Adaptive Artificial Intelligence

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.

• Series Preface xxiPreface xxiiiAcknowledgements xxviiPart 1: Adaptive Artificial Intelligence: Fundamentals 11 From Data to Diagnosis—Integrating Adaptive AI in Reshaping Healthcare 3Kumar Saurabh and Raghuraj Singh Suryavanshi1.1 Introduction 31.2 Literature Review 51.3 Benefits of Adaptive AI in Health Diagnostic 91.3.1 Personalized Treatment Plans Based on Individual Patient Data 91.3.2 Automated Health Monitoring Systems for Early Disease Identification 91.3.3 Reduction in Medical Errors and Misdiagnoses 91.4 Challenges and Limitations of Adaptive AI in Health Diagnostic 111.4.1 Privacy Concerns Related to Patient Data Usage 111.4.2 Lack of Standardized Regulations for AI in Healthcare 111.4.3 Potential Bias in AI Algorithms Leading to Inaccurate Diagnoses 121.5 Current Applications of Adaptive AI in Health Diagnostic 121.5.1 Disease Prediction and Risk Assessment 121.5.2 Image Recognition for Medical Imaging Analysis 121.5.3 Drug Discovery and Personalized Medicine 131.5.4 Automation of Administrative Tasks 141.6 Future Prospects of Adaptive AI in Health Diagnostic 151.7 Conclusion 15References 162 Transfer Learning in Adaptive AI 19Pradumn Kumar and Praveen Kumar Shukla2.1 Introduction: The Evolution of Adaptive Intelligence 202.2 Theoretical Foundations of Transfer Learning 212.2.1 Categorization of Transfer Learning Approaches: An In-Depth Exploration 222.3 Adaptive AI: Concepts and Challenges 282.3.1 What is Adaptive AI 282.3.2 Core Characteristics 302.3.2.1 Continual Learning 302.3.2.2 Generalization 312.3.2.3 Efficiency 322.3.3 Challenges 322.3.3.1 Catastrophic Forgetting 322.3.3.2 Data Scarcity 342.3.3.3 Domain Shift 362.4 Transfer Learning Techniques for Adaptive AI 382.4.1 Pre-Trained Models and Fine-Tuning 382.4.2 Domain Adaptation 382.4.3 Meta-Learning 392.4.4 Continual Learning 392.4.5 Multi-Task Learning 392.5 Applications of Transfer Learning in Adaptive AI 402.5.1 Natural Language Processing (NLP) 402.5.2 Computer Vision 402.5.3 Robotics 402.5.4 Healthcare 412.5.5 Tesla Autopilot 412.6 Conclusion 42References 423 Beyond Prediction: Adaptive AI as a Catalyst for Climate Change Mitigation and Understanding 45Deepak Gupta and Satyasundara Mahapatra3.1 Introduction 463.1.1 The Escalating Climate Crisis: A Data-Driven Perspective 463.1.2 The Evolution of Climate Modeling: From Traditional Methods to AI 473.1.3 Beyond AI: The Rise of Adaptive AI in Climate Science 473.1.4 Objectives and Significance of This Chapter 483.2 Foundations of Adaptive AI in Climate Science 483.2.1 Understanding Adaptive AI: A Paradigm Shift in Machine Learning 483.2.2 Core Mechanisms Enabling Adaptability 503.2.2.1 Reinforcement Learning for Dynamic Decision-Making 503.2.2.2 Continual Learning for Real-Time Model Updates 503.2.2.3 Meta-Learning 513.2.2.4 Evolutionary Algorithms and Neuroevolutionary 523.2.2.5 Transfer Learning to Leverage Knowledge Across Climate Domains 523.2.3 The Necessity of Adaptability in Climate Change Modeling 523.2.3.1 Coping with Evolving Climate Variables 523.2.3.2 Reducing Uncertainty in Long-Term Predictions 523.2.3.3 Enhancing Precision in Real-Time Climate Monitoring 533.2.4 Importance of Adaptation in Climate Models 533.2.4.1 Real-Time Learning and Parameter Updates 533.2.4.2 Handling Non-Stationary Climate Patterns 533.2.4.3 Reducing Uncertainties in Projections 533.3 Adaptive AI Frameworks for Climate Change Modeling 543.3.1 Dynamic Climate Forecasting Models 543.3.2 