Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ Discussion 24.6 Conclusion References 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

      11  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 СКАЧАТЬ