Emerging Technologies for Combatting Pandemics: AI, IoMT, and Analytics

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Acknowledgments
About the Editors
Contributors
Chapter 1 Artificial Intelligence Leveraged Internet of Medical Things and Continuous Health Monitoring and Combating …
1.1 Introduction
1.2 Defining the Role of AI From an IoMT Perspective
1.3 Relevance of AI During a Pandemic
1.3.1 Predictive Analytics
1.3.2 Current Challenges
1.4 Proposed Technology Framework for IoMT Mobile Apps
1.4.1 Example EHealth Standards for EHRs
1.4.2 EHRs in Developed Countries
1.4.2.1 Benefits
1.4.2.2 Limitations
1.4.3 EHR Resources for System Development
1.5 Kubernetes Advantages in AI-Based IoMT
1.5.1 Kubernetes Example
1.5.2 Distinctive Advantages of Kubernetes in the Medical Field
1.5.2.1 Password Management
1.5.2.2 Automated Deployment
1.5.2.3 Packaging Applications
1.5.2.4 Storage Management and Orchestration
1.5.2.5 Load-Balancing Mechanism
1.6 Prescription and Medicine Administration Using AI
1.7 Cybersecurity Challenges in AI-Based IoMT
1.7.1 Zero Trust Architecture for IoMT Devices
1.7.2 Role-Based Authentication in AI-Based IoMT
1.7.3 Workflow of Authentication
1.8 Scalability of IoMT
1.8.1 Introducing Edge Computing
1.8.2 Components of Edge Computing
1.8.3 Factors to Consider Before the Adoption of Edge Computing
1.8.4 Challenges in Implementing Edge Computing
1.9 Continuous Monitoring for the Availability of IoMT
1.9.1 Designing IoMT Systems for High Availability
1.10 Practical Uses of AI-Based IoMT in Pandemics
1.11 Predictive Analytics for Combating Pandemics
1.12 Conclusions
References
Chapter 2 Assessing the Economic Impact of COVID-19
2.1 Introduction
2.2 Economically Affected Indian Sectors By Pandemics
2.3 Impact of Medical Crisis On Economic Conditions
2.4 Start-Up Businesses
2.5 Effects On Indian GDP (2017–2021)
2.6 Comparing Inflation and Employment Between India and the UK
2.7 Stock Market Crashes Due to Pandemic
2.7.1 Black Days of the Stock Market
2.7.2 Effects On Travel and Tourism Services
2.7.9 Action Must Be Taken By the Government
2.7.10 Actions People Should Take
2.8 Challenges After Pandemics
2.8.1 Inflation
2.8.2 Market Recovery
2.8.3 Rise in Debt
2.8.4 Trade Impact
2.8.5 Sustainable Development
2.8.6 Social Issues
2.9 Future Scope
2.9.1 Possible Scope of Data Analysis
2.9.2 Change in Economic Structure
2.9.3 Self-Sufficiency
2.9.3.1 Atmanirbhar Bharat Rozgar Yojana
2.9.4 Digital Changes After COVID-19
2.10 Conclusions
References
Chapter 3 Assessing the Economic Impact of COVID-19 On the Implications of the Internet of Things Adoption On Small and …
3.1 Introduction
3.2 Methodology
3.3 SMEs Business Sustainability
3.4 The IoT
3.5 Contemporary Issues: A Malaysian’s Scenario
3.5.1 SMEs Ecology
3.5.1.1 Digitalisation
3.5.1.2 Cyber Security
3.5.2 Establishing a High-Level Tasks Force to Promote IoT/IR4.0
3.6 Discussion
3.6.1 Increasing SMEs’ Productivity Effectiveness
3.6.2 Adoption Challenges
3.7 Limitations
3.8 Suggestions for Future Research
3.9 Conclusions
References
Chapter 4 Impact of COVID-19: Insights From Key Sectors of the Indian Economy
4.1 Introduction
4.2 Sectoral Implications of the Pandemic
4.2.1 Primary Sector
4.2.2 Secondary Sector
4.2.2.1 Mega Challenge
4.2.3 Service Sector
4.3 Becoming a Global Hub
4.4 Thriving Amid COVID-19: Positive Implications
4.5 Impact of the Second Wave
4.5.1 Agriculture
4.5.2 Manufacturing
4.5.3 Services
4.6 Overall Impact On GDP
4.7 Revival Strategies for Businesses
4.8 Policies to Combat COVID-19
4.9 COVID-19 and Other Economies
4.10 Future Scope and Limitations
4.11 Conclusions
References
Chapter 5 Future Scope of Artificial Intelligence in Healthcare for COVID-19
5.1 Introduction
5.2 Basic Terminology
5.3 COVID-19 Diagnosis and Detection Using Chest CT/X-Ray Scans
5.4 Contactless Healthcare Services With AI
5.5 Future Scope
5.5.1 ML and DL in Drug Design
5.5.2 Addressing Security and Privacy Issues With Blockchain
5.5 Future Challenges
5.6 Conclusions
References
Chapter 6 Patient Recovery and Tracing Repercussions for COVID-19 in Discharged Patients
