Autorentext

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.

S. Balamurugan, PhD is the Director of Research, 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 experience using various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.

Klappentext

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. 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. 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.

  Inhalt

  Series Preface xxi

  Preface xxiii

  Acknowledgements xxvii

  Part 1: Adaptive Artificial Intelligence: Fundamentals 1

  1 From Data to Diagnosis-Integrating Adaptive AI in Reshaping Healthcare 3
  Kumar Saurabh and Raghuraj Singh Suryavanshi

  1.1 Introduction 3

  1.2 Literature Review 5

  1.3 Benefits of Adaptive AI in Health Diagnostic 9

  1.3.1 Personalized Treatment Plans Based on Individual Patient Data 9

  1.3.2 Automated Health Monitoring Systems for Early Disease Identification 9

  1.3.3 Reduction in Medical Errors and Misdiagnoses 9

  1.4 Challenges and Limitations of Adaptive AI in Health Diagnostic 11

  1.4.1 Privacy Concerns Related to Patient Data Usage 11

  1.4.2 Lack of Standardized Regulations for AI in Healthcare 11

  1.4.3 Potential Bias in AI Algorithms Leading to Inaccurate Diagnoses 12

  1.5 Current Applications of Adaptive AI in Health Diagnostic 12

  1.5.1 Disease Prediction and Risk Assessment 12

  1.5.2 Image Recognition for Medical Imaging Analysis 12

  1.5.3 Drug Discovery and Personalized Medicine 13

  1.5.4 Automation of Administrative Tasks 14

  1.6 Future Prospects of Adaptive AI in Health Diagnostic 15

  1.7 Conclusion 15

  References 16

  2 Transfer Learning in Adaptive AI 19
  Pradumn Kumar and Praveen Kumar Shukla

  2.1 Introduction: The Evolution of Adaptive Intelligence 20

  2.2 Theoretical Foundations of Transfer Learning 21

  2.2.1 Categorization of Transfer Learning Approaches: An In-Depth Exploration 22

  2.3 Adaptive AI: Concepts and Challenges 28

  2.3.1 What is Adaptive AI 28

  2.3.2 Core Characteristics 30

  2.3.2.1 Continual Learning 30

  2.3.2.2 Generalization 31

  2.3.2.3 Efficiency 32

  2.3.3 Challenges 32

  2.3.3.1 Catastrophic Forgetting 32

  2.3.3.2 Data Scarcity 34

  2.3.3.3 Domain Shift 36

  2.4 Transfer Learning Techniques for Adaptive AI 38

  2.4.1 Pre-Trained Models and Fine-Tuning 38

  2.4.2 Domain Adaptation 38

  2.4.3 Meta-Learning 39

  2.4.4 Continual Learning 39

  2.4.5 Multi-Task Learning 39

  2.5 Applications of Transfer Learning in Adaptive AI 40

  2.5.1 Natural Language Processing (NLP) 40

  2.5.2 Computer Vision 40

  2.5.3 Robotics 40

  2.5.4 Healthcare 41

  2.5.5 Tesla Autopilot 41

  2.6 Conclusion 42

  References 42

  3 Beyond Prediction: Adaptive AI as a Catalyst for Climate Change Mitigation and Understanding 45
  Deepak Gupta and Satyasundara Mahapatra

  3.1 Introduction 46

  3.1.1 The Escalating Climate Crisis: A Data-Driven Perspective 46

  3.1.2 The Evolution of Climate Modeling: From Traditional Methods to AI 47

  3.1.3 Beyond AI: The Rise of Adaptive AI in Climate Science 47

  3.1.4 Objectives and Significance of This Chapter 48

  3.2 Foundations of Adaptive AI in Climate Science 48

  3.2.1 Understanding Adaptive AI: A Paradigm Shift in Machine Learning 48

  3.2.2 Core Mechanisms Enabling Adaptability 50

  3.2.2.1 Reinforcement Learning for Dynamic Decision-Making 50

  3.2.2.2 Continual Learning for Real-Time Model Updates 50

  3.2.2.3 Meta-Learning 51

  3.2.2.4 Evolutionary Algorithms and Neuroevolutionary 52

  3.2.2.5 Transfer Learning to Leverage Knowledge Across Climate Domains 52

  3.2.3 The Necessity of Adaptability in Climate Change Modeling 52

  3.2.3.1 Coping with Evolving Climate Variables 52

  3.2.3.2 Reducing Uncertainty in Long-Term Predictions 52

  3.2.3.3 Enhancing Precision in Real-Time Climate Monitoring 53

  3.2.4 Importance of Adaptation in Climate Models 53

  3.2.4.1 Real-Time Learning and Parameter Updates 53

  3.2.4.2 Handling Non-Stationary Climate Patterns 53

  3.2.4.3 Reducing Uncertainties in Projections 53

  3.3 Adaptive AI Frameworks for Climate Change Modeling 54

  3.3.1 Dynamic Climate Forecasting Models 54

  3.3.2 Adaptive AI for Extreme Weather Prediction 55

  3.3.3 AI-Augmented Numerical and Physics-Based Climate M…