Оглавление
Группа авторов. Enabling Healthcare 4.0 for Pandemics
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Enabling Healthcare 4.0 for Pandemics. A Roadmap Using AI, Machine Learning, IoT and Cognitive Technologies
Preface
1. COVID-19 and Machine Learning Approaches to Deal With the Pandemic
1.1 Introduction
1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem
1.2 COVID-19 Diagnosis in Patients Using Machine Learning
1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19
1.2.2 Machine Learning to Speed Up Drug Development
1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19
1.3 AI and Machine Learning as a Support System for Robotic System and Drones
1.3.1 AI-Based Location Tracking of COVID-19 Patients
1.3.2 Increased Number of Screenings Using AI Approach
1.3.3 Artificial Intelligence in Management of Resources During COVID-19
1.3.4 Influence of AI on Manufacturing Industry During COVID-19
1.3.5 Artificial Intelligence and Mental Health in COVID-19
1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis?
1.3.7 Advantages and Disadvantages of AI in Post COVID Era
1.4 Conclusion
References
2. Healthcare System 4.0 Perspectives on COVID-19 Pandemic
2.1 Introduction
2.2 Key Techniques of HCS 4.0 for COVID-19
2.2.1 Artificial Intelligence (AI)
2.2.2 The Internet of Things (IoT)
2.2.3 Big Data
2.2.4 Virtual Reality (VR)
2.2.5 Holography
2.2.6 Cloud Computing
2.2.7 Autonomous Robots
2.2.8 3D Scanning
2.2.9 3D Printing Technology
2.2.10 Biosensors
2.3 Real World Applications of HCS 4.0 for COVID-19
2.4 Opportunities and Limitations
2.5 Future Perspectives
2.6 Conclusion
References
3. Analysis and Prediction on COVID-19 Using Machine Learning Techniques
3.1 Introduction
3.2 Literature Review
3.3 Types of Machine Learning
3.4 Machine Learning Algorithms
3.4.1 Linear Regression
3.4.2 Logistic Regression
3.4.3 K-NN or K Nearest Neighbor
3.4.4 Decision Tree
3.4.5 Random Forest
3.5 Analysis and Prediction of COVID-19 Data
3.5.1 Methodology Adopted
3.6 Analysis Using Machine Learning Models
3.6.1 Splitting of Data into Training and Testing Data Set
3.6.2 Training of Machine Learning Models
3.6.3 Calculating the Score
3.7 Conclusion & Future Scope
References
4. Rapid Forecasting of Pandemic Outbreak Using Machine Learning
4.1 Introduction
4.2 Effect of COVID-19 on Different Sections of Society. 4.2.1 Effect of COVID-19 on Mental Health of Elder People
4.2.2 Effect of COVID-19 on our Environment
4.2.3 Effect of COVID-19 on International Allies and Healthcare
4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19
4.2.5 Effect of COVID-19 on Labor Migrants
4.2.6 Impact of COVID-19 on our Economy
4.3 Definition and Types of Machine Learning
4.3.1 Machine Learning & Its Types
4.3.2 Applications of Machine Learning
4.4 Machine Learning Approaches for COVID-19
4.4.1 Enabling Organizations to Regulate and Scale
4.4.2 Understanding About COVID-19 Infections
4.4.3 Gearing Up Study and Finding Treatments
4.4.4 Predicting Treatment and Healing Outcomes
4.4.5 Testing Patients and Diagnosing COVID-19
References
5. Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19
5.1 Introduction
5.2 Related Work
5.3 Suggested Methodology
5.4 Models in Epidemiology
5.4.1 Bayesian Inference Models
5.4.1.1 Markov Chain (MCMC) Algorithm
5.5 Particle Filtering Algorithm
5.6 MCM Model Implementation
5.6.1 Reproduction Number
5.7 Diagnosis of COVID-19
5.7.1 Predicting Outbreaks Through Social Media Analysis
5.7.1.1 Risk of New Pandemics
5.8 Conclusion
References
6. Emerging Technologies for Handling Pandemic Challenges
6.1 Introduction
6.2 Technological Strategies to Support Society During the Pandemic
6.2.1 Online Shopping and Robot Deliveries
6.2.2 Digital and Contactless Payments
6.2.3 Remote Work
6.2.4 Telehealth
6.2.5 Online Entertainment
6.2.6 Supply Chain 4.0
6.2.7 3D Printing
6.2.8 Rapid Detection
6.2.9 QRT-PCR
6.2.10 Immunodiagnostic Test (Rapid Antibody Test)
6.2.11 Work From Home
6.2.12 Distance Learning
6.2.13 Surveillance
6.3 Feasible Prospective Technologies in Controlling the Pandemic
6.3.1 Robotics and Drones
6.3.2 5G and Information and Communications Technology (ICT)
