Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов
25 State-of-the-Art Optimization and metaheuristic Algorithms 25.1 Introduction 25.2 An Overview of Traditional Optimization Approaches 25.3 Properties of Metaheuristics 25.4 Classification of Single Objective Metaheuristic Algorithms 25.5 Applications of Single Objective metaheuristic Approaches 25.6 Classification of Multi-Objective Optimization Algorithms 25.7 Hybridization of MOPs Algorithms 25.8 Parallel Multi-Objective Optimization 25.9 Applications of Multi-Objective Optimization 25.10 Significant Contributions of Researchers in Various Metaheuristic Approaches 25.11 Conclusion 25.12 Major Findings, Future Scope of Metaheuristics and Its Applications 25.13 Limitations and Motivation of Metaheuristics Acknowledgements References 26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique 26.1 Introduction 26.2 Proposed Methodology 26.3 Simulation Results 26.4 Conclusions References 27 A New Parameter Estimation Technique of Three-Diode PV Cells 27.1 Introduction 27.2 Problem Statement 27.3 Proposed Method 27.4 Simulation Results and Discussions 27.5 Conclusions References
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Part IV SUSTAINABLE COMPUTING
28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network
28.1 Introduction
28.2 System Model and Preliminaries
28.3 Machine Learning Techniques of Decision Fusion
28.4 Optimum Quantization of Decision Statistic and Fusion
28.5 Measurement Setup
28.6 Performance Evaluation
28.7 Conclusion
28.8 Limitations and Scope for Future Work
References
29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies
29.1 Introduction
29.2 Green Approaches: Significance and Motivation
29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control
29.4 Green IoT–Based Smart Irrigation Monitoring
29.5 Technology Enablers for GIoT–Based Irrigation Monitoring
29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation
29.7 Other Recent Developments on GIoT–Based Smart Agriculture
29.8 Literature Review of Edge Computing–Based Irrigation Monitoring
29.9 LPWAN for GIoT–Based Smart Agriculture
29.10 Analysis and Discussion
29.11 Research Gap in GIoT–Based Precision Agriculture
29.12 Analysis of Merits and Shortcomings
29.13 Future Research Scope
29.14 Conclusion
References
30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification
30.1 Introduction
30.2 Related Works
30.3 Proposed Approach
30.4 Experimental Details and Results
30.5 Conclusion
References
31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission
31.1 Introduction
31.2 Related Work
31.3 Proposed Model
31.4 Complexity Analysis of Algorithms for Data Transmission
31.5 Experimental Analysis
31.6 Motivation and Limitations of Research
31.7