| Autor: |
Demir, Erdem, Canitez, Muhammed Nafiz, Elazab, Mohamed, Hameed, Alaa Ali, Jamil, Akhtar, Al-Dulaimi, Abdullah Ahmed |
| Přispěvatelé: |
İstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü, Hameed, Alaa Ali |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 2nd International Conference on Computing and Machine Intelligence (ICMI). |
| DOI: |
10.1109/icmi55296.2022.9873697 |
| Popis: |
Coronavirus (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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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