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Adaptive Artificial Intelligence

Fundamentals, Challenges, and Applications

Edited by P. Pavan Kumar, G. Suresh Kumar, Ajay Kumar Jena, Sandeep Kumar Panda and S. Balamurugan
Series: Leading-Edge Breakthroughs in Artificial Intelligence
Copyright: 2025   |   Expected Pub Date:2025/10/30
ISBN: 9781394389049  |  Hardcover  |  
466 pages

One Line Description
Master the next frontier of technology with this book, which provides an in-depth guide to adaptive artificial intelligence and its ability to create flexible, self-governed systems in dynamic industries.

Audience
Research scholars, IT professionals, engineering students, network administrators, artificial intelligence and deep learning experts, and government research agencies looking to innovate with the power of artificial intelligence.

Description
Adaptive artificial intelligence represents a significant advancement in the development of AI systems, particularly within various industries that require robust, flexible, and responsive technologies. Unlike traditional AI, which operates based on pre-defined models and static data, adaptive AI is designed to learn and evolve in real time, making it particularly valuable in dynamic and unpredictable environments. This capability is increasingly important in disciplines such as autonomous systems, healthcare, finance, and industrial automation, where the ability to adapt to new information and changing conditions is crucial.
In industry development, adaptive AI drives innovation by enabling systems that can continuously improve their performance and decision-making processes without the need for constant human intervention. This leads to more efficient operations, reduced downtime, and enhanced outcomes across sectors. As industries increasingly rely on AI for critical functions, the adaptive capability of these systems becomes a cornerstone for achieving higher levels of automation, reliability, and intelligence in technological solutions.
Readers will find the book:
• Introduces the emerging concept of adaptive artificial intelligence;
• Explores the many applications of adaptive artificial intelligence across various industries;
• Provides comprehensive coverage of reinforcement learning for different domains.

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Author / Editor Details
P. Pavan Kumar, PhD is an associate professor in the Department of Artificial Intelligence and Data Science at the ICFAI Foundation for Higher Education, Hyderabad, Telangana, India. He has published more than 20 scholarly peer-reviewed research articles in international journals and two Indian patents. His research interests include real-time systems, multi-core systems, high-performance systems, and computer vision.

Grandhi Suresh Kumar, PhD is an associate professor and Associate Dean of Academics in the School of Science and Technology at the ICFAI Foundation for Higher Education, Hyderabad, Telangana, India with more than ten years of experience. He has published one authored book, one edited book, one book chapter, and more than 15 articles. His research interests include intelligent manufacturing, robotics, sustainable energy solutions, CO2 capture, and applications of AI in mechanical engineering.

Ajay Kumar Jena, PhD is an assistant professor and Associate Dean in the School of Computer Engineering at the Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India. He has published three books, seven book chapters, and 61 research papers in various international journals and conferences. His
research interests include blockchain, object-oriented software testing, software engineering, data science, soft computing, and machine learning.

Sandeep Kumar Panda, PhD is a professor and an Associate Dean in the School of Science and Technology at the ICFAI Foundation for Higher Education, Hyderabad, Telangana, India. He has published six books, several book chapters, and 80 articles in international journals and conferences. His research interests include blockchain technology, W3, metaverse, the Internet of Things, AI, and cloud computing.

