in a given society (Sen,1985) [6]. All of these definitions of poverty point to it as a state in which a reasonable standard of living is not attained [7]. The World Bank has created a synthesis of the various positions and has defined poverty as the absence or inability to achieve a socially acceptable standard of living [8]. The variety of methods used to analyze poverty shows the relativity of this concept. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 3–10, 2023. https://doi.org/10.1007/978-3-031-29860-8_1
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The Sustainable Development Goals’ (SDGs) number one target, eradicating poverty in all of its forms, remains one of humanity’s greatest problems. Nearly half of the population in developing countries lived on less than $1.25 per day in 1990 [9]. In 2015, this figure declined to 14%. The number of people living in extreme poverty worldwide has decreased by more than half, from 1.9 billion in 1990 to 836 million in 2015, with the majority of progress occurring since 2000. Far too many individuals continue to struggle to meet even the most basic human requirements [10]. Like the international scale, both income poverty and multidimensional poverty are rapidly declining in Morocco, this downward trend shows great progress in the area of living conditions. The prevalence of multidimensional poverty has been reduced, in the period 2001–2014, from 25.0% to 8.2% at the national level, from 9.1% to 2.0% in urban areas, and from 44.6% to 17.7% in rural areas [11]. According to the study, a support vector machine can be utilized to quantify a wide variety of poverty and human component well-being [12]. According to the studies, a support vector system can be utilized to evaluate a range of characteristics of poverty and well-being[13]. Although policymakers and decision-makers have high hopes, there aren’t enough downstream applications just yet. One impediment, we believe, is the difficulty in conveying many aspects of the scientific method..
2 Support Vector Machine The Support Vector Machine (SVM) is a method for supervised learning that combines principles from both statistical learning theory and machine learning. Developed by Vapnik in 1993, it focuses on finding the optimal separator that separates two groups of data while maximizing the margin between them [14] [15]. The SVM approach starts with a linear function represented by f , where x represents the input vector in Rn , w represents the parameter vector (or weight) in Rn , and b represents the constant to be computed. f (x) =< w, x > +b
(1)
The weights norm w is minimized to verify the function’s platitude. The problem, therefore, becomes one of lowering this standard by ensuring that errors are fewer than ε and are able to record. 2 1 (2) min w 2 Under the constraint |yi − (< w, xi > +b)| < ε i = 1, . . . N
(3)
I The concept of soft margin is used in this case. It entails integrating slack variables ξi , ξi∗ to create the optimization problem’s restrictions. 2 1 (ξi + ξi∗ ) min w + C 2 N
(4)
i=1
(5)
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3 Data and Application 3.1 Data The data analyzed in this study was obtained from the Morocco National Household Living Standards Survey, conducted by the Household Survey Division of the High Commission for Planning. Before conducting the experiment and analysis, a pre-processing process was carried out, including cleaning, transforming, and filtering the data. Valid data from 12 regions in Morocco for the year 2014 was selected, resulting in a total of 11969 data points and 784 variables. 3.2 Instruments and Software This data analysis was performed using the R language, allowing for all statistical and econometric results to be generated through the R software. 3.3 Result of Support Vector Machine Application with Different Kernel • SVM Linear Kernel
Fig. 1. Confusion matrix Linear Kernel Source: Author’s output using R
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Fig. 2. Metrics Linear Kernel Source: Author’s output using R
• SVM Radial Kernel
Fig. 3. Confusion matrix Radial Kernel Source: Author’s output using R
Fig. 4. Metrics Radial Kernel Source: Author’s output using R
Study on Determining Household Poverty Status
• SVM Polynomial Kernel
Fig. 5. Confusion matrix Radial Kernel Source: Author’s output using R
Fig. 6. Metrics Radial Kernel Source: Author’s output using R
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• SVM Sigmoid Kernel
Fig. 7. Confusion matrix Sigmoid Kernel Source: Author’s output using R
Fig. 8. Metrics Sigmoid Kernel Source: Author’s output using R
4 Discussion and Interpretation of the Results The above Figures display the results of our analysis using the R programming language. The outputs show the confusion matrix and different metrics of the Support Vector Machine (SVM) model for different kernel functions. The purpose of this approach was to evaluate the reliability of the model’s forecasting accuracy and prediction. In the first stage, we tested the model using various kernels to determine its robustness. In the second stage, our goal was to use SVM to predict poverty status (poor or non-poor) from a database.
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The matrix of confusion for the linear SVM prediction displays the comparison of actual and predicted classifications. The matrix has two columns, representing the true classifications of poor (1) and non-poor (0), and two rows representing the predicted classifications. The model correctly identified 8054 households as non-poor, which are represented by the number in the first row and first column. However, it misclassified 37 households as poor, which are represented by the number in the first row and second column. The matrix of confusion for the prediction_svm_radial model shows that 8081 households have been classified as belonging to class 0 (non-poor), while all 297 households were classified as belonging to class 1 (poor). It’s important to note that there are no households that were actually poor but were misclassified as non-poor, as the value in the (2,1) position is 0. This means that the model has a high specificity in its classification of non-poor households, but poor sensitivity in its classification of poor households, as it failed to correctly identify any poor households. The same for the SVM with Sigmoid Kernel. For the true poor classification, the model correctly predicted 260 households as poor, which are represented by the number in the second row and second column. But, it also misclassified 27 households as non-poor, which are represented by the number in the second row and first column. For the Polynomial Kernel. Out of the total 8081 households, 8029 were correctly predicted as non-poor (class 0), while 250 were correctly predicted as poor (class 1). On the other hand, 52 households were wrongly predicted as poor, while 47 were wrongly predicted as non-poor. The results of the SVM predictions are shown in the figures above. The linear kernel had good results in both the prediction of non-poor (class 0) and poor (class 1) households. However, with the radial and sigmoid kernels, the model wasn’t effective in correctly identifying poor households. The polynomial kernel, on the other hand, had good results for both class 0 and class 1 households, with an overall accuracy of 0.9882 and a Cohen’s Kappa of 0.8286. We have analyzed the results of the SVM predictions using R programming and found that the performance of the model varies based on the choice of the kernel. The polynomial and linear kernels proved to be the best for classifying poor and non-poor households. Among these, the linear kernel showed the most accurate results. The other kernels, however, were not effective in correctly predicting the non-poor households.
5 Conclusion In our analysis of the output of our SVM predictions, we can see that the choice of the kernel has a significant impact on the results. The best kernel for classifying poor and non-poor households is the linear kernel. This is evident in the results of our database, which shows high accuracy and Cohen’s kappa value for the linear kernel. However, the radial and polynomial kernels did not perform as well in classifying poor households. As a result, it is important to carefully choose the appropriate kernel for SVM predictions. Our results, combined with the results obtained from the monetary approach-based classification of the households in the database, demonstrate the effectiveness of the support vector machine in this task.
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In conclusion, our results show that the SVM method can provide good results for the classification of households as poor or non-poor when the linear kernel is used.
References 1. Fisher, G.: The development and history of the poverty thresholds. Soc. Secur. Bull. 55, 3–14 (1992) 2. Lechheb, H., Ouakil, H., Jouilil, Y.: Economic growth, poverty, and income inequality: Implications for lower-And middle-income countries in the era of globalization. The Journal of Private Equity 23(1), 137–145 (2019) 3. B. Seillier. La lutte contre la pauvreté et l’exclusion : une responsabilité à partager. Rapport d’information n° 445. 2007 Rapport d’information n° 445 (2007–2008) 4. Ravallion, M.: Why don’t we see poverty convergence? American Economic Review 102(1), 504–523 (2012) 5. Sen, A.K.: Poverty: An ordinal approach to measurement. Econometrica 44, 219–231 (1976) 6. Sen, A.: Well being, agency and freedom: The dewey lectures. J. Philos. 82, 169–221 (1985) 7. Sindzingre, A.: Values, norms, and poverty: a consultation on WDR 2000/2001 poverty and development. World Bank Group, Washington, DC (1999) 8. World Bank, World development report 2000/2001, Attacking poverty, (Chapter 1: The Nature and Evolution of Poverty), 2001,15–21 9. World Bank, World development report 2000/2001, Attacking poverty, (Chapter 3 Growth, Inequality, and Poverty), 2001,45–57 10. UNDP Sustainable Development Summit Transforming Our World for People and Planet, Overview New Sustainable Development Agenda, Leaving No One Behind,(25–27 September 2015) 11. Haut Commissariat au Plan, Le développement socio-économique régional Niveau et disparités, 2001–2017. Oct. 2018 12. Rommel Melgaço Barbosa & Donald R. Nelson, 2016, The Use of Support Vector Machine to Analyze Food Security in a Region of Brazil, Applied Artificial Intelligence, Volume 30, 2016 - Issue 4 Pages 318–330 13. Ola Hall, Mattias Ohlsson, and Thorsteinn Rognvaldsson. A review of explainable AI in the satellite data, deep machine learning, and human poverty domain, Volume 3, Issue 10, 14 October 2022, 100600 14. Cortes, C., Vapnik, V.: Support-Vector Networks. Mach. Learn. 20, 273–297 (1995) 15. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
The Generation of Power from a Solar System Using a Variable Step Size P&O (VSS P&O) and Comparing It to a Grey Wolf Metaheuristic Algorithm (GWO) Mohammed Zerouali1(B) , Abdelghani El Ougli2 , and Belkassem Tidhaf1 1 Team of Embedded Systems, Renewable Energy and Artificial Intelligence – National School
of Applied Sciences, Mohammed First University Oujda, Oujda, Morocco [email protected] 2 Computer Science, Signal, Automation and Cognitivism Laboratory (LISAC), Faculty of Science Sidi Mohamed Ben Abdellah University, Fez, Morocco
Abstract. In order to arrive at a maximum power point, this paper compares a Grey wolf optimizer (GWO) meta-heuristic method against a Variable step size P&O algorithm based on fuzzy logic (VSS P&O).The design, implementation, and comparison of the two algorithms are the key goals of this research. The PV system consists of a PV panel, a DC-DC boost converter, and a load. The Matlab Simulink platform is used for all modeling and simulation operations. The simulation results demonstrate the limitations and performance of each approach as well as the superiority of the Grey Wolf Optimizer (GWO) over the variable step P&O. The findings show that while the VSS P&O can more precisely discover the global optimum, the GWO has greater search capability and converge speed. Keywords: PV System · Meta-heuristic · Grey wolf optimizer (GWO) · Perturb and observe P&O (P&O) · Fuzzy logic (FLC)
1 Introduction The need for electricity has skyrocketed, particularly in distant and hilly areas. Despite this, fossil fuels such as coal, oil, and gas are still used to provide electricity and heat. These energy sources, however, are limited and contribute significantly to CO2 emissions. Photovoltaic (PV) systems, on the other hand, are relatively inexpensive, durable, and ecologically friendly [1]. If excess energy is not used immediately, it can be stored in batteries. PV systems, on the other hand, have a limited efficiency range and are influenced by factors such as solar radiation and temperature. Tracking systems, which orient the panels towards the sun for maximum efficiency, reduce the number of panels required, and conserve land and resources, are one technique to improve their efficiency [2]. The nonlinear nature of photovoltaic (PV) cells means that they only produce electrical power at a single point on their power-voltage curve, called the Maximum Power Point (MPP). To maintain this MPP and optimize the power output of the Photovoltaic © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 11–20, 2023. https://doi.org/10.1007/978-3-031-29860-8_2
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system, a specific control method is required [3]. Traditional techniques like Fractional Open Circuit Voltage (FOCV) and Perturb and Observe (P&O) are commonly used, in addition to newer methods like neural networks and fuzzy logic [4]. In the literature, several works discuss the treated problem. In [5] proposes an optimized MPPT algorithm using the GWO algorithm as an alternative to traditional INC and P&O techniques. In [6] two new intelligent maximum Power Point tracking (MPPT) techniques for photovoltaic (PV) solar systems have been developed. The proposed algorithms reduce ripple, overshoot and response time, and improve MPPT performance in fast changing environment conditions, resulting in an overall reduction of lost energy. The previously mentioned studies have compared various methods for maximizing the produced power of a PV panel to the most often used algorithm, P&O. However, as these studies did not account for the potential of changing step sizes while implementing P&O, they are not entirely accurate. Furthermore, some of these studies simply look at reaction times and neglect the oscillations that occur around the MPP. The suggested system consists of a solar generator coupled with a Boost converter, which is controlled using a novel Maximum Power Point Tracking (MPPT) approach known as the Grey Wolf Optimizer (GWO) and is compared to a variable step size P&O employing fuzzy logic. The performance of the proposed system will be evaluated by comparing the power output, efficiency and stability of the GWO and the VSS P&O with fuzzy logic under different weather conditions and system configurations (Fig. 1).
Fig. 1. Functional diagram of the proposed system
2 PV Panel Modeling and Characteristics Solar energy is converted into usable electricity through the use of photovoltaic panels. Photovoltaic panels are made up of solar cells, which convert sunlight into direct current (DC) electricity. Two types of circuits are commonly used in photovoltaic systems: the series circuit and the parallel circuit. The series circuit uses a single path for the current to flow, while the parallel circuit uses multiple paths. The electrical model for these circuits can be seen in Fig. 2 [7]. Equations are used to describe the output current Iph (1) [8]: G Gref
(1)
q.(V + Rs I ) − 1) N .K.T
(2)
Iph = [Isc + KI (Tc − Tref )] The junction’s current is expressed by [8]: Id = Is (exp(
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Fig. 2. The equivalent circuit of a PV cell
Utilizing the equations from [9], the PV panel sizes are determined. The adopted PV panel’s specifications are summarized in Table 1. Table 1. PV array metric.
Table 2. Response time of different MPPT MPPT algorithms
Response Time (s) 0 to 1000 W/m2
GWO-MPPT
0.025
VSS P&O-MPPT
0.035
2.1 Effect of Solar Irradiation The behavior of Photovoltaic (PV) systems can be complex, as it is affected by various factors such as temperature and the amount of solar radiation it receives. The efficiency of the panel can be improved by increasing the level of solar radiation at a specific temperature. The current produced by the panel is directly proportional to the intensity of the solar radiation (as shown in Fig. 3). It is important to note that a decrease in solar radiation results in a decrease in output current. To ensure maximum efficiency, it is crucial to understand how different environmental conditions can affect the performance of PV systems. As such, it is essential to identify and monitor the optimal temperatures and solar intensities for PV systems [10]. 2.2 Effect of Temperature The output power of a photovoltaic (PV) panel is inversely related to temperature. As the temperature of the cells in the panel increases, the panel’s output power decreases.
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Fig. 3. P-V and I-V curves change as a result of solar radiation.
This relationship is known as the temperature coefficient of the panel and it is typically represented as a percentage per degree Celsius. Figure 4 illustrates the decrease in output power as the temperature of the cells in the PV panel increases. The temperature coefficient is usually provided by the panel’s manufacturer and should be taken into account when designing a PV system. Additionally, to mitigate the impact of temperature on the panel’s output power, PV systems can be designed to include cooling mechanisms, such as air or water-cooling [11].
Fig. 4. P-V and I-V curves change as a result of temperature.
3 DC-DC Converter The Boost converter is a type of DC-DC converter that is used to increase the voltage level of a power source. The basic circuit for the Boost converter is depicted in the figure above. It is made up of four main components: a capacitor, a diode, an inductor, and a controlled switch. The capacitor stores energy, the diode allows current to flow in one direction, the inductor resists changes in current, and the controlled switch regulates the flow of current [12] (Fig. 5).
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Fig. 5. Boost DC-DC converter.
4 The Analyzed System’s Control A solar generator converts solar energy into usable electricity by using photovoltaic panels. The available power from these panels is always extracted using the Maximum Power Point Tracking (MPPT) method. The MPPT method is used to track the maximum power point of the solar panel, which is the point on the power-voltage curve where the panel is able to generate the most power. The main goal of this study is to compare the performance of two intelligent MPPT techniques: the variable step size Perturb and Observe (P&O) method based on a fuzzy logic controller and the metaheuristic algorithm Grey Wolf Optimizer (GWO). Both methods are used to track the maximum power point of the solar panel and increase the power output from the panel. The proposed methods will be evaluated by comparing their performance in terms of power output, efficiency, and stability. The study will also analyze the computational complexity and robustness of the proposed methods under different weather conditions and system configurations. 4.1 Variable Step Size P&O (VSS P&O) The traditional Perturb and Observe (P&O) technique for photovoltaic (PV) module power output is limited by the Maximum Power Point (MPP) and its response time. The P&O technique is used to track the MPP of the PV module, but it can be affected by slow response time, steady-state oscillations, and low dynamic responsiveness. To improve the dynamic responsiveness and eliminate steady-state oscillations, we propose using a fuzzy logic approach to alter the step size of the MPPT P&O algorithm. This approach, called Variable Step Size P&O (VSS P&O), analyzes the input and output powers and panel temperature of the PV array to adjust the step size and reduce power fluctuations around the MPP. The VSS P&O algorithm uses fuzzy logic to evaluate the system’s current state and adjust the step size accordingly. The modified algorithm is illustrated in Fig. 8. This approach is expected to increase the power output and efficiency of the PV array while reducing the steady-state oscillations and improving the dynamic response time of the MPPT algorithm [13] (Fig. 6).
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Fig. 6. Schematic for P&O with variable step size (VSS P&O).
The VSS P&O algorithm is guided by the following rules.
Fig. 7. D and the dP/dV and membership functions
4.2 Grey Wolf Optimizer (GWO) Under non-linear conditions, a brand-new algorithm called GWO that is inspired by nature delivers the optimum MPPT solution. It contains four groups of wolves to carry out the essential task: alpha, beta, delta, and omega, as shown in Fig. 9. Wolves, while obedient to the GWO, are leaders among the other wolves and provide the finest solution. The third pack rules over the wolves. Encircling, hunting, and attacking the victim are the three actions that these wolves use when hunting. [13] In the same way, GWO has a three-stage operation when finding the optimal solution. The first stage is an iterative process known as the exploration phase. This process uses the four wolves to randomly search for potential solutions. The alpha wolf leads the other wolves and attempts to find the maximum value of the objective function [14, 15] (Fig. 8). The wolves are placed at random inside the wolves’ boundaries and dimensions, and the positions of the equations are as follows: [14] D = |C × XP (t) − X (t) |
(5)
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Fig. 8. Grey wolves’ social structure.
X (t + 1) = XP (t) − A × D
(6)
t is the current iteration, where X and XP represent the positions of grey wolves and their prey, respectively. According to the equations below, A and C stand in for the coefficient of vectors: [14] A = 2A × r1 − a(t)
(7)
c = 2 × r1
(8)
where r1 and r2 are random vectors that fall between 0 and 1. The number of “a” designated vector members gradually decreases from 2 to 0.The optimal position is updated since the hunting operation can be carried out by, and. The following are the hunting equations: [15] Dα = |C1 × Xα (t) − X (t) | Dβ = C2 × Xβ (t) − X (t)
(9)
Dγ = C3 × Xγ (t) − X (t) From where: X1 (t) = | Xα (t) − A1 × Dα (t)| X2 (t) = Xβ (t) − A2 × Dβ (t)
(10)
X3 (t) = Xγ (t) − A3 × Dγ (t) Using equation, the coefficients c1, c2, and c3 are determined [15]: X1(t) + X2(t) + X3(t) (11) 3 When the prey stops moving, the wolves attack and update their location, and the vector components in the attack reduce linearly from 2 to 0. The process spends half of its iterations exploring and half exploiting, but it suffers from early convergence, a lack of population heterogeneity, and an uneven exploration-to-exploitation ratio. X(t + 1) =
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5 Results and Discussion Figure 9 depicts the evaluation of the entire system using sun irradiance and temperature profiles. The sun’s irradiation varied while the temperature was held constant at 25 °C. Figure 10 displays the retrieved power profile and a comparison of the Grey Wolf Optimizer (GWO) and the variable step size P&O (VSS P&O) for time-varying irradiation at 25 °C. The GWO controller was found to be superior in terms of response time and power output when compared to other controllers. Results from Matlab/Simulink simulation showed that the GWO algorithm was more effective in obtaining the highest power point compared to other MPPT strategies. However, the VSS P&O algorithm was observed to have oscillation issues.
Fig. 9. Evolution of solar radiation level
Fig. 10. Evolution of produced power, current and voltage level
Figure 10 compares the results for current, voltage, and generated power between the GWO-MPPT and VSS P&O-MPPT. For a result for an actual profile for one day with the GWO-MPPT, see (d), (e), and (f). From all the results, the Grey wolf optimizer GWOMPPT offers better performances in terms of response time and the produced power. Therefore, it can be concluded that the GWO-MPPT is more efficient and a better choice compared to the VSS P&O-MPPT. The GWO-MPPT shows a faster response, smoother operation, and better tracking of maximum power compared to the VSS P&O-MPPT. Additionally, the GWO-MPPT has a higher efficiency in terms of power control and generates more power for a given profile. Overall, the GWO-MPPT is a more suitable approach for controlling a solar panel as seen from the results of this comparison.
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6 Conclusion To maximize power harvesting, the suggested solar system, which has a nonlinear characteristic, employs tracking algorithms. The Grey Wolf Optimizer surpasses a P&O with variable step size in terms of response time and oscillation around the MPP. The results of simulations in Matlab Simulink verify the efficacy of the proposed solution. The Grey Wolf Optimizer shows a more accurate and fast response to the changing irradiance. Moreover, the steady-state error was reduced significantly compared to the conventional P&O with variable step size. Thus, it can be concluded that the Grey Wolf Optimizer is an effective technique for solar power harvesting system. As future prospects of this work, different optimization algorithms and variable step sizes could be employed for the tracking of the MPP. Furthermore, the system can be generalized to be used in different climatic conditions. Additionally, further research could be done to compare the performance of the Grey Wolf Optimizer with other optimization techniques. Finally, the proposed technique can also be implemented in other renewable energy sources like wind energy to analyze the performance of different optimization techniques.
References 1. Ishaque, K., Salam, Z.: Syafaruddin. A comprehensive MATLAB Simulink PV system simulator with partial shading capability based on two-diode model. Sol. Energy 85, 2217–2227 (2011) 2. Singh, D., Yadav, R.: Jyotsana. Perturb and observe method MATLAB Simulink and design of PV system using buck boost converter. Int. J. Sci. Eng. Technol. Res. 3,1692–5 (2014) 3. Boutouba, M., Assahout, S., El Ougli, A., Tidhaf, B.: Improved maximum power point tracking using fuzzy logic control with SEPIC converter for photovoltaic systems. In: 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), Rabat, Morocco, pp. 1–8 (2018) 4. Chtita, S., Derouich, A., El Ghzizal, A., Motahhir, S.: An improved control strategy for charging solar batteries in off-grid photovoltaic systems. Sol. Energy 220, 927–941 (2021) 5. Abderrahim, Z., Eddine, H.K., Sabir, M.: A new improved variable step size MPPT method for photovoltaic systems using grey wolf and whale optimization Technique based PID controller. J. Eur. Des Systemes Automatises 54(1), 175-185S (2021) 6. Mohanty, B.S., Ray, P.K.: A Grey Wolf-Assisted Perturb & Observe MPPT Algorithm for a PV System. IEEE Trans. Energy Conversion 32(1), 340–347 (2017). https://doi.org/10.1109/ TEC.2016.2633722 7. Latif, T., Hussain, R.: Syed Design of a charge controller based on SEPIC and Buck topology using modified Incremental Conductance MPPT. In: Proceedings of the 8th international conference on electrical and computer engineering. Dhaka, Bangladesh; 20–22 December 2014 8. Boukezata, B., Chaoui, A., Gaubert, J.-P., Hachemi, M.: An improved fuzzy logic control MPPT based P&O method to solve fast irradiation change problem. J. Renewable Sustainable Energy 8(4) (2016) 9. Bouselham, L., Hajji, B., Mellit, A., Rabhi, A., Kassmi, K.: Hardware implementation of new irradiance equalization algorithm for reconfigurable PV architecture on a FPGA platform. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), 2019, pp. 1–8. https://doi.org/10.1109/WITS.2019.8723746
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10. Arunkumari, T., Indragandhi, V., Sreejith, S.: Topologies of a DC–DC Converter for Microgrid Application and Implementation of Parallel Quadratic Boost Converter. In: SenGupta, S., Zobaa, A.F., Sherpa, K.S., Bhoi, A.K. (eds.) Advances in Smart Grid and Renewable Energy. LNEE, vol. 435, pp. 633–644. Springer, Singapore (2018). https://doi.org/10.1007/978-98110-4286-7_63 11. D. G and S. N. Singh, “Selection of non-isolated DC-DC converters for solar photovoltaic system. Renew. Sustain. Energy Rev. 76, 1230–1247 (2017). https://doi.org/10.1016/j.rser. 2017.03.130 12. Fezai, S., Belhadj, J.: Sizing optimization of a stand-alone photovoltaic system using genetic algorithm. In: 18th Int. Conf. Sci. Tech. Autom. Control Comput. Eng. - STA’, pp. 499–504 (2017) 13. Dwivedi, B., Patro, B. D. K., Srivastava, V., Jadon, S.S.: LBR-GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks. Concurrency and Computation: Practice and Experience, 2022, vol. 34, no 4, p. e6603 (2022) 14. da Rocha, M.V., Sampaio, L.P., Oliveira da Silva, S.A.: Comparative Analysis of ABC, Bat, GWO and PSO Algorithms for MPPT in PV Systems. In: 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 347–352 (2019). https:// doi.org/10.1109/ICRERA47325.2019.8996520 15. Kishore, D.K., Mohamed, M.R., Sudhakar, K., Peddakapu, K.: An improved grey wolf optimization based MPPT algorithm for photovoltaic systems under diverse partial shading conditions. In: Journal of Physics: Conference Series. IOP Publishing, 2022, p. 012063, August 2022
Bio-Inspired Optimisation Algorithm for Congestion Control in Computer Networking Richard Nana Nketsiah1(B)
, Israel Edem Agbehadji2,3 and Emmanuel Freeman4
, Richard C. Millham1
,
1 ICT and Society Research Group, Department of Information Technology,
Durban University of Technology, Durban, South Africa [email protected] 2 Honorary Research Associate, Faculty of Accounting and Informatics, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa 3 University of KwaZulu Natal, Pietermaritzburg, South Africa 4 Faculty of Computing and Information Systems, Ghana Communication Technology University, PMB 100, Accra North, Ghana [email protected] Abstract. The development of internet technology gives consumers the chance to transfer packets across networks instantly. Such developments in internet technology are anticipated to ease congestion. Despite this, the majority of firms were bound by the expense of deploying cutting-edge technology or updating network infrastructure, necessitating efficient congestion management. Inherent distribution optimisation, which requires each source device on the network to continually adjust to its traffic load using the feedback information received or acknowledged from another source device, is unfortunately the problem with congestion management. This research addresses the issue of continually adjusting to a network traffic load by presenting a unique optimisation technique. This strategy is based on the features and hunting behaviour of foxes and rabbits on wild grass. A computer network congestion optimisation technique, named Fox Prey Optimisation, was created using a mathematical formulation of the fox predatory characteristics. The FPO algorithm is comparable to the Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Artificial Bee Colony (ABC), and Firefly (FA) algorithmic methods that are inspired by nature. In utilising a set of standard benchmark functions, the algorithms were assessed, and the findings demonstrate, using the Levy N. 13 multimodal benchmark function, that FPO guaranteed a global minimum value of 2.0055, while PSO, ACO, and FA provided 5.6683, 3.1795e+03, and 10 respectively. ABC though did fairly well by registering 2.0, almost the same as FPO. As a consequence, FPO was able to adjust to the load of network traffic by adopting an ideal value of 2.0055. Keywords: Packet sending rate · bio-inspired algorithms · network congestion · Fox Prey Optimisation (FPO) · network congestion control
1 Introduction Due to the proliferation of computing devices, which in turn require a lot of data, the number of networked computer users and the amount of network data traffic supported © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 21–32, 2023. https://doi.org/10.1007/978-3-031-29860-8_3
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by computing have recently expanded astronomically [1]. A well-known example of data traffic that has gradually expanded and often overwhelms the network with data packets is that of the internet. This circumstance reduces the network’s general efficiency and quality of service (QOS), which eventually impacts the network’s performance [2]. The process of monitoring and analyzing data about the whole network to determine how effectively the services it provides are working is known as network performance. Evaluation of network performance aids in efficient resource management and improves network service quality. Depending on the kind and design of the network, there are many methods for evaluating its performance. The capacity of the transmission medium, the nature of the network traffic it transports, and the network’s throughput are used to assess its efficiency [3]. Efficiency in computer networks refers to the effectiveness of information sharing. In a computer network, efficiency is defined as the ratio of data transmission rate (throughput) to bandwidth capacity [4]. Global efficiency measures information exchange over the whole network when information is simultaneously transferred on a global scale. However, the extreme data flow causes network congestion, which affects the throughput of the network. In this paper, we attempt to mitigate the problem of network congestion through an adaptive dynamic algorithm based on the mathematical formulation of the behavior and characteristics of foxes and rabbits on natural grass. The novelty of our approach is the consideration of the natural grass environment on which fox and rabbit cohabit. When the population of the rabbits increase, the grass depletes quickly. However, if the population of the rabbits reduces, the grass also regenerates proportionately. The section of the paper is organized as follows: Sect. 2 (related works), Sect. 3 (methodology), Sect. 4 (benchmark test functions), Sect. 5 (results and discussion) and Sect. 6 (conclusion).
2 Related Works 2.1 Network Congestion Network congestion is when a node or link carries more data than it can handle, which may reduce the quality of service for other network users. Typically, computer network congestion is unanticipated and may lead to the loss of important data, which degrades performance and causes network failures. Low buffer memory size and packet loss are only two of the numerous causes of computer network congestion. The speed of data exchange between a sender and a receiver on a network is measured by the data packet sending rate. Notably, data packet sending rate is one of the most practical variables for network management [5]. However, the network faces a hurdle since too many packets significantly impact the network’s performance, resulting in packet losses. When one or more data packets fail to arrive at their destination while moving across a computer network, this is known as packet loss. When packets with several bit errors reach their intended destination, they are determined to be corrupted. Occasionally, packets are lost because of unfavorable network characteristics like jitter and delay or
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when the network gets crowded. In this situation, the transmission control protocol (TCP) will force the retransmission of these lost packets [6]. Re-sending packets that were lost or damaged during first transmission is known as packet retransmission. Network congestion, which results in packet drops, and strict router quality of service policies are the two main causes of packet retransmissions. Managing congestion is essential since it focuses on improving the lifespan and performance of a network. Therefore, the primary goal of this research is to use bio-inspired optimisation to reduce computer network congestion [7]. 2.2 Bio-Inspired Optimisation Algorithms The phrase “nature-inspired” or “bio-inspired” optimisation refers to a broad range of computational techniques that are founded on the fundamentals of biological systems [8]. The Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Artificial Bee Colony (ABC), and Firefly Algorithm are some examples of nature-inspired optimisation methods. Particle swarm optimisation, is inspired by the movement of schools of fish and flocks of birds [9]. PSO is one of the bio-inspired algorithms that searches for the best solution in the solution area. The PSO method has the following key advantages: ease of implementation, resistance to control parameters, and computing efficiency. PSO method is limited by a poor merging speed throughout in the iterative phase and is prone to falling into local optimum in high-dimensional area. The application domain of PSO includes optimisation for the management of congestion in computer networks using queue length adjustment at active network nodes [10]. This technique also makes it possible to increase network performance by making good use of available bandwidth. Another nature-inspired population-based metaheuristic, Ant Colony Optimisation, may be used to solve challenging combinatorial optimisation issues [11]. It is an optimisation method that makes use of the probabilistic approach used to solve computational issues and to discover the best route using graphs. Based on how ants navigate between their colony and a food supply, this program seeks out the best route across a network. In order to determine the quickest route between a food source and their nest, ants use pheromone trails, a chemical substance that each ant leaves behind while functioning in its surroundings. The pheromone however tends to dissipate with time. The ACO’s ability to operate constantly and instantly to adjust to changes is its key benefit [12]. The ACO, however, has three major limitations. These are: the algorithm’s stagnation phase, exploration and exploitation rates and convergence speed. In high-performance telecommunications networks, a method called Multi-Protocol Label Switching (MPLS) uses labels to guide and carry data from one network node to the next. The construction of “virtual linkages” between distant nodes is made simple by MPLS. Here, the ACO method is used for traffic management in the MPLS networks [13]. ABC is a population-based function optimisation approach, based on the clever foraging behavior of honey-bee swarms [14]. The major goal of ABC is to enhance local search while preserving GA’s capacity for global search. The ABC algorithm benefits
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from excellent resilience, quick convergence, and high adaptability. However, premature convergence in the latter search phase is its disadvantage [15]. ABC has been used to mitigate congestion in some areas: it is used in a Congestion Management System Based on Artificial Bee Colonies in a Restructured Power System where the algorithm is used to handle transmission line overloads in deregulated environments. Again, it is also an efficient minimisation of re-dispatch cost which results in secure operating condition [16]. Lastly, the Firefly algorithm imitates how fireflies communicate by flashing their lights. Fireflies are successful because of their brief and rhythmic flashes [17]. The firefly method is mostly used to solve continuous and discrete optimisation problems. The Firefly method is simple to use and an efficient algorithm; again, parallel implementation is appropriate. For multimodal issues, research has shown that it is sluggish to converge and quickly falls into a local optimum. The FA algorithm has been employed in the field of congestion management in a few of these locations, including the deregulated environment, where FFA is effectively deployed to reduce the cost of rescheduling in order to entirely relieve congestion. It has also been used to control congestion in wireless sensor networks based on bioluminescent firefly behavior [18]. Also, the Firefly Algorithm is used to reduce queue length. Findings of the FA Algorithm thus show increase in network performance and longevity [19]. The application of these nature-inspired algorithms have been used in computer networks but in different areas including congestion management in wireless networks. This has necessitated the formulation of FPO to be used in wired computer networking in controlling network congestion using data packet sending rates.
3 Methodology This paper outlines the development of the Fox Prey algorithm to reduce network congestion. 3.1 Behavior and Characteristics of Rabbit, Fox, and Grass Generally, rabbits eat grass within a specific location for survival. Naturally, the increase in the population of rabbits is proportional to the quick depletion of the grasses in the environment. This is a trail that enables the foxes to pursue the rabbits. When foxes prey on rabbits, the rabbit population decreases over time. As a result, the fox population will grow and reproduce faster than the rabbit population. With time, as the population of rabbits reduces, re-vegetation of the grass also take place. The highlypopulated foxes will begin to starve which will lead to their death due to less prey in their environment for them to feed on. The rabbit population will also begin to multiply as they feed on the vegetation that has regrown and will become more populated with time. Now, the less-populated foxes will start feeding on the rabbits that have started increasing in population due to the re-vegetation of the grass.
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The characteristics of rabbits, foxes and grasses can be simplified as follows: • Foxes are predators that feed on rabbits • Rabbits eat grass within a specific location for survival. If the rabbit population is high, the grass within the location depletes quickly. The fox population will also increase. • When there are no rabbits, then the foxes will die or have a depleted population. Similarly, in the absence of grass, the rabbits will die or diminish. • Also, if the foxes feed on the rabbits, the rabbit population will decrease quickly, leading to the growth of the grass. Rabbits leave trail substances for foxes to follow. The assumption for fox, rabbit, and grass characteristics is as follows: • Every fox and rabbit has a specific position or location on the grass. • When there is no rabbit, foxes die or have a depleted population. In the same vein, the absence of grass leads to the diminishing or death of rabbits. • The rabbit population decrease quickly when foxes feed on them, allowing grass to grow rapidly. Rabbits leave trail substances for foxes to follow when grass depletes. 3.2 Relating Proposed Bio-Inspired Characteristics to Network Congestion Characteristics The predator-prey concept is used here, with bandwidth acting as the prey and data packets acting as the predator. With no packets or fewer packets on the network’s channel for transmission, the network’s congestion window (cwnd) is communicated to on the availability of free bandwidth. It will then let go of packets using the slow-start phase, which increases the packets exponentially after every ACK for each RTT. Characteristics of Network Congestion with Parameters • Packets depend on bandwidth for movement. Thus, packet movement is directly proportional to Bandwidth. • Throughput depends on the bandwidth of the network • Round trip time (RTT) is the total amount of time required for a data packet to travel from its origin to its destination and be acknowledged by its source. • An acknowledgement is when a packet is sent to a recipient, the receiver approves acceptance by sending the ACK back to the sender as feedback. Network, most often, depends on bandwidth size for better performance. As a result, the relationship between bandwidth and network size is straightforward. Additionally, when the bandwidth size increases, more packets are sent, leading to greater throughput and higher network efficiency. Hence, throughput is directly proportional to bandwidth. When there is no free bandwidth allocation, packets will drop or there will be packet losses. In the absence of packet drops, the congestion window would grow. The quantity of packets will grow as the cwnd’s size rises. In the event of packet drops, the congestion window size would decline. The closure of the congestion window is to ensure that no extra data sending packets come onto the network only to be dropped as the network is
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already overwhelmed. The period of time a request moves from the sending node to the receiver node is referred to as propagation delay. According to the formula RTT = 2 x propagation delay, propagation delay often makes up the majority of the RTT. A network request’s Round-Trip Time (RTT), measured in milliseconds (ms), is the amount of time it takes to go from the sender to the receiver and back (sender). At the slow start threshold, it goes into the congestion avoidance phase where packets are added (linearly) at each RTT for every acknowledgement. At the avoidance phase, packets cautiously probe for available bandwidth to avoid congestion. When all the available bandwidth is depleted, congestion sets in, and the incoming packets start to drop. The congestion window (cwnd) tends to close when informed leading to a reduction in the number of packets (predators) and freeing bandwidth (prey) space. With the number of packets now reduced, the bandwidth size will now start to grow. The congestion window will then let out packets exponentially, which however, starts at a slower pace with a segment (packet), hence the name slow-start phase. The packets can operate effectively because of the available bandwidth, which optimises throughput. The predator-prey relationship is directly analogous to the increase of the sent data packets, which makes use of the available bandwidth and thereby optimising throughput by the application. The following are the summary of the relationship between the fox, rabbit, and grass characteristics and network congestion parameters: • Foxes (predators) relate to packets. • Grass in a location relates to the network (size). • Rabbits (prey) relates to bandwidth. Whether there is packet loss or not, the trail indicates communication/link between fox and rabbit. The congestion window is triggered to either reduce or increase the number of packets and eventually the speed. Initially, it assumes there is packet loss, so the cwnd is set to a maximum segment size (MSS) of 1 segment (packet). 3.3 Mathematical Model of Rabbit, Fox (Predator-Prey) Characteristics Based on the simplified characteristics, a suitable mathematical formula is expressed as: • Position of fox-rabbit The position of every fox, and rabbit on the grass is expressed by: n b ∗ fj − q ∗ rj f (x) = wi ∗ k ai − t ∗ j=1
(1)
where w is the area of grass where foxes and rabbits feed (co-habitation). ai is the growth rate of wi in the absence of foxes and rabbits. k is rate of controlling the step size of both rabbit and fox. t is the trail that attracts fox which ranges between 0 and 1, where 1 signifies a trail is found or otherwise. b is the growth rate of foxes per encounter with rabbits. f j is the fox population. q is the death rate of rabbits per encounter with foxes. r j is the rabbit population while n is the number of iterations.
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• Area of grass formulation. The area of grass w is formulated as: w=
L2 + B2
(2)
where w denotes the area of grass, L is the length of the grass where fox and rabbit co-habit, B is the breadth of the environment of co-habitation. • Step size The step size of both rabbit and fox is controlled using k which is expressed by: k=
1 MSS ∗ (√ ) RTT PL
(3)
where k is step size. MSS represents maximum segment size, which relates to area of grass formulation (w). RTT is the time from which a packet is sent to the receiver and acknowledged and returned to the sender, which is expressed as: RTT = 2 ∗ Td
(4)
Td = Tt + Tp
(5)
where:
T t and T p are the transmission and propagation delay times. T d is the total delay time. • Data Packets PL = Psent − Prcvd
(6)
where PL refers to Loss of packets on the network, Psent and Prcvd are packets sent and packets received by sender and receiver respectively. • Objective function (f (x)) The objective function is expressed using root mean square as: 1 2 xi f (x) = n
(7)
i
where xi denotes the location of the fox prey and n denotes the population size. 3.4 Flow Chart The Flow chart for Fox Prey Optimisation (FPO) using this nature-inspired algorithm could be expressed as shown in Fig. 1.
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Fig. 1. The Analyse-Increase-Decrease (AID) Element
3.5 Pseudocode – Step by Step of the Fox Prey Optimisation Algorithm
1. Initialize parameters of FPO // set t=1, number of fox and prey 2. Compute w, k, RTT 3. Determine the position of fox, rabbit using equation (1) 4. Evaluate the area using equation (2) 5. while stopping criteria is not met do 6. search prey in the search area 7. if trail t = 1 then, prey is available 8. for each iteration 9. Randomly select the prey(s) among the searching agents 10. Evaluate and determine congestion level 11. If condition is Yes, decrease CWND (go back to analysis stage) 12. If condition is No, increase CWND equation (1) 13. end for 14. end while 15. End if 16. Generate random value for t in range 0 or 1 17. Finally, output the best throughput solution realized
4 Benchmark Test Functions The benchmark test functions are optimisation issues disguised as mathematical numerical functions. In order to reach the optimum result, these functions are optimised using a set of parameter values. Optimisation algorithm, including metaheuristic algorithms, tends to find the near best solution (though it is not always guaranteed to have obtained) as
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quickly as possible. The efficiency of any metaheuristic is gauged by its global searchability and local convergence ability [20]. The algorithms that have better global searchability are hard to be trapped in sub-optimal (local minima or maxima) locations. The test functions may be divided into unimodal and multimodal categories as showed in Table 1. The term “modality” refers to the number of peaks in the issue landscape, which may be local or global minima. Shifting and rotating these functions increases difficulty. Multimodal functions have more than one peak and are challenging to answer. These are useful problems for evaluating a computer’s capacity to search globally. In this study, the performance is evaluated using four benchmark functions in the categories of unimodal and multimodal as already captured in Table1 below. Table 1. Benchmark Test Functions Function Name Category
Mathematical Expression
Sphere
f (x) =
Unimodal
Range
d xi2
-5.12 to 5.12
i=1
Sum Squares
Unimodal
f (x) =
d i=1
Holder Table Levy N. 13
ixi2
-10 to 10
x12 +x22 Multimodal f (x) = −|sin(x1 )cos(x2 )exp 1 − -10 to 10 π Multimodal f (x) = sin2 (3π x1 ) + (x1 − 1)2 1 + sin2 (3π x2 ) + -10 to 10 (x2 − 1)2 1 + sin2 (2π x2 )
5 Results and Discussion During the experiment, the parameters of the FPO algorithm were set at random. The results of the FPO and the comparative algorithms are shown in Table 2. In this experiment, 100 iterations were considered as a termination criterion. Table 2. Parameters of the Five Bio Inspired Optimisation Algorithms Benchmarks
PSO
ACO
ABC
FA
FPO
Sphere
0.5593
1.9070e+03
2.8146e−44
8
0.1102
0.8389
2.8604e+03
4.2219e−44
12
0.1653
Holder Table
−0.9332
−1.7608e+05
−3.2247e−22
−0.4181
−0.5532
Levy N. 13
5.6683
3.1795e+03
2
10
2.0055
Sum Squares
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In Table 2, the multimodal Holder Table benchmark function recorded the lowest values of near global minima across all the five comparative algorithms. This indicates that the multimodal test function guaranteed a near optimal value. Aside this unique feature of the Holder Table benchmark test function, there were also minimum values that the test functions gave. PSO recorded the least optimal value using the Sphere test function. The ACO algorithm recorded the least optimal value with the Sphere benchmark test function. A least optimal value was captured by the Sum Square benchmark test function when the ABC algorithm was put to test. The Firefly algorithm recorded its least optimal value when subjected to the Sphere benchmark test function. Finally, the Fox Prey Optimisation algorithm (FPO) recorded and captured its least optimal value using the Sphere benchmark test function. Looking at these five least optimal captured values, FPO ranks second only to ABC as it triumphs over PSO, ACO, and FA respectively. This signifies that the Fox Prey Optimisation Algorithm can stand the test of time and deliver globally accepted results if used for optimisation. The analysis saw PSO trailing FPO in the conducted tests. Though it performed well in a few areas, PSO’s optimal local searchability is poor in comparison since it is insensitive to scaling design factors. The categorised analyses saw PSO, ACO, and FA trailing FPO when the unimodal Sphere benchmark function was used in the conducted tests. Using the multimodal Levy N13 benchmark function also for testing, FPO once again triumphed over PSO, ACO, and FA while being almost at par with ABC. The difference between ABC and FPO in this case being 0.005 which is insignificant. Although ABC outperformed FPO, it is a complex algorithm that consumes much time and is used in areas where response time is not critical. However, in a conventional computer network where response time is very critical, you need a lightweight quick response algorithm like FPO for effective congestion control. The ACO algorithm also recorded a comparatively poor results against FPO. Lastly, the outcome of the FA algorithm did not measure up to that of FPO. The performance of the FPO’s algorithm considered the leverage on the strength of the traditional nature inspired algorithms. A typical example is the ease of implementation and the ability to adapt to different domain, hence its ability to control computer network congestion by manipulating data packet sending rate.
6 Conclusions The purpose of this paper was to validate the modeled method that was built in light of the experimental data that our research team had previously produced. Although all the selected comparative algorithms of PSO, ACO, ABC, and FA have been used in computer networking in different ways, none of these comparative algorithms has been applied in the field of wired computer networking congestion management by controlling data packet sending rate. This has thus necessitated the need for this new FPO algorithm that can adapt to computer network congestion control by regulating data packet sending rate. In the future, the FPO would be tested against several benchmarked algorithms in the field of congestion control to ensure its dependability and use in managing computer networking congestion. This will also be an effort to try and enhance our model in comparison to a prior study in the literature.
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References 1. Amanullah, M.A., Habeeb, R.A.A., Nasaruddin, F.H., et al.: Deep learning and big data technologies for IoT security. Comput. Commun. 151, 495–517 (2020) 2. Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., Kannan, A.: Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Comput. Networks 151, 211–223 (2019) 3. Baumgärtner, L., Gardner-Stephen, P., Graubner, P., et al.: An experimental evaluation of delay-tolerant networking with Serval. In: 2016 IEEE Global Humanitarian Technology Conference (GHTC), pp. 70–79 (2016) 4. Xue, X., Yan, F., Prifti, K., et al.: ROTOS: a reconfigurable and cost-effective architecture for high-performance optical data center networks. J. Light Technol. 38(13), 3485–3494 (2020) 5. Al-Turjman, F.: Price-based data delivery framework for dynamic and pervasive IoT 1. In: Cognitive Sensors and IoT. CRC Press, pp. 187–222 (2017) 6. Bennouri, H., Berqia, A., et al.: U-NewReno transmission control protocol to improve TCP performance in Underwater Wireless Sensors Networks. J. King Saud Univ. Inf. Sci. 34(8), 5746–5758 (2022) 7. Xiao, Y., Zhang, N., Lou, W., Hou, Y.T.: A survey of distributed consensus protocols for blockchain networks. IEEE Commun. Surv. Tutorials 22(2), 1432–1465 (2020) 8. Song, B., Li, K., Orellana-Martin, D., Pérez-Jiménez, M.J., Pérez-Hurtado, I.: A survey of nature-inspired computing: Membrane computing. ACM Comput Surv. 54(1), 1–31 (2021) 9. Tang, Z., Gong, M., Xie, Y., Li, H., Qin, A.K.: Multi-task particle swarm optimization with dynamic neighbor and level-based inter-task learning. IEEE Trans. Emerg. Top. Comput. Intell. 6(2), 300–314 (2021) 10. Hu, T.: Analysis and Dynamic Dispatch of Energy Storage Systems in Electricity Markets Under Uncertainty. Wayne State University (2021) 11. Dorigo, M., Socha, K.: An introduction to ant colony optimization. In: Handbook of Approximation Algorithms and Metaheuristics, Second Edition. Chapman and Hall/CRC, pp. 395–408 (2018) 12. de Oliveira, S.M., Bezerra, L.C.T., Stützle, T., Dorigo, M., Wanner, E.F., de Souza, S.R.: A computational study on ant colony optimization for the traveling salesman problem with dynamic demands. Comput. Oper. Res. 135, 105359 (2021) 13. Ammal, R.A., Sajimon, P.C., Vinodchandra, S.S.: Termite inspired algorithm for traffic engineering in hybrid software defined networks. PeerJ Comput. Sci. 6, e283 (2020) 14. Sevim, O., Sonmez, M., Toprak, B.: Optimum design of truss structure using artificial bee colony algorithm. Int J Soft Comput Artif Intell. 4(1) (2016) 15. Yaseen, Z.M., et al.: A hybrid bat–swarm algorithm for optimizing dam and reservoir operation. Neural Comput. Appl. 31(12), 8807–8821 (2019). https://doi.org/10.1007/s00521-0183952-9 16. Li, J., Liu, F., Li, Z., Mei, S., He, G.: Impacts and benefits of UPFC to wind power integration in unit commitment. Renew Energy. 116, 570–583 (2018) 17. Elhoseny, M.: Intelligent firefly-based algorithm with Levy distribution (FF-L) for multicast routing in vehicular communications. Expert Syst Appl. 140, 112889 (2020) 18. Manshahia, M.S., Dave, M., Singh, S.B., et al.: Congestion control in wireless sensor networks based on bioluminescent firefly behavior. Wirel. Sens. Netw. 7(12), 149 (2015)
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R. N. Nketsiah et al.
19. Pitchaimanickam, B., Murugaboopathi, G.: A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Comput. Appl. 32(12), 7709–7723 (2019). https://doi.org/10.1007/s00521-019-044 41-0 20. Hussain, K., Salleh, M.N.M., Cheng, S., Shi, Y., Naseem, R.: Artificial bee colony algorithm: a component-wise analysis using diversity measurement. J. King Saud Univ. Inf. Sci. 32(7), 794–808 (2020)
Fingerprint Classification Models Based on Bioinspired Optimization Algorithm: A Systematic Review Alrasheed Mustafa1(B)
, Richard Millham1 , and Hongji Yang2
1 ICT and Society Research Group, Durban University of Technology, Durban, South Africa
[email protected], [email protected]
2 School of Computing and Mathematical Sciences, College of Science and Engineering
University of Leicester, Leicester, UK [email protected]
Abstract. This paper presents a systematic literature review based on fingerprint classification using a bioinspired optimization algorithm. Fingerprint classification is crucial to fingerprint characterization. Importantly, fingerprint classification is mostly employed in large databases such as identification systems, border access control, and forensic investigation. Therefore, fingerprint classification is utilized to reduce the search time in the database and achieve faster matching accuracy for humans to be verified and identified. In recent years, many fingerprint classification algorithms have been proposed that incorporate various approaches, such as deep learning and machine learning methods. However, little is known about fingerprint classification based on bioinspired optimization algorithms. Additionally, systematic analysis was used as a rigorous approach to select, analyse and assess journals. The research strategy was conducted to generate seminal research papers from 1998 to 2022, and the databases used are IEEEXplore and Scopus. We also conducted searches in additional databases such as Web of Science, but no publications were found pertaining to this study; therefore, we excluded the database. As a result, 290 articles were identified, 278 records were screened, 22 journal articles were assessed for quality, and 10 journal articles were qualified for systematic review. Our results show how rare fingerprint classification is based on bioinspired optimization algorithms as well as how a few literature reviews were selected for this paper. Keywords: Fingerprint · Classification · Bioinspired
1 Introduction Fingerprint classification is crucial in terms of the direct effect on fingerprint characterization and process identification and authentication. In fact, fingerprints are classified by their features, such as loops, whorls, arches, ridges, and minutiae, as shown in Fig. 1 [1, 2]. Recent advances in fingerprint classification approaches have become an important aspect of biometric classification in terms of fingerprint characterization and biometric © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 33–43, 2023. https://doi.org/10.1007/978-3-031-29860-8_4
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recognition. Typically, an automated fingerprint identification system (AFIS) is coined and derived from biometric characteristics that contain templates and stored fingerprint images. The AFIS recognition process primarily focuses on large-scale identification, and it is nevertheless considered to be a part of fingerprint detection and classification to establish accurate and reliable individual identity [2]. The classification of fingerprints is known as a systematic categorization that can be arranged in a reliable and consistent manner such that distinct impressions of the same fingerprint belong to the same group of fingerprints. Fingerprint classification was developed as a means of optimization to accelerate the search in a database. However, it can be considered coarse-level pre-matching if a query of a fingerprint can be further compared with a smaller subset of fingerprints in the database that are associated with the same group of fingerprints. In this regard, it is important to incorporate a classification module in fingerprint recognition systems to accelerate database searches and reduce the size of large fingerprint databases [3].
Fig. 1. Fingerprint classes.
In the early years, researchers have also introduced various classification schemes to ease the search for fingerprint verification and identification [3]. The classification of fingerprints aims to achieve the target of splitting fingerprints into different categories [2]. The fingerprint recognition system is used widely for the identification of individuals compared to the various biometrics techniques. Moreover, it is easy to capture fingerprint information, which is highly distinctiveness and persistent over time, and fingerprint sensors are cheaper than other biometric sensors [4]. In fingerprint identification, the classification of fingerprints becomes vital in terms of designing a computational pipeline procedure involving tasks such as fingerprint preprocessing, feature extraction, and classification. Fingerprint classification can be divided into two phases: feature extraction and classification. In the feature extraction phase, the fingerprint image is processed to obtain useful features with high distinguishability between the fingerprint classes [5]. Generally, the fingerprint classification approach requires preprocessing techniques such as machine learning and deep learning and bioinspired models that are usually used to improve the accuracy of fingerprint classification [1]. In the last two decades, several machine learning and deep learning approaches have been implemented in fingerprint classification [1]. Fingerprint classification algorithms are important for reducing the search time and computational complexity of fingerprint identification systems [6]. Fingerprint classification is also known or referred to as image classification, which means that the image is structured into a certain category of information [6]. The accuracy and robustness of fingerprint classification rely on the strength of the extracted
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features and the ability to deal with low-quality fingerprints [5]. In addition, AFIS is the most essential stage in fingerprint classification because it provides the indexing mechanism and facilitates the matching process in a large fingerprint database [7]. Several approaches have been developed for automatic fingerprint classification, such as deep learning, machine learning, and bioinspired optimization algorithms [8]. In this research paper, we present an up-to-date systematic literature review of fingerprint classification models based on bioinspired optimization algorithms. Alternatively, we discovered that there is a paucity of using bioinspired optimization algorithms in fingerprint classification [9]. Bioinspired algorithms are recognized as a superior technique to address the optimal solutions to tackle the problems of traditional optimization algorithms such as machine learning and deep learning techniques [10]. Over the past few decades, numerous research papers have concentrated on classifying fingerprints using deep learning and machine learning techniques [11]. Numerous techniques and algorithms have been created during the past few decades in a variety of domains, including genetic algorithms, artificial neural networks, and evolutionary and fuzzy algorithms [10]. Nature-inspired algorithms are metaheuristic algorithms that mimic nature for solving optimization problems, exploring new areas of application, and providing more opportunities in computing [12]. 1.1 Bioinspired Optimization Bioinspired algorithms are a field and discipline that seeks to identify the ideal solutions to a particular problem, such as optimization and classification [13]. Similarly, bioinspired algorithms are also referred to as metaheuristic algorithms. Typically, optimization approaches aim to increase system efficiency and optimize the value of an objective function. Furthermore, bioinspired optimization refers to a broad range of computational methods that are based on the basic principles of biological systems. Initially, humans simply knew nature as a source of food and shelter, and then humans began to observe and see nature objectively as well as subjectively. Humans are fascinated by a variety of physical phenomena, food foraging practices of insects and animals, and sociocultural conduct that humans realize and understand the functioning of nature and their efficiency. Thereafter, researchers, academics, and institutions called it a novel and natural computing revolution for closing the gap between computing and nature. In this nature-inspired algorithm, various nature-based algorithms are developed and used [14, 15]. For instance, bioinspired algorithms are also classified into various parts, such as evolutionary, swarm intelligence, ecology, and multi-objective algorithms [16]. Additionally, bioinspired algorithms are adaptable and straightforward due to their simple concept, easy implementation, and ability to be easily modified in accordance with particular optimization and classification problems [17–19]. This research paper aimed to explore how nature has inspired biometric technologies through the design and optimization of fingerprint classification. In this regard, metaheuristic algorithms or bioinspired algorithms have played a vital role in modeling and designing biometric systems and tackling various optimization and classification problems [17, 20]. Bioinspired approaches are efficient and robust strategies that have been elucidated to be computationally efficient in resolving fingerprint classification [12]. In addition, most of this research paper is prepared as follows: an overview of fingerprint classification
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is presented in Sect. (1). Furthermore, Sect. (1.1) presents the bioinspired optimization approach. In addition, Sect. (2) explains the method that was conducted to realize the research aim and objectives. Then, Sects. (3 & 4) indicate the results and discussion and include the conclusion and future research.
2 Methodology Within this section, the Preferred Reporting Items for Systematic Reviews and MetaAnalysis (PRISMA) guidelines were modified for the study selection. The outcomes of this research paper were limited because PRISMA requires a large amount of data to be examined and analysed. For this reason, we employed the Systematic Literature Review (SLR) method, as shown in Fig. 2. In this regard, a systematic literature search query was carried out using Scopus and IEEExplore, which are two large bibliographic databases that cover scholarly literature from nearly every discipline. Despite our search in the Web of Science database, no articles based on the classification of fingerprints using bioinspired algorithms were found. The SLR is a method utilized for conducting systematic reviews and gathering all available empirical research data to find the gap and explore a new technique to be developed. Similarly, SLR is a method for exploring a given topic and designing, reviewing, and analysing the results [21, 22]. SLR relies on the quality of evidence and evaluating the quality of evidence, which precisely helps in better extracting accurate, good-quality data from a flood of data that is being produced [23]. In this research paper, SLR was undertaken to elucidate the available knowledge for professional practice and to determine the gaps in this research topic, techniques, and unpublished sources [24]. Figure 2 outlines the six steps for performing a systematic literature review:
Fig. 2. Systematic literature review method.
2.1 Research Identification The research paper focuses on assessing bioinspired optimization algorithms for the classification of fingerprints. Therefore, we conducted a systematic literature analysis to identify the gap in using metaheuristic algorithms for fingerprint classification. 2.2 Research Strategy The journal articles generated from both databases, Scopus and IEEExplore, were organized using Endnote reference database systems for screening of articles and bibliographic management. Endnote systematically captures the reference details during the
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screening of journal articles and the search process from various sources [21]. Additionally, the following terms were used to perform this research paper: (Fingerprint classification algorithm AND Bioinspired OR Metaheuristic OR Swarm intelligence OR Particle Swarm Optimization OR Bee OR Ant Colony). 2.3 Study Selection In this step, based on particular criteria, we determined which studies were to be included or excluded. The systematic literature review criteria that were utilized are as follows: (1) the research paper was written in English and published in scientific journals, and (2) the research paper has relevant information about the research context and contains innovative solutions and further research studies. Moreover, journal articles that are not accessible or available are considered to be excluded. In this regard, the final list of the considered published journals and conference papers is 10 journal articles. Although the screening of the articles was based on reading the title, abstract, and the whole journal article, we determined if the journal articles were relevant to the research paper context. In this regard, journal articles were screened 278 articles and 256 articles were excluded. Furthermore, each included article contains quality information that is relevant to the research paper and represents the highest level of research as well as disseminating new findings [21]. 2.4 Quality Assessment In this step, we used quality criteria to evaluate the rigor and credibility of selected journal articles, and these criteria are as follows: (1) articles generated from the databases are adequate and linked to this research paper, (2) the literature review is adequate and addresses the research-related issues, and (3) data analysis is sufficiently rigorous. 2.5 Data Extraction In this step, we extracted journal articles, particularly from the Scopus and IEEExplore databases, as described in Sect. 2.2. However, we separated these journal articles into inclusion and exclusion criteria accordingly. Then, we extracted data from the qualified articles as explained in the next section. 2.6 Data Synthesis and Analysis In this step, Fig. 3 represents the processes for conducting the modified PRISMA flowchart and describes how the SLR in this research paper was conducted. The first step started with identifying the gap and the reason for conducting this research study, which is indicated in the first step as a Research Question (RQ). Step 2 indicates the keywords and databases used to conduct the research paper and is also mentioned in Sect. 2.2. In the study selection step, initially, we identified 290 articles from the IEEExplore and Scopus databases, and we found 6 journal articles that were duplicates that were removed; thereafter, approximately 278 journal article records were screened. In
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Fig. 3. Modified PRISMA flowchart describing the systematic literature review process.
step 4, quality assessment, we found 22 journal articles that were assessed and evaluated for the study context. Afterward, in step 5, we found 10 qualified journal articles utilized for this research paper, and SLR was applied.
3 Results and Discussion Following the comprehensive systematic review, 10 publications on fingerprint classification using bioinspired algorithms were found, and pertinent data were gathered using the methods described in Sect. 2. Approximately seven studies were electronic journal articles, and three studies were conference papers. The primary sources of information were journal papers published in scientific journals, as given in Table 1. In this section, we conducted an analysis of the geographical distribution of the research papers and discussed the frequency of those papers based on various fingerprint classification approaches. This was followed by the statistical analysis of the general information obtained from the fingerprint classification approach. Furthermore, we have identified many facts about fingerprint classification accuracy, methods, and techniques used to accomplish high accuracy based on a bioinspired optimization approach. In addition, most of the studies were published and have been carried out in India, which also indicates 60% compared to Iraq, South Korea, the United States, and China equally 10%. Figure 4 shows a detailed description of the percentage and total selected studies in these countries that are relevant to this research paper. For instance, as seen, the majority of the studies have been undertaken in India and are more than two times
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Table 1. Describes the classification approach and dataset. Author
Classification Method & Technique
Dataset
Accuracy
Sample Size
[26]
PSO-FLANN
Real-time online fingerprint
98%
50
[27]
PSO-FLANN
NIST-9
98%
50
[28]
PSO-Support Vector Machine (SVM) classifier
CASIA V5
N/A
[9]
Back Propagation Neural Network (BPN) and GA, PSO and Ant Colony Optimization (ACO)
NIST-4
GA = 95.00 PSO = 97,90 ACO = 93,10
150
[29]
Fuzzy Feed Forwards FVC2000 Neural Network (FFFNN) with Artificial Bee Colony
N/A
N/A
[30]
Artificial Bee Colony Optimization algorithm based on Feed Forwards Neural Network
FVC2000
N/A
N/A
[31]
Graph-based semi-supervised learning and swarm intelligence
FVC2000
N/A
N/A
[32]
Markov Model with GA
FVC2000 & FVC2002
DB1 = 81,9 DB2 = 82,3 DB1 = 83,0 DB2 = 82,4
N/A
[33]
Learning Algorithm Approach on Genetic Programming
NIST-4
93,3% & 91,6%
N/A
[34]
Feedback mechanism based on Genetic Algorithm
NIST-14
N/A
N/A
higher than the number of studies undertaken in the comparative research countries of Iraq, South Korea, the United States, and China. Although most of the research papers conducted in India are recent, such as those of Mishra, Dehuri, Mithuna, and Sasikala, were published in 2019, 2017, 2015, and 2014. One possible reason for this great variance in national research focuses is that India boasts the world’s most extensive national biometric identification system. It has a huge population base of 1.2 billion citizens. India uses biometrics to identify and verify individual citizens. Indian institutions are researching and using bioinspired algorithms to improve their country’s biometric identification systems. In this research paper, we
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discovered that Indian institutions have had tangible success in using bioinspired algorithms for the classification of fingerprints. Given this huge amount of research in India, it has been determined that bioinspired algorithms perform much better than classical or traditional algorithms [35].
