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001438337 020__ $$a9783030697440$$q(electronic bk.)
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001438337 0247_ $$a10.1007/978-3-030-69744-0$$2doi
001438337 035__ $$aSP(OCoLC)1261050998
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001438337 049__ $$aISEA
001438337 050_4 $$aRA644.C67
001438337 08204 $$a614.5/92414$$223
001438337 24500 $$aArtificial intelligence for COVID-19 /$$cDiego Oliva, Said Ali Hassan, Ali Mohamed, editors.
001438337 260__ $$aCham :$$bSpringer,$$c2021.
001438337 300__ $$a1 online resource
001438337 336__ $$atext$$btxt$$2rdacontent
001438337 337__ $$acomputer$$bc$$2rdamedia
001438337 338__ $$aonline resource$$bcr$$2rdacarrier
001438337 347__ $$atext file
001438337 347__ $$bPDF
001438337 4901_ $$aStudies in systems, decision and control ;$$vv. 358
001438337 504__ $$aReferences-Diagnosing COVID-19 Virus in the Cardiovascular System Using ANN-1 Introduction-1.1 Electrocardiography-1.2 Cardiovascular Risk Factors Associated with the Worse Outcomes of COVID-19-2 COVID-19 Cardiovascular Manifestations-2.1 COVID-19 and Cardiac Arrhythmia-2.2 COVID-19 Myocardial Injury and Heart Failure-2.3 COVID-19 and Myocarditis-2.4 Variability in Heart Rate-2.5 COVID-19 and Ischemic Heart Disease-3 Results and Discussion-4 Results from Artificial Neural Network-5 Conclusion-References.
001438337 5050_ $$aIntro -- 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
001438337 5058_ $$a2.1 Big Data Analytics Life Cycle -- 3 The Opportunities of Big Data and Big Data Analytics in COVID-19 Pandemic -- 4 Challenges of Big Data and Big Data Analytics During COVID-19 Pandemic -- 5 Conclusion -- References -- Application of COVID-19 Pandemic Using Artificial Intelligence -- 1 Introduction -- 2 Premature Detection of the Coronavirus (COVID-19) -- 3 Succinct Review on Transferable Syndrome Outburst in the Year 2020 -- 4 Applications of Artificial Intelligence in COVID-19 Pandemic -- 4.1 Premature Detection and Diagnosis of Infection -- 4.2 Protrusion of Suitcases and Transience
001438337 5058_ $$a4.3 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
001438337 5058_ $$aAn Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings -- 1 Introduction -- 2 Methodology -- 2.1 COVID-19 5-Class Balanced Dataset -- 2.2 The Pipeline of Deep Feature Extraction from Pretrained Networks and Machine Learning Classification -- 2.3 Performance Evaluation of the Proposed COVID-19 Detection Pipeline -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Understanding Role of Information and Communication Technology Application in Vietnam's Prevention and Control of COVID-19 Pandemic
001438337 506__ $$aAccess limited to authorized users.
001438337 520__ $$aThis book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.
001438337 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 6, 2021).
001438337 647_7 $$aCOVID-19 Pandemic$$d(2020- )$$2fast$$0(OCoLC)fst02024716
001438337 650_0 $$aCOVID-19 (Disease)$$xData processing.
001438337 650_0 $$aCOVID-19 Pandemic, 2020-$$xData processing.
001438337 650_0 $$aArtificial intelligence$$xMedical applications.
001438337 650_6 $$aCOVID-19$$xInformatique.
001438337 650_6 $$aPandémie de COVID-19, 2020-$$xInformatique.
001438337 650_6 $$aIntelligence artificielle en médecine.
001438337 655_7 $$aLlibres electrònics.$$2thub
001438337 655_0 $$aElectronic books.
001438337 7001_ $$aOliva, Diego.
001438337 7001_ $$aHassan, Said Ali,$$d1948-
001438337 7001_ $$aMohamed, Ali.
001438337 77608 $$iPrint version:$$tArtificial intelligence for COVID-19.$$dCham : Springer, 2021$$z3030697436$$z9783030697433$$w(OCoLC)1233163984
001438337 830_0 $$aStudies in systems, decision and control ;$$vv. 358.
001438337 852__ $$bebk
001438337 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-69744-0$$zOnline Access$$91397441.1
001438337 909CO $$ooai:library.usi.edu:1438337$$pGLOBAL_SET
001438337 980__ $$aBIB
001438337 980__ $$aEBOOK
001438337 982__ $$aEbook
001438337 983__ $$aOnline
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