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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOINCS (Journal of Informatics, Network, and Computer Science)
Murad, Hayder
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Computer Science & IT Engineering

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Artificial Intellegence In The Future Of Iraqi Healthcare System: Kecerdasan Buatan Di Masa Depan untuk Sistem Layanan Kesehatan Irak Murad, Hayder
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 7 No. 1 (2024): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v7i1.1635

Abstract

The expansion of healthcare AI in Iraq highlights the growing necessity for AI education among medical students. The aim of this research is to explore the perspectives of medical students in Iraq regarding artificial intelligence (AI), including their understanding of AI and their career aspirations. Methodologically, a group of Iraqi medical students were invited to participate in an anonymous electronic survey. The results indicate that a total of 318 responses were collected from 22 medical colleges. The majority of respondents (91.5%, s = 291) hold the belief that AI will have a significant impact on healthcare in the future. Specifically, their responses were categorized as strongly agreeing (33.6%, s = 107) or agreeing (57.9%, s = 184). This research reveals that Iraqi medical students recognize the significance of AI and are enthusiastic about engaging with this technology. Moreover, it suggests that there is a need to expand and enhance medical college training on AI to ensure that future healthcare professionals are well-prepared in this domain.
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 Mahdi, Mohammed Ibrahim Safie, Nurhizam Shakir, Yasir Hussein