Blockchain and Deep Learning for Smart Healthcare
BLOCKCHAIN and DEEP LEARNING for SMART HEALTHCARE The book discusses the popular use cases and applications of blockchain technology and deep learning in building smart healthcare. The book covers the integration of blockchain technology and deep learning for making smart healthcare systems. Blockch...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811323406719 |
Tabla de Contenidos:
- 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 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 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 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.
