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Table of Contents
Cover
Title
Copyright
End User License Agreement
Contents
Foreword I
FOREWORD II
Foreword II
Preface
COVID -19
1.1. INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION
1.2. SYMPTOMS
1.3. MEASURES
1.3.1. Demographic Information
1.3.2. Depressive Symptoms
1.3.3. Emotional Health
1.4. POTENTIAL IMPACT
1.4.1. Using Machine Learning
1.4.2. Using IoT Devices
1.5. OVERVIEW OF THE BOOK
CONCLUSION
REFERENCES
Supervised Learning Algorithms
2.1. INTRODUCTION
2.2. SUPERVISED LEARNING ALGORITHMS
2.2.1. Support Vector Machine
2.2.2. Artificial Neural Network
2.2.3. Naive Bayes Method
2.2.4. K-nearest Neighbor
2.2.5. Decision Support System
2.2.6. One Rule (Oner)
2.2.7. Zero Rule (Zeror)
2.3. LINEAR REGRESSION
2.3.1. Random Forest
2.3.2. Gradient Boosted Regression Tree
2.3.3. Perception Back-Propogation
2.4. DRAWBACKS
2.5. FUTURE DIRECTIONS
CONCLUSION
REFERENCES
Semi-Supervised Algorithms
3.1. INTRODUCTION
3.2. SEMI-SUPERVISED ALGORITHMS IN HEALTHCARE
3.2.1. Linear Regression
3.2.2. Multiple Regression
3.2.3. Logistic Regression
3.3. DRAWBACKS
CONCLUSION
REFERENCES
Unsupervised Algorithms
4.1. INTRODUCTION
4.2. CLUSTERING
4.3. DRAWBACKS
CONCLUSION
REFERENCES
Role of Internet-of-Things During Covid-19
5.1. INTRODUCTION
5.2. ARCHITECTURE
5.3. ROLE OF IOT IN COVID-19
5.3.1. IoT - Healthcare
5.3.2. Role of IOT-Transportation in Covid-19
5.3.3. Role of IOT-Entertainment During Covid-19
5.3.4. Role of IOT-Retail
5.3.5. Role of IOT-Education During Covid-19
5.4. ROLE OF CLOUD
5.5. CHALLENGES
5.5.1. Awareness
5.5.2. Accesibility
5.5.3. Human Power Crisis
5.6. AFFORDABILITY
5.7. ACCOUNTABILITY
5.8. DRAWBACKS
5.9. FUTURE DIRECTIONS.
5.9.1. Edge Architecture in H-IOT
5.9.2. Cryptography with Computing in H-IOT
5.9.3. Blockchain Based H-IOT
5.9.4. Machine Learning in H-IOT
5.9.5. Digital Twin in H-IOT
5.9.6. Unified Network Integration Framework
5.9.7. Context Aware Accessibility
5.9.8. Edge and Fog Computing
5.9.9. Sensors and Actuator Integration in H-IOT
CONCLUSION
REFERENCES
Subject Index
Back Cover.
Title
Copyright
End User License Agreement
Contents
Foreword I
FOREWORD II
Foreword II
Preface
COVID -19
1.1. INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION
1.2. SYMPTOMS
1.3. MEASURES
1.3.1. Demographic Information
1.3.2. Depressive Symptoms
1.3.3. Emotional Health
1.4. POTENTIAL IMPACT
1.4.1. Using Machine Learning
1.4.2. Using IoT Devices
1.5. OVERVIEW OF THE BOOK
CONCLUSION
REFERENCES
Supervised Learning Algorithms
2.1. INTRODUCTION
2.2. SUPERVISED LEARNING ALGORITHMS
2.2.1. Support Vector Machine
2.2.2. Artificial Neural Network
2.2.3. Naive Bayes Method
2.2.4. K-nearest Neighbor
2.2.5. Decision Support System
2.2.6. One Rule (Oner)
2.2.7. Zero Rule (Zeror)
2.3. LINEAR REGRESSION
2.3.1. Random Forest
2.3.2. Gradient Boosted Regression Tree
2.3.3. Perception Back-Propogation
2.4. DRAWBACKS
2.5. FUTURE DIRECTIONS
CONCLUSION
REFERENCES
Semi-Supervised Algorithms
3.1. INTRODUCTION
3.2. SEMI-SUPERVISED ALGORITHMS IN HEALTHCARE
3.2.1. Linear Regression
3.2.2. Multiple Regression
3.2.3. Logistic Regression
3.3. DRAWBACKS
CONCLUSION
REFERENCES
Unsupervised Algorithms
4.1. INTRODUCTION
4.2. CLUSTERING
4.3. DRAWBACKS
CONCLUSION
REFERENCES
Role of Internet-of-Things During Covid-19
5.1. INTRODUCTION
5.2. ARCHITECTURE
5.3. ROLE OF IOT IN COVID-19
5.3.1. IoT - Healthcare
5.3.2. Role of IOT-Transportation in Covid-19
5.3.3. Role of IOT-Entertainment During Covid-19
5.3.4. Role of IOT-Retail
5.3.5. Role of IOT-Education During Covid-19
5.4. ROLE OF CLOUD
5.5. CHALLENGES
5.5.1. Awareness
5.5.2. Accesibility
5.5.3. Human Power Crisis
5.6. AFFORDABILITY
5.7. ACCOUNTABILITY
5.8. DRAWBACKS
5.9. FUTURE DIRECTIONS.
5.9.1. Edge Architecture in H-IOT
5.9.2. Cryptography with Computing in H-IOT
5.9.3. Blockchain Based H-IOT
5.9.4. Machine Learning in H-IOT
5.9.5. Digital Twin in H-IOT
5.9.6. Unified Network Integration Framework
5.9.7. Context Aware Accessibility
5.9.8. Edge and Fog Computing
5.9.9. Sensors and Actuator Integration in H-IOT
CONCLUSION
REFERENCES
Subject Index
Back Cover.