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Table of Contents
Series Preface
Preface
Acknowledgements
Part 1: Adaptive Artificial Intelligence: Fundamentals
1. From Data to Diagnosis—Integrating Adaptive AI in Reshaping Healthcare
Kumar Saurabh and Raghuraj Singh Suryavanshi
1.1 Introduction
1.2 Literature Review
1.3 Benefits of Adaptive AI in Health Diagnostic
1.3.1 Personalized Treatment Plans Based on Individual Patient Data
1.3.2 Automated Health Monitoring Systems for Early Disease Identification
1.3.3 Reduction in Medical Errors and Misdiagnoses
1.4 Challenges and Limitations of Adaptive AI in Health Diagnostic
1.4.1 Privacy Concerns Related to Patient Data Usage
1.4.2 Lack of Standardized Regulations for AI in Healthcare
1.4.3 Potential Bias in AI Algorithms Leading to Inaccurate Diagnoses
1.5 Current Applications of Adaptive AI in Health Diagnostic
1.5.1 Disease Prediction and Risk Assessment
1.5.2 Image Recognition for Medical Imaging Analysis
1.5.3 Drug Discovery and Personalized Medicine
1.5.4 Automation of Administrative Tasks
1.6 Future Prospects of Adaptive AI in Health Diagnostic
1.7 Conclusion
References
2. Transfer Learning in Adaptive AI
Pradumn Kumar and Praveen Kumar Shukla
2.1 Introduction: The Evolution of Adaptive Intelligence
2.2 Theoretical Foundations of Transfer Learning
2.2.1 Categorization of Transfer Learning Approaches: An In-Depth Exploration
2.3 Adaptive AI: Concepts and Challenges
2.3.1 What is Adaptive AI
2.3.2 Core Characteristics
2.3.2.1 Continual Learning
2.3.2.2 Generalization
2.3.2.3 Efficiency
2.3.3 Challenges
2.3.3.1 Catastrophic Forgetting
2.3.3.2 Data Scarcity
2.3.3.3 Domain Shift
2.4 Transfer Learning Techniques for Adaptive AI
2.4.1 Pre-Trained Models and Fine-Tuning
2.4.2 Domain Adaptation
2.4.3 Meta-Learning
2.4.4 Continual Learning
2.4.5 Multi-Task Learning
2.5 Applications of Transfer Learning in Adaptive AI
2.5.1 Natural Language Processing (NLP)
2.5.2 Computer Vision
2.5.3 Robotics
2.5.4 Healthcare
2.5.5 Tesla Autopilot
2.6 Conclusion
References
3. Beyond Prediction: Adaptive AI as a Catalyst for Climate Change Mitigation and Understanding
Deepak Gupta and Satyasundara Mahapatra
3.1 Introduction
3.1.1 The Escalating Climate Crisis: A Data-Driven Perspective
3.1.2 The Evolution of Climate Modeling: From Traditional Methods to AI
3.1.3 Beyond AI: The Rise of Adaptive AI in Climate Science
3.1.4 Objectives and Significance of This Chapter
3.2 Foundations of Adaptive AI in Climate Science
3.2.1 Understanding Adaptive AI: A Paradigm Shift in Machine Learning
3.2.2 Core Mechanisms Enabling Adaptability
3.2.2.1 Reinforcement Learning for Dynamic Decision-Making
3.2.2.2 Continual Learning for Real-Time Model Updates
3.2.2.3 Meta-Learning
3.2.2.4 Evolutionary Algorithms and Neuroevolutionary
3.2.2.5 Transfer Learning to Leverage Knowledge Across Climate Domains
3.2.3 The Necessity of Adaptability in Climate Change Modeling
3.2.3.1 Coping with Evolving Climate Variables
3.2.3.2 Reducing Uncertainty in Long-Term Predictions
3.2.3.3 Enhancing Precision in Real-Time Climate Monitoring
3.2.4 Importance of Adaptation in Climate Models
3.2.4.1 Real-Time Learning and Parameter Updates
3.2.4.2 Handling Non-Stationary Climate Patterns
3.2.4.3 Reducing Uncertainties in Projections
3.3 Adaptive AI Frameworks for Climate Change Modeling