6.1 Introduction: Patient Recovery and Tracing Repercussions of Coronavirus in Discharged Patients
6.2 Post-COVID Conditions: What Are They?
6.2.1 COVID-19’s Multiorgan Impact
6.2.1.1 COVID-19 Organ Damage Over Time (Multiorgan Dysfunction)
6.2.1.2 Clots in the Blood and Difficulties With the Blood Vessels
6.2.2.3 Problems With Mood Changes and Exhaustion
6.2.1.4 COVID-19 Has Several Long-Term Consequences That Are Still Unclear
6.2.1.5 Inability to Tolerate Physical Activity
6.2.1.6 Complications in the Lungs Or Respiratory System
6.2.1.7 Cardiac Complications
6.2.1.8 Injury to Cardiac Muscle
6.2.1.9 Complications of Renal Injury Or Failure
6.2.1.10 Diabetes
6.2.1.11 Brain Disease: Acute Necrotising Encephalopathy
6.2.1.12 Complications in the Vascular System
6.2.1.13 Complications With Psychiatric Illness
6.2.1.14 Depression and Anxiety
6.2.1.15 Insomnia
6.2.1.16 Complications in Other Organs
6.2.1.17 Adult MIS and MIS-C
6.2.2 COVID-19 Illness Or Hospitalisation Effects
6.2.3 Long-Term Effects of Coronavirus After Recovery, Post-COVID Complications
6.2.3.1 What Is Long COVID Syndrome Or Post-COVID Syndrome?
6.2.3.2 Who Are at Risk of Post-COVID Problems, as Well as Long-Term Coronavirus Effects?
6.3 Diagnosis of Post-COVID Syndrome Or Long COVID
6.4 What Measures Should You Take After You’ve Recovered From COVID?
6.5 Importance of Self-Observation After Recovering From COVID-19
6.6 Conclusions
References
Chapter 7 The Impact of COVID-19 On the Maritime Economy: A Study On Bangladesh
7.1 Introduction
7.2 Maritime Economy of Bangladesh
7.2.1 Economy and Maritime Economy
7.2.2 Seaborne Trade in Bangladesh
7.3 The Empirical Model
7.3.1 EDA
7.3.2 Linear Regression Analysis
7.3.2.1 Import Volume and COVID-19
7.3.2.2 Export Volume and COVID-19
7.3.2.3 Total Seaborne Trade Volume and COVID-19
7.4 Discussion
7.5 Limitations and Future Research
7.6 Conclusions
References
Chapter 8 Intelligent Optimization and Computational Learning Techniques for Mitigating Pandemics
8.1 Introduction
8.2 Review On IOCLT for Mitigating Pandemics
8.2.1 AI
8.2.2 Blockchain
8.2.3 Open Source Technologies
8.2.4 3D Printing
8.2.5 Telehealth Technologies
8.2.6 Comparative Analysis of IOCT
8.3 Materials and Methods
8.3.1 Deep Learning for Computational Learning Techniques
8.3.2 Categorization of Deep Learning Algorithm for Computational Intelligence
8.3.2.1 CNNs
8.3.2.2 LSTMs
8.3.2.3 RNNs
8.3.2.4 Generative Adversarial Networks
8.3.3 Optimization Techniques
8.3.3.1 Genetic Algorithm
8.3.3.2 Adam Algorithm
8.3.3.3 GD Algorithm
8.3.3.4 Adaptive Gradient Algorithm
8.3.4 Evaluation Metrics
8.3.5 IOCLT
8.4 Results and Discussion
8.4.1 Data Description
8.4.2 Data Analysis and Modeling
8.4.3 Results Analysis
8.5 Limitations and Future Scopes
8.6 Conclusions
References
Chapter 9 Various Deep Learning Methodologies for COVID-19 Diagnosis
9.1 Introduction: Background and AI in COVID-19
9.1.1 AI and COVID-19
9.2 Materials and Diagnosis Methodologies for COVID-19
9.2.1 DL
9.2.2 Prerequisites for a Search to Diagnose COVID-19
9.2.3 Extraction of Data
9.3 COVID-19 Detection Using AI-Based ML and DL for Medical Image Inception
9.3.1 Detection From Chest CT Images
9.3.2 Using CXR Images for Detection
9.3.3 Various Severity Levels of COVID-19 Classification Using DL and CXR Images
9.4 Vaccine and Drug Development Using ML and DL Techniques for the Treatment of COVID-19
