Ph.D. graduate Industrial engineering &Engineering Management, and data scientist with strong skills in Statistics, and algorithms to big data sets related industries and health care engineering. Skilled in machine learning, data models, data mining, statistics process quality control, pre-processing, and visualization data to solve challenging business and industrial problems. Demonstrates experience with programming languages (e.g., R, SAS, Minitab, and Python).
EDUCATION
Ph.D. Industrial Engineering and Engineering Management from WMU, USA.
Master’s in Applied Statistics & Biostatistics from WMU, USA.
Master’s in Engineering Management from the University of Tripoli, Libya.
RESEARCH INTERESTS
Smart Manufacturing, Machine learning, Data Mining and Healthcare Management ,Data models, Statistics Process Quality Control, and Deep Learning
69
Scopus Publications
Scopus Publications
Explainable Machine Learning Models for Mortality Risk Prediction of Crimean-Congo Hemorrhagic Fever in Iraq Tiba Zaki Abdulhameed, Rabia Al Mamlook, Haider Ali Hantoosh, Hasnaa Imad Al-Shaikhli, Yasir Younis Majeed, et al. Al Nahrain Journal of Science, 2026 In mid-2022, Iraq experienced a massive outbreak of Crimean-Congo hemorrhagic fever (CCHF), resulting in high mortality rates. The outbreak began in Thi-Qarprovince and subsequently spread to other provinces. This research analyzes data collected from Thi-Qar province to investigate the key factors influencing patient life risk. This is accomplished by collecting a real dataset (HemoIraq24) and conducting a statistical analysis, followed by developing explainable patient outcome prediction models using several machine learning algorithms. The most important factors contributing to the decision of the predicted outcome are obtained using feature importance and SHAP techniques. In addition, a web-based application has been developed based on the best ML prediction model to assist healthcare providers in clinical decision-making. The ML algorithms tested include Decision Trees, Random Forests, Logistic Regression, Gradient Boosting, and K-nearest neighbor. The highest baseline prediction model accuracy achieved is 89%. Feature importance analysis and SHAP are utilized for further feature engineering, causing an enhancement of 3% in prediction accuracy, with up to8% enhancement in F1 score. It is found that the main factor contributing to the patient outcome is the days in the hospital, which means that the healthcare given in the hospitals is strong enough and can handle the endemic. The dataset can help with future research.
Efficient attention-based Ghost-ResNet for brain tumor classification in magnetic resonance imaging (MRI) Nahlah Shatnawi, Khalid M. O. Nahar, Rabia Emhamed Al Mamlook, Ali Saeed Almuflih, Abdullah Mohammed Al Fatais, et al. Frontiers in Neuroscience, 2026 Introduction Brain tumor classification from magnetic resonance imaging remains a challenging task in medical image analysis, particularly when high diagnostic performance must be achieved under limited computational resources. Effective models are therefore required to balance classification accuracy with efficiency to support practical clinical deployment. Methods This study addresses this challenge by proposing an efficiency-oriented deep learning architecture that integrates Ghost modules into a ResNet-50 backbone and enhances feature learning through Efficient Channel Attention (ECA) blocks. The proposed design aims to improve discriminative capability while reducing feature redundancy and computational overhead. The model was evaluated on the Bangladesh Brain Cancer MRI Dataset, which contains 6,056 MRI images representing three tumor categories: glioma, meningioma, and pituitary tumors. Preprocessing included contrast normalization using Contrast Limited Adaptive Histogram Equalization (CLAHE). Data augmentation was selectively applied to improve generalization while avoiding excessive artificial amplification of feature representations. Results Experimental results demonstrate the effectiveness of the proposed attention-assisted lightweight architecture. The model achieved an overall classification accuracy of 97.85%, while macro-averaged precision, recall (sensitivity), and specificity all exceeded 97.8% (as defined in the Methods section). This corresponds to a 1.65% absolute improvement in accuracy compared with the strongest baseline model, DenseNet121, while maintaining a low false-positive rate. These findings suggest that competitive performance can be achieved without increasing architectural complexity. Discussion The results highlight the potential of pursuing efficiency-driven architectural designs as an alternative to increasingly complex deep learning models. In particular, channel-attention-assisted feature generation appears to preserve high diagnostic accuracy while reducing representational and computational overhead, supporting its suitability for resource-constrained medical imaging applications.
