Data Science for COVID-19 Volume 1

Computational Perspectives
Erscheinungsjahr 2021
ISBN: 978-0-12-824536-1
Verlag: William Andrew Publishing

Buch, Englisch, 752 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1540 g

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.

Zielgruppe

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.

Autoren/Hrsg.

Kose, Utku

Gupta, Deepak

de Albuquerque, Victor Hugo Costa

Khanna, Ashish

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

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

Über Autor(innen)

de Albuquerque, Victor Hugo Costa
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.

Gupta, Deepak
Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision.

Kose, Utku
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.

Khanna, Ashish
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.