Metaheuristics for Machine Learning
Algorithms and Applications
1st Edition
Published on 16. April 2024
Book
Hardback
352 pages
978-1-394-23392-2 (ISBN)
Description
METAHEURISTICS for MACHINE LEARNING 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.
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.
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 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.
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.
More details
Series
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 231 mm
Width: 152 mm
Thickness: 23 mm
Weight
544 gr
ISBN-13
978-1-394-23392-2 (9781394233922)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kanak Kalita | Narayanan Ganesh | S. Balamurugan
Metaheuristics for Machine Learning
Algorithms and Applications
E-Book
03/2024
1st Edition
Wiley
€177.99
Available for download
Kanak Kalita | Narayanan Ganesh | S. Balamurugan
Metaheuristics for Machine Learning
Algorithms and Applications
E-Book
03/2024
1st Edition
Wiley
€177.99
Available for download
Persons
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.
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.
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.
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.
Content
Foreword xv
Preface xvii
1 Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review 1
Abrar Yaqoob, Navneet Kumar Verma and Rabia Musheer Aziz
1.1 Introduction 2
1.2 Types of Metaheuristic Algorithms 3
1.3 Application of Metaheuristic Algorithms 20
1.4 Future Direction 25
1.5 Conclusion 26
2 A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning 37
Ramachandran Narayanan and Narayanan Ganesh
2.1 Introduction 38
2.2 Fundamentals of Hyperparameter Optimization 39
2.3 Overview of Metaheuristic Optimization Techniques 42
2.4 Population-Based Metaheuristic Techniques 43
2.5 Single Solution-Based Metaheuristic Techniques 47
2.6 Hybrid Metaheuristic Techniques 49
2.7 Metaheuristics in Bayesian Optimization 50
2.8 Metaheuristics in Neural Architecture Search 53
2.9 Comparison of Metaheuristic Techniques for Hyperparameter Optimization 55
2.10 Applications of Metaheuristics in Machine Learning 61
2.11 Future Directions and Open Challenges 63
2.12 Conclusion 65
3 A Survey of Computer-Aided Diagnosis Systems for Breast Cancer Detection 73
Charu Anant Rajput, Leninisha Shanmugam and Parkavi K.
3.1 Introduction 73
3.2 Procedure for Research Survey 77
3.3 Imaging Modalities and Their Datasets 77
3.4 Research Survey 83
3.5 Conclusion 90
3.6 Acknowledgment 91
4 Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification 95
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz and Akash Saxena
4.1 Introduction 96
4.2 Related Work 99
4.3 Proposed Methodology 104
4.4 Experimental Setup 115
4.5 Results and Discussion 119
4.6 Conclusion 130
5 Anomaly Identification in Surveillance Video Using Regressive Bidirectional LSTM with Hyperparameter Optimization 135
Rajendran Shankar and Narayanan Ganesh
5.1 Introduction 136
5.2 Literature Survey 137
5.3 Proposed Methodology 138
5.4 Result and Discussion 143
5.5 Conclusion 146
6 Ensemble Machine Learning-Based Botnet Attack Detection for IoT Applications 149
Suchithra M.
6.1 Introduction 150
6.2 Literature Survey 151
6.3 Proposed System 152
6.4 Results and Discussion 156
6.5 Conclusion 160
7 Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks 163
Ilavendhan Anandaraj and Kaviarasan Ramu
7.1 Introduction 164
7.2 Literature Review 166
7.3 Proposed Methodology 168
7.4 Result and Discussion 173
7.5 Conclusion 177
8 Security Enhancement in IoMT--Assisted Smart Healthcare System Using the Machine Learning Approach 179
Jayalakshmi Sambandan, Bharanidharan Gurumurthy and Syed Jamalullah R.
8.1 Introduction 180
8.2 Literature Review 182
8.3 Proposed Methodology 184
8.4 Conclusion 192
9 Building Sustainable Communication: A Game-Theoretic Approach in 5G and 6G Cellular Networks 195
Puppala Ramya, Tulasidhar Mulakaluri, Chebrolu Yasmina, Pandi Bindu Madhavi and Vijay Guru Balaji K. S.
