Composite Artificial Intelligence
Fundamentals, Challenges, and Applications
1st Edition
Will be published approx. on 29. June 2026
Book
Hardback
400 pages
978-1-394-39303-9 (ISBN)
Description
Unlock the full potential of context-aware AI while navigating critical hurdles like bias mitigation and ethical governance with this definitive resource on the future of composite artificial intelligence.
In the rapidly evolving landscape of artificial intelligence, the demand for more adaptive, intelligent, and context-aware systems has led to the emergence of composite artificial intelligence: a paradigm that integrates multiple AI techniques to solve complex real-world problems with higher efficiency and intelligence. This book is a groundbreaking exploration of the next evolution in AI, where diverse methodologies like machine learning, symbolic reasoning, and cognitive computing converge to solve complex, real-world problems with unprecedented intelligence and adaptability. Unlike traditional AI approaches that rely on singular techniques, composite AI harnesses the strengths of multiple paradigms, enabling systems that are more robust, interpretable, and capable of human-like decision-making. This book provides a comprehensive roadmap for understanding and implementing these advanced systems, from foundational theories to cutting-edge applications across industries such as healthcare, finance, and smart manufacturing. It delves into critical challenges, including bias mitigation, integration hurdles, and ethical governance, while showcasing real-world case studies that demonstrate the transformative potential of composite AI. With its balanced blend of theory, technical depth, and actionable insights, this book is a definitive resource for unlocking the full potential of AI in an increasingly complex world.
Readers will find the volume:
Explores the intersection of machine learning, symbolic reasoning, and cognitive computing for solving real-world challenges smarter and faster;
Introduces cutting-edge techniques for bias reduction, optimization, and seamless multi-method integration;
Provides real-world case studies and scalable frameworks to demonstrate how composite AI is transforming industries;
Presents ethical implications and current innovations to build trustworthy, compliant AI systems that align with regulations;
Audience
Academics, policymakers, AI researchers, data scientists, AI and machine learning engineers and developers, and??industry professionals working in healthcare, finance, manufacturing, and cybersecurity who need robust, explainable, and adaptive AI solutions.
In the rapidly evolving landscape of artificial intelligence, the demand for more adaptive, intelligent, and context-aware systems has led to the emergence of composite artificial intelligence: a paradigm that integrates multiple AI techniques to solve complex real-world problems with higher efficiency and intelligence. This book is a groundbreaking exploration of the next evolution in AI, where diverse methodologies like machine learning, symbolic reasoning, and cognitive computing converge to solve complex, real-world problems with unprecedented intelligence and adaptability. Unlike traditional AI approaches that rely on singular techniques, composite AI harnesses the strengths of multiple paradigms, enabling systems that are more robust, interpretable, and capable of human-like decision-making. This book provides a comprehensive roadmap for understanding and implementing these advanced systems, from foundational theories to cutting-edge applications across industries such as healthcare, finance, and smart manufacturing. It delves into critical challenges, including bias mitigation, integration hurdles, and ethical governance, while showcasing real-world case studies that demonstrate the transformative potential of composite AI. With its balanced blend of theory, technical depth, and actionable insights, this book is a definitive resource for unlocking the full potential of AI in an increasingly complex world.
