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Record Nr. |
UNINA9910830708103321 |
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Titolo |
Blockchain and Deep Learning for Smart Healthcare / / edited by Akansha Singh, Anuradha Dhull, and Krishna Kant Singh |
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Pubbl/distr/stampa |
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Hoboken, NJ : , : John Wiley & Sons, Inc., , [2024] |
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©2024 |
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ISBN |
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1-119-79240-1 |
1-119-79239-8 |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (470 pages) |
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Disciplina |
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Soggetti |
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Cloud computing - Security measures |
Artificial intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Blockchain Fundamentals and Applications -- Chapter 1 Blockchain Technology: Concepts and Applications -- 1.1 Introduction -- 1.2 Blockchain Types -- 1.3 Consensus -- 1.4 How Does Blockchain Work? -- 1.5 Need of Blockchain -- 1.6 Uses of Blockchain -- 1.7 Evolution of Blockchain -- 1.8 Blockchain in Ethereum -- 1.9 Advantages of Smart Contracts -- 1.10 Use Cases of Smart Contracts -- 1.11 Real-Life Example of Smart Contracts -- 1.12 Blockchain in Decentralized Applications -- 1.12.1 Advantages of DApps -- 1.12.2 Role of Blockchain in Metaverse -- 1.12.3 Uses of Blockchain in Metaverse Applications -- 1.12.4 Some Popular Examples of Metaverse Applications -- 1.13 Decentraland -- 1.14 Challenges Faced by Blockchain -- 1.15 Weaknesses of Blockchain -- 1.16 Future of Blockchain -- 1.17 Conclusion -- References -- Chapter 2 Blockchain with Federated Learning for Secure Healthcare Applications -- 2.1 Introduction -- 2.2 Federated Learning -- 2.3 Motivation -- 2.4 Federated Machine Learning -- 2.5 Federated Learning Frameworks -- 2.6 FL Perspective for Blockchain and IoT -- 2.7 Federated Learning Applications -- 2.8 Limitations -- References -- Chapter 3 Futuristic Challenges in Blockchain Technologies -- 3.1 Introduction -- 3.2 Blockchain -- 3.2.1 Background of Blockchain -- 3.2.2 Introduction to |
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Cryptocurrencies: Bitcoin -- 3.2.3 Different Cryptocurrencies -- 3.2.4 Proof of Work (POW) -- 3.3 Issues and Challenges with Blockchain -- 3.4 Internet of Things (IoT) -- 3.5 Background of IoT -- 3.5.1 Issues and Challenges Faced by IoT -- 3.6 Conclusion -- References -- Chapter 4 AIML-Based Blockchain Solutions for IoMT -- 4.1 Introduction -- 4.2 Objective and Contribution -- 4.3 Security Challenges in Different Domains -- 4.4 Healthcare -- 4.5 Agriculture -- 4.6 Transportation. |
4.7 Smart Grid -- 4.8 Smart City -- 4.9 Smart Home -- 4.10 Communication -- 4.11 Security Attacks in IoT -- 4.12 Solutions for Addressing Security Using Machine Learning -- 4.13 Solutions for Addressing Security Using Artificial Intelligence -- 4.14 Solutions for Addressing Security Using Blockchain -- 4.15 Summary -- 4.16 Critical Analysis -- 4.17 Conclusion -- References -- Chapter 5 A Blockchain-Based Solution for Enhancing Security and Privacy in the Internet of Medical Things (IoMT) Used in e-Healthcare -- 5.1 Introduction: E-Health and Medical Services -- 5.1.1 What is Blockchain? -- 5.1.2 What are the