Data Science for COVID-19 Volume 1
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Data Science for COVID-19 Volume 1

Computational Perspectives

1st Edition - May 20, 2021

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  • Editors: Utku Kose, Deepak Gupta, Victor de Albuquerque, Ashish Khanna
  • Paperback ISBN: 9780128245361
  • eBook ISBN: 9780128245378

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Description

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.

Key Features

  • Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and treatment of the COVID-19 virus
  • Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including both positive and negative research findings
  • Provides insights into innovative data-oriented modeling and predictive techniques from COVID-19 researchers
  • Includes real-world feedback and user experiences from physicians and medical staff from around the world on the effectiveness of applied Data Science solutions

Readership

Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science and Engineering, Biomedical Engineering, Biology, Chemistry, Electronics and Communication Engineering, and Information Technology. The audience also includes interested professionals-experts from both public and private industries of medical, computer, data science, information technologies. The book may be used in Data Science, Medical, Biomedical, Artificial Intelligence, Machine Learning, Deep Learning, and even Data (i.e. Image, Signal) Processing oriented courses given at especially Health, Biology, Biomedical Engineering or similar programs of universities, institutions

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Foreword
  • Preface
  • 1. Predictive models to the COVID-19
  • 1. Introduction
  • 2. COVID-19 epidemic forecast
  • 3. Material and methods
  • 4. Methodology
  • 5. Results
  • 6. Final considerations
  • 2. An artificial intelligence–based decision support and resource management system for COVID-19 pandemic
  • 1. Introduction
  • 2. Fundamentals
  • 3. Related works
  • 4. System model
  • 5. Data resources
  • 6. Methods
  • 7. Conclusion
  • 3. Normalizing images is good to improve computer-assisted COVID-19 diagnosis
  • 1. Introduction
  • 2. Coronavirus disease 2019
  • 3. Proposed approach
  • 4. Methodology
  • 5. Experimental results
  • 6. Conclusions and future works
  • 4. Detection and screening of COVID-19 through chest computed tomography radiographs using deep neural networks.
  • 1. Introduction
  • 2. Symptoms and characteristics of COVID-19
  • 3. Screening for COVID-19
  • 4. Deep model for COVID-19 detection
  • 5. Preprocessing
  • 6. Experiment
  • 7. Discussion and conclusion
  • 8. Current research and future work
  • 5. Differential evolution to estimate the parameters of a SEIAR model with dynamic social distancing: the case of COVID-19 in Italy
  • 1. Introduction
  • 2. Related works
  • 3. The SEIAR model
  • 4. Simulations
  • 5. Conclusions
  • 6. Limitations and challenges on the diagnosis of COVID-19 using radiology images and deep learning
  • 1. Introduction
  • 2. COVID-19 radiology imaging dataset
  • 3. Recent works using radiology images for COVID-19
  • 4. Deep learning basics
  • 5. Limitations and challenges
  • 6. Summary and future perspective
  • 7. Deep convolutional neural network–based image classification for COVID-19 diagnosis
  • 1. Introduction
  • 2. Overview of data processing
  • 3. Overview on COVID-19 datasets
  • 4. Background study
  • 5. Proposed system for COVID-19 detection using image classification
  • 6. Materials and methods
  • 7. Model training
  • 8. Results and discussions
  • 9. Conclusion
  • 8. Statistical machine learning forecasting simulation for discipline prediction and cost estimation of COVID-19 pandemic
  • 1. Introduction
  • 2. Literature survey of situation report by World Health Organization
  • 3. Supervised model for discipline analysis within a country against COVID-19
  • 4. Limitations and future scope
  • 9. Application of machine learning for the diagnosis of COVID-19
  • 1. Introduction
  • 2. Visualization of the spread of coronavirus disease 2019
  • 3. Methodology
  • 4. Feature importance and feature scoring
  • 5. Classification using machine learning
  • 6. Performance parameters
  • 7. Conclusion
  • 10. PwCOV in cluster-based web server: an assessment of service-oriented computing for COVID-19 disease processing system
  • 1. Introduction
  • 2. Materials and method
  • 3. Focus of the study
  • 4. Testing of PwCOV
  • 5. Reliability of PwCOV
  • 6. Overall assessment of PwCOV
  • 7. Conclusion
  • 11. COVID-19–affected medical image analysis using DenserNet
  • 1. Introduction
  • 2. Related works
  • 3. Problem formulation
  • 4. Proposed methodology
  • 5. Experiments and discussions
  • 6. Conclusion
  • 12. uTakeCare: unlock full decentralization of personal data for a respectful decontainment in the context of COVID-19: toward a digitally empowered anonymous citizenship
  • 1. Introduction
  • 2. COVID-19 public safety applications
  • 3. Ethical and legal discussion on COVID-19 digital applications
  • 4. uTakeCare: a new concept of deconfinement applications
  • 5. Limitations, perspectives, and futures works
  • 6. Conclusion
  • 13. COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
  • 1. Introduction
  • 2. Materials and method
  • 3. Experimental results
  • 4. Conclusion
  • 14. Lexicon-based sentiment analysis using Twitter data: a case of COVID-19 outbreak in India and abroad
  • 1. Introduction
  • 2. Proposed methodology
  • 3. Discussion
  • 4. Conclusion
  • 15. Real-time social distance alerting and contact tracing using image processing
  • 1. Introduction
  • 2. Flattening the curve
  • 3. Contact tracing
  • 4. Proposed system for identification of susceptible members
  • 5. Conclusion
  • 16. Machine-learning models for predicting survivability in COVID-19 patients
  • 1. Introduction
