Advanced AI and Internet of Health Things for Combating Pandemics (Internet of Things)

This book presents the latest research, theoretical methods, and novel applications in the field of Health 5.0. The authors focus on combating COVID-19 or other pandemics through facilitating various technological services. The authors discuss new models, practical solutions, and technological advances related to detecting and analyzing COVID-19 or other pandemic based on machine intelligence models and communication technologies. The aim of the coverage is to help decision-makers, managers, professionals, and researchers design new paradigms considering the unique opportunities associated with computational intelligence and Internet of Medical Things (IoMT). This book emphasizes the need to analyze all the information through studies and research carried out in the field of computational intelligence, communication networks, and presents the best solutions to combat COVID and other pandemics. 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 Forecasting of COVID-19 Cases Using AI and Real-Time DataSet 1 Introduction 2 Literature Review 3 Materials and Methods 3.1 DATASET 3.2 Deep Learning Algorithms:LSTM 3.3 Evaluation Metrics 4 Methodology 4.1 Dataset Pre-processing 4.2 Model Configuration 5 Result and Discussion 6 Conclusion and Contribution References Predicting Covid-19 Using Cough Audio Recordings 1 Introduction 2 Related Work 3 Material and Method 3.1 Dataset 3.2 Methods 3.3 Data Preprocessing 3.4 Feature Extraction 3.4.1 Librosa 3.4.2 Spectrogram 3.4.3 Zero Crossing Rate 3.4.4 Spectral Centroid 3.4.5 Spectral Rolloff 3.4.6 Mel-Frequency Cepstral Coefficients 3.4.7 Chroma 3.4.8 Root Mean Square 3.5 Classification 3.5.1 Support Vector Machine 3.5.2 Naive Bayes 3.5.3 Random Forest 3.5.4 K-Nearest Neighbors 3.5.5 Deep Neural Network 3.6 Evaluation Criteria 4 Results 5 Discussion and Conclusion Declarations References Computational Linguistics Techniques in Measuring Genetic Distance of Living Organisms 1 Introduction 2 Related Work 3 Data 4 Methodology 4.1 String Character-Wise Comparison 4.2 Pivot Table 4.3 Vector Space Model 5 Experiments 5.1 Virus Nucleotide Sequence Pairwise Comparison 5.2 Vectorization of Virus Objects and Vector Space Models 5.3 Virus Relationships Graph 5.4 Virus Genetic Proximity Model (VGPM) 6 Results 7 Conclusions Appendix 1: Character-Wise String Compare Algorithm Character-Wise String Compare Function Python Code Character-Wise String Compare Algorithm Pseudocode Character-Wise String Compare Algorithm Flowchart Virus Genetic Proximity Model (VGPM) VGPM Function Python Code VGPM Closest Function Algorithm Pseudocode VGPM Closest Function Algorithm Flowchart References Explainable Artificial Intelligence (XAI) Based Analysis of Stress Among Tech Workers Amidst COVID-19 Pandemic 1 Introduction 2 Related Study 3 System Framework 3.1 Dataset Used 3.2 Stress Monitoring in Offices 3.3 Stress Prediction Through Questionnaires 4 Results & Discussion 4.1 Performance Measures of Different ML Models 4.2 Explainable Artificial Intelligence (XAI) Based Model Predictions 4.2.1 SHAP Global Explainability 4.2.2 SHAP Feature Importance 4.2.3 SHAP Summary Plot/Explain Model 4.2.4 SHAP Force Plot/Explain Prediction 4.2.5 SHAP Results Summary 5 Conclusion References Part III Deep Learning and COVID-19 Pandemic COVID-19 Disease Detection Using Deep Learning Techniques in CT Scan Images 1 Introduction 2 Related Works 3 Proposed Approach for COVID-19 CT Scan Images Segmentation 4 Experiments and Results 4.1 Data Augmentation and Training Details 4.2 Results and Discussions 5 Conclusion References Multimodal Diagnosis of COVID-19 Using Deep Wavelet Scattering Networks 1 Introduction 2 Related Work 3 Proposed Methodology 3.1 Deep Wavelet Scattering Decomposition 3.2 Constructing the Wavelet Scattering Network 3.3 Deep Scattering Feature Extraction 3.4 Subspace Feature Learning 3.4.1 Principal Component Analysis (PCA) 3.4.2 Joint Best Scattering Non-linear Approximation (JBSC_NA) 3.5 Classification Stage 4 Experiments and Results 4.1 Description of Datasets 4.1.1 COVID-19 Radiology Dataset (XrayDB1) 4.1.2 Augmented X-ray and CT COVID-19 Dataset 4.2 Evaluation Protocol and Metrics 4.3 Results 4.3.1 COVID-19 vs Non-COVID-19 Classification 4.3.2 X-ray COVID-19 vs Healthy Classification Task 4.3.3 Four-Class Classification Task 4.3.4 Evaluation of Subspace Learning Method 4.3.5 Multimodal COVID-19 Diagnosis 4.3.6 Comparison to Related Work 4.4 Summary and Conclusion References Development of Computer Aided Diagnosis System for Detection of COVID-19 Using Transfer Learning 1 Introduction 2 Literature Review 3 Transfer Learning 4 Regularization in Deep Learning 5 Materials and Methods 5.1 Materials 5.2 Methods 5.2.1 Chest X-Ray Image Classification Using Modified GoogLeNet Transfer Learning Network 5.2.2 Chest X-Ray Image Classification Using Modified SqueezeNet Transfer Learning Network 5.2.3 Proposed