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Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

dc.contributor.authorShafiq, Anum
dc.contributor.authorBatur Çolak, Andaç
dc.contributor.authorNaz Sindhu, Tabassum
dc.contributor.authorAhmad Lone, Showkat
dc.contributor.authorAlsubie, Abdelaziz
dc.contributor.authorJarad, Fahd
dc.contributor.authorID234808tr_TR
dc.date.accessioned2024-02-29T12:03:55Z
dc.date.available2024-02-29T12:03:55Z
dc.date.issued2022
dc.departmentÇankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractSince the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was −0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.en_US
dc.description.publishedMonth7
dc.identifier.citationShafiq, Anum;...et.al. (2022). "Comparative study of artificial neural network versus parametric method in COVID-19 data analysis", Results in Physics, Vol.38.en_US
dc.identifier.doi10.1016/j.rinp.2022.105613
dc.identifier.issn22113797
dc.identifier.urihttp://hdl.handle.net/20.500.12416/7390
dc.identifier.volume38en_US
dc.language.isoenen_US
dc.relation.ispartofResults in Physicsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectFailure Rate Functionen_US
dc.subjectMaximum Likelihood Estimationen_US
dc.subjectReliability Functionen_US
dc.titleComparative study of artificial neural network versus parametric method in COVID-19 data analysistr_TR
dc.titleComparative Study of Artificial Neural Network Versus Parametric Method in Covid-19 Data Analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication

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