{"product_id":"ai-driven-smart-healthcare-powered-by-hyperscale-computing-and-next-generation-networks-hardback-9781394297030","title":"AI-Driven Smart Healthcare; Powered by Hyperscale Computing and Next Generation Networks (Hardback) 9781394297030","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAI-Driven Smart Healthcare\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003ePowered by Hyperscale Computing and Next Generation Networks\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAjay Pratap (Author), Yashwant Singh Patel (Author), Ram Narayan Yadav (Author), Ali Ahmadian (Author), Ashok Kumar Yadav (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394297030, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 25 December 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e22.6 x 15 x 2.5 cm, 0.567 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eReimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eAI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, \u003c\/i\u003ea team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry. \u003c\/p\u003e\n\u003cp\u003eReaders will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions. \u003c\/p\u003e\n\u003cp\u003eThe book also includes: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA thorough introduction to the integration of AI and fog computing into smart healthcare systems\u003c\/li\u003e\n\u003cli\u003eComprehensive explorations of how these technologies enhance healthcare delivery, with examples like remote patient monitoring and advanced diagnostic models\u003c\/li\u003e\n\u003cli\u003ePractical discussions of the advantages, challenges, and potential solutions associated  with AI and fog computing\u003c\/li\u003e\n\u003cli\u003eAn interdisciplinary focus for professionals working at the intersection of AI, machine learning, fog computing, and healthcare\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for researchers, practitioners, and other healthcare stakeholders, \u003ci\u003eAI-Driven Smart Healthcare\u003c\/i\u003e will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eNotes on Contributors xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eList of Abbreviations xxi\u003c\/p\u003e \u003cp\u003eIntroduction xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Internet of Things, Edge, Fog, and Data Analytics in Smart Healthcare: Introduction, Benefits, and Challenges 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Use of Edge and Fog Computing for Healthcare Applications 3\u003c\/p\u003e \u003cp\u003e1.2.1 Role of Edge Computing in Resource Management 7\u003c\/p\u003e \u003cp\u003e1.3 Data Analytics in Healthcare Applications 8\u003c\/p\u003e \u003cp\u003e1.3.1 Components of BDA 9\u003c\/p\u003e \u003cp\u003e1.4 BDA Applications 10\u003c\/p\u003e \u003cp\u003e1.4.1 Challenge in Utilization of Big Data in Healthcare 11\u003c\/p\u003e \u003cp\u003e1.5 Use Case Scenarios 13\u003c\/p\u003e \u003cp\u003e1.5.1 Use of Data Analysis for Forecasting TB Prevalence Rates 13\u003c\/p\u003e \u003cp\u003e1.5.2 Skin Aging Estimation 14\u003c\/p\u003e \u003cp\u003e1.5.3 Other Use Cases in Smart Healthcare 16\u003c\/p\u003e \u003cp\u003e1.6 Current Challenges and Future Directions 17\u003c\/p\u003e \u003cp\u003e1.7 Future Direction: Opening Up Health Data for Research 19\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 20\u003c\/p\u003e \u003cp\u003eReferences 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Hyperscale Computing Paradigm in Healthcare 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 The Evolution of Computing in Healthcare 30\u003c\/p\u003e \u003cp\u003e2.2.1 Early Era of Digitalization 30\u003c\/p\u003e \u003cp\u003e2.2.2 Transition to Cloud Computing 31\u003c\/p\u003e \u003cp\u003e2.3 What Is Hyperscale Cloud? 