3.3.1 Dynamic Climate Forecasting Models
3.3.2 Adaptive AI for Extreme Weather Prediction
3.3.3 AI-Augmented Numerical and Physics-Based Climate Models
3.3.4 Hybrid Approaches: Integrating Big Data, IoT, and AI in Climate Prediction
3.3.5 Case Study: Adaptive AI in Global Climate Risk Assessment
3.4 Real-World Applications of Adaptive AI in Climate Resilience
3.4.1 Predicting and Mitigating Natural Disasters: Wildfire Prediction and Mitigation with Adaptive AI
3.4.2 Dynamic AI Models for Sustainable Agriculture and Food Security
3.4.3 Intelligent Water Management for Drought and Flood Prevention
3.4.4 Smart Energy Grids Optimized by Adaptive AI for Carbon Reduction
3.4.5 Monitoring and Protecting Marine and Terrestrial Ecosystems
3.5 Challenges and Limitations in Adaptive AI for Climate Science
3.5.1 Data Complexity and Computational Constraints
3.5.1.1 High-Dimensional, Spatiotemporal Datasets
3.5.1.2 Handling Incomplete and Uncertain Climate Data
3.5.2 Balancing Adaptability and Model Stability
3.5.3 Ethical Implications: Bias, Transparency, and AI Accountability
3.5.3.1 Algorithmic Bias in Climate Predictions
3.5.3.2 Ensuring Transparency in Adaptive Decision-Making
3.5.4 Policy and Regulatory Challenges in AI-Governed Climate Actions
3.5.4.1 Regulatory Frameworks for Adaptive AI in Environmental Monitoring
3.5.4.2 Collaboration Between Governments, AI Researchers, and Climate Scientists
3.6 The Future of Adaptive AI in Climate Change Mitigation
3.6.1 Quantum AI for Enhanced Climate Modeling
3.6.2 Federated Learning for Global Collaborative Climate Research
3.6.3 AI-Driven Policy Recommendations for Climate Adaptation
3.6.4 Towards a Unified Adaptive AI Framework for Climate Resilience
3.7 Conclusion
References
4. Adaptive AI: Transforming Natural Language Processing and Industry Applications
Meena Kumari P., Ramakrishna Reddy K. and Manikandakumar M.
4.1 Introduction
4.1.1 Benefits of NLP
4.1.2 Technologies Related to Natural Language Processing
4.1.3 Applications of Natural Language Processing (NLP)
4.2 Adaptive AI
4.2.1 What is Adaptive AI?
4.2.1.1 Key Characteristics of Adaptive AI
4.2.1.2 Traditional vs. Adaptive AI
4.2.2 How Does Adaptive AI Work?
4.3 Adaptive AI Use Cases with NLP
4.3.1 Chatbots and Virtual Assistants
4.3.2 Healthcare Industry
4.3.3 Personalized Education
4.4 Adaptive AI Use Cases in Other Industry
4.4.1 Healthcare
4.4.2 Finance
4.4.3 Transportation
4.4.4 Manufacturing
4.4.5 Environmental Sustainability
4.5 Ethical Considerations and Challenges
4.6 Conclusion
References
5. Optimizing Networking Systems with Machine Learning Approach
Cherukuri Gaurav Sushant, Tanishq Kumar, Lakshmi Ajay Veeramraju, Yuvraj Singh
and Sandeep Kumar Panda
5.1 Introduction
5.2 Networks
5.3 Computer Networks
5.4 Networking Software’s
5.4.1 Common Protocols
5.4.2 Network Topologies
5.4.3 OSI Model
5.4.4 Routing Algorithms
5.4.5 Internet Protocol (IP)
5.5 Hardware Devices
5.5.1 Network Interface Card (NIC)
5.5.2 Switch
5.5.3 Access Point
5.5.4 Router
5.5.5 Firewall
5.5.6 Gateway
5.5.7 Transmission Medium
5.6 Software-Defined Networks (SDN)
5.6.1 Management Plane
5.6.2 Control Plane
5.6.3 Data Plane
5.6.4 Definition
5.6.5 How Different is SDN from Traditional Systems
5.7 Machine Learning
5.7.1 Data Collection
5.7.2 Dimensionality Reduction
5.7.3 Performance Score
5.7.4 Regression