Percentage of Selected Studies Per Country 10% 10% 10%
60%
10%
India
Iraq
South Korea
United States
China
Fig. 4. Percentage of selected studies per country.
Figure 5 shows selected studies per year. In 2019 and 2015, there were 2 published journal articles for each year. However, in 2017, 2014, 2011, 2005, and 1998, there was one published journal article for each year.
Graph Presents Selected Studies Per Year 2.5 2 1.5 1 0.5 0 2019
2017
2015
2014
2018
2011
2005
1998
India
India
India
India
Iraq
South Korea
United States
China
Number of Publications Per Year Fig. 5. Graph presents selected studies per year.
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3.1 Fingerprint Classification Based on Bioinspired Algorithm Table 1 describes the classification approach, dataset, and sample size that was used in this selected study. Every study has used different classification approaches to measure the accuracy and to improve the performance accuracy of fingerprint classification. For instance, the study of real-time online fingerprint image classification has used adaptive hybrid techniques. The classification of online real-time fingerprints is a complex task because it involves adaptation and tuning of classifier parameters for better accuracy. For this task to be accomplished, sample images of 50 fingerprints were collected from 10 students to perform fingerprint classification [26]. However, the optimal adaptation of the parameters of the Functional Link Artificial Neural Network (FLANN) for real-time online fingerprint classification is proven, and an optimizer is established. Furthermore, the Biogeography-Based Optimizer (BBO), genetic algorithm, and PSO are effectively combined to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system [26]. The PSO-FLANN technique has shown superior performance compared to the GA-FLANN and BBO-FLANN techniques. In real-time online fingerprint classification, 98% is the highest accuracy recorded by the PSO-FLANN application [26].
4 Conclusion In this paper, we presented state-of-the-art systematic literature reviews for fingerprint classification based on a bioinspired optimization algorithm. Our findings demonstrate the rarity of fingerprint classification based on bioinspired optimization algorithms and the scarcity of literature reviews in the biometric field. We found that most of the selected studies did not accurately classify and assess accuracy using precision, recall, and fmeasure for accurate classification and evaluation of fingerprint accuracy. Additionally, researchers and academics may also employ a hybrid algorithm that combines bioinspired and classical techniques to address fingerprint optimization and classification accuracy in the future.
References 1. Win, K.N., Li, K., Chen, J., Viger, P.F., Li, K.: Fingerprint classification and identification algorithms for criminal investigation: a survey. Futur. Gener. Comput. Syst. 110, 758–771 (2020) 2. Militello, C., Rundo, L., Vitabile, S., Conti, V.: Fingerprint classification based on deep learning approaches: experimental findings and comparisons. Symmetry Multi. Digit. Publish. Inst. 13(5), 1–21 (2021) 3. Jiang, X.: Fingerprint classification. In: Encyclopedia of Biometrics (2009) 4. Ali, M.M.H., Mahale, V.H., Yannawar, P., Gaikwad, A.T.: Fingerprint recognition for person identification and verification based on minutiae matching. In: Paper presented at the IEEE 6th International Conference on Advanced Computing (IACC). Bhimavaram, India, 27–28 February, pp. 332–339 (2016)
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5. Zia, T., Ghafoor, M., Tariq, S.A., Taj, I.A.: Robust fingerprint classification with Bayesian convolutional networks. IET Image Proc. 13(8), 1280–1288 (2019) 6. Wu, H., Liu, Q., Liu, X.: A review on deep learning approaches to image classification and object segmentation. Comput. Mater. Continua 60(2), 575–597 (2019) 7. Abbood, A.A., Sulong, G.: Fingerprint classifiction techniques: a review. Int. J. Comput. Sci. (IJCSI) 11(1), 111–122 (2014) 8. Jain, A.K., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 348–359 (1999) 9. Mithuna, K.T., Sasirekha, K., Thangavel, K.: Metaheuristic optimization algorithms based feature selection for fingerprint image classification. In: Proceedings of Proceedings of the International Conference on Intelligent Computing Systems. Salem, Tamilnadu, India, 15–16 December, pp. 130–139 (2017) 10. Darwish, A.: Bioinspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput. Inform. J. 3(2), 231–246 (2018) 11. Binitha, S., Sathya, S.: A survey of bioinspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2), 137–151 (2012) 12. Selvaraj, C., et al.: A survey on application of bioinspired algorithms. Int. J. Comput. Sci. Inf. Technol. 5(1), 366–370 (2014) 13. Valdez, F., Castillo, O., Melin, P.: Bioinspired algorithms and its applications for optimization in fuzzy clustering. Algorithms – Multi. Digit. Publish. Inst. 14(4), 1–21 (2021) 14. Rai, D., Tyagi, K.: Bioinspired optimization techniques: a critical comparative study. ACM SIGSOFT Softw. Eng. Notes 38(4), 1–7 (2013) 15. Game, P.S., Vaze, V.M.M.: Bioinspired optimization: metaheuristic algorithms for optimization. Neural Evol. Comput. 1–9 (2020) 16. Fan, X., Sayers, W., Zhang, S., Han, Z., Ren, L., Chizari, H.: Review and classification of bioinspired algorithms and their application. J. Bionic Eng. 17, 611–631 (2020) 17. Abd-Alsabour, N.: Hybrid metaheuristics for classification problems (2016) 18. Bandaru, S., Deb, K.: Metaheuristic techniques. In: Decision Science: Theory and Practice, pp. 1–49. CRC Press, Boca Raton (2016) 19. Agrawal, P., Abutarboush, H.F., Ganesh, T., Mohamed, A.W.: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019). IEEE Access 9, 26766– 26791 (2021) 20. Zhang, G., Pan, L., Neri, F., Gong, M., Leporati, A.: Metaheuristic optimization: algorithmic design and applications. J. Optim. 1–2 (2017) 21. Henriette, E., Feki, M., Boughzala, I.: The shape of digital transformation: a systematic literature review. In: Association for Information Systems AIS Electronic Library (AISeL), pp. 1–14 (2015) 22. Shamseer, L., et al.: Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P): elaboration and explanation. Br. Med. J. (BMJ), 1–25 (2015) 23. Ahn, E., Kang, H.: Introduction to systematic review and meta-analysis. Nat. Libr. Med. 71(2), 103–112 (2018) 24. Okoli, C.: A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37, 879–910 (2015) 25. Bandara, W., Miskon, S., Fielt, E.: A systematic, tool-supported method for conducting literature reviews in information systems. In: Proceedings of 19th European Conference on Information Systems (ECIS). Helsinki, Finland, 09–11 June, pp. 1–14 (2011) 26. Mishra, A., Dehuri, S.: Real-time online fingerprint image classification using adaptive hybrid techniques. Int. J. Electr. Comput. Eng. 9(5), 4372–4381 (2019) 27. Mishra, A., Dehuri, S.: A novel hybrid flann-pso technique for real time fingerprint classification. Med.-Legal Update 19(2), 740–746 (2019)
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28. Al-sagheer, R.H.A., Mona, J., Abdulmohson, A., Abdulameer, M.H.: Fingerprint classification model based on new combination of particle swarm optimization and support vector machine. Int. J. Civil Eng. Technol. (IJCIET) 9(11), 78–87 (2018) 29. Sasikala, V., LakshmiPrabha, V.: A comparative study on the swarm intelligence based feature selection approaches for fake and real fingerprint classification. In: Paper presented at the International Conference on Soft-Computing and Networks Security (ICSNS). Coimbatore, India, 25–27 February, pp. 1–8 (2015) 30. Sasikala, V., Lakshmi, P.V.: Bee swarm based feature selection for fake and real fingerprint classification using neural network classifiers. IAENG Int. J. Comput. Sci. 42(4), 389–403 (2015) 31. Sasikala, V., Lakshmi Prabha, V.: A swarm intelligence based feature selection approach for efficient fake and real fingerprint classification using semisupervised learning. Int. J. Appl. Eng. Res. 9(23), 20611–20636 (2014) 32. Jung, H.W., Lee, J.H.: Live-scanned fingerprint classification with markov models modified by GA. Int. J. Control Autom. Syst. 9(5), 933–940 (2011) 33. Tan, X., Bhanu, B., Lin, Y.: Fingerprint classification based on learned features. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(3), 287–300 (2005) 34. Qi, Y., Tian, J., Dai, R.: Fingerprint classification system with feedback mechanism based on genetic algorithm. In: Proceedings of Fourteenth International Conference on Pattern Recognition (Cat. No. 98EX170). Brisbane, Queensland, Australia, 20 August, pp. 1–3 (1998) 35. Rao, U., Nair, V.: Aadhaar: governing with biometrics. South Asia J. South Asian Stud. 42(3), 469–481 (2019)
Inspired Nature Meta-Heuristics Minimizing Total Tardiness for Manufacturing Flow Shop Scheduling under Setup Time Constraint Omar Nejjarou1(B) , Said Aqil2 , and Mohamed Lahby1 1 Laboratory of Applied Mathematics and Computer Sciences, Higher Normal School Hassan II
University of Casablanca, 50069 Casablanca, Morocco [email protected], [email protected] 2 Laboratory Artificial Intelligence and Complex Systems Engineering, National Higher School of Arts And Crafts, University Hassan II of Casablanca, Casablanca, Morocco [email protected]
Abstract. The study of the variant under machine unavailability and setup time constraints is of great industrial importance. In this paper, we present an optimization of manufacturing flow shop scheduling under setup time constraints. The objective is to minimize the total tardiness for a set of jobs with deadline times to respect. This intention is aimed at satisfying customer requirements and gaining their trust. To investigate this problem, we applied three inspired-nature metaheuristics: the artificial bee colony algorithm, the genetic technique algorithm and finally the migratory bird optimization algorithm. A set of numerical simulations on a variety of instances is suggested to check the efficiency and strength of our algorithms. The results obtained show the strong success of the migratory bird optimization algorithm compared to other algorithms. Keywords: Inspired nature metaheuristics · Manufacturing flow shop · Optimization · Sequence dependent setup time · Total tardiness
1 Introduction The manufacturing flow shop scheduling (MFSS) optimization problem is widely present in several industries. In real industrial cases, manufacturing machines require prior tuning before processing a new job. This constraint has become prevalent in varieties of production machines. When a job requires a setup time dependent on its successor, this induces the sequence-dependent setup time (SDST) constraint. In this contribution, we study the MFSS_SDST issue to reduce the total tardiness (TT) criterion. Production managers are constrained to satisfy customer demands while respecting a delay (dj ) of one job or job batch. Fm|SDST, prum, dj|TT with respect to the notation adopted in the literature scheduling optimization area, will denote this problem. The first field α represents the manufacturing flow shop with Fm serial machines, the second field β designates the constraints: prmu, SDST, dj and the third field γ identifies the criterion to be minimized. The MFSS flow shop problem [1–3] has been the subject of many © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 44–54, 2023. https://doi.org/10.1007/978-3-031-29860-8_5
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solution approaches based on approximate and exact methods. The exact methods are essentially the mathematical formulation and the branch and bound technique [3, 4]. A mathematical formulation based on mixed-integer linear programming (MILP) is found in [5–7] solving the optimization MFSS manufacturing. Similarly, approximate techniques are based on heuristic and metaheuristic tools. The latter methods constitute the majority of the contributions to the solution of MFSS problems. The introduction of the SDST constraint is studied in numerous research works with different exact and approximate solution approaches. This constraint is taken into account in the paint industry, metallurgy, assembly/disassembly, etc. The MFSS_SDST problem is addressed in [8] by minimizing the makespan using the genetic algorithm (GA). In addition, this problem is solved using the iterated greedy algorithm (IG) to optimize the same criterion [9–11]. Other approaches are applied by using recent methods based on inspired nature meta-heuristics. Indeed, we find the artificial bee colony (ABC) algorithm to reduce the makespan [12]. In [13], the suggested simulated annealing algorithm solves the MFSS_SDST under no-wait constraint minimizing total tardiness and makespan criteria. On the other hand, we find the migratory bird optimization (MBO) method to minimize the maximum completion time [14]. We continue on the same path by applying approximate methods for solving the optimization MFSS_SDST problem. The high power of calculating machines by the technological advance of computers has pushed us to opt for metaheuristic algorithms. Recent studies show the great importance of iterative approaches for solving a large class of optimization scheduling problems. Indeed, the batch size of industrial problems is often large, which limits the application of exact methods to solve real industrial illustrations. In this contribution, we propose three inspired natural metaheuristic algorithms. Our goal is to apply the ABC, GA and MBO algorithms with a variety of local search methods. These methods make it possible to best explore the neighborhood of the current solution by a simple disruption of the sequence. Indeed, our improvement consists of generating sequences by insertion, permutation or a large neighborhood system. Our improvement therefore consists of better choosing the variable settings of each algorithm to obtain the best possible result. Such an approach allows having no redundancy on the solutions already treated during the evolution of the local search. We give in detail our exploration approach for each metaheuristic. Works analysing the industrial optimization problem MFSS are increasingly numerous. Recently the subject has been studied in [15– 17] by the application of metaheuristics and methods based on the MILP formulation. Taking into account the setup time constraint is mentioned in [18, 19] for the analysis of the MFSS_SDST problem. Motivated by these contributions, we continue our research by applying nature-inspired metaheuristics to solve the MFSS_SDST problem. The structure of the paper is as follows: in Sect. 2, we present the MFSS_SDST model. Section 3 gives the adopted methods. Section 4 shows the computational results and a comparative analysis of the developed methods. The section highlights the strengths of our work as well as avenues to explore in the future.
2 The Statement of MFSS_SDST Optimization Problem The MFSP_SDST optimization problem in the manufacturing process is formulated as follows. There is an R = {R1 „ Rm } resource machine in series, with a set of works W
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= {W1 ,..,Wn } that must be processed by these machines consecutively. The processing of these tasks started with the first machine R1 and finished its execution in the last machine Rm . For any work Wq : q = 1,..,n the processing time in machine Rl : l = 1,.., m that will be noted alq , with a sequence dependent setup time blqy . Where the work Wy is the successor of the work Wq in the machine Rl . The setup time between two consecutive works on the same machine depends only on the order of the works. The scheduling sequence will be noted as follows Ѵ= [Ѵ1 ,…, Ѵn ] for the production batch to be manufactured. The finishing time of a Work Wq on a machine Rl is designated by the variable FTlq . The mathematical model will be presented in subsequent equations:
2.1 Example In this part, we will present an example of an industrial case always with the goal of minimizing the total tardiness. Subsequently, we have schematized the problem with 3 machines R = {R1 ,…, R3 } executing 6 works W = {W1 ,…, W6 }, and the delays for all works are d1 = 38, d2 = 15, d3 = 26, d4 = 21, d5 = 32, d6 = 9. The sequence of execution is the following Ѵ = [W6 , W2 , W4 , W3 , W5 , W1 ]. The numerical data are expressed by the matrix [alq ] of the processing times and the setup time matrices [b1qy ], [b2qy ] and [b3qy ]. The data related to this problem are: ⎡ ⎤ 223323 ⎢2 4 2 3 5 2⎥ ⎡ ⎤ ⎢ ⎥ 2 3 6 2 3 2 ⎢ ⎥ 1 2 6 2 4 2 ⎢ ⎥ alq 3x6 = ⎣ 2 3 2 2 4 4 ⎦; b1qy 6x6 = ⎢ ⎥ ⎢1 3 2 1 2 1⎥ ⎢ ⎥ 443332 ⎣2 1 1 2 4 2⎦ 322322
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⎤ ⎤ ⎡ 114323 321241 ⎢2 2 2 3 5 2⎥ ⎢3 2 3 2 2 1⎥ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎢1 2 1 2 4 2⎥ ⎢2 3 4 4 4 3⎥ =⎢ ⎥; b3qy 6x6 = ⎢ ⎥ ⎢1 3 2 4 2 1⎥ ⎢1 1 2 3 3 2⎥ ⎥ ⎥ ⎢ ⎢ ⎣2 1 1 2 5 2⎦ ⎣2 2 2 3 4 1⎦ 322336 231426 ⎡
b2qy 6x6
In Fig. 1 we can see the tardiness of each work as well as the total tardiness is TT = 22 unit of time.
Fig. 1. Flowchart Gantt for three jobs and three machines.
3 Solution Approaches 3.1 The ABC Metaheuristic The ABC method is an optimization approximate algorithm based on population, which was proposed in [20]. The population is made up of three subsets of bees: workers, spectators and scouts. We propose a simulation model based on the behavior of finding food for the optimization of the scheduling-problem MFSS_SDST. We find in [21], the ABC method for solving the distributed flexible flow shop optimization problem. In addition, the ABC metaheuristic is used to investigate the optimization manufacturing flexible job shop problem minimizing the makespan [22]. In addition, this technique is suggested to solve the distributed manufacturing flow shop-scheduling problem [23]. The proposed study of the bicriteria job shop industrial problem is to optimize the makespan and total delay [24]. In our study, we propose a model of the ABC algorithm reinforced by diversification of the neighbor exploration system. We suggest the three-neighborhood N1 , N2 and N3 structure to explore the space neighborhood system. The step implementation for the general algorithm of the ABC metaheuristic is given in Algorithm 1. To illustrate the neighborhood system (see Fig. 2), the current sequence is Ѵ= [W5 , W7 , W9, W6 , W1 , W2 , W4 , W8 , W3 ], and we randomly generate two positions p = 2 and q = 7. In the first neighborhood, the work in position p is swapped with the work in position q.
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Fig. 2. Neighborhood structure for the ABC algorithm.
In the second neighborhood, we give the new sequence by inserting and shifting right. Finally, the third neighborhood of the neighboring sequence is generated by an insertion and shift to the left.
3.2 The Genetic Algorithm The genetic algorithm is inspired by biological nature; this metaheuristic is based on the evolution of the genetic code over time. It is characterized by three main phases: selection, crossing and mutation. In [25] the GA was proposed to solve the permutation flow shop scheduling problem. Likewise, an improvement of GA with a view to solving the flexible scheduling minimizing the makespan, the total setup and transport time [26]. In addition, we consider the sequence only for the ABC algorithm to use it in the GA algorithm. To illustrate this technique, in Fig. 3, we propose the neighborhood structure of the GA technique. In the selection phase, we start from two parent sequences PA1 =[W5,W7 ,W9 ,W6 ,W1,W2,W4 ,W8,W3] and PA2= [W6,W5,W1 ,W9,W4 ,W3,W8,W7,W2] to generate two childrenCH 2 in the crossing phase. Subsequently, the mutation procedure is applied to generate the two sequences MU 1 and MU 2 by inversion between the two positions.
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Fig. 3. Neighborhood structure for GA algorithms.
3.3 The MBO Metaheuristic The MBO technique is a powerful metaheuristic for solving optimization industrial problems. This iterative approach is based on the simulation of the night behavior of migratory birds. The MBO method proposed by Duman et al. [27] is a recent high performing method. This algorithm is implemented for various optimization problems [28, 29]. The principle is to divide the population θ into two subpopulations, one on the right θR and the other on the left θL . Algorithm 3 gives the implementation phases. In subpopulation θR, we apply neighborhood N1 and subpopulation θL to neighbor N2 . Our improvement for this metaheuristic is to use a total permutation of a subset of the sequence to the current good solution. Indeed this approach allows a vast local search denoted by the total neighborhood LN as indicated (see Fig. 4).
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Fig. 4. Neighborhood structure for the MBO algorithm.
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4 Computational Result To evaluate the effectiveness of our methods, we perform a comparison of a set of problems. We note that the processing times and the setup-times constraint are created based on uniform laws such as aql ∈ [1, 49] and bqly ∈ [1, 25]. The number of machines is defined by m ∈ {5, 10} and the number of works is n ∈ {10, 20… 120} forming our benchmark of relatively small and medium-sized instances. Table 1. The total tardiness value of various problems; the good results are in bold. λ = 25 n×m
ABC
λ = 50 GA
MBO
ABC
λ = 10 0 GA
MBO
ABC
GA
MBO
10 × 5
275
341
258
245
298
226
189
261
168
10 × 10
571
618
545
529
575
513
501
489
481
20 × 5
627
652
588
589
593
541
501
553
489
20 × 10
880
875
795
825
845
803
766
735
699
30 × 5
1241
1258
904
1189
1093
851
989
927
798
30 × 10
1483
1550
1445
1349
1355
1340
1138
1241
1104
40 × 5
2088
1985
2001
1977
1889
1943
1782
1780
1770
40 × 10
3148
3250
3084
2944
3157
2840
2838
2941
2735
50 × 5
3388
3372
3375
3277
3284
3273
3082
2984
3012
50 × 10
4148
4250
3945
3944
4035
3814
3738
3841
3630
60 × 5
4481
4420
4377
4317
4300
4209
4082
4010
3974
60 × 10
6148
6150
5945
5944
5855
5742
5738
5611
5605
70 × 5
6688
6580
6575
6477
6489
6475
6282
6280
6279
70 × 10
7248
7350
7205
6940
6999
6940
6738
6801
6735
80 × 5
7688
7792
7775
7577
7589
7583
7482
7451
7380
80 × 10
8141
8157
7940
7844
7825
7740
7738
7641
7705
90 × 5
11288
11080
11075
10957
10980
10873
10882
10780
10679
90 × 10
13448
13450
13445
13344
13249
13184
13138
13091
12975
100 × 5
13788
13680
13475
13577
13580
13373
13382
13428
13370
100 × 10
14568
14510
14433
14344
14496
14340
14338
14341
14307
110 × 5
15888
15980
15758
15777
15749
15603
15689
15680
15587
110 × 10
16588
16445
16345
16323
16355
16248
16138
16241
16169
120 × 5
17288
17221
17181
17177
17180
17001
17052
17000
16986
120 × 10
19948
19850
19854
19844
19747
19798
19738
19683
19654
Table 1 Provides simulation results for a variety of instances. According to the computation time limit. This time makes it possible to limit the search according to the
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size of the instance. The following expression gives the stopping criterion of the three meaheuristics in milliseconds. τlimit = λ × n × m
(7)
The results are given for values of λ ∈[25, 50, and 100]. According to this simulation, it will be noted that the MBO algorithm records the best performance. Indeed, out of 24 instances for three configuration for. In total, we simulate 72 test problems. This comparative study shows that the MBO algorithm realized 62 better results compared to other algorithms, i.e. a success rate of 86.11%. The tests are given for five replications for each algorithm and we retain the best result. All algorithms are well calibrated with a good choice of implementation parameters. We focus exclusively on population size and neighborhood system.
5 Conclusion In this work, we have presented three inspired nature meta-heuristics for resolving the manufacturing optimization flow-shop scheduling problem. This study is focused on minimizing the total tardiness under setup time constraints. To find a solution to this kind of problem we have enhanced these meta-heuristics with neighborhood search methods. We conducted a comparative simulation study to verify the robustness and efficiency of our methods. We performed a series of tests on instances of different sizes depending on the number of machines and jobs. Indeed, we generated a total of 72 problem test instances. The results obtained confirm that MBO is a powerful technique for solving the optimization scheduling issue. A perspective for the continuation of this work consists of hybridizing the three algorithms with other neighborhood exploration procedures. In addition, the study will be extended for other constraints such as blocking and other criteria such as the total flow time.
References 1. Zobolas, G.I., Tarantilis, C.D., Ioannou, G.: Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm. Comp. Oper. Res. 36(4), 1249– 1267 (2009) 2. Lian, Z., Gu, X., Jiao, B.: A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos Solit. Fract. 35(5), 851–861 (2008) 3. Gmys, J., Mezmaz, M., Melab, N., Tuyttens, D.: A computationally efficient Branch-andBound algorithm for the permutation flow-shop scheduling problem. Euro. J. Oper. Res. 284(3), 814–833 (2020) 4. Kim, H.J., Lee, J.H.: Three-machine flow shop scheduling with overlapping waiting time constraints. Comp. Oper. Res. 101, 93–102 (2019) 5. Brammer, J., Lutz, B., Neumann, D.: Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning. Euro. J. Oper. Res. 299(1), 75–86 (2022) 6. Ta, Q.C., Billaut, J.C., Bouquard, J.L.: Matheuristic algorithms for minimizing total tardiness in the m-machine flow-shop scheduling problem. J. Intel. Manuf. 29(3), 617–628 (2018) 7. Khatami, M., Salehipour, A., Hwang, F.J.: Makespan minimization for the m-machine ordered flow shop scheduling problem. Comp. Oper. Res. 111, 400–414 (2019)
Inspired Nature Meta-Heuristics Minimizing Total Tardiness
53
8. Aqil, S., Allali, K.: On a bi-criteria flow shop-scheduling problem under constraints of blocking and sequence dependent setup time. Ann. Oper. Res. 296(1), 615–637 (2021) 9. Aqil, S., Allali, K.: Three metaheuristics for solving the flow shop problem with permutation and sequence dependent setup time. In: 2018 4th International Conference on Optimization and Applications (ICOA), pp. 1–6. IEEE (2018) 10. Mohammadi, M., Shayannia, S.A., Lotfi, M., Rezaeian Zaidi, J.: Modeling and solving the flow-shop scheduling problem with sequence-dependent setup times by firefly algorithm (case study: automotive industry). Disc Dyn. Nat. Soc. (2022) 11. Ramezanian, R., Vali-Siar, M.M., Jalalian, M.: Green permutation flow shop scheduling problem with sequence-dependent setup times: a case study. Inter. J. Prod. Res. 57(10), 3311–3333 (2019) 12. Li, H., Gao, K., Duan, P.Y., Li, J.Q., Zhang, L.: An improved artificial bee colony algorithm with q-learning for solving permutation flow-shop scheduling problems. IEEE Trans. Syst. Man Cybern. Syst. (2022) 13. Allahverdi, A., Aydilek, H., Aydilek, A.: No-wait flow shop scheduling problem with separate setup times to minimize total tardiness subject to makespan. App. Math. Comp. 365, 124688 (2020) 14. Sioud, A., Gagné, C.: Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times. Euro. J. Oper. Res. 264(1), 66–73 (2018) 15. de Fátima Morais, M., Ribeiro, M.H.D.M., da Silva, R.G., Mariani, V.C., dos Santos Coelho, L.: Discrete differential evolution metaheuristics for permutation flow shop scheduling problems. Comp. Indus. Eng. 166, 107956 (2022) 16. Umam, M.S., Mustafid, M., Suryono, S.: A hybrid genetic algorithm and tabu search for minimizing makespan in flow shop scheduling problem. J. King Saud. Univ-Comp. Info. Sci. 34(9), 7459–7467 (2022) 17. Zhang, W., Hou, W., Li, C., Yang, W., Gen, M.: Multidirection update-based multiobjective particle swarm optimization for mixed no-idle flow-shop scheduling problem. Compl. Syst. Model Simul. 1(3), 176–197 (2021) 18. Almeida, F.S.D., Nagano, M.S.: Heuristics to optimize total completion time subject to makespan in no-wait flow shops with sequence-dependent setup times. J. Oper. Res. Soc. 1–12 (2022) 19. Rahman, H.F., Janardhanan, M.N., Chuen, L.P., Ponnambalam, S.G.: Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain. Comp. Ind. Eng. 158, 107378 (2021) 20. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007) 21. Li, Y., Li, F., Pan, Q.K., Gao, L., Tasgetiren, M.F.: An artificial bee colony algorithm for the distributed hybrid flow shop-scheduling problem. Proc. Manuf. 39, 1158–1166 (2019) 22. Wang, L., Zhou, G., Xu, Y., Wang, S., Liu, M.: An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Inter. J. Adv. Manuf. Tech. 60(1), 303–315 (2012) 23. Huang, J.P., Pan, Q.K., Miao, Z.H., Gao, L.: Effective constructive heuristics and discrete bee colony optimization for distributed flow shop with setup times. Eng. Appl. Artif. Intel. 97, 104016 (2021) 24. Scaria, A., George, K., Sebastian, J.: An artificial bee colony approach for multi-objective job shop scheduling. Proc. Tech. 25, 1030–1037 (2016) 25. Etiler, O., Toklu, B., Atak, M., Wilson, J.: A genetic algorithm for flow shop scheduling problems. J. Oper. Res. Soc. 55(8), 830–835 (2004) 26. Zhang, G., Hu, Y., Sun, J., Zhang, W.: An improved genetic algorithm for the flexible job shop-scheduling problem with multiple time constraints. Swarm. Evol. Comput. 54, 100664 (2020)
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27. Duman, E., Uysal, M., Alkaya, A.F.: Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf. Sci. 217, 65–77 (2012) 28. Cao, J., Guan, Z., Yue, L., Ullah, S., Sherwani, R.A.K.: A bottleneck degree-based migrating birds optimization algorithm for the PCB production scheduling. IEEE Access 8, 209579– 209593 (2020) 29. Ulker, E., Tongur, V.: Migrating birds optimization (MBO) algorithm to solve knapsack problem. Proc. Comp. Sci. 111, 71–76 (2017)
Punishing the Unpunishable: A Liability Framework for Artificial Intelligence Systems Rushil Chandra1,2(B)
and Karun Sanjaya1,3
1 Symbiosis Law School, Nagpur, A Constituent of Symbiosis, International (Deemed
University), Pune, India [email protected], [email protected] 2 Gujarat National Law University, Gandhinagar, India 3 VIT School of Law, Vellore Institute of Technology, Chennai, India
Abstract. Artificial Intelligence (AI) systems are increasingly taking over the day-to-day activities of human beings as a part of the recent technological revolution that has been set into motion ever since we, as a species, started harnessing the potential these systems have to offer. Even though legal research on AI is not a new phenomenon, due to the increasing “legal injuries” arising out of commercialization of AI, the need for legal regime/framework for the legal accountability of these artificial entities has become a very pertinent issue that needs to be addressed seriously. This research paper shall investigate the possibility of attaching civil as well as criminal liability to AI systems by analysing whether mens rea can be attributed to AI entities and, if so, what could be the legal framework/model(s) for such proposed culpability. The paper acknowledges the limitations of the law in general and criminal law in particular when it comes to holding AI systems criminally responsible. The paper also discusses the legal framework/legal liability model(s) that could be employed for extending the culpability to AI entities and understanding what forms of “punishments” or sanctions would make sense for these entities. Keywords: Artificial intelligence · legal personhood · liability · black-box algorithm · mens rea
1 Introduction The year 1979 witnessed a dreadful event in Michigan, United States, wherein Mr Robert Nicholas Williams, a factory worker employed at Ford Motor’s assembly plant in Michigan, was killed by an industrial robot arm. The same is believed to be the first known incident where a human was killed by a robot. The family of the deceased was awarded compensation of 10 million USD by the jury in 1983 [1]. This case answered a very crucial question – Who shall be held liable for the acts of a robot or an artificial intelligence entity? In the instant case, the manufacturers of the robotic arm, Litton Industries, were held liable for the unfortunate event. Two years after the Williams incident, another
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 55–64, 2023. https://doi.org/10.1007/978-3-031-29860-8_6
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shocking incident sparked a debate regarding the criminal liability of robots and AI systems. Kenji Urada, a maintenance worker at Kawasaki Heavy Industries, Akashi, Japan, was killed by an artificial intelligence robot working near him [2]. Yet another incident took place in 2015 when a robot named Random Darknet Shopper (RDS) bought drugs online and got caught. RDS was an Artificial Intelligence entity that was a part of a project wherein the items bought by RDS would be put up for exhibition in the Swiss art gallery. RDS purchased Ecstasy, and a Hungarian passport and the same was displayed in the art exhibition. Consequently, RDS was taken into custody [3]. A criminal action could be taken against the creators of RDS and the hosts of the exhibition, but can RDS be held criminally liable under the existing laws? Recently in 2018, a 49-year-old lady was killed by a self-driving Uber cab in Arizona, United States. Immediately after the incident, Uber announced the suspension of testing of its autonomous vehicles. Though it resulted in the death of an innocent pedestrian, Uber was not held criminally liable as the same was considered an accident [4]. Is a mere monetary liability proportionate for an act that culminated in the death of a person? Let us consider a hypothetical scenario in India wherein an individual driving a car causes the death of an innocent pedestrian. The same would attract criminal liability under Sect. 304 A of the Indian Penal Code, 1860. There are also other cases wherein AI entities have disrupted law and order. For instance, the allegedly racist algorithm of Amazon wherein Amazon Prime’s free same-day delivery service was not available in specific neighbourhoods where the African-Americans primarily resided [5]. These unfortunate incidents led to a debate as to the liability of Artificial Intelligence systems, and we are faced with the question of who or what should be blamed in case of an AI crime. Since there is no legal personhood attached to an AI entity, punishing the AI is not possible. At the same time, putting the entire liability on the developers or manufacturers of the AI system would seem unfair if they have taken due diligence and reasonable care. The same would not only be unfair but also discourage innovation in the field of AI. The paper, therefore, proposes to establish a liability framework in the event of an AI entity being involved in any crime or wrongdoing. This research paper can be broadly segregated into four parts. Section 1 of the paper shall provide an introduction to AI-based offences. Section 2 of the paper briefly summarizes the evolution of AI. Section 3 of the paper talks about black-box algorithms and the challenges put forth by them. Section 4 details the principles of robotics. Section 5 discusses the liability framework from both criminal and civil law perspectives. In Sect. 6, the authors conclude the paper with multiple approaches that can be adopted and the framework for each of the approaches.
2 Technological Advancements and Thinking Machines Einstein– “Technological progress is like an axe in the hands of a pathological criminal.” Criminality is a by-product of any societal machinery, irrespective of the form of governance. The primary objective of the law is to cater to the needs of society because, ultimately, the law is developed to regulate human conduct as well as the conduct of other subjects and variables influencing the actions or inactions of human being in a civilized setup [6]. One such influential variable is Artificial Intelligence (AI) which is bringing
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in a storm of technological revolution, thereby modifying societal interactions between humans and posing tough challenges with respect to its regulation to legal practitioners, scholars and academicians all over the globe. Intelligence or intellect, i.e., the capability to comprehend and think, is no longer a product of biological algorithms of the human brain. AI technology has proven that ‘intellect’ can be artificially created by using techniques like Natural Language Processing (NLP), Deep Learning/Machine Learning (ML) and Deep Neural Networks (DNN). The aforementioned makes it quite clear that AI algorithms are different from conventional computer algorithms primarily because of their ability to train themselves on the data provided. This unique feature allows AI to produce different outputs depending upon the situation at hand and upon the experiences learnt (patterns drawn) by running simulations on the data fed into the algorithm. This ability is what makes advanced AI systems become “Black-Boxes”, and thereafter, if a malfunction or an anomaly arises, resulting in an injury to a human, it becomes almost impossible for the programmer(s)/developer(s)/manufacturer(s) to reconstruct the code of the algorithm and locate the anomaly [7].
3 Black-Box Algorithms AI algorithms that lack transparency and have the capability of drafting their own code are usually referred to as “Black-Box algorithms” [8]. These types of algorithms are used when the data sets are enormous, and it is practically impossible for any programmer or even a corporation (having multiple teams of programmers working together) to come up with the kind of code required to make an algorithm take “intelligent decisions” on the go and therefore, they incorporate the technology of DNN into the initial draft of the algorithm itself. DNN work on the model of a human brain [9]. Just like there are millions of neurons in the human brain that are ultimately responsible for a plethora of conscious as well as subconscious decisions of a human being. Similarly, there are ‘artificial neurons’ that are responsible for the decision-making process in the case of an intelligent algorithm [10]. These algorithms are trained, rather than written, by programmers, on the data sets that are fed into them and by using the DNN, they are able to draft their own code as and when needed in order to enable themselves to perform certain functions for achieving the ultimate objective in the most efficient manner possible. Simply put, these intelligent algorithms train their own selves by running simulations on the Big Data provided, thereby drawing their own predictions and choosing the most appropriate outcome/decision/action out of the multiple patterns drawn [11]. The training process involves millions of interconnected variables, and the logic behind using them is too complicated for any human/corporation to deconstruct. This is the part where it becomes almost impossible for any developer or manufacturer of the algorithm to predict the exact stage of alleged malfunction (see Fig. 1) in the body of its code; hence, these intelligent algorithms are called opaque, non-transparent or, in other words, Black-Box algorithms. Input (In) represents Big Data that has been fed into the algorithm by the programmer(s), Hidden Layer (Hn) represents patterns drawn by the algorithm and Output (On) represents the final decision made by the algorithm. The stage of reaching Output from the patterns (Hn) is the ‘Black-Box’ i.e., the unpredictable part.
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Fig. 1. Artificial Neural Networks in decision-making process of the Black-Box Algorithm.
Techniques have been devised to combat their opacity, like the use of decision trees, linear regression models and Explainable AI [12]. These techniques can help reconstruct the decision-making process of any Black-Box algorithm and make it comprehensible by the standards of a human programmer. These techniques feed inputs with minor modifications and variations into the algorithm and analyse the changes in the outputs provided. After multiple iterations, judging these outputs, they attempt to build an explainable model as to how the code drafted by the algorithm is functioning in a particular scenario. The problem with these explainable models is that they are mere predictions rather than an accurate deconstruction of how the Black-Box algorithm operates, and hence the output could be a model that is similar but not the same, thereby creating at least two possibilities of explanation but not the exact explanation as to how the algorithm reached a particular conclusion.
4 Principles of Robotics Initially, the principles of robotics were given, for the first time, by Isaac Asimov in his fictional work “I, Robot” [13]. An extension of the same principles has been provided by the Engineering and Physical Sciences Research Council (EPSRC), UK, which has proposed five principles of Robotics that are recognized and followed across the globe when it comes to deciding the parameters that can be used to differentiate between ethical and unethical behaviour for any AI system. Out of these five principles, the second and fifth principles are the most important as far as the present discussion is concerned. The second principle states that “Humans, not robots, are responsible agents. Robots should be designed & operated as far as it is practicable to comply with existing laws and fundamental rights and freedoms, including privacy” [14]. Furthermore, the fifth principle states that “The person with legal responsibility for a robot should be attributed” [15]. The term ‘legal responsibility’ herein can be interpreted in light of the landmark judgment of Donoghue v. Stevenson [16] and the doctrine of vicarious liability [17]. So, going by this logic, if a legal injury is caused by an AI entity, the victim can always sue the corporation that owns the algorithm (going by the Fifth Principle of
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Robotics), or in other words, product liability would come in handy whenever a legal injury occurs. Even though it is simple to impute product liability straight away to the action or inactions of an AI algorithm but as far as product liability is concerned, holding one particular corporation liable in a court of law could prove to be very difficult since the one-corporation-per-algorithm scenario is quickly becoming an obsolete concept. In actual practical setups, there are multiple corporations involved that often have very specific distinctions in terms of their roles. (see Fig. 2) [18].
Fig. 2. Chain of corporations involved from the development stage till the commercialization of the algorithm
After applying the EPSRC principles of robotics, we could hold a corporation liable for the actions or inactions of an AI system owned by them, but this raises a lot of technical questions in a court of law. Simply imputing product liability would not solve the problem because the corporations could always argue from the perspective of fairness and this argument would make much sense because there are, in fact, multiple corporations involved at multiple stages. Identifying which corporation or corporations are actually liable and then imputing legal liability to them using the doctrine of vicarious liability would make sense only after considering factors like corporate gamesmanship [19] and the possibility of disincentivizing corporations from investing in technological advancements and thereby hampering the overall growth of the economy. It is important that the courts consider all the corporations involved and attribute liability and sanctions in such a manner that they are not too stringent and unfair for the owner/manufacturer of the algorithm alone.
5 Liability Framework Holding Artificial Intelligence entities accountable is arguably one of the biggest legal challenges. The increasing dependence on AI algorithms, especially the development of more and more Black-Box algorithms, raises very vital legal questions like: ‘Who would be responsible for the actions of an autonomous robot if such actions result in a grievous hurt, theft, illicit procurement of drugs online or something even worse like death? For understanding the issue, let us take a hypothetical scenario. Consider a situation in which an autonomous security robot having a Black-Box algorithm has been deployed at a public place, say, an airport. We are assuming that the programmer has already coded parameters into the algorithm, which defines what is ethical and what is unethical behaviour, keeping in mind the principles of robotics. So, the robot knows that it cannot indulge in certain kinds of behaviours which could result in harming a human in the pursuit of achieving its objectives. Now since this is a security robot, say some immediate crisis occurs at the airport, and now a decision has to be taken in real-time, which involves going from point A to point B in the least amount of time. Since this
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robot is autonomous and free to do its own coding to achieve its objectives, it could very well run over some human in its path, maybe step on their heads or limbs and approach the target location in the least amount of time possible, thereby causing fatal injuries to human(s) in the process. In such a situation, if only the owners of the robot are held legally responsible, it would, to a large extent, be unfair from the perspective of principles of natural justice. Our argument is based on a legitimate expectation that legal personhood needs to be attributed to AI systems so that a criminal liability framework is devised for holding autonomous AI systems directly liable for their actions. It would also be unfair from the perspective of the victims as well because the lack of criminal liability of AI would, at most, result in monetary compensation for the injury suffered or even for death. Another interesting issue that arises now is the question of deterrence. We believe that a sentient system would consider data deletion (in a way wiping its memory clean and resetting it) or being shut down as a punishment equivalent to a death sentence for a human being [20]. Artificial emotional intelligence is not science fiction anymore. AI systems have become advanced enough to sense and ‘feel’ emotions just like humans [21]. Since emotions are nothing but a product of biological algorithms of our brains which can be replicated artificially just like any other function of a human brain, therefore, emotions can be artificially developed by software as well [22]. Going by this logic, it makes much sense to legally recognize AI systems as persons and thereafter devise criminal liability models to punish them via proper sanctions and deterrence. 5.1 Criminal Liability Model: The Need for Legal Personhood We surveyed several works of literature, including research papers and books, and the most efficient criminal liability model that we found for AI is the one provided by Dr Prof. Gabriel Hallevy [23]. Hallevy strongly believes that “if an AI unit has established all the elements of a particular crime, both external and internal, there is no reason to prevent imposing criminal liability for that crime” [23]. Looking at the situation from a purely conceptual perspective, the line of difference between a corporation and an AI entity is very thin. Both entities lack natural personhood, and both have the capability to perform actions that could have financial, social and legal implications. A need was felt in law to create a legal fiction, thereby recognizing corporations as legal persons so that ease of doing business and legal accountability for financial decisions could be balanced. A similar need for a new legal fiction has arisen again as societies across the globe are experiencing a technological revolution due to the ever-increasing innovations in the field of Artificial Intelligence. We argue that right now, it is high time that we recognize AI entities as legal persons so that we can create a meaningful and viable legal regime that can bring harmony and fairness to the justice delivery systems with respect to offences involving AI. If AI is treated as ‘just a machine, or legally put: as a ‘thing’, then surely the basic principles of criminal law like “mens rea” and “actus reus” would be misplaced, and it might appear that the whole idea of punishing an Artificial Intelligence system would be inconsistent with criminal law. Keeping this premise in mind, let us analyse Hallevy’s three-fold liability framework, which provides the following models:
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Perpetration-via-another. This model treats AI as an innocent agent (though the actual perpetrator) and the programmer or the user as the one who intends to commit the crime through the former. In such a scenario, the situation could be compared to an offence being committed by a person who is mentally disabled, an animal or a child. The perpetrator here is held to be an innocent agent because it lacks the mental capacity to form mens rea. Therefore, if such an agent were instructed by another person to commit a crime, then the instructor could be held criminally accountable. Natural-probable-consequence. In this model, a part of the AI program, which was intended for good purposes, is activated inappropriately and performs a criminal action. So, the users/programmers could be held legally liable once again if they had the ‘knowledge’ that a criminal offence was a natural or the most probable consequence of such usage or application as intended by them. Direct Liability. This model is applicable to autonomous AI that does not require human intervention for taking decisions. It suggests imputing both the elements, i.e., mens rea and actus reus, to such a system. It is relatively simple to attribute actus reus to an AI system, but when it comes to attributing mens rea, the need for legal personhood arises. Assigning mens rea is the most complex challenge, and this model would find its application in scenarios of Black-Box algorithms and scenarios in which autonomous AI entities are involved in the commission of the crime in question.
5.2 Civil Liability Model: Strict Liability, Product Liability and Vicarious Liability The civil liability model encapsulates in itself liabilities based on the concepts of Strict Liability, Product Liability and Vicarious Liability. Strict Liability. The basic Strict Liability framework provides ways to punish an entity (be it a natural person or an artificial person) despite the lack of a culpable mental state. This principle takes into account a liability ‘without fault’ [24]. For imposing strict liability on AI, the following parameters need to be fulfilled: 1. 2. 3. 4.
There was a duty of care on the part of the Algorithm, This duty was breached by the Algorithm, Such a breach resulted in a legal injury, and This breach need not attract a fault-based liability, i.e., mere wrongdoing of the part of the AI entity could qualify as a civil offence.
In case of civil offences, the liability shall be imputed to the corporation that qualifies as the ‘software provider’ (the vendor) who owes a duty of care to the user/customer. There could be multifarious situations in which this duty of care could be breached, such as malfunctioning of the algorithm, incorrect parameters for calibrations, negligence in defining permissible and impermissible actions, errors and biases in the Big Data that has been fed into the system, etc. In almost all the cases of civil liability, we are assuming that
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the AI in question is not fully autonomous, i.e., a human element is always involved in influencing the causation because fully autonomous systems would create circumstances for the application of criminal law, as discussed earlier. Product Liability and Vicarious Liability. As an alternative to Hallevy’s model, the researchers suggest that a criminal liability model would not be operational as of now or till the time we are able to establish legal personhood of AI entities and recognize them as persons in the eyes of the law. Therefore, the solution to defining a liability framework for Artificial Intelligence systems stems from the doctrine of vicarious liability. This doctrine imputes the liability of an offence to a third party, thereby making it responsible for the actions of the perpetrator, and the victim may accordingly recover damages from this third party. Since the present-day AI algorithms are too comprehensive and complicated for a single corporation to develop, test, market, and vend the same to customers, the aforementioned functions are performed by multiple corporations. So, now if an offence is committed by the AI system, the simple argument of “product liability” sounds flawed in a court of law because imputing heavy sanctions on one corporation which had nothing to do with the coding is unfair. Therefore, merely suggesting a vicarious liability model would also not suffice, as a balance needs to be struck between the degree of the sanctions and the injury suffered by the victim [25]. Even though the vicarious liability model is not a very clear model as far as the liability framework for AI is concerned, it still provides a temporary solution to fill the void created in the liability framework for AI at the moment [26]. This framework aims to impute liability to the corporation(s) or the human(s) responsible for the development of the algorithm in question. The concept simply states that in the event of a legal injury arising out of the actions of an AI, the manufacturer or the owner of the algorithm would be held directly responsible. This framework operates keeping in mind the Second Principle of Robotics as formulated by EPSRC. The biggest issue while determining liability herein is that there is usually more than one corporation involved in the case of AI-based offences, and now it becomes challenging to determine who exactly is to be held liable. Like in the judgment of Williams v. Litton Industries [27], the manufacturer was held liable by the court even though the offence in question was committed by an AI employed by Ford Motors, which was a user of the AI technology developed by Litton Industries. A legal scholar could argue that the relief granted by any court ultimately depends on the question of law involved in that matter, and in this particular case, the question of law involved is the validity of imputing liability on Litton Industries, i.e., the manufacturer and not the corporation that owns the right to use the AI. Nevertheless, it creates a problem in law that the manufacturer, as well as the licensee, could be held liable for the action of an AI entity, and either of them could again argue in a court of law on the principles of justice, equity and fairness if a case is filed directly against them. Since there are multiple corporations involved, imputing vicarious liability to just one corporation in the entire chain of corporations could seem unfair.
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6 Conclusion and Suggestions The authors talk about three different approaches that can be adopted and propose suggestions for each of them. The first approach is to maintain the status quo, i.e. not to grant legal personhood to AI entities. In such a scenario, the present liability framework will have to undergo some changes. The present liability framework does not proportionately divide the responsibility, i.e. the ultimate responsibility regarding any problems caused by an AI is upon the developer or manufacturer. The same should be changed to joint and several liability wherein all the stakeholders involved, including the entity responsible for designing and assembling the module, testing the algorithm, marketing, licensing etc., should be asked to contribute to the monetary sanction imposed. As far as the criminal liability framework is concerned, the authors are of the view that Hallevy’s model will be the best-suited one. The second approach is to grant legal personhood to Autonomous AI. In such a scenario, the civil liability extends to holding the AI personally liable. Once legal personhood is granted, AI will be treated at par with humans, and all laws will be applied just like they apply to humans. However, if any human intervention is proved in the mishap caused by the AI, the doctrine of lifting the corporate veil may be applied to find the actual perpetrator. The third approach is to recognize Autonomous AI as special persons by creating a new form of person. They may be treated as a separate living entity, and a separate set of laws may be made to govern them.
References 1. Google News: $10 million awarded to family of plant worker killed by robot. https://news.goo gle.com/newspapers?id=7KMyAAAAIBAJ&sjid=Bu8FAAAAIBAJ&pg=3301. Accessed 15 Dec 2022 2. Weng, Y.-H., Chen, C.-H., Sun, C.-T.: Toward the human-robot co-existence society: on safety intelligence for next generation robots. Int. J. Soc. Robot. 1, 267–282 (2009). https://doi.org/ 10.1007/s12369-009-0019-1 3. Kharpal, A.: Robot with $100 bitcoin buys drugs, gets arrested. https://www.cnbc.com/2015/ 04/21/robot-with-100-bitcoin-buys-drugs-gets-arrested.html. Accessed 15 Dec 2022 4. BBC News: Uber not criminally liable for self-driving death (2019). https://www.bbc.com/ news/technology-47468391 5. Gralla, P.: Amazon Prime and the racist algorithms. https://www.computerworld.com/article/ 3068622/amazon-prime-and-the-racist-algorithms.html 6. Funk, D.: Major functions of law in modern society featured. 23 Case W. Res. L. Rev. 23, 257 (1972) 7. Yu, R., Alì, G.S.: What’s inside the black box? AI challenges for lawyers and researchers. Leg. Inf. Manag. 19, 2–13 (2019). https://doi.org/10.1017/S1472669619000021 8. GR, O.: Black box There’s no way to determine how the algorithm came to your decision. https://towardsdatascience.com/black-box-theres-no-way-to-determine-how-the-algori thm-came-to-your-decision-19c9ee185a8 9. Dickson, B.: Artificial neural networks are more similar to the brain than they get credit for. https://bdtechtalks.com/2020/06/22/direct-fit-artificial-neural-networks/
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10. Buettgenbach, M.H.: Explain like I’m five: Artificial neurons. https://towardsdatascience. com/explain-like-im-five-artificial-neurons-b7c475b56189 11. Hutson, M.: Self-taught artificial intelligence beats doctors at predicting heart attacks. https://www.science.org/content/article/self-taught-artificial-intelligence-beats-doc tors-predicting-heart-attacks. Accessed 15 Dec 2022 12. IBM: Explainable AI. https://www.ibm.com/watson/explainable-ai 13. Thomson, P.: Programming Morality: Isaac Asimov’s Three Laws of Robotics. https://learn. g2.com/laws-of-robotics 14. Müller, V.C.: Legal vs. ethical obligations – a comment on the EPSRC’s principles for robotics. Connect. Sci. 29, 137–141 (2017). https://doi.org/10.1080/09540091.2016.1276516 15. Bryson, J.J.: The meaning of the EPSRC principles of robotics. Connect. Sci. 29, 130–136 (2017). https://doi.org/10.1080/09540091.2017.1313817 16. Donoghue v. Stevenson (1932) 17. Bieber, C., Cetera, M.: Vicarious Liability: Legal Definition & Examples – Forbes Advisor. https://www.forbes.com/advisor/legal/personal-injury/vicarious-liability/ 18. Husak, D.: The criminal law as last resort. Oxf. J. Leg. Stud. 24, 207–235 (2004). https://doi. org/10.1093/ojls/24.2.207 19. Wines, L.: New takes on corporate gamesmanship. J. Bus. Strat. 17(2), 28-30 (1996) 20. Lemley, M.A., Casey, B.: Remedies for robots. SSRN Electr. J. 86 (2018). https://doi.org/10. 2139/ssrn.3223621 21. Shah, C.: Sentient AI — Is That What We Really Want?. https://papers.ssrn.com/sol3/papers. cfm?abstract_id=4213593. Accessed 15 Dec 2022 22. Mart´ınez-Miranda, J., Aldea, A.: Emotions in human and artificial intelligence. Comput. Hum. Behav. 21, 323–341 (2005). https://doi.org/10.1016/j.chb.2004.02.010 23. Hallevy, G.: The criminal liability of artificial intelligence entities. SSRN Electr. J. 4, 171 (2010). https://doi.org/10.2139/ssrn.1564096 24. Thayer, E.R.: Liability without fault. Harv. Law Rev. 29, 801 (1916). https://doi.org/10.2307/ 1326124 25. Harman, E.: Harming as causing harm. In: Wasserman, D., Roberts, M. (eds.) Harming Future Persons: Ethics, Genetics and the Nonidentity Problem. Springer (2009). https://doi.org/10. 1007/978-1-4020-5697-0_7 26. Evas, T.: Civil liability regime for artificial intelligence European added value assessment. European Parliamentary Research Service (2020) 27. Williams, V.: Litton Industries Inc. (1987)
Acceptability Aspects of Artificial Intelligence in Morocco: Managerial and Theoretical Contributions Marouane Mkik1(B) and Salwa Mkik2 1 FSJES Souissi Rabat, Morocco, (LARMODAD), University Mohammed V. Rabat, Rabat,
Morocco [email protected] 2 ENSA of Beni Mellal, University Sultan Moulay Slimane, Beni Mellal, (LAPREG), Morocco
Abstract. In our paper, we used factor analysis, a confirmatory method for identifying latent factors from observable variables. It does this by assigning a set of observable characteristics to each latent variable. Setting the parameters (loadings) to 0 in a confirmatory factor analysis allows for further analysis by allowing correlations between latent factors and, if necessary, additional correlations between residual errors. It provides a complete description of the hidden variables. What extent does digitalization drive the reduction of inequalities in technological acceptance in Morocco? Confirmatory factor analysis is a special case of structural equation modeling. In this type of approach, a model is fixed a priori that will specify the number of factors, the possible relationships between these factors, the relationships between these factors and the observed variables, the error terms attached to each observed variable and the possible correlations between them. Our sample focuses on the stakeholders of the Digital Development Agency (DDA), of which we selected a number of 60 stakeholders divided into 20 companies, 20 associations and 20 cooperatives. Through the UTAUT model we can provide estimates that ease of use, quality of service, anticipated performance and the influence of the professional body are all concepts that give psychological and motivational acceptance for the use of AI. Keywords: Artificial Intelligence · Acceptance model · Innovation · UTAUT model · TAM model
1 Introduction The context of our research lies in the modern foundations of technological acceptability as stated in the theories of Ajzen and the work of Venkash. They propose a detailed explanation based on behavioural and decisional variables and thus allow to establish the multifactorial axes of the technological influence on human performance. According to the most recent scientific work, the notion of acceptability announces the first stage of the dynamics of use. It is an approach to analysis and subjective representation based © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 65–74, 2023. https://doi.org/10.1007/978-3-031-29860-8_7
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on perceived usefulness, social influences and the context of use. They are part of an experimental framework which allows the evaluation of aspects linked to the nature of human behaviour. Alongside the advantages of technological acceptance, there are disadvantages relating to the phenomenon of creating technological inequalities. These are explained by modern research work on the behavioural aspects of individuals. The different technological inequalities that are announced have to be treated and analysed carefully in terms of technological acceptance models. In this study we will try to answer our research problematic which is presented as follows: What extent does digitalization drive the reduction of inequalities in technological acceptance in Morocco? At this stage two theoretical and practical foundations are presented to explain the causes of technological inequality saying that technological acceptance is automatically linked to the variables of motivation and requirement, which allows to determine major inequalities. Research work on the level of technological acceptability is currently positioned in the contemporary studies of Terrade et al. (2009) and Bobillier-Chaumon et al. (2017). They express the concepts of technological acceptability motivation and the choices of motivation appropriate to each individual. Similarly, Lee et al. (2003) have positioned their dimensions in parallel with technological acceptability saying that innovation is a fundamental instrument of acceptance and managerial relevance. The existence of two acceptance models (UTAUT and TAM model) describes this fundamental dimension of operationalization. They mention in their theories the interdisciplinary variables that can influence technological acceptance and create an implicit technological inequality, which allows us to answer our basic problem.
2 Literature Review Artificial intelligence is a set of algorithms that gives a machine analytical and decisionmaking capabilities that allow it to adapt intelligently to various situations by making predictions from acquired data. Using acceptance models, the design is inspired by the schematic functioning of psychological validations. The UTAUT model asserts a strong significance in conceptual studies dealing with the issues of technological and psychological motivations of human beings to use modern means. It considerably strengthens the capacity of the model to explain the observed phenomena. Like the explanatory factors, the moderating variables (gender, age, experience, and context of use) are crucial to identifying and understanding the unique characteristics of various user populations. Based on the two key research concepts. Our problem is as follows: To what extent does digitalization drive the reduction of inequalities in technological acceptance in Morocco. According to the two key concepts of the research. Our problem is as follows: what extent is digitalization driving the reduction of inequalities in technological acceptance in Morocco? According to this article, we have tried to analyze the technological acceptability through the variables of the UTAUT model practiced in particular in the confirmatory studies of Ajzen (quantitative approaches). Through this model, we define the framework
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of confirmation of the explanatory variables of the model based on the Moroccan territory given its technological specificity and its development positioning. 2.1 Artificial Intelligence: Conceptual Framework Artificial intelligence as “the construction of computer programs that perform tasks that are more satisfactorily accomplished by humans.“ This expression from 1956 poses a problem of an anthropomorphic nature, insofar as it alludes to the wills linked to a machine, which leads to a major societal disruption and is very questioning: Will the machine replace man? What would become of the man in the making if the machine became superior to him? These purely anxious questions legitimize the highlighting of ethical and legal limits. Artificial intelligence (AI) is a branch of science that has remained in the shadows for a long time. It has emerged as a technological form since the 2016. Artificial intelligence entered the digital ecosystem as soon as a critical threshold of development and diffusion was reached in information processing networks. Artificial intelligence is a set of algorithms that give a machine analytical and decision-making capabilities that allow it to adapt intelligently to various situations by making predictions from acquired data. by using neural networks whose design is inspired by the schematic functioning of biological neurons. 2.2 Artificial Intelligence (AI) Limitations and Hazards There may be drifts that weren’t anticipated at first. We are now faced with the limitations and dangers of artificial intelligence. Everyone is aware of the phishing phenomenon on the Internet, which is expertly tailored to a target, malware with a remarkable capacity for adaptation, acting somewhat like a robot diverted from its primary task of identifying and destroying a target, predictive systems of civil unrest, and “fake news”. In a very lengthy paper by 26 academics from 14 universities with a variety of backgrounds, who issued a combined text of over 100 pages in February 2018, they want to warn against the “malicious use of artificial intelligence” by providing some instances of the potential drifts of AI. Members of a number of academic institutions, such as Oxford University, the Open AI Alliance, the Center for the Study of Existential Risk (CSER), and the Electronic Frontier Foundation, will be among the speakers. 2.3 The UTAUT Model: Definition and Basic Model UTAUT is composed of four factors that are thought to strongly influence computer users’ actions, and four that are thought to reduce the impact of these four factors. Perceived ease of use, perceived usefulness, social influences, and enabling factors are found to be direct drivers of behavioral intention or user behavior in UTAUT. This significantly strengthens the model’s ability to explain the observed phenomena. Like the explanatory factors, the moderating variables (gender, age, experience, and context of use) are crucial to identifying and understanding the unique characteristics of various user populations. UTAUT can explain approximately 70% of the variation in goal. Comparisons with other
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models reveal that UTAUT performs better. In addition, it can provide a useful tool for managers to evaluate the performance of new technology. It represents the key tools of the UTAUT model, it is presented as follows: • Expected effort: it represents the degree of ease associated with the use of the system. • Innovation: refers to the introduction into the market of a new or significantly improved product or process compared to those previously developed by the legal unit. • Anticipated performance: represents the degree to which an individual believes that using the system will help him or her achieve gains in job performance. • Social influence: represents the degree to which an individual perceives that significant other believe they should use the new system. • Enabling conditions: defined as “The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system”. 2.4 The TAM Theory This ground breaking model originates in Fishbein and Ajzen’s (1975) theory of reasoned action and serves to elucidate the factors that influence people’s decisions to adopt or reject technological innovations. The purpose of this model is to explain how people react to new technologies (Fig. 1).
Fig. 1. User Acceptance of Computer Technology: a comparison of two theoretical models» (Davis et al. 1989)
People’s perceived usefulness and ease of use are the two most influential aspects of a computer’s adoption rate, both of which are outlined in the Technology Adoption Model (TAM). Davis defined perceived utility as “the prospective user’s subjective probability that adopting a certain application system will increase his or her job performance within an organizational environment.“ The perceived ease of use of a system is defined as the degree to which a prospective user expects little to no effort necessary from utilizing the intended system (Table 1).
