Оглавление
Группа авторов. Smart Healthcare System Design
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Smart Healthcare System Design. Security and Privacy Aspects
Dedications
Preface
Acknowledgments
1. Machine Learning Technologies in IoT EEG-Based Healthcare Prediction
1.1 Introduction
1.1.1 Descriptive Analytics
1.1.2 Analytical Methods
1.1.3 Predictive Analysis
1.1.4 Behavioral Analysis
1.1.5 Data Interpretation
1.1.6 Classification
1.2 Related Works
1.3 Problem Definition
1.4 Research Methodology
1.4.1 Components Used
1.4.2 Specifications and Description About Components. 1.4.2.1 Arduino
1.4.2.2 EEG Sensor—Mindwave Mobile Headset
1.4.2.3 Raspberry pi
1.4.2.4 Working
1.4.3 Cloud Feature Extraction
1.4.4 Feature Optimization
1.4.5 Classification and Validation
1.5 Result and Discussion. 1.5.1 Result
1.5.2 Discussion
1.6 Conclusion
1.6.1 Future Scope
References
2. Smart Health Application for Remote Tracking of Ambulatory Patients
2.1 Introduction
2.2 Literature Work
2.3 Smart Computing for Smart Health for Ambulatory Patients
2.4 Challenges With Smart Health
2.4.1 Emergency Support
2.4.2 The Issue With Chronic Disease Monitoring
2.4.3 An Issue With the Tele-Medication
2.4.4 Mobility of Doctor
2.4.5 Application User Interface Issue
2.5 Security Threats
2.5.1 Identity Privacy
2.5.2 Query Privacy
2.5.3 Location of Privacy
2.5.4 Footprint Privacy and Owner Privacy
2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems
2.7 Conclusion
References
3. Data-Driven Decision Making in IoT Healthcare Systems—COVID-19: A Case Study
3.1 Introduction
3.1.1 Pre-Processing
3.1.2 Classification Algorithms
3.1.2.1 Dummy Classifier
3.1.2.2 Support Vector Machine (SVM)
3.1.2.3 Gradient Boosting
3.1.2.4 Random Forest
3.1.2.5 Ada Boost
3.2 Experimental Analysis
3.3 Multi-Criteria Decision Making (MCDM) Procedure
3.3.1 Simple Multi Attribute Rating Technique (SMART)
3.3.1.1 COVID-19 Disease Classification Using SMART
3.3.2 Weighted Product Model (WPM)
3.3.2.1 COVID-19 Disease Classification Using WPM
3.3.3 Method for Order Preference by Similarity to the Ideal Solution (TOPSIS)
3.3.3.1 COVID-19 Disease Classification Using TOPSIS
3.4 Conclusion
References
4. Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID-19
4.1 Introduction and Motivation
4.1.1 Existing Interaction Approaches and Technology
4.1.2 Challenges and Gaps
4.2 Proposed Prototype of Touch and Voice-Assisted Multilingual Communication
4.3 A Sample Case Study
4.4 Conclusion
References
5. Cloud-Assisted IoT System for Epidemic Disease Detection and Spread Monitoring
5.1 Introduction
5.2 Background & Related Works
5.3 Proposed Model
5.3.1 ThinkSpeak
5.3.2 Blood Oxygen Saturation (SpO2)
5.3.3 Blood Pressure (BP)
5.3.4 Electrocardiogram (ECG)
5.3.5 Body Temperature (BT)
5.3.6 Respiration Rate (RR)
5.3.7 Environmental Parameters
5.4 Methodology
5.5 Performance Analysis
5.6 Future Research Direction
5.7 Conclusion
References
6. Impact of Healthcare 4.0 Technologies for Future Capacity Building to Control Epidemic Diseases
6.1 Introduction
6.2 Background and Related Works
6.3 System Design and Architecture
6.4 Methodology
6.5 Performance Analysis
