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

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Based 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 instant...

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Detalles Bibliográficos
Autor: Nieto-Chaupis, Huber
Formato: artículo
Fecha de Publicación:2021
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1817
Enlace del recurso:https://hdl.handle.net/20.500.13067/1817
https://doi.org/10.1109/TransAI51903.2021.00016
Nivel de acceso:acceso restringido
Materia:COVID-19
Correlation
Pandemics
Entropy
Software
Data models
Proposals
https://purl.org/pe-repo/ocde/ford#2.02.04
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spelling Nieto-Chaupis, Huber2022-04-29T20:50:29Z2022-04-29T20:50:29Z2021-10-18Nieto-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.978-1-6654-3412-6https://hdl.handle.net/20.500.13067/18172021 Third International Conference on Transdisciplinary AI (TransAI)https://doi.org/10.1109/TransAI51903.2021.00016Based 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.application/pdfengUniversidad Autónoma del PerúPEinfo:eu-repo/semantics/restrictedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/AUTONOMA4546reponame:AUTONOMA-Institucionalinstname:Universidad Autónoma del Perúinstacron:AUTONOMACOVID-19CorrelationPandemicsEntropySoftwareData modelsProposalshttps://purl.org/pe-repo/ocde/ford#2.02.04Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemicinfo:eu-repo/semantics/articlehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125747960&doi=10.1109%2fTransAI51903.2021.00016&partnerID=40LICENSElicense.txtlicense.txttext/plain; charset=utf-885http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1817/2/license.txt9243398ff393db1861c890baeaeee5f9MD52TEXTTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic.pdf.txtTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic.pdf.txtExtracted texttext/plain576http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1817/4/Theoretical%20Artificial%20Intelligence%20Based%20on%20Shannon%20Entropy%20to%20Identify%20Strains%20in%20Covid-19%20Pandemic.pdf.txt58a852ea2af14e480e4a6762f78134c6MD54THUMBNAILTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic.pdf.jpgTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic.pdf.jpgGenerated Thumbnailimage/jpeg5735http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1817/5/Theoretical%20Artificial%20Intelligence%20Based%20on%20Shannon%20Entropy%20to%20Identify%20Strains%20in%20Covid-19%20Pandemic.pdf.jpg175318c803481a71a6f7814afab273a7MD55ORIGINALTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic.pdfTheoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic.pdfVer fuenteapplication/pdf98381http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1817/3/Theoretical%20Artificial%20Intelligence%20Based%20on%20Shannon%20Entropy%20to%20Identify%20Strains%20in%20Covid-19%20Pandemic.pdfcdb444a5065df777998be887b52a6503MD5320.500.13067/1817oai:repositorio.autonoma.edu.pe:20.500.13067/18172022-04-30 03:00:21.451Repositorio de la Universidad Autonoma del Perúrepositorio@autonoma.pe
dc.title.es_PE.fl_str_mv Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
title Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
spellingShingle Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
Nieto-Chaupis, Huber
COVID-19
Correlation
Pandemics
Entropy
Software
Data models
Proposals
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
title_full Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
title_fullStr Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
title_full_unstemmed Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
title_sort Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
author Nieto-Chaupis, Huber
author_facet Nieto-Chaupis, Huber
author_role author
dc.contributor.author.fl_str_mv Nieto-Chaupis, Huber
dc.subject.es_PE.fl_str_mv COVID-19
Correlation
Pandemics
Entropy
Software
Data models
Proposals
topic COVID-19
Correlation
Pandemics
Entropy
Software
Data models
Proposals
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description Based 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.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2022-04-29T20:50:29Z
dc.date.available.none.fl_str_mv 2022-04-29T20:50:29Z
dc.date.issued.fl_str_mv 2021-10-18
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.es_PE.fl_str_mv Nieto-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.
dc.identifier.isbn.none.fl_str_mv 978-1-6654-3412-6
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13067/1817
dc.identifier.journal.es_PE.fl_str_mv 2021 Third International Conference on Transdisciplinary AI (TransAI)
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/TransAI51903.2021.00016
identifier_str_mv Nieto-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.
978-1-6654-3412-6
2021 Third International Conference on Transdisciplinary AI (TransAI)
url https://hdl.handle.net/20.500.13067/1817
https://doi.org/10.1109/TransAI51903.2021.00016
dc.language.iso.es_PE.fl_str_mv eng
language eng
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eu_rights_str_mv restrictedAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Universidad Autónoma del Perú
dc.publisher.country.es_PE.fl_str_mv PE
dc.source.es_PE.fl_str_mv AUTONOMA
dc.source.none.fl_str_mv reponame:AUTONOMA-Institucional
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instacron_str AUTONOMA
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dc.source.endpage.es_PE.fl_str_mv 46
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Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
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