The book covers a wide topic collection starting from essentials of Computational Intelligence to advance, and possible application types against COVID-19 as well as its effects on the field of medical, social, and different data-oriented research scopes. Among these topics, the book also covers very recently, vital topics in terms of fighting against COVID-19 and solutions for future pandemics. The book includes the use of computational intelligence for especially medical diagnosis and treatment, and also data-oriented tracking-predictive solutions, which are key components currently for fighting against COVID-19. In this way, the book will be a key reference work for understanding how computational intelligence and the most recent technologies (i.e. Internet of Healthcare Thing, big data, and data science techniques) can be employed in solution phases and how they change the way of future solutions.
The book also covers research works with negative results so that possible disadvantages of using computational intelligence solutions and/or experienced side-effects can be known widely for better future of medical solutions and use of intelligent systems against COVID-19 and pandemics. The book is considering both theoretical and applied views to enable readers to be informed about not only research works but also theoretical views about essentials/components of intelligent systems against COVID-19/pandemics, possible modeling scenarios with current and future perspective as well as solution strategies thought by researchers all over the world.
Author(s): Utku Kose, Junzo Watada, Omer Deperlioglu, Jose Antonio Marmolejo Saucedo
Series: Disruptive Technologies and Digital Transformations for Society 5.0
Publisher: Springer
Year: 2021
Language: English
Pages: 448
City: Cham
Foreword
Preface
Disclaimer
Short Description
Contents
Editors and Contributors
1 Can Different Impacts of Covid-19 on Different Countries Be Explained? a Preliminary Machine Learning Experiment
1 Introduction
2 The Data Set
3 DEREx Tool and Knowledge Extraction from the Data Set
4 The Experiments and the Results
4.1 Comparison
5 Discussion
5.1 Further Hypotheses
5.2 Time Period of the Data
5.3 Daily Parameters Versus Non-daily Parameters
5.4 Number of Classes
5.5 Value Range for Each Class
5.6 DEREx
5.7 Readability of the Solution
6 Conclusions and Future Work
References
2 Internet of Health Things (IoHT): The Significance of Virtual Tools Aiding to Overcome Novel Coronavirus (COVID-19) Pandemic
1 Introduction
2 Artificial Intelligence and Internet of Health Things
2.1 Artificial Intelligence
2.2 Internet of Health Things
3 Scope of IoHT in Healthcare
3.1 Vehicle Monitoring
3.2 Surveillance
3.3 Telemedicine and Healthcare
4 Objectives
5 Materials and Methods
6 Results and Discussion
6.1 IoHT-Driven Cluster Identification and Technology Advancements to Combat Covid-19
6.2 IoHT as a Tracing Tool
6.3 IoHT-Based Technology for Remote Screening
6.4 IoHT as a Useful Technique in the Diagnostic Procedure
6.5 IoHT for the Work Load Reduction During Corona Pandemic
7 Scope of Various IoHT Applications Used in Covid-19 Tackling
7.1 Early Diagnosis
7.2 Self-isolation or Quarantine Period
7.3 Post-recovery Phase
8 Future Pandemics and IoHT-based Emerging Applications and Strategies
8.1 SWOT Analysis of IoHT in Future Pandemics
8.2 Strategies to Be Adapted for Future Pandemics
9 Conclusions
References
3 Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques
1 Introduction
2 Materials and Method
2.1 Dataset
2.2 Machine Learning Algorithms
2.3 Description of Variables
2.4 Performance Measures
3 Results and Discussion
3.1 Application of Machine Learning to Future Pandemic
4 Conclusion and Future Work
References
4 Human-in-the-Loop Enhanced COVID-19 Detection in Transfer Learning-Based CNN Models
1 Introduction
2 Convolutional Neural Network (CNN)
2.1 Transfer Learning
3 Model Uncertainty in Bayesian Deep Learning
4 Data Collection, Explanation
5 System Model
6 Simulation Results
6.1 Dataset
6.2 Experiments
6.3 Discussion
7 Conclusion
References
5 Epidemiology Forecasting of COVID-19 Using AI—A Survey