Method (Modified Alexnet) for Classification of Chest X-Ray Image 6 Results and Analysis 6.1 Performance Comparison 6.2 Analysis 7 Conclusion and Future Scope References Part IV Internet of Health Things, Blockchain and COVID-19 Pandemic COVID-19 Detection System in a Smart Hospital Setting Using Transfer Learning and IoT-Based Model 1 Introduction 2 Materials and Methods 2.1 Image Pre-processing and Augmentation 2.2 Dataset Collection 2.3 Proposed Model 2.4 Performance Evaluation 2.4.1 Confusion Matrix 2.4.2 Cross Validation 2.4.3 AUC ROC Curve 3 Results of Implementation 3.1 Transfer Learning Models 3.1.1 InceptionV3 3.1.2 DenseNet169 3.1.3 ResNet50V2 3.1.4 Xception 3.2 Transfer Learning Classification Report 3.2.1 Confusion Matrix 3.2.2 TL Models Training and Validation 3.2.3 Comparative Analysis 4 Discussion 5 Conclusions References A Blockchain-Based Secure Framework for Homomorphic AI in IoHT for Tackling COVID-19 Pandemic 1 Introduction 2 Preliminaries 2.1 Blockchain 2.2 Homomorphic Encryption 3 Related Work 4 Proposed Approach 4.1 Security Requirements 4.2 Architecture 4.3 Platform Layer 4.4 Decentralized Application Layer 4.4.1 Distributed Storage Component 4.4.2 Smart Contract Component 4.5 IoT Gateway Component 4.5.1 Encryption Process 4.5.2 Anonymized Identification Process 4.6 Artificial Intelligence Component 4.7 Perception Layer 4.8 Workflow 5 Security Evaluation 5.1 Formal Description 5.1.1 Proposed Framework 5.1.2 Requirements 5.1.3 Assumptions 5.2 Formal Verification 5.2.1 Integrity 5.2.2 Ownership 5.2.3 Transparency 5.2.4 Confidentiality 5.2.5 Availability 6 Use Case: COVID-19 Monitoring 7 Conclusion References Blockchain-Based Solution for Patient Health Records Sharing by Adopting Private Decentralized Storage 1 Introduction 2 Background Knowledge 2.1 Ethereum Blockboard 2.2 Private Interplanetary File System (PIPFS) 2.3 NuCypher 2.4 Ethereum Smart Contracts 2.5 Proxy Re-encryption (PRE) 3 Motivation 4 Related Work 5 Developed Model and Its Implementation 5.1 Smart Contract Layer 5.2 Blockchain Layer 5.3 Data Layer 5.4 NuCypher Layer 6 Discussion 6.1 Analysis of the Security and Privacy Aspects 6.2 Data Security 6.3 Data Privacy 6.4 Data Integrity 6.5 Fine-Grained Access 6.6 Avoid a Single Point of Failure 6.7 Patient-Centric 6.8 Scalability 6.9 System Comparison 6.10 Perform the Vulnerability and Security Analysis on Smart Contract Code 7 Conclusion References Part V Case Studies and Frameworks On Natural Language Processing to Attack COVID-19 Pandemic: Experiences of Vietnam 1 The Outbreak of COVID-19 Disease 1.1 The Global Spread of COVID-19 1.2 COVID-19 Pandemic in Vietnam 2 Approaches Against COVID-19 2.1 Natural Language Processing 2.2 Named Entity Recognition 2.2.1 Approaches 2.2.2 Evaluation Metrics 2.3 COVID-19 Prevention Studies in NLP 3 Dataset for NER System Building 3.1 Data Collection 3.2 Entity Types 3.3 Data Overview 3.4 Annotation 4 Deep Learning Approach 4.1 Transformer and BERT-Style Models 4.1.1 Attention Mechanism 4.1.2 Transformer 4.2 BERT 4.3 Other Models 5 System Building 5.1 Data Processing 5.2 Training and Evaluation 5.3 Experimental Result 5.4 Error Analyst 5.5 In Reality 6 Conclusion References VacciNet: Towards a Reinforcement Learning Based Smart Framework for Predicting the Distribution Chain Optimization of Vaccines for a Pandemic 1 Introduction 2 Related Work 3 Our System: Overview and Design 3.1 Proposed Framework 3.1.1 Design Components and Operation 3.1.2 Choice of the Predictor Model and the RL-Agent 3.1.3 Model Architecture Details 3.2 Mathematical Formulations and Training Algorithm 4 Experiments and Evaluation 4.1 Data Preparation and Analysis 4.1.1 Vaccination Drive Data Preparation for RNN Based SRU Model 4.1.2 Cost Matrix Data Preparation for State Space of Q-Learning 4.2 Model Evaluation 4.2.1 SRU Predictor Evaluation 4.3 DQN RL-Agent Evaluation 5 Discussion and Conclusion References AI-Based Logistics Solutions to Tackle Covid-19 Pandemic and Ensure a Sustainable Financial Growth 1 Introduction 2 Era and Interests of Smart Solutions in Logistics Systems 2.1 AI Applications in Logistics 2.2 Roles of Learning Techniques 2.3 AI Techniques in Favor of Logistics Systems 3 Challenges of Optimization in the Logistics Sector 3.1 Combinatory Optimization 3.2 Continuous Optimization 3.3 Mixed Variable Optimization 3.4 Challenges in Choosing the Right Method 4 Results and Discussions 4.1 The Covid-19 Pandemic Accelerates Logistics Automation 4.2 Preventive Measures Against Coronavirus in the Workplace 4.3 AI-Based Solutions During the Covid-19 Crisis 4.4 The Importance of AI-Based Solutions in the Pandemic Context 4.5 Financial Insights 4.6 Limitations and Future Implications 5 Conclusion References A Comparative Modeling and Comprehensive Binding Site Analysis of the South African Beta COVID-19 Variant's Spike Protein Structure Abbreviations 1 Background 2 Related Work 3 Methods and Materials 4 Results and Discussion 5 Conclusion References Index