About the Book
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.
Table of Contents:
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
1.4 Challenges and Limitations of Adaptive AI in Health Diagnostic 11
1.5 Current Applications of Adaptive AI in Health Diagnostic 12
1.6 Future Prospects of Adaptive AI in Health Diagnostic 15
1.7 Conclusion 15
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
2.3 Adaptive AI: Concepts and Challenges 28
2.4 Transfer Learning Techniques for Adaptive AI 38
2.5 Applications of Transfer Learning in Adaptive AI 40
2.6 Conclusion 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
3.2 Foundations of Adaptive AI in Climate Science 48
3.3 Adaptive AI Frameworks for Climate Change Modeling 54
3.4 Real-World Applications of Adaptive AI in Climate Resilience 57
3.5 Challenges and Limitations in Adaptive AI for Climate Science 61
3.6 The Future of Adaptive AI in Climate Change Mitigation 65
3.7 Conclusion 68
4 Adaptive AI: Transforming Natural Language Processing and Industry Applications 73
Meena Kumari P., Ramakrishna Reddy K. and Manikandakumar M.
4.1 Introduction 74
4.2 Adaptive AI 78
4.3 Adaptive AI Use Cases with NLP 84
4.4 Adaptive AI Use Cases in Other Industry 90
4.5 Ethical Considerations and Challenges 96
4.6 Conclusion 97
5 Optimizing Networking Systems with Machine Learning Approach 101
Cherukuri Gaurav Sushant, Tanishq Kumar, Lakshmi Ajay Veeramraju, Yuvraj Singh and Sandeep Kumar Panda
5.1 Introduction 102
5.2 Networks 102
5.3 Computer Networks 102
5.4 Networking Software’s 103
5.5 Hardware Devices 105
5.6 Software-Defined Networks (SDN) 107
5.7 Machine Learning 108
5.8 Deep Learning 117
5.9 Applications of Machine Learning 121
5.10 Traditional Load Balancing Techniques 123
5.11 SDN Decision Making 124
5.12 Conclusion 126
Part 2: Adaptive Artificial Intelligence: Applications 135
6 Assessment of the Recurrent RBF Long-Range Forecasting Model for Estimating Net Asset Value 137
Minakhi Rout, Anjishnu Saw, Ajay Kumar Jena and Ajaya Kumar Parida
6.1 Introduction 137
6.2 Design of a Forecasting Model Using the Recurrent Radial Basis Function (RRBF) Neural Network 140
6.3 Extraction of Features and Construction of Input Data 143
6.4 Simulation Based Experiments 144
6.5 Conclusion 154
7 Reinforcement Learning in Network Optimization 157
M. Sandhya, L. Lakshmi and L. Anjaneyulu
7.1 Introduction 158
7.2 Related Works 160
7.3 Key Concepts of Network Optimization 161
7.4 Key Concepts of RL 163
7.5 Importance of RL in Network Optimization 175
7.6 Performance Evaluation and Benchmarking 179
7.7 Challenges and Future Directions in RL for Network Optimization 181
7.8 Conclusions 184
8 A Study on AI Adoption Methods in Industry 189
E. Sudarshan, K.S.R.K. Sarma and Karra Kishore
8.1 Types of Adaptive AI Techniques for Industrial Automation 190
8.2 Study: Predictive Maintenance in Industrial Automation 193
8.3 Study: Process Optimization in Industrial Automation 197
8.4 Study: Robotics and Autonomous Systems in Industrial Automation 201
8.5 Study: Quality Control and Inspection Systems in Industrial Automation 205
8.6 Study: Supply Chain Optimization in Industrial Automation 210
8.7 Study: Energy Management System (EMS) in Industrial Automation 215
8.8 Study: Human-Machine Collaboration System in Industrial Automation 220
8.9 Study: Fault Detection and Recovery System in Industrial Automation 224
8.10 Study: Intelligent Scheduling System in Industrial Automation 230
8.11 Study: Safety Systems in Industrial Automation 236
8.12 Study: Customisation and Flexibility in Industrial Automation 242
8.13 Study: Real-Time Monitoring and Analytics in Industrial Automation 249
9 Role of Artificial Intelligence for Real‑Time Systems and Smart Solutions 261
Gundala Jhansi Rani, Naresh Kumar Sripada, Sirikonda Shwetha and Erukala Sudarshan
9.1 Introduction 262
9.2 AI Techniques for Real-Time Systems 265
9.3 Applications of AI in Real-Time Systems 268
9.4 Challenges in AI for Real-Time Systems 278
9.5 Future Research Directions 278
9.6 Conclusion 279
10 Behavioral Analysis for Operational Efficiency in Coal Mines 285
Arunima Asthana and Tanmoy Kumar Banerjee
10.1 Introduction 285
10.2 Methodology 289
10.3 Rationale 292
10.4 Analysis and Future Research 301
10.5 Conclusion 302
Part 3: Adaptive Artificial Intelligence: Novel Practices 307
11 Society 5.0 – Study of Modern Smart Cities 309
Akash Raghuvanshi and Ravi Krishan Pandey
11.1 Introduction 310
11.2 Methods 317
11.3 What Exactly is the Smart City? 317
11.4 Energy Management System in Smart Cities 319
11.5 Citizen-Led Smart City to Society 5.0 322
11.6 Discussion: Risks and Challenges in Society 5.0 326
11.7 Conclusion 327
12 Artificial Intelligence Applications in Healthcare 329
Dileep Kumar Murala, Sandeep Kumar Panda, V.A. Sankar Ponnapalli and Pradosh Kumar Gantayat
12.1 Introduction 330
12.2 Literature Review 333
12.3 Role of AI in Healthcare 341
12.4 Examples and Applications of AI in Healthcare 346
12.5 Challenges, Advantages, & Feature Directions of AI in Healthcare 349
13 Cloud Manufacturing and Focus on Future Trends and Directions in Health Care Applications 359
Ravi Prasad Thati and Pranathi Kakaraparthi
13.1 Introduction 359
13.2 Challenges and Considerations in Cloud Manufacturing for Healthcare 364
13.3 Future Trends and Directions in Cloud Manufacturing for Healthcare 369
13.4 Conclusion 375
14 GAN Based Encryption to Secure Electronic Health Record 381
Alakananda Tripathy and Alok Ranjan Tripathy
14.1 Introduction 382
14.2 Background Study 383
14.3 Materials and Method 384
14.4 Result Analysis 389
14.5 Conclusion 393
15 Innovative AI-Driven Data Annotation Techniques 397
G. Viswanath, G. Kiran Kumar Reddy, K. Srinivasa Rao and C. Rambabu
15.1 Introduction 398
15.2 Machine Learning (ML): The Skeleton of AI-Driven Analytics 399
15.3 Knowledge-Based and Reasoning Methods 400
15.4 Decision-Making Algorithms 401
15.5 Search and Optimization Theory 401
15.6 Challenges in Text Annotation for Big Data 403
15.7 Related Work Comparison 404
15.8 Graph Descriptions 406
15.9 Conclusion 408
16 Empowering Sustainable Finance Through Education and Awareness: Fostering Responsible AI and Quantum Computing Usage for Enhanced ESG Analysis 411
Geetha N., Byreddy Sumanth Reddy, Valluri Hari Hara Teja, Keshav Khemka and U. M. Gopal Krishna
16.1 Introduction 412
16.2 Literature Review 416
16.3 Research Methodology 423
16.4 Interpretation and Analysis of Data 424
16.5 Conclusion 428
16.6 Limitation 428
16.7 Future Research 429
References 429
Index 433
About the Author :
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.