Readers will find the volume:
Explores the intersection of machine learning, symbolic reasoning, and cognitive computing for solving real-world challenges smarter and faster;
Introduces cutting-edge techniques for bias reduction, optimization, and seamless multi-method integration;
Provides real-world case studies and scalable frameworks to demonstrate how composite AI is transforming industries;
Presents ethical implications and current innovations to build trustworthy, compliant AI systems that align with regulations;
Audience
Academics, policymakers, AI researchers, data scientists, AI and machine learning engineers and developers, and??industry professionals working in healthcare, finance, manufacturing, and cybersecurity who need robust, explainable, and adaptive AI solutions.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
ISBN-13
978-1-394-39303-9 (9781394393039)
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
T. S. Arun Samuel | L. Jerart Julus | P. Kanimozhi
Composite Artificial Intelligence
Fundamentals, Challenges and Applications
E-Book
05/2026
1st Edition
Wiley
€188.99
Available for download
T. S. Arun Samuel | L. Jerart Julus | P. Kanimozhi
Composite Artificial Intelligence
Fundamentals, Challenges, and Applications
E-Book
05/2026
1st Edition
Wiley
€188.99
Available for download
Persons
T. S. Arun Samuel, PhD is a Professor on the Department of Electronics and Communication Engineering, National Engineering College, Tamil Nadu, India with more than two decades of experience. He has authored more than 65 research articles published in prestigious international journals, 15 articles in international conferences, one patent, and three edited books. His research interests are focused on advancing the frontiers of nanoelectronic device technologies through innovative modeling and simulation techniques.
L. Jerart Julus, PhD is an Assistant Professor in the Department of IT, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India. He is a member of IEEE, the Computer Society of India, and the Optical Society of India. His research interests include multicarrier communications systems, radio over fiber, and visible light communication.
P. Kanimozhi, PhD is a Professor of Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India with more than 19 years of teaching experience. She has published more than 17 research papers in national and international journals. Her current areas of interest include cloud computing security, data mining, and blockchain.
T. Ananth Kumar, PhD is an Associate Professor of Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India. He has presented papers in national and international conferences and journals, holds patents in various domains, and has edited six books and numerous book chapters. His fields of interest are networks on chips, computer architecture, and application-specific integrated circuit design.
S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published more than 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.
L. Jerart Julus, PhD is an Assistant Professor in the Department of IT, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India. He is a member of IEEE, the Computer Society of India, and the Optical Society of India. His research interests include multicarrier communications systems, radio over fiber, and visible light communication.
P. Kanimozhi, PhD is a Professor of Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India with more than 19 years of teaching experience. She has published more than 17 research papers in national and international journals. Her current areas of interest include cloud computing security, data mining, and blockchain.
T. Ananth Kumar, PhD is an Associate Professor of Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India. He has presented papers in national and international conferences and journals, holds patents in various domains, and has edited six books and numerous book chapters. His fields of interest are networks on chips, computer architecture, and application-specific integrated circuit design.
S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published more than 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.
Content
Series Preface xv
Preface xvii
Acknowledgement xix
Part I: Foundational Concepts and Emerging Trends in Composite AI 1
1 Data Fusion Techniques in Composite AI 3
S. Sowmyayani, D. Dhanya, J. Kavitha and R. Roselinkiruba
1.1 Introduction 1
1.2 Data Fusion Techniques in Composite AI 3
1.4 Proposed Methodology Using Composite AI 12
1.5 Experimental Results 17
1.6 Conclusion 26
2 Composite AI in Natural Language Processing: A Paradigm Shift in Understanding and Generating Human Language 33
Narendran M., M. Beema Meharaj, D. Diana Julie, Sowmya Banala, G. Umadevi and A. Devi
2.1 Introduction to Composite Artificial Intelligence (AI) 34
2.2 Role of Composite AI in NLP 37
2.3 Fundamental Elements of NLP Composite AI 42
2.4 Case Studies and Use Cases of Composite AI in NLP 49
2.5 Challenges and Future Directions 60
2.6 Conclusion 62
3 A Composite Artificial Intelligence Framework for Enhanced and Intelligent Word Recognition of Handwritten Hindi 65
R. S. Rampriya, Sabarinathan, SahayaBeni Prathiba, C. Renit, R. Arumuga Arun and S. Bhuvana
3.1 Introduction 66
3.2 Related Work 68
3.3 Proposed Methodology 69
3.4 Experimental Result and Analysis 75
3.5 Conclusion 81
4 Machine Learning-Driven Optimization for Composite AI in Wireless Body Area Networks (WBAN) 85
Krishna Kumar M., Pricilla Mary S., James Nesaratnam R. and Sharon Geege A.