32\u003c\/p\u003e \u003cp\u003e2.4 Components of Hyperscale Computing 32\u003c\/p\u003e \u003cp\u003e2.4.1 Distributed Systems 32\u003c\/p\u003e \u003cp\u003e2.4.2 Cloud Infrastructure 34\u003c\/p\u003e \u003cp\u003e2.4.3 Load Balancers 35\u003c\/p\u003e \u003cp\u003e2.4.4 Storage Systems 36\u003c\/p\u003e \u003cp\u003e2.4.5 Networking and Interconnectivity 36\u003c\/p\u003e \u003cp\u003e2.4.6 Compute Resources 36\u003c\/p\u003e \u003cp\u003e2.4.7 Automation and Orchestration 37\u003c\/p\u003e \u003cp\u003e2.4.8 Security and Compliance 37\u003c\/p\u003e \u003cp\u003e2.5 Challenges of Hyperscale Computing in Healthcare 37\u003c\/p\u003e \u003cp\u003e2.6 Hyperscale Data Centers 39\u003c\/p\u003e \u003cp\u003e2.6.1 Key Components of a Hyperscale Data Center 39\u003c\/p\u003e \u003cp\u003e2.7 Tech Giants Playing a Role in Hyperscaling 40\u003c\/p\u003e \u003cp\u003e2.8 Public Versus Private Hyperscale Clouds in Healthcare 42\u003c\/p\u003e \u003cp\u003e2.9 Depth and Future of Hyperscale Computing in Healthcare 42\u003c\/p\u003e \u003cp\u003e2.9.1 Integration of Connected Healthcare and Hyperscale Cloud 44\u003c\/p\u003e \u003cp\u003e2.10 Case Studies of Hyperscale Computing in Healthcare 45\u003c\/p\u003e \u003cp\u003eReferences 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Containerized Internet of Medical Things and Serverless Computing for Smart Healthcare Systems 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Wireless Technologies Empowering Internet of Medical Things 49\u003c\/p\u003e \u003cp\u003e3.2 IoMT and Its Role in Modern Healthcare 50\u003c\/p\u003e \u003cp\u003e3.3 Importance of Containerization 52\u003c\/p\u003e \u003cp\u003e3.3.1 Benefits of Containerization 52\u003c\/p\u003e \u003cp\u003e3.3.2 Use Cases of Containerization 53\u003c\/p\u003e \u003cp\u003e3.4 Serverless Computing: Concept and Overview 54\u003c\/p\u003e \u003cp\u003e3.4.1 Features of Serverless Computing 55\u003c\/p\u003e \u003cp\u003e3.5 Complementing Containerization for Healthcare Applications 55\u003c\/p\u003e \u003cp\u003e3.6 Real-world Use Cases 57\u003c\/p\u003e \u003cp\u003e3.7 Future Directions in Containerized IoMT and Serverless Healthcare 58\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Kubernetes Enabled Resource Management Architecture for Secure Innovation in Healthcare 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Overview of Kubernetes and Its Role in Healthcare 61\u003c\/p\u003e \u003cp\u003e4.2 Key Features of Kubernetes 62\u003c\/p\u003e \u003cp\u003e4.3 Key Kubernetes Concepts 63\u003c\/p\u003e \u003cp\u003e4.4 Kubernetes Control Plane and Nodes 64\u003c\/p\u003e \u003cp\u003e4.5 Kubernetes Resource Management 65\u003c\/p\u003e \u003cp\u003e4.6 Benefits of Kubernetes for the Healthcare Industry 66\u003c\/p\u003e \u003cp\u003e4.7 Use Cases of Cloud-native and Kubernetes in Healthcare 67\u003c\/p\u003e \u003cp\u003e4.8 Kubernetes as a Solution 68\u003c\/p\u003e \u003cp\u003e4.9 Conclusion 70\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Exploring Artificial Intelligence (AI) and Machine Learning (ML) for Performance and Predictive Analysis of Various Diseases Using Health-related Data 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 73\u003c\/p\u003e \u003cp\u003e5.2 Challenges in Healthcare 75\u003c\/p\u003e \u003cp\u003e5.3 Significance of AI and ML in Healthcare 76\u003c\/p\u003e \u003cp\u003e5.3.1 Early Diagnosis and Disease Detection 76\u003c\/p\u003e \u003cp\u003e5.3.2 Personalized Medicine 77\u003c\/p\u003e \u003cp\u003e5.3.3 Improving Clinical Decision Support 77\u003c\/p\u003e \u003cp\u003e5.3.4 Reducing Administrative Burden 77\u003c\/p\u003e \u003cp\u003e5.3.5 Predicting and Managing Disease Outbreaks 77\u003c\/p\u003e \u003cp\u003e5.3.6 Enhancing Drug Discovery and Development 77\u003c\/p\u003e \u003cp\u003e5.4 Application of AI\/ML in Healthcare System 78\u003c\/p\u003e \u003cp\u003e5.4.1 Prognosis 79\u003c\/p\u003e \u003cp\u003e5.4.2 Diagnosis 80\u003c\/p\u003e \u003cp\u003e5.4.3 Treatment 80\u003c\/p\u003e \u003cp\u003e5.4.4 Clinical Workflows 80\u003c\/p\u003e \u003cp\u003e5.5 Major Development Phases of AI\/ML-based Healthcare Systems 81\u003c\/p\u003e \u003cp\u003e5.5.1 Use Case Specification 83\u003c\/p\u003e \u003cp\u003e5.5.2 Data Access and Anonymization 83\u003c\/p\u003e \u003cp\u003e5.5.3 Data Annotation 83\u003c\/p\u003e \u003cp\u003e5.5.4 Model Development 83\u003c\/p\u003e \u003cp\u003e5.5.5 Model Testing and Auditing 84\u003c\/p\u003e \u003cp\u003e5.5.6 Multi-site Verification and Validation 84\u003c\/p\u003e \u003cp\u003e5.5.7 Regulatory Approvals 84\u003c\/p\u003e \u003cp\u003e5.5.8 Clinical Integration 84\u003c\/p\u003e \u003cp\u003e5.5.9 User Acceptance 85\u003c\/p\u003e \u003cp\u003e5.5.10 Real-world Surveillance 85\u003c\/p\u003e \u003cp\u003e5.6 Secure, Private, and Robust AI\/ML-based Healthcare: Challenges 85\u003c\/p\u003e \u003cp\u003e5.6.1 Vulnerabilities in Data Collection 85\u003c\/p\u003e \u003cp\u003e5.6.2 Vulnerabilities Due to Data Annotation 86\u003c\/p\u003e \u003cp\u003e5.6.3 Vulnerabilities in Model Training 86\u003c\/p\u003e \u003cp\u003e5.6.4 Vulnerabilities in Deployment Phase 87\u003c\/p\u003e \u003cp\u003e5.6.5 Vulnerabilities in Testing Phase 87\u003c\/p\u003e \u003cp\u003e5.7 Use Case: Diabetes 87\u003c\/p\u003e \u003cp\u003e5.7.1 Predictive Analysis in Disease Management 89\u003c\/p\u003e \u003cp\u003e5.7.2 Performance Analysis in Healthcare 89\u003c\/p\u003e \u003cp\u003e5.8 Challenges and Future Directions 93\u003c\/p\u003e \u003cp\u003e5.9 Conclusion 93\u003c\/p\u003e \u003cp\u003eReferences 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Algorithmic Frameworks for Cost Minimization in Criticality Aware Mobile Healthcare System 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 101\u003c\/p\u003e \u003cp\u003e6.2 Related Works 102\u003c\/p\u003e \u003cp\u003e6.3 System Model 104\u003c\/p\u003e \u003cp\u003e6.3.1 Data Criticality Index 106\u003c\/p\u003e \u003cp\u003e6.4 Problem Definition 108\u003c\/p\u003e \u003cp\u003e6.4.1 Auction Process 108\u003c\/p\u003e \u003cp\u003e6.4.2 LDPU Perspective 110\u003c\/p\u003e \u003cp\u003e6.4.3 CSP Perspective 110\u003c\/p\u003e \u003cp\u003e6.4.4 Mechanism Perspective 110\u003c\/p\u003e \u003cp\u003e6.4.5 Impact of Selfishness 111\u003c\/p\u003e \u003cp\u003e6.5 Proposed Auction Mechanism 111\u003c\/p\u003e \u003cp\u003e6.5.1 Cheating Detection Process at LDPU 112\u003c\/p\u003e \u003cp\u003e6.5.2 Decision Process at CSP 113\u003c\/p\u003e \u003cp\u003e6.5.3 Cheating Detection Process at CSP 113\u003c\/p\u003e \u003cp\u003e6.5.4 Decision Process at LDPU 114\u003c\/p\u003e \u003cp\u003e6.5.5 An Illustrative Example 114\u003c\/p\u003e \u003cp\u003e6.6 Analysis of Proposed Mechanism 117\u003c\/p\u003e \u003cp\u003e6.6.1 Truthful Analysis at LDPU Side 117\u003c\/p\u003e \u003cp\u003e6.6.2 Truthful Analysis at CSP Side 118\u003c\/p\u003e \u003cp\u003e6.7 Performance Study 120\u003c\/p\u003e \u003cp\u003e6.8 Conclusion 124\u003c\/p\u003e \u003cp\u003eReferences 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Utility-aware Edge Computing System for Remote Health Monitoring 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 127\u003c\/p\u003e \u003cp\u003e7.2 Related Works 129\u003c\/p\u003e \u003cp\u003e7.3 System Model and Problem Formulation 131\u003c\/p\u003e \u003cp\u003e7.3.1 Reputation Scheme 132\u003c\/p\u003e \u003cp\u003e7.4 Proposed Auction Model 136\u003c\/p\u003e \u003cp\u003e7.4.1 Auction Details 139\u003c\/p\u003e \u003cp\u003e7.5 Analysis of Proposed Auction Model 142\u003c\/p\u003e \u003cp\u003e7.6 Performance Evaluation 143\u003c\/p\u003e \u003cp\u003e7.6.1 Individual Rationality 144\u003c\/p\u003e \u003cp\u003e7.6.2 Budget Balance 145\u003c\/p\u003e \u003cp\u003e7.6.3 Utilities of Model Owners and Users 146\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 148\u003c\/p\u003e \u003cp\u003eReferences 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fog Computing-based WBAN and IoT Framework for Prediction of Various Diseases Using Big Data Analytics 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 The Role of Fog Computing in Healthcare 153\u003c\/p\u003e \u003cp\u003e8.3 Big Data Analytics in Healthcare 153\u003c\/p\u003e \u003cp\u003e8.4 Wireless Body Area Networks in Healthcare 155\u003c\/p\u003e \u003cp\u003e8.5 Framework Design 156\u003c\/p\u003e \u003cp\u003e8.6 Disease Prediction Use Case for Healthcare 158\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 163\u003c\/p\u003e \u003cp\u003eReferences 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Disease Spread Detection and Controlling with Fog-based Model in Wireless Body Area Networks 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Compartmental Model 169\u003c\/p\u003e \u003cp\u003e9.1.1 Applications of Compartmental Models 171\u003c\/p\u003e \u003cp\u003e9.1.2 Contact-based Models 172\u003c\/p\u003e \u003cp\u003e9.2 Simulation Tools 174\u003c\/p\u003e \u003cp\u003e9.3 Other Environmental Factors to Control Spread of Disease 177\u003c\/p\u003e \u003cp\u003e9.4 AI- and ML-based Health Prediction Approaches 178\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 180\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimized Doctor Recommendation System Using Machine Learning Approach 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 185\u003c\/p\u003e \u003cp\u003e10.2 Related Works 187\u003c\/p\u003e \u003cp\u003e10.3 System Model 190\u003c\/p\u003e \u003cp\u003e10.3.1 Selection Factor of Doctor 191\u003c\/p\u003e \u003cp\u003e10.3.2 Expertise Score of Doctor 192\u003c\/p\u003e \u003cp\u003e10.3.3 Expected Reward of Doctor 192\u003c\/p\u003e \u003cp\u003e10.3.4 Satisfaction Level of Patient 194\u003c\/p\u003e \u003cp\u003e10.3.5 Problem Formulation 194\u003c\/p\u003e \u003cp\u003e10.4 Proposed Approach 195\u003c\/p\u003e \u003cp\u003e10.4.1 Calculate Expertise Score 196\u003c\/p\u003e \u003cp\u003e10.4.2 Recommend an Ordered Assortment 198\u003c\/p\u003e \u003cp\u003e10.4.3 Update Doctor’s Information 199\u003c\/p\u003e \u003cp\u003e10.5 Performance Study 201\u003c\/p\u003e \u003cp\u003e10.6 Conclusion and Future Work 204\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 UAV-enabled Smart Healthcare Application for Next-generation Wireless Networks 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 209\u003c\/p\u003e \u003cp\u003e11.1.1 Motivation 209\u003c\/p\u003e \u003cp\u003e11.2 Related Works 211\u003c\/p\u003e \u003cp\u003e11.3 System Model 213\u003c\/p\u003e \u003cp\u003e11.3.1 System Components 214\u003c\/p\u003e \u003cp\u003e11.3.2 Cost Model 215\u003c\/p\u003e \u003cp\u003e11.3.3 Revenue Model 221\u003c\/p\u003e \u003cp\u003e11.3.4 Objective Function 222\u003c\/p\u003e \u003cp\u003e11.4 Federated Deep Reinforcement Learning 223\u003c\/p\u003e \u003cp\u003e11.4.1 Deep Reinforcement Learning 223\u003c\/p\u003e \u003cp\u003e11.4.2 Federated Advantage Actor Critic 226\u003c\/p\u003e \u003cp\u003e11.4.3 Federated Proximal Policy Optimization 227\u003c\/p\u003e \u003cp\u003e11.4.4 Proposed Solution 227\u003c\/p\u003e \u003cp\u003e11.5 A Direction for Performance Study 230\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 232\u003c\/p\u003e \u003cp\u003e11.6.1 Challenges 233\u003c\/p\u003e \u003cp\u003e11.6.2 