Rana Dhiaa Abdu-aljabar

@nahrainuniv.edu.iq

lecturer/systems engineering/college of information engineering
Al-Nahrain University

Rana Dhiaa Abdu-aljabar


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https://researchid.co/ranadhiaa1

EDUCATION

MsC. in computer engineering

RESEARCH INTERESTS

machine learning
7

Scopus Publications

133

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • YOLOv8-Edge: High-Accuracy Real-Time Social Distancing Monitoring on Resource-Constrained Embedded Systems
    Khansaa Aljafaar, Zinah Ameen, Rana Abdu-aljabar, Russul Al-anni
    Al Khwarizmi Engineering Journal, 2026
    Efforts to combat the recent spread of infectious diseases require innovative solutions for characterizing their transmission. This study introduces a system for helping people maintain safe distances from one another, thereby decreasing the transmission of infectious diseases in crowded public spaces. Real-time video footage from surveillance cameras was processed by the cutting-edge “You Only Look Once” ver. 8 (YOLOv8) computer vision model, which can detect and segment individuals in images. YOLOv8 was combined with an embedded system that includes an Atmel 8-bit AVR microcontroller and an Arduino Uno board, a buzzer, and an LCD. This proposed system can generate alerts when people breach the set social distancing limit of 0.5 m. Its new network design increases human detection to an accuracy range of 97%–98%. This proposed system was trained on 70% of the dataset, and validation and testing were performed on 15% and 15% of the dataset, respectively. Combining deep learning with embedded systems creates an intelligent vision-based monitoring system for crowded spaces, addressing key issues pertaining to disease transmission reduction and public health protection. The proposed system can be implemented at a low cost, such as by simply using a resource-constrained embedded device or a microcontroller. This approach overcomes the functional challenge of utilizing artificial intelligence-based surveillance in a scalable, decentralized, and economical manner. In general, the proposed system has a high frame per second rate, which is satisfactory for real-time operation on edge hardware. The novelty of this method relies on the use of the YOLOv8 model to achieve precise performance while balancing accuracy and speed on edge/embedded devices for practical, real-world epidemic control.
  • A COMPARATIVE STUDY OF BREAST CANCER DETECTION AND RECURRENCE PREDICTION USING CATBOOST CLASSIFIER
    Rana Dhia’a Abdu-aljabar, Khansaa Dheya Aljafaar, Zinah Jaffar Mohammed Ameen, Hala A. Naman
    Acta Polytechnica, 2025
    In 2019, breast cancer accounted for over one-third of all cancer cases in women in Iraq. It affects both men and women, though it is more common in women. This study delves into advanced machine learning techniques – CatBoost, XGBoost, Random Forest, SVM, KNN, and Naive Bayes – to improve the detection and prediction of breast cancer recurrence after healing. The goal is to evaluate models using key metrics (sensitivity, specificity, precision, F1 score, accuracy, ROC, and AUC score). Among all algorithms examined, CatBoost stood out, showcasing AUC values above 98 %, 90 %, and 83% on different datasets. This research demonstrates how machine learning techniques can significantly improve the accuracy of breast cancer detection and recurrence prediction, steering healthcare providers towards better patient care outcomes and more effective treatment plans.
  • An Electrocardiogram Classification Based on The Walrus Optimization Algorithm Improved The BP Neural Network Intelligence Model
    International Journal of Intelligent Engineering and Systems, 2025
    Heart diseases are one of the most common life-threatening factors, especially due to abnormal heart rhythms known as arrhythmias.Early detection of these arrhythmias can have a great impact on the prevention and successful treatment of heart diseases.An electrocardiogram (ECG) is a key tool for recording the electrical activity of the heart, and the automatic classification of ECG signals plays an important role in identifying arrhythmias and heart diseases.However, one of the challenges in this field is the low accuracy of heart disease prediction models and the inefficiency of feature selection methods.In this paper, a new technique named Walrus Optimization Algorithm (WaOA) in combination with Backpropagation Neural Network (BPNN) is presented to classify ECG signals.To improve the performance, wavelet transform with four different functions and adaptive thresholding techniques have been used to filter ECG signals and extract features.Then, with the help of Principal Component Analysis (PCA), the dimensions of the extracted features were reduced to minimize the computational complexity of the model.The Walrus optimization algorithm helps to improve the performance of the backpropagation neural network and allows the model to be optimized automatically.To evaluate this technique, the public MIT-BIH Arrhythmia Database has been used.Experiments have shown that the WaOA-BPNN technique achieves a high accuracy of 98.80% in the classification of ECG signals.
  • Improving Lung Cancer Relapse Prediction Using the Developed Optuna_XGB Classification Model
    Rana Dhia'a Abdu-Aljabar, Osama A. Awad, P Choi, S Jeong, S Yoon, et al.
    International Journal of Intelligent Engineering and Systems, 2023
    Lung cancer is more likely to relapse in the first five years following surgery; even though the operation may have been a complete success, there remains a chance that the lung cancer could return. This return may lead the patient to die after a successful surgery. Because there are no symptoms of lung cancer in its early stage, many researchers use intelligent systems to predict the relapse of lung cancer in its early stages. The outcome of previous works considering this issue still suffers from low prediction accuracy. This study proposed a method to predict lung cancer relapse more accurately. This method has multiple stages: 1st optimization system, feature selection stage, 2nd optimization stage, and extreme gradient boost (XGBoost) classifications stage. It used two datasets (GSE8894 and GSE68465) of a gene expression microarray for NSCLC with its clinical information on relapse state. We obtained three probes (3 genes) with clinical data combinations that can get good prediction results. These genes included 225389_at (BTBD6), 220239_at (KLHL7), and 204832_s_at (BMPR1A). A comparison between the proposed model and the original XGBoost with PSO and Hyperopt as hyperparameter optimization for the XGBoost classification model is performed. Extensive comparisons with four machine learning algorithms, including Deep Forest, K-nearest neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes, are conducted. The proposed model accuracies are 0.93 for the GSE8894 dataset and 0.81 for the GSE68465 dataset.
  • Parallel extreme gradient boosting classifier for lung cancer detection
    Rana Dhia’a Abdualjabar, Osama A. Awad
    Indonesian Journal of Electrical Engineering and Computer Science, 2021
    Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.
  • Prediction of NSCLC recurrence from microarray data with GEP
    Russul Al-Anni, Jingyu Hou, Rana Dhia'a Abdu-aljabar, Yong Xiang
    Iet Systems Biology, 2017
    Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.
  • Multiclass lung cancer diagnosis by gene expression programming and microarray datasets
    Hasseeb Azzawi, Jingyu Hou, Russul Alanni, Yong Xiang, Rana Abdu-Aljabar, Ali Azzawi
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017

