Deep Learning in Internet of Things for Next Generation Healthcare

Cover
Half Title
Title
Copyright
Dedication
Table of Contents
Preface
Editor Biographies
List of Contributors
Chapter 1 Rise of Communication Devices in IoT
	1.1 Introduction
	1.2 Internet of Things
	1.3 Existing Scenario of Communication Devices in IoT Systems
	1.4 Emerging Communication Devices in IoT Systems
	1.5 Conclusion
	References
Chapter 2 Architecture Framework for Deep Learning Systems and IoT: An Overview
	2.1 Introduction
	2.2 Architecture Framework for IoT
	2.3 Architecture Framework for Deep Learning Systems
	2.4 Applications
		2.4.1 Deep Learning Systems
		2.4.2 IoT
	2.5 Conclusions and Future Scope
	References
Chapter 3 Deep Learning and Human Vision in IoT
	3.1 Introduction
		3.1.1 Why Is Classical Machine Learning Less Effective Than Deep Learning?
	3.2 Importance of Human Vision in IoT Systems
	3.3 Challenges and Opportunities in Combining Deep Learning and Human Vision for IoT
		3.3.1 Challenges
		3.3.2 Opportunities
	3.4 Material Science and the IoT
	3.5 Visual Perception Processes
		3.5.1 Emerging Trends in Deep Learning and Human Vision in the IoT
		3.5.2 IoT Technologies for Sustainable Development
	References
Chapter 4 Impact of IoT on Big Data Analytics and Applications in Medical Images
	4.1 Introduction
	4.2 Techniques and Application Used in IoT
		4.2.1 Technological Aspects of the IoT
		4.2.2 IoT Connectivity
		4.2.3 Application of the IoT
		4.2.4 IoT Deployment
	4.3 Big Data
		4.3.1 Big Data Analytics
	4.4 Application of Big Data
	4.5 Big Data in the IoT
		4.5.1 IoT Big Data Processing
		4.5.2 Applications of Big Data Integrated with the IoT
	4.6 Impact of IoT and Big Data in Medical Images Using Deep Learning
	4.7 Challenges of the Impact of the IoT in Big Data
	4.8 Conclusion
	References
Chapter 5 Geospatial Data Collection Tools in Healthcare
	5.1 Introduction
	5.2 Geospatial Data Collection Devices
		5.2.1 Digitization
		5.2.2 Global Positioning Systems
		5.2.3 Mobile Technology
		5.2.4 Remote Sensing
		5.2.5 Sensors
		5.2.6 Social Media
	5.3 Geospatial Data Analysis
	5.4 Application Areas of Geospatial Data
	5.5 Future Scope
	References
Chapter 6 Geospatial Technology in Healthcare
	6.1 Introduction
		6.1.1 The Evolution of the Geospatial Sector
		6.1.2 The Rise of GIS Application in Healthcare
		6.1.3 The Role of the Geospatial Sector in Aiding the Delivery of Healthcare Services
		6.1.4 Challenges and Opportunities in Healthcare GIS
		6.1.5 Action Plan and the Way Forward
	6.2 The Evolution of the Geospatial Sector
	6.3 The Rise of GIS Applications in Healthcare
	6.4 The Role of the Geospatial Sector in Aiding the Delivery of Healthcare Services
		6.4.1 Aayushman Bharat Digital Health Mission
		6.4.2 Mapping Burden of Diseases
		6.4.3 AarogyaSetu—A Digital Initiative to Fight the Pandemic by Leveraging GIS Technology
	6.5 Challenges and Opportunities in Healthcare GIS
	6.6 Action Plan and the Way Forward
	References
Chapter 7 Advancement of Geospatial Technology in Healthcare Systems
	7.1 Introduction to Geospatial Technologies
	7.2 Application of Geospatial Technology in Public Health Systems
	7.3 Advancements in Geospatial Technologies in Private Healthcare Systems
	7.4 Challenges in Application of Geospatial Technologies in Healthcare Systems
	7.5 Future of Geospatial Technologies in Healthcare Systems
	References
Chapter 8 Implementation of Deep Learning in Assessment of Health-Hazardous Air Pollutants
	8.1 Introduction
	8.2 Air Pollution and Its Impact on Health
	8.3 Health Hazardous Pollutants
		8.3.1 Particulate Matter
		8.3.2 Sulfur Dioxide
		8.3.3 Oxides of Nitrogen