1 Introduction
1.1 Classical Approaches
1.2 Data-Driven Approaches
2 Gathering Data, Data Regularization, and Clustering
2.1 Data Regularization
2.2 Clustering Methods
3 Classical Approaches for Epidemiology Forecasting
3.1 SIR
3.2 SEIR
3.3 SEIRS
3.4 SIRD
3.5 ARIMA
4 Data-Driven Approaches to COVID-19 Dynamics Prediction
4.1 Long Short-Term Memory (LSTM)
4.2 Autoencoder (AE)
4.3 Convolutional Neural Network (CNN)
4.4 Random Forest
5 Data-Driven Versus Traditional
5.1 Our Implementations
6 Conclusion
References
6 Geographical Weighted Regression Approach: A Case Study on Covid-19 in India
1 Introduction
2 Methodology
2.1 Data Collection
2.2 Geographical Weighted Regression
3 Discussion and Results
3.1 Outputs from GWR: Case I
3.2 Case-II: Outputs of GWR
3.3 Case-III: Outputs of GWR
3.4 Tested Samples and Confirmed Cases
3.5 Results of GWR on Testing Samples Using STATA
3.6 GWR and OLS Results Using ARCGIS
4 Conclusions
Appendix-I
References
7 Integration of IoT and Fog Computing for the Development of COVID-19 Cluster Tracking System in Urban Cities
1 Introduction
2 Background
3 Proposed Design
3.1 Development of the Activity Detection System
3.2 Development of the Cluster Tracking System
4 Results and Analysis
4.1 Results of the Activity Detection System
4.2 Results of the COVID-19 Cluster Tracking System
5 Discussions
6 Conclusion
References
8 Reinforcement Learning Model for Pandemic Precautions in Healthcare Environment
1 Introduction
2 Related Works
3 System Model
4 Experimental Results
5 Conclusion
References
9 Transfer Learning-Based Economical and Rapid COVID-19 Detection Using X-Rays Images
1 Introduction
2 Literature Review
3 Material and Methods
3.1 Dataset
3.2 Transfer Learning
3.3 Deep Learning Image Classifiers
3.4 Model Performance Analysis
4 Results and Discussion
5 Conclusion
6 Animal/Human Involvement
References
10 A Predictive Modelling of Covid-19 Reoccurrence Using Recurrent Neural Network
1 Introduction
2 The Proposed Recurrent Neural Networks (RNN)
2.1 Long Short Term Memory (LSTM) and Its Variants
2.2 Evaluation Metrics
3 A Recurrent Neural Network Analysis of Covid-19
3.1 Data Analysis
3.2 Back Propagation Analysis of COVID-19
4 Analysis of Result
4.1 Dataset
4.2 Network Optimization and Training
4.3 Prediction Accuracy
4.4 Model Evaluation Criteria
4.5 Discussion
5 Conclusion
References
11 Computational Intelligence-Based Diagnosis of COVID-19
1 Introduction
2 Computational Intelligence Overview
3 Role of Computational Intelligence Against COVID-19
3.1 Role of Computational Intelligence in Transmission of COVID-19
3.2 Computational Intelligence in COVID-19 Surveillance and Prevention
3.3 Computational Intelligence in COVID-19 Treatment
3.4 Computational Intelligence in Drug Development and Repurposing for COVID-19
4 Computational Intelligence Techniques/Models for Diagnosis of COVID-19
4.1 Neural Networks
4.2 Fuzzy Logic
4.3 Evolutionary Computation
4.4 Computational Learning Theory
4.5 Probabilistic Methods
4.6 Real-World Systems and Tools
4.7 Big Data Analysis
4.8 Artificial Intelligence Models
4.9 Nature-Inspired Computing Models
5 Future Potential of Computational Intelligence and COVID-19
6 Conclusion
References
12 Short-term Forecasting of COVID-19
1 Introduction
2 Related Works
3 Methodology
4 Experimental Results
4.1 Evaluating Algorithms Over Dataset
5 Discussion
6 Conclusion
References
13 Internet of Health Things (IoHT) Against COVID-19: A Review of Recent Development
1 Introduction
2 Related Works
3 System Model
4 Results and Discussion
5 Conclusion
References
14 Drug Discovery with Computational Intelligence Against COVID-19
1 Introduction
1.1 Computer Intelligence in Biological Data
2 Bioinformatics and in Silico Approaches
2.1 Determination of SARS-COV 2 (Pathogen) Protein Target Characteristics
2.2 Determination and Identification of Protein Target Sites for SARS-COV 2 Tumor Necrosis Factor Receptor Associated Proteins 2 (TNF rp2)
2.3 Molecular Homology Modeling of Structures
2.4 Macromolecule (Target) Selection
2.5 Ligand Selection
2.6 Molecular Docking
2.7 Drug Design
