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Submit a ProposalMetaheuristics for Machine Learning
 | Algorithms and Applications Edited by Kanak Kalita, Narayanan Ganesh and S. Balamurugan Series: Artificial Intelligence and Soft Computing for Industrial Transformation Copyright: 2024 | Status: Published ISBN: 9781394233922 | Hardcover | 336 pages Price: $195 USD  |
One Line Description
The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications.
Audience
The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.
The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.
Description
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 machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems
in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a
pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field.
Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence.
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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 machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems
in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a
pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field.
Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence.
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Author / Editor Details
Kanak Kalita, PhD, is a professor in the Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India. He has more than 190 articles in international and national journals and 5 edited books. Dr. Kalita’s research interests include machine learning, fuzzy decision-making, metamodeling, process optimization,
finite element method, and composites.
Kanak Kalita, PhD, is a professor in the Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India. He has more than 190 articles in international and national journals and 5 edited books. Dr. Kalita’s research interests include machine learning, fuzzy decision-making, metamodeling, process optimization,
finite element method, and composites.
Narayanan Ganesh, PhD, is an associate professor at the Vellore Institute of Technology Chennai Campus. His extensive research encompasses a range of critical areas, including software engineering, agile software development, prediction and optimization techniques, deep learning, image processing,
and data analytics. He has published over 30 articles and written 8 textbooks and has been recognized for his contributions to the field with two international patents from Australia.
and data analytics. He has published over 30 articles and written 8 textbooks and has been recognized for his contributions to the field with two international patents from Australia.
S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI),
India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.
India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.
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Table of Contents
Foreword
Preface
1. Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review
Abrar Yaqoob, Navneet Kumar Verma and Rabia Musheer Aziz
1.1 Introduction
1.2 Types of Metaheuristic Algorithms
1.2.1 Genetic Algorithms
1.2.2 Simulated Annealing
1.2.3 Particle Swarm Optimization
1.2.4 Ant Colony Optimization
1.2.5 Tabu Search
1.2.6 Differential Evolution
1.2.7 Harmony Search
1.2.8 Artificial Bee Colony
1.2.9 Firefly Algorithm
1.2.10 Gray Wolf Optimizer
1.2.11 Imperialist Competitive Algorithm
1.2.12 Bat Algorithm
1.2.13 Cuckoo Search
1.2.14 Flower Pollination Algorithm
1.2.15 Krill Herd Algorithm
1.2.16 Whale Optimization Algorithm
1.2.17 Glowworm Swarm Optimization
1.2.18 Cat Swarm Optimization
1.2.19 Grasshopper Optimization Algorithm
1.2.20 Moth–Flame Optimization
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
Ramachandran Narayanan and Narayanan Ganesh
2.1 Introduction
2.1.1 Background and Motivation
2.1.2 Scope of the Review
2.1.3 Organization of the Paper
2.2 Fundamentals of Hyperparameter Optimization
2.2.1 Introduction to Hyperparameters
2.2.2 Importance of Hyperparameter Optimization
2.2.3 Performance Metrics for Hyperparameter Optimization
2.2.4 Challenges in Hyperparameter Optimization
2.3 Overview of Metaheuristic Optimization Techniques
2.3.1 Definition and Characteristics of Metaheuristics
2.3.2 Classification of Metaheuristic Techniques