		8.3.4 Ammonia
		8.3.5 Carbon Monoxide
		8.3.6 Ozone
		8.3.7 Benzene
		8.3.8 Toluene
		8.3.9 Xylene
		8.3.10 Arsenic
		8.3.11 Nickel
	8.4 New Trends in Computing
		8.4.1 Artificial Intelligence
		8.4.2 Machine Learning
		8.4.3 Deep Learning
	8.5 Application of AI/ML/DL in Estimation of Health-Hazardous Pollutants
	8.6 Conclusions
	References
Chapter 9 Technological Interventions in Healthcare
	9.1 Introduction
		9.1.1 Diagnostics and Sample Transportation
		9.1.2 Emergency Medical Services
		9.1.3 Telemedicine and Remote Patient Monitoring
		9.1.4 Challenges and Regulatory Considerations
	9.2 Diagnostics and Sample Transportation
		9.2.1 Types of Diagnostic Samples Transported by Drones
		9.2.2 Benefits of Drone-Based Diagnostics and Sample Transportation
		9.2.3 Challenges and Limitations of Drone-Based Diagnostics and Sample Transportation
		9.2.4 Impact on the Indian Healthcare System
	9.3 Emergency Medical Services
		9.3.1 Challenges and Limitations of the Current EMS System
		9.3.2 Potential Impact of Technology on EMS
		9.3.3 Future of EMS in India
	9.4 Telemedicine and Remote Patient Monitoring
		9.4.1 Benefits of Telemedicine and Remote Patient Monitoring
		9.4.2 Challenges of Telemedicine and Remote Patient Monitoring
		9.4.3 Role of Technology in Telemedicine and Remote Patient Monitoring
		9.4.4 Future of Telemedicine and Remote Patient Monitoring in India
	9.5 The Role of Public–Private Partnerships in Advancing Healthcare in India
		9.5.1 Benefits of Public–Private Partnerships in Healthcare
		9.5.2 Challenges of Public–Private Partnerships in Healthcare
		9.5.3 Role of Technology in Public–Private Partnerships
		9.5.4 Future of Public–Private Partnerships in Advancing Healthcare in India
	9.6 Challenges and Regulatory Considerations
		9.6.1 Challenges in the Indian Healthcare System
		9.6.2 Regulatory Considerations in the Indian Healthcare System
		9.6.3 Addressing Challenges and Regulatory Considerations
	9.7 Conclusion
	References
Chapter 10 Disaster and Emergency Healthcare
	10.1 Disaster
	10.2 Healthcare
	10.3 Emergency Healthcare
	10.4 Disaster Management Cycle
	10.5 Implementing Health Emergency and Disaster Risk Management
	10.6 Latest Technological Advancements in Emergency Healthcare
	References
Chapter 11 Deep Learning and IoT in Healthcare
	11.1 Introduction
	11.2 Big Data: Concept and Definition
		11.2.1 Big Data Engineering
		11.2.2 Non-Relational Model
		11.2.3 NoSQL
		11.2.4 Big Data Models
		11.2.5 Schema-on-Read
		11.2.6 Big Data Analytics
		11.2.7 Big Data Paradigm
	11.3 Using the Cloud for Data Management
	11.4 Managing Big Data in Environments of Cloud Computing
	11.5 Solutions and Techniques for Data Storage
	11.6 Big Data Frameworks
		11.6.1 Hadoop
		11.6.2 MapReduce
		11.6.3 Spark
		11.6.4 Hive
		11.6.5 Storm
		11.6.6 Flink
		11.6.7 Heron
		11.6.8 NoSQL Databases
		11.6.9 Challenges in the Visualisation of NoSQL Databases
	11.7 Advantages of Big Data Applications
	11.8 Factors of Big Data Frameworks
		11.8.1 Processing Speed
		11.8.2 Fault Tolerance
		11.8.3 Scalability
		11.8.4 Security
	11.9 Advantages of Big Data and Cloud Computing Frameworks
	11.10 Challenges and Risks of Big Data and Cloud Computing Frameworks
	11.11 Revolutionising Healthcare
		11.11.1 The Role of Big Data in Empowering Deep Learning
		11.11.2 Harnessing Cloud Computing for Data Storage and Processing
		11.11.3 IoT Devices: Augmenting Healthcare Data Collection
		11.11.4 Personalised Medicine and Customised Treatment Strategies
	References
Chapter 12 Improved Patient Care Using Robotics in the Healthcare Industry: Benefits, Real-Time Applications, and Challenges