6.3.3 Portable Applications
6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges
6.4.1 Remote Healthcare
6.4.2 Prevention Measures
6.4.3 Diagnostic Solutions
6.4.4 Hospital Care
6.4.5 Public Safety During Pandemic
6.4.6 Industry Adapting to the Lockdown
6.4.7 Cities Adapting to the Lockdown
6.4.8 Individuals Adapting to the Lockdown
6.5 The Golden Age of Drone Delivery
6.5.1 The Early Adopters are Winning
6.5.2 The Golden Age Will Require Collaboration and Drive
6.5.3 Standardization and Data Sharing Through the Smart City Network
6.5.4 The Procedure of AI and Non-AI-Based Applications
6.6 Technology Helps Pandemic Management
6.6.1 Tracking People With Facial Recognition and Big Data
6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots
6.6.3 Technology Supported Temperature Monitoring
6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity
6.7 Conclusion
References
7. Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19
7.1 Introduction
7.2 Review of Technologies Used During the Outbreak of Ebola and SARS
7.2.1 Technological Strategies and Tools Used at the Time of SARS
7.2.2 Technological Strategies and Tools Used at the Time of Ebola
7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis
7.3.1 Artificial Intelligence
7.3.1.1 Application of AI in Developed Countries
7.3.1.2 Application of AI in Developing Countries
7.3.2 IoT & Robotics
7.3.2.1 Application of IoT and Robotics in Developed Countries
7.3.2.2 Application of IoT and Robotics in Developing Countries
7.3.3 Telemedicine
7.3.3.1 Application of Telemedicine in Developed Countries
7.3.3.2 Application of Telemedicine in Developing Countries
7.3.4 Innovative Healthcare
7.3.4.1 Application of Innovative Healthcare in Developed Countries
7.3.4.2 Application of Innovative Healthcare in Developing Countries
7.3.4.3 Application of Innovative Healthcare in the Least Developed Countries
7.3.5 Nanotechnology
7.4 Conclusion
References
8. Advances in Technology: Preparedness for Handling Pandemic Challenges
8.1 Introduction
8.2 Issues and Challenges Due to Pandemic
8.2.1 Health Effect
8.2.2 Economic Impact
8.2.3 Social Impact
8.3 Digital Technology and Pandemic
8.3.1 Digital Healthcare
8.3.2 Network and Connectivity
8.3.3 Development of Potential Treatment
8.3.4 Online Platform for Learning and Interaction
8.3.5 Contactless Payment
8.3.6 Entertainment
8.4 Application of Technology for Handling Pandemic
8.4.1 Technology for Preparedness and Response
8.4.2 Machine Learning for Pandemic Forecast
8.5 Challenges with Digital Healthcare
8.6 Conclusion
References
9. Emerging Technologies for COVID-19
9.1 Introduction
9.2 Related Work
9.3 Technologies to Combat COVID-19
9.3.1 Blockchain
9.3.1.1 Challenges and Solutions
9.3.2 Unmanned Aerial Vehicle (UAV)
9.3.2.1 Challenges and Solutions
9.3.3 Mobile APK
9.3.3.1 Challenges and Solutions
9.3.4 Wearable Sensing
9.3.4.1 Challenges and Solutions
9.3.5 Internet of Healthcare Things
9.3.5.1 Challenges and Solutions
9.3.6 Artificial Intelligence
9.3.6.1 Challenges and Solutions
9.3.7 5G
9.3.7.1 Challenges and Solutions
9.3.8 Virtual Reality
9.3.8.1 Challenges and Solutions