4.1 Prelude 86
4.2 Architectural Framework of WBAN Integrated with Composite AI 87
4.3 Machine Learning Models for WBAN Optimization 89
4.4 Deep Learning for Signal Processing in WBAN 91
4.5 Security Challenges and AI-Based Solutions in WBAN 92
4.6 AI-Driven Antenna Optimization in WBANs 94
4.7 Conclusion and Research Trajectories 97
Part II: Advanced Methods and Technical Challenges in Composite AI 101
5 AI-Driven Hybrid Ant Colony and Golden Jackal Optimization Algorithm for Lung Disease Prediction and Classification 103
Karthikeyan A., Pradeep S., Boorneush M. and Dhivya P.
5.1 Introduction 104
5.2 Literature Survey 106
5.3 System Design 109
5.4 Results and Discussion 114
5.5 Conclusion 120
5.6 Future Scope 121
6 Removing Bias in Maritime Imagery: Advancing Gender Equality through Data-Driven Methods 125
Jordan Taylor and J. Padmapriya
6.1 Introduction 126
6.2 Literature Review 129
6.3 Methodology 134
6.4 Results and Discussion 143
6.5 Conclusion 146
7 Text-Based Analysis of Twitter Data with Machine Learning Models 151
N. Malathy, G. Sharmila, R. Yuvarshini and R. Lavanya
7.1 Introduction 152
7.2 Objective 157
7.3 Classification of Tweets 158
7.4 Evaluation Metrics 172
7.5 Conclusion and Future Work 175
8 Fingerprint Registration and Matching Based on Improved Convolutional Neural Network 181
Lakshmanan B., Selvakumar B., Kasthuri K., Nivashini S. and Swetha R.