Future Work 234\u003c\/p\u003e \u003cp\u003eReferences 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Road Map for Personalized Medicine: Challenges and Innovations 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 239\u003c\/p\u003e \u003cp\u003e12.2 Use of Genome Data to Develop Personalized Medicine 243\u003c\/p\u003e \u003cp\u003e12.2.1 Genetic Biomarkers 244\u003c\/p\u003e \u003cp\u003e12.2.2 Biochemical Biomarkers 244\u003c\/p\u003e \u003cp\u003e12.3 Use of Medical Imaging Data to Develop Personalized Medicine 245\u003c\/p\u003e \u003cp\u003e12.4 Use of AI and ML in Drug Development in Personalized Medicine 247\u003c\/p\u003e \u003cp\u003e12.4.1 AI Applications in Pharmacogenomics 248\u003c\/p\u003e \u003cp\u003e12.4.2 Use of ML Models for Predicting Drug Responses 249\u003c\/p\u003e \u003cp\u003e12.4.3 Use of Deep Learning Models for Analyzing Genomic Data 250\u003c\/p\u003e \u003cp\u003e12.4.4 Use of AI and ML in Computational Modeling in Personalized Medicine 250\u003c\/p\u003e \u003cp\u003e12.5 Use of Digital Twin for Personalized Medicine 251\u003c\/p\u003e \u003cp\u003e12.6 Current Challenges and Future Directions 253\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Delay-sensitive, Privacy-preserving Blockchain-enabled Fog-assisted Framework for Smart Healthcare 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 263\u003c\/p\u003e \u003cp\u003e13.2 Related Work 265\u003c\/p\u003e \u003cp\u003e13.3 System Model 267\u003c\/p\u003e \u003cp\u003e13.3.1 Founding Phase 268\u003c\/p\u003e \u003cp\u003e13.3.2 Bidding Phase 269\u003c\/p\u003e \u003cp\u003e13.3.3 Calculation 270\u003c\/p\u003e \u003cp\u003e13.4 Problem Formulation 271\u003c\/p\u003e \u003cp\u003e13.4.1 Criticality of the Data 271\u003c\/p\u003e \u003cp\u003e13.4.2 Data Transmission Cost 272\u003c\/p\u003e \u003cp\u003e13.4.3 Reputation Mechanism 272\u003c\/p\u003e \u003cp\u003e13.4.4 Profit of the Miners 276\u003c\/p\u003e \u003cp\u003e13.4.5 Profit Calculation for Patients 277\u003c\/p\u003e \u003cp\u003e13.4.6 Profit Calculation for Companies 277\u003c\/p\u003e \u003cp\u003e13.5 Proposed Solution 278\u003c\/p\u003e \u003cp\u003e13.5.1 Genetic Algorithm 278\u003c\/p\u003e \u003cp\u003e13.5.2 Problem Representation 280\u003c\/p\u003e \u003cp\u003e13.5.3 Problem Encoding 281\u003c\/p\u003e \u003cp\u003e13.5.4 Fitness Function 282\u003c\/p\u003e \u003cp\u003e13.5.5 Stopping Criteria 283\u003c\/p\u003e \u003cp\u003e13.5.6 Implementation Details 283\u003c\/p\u003e \u003cp\u003e13.6 Experimental Results 286\u003c\/p\u003e \u003cp\u003e13.6.1 Dataset 286\u003c\/p\u003e \u003cp\u003e13.6.2 Test and Results 288\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 294\u003c\/p\u003e \u003cp\u003eAppendix 13.A 294\u003c\/p\u003e \u003cp\u003e13.a.1 NP-hard Problems 294\u003c\/p\u003e \u003cp\u003e13.a.2 0\/1 Knapsack Problem 294\u003c\/p\u003e \u003cp\u003e13.a.3 NP-hard Proof 295\u003c\/p\u003e \u003cp\u003eReferences 296\u003c\/p\u003e \u003cp\u003eA Research Discussion, Tools, and Use Cases 299\u003c\/p\u003e \u003cp\u003eA. 1 Artificial Intelligence in Smart Healthcare 299\u003c\/p\u003e \u003cp\u003eA. 2 Research Trends in AI for Healthcare 300\u003c\/p\u003e \u003cp\u003eA. 3 Use Cases of AI in Smart Healthcare 300\u003c\/p\u003e \u003cp\u003eA. 4 Key Tools and Frameworks for AI-driven Healthcare 309\u003c\/p\u003e \u003cp\u003eA. 5 Challenges and Limitations in AI-driven Healthcare 312\u003c\/p\u003e \u003cp\u003eA. 6 Conclusion 313\u003c\/p\u003e \u003cp\u003eReferences 314\u003c\/p\u003e \u003cp\u003eIndex 319\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Other branches of medicine [\u003ca title=\"See our other books on Other branches of medicine\" 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