1
Administration & Finance Department University of Diyala, University of Diyala, Baqubah MJJ2+R9G, Iraq
(noor84abd84@uodiyala.edu.iq)
2
Al-Furat Al-Awsat Technical University, Technical Institute of Najaf, Iraq
(noor.hachame@atu.edu.iq)
3
Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk, Russia
(ammarka89@gmail.com)
4
Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, 35516, Mansoura, Egypt
(afai79@mans.edu.eg)
5
Computer Science Department, Faculty of Computer and Information Science, Ain Shams University, Cairo, 11566, Egypt
(abdelaziz@cis.asu.edu.eg)
Abstract :
The subset of manufactured insights (AI) known as machine learning starts in design acknowledgment, where information can be organized for human comprehension. For a long time, various applications utilizing machine learning have been created in healthcare, fund, military gear, and space investigation; presently, machine learning is a zone that's extending and progressing quickly. It utilizes information to optimize computer execution. AI is vital in combating modern coronaviruses in 2019 (COVID-19) -related matters and is used additionally in computer-assisted blend-making plans. Computer programs' settings are optimized based on preparing information or past encounters. It can moreover make future forecasts utilizing the information. With the assistance of machine learning, we are creating a numerical demonstration based on the data's measurements.
Numerous illustrations outline the viability of machine learning and counterfeit insights in this field. Counterfeit insights strategies can improve the consistency of forecasts and choices by making valuable calculations. AI is useful not for foreseeing people with COVID-19 but for assessing general wellbeing. It can screen the COVID-19 episode at different levels; in our paper, we use three machine learning calculations to analyze and predict. The leading precision was in XGP= 99%, but SVM and RF gave great precision at 98%.
Keywords :
Covid 19; SVM; RF; XGB; Machine learning; Internet of things; Prediction
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| Style | # |
|---|---|
| MLA | Noor abdulmuttaleb jaafar, Noor Razzaq Abbas , Ammar Kadi , Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid. "Develop application for prediction COVID-19 using artificial intelligence." Journal of Artificial Intelligence and Metaheuristics, Vol. 6, No. 1, 2023 ,PP. 27-34 (Doi : https://doi.org/10.54216/JAIM.060103) |
| APA | Noor abdulmuttaleb jaafar, Noor Razzaq Abbas , Ammar Kadi , Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid. (2023). Develop application for prediction COVID-19 using artificial intelligence. Journal of Journal of Artificial Intelligence and Metaheuristics, 6 ( 1 ), 27-34 (Doi : https://doi.org/10.54216/JAIM.060103) |
| Chicago | Noor abdulmuttaleb jaafar, Noor Razzaq Abbas , Ammar Kadi , Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid. "Develop application for prediction COVID-19 using artificial intelligence." Journal of Journal of Artificial Intelligence and Metaheuristics, 6 no. 1 (2023): 27-34 (Doi : https://doi.org/10.54216/JAIM.060103) |
| Harvard | Noor abdulmuttaleb jaafar, Noor Razzaq Abbas , Ammar Kadi , Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid. (2023). Develop application for prediction COVID-19 using artificial intelligence. Journal of Journal of Artificial Intelligence and Metaheuristics, 6 ( 1 ), 27-34 (Doi : https://doi.org/10.54216/JAIM.060103) |
| Vancouver | Noor abdulmuttaleb jaafar, Noor Razzaq Abbas , Ammar Kadi , Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid. Develop application for prediction COVID-19 using artificial intelligence. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 6 ( 1 ): 27-34 (Doi : https://doi.org/10.54216/JAIM.060103) |
| IEEE | Noor abdulmuttaleb jaafar, Noor Razzaq Abbas, Ammar Kadi, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Develop application for prediction COVID-19 using artificial intelligence, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 6 , No. 1 , (2023) : 27-34 (Doi : https://doi.org/10.54216/JAIM.060103) |