6.6 Future Research Direction
6.7 Conclusion
References
7. Security and Privacy of IoT Devices in Healthcare Systems
7.1 Introduction
7.2 Background and Related Works
7.3 Proposed System Design and Architecture
7.3.1 Modules. 7.3.1.1 Wireless Body Area Network
7.3.1.2 Centralized Network Coordinator
7.3.1.3 Local Server
7.3.1.4 Cloud Server
7.3.1.5 Dedicated Network Connection
7.4 Methodology
7.5 Performance Analysis
7.6 Future Research Direction
7.7 Conclusion
References
8. An IoT-Based Diet Monitoring Healthcare System for Women
8.1 Introduction
8.2 Background. 8.2.1 Food Consumption
8.2.2 Food Consumption Monitoring
8.2.3 Health Monitoring Methods Using Physical Methodology. 8.2.3.1 Traditional Form of Self-Report
8.2.3.2 Self-Reporting Methodology Through Smart Phones
8.2.3.3 Food Frequency Questionnaire
8.2.4 Methods for Health Tracking Using Automated Approach. 8.2.4.1 Pressure Process
8.2.4.2 Surveillance-Video Method
8.2.4.3 Method of Doppler Sensing
8.3 Necessity of Wearable Approach?
8.4 Different Approaches for Wearable Sensing
8.4.1 Approach of Acoustics. 8.4.1.1 Detection of Chewing
8.4.1.2 Detection of Swallowing
8.4.1.3 Shared Chewing/Swallowing Discovery
8.5 Description of the Methodology
8.6 Description of Various Components Used. 8.6.1 Sensors. 8.6.1.1 Sensors for Cardio-Vascular Monitoring
8.6.1.2 Sensors for Activity Monitoring
8.6.1.3 Sensors for Body Temperature Monitoring
8.6.1.4 Sensor for Galvanic Skin Response (GSR) Monitoring
8.6.1.5 Sensor for Monitoring the Blood Oxygen Saturation (SpO2)
8.7 Strategy of Communication for Wearable Systems
8.8 Conclusion
References
9. A Secure Framework for Protecting Clinical Data in Medical IoT Environment
9.1 Introduction
9.1.1 Medical IoT Background & Perspective. 9.1.1.1 Medical IoT Communication Network
9.2 Medical IoT Application Domains
9.2.1 Smart Doctor
9.2.2 Smart Medical Practitioner
9.2.3 Smart Technology
9.2.4 Smart Receptionist
9.2.5 Disaster Response Systems (DRS)
9.3 Medical IoT Concerns
9.3.1 Security Concerns
9.3.2 Privacy Concerns
9.3.3 Trust Concerns
9.4 Need for Security in Medical IoT
9.5 Components for Enhancing Data Security in Medical IoT
9.5.1 Confidentiality
9.5.2 Integrity
9.5.3 Authentication
9.5.4 Non-Repudiation
9.5.5 Privacy
9.6 Vulnerabilities in Medical IoT Environment. 9.6.1 Patient Privacy Protection
9.6.2 Patient Safety
9.6.3 Unauthorized Access
9.6.4 Medical IoT Security Constraints
9.7 Solutions for IoT Healthcare Cyber-Security
9.7.1 Architecture of the Smart Healthcare System
9.7.1.1 Data Perception Layer
9.7.1.2 Data Communication Layer
9.7.1.3 Data Storage Layer
9.7.1.4 Data Application Layer
9.8 Execution of Trusted Environment
9.8.1 Root of Trust Security Services
9.8.2 Chain of Trust Security Services
9.9 Patient Registration Using Medical IoT Devices
9.9.1 Encryption
9.9.2 Key Generation
9.9.3 Security by Isolation
9.9.4 Virtualization
9.10 Trusted Communication Using Block Chain
9.10.1 Record Creation Using IoT Gateways
9.10.2 Accessibility to Patient Medical History
9.10.3 Patient Enquiry With Hospital Authority
9.10.4 Block Chain Based IoT System Architecture
9.10.4.1 First Layer