9.1 Introduction 196
9.2 Related Works 196
9.3 Methodology 197
9.4 Result 207
9.5 Conclusion 211
10 Autonomous Vehicle Optimization: Striking a Balance Between Cost-Effectiveness and Sustainability 215
Vamsidhar Talasila, Sagi Venkata Lakshmi Narasimharaju, Neeli Veda Vyshnavi, Saketh Naga Sreenivas Kondaveeti, Garimella Surya Siva Teja and Kiran Kumar Kaveti
10.1 Introduction 216
10.2 Methods 219
10.3 Results 224
10.4 Conclusions 231
11 Adapting Underground Parking for the Future: Sustainability and Shared Autonomous Vehicles 235
Vamsidhar Talasila, Madala Pavan Pranav Sai, Gade Sri Raja Gopala Reddy, Vempati Pavan Kashyap, Gunda Karthik and K. V. Panduranga Rao
11.1 Introduction 236
11.2 Related Works 237
11.3 Methodology 238
11.4 Analysis 245
11.5 Conclusion 250
12 Big Data Analytics for a Sustainable Competitive Edge: An Impact Assessment 253
Rajyalakshmi K., Padma A., Varalakshmi M., Suhasini A. and Chiranjeevi P.
12.1 Introduction 254
12.2 Related Works 255
12.3 Hypothesis and Research Model 255
12.4 Results 259
12.5 Conclusion 264
13 Sustainability and Technological Innovation in Organizations: The Mediating Role of Green Practices 267
Rajyalakshmi K., Rajkumar G. V. S., Sulochana B., Rama Devi V. N. and Padma A.
13.1 Introduction 268
13.2 Related Work 269
13.4 Discussion 279
13.5 Conclusions 281
14 Optimal Cell Planning in Two Tier Heterogeneous Network through Meta-Heuristic Algorithms 285
Sanjoy Debnath, Amit Baran Dey and Wasim Arif
14.1 Introduction 285
14.2 System Model and Formulation of the Problem 288
14.3 Result and Discussion 295
14.4 Conclusion 298
15 Soil Aggregate Stability Prediction Using a Hybrid Machine Learning Algorithm 301
M. Balamurugan
15.1 Introduction 302
15.2 Related Works 303
15.3 Proposed Methodology 303
15.4 Result and Discussion 309
15.5 Conclusion 313
References 313
Index 315
Preface xvii
1 Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review 1
Abrar Yaqoob, Navneet Kumar Verma and Rabia Musheer Aziz
1.1 Introduction 2
1.2 Types of Metaheuristic Algorithms 3
1.3 Application of Metaheuristic Algorithms 20
1.4 Future Direction 25
1.5 Conclusion 26
2 A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning 37
Ramachandran Narayanan and Narayanan Ganesh
2.1 Introduction 38
2.2 Fundamentals of Hyperparameter Optimization 39
2.3 Overview of Metaheuristic Optimization Techniques 42
2.4 Population-Based Metaheuristic Techniques 43
2.5 Single Solution-Based Metaheuristic Techniques 47
2.6 Hybrid Metaheuristic Techniques 49
2.7 Metaheuristics in Bayesian Optimization 50
2.8 Metaheuristics in Neural Architecture Search 53
2.9 Comparison of Metaheuristic Techniques for Hyperparameter Optimization 55
2.10 Applications of Metaheuristics in Machine Learning 61
2.11 Future Directions and Open Challenges 63
2.12 Conclusion 65
3 A Survey of Computer-Aided Diagnosis Systems for Breast Cancer Detection 73
Charu Anant Rajput, Leninisha Shanmugam and Parkavi K.