Adaptive AI for Extreme Weather Prediction 553.3.3 AI-Augmented Numerical and Physics-Based Climate Models 553.3.4 Hybrid Approaches: Integrating Big Data, IoT, and AI in Climate Prediction 563.3.5 Case Study: Adaptive AI in Global Climate Risk Assessment 563.4 Real-World Applications of Adaptive AI in Climate Resilience 573.4.1 Predicting and Mitigating Natural Disasters: Wildfire Prediction and Mitigation with Adaptive AI 583.4.2 Dynamic AI Models for Sustainable Agriculture and Food Security 583.4.3 Intelligent Water Management for Drought and Flood Prevention 593.4.4 Smart Energy Grids Optimized by Adaptive AI for Carbon Reduction 603.4.5 Monitoring and Protecting Marine and Terrestrial Ecosystems 603.5 Challenges and Limitations in Adaptive AI for Climate Science 613.5.1 Data Complexity and Computational Constraints 613.5.1.1 High-Dimensional, Spatiotemporal Datasets 623.5.1.2 Handling Incomplete and Uncertain Climate Data 623.5.2 Balancing Adaptability and Model Stability 623.5.3 Ethical Implications: Bias, Transparency, and AI Accountability 633.5.3.1 Algorithmic Bias in Climate Predictions 633.5.3.2 Ensuring Transparency in Adaptive Decision-Making 633.5.4 Policy and Regulatory Challenges in AI-Governed Climate Actions 643.5.4.1 Regulatory Frameworks for Adaptive AI in Environmental Monitoring 643.5.4.2 Collaboration Between Governments, AI Researchers, and Climate Scientists 643.6 The Future of Adaptive AI in Climate Change Mitigation 653.6.1 Quantum AI for Enhanced Climate Modeling 653.6.2 Federated Learning for Global Collaborative Climate Research 663.6.3 AI-Driven Policy Recommendations for Climate Adaptation 663.6.4 Towards a Unified Adaptive AI Framework for Climate Resilience 673.7 Conclusion 68References 704 Adaptive AI: Transforming Natural Language Processing and Industry Applications 73Meena Kumari P., Ramakrishna Reddy K. and Manikandakumar M.4.1 Introduction 744.1.1 Benefits of NLP 744.1.2 Technologies Related to Natural Language Processing 754.1.3 Applications of Natural Language Processing (NLP) 764.2 Adaptive AI 784.2.1 What is Adaptive AI? 794.2.1.1 Key Characteristics of Adaptive AI 794.2.1.2 Traditional vs. Adaptive AI 814.2.2 How Does Adaptive AI Work? 814.3 Adaptive AI Use Cases with NLP 844.3.1 Chatbots and Virtual Assistants 844.3.2 Healthcare Industry 864.3.3 Personalized Education 884.4 Adaptive AI Use Cases in Other Industry 904.4.1 Healthcare 914.4.2 Finance 934.4.3 Transportation 944.4.4 Manufacturing 954.4.5 Environmental Sustainability 964.5 Ethical Considerations and Challenges 964.6 Conclusion 97References 985 Optimizing Networking Systems with Machine Learning Approach 101Cherukuri Gaurav Sushant, Tanishq Kumar, Lakshmi Ajay Veeramraju, Yuvraj Singh and Sandeep Kumar Panda5.1 Introduction 1025.2 Networks 1025.3 Computer Networks 1025.4 Networking Software’s 1035.4.1 Common Protocols 1035.4.2 Network Topologies 1035.4.3 OSI Model 1045.4.4 Routing Algorithms 1055.4.5 Internet Protocol (IP) 1055.5 Hardware Devices 1055.5.1 Network Interface Card (NIC) 1055.5.2 Switch 1055.5.3 Access Point 1065.5.4 Router 1065.5.5 Firewall 1065.5.6 Gateway 1065.5.7 Transmission Medium 1065.6 Software-Defined Networks (SDN) 1075.6.1 Management Plane 1075.6.2 Control Plane 1075.6.3 Data Plane 1075.6.4 Definition 1075.6.5 How Different is SDN from Traditional Systems 1085.7 Machine Learning 1085.7.1 Data Collection 1095.7.2 Dimensionality Reduction 1115.7.3 Performance Score 1115.7.4 Regression 1125.7.5 Classification in Machine Learning 1135.8 Deep Learning 1175.8.1 Long