Machine learning applications for COVID-19: a state-of-the-art review

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorCherukuri, Aswani Kumar
dc.contributor.authorSulieman, Hana
dc.contributor.authorThabtah, Fadi
dc.date.accessioned2023-10-04T13:58:26Z
dc.date.available2023-10-04T13:58:26Z
dc.date.copyright© 2023
dc.date.issued2022-01-01
dc.identifier.citationKamalov, F., Cherukuri, A. K., Sulieman, H., Thabtah, F., & Hossain, A. (2023). Machine learning applications for COVID-19: a state-of-the-art review. In A. K. Tyagi & A. Abraham (Eds.), Data Science for Genomics (pp. 277-289). Academic Press. https://doi.org/10.1016/B978-0-323-98352-5.00010-0
dc.identifier.isbn978-032398352-5, 978-032398576-5
dc.identifier.urihttps://doi.org/10.1016/B978-0-323-98352-5.00010-0
dc.identifier.urihttps://hdl.handle.net/20.500.12519/851
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofseriesData Science for Genomics
dc.rights.holderCopyright : © 2023 Elsevier Inc. All rights reserved.
dc.subjectApplications
dc.subjectCovid-19
dc.subjectDiagnosis
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectSurvey
dc.titleMachine learning applications for COVID-19: a state-of-the-art review
dc.typeBook chapter

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