3 Methodology In our paper, we used factor analysis, a confirmatory method for identifying latent factors from observable variables. It does this by assigning a set of observable characteristics
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Table 1. Comparison between the UTAUT model and the TAM model. Source: author’s construction UTAUT
TAM
Expected effort, Innovation, Anticipated performance, social influence, Enabling conditions
Innovation, ease of use
The applied variable
Technological acceptance
The perceived usefulness
The common variable
Innovation
The objective
Determining behavioral operation Set the objectives of the action
The key concept
Decision-making behavior
Measurement variables
reasoned action
to each latent variable. Setting the parameters (loadings) to 0 in a confirmatory factor analysis allows for further analysis by allowing correlations between latent factors and, if necessary, additional correlations between residual errors. It provides a complete description of the hidden variables. Confirmatory factor analysis is a special case of structural equation modeling. In this type of approach, a model is fixed a priori which will specify the number of factors, the possible relationships between these factors, the relationships between these factors and the observed variables, the error terms attached to each observed variable and the possible correlations between them. Our sample focuses on the stakeholders of the Digital Development Agency (DDA), of which we have chosen a number of 60 stakeholders divided into 20 companies, 20 associations and 20 cooperatives. Thus, our model is as follows (Fig. 2):
Fig. 2. Presentation of the initial research model
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4 Results and Discussion In light of these results, it is clear that the results of this research can contribute to the design of an automatic and efficient technological system that is acceptable to the majority of professionals. Through the UTAUT model we can bring estimates that ease of use, quality of service, anticipated performance and the influence of the professional body are all concepts that give psychological and motivational acceptance for the use of AI. Thus, our results are significant in several variables and items which allows us to obtain the following model (Fig. 3) (Table 2):
Fig. 3. Presentation of the final model after purification
Table 2. Description of factor analysis (PCA). Source: author’s construction AA AA1
0,889
AA2
0,903
AA3
0,891
AA4
0,871
AC
AC1
0,675
AC2
0,829
AD
AZ
TL
(continued)
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Table 2. (continued) AA
AC
AC3
0,919
AC4
0,812
AC5
0,798
AD
AD1
0,858
AD2
0,930
AD3
0,813
TAEFFICAC
AZ
TL
1,000
TL1
0,883
TL3
0,743
We note from the final purification model the existence of a deep significance between the ease of use which represents 0.911, so the relationship of innovation with technological acceptance represents 0.883 and the anticipated performance allowing to obtain a significant Cronbach’s alpha of 0.884 except for the professional influence and the quality of the service of which we have rejected the majority of the items what implies an immediate suppression of the variable. According to this study, we believe that technological acceptability is stimulated by innovation, service quality, anticipated performance, and ease of use. As illustrated in the final model, professional influence does not influence technological acceptance. This has been explained in the work on technological effectiveness and the impact of decisionmaking actions on practitioners’ decisions and concerns. To this observation, we state that the difficulty to bring together the conditions of experience and professionalism in the technological environment is conditioned by the daily life and experience of individuals as well as their motivations as in modern studies. 4.1 Goodness of Fit of a Statistical Model The goodness of fit of a statistical model refers to the degree to which the model fits the observed data. The indicators related to the coefficients of determination, it is presented in the degree of freedom (p), the GFI, the AGFI, the NFI and the CFI is an indicator of the quality of the fit used in the framework of a linear regression estimated by the method of least squares (Table 3). For all indicators, the analysis of our model is confirmed with a high quality. In this regard, we distinguish a GFI (Goodness-of-Fit Index), AGFI (Adjusted Goodness-of-Fit Index), NFI (Normed Fit Index) and CFI (Comparative Fit Index) higher than 0.90. This correlates with the measurement degrees applied by Kaya & Altınkurt 2018. Thus, the research hypotheses were accepted or rejected according to the final purification of the principal component analysis (Fig. 4).
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Index name
Values for the independent model
Chi-deux
4250,78
Degree of liberty (p)
584 (0,000)
Chi-deux/ddl (Saturated Khi-deux)
7,875
RMR
0,168
GFI
0,947
AGFI
0,995
RMSEA (p)
0,092 (0,000)
NFI
0,904
CFI
0,95
CAIC (Saturated model)
4850,452
Fig. 4. Diagram of the final model (acceptance or rejection of hypotheses)
5 Conclusion In conclusion, we were able to move to the Acceptance or Rejection phase of the hypotheses of which we accepted the majority of the variables except the hypothesis concerning the relationship between The influence of professional body with technological acceptance. Thus, we deduce from this presentation 3 contributions: theoretical, methodological and managerial.
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For the theoretical contribution, it should be noted that there is a multidisciplinary intersection between the foundations of the UTAUT model and technological acceptance, except for the variable of expected effort, which was not significant in relation to our sample. Concerning the Methodological contribution, we note a perfect confirmatory determination of the UTAUT model by the analysis of the structural equations. Thus, the Managerial Contribution is expressed in the fundamental concepts of technology acceptance which are affirmed as determinants of the reduction of technological inequalities in Morocco. Our work thus presents research limitations. The first limitation lies in the application of the research model. From a purely theoretical point of view, it is difficult to deal with all the aspects likely to determine the models for reducing inequalities in technological acceptance at once. The second limitation arises in the conceptual factors of the research: we say that the number of variables and determinants that can influence the treatment of technological inequalities is endless. The variables and determinants studied here are therefore only an outline to be completed. - We also consider that we have not been able to study them in sufficient depth. Moreover, if the variables influencing technological inequality are considered to be fluctuating data with respect to the territory, the precision of a larger sample is an important asset for future researchers.
References Ibrahim, R., Jaafar, A.: User acceptance of educational games: a revised unified theory of acceptance and use of technology (UTAUT). Int. J. Educ. Pedagogical Sci. 5(5), 557–563 (2011) Venkatesh, V., Zhang, X.: Unified theory of acceptance and use of technology: US vs. China. J. Glob. Inf. Technol. Manag. 13(1), 5–27 (2010) Huang, Y.C.: Integrated concepts of the UTAUT and TPB in virtual reality behavioral intention. J. Retail. Consum. Serv. 70, 103127 (2022) Rouidi, M., Abd Elmajid, E., Hamdoune, A., Choujtani, K., Chati, A.: TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: a systematic review. Inform. Med. Unlocked 32 (2022) Kaya, Ç., Altınkurt, Y.: Role of psychological and structural empowerment in the relationship between teachers’ psychological capital and their levels of burnout. Egitim ve Bilim, 43(193) (2018) Courbet, D., Fourquet-Courbet, M.P. : Modèles et mesures de l’influence de la communication : Nouvelles perspectives ouvertes par la psychologie sociale. Revue Int. de Psychosociologie 11(25), Automne, 171–195 (2005) Stéphane, L. : Laurens. Les relations d’influence et leurs représentations. Revue europ{\’e}enne des sciences sociales 52(2), 43–71 Varia Review (2014) Van Zanten, A.: L’influence des normes d’établissement dans la socialisation professionnelle des enseignants : le cas des professeurs des collèges périphériques français. Éducation et francophonie 29(1), 13–35 (2001) Daguzon, M. : L’influence de la prescription sur le développement professionnel des professeurs des écoles débutants (Doctoral dissertation, Université Blaise Pascal-Clermont-Ferrand II) (2010)
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Portnoff, A.Y., Soupizet, J.F.: Intelligence artificielle opportunités et risques. Futuribles 5, 5–26 (2018) Zhang, A., Chagla, Z., Nazy, I.: Characteristics of anti-SARS-CoV-2 antibodies in recovered COVID-19 subjects. Viruses 13(4), 697 (2021) Terrade, F., Pasquier, H., Reerinck-Boulanger, J., Guingouain, G., Somat, A.: L’acceptabilité sociale : la prise en compte des déterminants sociaux dans l’analyse de l’acceptabilité des systèmes technologiques. Le travail humain 72(4), 383–395 (2009) Bobillier Chaumon, M.E.: Emerging technologies and issues for activity and occupational health. Digit. Trans. Challenge Act. Work Underst. Supporting Technol. Changes 3, 1–19 (2021) Lee, Y., Kozar, K.A., Larsen, K.R.: The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst. 12(1), 50 (2003)
The Use of Learning Algorithms in Business Intelligence Tools to Enhance Customer Feedbacks Jai Keerthy Chowlur Revanna(B)
and Emine Arikan
Information Systems Engineering and Management, Harrisburg University of Science and Technology, 326 Market St, Harrisburg, PA 17101, USA {JChowlur,Earikan}@my.harrisburgu.edu
Abstract. Currently, many industries have operational processes, and many new firms need help understanding the importance of customer needs and feedback, which are crucial to the success of any business. There is plethora of tools in the cloud, and in this paper, we discuss them by surveying different Business Intelligence (BI) tools. It’s widely known that the customer is the real boss, and customer satisfaction is one of the most important factors for a successful business. The main research question addressed here is “How can artificial intelligence tools aid in data analysis so businesses can make important business decisions?” The paper talks about the benefits of adding machine learning and deep learning techniques in BI tools by showing experimental results. This paper also conducts a survey of six different industries and their IT managers on the effective use of BI tools for a successful business. Keywords: Business Intelligence · Artificial intelligence · Data Analysis · Intelligent decision · Analytics
1 Introduction Feedback is an integral part of business operations [1]. Companies that wish to remain successful in today’s globalized, dynamic, and intensely competitive business environment [2] must ensure that their feedback is both efficient and quick. This is because, in the current economic environment, the competitiveness of corporate strategies is largely dependent on how well the plans align with the market’s specific requirements [3]. Customers frequently discuss brands and their experiences and if a proper instrument exists for acquiring information, interpreting it, and incorporating it into strategic planning, corporate organizations may be able to attain competitive market positions [4], and this could be achieved by artificial intelligence learning algorithms like Machine learning (ML) and Deep Learning (DL). Today, informed, and dissatisfied clients demand higher-quality products and experiences, while market competitors strive to survive [5]. Simultaneously, organizations are amassing, generating, and being constantly inundated with large quantities of data and information from a variety of sources [6]. As mentioned in [7], for these firms to make strategic decisions, attain agility and efficiency in their © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 75–84, 2023. https://doi.org/10.1007/978-3-031-29860-8_8
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feedback, and be valuable while delivering to the client, they must have access to accurate and timely information [8]. At this stage, the business intelligence notion is required. It paints a clear picture of how they can improve the effectiveness of their feedback by leveraging data with modern AI systems [9]. This feedback indicates an increasing interest in the application of business intelligence solutions [10]. This study analyses the current work to decide the specific means through which firms might implement BI solutions customer happiness. Below in the paper in Sect. 2 it discusses literature review by comparing different business intelligence methodologies and how AI methods like ML and DL were utilized. Section 3 discusses methodology with different modern artificial intelligence techniques and tools are analyzed, it uses five different machines learning classifiers and two deep learning classifier algorithms to cross verify its quantitative analysis with real world BI tools to measure the effectives. Section 4 and 5 talk about ML and DL results. In Sect. 6 recommendations for future papers are discussed and Sect. 7 concludes by summarizing the results and recommending the future work.
2 Literature Review 2.1 The Concept of Customer Feedback Management In the contemporary business environment, customer feedback is an important concept. Businesses are increasingly interacting with savvy and well-informed customers. They demand better experiences and goods even though they possess the ability to make informed decisions [11]. Below in Fig. 2 shows the common operation of data movement with Metadata which has influence on a sample Power BI tool [12]. In the above Fig. 2 we have Five-Tran different tools compared with data management and Business intelligence impact. There are numerous tools available for customers to express their concerns about a company, such as Google reviews, public forums, and social networks, and negative feedback could have a negative impact on the industry; entrepreneurs should keep this in mind [13]. It is critical to understand the buyers’ experiences and expectations [14]. Hence, they need a customer feedback management system with respect to the software, channels and tools that allow companies to gather feedback from customers’ information and turn it into actionable advanced analytics [15]. These techniques and approaches assist business organizations in capturing pertinent reviews and improving the business [16]. In paper [17], the BI tools applications are cross verified with the effectiveness of supply chain tools. According to [18] business intelligence tools are designed to collect and analyze vast quantities of data quickly and effectively, here they mention about the Indonesian market analysis and in [19] the feedback effectiveness was compared. In this manner, these tools enable businesses to extract useful business information from datasets. 2.2 The Nature and Potential of Business Intelligence Various scholars and authors have defined business intelligence differently, but there is a need for large cloud software and industrial tools to form complex data analysis [19].
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Fig. 1. The Figure shows an example of Power BI database integration for data movement
These processes must be automated with data to produce actionable information and insights. Consequently, the availability of constructive, implementable, true intelligence can determine success or failure. It’s certainly questionable in view of the incontrovertible fact that, because of increased online usage and modernized tools for handling large data which are continuously being made, requiring businesses to make judgement calls, respond to feedback complexities, and implement systems [19]. 2.3 Usage of Machine Learning and Deep Learning in Business Intelligence Tools Utilizing two well-known approaches, Machine learning and Deep learning (DL) techniques, [20] a client satisfaction demeanor for big data emerged from online businesses on social media, namely Facebook and LinkedIn. There aren’t enough relevant datasets for this topic. Consequently, we obtained the dataset from industries. Using three of the most popular Natural Language Processing (NLP) tools, we have simplified the concept of intelligent tools for industries on Originating, regularization, and accuracy for social sites. For classifier evaluation, accuracy, loss, recall, and precision measures were calculated [20]. The Artificial neural network classifier has the highest accuracy (99.1%) and is therefore superior. Without preprocessing techniques, support vector machines loss and accuracy produced the much more reliable data (93.4%) [21]. In comparison, we evaluated the dataset using Deep Learning methods and the technique for extracting features, which involves bag of word meanings representations. In [22] The findings suggest that deep neural networks are exceptional.
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Many BI tools manage millions of records and process them, but it cannot be just managed with SQL and traditional methods. Hence, Deep learning is essential for intelligence information analysis systems due to the abundance of data sources and the rate at which businesses are flooded with information. According to the published literature [23], business intelligence systems can transform complex and otherwise disorganized data into meaningful information that managers and executives can use to inform their initiatives.
3 Methodology Here deep learning and machine learning techniques were implemented as primary research which I performed in python as shown in the below flowchart in Fig. 1
Fig. 2. Flowchart for Customer feedbacks and analytics using AI tools
Due to the nature of research and customer interaction, a qualitative research approach was also utilized for data collection, assessment and management which served
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as a secondary approach. A survey with industrial managers and directors was done and high-level information is obtained to analyze the current industrial issues. We can see in above Fig. 1 the implementation of customer feedback and business analytics in AI tools. 3.1 Description of the Research Issue -Machine Learning and Deep Learning Managers increasingly consider business intelligence as a subfield of information technology in the current workplace [23]. Most businesses are eager to implement intelligent systems so that they can make better business decisions. Intelligence refers to how a machine processes the data and the information as effectively as a person. Hence, AI is crucial in the customer feedback system to be successful in this regard. This research paper analyzes the BI and its tools to determine if Artificial intelligence techniques, such as ML and DL could help customer feedback and automate business processes. In this paper Bank Data sets from Decision support systems have been used [24]. Here dual inputs unorganized and organized data was used, and feature selection was based on the best classifier output from either DL or ML method. Machine learning Models: [21]
a. Artificial Neural network: In order to simulate the human brain, artificial neural networks (ANNs) include feedback, concealed, and output neurons with interconnected neurons (nodes) [20]. b. K nearest Neighbor: A Non-supervised learning technique to get clusters c. Vector Machine: This is a supervised machine learning method in which understands the data for classification and regression issues [20]. d. Logistic regression: This computation classifies a specified input data set as a specified tag. e. Naïve Bias: It anticipates the tag for an input data and provides the greatest chance label.
Deep Learning: [22]
a. BERT: Bidirectional Encoder Representations from Transformers [20] is a base model and a big model are included. Using a 12-layer, 768-hidden, 12-heads, 110 million-parameter neural network architecture as the base model, we have developed a classifier model. b. LSTM: Long Short-Term Memory is an algorithm for Deep learning. This is designed on the basis of Sequential class of keras, utilising[20] the Hidden layer as a bidirectional layer, in addition to the Segmentation and Thick layers. Data Pre-Processing: Here the data was collected from financial industries from bank data sets which was cleaned to match discretization to match the numerical features
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of the AI methods and perform quantitative analysis. Data Volume was also reduced normalize and aggregate them.
3.2 Survey of Different BI Tools Applying Machine learning algorithms helps predict the customer feedback systems. But once the data set is allotted and to learn the customer feedback from different areas we need qualitative approach, hence this secondary approach is implemented. Along with verbal feedback, the machines learning feedback system uses google reviews, public forums, and social media reviews to understand customer feedback which was used with K-means clustering methods [24]. This can help BI tools understand from customer feedback tools which is like the optimal design tools used in Ant colony which goes through nearest path [25]. To perform the survey, the authors of this paper interviewed and collected public data from Tableau, Salesforce, Domo, Yellowfin, Informatica and Microsoft BI. From this paper below are the expected outcomes to improve customer feedback. i. ii. iii. iv. v. vi.
Enhance Feedback system and analysis with AI integration in BI tools. Integrate real time customer feedback tools. Utilization of Deep learning for business decision approach Improve scalability and Revenue Predictive analytics using Machine learning Use AI tools and customer feedback to improve business processes.
4 Results The insights into BI tools and their integration and usage of AI learning methods in the system were studied. These facts were discovered through the analysis of the collected data. The two results listed below summarize the key findings regarding the research question. An example of Salesforce using Einstein analytics, it’s an AI tool that predicts performance and growth in BI tools, is provided in the following application. The below sentiment analysis was done in PyCharm in python. 4.1 The Results of Sentiment Analysis and Improvement by Deep Learning and Machine Learning Methods Here two AI methods are used the first is machine learning and second is Deep learning. To showcase the machine learning result, 5 classifiers are utilized including Vector machine, Logistic regression, K nearest neighbor, Artificial neural network, Support vector machine, to classify sentiment. The outcomes of these algorithms are listed in Table 1. For Deep Learning, LSTM and BERT are methods that were employed to improve the classification’s precision. The outcomes of these algorithms are shown in Table 2. For Deep learning, Validations have need to be performed by verification first, hence, post verification results the accuracy and loss is shown in Table 2 as shown below.
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Table 1. Machine learning results Classifier
Accuracy
Recall
Target
Artificial Neural network
0.963
0.96
0.96
K nearest Neighbor
0.961
0.93
0.93
Vector machine
0.931
0.93
0.934
Logistic regression
0.946
0.97
0.97
Naïve Bias
0.92
0.923
0.923
Table 2. Deep Learning results. Model
Verification Accuracy
Verification loss
Accuracy
Loss
LSTM
0.9855
0.0463
0.9911
0.0363
BERT
0.9891
0.0566
0.9963
0.0297
4.2 Survey of Different BI Tools The below pie chart in Fig. 3 shows the impact on scalability on the 6 industries mentioned in Table 3 which are using AI methods and we can see salesforce and Domo has the highest scalability options which provides a great flexibility and in turn also has impacted the revenue growth as shown below in Fig. 3. Table 3. Analysis of different BI tools. BI Tools
Scalability (%)
Revenue-growth (%)
Customer-Feedbacks
Salesforce
27.6
4.5
4.6 Stars
Domo
35
22.15
4.3 stars
Yellowfin
19
60
4.2 stars
Microsoft BI
23
4.5
4.0 stars
Informatica
26
17.3
4.2 stars
Tableu
16
18
4.4 stars
Even other industries have used the modernized methods and just the customer feedback has positively impacted the industry revenue growth. The usage of AI in customer feedback has significantly improved the business scalability and revenue as shown below in Table 3.
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Fig. 3. Shows Industrial scalability and Revenue Growth
5 Discussion of the Results The machine learning algorithm with the highest accuracy is artificial neural network, with 96.1%, while the Deep learning algorithm with the best accuracy is BERT, with 99.63%. We could thus draw the conclusion that BERT has the greatest precision with 98.91% percent, accompanied by LSTM with 98.55% specificity. The above accuracy is a significant factor for business stakeholders to understand customer needs. From the Pie chart visualization, salesforce has the highest revenue growth, this is because of its implementation of Salesforce Einstein Artificial intelligence tool. It helps in analyzing the customer scalability and firm’s scalability because AI tools guide them in correct business process identification. Within procedure, customer input could be fed into single inputs that could process the data and produce efficient results. The definition of business intelligence tools is the application of big data analytics tools to reshape inconsequential knowledge into action that can support data-driven strategic planning as per this definition the results are being aimed at. The sophistication of customer feedback, particularly in the manufacturing sector, necessitates that customer service representatives constantly address new issues. Among such obstacles may be the question of how to transform market, partner, and end-user information into a source of profitability.
6 Recommendations This recommendation here is for industries to implement modernized AI methods to ensure customers are happy. This truly increases the business revenue as shown in the above results and helps company’s grow. This data would then be used to select and implement the best business intelligence tool(s) to maximize value and return on investments. The team goal is to modernize and integrate the customer feedback system with Machine Learning and Deep Learning classifiers, thereby automating and enhancing the system. In this context, the corporate strategy would include periodic and regular customer feedback analysis as well as active user feedback data collection using shortest path method [26]. To maximize the processes, business intelligence tools should be utilized to convert massive amounts of data and highlight the most valuable insights through integration with intelligent methods and other cloud-based tools. Thus, industries should act on strategies which are driven by data and customer importance when aligned with business objectives, can increase the companies’ competitiveness and efficiency.
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In conclusion, the objective is to collect new data based on research to identify potential application and utilization chances.
7 Conclusion Business intelligence has been imagined as a prospective measure to reduce associated with dynamic, rapidly changing competitive environments, in which businesses must organize their feedback to compete profitably and effectively. Regarding the limitations of the article, our research focuses primarily on reviews written by consumers who have purchased a specific product. Even though the accuracy of the sentiment analysis performed on these reviews is positive, it can be enhanced by adding more than five classifiers. Despite this, above discussed issues there is correlation between the implementation of AI systems and BI tools to enhance the process in customer satisfaction tools and the results clearly indicate a positive impact on BI tools because of artificial intelligence methods. Therefore, it is recommended if the firms are clear with the requirements of the business and what customers review them in online digital tools. They must implement AI tools as per the specific needs. Therefore, businesses will benefit from BI systems accordingly.
References 1. Jakhar, P., Renu, Krishna, C.: Business Intelligence: As a Strategic Tool for Organization Development (A Literature Review) (2020) 2. Asree, S., Cherikh, M., Gopalan, S.: The impact of feedback responsiveness and strategic feedback collaboration on innovation performance. Int. J. Bus. Perform. Feedback Modell. 10(2), 131 (2018). https://doi.org/10.1504/ijbpscm.2018.098306 3. Kiruthika, J., Khaddaj, S.: Impact and challenges of using of virtual reality & artificial intelligence in businesses. In: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), pp. 165–168 (2017) 4. Chen, H., Chiang, A., Storey, D.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165 (2012) 5. Chen, Y., Chiu, Y.: Enhancing business intelligence for feedback operations through effective classification of supplier management. Uncertain Feedback Manag. 2(4), 229–236 (2014) 6. Ferreira, A., Pedrosa, I.: Data-driven management using business analytics: the case study of data sets for new business in tourism. In: 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–5 (2022) 7. Finch, B.: Customer expectations in online auction environments: an exploratory study of customer feedback and risk. J. Oper. Manag. 25(5), 985–997 (2006) 8. Bouaoula, W., Belgoum, F., Shaikh, A., Taleb-Berrouane, M., Bazan, C.: The impact of business intelligence through knowledge management. Bus. Inf. Rev. 36(3), 130–140 (2019) 9. Liu, R., Zhang, W.: Informational influence of online customer feedback: an empirical study. J. Database Mark. Cust. Strat. Manag. 17(2), 120–131 (2010) 10. Isik, A., Hamzehi, M., Hosseini, S.: Business intelligence using machine learning algorithms. Multimedia Tools Appl. (2022) 11. I¸sık, Ö., Jones, M., Sidorova, A.: Business intelligence success: the roles of BI capabilities and decision environments. Inf. Manag. 50(1), 13–23 (2013) 12. Wade, R.: Applying Machine Learning and AI to Your Power BI Data Models (2020)
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13. Wu, J. -Y.: Computational intelligence-based intelligent business intelligence system: concept and framework. In: 2010 Second International Conference on Computer and Network Technology, pp. 334–338, (2010) 14. Oliveira, M., McCormack, K., Trkman, P.: Business analytics in feedbacks. the contingent effect of business process maturity. Expert Syst. Appl. 39(5), 5488–5498 (2012) 15. Long, W.I., Ching-Hui, C.: Using the balanced scorecard in assessing the performance of e-SCM diffusion: a multi-stage perspective. Decis. Support Syst. 52(2), 474–485 (2012) 16. Kanan, T., Mughaid, A., Al-Shalabi, R., Al-Ayyoub, M., Elbes, M., Sadaqa, O.: Business intelligence using deep learning techniques for social media contents. Cluster Comput. 1–12 (2022) 17. Moniruzzaman, M., Kurnia, S., Parkes, A., Maynard, S.B.: Business intelligence and supply chain agility. arXiv preprint arXiv:1606.03511 (2016) 18. Rizan, H.O., Adiwinoto, B., Saputro, S.H., Perkasa, E.B.: Business intelligence to support visualization of Indonesian capital market investment gallery performance. In: 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), pp. 1–4, 9649610.( 2021) 19. Trkman, P., et al.: The impact of business analytics on feedback performance. Decis. Support Syst. 49(3), 318–327 (2010) 20. Desai, Z., Anklesaria, K., Balasubramaniam, H.: Business intelligence visualization using deep learning based sentiment analysis on amazon review data. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–7 (2021) 21. Tamang, M.D., Shukla, V.K., Anwar, S., Punhani, R.: Improving business intelligence through machine learning algorithms. In: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), pp. 63–68. IEEE (2021, April) 22. Bouguettaya, A., Zarzour, H., Kechida, A., Taberkit, A.M.: Machine learning and deep learning as new tools for business analytics. In: Handbook of Research on Foundations and Applications of Intelligent Business Analytics, pp. 166–188. IGI Global (2020) 23. Sapra, S.: Artificial neural networks: state of the art in business intelligence. Bus Eco J 7, e107 (2016) 24. Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014). https://doi.org/10.1016/j.dss.2014. 03.001 25. Revanna, J.K.C, Al-Nakash, N.Y.B.: Vehicle routing problem with time window constrain using KMeans clustering to obtain the closest customer. Global J. Comp. Sci. Technol. 22(D1), 25–37 (2022) 26. Revanna, J.K.C., Veerabhadrappa, R.: Analysis of optimal design model in vehicle routing problem based on hybrid optimization algorithm. In 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N–22). IEEE (2022)
Improving the Performance of Public Transport Bus Services: Analytics Approach to Revenue Forecasting Mohamed Amine Ben Rabia(B) and Adil Bellabdaoui ENSIAS, Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected]
Abstract. With the current energy crisis and the increase in the price of petroleum products, public bus transportation is an unavoidable choice for many vehicle users. Public bus companies face a dilemma that takes the form of a desire to serve more points to increase revenues on the one hand and reduce costs and expenses on the other. The main objective of this research work is to create a framework based on an analytics approach for bus line revenue forecasting. The novelty of the framework consists in its ability to integrate two methods for time series prediction. Two methods are used Multiple linear regression and SARIMAX models. Real data are used to confirm the robustness and feasibility of the framework. A comparison between the two techniques allows to select the most stable and performing one. This paper presents future directions to enrich academic work and suggests guidelines for professionals to take advantage of advances in terms of revenue forecasting. Keywords: revenue forecasting · public transport · simulation · artificial intelligence · predictive analytics · Multiple linear regression · SARIMAX
1 Introduction Public transport is a basic service for all, which all urban policies must aim to support and on which they must be based. This requires the reconciliation of three aspects that might seem contradictory: sharing and cohabitation on the road, the level of service and the efficiency of public transport, whether it is mass transit, such as public transport, or a more detailed service, such as supplementary public transport [1]. For public transport, first of all, this principle must be translated into a quality offer (frequency, network, speed, comfort). It is urban density that makes it possible to develop this quality and that constitutes the guarantee of the economic and social viability of the urban transport “system”. Public transport by bus must not be an additional service, even a rather well-supported one, to the implicit universal service of the car, but must truly constitute a basic service for all. Freedom to use the car or other forms of individual transport must not undermine the economy or degrade the quality of public transport [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 85–94, 2023. https://doi.org/10.1007/978-3-031-29860-8_9
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The question of financing public transport by bus is at the heart of the fight for the right to mobility. In concrete terms, it is a question of establishing an operation and a policy so that the service responds to the needs of users, rather than to capitalist logic. Raising transport prices means making a choice, that of making users pay for expenses rather than other financing structures. In the same way, considering free transport means recognizing that transport is an essential tool for everyone to access education, work and leisure. Therefore, an analytical approach pushes public bus companies to finance the activity intelligently by having visibility on the short, medium and long term [3, 4]. This method of action requires the tools and technologies to predict future scenarios [5, 6]. Business analytics addresses this issue by providing companies with a reliable and sustainable way to analyze data. The benefits of Business Analytics for companies: • • • • •
Better visibility into business performance, The ability to identify trends and predict results, The ability to make faster and more appropriate decisions, The ability to anticipate the unexpected and act accordingly, Insights that drive positive change and innovation.
The objective of our work is to implement a daily revenue simulation framework for public transport companies by bus. This will allow the decision maker to make decisions and improve the revenue of public transport lines. To achieve the final objective of the paper, our framework consists of three steps: The first step is data analysis, which is a process of inspecting, cleaning, transforming, and modeling the data to discover useful information, inform conclusions, and support decision making. The second step is to model and run the predictive models on the collected data. The final step is to compare models to arrive at a suitable choice based on the input data and which will be integrated into the final system. The rest of this paper is organized as follows: In Sect. 2 a briefing on the methods and techniques used is presented. In Sect. 3 a case study on real data of a bus company is given to validate the feasibility of the framework and finally Sect. 4 conclude the study and gives future directions.
2 Methodology 2.1 Time Series Forecasting Model A time series model is considered a set of observations. Each observation is associated with a unique time instant t. A time series can also be constructed by gathering a finite number of observations [7]. In this case, it can be represented by a vector of n random variables where the realizations are the actual observations. It is possible to consider the number of observations as an infinite number, in this case, we speak of discrete stochastic processes. In practice, the first representation is the most used, where time series are represented by vectors (indexed by time) of successive observations [8].
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It is also very important to consider multiple time series at the same time, since in practice, the variables modeled by the time series are often related to other variables. A multivariate time series X = X1 , . . . ., Xn is a finite set of uni-variate series with values that evolve at the same time, i.e. they can be indexed by a single time vector. Multi-variate time series are very useful in system modeling, especially when data are captured simultaneously, for example stock market data (prices of several items and indices), and neuroscience data to measure the electrical activities of the brain (electroencephalogram). Activities (electroencephalogram), etc. 2.2 Multiple Linear Regression We refer to a multiple regression model if we have more than two variables to predict. The general form of a multiple regression model is [9]: yt = β0 + β1 ∗ x1,t + β2 ∗ x2,t + · · · + βk ∗ xk,t + t where yt is the variable to be predicted and x1 , x2 , . . . , xk are the variables of the predictor k. The variables to be predicted must be numerical. The coefficients 1, 2 . . . , k evaluate the impact of each predictor after adjusting for the impact of all other variables in the model. Thus, the coefficients measure the marginal effects of the predictors. For seasonality, we can use dummy variables to capture the seasonal component in our model. In general, linear regression models give quite encouraging results when there is a strong linear link between inputs and outputs. As an example, the work of [10], which consists in comparing several machine learning models for the prediction of energy consumption, shows that the linear regression model gives satisfactory results. Multiple linear regression is a solution for identifying correlations between a result (the variable being explained) and several explanatory and independent variables. As mentioned above, it can be used to anticipate the sales performance of a given product (a high-end computer, for example) according to the profile of potential buyers: age, salary level, address…. Multiple linear regression has many applications [11]. This is due to the fact that it takes into account several variables in order to develop more complex models than a simple linear system. It can be used in the following areas of expertise [12]: Weather forecasting; Climate trends over a large or small geographical area; The spread of a viral infection across a country or according to a classification of contaminated or at-risk people; Financial and stock market studies; Econometrics; Statistical analysis. 2.3 The SARIMAX Model The SARIMAX model is considered an improvement to the ARIMA model, it is characterized by an additional set of moving averages as well as autoregressive components.
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The supplementary delays are compensated by the frequency of the seasonality. We conclude that SARIMAX model is a seasonal alternative model like SARIMA and Auto ARIMA. Univariate models predict a single time series, looking at the last values of the series to the last values of the series to estimate future values [13, 14]. In addition to these seasonal factors (P, D, Q, M), this model introduces the idea that external factors (environmental, economic, etc.) can also influence a time series and be used in forecasts. The knowledge of the stationarity of a series is essential if we want to find a model that best reflects our transport data [15]. Depending on whether the series is stationary or not, the modeling approaches will not be the same. Stationarity means that the structure of a stochastic process evolves or not with time. If the structure remains the same, the process is said to be stationary. Unlike ARIMA, SARIMA (for seasonal autoregressive integrated moving average) allows, as its name indicates, to predict a trend by integrating seasonal effects. In short, it is an ARIMA model that takes into account the seasonal component [16].
3 Application (Case Study) In this case study, we use real data from a bus company. The company we collaborated with is a large Moroccan company that operates in the economic capital of the country and serves a population of almost 5 million residents. 3.1 Data Collection and Analysis For our case study, we have an excel file containing data of several lines of a public transport company by bus, our objective now is to explore and analyze this file in order to specify the useful and not useful data. The data collected are daily records for each bus line for the period 2011/01/01 to 2016/06/30. The number of rows in this file is 12977 with 15 columns. Our data consists of 15 columns each illustrating information about the line in a day. Since we don’t have much detail in relation to the other columns, in our case we are interested in the following columns; • ‘Date’: The date of the working day of a line, • ‘LineNumber’: The number of a bus line, • ‘NBSERVICE’: Number of services assigned to this line, a service represents a mission departure/arrival between point A and B for eight hours of work of a team of Driver and Receiver on a bus, • ‘Revenue’: The revenue of a bus line in a working day. The choice of these columns is not arbitrary, the main variable we are trying to simulate is the revenue which varies with time and the number of services specified, the other columns do not provide the necessary data to be considered as parameters for our study.
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Fig. 1. Visualization of the revenues by line according to the time axis
From the figure above (Fig. 1) you will find that there is a seasonal component that resides in our data. We find the existence of a strong weekly seasonal component, which allows us to know that the line revenue has a seasonality included that varies by day during the week [17, 18]. 3.2 Forecasting Model In this part we will present the different steps followed for the application of the selected models and their comparison. Figure 2 shows the framework used in the study. The first step consists of collecting the company’s data over a period of 5 years. Thereafter we will proceed to a distribution of the data on training and test datasets. We divide our database into 2/3 training, 1/3 test. Multiple Linear Regression Model The multiple linear regression model is the most common statistical tool used to study multidimensional data [19]. A special case as a special case of a linear model, it is the natural generalization of simple regression [20]. After filtering our columns, it is time to try to prepare these data for the linear regression model. Our equation will have as variables to predict the revenue of a day for a specific line, which means that we will need to apply multiple regression on each line. We have created several datasets for each line, now it’s time to try to implement seasonality in our model, the approach we adopted was to use Dummy variable, we will try to convert the date into day of the week, each day of the week will be represented by a dummy variable. The number of services will represent our exogenous variable with time. Using the Scikit learn library we came out with the following models for each line, To evaluate we calculated also R2, RMSE and MSE (Fig. 3).
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Fig. 2. Framework of the revenue forecasting
Fig. 3. Visualization of original and predicted data for line L01
SARIMAX Model Our objective is to predict the revenue according to time by providing a value of number of services. In the case of the SARIMAX model we will not need the dummy variables, the seasonality is already taken into account, we just need to specify the frequency of seasonality. According to the data analysis done in the previous part we have a weekly seasonality. For our model we will not need any transformations on our data, only the date column becomes index, we will try to create the missing days for each row and fill it with the average of the days row and fill it with the average of the columns.
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For the specification of a set of parameters, we are directed to an automatic function “auto-arima” which returns the combination of parameters with the best AIC BIC. We will apply this method for each line to specify the parameters to be used. Using the Statsmodel learn library we came out with the following models for each line, To evaluate we calculated also RMSE and MSE: Line L01 (Fig. 4).
Fig. 4. Visualization of original and predicted data by SARIMAX for line L01
3.3 Comparison of the Two Models The choice between the various possible models is not always obvious, finally it is an art rather than a science. This Framework has allowed us to describe and compare two machine learning (ML) models in a practical case, and examines the trade-offs required depending on the approach chosen. We present the results of a series of experiments related to revenue prediction based on bus routes that we conducted to explore these different approaches and illustrate these trade-offs, both in the use of the multiple linear regression or SARIMAX model. This solution that we presented, allows data scientists who want to create machine learning models to compare more than one model and see their reliability. In this order, we will compare the evaluation metrics (MSE, RMSE) for each of the models for each line each model for each line. Following the Table 1, we find that the SARIMAX model is better in the majority of lines, only lines L01 and L02 where the multiple linear regression model has better MSE and RMSE, we keep the SARIMAX model to implement it in our web application.
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M. A. Ben Rabia and A. Bellabdaoui Table 1. RMSE and MSE for each model following each line.
Line number
RMSEMLR
MSEMLR
RMSESARIMAX
MSESARIMAX
L01
1450.58
2104199.79
1518.69
2306425.50
L02
2785.097
7756768.75
10540.97
111112215.78
L03
3763.89
14166897.9
3279.53
10755325.72
L04
2617.53
6851504.83
2047.95
4194119.34
L05
1120.91
1256459.74
1079.50
1165327.87
L06
1069.59
1144040.24
1033.18
1067461.97
L07
1577.82
2489523.66
1324.59
1754549.41
L09
5807.03
33721679.7
3863.08
14923411.52
L11
1292.69
1671055.63
449.43
201988.50
4 Conclusion and Future Directions Promoting an efficient bus network is essential. Economically, both in terms of bus and infrastructure costs, buses are an attractive alternative to streetcars and subways. Socially, public transport is mainly used by users who have no choice of mode of transport. The role of society is to provide these captive users with mobility that is equivalent, or at least comparable, to that of car users. Ecologically, this is an inevitable prerequisite for modal shift through massification of CT users. Thus, a reliable and efficient bus is the ideal means of transport. Managers must regulate their lines to avoid these situations. Many strategies have already been proposed in the literature to maintain bus frequencies. Most of them consist in slowing down the buses in advance. In the best case, these strategies are triggered by real-time information. They are rarely based on a short-term prediction of the system’s condition. They are therefore often activated once the situation has deteriorated too much, which sometimes makes them unsuitable to compensate for the instability of the lines. In addition, control strategies are applied mainly at the stop level through the drivers. Two main findings guided the research in two directions. The first is that they are not sufficiently developed to take explicit account of maintenance and operating costs. Yet these elements considerably affect the operation of the lines by delaying the buses and degrading their punctuality. Thus, classical bus analytics models can be progressively refined to take into account these two variables. The second observation is that these bus-centric models are quite relevant for real-time use by operators. They can be used as a decision aid for the choice of regulation strategies to be implemented. A research axis applying bus line modeling to concrete cases in a real time operation context would be highly desirable.
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References 1. Georgiadis, G., Politis, I., Papaioannou, P.: Measuring and improving the efficiency and effectiveness of bus public transport systems. Res. Transp. Econ. Compet. Ownersh. Land Passeng. Transp. 48, 84–91 (2014). https://doi.org/10.1016/j.retrec.2014.09.035. Selected papers from the Thredbo 13 conference 2. Kavanagh, P.: A case for negotiated performance-based contracting rather than competitive tendering in government public transport (bus) service procurement. Res. Transp. Econ. Compet. Ownersh. Land Passeng. Transp. 59, 313–322 (2016). https://doi.org/10.1016/j.ret rec.2016.06.006. (selected papers from the Thredbo 14 conference) 3. Better, M., Glover, F., Laguna, M.: Advances in analytics: integrating dynamic data mining with simulation optimization. IBM J. Res. Dev. 51, 477–487 (2007). https://doi.org/10.1147/ rd.513.0477 4. Cheema, M.A., Zhang, W., Chang, L. (eds.): Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, 28–29 September 2016, Proceedings. LNCS. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46922-5 5. Ben Rabia, M.A., Bellabdaoui, A.: A comparative analysis of predictive analytics tools with integrated What-if modules for transport industry. In: 2022 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA) (2022). https://doi.org/10.1109/ LOGISTIQUA55056.2022.9938042 6. Comi, A., Schiraldi, M.M., Buttarazzi, B.: Smart urban freight transport: tools for planning and optimising delivery operations. Simul. Model. Pract. Theory 88, 48–61 (2018). https:// doi.org/10.1016/j.simpat.2018.08.006 7. Vafeiadis, T., Papanikolaou, A., Ilioudis, C., Charchalakis, S.: Real-time network data analysis using time series models. Simul. Model. Pract. Theory 29, 173–180 (2012). https://doi.org/ 10.1016/j.simpat.2012.07.002 8. Baldán, F.J., Benítez, J.M.: Complexity measures and features for times series classification. Expert Syst. Appl. 213, 119227 (2023). https://doi.org/10.1016/j.eswa.2022.119227 9. Fang, T., Lahdelma, R.: Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system. Appl. Energy 179, 544–552 (2016). https://doi.org/10.1016/j.apenergy.2016.06.133 10. Bahij, M., Labbadi, M., Cherkaoui, M., Chatri, C., Lakrit, S.: A comparison study of machine learning methods for energy consumption forecasting in industry. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 165–175. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-73882-2_16 11. Ahtesham, M., Zulfiqar, J.: Used car price prediction with pyspark. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2022. LNNS, vol. 454, pp. 169–179. Springer, Cham (2022). https://doi.org/ 10.1007/978-3-031-01942-5_17 12. Breiman, L., Friedman, J.H.: Predicting multivariate responses in multiple linear regression. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 59(1), 3–54 (1997) 13. Cools, M., Moons, E., Wets, G.: Investigating the variability in daily traffic counts through use of ARIMAX and SARIMAX models. Transp. Res. Rec., 57–66 (2009). https://doi.org/ 10.3141/2136-0 14. Papaioannou, G.P., Dikaiakos, C., Dramountanis, A., Papaioannou, P.G.: Analysis and modeling for short-to medium-term load forecasting using a hybrid manifold learning principal component model and comparison with classical statistical models (SARIMAX, Exponential Smoothing) and artificial intelligence models (ANN, SVM): the case of Greek electricity market. Energies 9(8), 635 (2016)
94
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15. Vagropoulos, S.I., Chouliaras, G.I., Kardakos, E.G., Simoglou, C.K., Bakirtzis, A.G.: Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting. In: 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium, pp. 1–6 (2016). https://doi.org/10.1109/ENERGYCON.2016.7514029 16. Ampountolas, A.: Modeling and forecasting daily hotel demand: a comparison based on SARIMAX, neural networks, and GARCH models. Forecasting 3, 580–595 (2021). https:// doi.org/10.3390/forecast3030037 17. Kumar, S.V., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. 7(3), 1–9 (2015). https://doi.org/10.1007/s12 544-015-0170-8 18. Wesonga, R.: On multivariate imputation and forecasting of decadal wind speed missing data. Springerplus 4(1), 1–8 (2015). https://doi.org/10.1186/s40064-014-0774-9 19. Liagkou, V., et al.: A pricing model for container-as-a-service, based on hedonic indices. Simul. Model. Pract. Theory 115, 102441 (2022). ISSN 1569-190X. https://doi.org/10.1016/ j.simpat.2021.102441 20. Khashei, M., Hamadani, A.Z., Bijari, M.: A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Syst. Appl. 39(3), 2606–2620 (2012). ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2011.08.116
Multi-level Governance and Digitalization in Climate Change: A Bibliometric Analysis Ihyani Malik1 , Andi Luhur Prianto1(B) , Nur Ismi Roni1 , Arifeen Yama2 , and Tawakkal Baharuddin1 1 Universitas Muhammadiyah Makassar, Makassar, Indonesia
[email protected] 2 Chulalongkorn University, Bangkok, Thailand
Abstract. The purpose of this research is driven by the impact of climate change, which affects many aspects. Several mitigation efforts were made but were considered less than optimal. Multilevel governance adaptation and digitalization are starting to be considered for issues around climate change. This tendency prompted this study to search for available research documents to check on these topics. The method used is a bibliometric analysis by maximizing the Vosviewer analysis tool. The findings of this study indicate that research trends related to multilevel governance have only recently been seriously studied by global researchers because of a change in perspective on the understanding that mitigation that has been implemented thus far is not sufficient to avoid climate change. The arguments then target a new pattern of climate change adaptation by developing policies focusing on multilevel climate change governance. This focus targets intercross-policy development. The topic of digitalization has just begun to be associated with climate change, with arguments for digitalization opportunities that are considered capable of overcoming future sustainable development challenges. Digitalization is projected to be a key supporter of sustainable development with the potential to promote a climate-friendly environment. Future projections of this digitalization approach will likely influence climate change publications. The findings of this study assess the potential for global policy adaptation in climate change mitigation efforts by linking multilevel governance and digitalization simultaneously. Keywords: Climate Change · Multilevel Governance · Digitalization · Policy Adaptation
1 Introduction Climate change is a fairly popular topic and has influenced the attitudes of global researchers in many countries [1]. Climate change is closely related to environmental problems, changes in land use, increased pollution, and the problem of decreasing biodiversity. The domain is increasingly broad and interconnected with issues such as ecosystems, food, health, and security [2]. The impact of climate change can also affect people’s socioeconomic access [3]. The consequences that can be caused by climate change encourage responsive efforts, namely, by mitigation as a response to prevent or © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 95–104, 2023. https://doi.org/10.1007/978-3-031-29860-8_10
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slow down the occurrence of climate change and its impacts [4]. Climate change issues are a global challenge today, and other steps or approaches are needed [5], especially a more accommodative global perspective [6]. Some joint efforts include developing infrastructure, changing behavior, and alternative institutional arrangements [7]. In addition, using renewable energy sources is a possible priority option for preventing the impacts of climate change [8]. In mitigation efforts, government response and division of labor are also important aspects, especially policy-making actors, local governments to implement policies [9], and international institutions to accommodate common interests and strategies to promote climate change issues widely [10]. The division of labor should also be undertaken by private parties involved in the oil and gas industry or others to encourage emission reductions that can affect the environment and climate change [11]. Other studies have proposed combining institutions and multilevel governance or policy networks. This proposal was initiated to minimize communication and collaboration imbalances at various levels of governance related to climate change mitigation and policy implementation [12]. The concept of multilevel governance can be read narrowly as a shift in competence between local, national, and supranational government institutions. In simple terms, it means that governance not only focuses on traditional methods and state regulations but all series of actions that provide partnership opportunities, such as public–private, nonstate actors, and others [13]. Other studies have also assessed the potential of digitalization as an alternative option [14]. Utilizing digital technology is one of the mitigation efforts in a crisis. It is also part of industrial revolution 4.0, which demands a transformation toward improvement [15]. Much research has been done on climate change issues. However, there are still few studies that are specific and simultaneous in analyzing climate change using a multilevel governance approach and digitalization, especially the analysis process using bibliometric analysis to check available documents. However, there are still some studies that are considered quite relevant. First, the urgency of the consequences of climate change requires an adaptive management approach [16]. Second, climate change regulation can be carried out using a multilevel governance approach focusing on policy capacity among governments at several levels, namely, local, national and supranational governments [17]. Third, adaptation and mitigation of climate change can also consider an accommodative approach to digital development. Currently, several studies have looked at the potential of digital technology as an option to address climate change issues [18]. Fourth, a literature search can influence the understanding of climate adaptation for a global society in the future [19]. This study aims to fill the gaps in previous research with an approach to bibliometric analysis to map literature relevant to the discussion topics on climate change, multilevel governance, and digitalization approaches. The research questions are mapped as follows: (a) What are the trends in the development of literature related to multilevel governance approaches and digitalization on climate change issues? (b). How is there potential for multilevel governance and digitalization to mitigate climate change issues? These two questions make it possible to find trends and future research positions in discussing research opportunities on topics around climate change, especially those related to multilevel governance and digitalization. This trend simultaneously shows how the
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potential for future mitigation efforts is based on the search results of the available literature.
2 Method This study uses data sources in the form of research publication documents from the Scopus database. The Scopus database was chosen to help researchers observe the development of global studies related to trends in the development of literature on multilevel governance approaches and digitalization on climate change issues.
Search document: Database Scopus (Scopus.com)
Search topics: Multi-level governance, digitalization, and climate change
All document type
Export data to analysis tools (Vosviewer)
Number of documents: Multi-level governance and Climate change (56), Digitalization and Climate change (70). Total documents (126)
All subject area
Type of analysis: Co-occurrence Unit of analysis: All keywords
Based on analysis of search results in the Scopus database Data visualization
Data analysis
Based on Vosviewer analysis
Fig. 1. Data analysis process.
Figure 1 shows that the data source comes from searching documents through the Scopus database. The document search was carried out in December 2022. The selection of the Scopus database is due to its popular and selective track record, especially the global policy and process for selecting research papers or documents, which are rigorous by a board of independent scientists to ensure the best quality indexed. The data search was determined based on keywords focused on searching related literature or published documents (multilevel governance, digitization, climate change). All area types and subjects were included in the search process. The search results were carried out separately (Multilevel governance and Climate change) with 56 document results and (Digitalization and Climate Change) with 70 document results. The total number of relevant documents is 126. The document is then transferred into an analysis tool, namely, Vosviewer. Bibliometric analysis using Vosviewer aims to identify published literature or research documents in the Scopus database. The type of analysis used in Vosviewer is co-occurrence, while
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the unit of analysis is all keywords. Co-occurrence and all keywords are maximized to map themes or other research topics related to multilevel governance, digitalization, and climate change contained in the analyzed documents. The data are then visualized and analyzed in depth to answer the research questions.
3 Results and Discussion 3.1 Research Trends and Coauthorship Related to Multilevel Governance and Digitalization on Climate Change Issues Publication documents related to multilevel governance and digitalization research on climate change issues are identified in global publication documents. The identification process by observing the development literature aims to request potential global assistance on climate change issues in the future. This section explicitly describes publication trends based on the year of publication and the number of documents by the author (Figs. 2, 3, 4, and 5). The data visualization is based on analyzing search results in the Scopus database. The trend of published documents based on the year of publication is observed as follows:
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Fig. 2. Research development trends on multilevel governance and climate change.
Figure 2 shows the trend in published documents based on the year of publication. Publications related to multilevel governance with climate change started in 2008 with a total of 1 document. However, these documents influenced the interest of researchers and the development of subsequent studies. In 2010, there was a significant increase in the number of published documents by 7 documents. 2010 was also identified as a productive year, among others. This productivity is influenced by the view that the mitigation applied thus far is not enough to avoid climate change considering the existing emission levels. This view targets a new climate change adaptation pattern by developing specific policies in European countries that focus on multilevel climate change governance. This focus targets the perspective of policy development between central, regional, local, and supranational governments [20]. The development of research that links climate change issues with a multilevel governance approach has encouraged other researchers’ conceptions. Multilevel governance
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can bridge the increasing number of transnational networks, including various kinds of coalitions between actors from formal and informal institutions, which are considered to have potential as a space for discussing climate change in the future [21]. After passing through the 2010 period, the next period, namely, 2019–2020, was a turning point for developing multilevel governance and climate change topics with a total of 7 documents. This tendency is influenced by the perspective of researchers who propose innovative theoretical frameworks that combine institutional approaches and policy networks with studying multilevel governance. The framework is considered to find propositions about cross-level power imbalances shaping communication and collaboration at various levels of governance [12]. Trends in developing existing publication documents will influence the perspectives and attitudes of other researchers to find solutions or alternatives in developing relevant studies in the future [22]. The development of relevant topics has influenced the responses of other researchers on the same topic. It is observed in the number of published documents by authors on topics around multilevel governance and climate change.
Baron, N.
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Fig. 3. Top 5 authors with the most publications on multilevel governance and climate change.
Figure 3 shows that the trend of published documents is also identified as heavily influenced by the attitudes of researchers who publish more than one document on the same topic. Juhola and Keskitalo are the authors who publish their research the most compared to the others, each having a total of 4 documents. In general, Juhola focuses on climate change adaptation strategies at various governance levels that need more strength. Juhola identified several problems, especially the difference in concentration between parties. This difference in concentration results in adaptation measures being disrupted, slow, and fragmented at all levels of governance [23]. Keskitalo sees the potential of a centralized political system as an effort to influence other regional sectors to become involved in climate change issues [24]. The publication trends related to digitalization with climate change are as follows: Figure 4 shows the trend of published documents based on the year of publication. The publication of digitization with climate change started in 2013 with only 1 document. Unlike the previous topic (multilevel governance), the trend in the data above has only found a significant increase in the number of documents in the 2021–2022 period. This indicates that digitalization-climate change needed to be more popular to discuss climate change issues at the outset. This may be because the discussion of digitization
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was initially projected for future situations. Digitalization is projected to be a critical supporter of sustainable development with the potential to promote a climate-friendly environment [25]. Future projections of this digitalization approach will likely influence climate change publications. A significant increase in published documents occurred in the 2021–2022 period and was influenced by perspectives on digitalization opportunities to address sustainable development challenges. This perspective describes the opportunities that digitalization can provide for building sustainable societies in the future. Digitalization is envisioned as a path that benefits sustainable food production, access to clean drinking water, and the use of green energy. It continues, where digitalization is also considered to make a significant contribution to overcoming the severe challenges of climate change issues in the future[26]. The trend of developing publication documents on climate change by digitalization has also influenced the attitudes and contributions of other researchers. It was observed in the number of documents published by the authors on the same topic for several research documents.
Balogun, A.L. Sikarev, I. Shilin, M. Abramov, V. Petrov, Y.
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Fig. 5. Top 5 authors with the most publications on digitalization and climate change.
Figure 5 shows that the trend of published documents is heavily influenced by researchers who publish more than one document for the same topic. Petrov was identified as having the highest number of documents compared to other authors, with 6 documents. In general, Petrov focuses on developing digital tools for geo-information support in the context of climate change. Geo-information is information in which there
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are location data on the Earth’s surface. The digital tools considered are online platforms enabled for database creation, web technologies, and virtual reality tools. This digital approach is also helpful in examining, integrating, manipulating, analyzing, and displaying data related to climate change issues [27]. 3.2 Multi-level Governance and Digitalization in Climate Change: As Future Global Mitigation Efforts This study examines the potential for policy adaptation in mitigation efforts due to climate change by simultaneously linking multilevel governance with digitalization. These perspectives and arguments have been driven by the results of research documents that have been published. Previously, several published documents have described how the potential for policy adaptation to climate change can be carried out with multilevel governance [28] and digitalization [29]. This study can still develop because other interrelated topics are found in the documents analyzed.
Fig. 6. Global mitigation with multilevel governance and digitalization in climate change.
Figure 6 shows that the research topics in the published documents analyzed on multilevel governance and digitization related to climate change issues have formed the same suitability network regarding mitigation. Mitigation of climate change with these two topics also forms each cluster. Each cluster has a level of relationship that is quite dominant but does not limit each other with other clusters. This means that it is possible for each cluster to be analyzed and understood simultaneously. This tendency reinforces previous assumptions that there is future potential for how global mitigation efforts are carried out in the case of climate change. Other relevant topics that can be considered in linking topics around multilevel governance, digitalization, and climate change include decision-making, adaptive management, public policy, and others. From the data above, it is also known that digitalization is still relatively new to be researched in the context of climate change. This trend may
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encourage more accommodative research and publication interest in the future. The view that humanity is running out of time to reduce the impact of climate change encourages the digitalization approach to be entirely rational and relevant to consider [30]. This discussion, which is relatively new to research, is different from previous ideas regarding discussions on multilevel governance. However, the potential to develop these two topics in the case of climate change may influence the interests and positions of researchers, including the responses of stakeholders who concentrate on the same issues regarding climate change.
4 Conclusion The trend of research on multilevel governance and the digitalization of climate change still needs to be improved. Global researchers have seriously studied multilevel governance in certain periods. This change in perspective was influenced by the understanding that the mitigation implemented thus far is not optimal enough to avoid climate change. The argument then targets a new climate change adaptation pattern by developing policies focusing on multilevel climate change governance. The focus targets policy development between central, regional, local, and supranational governments. The topic of digitalization has just begun to be linked to climate change with a hypothesis on digitalization opportunities that are considered capable of addressing future sustainable development challenges. Digitalization is projected to be a key supporter of sustainable development with the potential to promote a climate-friendly environment. Future projections of digitalization are likely to influence publications on the topic of climate change. The findings of this study assess the potential for global policy adaptation to climate change mitigation efforts by linking multilevel governance and digitalization simultaneously. The search results of published research documents drive these perspectives and arguments. Research documents currently available may influence topic development and subsequent research positions.
References 1. Capstick, S., Whitmarsh, L., Poortinga, W., Pidgeon, N., Upham, P.: International trends in public perceptions of climate change over the past quarter century. Wiley Interdiscip. Rev. Clim. Change 6, 35–61 (2015). https://doi.org/10.1002/wcc.321 2. Cramer, W., et al.: Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018). https://doi.org/10.1038/s41558-0180299-2 3. Tol, R.S.J.: The economic impacts of climate change. Rev. Environ. Econ. Policy 12, 4–25 (2018). https://doi.org/10.1093/reep/rex027 4. Fawzy, S., Osman, A.I., Doran, J., Rooney, D.W.: Strategies for mitigation of climate change: a review. Environ. Chem. Lett. 18(6), 2069–2094 (2020). https://doi.org/10.1007/s10311020-01059-w 5. Malik, I., Prianto, A.L., Abdillah, A., Rusnaedy, Z., Amalia, A.A.: Urban resilience strategy in the climate change governance in Makassar City, Indonesia. J. Gov. Civ. Soc. 5, 31 (2021). https://doi.org/10.31000/jgcs.v5i1.3884
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6. Luhur Prianto, A., Nurmandi, A., Qodir, Z., Jubba, H.: Climate change and religion: from ethics to sustainability action. In: E3S Web of Conferences, p. 06011. EDP Sciences (2021). https://doi.org/10.1051/e3sconf/202127706011 7. Schwanen, T., Banister, D., Anable, J.: Scientific research about climate change mitigation in transport: a critical review. Transp. Res. Part A Policy Pract. 45, 993–1006 (2011). https:// doi.org/10.1016/j.tra.2011.09.005 8. Tang, K.H.D.: Climate change in Malaysia: trends, contributors, impacts, mitigation and adaptations. Sci. Total Environ. 650, 1858–1871 (2019). https://doi.org/10.1016/j.scitotenv. 2018.09.316 9. Rusnaedy, Z., Haris, A., Congge, U., Prianto, A.L.: Adaptive climate change governance in Makassar, Indonesia. J. Gov. 6, 244–258 (2021). https://doi.org/10.31506/jog.v6i2.12384 10. Thomas, K., et al.: Explaining differential vulnerability to climate change: a social science review. Wiley Interdiscip. Rev. Clim. Change 10, 1–18 (2019). https://doi.org/10.1002/ wcc.565 11. Grasso, M.: Oily politics: a critical assessment of the oil and gas industry’s contribution to climate change. Energy Res. Soc. Sci. 50, 106–115 (2019). https://doi.org/10.1016/j.erss. 2018.11.017 12. Di Gregorio, M., et al.: Multi-level governance and power in climate change policy networks. Glob. Environ. Change 54, 64–77 (2019). https://doi.org/10.1016/j.gloenvcha.2018.10.003 13. Kern, K., Bulkeley, H.: Cities, Europeanization and multi-level governance: governing climate change through transnational municipal networks. J. Common Mark. Stud. 47, 309–332 (2009). https://doi.org/10.1111/j.1468-5965.2009.00806.x 14. Pan, S.L., Carter, L., Tim, Y., Sandeep, M.S.: Digital sustainability, climate change, and information systems solutions: opportunities for future research. Int. J. Inf. Manag. 63, 102444 (2022). https://doi.org/10.1016/j.ijinfomgt.2021.102444 15. Prianto, A.L., Malik, I., Khaerah, N., Abdillah, A., Jermsittiparsert, K.: Government, digital society and industry 4.0: connective action against Covid-19 fake news. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2022. LNNS, vol. 454, pp. 480–491. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-01942-5_48 16. Greenhill, L., Kenter, J.O., Dannevig, H.: Adaptation to climate change–related ocean acidification: an adaptive governance approach. Ocean Coast. Manag. 191, 105176 (2020). https:// doi.org/10.1016/j.ocecoaman.2020.105176 17. Irepoglu Carreras, Y.: Problem-solving across literatures: comparative federalism and multilevel governance in climate change action. Eur. Policy Anal. 5, 117–134 (2019). https://doi. org/10.1002/epa2.1066 18. George, G., Merrill, R.K., Schillebeeckx, S.J.D.: Digital sustainability and entrepreneurship: how digital innovations are helping tackle climate change and sustainable development. Entrep. Theory Pract. 45, 999–1027 (2021). https://doi.org/10.1177/1042258719899425 19. Berrang-Ford, L., Ford, J.D., Paterson, J.: Are we adapting to climate change? Glob. Environ. Change 21, 25–33 (2011). https://doi.org/10.1016/j.gloenvcha.2010.09.012 20. Keskitalo, E.C.H.: Developing Adaptation Policy and Practice in Europe: Multi-Level Governance of Climate Change. Springer, Dordrecht (2010). https://doi.org/10.1007/978-90-4819325-7 21. Lidskog, R., Elander, I.: Addressing climate change democratically. Multi-level governance, transnational networks and governmental structures. Sustain. Dev. 18, 32–41 (2010). https:// doi.org/10.1002/sd.395 22. Baharuddin, T., Nurmandi, A., Qodir, Z., Jubba, H.: Bibliometric analysis of socio-political research on capital relocation: examining contributions to the case of Indonesia. J. Local Gov. Issues 5, 17–31 (2022). https://doi.org/10.22219/logos.v5i1.19468
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23. Juhola, S.: Mainstreaming climate change adaptation: the case of multi-level governance in Finland. In: Keskitalo, E. (ed.) Developing Adaptation Policy and Practice in Europe: Multilevel Governance of Climate Change, pp. 149–187. Springer, Dordrecht (2010). https://doi. org/10.1007/978-90-481-9325-7_4 24. Keskitalo, E.C.H.: Climate change adaptation in the United Kingdom: England and SouthEast England. In: Keskitalo, E. (ed.) Developing Adaptation Policy and Practice in Europe: Multi-level Governance of Climate Change, pp. 97–147. Springer, Dordrecht (2010). https:// doi.org/10.1007/978-90-481-9325-7_3 25. Balogun, A.L., et al.: Assessing the potentials of digitalization as a tool for climate change adaptation and sustainable development in urban centres. Sustain. Cities Soc. 53, 101888 (2020). https://doi.org/10.1016/j.scs.2019.101888 26. Mondejar, M.E., et al.: Digitalization to achieve sustainable development goals: steps towards a smart green planet. Sci. Total Environ. 794, 148539 (2021). https://doi.org/10.1016/j.scitot env.2021.148539 27. Istomin, E., Golosovskaya, V., Gogoberidze, G., Shevchuk, O., Petrov, Y.: Geo-information support digitalization for northern sea route logistics in the context of climate change and COVID-19. In: Beskopylny, A., Shamtsyan, M. (eds.) XIV International Scientific Conference “INTERAGROMASH 2021”. LNNS, vol. 246, pp. 638–646. Springer, Cham (2022). https:// doi.org/10.1007/978-3-030-81619-3_71 28. Gupta, J.: The multi-level governance challenge of climate change. Environ. Sci. 4, 131–137 (2007). https://doi.org/10.1080/15693430701742669 29. Munang, R., Nkem, J.N., Han, Z.: Using data digitalization to inform climate change adaptation policy: informing the future using the present. Weather Clim. Extrem. 1, 17–18 (2013). https://doi.org/10.1016/j.wace.2013.07.001 30. Guaita Martínez, J.M., Puertas, R., Martín Martín, J.M., Ribeiro-Soriano, D.: Digitalization, innovation and environmental policies aimed at achieving sustainable production. Sustain. Prod. Consum. 32, 92–100 (2022). https://doi.org/10.1016/j.spc.2022.03.035
Machine Learning Approach for Reference Evapotranspiration Estimation in the Region of Fes, Morocco Nisrine Lachgar(B) , Achraf Berrajaa, Moad Essabbar, and Hajar Saikouk Euromed Research Center, Euromed University of Fes, Fes, Morocco [email protected]
Abstract. In order to establish hydrological and agricultural applications, it is crucial to build a precise computational model for the prediction of the reference evapotranspiration. This element that has an indispensable role in the management of the irrigation, can be calculated using different empirical equations. However, due to its nonlinear process, artificial intelligence came in to estimate its value. In this perspective, five artificial intelligence models were investigated using the environmental dataset from January 2011 to December 2019. The implemented daily dataset is of the following collected data: max and min air temperature, solar radiation, relative humidity, wind speed of the region of Fes, located in the Northwestern Middle Atlas Mountain chain of Morocco. The explored models, the Decision Tree, Random Forest, Linear Regression, Support Vector Regression and K-Nearest Neighbor, showed good results. Their accuracy was tested using different quantitative criteria, such as Mean Squared Error ‘MSE’, Root Mean Squared Error ‘RMSE’, Mean Absolute Error ‘MAE’ and R-Squared ‘R2 ’. Having an RMSE ranging from 0.0095 mm/day to 0.0404 mm/day, MAE of 0.0666 mm/day to 0.1253 mm/day and R2 of 0.9793–0.9983, showing the good performances and that the regression models were able to capture the variance of our ET0 target. Keywords: Reference evapotranspiration · Machine Learning · Precise irrigation
1 Introduction A rising problem faced by countries around the world is the climate change. This phenomenon affects the life on earth by creating various challenges, such as the changes in precipitation and meteorological patterns, water scarcity among others. According to the world bank of data, 70% of freshwater is used by the agricultural field [1], where an important amount is lost by non-accurate irrigation systems. This water wastage coexists with systems low efficiency, the low crop yield, the increase of the food demand among other challenges [2]. Therefore, facing those problems would require using sustainable solutions and approaches to increase the productivity and decrease the water wastage. In this context, IoT nanotechnologies, AI technologies have gotten more attention in order to promote the effective irrigation, and act upon the faced challenges addressed in recent reviews of smart and precise irrigation systems [3–6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 105–113, 2023. https://doi.org/10.1007/978-3-031-29860-8_11
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Therefore, those systems interact with the surrounding environment by collecting specific parameters using sensors. Those parameters are essential for the water balance analysis [7], in which the estimation of evapotranspiration is a major element. Evapotranspiration is the process of water loss from the surface to the atmosphere [8]. The first step is to calculate the reference evapotranspiration related to the environment, usually referred as ET0. It uses information about the air temperature, radiation, wind speed, etc. Then, this value is multiplied by a certain coefficient in order to get the potential evapotranspiration related to the crop [9]. However, reference evapotranspiration is considered to be a non-linear process [10]. Therefore, to do ET0 estimations, many researches started using machine learning and deep learning models [11, 12]. The predicted value will be then taken into consideration in the decision and management irrigation system. However, we are still facing barriers in the exploitation of nanotechnologies in the agricultural field either due difficulties related to the communication, missing data, or other problems faced by the precise irrigation systems [13]. So, there is a need to remove those hurdles and create a win-win scenario in which agricultural production is increased, the amount of water is estimated beforehand and should be precise with no water wastage. In this perspective, we propose a comparison of different AI models that estimate the evapotranspiration in the region of Fes, Morocco using meteorological datasets. It will provide insights to overcome problems of data gaps during periods of the year that will impact the management process and help schedule the irrigation process.
