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All Journal International Journal of Electrical and Computer Engineering
Alsmadi, Mutasem K.
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Computer Science & IT Electrical & Electronics Engineering

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A novel population-based local search for nurse rostering problem Abuhamdah, Anmar; Boulila, Wadii; Jaradat, Ghaith M.; Quteishat, Anas M.; Alsmadi, Mutasem K.; Almarashdeh, Ibrahim A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (14.644 KB) | DOI: 10.11591/ijece.v11i1.pp471-480

Abstract

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.
A hybrid DMO-CNN-LSTM framework for feature selection and diabetes prediction: a deep learning perspective Alsmadi, Mutasem K.; Jaradat, Ghaith M.; Alsallak, Tariq; Alzaqebah, Malek; Jawarneh, Sana; Alfagham, Hayat; Alqurni, Jehad; Badawi, Usama A.; Almusfar, Latifa Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5555-5569

Abstract

The early and accurate prediction of diabetes mellitus remains a significant challenge in clinical decision-making due to the high dimensionality, noise, and heterogeneity of medical data. This study proposes a novel hybrid classification framework that integrates the dwarf mongoose optimization (DMO) algorithm for feature selection with a convolutional neural network–long short-term memory (CNN-LSTM) deep learning architecture for predictive modeling. The DMO algorithm is employed to intelligently select the most informative subset of features from a large-scale diabetes dataset collected from 130 U.S. hospitals over a 10-year period. These optimized features are then processed by the CNN-LSTM model, which combines spatial pattern recognition and temporal sequence learning to enhance predictive accuracy. Extensive experiments were conducted and compared against traditional machine learning models (logistic regression, random forest, XGBoost), baseline deep learning models (MLP, standalone CNN, standalone LSTM), and state-of-the-art hybrid classifiers. The proposed DMO-CNN-LSTM model achieved the highest classification performance with an accuracy of 96.1%, F1-score of 94.6%, and ROC-AUC of 0.96, significantly outperforming other models. Additional analyses, including confusion matrix, ROC curves, training convergence plots, and statistical evaluations confirm the robustness and generalizability of the approach. These findings suggest that the DMO-CNN-LSTM framework offers a powerful and interpretable tool for intelligent diabetes prediction, with strong potential for integration into real-world clinical decision-support systems.
Co-Authors Abuhamdah, Anmar Alfagham, Hayat Almarashdeh, Ibrahim A. Almusfar, Latifa Abdullah Alqurni, Jehad Alsallak, Tariq Alzaqebah, Malek Badawi, Usama A. Boulila, Wadii Jaradat, Ghaith M. Jawarneh, Sana Quteishat, Anas M.