Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Buletin Ilmiah Sarjana Teknik Elektro Sistem Pendukung Keputusan dengan Aplikasi
Al-Karaki, Jamal N.
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
The role of disruptive technologies in the metaverse worlds: state of the art survey Al-Karaki, Jamal N.; Gawanmeh, Amjad; Awad, Ahmed; Zerai Teklesenbet, Natnael
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2211-2223

Abstract

The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a state-of-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies’ potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.
Small Object Detection in Medical Imaging Using Enhanced CNN Architectures for Early Disease Screening Zangana, Hewa Majeed; Omar, Marwan; Li, Shuai; Al-Karaki, Jamal N.; Vitianingsih, Anik Vega
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14015

Abstract

Early detection of subtle pathological features in medical images is critical for improving patient outcomes but remains challenging due to low contrast, small lesion size, and limited annotated data. The research contribution is a hybrid attention-enhanced CNN specifically tailored for small object detection across mammography, CT, and retinal fundus images. Our method integrates a ResNet-50 backbone with a modified Feature Pyramid Network, dilated convolutions for contextual scale expansion, and combined channel–spatial attention modules to preserve and amplify fine-grained features. We evaluate the model on public benchmarks (DDSM, LUNA16, IDRiD) using standardized preprocessing, extensive augmentation, and cross-validated training. Results show consistent gains in detection and localization: ECNN achieves an F1-score of 88.2% (95% CI: 87.4–89.0), mAP@0.5 of 86.8%, IoU of 78.6%, and a low false positives per image (FPPI = 0.12) versus baseline detectors. Ablation studies confirm the individual contributions of dilated convolutions, attention modules, and multi-scale fusion.However, these gains involve higher computational costs (≈2× training time and increased memory footprint), and limited dataset diversity suggests caution regarding generalizability. In conclusion, the proposed ECNN advances small-object sensitivity for early disease screening while highlighting the need for broader clinical validation and interpretability tools before deployment.
Hybrid Decision Support Framework with Explainable AI and Multi-Criteria Optimization Zangana, Hewa Majeed; Hassan, Noor Salah; Omar, Marwan; Al-Karaki, Jamal N.
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1328

Abstract

Decision-making in domains such as healthcare, finance, and smart systems demands frameworks that combine model-driven expertise with data-driven adaptability. This paper proposes a hybrid decision support framework that integrates Explainable AI (XAI) with multi-criteria optimization to enhance transparency, robustness, and adaptability. Unlike traditional systems, our approach unifies mechanistic models with machine learning and embeds interpretability and optimization mechanisms. Comparative evaluation against state-of-the-art methods shows consistent performance gains, achieving 15–25% lower error rates compared with data-driven baselines and generating more diverse Pareto-optimal solutions. These improvements highlight the framework’s potential as a reliable, explainable, and scalable solution for complex, real-world decision-making
Co-Authors Anik Vega Vitianingsih Awad, Ahmed Gawanmeh, Amjad Hassan, Noor Salah Li, Shuai Omar, Marwan Zangana, Hewa Majeed Zerai Teklesenbet, Natnael