Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/76419
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dc.contributor.authorGupta, Amit Kumaren_US
dc.contributor.authorSingh, Vijanderen_US
dc.contributor.authorMathur, Priyaen_US
dc.contributor.authorTravieso-González, Carlos M.en_US
dc.date.accessioned2020-12-09T09:05:15Z-
dc.date.available2020-12-09T09:05:15Z-
dc.date.issued2021en_US
dc.identifier.issn0972-0502en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/76419-
dc.description.abstractThe whole world is embroiling the pandemic situation caused by COVID-19, which is spreading across all countries. As of mid-May, COVID-19 continues to increase the number of people affected and the number of deaths in each country. Each country’s administrations concerned are making endless efforts to maintain public health, mental health and to regulate the rate of illness of COVID-19. Analysis of COVID-19 data using the machine learning paradigm is becoming a major interest of the researcher in these situations. Several researchers analyzed data from COVID-19 to predict infection, death, cured persons in the future, which may lead to the planning of each country’s regulatory authority to maintain the public health of its people. The machine learning algorithm provides more accurate results when the data size is large due to the lower number of data sets available to COVID-19, making the most accurate predictions a challenging task to implement the machine learning algorithm. This paper was essentially designed to predict the active rate, the death rate, and the cured rate in India by analyzing the data of COVID-19. There are three models of machine learning Support Vector Machine (SVM), Prophet Forecasting Model, and Linear Regression Model for predicting active rate, death rate and cured rate. Prophet Forecasting Model has been shown to be the best predictive method for predicting active rate, death rate and cured rate compared to SVM and Linear Regression when the vast uncertain and small data sets.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Interdisciplinary Mathematicsen_US
dc.sourceJournal of Interdisciplinary Mathematics [ISSN 0972-0502], v. 24(1), p. 89-108en_US
dc.subject240401 Bioestadísticaen_US
dc.subject.other68T01en_US
dc.subject.otherCovid-19en_US
dc.subject.otherForecastingen_US
dc.subject.otherLinear Regression Modelen_US
dc.subject.otherMachine Learningen_US
dc.subject.otherProphet Forecasting Modelen_US
dc.subject.otherSupport Vector Machineen_US
dc.subject.otherTime Series Analysisen_US
dc.titlePrediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenarioen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/09720502.2020.1833458en_US
dc.identifier.scopus85096571816-
dc.contributor.authorscopusid57130244900-
dc.contributor.authorscopusid57191532025-
dc.contributor.authorscopusid57220025648-
dc.contributor.authorscopusid57219115631-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,221
dc.description.sjrqQ4
dc.description.esciESCI
dc.description.miaricds9,9
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
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