Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases
Marwa M. Eid,
El-Sayed M. El-Kenawy,
Nima Khodadadi,
Seyedali Mirjalili,
Ehsaneh Khodadadi,
Mostafa Abotaleb,
Amal H. Alharbi,
Abdelaziz A. Abdelhamid,
Abdelhameed Ibrahim,
Ghada M. Amer,
Ammar Kadi,
Doaa Sami Khafaga
Affiliations
Marwa M. Eid
Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
El-Sayed M. El-Kenawy
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Nima Khodadadi
Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USA
Seyedali Mirjalili
Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia
Ehsaneh Khodadadi
Department of Chemistry and Biochemistry, University of Arkansas—Fayetteville, Fayetteville, AR 72701, USA
Mostafa Abotaleb
Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia
Amal H. Alharbi
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdelaziz A. Abdelhamid
Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
Abdelhameed Ibrahim
Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Department of Food and Biotechnology, South Ural State University, Chelyabinsk 454080, Russia
Doaa Sami Khafaga
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach.