Short-Term Memory (LSTM) 1175.8.2 Autoencoders 1195.9 Applications of Machine Learning 1215.9.1 QOS – Quality of Service 1215.9.2 Machine Learning in Fault Management 1215.9.3 Machine Learning in Performance Prediction 1225.9.4 Machine Learning in Infrastructure Cost 1225.9.5 Load Balancing Using Machine Learning 1225.10 Traditional Load Balancing Techniques 1235.10.1 Machine Learning and Load Balancing 1235.11 SDN Decision Making 1245.11.1 Methods and Types of Decision Making in SDN 1245.11.2 Machine Learning in SDN Decision Making 1255.12 Conclusion 126References 126Part 2: Adaptive Artificial Intelligence: Applications 1356 Assessment of the Recurrent RBF Long-Range Forecasting Model for Estimating Net Asset Value 137Minakhi Rout, Anjishnu Saw, Ajay Kumar Jena and Ajaya Kumar Parida6.1 Introduction 1376.2 Design of a Forecasting Model Using the Recurrent Radial Basis Function (RRBF) Neural Network 1406.3 Extraction of Features and Construction of Input Data 1436.4 Simulation Based Experiments 1446.5 Conclusion 154References 1547 Reinforcement Learning in Network Optimization 157M. Sandhya, L. Lakshmi and L. Anjaneyulu7.1 Introduction 1587.2 Related Works 1607.3 Key Concepts of Network Optimization 1617.3.1 Traffic Routing 1617.3.2 Resource Allocation 1627.3.3 Load Balancing 1627.3.4 Quality of Service (QoS) 1627.4 Key Concepts of RL 1637.4.1 Fundamental Principles of RL 1637.4.1.1 States 1647.4.1.2 Actions 1647.4.1.3 Rewards 1647.4.1.4 Policies 1657.4.1.5 Value Functions 1657.4.2 Types of RL Algorithms 1657.4.2.1 Q-Learning 1667.4.2.2 Deep Q-Networks (DQN) 1677.4.2.3 Policy Gradient Methods 1687.4.2.4 Actor-Critic Method 1697.4.2.5 Deep Deterministic Policy Gradient (DDPG) 1707.4.2.6 Multi-Agent Reinforcement Learning 1717.4.2.7 Hierarchical Reinforcement Learning 1737.5 Importance of RL in Network Optimization 1757.5.1 Adaptability 1777.5.2 Expansion Capability 1777.5.3 Autonomous Operation 1787.5.4 Instantaneous Optimization 1787.6 Performance Evaluation and Benchmarking 1797.6.1 General Metrics 1797.6.2 Deep Q-Networks (DQN) 1807.6.3 Action-Critic Methods 1807.6.4 Multi-Agent Approaches 1807.6.5 DDPG (Deep Deterministic Policy Gradient) 1817.6.6 Hierarchical Reinforcement Learning (HRL) 1817.7 Challenges and Future Directions in RL for Network Optimization 1817.7.1 Challenges in RL for Network Optimization 1827.7.1.1 Scalability 1827.7.1.2 Real-Time Decision Making 1827.7.1.3 Data Availability and Quality 1827.7.1.4 Robustness and Reliability 1827.7.1.5 Integration with Existing Systems 1827.7.2 Future Directions in RL for Network Optimization 1837.7.2.1 Advanced RL Algorithms 1837.7.2.2 Efficient Training Techniques 1837.7.2.3 Real-Time and Low-Latency Solutions 1837.7.2.4 Robust and Adaptive RL Models 1837.7.2.5 Enhanced Simulation Environments 1847.7.2.6 Standardization and Benchmarking 1847.8 Conclusions 184References 1858 A Study on AI Adoption Methods in Industry 189E. Sudarshan, K.S.R.K. Sarma and Karra Kishore8.1 Types of Adaptive AI Techniques for Industrial Automation 1908.2 Study: Predictive Maintenance in Industrial Automation 1938.3 Study: Process Optimization in Industrial Automation 1978.4 Study: Robotics and Autonomous Systems in Industrial Automation 2018.5 Study: Quality Control and Inspection Systems in Industrial Automation 2058.6 Study: Supply Chain Optimization in Industrial Automation 2108.7 Study: Energy Management System (EMS) in Industrial Automation 2158.8 Study: Human-Machine Collaboration System in Industrial Automation 2208.9 Study: Fault Detection