3.1 Introduction 73
3.2 Procedure for Research Survey 77
3.3 Imaging Modalities and Their Datasets 77
3.4 Research Survey 83
3.5 Conclusion 90
3.6 Acknowledgment 91
4 Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification 95
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz and Akash Saxena
4.1 Introduction 96
4.2 Related Work 99
4.3 Proposed Methodology 104
4.4 Experimental Setup 115
4.5 Results and Discussion 119
4.6 Conclusion 130
5 Anomaly Identification in Surveillance Video Using Regressive Bidirectional LSTM with Hyperparameter Optimization 135
Rajendran Shankar and Narayanan Ganesh
5.1 Introduction 136
5.2 Literature Survey 137
5.3 Proposed Methodology 138
5.4 Result and Discussion 143
5.5 Conclusion 146
6 Ensemble Machine Learning-Based Botnet Attack Detection for IoT Applications 149
Suchithra M.
6.1 Introduction 150
6.2 Literature Survey 151
6.3 Proposed System 152
6.4 Results and Discussion 156
6.5 Conclusion 160
7 Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks 163
Ilavendhan Anandaraj and Kaviarasan Ramu
7.1 Introduction 164
7.2 Literature Review 166
7.3 Proposed Methodology 168
7.4 Result and Discussion 173
7.5 Conclusion 177
8 Security Enhancement in IoMT--Assisted Smart Healthcare System Using the Machine Learning Approach 179
Jayalakshmi Sambandan, Bharanidharan Gurumurthy and Syed Jamalullah R.
8.1 Introduction 180
8.2 Literature Review 182
8.3 Proposed Methodology 184
8.4 Conclusion 192
9 Building Sustainable Communication: A Game-Theoretic Approach in 5G and 6G Cellular Networks 195
Puppala Ramya, Tulasidhar Mulakaluri, Chebrolu Yasmina, Pandi Bindu Madhavi and Vijay Guru Balaji K. S.
9.1 Introduction 196
9.2 Related Works 196
9.3 Methodology 197
9.4 Result 207
9.5 Conclusion 211
10 Autonomous Vehicle Optimization: Striking a Balance Between Cost-Effectiveness and Sustainability 215
Vamsidhar Talasila, Sagi Venkata Lakshmi Narasimharaju, Neeli Veda Vyshnavi, Saketh Naga Sreenivas Kondaveeti, Garimella Surya Siva Teja and Kiran Kumar Kaveti
10.1 Introduction 216
10.2 Methods 219
10.3 Results 224
10.4 Conclusions 231
11 Adapting Underground Parking for the Future: Sustainability and Shared Autonomous Vehicles 235
Vamsidhar Talasila, Madala Pavan Pranav Sai, Gade Sri Raja Gopala Reddy, Vempati Pavan Kashyap, Gunda Karthik and K. V. Panduranga Rao
11.1 Introduction 236
11.2 Related Works 237
11.3 Methodology 238
11.4 Analysis 245
11.5 Conclusion 250
12 Big Data Analytics for a Sustainable Competitive Edge: An Impact Assessment 253
Rajyalakshmi K., Padma A., Varalakshmi M., Suhasini A. and Chiranjeevi P.
12.1 Introduction 254
12.2 Related Works 255
12.3 Hypothesis and Research Model 255
12.4 Results 259
12.5 Conclusion 264
13 Sustainability and Technological Innovation in Organizations: The Mediating Role of Green Practices 267
Rajyalakshmi K., Rajkumar G. V. S., Sulochana B., Rama Devi V. N. and Padma A.
13.1 Introduction 268
13.2 Related Work 269
13.4 Discussion 279
13.5 Conclusions 281
14 Optimal Cell Planning in Two Tier Heterogeneous Network through Meta-Heuristic Algorithms 285
Sanjoy Debnath, Amit Baran Dey and Wasim Arif
14.1 Introduction 285
14.2 System Model and Formulation of the Problem 288
14.3 Result and Discussion 295
14.4 Conclusion 298
15 Soil Aggregate Stability Prediction Using a Hybrid Machine Learning Algorithm 301
M. Balamurugan
15.1 Introduction 302
15.2 Related Works 303
15.3 Proposed Methodology 303
15.4 Result and Discussion 309
15.5 Conclusion 313
References 313
Index 315