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All Journal International Journal of Electrical and Computer Engineering
Abuhamdah, Anmar
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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.
Improving network security using deep learning for intrusion detection Al-Shabi, Mohammed; Abuhamdah, Anmar; Alzaqebah, Malek
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.pp5570-5583

Abstract

As cyber threats and network complexity grow, it is crucial to implement effective intrusion detection systems (IDS) to safeguard sensitive data and infrastructure. Traditional methods often struggle to identify sophisticated attacks, necessitating advanced approaches like machine learning (ML) and deep learning (DL). This study explores the application of ML and DL algorithms in IDS. Feature selection techniques, such as correlation and variance analysis, were employed to identify key factors contributing to accurate classification. Tools like WEKA and MATLAB supported data pre-processing and model development. Using the UNSW-NB15 and NSL-KDD datasets, the study highlights the superior performance of random forest (RF) and multi-layer perceptron (MLP) algorithms. RF ensemble decision trees and MLP multi-layered architecture enable accurate attack detection, demonstrating the potential of these advanced techniques for enhanced network security.
Co-Authors Al-Shabi, Mohammed Almarashdeh, Ibrahim A. Alsmadi, Mutasem K. Alzaqebah, Malek Boulila, Wadii Jaradat, Ghaith M. Quteishat, Anas M.