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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Anas Abu Taleb
Princess Sumaya University for Technology
Computer Science & IT Engineering

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Utilizing linear regression and random forest models for money laundering identification Ammar Odeh; Anas Abu Taleb
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26163

Abstract

This paper investigates the effectiveness of traditional machine learning techniques, namely linear regression and random forest, in enhancing the detection of money laundering (ML) activities within financial systems. As ML schemes evolve in complexity, traditional rule-based methods struggle with high false favorable rates and a lack of adaptability, prompting the need for more sophisticated analytical approaches. In contrast to the complexities of deep learning models, this study explores the potential of these more accessible machine learning methods in identifying and analyzing suspicious transactional patterns. We apply linear regression and random forest (RF) models to transactional data to detect anomalous activities that could indicate ML. Our research thoroughly compares these models based on key performance metrics such as accuracy, precision, and recall. The findings suggest that while less complex than deep learning frameworks, linear regression, and RF models offer substantial benefits. They provide a more streamlined, interpretable, and efficient alternative to conventional rule-based systems in the context of ML detection. This study contributes to the ongoing discourse on the application of machine learning in financial crime detection, demonstrating the practicality and effectiveness of these methods in a critical area of financial security.
A comprehensive survey of cyberbullying on social media: challenges, detection, and AI-based prevention Ammar Odeh; Osama Alhaj Hassan; Anas Abu Taleb; Abobakr Aboshgifa; Nabil Belhaj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp86-96

Abstract

Cyberbullying is a pervasive issue in the digital landscape, particularly on social media platforms, where individuals engage in online harassment, intimidation, and abuse. Unlike traditional bullying, cyberbullying has a broader reach, anonymity, and persistence, making it a growing concern for mental health, social well-being, and online safety. This paper provides a comprehensive survey of cyberbullying trends, its psychological and social impacts, and the role of social media in amplifying the problem. It explores existing detection and prevention strategies, including artificial intelligence (AI)-driven approaches, policy frameworks, and platform-based moderation techniques. Furthermore, it discusses challenges in enforcement, the limitations of automated detection systems, and the need for improved legal measures. This paper uniquely contributes an integrated perspective on cyberbullying detection and prevention by synthesizing current research across psychological, sociocultural, and technical dimensions. It emphasizes underexplored gaps such as multilingual detection, real-time moderation, and cross-platform enforcement, and proposes a layered framework to guide future research and policy.
Co-Authors Abobakr Aboshgifa Ammar Odeh Ammar Odeh Nabil Belhaj Osama Alhaj Hassan