{"product_id":"generative-artificial-intelligence-for-biomedical-and-smart-health-informatics-isbn-9781394280704","title":"Generative Artificial Intelligence for Biomedical and Smart Health Informatics","description":"\u003cp\u003e\u003cb\u003eEnables readers to understand the future of medical applications with generative AI and related applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eGenerative Artificial Intelligence for Biomedical and Smart Health Informatics\u003c\/i\u003e delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. \u003c\/p\u003e\u003cp\u003eThe book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. \u003c\/p\u003e\u003cp\u003eThe book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. \u003c\/p\u003e\u003cp\u003eTopics covered include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eTreatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders\u003c\/li\u003e\n\u003cli\u003eBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems\u003c\/li\u003e\n\u003cli\u003eTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring\u003c\/li\u003e\n\u003cli\u003eEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eGenerative Artificial Intelligence for Biomedical and Smart Health Informatics\u003c\/i\u003e is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare. \u003c\/p\u003e\u003cp\u003eAbout the Editors xxvii\u003c\/p\u003e \u003cp\u003eList of Contributors xxix\u003c\/p\u003e \u003cp\u003ePreface xxxix\u003c\/p\u003e \u003cp\u003eAcknowledgments xli\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Generative AI in Wearables: Exploring the Impact of GANs, VAEs, and Transformers 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDiwakar Diwakar and Deepa Raj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Theoretical Foundations 7\u003c\/p\u003e \u003cp\u003e1.3 Opportunities of Integration 14\u003c\/p\u003e \u003cp\u003e1.4 Research and Development Insights 16\u003c\/p\u003e \u003cp\u003e1.5 Ethical and Regulatory Considerations 24\u003c\/p\u003e \u003cp\u003e1.6 Case Studies and Applications 26\u003c\/p\u003e \u003cp\u003e1.7 Future Directions and Emerging Trends 27\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 31\u003c\/p\u003e \u003cp\u003eReferences 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Safeguarding Privacy and Security in AI-Enabled Healthcare Informatics 35\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAkanksha Kochhar, Ganeev Kaur Chhabra, Toshika Goswami, and Moolchand Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 35\u003c\/p\u003e \u003cp\u003e2.2 Drawbacks and Their Possible Solutions 38\u003c\/p\u003e \u003cp\u003e2.3 Applications 43\u003c\/p\u003e \u003cp\u003e2.4 Devices 44\u003c\/p\u003e \u003cp\u003e2.5 Future Scope 46\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 47\u003c\/p\u003e \u003cp\u003e2.7 Future Scope 48\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Generating Synthetic Medical Data Using GAI 51\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSudhanshu Singh, Suruchi Singh, and C.S. Raghuvanshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 51\u003c\/p\u003e \u003cp\u003e3.2 Uncloaking the GAI Orchestra: A Compendium of Techniques 53\u003c\/p\u003e \u003cp\u003e3.3 Beyond the Notes: Ethical Considerations and Responsible Use 66\u003c\/p\u003e \u003cp\u003e3.4 Conclusion 70\u003c\/p\u003e \u003cp\u003eReferences 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Automation of Drug Design and Development 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSudhanshu Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 73\u003c\/p\u003e \u003cp\u003e4.2 High-Throughput Screening (HTS) 74\u003c\/p\u003e \u003cp\u003e4.3 Artificial Intelligence (AI) and Machine Learning (ML) 77\u003c\/p\u003e \u003cp\u003e4.4 Automation in Drug Synthesis and Optimization 80\u003c\/p\u003e \u003cp\u003e4.5 Automation in Clinical Trials 81\u003c\/p\u003e \u003cp\u003e4.6 Challenges and Opportunities 83\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 