Blockchain and Deep Learning for Smart Healthcare

Name: Blockchain and Deep Learning for Smart Healthcare
Brand: Wiley-ISTE
Price: 194.99 EUR
Availability: OnlineOnly

Akansha Singh Anuradha Dhull Krishna Kant Singh(Herausgeber*in)

Wiley-ISTE (Verlag)

1. Auflage

Erschienen am 10. November 2023

480 Seiten

E-Book

PDF mit Adobe-DRM

Systemvoraussetzungen

978-1-119-79239-0 (ISBN)

194,99 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Personen

Inhalt

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
15.2.3 Data Pre-Processing
15.2.4 Android Application
15.2.5 Data Encryption
15.2.6 Cloud Storage
15.2.7 Machine Learning Models
15.2.8 Proposed Algorithm
15.3 Evaluation Criteria
15.4 Results
15.5 Conclusion and Future Scope
References
Chapter 16 An Improved Random Forest Feature Selection Method for Predicting the Patient's Characteristics
16.1 Introduction
16.2 Literature Survey
16.3 Dataset
16.4 Data Analysis
16.5 Data Pre-Processing
16.6 Feature Selection Methods
16.6.1 Boruta Algorithm
16.6.2 Rank Features by Importance
16.6.3 Recursive Feature Elimination
16.7 Variable Importance by Machine Learning Methods
16.8 Random Forest Feature Selection
16.9 Proposed Methodology
16.10 Results and Discussion
16.11 Conclusion
References
Chapter 17 Blockchain and Deep Learning: Research Challenges, Open Problems, and Future
17.1 Introduction
17.2 Research Challenges
17.3 Open Problems
17.4 Future Possibilities
17.5 Conclusion
References
Index
EULA

Systemvoraussetzungen

Als PDF speichern Als Link merken