Search

Browse Subject Areas

For Authors

Submit a Proposal

Composite Artificial Intelligence

Fundamentals, Challenges, and Applications
Edited by T. S. Arun Samuel, L. Jerart Julus, P. Kanimozhi, T. Ananth Kumar, and S. Balamurugan
Series: Leading-Edge Breakthroughs in Artificial Intelligence
Copyright: 2026   |   Expected Pub Date:2025/12/30
ISBN: 9781394393039  |  Hardcover  |  
394 pages

One Line 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.

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.

Description
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.

Back to Top
Author / Editor Details
T. S. Arun Samuel, PhD is a Professor in 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 communication 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

Back to Top

Table of Contents
Series Preface
Preface
Acknowledgement
Part I: Foundational Concepts and Emerging Trends in Composite AI
1. Data Fusion Techniques in Composite AI
S. Sowmyayani, D. Dhanya, J. Kavitha and R. Roselinkiruba
1.1 Introduction
1.2 Data Fusion Techniques in Composite AI
1.2.1 Data Fusion
1.2.2 Feature Fusion
1.2.3 Decision Fusion
1.2.4 Hybrid Fusion
1.3 Literature Survey
1.4 Proposed Methodology Using Composite AI
1.4.1 Keyframe Extraction
1.4.2 Feature Extraction
1.4.3 Implemented Fusion Technique and Classification
1.4.4 Proposed CBVR System
1.5 Experimental Results
1.5.1 Implementation Details and Performance Measures
1.5.2 Comparison of Both Fusion Methods
1.5.3 Analyses of All the Proposed Methods
1.6 Conclusion
References
2. Composite AI in Natural Language Processing: A Paradigm Shift in Understanding and Generating Human Language
Narendran M., M. Beema Meharaj, D. Diana Julie, Sowmya Banala, G. Umadevi and A. Devi
2.1 Introduction to Composite Artificial Intelligence (AI)
2.1.1 What is Composite AI?
2.1.2 Why Composite AI is Important?
2.2 Role of Composite AI in NLP
2.2.1 Challenges in NLP that Composite AI Addresses
2.2.1.1 Word Sense Disambiguation
2.2.1.2 Handling Diverse Data Types
2.2.1.3 Multi-Lingual and Cross-Lingual Processing
2.2.1.4 Handling Noisy and Unstructured Data
2.2.1.5 Sentiment and Emotional Analysis
2.2.1.6 Bias Detection and Mitigation
2.2.1.7 Low-Resource Language Processing
2.2.2 Benefits of Leveraging Multiple AI Techniques in NLP
2.2.2.1 Greater Precision
2.2.2.2 Heightened Contextual Awareness
2.2.2.3 Powerful Multimodal Analysis
2.2.2.4 Scalability Across Domains
2.2.2.5 Explainability and Trust
2.3 Fundamental Elements of NLP Composite AI
2.3.1 DL for Presentation Learning
2.3.1.1 Word Embeddings
2.3.1.2 Sentence and Document-Level Representations
2.3.2 Symbolic AI for Logical Reasoning
2.3.2.1 Fundaments of Symbolic AI
2.4 Case Studies and Use Cases of Composite AI in NLP
2.4.1 Conventional AI and Virtual Assistants
2.4.1.1 Amazon Alexa
2.4.1.2 Google Assistant
2.4.1.3 IBM Watson Assistant
2.4.2 Healthcare Chatbots for Patient Interaction
2.4.2.1 IBM Watson for Healthcare Case Study
2.4.2.2 Case Study: Ada Health – AI-Powered Symptom Checker
2.4.2.3 Case Study: Babylon Health – AI-Driven Telemedicine Chatbot
2.4.3 Automated Content Moderation Systems
2.4.3.1 Case Study on Facebook’s AI-Powered Content Moderation System
2.4.3.2 OpenAI GPT-Based Customer Service Chatbots Case Study
2.5 Challenges and Future Directions
2.5.1 Challenges in Composite AI for NLP
2.5.2 Future Trends in Composite AI for NLP
2.6 Conclusion
References
3. A Composite Artificial Intelligence Framework for Enhanced and Intelligent Word Recognition of Handwritten Hindi
R. S. Rampriya, Sabarinathan, SahayaBeni Prathiba, C. Renit, R. Arumuga Arun and S. Bhuvana
3.1 Introduction
3.2 Related Work
3.3 Proposed Methodology
3.3.1 Encoder - Vision Transformer
3.3.2 Decoder –HindRoBERTa
3.4 Experimental Result and Analysis
3.4.1 Experimental Data
3.4.2 Implementation Details
3.4.3 Evaluation Metrics
3.4.4 Result Analysis
3.5 Conclusion
References
4. Machine Learning-Driven Optimization for Composite AI in Wireless Body Area Networks (WBAN)
