dc.contributor.author | Alsharif, Mohammed H. | |
dc.contributor.author | Alsharif, Yahia H. | |
dc.contributor.author | Chaudhry, Shehzad Ashraf | |
dc.contributor.author | Albreem, Mahmoud A. M. | |
dc.contributor.author | Jahid, Abu | |
dc.contributor.author | Hwang, Eenjun | |
dc.date.accessioned | 2023-08-17T07:52:19Z | |
dc.date.available | 2023-08-17T07:52:19Z | |
dc.date.issued | 2020 | en_US |
dc.identifier.issn | 1128-3602 | |
dc.identifier.uri | https://hdl.handle.net/11363/5362 | |
dc.description.abstract | Today, the world suffers from the
rapid spread of COVID-19, which has claimed
thousands of lives. Unfortunately, its treatment
is yet to be developed. Nevertheless, this phenomenon can be decelerated by diagnosing
and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. In
this study, the early diagnosis of this disease
through artificial intelligence (AI) technology
is explored. AI is a revolutionizing technology
that drives new research opportunities in various fields. Although this study does not provide
a final solution, it highlights the most promising lines of research on AI technology for the diagnosis of COVID-19. The major contribution of
this work is a discussion on the following substantial issues of AI technology for preventing
the severe effects of COVID-19: (1) rapid diagnosis and detection, (2) outbreak and prediction of
virus spread, and (3) potential treatments. This
study profoundly investigates these controversial research topics to achieve a precise, concrete, and concise conclusion. Thus, this study
provides significant recommendations on future
research directions related to COVID-19. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY | en_US |
dc.relation.isversionof | 10.26355/eurrev_202009_22875 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Artificial intelligence | en_US |
dc.subject | Coronavirus pandemic | en_US |
dc.subject | AI | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Big data | en_US |
dc.title | Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues | en_US |
dc.type | article | en_US |
dc.relation.ispartof | European Review for Medical and Pharmacological Sciences | en_US |
dc.department | Mühendislik ve Mimarlık Fakültesi | en_US |
dc.authorid | https://orcid.org/0000-0001-8579-5444 | en_US |
dc.authorid | https://orcid.org/0000-0002-9321-6956 | en_US |
dc.authorid | https://orcid.org/0000-0002-6464-1101 | en_US |
dc.identifier.volume | 24 | en_US |
dc.identifier.issue | 17 | en_US |
dc.identifier.startpage | 9226 | en_US |
dc.identifier.endpage | 9233 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.institutionauthor | Chaudhry, Shehzad Ashraf | |