Metaheuristics for Machine Learning - Algorithms and Applications

✍ Scribed by Kanak Kalita; Narayanan Ganesh; S. Balamurugan

• Publisher: WILEY
• Year: 2024
• Tongue: English
• Leaves: 352
• Category: Library

✦ Synopsis

The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing

✦ Table of Contents

Cover
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
1 Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review
1.1 Introduction
1.2 Types of Metaheuristic Algorithms
1.3 Application of Metaheuristic Algorithms
1.4 Future Direction
1.5 Conclusion
References
2 A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning
2.1 Introduction
2.2 Fundamentals of Hyperparameter Optimization
2.3 Overview of Metaheuristic Optimization Techniques
2.4 Population-Based Metaheuristic Techniques
2.5 Single Solution-Based Metaheuristic Techniques
2.6 Hybrid Metaheuristic Techniques
2.7 Metaheuristics in Bayesian Optimization
2.8 Metaheuristics in Neural Architecture Search
2.9 Comparison of Metaheuristic Techniques for Hyperparameter Optimization
2.10 Applications of Metaheuristics in Machine Learning
2.11 Future Directions and Open Challenges
2.12 Conclusion
References
3 A Survey of Computer-Aided Diagnosis Systems for Breast Cancer Detection
3.1 Introduction
3.2 Procedure for Research Survey
3.3 Imaging Modalities and Their Datasets
3.4 Research Survey
3.5 Conclusion
3.6 Acknowledgment
References
4 Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification
4.1 Introduction
4.2 Related Work
4.3 Proposed Methodology
4.4 Experimental Setup
4.5 Results and Discussion
4.6 Conclusion
References
5 Anomaly Identification in Surveillance Video Using Regressive Bidirectional LSTM with Hyperparameter Optimization
5.1 Introduction
5.2 Literature Survey
5.3 Proposed Methodology
5.4 Result and Discussion
5.5 Conclusion
References
6 Ensemble Machine Learning-Based Botnet Attack Detection for IoT Applications
6.1 Introduction
6.2 Literature Survey
6.3 Proposed System
6.4 Results and Discussion
6.5 Conclusion
References
7 Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Result and Discussion
7.5 Conclusion
References
8 Security Enhancement in IoMT‑Assisted Smart Healthcare System Using the Machine Learning Approach
8.1 Introduction
8.2 Literature Review
8.3 Proposed Methodology
8.4 Conclusion
References
9 Building Sustainable Communication: A Game-Theoretic Approach in 5G and 6G Cellular Networks
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.4 Result
9.5 Conclusion
References
10 Autonomous Vehicle Optimization: Striking a Balance Between Cost-Effectiveness and Sustainability
10.1 Introduction
10.2 Methods
10.3 Results
10.4 Conclusions
References
11 Adapting Underground Parking for the Future: Sustainability and Shared Autonomous Vehicles
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.4 Analysis
11.5 Conclusion
References
12 Big Data Analytics for a Sustainable Competitive Edge: An Impact Assessment
12.1 Introduction
12.2 Related Works
12.3 Hypothesis and Research Model
12.4 Results
12.5 Conclusion
References
13 Sustainability and Technological Innovation in Organizations: The Mediating Role of Green Practices
13.1 Introduction
13.2 Related Work
13.3 Methodology
13.4 Discussion
13.5 Conclusions
References
14 Optimal Cell Planning in Two Tier Heterogeneous Network through Meta-Heuristic Algorithms
14.1 Introduction
14.2 System Model and Formulation of the Problem
14.3 Result and Discussion
14.4 Conclusion
References
15 Soil Aggregate Stability Prediction Using a Hybrid Machine Learning Algorithm
15.1 Introduction
15.2 Related Works
15.3 Proposed Methodology
15.4 Result and Discussion
15.5 Conclusion
References
Index
Also of Interest
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