8.1 Introduction 182
8.2 Related Works 182
8.3 Dataset Description 185
8.4 Proposed Work 185
8.5 Results and Discussion 191
8.6 Conclusion 195
Part III: Real-World Applications of Composite AI in Healthcare and Beyond 199
9 A Novel Transfer Learning-Based Composite AI Model for Skin Disease Classification 201
R. Karthick Manoj and S. Aasha Nandhini
9.1 Introduction 202
9.2 Literature Survey 203
9.3 Proposed Methodology 208
9.4 Result and Discussion 213
9.5 Conclusion 218
10 Composite AI-Driven Music Recommendation: Integrating Emotion, Aural Analysis and Song Similarity 223
Ayushmaan Das and Rajalakshmi Shenbaga Moorthy
10.1 Introduction 224
10.2 Literature Survey 228
10.3 Proposed Composite AI-Driven Music Recommendation Engine 232
10.4 Results and Discussions 239
10.5 Conclusion 244
11 A Composite Artificial Intelligence Based Framework for Heart Disease Prediction 249
M. Suresh, M. S. Anbarasi, R. Rajmohan and A. Anbarasi
11.1 Introduction 250
11.2 Smart Health Monitoring Systems 254
11.3 Materials and Methods 257
11.4 Results and Discussions 270
11.5 Conclusions 275
12 Composite AI for Predictive Analysis of Autism Spectrum Disorder Using Facial Features 279
S. Usharani, A. Ganesh, N. Muralidharan and G. Glorindal
12.1 Introduction 280
12.2 Related Work 280
12.3 Overview of Composite AI in Predictive Analysis 284
12.4 Proposed Methodology 285
12.5 Experimental Setup 291
12.6 Results and Outputs 295
12.7 Conclusion 296
13 Integrating Imaging and Genomic Data with Composite AI to Enhance Breast Cancer Diagnosis and Early Detection 303
P. Manju Bala, S. Usharani, A. Balachandar, Sunday Adeola Ajagbe and Matthew Olusegun Adigun
13.1 Introduction 304
13.2 Materials and Methods 307
13.3 Model Training and Evaluation 313
13.4 Evaluation Results 314
13.5 Conclusion 319
14 Cognitive Analytics AI for Predictive Diagnostics and Neurological Forecasting in Brain Tumor Management 325
S. Usharani, P. Manju Bala, A. Balachandar and Olukayode A.
14.1 Introduction 326
14.2 Related Works 327
14.3 Proposed Predictive Analytics Framework for Predicting Brain Tumors and Neurological Disorders 331
14.4 Experimental Setup for Predictive Analytics in Prediction of Brain Tumor and Neurological Disorder 334
14.5 Results and Analysis 340
14.6 Conclusion 341
15 Applications for Composite AI in Healthcare 345
R. Vijayarajeswari, David Samuel Azariya S., Anto Lourdu Xavier Raj Arockia Selvarathinam, Priyabrata Thatoi, Abhinaya Saravanan and Nisha Soms
15.1 Introduction 346
15.2 Fundamentals of Composite AI 347
15.3 Applications for Composite AI in Healthcare 351
15.4 Case Studies in Composite AI Applications in Healthcare 356
15.5 Challenges and Future Directions 357
15.6 Conclusion 362
References 364
Index 367
Preface xvii
Acknowledgement xix
Part I: Foundational Concepts and Emerging Trends in Composite AI 1
1 Data Fusion Techniques in Composite AI 3
S. Sowmyayani, D. Dhanya, J. Kavitha and R. Roselinkiruba
1.1 Introduction 1
1.2 Data Fusion Techniques in Composite AI 3
1.4 Proposed Methodology Using Composite AI 12
1.5 Experimental Results 17
1.6 Conclusion 26
2 Composite AI in Natural Language Processing: A Paradigm Shift in Understanding and Generating Human Language 33
Narendran M., M. Beema Meharaj, D. Diana Julie, Sowmya Banala, G. Umadevi and A. Devi
2.1 Introduction to Composite Artificial Intelligence (AI) 34
2.2 Role of Composite AI in NLP 37
2.3 Fundamental Elements of NLP Composite AI 42
2.4 Case Studies and Use Cases of Composite AI in NLP 49
2.5 Challenges and Future Directions 60
2.6 Conclusion 62
3 A Composite Artificial Intelligence Framework for Enhanced and Intelligent Word Recognition of Handwritten Hindi 65
R. S. Rampriya, Sabarinathan, SahayaBeni Prathiba, C. Renit, R. Arumuga Arun and S. Bhuvana
3.1 Introduction 66
3.2 Related Work 68
3.3 Proposed Methodology 69
3.4 Experimental Result and Analysis 75
3.5 Conclusion 81
4 Machine Learning-Driven Optimization for Composite AI in Wireless Body Area Networks (WBAN) 85
Krishna Kumar M., Pricilla Mary S., James Nesaratnam R. and Sharon Geege A.
4.1 Prelude 86
4.2 Architectural Framework of WBAN Integrated with Composite AI 87
4.3 Machine Learning Models for WBAN Optimization 89
4.4 Deep Learning for Signal Processing in WBAN 91
4.5 Security Challenges and AI-Based Solutions in WBAN 92
4.6 AI-Driven Antenna Optimization in WBANs 94
4.7 Conclusion and Research Trajectories 97
Part II: Advanced Methods and Technical Challenges in Composite AI 101
5 AI-Driven Hybrid Ant Colony and Golden Jackal Optimization Algorithm for Lung Disease Prediction and Classification 103
Karthikeyan A., Pradeep S., Boorneush M. and Dhivya P.