2.4 Population-Based Metaheuristic Techniques
2.4.1 Genetic Algorithms
2.4.2 Particle Swarm Optimization
2.4.3 Differential Evolution
2.4.4 Ant Colony Optimization
2.4.5 Biogeography-Based Optimization
2.4.6 Cuckoo Search
2.4.7 Gray Wolf Optimizer
2.4.8 Whale Optimization Algorithm
2.4.9 Recent Developments in Population-Based Metaheuristics
2.5 Single Solution-Based Metaheuristic Techniques
2.5.1 Simulated Annealing
2.5.2 Tabu Search
2.5.3 Harmony Search
2.5.4 Bat Algorithm
2.5.5 Recent Developments in Single Solution-Based Metaheuristics
2.6 Hybrid Metaheuristic Techniques
2.6.1 Genetic Algorithm and Particle Swarm Optimization Hybrid
2.6.2 Genetic Algorithm and Simulated Annealing Hybrid
2.6.3 Tabu Search and Particle Swarm Optimization Hybrid
2.6.4 Recent Developments in Hybrid Metaheuristics
2.7 Metaheuristics in Bayesian Optimization
2.7.1 Background of Bayesian Optimization
2.7.2 Gaussian Process Regression
2.7.3 Acquisition Functions
2.7.4 Recent Developments in Metaheuristic-Based Bayesian Optimization
2.8 Metaheuristics in Neural Architecture Search
2.8.1 Introduction to Neural Architecture Search
2.8.2 Applications of Metaheuristics in Neural Architecture Search
2.8.3 Recent Developments in Metaheuristic-Based Neural Architecture Search
2.9 Comparison of Metaheuristic Techniques for Hyperparameter Optimization
2.9.1 Criteria for Comparison
2.9.2 Comparative Analysis of Metaheuristic Techniques
2.9.3 Performance Evaluation of Metaheuristic Techniques
2.10 Applications of Metaheuristics in Machine Learning
2.10.1 Supervised Learning
2.10.2 Unsupervised Learning
2.10.3 Reinforcement Learning
2.10.4 Deep Learning
2.11 Future Directions and Open Challenges
2.11.1 Opportunities for Improvement in Metaheuristics
2.11.2 Adapting Metaheuristics to New Machine Learning Paradigms
2.11.3 Addressing the Computational Complexity and Scalability
2.12 Conclusion
References
3. A Survey of Computer-Aided Diagnosis Systems for Breast Cancer Detection
Charu Anant Rajput, Leninisha Shanmugam and Parkavi K.
3.1 Introduction
3.2 Procedure for Research Survey
3.3 Imaging Modalities and Their Datasets
3.3.1 Histopathological WSI
3.3.2 Digital Mammography
3.3.3 Ultrasound
3.3.4 Magnetic Resonance Imaging
3.3.5 Infrared Breast Thermal Images
3.4 Research Survey
3.4.1 Histopathological WSI
3.4.1.1 Machine Learning-Based Histopathological WSI
3.4.1.2 Deep Learning-Based Histopathological WSI
3.4.2 Digital Mammogram
3.4.2.1 Machine Learning-Based Digital Mammogram
3.4.2.2 Deep Learning-Based Digital Mammogram
3.4.3 Ultrasound
3.4.3.1 Machine Learning-Based Ultrasound
3.4.3.2 Deep Learning-Based Ultrasound
3.4.4 MRI-Based
3.4.4.1 Machine Learning-Based MRI Analysis
3.4.4.2 Deep Learning-Based MRI Analysis
3.4.5 Thermography-Based
3.4.5.1 Machine Learning-Based Thermography Analysis
3.4.5.2 Deep Learning-Based Thermography Analysis
3.5 Conclusion
3.6 Acknowledgment
References
4. Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz and Akash Saxena
4.1 Introduction
4.2 Related Work
4.3 Proposed Methodology
4.3.1 Cuckoo Search Algorithm
4.3.2 Harris Hawks Algorithm
4.3.3 The Proposed Hybrid Algorithm
4.3.4 Classifiers Used
4.3.4.1 KNN Classifier
4.3.4.2 SVM Classifier
4.3.4.3 NB Classifier
4.3.4.4 mRMR
4.4 Experimental Setup
4.4.1 The Compared Algorithms
4.4.2 Parameter Setting
4.5 Results and Discussion
4.5.1 Experimental Results of the Proposed Algorithm With the SVM Classifier
4.5.2 Experimental Results of the Proposed Algorithm With the KNN Classifier
4.5.3 Experimental Results of the Proposed Algorithm With the NB Classifier
4.5.4 Comparison of the Proposed Algorithm Compared to Other Recently Published and Popular Algorithms for Cancer Classification
4.6 Conclusion
References
5. Anomaly Identification in Surveillance Video Using Regressive Bidirectional LSTM with Hyperparameter Optimization
Rajendran Shankar and Narayanan Ganesh
5.1 Introduction
5.2 Literature Survey
5.3 Proposed Methodology
5.3.1 Dataset
5.3.2 Preprocessing Using Normalization
5.3.3 Feature Extraction Using Video Swin Transformer
5.3.4 Anomaly Detection
5.3.4.1 Regressive Bidirectional LSTM
5.3.4.2 Hyperparameter Optimization
5.4 Result and Discussion
5.5 Conclusion
References
6. Ensemble Machine Learning-Based Botnet Attack Detection for IoT Applications
Suchithra M.
