Artificial intelligence for COVID-19 / Diego Oliva, Said Ali Hassan, Ali Mohamed, editors.
Oliva, Diego.; Hassan, Said Ali, 1948-; Mohamed, Ali.
2021
RA644.C67
Available Online

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Title Artificial intelligence for COVID-19 / Diego Oliva, Said Ali Hassan, Ali Mohamed, editors.
ISBN 9783030697440 (electronic bk.)
3030697444 (electronic bk.)
3030697436
9783030697433
DOI https://doi.org/10.1007/978-3-030-69744-0
Imprint Cham : Springer, 2021.
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-030-69744-0 doi
Call Number RA644.C67
Dewey Decimal Classification 614.5/92414
Summary This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.
Bibliography, etc. Note References-Diagnosing COVID-19 Virus in the Cardiovascular System Using ANN-1 Introduction-1.1 Electrocardiography-1.2 Cardiovascular Risk Factors Associated with the Worse Outcomes of COVID-19-2 COVID-19 Cardiovascular Manifestations-2.1 COVID-19 and Cardiac Arrhythmia-2.2 COVID-19 Myocardial Injury and Heart Failure-2.3 COVID-19 and Myocarditis-2.4 Variability in Heart Rate-2.5 COVID-19 and Ischemic Heart Disease-3 Results and Discussion-4 Results from Artificial Neural Network-5 Conclusion-References.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 6, 2021).
Added Author Oliva, Diego.
Hassan, Said Ali, 1948-
Mohamed, Ali.
Series Studies in systems, decision and control ; v. 358.
Available in Other Form Artificial intelligence for COVID-19.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro
Preface
Contents
Simulation of the Relation Between the Number of COVID-19 Death Cases as a Result of the Number of Handwashing Facilities by Using Artificial Intelligence
1 Literature Review
2 Aim of the Research
2.1 Statement of the Problem
2.2 Research Methodology
2.3 Research Setting and Research Paradigm
2.4 Limitations of the Method
3 Results and Discussion
4 Conclusion
References
Big Data and Data Analytics for an Enhanced COVID-19 Epidemic Management
1 Introduction
2 Big Data and Big Data Analytics for COVID-19

2.1 Big Data Analytics Life Cycle
3 The Opportunities of Big Data and Big Data Analytics in COVID-19 Pandemic
4 Challenges of Big Data and Big Data Analytics During COVID-19 Pandemic
5 Conclusion
References
Application of COVID-19 Pandemic Using Artificial Intelligence
1 Introduction
2 Premature Detection of the Coronavirus (COVID-19)
3 Succinct Review on Transferable Syndrome Outburst in the Year 2020
4 Applications of Artificial Intelligence in COVID-19 Pandemic
4.1 Premature Detection and Diagnosis of Infection
4.2 Protrusion of Suitcases and Transience

4.3 Progress of Drugs and Vaccines
4.4 Tumbling the Work of Healthcare Employees
5 The Original AI Capability of Bluedot and Metabiota
5.1 Bluedot
5.2 Metabiota
6 Conclusion
References
Application of Artificial Intelligence for COVID-19 Epidemic: An Exploratory Study, Opportunities, Challenges, and Future Prospects
1 Introduction
2 Artificial Intelligence (AI) Techniques in COVID-19 Outbreak
3 The Applicability of Artificial Intelligence During COVID-19 Pandemic
4 The Challenges Applying Artificial Intelligence During COVID-19 Pandemic
5 Conclusion

An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
1 Introduction
2 Methodology
2.1 COVID-19 5-Class Balanced Dataset
2.2 The Pipeline of Deep Feature Extraction from Pretrained Networks and Machine Learning Classification
2.3 Performance Evaluation of the Proposed COVID-19 Detection Pipeline
3 Results
4 Discussion
5 Conclusion
References
Understanding Role of Information and Communication Technology Application in Vietnam's Prevention and Control of COVID-19 Pandemic

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