9.10.4.2 Second Layer
9.10.4.3 Third Layer
9.11 Conclusion
References
10. Efficient Data Transmission and Remote Monitoring System for IoT Applications
10.1 Introduction
10.2 Network Configuration
10.2.1 Message Queuing Telemetry Transport (MQTT) Protocol
10.2.2 Embedded Database SQLite
10.2.3 Eclipse Paho Library
10.2.4 Raspberry Pi Single Board Computer
10.2.5 Custard Pi Add-On Board
10.2.6 Pressure Transmitter (Type 663)
10.3 Data Filtering and Predicting Processes
10.3.1 Filtering Process
10.3.2 Predicting Process
10.3.3 Remote Monitoring Systems
10.4 Experimental Setup
10.4.1 Implementation Using Python
10.4.1.1 Prerequisites
10.4.2 Monitoring Data
10.4.3 Experimental Results
10.4.3.1 IoT Device Results
10.4.3.2 Traditional Network Results
10.5 Conclusion
References
11. IoT in the Current Times and its Prospective Advancements
11.1 Introduction. 11.1.1 Introduction to Industry 4.0
11.1.2 Introduction to IoT
11.1.3 Introduction to IIoT
11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era
11.3 IoT and its Current Applications
11.3.1 Home Automation
11.3.2 Wearables
11.3.3 Connected Cars
11.3.4 Smart Grid
11.4 Application Areas of IIoT. 11.4.1 IIoT in Healthcare
11.4.2 IIoT in Mining
11.4.3 IIoT in Agriculture
11.4.4 IIoT in Aerospace
11.4.5 IIoT in Smart Cities
11.4.6 IIoT in Supply Chain Management
11.5 Challenges of Existing Systems
11.5.1 Security
11.5.2 Integration
11.5.3 Connectivity Issues
11.6 Future Advancements
11.6.1 Data Analytics in IoT
11.6.2 Edge Computing
11.6.3 Secured IoT Through Blockchain
11.6.4 A Fusion of AR and IoT
11.6.5 Accelerating IoT Through 5G
11.7 Case Study of DeWalt
11.8 Conclusion
References
12. Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0
12.1 Introduction to Artificial Intelligence
12.1.1 History of AI
12.1.2 Views of AI
12.1.3 Types of AI
12.1.4 Intelligent Agents
12.2 AI and its Related Fields
12.3 What is Industry 4.0?
12.4 Industrial Revolutions
12.4.1 First Industrial Revolution (1765)
12.4.2 Second Industrial Revolution (1870)
12.4.3 Third Industrial Revolution (1969)
12.4.4 Fourth Industrial Revolution
12.5 Reasons for Shifting Towards Industry 4.0
12.6 Role of AI in Industry 4.0
12.7 Role of ML in Industry 4.0
12.8 Role of Deep Learning in Industry 4.0
12.9 Applications of AI, ML, and DL in Industry 4.0
12.10 Challenges
12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0
12.12 Conclusion
References
13. The Implementation of AI and AI-Empowered Imaging Systems to Fight Against COVID-19—A Review
13.1 Introduction
13.2 AI-Assisted Methods
13.2.1 AI-Driven Tools to Diagnose COVID-19 and Drug Discovery
13.2.2 AI-Empowered Image Processing to Diagnosis
13.3 Optimistic Treatments and Cures
13.4 Challenges and Future Research Issues
13.5 Conclusion
References
14. Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19
14.1 Introduction
14.2 Data Analysis
14.3 Methodology. 14.3.1 Linear Regression Model
14.3.2 Time Series Model
14.4 Results and Discussions
14.4.1 Model Estimation and Studying its Adequacy
14.4.2 Regression Model for Daily New Cases and New Deaths
14.5 Conclusions
References
Index
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