6.1 Introduction
6.2 Literature Survey
6.3 Proposed System
6.3.1 Dataset
6.3.2 Data Processing Using Normalization
6.3.3 Feature Extraction Using Principal Component Analysis
6.3.4 Random Tree-Adaptive Artificial Neural Network
6.4 Results and Discussion
6.4.1 Accuracy
6.4.2 Precision
6.4.3 Recall
6.4.4 F-Measure
6.5 Conclusion
References
7. Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks
Ilavendhan Anandaraj and Kaviarasan Ramu
7.1 Introduction
7.1.1 Intrusion Detection System
7.1.2 Machine Learning
7.2 Literature Review
7.3 Proposed Methodology
7.3.1 Problem Statement
7.3.2 Methodology
7.3.3 Data Collection
7.3.4 Data Preprocessing
7.3.5 Support Vector Machine
7.3.6 Tuned Spider Monkey Optimization
7.4 Result and Discussion
7.4.1 Accuracy
7.4.2 Precision
7.4.3 Recall
7.4.4 F1 Score
7.5 Conclusion
References
8. Security Enhancement in IoMT‑Assisted Smart Healthcare System Using the Machine Learning Approach
Jayalakshmi Sambandan, Bharanidharan Gurumurthy and Syed Jamalullah R.
8.1 Introduction
8.2 Literature Review
8.3 Proposed Methodology
8.3.1 Data Collection
8.3.2 Data Preprocessing
8.3.3 Support Vector Machine
8.3.4 Multilayer Particle Swarm Optimization
8.3.5 Performance Evaluation
8.3.5.1 Accuracy
8.3.5.2 Precision
8.3.5.3 Sensitivity
8.3.5.4 Specificity
8.3.5.5 Security
8.4 Conclusion
References
9. Building Sustainable Communication: A Game-Theoretic Approach in 5G and 6G Cellular Networks
Puppala Ramya, Tulasidhar Mulakaluri, Chebrolu Yasmina, Pandi Bindu Madhavi and Vijay Guru Balaji K. S.
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.3.1 Nash Equilibrium
9.3.2 Unique Nash Equilibrium
9.3.3 Total Unique Equilibrium
9.4 Result
9.5 Conclusion
References
10. Autonomous Vehicle Optimization: Striking a Balance Between Cost-Effectiveness and Sustainability
Vamsidhar Talasila, Sagi Venkata Lakshmi Narasimharaju, Neeli Veda Vyshnavi, Saketh Naga Sreenivas Kondaveeti, Garimella Surya Siva Teja and Kiran Kumar Kaveti
10.1 Introduction
10.2 Methods
10.2.1 Competition and Sustainable Supply Chains at Odds
10.2.2 Industry’s Effect Application of the 4.0 Idea to Business Operations
10.3 Results
10.4 Conclusions
References
11. Adapting Underground Parking for the Future: Sustainability and Shared Autonomous Vehicles
Vamsidhar Talasila, Madala Pavan Pranav Sai, Gade Sri Raja Gopala Reddy, Vempati Pavan Kashyap, Gunda Karthik and K. V. Panduranga Rao
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.3.1 Framework for Research
11.3.2 Area of Research
11.3.3 Collection of Data
11.3.4 Analysis of Clusters
11.3.5 DSR Model
11.3.6 Model with Multiple Objectives
11.3.7 Statistical Data
11.4 Analysis
11.4.1 Feature Types
11.4.2 Timing of Renewal
11.4.3 Replacement of Function
11.5 Conclusion
References
12. Big Data Analytics for a Sustainable Competitive Edge: An Impact Assessment
Rajyalakshmi K., Padma A., Varalakshmi M., Suhasini A. and Chiranjeevi P.
