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

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dc.contributor.author Shafiq, Anum
dc.contributor.author Batur Çolak, Andaç
dc.contributor.author Naz Sindhu, Tabassum
dc.contributor.author Ahmad Lone, Showkat
dc.contributor.author Alsubie, Abdelaziz
dc.contributor.author Jarad, Fahd
dc.date.accessioned 2024-02-29T12:03:55Z
dc.date.available 2024-02-29T12:03:55Z
dc.date.issued 2022-07
dc.identifier.citation Shafiq, Anum;...et.al. (2022). "Comparative study of artificial neural network versus parametric method in COVID-19 data analysis", Results in Physics, Vol.38. tr_TR
dc.identifier.issn 22113797
dc.identifier.uri http://hdl.handle.net/20.500.12416/7390
dc.description.abstract Since 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. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.rinp.2022.105613 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Artificial Neural Network tr_TR
dc.subject Failure Rate Function tr_TR
dc.subject Maximum Likelihood Estimation tr_TR
dc.subject Reliability Function tr_TR
dc.title Comparative study of artificial neural network versus parametric method in COVID-19 data analysis tr_TR
dc.type article tr_TR
dc.relation.journal Results in Physics tr_TR
dc.contributor.authorID 234808 tr_TR
dc.identifier.volume 38 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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