Evolutionary Computation with Intelligent Systems: A Multidisciplinary Approach to Society 5.0

Description

This book focuses on cutting-edge innovations and core theories, principles, and algorithms applicable to a wide area. Real-life applications, case studies, and examples are included along with emerging trends, design, and optimized solutions pivoting around the needs of Society 5.0. Evolutionary Computation with Intelligent Systems: A Multidisciplinary Approach to Society 5.0 provides a holistic view of evolutionary computation techniques including principles, procedures, and future applications with real-life examples. The book comprehensively explains evolutionary computation, design, principles, development trends, and optimization and describes how it can transform the operating context of the organization. It exemplifies the potential of evolutionary computation for the next generation and the role of cloud computing in shaping Society 5.0. It also provides insight into various platforms, paradigms, techniques, and tools used in diverse fields. This book appeals to a variety of readers such as academicians, researchers, research scholars, and postgraduates. Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors Chapter 1 Evolutionary Trends in Smart City Initiatives: A Consumer Perspective 1.1 Introduction 1.2 Literature Review 1.3 Methodology 1.4 Smart Housing and Smart Infrastructure 1.4.1 Smart Housing 1.4.2 Smart Buildings and Infrastructure 1.4.3 Smart Health Sector 1.5 Smart Transport and Smart Vehicles 1.5.1 Smart Transport 1.5.2 Public Vehicles 1.6 Smart Community and Smart Waste Management 1.6.1 Smart Community Services 1.6.2 Smart Water Management 1.6.3 Smart Waste Management 1.7 Conclusion References Chapter 2 Human-Feedback Adaptive Learning Using Interpretable and Interactive Intelligent Systems 2.1 Introduction 2.2 Related Work 2.3 Algorithmic Description of an Interactive Intelligent System 2.4 Proposed Framework 2.5 Experimentation 2.5.1 Material (Dataset) 2.5.2 Experiments Designed 2.5.3 Methods 2.5.4 Learning Algorithm and Evaluation Metrics 2.5.5 Listening to the Dataset by Identifying Important Features 2.5.6 Listening to the ML Model by Interpreting Its Behavior 2.5.7 Listening to Human Domain Experts 2.5.8 Statistical Tests and Hypothesis Testing 2.6 Results and Discussion 2.6.1 Evaluating ML Model Performance 2.6.2 Listening to the Dataset in Terms of Dataset Characteristics 2.6.3 Listening to the Model through a Diagnosis of Its Decision-Making Behavior 2.6.4 Feedback from Human Domain Experts 2.6.5 Agreement between Dataset, Model, and Human Experts in Terms of Ranking of Features 2.6.6 Observations 2.6.7 Revising ML Model to Adapt to Human Expert Feedback 2.6.8 Observation(s) 2.7 Metrics for Evaluation of Interactive ML Systems 2.8 Other Novel Opportunities 2.8.1 Human-Feedback Adaptive Random Forest (HARF) – Weighted Selection of Features 2.8.2 User Interface That Can Adapt to Human Capability 2.9 Conclusions and Future Work References Chapter 3 Advertisement Detection: Image Processing and Deep Learning Approach for Effective Information Extraction from Online English Newspapers 3.1 Introduction 3.1.1 Newspaper Layout Segmentation 3.1.2 Image Classification into Advertisements and Non-advertisements 3.2 Related Literature 3.2.1 Review of Newspaper Layout Segmentation 3.2.2 Review of Advertisement Image Classification 3.3 Image Extraction from Online English Newspapers 3.3.1 Image Extraction Technique 3.3.2 Image Extraction Results and Discussion 3.4 Advertisement Image