Ahmed Hassan Mohammed Hassan

Work place: Faculty of Engineering, Mashreq University, Khartoum North, Sudan

E-mail: dr.ahmed@mashreq.edu.sd


Research Interests: Machine Learning, Wireless Networks


Ahmed Hassan M. Hassan: Associate Professor for Communication Engineering at Mashreq University, Sudan. His research interesting in Wireless Networks, and Machine Learning.

Author Articles
Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

By Ahmed Hassan Mohammed Hassan Arfan Ali Mohammed Qasem Walaa Faisal Mohammed Abdalla Omer H. Elhassan

DOI: https://doi.org/10.5815/ijitcs.2021.03.02, Pub. Date: 8 Jun. 2021

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID-19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current scenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

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