Blockchain and Deep Learning for Smart Healthcare / edited by Akansha Singh, Anuradha Dhull, and Krishna Kant Singh.

Format:

Book

Contributor:

Singh, Akansha, editor.

Dhull, Anuradha, editor.

Singh, Krishna Kant, editor.

Language:

English

Subjects (All):

Medicine--Data processing.

Medicine.

Blockchains (Databases).

Physical Description:

1 online resource (470 pages)

Edition:

First edition.

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., [2024]

Summary:

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. Blockchain is used for health record-keeping, clinical trials, patient monitoring, improving safety, displaying information, and transparency. Deep learning is also showing vast potential in the healthcare domain. With the collection of large quantities of patient records and data, and a trend toward personalized treatments. there is a great need for automated and reliable processing and analysis of health information. This book covers the popular use cases and applications of both the above-mentioned technologies in making smart healthcare. Audience Comprises professionals and researchers working in the fields of deep learning, blockchain technology, healthcare & medical informatics. In addition, as the book provides insights into the convergence of deep learning and blockchain technology in healthcare systems and services, medical practitioners as well as healthcare professionals will find this essential reading.

Contents:

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

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

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Singh, Akansha Blockchain and Deep Learning for Smart Healthcare

ISBN:

9781119792406

1119792401

9781119792390

1119792398

OCLC:

1409396783