	12.1 Introduction
	12.2 Major Benefits of Robotics in the Healthcare Industry
		12.2.1 High-End Healthcare
		12.2.2 Safer Work Environment
		12.2.3 Simplified Hospital Workflows
		12.2.4 Surgical Robots in Operating Theatres
	12.3 Examples of Robotics
		12.3.1 da Vinci Surgical Robots
		12.3.2 Capsule Endoscope Robots
		12.3.3 Orthoses (a.k.a. Exoskeletons)
		12.3.4 Disinfectant Robots
		12.3.5 Companion Robots
		12.3.6 Robotic Nurses
		12.3.7 Robotic-Assisted Biopsy
		12.3.8 Antibacterial Nanorobots
	12.4 Challenging Issues in Adopting Robotics
	12.5 Future of Robotics
	12.6 Conclusion
	References
Chapter 13 Deep Learning Processes in MRI Images
	13.1 Introduction
	13.2 Processing of MRI Images
		13.2.1 Preprocessing
		13.2.2 Segmentation
		13.2.3 Classification
	13.3 MRI Image Processing Using Deep Learning Techniques
		13.3.1 Input Layer
		13.3.2 Hidden Layer
		13.3.3 Output Layer
	13.4 Deep Learning Applications in MRI Images
		13.4.1 Pre-Processing of MRI Images Using Deep Learning
		13.4.2 MRI Image Segmentation Using Deep Learning
		13.4.3 MRI Image Classification Using Deep Learning
	13.5 Application of Deep Learning in MRI Image Preprocessing
	13.6 Conclusion
	References
Chapter 14 Artificial Intelligence and Robotics in Healthcare: Transforming the Indian Landscape
	14.1 Introduction
		14.1.1 Factors Contributing to the Growing Interest in AI and Robotics in Indian Healthcare
		14.1.2 Key Players in the Indian AI and Robotics Healthcare Ecosystem
		14.1.3 Potential Impact of AI and Robotics on Indian Healthcare
	14.2 The Emergence of AI and Robotics in Indian Healthcare
		14.2.1 Need for Cost-Effective Solutions
		14.2.2 Rise of Digital Health
		14.2.3 Increasing Prevalence of Chronic Diseases
		14.2.4 Government Initiatives
		14.2.5 Key Players in the Indian AI and Robotics Healthcare Ecosystem
	14.3 AI and Robotics Applications in Indian Healthcare
		14.3.1 Diagnostics
		14.3.2 Treatment
		14.3.3 Patient Care
		14.3.4 Research
	14.4 Challenges and Ethical Considerations
		14.4.1 Challenges
		14.4.2 Ethical Considerations
	14.5 The Future of AI and Robotics in Indian Healthcare
		14.5.1 Key Trends and Developments
		14.5.2 Emerging Applications
		14.5.3 Potential Impact on Indian Healthcare
	14.6 Conclusion
	References
Chapter 15 Medical Insurance Fraud Detection
	15.1 Medical Insurance: Introduction and Its Benefits
	15.2 Medical Insurance Fraud
	15.3 Types of Medical Insurance Fraud
		15.3.1 Fraud by Service Providers
		15.3.2 Fraud by Subscribers
		15.3.3 Fraud by Insurance Carriers
		15.3.4 Conspiracy Fraud
	15.4 Traditional Methods of Medical Insurance Fraud Detection
		15.4.1 Auditing
		15.4.2 Whistleblowing
		15.4.3 Manual Review of Claims
	15.5 Technological Methods of Medical Insurance Fraud Detection
	15.6 Use of Technologies in Mitigating the Challenges Identified
	15.7 Role of Laws, Regulations, and Policy Measures in Fraud Detection
	15.8 Future Trends and Challenges
	15.9 Conclusion and Way Forward
	References
Chapter 16 Privacy and Security Issues for IoT and Deep Learning in Next-Generation Healthcare: An Indian Perspective
	16.1 Introduction
		16.1.1 The Indian Healthcare Landscape
		16.1.2 IoT and Deep Learning in Healthcare
		16.1.3 Privacy and Security Concerns
		16.1.4 Addressing Privacy and Security Challenges
		16.1.5 The Way Forward
	16.2 The Indian Healthcare Landscape
		16.2.1 Public Healthcare System
		16.2.2 Challenges in Public Healthcare Systems
		16.2.3 Private Healthcare System
		16.2.4 Rural–Urban Divide
		16.2.5 Role of Technology in Indian Healthcare
		16.2.6 Opportunities for IoT and Deep Learning in Indian Healthcare
	16.3 IoT and Deep Learning in Healthcare