  • 2. Materials and method
  • 3. Comparative analysis and results
  • 4. Discussion
  • 5. Conclusion
  • 17. Robust and secured telehealth system for COVID-19 patients
  • 1. Introduction
  • 2. Error mitigation codes for telehealth system
  • 3. Conclusion
  • 18. A novel approach to predict COVID-19 using support vector machine
  • 1. Introduction
  • 2. Related studies
  • 3. Proposed COVID-19 detection methodology
  • 4. Experimental results and discussions
  • 5. Performance analysis of other supervised learning models using visual programming
  • 6. Concluding remarks
  • 19. An ensemble predictive analytics of COVID-19 infodemic tweets using bag of words
  • 1. Introduction
  • 2. Related models
  • 3. Research methodology
  • 4. Results and discussion
  • 5. Conclusion and recommendation
  • 20. Forecast and prediction of COVID-19 using machine learning
  • 1. Introduction
  • 2. Introduction to COVID-19
  • 3. Introduction to machine learning
  • 4. Use of machine learning in COVID-19
  • 5. Different techniques for prediction and forecasting
  • 6. Proposed method for prediction
  • 7. Forecasting
  • 8. Conclusion and future work
  • 21. Time series analysis of the COVID-19 pandemic in Australia using genetic programming
  • 1. Introduction
  • 2. Technical preliminaries and model calibration
  • 3. Proposed gene expression programming–based formulation for best OBJ
  • 4. Model validity and comparative study
  • 5. Variable importance
  • 6. Conclusion
  • 22. Image analysis and data processing for COVID-19
  • 1. Introduction
  • 2. Explanations regarding detection and analysis for COVID-19
  • 3. Data processing to analyze the number of COVID-19 patients
  • 4. Explanation of patient chest computed tomography scan imaging analysis using deep learning
  • 5. Conclusion
  • 23. A demystifying convolutional neural networks using Grad-CAM for prediction of coronavirus disease (COVID-19) on X-ray images
  • 1. Introduction
  • 2. Literature survey
  • 3. Materials and method
  • 4. Implementation workflow
  • 5. Gradient-based activation model
  • 6. Results discussion
  • 7. Conclusion
  • 8. Future work
  • 9. Summary of work carried out so far
  • 10. Application program interface for COVID-19 testing
  • 24. Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images
  • 1. Introduction
  • 2. Convolutional neural network
  • 3. Materials and method
  • 4. Conclusion
  • 25. Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
  • 1. Introduction
  • 2. Material and methods
  • 3. Results
  • 4. Discussion
  • 5. Conclusion
  • 26. Received signal strength indication—based COVID-19 mobile application to comply with social distancing using bluetooth signals from smartphones
  • 1. Introduction
  • 2. Literature review
  • 3. Experiment overview
  • 4. Analysis of results
  • 5. Discussion
  • 6. Conclusions and future work
  • 27. COVID-19 pandemic in India: Forecasting using machine learning techniques
  • 1. Introduction
  • 2. Material and methods
  • 3. Machine learning techniques
  • 4. Results and discussion
  • 5. Conclusion
  • 28. Mathematical recipe for curbing coronavirus (COVID-19) transmition dynamics
  • 1. Introduction
  • 2. Materials and methods
  • 3. Proposed model
  • 4. Existence and uniqueness of solution of the model
  • 5. Stability analysis (positivity solution)
  • 6. Model equilibrium point
  • 7. Results
  • 8. Discussion
  • 9. Conclusion
  • 29. Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia
  • 1. Introduction
  • 2. Related work
  • 3. Sliding window technique for temporal data analytics
  • 4. Trend analysis and forecast
  • 5. Discussion
  • 6. Conclusion
  • 30. A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa
  • 1. Introduction
  • 2. Proposed two-level deterministic reasoning pattern for COVID-19
  • 3. Determining distribution function for Petri net with COVID-19 cases
  • 4. Conclusion
  • 31. Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model
  • 1. Introduction
  • 2. Material and methods
  • 3. Results and discussion
  • 4. Conclusion
  • 32. A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network
  • 1. Introduction
  • 2. The proposed discrete wavelet transform–rough neural network model
  • 3. Performance validation
  • 4. Conclusion
  • 33. Artificial intelligence–based solutions for early identification and classification of COVID-19 and acute respiratory distress syndrome
  • 1. Introduction
  • 2. The proposed enhanced kernel support vector machine model
  • 3. Experimental validation
  • 4. Conclusion
  • 34. Internet of Medical Things (IoMT) with machine learning–based COVID-19 diagnosis model using chest X-ray images
  • 1. Introduction
  • 2. The proposed model
  • 3. Performance validation
  • 4. Conclusion
  • 35. The growth of COVID-19 in Spain. A view based on time-series forecasting methods
  • 1. Introduction
  • 2. Materials and method
  • 3. Analysis of the daily death toll
  • 4. Analysis of the relationship between deaths and intensive care unit figures
  • 5. Relationship between infected and recovered
  • 6. Conclusions and final comments
  • Annex A. Data
  • 36. On privacy enhancement using u-indistinguishability to COVID-19 contact tracing approach in Korea
  • 1. Introduction
  • 2. Related technologies
  • 3. Contact tracing in South Korea
  • 4. Problems of contact data disclosure
  • 5. u-indistinguishability
  • 6. Conclusion
  • 37. Scheduling shuttle ambulance vehicles for COVID-19 quarantine cases, a multi-objective multiple 0–1 knapsack model with a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm
  • 1. Introduction
  • 2. Scheduling shuttle ambulance for COVID-19 patients
  • 3. Multi-objective Multiple Knapsack Problem: an overview
  • 4. Mathematical model for scheduling the shuttle ambulance vehicles
  • 5. An illustrated case study
  • 6. Proposed methodology
  • 7. Experimental results
  • 8. Conclusions and points for future researches
  • Index