Krishna Kumar M., Pricilla Mary S., James Nesaratnam R. and Sharon Geege A.
4.1 Prelude
4.2 Architectural Framework of WBAN Integrated with Composite AI
4.3 Machine Learning Models for WBAN Optimization
4.4 Deep Learning for Signal Processing in WBAN
4.5 Security Challenges and AI-Based Solutions in WBAN
4.6 AI-Driven Antenna Optimization in WBANs
4.7 Conclusion and Research Trajectories
References
Part II: Advanced Methods and Technical Challenges in Composite AI
5. AI-Driven Hybrid Ant Colony and Golden Jackal Optimization Algorithm for Lung Disease Prediction and Classification
Karthikeyan A., Pradeep S., Boorneush M. and Dhivya P.
5.1 Introduction
5.2 Literature Survey
5.3 System Design
5.3.1 Image Filtration Using a Statistical Median Filter
5.3.2 Statistical Median Filter Process
5.3.3 Image Segmentation Using Local Binary Active Contour Integration
5.3.4 Classification Using Hybrid ACO and GJO Optimization Algorithm
5.3.5 Ant Colony Optimization (ACO)
5.3.6 Golden Jackal Optimization (GJO)
5.3.7 Hybrid ACO-GJO Algorithm for Classification
5.4 Results and Discussion
5.4.1 Accuracy
5.4.2 Precision
5.4.3 Sensitivity
5.5 Conclusion
5.6 Future Scope
References
6. Removing Bias in Maritime Imagery: Advancing Gender Equality through Data-Driven Methods
Jordan Taylor and J. Padmapriya
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.4 Results and Discussion
6.5 Conclusion
References
7. Text-Based Analysis of Twitter Data with Machine Learning Models
N. Malathy, G. Sharmila, R. Yuvarshini and R. Lavanya
7.1 Introduction
7.1.1 Inspiration
7.1.2 Related Work
7.1.3 Contribution
7.2 Objective
7.3 Classification of Tweets
7.3.1 Data Pre-Processing
7.3.1.1 Stop Word Removal
7.3.1.2 Tokenization
7.3.1.3 Lemmatization
7.3.1.4 Stemming
7.3.2 Feature Selection
7.3.3 Sentiment Analysis
7.3.4 Sentiment Analysis Using BERT
7.3.5 Labeling the Data
7.3.6 ML Model
7.4 Evaluation Metrics
7.4.1 Dataset
7.4.2 Performance Metric
7.4.3 Results
7.4.3.1 Labeling of Sentiment
7.4.3.2 Performance of ML Models
7.5 Conclusion and Future Work
Bibliography
8. Fingerprint Registration and Matching Based on Improved Convolutional Neural Network
Lakshmanan B., Selvakumar B., Kasthuri K., Nivashini S. and Swetha R.
8.1 Introduction
8.2 Related Works
8.3 Dataset Description
8.4 Proposed Work
8.4.1 Image Pre-Processing
8.4.2 Registration
8.4.3 Feature Extraction Based on Improved Convolutional Neural Network
8.4.4 Local Patch Alignment
8.4.5 Descriptor Extraction
8.4.6 Patch Matching
8.5 Results and Discussion
8.5.1 Evaluation Metrics
8.6 Conclusion
References
Part III: Real-World Applications of Composite AI in Healthcare and Beyond
9. A Novel Transfer Learning-Based Composite AI Model for Skin Disease Classification
R. Karthick Manoj and S. Aasha Nandhini
9.1 Introduction
9.2 Literature Survey
9.3 Proposed Methodology
9.4 Result and Discussion
9.5 Conclusion
References
10. Composite AI-Driven Music Recommendation: Integrating Emotion, Aural Analysis and Song Similarity
Ayushmaan Das and Rajalakshmi Shenbaga Moorthy
10.1 Introduction
10.2 Literature Survey
10.3 Proposed Composite AI-Driven Music Recommendation Engine
10.3.1 Recommendations Using Cosine Similarity
10.3.2 Emotion–Based Music Recommendation
10.3.3 Lyrics Generation Using LSTM
10.3.4 Chatbot Implementation Powered by Pandas AI
10.4 Results and Discussions
10.4.1 Datasets Used
10.4.2 Parameter Settings
10.4.3 Implementation
10.5 Conclusion
References
11. A Composite Artificial Intelligence Based Framework for Heart Disease Prediction
M. Suresh, M. S. Anbarasi, R. Rajmohan and A. Anbarasi
11.1 Introduction
11.1.1 Problem Statement
11.1.2 Motivation
11.2 Smart Health Monitoring Systems
11.3 Materials and Methods
11.3.1 Dataset Description
11.3.2 Pre-Processing
11.3.3 Feature Selection
11.3.4 Composite Artificial Intelligence in Smart Healthcare Predictions
11.3.4.1 Attention-Based Convolutional Neural Network
11.4 Results and Discussions
11.5 Conclusions
References