and Recovery System in Industrial Automation 2248.10 Study: Intelligent Scheduling System in Industrial Automation 2308.11 Study: Safety Systems in Industrial Automation 2368.12 Study: Customisation and Flexibility in Industrial Automation 2428.13 Study: Real-Time Monitoring and Analytics in Industrial Automation 249References 2569 Role of Artificial Intelligence for Real‐Time Systems and Smart Solutions 261Gundala Jhansi Rani, Naresh Kumar Sripada, Sirikonda Shwetha and Erukala Sudarshan9.1 Introduction 2629.1.1 Objectives of the Book Chapter 2629.1.2 Real-Time Systems and Smart Solution 2629.2 AI Techniques for Real-Time Systems 2659.2.1 Machine Learning for Real-Time Analytics 2669.2.1.1 Supervised Learning 2669.2.1.2 Reinforcement Learning 2679.2.2 Neural Networks and Deep Learning 2679.2.2.1 CNNs for Image Recognition 2679.2.2.2 LSTMs for Sequential Data 2689.2.3 Edge Computing and Federated Learning 2689.2.4 Natural Language Processing for Smart Interfaces 2689.3 Applications of AI in Real-Time Systems 2689.3.1 Autonomous Vehicles (AV) 2699.3.2 Smart Cities 2719.3.3 Healthcare 2739.3.4 Industrial Automation 2769.4 Challenges in AI for Real-Time Systems 2789.5 Future Research Directions 2789.6 Conclusion 279References 28010 Behavioral Analysis for Operational Efficiency in Coal Mines 285Arunima Asthana and Tanmoy Kumar Banerjee10.1 Introduction 28510.1.1 Background of Behavioral Analysis 28710.1.2 Importance of Behavioral Analysis 28810.1.3 Research Motivation 28810.2 Methodology 28910.3 Rationale 29210.4 Analysis and Future Research 30110.5 Conclusion 302References 303Part 3: Adaptive Artificial Intelligence: Novel Practices 30711 Society 5.0 – Study of Modern Smart Cities 309Akash Raghuvanshi and Ravi Krishan Pandey11.1 Introduction 31011.1.1 Knowledge-Intensive Society 31111.1.2 Data, Information, and Knowledge 31111.1.3 What is a Data-Driven Society? 31211.1.4 From the Information Society to the Data-Driven Society 31411.1.5 Comparative Aims of Industrie 4.0 and Society 5.0 31511.2 Methods 31711.2.1 Data Source and Data Collection 31711.2.2 Classical Content Analysis 31711.3 What Exactly is the Smart City? 31711.3.1 Demonstrating the Word Smart City 31711.3.2 Smart City and Common Urban Infrastructure 31811.3.3 Integrating Information Technologies to Urban Infrastructure to Smart Cities 31911.4 Energy Management System in Smart Cities 31911.4.1 Smart Energy Supply System 31911.4.2 Smart-Grid 32011.4.3 Micro-Grid 32011.4.4 Smart-House 32011.4.5 The Smart City Concept in Large Urban Development Projects 32011.5 Citizen-Led Smart City to Society 5.0 32211.5.1 New York, US 32211.5.2 Boston, US 32311.5.3 San Jose, Northern California 32411.5.4 Smart City: Barcelona 32411.5.5 The Sensing City, Chicago 32511.6 Discussion: Risks and Challenges in Society 5.0 32611.6.1 Cyber Security 32611.6.2 Data Elite 32611.6.3 Digital Divide 32711.7 Conclusion 327Acknowledgment and Author Contributions 327References 32812 Artificial Intelligence Applications in Healthcare 329Dileep Kumar Murala, Sandeep Kumar Panda, V.A. Sankar Ponnapalli and Pradosh Kumar Gantayat12.1 Introduction 33012.1.1 Types of AI Relevance to Healthcare 33012.2 Literature Review 33312.2.1 Robotic Process Automation (RPA) 33312.2.2 AI-Based Medical Imaging 33512.2.3 Artificial Intelligence and Big Data in Precision Oncology 33712.2.4 Artificial Intelligence in Digital Pathology and Drug Discovery 33812.2.5 AI will Figure Out the Molecular Signaling Chain and How Cancer Works 33912.2.6 AI in Surgery 34012.3 Role of AI in