5.1 Introduction 104
5.2 Literature Survey 106
5.3 System Design 109
5.4 Results and Discussion 114
5.5 Conclusion 120
5.6 Future Scope 121
6 Removing Bias in Maritime Imagery: Advancing Gender Equality through Data-Driven Methods 125
Jordan Taylor and J. Padmapriya
6.1 Introduction 126
6.2 Literature Review 129
6.3 Methodology 134
6.4 Results and Discussion 143
6.5 Conclusion 146
7 Text-Based Analysis of Twitter Data with Machine Learning Models 151
N. Malathy, G. Sharmila, R. Yuvarshini and R. Lavanya
7.1 Introduction 152
7.2 Objective 157
7.3 Classification of Tweets 158
7.4 Evaluation Metrics 172
7.5 Conclusion and Future Work 175
8 Fingerprint Registration and Matching Based on Improved Convolutional Neural Network 181
Lakshmanan B., Selvakumar B., Kasthuri K., Nivashini S. and Swetha R.
8.1 Introduction 182
8.2 Related Works 182
8.3 Dataset Description 185
8.4 Proposed Work 185
8.5 Results and Discussion 191
8.6 Conclusion 195
Part III: Real-World Applications of Composite AI in Healthcare and Beyond 199
9 A Novel Transfer Learning-Based Composite AI Model for Skin Disease Classification 201
R. Karthick Manoj and S. Aasha Nandhini
9.1 Introduction 202
9.2 Literature Survey 203
9.3 Proposed Methodology 208
9.4 Result and Discussion 213
9.5 Conclusion 218
10 Composite AI-Driven Music Recommendation: Integrating Emotion, Aural Analysis and Song Similarity 223
Ayushmaan Das and Rajalakshmi Shenbaga Moorthy
10.1 Introduction 224
10.2 Literature Survey 228
10.3 Proposed Composite AI-Driven Music Recommendation Engine 232
10.4 Results and Discussions 239
10.5 Conclusion 244
11 A Composite Artificial Intelligence Based Framework for Heart Disease Prediction 249
M. Suresh, M. S. Anbarasi, R. Rajmohan and A. Anbarasi
11.1 Introduction 250
11.2 Smart Health Monitoring Systems 254
11.3 Materials and Methods 257
11.4 Results and Discussions 270
11.5 Conclusions 275
12 Composite AI for Predictive Analysis of Autism Spectrum Disorder Using Facial Features 279
S. Usharani, A. Ganesh, N. Muralidharan and G. Glorindal
12.1 Introduction 280
12.2 Related Work 280
12.3 Overview of Composite AI in Predictive Analysis 284
12.4 Proposed Methodology 285
12.5 Experimental Setup 291
12.6 Results and Outputs 295
12.7 Conclusion 296
13 Integrating Imaging and Genomic Data with Composite AI to Enhance Breast Cancer Diagnosis and Early Detection 303
P. Manju Bala, S. Usharani, A. Balachandar, Sunday Adeola Ajagbe and Matthew Olusegun Adigun
13.1 Introduction 304
13.2 Materials and Methods 307
13.3 Model Training and Evaluation 313
13.4 Evaluation Results 314
13.5 Conclusion 319
14 Cognitive Analytics AI for Predictive Diagnostics and Neurological Forecasting in Brain Tumor Management 325
S. Usharani, P. Manju Bala, A. Balachandar and Olukayode A.
14.1 Introduction 326
14.2 Related Works 327
14.3 Proposed Predictive Analytics Framework for Predicting Brain Tumors and Neurological Disorders 331
14.4 Experimental Setup for Predictive Analytics in Prediction of Brain Tumor and Neurological Disorder 334
14.5 Results and Analysis 340
14.6 Conclusion 341
15 Applications for Composite AI in Healthcare 345
R. Vijayarajeswari, David Samuel Azariya S., Anto Lourdu Xavier Raj Arockia Selvarathinam, Priyabrata Thatoi, Abhinaya Saravanan and Nisha Soms
15.1 Introduction 346
15.2 Fundamentals of Composite AI 347
15.3 Applications for Composite AI in Healthcare 351
15.4 Case Studies in Composite AI Applications in Healthcare 356
15.5 Challenges and Future Directions 357
15.6 Conclusion 362
References 364
Index 367