Computational Linguistics Meets Libyan Dialect: A Study on Dialect Identification Mansour Essgaer, Khamis Massud, Rabia Al Mamlook, Najah Ghmaid International Journal of Intelligent Systems and Applications, 2025 This study investigates logistic regression, linear support vector machine, multinomial Naive Bayes, and Bernoulli Naive Bayes for classifying Libyan dialect utterances gathered from Twitter. The dataset used is the QADI corpus, which consists of 540,000 sentences across 18 Arabic dialects. Preprocessing challenges include handling inconsistent orthographic variations and non-standard spellings typical of the Libyan dialect. The chi-square analysis revealed that certain features, such as email mentions and emotion indicators, were not significantly associated with dialect classification and were thus excluded from further analysis. Two main experiments were conducted: (1) evaluating the significance of meta-features extracted from the corpus using the chi-square test and (2) assessing classifier performance using different word and character n-gram representations. The classification experiments showed that Multinomial Naive Bayes (MNB) achieved the highest accuracy of 85.89% and an F1-score of 0.85741 when using a (1,2) word n-gram and (1,5) character n-gram representation. In contrast, Logistic Regression and Linear SVM exhibited slightly lower performance, with maximum accuracies of 84.41% and 84.73%, respectively. Additional evaluation metrics, including log loss, Cohen’s kappa, and Matthew’s correlation coefficient, further supported the effectiveness of MNB in this task. The results indicate that carefully selected n-gram representations and classification models play a crucial role in improving the accuracy of Libyan dialect identification. This study provides empirical benchmarks and insights for future research in Arabic dialect NLP applications.
A Hybrid Brain Stroke Prediction Framework: Integrating Feature Selection, Classification, and Hyperparameter Optimization Mohammad Amin, Khalid M. O. Nahar, Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, et al. Engineering Reports, 2025 Stroke is a leading cause of death and disability worldwide, requiring accurate and early prediction to ensure timely medical intervention. This study proposes a hybrid system that combines optimal feature selection and advanced classification techniques to improve stroke prediction performance. We used a publicly available Harvard Stroke Prediction Data Warehouse dataset, applying multiple feature selection methods: ANOVA, chi‐square, mutual information classification, and analysis of variance to identify relevant features. Five classifiers were examined: Random Forest (RF), K‐Nearest Neighbors (KNN), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). MLP was also used for feature selection through its internal representation learning capabilities. The parameters were fine‐tuned using GridSearchCV. The most effective configuration used selected features from a RF with MLP as a classifier, achieving 99.86% accuracy, 1.00% recall, 99.73% precision, and an F1 score of 99.86%. Compared with existing state‐of‐the‐art models, our proposed system demonstrates superior performance. This approach enables earlier and more accurate stroke detection, supporting healthcare providers in delivering personalized and proactive care to at‐risk individuals.
VRDeepSafety: A Scalable VR Simulation Platform with V2X Communication for Enhanced Accident Prediction in Autonomous Vehicles Mohammad BaniSalman, Mohammad Aljaidi, Najat Elgeberi, Ayoub Alsarhan, Rabia Emhamed Al Mamlook World Electric Vehicle Journal, 2025 Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, and insufficient coverage of critical edge cases in multi-vehicle interactions. This paper introduces VRDeepSafety, a novel and scalable VR simulation platform that overcomes these limitations by integrating Vehicle-to-Everything (V2X) communication, including realistic latency, packet loss, and signal prioritization, to enhance AV accident prediction and mitigation. VRDeepSafety generates comprehensive datasets featuring synchronized multi-vehicle interactions, coordinated V2X scenarios, and diverse sensor data, including visual, LiDAR, radar, and V2X streams. Evaluated with our novel deep-learning model, VRFormer, which uniquely fuses VR sensor data with V2X using a probabilistic Bayesian inference, as well as a hierarchical Kalman and particle filter structure, VRDeepSafety achieved an 85% accident prediction accuracy (APA) at a 2 s horizon, a 17% increase in 3D object detection precision (mAP), and a 0.3 s reduction in response time, outperforming a single-vehicle baseline. Furthermore, V2X integration increased APA by 15%. Extending the prediction horizon to 3–4 s reduced APA to 70%, highlighting the trade-off between prediction time and accuracy. The VRDeepSafety high-fidelity simulation and integrated V2X provide a valuable and rigorous tool for developing safer and more responsive AVs.
CLIMATE-RESPONSIVE PAVEMENT MANAGEMENT IN JORDAN: LEVERAGING MACHINE LEARNING FOR SUSTAINABLE INFRASTRUCTURE Odey Alsbhoul, Ali Shehadeh, Khaled Al-Shboul, Ghassan Almasabha, Rabia Al Mamlook International Multidisciplinary Scientific Geoconference Surveying Geology and Mining Ecology Management Sgem, 2025 Climate change poses a significant challenge to infrastructure worldwide, with asphalt pavements particularly vulnerable due to their temperature-sensitive properties. Jordan�s pavement infrastructure faces escalating challenges due to climate change, including rising temperatures and increasing environmental stressors. This study investigates the impact of climate change on asphalt road pavements in Jordan, focusing on the period from 2020 to 2050. Using advanced machine learning algorithms integrated with mathematical models, the research develops predictive maintenance plans tailored to Jordan�s unique climatic and environmental conditions. The findings highlight significant temperature fluctuations over the study period, emphasizing the need for innovative, climate-adaptive solutions in pavement design and management. The proposed strategies aim to enhance pavement resilience, optimize resource allocation, and reduce long-term maintenance costs. This research underscores the critical importance of incorporating climate-responsive planning into infrastructure management to support sustainable development in Jordan.