9.4.1 Vaccine Development Using ML and DL
9.4.2 Drug Development
9.5 Limitations and Future Direction
9.5.1 Limitations
9.5.1.1 Regulations
9.5.1.2 Large-Scale Training Data Is Scarce and Unavailable
9.5.1.3 Data That Is Noisy, as Well as Fake Rumors
9.5.1.4 There Is a Gap Between Medicine and Computer Science
9.5.1.5 Data Security and Privacy
9.5.1.6 Early Detection of COVID-19 Through CXRs and CT Scans
9.5.1.7 Structurally Incorrect Data and Data That Is Not Structurally Correct (E.g., Image, Text, and Numerical Data)
9.5.1.8 Patients Are Screened and Triaged, Functional Therapy and Cures Are Sought, and Risk Assessments Are Conducted
9.5.2 Future Research Direction
9.5.2.1 Contactless Detection of Diseases During a Pandemic
9.5.2.2 Video Diagnostics and Consultations Via the Internet
9.5.2.3 Research in Biological Field
9.5.2.4 Vaccination and Drug Development
9.5.2.5 Fake Information Is Identified and Screened
9.5.5.6 Assessment and Evaluation of the Impact
9.5.2.7 COVID-19 Patient Tracking
9.5.2.8 AI Robots
9.5.2.9 More Research Work On Descriptive AI-Based DL Algorithms in the Future
9.5.2.10 What Is the Significant Aspect of COVID-19 Diagnosis and Treatment?
9.6 Conclusions
References
Chapter 10 Hybridization of Decision Tree Algorithm Using Sequencing Predictive Model for COVID-19
10.1 Introduction
10.2 COVID-19 Virus
10.3 Detection and Diagnosis of COVID-19 Infection
10.3.1 Molecular Detection
10.3.2 Medical Imagery Detection
10.3.3 Symptomatic Detection
10.4 The Role of ML
10.5 ML
10.5.1 Types of ML
10.5.2 Statistical Inference
10.5.3 Learning Techniques
10.5.4 Learning Problems
10.5.5 Hybrid Learning Problems
10.5.6 DL
10.5.7 Evolutionary Learning
10.6 DT Algorithm
10.6.1 Regression Tree Modeling
10.6.2 Classification Trees
10.6.3 BART
10.7 DT Hybridized Ensembles
10.8 DT Hybridized Ensemble Modeling
10.8.1 Single DT Ensemble
10.8.2 Multilevel DT Ensemble
10.8.3 Two-Level DT Ensemble
10.8.4 Hybrid DT Ensemble
10.9 HDT Disease Detection
10.10 Results and Discussion
10.10.1 Fitting Classification Trees
10.10.2 Fitting Regression Trees
10.10.3 Hybridization of Bag and Random Forest
10.10.4 Boosting
10.10.5 BART
10.11 Limitations and Future Work
10.12 Conclusions
References
Chapter 11 CoVICU: A Smart Model for Predicting the Intensive Care Unit Stay of COVID-19 Patients Using Machine Learning ...
11.1 Introduction
11.2 Related Works
11.3 Proposed System
11.3.1 Data Preprocessing
11.3.2 Classification
11.4 Implementation and Results
11.5 Conclusions
11.6 Future Work
References
Chapter 12 Long Short-Term Memory-Based Recurrent Neural Network Model for COVID-19 Prediction in Different States of India
12.1 Introduction
12.2 Related Work
12.3 Methodology
12.3.1 Dataset Description
12.3.2 Proposed Methodology
12.4 Experimental Results and Discussion
12.4.1 Prediction for Active Cases Per Day
12.4.2 Prediction for Confirmed Cases Per Day
12.4.3 Prediction for Cumulative Confirmed Cases
12.5 Conclusions
12.6 Limitation and Future Scope
References
Chapter 13 Dengue in the Presence of COVID-19: Evaluation of Tree-Based Classifiers Using Stratified K-Fold On Dengue Dataset
13.1 Introduction
13.2 Related Works
13.3 Proposed Model
13.3.1 Model Architecture
13.4 Data Collection
13.5 Train the Test Dataset
13.6 Results
13.7 Discussion
13.8 Conclusions
References
Index