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All Journal International Journal of Electrical and Computer Engineering
Atiewi, Saleh
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Computer Science & IT Electrical & Electronics Engineering

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Enhancing online exam security: encryption and authentication in Jordanian and international universities Al-Ghonmein, Ali M.; Alemami, Yahia; Al-Moghrabi, Khaldun G.; Atiewi, Saleh
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.pp719-727

Abstract

In today's educational landscape, the online examination system has become crucial, particularly due to the challenges posed by the coronavirus disease 2019 (COVID-19) pandemic. Despite its advantages in expediting result dissemination and reducing resource consumption, online examinations face significant security threats like leakage, cheating, fraud, and hacking, which hinder their widespread adoption. This paper addresses these security concerns by proposing integrating advanced security algorithms and biometric devices. It presents a comprehensive literature review on existing online examination systems, focusing on their security mechanisms, and compares these findings with a proposed framework. Additionally, a questionnaire was administered across Jordanian governmental and private universities to explore strategies for safeguarding computerized tests through encryption and authentication methods. The results reveal that Jordanian institutions lack adequate security safeguards and procedural standards. Key recommendations include encrypting the question bank stored in databases and employing biometric identification techniques to enhance the security and effectiveness of student verification. The proposed framework aims to improve the overall security, speed, and secrecy of the online examination process, addressing the critical gaps identified in current systems. This research contributes to developing more secure and reliable online examination systems in higher education.
Gene set imputation method-based rule for recovering missing data using deep learning approach Al-Rahayfeh, Amer; Atiewi, Saleh; Almiani, Muder; Mughaid, Ala; Razaque, Abdul; Abu-Salih, Bilal; Alweshah, Mohammed; Alrawajfeh, Alaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4296-4317

Abstract

Data imputation enhances dataset completeness, enabling accurate analysis and informed decision-making across various domains. In this research, we propose a novel imputation method, a spectral clustering based on a gene set using adaptive weighted k-nearest neighbor (AWKNN), and an imputation of missing data using a convolutional neural network algorithm for accurate imputed data. In this research, we have considered the Kaggle water quality dataset for the imputation of missing values in water quality monitoring. Data cleaning detects inaccurate data from the dataset by using the median modified Weiner filter (MMWFILT). The normalization technique is based on the Z-score normalization (Z-SN) approach, which improves data organization and management for accurate imputation. Data reduction minimizes unwanted data and the amount of capacity required to store data using an improved kernel correlation filter (IKCF). The characteristics and patterns of data with specific columns are analyzed using enhanced principal component analysis (EPCA) to reduce overfitting. The dataset is classified into complete data and missing data using the light- DenseNet (LIGHT DN) approach. Results show the proposed outperforms traditional techniques in recovering missing data while preserving data distribution. Evaluation based on pH concentration, chloramine concentration, sulfate concentration, water level, and accuracy.
A systematic review of heuristic and meta-heuristic methods for dynamic task scheduling in fog computing environments Talhouni, Hamed; Ali, Noraida Haji; Yunus, Farizah; Atiewi, Saleh; Yahya, Yazrina
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.pp5986-6000

Abstract

The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.
Co-Authors Abu-Salih, Bilal Al-Ghonmein, Ali M. Al-Moghrabi, Khaldun G. Al-Rahayfeh, Amer Alemami, Yahia Ali, Noraida Haji Almiani, Muder Alrawajfeh, Alaa Alweshah, Mohammed Mughaid, Ala Razaque, Abdul Talhouni, Hamed Yazrina Yahya Yunus, Farizah