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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Shakir, Yasir Hussein
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Leveraging Machine Learning for Early Risk Prediction in Cirrhosis Outcome Patients Shakir, Yasir Hussein; Mandhari, Eshaq Aziz Awadh AL; Alkhazraji, Ali
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.2015

Abstract

Millions of individuals worldwide suffer from liver cirrhosis, which is one of the primary causes of mortality. Healthcare professionals may have more opportunities to treat cirrhosis patients effectively if early death prediction is made and it is postulated that death in this cohort would be correlated with laboratory test findings and other relevant diagnoses. In this study five machine learning models, including LR, SVM, XGBoost, AdaBoost and KNN, are implemented and evaluated. The preprocessing steps included feature selection, categorical data encoding, and data balancing using SVMSMOTE. The XGBoost model demonstrated superior performance, achieving 89.55% accuracy, 89.69% precision, 89.55% recall, and an F1-score of 89.59% after balancing. These findings highlight the potential of machine learning models in accurate risk detection in patients with cirrhosis and providing valuable support in clinical decision-making and improving patient treatment.
Hybrid DL and ML approach for MRI-based classification of bone marrow changes in lumbar vertebrae Shakir, Yasir Hussein; Kiong, Tiong Sieh; Chen, Chai Phing; Kumar, Sachin Sharma Ashok
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10617

Abstract

Alterations in the bone marrow changes lumbar vertebrae (BMCLVB) are considered important markers of chronic low back pain severity, particularly among patients with coexisting conditions like osteoporosis or cancer. Realizing these associations informs healthcare and insurance frameworks but also supports early intrusion planning for high-risk populations. This study aims to classification (BMCLVB) as normal or abnormal used magnetic resonance imaging (MRI) with machine learning (ML) model. A novel dataset comprising 1,018 BMCLVB MRI images was utilized to extract deep features via a pre-trained ConvNeXt-XLarge model. These features were then classified using different types in individual and ensemble ML algorithms. To ensure a comprehensive performance evaluation, all models were tested using accuracy, precision, recall, and F1-score. The combination of ConvNeXt-XLarge and logistic regression (LR) achieved a classification accuracy 93.14%, precision 93.22%, recall 94.83%, and F1-score 94.02%. These results highlight that the proposed model provides a fast and cost-efficient solution supporting the diagnosis of BMCLVB and potential to significantly improve clinical decision-making and patient care outcomes.
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.
An efficient approach for cyber-attack detection by using machine learning and deep learning algorithms Shakir, Yasir Hussein; Abdelhamied, Mahmoud Mohamed; Aziz Awadh AL Mandhari, Eshaq; Alkhazraji, Ali; Reda, Naglaa M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1219-1235

Abstract

Cybercrime gained traction in the late 20th century. The capabilities of cyber-attackers have improved dramatically. One of the biggest challenges facing cybersecurity developers is safeguarding consumers' security and privacy. Interest in using AI approaches in cybersecurity has grown significantly because of the incredible proficiency these techniques have demonstrated across all domains. Even while machine learning algorithms are very effective at identifying malicious activity, there are still certain issues that lower performance accuracy. This paper has the novelty of deploying the Artificial Bee Colony (ABC) meta-heuristic algorithm with the K-Nearest Neighbors (KNN) classifier to detect cyber-attacks. It proposes a variant approach called KNN+Bee that detects attacks efficiently, achieving 99.86% overall accuracy. The NSL-KDD dataset of cyberattacks has been leveraged in the training and testing phases. The proposed approach has been contrasted with the most popular machine learning. According to experimental findings, the suggested model delves deeper into the identification of cyberattacks. It achieves unprecedented performance, outperforming other models in terms of precision, Recall, F-score and MCC. Furthermore, popular deep learning models have been implemented and examined on the same dataset. Results prove that GRU is the most accurate, reaching 99.71%.
DCNNVA: a deep convolutional neural network for volcanic activity classification using satellite imagery Shakir, Yasir Hussein; Mutlag, Reem Ali; AL Mandhari, Eshaq Aziz Awadh; Abdulnabi, Mohamed Shabbir
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp281-292

Abstract

Monitoring and classifying volcanic activity are a critical task for disaster risk reduction and hazard management. Recent discoveries in machine learning and deep learning have proved excellent satellite image classification and volcanic anomaly identification capabilities, yet the majority of existing methods suffer from small datasets, particularly on solitary data modalities or particular cases, merely as examples. In this research work, we put forward develop deep convolutional neural network for volcanic activity (DCNNVA) classification specifically designed for satellite imagery on volcanic activity. We rigorously benchmarked DCNNVA model's strength against a total of eight state-of-the-art transfer learning models: ResNet50, NASNetLarge, DenseNet121, MobileNet, InceptionV3, Xception, VGG19, and VGG16. Comparative experimental results show that proposed DCNNVA framework's overall performance significantly surpasses its competitors with an accuracy of 99.33%, precision of 100%, recall of 98.67%, and F1-score of 99.33%, significantly beating existing state-of-the-art methods. Also, we create a deployable graphical user interface (GUI) system that is capable of real-time monitoring on volcanic activity and generates multi-modal alert processing that can make this research directly applicable for practical use on disaster management as well as in early warning systems. This research contributes a scalable, strong, as well as practical solution towards volcanic hazard identification as well as a baseline system toward developing future multi-modal as well as real-time geohazard tracking system frameworks.
Co-Authors Abbas, Ali Hashim Abdelhamied, Mahmoud Mohamed Abdulnabi, Mohamed Shabbir Ajlan, Iman Kadhim AL Mandhari, Eshaq Aziz Awadh Al-Dhief, Fahad Taha Alkhazraji, Ali Aziz Awadh AL Mandhari, Eshaq Chen, Chai Phing Kiong, Tiong Sieh Kumar, Sachin Sharma Ashok Mahdi, Mohammed Ibrahim Mandhari, Eshaq Aziz Awadh AL Murad, Hayder Mutlag, Reem Ali Reda, Naglaa M. Safie, Nurhizam