Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/14845
Full metadata record
DC FieldValueLanguage
dc.rights.licenseopenAccess-
dc.contributor.authorJovanović, Nemanja-
dc.date.accessioned2022-09-13T08:49:31Z-
dc.date.available2022-09-13T08:49:31Z-
dc.date.issued2022-
dc.identifier.isbn9788677762629en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/14845-
dc.description.abstractWith the appearance of the first registered case of corona, as one of the world's most widespread and most dangerous viral infections, the need to monitor and predict the epidemiological situation is growing, both in the world and in our country. In this paper, the epidemiological data of the Republic of Serbia regarding the Corona virus in the period from 2020 to June 2021 are analyzed. Data were analyzed by regression methods, as one of the data mining techniques. Depending on the choice of regression method (simple, multiple and linear), a number of parameters were selected that include the number of persons (positive, tested, deceased, hospitalized and respirator) in relation to the time of the pandemic to make the most accurate prediction. As a result of the research using regression methods, it was found that the trend of development of the Corona virus epidemic is decreasing, i.e. (id est.) that preventive measures as well as the process of vaccination and revaccination have had an effect in the fight against Corona virus.en_US
dc.language.isoenen_US
dc.publisherUniversity of Kragujevac, Faculty od Technical Sciences, Čačaken_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.source“Technics and Informatics in Education – TIE 2022”en_US
dc.sourceProceedings TIE 2022 9th International Scientific Conference Technics and Informatics in Educationen_US
dc.subjectregressionen_US
dc.subjectcoronaen_US
dc.subjectdata miningen_US
dc.subjectanalysisen_US
dc.subjectthe dataen_US
dc.titleData analysis for COVID-19 using regression methodsen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.identifier.doi10.46793/TIE22.257Jen_US
dc.type.versionPublishedVersionen_US
Appears in Collections:Faculty of Technical Sciences, Čačak

Page views(s)

401

Downloads(s)

20

Files in This Item:
File Description SizeFormat 
S406_16.pdf863.96 kBAdobe PDFThumbnail
View/Open


This item is licensed under a Creative Commons License Creative Commons