Aditya Khamparia, Assistant Professor, Department of Computer Science at Babasaheb Bhimrao Ambedkar University, India. His research areas include Artificial Intelligence, Intelligent Data Analysis, Machine Learning, Deep Learning, and Soft Computing. Deepak Gupta, Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India. His research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.

• About the Editors xxviiList of Contributors xxixPreface xxxixAcknowledgments xli1 Generative AI in Wearables: Exploring the Impact of GANs, VAEs, and Transformers 1Diwakar Diwakar and Deepa Raj1.1 Introduction 11.2 Theoretical Foundations 71.3 Opportunities of Integration 141.4 Research and Development Insights 161.5 Ethical and Regulatory Considerations 241.6 Case Studies and Applications 261.7 Future Directions and Emerging Trends 271.8 Conclusion 31References 322 Safeguarding Privacy and Security in AI-Enabled Healthcare Informatics 35Akanksha Kochhar, Ganeev Kaur Chhabra, Toshika Goswami, and Moolchand Sharma2.1 Introduction 352.2 Drawbacks and Their Possible Solutions 382.3 Applications 432.4 Devices 442.5 Future Scope 462.6 Conclusion 472.7 Future Scope 48References 493 Generating Synthetic Medical Data Using GAI 51Sudhanshu Singh, Suruchi Singh, and C.S. Raghuvanshi3.1 Introduction 513.2 Uncloaking the GAI Orchestra: A Compendium of Techniques 533.3 Beyond the Notes: Ethical Considerations and Responsible Use 663.4 Conclusion 70References 704 Automation of Drug Design and Development 73Sudhanshu Singh4.1 Introduction 734.2 High-Throughput Screening (HTS) 744.3 Artificial Intelligence (AI) and Machine Learning (ML) 774.4 Automation in Drug Synthesis and Optimization 804.5 Automation in Clinical Trials 814.6 Challenges and Opportunities 834.7 Conclusion 85References 875 Autism Spectrum Disorder Diagnosis: A Comprehensive Review of Machine Learning Approaches 89Deepti Prasad and Suman Bhatia5.1 Introduction 895.2 Machine Learning and Deep Learning Algorithms 925.3 Discussion 985.4 Future Work 995.5 Conclusion 99References 1006 Temporal Normalization and Brain Image Analysis for Early-Stage Prediction of Attention Deficit Hyperactivity Disorder (ADHD) 103Poonam Chaudhary, Nikki Rani, Diksha Aggarwal, and Srishti Sharma6.1 Introduction 1036.2 Exploratory Data Analysis 1056.3 Methodology 1096.4 Results and Discussion 1156.5 Conclusion 116References 1177 Sustainable Agriculture Through Advanced Crop Management: VGG16-Based Tea Leaf Disease Recognition 121R. Sivaraman, S. Praveena, and H. Naresh Kumar7.1 Introduction 1217.2 Literature Survey 1227.3 Proposed Methodology for Tea Leaf Diseases Detection 1257.4 Results and Discussion 1307.5 Conclusion 131References 1328 Advancing Colorectal Cancer Diagnosis: Integrating Synthetic Data and Machine Learning for Microbiome Analysis 135Alessio Rotelli and Ernesto Iadanza8.1 Colorectal Cancer (CRC) 1358.2 Understanding the Gut Microbiome 1368.3 Influence of the Gut Microbiome Dysbiosis on Colorectal Adenomas and CRC 1368.4 Differentiating Adenomatous Polyps (AP) from CRC 1378.5 Use of Data Augmentation 1388.6 Data Evaluation Metrics 1388.7 Feature Extraction by Later-Wise Relevance Propagation 1398.8 Beta Diversity Analysis 1408.9 Machine Learning and SHAP Analysis to Classify AP and CRC Samples 1418.10 Results of Classification and SHAP Analysis 1438.11 Key Bacterial Taxa Discriminating Between AP and CRC: Insights from Feature Extraction and SHAP Analysis 1498.12 Conclusion 149References 1509 Recent Knowledge in Drug Design and Development: Automation and Advancement 153Kusum Gurung, Saurav K. Mishra, Tabsum Chhetri, Sneha Roy, Anagha Balakrishnan, and John J. Georrge9.1 Introduction 1539.2 Automation in Drug Design and Development 1569.3 Tools and Database for Drug Design, including Algorithm and Application 1589.4 Automation in Drug Design and Its Impact on the Pharmaceutical Sector 1609.5 Automation-Assisted Successful Studies in Drug Design 1659.6 Advancement and Challenges 1709.7 Conclusion 171References 17210 Machine Learning and Generative AI Techniques