		16.3.1 IoT in Healthcare
		16.3.2 Deep Learning in Healthcare
		16.3.3 Integration of IoT and Deep Learning in Healthcare
		16.3.4 Challenges and Barriers to Adoption
	16.4 Privacy and Security Concerns
		16.4.1 Data Privacy Concerns
		16.4.2 Data Security Concerns
		16.4.3 Regulatory Landscape
		16.4.4 Strategies for Addressing Privacy and Security Concerns
	16.5 Addressing Privacy and Security Challenges
		16.5.1 Technological Solutions
		16.5.2 Policy Development
		16.5.3 Collaboration among Stakeholders
		16.5.4 Education and Training
		16.5.5 Continuous Improvement and Adaptation
		16.5.6 Legal and Regulatory Considerations
	16.6 The Way Forward
		16.6.1 Future Trends in Healthcare IoT and Deep Learning
		16.6.2 Emerging Technologies and Their Impact on Privacy and Security
		16.6.3 Strategies for Navigating the Evolving Privacy and Security Landscape
	16.7 Conclusion
	References
Chapter 17 A Systematic Review on the Future of Internet of Things Applications in Healthcare
	17.1 Introduction
	17.2 Literature Review
	17.3 Literature Summary
	17.4 Conclusion
	References
Chapter 18 The Extraordinary Importance of 6G Network Development and 3D Holography in Future Healthcare
	18.1 Introduction
	18.2 6G Technology
		18.2.1 Background
		18.2.2 Edge Technology
	18.3 Holographic Communication
		18.3.1 History of Holography and Development
		18.3.2 Hologram Recording
		18.3.3 Reconstruction of the Hologram
		18.3.4 Holography and Artificial Intelligence
	18.4 Augmented and Virtual Reality
	18.5 Tactile/Haptic Internet
	18.6 Intelligent Internet of Medical Things
	18.7 Telesurgery, Epidemics and Pandemics and Precision Medicine
	18.8 The Metaverse and Holographic Simulation
	18.9 Conclusion
	References
Chapter 19 Tracking of Disease—A Review of the State of the Art of Technology for Next Generation Healthcare
	19.1 Introduction
	19.2 Approaches for Tracking of Disease
		19.2.1 Conventional Tracking of Disease
		19.2.2 Sustainable Tracking of Disease
		19.2.3 Methods of Tracking of Disease
	19.3 Role of the IoT in Tracking of Disease
		19.3.1 IoT-Enabled Wearable Devices for Health Monitoring
		19.3.2 IoT in Environmental Monitoring for Prediction of Disease Outbreak
		19.3.3 IoT-Based Predictive Analysis for Tracking of Disease
	19.4 Deep Learning Techniques for Tracking of Disease
		19.4.1 Deep Learning Algorithms Used in Tracking of Disease
		19.4.2 Applications of Deep Learning in Tracking of Disease
	19.5 Integration of IoT and Deep Learning in Tracking of Disease
		19.5.1 Leveraging IoT Data for Deep Learning Models
		19.5.2 Real-Time Tracking of Disease and Early Warning Systems
		19.5.3 Data Fusion and Integration for Enhanced Tracking of Disease
	19.6 Challenges and Future Directions
		19.6.1 Ethical Considerations
		19.6.2 Scalability and Interoperability Challenges
		19.6.3 Newly Emerging Diseases
		19.6.4 Emerging Trends and Future Directions in Tracking of Disease
	19.7 Conclusion
	References
Chapter 20 Disease Detection Using TensorFlow Methodology
	20.1 Introduction
	20.2 Objective
	20.3 Data, Algorithms, and Methods
	20.4 Methodology
		20.4.1 Data Processing System
		20.4.2 Data Architecture Using Machine Learning Techniques
		20.4.3 Calculating Feature Importance
		20.4.4 Training and Validation Loss Curves
	20.5 Conclusion
	References
Chapter 21 AI and Deep Learning: Applications in Healthcare
	21.1 Introduction
	21.2 Understanding AI, Machine Learning, and Deep Learning in Healthcare
		21.2.1 Scopes of Applying AI in Healthcare
		21.2.2 Real-Time Case Studies
	21.3 Challenges and Opportunities
	21.4 Future Trends
	21.5 Conclusion
	References
Index