Factor Analysis Approach To Classify Covid-19 Datasets in Several Regions
| dc.contributor.author | Baleanu, D. | |
| dc.contributor.author | Band, S.S. | |
| dc.contributor.author | Mosavi, A. | |
| dc.contributor.author | Mahmoudi, M.R. | |
| dc.date.accessioned | 2022-04-22T12:51:11Z | |
| dc.date.accessioned | 2025-09-18T12:49:17Z | |
| dc.date.available | 2022-04-22T12:51:11Z | |
| dc.date.available | 2025-09-18T12:49:17Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships. © 2021 The Authors | en_US |
| dc.description.sponsorship | UK Research and Innovation, UKRI, (104071) | en_US |
| dc.identifier.citation | Mahmoudi, Mohammad Reza...et al. (2021). "Factor analysis approach to classify COVID-19 datasets in several regions", Results in Physics, Vol. 25. | en_US |
| dc.identifier.doi | 10.1016/j.rinp.2021.104071 | |
| dc.identifier.issn | 2211-3797 | |
| dc.identifier.issn | 1556-5068 | |
| dc.identifier.scopus | 2-s2.0-85105033176 | |
| dc.identifier.uri | https://doi.org/10.1016/j.rinp.2021.104071 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12416/12321 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier B.V. | en_US |
| dc.relation.ispartof | Results in Physics | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Coronaviruses | en_US |
| dc.subject | Correlation | en_US |
| dc.subject | Covid-19 | en_US |
| dc.subject | Factor Analysis | en_US |
| dc.title | Factor Analysis Approach To Classify Covid-19 Datasets in Several Regions | en_US |
| dc.title | Factor analysis approach to classify COVID-19 datasets in several regions | tr_TR |
| dc.type | Article | en_US |
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| gdc.description.department | Çankaya University | en_US |
| gdc.description.departmenttemp | Mahmoudi M.R., Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran; Baleanu D., Department of Mathematics, Faculty of Art and Sciences, Cankaya University, Balgat, 06530, Ankara, Turkey, Institute of Space Sciences, Magurele-Bucharest, Romania; Band S.S., Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan; Mosavi A., John von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary, School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway | en_US |
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| gdc.description.volume | 25 | en_US |
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| gdc.oaire.keywords | Radiology, Nuclear Medicine and Imaging | |
| gdc.oaire.keywords | Artificial intelligence | |
| gdc.oaire.keywords | Statistical Analysis in Various Fields | |
| gdc.oaire.keywords | Bioinformatics | |
| gdc.oaire.keywords | Coronaviruses | |
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| gdc.oaire.keywords | Pandemic | |
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| gdc.oaire.keywords | Modeling the Dynamics of COVID-19 Pandemic | |
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| gdc.oaire.keywords | Applications of Deep Learning in Medical Imaging | |
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| gdc.oaire.keywords | Covid-19 | |
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| gdc.virtual.author | Baleanu, Dumitru | |
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