Assessing the spreading behavior of the Covid-19 epidemic: a case study of Turkey

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dc.authoridAlaa Ali Hameed / 0000-0002-8514-9255en_US
dc.authorscopusidAlaa Ali Hameed / 56338374100en_US
dc.contributor.authorDemir, Erdem
dc.contributor.authorCanıtez, Muhammed Nafiz
dc.contributor.authorElazab, Mohamed
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorJamil, Akhtar
dc.contributor.authorAl-Dulaimi, Abdullah Ahmed
dc.date.accessioned2022-11-07T07:24:40Z
dc.date.available2022-11-07T07:24:40Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractCoronavirus (Covid-19) disease is a rapidly spreading type of virus that was discovered in Wuhan, China, and emerged towards the end of 2019. During this period, various studies were conducted, and intensive studies are continued in different fields regarding coronavirus, especially in the field of medicine. The virus continues to spread and is yet to be controlled fully. Machine learning is a well-explored field in the domain of computer science that can learn patterns based on existing data and make predictions on new data. This study focused on using various machine learning approaches for predicting the spreading behavior of the COVID-19 virus. The models that were considered include SARIMAX, Extreme Gradient Boosting (XGBoost), Linear Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Artificial Neural Network (ANN). The models were trained and then predictions were made by applying these models to the daily updated data provided by the Turkish Ministry of Health. Experiments on the test data showed that both XGBoost and Decision Tree models outperformed other models.en_US
dc.identifier.citationDemir, E., Canitez, M. N., Elazab, M., Hameed, A. A., Jamil, A., & Al-Dulaimi, A. A. (2022). Assessing the spreading behavior of the covid-19 epidemic: A case study of turkey. Paper presented at the 2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings, doi:10.1109/ICMI55296.2022.9873697 Retrieved from www.scopus.comen_US
dc.identifier.doi10.1109/ICMI55296.2022.9873697en_US
dc.identifier.scopus2-s2.0-85139003005en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ICMI55296.2022.9873697
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3242
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHameed, Alaa Ali
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutomatic Prediction of COVID-19en_US
dc.subjectCOIVD-19 Predictionen_US
dc.subjectMachine Learningen_US
dc.subjectSARS-CoV2en_US
dc.titleAssessing the spreading behavior of the Covid-19 epidemic: a case study of Turkeyen_US
dc.typeConference Objecten_US

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