2 Empirical Equation and AI Related Work 2.1 Empirical Equations for Evapotranspiration Evapotranspiration is a major element of the hydrological cycle, making it indispensable for the design of the precise irrigation system. In this context, a study needs to be done to ensure the water balance analysis [7] using the following equation: θ˙ (t) + ET p (t) + Dp (t) + Ro (t) = Irr (t) + Rf (t) + Cr (t)
(1)
where, θ˙ is the soil moisture in the root zone, ET p is the potential evapotranspiration of the crop, Dp is the deep percolation, Ro is the run off, Irr is the irrigation, Rf is the rainfall, Cr is the capillary rise for deep water. The reference evapotranspiration is a process that depends on the weather primary effects such as solar radiation, air temperature, relative humidity and the wind speed, collected by sensors. Various empirical equations were developed through the years to estimate the ET using the energy balance and aerodynamic formula. Each model according to the used parameters can be categorized into a type: temperature based, radiation based or mass transform based that combine both [14]. However, the PenmanMonteith equation is the one recommended by the Food and Agriculture Organization due to its precise results [15]. The following model is the Penman Monteith equation and it includes all of the climatic parameters that affects the evapotranspiration process. ET 0 =
900 u2 (es − ea ) 0.408(Rn − G) + γ T +273
+ γ (1 + 0.34u2 )
(2)
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where ET0 is the reference evapotranspiration rate (mm/day), T is the mean temperature (°C), u2 is the wind speed at 2 m above the ground (m/s), es is the mean saturated vapor pressure (kPa), ea is the mean daily ambient vapor pressure (kPa), is the slope of saturation vapor pressure, Rn is the net radiation flux (MJ/m2 .d), G is the sensible heat flux density into the soil (MJ/m2 .d) and γ is the psychometric constant (kPa/°C). However, due to the limitation of availability of climatic parameters data, the computation cannot be done. In this case, other models can estimate approximately the reference evapotranspiration. The Table 1 shows some of the most used empirical models that can be applied for the estimation of ET 0 : Table 1. Selected models according to their type for the estimation of the reference evapotranspiration Model
Formula
Type
Hargreaves-Samani [16]
ET 0 = 0.00102Ra (Tmax − Tmin )0.5 (Tmean + 16.8)
Temperature based
Blaney-Criddle [17]
ET 0 = a + bP(0.46Tmean + 8.13)(1 + 0.0001H )
Temperature based
Thornthwaite [18]
ET 0 = 16(10 TI )
Temperature based
Turc [19]
ET 0 = (0.3107Rs + 0.65) TTmean at mean Rn −C ET 0 = 1.26 +γ λ
Radiation based
Priestly-Taylor [20]
m
Radiation based
Ra is the extraterrestrial radiation (MJ/m2 d), a + b is the extraterrestrial radiation fraction reaching the earth on a clear day, P is the mean annual percentage of daytime hours, H is the elevation (m), λ is the latent heat of vaporization (MJ/kg), at is a constant related to the relative humidity value, m is related to the value of I which is the sum of temperature. 2.2 Artificial Intelligence for the ET0 Prediction Artificial intelligence models have been applied into various fields, the agricultural one included. The advantages of those models are various, for example finding the complex mapping, make up with the system glitches, incoherence of data and incomplete datasets, etc. They help translating the collected raw data into decisions for the irrigation management. Various papers and reviews have investigated the role of those algorithms in the estimation of the evapotranspiration necessary for the hydric cycle. For example, the following study compared the machine learning methods with the empirical ones for the evapotranspiration estimation [21]. Where they explained various models and hybrid ones such as support vector machine, ‘SVR’ with grey wolf optimizer, particle swarm optimizer and also compared it with artificial neural network and different empirical models (Turc, Thornwaite, Penman Monteith, etc.) in Algeria. KNN, Decision Tree and Polynomial Regression were investigated in [22] to do an
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exploratory study with 3 datasets for different tasks of forecasting using a large dataset, short term prediction using few data and cluster to detect the hardware failure. In [23], they used Decision Tree, Random Forest and MLP to forecast the irrigation using hourly data of Temperature, Humidity and soil moisture. The evaluation of deep learning and machine learning models were done in [11], where the study used 2 scenarios, one with a full dataset and one that splits the dataset into groups using k-means method.
3 Materials and Methods 3.1 Study Area and Climate Data This study was done in the region of Fes-Meknes, Morocco. It has a diversity of soils, which range from Volcanic ash to Triassic clays and also from primary schist to alluvial deposits [24]. Making it unique with a great potential for the agricultural production. More specifically, the study area is Fes, it has a mediterranean climate with a continental influence, characterized by cold/wet winters and dry/hot summers. The climatic Fes station (latitude 34° 08 , , elevation 78 m) is located in the North west of the middle Atlas Mountain chain of Morocco, North of Africa. The obtained daily data of maximum and minimum air temperatures (Tmax and Tmin ), relative humidity (RH), wind speed (U2 ), solar radiation (Rs ) and precipitation cover the period from January 2011 to December 2019. The Table 2 is a descriptive of statistical characteristics of the input data. Table 2. Statistical description of meteorological input characteristics Parameter
MIN
MAX
MEAN
SKEWNESS
STANDARD DEVIATION
Tmin (°C)
−4,3
29,5
11,14
0,079709
6,06871
Tmax (°C)
2
46,4
25,97
0,214088
8,109764
RH (%)
6,6
98,9
58,08
−0,27853
17,92396
Vmoy (m/s)
0.0
11.63
3.21
5.019588 —
Rs (w/m2 )
0
471
228.55
−0.0817
90.707
3.2 Missing Data and Analysis In the Fig. 1, we can see the variation of the ET0 through the years, with an average of 3.83 mm/day. And in order to have a deep analysis, we zoom in one year of the dataset. According to the correlation values that we obtained between ET0-PM and our inputs, we found that our process is highly affected by the temperature. Where we have a clear increase of the temperature that happens from April until end of September, making the reference evapotranspiration follow the curve of the temperature. However, we notice some discontinuous parts monthly evapotranspiration of 2011. Which means that we have missing data. In order to know the NaN values and corresponding lines, we used the heatmap as shown in Fig. 2.
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Fig. 1. Distribution of ET0 variation through the years and in 2011 in the region of Fes.
Fig. 2. Heatmap of Missing data and corresponding lines in the Dataset
From this figure, we can notice that there is a small amount of missing data, all from the same rows. Therefore, we have decided to drop those lines from our dataset. The reason behind this decision was to maintain the actual true state of the dataset and not cause any disturbances that may affect the process.
4 Results and Discussion 4.1 Performance Metrics In order to evaluate the performances of the regression models, we employ error metrics created for evaluating those predictions. The most popular ones are Root Mean Squared Error ‘RMSE’, Mean Absolute Error ‘MAE’, R-Squared ‘R2 ’. Starting with the RMSE, it is the square root of Mean Squared Error ‘MSE’. It penalizes even the small errors between the predicted and expected values by squaring them. Unlike this metric, the changes in MAE are linear. It calculates the average of the absolute errors. And finally,
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R2 also called the coefficient of determination. It shows through its value if the model was able to capture the variance of the target. Their equations are as follows: 1 N |yj − yj |2 (3) RMSE = j=1 N 1 N MAE = |yj − yj | (4) j=1 N N yj − yj 2 j=1 (5) R2 = 1 − 2 N j=1 yj − ymean
where, yj is the expected value in our dataset and yj is the predicted value. They were performed on the following models: Linear Regression, K-Nearest Neighbor ‘KNN’, Support Vector Regression ‘SVR’, Decision Tree ‘DT’, Random Forest ‘RF’. For the SVR model, 3 kernels (Linear, polynomial, Radial Basis Function) were tested in order to choose the best one. RBF exhibited the best performances. 4.2 Choice of the Best Model In this part of the study, we are comparing the cited models in order to predicted the value of ET0 . In order to do so, 30% of the dataset was used for the testing, with an average MSE of 0.009 mm2 /day2 –0.0404 mm2 /day2 , RMSE of 0.0975 mm/day–0.201 mm/day, MAE of 0.066 mm/day–0.125 mm/day and R2 of 0.959–0.998. The results of the comparison between the performances are illustrated on Table 3. And in order to know if overfitting occurs, we add the value of the R2 for the training too. Table 3. Comparison of performances of Linear Regression, KNN, SVR, DT, RF for ETo-PM estimation R2 training
R2 testing
MSE
RMSE
MAE
Linear Reg
0.9793
0.9793
0.0206
0.1438
0.0869
KNN
0.9887
0.9847
0.0153
0.1237
0.0928
SVR
0.9911
0.9595
0.0404
0.2011
0.1198
DT
0.9997
0.9687
0.0312
0.1768
0.1253
RF
0.9983
0.9983
0.0095
0.0975
0.0666
From the analysis of Table 3, we notice that SVR showed the poorest results with an RMSE of 0.201 mm/day and MAE of 0.119 mm/day. Followed by the DT with an RMSE of 0.176 mm/day and MAE of 0.125 mm/day. Linear Regression and KNN showed better results than the previous models with an RMSE of 0.1438 mm/day–0.1237 mm/day and MAE 0.0869 mm/day–0.0928 mm/day respectively. However, RF outperformed all four models in the ET0-PM prediction, with an RMSE of 0.0975 mm/day and MAE of
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0.066 mm/day. Also, we should note that according to the coefficient of determination values of both training and testing resulting on R2 equal to 0.998, the model didn’t face an overfitting. To make further analysis for the rest of the study, we generated the predicted ET0 series over the expected Eto-PM value. The Fig. 3 represent the regression plot of the ET0 predicted using RF that has the best performances according to the metrics.
Fig. 3. Scatter plot of the predicted ET0 using Random Forest and ET0-PM
The graphical representation of the RF scatter plot shows small differences between the predicted and expected values of evapotranspiration, having therefore few outliers and that the predictions form a linear line. Therefore, with regard to those performances, the hierarchical performance of the scatter plots and metrics results is as the following: RF > KNN > Linear Regression > DT > SVR. We should also keep in mind that our aim is to estimate the reference evapotranspiration for an autonomous irrigation management system over the years. Therefore, needing a bigger dataset. RF, as the model that showed its superiority in our study, is considered to be a powerful algorithm in Machine Learning. It allows creating as many trees on the subsets of our dataset and combining their outputs. By this, overfitting is reduced, the model improves its accuracy and catches as many variances of the ET0 target as possible, as we can see from the R2 value and in the Table 3. RF can also handle nonlinear datasets efficiently and this is the case of our evapotranspiration process. Moreover, this type of model can handle missing values and outliers which can be caused by the sensors’ faults, therefore making up with the irrigation system’s glitches. And finally, another advantage among the others is tuning the inputs. The inputs of our dataset have different scales and in the chosen Machine Learning model, scaling isn’t necessary since RF applies a rule-based approach instead of a distance calculation approach used by the other models.
5 Conclusion Reference evapotranspiration is considered to be a major element of the hydrological cycle needed for the irrigation management and control. The main purpose of the paper was about investigating the ability of artificial intelligence model to estimate this value.
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In this perspective, five predictive machine learning models were constructed using daily data of temperature, radiation, humidity, wind speed and other calculated parameters needed in the recommended Penman Monteith reference evapotranspiration equation, in the region of Fes, Morocco. The developed and tested models are the following: Linear Regression, K-Nearest Neighbor, Support Vector Regression, Decision Tree and Random Forest. Their results were compared with each other and with the expected value of ET0-PM , providing accurate estimation with R2 of 0.9793–0.9983 and 0.9595–0.9983 for training and testing respectively. One of the suggested models, nevertheless, outperformed the others with its various advantages of good accuracy, no overfitting, robustness to outliers, ability to handle the non-linear inputs efficiently, etc. It is the Random Forest with an RMSE of 0.0095 mm/day and MAE of 0.0666 mm/day, demonstrating that RF is better suited to estimate the reference evapotranspiration for the region of Fes and its climatic pattern. Moreover, we should note that the primary shortcoming of the study was its inability to offer the complete dataset required for the Penman Monteith equation, resulting, therefore, to drop lines from the dataset.
References 1. Water in Agriculture. https://www.worldbank.org/en/topic/water-in-agriculture. Accessed 04 Oct 2022 2. Farooq, M., Hussain, A., Hashim, S., Yang, L., Ali, M.: Automated irrigation system based on irrigation gates using fuzzy logic. In: 2020 International Conference on Internet of Things and Intelligent Applications, ITIA 2020, pp. 1–5. Institute of Electrical and Electronics Engineers Inc. (2020). https://doi.org/10.1109/ITIA50152.2020.9312344 3. Adeyemi, O., Grove, I., Peets, S., Norton, T.: Advanced monitoring and management systems for improving sustainability in precision irrigation. Sustainability 9, 353 (2017). https://doi. org/10.3390/su9030353 4. Jawad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., Ismail, M.: Energy-efficient wireless sensor networks for precision agriculture: a review (2017). https://doi.org/10.3390/s17081781 5. Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017). https://doi.org/10.1016/j.agsy.2017.01.023 6. Abioye, E.A., et al.: Precision irrigation management using machine learning and digital farming solutions. AgriEngineering 4, 70–103 (2022). https://doi.org/10.3390/agriengineer ing4010006 7. Lozoya, C., Mendoza, C., Aguilar, A., Román, A., Castelló, R.: Sensor-based model driven control strategy for precision irrigation. J. Sens. 2016 (2016). https://doi.org/10.1155/2016/ 9784071 8. Zotarelli, L., Dukes, M.D., Romero, C.C., Migliaccio, K.W., Morgan, K.T.: Step by Step Calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method). Institute of Food and Agricultural Sciences. University of Florida, p. 8 (2010) 9. Paparrizos, S., Maris, F., Matzarakis, A.: Sensitivity analysis and comparison of various potential evapotranspiration formulae for selected Greek areas with different climate conditions. Theor. Appl. Climatol. 128(3–4), 745–759 (2016). https://doi.org/10.1007/s00704015-1728-z 10. Sanikhani, H., Kisi, O., Maroufpoor, E., Yaseen, Z.M.: Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theor. Appl. Climatol. 135(1–2), 449–462 (2018). https://doi.org/10. 1007/s00704-018-2390-z
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11. Chen, Z., Zhu, Z., Jiang, H., Sun, S.: Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. J. Hydrol. (Amst) 591, 125286 (2020). https://doi.org/10.1016/j.jhydrol.2020.125286 12. Ferreira, L.B., da Cunha, F.F.: New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agric. Water Manag. 234, 106113 (2020). https://doi.org/10.1016/j.agwat.2020.106113 13. Ojha, T., Misra, S., Raghuwanshi, N.S.: Wireless sensor networks for agriculture: the state-ofthe-art in practice and future challenges. Comput. Electron. Agric. 118, 66–84 (2015). https:// doi.org/10.1016/j.compag.2015.08.011 14. Valipour, M., Eslamian, S.: Analysis of potential evapotranspiration using 11 modified temperature-based models. Int. J. Hydrol. Sci. Technol. 4, 192–207 (2014) 15. Allen, R.G., Pereira, L.S., Raes, D., Smith, M.: FAO irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, vol. 56, p. e156 (1998) 16. Hargreaves, G.H., Samani, Z.A., Abstract, A.: Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1, 96–99 (1985) 17. Blaney, H.F., Criddle, W.D.: Determining consumptive use and irrigation water requirements. US Department of Agriculture, p. 1275 (1962) 18. Thornthwaite, C.W.: An approach toward a rational classification of climate. Geogr. Rev. 38, 55–94 (1948) 19. Turc, L.: Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann. Agron. 12, 13–49 (1961) 20. Priestley, C.H.B., Taylor, R.J.: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100, 81–92 (1972) 21. Tikhamarine, Y., Malik, A., Souag-Gamane, D., Kisi, O.: Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environ. Sci. Pollut. Res. 27(24), 30001–30019 (2020). https://doi.org/10.1007/s11356-020-08792-3 22. Balducci, F., Impedovo, D., Pirlo, G.: Machine learning applications on agricultural datasets for smart farm enhancement. Machines 6, 38 (2018). https://doi.org/10.3390/machines6 030038 23. Glória, A., Cardoso, J., Sebastião, P.: Sustainable irrigation system for farming supported by machine learning and real-time sensor data. Sensors 21, 3079 (2021). https://doi.org/10.3390/ s21093079 24. Moinina, A., Lahlali, R., Maclean, D., Boulif, M.: Farmers’ knowledge, perception and practices in apple pest management and climate change in the Fes-Meknes region, Morocco. Horticulturae 4, 42 (2018). https://doi.org/10.3390/horticulturae4040042
Data Augmentation and Deep Learning Applied for Traffic Signs Image Classification Ismail Nasri1(B) , Mohammed Karrouchi1 , Abdelhafid Messaoudi2 Kamal Kassmi1 , and Soufian Zerouali2
,
1 Laboratory of Electrical Engineering and Maintenance, High School of Technology,
Mohammed First University, Oujda, Morocco [email protected] 2 Laboratory of Energy, Embedded Systems and Information Processing, National School of Applied Sciences, Mohammed First University, Oujda, Morocco
Abstract. Traffic sign recognition is an essential phase for intelligent autonomous driving systems. In this work, we have presented a recognition solution which is based on the classification performed by supervised learning method through deep learning an artificial using Convolutional Neural Network (CNN). Three pretrained neural networks such as, GoogLeNet, VGG-16, AlexNet, were adapted for transfer learning algorithm. In order to improve the classification accuracy and to solve the problem of the insufficient amount of training dataset or the unequal number of images for each class, we have used the data augmentation technique applied on German Traffic Sign Benchmark dataset (GTSRB). The obtained experimental results show that the size of the dataset as the number of training images has a significant impact on the classification accuracy of the CNN model. The number of images was increased from 51,840 to 96,750 images by using data augmentation technique. Therefore the classification accuracy was increased from 86.72% to 97.33%. Keywords: Traffic Signs Image Classification · Supervised Learning · Data Requirements · Data Augmentation · CNN · Deep Learning
1 Introduction Deep learning is currently considered the fastest-growing field in machine learning (ML); particularly for image processing and classification problems. The most of machine learning algorithms such as ANN and CNN models [1], require a large dataset for training process. Deep learning models, in particular, usually require millions of dataset to generate significant results. These datasets requirements are often difficult to achieve in practice. Image data augmentation [2] is one of the best solutions for this kind of problem. Instead of trying to find and label more datasets, we build new ones based on what we have by creating modified versions of images from the existing training images. This technique can then effectively improve the learning and validation process as these results in more training samples for the CNN model. In [3], the authors have provided a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 114–123, 2023. https://doi.org/10.1007/978-3-031-29860-8_12
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classification method applied to classify traffic signs images and discussed the effect of adaptive fine-tuning on training options such as learning rate and number of epoch for CNNs model in the transfer learning. The authors in [4], have proposed a method of data augmentation to create new images for training process. This method based on image style transfer by combining the content of an input image with the appearance of another one. The authors in [5] have proposed a new automatic and adaptive algorithm to choose the transformations of the samples used in the data augmentation. Their main idea is to search for a small transformation that produces a maximum classification loss on the transformed image. The rest of this paper is organized as follows: The Sect. 2 introduces the methods adopted for data augmentation and training of CNN models by transfer learning and training from scratch. In Sect. 3, we have presented the experimental results of the training process of CNNs and the impact of data augmentation on classification accuracy and training time. The last section provides a summary of the main findings of the work.
2 Materials and Methods 2.1 Data Description Several databases have been used for detection and classification of traffic signs, the most popular is the German Traffic Sign Benchmark (GTSRB) [6]. It is commonly used in the traffic sign detection problems. This facilitates authors to compare their proposed approaches with the state of the art. It consists of two different datasets. The firs one is GTSDB (German Traffic Sign Detection Benchmark), containing 900 images of 1360 × 800 pixels. The second is GTSRB (German Traffic Sign Recognition Benchmark) which contain 43 classes for a total of 51,840 images of diverse types of road signs, including regulatory, warning, and guide signs. The size of the images ranges from 15 × 15 to 250 × 250 pixels. As shown in Fig. 1, the number of images per class in the GTSRB dataset is not equal, some classes contain more than 2000 images and some contain less than 250 images. We have used the GTSB database, to test our proposed system because of the diversity of its classes (43 classes), and also its large size. The available databases of traffic signs are summarised in Table 1 below: 2.2 Data Augmentation Data augmentation technique aimed to increase and create of image variations that can improve the effectiveness of deep learning models. The significant difference in the number of images for each class may affect the training process and the classification results, particularly for classes with a limited number of images. In order to solve this problem, we have applied data augmentation for the limited representation classes to have a number of 2250 images for each class, which is a total of 96,750 images. We have applied each transformation on the same input image (see Fig. 2(a)) to compare the effect of the different types of image augmentation, as shown in Fig. 2. The different types of transformation used in this paper for image data augmentation have been described in Table 2.
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Number of Samples
Number of samples per class
Class
Fig. 1. Total number of samples in the GTSRB dataset for each class.
Table 1. Available datasets of traffic signs. Dataset
Number of samples Size of images
GTSRB [6]
51 840
15 × 15 to 250 × 250 Germany
BTSD (Belgium Traffic Sign Dataset) [7]
25 634
1 628 × 1 236
Belgium
STS (Swedish Traffic Sign) [8]
20 000
1 280 × 960
Sweden
1 024 × 522
United States
LISA (Laboratory for Intelligent 7 855 and Safe Automobiles dataset) [9]
Country
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
Fig. 2. Examples of multiple types of transformations for data augmentation.
2.3 Training from Scratch The first CNN was introduced in 1989, named LeNet [10], for handwritten digit recognition using a supervised learning and trained by back-propagation algorithms. This
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Table 2. Different types of data augmentation techniques. Transformations
Techniques
Description
Random Image Warping Transformations
Rotation
Rotate the input image by an angle of −45°. (see Fig. 2(b))
Translation
Translation the input image horizontally and vertically by a distance of [−50, 50] pixels. (see Fig. 2(c))
Scale
Resize the input image using a scale factor from the range [1.2, 1.5]. (see Fig. 2(d))
Shear
Create a horizontal shear transformation with the shear angle from the range [−30, 30]. (see Fig. 2(e))
Control Fill Value
Create a random rotation transformation, then apply the transformation and specify a gray fill value. (see Fig. 2(f))
Hue Jitter
Adjust the hue of the input image from the range [0.05, 0.15]. (see Fig. 2(g))
Saturation Jitter
Adjust the saturation from the range [−0.4, −0.1]. (see Fig. 2(h))
Brightness Jitter
Adjust the brightness from the range [−0.3, −0.1]. (see Fig. 2(i))
Contrast Jitter
Adjust the contrast by a scale factor from the range [1.2, 1.4]. (see Fig. 2(j))
Synthetic Noise
Apply synthetic noise to the input image. (see Fig. 2(k))
Synthetic Blur
Apply synthetic blur to the input image (Gaussian). (see Fig. 2(l))
Color Transformations
Other Image Processing Operations
architecture was successfully applied to the identification of handwritten postal codes provided by the US Postal Service in their paper [11]. Test results on numerical data of postcodes given by the United States Postal Service (USPS) showed that the proposed CNN architecture LeNet had only 1% of error rate [12]. In 2012, Krizhesky et al. presented AlexNet architecture [13], which exceeded the previous approaches by significant performances in ILSVRC (ImageNet Large-Scale Visual Recognition Challenge) [14]. The proposed convolutional neural network, inspired by the LeNet architecture, contains many layers, including input layer, seven hidden layers, and output layer as indicated in Table 3. The features in CNN were detected by convolution and Max pooling layers
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[15]. The input image was represented by the first layer with the dimensions 227 × 227 × 3 processed through a convolution layer with 16 kernels of size 3 × 3 by a convolution layer with 16 kernels of size 3 × 3 producing a dimension of 227 × 227 × 16. Three convolutional layers have been used to extract the different features like lines, curves, colors, etc. These features were learned by the convolutional neural network itself during the learning process. Figure 3 shows what these features look like through different layers of the CNN. The third layer is the Max Pooling layer which has a kernel size of 2 × 2 and a stride of 2, producing an image of 113 × 113 × 16. Similarly, the sixth layer consists of a convolutional with a kernel size 64 of 3 × 3 and a stride of 1 to get an image size of 56 × 56 × 32. Finally, after several feature detection layers, the fully connected layer (FC) has been used for the classification task. The last layer of the proposed architecture contains the softmax function, to provide which class the input image corresponds and the probability of corresponding to each class. Table 3. Parameters of each layer in CNN trained from scratch. Layer
Size
Maps
Kernel Size
Stride
Padding
Activation
Image Input
227 × 227 × 3
–
–
–
–
–
Convolution1
227 × 227 × 16
16
3×3
1
[1 1 1 1]
ReLU
Max Pooling1
113 × 113 × 16
16
2×2
2
[0 0 0 0]
–
Convolution 2
113 × 133 × 32
32
3×3
1
[1 1 1 1]
ReLU
Max Pooling 2
56 × 56 × 32
32
2×2
2
[0 0 0 0]
–
Convolution3
56 × 56 × 64
64
3×3
1
[1 1 1 1]
ReLU
FC
–
–
–
–
–
–
Classoutput
–
–
–
–
–
Softmax
During the learning process, as illustrated in Fig. 4, the deep learning model aimed to minimize the loss function value. The process of reducing the value of the loss function is called “optimization”. The Mean Squared Error (MSE) is a loss function used for classification problems. The value of loss is the differences between actual and predicted values calculated as an average squared value, as shown in Eq. (1): 1 n 2 ytrue − ypred L ytrue , ypred = i=1 n
(1)
where L ytrue , ypred is the MSE, ytrue is the actual value, ypred is the predicted value and n is the total number of training dataset. The processing platform for the CNN learning and testing algorithm has the following performances: 8th Generation Core i5 processors, 8 GB memory, and 256 GB SSD. The algorithm of training CNNs model was developed in MATLAB R2018 software.
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(a)
(b)
(c)
(d)
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Fig. 3. Maps of features learned by the CNN layers. (a) Convolution1, (b) Convolution 2, (c) Convolution, (d) Fully connected layer.
2.4 Transfer Learning Transfer learning [16, 17] is the most commonly algorithm currently used for deep learning where the training dataset is limited or not sufficient. The main objective of this technique is using a previously pre-trained CNN architecture for new classification problems. This approach reduces the model learning time to achieve good performance, as this pre-training phase on a similar dataset provides the parameter values associated with the extraction of conventional image descriptors. In this study, tree pre-trained models have been used, such as AlexNet [13], GoogLeNet [18], and VGG-16 [19]. GoogLeNet contains 22 deep layers. This variant of the Inception network was developed by Google researchers in 2014. This CNN has two different versions. The first is trained by the ImageNet dataset [14] and can classify images in 1000 classes. The other version was trained on the places365 dataset [20] to classify images in 365 different location categories. The size of the image input is 224 × 224 (RGB). VGG-16 is a CNN model that achieved an accuracy of 92.7% on the ImageNet database which contains more than 14 million images divided into 1000 classes. The input image has a size of 224 × 224 × 3 (RGB). AlexNet, created in 2012, is CNN architecture, trained using graphics processing units (GPUs). The model contains eight layers (5 convolutional layers and 3 fully connected layers). ReLU has been used as the activation function in each of these layers except the output layer which uses the softmax function. The AlexNet has been trained using ImageNet datasets [14].
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(a)
(b)
Fig. 4. Training process of CNN trained from scratch. (a) Accuracy, (b) Loss.
3 Results and Analysis This section provides the experimental results of the learning process and also the performances of the pre-trained CNNs and CNN trained from scratch. The performance of the CNN model was evaluated by verifying its ability to classify the test images (30% of the dataset) correctly. We have calculated a parameter called Accuracy using the Confusion Matrix [21]. This parameter represents the ratio of the correct classifications among the total number of test images (correct classifications + incorrect classification). The correctly predicted classifications were mentioned on the diagonal from bottom left to top right, the other values in the confusion matrix correspond to the incorrect classifications, as shown in Fig. 5. We have also demonstrated the efficient impact of the data augmentation technique on the classification accuracy of the CNNs model. Table 4 shows the process of training the CNN network from scratch using the augmented dataset (GTSRB). The number of training images represents 70% of the total number of dataset for one epoch only, 214 iterations, and a learning rate of value 0.001. Table 5 provides performances comparison between all CNNs, including pre-trained and trained from scratch. Before data augmentation the best accuracy score of all CNNs was obtained by the CNN trained from scratch, and AlexNet for the pre-trained networks. CNN trained from scratch and AlexNet have achieved 86.72% and 85.93% of accuracy for 62 min and 49 min of training time respectively. After data augmentation the best accuracy score has been achieved the CNN trained from scratch and AlexNet from all
Data Augmentation and Deep Learning Applied Pretrained CNN GoogLeNet
True Class
True Class
CNN trained from scratch 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0464 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04690 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 05020 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 04880 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 410152 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 34580 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05113 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27553 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05210 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05980 0 3 3 3 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04690 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 0 06280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34753 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35550 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04684 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 2 05760 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 08450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 34750 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05100 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 4 2 04910 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 54994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24592 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 07520 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 07300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 45190 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05870 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06300 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 210420 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 010240 0 0 0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07900 0 0 0 0 0 3 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 39910 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 08327 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 3 0 38150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 3 0 082510 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 45200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 09512 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 09892 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 08150 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 310672 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 310603 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4600 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Predicted Class
Predicted Class
(b)
(a)
Fig. 5. Confusion Matrix of CNNs model. (a) CNN trained from Scratch, (b) Pre-trained CNN GoogLeNet.
Table 4. Training process of CNN trained from scratch using augmented dataset. Epoch
Iteration
Time Elapsed
Accuracy
Loss
Learning Rate
1
1
00:14:02
47.66%
1.8177
0.001
1
50
00:39:14
85.16%
0.4919
0.001
1
100
00:50:32
87.50%
0.4291
0.001
1
150
01:05:26
89.84%
0.2629
0.001
1
200
01:30:54
94.53%
0.1800
0.001
1
214
01:49:35
97.33%
0.1291
0.001
the pre-trained networks. The accuracy of CNN trained from scratch and AlexNet was 97.33% and 95.63% for 109 min and 78 min of training time respectively, as shown in Table 6. The obtained results show that, in deep learning, precisely in the CNN algorithm, the accuracy is highly influenced by the size of images data training. A large amount of image dataset is required to obtain reasonable results [22]. The accuracy rate was increased significantly after using data augmentation from 86.72% to 97.33%.
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Table 5. Performances comparison between all CNNs before and after Data Augmentation. Before Data Augmentation (Original dataset (GTSRB))
After Data Augmentation (Augmented dataset)
Accuracy
Training time
Accuracy
Training time
GoogLeNet
85.13%
00:52:27
94.84%
01:36:12
VGG-16
83.54%
00:43:11
92.02%
01:03:47
AlexNet
85.93%
00:49:18
95.63%
01:18:05
CNN from scratch
86.72%
01:02:30
97.33%
01:49:35
4 Conclusion We have aimed, through this work, to apply supervised learning techniques to a traffic signs classification problem. The classification models are based on CNNs trained by the transfer learning algorithms and training from scratch. Three pre-trained CNNs such as GoogLeNet, VGG-16, and AlexNet were used and evaluated using the Matrix of Confusion. We have also demonstrated the significant impact of the number of images dataset on the classification accuracy. For that, we have augmented the original dataset (GTSRB), from 51,840 to 96,750 images; by different traditional types of data augmentation and transformations techniques such as Random Image Warping Transformations, Color Transformations, etc. Data augmentation can improve the performance of CNN models and make datasets, which have a limited number of images, useful for supervised learning algorithms, which require a large dataset to achieve relevant results. The obtained results show that the accuracy of classification increases from 86.72% to 97.33% using data augmentation.
References 1. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), August 2017, pp. 1–6 (2017). https://doi.org/10.1109/ICEngTechnol.2017.8308186 2. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0 3. Nasri, I., Messaoudi, A., Kassmi, K., Karrouchi, M., Snoussi, H.: Adaptive fine-tuning for deep transfer learning based traffic signs classification. In: 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), December 2021 pp. 1–5 (2021). https://doi.org/10.1109/ISAECT53699.2021.9668592 4. Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary Ph.D. Workshop (IIPhDW), May 2018, pp. 117–122 (2018). https://doi.org/10.1109/IIPHDW.2018.8388338 5. Fawzi, A., Samulowitz, H., Turaga, D., Frossard, P.: Adaptive data augmentation for image classification. In: 2016 IEEE International Conference on Image Processing (ICIP), September 2016, pp. 3688–3692 (2016). https://doi.org/10.1109/ICIP.2016.7533048
Data Augmentation and Deep Learning Applied
123
6. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 International Joint Conference on Neural Networks, July 2011, pp. 1453–1460 (2011). https://doi.org/10.1109/IJCNN.2011. 6033395 7. Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition, and 3D localisation. Mach. Vis. Appl. 25(3), 633–647 (2011). https://doi.org/10.1007/s00 138-011-0391-3 8. Larsson, F., Felsberg, M.: Using Fourier descriptors and spatial models for traffic sign recognition. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 238–249. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_23 9. Møgelmose, A., Trivedi, M.M., Moeslund, T.B.: Learning to detect traffic signs: comparative evaluation of synthetic and real-world datasets. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), November 2012, pp. 3452–3455 (2012) 10. LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. Adv. Neural Inf. Process. Syst. vol. 2 (1989) 11. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541 12. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791 13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386 14. Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, June 2009, pp. 248–255 (2009). https:// doi.org/10.1109/CVPR.2009.5206848 15. Nasri, I., Karrouchi, M., Snoussi, H., Kassmi, K., Messaoudi, A.: DistractNet: a deep convolutional neural network architecture for distracted driver classification. IAES Int. J. Artif. Intell. IJ-AI 11(2), 494–503 (2022). https://doi.org/10.11591/ijai.v11.i2.pp494-503 16. Shaha, M., Pawar, M.: Transfer learning for image classification. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), March 2018, pp. 656–660 (2018). https://doi.org/10.1109/ICECA.2018.8474802 17. Nasri, I., Karrouchi, M., Snoussi, H., Kassmi, K., Messaoudi, A.: Detection and prediction of driver drowsiness for the prevention of road accidents using deep neural networks techniques. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds.) WITS 2020. LNEE, vol. 745, pp. 57–64. Springer, Singapore (2022). https://doi.org/10.1007/978-981-336893-4_6 18. Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 1–9 (2015). https://doi.org/10.1109/ CVPR.2015.7298594 19. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2014). https://doi.org/10. 48550/arXiv.1409.1556 20. Zhou, B., Lapedriza, A., Torralba, A., Oliva, A.: Places: an image database for deep scene understanding. J. Vis. 17(10), 296 (2017). https://doi.org/10.1167/17.10.296 21. Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-54031865-1_25 22. Nasri, I., Karrouchi, M., Snoussi, H., Messaoudi, A., Kassmi, K.: MaskNet: CNN for realtime face mask detection based on deep learning techniques. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 87–97. Springer, Cham (2021). https://doi.org/10. 1007/978-3-030-73882-2_9
Arabic Speech Emotion Recognition Using Deep Neural Network Omayma Mahmoudi(B) and Mouncef Filali Bouami Laboratory of Applied Mathematics and Information Systems, Multidisciplinary Faculty of Nador, Mohammed Premier University Morocco, Oujda, Morocco {mahmoudi.omayma,m.filalibouami}@ump.ac.ma
Abstract. Audio recordings have become a fundamental tool to outclass text messages in social networks. Spontaneity and naturalness in human behavior on the one hand and the expansion of smart devices on the other explain such behavior. Since the recordings can contain, beyond the verbal expression, the feelings of the sender, it is essential to take into account and process this kind of information in data classification tasks. As a result, one of the most important research topics at the moment is speech emotions recognition (SER) using deep learning (DL). Some research treats this context in a generic way, while others specialize in a specific language, such as the English speech case, unlike Arabic, which is barely targeted by research of this kind. This is due principally to a scarcity of Arabic speech emotion databases. This research aims to identify the emotions in spoken Arabic, for which we have adopted as a database a group of recordings of Arab students, with the aim of revealing their feelings during their communication with their teacher. In this paper, we suggest comparing the LSTM model for emotion recognition in Arabic speech with the following models: GRU, RNN, and CNN. Keywords: Arabic Speech Emotion recognition · MFCC · Deep learning · LSTM · GRU · RNN · CNN
1 Introduction People use many ways to communicate. Some prefer to send short messages, and some prefer to chat. There is also an increasing fraction who prefer to communicate through voice messages, video calls, etc. Since many consumer applications also contribute to distance education, we targeted WhatsApp in our work. The students express their feelings through their audio recordings when learning remotely. For example, when a student sends an audio recording without any information about his facial expression or physiological state, the teacher may obtain a rough idea of his emotional state by listening to his voice. Emotion is a state of mind that includes human feelings, ideas, and behaviors. It encompasses the emotional responses of pupils toward study and teachers, along with physiological responses associated with this psychological interaction [1]. For this, we apply SER, which aims to use strategies to better understand a student’s emotions by extracting his emotional state from vocal messages [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 124–133, 2023. https://doi.org/10.1007/978-3-031-29860-8_13
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where student speech emotion recognition is critical for knowing how to interact with them and become close to them, especially with their instructor, for the success of the learning process. Studies targeting SER have varied in different speaker languages, particularly English. Due to the scarcity of datasets, Arabic was among the languages that were not popular in this field, so we tried to apply SER to Arabic [3]. We used as a dataset a recording set of Arab students’ communications with their teacher during distance learning. The participants are boys and girls aged from 13 to 17. In this paper, we have applied a set of DL [4] models such as LSTM [5], RNN [6], GRU [7], and CNN [8] to identify Arabic emotions. Then, five features were extracted using MFCC [9], RMSV [10], MSS [11], Chroma STFT [12], and ZCR [13]. The rest of the structure of this paper is as follows: The related works are described in the second section, and the Arabic SER approach is presented in the third section. The main topic of the fourth section is the DNN [14] classifier and some of its algorithms. The fifth section presents our methodology and results. The final section is the conclusion.
2 Related Work This section presents an overview of the many SER approaches that have been published by different researchers. Many of them use numerous and different feature vectors, and others apply various models of classification to improve emotion identification outcomes. These can be shown in the following studies. In [15], Nada et al. introduced the subject of recognizing the emotional state of an Arabic user by analyzing the speech signal. They created an Arabic SER corpus that encompassed five emotions: happiness, rage, sadness, surprise, and neutrality. They used supervised learning techniques for classification and constructed multiple classification algorithms, including support vector machine algorithms (SVMs) with the radial basis function (RBF) kernel, neural networks (NNs), and DL RNNs. They recorded information on the frequency and spectrum of the speech signal. They generated MFCC after applying an HPF and normalized the features’ length using PCA. When the different classification approaches on the Arabic speech corpus were compared, the SVM approach outperformed the other two methods. In [16], Tajalsir et al. proposed a model based on the LSTM model for Arabic speech emotion recognition. MFCC features, chromatogram, Melscaled spectrogram, spectral contrast, and tonal centroid characteristics were recovered from the speech (tonnetz). They tested the model using the Basic Arabic Expressive Speech corpus, an Arabic speech dataset (BAES-DB). In addition, they build a DNN to identify emotions and compare the accuracy of the LSTM and DNN models. In [17], Reem Hamed Aljuhani et al. studied emotion recognition based on Arabic Saudi dialect spoken data. The dataset was constructed by labeling freely available YouTube films with four perceived emotions: anger, happiness, sadness, and neutral. Various spectrum features were retrieved, including the MFCC and Mel spectrogram, and they used classification algorithms such as SVM, MLP, and KNN. In the end, they examined and assessed the data and then compared the three models using various feature extractions.
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In [18] Ali & Mohammed. Presented a study on SER using the KSUEmotions Arabic speech emotion corpus, followed by feature extraction and classification algorithms such as the KNN and SVM. Python was used to conduct the studies. The trials found that KNN outperformed SVM in this corpus, and they found the highest accuracy for the emotion of melancholy, followed by happiness, and finally surprise.
3 Arabic Speech Emotion Recognition We, as humans, have the innate ability to employ all of our senses to fully comprehend the message we are receiving. We are naturally good at detecting emotions, while machines struggle with them. The objective of Arabic SER is to recognize the emotion in Arabic speech, regardless of the semantic information. Speech input, preprocessing, feature extraction, feature vectors, classification and recognized emotional output are the six primary components of the speech emotion detection system. As shown in Fig. 1
Fig. 1. Architecture of the speech emotion recognition system
Arabic Audio Input: Enters the processed records and classifies them separately. Preprocessing: Ensures the quality of the recordings by representing them through the use of several features, including MFCC, and then deleting or modifying what needs to be modified [19]. Feature Vector Extraction: To extract useful features from the audio data, we will use the Librosa library, which provides several methods to extract a variety of features from the audio [20]. Deep Neural Network Classifier: Performs a classification of recordings using LSTM, RNN, GRU, and CNN. In addition, a comparison with each other [21].
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Emotion Recognition: A major challenge in SER systems is the necessity to identify a set of meaningful emotions that can be identified by an automatic emotion recognizer. There is no counted number of emotional states in a typical set of feelings; as a result, classifying such a large variety of emotions is extremely difficult. That is why we will measure the prevalent feelings among the students, such as shyness, happiness, sadness, and neutrality [22]. The models cannot interpret the audio data presented and must convert it to a format that they can understand. As a result, it employs features of extraction. 3.1 Feature Extraction The extraction of features is a crucial step in studying and discovering relationships between various objects. For the algorithms that classify, predict, and suggest, a previous step of feature extraction is needed. We will go over various strategies for extracting audio elements. The audio signal has three axes that describe time, amplitude, and frequency. We may show audio files in a variety of ways, such as a wave plot, spectrogram, or color map. Wave charts show us the loudness of the audio at a particular time. The spectrogram displays the frequencies that are active at a given time, as well as their amplitude. Amplitude and frequency are important properties of sound that are unique to each audio. Table 1 presents five different features used in our study. Table 1. Different models used for feature extraction Features
Definition
Mel spectrogram
A Mel spectrogram is one in which the frequencies have been translated to the Mel scale.
MFCC
Chroma-stft
Root Mean Square Value Zero Crossing Rate
Example
MFCC is calculated by discrete cosine transform of the Mel scale log power spectrum. These features make it possible to imitate the human perception of the spectral envelope. represent the twelve semitones of a musical octave. They are obtained by simple projection of the spectrum in the corresponding frequency zones. By definition, the RMS is the effective value of a periodic signal or an ergodic random signal and is the square root of the moment of order two of the signal. is the sign change rate of a signal. ZCR has been widely used in speech recognition and music information retrieval.
4 Recurrent Neural Network Classifier Traditional neural networks analyze labeled data but are not designed to make timeseries predictions (data changing over time). To perform this type of calculation, there are three main types of RNN: simple RNN, LSTM, and GRU. A simple RNN is the
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most basic form of RNN. It has no gates, which means that the flow of information is not controlled and the essential information for the task at hand can be overwritten by redundant or irrelevant information. In practice, vanilla RNNs are not used and are only studied for teaching purposes. LSTM and GRU consist of several gates (3 and 2, respectively) that allow forgetting or selectively storing information from the previous time sequence in dynamic memory. As with traditional NNs, RNNs can contain multiple layers, which allows them to capture more nonlinearity among the data but also increases computation time in the training phase (see Fig. 2).
Fig. 2. Three different architectures of RNN models
5 Methodology and Results 5.1 Dataset An appropriate database is a key element in any machine-learning problem. Due to the scarcity of audio recordings in the Arabic language, we have created one of our own, which includes 565 audio recordings of male and female students, aged from 13 to 17 years, while communicating through the WhatsApp application with their teacher during distance learning. The recordings are the beginning of the conversation, with a duration of 2 to 3 s. Since Arab students are characterized by feelings of shyness, happiness, neutrality, and sometimes sadness, these emotions were chosen as the output classes of our model. As we show in Fig. 3 (REF), our dataset is almost homogeneous, with a slight discrepancy between the four emotions, before starting the processing. The F1-score [23] has in common the accuracy [24] of summarizing the performance of a model in a single indicator for each classification threshold. It has the advantage of being robust in the presence of unbalanced data. 5.2 Data Preparation and Preprocessing: We named the audio files in such a way that the prefix letters describe the emotion classes as follows: ‘h’ = ‘happiness’
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Fig. 3. Histogram count of Emotion
‘n’ = ‘neutral’ ‘sa’ = ‘sadness’ ‘t’ = ‘timid’ Take, for example, EV_n20.wav, which is the neutral sentence number 20 that a student said. This makes it easier to deal with the recordings. As the first stage is preprocessing, it seeks to discriminate between voiced and unvoiced signals, generate feature vectors, and alter the speech signal to make it more suitable for the next stage of feature extraction. For this article, we use the silent remover algorithm ref REF to preprocess the data. 5.3 Feature Extraction The next step is to extract the features. We are looking for structured data to extract from our audio tracks that contain enough information to be able to separate the recordings as best as possible. In our case, we collected five different features from each audio and fused them into a vector. That allows us to expect better results, these are Mel spectrogram, MFCC, chroma-stft, Root Mean Square Value, and Zero Crossing Rate. Therefore, each audio file will have a fixed vector size of 161 features. 5.4 Data Vectorization Next, we describe two functions that will extract both the features and emotion labels from the audio recordings. Because this is a multiclass classification problem, we must (one hot encoder) encode the audio labels. The following step is to invoke the functions using the appropriate directories. All of our audio features will be saved to variable ‘X’, and all of our labels will be saved to variable ‘y’.
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5.5 Modeling After splitting the data into two parts, one for training and the other for validation. To ensure the model’s performance measure at its true accuracy, and as we preprocessed our dataset, we implemented a feedforward neural network in Keras (TensorFlow) [25] to classify each sound clip into a different category after training and evaluating the network with the extracted features. This process is straightforward. Then, we read the data from all the training, combine them, and train the network. After evaluating its performance on the held-out fold, we will repeat this process fifty times, covering all the folds and averaging the obtained accuracies to obtain the final performance estimate. 5.6 Result The Adam optimization approach was used to train the LSTM model, with a dropout rate of 0.4. The categorical cross-entropy loss function was employed with an initial learning rate of 0.001 and a batch size of 64 for 50 epochs. Figure 4 shows the accuracy and loss curves for the two models.
Fig. 4. Training and testing history-plot of loss and accuracy
Therefore, we can see that the accuracy starts to plateau at approximately 20 epochs. Regardless, we will keep it at 50 as the final model. Table 2. Accuracy of our model on test data Classifier
accuracy
TIME TAKEN
LSTM
92.09%
4 min
RNN
84.02%
2 min
GRU
87.74%
2,5 min
CNN
81.50%
0.5 min
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Table 3. The precision of our model on test data Model
happy
neutral
sad
timid
RNN
0.89
0.86
0.85
0.86
LSTM
0.92
0.91
0.87
0.97
GRU
0.89
0.87
0.67
0.69
CNN
0.90
0.88
0.78
0.88
Table 4. Recall the measure of our model on test data Model
happy
neutral
sad
timid
RNN
0.90
0.83
0.61
0.93
LSTM
0.98
0.86
0.82
0.88
GRU
0.76
0.77
0.75
0.73
CNN
0.93
0.89
0.55
0.93
Table 5. f1-score of our model on test data Model
happy
neutral
sad
timid
RNN
0.92
0.84
0.72
0.90
LSTM
0.95
0.88
0.84
0.96
GRU
0.82
0.77
0.71
0.71
CNN
0.92
0.88
0.65
0.91
6 Comparative and Discussion According to the results shown in Table 2, we can notice that the best accuracy was obtained by LSTM with 92.09%, followed by GRU with 87.74%. The RNN and CNN models had the lowest accuracy rates at 84.04% and 81.50%, respectively. In Table 3, we can observe that the happy emotion has the highest precision rate for the RNN model, and the sad emotion has the lowest precision rate for the RNN and GRU models. The same result is nearly in Table 4. The recall rate is high for happy emotions, with 98% for the LSTM model and the lowest value for the GRU model, with 76%. For Table 5, we can say the value for the F1-score is very high for timid emotion by 96% and lowest for GRU emotion by 71%. Such as the results being the same when we execute the code by using the same methods and under the same circumstances. From the previous results, it became clear to us that we should rely on the LSTM model for its accuracy in Arabic speech emotion recognition, despite its slowness during model training, unlike the CNN model, which is considered the fastest during training but whose accuracy is somewhat weak.
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7 Conclusion There are still many uncertainties in regard to the optimum algorithm for classifying Arabic speech emotions. Emotion recognition rates vary depending on the combination of emotional features used, where the experts are still arguing what features affect emotion perception in speech and the perfect algorithms to predict. In this study, the best accuracy was 92.09%, attained by integrating the five characteristics MFCC, RMSV, MSS, chroma STFT, and ZCR for the LSTM model in the Arabic audio database. The accuracy was lower for other algorithms: RNN, GRU, and CNN. Furthermore, greater precision can be attained by combining more features. In addition, the quest for strong feature representation, as well as effective classification algorithms for automatic speech emotion identification, is part of continuing studies. Our future work will be to use more features that provide an alternative interpretation of frequency and an alternative understanding of nonlinear and nonstationary processes. Furthermore, we intend to perform more comprehensive learning studies with much larger datasets.
References 1. Drew, C.: What is the Importance of Emotions in Education & Learning? Helpful professor, 6 July 2022. helpfulprofessor.com/emotion-in-education/ 2. Ingale, A.B., Chaudhari, D.S.: Speech emotion recognition, March 2012 3. Klaylat, S., Osman, Z., Hamandi, L., Zantout, R.: Emotion recognition in Arabic speech (2018) 4. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning, 27 May 2015 5. Li, S., Xu, B., Chung, T.L.: Definition extraction with LSTM recurrent neural networks, 10 October 2016 6. Merrill, W., et al.: A formal hierarchy of RNN architectures, 19 September 2020 7. Kurochkin, I., Volkov, S.S.: Using GRU-based deep neural network for intrusion detection in software-defined networks (2020) 8. Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, 31 March 2021 9. Zheng, F., Zhang, G., Song, Z.: Comparison of different implementations of MFCC, November 2001 10. Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? (2014). https://doi.org/10.5194/gmdd-7-1525-2014 11. Ramalingam, A., Kedari, S., Vuppalapati, C.: IEEE FEMH voice data challenge 2018 (2018) 12. Kattel, M., Nepal, A., Shah, A.K., Shrestha, D.: Chroma feature extraction (2019) 13. Shete, D.S., Patil, S.B.: Zero crossing rate and Energy of the speech signal of Devanagari script (2014) 14. Li, G., Hari, S.K.S.: Understanding error propagation in deep learning neural network (DNN) accelerators and applications, November 2017 15. Al-Faham, A., Ghneim, N.: Towards enhanced Arabic speech emotion recognition: comparison between three methodologies. ISSN: 0976-3376 16. Logan, H.: ASERS-LSTM: Arabic speech emotion recognition system based on LSTM model, 23 March 2022. SSRN: https://ssrn.com/abstract= 17. Aljuhani, R.H., Alshutayri, A., Alahdal, S.: Arabic speech emotion recognition from Saudi dialect corpus, 7 September 2021. https://doi.org/10.1109/ACCESS.2021.3110992
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18. Meftah, A., Qamhan, M., Alotaibi, Y.A., Zakariah, M.: Arabic speech emotion recognition using KNN and KSUEmotions corpus. https://doi.org/10.5013/IJSSST.a.21.02.21 19. Berdibaeva, G.K., et al. : Pre-processing voice signals for voice recognition systems, 03 July 2017. https://doi.org/10.1109/EDM.2017.7981748 20. Costa, C.H.L., Valle, J.D., Koerich, A.L.: Automatic classification of audio data, March 2005 21. Geifman, Y., El-Yaniv, R.: Selective classification for deep neural networks (2017) 22. Basu, S., et al.: A review on emotion recognition using speech, 17 July 2017. https://doi.org/ 10.1109/ICICCT.2017.7975169 23. Badrani, M., Marouan, A., Kannouf, N., Chetouani, A.: Smart techniques for Moroccan students’ orientation. In: Al-Sharafi, M.A., Al-Emran, M., Al-Kabi, M.N., Shaalan, K. (eds.) ICETIS 2022. LNNS, vol. 573, pp. 361–366. Springer, Cham (2023). https://doi.org/10.1007/ 978-3-031-20429-6_33 24. Mahmoudi, O., Bouami, M.F., Badri, M.: Arabic language modeling based on supervised machine learning. Revue d’Intelligence Artificielle 36(3), 467–473 (2022). https://doi.org/ 10.18280/ria.360315 25. Ziebell, N., Abundez-Arce, A., Johnson, C.R.: Sensorflow: learning through motion (2018)
A Chatbot’s Architecture for Customized Services for Developmental and Mental Health Disorders: Autism Fatima Ezzahrae El Rhatassi1(B) , Btihal El Ghali1 , and Najima Daoudi1,2 1 Information Sciences School (ESI), ITQAN Team, LYRICA Lab, Rabat, Morocco
{fatima-ezzahrae.el-rhatassi,bel-ghali,ndaoudi}@esi.ac.ma 2 SSLab, ENSIAS, Mohammed V University, Rabat, Morocco
Abstract. The objective of this article is to position the issue of personalized medicine, particularly in detecting and treating various problems like developmental and mental challenges. Nowadays, chatbot has become a revolutionary solution in various fields. There are several instances of chatbots dealing with autism, but they are either object-constrained, not sufficiently trained to converse like a human, or not genuinely tailored to an autistic person. We have proposed an architecture complete enough to offer a personalized assistance to autistic people and mental patients based on their preferences and needs. This architecture allows detecting the developmental and mental health disorder, aiding and guiding patients in controlling their conditions and developing their conversational skills in order to better integrate them into society. Keywords: Chatbot · Architecture · Natural Language Processing · Machine learning · Deep learning · Developmental disorders · Mental disease
1 Introduction Developmental disorders are some difficulties conditions that people have in the physical, cognitive, linguistic, or behavioral domains [1]. These ailments start throughout the formative stage, might affect day-to-day functioning, and typically last for the entirety of a person’s life. Autistic or ADHD (Attention Deficit/Hyperactivity Disorder) persons as an example, differ greatly from persons who do not have the disease. There are several indications that someone is autistic, such as a slower speech tempo, delayed word formation, the tendency to repeat words or phrases unnecessarily [2] and avoiding maintaining eye contact with others with demonstrating no or less response or facial expressions to the angry or happy faces. Today, parents and educational families are more aware of this condition and the irregularities in their children and students. Since more cases are being diagnosed, the number is growing over time. There is no medical test or diagnostic procedure that can determine whether a person has autism spectrum disorder. Some of the precited signs may exist in a normal person © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 134–141, 2023. https://doi.org/10.1007/978-3-031-29860-8_14
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that why an expert is the only one who can identify this illness by looking at the developmental trajectory and behavioral changes to detect some certain autism criteria [3]. Conversational agents will be helpful to these people in maintaining constant touch with specialists, observing their behavior and mental condition in real time, assisting in their monitoring, integration and socialization [4–6]. The continuity of this paper is structured as follows. In Sect. 2, we’ll show the importance of chatbots to a patient who suffers from developmental disorders or mental diseases. Section 3 will define the types of categorizing chatbots and the general architecture used by different works in health care especially in these two subfields: Developmental and mental diseases. Section 4 gives a framework for the communication with the patient that we will be suggesting (autist or mental patient). In the final section, other areas of application of this chatbot will be discussed in order to suggest a continuous course of action for the upcoming research.
2 Mental and Developmental Health Issues and Chatbots Those with mental or developmental diseases or both are a vulnerable population that needs specialized assistance for their particular needs [1]. Autistic and ADHD people or both are more likely to have anxiety, bipolar, and personality disorders than adults with either ailment alone. Their uniqueness is in their mental state that requires first getting to know the person and trying to gain a full picture of his life, as well as getting a feel of his play and daily living way and his social and verbal abilities which necessitate after the detection of the disease a daily companionship to put him at ease, engage him in conversations, enhance his condition and take action in urgent situations. Numerous studies have connected mental illness to the need to step in on behalf of elderly people, children, individuals with autism and others to assist them overcome anxiety and a depressive state of mind, improve their communication skills and aid them in societal integration [7–10]. While some of them did well, others required more effort. There are now many chatbots available to help individuals deal with these ailments; however, each one focuses on a different target in order to achieve a different goal. Lana and other chatbots are committed to help autistic patients (children) learn by creating a tutoring system that is tailored to their needs and preferred methods of learning. Children with autism are renowned for being unable to look at faces or engage with people. Therefore, the concept of the virtual tutor being represented by a cartoon character makes the child feel more comfortable and at ease while learning and interacting. For autistic children, it is advantageous to integrate three different learning styles through the VAK style: "Visual-Auditory-Kinesthetic" to improve their learning. These sorts of systems focus on each child individually and recommend an appropriate learning method based on his behavior and training [11, 12]. While some chatbots, who are more interested in the psychological aspects of these illnesses, have chosen to focus on diagnosing the severity of particular psychological issues that patients face and assisting them in getting better by providing psychoeducation via cognitive behavioral therapy (CBT) [13, 14]. Certain chatbots have as goal to converse with patients and provide feedback on their conduct in order to help them develop their conversational skills and assist them in integrating into society [15]. Because they
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were not sufficiently trained, almost all of these conversational agents missed the opportunity to become more human-like. We’re talking about dealing with a patient who is experiencing mental issues. Passing time conversing, comprehending the patient’s state of mind requires analyzing his emotions in the context of the conversation, making him feel better and creating more personalized conversations emotionally charged and not just offering a service with a predefined responses or selecting many criteria to detect the conditions and generate a response.
3 Chatbot’s Considerations 3.1 Personalized Chatbots Since Alan Turing argues the thinking of machines in 1950 by proposing the Turing Test (“Can machines think?”) [16], chatbots have revolutionized the world from Eliza in 1966 to many other chatbots as Ally, Healthy lifestyle coaching, Tess and others until now. In order to design a personalized chatbot, personalization is defined in terms of three factors from what is personalized as contents, user interface, how the chatbot is functioning to what is delivered as a service. Also, for whom is it personalized by defining the target (a person or a group) and how to gather the information required for user modeling [6]. For this reason, it’s important to comprehend each user’s background from his or her behavior elements and habits (such as sociodemographic features, environment, and personality) to design a tailored chatbot system. There are several chatbots in numerous fields but the goal of every chatbot, independent of its field of application, is to intervene in a personalized way by adapting the outcome to the user’s backgrounds and needs in terms of behavior modification tactics and generating a convincing message [7]. 3.2 Chatbot Types Numerous factors, including knowledge domain, service given, goal-based, response generation can be used to classify chatbots [17]. When it comes to the knowledge domain, closed domain chatbots are constrained to the knowledge of that domain while open domain chatbots can answer inquiries from any domain [17]. Concerning the service given, if the chatbot is interpersonal, it will provide services like booking without engaging in close communication with the user. Intrapersonal chatbots go further with the user by comprehending him like a human does, but interagent chatbots finish another chatbot’s mission by communicating with it [17]. The process of producing responses is taken into account in classification based on the response generation method. Chatbots can be categorized as rule-based, retrieval-based, or generative-based. The rule-based approach selects the appropriate text response from a set of rules rather than creating new text responses. While generative models employ deep learning techniques to generate the response and these models are able to improve from previous messages and retrieval-based models entail selecting the best response from a pool of responses [17]. Whatever the chatbot’s level of personalization, it has a clear architecture that enables it to deliver the required service.