12.1 Introduction
12.2 Related Works
12.3 Hypothesis and Research Model
12.3.1 Theoretical Model
12.3.1.1 Hypothesis 1 (H1)
12.3.1.2 BDAC as a Facilitator for IC (H2)
12.3.1.3 The Contribution of BDAC and IC to Raising SCA (H3) and (H4)
12.3.1.4 Sustainable Competitive Advantage
12.3.2 Proposed Method
12.3.2.1 Layout Assessment
12.3.2.2 Analysis Method and Data Collection
12.4 Results
12.4.1 Evaluation of Validity and Reliability
12.4.2 Structural Model of Coefficient of Determination (R2)
12.4.3 Discussion
12.5 Conclusion
References
13. Sustainability and Technological Innovation in Organizations: The Mediating Role of Green Practices
Rajyalakshmi K., Rajkumar G. V. S., Sulochana B., Rama Devi V. N. and Padma A.
13.1 Introduction
13.2 Related Work
13.2.1 Hypothesis Development
13.3 Methodology
13.3.1 Sample and Population
13.3.2 Techniques
13.3.3 Control
13.3.4 Data Analysis
13.3.5 Verifying Factor Evaluation
13.3.6 Standard Technique Bias
13.3.7 Correlation
13.3.8 Robustness Checks
13.3.9 Interaction Analysis
13.4 Discussion
13.5 Conclusions
References
14. Optimal Cell Planning in Two Tier Heterogeneous Network through Meta-Heuristic Algorithms
Sanjoy Debnath, Amit Baran Dey and Wasim Arif
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
M. Balamurugan
15.1 Introduction
15.2 Related Works
15.3 Proposed Methodology
15.3.1 Soil Samples and Characteristics
15.3.2 Analyzing Soil Samples
15.3.3 Hybrid Tree-Based Twin-Bounded Support Vector Machine-Based Model
15.3.3.1 Hybrid Tree Algorithm-C5.0
15.3.3.2 Twin-Bounded Support Vector Machine-Based Model
15.4 Result and Discussion
15.5 Conclusion
References
Index
Back to Top
Foreword
Preface
1. Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review
Abrar Yaqoob, Navneet Kumar Verma and Rabia Musheer Aziz
1.1 Introduction
1.2 Types of Metaheuristic Algorithms
1.2.1 Genetic Algorithms
1.2.2 Simulated Annealing
1.2.3 Particle Swarm Optimization
1.2.4 Ant Colony Optimization
1.2.5 Tabu Search
1.2.6 Differential Evolution
1.2.7 Harmony Search
1.2.8 Artificial Bee Colony
1.2.9 Firefly Algorithm
1.2.10 Gray Wolf Optimizer
1.2.11 Imperialist Competitive Algorithm
1.2.12 Bat Algorithm
1.2.13 Cuckoo Search
1.2.14 Flower Pollination Algorithm
1.2.15 Krill Herd Algorithm
1.2.16 Whale Optimization Algorithm
1.2.17 Glowworm Swarm Optimization
1.2.18 Cat Swarm Optimization
1.2.19 Grasshopper Optimization Algorithm
1.2.20 Moth–Flame Optimization
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
Ramachandran Narayanan and Narayanan Ganesh