85\u003c\/p\u003e \u003cp\u003eReferences 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Autism Spectrum Disorder Diagnosis: A Comprehensive Review of Machine Learning Approaches 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDeepti Prasad and Suman Bhatia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 89\u003c\/p\u003e \u003cp\u003e5.2 Machine Learning and Deep Learning Algorithms 92\u003c\/p\u003e \u003cp\u003e5.3 Discussion 98\u003c\/p\u003e \u003cp\u003e5.4 Future Work 99\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 99\u003c\/p\u003e \u003cp\u003eReferences 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Temporal Normalization and Brain Image Analysis for Early-Stage Prediction of Attention Deficit Hyperactivity Disorder (ADHD) 103\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePoonam Chaudhary, Nikki Rani, Diksha Aggarwal, and Srishti Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 103\u003c\/p\u003e \u003cp\u003e6.2 Exploratory Data Analysis 105\u003c\/p\u003e \u003cp\u003e6.3 Methodology 109\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussion 115\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 116\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Sustainable Agriculture Through Advanced Crop Management: VGG16-Based Tea Leaf Disease Recognition 121\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Sivaraman, S. Praveena, and H. Naresh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 121\u003c\/p\u003e \u003cp\u003e7.2 Literature Survey 122\u003c\/p\u003e \u003cp\u003e7.3 Proposed Methodology for Tea Leaf Diseases Detection 125\u003c\/p\u003e \u003cp\u003e7.4 Results and Discussion 130\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 131\u003c\/p\u003e \u003cp\u003eReferences 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Advancing Colorectal Cancer Diagnosis: Integrating Synthetic Data and Machine Learning for Microbiome Analysis 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlessio Rotelli and Ernesto Iadanza\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Colorectal Cancer (CRC) 135\u003c\/p\u003e \u003cp\u003e8.2 Understanding the Gut Microbiome 136\u003c\/p\u003e \u003cp\u003e8.3 Influence of the Gut Microbiome Dysbiosis on Colorectal Adenomas and CRC 136\u003c\/p\u003e \u003cp\u003e8.4 Differentiating Adenomatous Polyps (AP) from CRC 137\u003c\/p\u003e \u003cp\u003e8.5 Use of Data Augmentation 138\u003c\/p\u003e \u003cp\u003e8.6 Data Evaluation Metrics 138\u003c\/p\u003e \u003cp\u003e8.7 Feature Extraction by Later-Wise Relevance Propagation 139\u003c\/p\u003e \u003cp\u003e8.8 Beta Diversity Analysis 140\u003c\/p\u003e \u003cp\u003e8.9 Machine Learning and SHAP Analysis to Classify AP and CRC Samples 141\u003c\/p\u003e \u003cp\u003e8.10 Results of Classification and SHAP Analysis 143\u003c\/p\u003e \u003cp\u003e8.11 Key Bacterial Taxa Discriminating Between AP and CRC: Insights from Feature Extraction and SHAP Analysis 149\u003c\/p\u003e \u003cp\u003e8.12 Conclusion 149\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Recent Knowledge in Drug Design and Development: Automation and Advancement 153\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKusum Gurung, Saurav K. Mishra, Tabsum Chhetri, Sneha Roy, Anagha Balakrishnan, and John J. Georrge\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 153\u003c\/p\u003e \u003cp\u003e9.2 Automation in Drug Design and Development 156\u003c\/p\u003e \u003cp\u003e9.3 Tools and Database for Drug Design, including Algorithm and Application 158\u003c\/p\u003e \u003cp\u003e9.4 Automation in Drug Design and Its Impact on the Pharmaceutical Sector 160\u003c\/p\u003e \u003cp\u003e9.5 Automation-Assisted Successful Studies in Drug Design 165\u003c\/p\u003e \u003cp\u003e9.6 Advancement and Challenges 170\u003c\/p\u003e \u003cp\u003e9.7 Conclusion 171\u003c\/p\u003e \u003cp\u003eReferences 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Machine Learning and Generative AI Techniques for Sentiment Analysis