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3.3 General Architecture We outline the common design of the chatbot utilized in various researches in this section. As seen in Fig. 1, it comprises of the user’s interface, through which the user submits a request in either text or speech format. The second component, the input understanding component, receives and interprets the user’s request. The message is examined by determining its intent and entities, after which it is delivered to the section that generates the answer, where it is examined for the information required in the response and how to present it to the user. The requested operations are carried out, and the relevant data are obtained from the database or some other external sources. Each component in the architecture will be more detailed in the next section.
Fig. 1. General architecture of chatbots
4 Architecture’s Chatbot for a Developmental Disease The chatbot that we’re proposing aims to diagnose illness by interpreting the patient’s emotional expressions and the diseases features, as well as aiding in improved communication and general behavioral skills. Our chatbot’s architecture is composed of three main components: request management, research response and response generation components as it is summarized in Fig. 2. The user’s message is received in the first component, the request management component. As we previously mentioned, our user has autism. At some time, a large segment of this population is nonverbal. While some of them learn to speak, others remain nonverbal their entire lives. For this reason, we considered incorporating all of these message formats: text, voice, picture, visual symbols (Emojis), and video to make him feel comfortable to choose his way of expressing and chatting. Natural language processing (NLP) is used to capture and analyze the message that the request management has received. A branch of computer science, specifically the artificial intelligence subfield, is natural language processing (NLP). It focuses on giving computers the ability to understand spoken and written words similarly to humans. When a speech-based communication is entered, a speech recognition system first converts it to text. The information is then
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Fig. 2. The chatbot’s architecture
retrieved from the inputs, and the NLP creates a representation of its meaning. The stage of natural language understanding (NLU) is included in natural language processing. The NLU module firstly detects the language of the user query [18] to deal after that with comprehending it. Afterward, it classifies user intents using Support Vector Machine (SVM) to identify the user’s goal and his intent’s domain and to gather any additional information that can help to better understand the user’s query as it is shown in Fig. 3.
Fig. 3. Natural language understanding
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To comprehend the patient’s needs, a variety of approaches will be utilized, including voice recognition, image analysis, text analysis, sentiment analysis, and emotional recognition to pick up the important elements even in the smallest details from the gestures, emotions, and text message. Voice recognition, also known as speech-to-text is the ability of a machine or program to recognize words spoken aloud and translate them into legible text. Image analysis is the extraction of meaningful information from images. Text analysis is a technique of extracting meaningful information from unstructured data. Sentiment analysis is used to examine the user’s input and to figure whether the user is satisfied or annoyed to respond emotionally to him [17] and emotional recognition helps to determine the user’s emotions from the patient’s facial expressions. All these elements are essential to understand the request holistically and choosing the intent and entities that construct the request to determine the nature of the response. The features in relation with the disease will be extracted as the eye limited contact, the repetitive movement, the voice tone, the speech tempo and the repetitive words and phrases… As a result, these features can be examined to ascertain the user’s mental state, gauge how serious his situation is, and then decide how to respond. The graphical user interface will be customized to the user’s needs, and the knowledge base will be updated with new information on the patient and its characteristics so we may have a global image detailed with its mood and information in real time. In order to produce a more consistent and genuine response—exactly what is expected from a chatbot—the data stored in databases will be used to construct responses that take into account what has been said in previous messages and what has been predefined as knowledge about the user. After extracting and understanding the user’s request, machine learning and deep learning are being used in the research of the response component. These two techniques help in dealing with information coming from the previous component and they are responsible of controlling the conversation and how to confirm the meaning of understanding the user’s request to better define the information needed in the response. Machine learning with its two approaches; Reinforcement learning specifies the action of the system in each state of the conversation and the corpus bases approach helps generating the next response through a classification process using the complete dialogue history. Once the right information has been retrieved, the next step for the response generation component is to determine the content of the response and the best way to express it. Five stages of processing are included: signal analysis, data interpretation, document planning, microplanning and realization [19]. • Signal analysis locates the patterns in numerical data and other types of data using pattern matching and helps differing between noise data and real data. • Data interpretation analyzes complex data and studies the relation between messages. • Document planning identifies the content that should be mentioned in the response and also how the structure of the message should be without changing its meaning including some operations as filtering and summarizing…
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• Microplanning and realization complete the role of the previous step by joining the lexical selection to the message and applying word ordering rules to generate the final answer [17]. Once the output sequence is generated, if the input of the user was a speech one, the message is then converted to speech to keep up with the preferences of the user.
5 Conclusion and Future Work In this study, we have shown how chatbots can be used in the medical field, particularly in the detection, management, and treatment of several developmental and mental diseases. A chatbot can be created in a variety of ways while adhering to a specific architecture. Then, in order to improve the patient’s conditions and detect the ailment more accurately, we suggested a thorough architecture for our chatbot. In the area of mental illnesses or developmental disorders where the stability of the mind is impacted. By communicating with the patient and learning his intention in real time, chatbots are an effective tool for helping these patients learn about their illness, receive therapy through cognitive behavioral intervention and avert some suicidal scenarios. Especially for those who need to talk and feel comfortable sharing without being judged or uncomfortable, chatbots make the mental health treatment system more accessible. It should also be necessary for the patient’s families to receive assistance from the chatbot because raising a child who has autism or other mental or developmental health issues is difficult. Families and parents must receive psychological support in order to care for their sick children, keep going and maintain their health in the face of inappropriate reactions. Numerous tests might be integrated into the chatbot to identify the child’s areas of strength and weakness and help him focus on developing his strong points and assist him in his tutoring by providing him an adequate way of learning.
References 1. Hodges, H., Fealko, C., Soares, N.: Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl. Pediatr. 9, S55 (2020) 2. Eni, M., Dinstein, I., Ilan, M., Menashe, I., Meiri, G., Zigel, Y.: Estimating autism severity in young children from speech signals using a deep neural network. IEEE Access 8, 139489– 139500 (2020) 3. Huang, Y., Arnold, S.R., Foley, K.R., Trollor, J.N.: Diagnosis of autism in adulthood: a scoping review. Autism: Int. J. Res. Pract. 24(6), 1311–1327 (2020) 4. Zhong, H., Li, X., Zhang, B., Zhang, J.: A general Chinese chatbot based on deep learning and its’ application for children with ASD. Int. J. Mach. Learn. Comput. 10(4), 519–526 (2020) 5. Scassellati, B., et al.: Improving social skills in children with ASD using a long-term, in-home social robot. Sci. Robot. 3(21), eaat7544 (2018) 6. Rakhymbayeva, N., Amirova, A., Sandygulova, A.: A long-term engagement with a social robot for autism therapy. Front. Robot. AI 8, 669972 (2021)
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7. Ali, M.R., et al.: Aging and engaging: a social conversational skills training program for older adults. In: 23rd International Conference on Intelligent User Interfaces (2018) 8. Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., Rauws, M.: Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Mental Health 5(4), e64 (2018) 9. Pola, S., Sheela, M.R.C.: Behavioral therapy using conversational chatbot for depression treatment using advanced RNN and pretrained word embeddings. Mater. Today: Proc. (2021) 10. Cooper, A., Ireland, D.: Designing a chat-bot for non-verbal children on the autism spectrum. In: Health Informatics Conference (2018) 11. Mujeeb, S., Javed, M.H., Arshad, T.: Aquabot: a diagnostic chatbot for achluophobia and autism. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8, 39–46 (2017) 12. Vijayan, A., Janmasree, S., Keerthana, C., Baby Syla, L.: A framework for intelligent learning assistant platform based on cognitive computing for children with autism spectrum disorder. In: 2018 International CET Conference on Control, Communication, and Computing (IC4), pp. 361–365 (2018) 13. Jang, S., Kim, J.J., Kim, S.J., Hong, J., Kim, S., Kim, E.: Mobile app-based chatbot to deliver cognitive behavioral therapy and psychoeducation for adults with attention deficit: a development and feasibility/usability study. Int. J. Med. Inform. 150, 104440 (2021) 14. Ali, M.R., et al.: In a virtual conversational agent for teens with autism spectrum disorder: experimental results and design lessons. In: ACM International Conference on Intelligent Virtual Agents (IVA 2020) (2020) 15. El rhatassi, F., EL Ghali, B., Daoudi, N.: Improving health care services via personalized medicine. In: The International Conference on Big Data and Internet of Things (2022) 16. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950) 17. Adamopoulou, E., Moussiades, L.: Chatbots: history, technology, and applications. Mach. Learn. Appl. 2, 100006 (2020) 18. Ait-Mlouk, A., Jiang, L.: KBot: a knowledge graph based chatbot for natural language understanding over linked data. IEEE Access 8, 149220–149230 (2020) 19. Reiter, E.: An architecture for data-to-text systems. In: Proceedings of ENLG-2007 11th European Workshop on Natural Language Generation, pp 97–104. Schloss Dagstuhl, Germany (2007)
Machine Learning with Reinforcement for Optimal and Adaptive Learning Fatima Rahioui1(B) , Mohammed El Ghzaoui1 , Mohammed Ali Tahri Jouti1 , Mohammed Ouazzani Jamil2 , and Hassan Qjidaa1,2 1 Sidi Mohamed Ben Abdellah University, Fez, Morocco
[email protected] 2 LSEED Laboratory, UPF, Fez, Morocco
Abstract. In this article, we focus on Artificial Intelligence (AI) as a teaching topic. At the moment, it is important that every citizen is not a simple consumer of technology. We have challenges to overcome around digital sciences which must have the same place as life and earth sciences in the training of an individual in school curricula. Artificial intelligence is particularly concerned. How to overcome the initial representations of students who may think that these little machines work like magic. It is crucial that students can understand how these voices can inform us about time. The school has a big role to play. As for the teaching and learning of programming which has experienced an exceptional boom in recent years, the idea is not to make young people trained at the school of computer engineers. Rather, it is a question of fully preparing them for the world of tomorrow and understanding what AI is by asking these crucial questions. Beyond the learning mode (individual and / or collaborative), the modeling of user profiles and educational resources of a training domain represent essential parameters for the realization of adaptation to a user in order to recommend relevant educational resources and more finely customize a learning path for a single user in individual mode, but also for a group of users during the synchronization of events in collaborative mode. In this article, after an overview of the existing AI in education, we propose an architecture for the management of this type of adaptive learning. Keywords: Machine Learning · Adaptive Learning · Artificial Intelligence · AI in Education · Teaching and Learning
1 Introduction The arrival of Artificial Intelligence in education must be accompanied by a preparation of future teachers [1, 2]. Because AI influences our individual and collective life and it is necessary to develop a critical mind in the face of its use. Because training teachers in AI also means, in a way, working to prevent potential drifts that could occur in the near future. Because if we really want AI to be able to contribute to the academic success of all students, the role of teachers has never been more important. Because the arrival of intelligent robots will transform future jobs, and it is necessary to prepare students for reality from elementary school. Because it is important not to leave this important area to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 142–149, 2023. https://doi.org/10.1007/978-3-031-29860-8_15
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technology companies alone. As we have seen, AI is already omnipresent in education: smart books, search engines, educational applications, learning platforms [3, 4]. But, despite this omnipresence at school, under the entry artificial intelligence in Wikipedia, we do not find the word “education” once. So we have to ask ourselves the question: what kind of future do we want in our schools? Schools where technology companies are responsible for the use of artificial intelligence in education or rather schools where students and teachers are able to ask questions and influence, in a constructive, informed, responsible and ethical way, our future full of technologies. The incursion of AI in education cannot resolutely be decided only by those who benefit from it financially. Its arrival must be clearly marked by different educational actors of our entire society, starting with teachers and learners. On the other hand, adaptive learning is the process that consists of “building a model of the learner’s objectives, preferences and knowledge, and using it throughout his interaction with the environment in order to offer personalized feedback or adapt the contents, and the interface to his learning needs” [5]. The adaptation is carried out in real time thanks to algorithms (machine) which make inferences from the actions of the learner during the learning session. An example of inference is the diagnosis of their errors. The more the machine simulates the behavior of a human tutor, the more it is called “intelligent”. We then speak of intelligent tutor and intelligent adaptive learning. The “intelligent” character of a learning environment lies in the fact that it can adapt to the learner. Adaptability is understood to mean the ability of the learning environment to modify its behavior based on inferences made according to the updated content of a model of the learner, whether on his cognitive, metacognitive or affective state.
2 Litérature Review In [6], Agbanglanon et al. proposed digital tools in the learning of mechanical design. The purpose of the study presented in [6] is to explore the characteristics of the ongoing process in the co-localized collaborative activity of mechanical design in relation to the visual-spatial abilities of the learners and the attributes of the external representations that are put to use. The work carried out in [6] is based on the measurement of the visual-spatial abilities of 63 Senegalese learners in electromechanics, at bac +2 and bac +5 level, through tests. The results show that learners with the weakest spatial abilities invest more time in actions to represent solutions and in those aimed at understanding the problem and the solutions. On the other hand, the actions related to the evaluation of solutions and the incentive to the genesis of solutions are more the work of the learners with the highest spatial visualization scores. The authors of [7] have presented a contribution of technologies to the learning of written language in primary school. The research presented in [7] was interested in pedagogical practices instrumented in primary school at the service of reading and writing in France and Chile. in Chile, a limited margin of maneuver for teachers and a palliative use of technologies is observed while in France these uses are built in a more autonomous and less directed way. In [8], Fotsing et al. analyzed the use of simulation software in hybrid devices in science education for engineering students. The work carried out in [8] has two objectives. The first object concerns the study and evaluation of hybrid devices. The second object relates to the
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study of the impacts arising from the uses of simulation software used by the educational contents of said devices on the effectiveness of training in order to assess the development of the skills of engineering students in the context of distance education. In [9], the authors studied the activity of organizing educational resources by teachers. It is a question of understanding the daily management processes and the singular activities and tasks (research, choice, storage, storage, classification, use, modification, reappropriation, creation, dissemination, pooling…) of secondary school teachers around educational resources. In [10], Messaoui et al. analyzes the development of the documentary expertise of teachers in situations of mutation of their teaching: The case of teachers of mathematics and English. The transformations of the information environment are changing the relationship of teachers to resources. They involve the development of new information, media and digital knowledge, necessary for the exercise of their professional activity. The work carried out in [11] focuses on the mechanisms for resolving the tensions induced by the arrival of XO computers in their schools, by the teachers of the Pilot Project for the improvement of the quality of basic education (PAQUEB) in Cameroon. Pauty et al. Gives in [12] a systemic analysis of the process of appropriation of digital cultures. The case of the Ordival operation. In [12], Perret et al. Discusses a didactic approach to the influence of digital technology on the training of craft trades.
3 Artificial Intelligence in Education Examples of the application of AI in education Artificial intelligence has already been used for some time in education, and this in several ways, including so-called intelligent tutorial systems, automatic assessment systems, collaborative learning environments and games aimed at learning. It can contribute to meeting certain challenges, including that of adequately responding to the diverse needs of people [13]. Intelligent tutorial systems are characterized by knowledge of the educational content, of the student or of the student (of his profile) as well as knowledge of the learning processes and the methodology. They make suggestions when people doubt or are stuck while solving a problem. They guide them in their learning. For example, people with disabilities could have a better learning experience thanks to these systems. Through automatically corrected tests, automatic assessment systems target people’s strengths and weaknesses in their learning activities. They provide useful information about the skills and abilities they have developed. For their part, collaborative learning environments are intended to facilitate the process by providing both the context and the tools to interact and work collaboratively. In intelligent systems, collaboration usually takes place using software agents responsible for mediating and supporting student interactions to achieve the proposed objectives [14]. Learning through play, on the other hand, makes it possible to carry out activities that would be impossible to carry out with traditional resources due to the allocated budget, the time and the available infrastructures as well as security [14]. The challenge is to train the teaching staff to use these systems in addition to their own teaching. This requires reviewing the formula of the courses, improving the didactic material and enriching the pedagogical design. It is an opportunity to have a collective discussion on the subject to be covered and the skills to be developed, and to offer students and the student population personalized
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monitoring and support. In other words, learning data allows personalized monitoring, which consists of identifying a person’s difficulties in order to react to them in real time. Moreover, by alerting the teaching staff when some students show a loss of interest, intelligent tutors can lead to the improvement of pedagogy. Some tools can help prevent dropping out and also play a supporting role in school and career guidance. In addition, AI promotes pedagogical differentiation; it makes it possible to match the people most likely to help each other and to ensure that their exchanges remain pedagogically relevant [15]. Figure 1 shows the process of data learning.
Fig. 1. Subject to be covered and skills to be developed
4 Contributions of Adaptive Learning: The Personalization of Courses Each student is different with a learning rhythm of his own. However, taking into account the diversity of students, adapting its teaching and its educational action to the diversity of students are skills expected of teachers. The latter are not always easy to implement in the context of the heterogeneity of classes. The teacher, within the framework of standardized school programs, is responsible for accompanying and responding to the individual needs of students. But with AI, the personalization of courses can be facilitated with “adaptive learning” which allows students to complete tasks at their own pace. 4.1 Adaptive Learning System The latest OECD report of 2021 devotes a special chapter to adaptive learning and gives us a definition of it. “Adaptive learning” is one of the new young disciplines of data science [16]. This system studies how to use data mining, machine learning, natural language processing, visualization and human-computer interaction approaches, among others [17, 18]. And it aims to provide educators and learners with information that can improve learning processes and teaching practices”. In addition, the aim is to adapt educational decisions to the particular skills and needs of each learner. To do this, the teaching takes into consideration the profile of the learner (his knowledge, his preferences, his aptitudes, his objectives…) in the construction of a unique educational path. Moreover, this brings a great help in the monitoring of students. Figure 2 show the adaptive learning system corposants.
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Fig. 2. Adaptive Engine
Finally, the Deloitte AI report describes the principle taking into account the student learning model, the teaching model, the assessments with regard to the skills of the curriculum, content with learning objects. 4.2 Development of Adaptive AI Learning Solutions in Education For the development of adaptive AI learning solutions in education, it is both necessary to have quality data and algorithms allowing their processing. On the one hand, the educational data produced and accessible are a major issue. The quality of these data is linked both to the relevance in relation to the disciplinary field, the curriculum, and the learning task addressed, to the learner’s model and to the pedagogical model. On the other hand, dashboards generated by AI applications make it possible to synthesize a lot of data and recommend steps to follow to teachers. With the data collected on student results, AI will make it possible to better personalize teaching by helping students improve their learning and by providing assistance to teachers. AI-powered adaptive learning could be compared to learning companions capable of accompanying and supporting individual learners throughout the curriculum with new forms of assessment that measure learning while it takes place, shaping the learning experience in real time. We depicted in Fig. 3 the external and internal loop of adaptive learning cycle.
5 Artificial Intelligence at the Service of Education One of the objectives of artificial intelligence at the service of education is to improve the memorization of the course. Indeed, according to the studies carried out, longterm memorization is favorable when the revisions are spaced out. It also depends on the specifics of each student: his attention, his abilities and difficulties. This is where artificial intelligence comes into play. Technology can learn when to propose “reminder bites” adapted according to the student’s history. In this case, we will talk about machine learning or machine learning, which is a branch of AI in full evolution. This learning is based on the recording of past events, that is to say that its approach is oriented towards
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Fig. 3. Adaptive AI learning solutions in education
the experience acquired and interpretable in the data. Machine learning calculates the “average answers” to predict the behavior of the new student. A large volume of data is therefore necessary, the more data we have, the more precise groups with similar pedagogical characteristics can be formed. Despite the advantages of artificial intelligence for the education sector, it is nevertheless necessary to take into account the current technological advance. Today, any teaching that is both “human” and automatic is impossible. It is therefore more sensible to find a middle ground between technology and humans, for example by using AI outside the classroom for revisions and keeping face-to-face in class.
6 Conclusion In order to combat school failure, many solutions have been put in place in recent years: specialized classes, school support, new pedagogies… However, these solutions have led to problems at the level of the link between the individual and the collective. It is difficult for the teaching teams to take into account the specificities of each student, without weakening the collective. For some, the solution would be hidden in new technologies, in particular Artificial Intelligence (AI), which could propose educational strategies adapted to each of the students. Finally, adaptive learning is proving to be a promising approach to make learning environments smarter. The need to adapt learning will not disappear. On the contrary, it will become more and more present as MOOCs develop and the technology to support it will follow.
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References 1. Pereira, S.W., Fishman, E.K., Rowe, S.P.: The future is now: how technology and entertainment are transforming education in the artificial intelligence era. J. Am. Coll. Radiol. 19(9), 1077–1078 (2022). https://doi.org/10.1016/j.jacr.2022.06.015 2. Zhu, X.: College English assisted teaching based on artificial intelligence. In: 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), pp. 18–21 (2018). https://doi.org/10.1109/ICVRIS.2018.00013 3. Sanusi, I.T., Olaleye, S.A., Oyelere, S.S., Dixon, R.A.: Investigating learners’ competencies for artificial intelligence education in an African K-12 setting. Comput. Educ. Open 3 (2022). https://doi.org/10.1016/j.caeo.2022.100083 4. Shaikh, A.A., Kumar, A., Jani, K., Mitra, S., García-Tadeo, D.A., Devarajan, A.: The role of machine learning and artificial intelligence for making a digital classroom and its sustainable impact on education during covid-19. Mater. Today: Proc. 56(6), 3211–3215 (2022). https:// doi.org/10.1016/j.matpr.2021.09.368 5. Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. (IJAIED) 13, 159–172 6. Agbanglanon, S.: Numerical tools in the learning of mechanical design: analysis of the links between external representations and visuo-spatial capacities in the design process. CergyPontoise University, Val-d’Oise (2019). http://www.theses.fr/2019CERG1013 7. Carreño Valdivia, Y.: Contribution of technologies to the learning of written language in primary school: comparative approach of educational policies and teaching practices between France and Chile. Sorbonne University, Paris (2018). http://www.theses.fr/2018USPCB160 8. Fotsing, J.: The use of simulation software in hybrid devices in science training for engineering students: evaluative study at the ENSP - Ecole Nationale Supérieure Polytechnique - of Yaoundé, Cergy-Pontoise university, Val-d’Oise (2019). http://www.theses.fr/2019CE RG1040 9. Boullé Loffreda, M.: The activity of organizing educational resources by teachers ENS ParisSaclay, Paris (2021). http://www.theses.fr/s174473 10. Messaoui, A.: Development of the documentary expertise of teachers in situations of change in their teaching: the case of teachers of mathematics and English, ENS de Lyon, Lyon (2020). http://www.theses.fr/2019LYSEN052 11. Nyebe Atangana, S.: Effects of the use of XO computers on pedagogical practices in primary school in Cameroon: case of the public school of application of angale. CY Cergy University, Paris (2015). http://www.theses.fr/s144564 12. Pauty, C.: Systemic analysis of the process of appropriation of digital cultures. The Case of Operation Ordival. Paris University, Paris (2020). http://www.theses.fr/s176724 13. UNESCO: Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development, Working Papers on Education Policy, Paris, p. 46. UNESCO (2019) 14. Sanchez, E., Lama, M.: Artificial intelligence and education. In: Rabuñal Dopico, J.R., Dorado, J., Pazos, A. (eds.) Encyclopedia of Artificial Intelligence, Hershey (PA), Information Science Reference, pp. 138–143 (2008) 15. Van Ranst, J: The ethical issues of AI in education, education carrefour, Ref. of december 2020 (2019). https://carrefour-education.qc.ca/dossiers/lintelligence_artificielle_en_edu cation/les_enjeux_ethiques_de_lia_en_education 16. Hou, J.-W., Jia, K., Jiao, X.-J.: Teaching evaluation on a WebGIS course based on dynamic self-adaptive teaching-learning-based optimization. J. Central South Univ. 26(3), 640–653 (2019). https://doi.org/10.1007/s11771-019-4035-5
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17. Rahayu, N.W., Ferdiana, R., Kusumawardani, S.S.: A systematic review of learning path recommender systems. Educ. Inf. Technol. (2022). https://doi.org/10.1007/s10639-022-114 60-3 18. Kanya Kumari, L., Naga Jagadesh, B.: An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection. Int. J. Syst. Assur. Eng. Manag. (2022). https://doi.org/10.1007/s13198-021-015 98-7
Incorporating Deep Learning and Sentiment Analysis on Twitter Data to Improve Tourism Demand Forecasting Houria Laaroussi(B) , Fatima Guerouate, and Mohamed Sbihi EST SALE, Mohammed V University, Rabat, Morocco [email protected]
Abstract. Tourist arrivals forecasting has become an increasingly prominent topic due to its important role in the tourism industry. To increase the prediction accuracy of tourism demand, this study presents a hybrid model that combines a sentiment analysis model with a deep learning model to forecast monthly tourist arrivals to Hong Kong. A VADER model is employed to get sentimental scores of tweets extracted from Twitter. Then, the Long Short-Term Memory (LSTM) is used for forecasting future tourist arrivals. Our proposed model has achieved excellent performance in predicting tourism demand compared to other models and the models without sentiment analysis. Keywords: Sentiment analysis · Deep Learning · LSTM · VADER · Tourism Demand Forecasting
1 Introduction The need to predict accurately is crucial in the planning process to make better business decisions. Because of the perishable characteristic of the tourism industry [1]. Therefore, intelligent and accurate tourism demand forecasting tools are required to enhance the tourism demand forecasting performance. Recently, researchers have drawn upon Internet big data, such as search engine data and social media, to forecast tourism demand [3]. One advantage of the search query is it can be accessed at low costs, but it is not always simple for tourists to dis-cover what they are seeking for with keyword search. Social media data allows tourists to create and publish their travel experiences in real-time. Only a few studies have tried to forecast tourism demand using social media data. Twitter is a useful source of data and it is the most popular social media platform on which tourists can share their opinions as tweets about their travels. Twitter attracted considerable attention from sentiment analysis (SA) researchers, Tweets can be used as the primary collection for SA which is a subfield of natural language processing. It has an essential impact in prediction in several sectors: healthcare [4, 5], financial [6, 7] etc. It comprises converting tweet text into a sentiment score that is representative of its emotion. It is a computational method that It specifies what the tourist like and dislike. Techniques of SA can be classified into two categories: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 150–158, 2023. https://doi.org/10.1007/978-3-031-29860-8_16
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dictionary-based techniques, such as VADER, and machine learning based approaches. VADER is the most famous SA method for Social Media reviews. It is a lexicon-based approach adapted to sentiments that are declared in social media specifically. [8] Hutto et al. compare the effectiveness of the VADER model to eleven strategies benchmarks, the comparison demonstrates that the model achieves better results than other models. A VADER meth-od is especially destined for social media context. Forecasting models are mainly divided into three groups: time series, econometric, and artificial intelligence (AI) [2]. AI has caught major attention due to its ability to find nonlinear relationships and excellent performance. Artificial intelligence (AI) methods are categorized into two subgroups: shallow learning and deep learning (DL) models, specifically DL has higher generalization capacity compared to shallow models. LSTM is one of the popular DL methods. It has a superior capacity to capture long-term dependencies and the time series forecasting problems can be tackled by it in a great way. The main of this study is to construct a more accurate model to forecast the tourist arrivals in Hong Kong, in our study we used Vader method to calculate the sentiment score from each tweet. We aggregate individual tweet sentiment score into a com-binned score for each month. The association between a number of tourists in each month and the mean sentiment score for each month was used as inputs in the LSTM model and other machine learning (ML) models and deep learning (DL) models. The paper is structured as follows. Related work about sentiment analysis on tourism demand forecasting is described in Sect. 2. In Sect. 3, Methodology is presented. The results of experiments are depicted in Sect. 4. Conclusions are delivered in Sect. 5.
2 Literature Review A lot of artificial intelligence techniques have been utilized for forecasting tour-ism demand. Particularly, LSTMs have demonstrated the best accuracy in tourism demand forecasting. Law et al. [9] used several models to forecast tourist volumes. Experiments indicated that the LSTM performs better than two shallow models. Zhang et al. [10] applied LSTM to predict daily tourist flow in Jiuzhaigou, the experiment showed that LSTM is an efficient method. Wu et al. [11] predict tourist arrivals to Macau using hybrid model that combines LSTM and SARIMA, the experiments showed that the proposed approach is more accurate than other models. Peng et al. [12] Combine the search query index data, Random forest for dimensionality reduction and LSTM method for forecasting tourist arrivals. The proposed model achieves better accuracy than the other methods. Social media plays an important role in information search. Tourists can ex-press their opinions on social media. These interactions leave various kinds of footprints that specify their interest preferences and satisfaction. They are two techniques to evaluate SA namely Machine Learning and the Lexicon-based Approach [13]. Many studies used machine learning to classify the sentiment of tourists. For example, [14] Ye used several machine learning models that classify the sentiment of the reviews on online platforms for travel destinations. [15] Applied artificial neural network to analyse the influence of the sentiment in the German-speaking media in popular tourist destinations
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for Europeans on tourist arrivals. The results demonstrated that the sentiment in online media is a best factor of the number of tourist arrivals. Limited researches have attempted to predict tourism demand based on social media data and sentiment analysis. Existing research has not used VADER model to analyze sentiments. For instance; [16] Peng et al. implemented social net-work data, SA and Gradient Boosting Regression Trees to forecast Huang Shan tourism demand and which have always resulted in good forecasting performance. They used BERT model for sentiment analysis. [17] combine SA and the Neural Networks (NN) to predict tourists flow. The results indicated that the proposed model with SA outperforms the previous obtained results.
3 Materials and Methods 3.1 Methodology The proposed Framework in this paper suggests a forecasting model that combines prediction model that combines a SA with DL model. The methodology includes six step that are showed in Fig. 1, comprising data collection, preprocessing, sentiment analysis, Forecasting Model and evaluation.
Fig. 1. Proposed Framework.
3.2 Historical Data We gathered historical data from the official “https://partnernet.hktb.com”. We collected the tourist arrivals data from January 2013 to January 2022.
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3.3 Social Media Data The dataset employed is the collection of reviews extracted from Twitter, which is gathered using Twitterscraper Python package. The Twitter REST API has some disadvantages regarding the number of collected data (only 100 tweets per request are returned) [18].to gather tweets published between 2013 to January 2022 we used 12 tourism related keywords (Hong King, trip, travel, travelling, Tourist, holiday, vacation, Adventure) [19]. We retrieved the tweet text, and date of creation. 7440 tweets were retrieved. 3.4 Preprocessing Preprocessing phase consists of delete irrelevant information from the tweets. • Symbol Removal: Tweets often contain special characteristics such as URLs, retweet, user mention, we use a regular expression (Regex) in Python to find and eliminate these symbols. • Convert the tweets to lowercase • Remove duplicate tweet: To avoid the annotation of duplicates, all retweets with the same text were removed. • Stopword removal: we eliminate stop words the words that contain little information. • Tokenisation: we split the given review into symbols or words. 3.5 Normalization It is essential to normalize the tourism demand data. We applied “min-max” normalization method. 3.6 Sentiment Analysis Model: VADER (Valence Aware Dictionary and sEntiment Reasoner) Gilbert [8] developed VADER that can deal with the syntax usually designed to social media. It defines the polarity score of the given review. The main advantage of VADER is that the training data is not necessary [8]. It can be employed on the social media data. VADER provides a dictionary of positive, negative, neutral and compound scores. The sentiment is a value in the interval [−1, 1], If it is 0, it is classified as neutral. if it is −1 the sentiment is negative and the value 1 represents the positive sentiment. We assigned each tweet a sentiment score. The mean sentiment score for each month will be used as one of the features of the prediction model in the next step. The LSTM model used the sentiment values as a feature and the number of tourists as the goal variable (see Fig. 2). 3.7 Forecasting Model Our study requires the sentiment score of tweets and the number of tourists to predict the tourism demand. The aim is to combine the number of tourists and the sentiment scores into a single dataset and forecast tourism demand by employing DL and ML methods
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Fig. 2. Detailed processes of framework.
on these data. We have applied several algorithms to forecast tourism demand namely Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), Recurrent Neural Network (RNN), SVR and ANN. • LSTM is a kind of RNN, it was proposed by [20], it avoids a vanishing gradient. It has three gates -input, output and forget gate. Therefore, it manages when to remember and when to forget. Hence, Fig. 3 illustrates the internal structure of LSTM model. • RNN can learn the temporal patterns [21]. It can have applied arbitrarily long sequence information. • GRU developed by [22]. It models long-term dependencies of different time scales. The internal structure of GRU is similar to LSTM.
Fig. 3. LSTM cell.
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3.8 Model Performance Evaluation Two metrics indices i.e., the mean absolute error (MAE), and the root mean square error (RMSE), are applied to evaluate performance and to compare forecasting models, which are given as: 1 i=n yi − yi i=1 n 2 1 i=n RMSE = yi − yi i=1 n MAE =
(1)
(2)
where yi is the actual value and yi is the prediction.
4 Results – The monthly number of tourists is showed in Fig. 4. Between January 2013 and January 2022, we gathered 34323 tweets written in English.
Fig. 4. Number of tourist arrivals in Hong Kong.
– The most of tweets were identified to be positive. Specially, 45.8% of tweets were positive, 42.8% were negative, and 13.0% were neutral (see Fig. 5).
– The data is divided into two parts. The 80% of the data will be employed as a training set; 20% is used to test our model prediction accuracy. – After the experiments, The LSTM model has two hidden layers. The number of neurons in each layer 15, The sigmoid activation function is used, Furthermore, the ADAM is used as optimizer, Learning rate was 0.001, we used 500 training epochs.
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Fig. 5. Sentiment analysis using VADER.
– Figure 6. Illustrates the training and validation loss curves of the LSTM model. X-axis presents the number of epochs and the Y-axis Reveals Loss. The orange line represents the validation loss, however the blue line exhibits the training loss. The losses progressively diminuted and the lines stabilized after 200 epochs. – Tables 1 and 2 illustrated that the results obtained using LSTM with sentiment score achieve the best performance in terms of RMSE and MAE. LSTM model with sentiment score produces less MAE, RMSE than RNN, GRU, ANN, SVR models. The training RMSE was observed to be 956. The MAE was found to be 843 in the training model. While the RMSE value obtained was1036 and MAE value was 887 during the testing model.
Fig. 6. Curves of loss function during LSTM training and testing.
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Table 1. Results without review. Training data
Test data
RMSE
MAE
RMSE
MAPE
LSTM
1123
1021
1207
1092
ANN
1370
1275
1331
1348
GRU
1349
1217
1384
1272
SVR
1459
1393
1485
1420
Table 2. Results with sentiment analysis review. Training data
Test data
RMSE
MAE
RMSE
MAPE
LSTM
956
843
1036
887
ANN
1187
1064
1201
1081
GRU
1093
984
1128
1001
SVR
1267
1198
1292
1218
5 Conclusions In this paper, we incorporated sentiment score into the deep neural network to predict tourism demand in Hong Kong. We first extracted the tweets form twitter. Then, we used VADER for the sentiment analysis, we identified the sentiment score within a given tweet. Therefore, a LSTM deep neural network was proposed to predict tourism demand, which combines the number of tourists arrival, the sentiment score of tweets. The result showed that our proposed model outperforms all other models and the models without sentiment analysis.
References 1. Law, R.: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tour. Manag. 21(4), 331–340 (2000). https://doi.org/10.1016/ S0261-5177(99)00067-9 2. Song, H., Li, G.: Tourism demand modelling and forecasting-a review of recent research. Tour. Manag. 29(2), 203–220 (2008). https://doi.org/10.1016/j.tourman.2007.07.016 3. Sun, S., Wei, Y., Tsui, K.L., Wang, S.: Forecasting tourist arrivals with machine learning and internet search index. Tour. Manag. 70, 1–10 (2019). https://doi.org/10.1016/j.tourman.2018. 07.010 4. Yang, F.-C., Lee, A.J.T., Kuo, S.-C.: Mining health social media with sentiment analysis. J. Med. Syst. 40(11), 1–8 (2016). https://doi.org/10.1007/s10916-016-0604-4 5. Ramírez-Tinoco, F.J., Alor-Hernández, G., Sánchez-Cervantes, J.L., Salas-Zárate, M.D., Valencia-García, R.: Use of sentiment analysis techniques in healthcare domain. Stud. Comput. Intell. 815, 189–212 (2019). https://doi.org/10.1007/978-3-030-06149-4_8/COVER
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H. Laaroussi et al.
6. Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big data: deep learning for financial sentiment analysis. J. Big Data 5(1), 1–25 (2018). https://doi.org/10.1186/S40537017-0111-6/TABLES/8 7. Day, M.Y., Lee, C.C.: Deep learning for financial sentiment analysis on finance news providers. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, pp. 1127–1134, November 2016. https://doi.org/10.1109/ASONAM.2016.7752381 8. Hutto, C.J., Gilbert, E.: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media, Ann Arbor, MI, 1–4 June 2014 (2014). http://eegilbert.org/papers/icwsm14.vader.hutto.pdf 9. Law, R., Li, G., Fong, D.K.C., Han, X.: Tourism demand forecasting: a deep learning approach. Ann. Tour. Res. 75, 410–423 (2019). https://doi.org/10.1016/j.annals.2019.01.014 10. Zhang, B., Li, N., Shi, F., Law, R.: A deep learning approach for daily tourist flow forecasting with consumer search data. 25(3), 323–339 (2020). https://doi.org/10.1080/10941665.2019. 1709876 11. Wu, D.C.W., Ji, L., He, K., Tso, K.F.G.: Forecasting tourist daily arrivals with a hybrid SarimaLSTM approach. J. Hospitality Tour. Res. 45(1), 52–67 (2021). https://doi.org/10.1177/109 6348020934046/ASSET/IMAGES/LARGE/10.1177_1096348020934046-FIG1.JPEG 12. Peng, L., Wang, L., Ai, X.-Y., Zeng, Y.-R.: Forecasting tourist arrivals via random forest and long short-term memory. Cogn. Comput. 13(1), 125–138 (2020). https://doi.org/10.1007/s12 559-020-09747-z 13. Fronzetti Colladon, A., Guardabascio, B., Innarella, R.: Using social network and semantic analysis to analyze online travel forums and forecast tourism demand. Decis. Support Syst. 123 (2019). https://doi.org/10.1016/j.dss.2019.113075 14. Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36(3), 6527–6535 (2009). https://doi.org/10.1016/J.ESWA.2008.07.035 15. Starosta, K., Budz, S., Krutwig, M.: The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans. Appl. Econ. 51(14), 1558–1573 (2019). https://doi.org/10.1080/00036846.2018.1527463 16. Peng, T., Chen, J., Wang, C., Cao, Y.: A forecast model of tourism demand driven by social network data. IEEE Access 9, 109488–109496 (2021). https://doi.org/10.1109/ACCESS.2021. 3102616 17. Serda, M., et al.: Sentiment analysis and artificial neural networks-based econometric models for tourism demand forecasting. Uniwersytet s´l˛aski 7(1), 88–97 (2020) 18. Steinert-Threlkeld, Z.C.: Twitter as data. 1–122 (2018). https://doi.org/10.1017/978110852 9327 19. Scholz, J., Jeznik, J.: Evaluating geo-tagged twitter data to analyze tourist flows in Styria, Austria. ISPRS Int. J. Geo-Info. 9(11), 681 (2020). https://doi.org/10.3390/IJGI9110681 20. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/NECO.1997.9.8.1735 21. Parmezan, A.R.S., Souza, V.M., Batista, G.E.: Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model. Inf. Sci. 484, 302–337 (2019) 22. Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Empirical Methods in Natural Language Processing, pp 1724–1734 (2014)
A Deep Learning Model for Diabetic Retinopathy Classification Mohamed Touati1,2(B) , Laurent Nana1 , and Faouzi Benzarti2 1 Univ Brest, Lab-STICC/UMR CNRS 6285, 29238 Brest, France
{Mohamed.touati,laurent.nana}@univ-brest.fr
2 University Tunis El Manar, National School of Engineers of Tunis, SITI Laboratory, Tunis,
Tunisia [email protected], [email protected]
Abstract. An early diabetic retinopathy (DR) diagnosis leads to a reduction in the risk of blindness in diabetic disease patients. With the help of artificial intelligence, the efficiency is increased, and the cost is reduced for DR disease. One of the central ideas of deep learning is to take knowledge from a trained model on a dataset and apply it to a similar dataset with the same problem. This technique is called transfer learning. In this paper, we present a deep learning workflow that uses transfer learning to train an Xception model on our dataset with new customized layers. Transfer learning is used to reduce the required training data and minimize the learning time. We improved our model to obtain a high score of training accuracy (94%), test accuracy (0.89) and F1 Score (0.94) dealing with the unbalanced dataset of APTOS 2019 Blindness Detection. Keywords: Diabetic Retinopathy · Deep Learning · Xception · Transfer Learning · Classification
1 Introduction Diabetic Retinopathy (DR) is a silent disease that becomes symptomatic at a late and sometimes outdated stage (vitreous hemorrhage, OMD, retinal detachment, neovascular glaucoma, etc.). There are three main levels of clinical severity of DR, namely, minimal to moderate non-proliferative DR (isolated microaneurysms), severe non-proliferative DR (sign of ischemia), and proliferative DR, which can lead to blindness. The first clinically apparent stage of DR is mild non-proliferative diabetic retinopathy (NPDR), which is characterized by the development of microaneurysms. The disease can then progress to moderate or severe NPDR depending on changes in venous caliber and intraretinal microvascular abnormalities and then to proliferative DR. One of the problems that arises is therefore the rapid and early detection of DR to avoid reaching advanced stages with serious consequences for the health of patients. In recent years, experiments that are based on artificial intelligence have shown that the use of deep learning models is a possibility to avoid the risks of late analysis and slow diagnosis of diabetic retinopathy. Therefore, doctors are racing to take the best and most effective models to apply in their diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 159–168, 2023. https://doi.org/10.1007/978-3-031-29860-8_17
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This challenge shows us the power of CNN models for DR classification. Deep learning models with many layers reach the human level of data and inventory and provide more features that motivate researchers to classify medical images on mobile devices and smart systems [1]. Classical machine learning methods such as Gaussian processes to deep neural networks highlight the advantages of GP, such as better performance with small datasets, a lower number of parameters to train, the ability to incorporate domain knowledge, and the calculation of prediction uncertainty. The ability to calculate uncertainty is especially valuable in medical applications [2]. The image processing process involves separating the parts of an image that contain objects of interest from the background using measures of similarity and homogeneity. To improve the contrast of the image and enhance the accuracy of the segmentation and DR classification, the CLAHE method is applied in [3]. This paper describes the performance of using transfer learning of a robust model developed by Google called Xception, which involves depthwise separable techniques trained after a preprocessing phase on fundus images. In the next section, we will present the DR research work. In Sect. 3, we describe our proposed model with an explanation of its workflow. The last section gives us a conclusion on the developed approach, and we discuss the challenge of using deep learning in medical imaging.
2 State of the Art The authors in [4] and [5] proposed a method using image preprocessing and blob detection to extract features of retinal images captured in fundus photography. The proposed model would help to detect diabetic retinopathy more quickly. Various classification algorithms, such as Naive Bayes classifiers, artificial neural networks, and support vector machines, are provided from the extracted features, and comparative performance studies are also performed. The Naïve Bayes classifier was identified as one of the most efficient classifiers compared to others, with an accuracy of 83%. The authors of the study [5] introduced a cross-attention network, called CANet, for the simultaneous classification of two diseases called DR and DME. The CANet utilized two attention modules to examine the individual characteristics of each disease and the relationship between the two diseases. The first module focused on learning disease-specific features, while the second module focused on learning features that were dependent on both diseases. Then, the connectionist model used both characteristics together to classify DR and DME to maximize the overall precision of the model when classifying data. The model had a very high success rate of 99.1% for correctly classifying images as having DR. The model has a 35.9% error rate for identifying images without DR. This accuracy is high, which means that 86% of the positively classified features are true positives. The transfer-based learning approach of reference [1] used a pre-trained MobileNetV2 to train the data and added a softmax for the classification of the five fundus image categories. The first model they trained reached 70% accuracy, but they were able to improve it to 91% by refining the model further. The improved model performed well on unseen data and was able to learn effectively from a small dataset of fundus images. The model achieved higher accuracy for categorical classification. This approach can
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be transferred to other biomedical engineering image classification problems using the same technique of a small training dataset. In their work [6], the researchers used the same MobileNetV2 architecture as in [7] but on a different dataset. When divided into a 2-grade DR and RDR (referable DR) problem, grades 0 and 1 are classified as DR (diabetic retinopathy), while grades 2 and 3 are considered “referred DR” types and require immediate surveillance. The MobileNetV2 model was able to correctly identify the DR class with 90.8% accuracy and the RDR class with 92.3% accuracy. The study looked at the problem as a five-class case with classes ranging from 0 to 4. Using a customized dataset, they were able to achieve an average accuracy of 91.68% and an AUC of 0.9 when training and testing on 50% of the Messidor-2 dataset, with the remaining half serving as an “invisible” test set. In [8], the authors suggested a brain inspired system named Zoom-in Net that performs a pair of tasks synchronously, i.e., it emulates the clinician’s technique of zooming to examine the retinal images more closely by creating an attention map and identifying suspicious areas and makes estimations derived from them. Owing to different annotation scales used in the study dataset, they also used a technique such as in [7], and the problem was turned into a non-referential versus referential binary classification task. The method achieved 91.1% accuracy on the Messidor dataset and 90.5% accuracy on the EyePACS dataset. Consequently, their model performance is comparable to the latest technology and can be applied to clinical settings using mobile lens inspection applications. The authors in [3] proposed a new model (HPTI-v4) for identifying and categorizing DR from fundus color. They first use a contrast-limited adaptive histogram equalization model at the preliminary processing to improve the contrast of the image and a histogram-based approach for segmentation; then, they apply the HPTIv4 model for feature extraction and a multilayer perceptron (MLP) for classification. Their model demonstrated extraordinary classification performance in comparison to existing approaches, with maximum sensitivity, accuracy, and precision of 98.83%, 99.49%, and 99.68%, respectively. In [2], Toledo-Cortés et al. proposed a refined deep learning model called DLGP- DR. This model was applied to the EyePACS dataset to extract features. The final DR classification and ranking tasks were conveyed by using a Gaussian function. The results shown in the paper prove that the DLGP-DR outperformed the accuracy and AUC score of the NN model (for binary DR classification) and the measures reported by Voetset al. [9]. The standard deviation for false positives and false negatives is higher. Their DLGP-DR model contains a larger uncertainty for poorly classified patients than for well-classified patients, which provides the user with a tool to identify mispredictions. In [10], a multi-channel GAN with semi-supervised learning was developed to evaluate DR. The proposed model could handle a DR identification task with mismatched labeled data. A multichannel generative method is proposed to create a series of new batch images that correspond to scattering DR features. The proposed multichannel GAN model with semi-supervision can effectively use both labeled and unlabeled data. In the proposed model, a DR feature extractor is incorporated to reduce noise and identify important DR features. These features are often subtle and spread across the high-resolution eye image. The study uses the Messidor dataset to test the developed model, and the results indicate that it performs better than other comparable models [8, 11, 12] in terms of accuracy, AuC, and sensitivity when only 100 labeled samples are used. However, this paper has some limitations, such as not considering
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biological methods and pathological analysis. In [13], the authors present the results of evaluating the severity levels of diabetic retinopathy using the proposed method on a validation set and two test sets. The performance of the method is determined by the kappa coefficient. To provide context, the authors also present results from a multi-task approach. It is noteworthy that when compared to the multi-Task approach, the proposed method had better kappa scores: 0.8083 (std = 0.0097) for the validation set, 0.7421 (std = 0.0172) for the first test, and 0.7187 (std = 0.0183) for the second. The validator’s improvement was 0.0293, test-1 had an improvement of 0.0495, and test-2 had an improvement of 0.0129. These results suggest that incorporating a random relationship between traits related to DR and the severity of DR improves performance. In summary, current research demonstrates that various traditional and advanced machine learning methods have been developed to address the challenge of detecting diabetic retinopathy. There are limitations to the existing approaches. In most cases, the images used for evaluation require ophthalmologist experts for the score values. This implies that the algorithm may not perform effectively for pictures that depict subtle symptoms, which may not be easily recognizable by the doctor’s eyes. Another limitation that stems from the characteristics of deep networks is also present. In many of the studies previously discussed, the neural network was given only images and the corresponding scores, without any specific instructions on what features, such as microaneurysms or exudates, to focus on. It could be the case that the algorithm identifies features that were previously unknown or not taken into consideration by humans. The approach proposed in our work addresses some of the shortcomings observed in existing DR detection methods and is outlined in the following section.
3 Our Solution 3.1 Data Understanding Data collection is essential in the medical field, which is why we worked on a very popular DR from a dataset Image taken from a contest “APTOS 2019 Blindness Detection”. This dataset includes images with five class labels from the Kaggle APTOS 2019 competition. The dataset contains 3662 pieces of training and 1928 validations. 3.2 Image Preprocessing All images have been converted to hierarchical data structures to enable preprocessing, improve the dataset by adding more types, and prepare for training. The preprocessing involves a series of steps: the image is cropped using the Otsu method to separate the retinal image with circular color. The image is normalized by subtracting the minimum pixel intensity value per channel and dividing it by the average pixel intensity, obtaining a range from 0 to 1. Brightness adjustment is performed through the filter method. Adaptive Histogram Equalization (CLAHE). All images have been scaled down to 256. Although it takes more time to practice, the detail present in images of this size is much larger than 128 × 128. In addition, few images were removed from the training set. The python function produced some alerts while adjusting the image sizes due to the absence
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of color space in these images. As a result, all completely black images were removed from the training dataset. To increase the diversity of the data, all images were flipped and rotated: as shown in Fig. 1, the images without retinopathy were duplicated, while those with retinopathy were flipped and rotated by angles of 90, 120, 180, and 270°.
Fig. 1. Crop and resize all images
This is the first preprocessing step before feeding the input dataset into our learning network. The fundus images are first normalized using image enhancement and restoration techniques to improve their quality. Therefore, the objects and background in the image are more suitable for feature extraction in the next steps of the process. A Gaussian low-pass filter is used to remove blocking artifacts in the image, possibly caused by a high compression ratio. The retina’s RGB image is then converted to a multi-channel image, including grayscale, R, G, and B images, to distinguish the object more effectively from the background. The contrast of local areas in the grayscale image is further enhanced using contrast-limited adaptive histogram equalization (CLAHE), after which a 2D median filter is applied to reduce noise in the image.
Fig. 2. Normalization of the fundus image
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In Fig. 2 above, feature extraction from the general fundus image is performed, which can be divided into three features: optic disc, visual fossa, and vascular structure. 3.3 CNN Modeling Approach The Convolutional Neural Network (ConvNet) is a deep learning technique that is most commonly applied to image datasets. There are several commonly used class types in ConvNets: - Convolution layer - This layer applies a convolution operation on an image with a defined stride and padding - Pooling Layer - This layer is used to reduce the size of the feature map by defining the mask and the action to take and then moving the mask over the image based on the defined stride. No weights are learned in this class - Dropout Layer - Used to reduce overfitting. It randomly turns off some neurons during exercise for each run. - Batch Normalization - Normalize output values, reduce calculation time. It also introduces a regularization effect. - FCL – a connected layer used at the end of the neural network. Rarely used currently due to the large number of parameters it uses. The model used to classify the DR is an Xception network pretrained on the ImageNet dataset whose weights were not freezed. The Xception architecture is similar to InceptionV3 but adds skip connections and uses depthwise separable convolutions, a dense top block with 3 dense layers, each preceded by dropout and batch normalization. All in all, we have 88.564.013 parameters, including 317.952 nontrainable ones. As the figure below shows, our deep learning workflow is based on the transfer learning of Xception.
Fig. 3. Figure of the transfer learning workflow
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The model with a customized configuration based on the newly learned feature forms the old model (Fig. 3). Feature Extraction from Layers Feature extraction and generic classification are used to study several scenarios of our DR classes. The patches provided by the preprocessing step are fed into a convolutional network (CNN), and each feature is extracted and then fed into a new customized model (Fig. 4).
Fig. 4. Figure caption of Hitmap Visualization of the features
Transfer Learning Transfer learning from other trained models is used to speed up the training process. Therefore, we increase the training dataset by image processing using the ImageDataGenerator function n, implement a new model in a deep learning architecture and compare its result with the latest research. We used the accuracy metric to define the number of test samples correctly classified divided by the total number of test samples: Accuracy =
(TP + TN ) TP + TN + FP + FN
(1)
Using Eq. (1), we obtain good results that reach training accuracy of 94% and testing of 89%. In addition, for the values of the AuC and the kappa metrics, the table below shows the following (Table 1): The following table concerns the whole APTOS dataset: 20% Val and 80% Train (Table 2). We use Eq. (2) of precision as a metric to identify the correctness of classification. Precision = TP (TP + FP)
(2)
The F1 score is needed to measure the model dealing with the problem of unbalanced datasets and help us to solve the problem of the large number of false negatives. F1score = 2 × (precision × recall)/(precision + recall)
(3)
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F1 Score
Kappa
AuC
Training
0.94
0.94
0.96
0.99
Testing
0.89
0.89
0.89
0.98
Table 2. Table of indicators metric according to the dataset distribution 0
1
2
3
4
Precision
0.98
0.68
0.89
0.86
0.88
Recall
0.99
0.87
0.88
0.73
0.74
F1 Score
0.99
0.76
0.89
0.79
0.81
Support
1784
291
1011
226
350
In our case, the F1 score reaches a very high value of 0.94 (Fig. 5).
Fig. 5. Confusion matrix of the training model on the ATOS dataset
In relation to the numbers of false negatives and false positives, we can say that despite the resemblance of classes 1 and 2 and classes 2 and 3, the models generate a
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high rate of true positives and true negatives compared to most of class 0, which has several supports of 1784. 3.4 Discussion and Comparison After evaluating various developer models, we ultimately decided to utilize the Xception pretrained model with some modifications made to certain layers, particularly the last two. This selection was deemed appropriate for scenarios involving small data and similar datasets, and it was deemed more efficient in terms of time compared to alternative pre-trained models. Adapting a preprocessing stage in our workflow gives us a more comfortable situation on the training data, which helps to clearly visualize the features. In recent works, the authors concentrate on the architecture; however, medical imaging requires high-quality data processing and filtering. In all the works presented in the state of the art, the value of the F1 score has a maximum value of 0.95 that we almost reached. The values of 94% and 89% on training and testing are not the best results but are better than the scores in [4, 8, 6, 17]. Additionally, we are much better than the work of [6] (they reached values of 90.8% DR and 92.3% for the RDR class), and we are far from the results of the works presented in [4] and [8]. AUC, Kappa, and F1 score are all measures that are commonly used to evaluate the performance of a classification model. Compared to kappa scores in [13] (0.8083 for the validation set, 0.7421 for the training), our model gives a more confirmed value. In addition to our achievements, in comparison to [8, 11, 12] in terms of AuC, our value is the best, which is close to the perfect value of 0.99.
4 Conclusion In summary, we can conclude that deep learning has achieved exceptional performance in smart diagnosis, especially in the classification of diabetic retinopathy using fundus images. CNN models require considerable hyperparameters tuning when dealing with big data. In this case, we chose the transfer learning technique with a very accurate model to learn the feature with very numerous numbers of weights. Some research on changing hyperparameters can cause disproportionate changes in network output. Although certain techniques, such as backpropagation and Adam optimization, can automatically determine the weights of a network, there are still several hyperparameters that are selected through trial and error, including the number of layers, parameter regularization, and dropout rates. A significant amount of research has been conducted to address the issues that arise from these heuristic decisions. However, deep learning methods are still not fully optimized. There are numerous clinical concerns that remain to be addressed. Moving forward, it is extremely important to carefully consider the potential limitations of deep learning methodologies. One of the most important dependencies is the availability of high-quality, annotated medical imaging data. These data are typically used to train and evaluate deep learning models. In this project, we did not concentrate only on classical metrics such as accuracy and precision; we paid attention to other metrics that are used to evaluate the performance of deep learning models in medical imaging, such as AUC, sensitivity, specificity, and F1 score, which deduce good understanding from the medical expert and help us when building deep learning models.
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References 1. Patel, R., Chaware, A.: Transfer learning with fine-tuned mobilenetv2 for diabetic retinopathy. In: 2020 International Conference for Emerging Technology (INCET), pp. 1–4. IEEE (2020) 2. Toledo-Cortés, S., de la Pava, M., Perdomo, O., González, F.A.: Hybrid deep learning Gaussian process for diabetic retinopathy diagnosis and uncertainty quantification. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) Ophthalmic Medical Image Analysis. LNCS, vol. 12069, pp. 206–215. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-634193_21 3. Shankar, K., Zhang, Y., Liu, Y., Wu, L., Chen, C.H.: Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 8, 118164–118173 (2020) 4. Dharmana, M.M., Aiswarya, M.S.: Pre-diagnosis of diabetic retinopathy using blob detection. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 98–101. IEEE (2020) 5. Li, X., Hu, X., Yu, L., Zhu, L., Fu, C.W., Heng, P.A.: CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans. Med. Imaging 39(5), 1483–1493 (2019) 6. Sheikh, S., Qidwai, U.: Using mobilenetv2 to classify the severity of diabetic retinopathy. Int. J. Simul. Syst. Sci. Technol. 21(2) (2020) 7. Gao, J., Leung, C., Miao, C.: Diabetic retinopathy classification using an efficient convolutional neural network. In: 2019 IEEE International Conference on Agents (ICA) (2019) 8. Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., Wang, X.: Zoom-in-net: deep mining lesions for diabetic retinopathy detection. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2017. LNCS, vol. 10435, pp. 267–275. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_31 9. Voets, M., Møllersen, K., Bongo, L.A.: Reproduction study using public data of: development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS ONE 14(6), e0217541 (2019) 10. Wang, S., et al.: Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Trans. Autom. Sci. Eng. 18, 574–585 (2020) 11. Odena, A.: Semi-supervised “learning with generative adversarial networks arXiv:1606. 01583 http://arxiv.og/abs/1606.01583 (2016) 12. Vo, H.H., Verma, A.: New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space. In: Proceedings of IEEE International Symposium Multimedia (ISM), pp. 209–215 (2016) 13. Wang, J., Bai, Y., Xia, B.: Simultaneous diagnosis of severity and features of diabetic retinopathy in fundus photography using deep learning. IEEE J. Biomed. Health Inform. 24(12), 3397–3407 (2020)
A Forecasting Model for the Energy Requirements of an Office Building Based on Energy Modeling and Machine Learning Models – A Case Study of Morocco Khaoula Boumais(B)
and Fayçal Messaoudi
Laboratory of Artificial Intelligence and Data Science and Emerging Systems, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco {khaoula.boumais,faycal.messaoudi}@usmba.ac.ma
Abstract. In Morocco, where buildings consume more than 35% of the country’s energy, the decision to integrate energy efficiency practices into the building from the design phase can avoid any energy waste. In this study, we examine the impact of integrating insulation on the energy efficiency of an office building using two approaches, with a focus on predicting the energy demand of an office building in Casablanca. The first method uses the Trnsys software for dynamic thermal modeling and the result is 37.45 kWh/m2 .year on total energy demand modeling over one year, which has allowed us to meet the requirements of the Moroccan Thermal Regulation for Buildings (RTCM). The second approach is based on the use of two machine learning models, and the results show that the SVM is more efficient than the RF model. It achieved a coefficient of determination of 97%. Keywords: Office building energy demand · dynamic thermal simulation · machine learning · Moroccan Thermal Regulation for Buildings (RTCM)
1 Introduction Energy is the most crucial issue in the world, and it plays an essential role in economic development and the satisfaction of all basic human needs. The building sector is one of the most vulnerable sectors, accounting for 40% of global energy consumption, with buildings in Morocco accounting for 35% of energy consumption. In addition, energy consumption for lighting accounts for more than 20% of office buildings and is expected to increase by 1.3% due to the growth in urbanization rates by 2050 [1]. Today we are living in the emergency of the digital and energy transition, where the Moroccan authorities have developed energy strategies that aim to reduce energy consumption by 15% by 2030, based on increasing the share of renewable energy, improving energy efficiency and energy management. The introduction of intelligent systems in buildings for energy management by using model predictive control technology (MPC), smart meters, and artificial intelligence affected positively affected energy efficiency and energy savings in buildings. In this context, the implementation of machine learning is © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 169–178, 2023. https://doi.org/10.1007/978-3-031-29860-8_18
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one of the most advanced techniques in AI used for forecasting energy and the control of buildings. This study aims to predict the energy requirements for the thermal comfort of an office building under construction in Casablanca city using dynamic thermal simulation and machine learning modules and to assess the impact of integrating thermal insulation to reduce the energy requirements of the building and meet the requirements of the Moroccan Thermal Code, which states that the annual thermal energy demand must be less than 45 kWh/m2 .year. Part of this paper is organized in this way. After an overview discussion of the world energy issue in buildings and a review of the literature on machine learning algorithms in building energy management, Sect. 2 presents case studies and building energy modeling, Sect. 3 includes the ML methodology used in this study, and Sect. 4 discusses the investigation’s findings. Section 5 concludes, and identifies policy solutions for the study.
2 Prediction ML Model for Building Energy Efficiency and Thermal Comfort There is a large quantity of literature, research, and review articles on energy forecasting using machine learning for thermal comfort and energy savings. 2.1 Utilization of the ML Model in Occupancy Forecasting for Energy Savings For the energy efficiency and thermal comfort of the residential building HVAC system, the authors [2] investigated the effect of occupancy prediction on building energy control based on a decision tree, gated recurrent unit, K-nearest neighbors, and multilayer perceptron models of ML. By using MAE and accuracy, the authors evaluate occupancystate performance and prediction time where the performance metrics provided greater accuracy of the KNN model in occupancy prediction with a value of 75.92% and 1 h time prediction shown by the MLP model. Furthermore, a study proved the impact of occupancy on the prediction of load electrical energy consumption in buildings and how it positively affects the efficiency of buildings. The authors of this study developed a new model of energy consumption forecasting based on various machine learning-based modeling techniques and multiple nonlinear regressions: SVR, RF, GB, ANN-DN, and ANN-FF. The results show a higher accuracy by using RF with 56.26 of RMSE and 0.87 on R2 instead of other ML algorithms. The correlation between the occupant rate and indoor environmental data (e.g., CO2 concentrations and electricity consumption of lighting systems and appliances) is found to be between 85 and 93.2% for a public building by using the decision tree and hidden Markov models [3]. Some of the experimental and modeling studies found good potential for energy saving by using ML models for predicting the occupancy in buildings. Thirty-five percent of energy consumption was saved by using real-time occupancy data [1], and other experimental studies found potential energy savings between 26.9% and 59.5% by using intelligent control of the occupancy and other systems [4].