5.7.5 Classification in Machine Learning
5.8 Deep Learning
5.8.1 Long Short-Term Memory (LSTM)
5.8.2 Autoencoders
5.9 Applications of Machine Learning
5.9.1 QOS – Quality of Service
5.9.2 Machine Learning in Fault Management
5.9.3 Machine Learning in Performance Prediction
5.9.4 Machine Learning in Infrastructure Cost
5.9.5 Load Balancing Using Machine Learning
5.10 Traditional Load Balancing Techniques
5.10.1 Machine Learning and Load Balancing
5.11 SDN Decision Making
5.11.1 Methods and Types of Decision Making in SDN
5.11.2 Machine Learning in SDN Decision Making
5.12 Conclusion
References
Part 2: Adaptive Artificial Intelligence: Applications
6. Assessment of the Recurrent RBF Long-Range Forecasting Model for Estimating Net Asset Value
Minakhi Rout, Anjishnu Saw, Ajay Kumar Jena and Ajaya Kumar Parida
6.1 Introduction
6.2 Design of a Forecasting Model Using the Recurrent Radial Basis Function (RRBF) Neural Network
6.3 Extraction of Features and Construction of Input Data
6.4 Simulation Based Experiments
6.5 Conclusion
References
7. Reinforcement Learning in Network Optimization
M. Sandhya, L. Lakshmi and L. Anjaneyulu
7.1 Introduction
7.2 Related Works
7.3 Key Concepts of Network Optimization
7.3.1 Traffic Routing
7.3.2 Resource Allocation
7.3.3 Load Balancing
7.3.4 Quality of Service (QoS)
7.4 Key Concepts of RL
7.4.1 Fundamental Principles of RL
7.4.1.1 States
7.4.1.2 Actions
7.4.1.3 Rewards
7.4.1.4 Policies
7.4.1.5 Value Functions
7.4.2 Types of RL Algorithms
7.4.2.1 Q-Learning
7.4.2.2 Deep Q-Networks (DQN)
7.4.2.3 Policy Gradient Methods
7.4.2.4 Actor-Critic Method
7.4.2.5 Deep Deterministic Policy Gradient (DDPG)
7.4.2.6 Multi-Agent Reinforcement Learning
7.4.2.7 Hierarchical Reinforcement Learning
7.5 Importance of RL in Network Optimization
7.5.1 Adaptability
7.5.2 Expansion Capability
7.5.3 Autonomous Operation
7.5.4 Instantaneous Optimization
7.6 Performance Evaluation and Benchmarking
7.6.1 General Metrics
7.6.2 Deep Q-Networks (DQN)
7.6.3 Action-Critic Methods
7.6.4 Multi-Agent Approaches
7.6.5 DDPG (Deep Deterministic Policy Gradient)
7.6.6 Hierarchical Reinforcement Learning (HRL)
7.7 Challenges and Future Directions in RL for Network Optimization
7.7.1 Challenges in RL for Network Optimization
7.7.1.1 Scalability
7.7.1.2 Real-Time Decision Making
7.7.1.3 Data Availability and Quality
7.7.1.4 Robustness and Reliability
7.7.1.5 Integration with Existing Systems
7.7.2 Future Directions in RL for Network Optimization
7.7.2.1 Advanced RL Algorithms
7.7.2.2 Efficient Training Techniques
7.7.2.3 Real-Time and Low-Latency Solutions
7.7.2.4 Robust and Adaptive RL Models
7.7.2.5 Enhanced Simulation Environments
7.7.2.6 Standardization and Benchmarking
7.8 Conclusions
References
8. A Study on AI Adoption Methods in Industry
E. Sudarshan, K.S.R.K. Sarma and Karra Kishore
8.1 Types of Adaptive AI Techniques for Industrial Automation
8.2 Study: Predictive Maintenance in Industrial Automation
8.3 Study: Process Optimization in Industrial Automation
8.4 Study: Robotics and Autonomous Systems in Industrial Automation
8.5 Study: Quality Control and Inspection Systems in Industrial Automation
8.6 Study: Supply Chain Optimization in Industrial Automation
8.7 Study: Energy Management System (EMS) in Industrial Automation
8.8 Study: Human-Machine Collaboration System in Industrial Automation