9.4 Comparison of Various Technologies to Combat COVID-19
9.5 Conclusion
References
10. Emerging Techniques for Handling Pandemic Challenges
10.1 Introduction to Pandemic
10.1.1 How Pandemic Spreads?
10.1.2 Background History
10.1.3 Corona
10.2 Technique Used to Handle Pandemic Challenges. 10.2.1 Smart Techniques in Cities
10.2.2 Smart Technologies in Western Democracies
10.2.3 Techno- or Human-Driven Approach
10.3 Working Process of Techniques
10.4 Data Analysis
10.5 Rapid Development Structure
10.6 Conclusion & Future Scope
References
11. A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling
11.1 Introduction
11.2 Methodology. 11.2.1 Data Collection
11.2.2 Mathematical Model Development
11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm
11.2.4 Discrete Version of APGWO
11.2.4.1 Population Initialization
11.2.4.2 Discrete Search Operator for PSO Main Loop
11.2.4.3 Discrete Search Strategy for GWO Nested Loop
11.2.4.4 Constraint Handling
11.3 Computational Results
11.4 Conclusion
References
12. Multi-Purpose Robotic Sensing Device for Healthcare Services
12.1 Introduction
12.2 Background and Objectives
12.3 The Functioning of Multi-Purpose Robot
12.4 Discussion and Conclusions
References
13. Prevalence of Internet of Things in Pandemic
13.1 Introduction
13.2 What is IoT?
13.2.1 History of IoT
13.2.2 Background of IoT for COVID-19 Pandemic
13.2.3 Operations Involved in IoT for COVID-19
13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19?
13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT
13.3.1 Smart Disease Surveillance Based on Internet of Things
13.3.1.1 Smart Disease Surveillance
13.3.2 IoT PCR for Spread Disease Monitoring and Controlling
13.4 Global Technological Developments to Overcome Cases of COVID-19
13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic
13.4.2 Key Benefits of Using IoT in COVID-19
13.4.3 A Last Word About Industrial Maintenance and IoT
13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic
13.5 Results & Discussions
13.6 Conclusion
References
14. Mathematical Insight of COVID-19 Infection—A Modeling Approach
14.1 Introduction
14.1.1 A Brief on Coronaviruses
14.2 Epidemiology and Etiology
14.3 Transmission of Infection and Available Treatments
14.4 COVID-19 Infection and Immune Responses
14.5 Mathematical Modeling
14.5.1 Simple Mathematical Models. 14.5.1.1 Basic Model
14.5.1.2 Logistic Model
14.5.2 Differential Equations Models. 14.5.2.1 Temporal Model (Linear Differential Equation Model, Logistic Model)
14.5.2.2 SIR Model
14.5.2.3 SEIR Model
14.5.2.4 Improved SEIR Model
14.5.3 Stochastic Models. 14.5.3.1 Basic Model
14.5.3.2 Simple Stochastic SI Model
14.5.3.3 SIR Stochastic Differential Equations
14.5.3.4 SIR Continuous Time Markov Chain
14.5.3.5 Stochastic SIR Model
14.5.3.6 Stochastic SIR With Demography
14.6 Conclusion
References
15. Machine Learning: A Tool to Combat COVID-19
15.1 Introduction
15.1.1 Recent Survey and Analysis
15.2 Our Contribution
15.3 State-Wise Data Set and Analysis
15.4 Neural Network
15.4.1 M5P Model Tree
15.5 Results and Discussion
15.6 Conclusion
15.7 Future Scope
References
Index
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