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dc.contributor.authorGupta, Yogesh
dc.contributor.authorRaghuwanshi, Ghanshyam
dc.contributor.authorAhmadini, Abdullah Ali H.
dc.contributor.authorGöktaş, Pınar
dc.date.accessioned2022-01-14T08:04:10Z
dc.date.available2022-01-14T08:04:10Z
dc.date.issued2021en_US
dc.identifier.citationGupta, Y., Raghuwanshi, G., Ahmadini, A. A. H., Sharma, U., Mishra, A. K., Mashwani, W. K., . . . Balogun, O. S. (2021). Impact of weather predictions on COVID-19 infection rate by using deep learning models. Complexity, 2021 doi:10.1155/2021/5520663en_US
dc.identifier.issn10762787
dc.identifier.urihttps://doi.org/10.1155/2021/5520663
dc.identifier.urihttps://hdl.handle.net/20.500.12809/9763
dc.description.abstractNowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.en_US
dc.item-language.isoengen_US
dc.publisherHindawi Limiteden_US
dc.relation.isversionof10.1155/2021/5520663en_US
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoronavirus diseasesen_US
dc.titleImpact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Modelsen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Rektörlük, Strateji Geliştirme Daire Başkanlığıen_US
dc.contributor.authorID0000-0001-5552-1813en_US
dc.contributor.institutionauthorGöktaş, Pınar
dc.relation.journalComplexityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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