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Alfagham, Hayat
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Computer Science & IT Electrical & Electronics Engineering

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Enhancing routing efficiency in highway environments of vehicular ad hoc networks through fuzzy logic-based protocols Al Shugran, Mahmoud A.; Abu-Al-Aish, Ahmad; Jaradat, Ghaith M.; Alghamdi, Fahad Ali; Alqurni, Jehad Saad; Alsmadi, Mutasem Khalil; AL Hawamdeh, Majd; Alfagham, Hayat; Badawi, Usama A.; Gharaibeh, Mutaz Falah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp493-504

Abstract

The predictive directional greedy routing (PDGR) protocol is widely utilized in highway settings within vehicular ad hoc networks (VANETs). However, PDGR encounters a notable challenge when packets lack a suitable vehicle directionally, leading to network disconnections. This triggers a shift to carry and forward recovery mode due to outdated neighbor information in the vehicle's neighbor table (VNT). To address this, our study proposes an improved fuzzy logic-based improved PDGR (IPDGR). This novel algorithm dynamically adjusts beaconing intervals based on real-time network dynamics. Through comprehensive evaluation using VANET simulators, IPDGR demonstrates superior performance compared to PDGR and directional greedy routing (DGR) protocols across various metrics including Inconsistency of vehicle's neighbor's table (IVNT), packet delivery ratio (PDR), routing path length (RPL), and number of hole problem occurrence (NHPO).
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 Abu-Al-Aish, Ahmad AL Hawamdeh, Majd Al Shugran, Mahmoud A. Alghamdi, Fahad Ali Almusfar, Latifa Abdullah Alqurni, Jehad Alqurni, Jehad Saad Alsallak, Tariq Alsmadi, Mutasem K. Alsmadi, Mutasem Khalil Alzaqebah, Malek Badawi, Usama A. Gharaibeh, Mutaz Falah Jaradat, Ghaith M. Jawarneh, Sana