Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases
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 public...
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MDPI AG
2022-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/20/3845 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Marwa M. Eid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:45:33Z |
| format | Article |
| id | doaj.art-2e8bcd657c184efcaea1f884d4b9baba |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T09:45:33Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2e8bcd657c184efcaea1f884d4b9baba2023-12-02T00:36:09ZengMDPI AGMathematics2227-73902022-10-011020384510.3390/math10203845Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox CasesMarwa M. Eid0El-Sayed M. El-Kenawy1Nima Khodadadi2Seyedali Mirjalili3Ehsaneh Khodadadi4Mostafa Abotaleb5Amal H. Alharbi6Abdelaziz A. Abdelhamid7Abdelhameed Ibrahim8Ghada M. Amer9Ammar Kadi10Doaa Sami Khafaga11Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, EgyptDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, EgyptDepartment of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USACentre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, AustraliaDepartment of Chemistry and Biochemistry, University of Arkansas—Fayetteville, Fayetteville, AR 72701, USADepartment of System Programming, South Ural State University, Chelyabinsk 454080, RussiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaComputer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, EgyptElectrical Engineering Department, Faculty of Engineering, Benha University, Benha 13518, EgyptDepartment of Food and Biotechnology, South Ural State University, Chelyabinsk 454080, RussiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaRecent 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.https://www.mdpi.com/2227-7390/10/20/3845monkeypoxmeta-heuristic optimizationLSTMdeep learning |
| spellingShingle | 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 Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases Mathematics monkeypox meta-heuristic optimization LSTM deep learning |
| title | Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases |
| title_full | Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases |
| title_fullStr | Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases |
| title_full_unstemmed | Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases |
| title_short | Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases |
| title_sort | meta heuristic optimization of lstm based deep network for boosting the prediction of monkeypox cases |
| topic | monkeypox meta-heuristic optimization LSTM deep learning |
| url | https://www.mdpi.com/2227-7390/10/20/3845 |
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