12. Composite AI for Predictive Analysis of Autism Spectrum Disorder Using Facial Features
S. Usharani, A. Ganesh, N. Muralidharan and G. Glorindal
12.1 Introduction
12.2 Related Work
12.3 Overview of Composite AI in Predictive Analysis
12.4 Proposed Methodology
12.4.1 Data Acquisition
12.4.2 Data Preprocessing
12.4.3 Facial Feature Extraction
12.4.4 Deep Learning Models (CNNs)
12.4.4.1 Xception
12.4.4.2 VGG 19
12.4.5 Model Evaluation
12.4.6 Composite AI Integration
12.4.7 Prediction and Diagnosis
12.5 Experimental Setup
12.5.1 Data Collection
12.5.2 Data Pre-Processing
12.5.3 Model Training Environment
12.5.4 Training and Hyperparameter Tuning
12.5.5 Evaluation Metrics
12.5.6 Experimental Framework and Composite AI
12.6 Results and Outputs
12.7 Conclusion
References
13. Integrating Imaging and Genomic Data with Composite AI to Enhance Breast Cancer Diagnosis and Early Detection
P. Manju Bala, S. Usharani, A. Balachandar, Sunday Adeola Ajagbe and Matthew Olusegun Adigun
13.1 Introduction
13.1.1 Importance of Imaging and Genomic Integration in Breast Cancer Diagnostics
13.1.2 Role of AI in Multimodal Data Analysis for Breast Cancer
13.1.3 Composite AI Framework for Breast Cancer Diagnosis
13.1.4 Validation and Clinical Implications of Composite AI
13.2 Materials and Methods
13.2.1 Data Collection and Description
13.2.2 Data Pre-Processing
13.2.3 Composite AI Framework Architecture
13.2.4 Machine Learning Models for Genomic Data Analysis
13.3 Model Training and Evaluation
13.3.1 Training Procedure
13.3.2 Evaluation Metrics
13.4 Evaluation Results
13.4.1 Model Performance
13.4.2 Cross-Validation and Robustness
13.4.3 ROC Curve Analysis
13.4.4 Comparative Analysis of Individual Model and Composite AI Model
13.5 Conclusion
References
14. Cognitive Analytics AI for Predictive Diagnostics and Neurological Forecasting in Brain Tumor Management
S. Usharani, P. Manju Bala, A. Balachandar and Olukayode A.
14.1 Introduction
14.2 Related Works
14.3 Proposed Predictive Analytics Framework for Predicting Brain Tumors and Neurological Disorders
14.3.1 Data Collection and Preprocessing
14.3.2 Feature Extraction and Radiomics
14.3.3 Multimodal Data Fusion
14.3.4 Predictive Model
14.3.5 Model Evaluation and Performance Metrics
14.3.6 Explainability and Interpretability
14.3.7 Ethical Considerations and Privacy
14.4 Experimental Setup for Predictive Analytics in Prediction of Brain Tumor and Neurological Disorder
14.4.1 Data Collection
14.4.2 Preprocessing of Data
14.4.3 Model Development and Training
14.4.4 Evaluation Metrics
14.4.4.1 Tumor Classification
14.4.4.2 Survival Prediction
14.4.4.3 Cross-Validation Results
14.4.4.4 Explainability and Interpretability
14.4.4.5 Ethical Considerations
14.5 Results and Analysis
14.6 Conclusion
References
15. Applications for Composite AI in Healthcare
R.Vijayarajeswari, David Samuel Azariya S., Anto Lourdu Xavier Raj Arockia Selvarathinam, Priyabrata Thatoi, Abhinaya Saravanan and Nisha Soms
15.1 Introduction
15.1.1 Overview of AI in Healthcare
15.1.2 Composite AI Definition and Concept
15.1.3 Importance of Composite AI in the Healthcare Domain
15.1.4 The Architecture of Composite AI in Healthcare
15.2 Fundamentals of Composite AI
15.2.1 Description of the Different AI Techniques Involved
15.2.2 How Composite AI Integrates These Techniques
15.3 Applications for Composite AI in Healthcare
15.3.1 Early Disease Detection and Diagnosis
15.3.2 Personalized Medicine
15.3.3 Patient Monitoring and Management
15.3.4 Clinical Decision Support Systems (CDSS)
15.4 Case Studies in Composite AI Applications in Healthcare
15.4.1 Case Study 1: Composite AI in Hospital Diagnostics
15.4.2 Case Study 2: Telemedicine for Chronic Disease Management
15.4.3 Case Study 3: Public Health Initiatives
15.5 Challenges and Future Directions
15.5.1 Data Privacy, Security and Ethical Considerations
15.5.2 Scalability and Deployment in Healthcare Institutions
15.5.3 Future Trends and Research Opportunities
15.6 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page