Classification 3.4.1 Using CNN for Advertisement Image Classification 3.4.1.1 Working of CNN 3.4.2 English Newspaper Image Dataset 3.4.3 Transfer Learning 3.4.4 Pre-Trained ResNet50 Model for Transfer Learning 3.4.5 CNN Models for Advertisement Classification 3.4.5.1 Model Building 3.4.5.2 Model Evaluation 3.4.5.3 Model 1 (Feature Extractor + Classifier) 3.4.5.4 Model 2 (Fine-Tuning Top Most Conv Block) 3.4.5.5 Model 3 (Fine-Tuning Top Two Conv Blocks) 3.4.5.6 Model 4(Fine-Tuning Top Three Conv Blocks) 3.4.5.7 Model 5 (Fine-Tuning Top Four Conv Blocks) 3.4.5.8 Model 6 (Re-training Whole Model + Classifier) 3.4.6 Classification Results and Analysis 3.5 Conclusion References Chapter 4 Evolutionary Computation Framework for Handling Resource and Optimization of Solar Energy Harvesting System for WSN 4.1 Introduction 4.2 Literature Survey 4.3 PV Cell Principal, Characteristic, and Module 4.3.1 Solar Radiation Effect (G) 4.3.2 The Temperature Effect (T) 4.4 System for Harvesting Solar Energy 4.4.1 DC-DC Converter Modeling 4.4.2 DC-DC Buck Converter Power Losses 4.5 Maximum Power Point Tracking (MPPT) Modeling Technique 4.6 Incremental Conductance (INC) Algorithm 4.7 Simulation Experiment Setup 4.8 Simulation Results 4.9 Energy Harvester Systems Efficiency ( ŋ[sub(sys)]) Calculation 4.9.1 P&O-MPPT Efficiency 4.9.2 INC-MPPT Efficiency 4.10 Conclusion 4.11 Future Scope References Chapter 5 Smart Systems for Global Sustainability with Enhanced Computing 5.1 Introduction 5.2 History and Background 5.2.1 Parking Management System Based on WSN 5.2.2 Parking Management System Based on RFID 5.2.3 Cloud- Based Parking Management System using VANETs 5.2.4 Cloud- Based Parking Management System Based on IoT 5.2.5 Smart Parking Management System Based on Parallel Theory 5.2.6 Smart Parking Management System Using Image Processing and Artificial Intelligence 5.3 Strengths of the Existing Technologies for Smart Parking Management System 5.4 Scopes of Improvement in the Existing Technologies for Smart Parking Management System 5.5 Proposed Sustainable Smart Parking System with Security 5.6 Results 5.6.1 Throughput vs Number of Rounds 5.6.2 Residual Energy 5.7 Conclusion References Chapter 6 Intelligent Systems: Techniques for Optimized Decision Making 6.1 Introduction 6.2 Motion Prediction 6.2.1 The Extended Social Force Model (ESFM) 6.2.2 Robot Navigation Using the Extended Social Force Model (ESFM) 6.3 Autonomous Mobile Robot Navigation and Control 6.3.1 Perception 6.3.2 Localization/Pose Estimation 6.3.3 Map Building 6.3.4 Path Planning 6.3.5 State-of-the Art Path Planning Approaches 6.4 Q-learning Strategy 6.5 Results and Discussion 6.6 Conclusion Acknowledgement Disclosure Statement References Chapter 7 Innovations in Healthcare Using Smart Systems Equipped with Evolutionary Computation 7.1 Introduction 7.2 Literature Review 7.3 Evolutionary C omputation and Society 5.0 7.4 Experimental Setup 7.5 Results and Discussion 7.6 Conclusion 7.7 Future Scope References Chapter 8 Exploiting Evolutionary Computation Techniques for Service Industries 8.1 Introduction 8.2 Framework 8.2.1 Business Understanding 8.2.2 Data Understanding 8.2.3 Data Preparation 8.2.4 Modeling 8.2.5 Evaluation 8.2.6 Deployment 8.3 Study Cases 8.3.1 Position Regulation with the Simple Pendulum 8.3.1.1 Business Understanding 8.3.1.2 Data Understanding 8.3.1.3 Data Preparation 8.3.1.4 Modeling 8.3.1.5 Evaluation 8.3.1.6 Deployment 8.3.2 Trajectory Tracking with the Fully-Actuated Inverted Pendulum 8.3.2.1 Business Understanding 8.3.2.2 Data Understanding 8.3.2.3 Data