8.9 Study: Fault Detection and Recovery System in Industrial Automation
8.10 Study: Intelligent Scheduling System in Industrial Automation
8.11 Study: Safety Systems in Industrial Automation
8.12 Study: Customisation and Flexibility in Industrial Automation
8.13 Study: Real-Time Monitoring and Analytics in Industrial Automation
References
9. Role of Artificial Intelligence for Real‑Time Systems and Smart Solutions
Gundala Jhansi Rani, Naresh Kumar Sripada, Sirikonda Shwetha and Erukala Sudarshan
9.1 Introduction
9.1.1 Objectives of the Book Chapter
9.1.2 Real-Time Systems and Smart Solution
9.2 AI Techniques for Real-Time Systems
9.2.1 Machine Learning for Real-Time Analytics
9.2.1.1 Supervised Learning
9.2.1.2 Reinforcement Learning
9.2.2 Neural Networks and Deep Learning
9.2.2.1 CNNs for Image Recognition
9.2.2.2 LSTMs for Sequential Data
9.2.3 Edge Computing and Federated Learning
9.2.4 Natural Language Processing for Smart Interfaces
9.3 Applications of AI in Real-Time Systems
9.3.1 Autonomous Vehicles (AV)
9.3.2 Smart Cities
9.3.3 Healthcare
9.3.4 Industrial Automation
9.4 Challenges in AI for Real-Time Systems
9.5 Future Research Directions
9.6 Conclusion
References
10. Behavioral Analysis for Operational Efficiency in Coal Mines
Arunima Asthana and Tanmoy Kumar Banerjee
10.1 Introduction
10.1.1 Background of Behavioral Analysis
10.1.2 Importance of Behavioral Analysis
10.1.3 Research Motivation
10.2 Methodology
10.3 Rationale
10.4 Analysis and Future Research
10.5 Conclusion
References
Part 3: Adaptive Artificial Intelligence: Novel Practices
11. Society 5.0 – Study of Modern Smart Cities
Akash Raghuvanshi and Ravi Krishan Pandey
11.1 Introduction
11.1.1 Knowledge-Intensive Society
11.1.2 Data, Information, and Knowledge
11.1.3 What is a Data-Driven Society?
11.1.4 From the Information Society to the Data-Driven Society
11.1.5 Comparative Aims of Industrie 4.0 and Society 5.0
11.2 Methods
11.2.1 Data Source and Data Collection
11.2.2 Classical Content Analysis
11.3 What Exactly is the Smart City?
11.3.1 Demonstrating the Word Smart City
11.3.2 Smart City and Common Urban Infrastructure
11.3.3 Integrating Information Technologies to Urban Infrastructure to Smart Cities
11.4 Energy Management System in Smart Cities
11.4.1 Smart Energy Supply System
11.4.2 Smart-Grid
11.4.3 Micro-Grid
11.4.4 Smart-House
11.4.5 The Smart City Concept in Large Urban Development Projects
11.5 Citizen-Led Smart City to Society 5.0
11.5.1 New York, US
11.5.2 Boston, US
11.5.3 San Jose, Northern California
11.5.4 Smart City: Barcelona
11.5.5 The Sensing City, Chicago
11.6 Discussion: Risks and Challenges in Society 5.0
11.6.1 Cyber Security
11.6.2 Data Elite
11.6.3 Digital Divide
11.7 Conclusion
Acknowledgment and Author Contributions
References
12. Artificial Intelligence Applications in Healthcare
Dileep Kumar Murala, Sandeep Kumar Panda, V.A. Sankar Ponnapalli and Pradosh Kumar Gantayat
12.1 Introduction
12.1.1 Types of AI Relevance to Healthcare
12.2 Literature Review
12.2.1 Robotic Process Automation (RPA)
12.2.2 AI-Based Medical Imaging
12.2.3 Artificial Intelligence and Big Data in Precision Oncology
12.2.4 Artificial Intelligence in Digital Pathology and Drug Discovery
12.2.5 AI will Figure Out the Molecular Signaling Chain and How Cancer Works
12.2.6 AI in Surgery
12.3 Role of AI in Healthcare
