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Data Science for COVID-19

  • ID: 5180549
  • Book
  • April 2021
  • 638 Pages
  • Elsevier Science and Technology
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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.

  • 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
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1. Predictive models to the COVID-19
2. An AI Based Decision Support and Resource Management System for COVID-19 Pandemic
3. Normalizing Images is Good to Improve Computer-Assisted COVID-19 Diagnosis
4. Detection and Screening of COVID-19 Through Chest CT Radiographs Using Deep Neural Networks
5. Differential Evolution to improve the effectiveness of the epidemiological SEIR model enhanced with dynamic social distancing: the case of COVID-19 and Italy
6. Limitations and Challenges on the Diagnosis of COVID-19 Using Radiology Images and Deep Learning
7. Deep Convolutional Neural Network Based Image Classification for Covid-19 Diagnosis
8. Statistical Machine Learning Forecasting Simulation for Discipline Prediction and Cost Estimation of COVID-19 Pandemic
9. Application of Machine Learning for the Diagnosis of COVID-19
10. PwCOV in Cluster Based Web Server: An Assessment of Service Oriented Computing for COVID-19 Disease Processing System
11. COVID-19-affected Medical Image Analysis using Denser Net
12. uTakeCare: unlock full decentralization of personal data for a respectful decontainment in the context of COVID-19: toward a digitally empowered anonymous citizenship
13. COVID-19 Detection from Chest X-Rays Using Transfer Learning with Deep CNN
14. Lexicon Based Sentiment Analysis Using Twitter Data: A Case of COVID-19 Outbreak in India and Abroad
15. Real time social distancing alerting and contact tracing using image processing
16. Machine Learning Models for Predicting Survivability in COVID-19 Patients
17. Robust and Secured Telehealth System for COVID-19 Patients
18. A Novel Approach to Predict COVID-19 Using Support Vector Machine
19. An Ensemble Predictive Analytics of Covid-19 Infodemic Tweets Using Bag of Words
20. Forecast & Prediction of Covid-19 Using Machine Learning
21. Time Series Analysis of the COVID-19 Pandemic in Australia using Genetic Programming
22. Image Analysis and Data Processing for COVID-19
23. A Demystifying Convolutional Neural Networks using Gradcam for Prediction of Coronavirus Disease (Covid-19) On X-Ray Images
24. Transfer Learning Based Convolutional Neural Network for Covid-19 Detection with X-Ray Images
25. Computational Modelling of the Pharmacological actions of some anti-viral agents against SARS-CoV-2
26. Mobile Technology Solutions for COVID-19: RSSI-based COVID-19 mobile app to comply with social distancing using bluetooth signals from smartphones
27. COVID-19 Pandemic in India: Forecasting Using Machine Learning Techniques
28. Mathematical Recipe for Curbing Corona Virus (Covid-19) Transmition Dynamics
29. Sliding Window Time Series Forecasting with Multi-Layer Perceptron and Multi Regression of COVID-19 outbreak in Malaysia
30. A Two-Level Deterministic Reasoning Pattern to Curb the Spread of Covid-19 in Africa
31. Data-driven approach to covid-19 infection forecast in Nigeria using negative binomial regression model
32. A Novel Machine Learning Based Detection and Diagnosis Model for Corona Virus Disease (Covid-19) using Discrete Wavelet Transform (DWT) with Rough Neural Network (RNN)
33. Artificial Intelligence Based Solutions for Early Identification and Classification of COVID-19 and Acute Respiratory Distress Syndrome
34. Internet of Medical Things (IoMT) with Machine Learning based COVID-19 Diagnosis Model using Chest X-Ray Images
35. The growth of COVID-19 in Spain. A view based on time-series forecasting methods
36. On Privacy Enhancement Using u-Indistinguishability to COVID19 Contact Tracing Approach in Korea
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
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Kose, Utku
Dr. Kose received the BSc. degree from Gazi University, Turkey (2008). He received MSc. degree from Afyon Kocatepe University, Turkey (2010) and PhD. degree from Selcuk University, Turkey (2017). Currently, he is an Associate Professor in Suleyman Demirel University, Turkey. He has more than 100 publications including articles, books, proceedings, and reports. He is also in editorial boards of scientific journals and serves as one of the editors of the Biomedical and Robotics Healthcare book series (CRC Press). His research interest includes artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos, distance education, e-learning, computer education, and computer science.
Gupta, Deepak
Dr. Gupta is an eminent academician; plays versatile roles and responsibilities within lectures, research, publications, consultancy, and community service. He has 13-years of rich expertise in teaching and two years in the industry with focus on rational-practical learning. He has served as Editor-in-Chief, Guest Editor, Associate Editor in SCI and various reputed journals. He has actively been an organizing end of various reputed international conferences. He has completed Post-Doc. from Inatel, Brazil, and PhD. from Dr. APJ Abdul Kalam Technical University. He authored / edited over 43 books, and published over 144 papers in reputed journals and conferences (including 73 SCI indexed journals). He acts also as faculty resource person, session chair, reviewer, TPC member in FDPs, conferences, and journals..
de Albuquerque, Victor Hugo Costa
Dr. Albuquerque [M'17, SM'19] is a Professor and senior researcher at the University of Fortaleza, Brazil, and Data Science Director at the Superintendency for Research and Public Safety Strategy of Ceará State, Brazil. He has PhD. in Mechanical Engineering from the Federal University of Paraíba, MSc. in Teleinformatics Engineering from the Federal University of Ceará, and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará. He is at the Applied Informatics of UNIFOR and leader of the Industrial Informatics, Electronics and Health Research Group. He is a specialist in IoT, machine / deep learning, pattern recognition, and robotics.
Khanna, Ashish
Dr. Khanna has received his PhD. degree from National Institute of Technology, Kurukshetra (2017). He has completed his M. Tech. (2009) and B. Tech. from GGSIPU, Delhi (2004). He has completed his PDF from Internet of Things Lab at Inatel, Brazil. He is serving the research and academics as a teacher, researcher, guide, co-guide, keynote speaker, consultant, book author and editor, convener, and project consultant. He has around 125 research papers, book chapters, 28 books. He also has published a patent. His research interest includes image processing, distributed Systems and its variants, machine learning, evolutionary computing and many more.
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