Preparation 8.3.2.4 Modeling 8.3.2.5 Evaluation 8.3.2.6 Deployment 8.4 Conclusion References Chapter 9 Evolutionary Computation Techniques for Strengthening Performance of Commercial MANETs in Society 5.0 9.1 Introduction 9.2 Commercial MANET: Issues and Challenges in Society 5.0 9.3 Vulnerabilities and Threats to Routing in Commercial MANET in Society 5.0 9.4 Computational Intelligence for Effective Routing in Commercial MANET 9.5 Evolutionary Computation Techniques for Commercial MANETs in Society 5.0: A Comparative Study 9.6 Areas for Strengthening MANET Performance with Evolutionary Computation Techniques 9.7 Conclusion References Chapter 10 Availability Optimization of a Rice Finishing and Grading System Using Evolutionary Computation Techniques 10.1 Introduction 10.2 Industrial System Description 10.3 Notations and Assumptions 10.4 Availability Modeling of Rice Finishing and Grading System 10.5 Steady State Availability (SSA) of RFGS 10.6 Availability Optimization 10.7 Results and Discussion 10.8 Conclusion 10.9 Scope for Future Work Acknowledgment References Chapter 11 Analysis of Sign Language Recognition System for Society 5.0 for Sensory-Impaired People 11.1 Introduction 11.2 Literature Survey 11.3 Sign Language Recognition System 11.3.1 Validated Dataset 11.3.2 Conditioning Stage 11.3.3 Segmentation Stage 11.3.4 Feature Extraction and Selection 11.3.5 Classification Stage 11.4 Conclusion and Future Work References Chapter 12 Study and Control of Shrinkage in Gearbox Sand Casting Using Simulation and Experimental Validation 12.1 Introduction 12.2 Literature Review 12.3 Research Material and Properties 12.4 Research Work Methodology Taken for the Work 12.4.1 Identification of Vital Few Factors through Pareto 12.4.2 Listing of Parameters Responsible for Turbulence 12.4.3 Numerical Simulation for the Existing and Proposed Casting System Parameters 12.4.4 Tooling Modification and Bulk Production 12.4.5 Validation of Experimental and Numerical Study 12.4.6 Validation for the Various Parameters Responsible for Shrinkage Defect 12.5 Casting Process Simulation Study 12.6 Results and Discussions 12.7 Conclusion and Future Scope Acknowledgments References Chapter 13 An Integrated Approach Based on Structural Modeling for Development of Risk Assessment Framework for Drivers Involved in Green Supply Chain Management in India 13.1 Introduction 13.2 Literature Review 13.3 Interpretive Structural Modeling 13.4 Case Study 13.4.1 Structural Self-Interaction Matrix 13.4.2 Reachability Matrix 13.4.3 Final Reachability Formation Matrix 13.4.4 Level Partitions 13.5 Discussion 13.6 Conclusion 13.7 Limitations of the Study References Chapter 14 Human Resource Intelligent Systems: Rewriting the DNA of HR Function 14.1 Introduction 14.1.1 Adoption of HR Intelligent Systems: Journey of “HRM” to “e-HRM” 14.2 Literature Review 14.3 Research Methodology 14.3.1 Objectives of Research 14.3.2 Procedure and Participants 14.4 Results and Discussion 14.4.1 Sector-wise Comparisons 14.4.1.1 IT sector 14.4.1.2 Automobile Sector 14.4.1.3 Banking and Finance Sector 14.5 Conclusion 14.6 Future Scope 14.7 Limitations of the Study References Appendix Chapter 15 Role of Servitization in Society 5.0 15.1 Introduction 15.2 Review of the Literature 15.3 Shift to Servitization 15.4 Technology 15.4.1 PR Actioners for Servitization 15.5 Servitization Drivers and Models 15.5.1 Servitization Model 15.5.2 Incremental Servitization 15.6 Reducing Barriers 15.6.1 Ownership Barriers 15.6.2 Operational Barrier 15.6.3 Risk Barrier 15.7 Conclusion References Index