| Autor: |
Tarek Z; Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt., Shams MY; Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt., Towfek SK; Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA.; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt., Alkahtani HK; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia., Ibrahim A; Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt., Abdelhamid AA; Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt.; Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia., Eid MM; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt.; Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt., Khodadadi N; Department of Civil and Architectural Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33146, USA., Abualigah L; Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan.; College of Engineering, Yuan Ze University, Taoyuan 32003, Taiwan.; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan.; MEU Research Unit, Middle East University, Amman 11831, Jordan.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.; School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia.; School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia., Khafaga DS; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia., Elshewey AM; Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt. |
| Abstrakt: |
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R 2 ). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction. |