3 Results
3.1 Physiochemical Characteristics of SAR-COV 2 Isolates Protein Targets
3.2 Number of Reaction Sites for Protein and Drug Targets for SARS-COV 2 Isolate
3.3 Results of Molecular Docking Calculations and Implication on Drug Discovery
3.4 Drug Discovery Using Molecular Docking Calculations and Results
4 Antimalarial Drug–Protein Interactions in Malaria Parasite (Plasmodium Falciparum)
4.1 Results of Chemical–protein Interactions of Antimalarial Drug, Artesunate Ligand, and Receptors Proteins of the Malaria Parasite (Plasmodium Falciparum)
4.2 Results of Chemical–Protein Interactions of Antimalarial Drug, Chloroquine Ligand, and Receptors Proteins of the Malaria Parasite (Plasmodium Falciparum)
4.3 Results of Chemical–protein Interactions of Antimalarial Drug, Quinine Ligand, and Receptor Proteins of the Malaria Parasite (Plasmodium Falciparum)
4.4 Discussion
5 Conclusion and Suggestions
References
15 Explainable AI for Fighting COVID-19 Pandemic: Opportunities, Challenges, and Future Prospects
1 Introduction
2 eXplainable AI (XAI)
3 The Artificial Intelligence
3.1 Application of AI in COVID-19
4 Review of XAI for COVID-19 Pandemic
5 The Challenges of XAI
6 Prospect of XAI in COVID-19 Pandemic
6.1 Framework/Steps to Follow in Building XAI
7 Conclusion
References
16 Investigation on Some Aspects of Modeling, Forecasting, and Evaluating the Impact of Global Coronavirus Disease 2019
1 Introduction
1.1 Global Pandemic in Different Regions of the World
1.2 Related Work
1.3 Computational Intelligence
2 Materials and Methods for Global Fight to Handle Pandemics, Poverty, Famine, Economic Impact, Vaccine Development, and Treatment
3 Development of International Relation and Communal Behavior
4 Overall Assessment
5 Conclusion
References
17 “Horizontal World Treatment” and a Smart City Oriented Project for Total Recovery from COVID Outbreak: CORONAPOLISES
1 Introduction
1.1 “Fear of Norm” ® and “Dictator Societies” ®
1.2 The Power Defeating the Kings
1.3 From Antique to Present
2 Looting Culture and Perspectives with Phenomenology
2.1 Looting Culture and the “Extortion of the Need” ®
2.2 Understanding with the Phenomenology
3 Un-ethical Criminal and the “Produced Anxiety” ®
3.1 “Ethical Crime” and “Un-ethical Criminal” Against the Social Panic®
3.2 Risk Society and the “Produced Anxiety” ®
4 Self-Ostracism® and the Coronavirus Hallucination
4.1 Self-Ostracism ®
4.2 Hallucination of the Invisible
5 Political Structure of the Coronavirus and the Cultural Racism
5.1 “Demo-Fascist” ®: Political Structure of a Virus
5.2 “Age Discrimination” as Cultural Racism and the Mistake: “Social Distance” Instead of “Physical Distance”
5.3 Disposal Communism Dystopia
6 Foucault, “Big Locking Down” and “the World is a Great Madhouse”
6.1 Urban Empires
6.2 “Urban Empire of Istanbul” in Turkey
6.3 Marginalization and Depression Locations
6.4 Gentrification
6.5 Power as a Body Technology ®
6.6 Technical and Technological Body
7 A Total Solution: “Horizontal World Treatment” and a Smart City Oriented Project: Coronapolises
7.1 Sheltered Smart Cities Against the Epidemic (a Model)
7.2 Politicalized Life Preferences
7.3 Computational Intelligence and Internet of Things (IoT)-Based Background
7.4 Control of Coronapolises
8 Conclusions
References
18 Selection of the Best Neonatal Ventilator in Patients with COVID-19 Using Multicriteria Analysis
1 Introduction
1.1 Conventional Mechanical Ventilation
1.2 High-Frequency Ventilation
2 Introduction
3 Results
3.1 Analytic Hierarchy Process
3.2 Grand Prix Model
4 Further Results
5 Discussion
6 Conclusions
References
19 Blockchain-Based Secure Biomedical Data-as-a-Service for Effective Internet of Health Things Enabled Epidemic Management
1 Introduction
2 Related Works
2.1 COVID-19 and Computational Intelligence
2.2 Blockchain for COVID-19 and Privacy
3 Blockchain-Based Internet of Health Things
3.1 Preliminaries
3.2 The BDaaS+MTaaS+CIaaS Framework
4 Experimental Results and Discussion
4.1 Deep Learning-Based Modeling and Forecasting of Epidemic Spread and Infection Dynamics of COVID-19
4.2 Results
5 Conclusion
References