12.4 Examples and Applications of AI in Healthcare
12.5 Challenges, Advantages, & Feature Directions of AI in Healthcare
12.5.1 Challenges
12.5.2 Advantages of AI in the Health Care Sector
12.5.3 The Future Directions of AI in Healthcare
Conclusion
References
13. Cloud Manufacturing and Focus on Future Trends and Directions in Health Care Applications
Ravi Prasad Thati and Pranathi Kakaraparthi
13.1 Introduction
13.1.1 Operational Quality Simplifying the Process
13.1.2 Reduce Costs
13.1.3 Personalized Medicine Customized Treatment
13.1.4 Customized Medical Equipment
13.1.5 Patient-Centered Care Enhance Patient Engagement
13.1.6 Scalability
13.1.7 Flexibility
13.1.8 Conclusion
13.2 Challenges and Considerations in Cloud Manufacturing for Healthcare
13.2.1 Data Breaches and Cyber Security Threats
13.2.2 Data Privacy and Patient Consent
13.2.3 Health Care Product Regulatory Standards
13.2.4 Global Regulatory Changes
13.2.5 Integrate with Existing Systems
13.2.6 Data Standardization and Interoperability
13.2.7 Supplier Lock-In and Flexibility
13.3 Future Trends and Directions in Cloud Manufacturing for Healthcare
13.3.1 Artificial Intelligence (AI) and Machine Learning
13.3.2 Block Chain Technology
13.3.3 Internet of Things (IoT)
13.3.4 Personalized Medicine and Customized Treatment
13.3.5 Advanced Telemedicine and Telemedicine
13.3.6 Regenerative Medicine and Bio Printing
13.3.7 Accessibility and Coverage
13.3.8 Innovation and Collaboration
13.4 Conclusion
13.4.1 Overview of Medical Cloud Manufacturing
13.4.2 Technical Basis
13.4.3 Healthcare Provider
13.4.4 Final Thoughts
References
14. GAN Based Encryption to Secure Electronic Health Record
Alakananda Tripathy and Alok Ranjan Tripathy
14.1 Introduction
14.2 Background Study
14.3 Materials and Method
14.3.1 Dataset
14.3.2 Different Stages of the Model
14.4 Result Analysis
14.5 Conclusion
References
15. Innovative AI-Driven Data Annotation Techniques
G. Viswanath, G. Kiran Kumar Reddy, K. Srinivasa Rao and C. Rambabu
15.1 Introduction
15.2 Machine Learning (ML): The Skeleton of AI-Driven Analytics
15.2.1 Supervised Learning
15.2.2 Unsupervised Learning
15.2.3 Reinforcement Learning
15.3 Knowledge-Based and Reasoning Methods
15.3.1 Expert Systems
15.3.2 Ontologies
15.4 Decision-Making Algorithms
15.4.1 Fuzzy Logic
15.4.2 Game Theory
15.4.3 Multi-Agent Systems (MAS)
15.5 Search and Optimization Theory
15.5.1 Genetic Algorithms
15.5.2 Swarm Intelligence
15.5.3 Sentiment Analysis
15.5.4 Named Entity Recognition (NER)
15.5.5 Part-of-Speech (POS) Tagging
15.5.6 Intent Recognition
15.5.7 Spam Detection
15.6 Challenges in Text Annotation for Big Data
15.7 Related Work Comparison
15.8 Graph Descriptions
15.9 Conclusion
References
16. Empowering Sustainable Finance Through Education and Awareness: Fostering Responsible AI and Quantum Computing Usage for Enhanced ESG Analysis
Geetha N., Byreddy Sumanth Reddy, Valluri Hari Hara Teja, Keshav Khemka and U. M. Gopal Krishna
16.1 Introduction
16.2 Literature Review
16.3 Research Methodology
16.4 Interpretation and Analysis of Data
16.4.1 Validity of Measurement
16.4.1.1 Root-Mean-Square Residual (RMR) and Goodness-of-Fit (GFI)
16.4.1.2 Root Mean Square Error of Approximation
16.5 Conclusion
16.6 Limitation
16.7 Future Research
References
Index

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