2.1 Introduction
2.1.1 Background and Motivation
2.1.2 Scope of the Review
2.1.3 Organization of the Paper
2.2 Fundamentals of Hyperparameter Optimization
2.2.1 Introduction to Hyperparameters
2.2.2 Importance of Hyperparameter Optimization
2.2.3 Performance Metrics for Hyperparameter Optimization
2.2.4 Challenges in Hyperparameter Optimization
2.3 Overview of Metaheuristic Optimization Techniques
2.3.1 Definition and Characteristics of Metaheuristics
2.3.2 Classification of Metaheuristic Techniques
2.4 Population-Based Metaheuristic Techniques
2.4.1 Genetic Algorithms
2.4.2 Particle Swarm Optimization
2.4.3 Differential Evolution
2.4.4 Ant Colony Optimization
2.4.5 Biogeography-Based Optimization
2.4.6 Cuckoo Search
2.4.7 Gray Wolf Optimizer
2.4.8 Whale Optimization Algorithm
2.4.9 Recent Developments in Population-Based Metaheuristics
2.5 Single Solution-Based Metaheuristic Techniques
2.5.1 Simulated Annealing
2.5.2 Tabu Search
2.5.3 Harmony Search
2.5.4 Bat Algorithm
2.5.5 Recent Developments in Single Solution-Based Metaheuristics
2.6 Hybrid Metaheuristic Techniques
2.6.1 Genetic Algorithm and Particle Swarm Optimization Hybrid
2.6.2 Genetic Algorithm and Simulated Annealing Hybrid
2.6.3 Tabu Search and Particle Swarm Optimization Hybrid
2.6.4 Recent Developments in Hybrid Metaheuristics
2.7 Metaheuristics in Bayesian Optimization
2.7.1 Background of Bayesian Optimization
2.7.2 Gaussian Process Regression
2.7.3 Acquisition Functions
2.7.4 Recent Developments in Metaheuristic-Based Bayesian Optimization
2.8 Metaheuristics in Neural Architecture Search
2.8.1 Introduction to Neural Architecture Search
2.8.2 Applications of Metaheuristics in Neural Architecture Search
2.8.3 Recent Developments in Metaheuristic-Based Neural Architecture Search
2.9 Comparison of Metaheuristic Techniques for Hyperparameter Optimization
2.9.1 Criteria for Comparison
2.9.2 Comparative Analysis of Metaheuristic Techniques
2.9.3 Performance Evaluation of Metaheuristic Techniques
2.10 Applications of Metaheuristics in Machine Learning
2.10.1 Supervised Learning
2.10.2 Unsupervised Learning
2.10.3 Reinforcement Learning
2.10.4 Deep Learning
2.11 Future Directions and Open Challenges
2.11.1 Opportunities for Improvement in Metaheuristics
2.11.2 Adapting Metaheuristics to New Machine Learning Paradigms
2.11.3 Addressing the Computational Complexity and Scalability
2.12 Conclusion
References
3. A Survey of Computer-Aided Diagnosis Systems for Breast Cancer Detection
Charu Anant Rajput, Leninisha Shanmugam and Parkavi K.
