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dc.contributor.authorJamshidi, Mohammad
dc.contributor.authorLalbakhsh, Ali
dc.contributor.authorTalla, Jakub
dc.contributor.authorPeroutka, Zdeněk
dc.contributor.authorHadjilooei, Farimah
dc.contributor.authorLalbakhsh, Pedram
dc.contributor.authorJamshidi, Morteza
dc.contributor.authorLa Spada, Luigi
dc.contributor.authorMirmozafari, Mirhamed
dc.contributor.authorDehghani, Mojgan
dc.contributor.authorSabet, Asal
dc.contributor.authorRoshani, Saeed
dc.contributor.authorRoshani, Sobhan
dc.contributor.authorBayat-Makou, Nima
dc.contributor.authorMohamadzade, Bahare
dc.contributor.authorMalek, Zahra
dc.contributor.authorJamshidi, Alireza
dc.contributor.authorKiani, Sarah
dc.contributor.authorHashemi Dezaki, Hamed
dc.contributor.authorMohyuddin, Wahab
dc.date.accessioned2020-09-21T10:00:14Z-
dc.date.available2020-09-21T10:00:14Z-
dc.date.issued2020
dc.identifier.citationJAMSHIDI, M., LALBAKHSH, A., TALLA, J., PEROUTKA, Z., HADJILOOEI, F., LALBAKHSH, P., JAMSHIDI, M., LA SPADA, L., MIRMOZAFARI, M., DEHGHANI, M., SABET, A., ROSHANI, S., ROSHANI, S., BAYAT-MAKOU, N., MOHAMADZADE, B., MALEK, Z., JAMSHIDI, A., KIANI, S., HASHEMI DEZAKI, H., MOHYUDDIN, W. Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access, 2020, roč. 8, č. 2020, s. 109581-109595. ISSN 2169-3536.en
dc.identifier.issn2169-3536
dc.identifier.uri2-s2.0-85087668610
dc.identifier.urihttp://hdl.handle.net/11025/39669
dc.format15 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Accessen
dc.rights© IEEEen
dc.titleArtificial intelligence and COVID-19: deep learning approaches for diagnosis and treatmenten
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedCOVID-19 outbreak has put the whole world in an unprecedented difcult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Articial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Articial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.en
dc.subject.translatedartificial intelligenceen
dc.subject.translatedbig dataen
dc.subject.translatedbioinformaticsen
dc.subject.translatedbiomedical informaticsen
dc.subject.translatedCOVID-19en
dc.subject.translateddeep learningen
dc.subject.translateddiagnosisen
dc.subject.translatedmachine learningen
dc.subject.translatedtreatmenten
dc.identifier.doi10.1109/ACCESS.2020.3001973
dc.type.statusPeer-revieweden
dc.identifier.document-number546414000007
dc.identifier.obd43930115
dc.project.IDEF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligencecs
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