Healthcare 34112.4 Examples and Applications of AI in Healthcare 34612.5 Challenges, Advantages, & Feature Directions of AI in Healthcare 34912.5.1 Challenges 34912.5.2 Advantages of AI in the Health Care Sector 35012.5.3 The Future Directions of AI in Healthcare 351Conclusion 352References 35313 Cloud Manufacturing and Focus on Future Trends and Directions in Health Care Applications 359Ravi Prasad Thati and Pranathi Kakaraparthi13.1 Introduction 35913.1.1 Operational Quality Simplifying the Process 36013.1.2 Reduce Costs 36013.1.3 Personalized Medicine Customized Treatment 36113.1.4 Customized Medical Equipment 36213.1.5 Patient-Centered Care Enhance Patient Engagement 36213.1.6 Scalability 36313.1.7 Flexibility 36313.1.8 Conclusion 36413.2 Challenges and Considerations in Cloud Manufacturing for Healthcare 36413.2.1 Data Breaches and Cyber Security Threats 36513.2.2 Data Privacy and Patient Consent 36513.2.3 Health Care Product Regulatory Standards 36613.2.4 Global Regulatory Changes 36713.2.5 Integrate with Existing Systems 36813.2.6 Data Standardization and Interoperability 36813.2.7 Supplier Lock-In and Flexibility 36813.3 Future Trends and Directions in Cloud Manufacturing for Healthcare 36913.3.1 Artificial Intelligence (AI) and Machine Learning 36913.3.2 Block Chain Technology 37013.3.3 Internet of Things (IoT) 37113.3.4 Personalized Medicine and Customized Treatment 37213.3.5 Advanced Telemedicine and Telemedicine 37213.3.6 Regenerative Medicine and Bio Printing 37213.3.7 Accessibility and Coverage 37313.3.8 Innovation and Collaboration 37413.4 Conclusion 37513.4.1 Overview of Medical Cloud Manufacturing 37513.4.2 Technical Basis 37513.4.3 Healthcare Provider 37613.4.4 Final Thoughts 377References 37814 GAN Based Encryption to Secure Electronic Health Record 381Alakananda Tripathy and Alok Ranjan Tripathy14.1 Introduction 38214.2 Background Study 38314.3 Materials and Method 38414.3.1 Dataset 38414.3.2 Different Stages of the Model 38414.4 Result Analysis 38914.5 Conclusion 393References 39415 Innovative AI-Driven Data Annotation Techniques 397G. Viswanath, G. Kiran Kumar Reddy, K. Srinivasa Rao and C. Rambabu15.1 Introduction 39815.2 Machine Learning (ML): The Skeleton of AI-Driven Analytics 39915.2.1 Supervised Learning 39915.2.2 Unsupervised Learning 39915.2.3 Reinforcement Learning 40015.3 Knowledge-Based and Reasoning Methods 40015.3.1 Expert Systems 40015.3.2 Ontologies 40015.4 Decision-Making Algorithms 40115.4.1 Fuzzy Logic 40115.4.2 Game Theory 40115.4.3 Multi-Agent Systems (MAS) 40115.5 Search and Optimization Theory 40115.5.1 Genetic Algorithms 40215.5.2 Swarm Intelligence 40215.5.3 Sentiment Analysis 40215.5.4 Named Entity Recognition (NER) 40215.5.5 Part-of-Speech (POS) Tagging 40215.5.6 Intent Recognition 40215.5.7 Spam Detection 40315.6 Challenges in Text Annotation for Big Data 40315.7 Related Work Comparison 40415.8 Graph Descriptions 40615.9 Conclusion 408References 40816 Empowering Sustainable Finance Through Education and Awareness: Fostering Responsible AI and Quantum Computing Usage for Enhanced ESG Analysis 411Geetha N., Byreddy Sumanth Reddy, Valluri Hari Hara Teja, Keshav Khemka and U. M. Gopal Krishna16.1 Introduction 41216.2 Literature Review 41616.3 Research Methodology 42316.4 Interpretation and Analysis of Data 42416.4.1 Validity of Measurement 42516.4.1.1 Root-Mean-Square Residual (RMR) and Goodness-of-Fit (GFI) 42616.4.1.2 Root Mean Square Error of Approximation 42716.5 Conclusion 42816.6 Limitation 42816.7 Future Research 429References 429Index 433