for Sentiment Analysis with Applications 183Riya Sharma, Balraj Singh, and Aditya Khamparia10.1 Introduction 18310.2 Literature Review 18710.3 Machine Learning Techniques for Sentiment Analysis 18710.4 Generative AI Techniques for Sentiment Analysis 19610.5 Conclusion 202References 20311 Use of AI with Optimization Techniques: Case Study, Challenges, and Future Trends 209Ayushi Mittal, Parul Parul, Charu Gupta, and Devendra K. Tayal11.1 Introduction 20911.2 Overview of Medical Disease Prediction Models 21311.3 Importance of Optimization in Enhancing Prediction Accuracy 21411.4 Commonly Used Optimization Algorithms in Medical Predictive Modeling 21411.5 Integration of ML and Optimization for Disease Prediction 22211.6 Challenges and Considerations in Applying Optimization Techniques to Medical Data 22311.7 Case Studies: Successful Applications of Optimization in Disease Prediction 22611.8 Future Directions and Emerging Trends in Optimizing Medical Prediction Models 22811.9 Ethical and Regulatory Implications of Optimized Disease Prediction Systems 23111.10 Conclusion: Harnessing Optimization for Advancements in Medical Predictive Analytics 23311.11 Future Scope 234References 23412 Inclusive Role of Internet of (Healthcare) Things in Digital Health: Challenges, Methods, and Future Directions 239Mohammed Abdalla12.1 Introduction 23912.2 The Internet of Medical Things’ (IoMT) Revolution in Healthcare 24212.3 The Integration Between Internet of (Healthcare) Things and Digital Health 24312.4 Blockchain Applications in the Healthcare Systems 24812.5 Healthcare IoT Future Directions: For Digital Health 24912.6 Conclusion 252References 25313 Generating Synthetic Medical Dataset Using Generative AI: ACaseStudy 259Partha Pratim Ray13.1 Introduction 25913.2 Methodology 26013.3 Results 26513.4 Conclusion 270References 27014 A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques 275Anuj Gupta, Vikas Kumar, and Aryan Nakhale14.1 Introduction 27514.2 Literature Review 27614.3 Machine Learning Methods 27814.4 Proposed System 27914.5 Mathematical Model 28214.6 Data Preparation 28414.7 Results and Discussion 28614.8 Conclusion and Future Work 292References 29315 Classification Methods of Deep Learning for Detecting Autism Spectrum Disorder in Children (4–12 Years) 297Yashashwini Reddy, Chinthala Kishor Kumar Reddy, Kari Lippert, and Sahithi Reddy15.1 Introduction 29715.2 Relevant Work 30215.3 Proposed Methodology 30515.4 Results 31215.5 Conclusion 314References 31716 Deep Learning Model for Resolution Enhancement of Biomedical Images for Biometrics 321Bhallamudi RaviKrishna, Madireddy Vijay Reddy, Mukesh Soni, Haewon Byeon, Sagar D. Pande, and Maher A. Rusho16.1 Introduction 32116.2 Model 32416.3 Experiments and Results 33216.4 Conclusion 338References 33817 Tackling the Complexities of Federated Learning 343Raj Thakur, Shreyansh Patel, Neelesh Singh, Aaryan Barde, and Snehlata Barde17.1 Introduction 34317.2 Why We Come to Federated Learning 34417.3 Related Work 34417.4 Challenges in Federated Learning 34517.5 Techniques Used in Federated Learning 34717.6 Applications 35017.7 Result and Analysis 35117.8 Conclusion 351References 35218 Revolutionizing Healthcare: The Impact of AI-Powered Sensors 355Veenadhari Bhamidipaty, Durgananda Lahari Bhamidipaty, Indira Guntoory, Kanaka Durga Prasad Bhamidipaty, Karthikeyan P. Iyengar, Bhuvan Botchu, and Rajesh Botchu18.1 Introduction 35518.2 Evolution of Healthcare Technology 35618.3 Understanding AI-Powered Sensors 35818.4 Enhancing Patient Monitoring and Diagnosis 35918.5 Improving Treatment Outcomes 36118.6 Remote Healthcare and Telemedicine 36218.7 Challenges and Ethical Considerations 36318.8 Regulatory Landscape 36518.9 Future Directions and Opportunities 36618.10 Case Studies and Success Stories 367References 37019 GAI and Deep Learning-Based Medical Sensor Data Relationship Model for Health Informatics 375Kirti Shukla, Pramod Kumar, Mukesh Soni, Haewon Byeon, Sagar Dhanraj Pande, and Ismail Keshta19.1 Introduction 37519.2 Related Work 37919.3 DSRF Based