with Applications 183\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRiya Sharma, Balraj Singh, and Aditya Khamparia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 183\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 187\u003c\/p\u003e \u003cp\u003e10.3 Machine Learning Techniques for Sentiment Analysis 187\u003c\/p\u003e \u003cp\u003e10.4 Generative AI Techniques for Sentiment Analysis 196\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 202\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Use of AI with Optimization Techniques: Case Study, Challenges, and Future Trends 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAyushi Mittal, Parul Parul, Charu Gupta, and Devendra K. Tayal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 209\u003c\/p\u003e \u003cp\u003e11.2 Overview of Medical Disease Prediction Models 213\u003c\/p\u003e \u003cp\u003e11.3 Importance of Optimization in Enhancing Prediction Accuracy 214\u003c\/p\u003e \u003cp\u003e11.4 Commonly Used Optimization Algorithms in Medical Predictive Modeling 214\u003c\/p\u003e \u003cp\u003e11.5 Integration of ML and Optimization for Disease Prediction 222\u003c\/p\u003e \u003cp\u003e11.6 Challenges and Considerations in Applying Optimization Techniques to Medical Data 223\u003c\/p\u003e \u003cp\u003e11.7 Case Studies: Successful Applications of Optimization in Disease Prediction 226\u003c\/p\u003e \u003cp\u003e11.8 Future Directions and Emerging Trends in Optimizing Medical Prediction Models 228\u003c\/p\u003e \u003cp\u003e11.9 Ethical and Regulatory Implications of Optimized Disease Prediction Systems 231\u003c\/p\u003e \u003cp\u003e11.10 Conclusion: Harnessing Optimization for Advancements in Medical Predictive Analytics 233\u003c\/p\u003e \u003cp\u003e11.11 Future Scope 234\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Inclusive Role of Internet of (Healthcare) Things in Digital Health: Challenges, Methods, and Future Directions 239\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMohammed Abdalla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 239\u003c\/p\u003e \u003cp\u003e12.2 The Internet of Medical Things’ (IoMT) Revolution in Healthcare 242\u003c\/p\u003e \u003cp\u003e12.3 The Integration Between Internet of (Healthcare) Things and Digital Health 243\u003c\/p\u003e \u003cp\u003e12.4 Blockchain Applications in the Healthcare Systems 248\u003c\/p\u003e \u003cp\u003e12.5 Healthcare IoT Future Directions: For Digital Health 249\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 252\u003c\/p\u003e \u003cp\u003eReferences 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Generating Synthetic Medical Dataset Using Generative AI: ACaseStudy 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePartha Pratim Ray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 259\u003c\/p\u003e \u003cp\u003e13.2 Methodology 260\u003c\/p\u003e \u003cp\u003e13.3 Results 265\u003c\/p\u003e \u003cp\u003e13.4 Conclusion 270\u003c\/p\u003e \u003cp\u003eReferences 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques 275\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnuj Gupta, Vikas Kumar, and Aryan Nakhale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 275\u003c\/p\u003e \u003cp\u003e14.2 Literature Review 276\u003c\/p\u003e \u003cp\u003e14.3 Machine Learning Methods 278\u003c\/p\u003e \u003cp\u003e14.4 Proposed System 279\u003c\/p\u003e \u003cp\u003e14.5 Mathematical Model 282\u003c\/p\u003e \u003cp\u003e14.6 Data Preparation 284\u003c\/p\u003e \u003cp\u003e14.7 Results and Discussion 286\u003c\/p\u003e \u003cp\u003e14.8 Conclusion and Future Work 292\u003c\/p\u003e \u003cp\u003eReferences 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Classification Methods of Deep Learning for Detecting Autism Spectrum Disorder in Children (4–12 Years) 297\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYashashwini Reddy, Chinthala Kishor Kumar Reddy, Kari Lippert, and Sahithi Reddy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 297\u003c\/p\u003e \u003cp\u003e15.2 Relevant Work 302\u003c\/p\u003e \u003cp\u003e15.3 Proposed Methodology 305\u003c\/p\u003e \u003cp\u003e15.4 Results 312\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 314\u003c\/p\u003e \u003cp\u003eReferences 317\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Deep Learning Model for Resolution Enhancement of Biomedical Images for Biometrics 321\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBhallamudi RaviKrishna, Madireddy Vijay Reddy, Mukesh Soni, Haewon Byeon, Sagar D. Pande, and Maher A. Rusho\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 321\u003c\/p\u003e \u003cp\u003e16.2 Model 324\u003c\/p\u003e \u003cp\u003e16.3 Experiments and Results 332\u003c\/p\u003e \u003cp\u003e16.4 Conclusion 338\u003c\/p\u003e \u003cp\u003eReferences 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Tackling the Complexities of Federated Learning 343\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRaj Thakur, Shreyansh Patel, Neelesh Singh, Aaryan Barde, and Snehlata Barde\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 343\u003c\/p\u003e \u003cp\u003e17.2 Why We Come to Federated Learning 344\u003c\/p\u003e \u003cp\u003e17.3 Related Work 344\u003c\/p\u003e \u003cp\u003e17.4 Challenges in Federated Learning 345\u003c\/p\u003e \u003cp\u003e17.5 Techniques Used in Federated Learning 347\u003c\/p\u003e \u003cp\u003e17.6 Applications 350\u003c\/p\u003e \u003cp\u003e17.7 Result and Analysis 351\u003c\/p\u003e \u003cp\u003e17.8 Conclusion 351\u003c\/p\u003e \u003cp\u003eReferences 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Revolutionizing Healthcare: The Impact of AI-Powered Sensors 355\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVeenadhari Bhamidipaty, Durgananda Lahari Bhamidipaty, Indira Guntoory, Kanaka Durga Prasad Bhamidipaty, Karthikeyan P. Iyengar, Bhuvan Botchu, and Rajesh Botchu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 355\u003c\/p\u003e \u003cp\u003e18.2 Evolution of Healthcare Technology 356\u003c\/p\u003e \u003cp\u003e18.3 Understanding AI-Powered Sensors 358\u003c\/p\u003e \u003cp\u003e18.4 Enhancing Patient Monitoring and Diagnosis 359\u003c\/p\u003e \u003cp\u003e18.5 Improving Treatment Outcomes 361\u003c\/p\u003e \u003cp\u003e18.6 Remote Healthcare and Telemedicine 362\u003c\/p\u003e \u003cp\u003e18.7 Challenges and Ethical Considerations 363\u003c\/p\u003e \u003cp\u003e18.8 Regulatory Landscape 365\u003c\/p\u003e \u003cp\u003e18.9 Future Directions and Opportunities 366\u003c\/p\u003e \u003cp\u003e18.10 Case Studies and Success Stories 367\u003c\/p\u003e \u003cp\u003eReferences 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 GAI and Deep Learning-Based Medical Sensor Data Relationship Model for Health Informatics 375\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKirti Shukla, Pramod Kumar, Mukesh Soni, Haewon Byeon, Sagar Dhanraj Pande, and Ismail Keshta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 375\u003c\/p\u003e \u003cp\u003e19.2 Related Work 379\u003c\/p\u003e \u003cp\u003e19.3 DSRF Based on Dynamic and Static Relationships Fusion of Multisource Health Sensing Data 381\u003c\/p\u003e \u003cp\u003e19.4 Experiments and Analysis 388\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 397\u003c\/p\u003e \u003cp\u003eReferences 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Leveraging Generative Adversarial Networks for Image Augmentation in Deep Learning 401\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRavi Kumar, Akshay Kanwar, Amritpal Singh, and Aditya Khamparia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 401\u003c\/p\u003e \u003cp\u003e20.2 Literature Review 403\u003c\/p\u003e \u003cp\u003e20.3 Material and Method 411\u003c\/p\u003e \u003cp\u003e20.4 Result and Discussion 413\u003c\/p\u003e \u003cp\u003e20.5 Conclusion 414\u003c\/p\u003e \u003cp\u003eReferences 414\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Exploring Trust and Mistrust Dynamics: Generative Ai-curated Narratives in Health Communication Media Content