Product details

  • No. of pages: 752
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: May 20, 2021
  • Imprint: Academic Press
  • Paperback ISBN: 9780128245361
  • eBook ISBN: 9780128245378

About the Editors

Utku Kose

Dr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medical Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science.

Affiliations and Expertise

Associate Professor, Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

Deepak Gupta

Dr. Deepak Gupta is an assistant professor at Maharaja Agrasen Institute of Technology, Delhi, India. He is an eminent academician, including roles as lecturer, researcher, consultant, community service, PhD, and post-doctorate supervision. Dr. Gupta focuses on rational and practical learning and has contributed important literature in the fields of Human-Computer Interaction, Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning, and Soft Computing. Dr. Gupta has authored/edited a number of books, including Emerging Trends and Roles of Fog, Edge, and Pervasive Computing in Intelligent IoT-Driven Applications, Wiley; Advanced Machine Intelligence and Signal Processing, Springer; Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press; Explainable Edge AI: A Futuristic Computing Perspective, Springer; Applications of Big Data in Healthcare, Elsevier/Academic Press; and Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; among others.

Affiliations and Expertise

Assistant Professor, Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India.

Victor de Albuquerque

Victor Hugo C. de Albuquerque [M’17, SM’19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic.

Affiliations and Expertise

Professor and Senior Researcher, Federal University of Ceara, Fortaleza, Graduate Program on Teleinformatics Engineering,Fortaleza/CE, Brazil

Ashish Khanna

Dr. Ashish Khanna has 16 years of expertise in teaching, entrepreneurship, and research and development. He received his PhD from the National Institute of Technology, Kurukshetra, India, and completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil. He has published around 40 SCI-indexed papers in 'IEEE Transactions', and in other reputed journals by Springer, Elsevier, and Wiley, with a cumulative impact factor of above 100. He has published around 90 research articles in top SCI/Scopus journals, conferences, and book chapters. He is co-author or editor of numerous books, including 'Advanced Computational Techniques for Virtual Reality in Healthcare' (Springer), 'Intelligent Data Analysis: From Data Gathering to Data Comprehension' (Wiley), and 'Hybrid Computational Intelligence: Challenges and Applications' (Elsevier). His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning. He is one of the founders of Bhavya Publications and the Universal Innovator Lab, which is actively involved in research, innovation, conferences, start-up funding events, and workshops. He is currently working at the Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain.

Affiliations and Expertise

Sr. Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, India

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