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Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic

dc.contributor.authorNieto-Chaupis, Huber
dc.date.accessioned2022-04-29T20:50:29Z
dc.date.available2022-04-29T20:50:29Z
dc.date.issued2021-10-18
dc.identifier.citationNieto-Chaupis, H. (2021). Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic. In 2021 Third International Conference on Transdisciplinary AI (TransAI) (pp. 45-46). IEEE.es_PE
dc.identifier.isbn978-1-6654-3412-6
dc.identifier.urihttps://hdl.handle.net/20.500.13067/1817
dc.description.abstractBased in the fact that ongoing pandemic is caused by a kind of disorder, this paper employs the concept of Shannon entropy to model data of infections by Covid-19. The usage of this represents a proposal as a type of artificial intelligence that might be used in advanced softwares to perform instantaneous measurements of new infections. The presented theory is applied to the case of UK data, yielding an interesting matching. Therefore, it is seen that waves of pandemics can be explained in terms of apparition of strains and entropy.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherUniversidad Autónoma del Perúes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.sourceAUTONOMAes_PE
dc.subjectCOVID-19es_PE
dc.subjectCorrelationes_PE
dc.subjectPandemicses_PE
dc.subjectEntropyes_PE
dc.subjectSoftwarees_PE
dc.subjectData modelses_PE
dc.subjectProposalses_PE
dc.titleTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemices_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journal2021 Third International Conference on Transdisciplinary AI (TransAI)es_PE
dc.identifier.doihttps://doi.org/10.1109/TransAI51903.2021.00016
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.publisher.countryPEes_PE
dc.relation.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125747960&doi=10.1109%2fTransAI51903.2021.00016&partnerID=40es_PE
dc.source.beginpage45es_PE
dc.source.endpage46es_PE


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