Advantages and Challenges of Blockchain in Healthcare? -- 5.2 Literature Review -- 5.3 Architecture of Blockchain-Enabled IoMT -- 5.3.1 Opportunities of Blockchain-Enabled IoMT -- 5.3.2 Security Improvement of IoMT -- 5.3.3 Privacy Preservation of IoMT Data -- 5.3.4 Traceability of IoMT Data -- 5.4 Proposed Methodology -- 5.4.1 Overview of the Proposed Architecture -- 5.4.2 Blockchain-Enabled IoMT Architecture -- 5.5 Conclusion and Future Work -- References -- Chapter 6 A Review on the Role of Blockchain Technology in the Healthcare Domain -- 6.1 Introduction -- 6.2 Systematic Literature Methodology -- 6.2.1 Data Sources -- 6.2.2 Selection of Studies -- 6.2.3 Data Extraction and Mapping Process -- 6.2.4 Results -- 6.3 Applications of Blockchain in the Healthcare Domain -- 6.3.1 Blockchains in Electronic Health Records (EHRs) -- 6.3.2 Blockchains in Clinical Research -- 6.3.3 Blockchains in Medical Fraud Detection -- 6.3.4 Blockchains in Neuroscience -- 6.3.5 Blockchains in Pharmaceutical Industry and Research -- 6.3.6 Electronic Medical Records Management -- 6.3.7 Remote Patient Monitoring -- 6.3.8 Drug Traceability -- 6.3.9 Securing IoT Medical Devices -- 6.3.10 Tracking Infectious Disease -- 6.4 Blockchain Challenges. |
6.4.1 Resource Limitations and Bandwidth -- 6.4.2 Scalability -- 6.4.3 Lack of Standardization -- 6.4.4 Privacy Leakage -- 6.4.5 Interoperability -- 6.4.6 Security and Privacy of Data -- 6.4.7 Managing Storage Capacity -- 6.4.8 Standardization Challenges -- 6.4.9 Social Challenges -- 6.5 Future Research Directions and Perspectives -- 6.6 Implications and Conclusion -- References -- Chapter 7 Blockchain in Healthcare: Use Cases -- 7.1 Introduction -- 7.1.1 Features of Blockchains -- 7.2 Challenges Faced in the Healthcare Sector -- 7.3 Use Cases of Blockchains in the Healthcare Sector -- 7.3.1 Blockchains for Maintaining Electronic Health Records -- 7.3.2 Electronic Health Record Applications -- 7.3.3 Blockchains in Clinical Trials -- 7.3.4 Blockchains in Improving Patient-Doctor Interactions -- 7.4 What is Medicalchain? -- 7.4.1 Features of Medicalchain -- 7.4.2 Flow of the Processes in Medicalchain -- 7.4.3 The Medicalchain Currency -- 7.5 Implementing Blockchain in SCM -- 7.5.1 Working of this Technique -- 7.6 Why Use Blockchain in SCM -- References -- Part 2: Smart Healthcare -- Chapter 8 Potential of Blockchain Technology in Healthcare, Finance, and IoT: Past, Present, and Future -- 8.1 Introduction -- 8.2 Types of Blockchain -- 8.3 Literature Review -- 8.3.1 Challenges of Blockchain -- 8.3.2 Working of Blockchain -- 8.4 Methodology and Data Sources -- 8.4.1 Eligibility Criteria -- 8.4.2 Search Strategy -- 8.4.3 Study Selection Process -- 8.5 The Application |
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of Blockchain Technology Across Various Industries -- 8.5.1 Finance -- 8.5.2 Healthcare -- 8.5.3 Internet of Things (IoT) -- 8.6 Conclusion -- References -- Chapter 9 AI-Enabled Techniques for Intelligent Transportation System for Smarter Use of the Transport Network for Healthcare Services -- 9.1 Introduction -- 9.2 Artificial Intelligence. |