Among Gen X 417\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSeema Shukla, Babita Pandey, Devendra Kumar Pandey, Brijendra Pratap Mishra, and Aditya Khamparia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Background 417\u003c\/p\u003e \u003cp\u003e21.2 Related Work 418\u003c\/p\u003e \u003cp\u003e21.3 Theoretical Framework 420\u003c\/p\u003e \u003cp\u003e21.4 Research Methodology 420\u003c\/p\u003e \u003cp\u003e21.5 Data Analysis 423\u003c\/p\u003e \u003cp\u003e21.6 Results 424\u003c\/p\u003e \u003cp\u003e21.7 Conclusions and Discussion 428\u003c\/p\u003e \u003cp\u003eReferences 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Generative Intelligence-Based Federated Learning Model for Brain Tumor Classification in Smart Health 435\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNiladri Maiti, Riddhi Chawla, Aadam Quraishi, Mukesh Soni, Maher Ali Rusho, and Sagar Dhanraj Pande\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 435\u003c\/p\u003e \u003cp\u003e22.2 Classification Model 438\u003c\/p\u003e \u003cp\u003e22.3 Experiment 444\u003c\/p\u003e \u003cp\u003e22.4 Conclusion 449\u003c\/p\u003e \u003cp\u003eReferences 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 AI-Based Emotion Detection System in Healthcare for Patient 455\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAti Jain and Amiyavardhan Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 455\u003c\/p\u003e \u003cp\u003e23.2 Literature Survey 456\u003c\/p\u003e \u003cp\u003e23.3 AI in Healthcare Sector 458\u003c\/p\u003e \u003cp\u003e23.4 Methodology 465\u003c\/p\u003e \u003cp\u003e23.5 Conclusion 465\u003c\/p\u003e \u003cp\u003eReferences 467\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Leveraging Process Mining for Enhanced Efficiency and Precision in Healthcare 471\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eParth Sharma, Sohan Kumar, Tanay Falor, Om Dabral, Abhinav Upadhyay, Rishik Gupta, and Vanshika Singh Andotra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 471\u003c\/p\u003e \u003cp\u003e24.2 Process Mining 472\u003c\/p\u003e \u003cp\u003e24.3 Main Focus of the Chapter 474\u003c\/p\u003e \u003cp\u003e24.4 Problems 476\u003c\/p\u003e \u003cp\u003e24.5 Solution 476\u003c\/p\u003e \u003cp\u003e24.6 Tools 477\u003c\/p\u003e \u003cp\u003e24.7 Ways Process Mining Solves Healthcare 479\u003c\/p\u003e \u003cp\u003e24.8 One Solution: Robotic Process Automation (RPA) 482\u003c\/p\u003e \u003cp\u003e24.9 Case Study: Process Mining for Optimized COVID-19 ICU Care 483\u003c\/p\u003e \u003cp\u003e24.10 Conclusion 486\u003c\/p\u003e \u003cp\u003eReferences 487\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Transform Drug Discovery and Development With Generative Artificial Intelligence 489\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAntonio Lavecchia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 489\u003c\/p\u003e \u003cp\u003e25.2 Dataset, Molecular Representation, and Benchmark Platforms in Molecular Generation 491\u003c\/p\u003e \u003cp\u003e25.3 Deep Generative Model Architectures 499\u003c\/p\u003e \u003cp\u003e25.4 AI Applications in Drug Discovery and Development 511\u003c\/p\u003e \u003cp\u003e25.5 Challenges and Future Outlooks 516\u003c\/p\u003e \u003cp\u003eAcknowledgments 519\u003c\/p\u003e \u003cp\u003eReferences 520\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics 539\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDharmendra Dangi, Arish Mallick, Amit Bhagat, and Dheeraj Kumar Dixit\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 539\u003c\/p\u003e \u003cp\u003e26.2 Medical Imaging 541\u003c\/p\u003e \u003cp\u003e26.3 Various Types of Modalities 543\u003c\/p\u003e \u003cp\u003e26.4 Medical Imaging Analysis 549\u003c\/p\u003e \u003cp\u003e26.5 Conventional Morphological Image Processing 551\u003c\/p\u003e \u003cp\u003e26.6 Rotational Morphological Processing 553\u003c\/p\u003e \u003cp\u003eReferences 560\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Machine Learning Applications