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All Journal Data Science: Journal of Computing and Applied Informatics
Abdolrazzagh-Nezhad, Majid
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Computer Science & IT

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A Review on Metaheuristic Approaches for Job-Shop Scheduling Problems Abdolrazzagh-Nezhad, Majid; Abdullah, Salwani
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v8.i1-17138

Abstract

Over the past several decades, interest in metaheuristic approaches to address job-shop scheduling problems (JSSPs) has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides a significant attention on reviewing state-of-the-art metaheuristic approaches that have been developed to solve JSSPs. These approaches are analysed with respect to three steps: (i) preprocessing, (ii) initialization procedures and (iii) improvement algorithms. Through this review, the paper highlights the gaps in the literature and potential avenues for further research.
Phishing Detection Techniques: A review Abdolrazzagh-Nezhad, Majid; Langarib, Nafise
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 1 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i1-19904

Abstract

Phishing remains one of the most pervasive and sophisticated threats to cybersecurity, exploiting human and system vulnerabilities to compromise sensitive information. This study systematically reviews and categorizes phishing detection techniques into four groups: anti-phishing tools, heuristic approaches, machine learning-based techniques, and metaheuristic algorithms. Each method is critically analyzed for its effectiveness, highlighting their strengths and limitations. The review identifies significant advancements in phishing detection, such as the adoption of hybrid techniques and real-time detection algorithms, while also addressing gaps, including handling zero-day phishing attacks and scalability in large datasets. The findings provide a roadmap for future research, encouraging the development of more robust, adaptive, and efficient solutions. This comprehensive analysis not only synthesizes the state-of-the-art in phishing detection but also lays the groundwork for designing next-generation defense mechanisms.
Co-Authors Abdullah, Salwani Langarib, Nafise