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2.2 The Effect of the Application ML Model on Thermal Comfort Several research and review articles on the application of ML models for building thermal comfort have been published (e.g., Zahra Qavidel Fard et al. [5] discovered 62% of review articles focusing on comfort models and 35% of reviewed studies focusing on developing group-based comfort models). Shiyu Yanga et al. [6] identified the efficiency of using a model predictive control (MPC) system based on ANN for the automatic collection of data instead of improving the indoor thermal comfort of an office and a lecture theatre building. The results showed an improvement in indoor thermal comfort and hull energy savings of 58.5% in terms of cooling for the office and 36.7% in terms of electricity consumption in the lecture theatre. In another study focusing on thermal comfort indices in a room for improving the energy management of the lighting and HVAC system, by employing various ML classification models, KNN outperformed the DT and AO-ANN(BP) models in terms of accuracy (99.5%) [7]. 2.3 The Impact of Incorporating the Smart Meters for Tracking Energy Management To improve the performance of buildings, the integration of smart meters is one of the deep learning-based methods used [8]. In this study, the authors [9] recognized the role of IoT systems as smart systems used in monitoring and controlling the energy usage and energy efficiency of a commercial building, and by integrating the smart energy management system, the overall load demand of the building was reduced by 13.69 kW with a higher accuracy rate in terms of sensing and informing the IoT system. The shortterm power load prediction technique is a good strategy for building energy management, using the 1-D CNN-based RICNN model to calibrate the prediction time and the RNN model to preserve past information. Additionally, several smart meters are used as inputs for energy consumption prediction, and the proposed model provides high performance for load prediction better than other models in building an application [10]. 2.4 The Effect of Predicting Energy Consumption on Building Performance Several types of research have been conducted to predict energy consumption to ensure the energy efficiency of a building based on machine learning models and data-driven methods [11]. In the study of Jiwon Kim et al. [12] Three data-driven prediction models, RF, MLP, and SVM are used to predict the electrical energy consumption of a building. For data collection, a monitoring system and smart meter are used for the indoor environment, lighting, A/C equipment, and occupancy rate, and the R2 and t value are used for the prediction accuracy, where the MLR method provides a higher accuracy with 1 h of temporal resolution. Another study combined three methods, BIM, ML, and NSGA II, to reduce energy consumption and optimize interior thermal comfort. The evolution performance showed a high accuracy of 99% in terms of R2 for the GLSSVM predictive module, with 38% energy savings and a 40% improvement in thermal comfort in the building [13].
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3 Materials and Methods 3.1 Case Studies The Casa-shore office building is located in the city of Casablanca, Morocco. With a gross area of 62 121 m2 , the building provides approximately 36 workplaces. This office is in the design phase and situated on 4 buildings in two basements, a ground floor, and seven office floors. The building is expected to be occupied by 3 194 people, and each person occupies approximately 10 m2 of floor area from 8:00 a.m. to 6:00 p.m., from Monday to Friday. In this study, gender was treated equally in terms of thermal comfort. The building envelope features are selected according to three criteria (e.g., budget, technical, and environmental) based on the multicriteria analysis for the best selection of building materials, the lighting system, VRF system-based heat pump, and the setpoints were selected for an office building that complied with The Thermal Construction Regulation in Morocco (RTCM) [14, 15]. To obtain the simulation model of energy demand and comfort temperature of the building, the dynamic thermal simulation model of energy flows and thermal performance of the office building is carried out using the TRNSYS computer program considering a typical meteorological year (TMY) of the city of Casablanca Fig. 1 and Table 1 shows the general information about the reference case building, all initial values in Table 1 by the theoretical calculation and the normalised value for an office building, and all characteristics were confirmed with RTCM for the Casablanca region.
Fig. 1. Simulation of the energy requirements for heating and cooling of the office building with TRNSYS.
3.2 Data Analysis Process For the data analysis process of our study, we used a flowchart shown in Fig. 2. We start by modelling the building in 3D on SketchUp, then we simulate the building using TRNSYS software to estimate the heating/cooling loads under two scenarios: without
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Table 1. Global heat transfer coefficients of structural elements. Feature structure/Parameter
Features
The double wall on heated room, hollow brick 8T and 12T in other side, 1.25 cm plaster on each side, and 4 cm polystyrene insulation panel U value (W/ m2 .K)
0.33
Wall on unheated room in double 8T partitions with 4 cm air space U-value (W/m2 . K)
0.58
20 cm PSE slab, 7cm screed, 40 × 25 cm porcelain stoneware tile floor covering U-value (W/m2 .K)
0.61
Continuous plaster ceiling, 20 cm polystyrene slab, 7 cm screed and gravel asphalt 0.55 base layer U (W/m2 .K) Doors U-value (W/m2 .K)
2.2
Windows and single glassing
5.66
Minimum fresh air flow rate per occupant (m3 /h)
25
insulation/with wall insulation in order to justify the thermal performance of the building against the RTCM guidelines. The data obtained from the dynamic simulation is then used to predict the energy requirements of the building using RF and SVM machine learning regression methods. By training and testing our data on Spyder, we can find the best prediction method. Finally, for the performance evaluation of our model, we used R-Squared (R2 ), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Error (MSE) which are the most used for energy prediction as justified by our literature review. The following equations show the method of calculating the performance of the index [16, 17]. (1) MAE = 1n ni=1 |AEi − PEi | MSE =
1 n
RMSE =
R2
=1−
n
(AEi − PEi )2
(2)
i=1 n
(AEi − PEi )2
(3)
2 n i=1 ypredect,i − ydata, i n 2 i=1 (ydata,i − ydata )
(4)
1 n
i=1
4 Results and Discussion This section presents the results obtained from the dynamic thermal simulation and the ML for energy prediction methods.
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m2
Fig. 2. The flowchart process for developing a forecasting energy model of the office building.
4.1 The Result of Forecasting Annual Energy Needs Using Simulation in Trnsys In this research, the efficiency of using dynamic simulation for the prediction of the energy load of an office building in the construction phase has been shown, acting on several parameters which are orientation, use of thermal insulation, and internal thermal power (e.g., metabolism, correspondence, lighting…), Table 2 shows that the total energy requirement is 37.45 kWh/m2 .year ≤45 kWh/m2 .year is in line with the RTCM requirement, which shows that the large amount of energy required for occupant comfort is in cooling for Casablanca. The illustrations below show the effect of integrating insulation into the walls on the energy needed to achieve thermal comfort and compliance with the RTCM requirements.
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Table 2. The total internal energy needs for cooling and heating for the thermal comfort of the occupants. Energy
Energy need (kWh/m2 .year)
Heating Colling Total energy need
0.57 36.86 37.43
In Fig. 3, which illustrates the distribution of the thermal load, it can be seen that the two curves converge towards the same values and that the maximum heating demand is generally for the month of January, which shows that the insulation has no effect on the cooling energy demand in Casablanca due to its climate, with an external temperature that reaches the thermal comfort range, and the orientation of the building towards the south, which allows the penetration of solar radiation through its large bay windows, representing a free natural thermal input. However, the choice of insulation has a significant effect on the cooling energy demand. In Fig. 4 shows that after the integration of the insulation in the walls, the energy demand decreases by 77% compared to the previous system, which managed to reduce the energy demand of the previous building system. Compared to the study of Toktam Bashirzadeh Tabrizi et al. [18], the integration of the insulation has a significant influence on the cooling and heating energy demand, where we can save 60% of the energy demand of the building. In addition, good dynamic simulation software remains a dependable option that allows us to adjust a number of variables while applying basic energy efficiency measures to estimate the energy demand required to maintain the thermal comfort of our building.
Fig. 3. Annual energy requirements for heating with and without building insulation.
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Fig. 4. Annual energy requirements for cooling with and without building insulation.
4.2 The Result of Forecasting Annual Energy Needs Implementation of ML Model The main objective of our program is to improve the thermal performance of the office building, and to do this, we need to predict the load required for the comfort of our building. In this study, we looked at the effectiveness of using two machine learning models, random forest (RF) and support vector machine (SVM), to predict the need for energy. The analysis of the performance of the two models is shown in Table 3. For RMSE, MAE and MSE, a good predictive model should have values close to zero, and for R-squared with global training time, values close to one. Our research showed that the SVM outperformed the RF model in terms of effectiveness. It had a coefficient of determination of 97% and relatively few errors (MAE, RMSE and MSE), putting it at the top of the list. Table 3. Energy need forecasting results based on the RF and SVM of ML methods. Model
R2
RMSE
MSE
MAE
Training time (seconds)
RF SVM
0.91 0.97
4.01 2.04
13.23 4.97
2.50 1.68
3.10 14.03
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5 Conclusion By using dynamic simulation and machine learning techniques in this study, we were able to predict the energy requirements necessary for the thermal comfort of a building housing a user’s office in Casablanca. As a result, we can draw the following conclusion: – The project complies with Moroccan thermal building regulations (RTCM). – The use of insulation reduces the need for air conditioning by 77%. – The SVM outperformed the RF model in terms of effectiveness in detecting an energy demand in the building, according to the machine learning performance. It had a coefficient of determination of 97% and only a small number of errors.
References 1. Brambilla, A., et al.: The potential of harnessing real-time occupancy data for improving energy performance of activity-based workplaces. Energies 15, 230 (2021) 2. Ryu, S.H., Moon, H.J.: Development of an occupancy prediction model using indoor environmental data based on machine learning techniques. Build. Environ. 107, 1–9 (2016) 3. Esrafilian-Najafabadi, M., Haghighat, F.: Impact of occupancy prediction models on building HVAC control system performance: application of machine learning techniques. Energy Build. 257, 111808 (2022) 4. De Bock, Y., Auquilla, A., Bracquené, E., Nowé, A., Duflou, J.R.: The energy saving potential of retrofitting a smart heating system: a residence hall pilot study. Sustain. Comput. Inform. Syst. 31, 100585 (2021) 5. Qavidel Fard, Z., Zomorodian, Z.S., Korsavi, S.S.: Application of machine learning in thermal comfort studies: a review of methods, performance and challenges. Energy Build. 256, 111771 (2022) 6. Yang, S., Wan, M.P., Chen, W., Ng, B.F., Dubey, S.: Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Appl. Energy 271, 115147 (2020) 7. Abdel-Razek, S.A., Marie, H.S., Alshehri, A., Elzeki, O.M.: Energy efficiency through the implementation of an AI model to predict room occupancy based on thermal comfort parameters. Sustainability 14, 7734 (2022) 8. Tien, P.W., Wei, S., Darkwa, J., Wood, C., Calautit, J.K.: Machine learning and deep learning methods for enhancing building energy efficiency and indoor environmental quality – a review. Energy AI. 10, 100198 (2022) 9. Karthick, T., Charles Raja, S., Jeslin Drusila Nesamalar, J., Chandrasekaran, K.: Design of IOT based smart compact energy meter for monitoring and controlling the usage of energy and power quality issues with demand side management for a commercial building. Sustain. Energy Grids Netw. 26, 100454 (2021) 10. Kim, J., Moon, J., Hwang, E., Kang, P.: Recurrent inception convolution neural network for multi short-term load forecasting. Energy Build. 194, 328–341 (2019) 11. Wei, Y., et al.: A review of data-driven approaches for prediction and classification of building energy consumption. Renew. Sustain. Energy Rev. 82, 1027–1047 (2018) 12. Kim, J., Kwak, Y., Mun, S.-H., Huh, J.-H.: Electric energy consumption predictions for residential buildings: Impact of data-driven model and temporal resolution on prediction accuracy. J. Build. Eng. 62, 105361 (2022)
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13. Hosamo, H.H., Tingstveit, M.S., Nielsen, H.K., Svennevig, P.R., Svidt, K.: Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II. Energy Build. 277, 112479 (2022) 14. M’lahfi, B., Amegouz, D., El Qandil, M.: A new approach for the mandatory application of the thermal regulation of construction (RTCM) in the future Moroccan buildings. SN Appl. Sci. 2, 1656 (2020). https://doi.org/10.1007/s42452-020-03367-w 15. Reglement Thermique de Construction au Maroc (RTCM) Agence Nationale pour le Développement des Energies Renouvelables et de l’Efficacité Energétique (ADEREE) (2014). https://aust.ma/images/aust/reglementation/Decrets/R/%e2%80%98eglement_ther mique_de_construction_au_Maroc.pdf 16. Huang, C.-J., Kuo, P.-H.: Multiple-input deep convolutional neural network model for shortterm photovoltaic power forecasting. IEEE Access 7, 74822–74834 (2019) 17. Wang, Z., Wang, Y., Zeng, R., Srinivasan, R.S., Ahrentzen, S.: Random forest based hourly building energy prediction. Energy Build. 171, 11–25 (2018) 18. Tabrizi, T.B., Hill, G., Aitchison, M.: The impact of different insulation options on the life cycle energy demands of a hypothetical residential building. Procedia Eng. 180, 128–135 (2017)
Internet of Things, Blockchain and Security and Network Technology
Blockchain Technology for Audit Firms: Perspectives and Challenges Tilila Taj(B) Faculty of Legal, Economic and Social Sciences Ain Chock, University of Hassan II, Casablanca, Morocco [email protected]
Abstract. Blockchain technology has spread to different business areas beyond cryptocurrency since 2008. Its operating mode has generated innovative and secure practices to insure reliable, immutable and ‘real-time’ information within an organization. This paper is a research on Blockchain technology, discussing its implication in audit procedures such as planning, gathering audit evidence and finally, exercising the professional judgement on the financial statements. The conducted study investigates the operation of external auditing by revealing the fundamental shift operated by the implementation of Blockchain within audit procedures. Empirical data were collected through semi-directive interviews with 25 subject audit experts. Lastly, the study exhibits the requirements for the application of Blockchain in audit firms. Keywords: Audit firms · Audit procedures · Blockchain technology · Financial statements · Professional judgement
1 Introduction 1.1 The Emergence of Blokchain Technology Blockchain technology is revolutionizing the way in which business and financial information are recorded, processed, stored and communicated. It is used as a technological tool that considers all the instructions and procedures within a profession to monitor all the operational processes within an organization. Blokchain technology is considered as both a foundational and disruptive technology that is impacting businesses and data in ways not seen since the emergence of internet (Casey and Vigna 2018) [1]. According to Yermack (2017) [2], it is considered as the most powerful technology after the internet. Blockchain technology is defined as a digital ledger used to record and share information through a peer-to-peer network (Evangeline & Wilner 2017) [3]. Identical copies of the virtual “notebook” are collectively validated by the concerned network members, with accepted information that is aggregated into blocks and added to an existing interrelated chain of previously validated blocks. The main attribute of this technology is it’s the digital authenticity of the transaction without the intervention of an intermediate party. It allows quick and low-cost distribution of information across vast networks of actors. Blockchain technology creates an interlocking immutable system where trust arise to all the members of the Blockchain network (Tan and Low 2017) [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 181–190, 2023. https://doi.org/10.1007/978-3-031-29860-8_19
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1.2 Blokchain Technology for Audit Profession External audit firms are concerned by this revolutionary technology. It will change the way they plan, design and perform their profession. In today’s ecosystem, external auditors are considered as the trusted professionals who provide an assurance that the financial statements have been properly drawn up in accordance with the accounting standards in effect within a nation. Recent reports established by Big Four audit firms demonstrates that auditors and regulators will be considerably affected by the adoption of Blockchain technology (KPMG 2016 [5], Deloitte 2016 [6], PwC 2017 [7], E&Y 2017 [8]), especially the way procedures are performed, recorded, audited and reported. They suggest that the technology makes accounting and financial information timelier and trustworthy…an alternative to existing auditing systems. For instance, Deloitte was the first audit firm that launched “Rubix” a Blokchain initiative in 2014. Then, EY was the first audit firm that allows client to settle their invoices for auditing and advisory services using Bitcoin. In 2017, EY also initiated “Ops Chain”, a set of services to enable the commercial use of Blockchain through the firms. However, this technology has created number of uncertainties on how the audit firms will shape up in the future and what will be the impacts of the use of this technology on the audit profession. The purpose of this paper, is to identify whether Blockchain technology will effectively, assist external audit firms in the various audit practices such as planning, gathering audit evident, test of controls and analytical reviews. For that, the objective is to answer the following questions: • How Blockchain technology can improve the final professional judgement of whether the financial statements are reliable, compliant and regular. • And what are the necessary skills that auditors must detain for the next years to be able to exploit this technology? The paper consists of the following parts: the first discusses the operational mechanism of Blockchain technology and demonstrates the value of this fast growing digital transformation to improve the procedures of performing an external audit. The second, illustrates the application of Blockchain system to audit procedures and its requirements. For that, 25 external auditors have been interviewed from the Big Four audit firms The data collected was treated in thematic grouping according to the research objective. The results of the conducted study have proven that Blockchain have a significant influence on the audit profession. It has both strategic and procedural implication that requires some strategic changes on the audit approach. The study has also showed that external auditor must be equipped with managerial and technological related skills to conduct correctly the audit mission which is being transformed to a data/technological profession.
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2 Blockchain Technology: Mechanisms of Application 2.1 Blockchain Mechanisms The Several previous researchers have defined Blockchain as a network of nodes working as peers to produce an immutable distributed ledger that can be accessible to all the users and for which no information can be erased (Delahaye 2014 [9]; Drescher 2017 [10]; Sheldon 2018 [11]). It created a breakthrough to the extent of not needing a trusted third party to authenticate and assess the viability of the transactions. The recorded transactions are unchangeable and timely registered by the concerned parties (Bishop et al. 2019) [12]. Blockchain technology is considered as a digital record that will distribute the information among all the connected participant through a network who have approved the rules to update their ledger. All the records are compiled and put together into “blocks” of data that belongs to a long chain of blocks that represent the system of Blockchain. The contained Data will be, systematically, arranged in a chronological range so that each block is related to the previous created block. A block can be thought as a table that comprises four columns. The fist column contains the “Block header”, the second column stores the “Hash value” of the previous block, the third column represents all the transaction’s timestamp and information, and finally, the fourth stores the proof of work. The following figure illustrates the functioning of the blocks within a Blockchain system (see Fig. 1):
Block (α) Block Header
Block (α+1) Block Header
Block (α+2) Block Header
Hash Value of Block α-1
Hash Value of Block α
Hash Value of Block α+1
Timestamp and information’s transaction
Timestamp and information’s transaction
Timestamp and information’s transaction
Proof of work
Proof of work
Proof of work
Hash Value of Block α
Hash Value of Block α+1
Hash Value of Block α+2
Fig. 1. Mechanism of the creation of the blocks of the Blockchain
(1) The first element “Block Header” identifies the block on the entire Blockchain and summarizes the block’s transactions. (2) The hash value is a mathematical function that convert an input string of any length to a fixed-length output string. It is an encrypted version that is calculated mathematically by a “Hash function”. The first component is defined by the following
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formula: Hash Value of Block (α) = H [ Information recorded in Block header of Block (α)] = H [ Hash Value of Block (α-1), timestamp and all the transaction’s information]
(3) The transaction’s information contains all the instructions, agreements, supporting documents contained in “Smart contracts”. This application executes the tasks and adjust contracts by controlling, timely, the changing conditions within the existing contract’s terms. (4) Finally, for the proof of work (PoW): It allows each node of the Blockchain to have the right of mining. The process of mining consists to validate and certify each data entry within the Blockchain system. It is a method that requires from the users to run constantly hashing algorithms to validate new electronic transaction timely. 2.2 Blockchain Types The literature emphasizes two types of Blockchain: Permissionless and permissioned Blockchain. The following table summarize the main differences between the two types of Blokchain technology in terms of attributes, advantages and drawbacks (Table 1). Table 1. Comparison between the two types of Blockchain technology
Type of network Key attributes
-
Advantages
-
Drawbacks
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Permissionless Public Network Full transparency of the transaction No central authority Privacy of data depends on the technological limitation of the system Decentralization of the information Accessibility across locations and nationalities Time-wasting record reconciliation is eliminated - Resilient system Limited user privacy Uncontrolled information
Permissioned Private Network Controlled transparence of the transaction Private group authorizes participation Privacy of data depends on the technological innovation of the system and its application Stronger information privacy Greater security Time-wasting record reconciliation is eliminated Faster Network Resilient system
-
-
Limited decentralization : Risk of collusion
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The permissionless Blockchain enables information to be shared to all the network members and records can be controlled and monitored by all the concerned parties. For the second type, the access to information is controlled by defining each participant roles on the network. A permissioned Blockchain network is considered as a partially decentralized system. 2.3 The Application of Blockchain System for Audit Procedures Blockchain system is based on four steps that transform a request within the data system and record the supporting documents related to a financial or accounting entries. The following figure represents the process of preparing an audit mission with Blockchain technology (Fig. 2):
Company (CLIENT)
S
1.The audit evidence required is encrypted in a message
External Audit firm
3.Approve the creation of the block by the nodes of the network
2. Register the accounting entries related to the supporting document recorded
S
5.Reception of the audit evidence
S
4.Add the block to the end of the chain in a public and immutable ledger
Fig. 2. The design of Blockchain mechanism for audit profession
The first step consists to insert the required audit evidence for the concerned parties in the public shared ledger of Blokchain system (e.g. the financial statements, contracts, invoices, balance sheets, commercial agreement…). The company encrypt the required information via a software interface (Internet) connected to the whole network. For the second step, it is about to register the request related to the creation of the audit evidence by adding a Block that contains all the information verified by the users of the network (decentralized system of evidence). The block will be disposed in “the queuing system”. Then, the block will be approved by the nodes of the network (Step3). The block will be added in a chronological order in the immutable public ledger. For the fourth step, each Block must be joined to the Blockchain by verifying the viability of the contained information1 . When the users of Blockchain technology validate conjointly 1 See Fig. 1 that illustrates the mechanism of the creation of a block.
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the exactitude of the transactions, the Block is added. Finally, the external auditor can consult and exploit at different intervals all the information related to the audit evidence. Identical copies are maintained by all the network members.
3 Blockchain Technology: Mechanisms of Application in Audit Procedures 3.1 Research Methodology To fulfil their mission, external auditors need a good understanding of their client’s business. For that, they must collect all the DATA related to the control environment. Jackson (2018) [13] and Brender et al. (2019) [14] were the first researchers that have conducted studies regarding the implication of the use of Blockchain systems on the audit profession. The qualitative study was conducted with 25 external auditors from international audit firms in Morocco. The first phase was to contact them via email to fixe semi-directive interviews in order to fulfil the survey. The survey was structured in five themes. The fist domain contained questions related to the perception of auditors toward the use of Blockchain technology. The second theme contained questions about the challenges and opportunities of Blockchain for audit firms and organizations in general. The third party was about the implications of Blockchain for audit firms and external auditors. Finally, the last theme contained open questions about their perceptions and recommendations for audit firms toward the adoption of Blockchain and what can be the limit of the use of this technology (Table 2). Table 2. The description of the sample BIG FOUR Deloitte PwC2 EY3 KPMG Total
Number of interviews 5
Average year of experience 5
4
4
12
17
4
8
25
8,5
The data analysis was assembled via Google Forms, for the interviewers that answered the survey alone. For the other respondents that accepted to do the semidirective interview, data was transcript from the audio records. 3.2 Findings: Blockchain’s Implication in Audit Procedures The results of the conducted survey have shown that Blockchain technology have a significant influence on the audit profession. It has both strategic and procedural implication (Fig. 3): 2 PriceWaterhouseCoopers. 3 Ernst & Young.
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New audit practices Strategic implication
New advisory services
Blockchain
IT related skills Audit based on Testing of controls Procedural implication
Continious audit process Time saving Audit covering whole information
Fig. 3. The implications of Blockchain technology for audit firms
New Audit Practices Auditors should consider the long-run prospects related to the application of Blockchain while auditing. The following table describes the main impact revealed in the collected responses of auditors (Table 3). Table 3. The impact of Blockchain on audit practices Audit Practices
Blockchain’s impact
Planning
- Provide exhaustive records for external auditors - Quick spot of problems and improved prioritization of plans to find the right diligence
Audit evidence gathering
- Instant access to digital strategic data of the organization - Collect approved and accurate digital records and full disclosure of reports
Test of controls
- No errors - Less risks of fraud
Compliance assessment
- The review and verification of the existing digital assets - The verification of the consistency between information on the Blockchain system and the physical reality
Reconciliation and validation of transactions
- Facilitate smart contracts by setting up integrated analytical models - Digitalization of the inter-company reconciliations
Analytical review
- Real-time financial reporting - Better predictions and analysis of risks
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New Advisory Practices Blockchain technology can offer new opportunities for auditors to extend their areas of intervention by proposing new services. Indeed, new advisory services can be developed such as Blokchain management, assistance of the client to set up Blockchain system, the assessment of the right participation of the members on the network …. In the next years, Blokchain can develop considerably the audit profession and audit firm’s business. IT Related Skills Most of the auditors interviewed agreed that auditing will head toward a data driven approach which will require some specific technological skills. The challenge behind the use of Blokchain involves the development of IT related skills within audit firms. They must invest in personnel training to be able to master the technology and insure its right use. It also requires to recruit auditors with high technological skills so they can guarantee the development of their audit and advisory services. Audit Based on Testing of Controls The audit approach will no longer be based on testing transaction but more on testing controls. For example, the action of sending out third party confirmation for the validation of some accounting entries will not be required anymore as the transaction is already approved and verified by the system. The transactions are approved and verified by the Blockchain system, so the external auditor will focus its analysis on the control system implemented such as: the right distribution of roles between users, the completeness of Blockchain codes, the viability of the process of confirmation… This is in accordance with Smith & Castonguay (2020) [15], who recognise that auditing using Blockchain depends on the right control and governance of the system with technical and managerial standards. Continuous Audit Process Performing external audit engagement requires to gather continuously, journal entries, financial statements, balance sheets, supporting documents of some economic transactions … the real-time access to these documents will allow external auditors to conduct a continuous audit. This new reconfiguration considers auditors as the principal actor in the Blockchain process. All the respondents approved that the technology of Blockchain can be used by audit firms to perform their audit frequently and in real-time transforming their profession to a new form of auditing that can be named “AUDIT 4.0”. Time Saving Blockchain technology provides secure and verified digital records allowing a higher standardization and full disclosure of strategic and accounting information. Traditionally, external auditors make copies of audit evidence provided by the clients and create folders to store these files. However, Blockchain will considerably reduce these unnecessary workloads in order to increase the time for external auditor to perform on their audit verification. In this new reconfiguration, clients will be required to submit the documents into the Blockchain system such as invoices, contracts, agreements and all the supporting documents. The findings are in line with some previous case studies (Rozario & Thomas
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2019 [16]; Liu et al. 2019 [17]) that have shown that Blockchain ensures a full coverage of data and make the process of auditing more efficient. Audit Covering Whole Information Blockchain technology will allow audit firms to change their methods while testing, by auditing all the data of the client rather than sampling. Hence, it will improve the capability to identify the anomalies and risks in the client’s financial statements. The audit mission carried out will cover the whole transactions of the organization within the Blokchain system which will improve the quality of the judgement.
4 Conclusion Because Blockchain afford interesting characteristics such as transparency, traceability and immutability of transactions, its adoption to audit profession will be rapidly rooted within the next years. The aim of this article was to identify the implications of Blockchain to the audit profession and to define the main changes for which the external auditors and regulators must think about. It shows that Blokchain is creating new challenging tasks to external audit firms such as the verification of the reality of the digital assets, the compliance and exhaustiveness of the recorded transactions. However; due to the fact that the technology is still in the experimental stage, there are not enough standards for the use of Blockchain in audit profession. According to some audit experts, it is urgent to define consensus, regulations and standards for the application of Blockchain in audit firms. The system must take into consideration all the requirements of the profession. Moreover, audit firms must be able to identify the information that should be kept in private. They must also define which information should be encoded into smart contracts so as to register them in the Blockchain system. Finally, this research has some limitations because of the limited number of the chosen sample (25 auditors interviewed) which might be increased for future analysis. Furthermore, the disadvantages of the Blockchain on the audit profession have not been mentioned neither the question of cost-advantages. Some respondents in the conducted study have revealed some negative implication of Blockchain such as the lack of standards that regulate the application of Blockchain for auditing and accounting procedures. New lines of research are expected about the application policies of Blockchain within audit firms. In addition to that, a case study must be conducted to assist an audit firm while implementing Blockchain, so as to identify clearly the inconsistencies and the main ways of improvements.
References 1. Casey, M., Vigna, P.: The Truth Machine: The Blockchain and the Future of Everything. St. Martin’s press (2018) 2. Yermack, D.: Corporate governance and blockchains. Rev. Finan. 21(1), 7–31 (2017) 3. Evangeline, D., Wilner, A.: The security and financial implications of Blockchain technologies: regulating emerging technologies in Canada. Int. J. 72(4), 538–562 (2017) 4. Tan, B.S., Low, K.Y.: Bitcoin: its economics for financial reporting. Aust. Account. Rev. 27(2), 220–227 (2017)
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5. KPMG: KPMG’s Distributed Ledger Services Meet Luxembourg, Luxembourg (2017) 6. Deloitte, Blockchain Technology: A Game-changer in accounting (2016). https://www. deloitte.com/content/dam/Deloitte/de/Documents/Innovation/Blockchain. Accessed 12 Sept 2022 7. PwC: ‘Auditing Blockchain: A New Frontier’ (2017). https://www.pwc.com/us/en/industries/ financial-services/research-institute/blog/Blockchain-audit-a-Michael-smith.html. Accessed 13 Sept 2022 8. EY, How Blockchain Will Revolutionize Finance and Auditing’ (2017). https://better workingworld.ey.com/digital/blockchain-why-finance-and-auditing-willnever-be-the-same. Accessed 12 Sept 2022 9. Delahaye, J.P.: La cryptographie réinvente la monnaie: le Bitcoin, Science et société, LNA 66 (2014) 10. Drescher, D.: Using the Blockchain, in Blockchain Basics, pp. 223–233. Berkeley, CA (2017) 11. Sheldon, M.D.: Using blockchain to aggregate and share misconduct issues across the accounting profession. Curr. Issues Audit. 12(2), 27–35 (2018) 12. Bishop, A.H., Chen, Y., Johnson, T.E., Nunez, A.S.: Know more about Blockchain. Internal Audit. 34(3), 6–12 (2019) 13. Jackson, B.: Understanding the Implication of Blockchain Technology on the Audit Profession. University of Central Florida (2018) 14. Brender, N., Gauthier, M., Morin, J., Salihi, A.: The potential impact of Blockchain technology on audit practice. J. Strat. Innov. Sustain. 14(2), 35–59 (2019) 15. Smith, S.S., Castonguay, J.: J: Blockchain and accounting governance: emerging issues and considerations for accounting and assurance professionals. J. Emerg. Technol. Account. 17(1), 119–131 (2020) 16. Rozario, A.M., Thomas, C.: Reengineering the audit with blockchain and smart contracts. J. Emerg. Technol. Account. 16(1), 21–35 (2019) 17. Liu, M., Wu, K., Xu, J.: J: how will blockchain technology impact auditing and accounting: permissionless versus permissioned Blockchain. Curr. Issues Audit. 13(2), 19–29 (2019)
Blockchain Security Attacks: A Review Study Dahhak Hajar1(B) , Imane Hilal2 , and Nadia Afifi3 1 Hajar Dahhak C3S Laboratory, ENSEM, Hassan II University Casablanca, Casablanca,
Morocco [email protected] 2 Imane Hilal ITQAN TEAM, LyRICA Lab, School of Information Sciences, Rabat, Morocco [email protected] 3 Nadia Afifi C3S Laboratory, EST, Hassan II University Casablanca, Casablanca, Morocco [email protected]
Abstract. Blockchain is the biggest revolution affecting different sectors of activity: industrial, economic and civic. It offers its users essential security measures based on solid principles such as decentralization, consensus and cryptographic algorithms to guarantee the security of transactions. Moreover, this technology is considered one of the most secure in the world of information systems. However, blockchain has become the focus of cybercriminals because of the profits they can amass. The objective of this article is to present the different vulnerabilities of the Blockchain structure regularly exploited by hackers to cause irreparable damage as well as establishing a comparative approach between 06 cryptocurrencies in terms of: the algorithm of the cryptography, the type of consensus, the security mechanisms and the different attacks suffered between 2017 and 2022. The above approach has shown that even though the cryptocurrency market has undergone a great evolution, the inefficiency of the different structures of the blockchain algorithms still presents itself. Keywords: blockchain · cryptography · attacks of security · consensus
1 Introduction Today, blockchain [1] is the most significant revolution affecting various industries [2]. It provides its users with critical security measures based on solid principles such as decentralization [3], consensus, and cryptographic algorithms to ensure transaction security. Asymmetric cryptography is used in each transaction to ensure the origin of data, the reliability of information, and the confidentiality of information. This type of cryptography makes use of two pairs, one private and one public. This pair of keys is very important because it is used to sign messages. In the system described below, a user generates a random sequence of numbers known as a private key. An algorithm (for example, in the case of Bitcoin, the Elliptic Curve Digital Signature Algorithm (ECDSA)) [4] generates a second key called the public key from it. This private key will later be used to sign a message. Other network users who have the corresponding public key will be able to verify that he is the author of this message [5]. These benefits © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 191–199, 2023. https://doi.org/10.1007/978-3-031-29860-8_20
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have prompted people to invest in crypto-currency, which is regarded as one of the most important technologies in the global financial sector in the digital age. They can be used to purchase goods or invested in. A crypto-currency is a digital currency that can be used instead of traditional currencies. It is generated by cryptographic algorithms and stored in a system that records transactions made anywhere in the world at any time [6]. In light of this, blockchains and crypto-currencies are related [7]. Blockchains are regarded as the crypto-foundation; currency’s they can be used to create and manage cryptocurrency transactions, as well as to validate and track their movements. By creating a more efficient and secure environment for conducting transactions, both blockchain and crypto-currencies have become the driving force of today’s global financial sector. Blockchain technology, on the other hand, has become a target for hackers. It still has vulnerabilities that hackers can exploit to launch serious security attacks. The FBI’s 2021 report lists criminal use of crypto-currency as one of the top three incidents reported this year. In 2021, these criminal acts will cost more than $3.2 billion in reported losses. According to Chainalysis [8] January 2022 report, crime in 2021 reached a new all-time high. However, 2022 got off to a shaky start, with 125 hacks costing $3 billion in total losses. This paper is structured as follows: Beginning with an introduction and the Background, the types of security attacks on the blockchain are studied in the second section, the third section discuss the failure of ECDSA [4]. The final section is devoted to a comparison of seven crypto-currencies in order to select the most secure method.
2 Background Blockchain [1] is a technology that enables data storage: • Perennial • Unforgeable • Distributed The blockchain is a decentralized system that enables global transactions to be carried out in complete security [9], transparency, and without the need for a central control body [10]. It amasses a massive database containing the history of all transactions between its users since its inception. The blockchain can be used either: • To perform asset transfers. • To ensure better traceability of assets. • To automatically execute smart contracts. It is a method of transmitting information from one user to another without going through a trusted third party verification process. The blockchain architecture is built on a network of users [10]. Since the blockchain’s inception, each user has kept a complete copy of all transactions. These users are real and equal users in the blockchain system, This brings us back to the topic of decentralized trust [11]. Thousands of miners check a transaction before it is approved [10]. Once the transaction is approved, it is recorded
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in the current block with a unique history, ensuring that no one can change the data contained within the blocks. The blockchain is built primarily on cryptographic hashing algorithms. Because of these algorithms, a simple change has a dramatic effect on the hash result. The smallest change will completely alter the chain. The blockchain is defined as a collection of time-stamped records of unchangeable or immutable data that are managed by a computer cluster that is independent of any central unit. The security of user information and transaction data is a prerequisite for blockchain adoption. The blockchain has five layers [11]: the data layer, network layer, consensus layer, contract layer, and application layer. The consensus [11] layer is primarily based on consensus algorithms, which bring processes together to enable the creation of an environment characterized by decentralized system security, trust, collaboration, and cooperation. These algorithms also enable the group of miners to reach an agreement and have equal rights among all blockchain users. The contract layer [11] is primarily comprised of the smart contract, which contains the conditions required to initiate a transaction in the decentralized system. It is a program that allows the terms of predefined conditions in a block-chain to be executed automatically. Only this program determines whether a contract is fully or partially executed. When the program determines that the conditions have been met, each contracting party’s obligation is debited. To ensure the integrity of data storage, the data layer primarily employs the block data structure. Each network node encapsulates data transactions received over time into a time-stamped data block and links the block to the longest master blockchain for storage. This layer contains the primary block storage techniques, such as chain structure, hash algorithm, Merkle tree, timestamp [11], and so on. The blockchain is a typical peer-to-peer network [1]. There are no central nodes and all nodes are connected by a planar topol-ogy. Any node can join or leave the network at any time, and two nodes can freely exchange information. The blockchain’s P2P protocol is primarily used for information transmission between nodes. The blockchain is regarded as a massive distributed database. This database stores the various transactions made by blockchain users in blocks [10]. The following information is contained in each block: nonce, previous hash, timestamp, Merkle tree, and target hash. Previous Hash: this field contains the hash value of the previous block data [12]. Nonce: The blockchain’s operating principle is based on a network of miners creating and validating new blocks before adding them to the chain. Miners must meet the blockchain’s consensus requirements in order for the network to validate these blocks. The nonce is one of these requirements [12]. The nonce is a random number with a starting value of 0. The mining node continuously performs a SHA256 operation on the global data of the block. When the SHA256 value calculated by the current random number fails to satisfy the requirements, the random number is increased by one and the SHA256 operation is repeated. Until the SHA256 value is less than the target difficulty, a new data block is generated, and the P2P network accepts the new data block.. This process of generating data blocks is known as proof of work.
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Timestamp The blockchain technique requires the node to include a timestamp [12] in the header of the current data block to indicate when the data in the block was written [12]. Target Hash This is a value found in the block header [12]. The block-encryption chain’s algorithm generates this value. This algorithm generates verifiable random numbers in a predictable amount of computer processing power. As previously stated, consensus is an essential component in the architecture of a blockchain, which makes it secure. It is a method of reaching an agreement so that the network of nodes can determine whether or not the transactions are valid. We find several types of consensus mechanisms, including: Proof of Work This is a consensus algorithm that requires miners to solve complex computer problems with a large amount of computing power. The first miner to solve the problem and generate a hash that meets the criteria will be able to create a new block and confirm transactions. This miner will be rewarded with crypto-currency as well [6]. Proof of Stack To create and validate transactions in this type of consensus, nodes must have a certain amount of crypto-currency. The greater this amount, the more likely the miner will be chosen to update the blockchain [12]. Xrp Ledger Consensus This mechanism prompts network nodes to select a validator from a list. The node listens to its trusted list during a transaction. The server declares a consensus when a large enough percentage of them agree that a set of transactions should take place and that a given ledger is the result. If they disagree, the validators modify their proposals to better match the other validators they trust, and the process is repeated several times until they reach agreement [12].
3 Types of Security Attacks on the Blockchain 3.1 Blockchain Network Attacks Distributed Denial of Service (DDOS) A DDoS attack attempts to flood the blockchain with spam transactions, reducing the blockchain’s ability to add legitimate transactions. Given the distributed nature of the blockchain, it is possible that many of the nodes will crash. Malleability Attacks on Transactions Transaction malleability is an attack in which the attacker changes the ID of a transaction before it is confirmed in the blockchain network, causing the user to pay twice. This modification enables the attacker to pretend that the transaction did not occur, resulting in the same amount being debited twice. Time Jacking Time jacking is an attack in which the attacker creates bogus nodes on the blockchain
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network with bogus timestamps in order to isolate a legitimate node and modify its time counter in order to force it to accept an alternative blockchain. Routing Attack The routing attack divides the network into two distinct components. The attacker disables communication between the nodes, making it impossible for them to detect transaction tampering. Sybil Attack A Sybil attack allows the attacker to gain control of the network by spawning multiple nodes with fictitious identities and attempting to influence the network [13]. Eclipse Attack An eclipse attack [14] takes advantage of the fact that a node in a blockchain cannot connect to other nodes at the same time due to bandwidth constraints. This means that each node must connect to a small number of neighboring nodes. In this case, the attacker creates a simulated environment around a node in order to manipulate it and force it to validate illegitimate transactions. 3.2 User Portfolio Attacks Phishing Phishing is the practice of tricking users into disclosing their private keys or personal information. To gain trust, the attacker gives the impression that he represents a legitimate organization. The attacker then uses the victims’ information to steal their funds. Dictionary Attacks A dictionary attack employs a predetermined set of words. Each word on the list has been hashed. If the hash output of the dictionary attack matches the hash of the password, the attacker has successfully identified the original password. Vulnerable Signatures Digital signatures are becoming more powerful. However, attackers have discovered loopholes to exploit. Attackers can sign messages and documents with users’ private keys. Alternatively, they can present users with messages and documents that cause the signer to see things other than what he or she intended to sign. 3.3 Smart Contract Attacks Smart contracts [15] are computer programs that are stored on the blockchain. The goal of these programs is to automate the agreement so that all parties can be confident in the outcome without the need for a third party. Known flaws include: • reentrancy; • timestamp dependence • ERC20 API violation
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• • • •
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style guide violation unchecked external call – unchecked math compile version not fixed uninitialized state/storage/local variables
3.4 Attacks on the Transaction Verification Mechanism This type of attack takes advantage of the fact that transactions cannot be validated unless all network nodes agree. However, the network’s verification of these transactions takes time, giving attackers the opportunity to carry out security attacks [3] such as double spending, 51% or majority attacks….
4 ECDSA Failure As the standard for digital signature systems, the Elliptic Curve Digital Signature Algorithm (ECDSA) is the most widely used algorithm in blockchain protocols. Asymmetric cryptography is used in this algorithm [16]. Its basic idea is to generate two keys: public and private. ECDSA is founded on a mathematical problem known as “discrete logarithm”. to solve it, it is necessary to calculate n knowing a, b and c and knowing that a = bn modc
(1)
This algorithm’s security is poor, but it has been known to have some security flaws, including a vulnerability similar to that of the PlayStation 3. As previously stated, the randomly generated number represents the private key and can only be generated once. The mistake made in the Anyswap hack in July 2021 was that the same value was used to generate multiple digital signatures. As a result, the attackers were able to calculate the private key and thus sign any transaction.
5 Comparison of Security Attacks Against Crypto-Currencies We have provided a detailed study of two parts in previous parts of this paper: the first part concerns the infrastructure of the blockchain, and the second part concerns the various security flaws of this technology and the types of attacks that the blockchain can suffer. In this table, we present six crypto-currencies along with their consensus protocol, hash algorithm, security mechanism, and recent security attacks to identify the most common type of attack. The growth of the crypto-currency market has prompted innovators to create new crypto-currencies with characteristics distinct from Bitcoin. Table 1 examines the properties of the six crypto-currencies in terms of consensus mechanism, hashing algorithm, security mechanism, and major security attacks detected between 2017 and 2022. The main difference, according to this table, is in the consensus mechanism and the encryption algorithm. This algorithm has an impact on the process of creating new coins. Bitcoin, Ripple, and Solana use the SHA-256 algorithm for encryption, whereas
proof-of-stake
XRP ledger
proof-of-stake
Fast Probabilistic Consensus Winternitz
proof-of-history
Ethereum
Ripple
Cardano
Iota
Solana
Security Attacks
Keccak-256
• In August 2020, an attack occurred against Euthereum when a miner used old software and lost internet access for a while during a mining operation. a reorganization occurred when two versions of the blockchain competed for validity and resulted in an insertion of about 3000 blocks • In the first two quarters of 2022, 1.9 billion cryptocurrencies were stolen up 60 via a sharkbot • Between January 2022 and March 2022, the DEFi platform suffered a security attack by exploiting a signature verification vulnerability. The attackers stole about 320 dollars million according to the FBI report. According to the FBI, during 2022, the DEFI platform lost 780 million dollars to Flash loans
SHA-256
Black2b/ed25519
SHA-256
None
None
• solana suffered a ddos attack in 14 September 2021 [18] • solana also suffered another attack in August 2022, exploiting vulnerabilities in digital portfolios on the network
Iota suffered a 51 percent attack on its network
According to Atlas VPN, Cardano ranks as the 3rd most targeted crypto-currency project over the past three months. The protocol has suffered 191 phishing attacks since late March
First half of SHA256 SHA256, RIPEMD160 In June 2019, an attack against gatehub resulted in unauthorized access to dozens of XRP wallets and the theft of cryptographic assets
Keccak-256
proof of work (PoW) [17]
Bitcoin
SHA256, RIPEMD160 • Bitfinix suffered another DDos attack in 2017 • February 2020, Bitfinex also suffered a DDOS attack
Encryption algorithm Security Mechanism
SHA256
Crypto-currency Consensus Protocol
Table 1. Comparison of security attacks against crypto-currencies.
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Ethereum Network, Cardano, and IOTA use Keccak-256, Black2b/ed25519, and Winternitz, respectively. Cybercriminals continue to aggressively exploit blockchain technology and launch scams. In the third quarter of 2022, hackers stole approximately $483 million [17]: Etheruem: 11 attacks with a loss of $348 Million Ecosystem Polkadot: 2 attacks with a loss of 52 Million Dollars Ecosystem Binance smart chain: 13 attacks with a loss of 28 Million Dollars Ecosystem Solana: 3 attacks with a loss of 6 Million Dollars NFT: 7 attacks with a loss of 4 Million Dollars
6 Analysis The security of blockchain technology is the subject of this paper. As we can see, blockchain technology has some security issues. It is clear that hackers have been able to exploit all types of flaws to organize a security attack. These security issues have an impact on transactions as well. This paper provides a detailed examination of the various components of blockchain technology as well as the various flaws in the infrastructure. It then uses a comparative table to present a brief comparison of six types of cryptocurrencies in terms of their structure and the various attacks and financial damages. This paper presents statistics on security attacks and the types of attacks used on the world’s six most popular crypto currencies. Researchers can use this paper to learn about the most common types of attacks over the last two years and begin looking for ways to improve the blockchain infrastructure.
7 Conclusion This paper presented a bibliometric analysis to better understand blockchain components and allowed researchers to delve into the world of security attacks and vulnerabilities in blockchain infrastructure. The paper demonstrates, through a comparative study of various security attacks against crypto currencies, that more efforts are required to improve blockchain infrastructure and protect it from cyber-criminals now and from quantum security attacks in the future.
References 1. Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008). https://bitcoin.org/bit coin.pdf 2. Nofer, M., Gomber, P., Hinz, O., Schiereck, D.: Blockchain. Bus. Inform. Syst. Eng. 59(3), 183–187 (2017). https://doi.org/10.1007/s12599-017-0467-3. Accessed 6 Dec 2022 3. Gervais, A., Karame, G.O., Capkun, V., Capkun, S.: Is bitcoin a decentralized currency? IEEE 4. Miyaji, A.: Elliptic curves suitable for cryptosystems. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 77, 98–105 (1994) 5. Wang, H.Q., Wu, T.: Cryptography in Blockchain. J. Nanjing Univ. Posts Telecommun. 37, 61–67 (2017)
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6. Courtois, N.T., Bahack, L.: On subversive miner strategies and block withholding attack in bitcoin digital currency. CoRR, abs/1402.1718 (2014) 7. Puthal, D., Malik, N., Mohanty, S., Kougianos, E., Yang, C.: The Blockchain as a Decentralized Security Framework [Future Directions]. IEEE Consum. Electron. Magaz. 7(2), 18–21 (2018). https://doi.org/10.1109/mce.2017.2776459. Accessed 9 Dec 2022 8. Team, C.: Defi hacks are stealing more crypto than ever before. In: Chainalysis (2022). https:// blog.chainalysis.com/reports/2022-defi-hacks/. Accessed 10 Oct 2022 9. Zhai, S.P., Li, Z.Z.: The data blockchain of the key technologies Consistency. Comput. Technol. Develop. 8, 1–6 (2018) 10. He, P., Yu, G., Zhang, Y.F.: Prospective review of blockchain technology and application. Comput. Sci. 44, 1–7 (2017) 11. Zyskind, G., Nathan, O., Pentland, A.: Decentralizing decentra: Using Blockchain to Protect Personal Data - IEEE Conference Publication, Ieeexplore.ieee.org (2015). https://ieeexplore. ieee.org/document/7163223/. Accessed 04 Dec 2022 12. Zheng, Z., et al.: An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData Congress). IEEE (2017) 13. Alachkar, K., Gaastra, D.: Blockchain-based Sybil Attack Mitigation: A Case Study of the I2PNetwork (2018) 14. Heilman, E., Kendler, A., Zohar, A., Goldberg, S.: Eclipse attacks on bitcoin’s peer-to-peer network (2015) 15. Zhu, Y., Gan, G.H., Deng, D.: Security research in key technologies of Blockchain. Inform. Secur. Res. 12, 1090–1097 (2016) 16. Al-Zubaidie, M., Zhang, Z., Zhang, J.: Efficient and Secure ECDSA Algorithm and its Applications: A Survey (2019) 17. Cyber criminals increasingly exploit vulnerabilities in decentralized finance platforms to obtain cryptocurrency, causing investors to Lose Money. https://www.ic3.gov/Media/Y2022/ PSA220829. Accessed 10 Oct 2022 18. Network Outage Initial Overview. https://solana.com/news/9-14-network-outage-initial-ove rview. Accessed 10 Oct 2022
Classification of Coordinators’ Limitations in Cyber-Physical Production System Management Abdelaziz Ouazzani-Chahidi1,3(B) , Jose-Fernando Jimenez2 , Lamia Berrah1 , and Abdellatif Loukili3 1 LISTIC Laboratory, Université Savoie Mont Blanc., Annecy, France {abdelaziz.ouazzani-chahidi,lamia.berrah}@univ-smb.fr 2 SYMME Laboratory, Université Savoie Mont Blanc., Annecy, France [email protected] 3 Industrial Technologies and Services Laboratory, Sidi Mohamed Ben Abdallah University, Fez, Morocco [email protected]
Abstract. A Cyber-Physical Production System (CPPS) is represented by the interconnection between two kinds of systems: the first is virtual, which has increased computational capacities, and the second is physical, represented by a production workshop. In particular, this interconnection is intended to guarantee real-time monitoring of the execution of production operations, providing coordinators with relevant information to enable them to control the system and achieve the planned performance. However, the distributed architecture of the system and the massive data from each physical entity create certain limitations for the coordinators. This is a limitation in terms of medium and long-term perspective, mainly linked to the local decision-making action taken by the coordinator, which affects the performance of the CPPS. To help the coordinator in his decisions, it is first necessary to understand his limitations and incapacities vis-à-vis the CPPS system. The objective of this paper is to classify the limitations in terms of understanding the situation and the behaviors needed by the coordinators to deal with a dysfunctional situation that requires intervention. Keywords: Coordinators · limitations · Cyber-physical systems · industrial performance · performance management systems
1 Introduction Nowadays, the manufacturing industry is faced with challenging such as high product customization, increased complexity of technologies used and momentary life cycles. In response to these challenges, the fourth industrial revolution, also known as Industry 4.0, erupted in 2012 [2]. A major change in Industry 4.0 is the convergence of the physical and virtual worlds, due in part to cyber-physical systems (CPS) [3]. A specific application of CPS in a production environment, known as Cyber-Physical Production © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 200–209, 2023. https://doi.org/10.1007/978-3-031-29860-8_21
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Systems (CPPS). A CPPS consists of a set of computing devices communicating with each other and interacting with the physical world, which represents the production floor, via sensors and actuators in a feedback loop [4]. In these systems, there are implicit feedback connections where the computer and network devices directly affect the physical system [5]. Traditionally, the control of industrial production systems has been achieved using dedicated electronic systems, with analog process variables and control signals communicated via twisted pair cables. As automation functions evolve, so do communication architectures. With the advent of the Internet and cloud computing, the Internet Protocol has been used for more advanced data collection and control applications [6]. These technologies, among others, have expanded the concepts used in factories and addressed a new concept that is the “smart factory” [7]. The smart factory is a term used to describe the new industrial environment, combining recent technological advances and design principles in computing, storage and communication (such as interoperability, virtualization, decentralized control, distributed control), real-time processing and communication, service-oriented architecture, predictive maintenance, and modular design [8–10]. In the context of Industry 4.0, the Industrial Internet of Things (IIoT) and CPPS are expected to bring enormous benefits in terms of efficiency, reliability, and productivity in smart factories [11]. The advent of CPPS offers new ways to compete with evolving and unpredictable market demands. However, due to transformation barriers, the widespread implementation of CPPS in industrial practice is still in its infancy [5]. In fact, when CPPS is implemented in a plant, old systems are not easily replaced by new ones because they are expensive to acquire and operate [12]. As a result, CPPS adaptation is not yet triggered for most industrial plants, which provides an opportunity for researchers to propose models and new architectures to facilitate the operation of CPPSs [2]. In a CPPS, we can encounter several problems, concerning firstly the architecture of the system, in fact, the CPPS is considered as a distributed system, in which the decisionmaking is difficult. Furthermore, the data from the physical system is of a heterogeneous nature, in large quantities and changing quickly, making the selection of data to be presented to the coordinator complicated. [14]. In addition, several places can be found to affect the CPPS, we talk about how the coordinator can launch instructions to modify the system’s behavior [15]. Talking about instructions, several types of instructions can be launched, such as coercive, restrictive, directive, permissive, and influential. The instructions necessarily depend on the roles of decision makers within the manufacturing management [16]. So, a CPPS is represented by an interconnection between a cybersystem and a physical production system. Moreover, this interconnection ensures realtime monitoring of production execution for intelligent decision-making, thus improving industrial performance [17]. To help the coordinator in his decisions, it is first necessary to know his limitations and incapacities vis-à-vis the CPPS system. Therefore, the objective of this paper is to classify the limitations in terms of understanding the situation and the behavior of coordinators to deal with a dysfunctional situation that requires intervention. This paper is organized as follows. Section 2 introduces the role of the coordinators as main actors in the management of the CPPS and in decision-making in the event of dysfunction. Section 3 introduces different architectures of a CPPS and different locations to integrate a PMS to evaluate industrial performance. Section 4 presents some
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coordinators’ limitations in understanding and behaviors to react with the system. Finally, Sect. 5 states the preliminary conclusions and the future work to follow this research.
2 The Role of the Coordinator in CPPS Management The coordinator has a key role in steering the cyber-physical production system [16]. He has interesting managerial skills in collaboration with the digital coordination system; the latter provides the coordinator with the data from the cyber system after processing. To help the coordinator in his decisions, it is first necessary to know his limitations and incapacities vis-à-vis the CPPS system. Nevertheless, the data generated by the system are massive and come from a heterogeneous nature, which makes the task of the coordinator complicated. The coordinator does not have enough time to process and analyze all the information and the behavior of the system, which can cause decision delays or even a lack of reaction [18]. In the literature, this inability of the coordinator to comprehend big data is related to certain limitations in understanding the situation and behaving in a way that can effectively deal with the situation in real time. A CPPS is therefore represented by an interconnection between a cyber-system and a physical production system. Moreover, this interconnection ensures real-time monitoring of production execution, for intelligent decision-making, thus improving industrial performance [17]. In traditional production systems, performance is managed by indicator systems displayed in a Performance Management System (PMS), and driven by coordinators. The PMS can be defined as the set of procedures and indicators that make it possible to precisely and continuously measure the performance of a company. According to [19] PMS is the set of structured measures used to quantify the efficiency and effectiveness of actions. In fact, the exploitation of the PMS in a CPPS is limited for several reasons. First of all, the unsuitable periodicity of the calculation of the indicators with the imposed notion of real time. Secondly, the limits of the human capacity for real time decision making in front of massive and incoherent data [20], and finally, the need for a fast analysis and decision making in dysfunctional situations. Especially in semi-automated CPPS, where human intervention is necessary, the limitations of coordinator are widespread [21]. Indeed, human decision making is not synchronized with the execution of the production system, which can generate delays or decisions that are not consistent with the achievement of the objectives.
3 Approaches of PMS Integration: Characteristics Several architectures of a CPPS have been proposed in the literature, explaining how this type of system works [3, 11]. The design considered in this paper, as presented in (Fig. 1), is based on structural considerations, given that the cyber-physical system of production as the controlled system, and the coordination system as the control system, integrating the intervention of the human being in the management and decision-making process. A cyber-physical production system (CPPS) constitutes the controlled system, it is composed of a physical level, a cyber-level, and a digital coordination interface. The physical system can communicate and work with the cyber system without or with
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human mediation by means of digital coordination interface, considered as a system of virtual objects that represents automatic processes in production execution (planning, scheduling, etc.). The physical world refers to objects, called physical entities, such as operators, machines, products, activities or the environment, which need to be monitored and controlled in the real world. The communication between the physical level and the cyber level takes place via sensors and actuators. Sensors collect real-time data from the physical world and automatically transmit it to the cyber-system while actuators act on the physical world based on outputs from the cyber-system. The cybernetic element of CPS provides real-time functions such as task analysis and planning, data processing and storage, and decision making. In the cyber level, we find virtual objects, considered as a digital twin, which is a virtual model designed to accurately reflect a physical object [24]. The coordination system is made up of one or more coordinators, who are responsible for monitoring the production system remotely, based on the data displayed in the digital coordination level. The latter is considered a bridge between the system control and the controlled system. The coordinators make decisions based on the data provided and issue instructions to deal with situations of anomalies or malfunctions. There are several types of instructions. Therefore, we can classify the coordinators’ limitations into two categories (Fig. 1): those related to the coordinators’ understanding and those related to their behaviors. For the first category, the coordinators should analyze and understand the situation based on the data generated by the system. This requires them to have a good understanding of the data and the system, as well as the ability to interpret the data correctly. For the second category, the limitations are related to the coordinators’ behaviors. This includes their ability to make decisions based on the data, their ability to communicate effectively with other coordinators, and their ability to manage the system effectively. To manage performance in CPPS, we have proposed in our previous work different locations to integrate PMS based mechanism [23]. It is identified 5 types of inclusion derived from the structure and behaviour from the CPPS. Although, the types of a PMSbased mechanism are 1) Manual performance management, 2) Computer system-aided performance management, 3) coordination system performance management, 4) distributed system performance management, and 5) coordination-distributed performance management. Figure 1 shows the localization of each type of mechanism. 1) Type 1 - Manual performance management: the first model is manufacturing management reference model, the coordinator acts directly on the physical system, there is no cyber intervention. We find a complete control of manufacturing process and actions from the performance team. 2) Type 2 - Computer system-aided: the digital coordination system supports the coordinator in his decisions, it can propose recommendations, alternatives to help the coordinator in his decision-making. The coordinator makes the ultimate decision. 3) Type 3 - Coordinated management: Human intervention is not always necessary, as the cyber system can do almost all the tasks and provide coordinators with useful information for decisions. 4) Type 4 - Distributed management: We find virtual entities with shared and autonomous goals that allow for fully local objectives, looking for reactive behaviour.
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Fig. 1. Reference framework of a CPPS [24]
5) Type 5 - coordination-distributed management: Decision-making is shared between the cyber system and the coordinator; system management is automatic and includes management of global and local performance of the system and virtual entities.
4 Approaches Comparison: Coordinators and System Limitation Coordinators’ limitations can be related to several activities; first, the interaction of the coordinator with the system. The coordinator can declare objectives, launch action plans, and give instructions. However, due to lack of time to process and analyze the information and the behavior of the system, the coordinator is unable to interact with the system effectively. There are multiple places that can affect the controlled system, and locations to launch instructions to drive the system [4]. In addition, there are several types of instructions, such as coercive, limiting, directive, permissive, influencing. The instructions depend on the roles of decision makers within manufacturing management. And the interaction can be instructions, policies, strategies, standards, priority rules. Second, the interaction of the system with the coordinator, the digital coordination system provides the coordinator with massive, heterogeneous data in real time, which can quickly change depending on the manufacturing execution, causing some limitations for the coordinator [28]. As well as the selection of data to present to the coordination system being a delicate task (immediate, prediction, grouping, aggregated, etc.), the
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temporality of updating the data (long/medium/short/real time, etc.) and the complexity of the interface (from a dashboard of raw data to a dashboard of complex and constructed data) must also be taken into consideration [29]. Figure 2 shows the limitations of the coordinator and the system, which can cause limitations in their interaction and impact the performance of the system.
Fig. 2. Coordinators and system limitations
In the (Table 1), we present the set of limitations in each category and a comparison of the five possible PMS functioning in a CPPS, starting with the understanding of coordinators. We can find limitations such as knowledge, cognition, intelligence, and so on. For the behavior of the coordinators, we speak of limitations such as Definition of objectives, Analysis of the situation, Adaptability in situation, and many others. Additionally, we can also consider limitations such as communication skills, problem-solving abilities, and decision-making capabilities.
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A. Ouazzani-Chahidi et al. Table 1. Classification of coordinators’ limitations Table 1: classification of coordinators’ limitations Models Traditional Performance management
Computer-Sy stem aided Performance Management
Type 1
Limitations
Coordinated Sy stem Performance Management
Type 2
Distributed sy stem Performance Management
Type 3
Coordination distributed Performance management Type 5
Type 4
Knowledge [30]
Standard technical knowledge of coordinators.
Technical parameters configured in the information sy stems
Knowledge is shared between the coordinator and the sy stem
Parameterized knowledge of the sy stem
Cognition [31]
Normal abilities to interact with the environment. Direct interaction
The coordinator has the ability to interact with the phy sical sy stem via the computer sy stem, or directly.
