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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Mahran M. Al-Zyoud
Al-Ahliyya Amman University
Computer Science & IT Electrical & Electronics Engineering

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A doctor recommender system based on collaborative and content filtering Qusai Y. Shambour; Mahran M. Al-Zyoud; Abdelrahman H. Hussein; Qasem M. Kharma
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp884-893

Abstract

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity.
A firewall model for attack detection using machine learning and metaheuristic feature selection algorithms Mosleh M. Abualhaj; Sumaya Nabil Al-Khatib; Nida Al-Shafi; Mohammad O. Hiari; Mohammad Sh. Daoud; Mohammed Anbar; Mahran M. Al-Zyoud
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9887

Abstract

This research presents a firewall model designed to enhance network attack detection by integrating machine learning (ML) and advanced feature selection techniques. The study introduces a union-based (DAUBA) feature selection method that combines the exploratory capability of the Dragonfly Algorithm (DA) with the exploitation efficiency of the Bat Algorithm (BA). By combining these two bio-inspired optimizers, the method generates complementary feature subsets that enhance both accuracy and efficiency. The proposed DA?BA feature selection method is incorporated into a ML–based firewall and evaluated on the UNSW-NB15 dataset using three classifiers: adaptive boosting (AdaBoost), K-nearest neighbor (KNN), and Naïve Bayes (NB). Experimental results demonstrate that the approach achieves near-perfect accuracy (100% with AdaBoost), along with strong precision, recall, and F1-scores, while maintaining computational costs compatible with real-time deployment. These findings highlight the novelty and practical value of combining DA and BA in feature selection for next-generation firewall systems.
Co-Authors Abdelrahman H. Hussein Mohammad O. Hiari Mohammad Sh. Daoud Mohammed Anbar Mosleh M. Abualhaj Nida Al-Shafi Qasem M. Kharma Qusai Y. Shambour Sumaya Nabil Al-Khatib