Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

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dc.creatorKavadi, Durga Prasad
dc.creatorPatan, Rizwan
dc.creatorRamachandran, Manikandan
dc.creatorGandomi, Amir H.
dc.date.accessioned2020-07-29T22:14:16Z
dc.date.available2020-07-29T22:14:16Z
dc.date.created2020-06-25
dc.description.abstractenglishThe recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.spa
dc.format.extent7 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1016/j.chaos.2020.110056spa
dc.identifier.issn0960-0779spa
dc.identifier.otherhttps://www.sciencedirect.com/science/article/pii/S0960077920304537?via%3Dihubspa
dc.identifier.urihttps://hdl.handle.net/20.500.12010/11384
dc.publisherChaos, Solitons & Fractalseng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectMachine Learningspa
dc.subjectProgressivespa
dc.subjectPartial Derivativespa
dc.subjectLinear Regressionspa
dc.subjectNonlinearspa
dc.subjectGlobal Pandemicspa
dc.subjectKuhn-tuckerspa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.titlePartial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19spa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.localArtículospa

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