on Dynamic and Static Relationships Fusion of Multisource Health Sensing Data 38119.4 Experiments and Analysis 38819.5 Conclusion 397References 39720 Leveraging Generative Adversarial Networks for Image Augmentation in Deep Learning 401Ravi Kumar, Akshay Kanwar, Amritpal Singh, and Aditya Khamparia20.1 Introduction 40120.2 Literature Review 40320.3 Material and Method 41120.4 Result and Discussion 41320.5 Conclusion 414References 41421 Exploring Trust and Mistrust Dynamics: Generative Ai-curated Narratives in Health Communication Media Content Among Gen X 417Seema Shukla, Babita Pandey, Devendra Kumar Pandey, Brijendra Pratap Mishra, and Aditya Khamparia21.1 Background 41721.2 Related Work 41821.3 Theoretical Framework 42021.4 Research Methodology 42021.5 Data Analysis 42321.6 Results 42421.7 Conclusions and Discussion 428References 43022 Generative Intelligence-Based Federated Learning Model for Brain Tumor Classification in Smart Health 435Niladri Maiti, Riddhi Chawla, Aadam Quraishi, Mukesh Soni, Maher Ali Rusho, and Sagar Dhanraj Pande22.1 Introduction 43522.2 Classification Model 43822.3 Experiment 44422.4 Conclusion 449References 45023 AI-Based Emotion Detection System in Healthcare for Patient 455Ati Jain and Amiyavardhan Jain23.1 Introduction 45523.2 Literature Survey 45623.3 AI in Healthcare Sector 45823.4 Methodology 46523.5 Conclusion 465References 46724 Leveraging Process Mining for Enhanced Efficiency and Precision in Healthcare 471Parth Sharma, Sohan Kumar, Tanay Falor, Om Dabral, Abhinav Upadhyay, Rishik Gupta, and Vanshika Singh Andotra24.1 Introduction 47124.2 Process Mining 47224.3 Main Focus of the Chapter 47424.4 Problems 47624.5 Solution 47624.6 Tools 47724.7 Ways Process Mining Solves Healthcare 47924.8 One Solution: Robotic Process Automation (RPA) 48224.9 Case Study: Process Mining for Optimized COVID-19 ICU Care 48324.10 Conclusion 486References 48725 Transform Drug Discovery and Development With Generative Artificial Intelligence 489Antonio Lavecchia25.1 Introduction 48925.2 Dataset, Molecular Representation, and Benchmark Platforms in Molecular Generation 49125.3 Deep Generative Model Architectures 49925.4 AI Applications in Drug Discovery and Development 51125.5 Challenges and Future Outlooks 516Acknowledgments 519References 52026 Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics 539Dharmendra Dangi, Arish Mallick, Amit Bhagat, and Dheeraj Kumar Dixit26.1 Introduction 53926.2 Medical Imaging 54126.3 Various Types of Modalities 54326.4 Medical Imaging Analysis 54926.5 Conventional Morphological Image Processing 55126.6 Rotational Morphological Processing 553References 56027 Machine Learning Applications in the Prediction of Polycystic Ovarian Syndrome 565Ardra Nair, Virrat Devaser, and Komal Arora27.1 Introduction 56527.2 Literature Review 56927.3 ml Techniques for Polycystic Ovarian Syndrome 56927.4 Artificial Neural Network and Deep Learning 58027.5 Challenges 58427.6 Conclusion 585References 58528 Diagnosis and Classification of Skin Cancer Using Generative Artificial Intelligence (Gen AI) 591Niveditha N. Reddy and Pooja Agarwal28.1 Introduction 59128.2 Factors Affecting Skin Cancer Detection 59228.3 Different Types of Skin Cancer 59228.4 How Common Is Skin Cancer? 59228.5 Dermatological Images and Datasets 59528.6 Datasets 59928.7 Skin Cancer Classification in Typical CNN Frameworks 59928.8 Imbalance in Data and Limitations in Disease in Skin Databases 60028.9 ml Techniques for Skin Cancer Diagnosis 60128.10 Conclusion 604References 60429 Secure Decentralized ECG Prediction: Balancing Privacy, Performance, and Heterogeneity 607Bagesh Kumar, Sohan Kumar, Yash Vikram Singh Rathore, Akash Raj, Vanshika Singh Andotra, Rishik Gupta, and Prakhar Shukla29.1 Introduction 60729.2 Parsing ECG Data 60929.3 FL for Decentralized ECG Prediction 61229.4 Security and Privacy in FL 61329.5 Addressing Heterogeneity in ECG Dataset 61529.6 Case Study: Advancing Heart Disease Prediction with Asynchronous Federated Deep Learning 61729.7 Conclusion 619References 619Index 623