Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mahdi, Mohammed Ibrahim
Unknown Affiliation
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Enhancing credit card fraud detection with synthetic minority over-sampling technique-integrated extreme learning machine Ajlan, Iman Kadhim; Mahdi, Mohammed Ibrahim; Murad, Hayder; AL-Dhief, Fahad Taha; Safie, Nurhizam; Shakir, Yasir Hussein; Abbas, Ali Hashim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4749-4762

Abstract

Many works in cybersecurity detection suffer from low accuracy rates, particularly in real-world applications, where imbalanced datasets and evolving fraud strategies pose significant hurdles. This study introduces an optimized extreme learning machine (ELM) algorithm to address these challenges by dynamically adjusting hidden nodes ranging from 10 to 100 with an increment step of 10 and integrating two activation functions. The proposed method utilizes the synthetic minority over-sampling technique (SMOTE) to handle class imbalance effectively and incorporates a comprehensive evaluation using descriptive statistics, visualization, and significance testing. The proposed ELM-SMOTE method achieves the highest results including an accuracy of 99.710%, recall of 85.811%, specificity of 99.743%, and G-mean of 92.068%. These outcomes reflect the robustness and adaptability of the proposed ELM algorithm in detecting fraudulent transactions. This study emphasizes the importance of a holistic performance analysis, addressing gaps in existing methods and providing a scalable framework for real-world fraud detection applications.
Co-Authors Abbas, Ali Hashim Ajlan, Iman Kadhim Al-Dhief, Fahad Taha Murad, Hayder Safie, Nurhizam Shakir, Yasir Hussein