The capacities of the coordinators to interact with the sy stem must be increased, the coordinator must always monitor the sy stem to validate the proposals
The coordinators cannot follow the sy stem and interact with it
Confusion between situations [32]
Lack of communication of information between entities
The sy stem just display s the data and it is up to the coordinators to understand the situation
Sy stem recommendations are clear, and are based on logical calculations
Omission or concealment of information [33]
Performance indicators display ed in the classic dashboards
Data deduced from the computer sy stem, display ed in spreadsheets
The indicators are calculated automatically and displayed in digital dashboards
Clear, non-complex information
The data is massive and it is up to the coordinators to seek the information
The display of data is done according to the needs of the coordinators and according to each situation
Problem not too complicated but it is global, it concerns an entity
Detected issues relate to multiple entities
The sy stem offers simplifications of the problems, which are not alway s reliable, the coordinator must check
The sy stem can be lost in situations never seen before
The coordinators and the sy stem can find themselves lost in situations never seen before
Traditional calculation Logical, not many sensors
Sensors installed throughout the sy stem
Uses of smart and interconnected sensors
Uses of smart and interconnected sensors
Uses of smart and interconnected sensors
Reasoning
The coordinator takes his time in reasoning Delay in decision
The coordinators take their time to understand the situation and interact delay in the decision
The coordinator must react in real time with the sy stem, he does not have enough time to reason
The coordinator is content with monitoring
The coordinator must reason and make decisions in real time
Intelligence [36]
Normal human intelligence
Normal human intelligence
The sy stem is equipped with augmented artificial intelligence
The sy stem is equipped with augmented artificial intelligence
Interpretations of data [37]
Simplified, no complex data, reduced data
Simplified, no complex massive data, difficult to deal with by the coordinator
The sy stem is responsible for analy zing and interpreting the data
The sy stem is responsible for analy zing and interpreting the data
Consistency of information with coordinators [38]
Centralized decision sy stem, Coordinators have time to communicate with each other before reacting
Centralized decision sy stem, Coordinators have time to communicate with each other via the sy stem before
Semi-centralized decision sy stem, each coordinator reacts according to his vision
Distributed decision sy stem, each coordinator reacts according to his vision
Semi-centralized decision sy stem, each coordinator reacts according to his vision
Roles are well defined
Roles are well defined
There may be confusion in the roles of the coordinators
There may be confusion in the roles of the coordinators
There may be confusion in the roles of the coordinators
Global objectives
Global objectives
Global and local objectives
Global objectives
Global objectives
By traditional tools
Uses of standardized tools
Use of computer system
Use of computer system
Use of the computer sy stem
Weak
Weak
Mean
Augmented
Augmented
Error rate can be increased
Error rate can be increased
The error rate is low
Decentralization of decision-making [26]
Decision must follow a hierarchical order
Reliable decisions based on exact analy zes Decision must follow a distributed order
Consistency of decisions [41]
Coordinators have time to rectify their decisions
Decision must follow a hierarchical order Coordinators have time to rectify their decisions and influence the sy stem Possible, the data is real time
Reliable decisions based on accurate analy zes Decision must follow a distributed order
Coordinators do not have time to rectify their decisions
Coordinators do not have time to rectify their decisions
Coordinators do not have time to rectify their decisions
Possible, the data is real time
Possible, the data is real time
Possible, the data is real time
Decry ption of information [15] Understanding
Understanding of the issue [34]
Measurement [35]
Role of coordinators [39]
Behaviours
Definition of objectives [40] Analy sis of the situation [32] Adaptability in situation [7] Reliability of action [36]
Real-time interaction [42]
Impossible, no real-time data
Decision must follow a semihierarchical order
The sy stem has no conscience to understand the situation, it only relies on the data it has received Artificial intelligence used to calculate performance indicators and determine the periodicity of the dashboard according to the situation The data comes from a heterogeneous nature, which makes understanding difficult
Knowledge is shared between the coordinator and the sy stem The capacities of the coordinators to interact with the sy stem must be increased, the coordinator can rely on the sy stems in certain decisions The sy stem has no awareness to understand the situation, the coordinator must pilot the sy stem Artificial intelligence used to produce indicators and prepare the coordinators for possible situations in the near future Data is real-time and changes quickly
The sy stem is equipped with an augmented artificial intelligence, moreover the intelligence of the coordinator The sy stem is responsible for making analy zes and the coordinators must interpret the data
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5 Conclusion Considering that a CPPS is composed of a digital autonomous part and another which requires human intervention for monitoring and validation, the characteristics of a CPPS require processing massive data and reacting in real-time depending on the anomalies detected, thanks to its high computational capacities. However, the aptitude of the coordinators is very limited in this regard, and they cannot analyze the situation and initiate corrective actions in the time. In this way, the coordinators are unable to interact with the CPPS. To address this issue, it is necessary to develop collaboration protocols with coordinators for distributed approaches, in order to mitigate the impact of limitations on industrial performance. To achieve efficiency in performance management, a coupled coordinator-system function must be developed to obtain optimal efficiency in production management. This function should be based on a Performance Management System (PMS) that is either fully automated or semi-automated (Type 2 or Type 3). Additionally, distributed approaches (Type 4) should be considered to contribute to system responsiveness and adaptability for productivity, as well as performance. However, distributed performance management will not necessarily contribute to overall performance, so it is important to develop collaboration protocols with coordinators to ensure synergy with the autonomous intelligent production systems. In the future, a coupled centralized and distributed approach to performance management (Type 5) may be necessary. This approach is still under development and its characteristics and operation are unclear. However, it will be an appropriate contribution to propose a mechanism based on PMS for this type of architectures. To ensure the effectiveness of the PMS-based mechanism, it is important to consider the specific needs of the coordinators and the CPPS. This includes identifying the general steps of a performance management system that should be allocated to facilitate the tasks of the coordinators, and allocating these features to the PMS-based mechanism. Additionally, it is recommended to compare the different types of performance management approaches in a real industrial environment, to determine which is the most optimal to drive CPPS performance, and help to reduce the coordinators’ limitations. By doing so, the CPPS will be able to interact with the coordinators in a more efficient manner, and the overall performance of the system will be improved.
References 1. Antons, O., Arlinghaus, J.C.: Distributed control for Industry 4.0 - a comparative simulation study. IFAC-Paper, vol. 54, no 1, pp. 516–521 (2021) 2. Almada-Lobo, F.: The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). J. Innov. Manag. 3(4), 16–21 (2016) 3. Cui, X.: Cyber-Physical System (CPS) architecture for real-time water sustainability management in manufacturing industry. Procedia CIRP 99, 543–548 (2021) 4. Ribeiro, L., Bjorkman, M.: Transitioning from standard automation solutions to cyberphysical production systems: an assessment of critical conceptual and technical challenges. IEEE Syst. J. 12(4), 3816–3827 (2018) 5. Trappey, A.J.C., Trappey, C.V., Govindarajan, U.H., Sun, J.J., Chuang, A.C.: A review of technology standards and patent portfolios for enabling cyber-physical systems in advanced manufacturing. IEEE Access 4, 7356–7382 (2016)
208
A. Ouazzani-Chahidi et al.
6. Dalenogare, L.S., Benitez, G.B., Ayala, N.F., Frank, A.G.: The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 204, 383–394 (2018) 7. Antons, O., Arlinghaus, J.C.: Adaptive self-learning distributed and centralized control approaches for smart factories. Procedia CIRP 104, 1577–1582 (2021) 8. Büchi, G., Cugno, M., Castagnoli, R.: Smart factory performance and Industry 4.0. Technol. Forecast. Soc. Change 150, 119790 (2020) 9. Osterrieder, P., Budde, L., Friedli, T.: The smart factory as a key construct of industry 4.0: a systematic literature review. Int. J. Prod. Econ. 221, 107476 (2020) 10. Elvis, H.: Smart factory for industry 4.0: a review. Int. J. Mod. Manuf. Technol. (2015) 11. Darwish, L.R., El-Wakad, M.T., Farag, M.M.: Towards sustainable industry 4.0: a green realtime IIoT multitask scheduling architecture for distributed 3D printing services. J. Manuf. Syst. 61, 196–209 (2021). https://doi.org/10.1016/j.jmsy.2021.09.004 12. Yang, Y.: MES technology analysis and optimization for electronic manufacturing industry. In: présenté à International Conference on Robots & Intelligent System (ICRIS) (2017) 13. Kim, T.H., Jeong, J., Kim, Y.: A conceptual model of smart manufacturing execution system for rolling stock manufacturer. Procedia Comput. Sci. 151, 600–606 (2019) 14. Antons, O.: Data-driven and autonomous manufacturing control in cyber-physical production systems. Comput. Ind. 9 (2022) 15. Zimmermann, E.: Multicriteria decision-making method for scheduling problem based on smart batches and their quality prediction capability. Comput. Ind. 12 (2021) 16. Antons, O., Arlinghaus, J.C.: Designing decision-making authorities for smart factories. Procedia CIRP 93, 316–322 (2020). https://doi.org/10.1016/j.procir.2020.04.047 17. Ante, G., Facchini, F., Mossa, G., Digiesi, S.: Developing a key performance indicators tree for lean and smart production systems. IFAC-Paper, vol. 51, no 11, pp. 13–18 (2018) 18. Murmura, F., Bravi, L., Santos, G.: Sustainable process and product innovation in the eyewear sector: the role of industry 4.0 enabling technologies. Sustainability 13(1), 365 (2021) 19. Neely, A., Adams, C., Crowe, P.: The performance prism in practice Meas. Bus. Excell. 5(2), 6–13 (2001) 20. João, M.R.: The Human in the loop in Cyber-Physical Systems: the case of Building Automation, p. 126 (2019) 21. Fuso Nerini, F., Keppo, I., Strachan, N.: Myopic decision making in energy system decarbonisation pathways. A UK case study. Energy Strategy Rev. 17, 19–26 (2017) 22. Mörth, O., Emmanouilidis, C., Hafner, N., Schadler, M.: Cyber-physical systems for performance monitoring in production intralogistics Comput. Ind. Eng. 142, 106333 (2020) 23. Ouazzani-Chahidi, A., Jimenez, J.-F., Berrah, L., Loukili, A.: Towards an inclusion of a PMS-based mechanism for Cyber-Physical Production Systems, p. 8 24. Lee, J., Bagheri, B., Kao, H.-A.: A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015) 25. Wassim, B., Sallez, Y., Nassima, A., Beldjilali, B.: A model for manufacturing scheduling optimization using learning intelligent products, p. 9 (2015) 26. Wagner, T., Hausner, Elger, J., Lowen, U., Luder, A.: Engineering Processes for Decentralized Factory Automation Systems. In Factory Automation. J. Silvestre-Blanes 27. Mezgebe, T.T., Bril El Haouzi, H., Demesure, G., Thomas, A.: A Negotiation-based control approach for disturbed industrial context. IFAC-Paper, vol. 51, no 11, pp. 1255–1260 (2018) 28. Zambrano, G., Pach, C., Adam, E., Berger, T., Trentesaux, D.: Myopic Behavior in Heterarchical Control of FMS, p. 11 (2012) 29. Souifi, A., Boulanger, Z.C., Zolghadri, M., Barkallah, M., Haddar, M.: Uncertainty of key performance indicators for Industry 4.0: A methodology based on the theory of belief functions. Comput. Ind. 140, 103666 (2022) 30. Rutkowska, M., Sulich, A.: Green Jobs on the background of Industry 4.0. Procedia Comput. Sci. 176, 1231–1240 (2020)
Classification of Coordinators’ Limitations in Cyber-Physical Production
209
31. Janikova, D., Bezak, P.: Prediction of production line performance using neural networks. In: 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) , p. 1–5. Lodz (2016) 32. Kang, N., Zhao, C., Li, J., Horst, J.A.: Analysis of key operation performance data in manufacturing systems. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2767–2770. Santa Clara, CA, USA (2015) 33. Gawankar, S.A., Gunasekaran, A., Kamble, S.: A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context. Int. J. Prod. Res. 58(5), 1574–1593 (2020) 34. Bouazza, W., Sallez, Y., Beldjilali, B.: A distributed approach solving partially flexible jobshop scheduling problem with a Q-learning effect. IFAC-Paper, vol. 50, no 1, pp. 15890–15895 (2017) 35. Mezgebe, T.T., Demesure, G., Bril El Haouzi, H., Pannequin, R., Thomas, A.: CoMM: a consensus algorithm for multi-agent-based manufacturing system to deal with perturbation. Int. J. Adv. Manuf. Technol. 105(9), 3911–3926 (2019) 36. Bhatia, M., Ahanger, T.A.: Intelligent decision-making in Smart Food Industry: Quality perspective. Pervasive Mob. Comput. 72, 101304 (2021) 37. Apiletti, D., et al.: iSTEP, an Integrated Self-Tuning Engine for Predictive Maintenance in Industry 4.0. In: 2018 IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), Melbourne, Australia (2018) 38. Sardi, A., Sorano, E., Cantino, V, Garengo, P.: Big data and performance measurement research: trends, evolution and future opportunities. Meas. Bus. Excell. vol. ahead-of-print, no ahead-of-print (2020). https://doi.org/10.1108/MBE-06-2019-0053 39. Gallo, T., Santolamazza, A.: Industry 4.0 and human factor: How is technology changing the role of the maintenance operator?. Procedia Comput. Sci. 180, 388–393 (2021) 40. Osak-Sidoruk, M., Gola, A.: A method for modelling the flow of objects to be machined in FMS using enterprise dynamics, p. 11 41. Shah, L.A., Etienne, A., Siadat, A., Vernadat, F.B.: Performance visualization in industrial systems for informed decision making. IFAC-Paper, vol. 51, no 11, pp. 552–557 (2018) 42. Leitão, P., Alves, J., Pereira, A.I.: Solving myopia in real-time decision-making using petri nets models’ knowledge for service-oriented manufacturing systems. IFAC Proc. 43(4), 144–149 (2013)
Cryptography in Distributed Ledger Technologies from a Layered Perspective: A State of the Art Sara Barj(B)
, Aafaf Ouaddah , and Abdellatif Mezrioui
National Institute of Posts and Telecommunications, Rabat, Morocco [email protected]
Abstract. Cryptography is the practice of secure communication to protect data’s confidentiality, integrity, and authenticity. In the context of blockchains, cryptography is used to secure transactions, protect users’ privacy, and ensure the integrity and security of the network. Nevertheless, this later is subject to many attacks, such as post-quantum attacks. In this paper, we survey and extract how cryptography is used in blockchains from a layered perspective: data, Consensus, and Execution to Application layers. We summarize our findings and results using a table containing the functions of cryptographic protocols, families, functions, primitives, and schemes and how they are utilized for each studied blockchain platform. We discuss each cryptographic technique’s related DCEA layer in the forecited table. Therefore, this work helps assess current cryptographic techniques used in current Distributed Ledger Technologies or design and develop new hardened ones by choosing post-quantum-related cryptographic techniques for each layer. Keywords: Cryptographic Protocols · Privacy · Data Security · Distributed Computing · Blockchain · Design Improvement
1 Introduction Blockchain relies on cryptography to ensure a high level of security and integrity of the network, enabling a reliable and verifiable execution of transactions and other types of data. It is a Distributed Ledger Technology (DLT). The DCEA reference model could help to design such systems [1]. On the one hand, blockchain technologies can be adopted in many domains, such as e-learning, e-health, access control, finance, and logistics [2–6]. Its adoption in these domains is for multiple reasons, mainly because of its immutability property. On the other hand, cryptography, the critical component of blockchain systems and DLTS, includes techniques such as hashes, which could be used for data storage and integrity purposes, and Secret Sharing Algorithms, which could be used to deliver strong anonymity and privacy-preserving [7, 8]. Nevertheless, many blockchain platforms struggle to handle cryptographic attacks, such as post-quantum attacks. One of many post-quantum proposed solutions is to use lattice-based cryptographic techniques that are the application of the Learning With Errors (LWE) hard problem [9, 10]. Many © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 210–220, 2023. https://doi.org/10.1007/978-3-031-29860-8_22
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cryptographic techniques verify the precited requirements, for example, the implementations proposed by these papers [11–14]. The problem is the applicability of lattice-based cryptography in Distributed Ledger Technologies. And, if it is applicable, the arising problem is how it is applied and in which DCEA Layer [1] they could be employed. In this direction, it is still essential to understand how cryptography is used in blockchain platforms to achieve the goal of resistance to post-quantum attacks using lattice-based cryptography. Besides, it is crucial to know the layers and the functions concerned by cryptographic protocols, schemes, families, functions, and primitives. This work aims to help blockchain designers accomplish the forecited objective by analyzing the utilization of cryptography within the DCEA Layers. The rest of the paper is structured as follows: Sect. 2 describes the DCEA Framework. Section 3 analyzes and discusses the finding related to the usage of cryptography in some blockchain platforms, throughout the DCEA layers. Finally, Sect. 4 concludes the paper.
2 Materials and Methods: The DCEA Framework This section introduces and explains the DCEA framework, a reference model related to DLTS [1]. Four layers compose this framework. These layers are the data layer, the consensus layer, the execution layer, and the application layer. In this direction, we use the forecited reference model to analyze the cryptographic protocols, primitives, and schemes used in the Blockchain from a layered perspective. 2.1 Data Layer In distributed ledger technology (DLT), a data layer is the system’s layer responsible for structuring (chain of blocks or DAGs), storing (on-chain or off-chain), and managing the data or transactions recorded on the ledger. And cryptography is used to ensure the security and integrity of this data. In DLTs, transactions are recorded and stored in blocks linked together in a chain. Each block contains a cryptographic hash of the previous block and the transaction data. This configuration creates a tamper-evident record of all transactions, as any attempt to modify a block would result in a different hash being generated, which would be detected by the network. 2.2 Consensus Layer The consensus layer is usually implemented using a consensus mechanism, a set of rules or protocols that all participants in the DLT network must follow to reach a consensus on the state of the ledger. The consensus mechanism is designed to ensure that all participants agree on the ledger’s contents and that the ledger is accurate, reliable, and tamper-evident. The consensus layer is an important part of a DLT system, as it ensures that all participants in the network can trust the contents of the ledger and can rely on the system’s integrity. It also plays a critical role in ensuring the security and reliability of the DLT system, as it ensures that the ledger is protected against tampering and other types of attacks.
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2.3 Execution Layer The execution layer is the system’s layer responsible for executing and processing transactions and other operations recorded on the ledger. The execution layer is typically implemented using smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. Smart contracts are stored on the ledger and are executed automatically when certain conditions are met, allowing for the automation of complex processes and the creation of decentralized applications (DApps). The execution layer is an essential part of a DLT system, as it allows the system to process and execute transactions and other operations in a decentralized and automated manner. It also enables the creation of DApps, which can be used to build various types of decentralized systems and applications on top of the DLT platform. 2.4 Application Layer The application layer is the layer of the system that is responsible for providing interfaces and tools for users to interact with the DLT network and use the various services and applications that are built on top of it. The application layer is the topmost layer of a DLT system and is typically composed of various types of user-facing applications, such as wallets, exchanges, and other types of applications that allow users to access and use the DLT network. The application layer is an important part of a DLT system, as it provides the interface through which users can interact with the DLT system and access the various services and applications that are built on top of it. It also enables the creation of a wide range of decentralized applications (DApps), which can be used to build various types of decentralized systems and applications on top of the DLT platform. In addition to providing interfaces for users to interact with the DLT network, the application layer
Fig. 1. DCEA framework
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may also be responsible for other tasks, such as managing user accounts and providing access to data and other resources on the DLT network. To summarize, the application layer processes the blockchain services, such as the payment and the wallet, and helps to manage the end user identity. Finally, the DCEA framework helps to get a layered view of the identified cryptographic functions, families, primitives, protocols, and schemes used in Blockchain platforms. Figure 1 summarizes the scope of each layer of the DCEA framework.
3 Analysis and Discussion: Cryptographic Techniques in Blockchain Platforms from a Layered Perspective This section extracts the cryptographic techniques used throughout the DCEA layers. 3.1 Data Layer Cryptography can be used to overcome many of the privacy-preserving challenges related to the data layer of Blockchain. Some ways in which cryptography is used for this purpose include: Integrity and immutability of on-chain stored transaction states: Cryptographic hashing is used to create a unique, fixed-size representation, or “fingerprint,” of a transaction or data. This fingerprint, known as a hash, is then included in the Blockchain, ensuring the integrity and immutability of the transaction or data. Traceability and accountability of transactions: Cryptographic signatures, such as the Elliptic Curve Digital Signature Algorithm (ECDSA), are used to verify the transactions’ authenticity and provide traceability and accountability. These signatures are generated using a private key that is known only to the signer and can be verified using a corresponding public key. Pseudonymization of transaction addresses and history: Cryptography can be used to create pseudonymous transaction addresses that are not directly linked to the identity of the individual or entity making the transaction. This pseudonymity can be achieved through the use of public/private key pairs, where the public key is used as the transaction address, and the private key is kept secret to maintain anonymity. 3.2 Consensus Layer Cryptography can be used to overcome many of the privacy-preserving challenges related to the consensus layer of Blockchain. Some ways in which cryptography is used for this purpose include: Ensuring the integrity and security of consensus protocols: Cryptographic techniques such as proof-of-work (PoW) and proof-of-stake (PoS) are used to ensure the integrity and security of consensus protocols. These techniques use cryptographic hashes and digital signatures to verify the authenticity of transactions and block proposals and prevent malicious attacks. Maintaining anonymity of participants: Cryptographic techniques such as zero-knowledge proofs and ring signatures can be used to maintain the anonymity of participants in the consensus process. These techniques allow users to prove their involvement in a consensus process without revealing their identity. Protecting the privacy of transactions: Cryptographic techniques such as confidential transactions and zero-knowledge proofs can be used to protect the privacy of transactions included in blocks. These techniques allow users to prove the validity of transactions without revealing the underlying data or amounts involved.
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3.3 Execution Layer Cryptography can be used to overcome many of the privacy-preserving challenges related to the execution layer of Blockchain. Some ways in which cryptography is used for this purpose include: Hiding the inputs to this layer from every user and administrator: Cryptographic techniques, such as zero-knowledge proofs and secure multi-party computation (MPC), can be used to allow users to prove the validity of certain statements or computations without revealing the underlying inputs or data. This operation can be useful for hiding the inputs to the execution layer from every user and administrator. Executing operations on encrypted and hidden data: Cryptographic techniques such as homomorphic encryption and functional encryption can be used to allow operations to be performed on encrypted data without first decrypting it. This task can be useful for executing operations on encrypted and hidden data in the execution layer, as it allows users to perform calculations without revealing the underlying data. 3.4 Application Layer Protecting User Privacy: Cryptographic techniques such as anonymous credentials and privacy-preserving protocols can be used to protect users’ privacy. These techniques allow users to prove certain attributes or characteristics without revealing their identity. Elliptic curve-based cryptography and lattice-based one are used for address generation, Table 1 describes the finding. It evaluates using cryptography in post-quantum Blockchain with lattices, examines the use of cryptography in Monero, analyzes the use of cryptography in Zerocash, and investigates the use of cryptography in Bitcoin. Table 1. Analysis of the cryptography usage in Blockchain platforms per DLT layer DLT Layer
Blockchain Platform
Cryptographic technique
Application Layer
Post-Quantum Lattice-based functions Blockchain with Lattices [10] RIPEMD160; Base58Check encoding; SHA256
Utilization Keys generation; Address generation Address Generation
Hash function
Transaction security
Lattice-based signatures
Transaction security (continued)
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Table 1. (continued) DLT Layer
Blockchain Platform
Cryptographic technique
Utilization
Monero [15]
Elliptic curve used by Monero: Monero uses the Twisted Edwards curves defined as: ax2 + y2 = 1 + dx2 y2 where a, d, x, y ∈ Fq The cryptographic family is the public key cryptography using elliptic curves
Keys generation: User address (use public keys (two)) and two private keys: (kv the view key, and ks the spend key) Transaction public key generation Subaddress generation
The used Hash function is Keccak
One-time addresses created for each transaction output to be paid to the user It is used for strong anonymization purposes Multi-output transactions Subaddress generation Transaction ID
Diffie-Hellman key exchange with elliptic curves
One-time addresses created for each transaction output to be paid to the user It is used for strong anonymization purposes Multi-output transactions
MLSAG signatures
Transaction ID
Zerocash [16, 17] zk-SNARKs
SHA256
Create addresses; Transaction operations: Mint; Verify transactions; Pour; Receive Address generation; Transaction operations: Mint; Verify transactions; Pour; Receive (continued)
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DLT Layer
Blockchain Platform
Cryptographic technique
Utilization
ECDSA signatures
Transaction operations: Mint; Verify transactions; Pour; Receive
Elliptic-Curve Integrated Address Encryption Scheme generation;Transaction (ECIES) operations: Mint; Pour; Receive Bitcoin [9, 18, 19] Elliptic Curve Digital Signature Algorithm (ECDSA) Secp256k1 describes the elliptic curve parameters as follows: The prime number is:
Address generation (addresses are the hashes of the ECDSA public keys) Verify the transaction ownership
p = 2256 − 232 − 29 − 27 − 26 − 24 − 1 The equation of the curve is y2 = x3 + 7(modp) The keys have a maximum length: log 2 (p) = 256 Hash-based cryptography Address generation; Transaction operations Execution layer
Post-Quantum Blockchain with Lattices [10]
Hash function
Transaction security
Lattice-based signatures
Transaction security
Monero [15]
Pedersen commitments
Output amounts storage in transactions
Ring Confidential Transactions (RingCT)
Transaction fee
MLSAG signature
Transaction security
Zerocash [16, 17] zk-SNARKs
Transaction operations: Mint; Verify transactions; Pour; Receive (continued)
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Table 1. (continued) DLT Layer
Blockchain Platform
Cryptographic technique
Utilization
SHA256
Transaction operations: Mint; Verify transactions; Pour; Receive
ECDSA signatures
Transaction operations: Mint; Verify transactions; Pour; Receive
Elliptic-Curve Integrated Transaction operations: Encryption Scheme Mint; Pour; Receive (ECIES) Bitcoin [9, 18, 19] Elliptic Curve Digital Verify the transaction Signature Algorithm ownership (ECDSA) Secp256k1 describes the elliptic curve parameters as follows: The prime number is p = 2256 − 232 − 29 − 27 − 26 − 24 − 1 The equation of the curve is y2 = x3 + 7(modp) The keys have a maximum length: log 2 (p) = 256 Hash-based cryptography Transaction operations Consensus layer
Monero [15]
Cryptonight
Monero proof of work (PoW)
Data layer
Post-Quantum Hash-based cryptography Merkle Tree Blockchain with Lattices [10]
Bitcoin [9, 18, 19] Hash-based cryptography Bitcoin proof of work
Monero [15]
Hash-based cryptography Merkle Tree
Zerocash [16, 17] Hash-based cryptography Merkle Tree Bitcoin [9, 18, 19] Hash-based cryptography Merkle Tree
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Fig. 2. Cryptography Techniques from a Layered Perspective
Figure 2 illustrates the discussion. 3.5 Recommendations The encryption cryptosystem implemented in the paper [11] could be selected to harden lattice-based blockchains when calling the python code using Oraclize’s smart contracts. It is important to know that Oraclize is written in Solidity [20]. In addition, the key exchange protocol implemented in the paper [12] could be taken as a choice to design post-quantum and privacy-preserving blockchains. For signatures, it is possible to select TESLA-768 as the signature scheme [13]. Lastly, SHA3 is an excellent choice against post-quantum attacks, as described in this reference [14]. To conclude, several approaches can be taken to make cryptographic protocols postquantum safe, which refer to protocols that are resistant to attacks by quantum computers. Some recommendations for post-quantum security include: Using post-quantum cryptographic algorithms: Several cryptographic algorithms have been specifically designed to resist attacks by quantum computers. Some examples of these algorithms include lattice-based cryptography, code-based cryptography, and hash-based cryptography. Or, Using hybrid approaches: One way to achieve post-quantum security is by combining classical and quantum-resistant cryptographic algorithms. For example, we could use a classical algorithm for encryption and a quantum-resistant algorithm for digital signatures. Table 2 recapitulates the recommendations as a demonstration of this work’s applicability.
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Table 2. Post-quantum recommended techniques for DLTS design Cryptographic techniques
Recommended cryptographic implementation
Encryption cryptosystem
Encryption cryptosystem implemented in [11]
Key exchange protocol
Key exchange protocol implemented in [12]
Hash primitive
SHA3
Signature scheme
TESLA-768
This paper helps with the forecited recommendations as examples for design improvement.
4 Conclusion This work analyzes the cryptographic techniques used in some Distributed Ledger Technologies (DLTS) from a layered perspective within the DCEA framework. Cryptographic techniques, such as hashing and encryption, are used to secure the data stored on the Blockchain and to ensure that it cannot be tampered with or altered by unauthorized parties. Cryptographic protocols are also used in the Blockchain consensus layer to ensure that all nodes in the network agree on the Blockchain state. However, there are limitations to using cryptography in the context of Blockchain. One of the main limitations is the vulnerability of cryptographic protocols to quantum attacks. In this direction, we propose to select and choose post-quantum lattice-based cryptographic techniques, which are optimized and utilize LWE, such as Regev’s and/or Tesla’s techniques. Hence, this work helps harden current blockchain platforms or design better distributed ledger technologies.
References 1. Bellaj, B., Ouaddah, A., Bertin, E., Crespi, N., Mezrioui, A.: DCEA: A reference model for distributed ledger technologies. In: IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2021 (2021) 2. Athavale, V.A., Arora, S., Athavale, A.: Lecture Notes in Electrical Engineering, vol. 907. Springer, Singapore (2022) 3. Khatoon, A.: A blockchain-based smart contract system for healthcare management. Electronics (Switzerland) 9 (2020) 4. Ouaddah, A., Bellaj, B.: FairAccess2.0: a smart contract-based authorisation framework for enabling granular access control in IoT. Int. J. Inform. Comput. Secur. 15, 18–48 (2021) 5. Treleaven, P., Yang, D.: Richad Gendal Brown: Blockchain Technology in Finance. Computer published by the IEEE Computer Society (2017) 6. Tijan, E., Aksentijevi´c, S., Ivani´c, K., Jardas, M.: Blockchain technology implementation in logistics. Sustainability (Switzerland) 11 (2019) 7. Athavale, V.A., Arora, S., Athavale, A., Yadav, R.: One-way cryptographic hash function securing networks. In: Gupta, G., Wang, L., Yadav, A., Rana, P., Wang, Z. (eds.) Proceedings of Academia-Industry Consortium for Data Science. AISC, vol. 1411. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-6887-6_10
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8. Arora, S., Verma, D.C., Athavale, V.A.: A secured automated Attendance Management System implemented with Secret Sharing Algorithm. In: 6th International Conference on Parallel, Distributed and Grid Computing, pp. 141–145. Institute of Electrical and Electronics Engineers Inc. (2020) 9. Semmouni, M.C., Nitaj, A., Belkasmi, M.: Bitcoin security with post quantum cryptography. In: Atig, M.F., Schwarzmann, A.A. (eds.) NETYS 2019. LNCS, vol. 11704, pp. 281–288. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31277-0_19 10. Zhang, X., Wu, F., Yao, W., Wang, W., Zheng, Z.: Post-quantum blockchain over lattice. Comput. Mater. Continua. 63, 845–859 (2020) 11. Barj, S.: Towards a generalized Regev’ s cryptosystem and its related signing cryptosystem: an implementation for hashes and simple ASCII texts in Python and its related signing cryptosystem, pp. 0–15 (2022) 12. Singh, V.: A practical key exchange for the internet using lattice cryptography. IACR Cryptol. ePrint Archive 2015, 138 (2015) 13. Alkim, E., Bindel, N., Buchmann, J., Dagdelen, Ö.: TESLA: Tightly-Secure Efficient Signatures from Standard Lattices. Cryptology ePrint Archive (2015) 14. Nakov, S.: Quantum-Safe Cryptography - Practical Cryptography for Developers 15. June, P., Alonso, K.M.: Zero to Monero: First Edition a technical guide to a private digital currency; for beginners, amateurs, and experts, vol. 2018 (2018) 16. Ben-sasson, E., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E.: Zerocash: Decentralized Anonymous Payments from Bitcoin (extended version), pp. 1–56 (2014) 17. Ben-Sasson, E., et al.: Zerocash: decentralized anonymous payments from bitcoin. In: Proceedings - IEEE Symposium on Security and Privacy, pp. 459–474. Institute of Electrical and Electronics Engineers Inc. (2014) 18. Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008) 19. Wang, Q., Li, X., Yu, Y.: Anonymity for bitcoin from secure escrow address. IEEE Access 6, 12336–12341 (2017) 20. GitHub - johnhckuo/Oraclize-Tutorial: Intro to oracle service
New Topology of WSN for Smart Irrigation with Low Consumption and Long Range Yassine Ayat(B)
, Ali El Moussati, Mohammed Benzaouia, and Ismail Mir
Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), 60000 Oujda, Morocco [email protected]
Abstract. Meteorological stations are a set of measuring devices, consisting of different sensors that record and provide information about physical measurements related to climate variations. These physical quantities can be temperature, wind speed, rainfall, etc. This paper proposes an optimal solution to monitor weather conditions at low cost, power consumption, and long-range based on the Internet of Things (IoT). The wireless communication protocol is used for the installed sensor network to know the amount of irrigation water in an agricultural field based on the values acquired by the station and soil sensors for controlling the state of solenoid valves. This station mainly detects temperature, pressure, humidity, solar radiation, precipitation value, and wind speed. With that information, other data parameters can be calculated, such as Forest fire Weather Index (FWI ) and evapotranspiration (ET0 ). The different data are sent to the database and web server, to be visualized at the developed applications. Keywords: Smart irrigation · Connected objects · Wireless Sensor Network WSN · Soil Moisture · Weather station
1 Introduction Today meteorology plays a very important role for many human activities which requires perfect knowledge on the variations of the climatic phenomena of the environment of the planet, agriculture is essential for sustainable development and poverty reduction in many developing countries [1]. Weather measuring stations usually consist of a model on which sensors are installed [2]. These are connected to the box that makes the recording on a database and sending the measurements, usually via mobile network [3]. IoT used to be an emerging agricultural technology and has now taken a dominant position through wider adoption. By the simplest definition, IoT in agriculture is the Internet that controls things [4]. The application of information technology in the agricultural field can play a major role in developing the rural economy, which has been widely used in industry, environmental monitoring, and other aspects of life [5]. Generally, farmers need information on the amount of water required for the plants. They use
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only their knowledge and observation, unaware that unnecessary amounts of water affect soil fertility [6]. In the traditional irrigation process, a huge amount of water is needed, which leads to water wastage. Water scarcity is a hot topic, and especially at present, water crises are the most important risks for the economy and society in the coming years. A smart irrigation system is urgently needed. Calling on connected objects (IoT) is necessary to monitor and control the system remotely. In this paper, the proposed smart irrigation solution uses wireless sensors to collect several data on the soil of the cultivated areas to decide on the opening duration of the solenoid valve based on the weather station that acquires data on the ambient climate. The sensors send data via a wireless, energy-efficient Low Power Wide Area Network connection to the developed monitoring platform. There, allowing the farmer the visualization the variance of soil and climatic parameters in real-time for scheduled irrigation work according to time or volume. Based on the collected data and specified thresholds, calculations are made to determine the amount of water and the irrigation time by specifying the time, and the system automatically starts the irrigation process. The proposed solution proves it efficiency under random tests to program irrigation duration, thus allowing the improvement of crop yields and the reduction of water losses resulting in sustainable use of water. This paper is divided into 6 sections. Section 2 deals with the architecture of the proposed solution, information on the evapotranspiration coefficient is presented in Sect. 3, and the experimental setup’s proposed material, methods, and platform are introduced in Sect. 4. The obtained results from field tests are discussed in Sect. 5, and Sect. 6 concludes the paper.
2 Proposed System The principal units of the proposed solution are shown in Fig. 1. The system is designed with the aim of ensuring low power consumption and high coverage distance at the lowest deployment cost. The objective is to automate and monitor the irrigation process based on an architecture that meets the needs of farmers and rationalizes the use of water.
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Fig. 1. Proposed solution architecture.
3 Evapotranspiration In the water cycle, there is a phenomenon that goes in the opposite direction of precipitation: evapotranspiration, that is, by definition, the water that goes back into the air. It is the result of two different reactions, the physical phenomenon of evaporation on one part and the transpiration of plants on the other [7]. Potential evapotranspiration is the amount of water that can be evaporated from an open water surface or from a vegetation cover where water supply is not the limiting factor. The limit value of the ETP is a function of the available energy. (J. Margat 1997) [8]. ET0 can be calculated from meteorological data. Following an expert consultation held in May 1990, the FAO Penman-Monteith method is recommended as the only standard method for defining and calculating reference evapotranspiration. Based on the calculated ET0 value (Eq. 1), the amount of water and the irrigation duration are fixed, specifying the time of the command to the solenoid valves. 900 0.408 ∗ (Rn − G) + γ T +273 u2 (es − ea ) (1) ET0 = + γ (1 + 0.34u2 ) where: ET0 Rn G T u2 es
: reference evapotranspiration [mm day−1 ], : net radiation at the crop surface [MJ m−2 day−1 ], : soil heat flux density [MJ m−2 day−1 ], : air temperature at 2 m height [°C], : wind speed at 2 m height [m s−1 ], : saturation vapour pressure [kPa],
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: actual vapour pressure [kPa], : saturation vapour pressure deficit [kPa], : slope vapour pressure curve [kPa °C−1 ], : psychrometric constant [kPa °C−1 ]
4 Description of Hardware Platform and Method This section focuses on the hardware platform using the development method. 4.1 Hardware Platform Our objective is to develop a wireless sensor network based on Low Power Wide Area Network (LPWAN) technology to make this network autonomous with low power consumption and long-range communication. Our sensor network consists of several layers: Nodes Layer. Represent the sensors constituting the network. In our case, the sensors used are: Soil Sensor. The RS-SD-N01-TR soil moisture sensor is a high-precision, highsensitivity sensor for measuring soil moisture. By measuring the dielectric constant of the soil, the volume percentage of soil moisture can be measured, and the soil moisture measurement method conforming to the current international standard can directly and stably reflect the actual moisture content of various soils. Our method of measurement is to insert the sensor into a 30 cm deep pit and place the steel needle of the sensor horizontally in the pit (Fig. 2a). Solenoid Valve. Is an electrically controlled valve; thanks to this device, it can act on the fluid flow in a circuit using an electrical signal. Weather Station. SEN-15901 This kit (Fig. 2b). Includes a wind vane, cup anemometer, and tipping bucket rain gauge, with associated mounting hardware. These sensors contain no active electronics, instead using sealed magnetic reed switches and magnets to take measurements. A voltage must be supplied to each instrument to produce an output. Rain Gauge. The rain gauge is a self-emptying tipping bucket type. Each 0.2794 mm of rain cause one momentary contact closure that can be recorded with a digital counter or microcontroller interrupt input. The gauge’s switch is connected to the two center conductors of the attached RJ 11-terminated cable (See Fig. 2c). Anemometer. The cup-type anemometer measures wind speed by closing a contact as a magnet moves past a switch. A wind speed of 2.4 km/h causes the switch to close once per second. Pyranometer. The PYR20 pyranometer (Fig. 2d), or solar radiation sensor, measures the global radiation of both direct radiation and solar irradiation scattering. Measuring range at 2000 W/m2 , spectrum range 400–1100 nm also high precision and consistency with excellent stability.
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(b)
(c)
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Fig. 1. a) Soil sensor. b) Weather station. c) Connection of the rain gauge. d) PYR20 pyranometer.
Gateway Layer. Receive messages from end devices and forward them to the Network Server using the internet. In our case, the raspberry (Fig. 3) board represents the gateway layer with Dorji DRF1276DM (865–915 MHz) to receive and send data to the node using LoRa protocol.
Fig. 3. Raspberry & DRF1276DM.
Server and Application Layer. The network server has all the intelligence. It filters the duplicate packets from different gateways and sends ACKs to the gateways. The end packet is intended for an application. The network server sends the packet to the specific application. MQTT Protocol. MQTT is an OASIS standard messaging protocol for the Internet of Things (IoT). It is designed as an extremely lightweight publish/subscribe messaging transport that is ideal for connecting remote devices with a small code footprint and minimal network bandwidth. Django. Is a high-level Python web framework that prioritizes rapid development and practical design. Developed by experienced programmers, it handles many of the challenges and complexities of web development, allowing you to focus on creating your application without needing to start from scratch. It is free to use and open-source. Each sensor is connected to a microcontroller for measurement processing and to a LoRa communication chip with the gateway. Adopting an ESP32 microcontroller because of its very low power consumption (Fig. 4), with two modes of working. Running ESP32 in active mode with batteries is not ideal, as the batteries drain very quickly. Otherwise ESP32 in deep sleep mode, this will reduce power consumption and your batteries will last longer. For the visualization platform has been developed based on the use of Django framework.
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Fig. 4. ESP32 and used protocol.
4.2 Development Method The prototype is in the phase of testing the main functions (calibration of soil sensors, sending, reliability of data,…) in a field. For this purpose, the weather station, soil sensors, and valve are installed with 4 drippers discharging 50 ml/min in a m2 . According to the station data ET0 is calculated daily in a specific time from 1 : 00am the solenoid valve receives the duration of irrigation for a conversion of ET0 l/m2 in min. The algorithm of this purpose (see Fig. 5).
Fig. 5. Algorithm for irrigation (test phase).
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Then the evolution of the soil moisture after the irrigation of the value of ET0 will be used to determine an interval then after the irrigation will be conditional to the moisture or a correlation between ET0 and moisture according to the results obtained. The objective is to ensure the reliability of the measurements by comparing them with probes of soil moisture as well as a calibrated meteorological station of supplier Visio-green with the aim of installing them in agricultural fields of the area (See Fig. 7 and Fig. 8). 4.3 Node Consumption The node has three modes of operation - measurement, transmission, and deep sleep mode. Each of these modes has a specific power consumption (Eq. (2)) in a specific time. Figure 6 illustrates the three operating modes and their power consumption.
Measurement
Transmission
Deep sleep
Fig. 6. Graph of soil sensor consumption.
consumption =
CWU × TWU + CDS × TDS TP
(2)
where: CWU = Power consumption in operating mode (measurement and transmission). CDS = Power consumption in deep sleep mode. TWU = Duration of operation. TDS = Deep sleep mode duration. TP = Duration of the period 43 × 55 × 10−3 + 125 × 79 × 10−3 + 3599.86 × 25 × 10−3 consumption = 60 × 60 = 0.24 mA Then using battery C = 13Ah: life of battery =
capacity(ah) 13 = = 54166 h ≈ 6.18 ans consumption(A) 0.24 × 10−3
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5 Implementation and Results The proposed systems in the previous section differ with systems presented in the literature depending on the technology and the processors used. The common drawbacks of those systems are principally: the energy, due to the use of technologies like WiFi between sensor and microcontroller for example the NodeMCU board it consumes more energy also the use of open access platform like ThingSpeak present limited space and limited functionality and common advantage Low cost. The Table 1 summarize the several techniques used in each system and the differences between them.
A
B
C
Fig. 7. A: Box of weather, B: Soil sensor, C: Solenoid valve box.
Fig. 8. Weather station box and sensors.
Table 1. Previous Work. Ref
Processing
Parameters
Technology
Monitoring station
[9]
NodeMCU (µC)
Light intensity Relative humidity Soil moisture Temperature
Wi-Fi
Mobile Thingspeak
(continued)
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Table 1. (continued) Ref
Processing
Parameters
Technology
Monitoring station
[10]
Raspberry Pi 3 Arduino
Soil moisture Humidity Temperature
MQTT
Thingspeak Firebase
[11]
Libelium
Leaf wetness Solar radiation Temperature Relative humidity Relative of soil
GPRS WiFi 3G
Meshlium Xterm gatway
[12]
Microcontroller
Rainfall detection Humidity-PH-Air-quality-Motion detection-Temperature
GSM WiFi
PC Mobile
Proposed system
ESP32 Raspberry
Temperature (Soil and weather) Humidity (Soil and weather)-Radiation Rainfall-Windspeed Duration of irrigation
LoRa Ethernet MQTT
Web platform
5.1 Visualization Platform The data acquired from the sensors are sent via radio frequency network to the gateways using LPWAN protocol and then transferred to our server based on MQTT protocol which is a protocol for sharing the most recommended data in the field of IoT and the platform is developed using the Django framework based on python programming. The Fig. 9a) represents the home interface containing the data acquired from the two ground sensors as well as the manual control of the solenoid valve and the state of the battery and the Fig. 9b) Dashboard of weather. Figure 10a) represents the ambient temperature during the last three days noting that the highest during this period was marked on 13th November (28.78 °C). Figure 10b) Represents the evolution of the value of the evapotranspiration during the last 7 days the highest was marked during the day of 12th November. Figure 10c) the figure illustrates the ambient humidity during the three previous days the maximum was marked on the
a)
b)
Fig. 9. a) Home interface. b) Dashboard of weather station.
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13th of November (81.20%). The figure Fig. 10d). indicates the soil moisture during a period of three days.
a)
b)
c)
d)
Fig. 10. a) Temperature graph for 3 days ago. b) Daily ET0 for 7days ago Oujda city. c) Ambient humidity for 3 days ago. d) Soil moisture for 3 days ago.
6 Conclusion This article proposes a novel technic based on the integration of IoT in agriculture, especially for the management of irrigation according to the climatic parameters in correlation with the soil parameters in the aim of rationalizing the use of water also for the agricultural yield of Morocco. The prototype is in the test phase. In the future the prototype will be installed in an agricultural field of the region. As perspective, artificial intelligence algorithm will be used and implemented to predict the need in water of soil to protect the resources in water, that requires more data to have a precise model.
References 1. Halder, B.: Investigation of the extreme weather conditions and anthropogenic activities in Island ecosystem. Safe. Extreme Environ. 1–20 (2022) 2. Ferla, M., Nardone, G., Orasi, A., Picone, M., Falco, P., Zambianchi, E.: Sea monitoring networks. In: Daponte, P., Rossi, G.B., Piscopo, V. (eds.) Measurement for the Sea. SSMST, pp. 211–235. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82024-4_9 3. Diène, B., Rodrigues, J.J.P.C., Diallo, O.: Data management techniques for Internet of Things. Mech. Syst. Sign. Process. 138 (2020) 4. Haseeb, K., Din, I.U., Almogren, A., Islam, N.: An energy efficient and secure IoT-Based wsn framework: an application to smart agriculture. Sensors 20, 208 (2020)
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5. Marwa, C., Othman, S.B., Sakli, H.: IoT based low-cost weather station and monitoring system for smart agriculture. In: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 349–354. IEEE (2020) 6. Simitha, K.M., et al.: IoT and WSN based water quality monitoring system. In: 3rd International Conference on Electronics Communication and Aerospace Technology, 12–14 June 2019, Coimbatore, Tamil Nadu, India (2019) 7. Barrutia, O., Ruíz-González, A., Villarroel, J.D., Díez, J.R.: Primary and secondary students’ understanding of the rainfall phenomenon and related water systems: a comparative study of two methodological approaches. Res. Sci. Educ. 51(2), 823–844 (2019). https://doi.org/10. 1007/s11165-019-9831-2 8. Xiang, K., Li, Y., Horton, R., et al. : Similarity and difference of potential evapotranspiration and reference crop evapotranspiration–a review. Agric. Water Manage. 232, 106043 (2020) 9. Dholu, M., Ghodinde, K.A.: Internet of Things (IoT) for precision agriculture application. In: Proceedings of the 2nd International Conference on Trends in Electronics and Informatics (ICOEI 2018), 11–12 May 2018 at Tirunelveli, India (2018) 10. Nagaraja, G.S., et al.: IoT based smart agriculture management system”, 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS). Bengaluru, India, India, 20–21 Dec 2019 11. Marcu, I.M., et al.: IoT based system for smart agriculture. In: ECAI 2019 - International Conference – 11th Edition Electronics, Computers and Artificial Intelligence 27 June–29 June 2019, Pitesti, ROMÂNIA (2019) 12. Mahbub, M.:A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network. In: 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (2017)
A Literature Review of Internet of Vehicle Based Blockchain Fatima Zohra Fassi Fihri(B) , Mohammed Benbrahim, and Mohammed Nabil Kabbaj LIMAS Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco {fatimazohra.fassifihri,mohammed.benbrahim,n.kabbaj}@usmba.ac.ma
Abstract. The Internet of Vehicles (IoV) is an emerging concept that helps to realize intelligent transportation systems (ITSs). IoV is an area of great interest given the rapid evolution of vehicle technologies, which enables communication between intelligent vehicles and the various system components related to their environment. However, the IoV suffers from security issues regarding the storage and management of exchanged information, in addition to the inability to manage traffic due to the increasing number of connected vehicles. To overcome these constraints, blockchain represents a promising technology in the IoV that provides a distributed, transparent and highly confidential database by recording immutable transactions. This study proposes a systematic review of the literature on the application of blockchain technology in the IoV. The objective is to identify the current challenges in the IoV and highlight the value of blockchain in solving these challenges based on papers published from 2017 to 2022 in Web of Science (WoS)-indexed scientific journals. In addition, the limitations removed from the theoretical foundations of several research works were identified, as well as the perspectives and directions to be addressed for future research. Keywords: Blockchain · Internet of Vehicles · Intelligent Transportation Systems · Systematic Literature Review
1 Introduction With the design of smart cities, the adaptation of ITSs integrating advanced communication technologies into vehicles and transportation infrastructure has become essential. To cope with the increasing number of vehicles and their use, the Vehicle Ad hoc Networks (VANETs) have been developed to improve vehicle and road safety and traffic efficiency [1]. VANET is based on the principle of connectivity and exchange of information and data between vehicles and their environment. The innovation in VANETS has led to the definition of the IoV concept, which integrates Internet of Things (IoT) technology to enable interconnection between smart vehicles, roadside infrastructures and even pedestrians to ensure Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) and thus the Vehicle-to-Everything (V2X) paradigm and to implement the functional requirements of ITS [2]. However, the IoV has limitations in terms of security and privacy. In fact, communications that are used for message sharing and that contain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 232–241, 2023. https://doi.org/10.1007/978-3-031-29860-8_24
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confidential information, personal data, and sensitive navigational information are vulnerable to external attacks or malicious users and thus become accessible and can even be altered, causing serious problems that threaten the safety of the road and its users, namely, drivers, pedestrians and vehicles. Another problem with IoVs is the inability to handle a large traffic load and data storage, especially with the increasing number of nodes accessing the same centralized network. Blockchain is considered an effective way to solve these IoV problems, being a technology based on immutable and distributed ledgers, providing transactional databases that serve both as data protection and storage of valid transaction information engaged by each peer in a network of peer-to-peer systems [3]. The remainder of this article is structured as follows: Sect. 2 describes the three steps of the research methodology. Then, in Sect. 3, the main results and ideas extracted from the analysis of the selected papers are cited. In Sect. 4, the existing gaps and limitations in the current work are highlighted to serve as a basis for the orientation of future research. Finally, Sect. 5 concludes the paper.
2 Systematic Literature Review Methodology In this section, we propose a systematic literature review to study the integration of blockchain for the IoV based on Kitchenham’s approach [4]. 2.1 Plan the Review The basis of the study presented in this paper is formed by previous works. The aim of this Systematic Literature Review (SLR) is to highlight the research that integrates blockchain technology and emphasizes its use and interest in application to the IoV, as well as to identify the present limitations to be able to extract potential areas of research in the same discipline and to direct future research. The specific questions suggested in this study are related to the identification of needs and gaps between existing studies and future research opportunities: – – – –
SLR. Q1: How can blockchain be applied in the concept of IoV in particular? SLR. Q2: What are the current issues and challenges of the IoV? SLR. Q3: What are the potentialities of blockchain in the field of IoV? SLR. Q4: What are the limitations of current research?
There are several sources of research databases, such as Scopus, IEEEXplore, SpringerLink, ScienceDirect, Google Scholar and others. In this SLR, we used WoS to collect all the works that dealt with the IoV based on blockchain. To achieve this, while planning the review, we used a set of keywords to form a query that served to identify all the existing works for our research topic. 2.2 Conduct the Review As a result of the search request “(Internet of Vehicles OR IoV) AND Blockchain”, 7496 studies published since 2017 were identified. The processing of these results is necessary
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and is divided into several stages, starting with the elimination of duplicate works (256 papers). Then, we removed some papers that represented exclusion criteria, such as the divergence of the scope of the application of blockchain for IoV (3549 papers) and the unavailability of the full version of the article (1750 papers). At the end of this sorting stage, 1741 papers were selected after full text validation (Fig. 1). Finally, 301 articles are used in this SLR to accomplish its goal and answer the research questions.
Records identified through WoS
Records after duplicates removed N= 7240 papers
Records after screening process N= 1941 papers
Total studies included in the review
Records after full text validation N= 1741 papers
Fig. 1. Procedure to select studies
2.3 Report the Review The objective of this step is to use selected research to enrich the present study. The selection and sorting of existing studies is based on the following information: – Article authors, type, year of publication, and by Digital Object Identifier (DOI). – Review of abstracts to determine the research focus. – Examination of article content, namely, the relevant problems and opportunities addressed and the work proposed as solutions. – Review of results and limitations to define research directions. Referring to the type of paper, we can see that the majority goes to articles, followed by proceeding papers, review articles and books. Their representation in terms of publication date is shown in Fig. 2. 1800 1600 1400 1200 1000 800 600 400 200 0 2017
2018
2019
2020
2021
Review article
Article
Proceeding paper
Book paper
Fig. 2. Distribution of studies from 2017 to 2022
2022
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Based on a search of the most relevant articles according to their number of citations, the most interesting articles were grouped among the 301 selected articles, excluding those from surveys and literature reviews. For each study, an examination was evaluated based on the topic and its application area in relation to blockchain for IoV. A definition of the results obtained by each study as well as the strengths and limitations were highlighted. After the analysis of all the selected files, they were categorized according to their fields of application of blockchain technology in the IoV into seven broad categories based on their purpose (Fig. 3): 1. 2. 3. 4. 5. 6. 7.
Privacy and security IoV management Artificial Intelligence (AI) and Machine Learning (ML) for the IoV Blockchain performance in the IoV Energy trading Demand response General purpose
8% 4%
1%
AI and ML for IoV 6% Privacy and security
3% General purpose 9%
Energy trading Blockchain performance in IoV IoV management 69% Demand response
Fig. 3. Repartition of studies from 2017 to 2022 according to IoV application areas
3 Research on Blockchain-Based IoV In this section, we present the body of research on which this SLR is based, taking into account the application areas of blockchain for the IoV. 3.1 General purpose The articles belonging to this category are mainly surveys or literature reviews made on this study topic. To ensure security, privacy and trust management, studies [5–8] have focused on the interest of decentralized blockchain technology while explaining its ability to enhance the performance of IoVs.
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In [9, 10], the issues and challenges for the application of blockchain in the IoV domain were presented. Some reviews have separately addressed the benefits of blockchain, namely, authentication and security assurance [11], which are essential in view of the fact that for the IoV, several communications and applications must be available to provide all the necessary roadside services, leading to the exchange of a large set of data between the entities of the vehicular network. These data can be targeted by several attacks that lead to potential problems, hence the interest of the blockchain to ensure security. The authors of [12] cited the security techniques used, namely, identification authentication, privacy protection, trust management and access control. 3.2 AI and ML for IoV In coordination with blockchain, a solution was presented for trust management using deep learning [13]. When communicating between nodes in the vehicular network, each message exchanged is assigned a trust score to judge its credibility, with the goal of eliminating malicious vehicles. The authors of [14] propose a Classification Trust Model (CTM) to judge the reliability of vehicles and exchanged data based on blockchain and machine learning. The use of blockchain technology and deep learning for the IoV has been introduced in [15]. The proposed work aims to improve the detection of the vehicle’s position by using its proximity to fixed landmarks such as traffic signs using Deep Neural Network (DNN), subsequently in order to share this information, blockchain technology is used to ensure data security and privacy. Given the immense growth of the IoV and in situations of high-speed scenarios, in [16], we propose Deep Reinforcement Learning (DRL) a new framework to manage the scalability problem for IoV systems based on blockchain while keeping its main characteristics, namely, decentralization, latency and security. 3.3 Blockchain Performance in the IoV The improvement of blockchain performance in the IoV consists of setting up consensus algorithms on which to base transaction management and security assurance. Several papers have proposed different types of consensus. In [17], an Efficient Coherence Consensus Algorithm (ECCA) for blockchain technology was proposed that solves the problems of low debit and increased transaction delay. ECCA proposes classifying vehicular network nodes into 3 categories to designate them as normal, malicious or verification. This work has allowed us to increase the speed of transactions and therefore decrease transaction delays by eliminating and reducing the impact of malicious nodes and those with low performance. The SG-PBFT consensus (Score Grouping-PBFT) was initiated in [18], which presents an improvement over a traditional [19] consensus algorithm by calculating scores for each node, thus reducing attacks and identifying malicious nodes. To improve security in vehicle networks and reduce the complexity of communication, the bottom-up consensus protocol of the Road Side Unit (RSU) with blockchain technology based on a trust value has been proposed [20]. The peculiarity of this
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consensus is that it is established in 2 steps for defining a head node and a primary node. 3.4 IoV Management IoV management includes vehicle management, ride sharing and traffic control. The use of blockchain allows the management of the data and their sharing between the different nodes constituting the vehicular network with the use of signatures to guarantee the confidentiality and security of the shared information without the need to go through a third-party certification [21]. In [22], a scheduling scheme based on Blockchain technology was used to solve the problems related to the centralization of IoVs, such as the readiness to attack, improve performance and enhance the efficiency of resource allocation. The IoV has made it possible to solve traffic and road safety problems using intervehicle communications that must be fast and give reliable results in the shortest possible time. In [23], we present a solution for these challenges based on blockchain technology for a real-time application. 3.5 Privacy and Security Several papers have discussed the security problems of the IoV [2], and others have introduced in general the use of blockchain and its interest for the IoV and proposed a set of architectures [24–29]. Among the proposed solutions is the integration of Software-Defined Networking (SDN) to manage the challenges related to high mobility for the IoV but with the integration of blockchain to ensure security against malicious attacks [30–32]. Following the same approach of using blockchain to ensure the security and privacy of shared data and resources, several consensus mechanisms have been proposed, namely, proof-of-reputation-based blockchain architecture [33], proof of event consensus, proof of authority [34], and dynamic Proof-of-Work (dPoW) [35]. The use of the trust bit has been one of the means used for the security established by the blockchain [36]. This method consists of assigning to each node of the network, as well as to the information it disseminates, a trust bit allowing us to judge its reliability and trustworthiness to eliminate those that present attacks or malicious behavior. With the use of blockchain with an encoding mechanism, the authentication of data in V2V communication is realizable in real time and reliable. The method proposed in [37] consists of comparing the hash values generated from the images broadcasted on the transfer and reception side of the corresponding vehicles. To overcome the centralization problem of the IoV-based system, blockchain has been implemented with a Byzantine consensus algorithm to ensure security authentication in the IoV by improving the efficiency of node and data verification [38, 39].
4 Limitations and Future Directions There are still gaps in terms of managing nonfunctional requirements for the application of blockchain to the IoV. Research is still ongoing, as this is a new area of research,
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but based on the existing papers, few have focused on performance improvement. The implementation of the IoV requires the management of a large amount of data and transactions, taking into account the change in this information due to the mobility of the vehicular environment and the frequent changes in terms of number and information. The evaluation of latency and throughput requires the calculation of the speed of the transactions made per second knowing that the response and exchange must be done in real time. In addition, based on the present research, there is a capacity to manage a large number of nodes integrating the network at the same time. In terms of privacy and security, the blockchain with its used techniques allows the selection of malicious nodes and the prevention or detection of malicious attacks, and it forbids the manipulation of certain data in the IoV. That is why this designation of nodes and data must be done with great precision and efficiency, hence the need to have cryptographic techniques and devices for the blockchain very advanced to preserve the security contained in the blocks and those confidential. On the resource management side, there is a lack of efficient features for IoVs, such as sensors, RSUs, battery power, and limited storage space. Signing consumes a significant amount of energy, and for IoV devices, the high energy consumption may be impractical, which limits the use of blockchain. This is why the definition of an algorithm to sort the data and limit their storage according to criteria and thresholds is indispensable. As found since the analysis of the current studies, several consensus algorithms have been used for the application of blockchain to the IoV. These algorithms have issues such as the high computational cost required to validate a transaction and judge its compliance, the complexity of their application due to the high number of conditions and rules that are implemented, or other consensus that has limitations in terms of the number of nodes that support at once. Although they are numerous and consistent with many applications to solve IoV problems, consensus algorithms are developed for a single purpose, and therefore, there is no way to reuse them. They still lack thorough evaluation, and research to allow their optimization is needed. On the other hand, the use of Artificial Intelligence, Machine learning and deep learning techniques has been initiated for blockchain applied to the IoV as well as for the improvement of the position of vehicles. It will be interesting to take advantage of the opportunities and benefits that AI, ML and Deep Learning (DL) offer for more applications in the IoV for data processing and storage, for example.
5 Conclusion This SLR explored all papers dealing with the application of blockchain for the IoV between 2017 and 2022. The selection of relevant research was made by referring to the WoS database with the use of a specific query encompassing the keywords related to this study. Initially, the value of blockchain for the IoV was explained, and then a study of the existing challenges in the field of blockchain-enabled IoV was established based on a selection of articles. The fields of application of blockchain have been identified and divided into main categories, namely, privacy and security, IoV management, AI and ML for the IoV, blockchain performance in the IoV, energy trading, IoV management and general purpose, which contains literature and surveys. The analysis of each specificity
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that the use of blockchain for IoV systems offers has been established, and as a result of the findings, the limitations of each area have been highlighted in addition to some proposals to orient future research. The continuation of this article will be a large version based on the use of WoS and Scopus for research.
