Intro
Preface
Contents
About the Editors
Part I State-of-the-Art
Knowledge Graphs for COVID-19: A Survey
1 Introduction
2 Background
2.1 Knowledge Graph
2.2 Transformer
2.2.1 Bidirectional Encoder Representations from Transformers (BERT)
3 Knowledge Graph Construction
3.1 Entity Extraction
3.2 Relation Extraction
3.3 Knowledge Fusion/Coreference Resolution
3.4 Knowledge Graph Storage
3.5 Knowledge Graph Visualization
4 Application of Knowledge Graphs in Covid-19
5 Challenges in Knowledge Graph Construction

5.1 Quality of Knowledge Graph Construction
5.2 Visualizing a Knowledge Graph
6 Conclusion
References
Mapping Effective Practices and Frameworks During the AEC Industry's Combat with COVID-19: Scientometric Analysis
1 Introduction
1.1 Research Gap and Objective
2 Research Method
2.1 Phase One: Searching Publications
2.2 Screening
2.3 Data Analysis
2.4 Scientometric Analysis
2.5 Thematic Analysis
3 Results of Scientometric Analysis
3.1 Number of Publications and Citations
3.2 Publication Source
3.3 Origin of Publications
3.4 Keywords Analysis

3.5 Co-occurrence of Keywords Analysis
3.6 Author Co-citation Network
3.7 Active Countries
3.8 Most-Cited Publications
4 Results of Thematic Analysis
4.1 Technology Solutions for COVID-19
4.2 Management Frameworks for Health Technology
4.3 Educational Technology
5 Discussion
6 Conclusion
References
Deep Learning for Combating COVID-19 Pandemic in Internet of Medical Things (IoMT) Networks: A Comprehensive Review
1 Introduction
2 Wireless Body Sensor Networks
3 The WBSN Architecture
4 WBSN Applications
4.1 Telemedicine and Remote Patient Monitoring

4.2 Rehabilitation and Therapy
4.3 Biofeedback
4.4 Assisted Living Technologies
5 Main Challenges of WBSNs
6 Health Surveillance System
6.1 Biosensor Devices
6.2 Gateway Device
6.3 Back-End Component
7 Data Gathering and Fusion
8 The Covid-19 Pandemic
9 Telemedicine, Remote Patient Monitoring, and Decision Making
10 Covid-19 Pandemic Combating: Deep Learning Approaches
10.1 Convolutional Neural Network (CNN)
10.2 Recurrent Neural Network (RNN))
10.3 LSTM (Long Short-Term Memory)
10.4 GAN
10.5 Auto-Encoder-Decoder
11 Discussions

12 Conclusions
References
Part II Machine Learning and COVID-19 Pandemic
Machine Learning Algorithms for Classification of COVID-19 Using Chest X-Ray Images
1 Introduction
2 Literature Review
3 Methodology
3.1 Pre-processing
3.2 Feature Extraction
3.3 Classification Techniques
3.3.1 Naïve Baye Classifier
3.3.2 Decision Tree Classifier
3.3.3 KNN Classifier
3.3.4 Logistic Regression
3.3.5 ANN Classifier
4 Result and Discussion
4.1 Dataset Description
4.2 Performance Matrix
5 Overall Performance Accuracy
6 Conclusions
References