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Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases

Author

Listed:
  • 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
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

  • 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
    Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Abdelhameed Ibrahim

    (Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

  • Ghada M. Amer

    (Electrical Engineering Department, Faculty of Engineering, Benha University, Benha 13518, Egypt)

  • Ammar Kadi

    (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)

Abstract

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.

Suggested Citation

  • 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 & Am, 2022. "Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3845-:d:945080
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    References listed on IDEAS

    as
    1. Abdelaziz A. Abdelhamid & El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Doaa Sami Khafaga & Amal H. Alharbi & Abdelhameed Ibrahim & Marwa M. Eid & Mohamed Saber, 2022. "Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-29, October.
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