References 1. Nikhil, R., Sahil, V., Kavita, Jhanjhi, N.Z., Talib, M.N.: Securing VANET using blockchain technology. J. Phys.: Conf. Ser. 1979, 012035 (2021) 2. Bai, H., Wu, C., Yang, Y., Xia, G., Jiang, Y.: A blockchain-based traffic conditions and driving behaviors warning scheme in the internet of vehicles. In: International Conference on Communication Technology Proceedings, ICCT, art. no. 8947312, pp. 1160–1164 (2019) 3. Qureshi, K.N., et al.: A blockchain-based efficient, secure and anonymous conditional privacypreserving and authentication scheme for the internet of vehicles. Appl. Sci. 12, 476 (2022) 4. Brereton, P., Kitchenham, B.A., Budgen, D., Turner, M., Khalil, M.: Lessons from applying the systematic literature review process within the software engineering domain. J. Syst. Softw. 80, 571–583 (2007) 5. Mendiboure, L., Chalouf, M.A., Krief, F.: Survey on blockchain-based applications in internet of vehicles. Comput. Electr. Eng. 84, 106646 (2020) 6. Wang, C., Cheng, X., Li, J., He, Y., Xiao, K.: A survey: applications of blockchain in the Internet of Vehicles. Eurasip J. Wirel. Commun. Networking 2021, 77 (2021) 7. Abbar, R., et al.: Blockchain technology for intelligent transportation systems: a systematic literature review. IEEE Access 10, 20995–21031 (2022) 8. Queiroz, A., Oliveira, E., Barbosa, M., Dias, K.: A survey on blockchain and edge computing applied to the internet of vehicles. In: International Symposium on Advanced Networks and Telecommunication Systems, ANTS, 2020-December, art. no. 9342818 (2020) 9. Tripathi, G., Ahad, M.A., Sathiyanarayanan, M.: The role of blockchain in internet of vehicles (IoV): issues, challenges and opportunities. In: Proceedings of the 4th International Conference on Contemporary Computing and Informatics, IC3I 2019, art. no. 9055613, pp. 26–31 (2019) 10. Kapassa, E., Themistocleous, M., Christodoulou, K., Iosif, E.: Blockchain application in internet of vehicles: challenges, contributions and current limitations. Future Internet 13(12), 313 (2021) 11. Kumar, S., Velliangiri, S., Karthikeyan, P., Kumari, S., Kumar, S., Khan, M.K.: A survey on the blockchain techniques for the Internet of Vehicles security. Trans. Emerg. Telecommun. Technol. e4317 (2021) 12. Chen, C., Quan, S.: A summary of security techniques-based blockchain in IoV. Secur. Commun. Network. 2022, 8689651 (2022) 13. Wang, S., Hu, Y., Qi, G.: Blockchain and deep learning based trust management for Internet of Vehicles. Simul. Model. Pract. Theory 120, 102627 (2022) 14. Manogaran, G., Rawal, B.S.: Machine learning based trust model for secure internet of vehicle data exchange. In: 2020 IEEE Globecom Workshops, GC Wkshps 2020 – Proceedings, art. no. 367424 (2020) 15. Song, Y., Fu, Y., Yu, F.R., Zhou, L.: Blockchain-enabled internet of vehicles with cooperative positioning: a deep neural network approach. IEEE Internet Things J. 7(4), 3485–3498 (2020) 16. Liu, M., Teng, Y., Yu, F.R., Leung, V.C.M., Song, M.: Deep reinforcement learning based performance optimization in blockchain-enabled internet of vehicle. In: IEEE International Conference on Communications, art. no. 8761206 (2019)
240
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17. Chen, Y., Zhang, Y., Chen, H., Han, M., Liu, B., Ren, T.: Efficient consistency consens algorithm of blockchain for heterogeneous nodes in the internet of vehicles. Dianzi Yu Xinxi Xuebao/J. Electron. Inform. Technol. 44(1), 314–323 (2022) 18. Xu, G., et al.: SG-PBFT a secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles. J. Parallel Distrib. Comput. 164, 11 (2022) 19. Chai, H., Leng, S., Zeng, M., Liang, H.: A hierarchical blockchain aided proactive caching scheme for internet of vehicles. In: IEEE International Conference on Communications, art. no. 8761482 (2019) 20. Zhai, B., Wang, J., Han, L., Liu, J., He, J., Liu, T.: Hierarchical proxy consensus optimization for IoV based on blockchain and trust value. Chinese J. Network Inform. Secur. 8(3), 142–153 (2022) 21. Gao, J.L., et al.: Toward efficient blockchain for the internet of vehicles with hierarchical blockchain resource scheduling. Electronics (Switzerland) 11(5), 832 (2022) 22. Abbar, R., Kharbeche, M., Al-Khalifa, K., Krichen, M., Barkaoui, A.K.: Blockchain for the internet of vehicles: a decentralized IoT solution for vehicles communication using ethereum. Sensors (Switzerland) 20(14), 3928 (2020) 23. Gafencu, L., Scripcariu, L.: Security issues in the internet of vehicles. In: 2018 12th International Conference on Communications, COMM 2018 – Proceedings, art. no. 8430112, pp. 441–446 (2018) 24. Wang, X., Zeng, P., Patterson, N., Jiang, F., Doss, R.: An improved authentication scheme for internet of vehicles based on blockchain technology. IEEE Access 7, 45061–45072 (2019) 25. Hatim, S.M., Elias, S.J., Ali, R.M., Jasmis, J., Aziz, A.A., Mansor, S.: Blockchain-based internet of vehicles (BIoV): an approach toward smart cities development. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2020 – Proceeding, art. no. 9358355 (2020) 26. Ayaz, F., Sheng, Z., Tian, D., Leung, V.C.M.: Blockchain-enabled security and privacy for internet-of-vehicles. In: Gupta, N., Prakash, A., Tripathi, R. (eds.) Internet of Vehicles and its Applications in Autonomous Driving. UST, pp. 123–148. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-46335-9_9 27. Huang, P., Huang, B., Zhao, Y., Qiang, Z., Qing, M.: BCoV: a convergence of blockchain and IoV. In: Proceedings – Companion of the 2020 IEEE 20th International Conference on Software Quality, Reliability, and Security, QRS-C 2020,art. no. 9282634, pp. 636–643 (2020) 28. Wu, J., Jin, Z., Li, G., Xu, Z., Fan, C., Zheng, Y.: Design of vehicle certification schemes in IoV based on blockchain. Worldwide Web 25, 2241–2263 (2022) 29. Eltahlawy, A.M., Azer, M.A.: Using blockchain technology for the internet of vehicles. In: 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021, art. no. 9447622, pp. 54–61 (2021) 30. Mendiboure, L., Chalouf, M.A., Krief, F.: Toward a blockchain-based SDIoV for applications authentication and trust management. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11253 LNCS, pp. 265–277 (2018) 31. Gao, J., et al.: A blockchain-SDN-enabled internet of vehicles environment for fog computing and 5G networks. IEEE Internet of Things J. 7(5), 4278–4291 (2020) 32. Xiao, Y., Liu, Y., Li, T.: Edge computing and blockchain for quick fake news detection in IoV. Sensors (Switzerland) 20(16), 4360 (2020) 33. Chai, H., Leng, S., Zhang, K., Mao, S.: Proof-of-reputation based-consortium blockchain for trust resource sharing in internet of vehicles. IEEE Access 7, 175744–175757 (2019) 34. Al-Ali, M.S., Al-Mohammed, H.A., Alkaeed, M.: Reputation based traffic event validation and vehicle authentication using blockchain technology. In: IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020, art. no. 9089639, pp. 451–456 (2020)
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35. Gupta, M., et al.: Game theory-based authentication framework to secure internet of vehicles with blockchain. Sensors 22(14), 5119 (2022) 36. Singh, M., Kim, S.: Trust Bit: Reward-based intelligent vehicle commination using blockchain paper. In: IEEE World Forum on Internet of Things, WF-IoT 2018 – Proceedings, 2018January, pp. (2018) 37. Kamal, M., Srivastava, G., Tariq, M.: Blockchain-based lightweight and secured V2V communication in the internet of vehicles. IEEE Trans. Intell. Transport. Syst. 22(7), 3997–4004 (2021) 38. Hu, W., Hu, Y., Yao, W., Li, H.: A blockchain-based byzantine consensus algorithm for information authentication of the internet of vehicles. IEEE Access 7, 139703–139711 (2019) 39. Cinque, M., Esposito, C., Russo, S., Tamburis, O.: Blockchain-empowered decentralized trust management for the Internet of Vehicles security. Comput. Electr. Eng 86, 106722 (2020)
Tiny Machine Learning for IoT and eHealth Applications: Epileptic Seizure Prediction Use Case Mohamed Ould-Elhassen Aoueileyine(B) Innov’COM Lab, SUP’COM, University of Carthage, Tunis, Tunisia [email protected]
Abstract. Tiny Machine Learning (TinyML) and the Internet of Things offer a new opportunity for resource-constrained embedded systems. Conventional machine learning requires a lot of processing power to be implemented; the TinyML approach shifts the need for processing power to the cloud and produces lightweight models to run on an embedded device. This paper introduces a TinyML workflow and explains the steps involved in creating a model and deploying it in a resource-constrained environment for an eHealth use case. A dataset of epileptic patients was used to build a model and then implement it on a microcontroller using TinyML A good performance was obtained using a Microcontroller for epileptic seizure prediction. Keywords: TinyML · IoT · Embedded Systems · E-health · epileptic seizure
1 Introduction Few of us have not met people who have had sudden seizures, during which they make strange movements, and sometimes the case progresses to serious complications that affect their physical and psychological health: neurologists call these epileptic seizures. Epilepsy is a chronic disease that affects the brain and affects approximately 50 million people worldwide, and is characterized by recurrent seizures, in which brain activity becomes abnormal, causing seizures or periods of unusual behavior and sometimes accompanied by loss of consciousness and loss of bowel control or bladder function [1]. For many years, neuroscientists believed epileptic seizures began abruptly, just seconds before clinical seizures. Now scientists have been able to monitor the brain activity of a specific type of cell to predict epileptic seizures minutes before they occur in humans. Studies indicate that if seizures can be reliably predicted, people with epilepsy can modify their activities, turn on their neurostimulator, or take a fast-acting medication to prevent a seizure or reduce its effects. Nevertheless, how can we predict crises quickly and effectively? The answer to this question came from using monitoring devices and data collection from the Internet of Things (IoT) ecosystem combined with artificial intelligence [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 242–251, 2023. https://doi.org/10.1007/978-3-031-29860-8_25
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In this paper, we present an implementation of a TinyML workflow used for a realtime epileptic seizure. We start by choosing a dataset and following the machine learning process; we produce an optimized model ready to be deployed on resource-constrained embedded devices. A final deployment test was conducted using a Raspberry Pi device, and good performance was noticed.
2 Background Several technologies are used in the collection and monitoring of epileptic seizures; in the following, we discuss these technologies and discuss their relationship to the world of medical monitoring. 2.1 Internet of Things The IoT describes the network of physical devices consisting of sensors, actuators and software to exchange data without requiring human interaction [2]. A thing in IoT is a physical object that has an embedded device that can communicate with the internet. This can range from agricultural equipment (a tractor, an irrigation system, a drone) to “wearables” for medical purposes (blood pressure and heart rate monitoring bracelets, glucometer, etc.), to your “connected car” or any other device capable of obtaining, processing and communicating with data from environments to be controlled or communicating information. Although several currently proposed applications affect different areas of our lives, connected health is positioned in the most advanced areas of the IoT. 2.2 e-Health One of the areas that IoT applications cover the most is e-health. According to the Encyclopedia Britannica [2], it is defined as the application of digital and telecommunications technologies, such as computers., the internet and mobile devices, to facilitate health care services. The term belongs to the family of “e-words”, and its uses date back to the end of the last century, but it has evolved a lot in the last decade due to the exponential growth in the amount of data everywhere and the rapid development of IoT and artificial intelligence. 2.3 Artificial Intelligence for IoT and eHealth Artificial Intelligence (AI) is the art of making intelligent machines, especially intelligent computer programs. It is related to performing tasks commonly associated with intelligent beings. We can limit AI to branches of human intelligence that focus on learning, reasoning, problem-solving, cognition, and language use. A discipline of AI describes how a computer system develops its intelligence by using statistical learning algorithms to build intelligent systems: it is Machine Learning. It is a method of learning algorithms to learn how to make decisions [4].
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2.4 Machine Learning in e-Health Machine learning applications are spreading everywhere; e-health is one of its most important areas. For many years, using information systems in health services and digitizing patient information has generated massive amounts of data. This paved the way for optimizing machine learning algorithms to improve diagnostic accuracy and develop a more accurate treatment plan. However, the health field remains the most sensitive to the consequences of inaccurate predictions about human lives and breaches in patient data privacy. This poses a significant challenge to researchers working on machine learning applications in e-health [4]. 2.5 Machine Learning for Epileptic Seizure Prediction One of the main goals of e-health is the early detection and prediction of diseases to provide timely preventive interventions using technology [5]. Predicting epileptic seizures is one of the most important applications in this field, which for many years remained an intractable problem. However, through many years of research, neuroscientists have linked the behavior of nerve cells in patients’ brains to the likelihood of a seizure. With the expansion of epilepsy databases and exciting new developments in MLbased algorithms, researchers are on the cusp of revolutionizing the early and accurate prediction of epileptic seizures [5]. Machine learning applications to treat epilepsy include determining the epilepsy region, predicting medical or surgical outcomes for epilepsy and predicting seizures using electroencephalogram (EEG) analysis [5]. 2.6 TinyML as a Candidate for Embedded Intelligence in e-Health Since each patient has his unique pattern of seizure formation, and since speed and respect for privacy are key factors that must be present in any seizure prediction method, Edge computing, in particular TinyML, is mostly considered a candidate for an optimal solution [3]. It can be defined as the meeting point of machine learning and embedded systems that allow ML models to run on small, low-power devices such as microcontrollers. TinyML connects machine learning with microcontrollers and Internet of Things (IoT) devices to perform analytics locally by optimizing ML models and taking advantage of the vast amounts of data collected by them without having to send the data to a central server over the cloud. TinyML also guarantees the privacy of patient data with the advantage of being independent of an Internet connection. However, other challenges will appear on the interface, including limited memory and the hardness of troubleshooting [3].
3 Related Works In paper [1], the authors compare the dynamical properties of brain electrical activity from different recording regions and physiological and pathological brain states. Using
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the nonlinear prediction error and an estimate of an effective correlation dimension in combination with the method of iterative amplitude-adjusted surrogate data, they analyze datasets of EEG time series: surface EEG recordings from healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from epilepsy patients during the seizure-free interval from within and from outside the seizure generating area as well as intracranial EEG recordings of epileptic seizures. In the paper [5], the authors present various techniques of detection methods that depend on various features and classifiers that have been considered over the last few years. The methods presented give a detailed understanding and ideas about seizure prediction and future research directions. In [6], the author gives a study of applied machine-learning algorithms that are used to categorize EEG dynamics according to self-reported emotional states during music listening. This paper proposed a framework allowing the optimization of EEG-based emotion recognition by systematically seeking emotion-specific EEG features.
4 Machine Learning Workflow We should determine if ML is the best solution to predict an epileptic seizure before we start using it to detect epileptic seizures. As mentioned, neuroscientists have recently hinted at discovering unusual behavior in neurons minutes before seizure formation. So, by gathering a sizable set of data records on brain activity from multiple epileptic patients and building a statistical model by analyzing time-series EEG signals, we could classify and predict seizures minutes before: a classification problem. Figure 1 shows the ML workflow used for seizure characterization and prediction. Real-time prediction uses data captured from patients as input to ML block that allows analyzing data and gives a prediction.
Fig. 1. Machine learning workflow for data collection, training, and deployment.
4.1 Data Acquisition and Collection Data acquisition is one of the most critical stages of the machine learning workflow. The data collected consists of an EEG dataset for epilepsy patients gathered by a recorder of the brain’s electrical activity. Due to the limitation of epilepsy data, we chose the Bonn
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EEG time-series database, mainly because it is open and a very commonly used dataset featuring epileptic seizure detection. It was built in 2001 [1]. This dataset is built based on Five sets denoted (A–E), each containing 100 single-channel EEG segments of 23.6-s duration, composed for the study. In addition, the segments had to fulfil a stationarity criterion. The data collection A and B consisted of segments taken from surface EEG recordings on five healthy volunteers using a standardized electrode placement. Volunteers have been relaxed in an unsleeping state with eyes open A and eyes closed B, respectively. The dataset part C, D, and E are originated from the EEG archive of presurgical diagnosis. For our work, the EEG signal is selected from five patients [1]. 4.2 Data Preparation Our dataset consists of 11,500 samples, each with 178 features. The samples are classified into five different classes Y = {A, B, C, D, E}. Only Class A represents seizure activity recording. Fortunately, there are not any missed values, the samples are normally distributed, and hence no prior pre-processing is required for the classification of healthy (non-epileptic) and unhealthy (epileptic) signals [1]. 4.3 Algorithm Choice Since it is a binary classification problem and we have labeled data, we should train our data with classifier models based on supervised learning techniques. Therefore, we decided to train our model with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm. 4.4 Training and Testing The classification process starts by splitting the data into three sections: ‘Train’, ‘Validation’ and ‘Test’. Then, we start by designing our neural network architecture by creating necessary layers and feeding it sample vectors of EEG signals for which it already knows their Y value. Finally, we run the model to predict whether it is an epileptic case by calculating the loss each time and updating the weights of the Neural Network. 4.5 Evaluation To evaluate the model, we compare the results of the model’s predictions to the actual values to find the fraction of cases predicted correctly out of the complete dataset by using statistical techniques appropriate: Confusion matrix, F1 score, ROC curve, and AUC, To improve the model, we tune the model by manipulating the LSTM Hyperparameters: the number of training epochs, the size of training batches and the number of neurons [6].
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4.6 Inference and Prediction Once the model has been trained, we deploy it as part of a tiny computer, with sensors responsible for capturing EEG signals, pre-processing them, and applying the training model to predict an epileptic seizure minutes before it occurs and inform the patient or anyone interested in it. For experimentation, we proposed to deploy the model on the ARM-based Raspberry pi 4 nano-computer, designed by professors from the Department of Computer Science at the University of Cambridge as part of the Raspberry Pi Foundation. It is a fast and versatile microcontroller board (Fig. 2).
Fig. 2. Proposed embedded board for data acquisition.
Embedded systems and connected devices make our lives easier and provide us with many important services based on machine learning models, especially in the field of e-health, where their role was to capture signals using sensors, whether audio, thermal, or visual, then pre-process and send them to other systems or servers to decide what to do next. However, if this thing is smart, what does that mean? It means that it will not need to send data to the cloud to receive what to do, but rather the processing data and running of the model will take place between the corridors of that microscopic chip. In short, TinyML provides us with a lower response time, independence from the internet, and greater data security.
5 TinyML Workflow TinyML is the combination of the embedded systems component and the machine learning component Fig. 3. In other words, TinyML enables to run of complex machine learning models on tinyML machines [3]. The capabilities of the giant processors are different from those of microcontrollers since they cannot run the same instructions and operations.
Fig. 3. TinyML as part of Machine Learning and Embedded system
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Here comes the role of TinyML, which transforms the model through a series of stages into a light version that can be run on edge devices, using less space and less computing. Table 1 gives a comparison between Tiny Microcontrollers and Cloud. Table 1. Cloud and Mobile ML versus TinyML systems. Platform
Architecture
Memory
Storage
Power
Price
Cloud E.g. Nvdia
GPU Nvidia Volta
16 GB
SSD/disk TB-PB
250 W
~$9.000
Mobile
CPU Arm Cortex-A78
DRAM 4 GB
Flash 64 GB
~8 W
~$750
Tiny E.g. Arduino Nano 33 BLE Sensor
MCU Arm Cortex-M4
SRAM 256 kB
eFlash 1 MB
0.05 W
~$3
5.1 Convert Our Model To run the ML model on a Microcontroller, we use a specified Framework known as TensorFlow. The TensorFlow model represents a set of instructions that tell an interpreter how to deal with input samples to produce an output. However, TensorFlow’s interpreter needs powerful desktop computers and servers to run models. Since we want to run our models on microcontrollers, we need a different suitable interpreter.
Fig. 4. TinyML workflow using TensorFlow Lite.
There is TensorFlow Lite that can run models on small devices like wearables. However, before that, we need to convert our model into a TensorFlow Lite formula via Tensorflow Lite Converter’s Python API Fig. 4. The converter applies special optimizations to reduce the size of the model and help it run faster, often without sacrificing performance. Pruning: the idea is to take some of your neural network’s connections away or remove certain neurons.
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Quantization: It will reduce the precision of our neural network’s weight and bias values to fit into 8-bit integers. Those techniques can come at the cost of reducing accuracy, but often this reduction is small enough to be useful. Finally, to deploy our model on the Raspberry pi 4 we need to convert it to a C source file that can be included in our binary and loaded directly into memory because most microcontrollers do not have a filesystem. 5.2 Deployment, Inference, and Real-Time Data Streaming Figure 5 shows the deployment workflow. The initial step is data collection of brain activity. For this, diverse checking challenges are used. To record the EEG, electrodes are fixed with the help of glue on the outer part of the scalp as per 10–20 standard universal systems at distinct lobes. Then, all electrodes are wired, joining the EEG device, which provides suitable statistics of the deviations in potential, laterally with temporal and spatial info. Electrodes are located on the skull or scalp, and the monitoring tool records the corresponding EEG signal, then carefully checked by the expert and categorized into “seizure” and “non-seizure” conditions [6]. Extracting features from data is crucial to removing non-useful information from unprocessed data. Therefore, these approaches are usually helpful to the extracted EEG signal dataset. After completing the feature extraction process, it is more revealing to support the classifier for discriminating features [7–9].
Fig. 5. Final Real-time seizure detection.
6 Results and Discussion Embedded hardware is extremely limited in performance, power consumption, and storage. However, TinyML allows turning these characteristics into strengths even if there is no doubt that the most powerful version of the model is the original one applied to the cloud. Furthermore, in ML applications, we use many cloud TPUs or GPUs simultaneously and get the machine learning algorithm to learn the patterns it finds in the data. Nevertheless, in TinyML, we are not learning things on the embedded device. Instead, we are just exercising what we have already learned (Table 2).
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Accuracy
Latency
Original model
3110 Kb
99%
0.05400 s
Optimized model
261 Kb (4x smaller due to post-training integer quantization)
99%
0.00015 s
Size memory
The table above compares the two versions of the model: the original and the converted. The findings demonstrated that the accuracy remained relatively unchanged even after the conversion process, which includes pruning and quantization and reduces the number of neurons as well as transforming the set of values to fit int8 rather than float32. We also note that the size and latency comparison results are in favor of the converted version, as the model size has been reduced by about ten times and reached the 256 KB that a microcontroller can fit as it generally does not exceed 1 MB and the time to complete the inference operation has been dramatically reduced.
7 Conclusion TinyML and IoT ecosystems can leverage smart decision capability at the edge using a constrained performance device and involving machine learning. This trend is becoming an important need in future time-constrained applications. This work presents intuitive detailing about TinyML used for eHealth applications, and good results were donated for the used dataset.
References 1. Andrzejak, R.G., Lehnertz, K., Rieke, C., Mormann, F., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64, 061907 (2001) 2. Scarpato, N., Pieroni, A., Di Nunzio, L., Fallucchi, F.: E-health-IoT universe: a review. Int. J. Adv. Sci., Eng. Inform. Technol. 7(6), 2328–2336 (2017) 3. Pratim Ray, P.: A review on TinyML: state-of-the-art and prospects. J. King Saud Univ. – Comput. Inform. Sci. 34(4), 1595–1623 (2022). https://doi.org/10.1016/j.jksuci.2021.11.019 4. Cabestany, J., Rodriguez-Martín, D., Pérez, C., Sama, A.: Artificial intelligence contribution to ehealth application. In: 2018 25th International Conference “Mixed Design of Integrated Circuits and System” (MIXDES), pp. 15–21 (2018). https://doi.org/10.23919/MIXDES.2018. 8436743 5. Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Comput. Math. Methods Med. 2022, 7751263 (2022). https://doi.org/10.1155/2022/7751263 6. Lin, Y.P., Wang, C.H., Jung, T.P., et al.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)
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7. Teixeira, C.A., Direito, B., Bandarabadi, M., et al.: Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients. Comput. Methods Programs Biomed. 114(3), 324–336 (2012) 8. Ahmad, I., et al.: EEG-based epileptic seizure detection via machine/deep learning approaches: a systematic review. Comput. Intell. Neurosci. 2022, 1–20 (2022). https://doi.org/10.1155/ 2022/6486570 9. Dhar, P., Garg, V.K., Rahman, M.A.: Enhanced feature extraction-based CNN approach for epileptic seizure detection from EEG signals. J. Healthcare Eng. 2022, 3491828 (2022). https:// doi.org/10.1155/2022/3491828
A Simulation Framework for IoT Networks Intrusion and Penetration Testing Khalil Ben Kalboussi1(B) , Farah Barika Ktata2 , and Ikram Amous3 1 Miracl Laboratory, FSEGS, University of Sfax, Sfax, Tunisia
[email protected]
2 Miracl Laboratory, ISSATSo, University of Sousse, Sousse, Tunisia
[email protected]
3 Miracl Laboratory, Enet’Com, University of Sfax, Sfax, Tunisia
[email protected]
Abstract. IoT systems are progressively becoming important components of the civil and industrial structure. With the spreading employment of these IoT ecosystems, attacks and risks are growing consequently. In this paper we demonstrate the challenges of testing and evaluating the security and vulnerabilities of IoT networks. We propose in our work a new approach of combining both virtual (simulated) and real (physical) IoT modules. INIT is a simulation environment that’s capable of generating real use case data of IoT applications, as it offers access to the totality of layers within the simulator in a way that all traffic packets at any part of the system can be captured and monitored. INIT allows real IoT modules to be connected over the internet or via a special VLAN. This enables the simulator system to integrate physical attacks and to be able to analyze its characteristics. Because we have implemented a virtual Server running penetration testing tools, INIT is also capable of running attacks on the simulated IoT application from within the system. Keywords: Simulator · IoT · Monitoring · Intrusion · Cyberattack
1 Introduction Internet of Things (IoT) has a transformative effect on society and the environment through a range of application sectors including smart homes, healthcare, agriculture, and Industry [1]. The ongoing networking of an increasing number of heterogeneous IoT devices enables real-time monitoring and actuation across numerous domains. The objective of IoT is to create a brilliant environment by utilizing items, objects, and gadgets that have the physical and functional capacity to generate information on their own and transfer it over the Internet for archiving, analysing, and making decisions. These options are used to address problems related to human life, such as energy management, transportation, health care, logistics, and others. The collected data and the choices made within the IoT environment are sometimes critical and error intolerant as it may be related to human health and wellbeing or expensive and highly valued goods and equipment © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 252–263, 2023. https://doi.org/10.1007/978-3-031-29860-8_26
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[2]. Thus, it is very important to have a high level of security in IoT systems. In order to develop security systems (such as intrusion prevention IPS, and intrusion detection systems IDS) researchers must have access to an IoT system in order to test Attacks and Vulnerabilities and test IDS architectures which may not be available. As a solution we propose INIT, an IoT Networks Intrusion and penetration Testing framework that combines the ability of modelling and simulating IoT architectures, launching attacks on the system and assessing the results with precise monitoring of the totality of the system. 1.1 Challenges IoT simulation is difficult for a variety of reasons, including I the need for applicationrelated nodes to be connected to the cloud or to edge computing; (ii) the difficulty of abstractly modelling the networking graph between various types of IoT and edge computing devices; and (iii) the complexity of modelling the dependencies between data and control flows between IoT and edge layers to support various data analysis workflow structures. (iv) Because it depends on a variety of configuration factors, such as data volume, data velocity, and upstream/downstream network bandwidth, to mention a few, capacity planning across the edge computing layer is difficult; (v) IoT and edge device communication differ significantly from cloud datacentre communication, which is often based on wired and/or wireless protocol. We examine various types of communication between IoT and edge computing layers further in the study. Certain IoT related applications like intrusions detection systems utilize advanced and resource demanding technologies such as Big Data which is difficult to simulate [3]. As a result, they are exceedingly challenging to abstractly represent without losing their expressiveness, including lower-level information about protocol latency, the effect of protocols on the IoT device’s battery depletion rate, etc.; (vi) Mobility is another important parameter of IoT devices as sensors embedded to many physical devices are moving. Since the range of IoT devices is limited, the movement of the sensor may lead to handoff. Also, the data sent from a server to a node may not be in the current range of the target IoT devices. Modelling the mobility and handoff for a large number of IoT devices with varying velocity is very challenging; (vii) Dynamicity of the IoT environment leads to addition and removal of IoT devices very frequently. This may be caused by numerous factors e.g., device failure, network link failure, and may also be the result of a cyber-attack. Modelling the scalability of IoT devices with heterogeneous features at a fast rate is very challenging; and (viii) Since IoT environment is an emerging area, new applications might be developed in future. It is very important for a simulator to allow users to customize and extend the framework based on their requirements. Making a general simulator that allows easy customization is very challenging. Different simulators are proposed in the literature. However, from the best of our knowledge, we could not find any simulator that addresses all the above challenges.
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1.2 Contributions In System Attack Simulation: In our study we concentrate on cybersecurity. We integrate a ready to use open-source penetration-testing framework (Kali Linux) in the purpose of launching and monitoring attacks on the simulated IoT systems. Multi-Layer Monitoring: Because our system is modular it is possible to simulate the totality of the five layers of IoT discussed later, giving an important advantage of monitoring the inter-layer traffic and analysing the performance of any IPS/IDS on a distributed scale independently from physical of software limitations. Hybrid Simulation: Physical cyberattacks are extremely difficult to simulate using IoT systems with purely virtual nodes as it is necessary to explicitly program the effects of the attack in the simulator which is difficult and can give unprecise results. In this work we demonstrate an effective method of combining virtual (simulated) and physical (real) devices in one application, creating a combination of a simulator/testbed environment.
2 Background 2.1 Internet of Things IEEE has outlined the IoT’s architectural framework. The goal is to describe IoT domains, and the various applications found therein [4]. This Internet of Things design is divided into three layers: the application layer, the network and data transmission layer, and the detection layer. According to [5, 6] and [7], the general design of the IoT is divided into five layers that cover three domains, namely the application, network, and physical domains. The new five layers model contains two additional business and middleware layers. As a result, the IoT can be customized to meet the needs of various intelligent environments. The management and utilization fall under the application domain. Data transmission is the responsibility of the network domain. Information gathering is the responsibility of the physical sciences. 2.2 Low-Level Networking Simulation To evaluate low-level components of specialized IoT systems, numerous simulators are available. They frequently have connections to IoT operating systems. For instance, the operating systems Contiki and TinyOs each feature a unique simulator: Cooja and Tossim, respectively. There are numerous generic simulators, such as ns-3 and Omnet++ in addition to these system level simulators. These simulators are designed to model the low-level behaviour of a single node, such as its network, communication, and power systems. This eliminates the ability to mimic a large number of IoT nodes, according to Brambilla et al. [8].
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2.3 Security Threats We conducted a thorough analysis of potential IoT system attacks in order to identify the security threats to IoT networks. We’ve divided the Internet of Things threats into the following five families: • • • • •
Access control Applications’ susceptibilities Threat on the device or network Inter-applications or platform threat Firmware related threat
Below is a comparison of some attacks specific to IoT systems citing the target layer of each. The study of the details of these threats is crucial for the continuity of our work (Table 1). Table 1. Attacks on IoT systems Reference
Attack/Vulnerability
Layer
Family
Fu [9] Chi [10]
False event/False commands
Application
Access Control
Fu [11]
Event/Command Interception
Perception/Network
Access Control
Chi [10]
False Commands
Perception/Network
Access Control
Chi [10]
Denial of Execution (DoE)
Network
Access Control
Chi [11]
Cross-App Interference Application (CAI)
Access Control
Babun [12]
Data Privacy Vulnerability
Application
Applications’ susceptibilities
Manandhar [13]
Vulnerability of data collected on cloud
Network/ Application
Threat on the device or network
Wu [14]
Restricted Security Policy
Application
Inter-applications or platform threat
Liu,X [15]
Firmware manipulation Perception
Firmware related threat
3 Related Work In this section we analyse and criticize different IoT simulation platforms and testbeds discussed in related studies. Numerous simulators have been created with a Graphical User Interface (GUI) and a primary core, making it possible for researchers to start
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simulating rapidly. It also helps in evaluating contribution to the current existing IoT simulators. The Internet of Things simulator enables users to trouble-free test hundreds of connected devices that they define. Simulators provide several benefits, including control, manageability, scalability, integration, and configurability. IoT simulators enable numerous devices to continually interact or communicate with one another. Testing and examining IoT devices are a way to identify or categorize breakdowns, where a breakdown is any difference between the actual results and the outcomes that were expected. 3.1 Iot Simulators As opposed to the challenging setup of physical IoT devices, an IoT simulator makes it possible to replicate an actual IoT environment with thousands of connected devices. In Table 2, a comparison of the top sixteen simulators is provided. The following characteristics are included in this table: scope, type, programming language, layers of IoT architecture, built-in IoT standards, simulation of cyber resilience, service domain, and security measures. DPWsim: A device profile for web services called DPWSim emphasizes secure messaging, precise identification, and service categorization. It is a simulation toolset for Internet of Things systems and programs. Devices test two different service types: hosted services and hosting services, according to a paradigm established by DPWS. It describes resource-limited devices, secure web services messaging, and detection and interpretation. [16]. iFogSim: IoT, fog data centres, edge devices, network connections, cloud analysis, and performing metrics assessment toward them may all be simulated using iFogSim, an edge and fog computing simulator built on top of a cloud simulator. Among these features, it enables an analysis and comparison of system management methods and techniques based on QoS standards (e.g., network congestion, latency, bandwidth, task scheduling, etc.). [17]. IOTSim: A cloud-based Internet of Things simulator called IOTSim was created on top of CloudSim. It has been developed to aid in the examination of Big Data processing and address using the MapReduce technique. It makes the recognition and analysis of results as well as the execution of IoT-based objectives through researchers and lucrative company industries simpler by genetically assisting significant information policies. [18]. CupCarbon: It is a discrete Wireless Sensor Network (WSN) event debug simulator with multi-agent, 2D/3D visualization. It verifies the distributed algorithm and the industrial automation of specific repetitive operations. It can assist instructors in demonstrating the fundamental standards and the operation of smart sensor networks. The analyst can use it to test out wireless topology, communication, protocols, routing, etc. [19]. SimIoT: SimIoT focuses on big data processing and cloud performance. It can be used to assess job processing times in a particular cloud-based data processing system configuration based on information provided by sensors or IoT service users. This simulator’s
Scope
IoT
Fog
Data analysis
Network
Data analysis
Edge WLAN
IoT Device
Simulators
DPWSim [16]
iFogSim [17]
IOTSim [18]
CupCarbon [19]
SimIoT [20]
EdgeCloudSim [21]
Bevywise [22]
Broker
Realistic
Discreteevent
Agent-based discreteevent
MapReduce Model
Discreteevent
Open Source
Type
Python / Java
Matlab
Java
Java Custom scripting
Java
Java
Java
Programming Language
Network
Network
Application
Perceptual Network
Application
Perceptual Network Application
Application
IoT Architecture Layers
real Time
Mist Computing
No
802.15.4 LoRaWAN
No
No
Secure Web Services Messaging
Built-in IoT Standards
Table 2. Iot simulators comparaison
No
No
No
No
No
No
No
Attacks simulation
Smart City
Edge Orchestrator
Generic
Smart City
Generic
Generic
Generic
Service Domain
Medium
High
High
High
Medium
Medium
Medium
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current primary focus (as of the time of writing) is on evaluating the performance of data centres, with plans to include support for sensor heterogeneity and topology management in the future [20]. EdgeCloudSim: EdgeCloudSim takes into account the unique requirements of the Edge Computing tasks and redesigns the required utility in terms of calculation and systems administration capabilities. A test arrangement based on unique viewpoint designs is recreated to showcase the capabilities of EdgeCloudSim, and the effects of the computational and systems administration machine parameters on the outcomes are shown [21]. Bevywise: Experimentations with an on-premises MQTT (virtual clients) application in a fog computing environment is supported by the Bevywise IoT Simulator for user testing and functional cloud testing. Users are able to imitate thousands of common servers. A complete product and solution for IoT and Industrial-IoT is offered by Bevywise Simulator. It can be used to demonstrate, build, and test IoT applications in a live setting [22].
3.2 Analysis and Discussion IoT simulators are used to test and experiment with early-stage designs and concepts. It enables users to set up a virtual framework to test and analyse the behaviour and performance of new IoT topologies and architectures without the need to invest in high priced physical nodes. Although, simulators are limited in terms of scopes, extensibility and supported technologies. Because it is often a purely virtual environment, it is possible to have inaccurate results while the experimenting using simulators specially in the context of cybersecurity where attacks can be performed physically on the nodes or the network components (Firmware manipulation, Command interception, …). Another method is to use IoT testbeds to test ready-to-implement solutions and architectures in realistic environments. It provides a framework for IoT testing and evaluation that is comparable to actual implementation. Test-bed results are more accurate and reliable than those from software-based simulators. Testbeds are basically a combination of hardware equipment that are often expensive and that compose a miniature scope of the real environment.
4 Proposed System In this work we propose an IoT simulation platform that is reliable and inclusive. It combines the abstraction and virtuality of a simulator and the reality and accuracy of a testbed. The platform is built in a modular architecture to add flexibility and extensibility. Discussed later, each module is responsible of a task. Although the good operation of the system is guaranteed when all modules are running, it is not necessary for minimal operation. Proposed modules are as follows:
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• The main core: based on EVE-NG network simulator. It is released as an OVF files and ISO. An OVF is an Open Virtualization File for the virtual machine. EVE-NG can be directly installed on the physical hardware, without any virtualization by use of ISO image. • Packets monitor: Wireshark is a free and open-source packet analyser. It is used in our system to be able to trace and analyse network performance. Because we have full access to the previously mentioned main core, Wireshark is able to monitor the totality of the system including all modules (running all three main layers) of the simulated IoT application. • Virtual nodes: the system is capable of simulating the behaviour of multiple IoT nodes and connected devices like smart light controls, temperature and atmosphere sensors, smart watches, etc. • Gateway: an important contribution is the integration of a gateway that enables users to connect physical nodes to the simulator, which is extremely useful to test response time and quality of service in relation to position and physical cyberattacks. The gateway is also used to connect the system to cloud services. • Attacker node: another contribution is implementing a fully functional attacker computer inside the simulator, running the Open-Source Kali Linux which contains a precious set of penetration testing tools. 4.1 General Architecture Figures 1 and 2 illustrates the composition of our system in relation with the previously mentioned IoT layers. We clarify that the mentioned example is the minimal configuration for a fully functional system. It is possible to clone and multiply any node from those inside the defined layers. It is impossible, of course, to multiply the Main Core or the packet monitor. Figure 3 demonstrates the actual graphical user interface of the proposed system with an attacker node, network switches, virtual nodes, and the gateway to connect physical nodes. The Discrete Event Simulation (DES) simulation methodology is one of the most widely utilized for IoT and Wireless Network Sensor (WSN) simulation. A system is modelled using DES as a discrete series of events. The entire simulation is managed by a central event data structure and a global clock. This enables very effective simulation at a small scale, with 100 to 1000 simulated entities depending on the resources available and the computational complexity of the entities. The Agent Based Simulation methodology offers a distinct strategy. Each simulated creature, also known as an agent, acts independently while using this method. By interacting with other agents and the environment, it can change and evolve its unique state and behaviour. In comparison to DES, this naturally modular approach enables a simpler modelling procedure and a better fit for simulation distribution. Because of all of this, ABS is a perfect choice for modelling and simulating large-scale IoT applications. In our approach we join both methods. The application layer is responsible for discrete event simulation with pre-set time triggered actions while a global clock is already configured in the eve-ng core itself. Yet we have also adapted the ABS method by implementing the modules discussed in Sect. 4.
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Fig. 1. INIT’s General architecture
Fig. 2. Simulated IoT environment example
For the physical nodes an additional configuration step is required. A gateway must be setup inside network layer and devices have to be connected to the application layer through this gateway. For simulated devices, an environment running on HTTP and MQTT protocol is integrated into the previously virtualization module. This module is executing an IoT Open-Source firmware called Tasmota. It is simulating the functioning of a light switch composed of a relay connected to a ESP8266 microcontroller. 4.2 Use Case In this section we present an example use case of our proposed simulator. We setup a simple home automation system including two physical and two virtual light switches
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(relays). For the physical nodes we used Sonoff R3 WIFI relays shown in Fig. 4. The use of these nodes is beneficial to test real time performance, reliability and security features. An application server is then installed on a Linux machine running in-side the simulator. We have chosen to install “Domoticz” framework along with “Mosquitto” MQTT broker in order to coordinate and control the switches. The Discrete Event Simulation is explicitly made in this module as we setup schedules and scenarios to be executed at a certain time or as a response to certain actions, for example temperature rise automatically turns on AC. Mosquitto is the link between Domoticz and each device connected to the platform. Through it’s command prompt we are able to see requests and information passing thru. To be able to connect the system to mobile google application “Home” an intermediate framework called “Homebridge” is setup on a different module. After establishing a connection with the MQTT broker, it is able to link with Home application after an email verification security method. After this step the user is able to successfully control the switches from the mobile application. An important contribution in our work is the integration of a pre-configured attacker node and giving the ability to test the security of the total-ity of the system. The simulated environment is put under attack and packets were monitored and saved for analysing.
Fig. 3. Use case
Fig. 4. Sonoff R3
5 Conclusion This paper indicates that during the past five years, the IoT paradigm has been the focus of much research. According to IoT research trends presented in this paper, scientists are working more and more on enhancing current IoT simulation frameworks to enable researchers, engineers and industries create more reliable and secure IoT ecosystems. The IoT research community is progressively utilizing ideas from allied disciplines, including cloud computing, blockchain technology, artificial intelligence, and others. Our work yields concrete results that IoT device makers, industries, and service providers may use, leverage, and exploit to improve the development of a smart and secure IoT environments. Utilizing simulators, a virtual proof-of-concept can be produced. However,
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current simulators typically have limited capabilities in security testing, not being able to simulate an end-to-end IoT platform securely and accurately and since they concentrate on a certain architectural layer (such as the network layer or the perceptual layer). In this paper we have proposed an ultimate solution to not only simulate and test an IoT environment but also to test its security aspect without the need for any extra tools. Our system has been prototyped and tested on both cloud infrastructure and a laptop with average configuration. Further testing and extensions will be done in the future. The code, user manual, configuration files and all resources used in this work are available at: https://github.com/kalboussik/INIT.git.
References 1. Delgosha, M.S., Hajiheydari, N., Talafidaryani, M.: Discovering IoT implications in business and management: a computational thematic analysis. Technovation 118, 102236 (2022) 2. Sitesh, M., Kathryn, C., Chandrasekhar, D.: Analysis of critical success factors to mitigate privacy risks in IoT Devices. Procedia Comput. Sci. 196, P191-198 (2022) 3. Louati, F., Ktata, F.B,. Amor I.A.: A distributed intelligent intrusion detection system based on parallel machine learning and big data analysis. In: SENSORNETS 2022: 11th International Conference on Sensor Networks, pp. 152–157 (2022) 4. IEEE: Define IoT. https://iot.ieee.org/definition.html. Last accessed on 13 Sep 2022 5. Han, C., Jornet, J.M., Fadel, E., Akyildiz, I.F.: A cross-layer communication module for the internet of things. Comput. Netw. 57(3), 622–633 (2013) 6. Bandyopadhyay, D., Sen, J.: Internet of things: applications and challenges in technology and standardization. Wirel. Pers. Commun. 58(1), 49–69 (2011) 7. Noor, M.M., Hassan, W.H.: Current Research on Internet of Things (IoT) Security: A Survey. Comput. Netw. 148, 283–294 (2019) 8. Brambilla, G., Picone, M., Cirani, S., Amoretti, M., Zanichelli, F.: A simulation platform for large-scale internet of things scenarios in urban environments. In: Proceedings of the First International Conference on IoT in Urban Space, pp. 50–55 (2014) 9. Fu, C., Zeng, Q., Du, X.: Semantics-aware anomaly detection for appified smart homes. In: 30th USENIX Security Symposium (USENIX Security 21) (2021) 10. Chi, H., Zeng, Q., Du X., Luo, L.: PFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems. arXiv, vol. 1910.07987 (2019) 11. Chi, H., Zeng, Q., Du, X., Yu, J.: Cross-App Interference Threats in Smart Homes: Categorization, Detection and Handling. arXiv, vol. 1808.02125v5 (2021) 12. Babun, L., Celik, Z.B., McDaniel, P., Uluagac, A.S.: Real-time Analysis of Privacy-(un)aware IoT Applications. arXiv, vol. 1911.10461v1 (2019) 13. Manandhar, S., Moran, K., Kafle, K., Tang, R., Poshyvanyk, D., Nadkarni, A.: Towards a natural perspective of smart homes for practical security and safety analyses. In: 2020 IEEE Symposium on Security and Privacy (SP), San Francisco, CA (2020) 14. Wu, Y.C., Sun, R., Wu, Y.J.: Smart city development in Taiwan: from the perspective of the information security policy. Sustainability 12, 2916 (2020) 15. Liu, X., Qian, C., Hatcher, W.G., Xu, H., Liao, W., Yu, W.: Secure internet of things (iot)based smart-world critical infrastructures: survey, case study and research opportunities. IEEE Access 7, 79523–79544 (2019) 16. Han, S.N., et al.: Dpwsim: A simulation toolkit for IoT applications using devices profile for web services. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 544–547 (2014)
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17. Gupta, H., Dastjerdi, A.V., Ghosh, S.K., Buyya, R.: Ifogsim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw. Pract. Experience 47, 1275–1296 (2016) 18. Zeng, X., Garg, S.K., Strazdins, P., Jayaraman, P.P., Georgakopoulos, D., Ranjan, R.: Iotsim: a simulator for analysing iot applications. J. Syst. Architect. 72, 93–107 (2017) 19. Mehdi K., Lounis M., Bounceur A., Kechadi T.,: Cupcarbon: A multi-agent and discrete event wireless sensor network design and simulation tool. In: 7th International ICST Conference on Simulation Tools and Techniques, Lisbon, Portugal, p. 1 (2014) 20. Sotiriadis S., Bessis N., Asimakopoulou E.; Mustafee N.: Towards simulating the Internet of Things. In: Proceedings 28th International Conference Advanced Information Networking Applications Workshops. Victoria, BC, Canada, pp. 444–448 (2014) 21. Sonmez, C., Ozgovde, A., Ersoy, C.: EdgeCloudSim: an environment for performance evaluation of edge computing systems. Trans. Emerg. Telecommun. Technol. 29, e3493 (2018). https://doi.org/10.1002/ett.3493 22. Pieta, P., Deniziak S., Belka R., Plaza, M., Plaza, M.: Multi-domain model for simulating smart IoT-based theme parks. In: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, International Society for Optics and Photonics (2018)
A New Four Ports Multiple Input Multiple Output Antenna with High Isolation for 5G Mobile Application Nour El Houda Nasri1(B) , Mohammed El Ghzaoui2 , Mohammed Fattah1 , Mohammed Ouazzani Jamil3 , and Hassan Qjidaa2,3 1 IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco
[email protected]
2 Faculty of Sciences Dhar El Mahraz-Fes, Sidi Mohamed Ben Abdellah University, Fes,
Morocco 3 LSEED Laboratory, UPF, Fez, Morocco
Abstract. This paper introduces a new 4-port multiple-input multiple-output (MIMO) antenna for future 5G applications. The proposed MIMO antenna is formed of 4 identical hexagonal patches, and each patch is hexagonally slotted to achieve the desired results. The ground plane of the antenna is made up of 4 similar rectangles, each with a size of 15 × 22 mm2 . The substrate of the antenna is made using FR4-epoxy which has a dielectric constant of 4.4 and a thickness of 1.08 mm. The proposed MIMO antenna has dimensions of 50× 55× 1.08 mm3 . The suggested antenna has been simulated and analyzed using the high-frequency structure simulator (HFSS). The suggested antenna resonates from 23.4 to 27.6 GHz with a wide bandwidth of 4.14 GHz and a return loss of about −43.50 dB. Furthermore, the antenna offers a high isolation which is largely sufficient for 5G future applications. The suggested MIMO antenna covers the European band (24.25– 27.50 GHz) that has been widely used since 2020. All the captured results in terms of return loss, isolation, and other parameters and also the compact size of the antenna prove that the proposed MIMO antenna can be of a great use for 5G applications. Keywords: MIMO · 5G · Wide Bandwidth · Isolation
1 Introduction The abundant use of internet mobile has increased the needs of a more performing generation, leading to the development of the 5th generation. This new generation provides a higher capacity, lower latency, and higher reliability, and all these criteria meet the requirements of new applications in contrast to the existing generations [1, 2]. The antenna is an important element in the telecommunication field, and developing a new generation means new specifications for antennas. For antennas to be 5G convenient they must be larger so they can function on the smaller bands. With the immense use of the multiple-input multiple-output systems (MIMO), this type of antenna guarantees a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 264–271, 2023. https://doi.org/10.1007/978-3-031-29860-8_27
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higher data rate and a wider bandwidth. MIMO antennas have several telecommunication issues such as RF-multipaths, and offer techniques that enhance widely the antenna performance such as the multipath propagation [3, 4]. With all these specifications such as higher data rate, lower latency and lower profile, the 5th generation has brought up new challenges for the telecommunications research field which triggers a large series of researches in this direction. With the new specifications brought by the 5G into the telecommunication industry, several researches have been led to design appropriate antennas for the new generation [5–11]. Arizaca-Cusicuna et al. in [9] proposed a 4x4 patch array antenna with an acceptable bandwidth of 0.3 GHz at 28 GHz. In [6] authors designed a dual- band antenna with maximum gains of 5.51 dBi for 10.15 GHz and 8.03 dBi for 28 GHz. The authors in [11] proposed a Ka-band antenna that has an isolation of 20 dB and resonates on a range from 26.8 to 28.4 GHz with a size of 39.8 mm × 33.4 mm2 . In the ref [12], two antipodal Fermi-based tapered slot antennas utilizing metasurface corrugation designed on a 20mil Rogers substrate (εr = 3.55) with a surface of 41 mm × 85 mm with an S11 of 37.1 dB and a bandwidth of 5 GHz. In [13], Aghoutane et al. proposed a square slot-loaded millimeter-wave patch antenna array which has a maximum gain of 7.81 dB at 38.1 GHz. In the ref [14], Ghazaoui et al. designed a millimeter-wave antenna for a 5G application with a wide bandwidth of 4.10 GHz at 28 GHz. Aghoutane et al. in [15] have proposed a dual-band high gain 4-port millimeter wave MIMO antenna array functioning at 28/37 GHz for 5G applications having a significant gain of 7.9 and 13.7 dB at 28 and 37.3 GHz, respectively. In [16], a Circularly Polarized Antenna resonating at 28 GHz for 5G applications is presented with a bandwidth of 3.15 GHz (27.2–30.35 GHz) and a gain of about 11.65 dBi. In [12] a 4-element array antenna is designed for 5G applications offering a peak gain of 14 dB and an impedance bandwidth of 1.14 GHz. Sandeep et al. presented a dual element bowtie shaped antenna with reflector and DGS operates at 28 GHz and offers a peak gain of 12 dB in ref [14]. Authors in [17] presented a square loop antenna at 38.5 GHz and has a bandwidth of 13% and a gain of 2.9 dBi. All the previous works have dedicated to design new antennas for the 5th generation. In this framework, the antenna in the following communication paper is proposed. In this paper, a new 4-port multiple-input multiple-output (MIMO) microstrip antenna is proposed. The proposed MIMO antenna is composed of 4 similar hexagonal patches which are hexagonally slotted, and a ground plane of the antenna is made up of 4 identical rectangles. The proposed antenna has a wide bandwidth of 4.2 GHz (23.40 GHz–27.60 GHz) with a size of 50 × 55 × 1.08 mm3 with a very good return loss of about −43.50 dB, and a high isolation. The proposed antenna is carried out on FR-epoxy and fed by a microstrip line with a power port adapted to 50 . The 4-port MIMO antenna is made with the intention to function in the European bandwidth 24.25–27.5 GHz deployed since 2020 which is largely achieved using the MIMO technique.
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2 Antenna Design The proposed MIMO antenna is formed of 4 hexagonal patches each segment has a size of a = 7 mm and each patch is hexagonally slotted, the size of the slot segment is b = 3 mm as shown in Fig. 1a. The patches are fed using microstrip lines which have a length of Lf1 = 8.40 mm and a width of Wf 1 = 1.60 mm, the power ports are adapted to 50 . The antenna has a size of W 1 × L1 × h = 50 × 55 × 1.08 mm3 . The ground plane of the proposed antenna is made of 4 rectangles with a size of Wg × Lg = 15 × 22 mm2 as shown in Fig. 1b. All dimensions of the proposed antenna are described in Fig. 1.
(a)
(b)
Fig. 1. The proposed MIMO antenna design (a) Top view, (b) Bottom view.
3 Results and Simulations The proposed 4-port MIMO antenna is designed and simulated using HFSS. The antenna results are evaluated in terms of return loss, isolation, gain, E-plane, H-plane radiation patterns and VSWR. The return loss of the proposed antenna is depicted in Fig. 1 which shows that the proposed antenna S11 has a minimum value of about –43.50 dB. The antenna has a wide bandwidth of about 4.20 GHz (23.4–27.6 GHz) (Fig. 2).
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Fig. 2. Return loss of the antenna (S11, S22, S33, and S44).
The isolation parameter is also an important parameter to judge the quality of a MIMO antenna. As depicted in Fig. 3, the isolation parameters have very small values with a maximum of about –36.41 dB which demonstrates the good isolation between the proposed MIMO antenna elements.
Fig. 3. Antenna Isolation.
Adding to the previous parameters, the E-plane radiation pattern and H-plane radiation pattern are depicted in Fig. 4, and the radiation patterns of the proposed MIMO antenna are almost omnidirectional.
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(b)
(a)
Fig. 4. (a) E-plane radiation pattern (b) H-plane radiation pattern.
In order to judge the reflection of the antenna the Voltage Standing Wave Ratio (VSWR) is displayed in Fig. 5. For this proposed MIMO antenna, the VSWR has very good values which proves that the antenna transmits most of the power received.
Fig. 5. VSWR of the antenna.
4 Parametric Study A parametric study has been led to analyze the structural parameter variation’s effects on the results (Figs. 6 and 7).
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Fig. 6. Impact of the antenna height S11 comparison.
As observed in Fig. 6a the bandwidth of the antenna is almost the same but the value of S11 is optimal when h = 1.08 mm, which is not the case for h = 1.04 mm and 1.12 mm. Hence, h = 1.08 mm is proven to be the best value to ameliorate the performance of the antenna. The width of the ground Wg is another parameter that has been analyzed, and its impact on the antenna performance is studied. As shown in Fig. 7 the proposed antenna has been simulated using the most convenient value of Wg = 15 mm compared the two other values Wg = 12.5 and 17.5 mm, for return loss in Fig. 7. Therefore, the proposed antenna was designed and simulated using Wg = 15 mm.
Fig. 7. Impact of the ground plane width S11 comparison.
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5 Comparison Study A comparison study is detailed in Table 1, it proves that the proposed antenna is a good performing antenna for 5G applications with a compact size and a simple design. Table 1. Comparison study Ref
Bandwidth (dB)
Isolation (dB)
[13]
3.7
>34
[16]
2.4
–
[17]
1.14
–
[18]
2.02
–
[19]
4.6
>20
Proposed
4.2
>36.41
6 Conclusion In this paper, a new 4-port MIMO antenna is proposed. The suggested MIMO antenna has a wide bandwidth of about 4.20 GHz (23.40–27.60 GHz) an S11 equals to 43.50 dB, a very high gain of about 33,20 dB, and a high isolation with a maximum of –36.41 dB. The proposed antenna has a compact size of 50 × 55 × 1.08 mm3 . The performance results of the antenna in terms of return loss, isolation, and other important parameters and also its compact size make the antenna a good candidate for the future 5G applications. The proposed antenna covers the European bandwidth (24.25–27.50 GHz).
References 1. Hong, W.: Solving the 5G mobile antenna puzzle: assessing future directions for the 5G mobile antenna paradigm shift. IEEE Microwave Mag. 18, 86 (2017) 2. El Ghzaoui, M., Hmamou, A., Foshi, J., Mestoui, J.: Compensation of non-linear distortion effects in MIMO-OFDM systems using constant envelope OFDM for 5G applications. J. Circuit Syst. Comp. 29, 2050257 (2020) 3. Venkateswara Rao, M., Madhav, B.T.P., Krishna, J., Usha Devi, Y., Anilkumar, T., Prudhvi Nadh, B.: CSRR-loaded T-shaped MIMO antenna for 5G cellular networks and vehicular communications. Int. J. RF Microw. Comput. Aided Eng. 29, e21799 (2019) 4. Park, S., Lee, H.-S., Yook, J.-G.: 28/39-GHz dual-band dual-polarized millimeter wave stacked patch antenna array for 5G applications. IEEE Trans. Antennas Propag. 68, 4007 (2020) 5. Awan, W.A., Zaidi, A., Baghdad, A.: Patch antenna with improved performance using DGS for 28GHz applications. In: International Conference on Wireless Technologies, Embe and Intelligent Systems (WITS), pp. 1–4 (2019)
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6. Jandi, Y., Gharnati, F., Oulad Said, A.: Design of a compact dual bands patch antenna for 5G applications. In: International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp. 1–4 (2017) 7. Aghoutane, B., El Ghzaoui, M., Das, S., Ali, W., El Faylali, H.: A dual wideband high gain 2 × 2 multiple-input-multiple-output monopole antenna with an end-launch connector model for 5G millimeter-wave mobile applications. Int. J. RF Microwave Comput. Aided Eng. 32, e23088 (2022) 8. El Alami, A., Ghazaoui, Y., Das, S., Bennani, S.D., El Ghzaoui, M.: Design and simulation of RFID array antenna 2 × 1 for detection system of objects or living things in motion. Procedia Comput. Sci. 151, 1010–1015 (2019). https://doi.org/10.1016/j.procs.2019.04.142 9. Arizaca-Cusicuna, D.N., Luis Arizaca-Cusicuna, J., Clemente-Arenas, M.: High gain 4x4 rectangular patch antenna array at 28 GHz for future 5G applications. In: 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Lima, Peru, pp. 1–4 (2018) 10. Ghazaoui, Y., El Alami, A., Das, S., El Ghzaoui, M.: A modified e-shaped compact printed antenna for 28 GHz 5G applications. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds.) WITS 2020. LNEE, vol. 745, pp. 961–969. Springer, Singapore (2022). https://doi.org/10.1007/978-981-33-6893-4_87 11. Lin, M., Liu, P., Guo, Z.: Gain-enhanced Ka-band MIMO antennas based on the SIW corrugated technique. IEEE Antennas Wirel. Propag. Lett. 16, 3084 (2017) 12. Gupta, S., Briqech, Z., Sebak, A.R., Denidni, T.A.: Mutual-coupling reduction using meta surface corrugations for 28 GHz MIMO applications. IEEE Antennas Wirel. Propag. Lett. 16, 2763–2766 (2017) 13. Aghoutane, B., Das, S., El Faylali, H., Madhav, B.T.P., El Ghzaoui, M., El Alami, A.: Analysis, design and fabrication of a square slot loaded (SSL) millimeter-wave patch antenna array for 5G applications. J. Circuit Syst. Comp. 30, 2150086 (2021) 14. Ghazaoui, Y., Alami, A.E., Ghzaoui, M.E., Das, S., Barad, D., Mohapatra, S.: Millimeter wave antenna with enhanced bandwidth for 5G wireless application. J. Inst. 15, T01003 (2020) 15. Aghoutane, B., Das, S., EL Ghzaoui, M., Madhav, B.T.P., El Faylali, H.: A novel dual band high gain 4-port millimeter wave MIMO antenna array for 28/37 GHz 5G applications. AEU-Int. J. Electron. C. 145, 154071 (2022) 16. Ullah, U., Al-Hasan, M., Koziel, S., Mabrouk, I.B.: A series inclined slot-fed circularly polarized antenna for 5G 28 GHz applications. IEEE Antennas Wirel. Propag. Lett. 20(3), 351–355 (2021) 17. Nabil, M., Faisal, M.M.A.: Design, simulation and analysis of a high gain small size array antenna for 5G wireless communication. Wirel. Pers. Commun. 116(4), 2761–2776 (2021) 18. Sharma, S., Mainuddin, Kanaujia, B.K., Khandelwal, M.K.: Analysis and design of single and dual element bowtie microstrip antenna embedded with planar long wire for 5G wireless applications. Microw. Opt. Technol. Lett. 62(3), 1281–1290 (2020) 19. Li, S., Chi, T., Wang, Y., Wang, H.: A millimeter-wave dual-feed square loop antenna for 5G communications. IEEE Trans. Antennas Propag. 65(12), 6317–6328 (2017)
Isolation Improvement Using a Protruded Ground Structure in a 2 * 2 MIMO Antenna Aicha Mchbal(B) , Naima Amar Touhami, and Abdelhafid Marroun Information Systems and Telecommunications Laboratory, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco [email protected]
Abstract. A compact multiple input multiple output (MIMO) antenna for Ultra Wideband (UWB) applications is studied. The presented antenna is characterized by an overall dimension of 31 * 26 * 0.8 mm3 . The UWB MIMO antenna is composed of two similar monopole radiating elements with a protruded ground branch structure on the ground plane in order to enhance the adaptation and reduce mutual coupling. Results prove that the presented antenna has a frequency range of 3.1 to 11.12 GHz, mutual coupling between two ports lower than −20 dB, an envelope correlation coefficient less than 0.002, and a maximum gain of about 5.22 dBi. Keywords: MIMO · UWB · High isolation · Monopole antenna
1 Introduction The main advantages of an UWB system are low power and high data rate interference immunity as well as small size, low profile and simple manufacturing [1]. But, one noteworthy problem of UWB system is multipath fading. To deal with this issue, a robust technique called MIMO has been presented to enhance UWB system, since it is well known as one of the effective ways for broadband communication systems performance, by increasing its channel capabilities of improving wireless communication systems performance, by increasing its channel capacity without the need of additional frequency band or consuming additional power [2]. The main objective that has to be taken into account once we start a MIMO antenna design, is to maintain a low electromagnetic coupling between radiators, that can significantly affects the overall MIMO antenna system performance [3]. Based on this, designing UWB-MIMO antennas with a low inter-elements electromagnetic coupling is mandatory. To this end, diverse techniques have been proposed for the reduction of electromagnetic coupling between antenna elements and various techniques have been utilized to enhance isolation. With the aim of eliminating correlation amid narrow and ultra wide bands applications, two notched bands UWB MIMO antenna has been introduced in [4]. In [5], in order to suppress the electromagnetic coupling between the investigated MIMO antenna elements, polarization diversity technique has been employed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2023, LNNS 669, pp. 272–277, 2023. https://doi.org/10.1007/978-3-031-29860-8_28
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in combination with two band rejection approaches that are based on the implementation of a C-formed stub as well as a L-formed slot on the radiating elements. In [6], the investigation of the impact of two different techniques on mutual coupling between two radiators of a MIMO assembly is presented. While in [7], the suppression of the mutual coupling amid an UWB MIMO antenna elements has been achieved by using two different defected ground structure. In this paper, a MIMO antenna for UWB applications is presented. The proposed antenna exploits the approach of using slots on the ground in order to reduce mutual coupling as well as a decoupling structure acting as an electromagnetic band-gap structure was employed to suppress the coupling amid the two elements. Results show a bandwidth ranging from 3.1 to 11.12 GHz, a mutual coupling lower than −20 dB and a correlation of less than 0.002 in the UWB range.
2 Design Steps The studied antenna with detailed dimensions is depicted in Fig. 1. The antenna is composed of two symmetrical monopoles, and is etched on a FR4 laminate with dimensions of 31 * 26 * 0.8 mm3 and a dielectric constant of 4.3. A reduced ground plane with three defected ground structures has been employed with an overall dimension of about 31 * 8 mm2 . The main objective behind using a partial ground plane with DGS technique is to enhance the impedance bandwidth of the presented UWB MIMO antenna. With the aim of enhancing the isolation amid antenna elements, a decoupling structure was employed in order to decrease the electromagnetic coupling. Hence the mutual coupling of less than −20 dB and a bandwidth ranging from 3.1 to 11.12 GHz are obtained. Figure 2 (a) and (b) represents MIMO antenna s-parameters.
Fig. 1. The studied MIMO antenna array.
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3 Results and Discussion The major purpose behind the used decoupling technique, is to enhance the isolation between the ports of the constituent radiators; hence, its importance can be estimated by noticing the S parameters of the studied UWB MIMO antenna, with and without the decoupling structure. So, as we can see from Fig. 2 (a) and (b), we notice that after employing the protruded ground branch structure at the ground plane level as a DGS, we have no only a noticeable improvement at isolation level, but also at impedance matching level too. Which prove nothing but the used decoupling structure good efficiency.
(a)
(b) Fig. 2. S-parameters with and without the decoupling structure. (a) S11, (b) S21
Isolation Improvement Using a Protruded Ground Structure
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Since the correlation is regarded as a key parameter that give an idea about antenna diversity performance on a scattering environment. The ECC can be provided based on the scattering parameters data as below [8]: ∗ S S12 + S ∗ S22 2 11 21 ECC = 1 − |S11 |2 − |S21 |2 1 − |S22 |2 − |S21 |2
(1)
Simulated result depicted in Fig. 3. Which represent the studied antenna simulated correlation coefficient, shows that the presented antenna is characterized by an ECC lower than 0.002 over the frequency band of interest which is by its turn lower than the threshold that is defined as 0.5, which proves the studied antenna good diversity performance. And As can be seen from Fig. 4. That illustrates the presented antenna simulated gain, we observe that the proposed UWB MIMO antenna has a maximum realized gain of about 5.3 dBi.
Fig. 3. The simulated correlation coefficient
4 Performance Comparison The comparison of the studied MIMO antenna with reference antennas, shows that the antenna of interest is characterized by a compact size and better isolation compared with others, which makes it a potential candidate for UWB applications such as military, radar, localization, sensing, and many other applications (Table 1).
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Fig. 4. The realized gain
Table 1. Comparison of the proposed design with previous designs Ref
Size (mm2 )
Bandwidth (GHz)
Isolation (dB)
ECC
No. of Ants
[9]
26 * 40
3.1–10.6