3.1 Introduction
3.2 Procedure for Research Survey
3.3 Imaging Modalities and Their Datasets
3.3.1 Histopathological WSI
3.3.2 Digital Mammography
3.3.3 Ultrasound
3.3.4 Magnetic Resonance Imaging
3.3.5 Infrared Breast Thermal Images
3.4 Research Survey
3.4.1 Histopathological WSI
3.4.1.1 Machine Learning-Based Histopathological WSI
3.4.1.2 Deep Learning-Based Histopathological WSI
3.4.2 Digital Mammogram
3.4.2.1 Machine Learning-Based Digital Mammogram
3.4.2.2 Deep Learning-Based Digital Mammogram
3.4.3 Ultrasound
3.4.3.1 Machine Learning-Based Ultrasound
3.4.3.2 Deep Learning-Based Ultrasound
3.4.4 MRI-Based
3.4.4.1 Machine Learning-Based MRI Analysis
3.4.4.2 Deep Learning-Based MRI Analysis
3.4.5 Thermography-Based
3.4.5.1 Machine Learning-Based Thermography Analysis
3.4.5.2 Deep Learning-Based Thermography Analysis
3.5 Conclusion
3.6 Acknowledgment
References
4. Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz and Akash Saxena
4.1 Introduction
4.2 Related Work
4.3 Proposed Methodology
4.3.1 Cuckoo Search Algorithm
4.3.2 Harris Hawks Algorithm
4.3.3 The Proposed Hybrid Algorithm
4.3.4 Classifiers Used
4.3.4.1 KNN Classifier
4.3.4.2 SVM Classifier
4.3.4.3 NB Classifier
4.3.4.4 mRMR
4.4 Experimental Setup
4.4.1 The Compared Algorithms
4.4.2 Parameter Setting
4.5 Results and Discussion
4.5.1 Experimental Results of the Proposed Algorithm With the SVM Classifier
4.5.2 Experimental Results of the Proposed Algorithm With the KNN Classifier
4.5.3 Experimental Results of the Proposed Algorithm With the NB Classifier
4.5.4 Comparison of the Proposed Algorithm Compared to Other Recently Published and Popular Algorithms for Cancer Classification
4.6 Conclusion
References
5. Anomaly Identification in Surveillance Video Using Regressive Bidirectional LSTM with Hyperparameter Optimization
Rajendran Shankar and Narayanan Ganesh
5.1 Introduction
5.2 Literature Survey
5.3 Proposed Methodology
5.3.1 Dataset
5.3.2 Preprocessing Using Normalization
5.3.3 Feature Extraction Using Video Swin Transformer
5.3.4 Anomaly Detection
5.3.4.1 Regressive Bidirectional LSTM
5.3.4.2 Hyperparameter Optimization
5.4 Result and Discussion
5.5 Conclusion
References
6. Ensemble Machine Learning-Based Botnet Attack Detection for IoT Applications
Suchithra M.
6.1 Introduction
6.2 Literature Survey
6.3 Proposed System
6.3.1 Dataset
6.3.2 Data Processing Using Normalization
6.3.3 Feature Extraction Using Principal Component Analysis
6.3.4 Random Tree-Adaptive Artificial Neural Network
6.4 Results and Discussion
6.4.1 Accuracy
6.4.2 Precision
6.4.3 Recall
6.4.4 F-Measure
6.5 Conclusion
References
7. Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks
Ilavendhan Anandaraj and Kaviarasan Ramu
7.1 Introduction
7.1.1 Intrusion Detection System
7.1.2 Machine Learning
7.2 Literature Review
7.3 Proposed Methodology
7.3.1 Problem Statement
7.3.2 Methodology
7.3.3 Data Collection
7.3.4 Data Preprocessing
7.3.5 Support Vector Machine
7.3.6 Tuned Spider Monkey Optimization
7.4 Result and Discussion
7.4.1 Accuracy
7.4.2 Precision
7.4.3 Recall
7.4.4 F1 Score
7.5 Conclusion
References
8. Security Enhancement in IoMT‑Assisted Smart Healthcare System Using the Machine Learning Approach
Jayalakshmi Sambandan, Bharanidharan Gurumurthy and Syed Jamalullah R.
8.1 Introduction
8.2 Literature Review
8.3 Proposed Methodology
8.3.1 Data Collection
8.3.2 Data Preprocessing
8.3.3 Support Vector Machine
8.3.4 Multilayer Particle Swarm Optimization
8.3.5 Performance Evaluation
8.3.5.1 Accuracy
8.3.5.2 Precision
8.3.5.3 Sensitivity
8.3.5.4 Specificity
8.3.5.5 Security
8.4 Conclusion
References
9. Building Sustainable Communication: A Game-Theoretic Approach in 5G and 6G Cellular Networks
Puppala Ramya, Tulasidhar Mulakaluri, Chebrolu Yasmina, Pandi Bindu Madhavi and Vijay Guru Balaji K. S.
