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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Choukri, Ali
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Computer Science & IT Engineering

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Comparative analysis of machine learning models for fake news detection in social media Eddine Elbaghazaoui, Bahaa; Amnai, Mohamed; Fakhri, Youssef; Choukri, Ali; Gherabi, Noreddine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1951-1959

Abstract

The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting text based fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Infor mation Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a signifi cantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical de tection systems and future research in the field.
Explainable deep learning for scalable record linkage: a TabNet-based framework for structured data integration Zahrae Saber, Fatima; Choukri, Ali; Amnai, Mohamed; Waga, Abderrahim
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.pp725-743

Abstract

Record linkage is considered a fundamental process for ensuring data quality and reliability, with critical applications in domains such as healthcare, finance, and commerce. A machine learning-based approach for optimizing record linkage in structured datasets is presented in this paper. By integrating hybrid blocking methods (combining standard blocking and sorted neighborhood approaches) with advanced similarity measures, computational overhead is significantly reduced while high accuracy is maintained. The performance of TabNet, a deep learning model designed for tabular data, is compared with traditional deep neural networks (DNNs) in the classification phase. Experimental results on a synthetic dataset of 5,000 records demonstrate that comparable precision and recall are achieved by TabNet to DNNs while execution time is reduced by over 79%. The scalability and efficiency of the proposed method are highlighted by these findings, making it well-suited for large-scale data management tasks. Practical and computationally efficient solutions for record linkage in the era of big data are contributed to by this work.
Co-Authors Amnai, Mohamed Eddine Elbaghazaoui, Bahaa Fakhri, Youssef Gherabi, Noreddine Waga, Abderrahim Zahrae Saber, Fatima