9.3 Artificial Intelligence: Transport System and Healthcare -- 9.4 Artificial Intelligence Algorithms -- 9.5 AI Workflow -- 9.6 AI for ITS and e-Healthcare Tasks -- 9.7 Intelligent Transportation, Healthcare, and IoT -- 9.8 AI Techniques Used in ITS and e-Healthcare -- 9.9 Challenges of AI and ML in ITS and e-Healthcare -- 9.10 Conclusions -- References -- Chapter 10 Classification of Dementia Using Statistical First-Order and Second-Order Features -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Dataset -- 10.2.2 Image Pre-Processing -- 10.3 Proposed Framework -- 10.3.1 Discrete Wavelet Transform -- 10.3.1.1 Statistical Features -- 10.3.2 Classification -- 10.3.2.1 K-Nearest Neighbor -- 10.3.2.2 Linear Discriminant Analysis -- 10.3.2.3 Support Vector Machine -- 10.3.3 Performance Measure -- 10.4 Experimental Results and Discussion -- 10.5 Conclusion -- References -- Chapter 11 Pulmonary Embolism Detection Using Machine and Deep Learning Techniques -- 11.1 Introduction -- 11.2 The State-of-the-Art of PE Detection Models -- 11.3 Literature Survey -- 11.4 Publications Analysis -- 11.5 Conclusion -- References -- Chapter 12 Computer Vision Techniques for Smart Healthcare Infrastructure -- 12.1 Introduction -- 12.2 Literature Survey -- 12.2.1 Computer Vision -- 12.2.1.1 Computer Vision Techniques for Safety and Driver Assistance -- 12.2.1.2 Types of Optical Character Recognition Systems -- 12.2.1.3 Phases of Optical Character Recognition -- 12.2.1.4 Threshold Segmentation -- 12.2.1.5 Edge Detection Operator -- 12.2.1.6 Use Cases of OCR -- 12.2.1.7 List of Research Papers -- 12.2.2 How is IoT Changing the Face of Information Science? -- 12.3 Proposed Idea -- 12.3.1 Phases of OCR Processing -- 12.3.1.1 Pre-Processing -- 12.3.1.2 Segmentation -- 12.4 Results -- 12.5 Conclusion -- References. |
Chapter 13 Energy-Efficient Fog-Assisted System for Monitoring Diabetic Patients with Cardiovascular Disease -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Architectural Design of the Proposed Framework -- 13.4 Fog Services -- 13.4.1 Information Processing -- 13.4.2 Algorithm for Extracting Heart Rate and QT Interval -- 13.4.3 Activity Status Categorization and Fall Detection Algorithm -- 13.4.4 Interoperability -- 13.4.5 Security -- 13.4.6 Implementation of the Framework and Testbed Scenario -- 13.4.7 Sensor Layer Implementation -- 13.5 Smart Gateway and Fog Services Implementation -- 13.6 Cloud Servers -- 13.7 Experimental Results -- 13.8 Future Directions -- 13.9 Conclusion -- References -- Chapter 14 Medical Appliances Energy Consumption Prediction Using Various Machine Learning Algorithms -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Methodology -- 14.3.1 Dataset -- 14.3.2 Data Analysis and Pre-Processing -- 14.3.3 Descriptive Statistics -- 14.3.4 Correlation Matrix -- 14.3.5 Feature Selection -- 14.3.6 Data Scaling -- 14.4 Machine Learning Algorithms Used -- 14.4.1 Multiple Linear Regressor -- 14.4.2 Kernel Ridge Regression -- 14.4.3 Stochastic Gradient Descent (SGD) -- 14.4.4 Support Vector Machine (Support Vector Regression) -- 14.4.5 K-Nearest Neighbor Regressor (KNN) -- 14.4.6 Random Forest Regressor -- 14.4.7 Extremely Randomized Trees Regressor (Extra Trees Regressor) -- 14.4.8 Gradient Boosting Machine/Regressor (GBM) -- 14.4.9 Light GBM (LGBM) -- 14.4.10 Multilayer Perceptron Regressor (MLP) -- 14.4.11 Implementation -- 14.5 Results and Analysis -- 14.6 Model Analysis -- 14.7 Conclusion |
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and Future Work -- References -- Part 3: Future of Blockchain and Deep Learning -- Chapter 15 Deep Learning-Based Smart e-Healthcare for Critical Babies in Hospitals -- 15.1 Introduction -- 15.2 Literature Survey -- 15.2.1 Methodology. |
15.2.2 Data Collection. |
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