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.3.1 Nash Equilibrium
9.3.2 Unique Nash Equilibrium
9.3.3 Total Unique Equilibrium
9.4 Result
9.5 Conclusion
References
10. Autonomous Vehicle Optimization: Striking a Balance Between Cost-Effectiveness and Sustainability
Vamsidhar Talasila, Sagi Venkata Lakshmi Narasimharaju, Neeli Veda Vyshnavi, Saketh Naga Sreenivas Kondaveeti, Garimella Surya Siva Teja and Kiran Kumar Kaveti
10.1 Introduction
10.2 Methods
10.2.1 Competition and Sustainable Supply Chains at Odds
10.2.2 Industry’s Effect Application of the 4.0 Idea to Business Operations
10.3 Results
10.4 Conclusions
References
11. Adapting Underground Parking for the Future: Sustainability and Shared Autonomous Vehicles
Vamsidhar Talasila, Madala Pavan Pranav Sai, Gade Sri Raja Gopala Reddy, Vempati Pavan Kashyap, Gunda Karthik and K. V. Panduranga Rao
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.3.1 Framework for Research
11.3.2 Area of Research
11.3.3 Collection of Data
11.3.4 Analysis of Clusters
11.3.5 DSR Model
11.3.6 Model with Multiple Objectives
11.3.7 Statistical Data
11.4 Analysis
11.4.1 Feature Types
11.4.2 Timing of Renewal
11.4.3 Replacement of Function
11.5 Conclusion
References
12. Big Data Analytics for a Sustainable Competitive Edge: An Impact Assessment
Rajyalakshmi K., Padma A., Varalakshmi M., Suhasini A. and Chiranjeevi P.
12.1 Introduction
12.2 Related Works
12.3 Hypothesis and Research Model
12.3.1 Theoretical Model
12.3.1.1 Hypothesis 1 (H1)
12.3.1.2 BDAC as a Facilitator for IC (H2)
12.3.1.3 The Contribution of BDAC and IC to Raising SCA (H3) and (H4)
12.3.1.4 Sustainable Competitive Advantage
12.3.2 Proposed Method
12.3.2.1 Layout Assessment
12.3.2.2 Analysis Method and Data Collection
12.4 Results
12.4.1 Evaluation of Validity and Reliability
12.4.2 Structural Model of Coefficient of Determination (R2)
12.4.3 Discussion
12.5 Conclusion
References
13. Sustainability and Technological Innovation in Organizations: The Mediating Role of Green Practices
Rajyalakshmi K., Rajkumar G. V. S., Sulochana B., Rama Devi V. N. and Padma A.
13.1 Introduction
13.2 Related Work
13.2.1 Hypothesis Development
13.3 Methodology
13.3.1 Sample and Population
13.3.2 Techniques
13.3.3 Control
13.3.4 Data Analysis
13.3.5 Verifying Factor Evaluation
13.3.6 Standard Technique Bias
13.3.7 Correlation
13.3.8 Robustness Checks
13.3.9 Interaction Analysis
13.4 Discussion
13.5 Conclusions
References
14. Optimal Cell Planning in Two Tier Heterogeneous Network through Meta-Heuristic Algorithms
Sanjoy Debnath, Amit Baran Dey and Wasim Arif
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
M. Balamurugan
15.1 Introduction
15.2 Related Works
15.3 Proposed Methodology
15.3.1 Soil Samples and Characteristics
15.3.2 Analyzing Soil Samples
15.3.3 Hybrid Tree-Based Twin-Bounded Support Vector Machine-Based Model
15.3.3.1 Hybrid Tree Algorithm-C5.0
15.3.3.2 Twin-Bounded Support Vector Machine-Based Model
15.4 Result and Discussion
15.5 Conclusion
References
Index
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