in the Prediction of Polycystic Ovarian Syndrome 565\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArdra Nair, Virrat Devaser, and Komal Arora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 565\u003c\/p\u003e \u003cp\u003e27.2 Literature Review 569\u003c\/p\u003e \u003cp\u003e27.3 ml Techniques for Polycystic Ovarian Syndrome 569\u003c\/p\u003e \u003cp\u003e27.4 Artificial Neural Network and Deep Learning 580\u003c\/p\u003e \u003cp\u003e27.5 Challenges 584\u003c\/p\u003e \u003cp\u003e27.6 Conclusion 585\u003c\/p\u003e \u003cp\u003eReferences 585\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Diagnosis and Classification of Skin Cancer Using Generative Artificial Intelligence (Gen AI) 591\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNiveditha N. Reddy and Pooja Agarwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 591\u003c\/p\u003e \u003cp\u003e28.2 Factors Affecting Skin Cancer Detection 592\u003c\/p\u003e \u003cp\u003e28.3 Different Types of Skin Cancer 592\u003c\/p\u003e \u003cp\u003e28.4 How Common Is Skin Cancer? 592\u003c\/p\u003e \u003cp\u003e28.5 Dermatological Images and Datasets 595\u003c\/p\u003e \u003cp\u003e28.6 Datasets 599\u003c\/p\u003e \u003cp\u003e28.7 Skin Cancer Classification in Typical CNN Frameworks 599\u003c\/p\u003e \u003cp\u003e28.8 Imbalance in Data and Limitations in Disease in Skin Databases 600\u003c\/p\u003e \u003cp\u003e28.9 ml Techniques for Skin Cancer Diagnosis 601\u003c\/p\u003e \u003cp\u003e28.10 Conclusion 604\u003c\/p\u003e \u003cp\u003eReferences 604\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Secure Decentralized ECG Prediction: Balancing Privacy, Performance, and Heterogeneity 607\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBagesh Kumar, Sohan Kumar, Yash Vikram Singh Rathore, Akash Raj, Vanshika Singh Andotra, Rishik Gupta, and Prakhar Shukla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 607\u003c\/p\u003e \u003cp\u003e29.2 Parsing ECG Data 609\u003c\/p\u003e \u003cp\u003e29.3 FL for Decentralized ECG Prediction 612\u003c\/p\u003e \u003cp\u003e29.4 Security and Privacy in FL 613\u003c\/p\u003e \u003cp\u003e29.5 Addressing Heterogeneity in ECG Dataset 615\u003c\/p\u003e \u003cp\u003e29.6 Case Study: Advancing Heart Disease Prediction with Asynchronous Federated Deep Learning 617\u003c\/p\u003e \u003cp\u003e29.7 Conclusion 619\u003c\/p\u003e \u003cp\u003eReferences 619\u003c\/p\u003e \u003cp\u003eIndex 623\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAditya Khamparia,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDeepak Gupta,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eEnables readers to understand the future of medical applications with generative AI and related applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eGenerative Artificial Intelligence for Biomedical and Smart Health Informatics\u003c\/i\u003e delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. \u003c\/p\u003e\u003cp\u003eThe book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. \u003c\/p\u003e\u003cp\u003eThe book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. \u003c\/p\u003e\u003cp\u003eTopics covered include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eTreatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders\u003c\/li\u003e\n\u003cli\u003eBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems\u003c\/li\u003e\n\u003cli\u003eTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring\u003c\/li\u003e\n\u003cli\u003eEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eGenerative Artificial Intelligence for Biomedical and Smart Health Informatics\u003c\/i\u003e is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989277950181,"sku":"NP9781394280704","price":165.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394280704.jpg?v=1761783488","url":"https:\/\/k12savings.com\/products\/generative-artificial-intelligence-for-biomedical-and-smart-health-informatics-isbn-9781394280704","provider":"K12savings","version":"1.0","type":"link"}