RECENT SCHOLAR PUBLICATIONS

  • YOLOv8-Edge: High-Accuracy Real-Time Social Distancing Monitoring on Resource-Constrained Embedded Systems
    K Aljafaar, Z Ameen, R Abdu-aljabar, R Al-anni
    Al-Khwarizmi Engineering Journal 22 (1), 44-54 , 2026
    2026
  • A comparative study of breast cancer detection and recurrence prediction using CatBoost classifier
    RD Abdu-aljabar, KD Aljafaar, ZJM Ameen, HA Naman
    Acta Polytechnica 65 (2), 136-142 , 2025
    2025
    Citations: 3
  • An Electrocardiogram Classification Based on The Walrus Optimization Algorithm Improved The BP Neural Network Intelligence Model.
    ZJ Mohammed Ameen, R Abdu-aljabar, K Aljafaar
    International Journal of Intelligent Engineering & Systems 18 (4) , 2025
    2025
  • Improving Lung Cancer Relapse Prediction Using the Developed Optuna_XGB Classification Model.
    RD Abdu-Aljabar, OA Awad
    International Journal of Intelligent Engineering & Systems 16 (1) , 2023
    2023
    Citations: 8
  • Lung Cancer Relapse Prediction Using Parallel XGBOOST: Bioinformation
    RD Abdu-Aljabar, OA Awad
    Iraqi Journal of Information and Communication Technology 5 (2), 10-20 , 2022
    2022
    Citations: 4
  • LUNG CANCER RELAPSE PREDICTION USING PARALLEL XGBOOST
    RD Abdu-Aljabar, OA Awad
    Iraqi Journal of Information and Communication Technology (IJICT) 5 (2) , 2022
    2022
  • Parallel extreme gradient boosting classifier for lung cancer detection
    OAA Rana Dhia’a Abdualjabar
    Indonesian Journal of Electrical Engineering and Computer Science 24, 1610~1617 , 2021
    2021
    Citations: 4
  • A comparative analysis study of lung cancer detection and relapse prediction using XGBoost classifier
    RD Abdu-Aljabar, OA Awad
    IOP conference series: materials science and engineering 1076 (1), 012048 , 2021
    2021
    Citations: 49
  • Multiclass lung cancer diagnosis by gene expression programming and microarray datasets
    H Azzawi, J Hou, R Alanni, Y Xiang, R Abdu-Aljabar, A Azzawi
    International Conference on Advanced Data Mining and Applications, 541-553 , 2017
    2017
    Citations: 20
  • Prediction of NSCLC recurrence from microarray data with GEP
    YX Russul Al-Anni , Jingyu Hou, Rana Dhia'a Abdu-aljabar
    IET Systems Biology 11 (3), 77-85 , 2017
    2017
    Citations: 15
  • student absence registration and reporting
    RD Alanni, AM Al-Saraaf, MM Noori
    i-Manager's Journal on Information Technology 4 (4), 24-32 , 2015
    2015
  • Design and Implementation of Neural Network in FPG
    RD Abdu-aljabar
    2012
    Citations: 28
  • Design a Security Network System against Internet Worms
    RD Abdu-Aljabar
    2012
    Citations: 2
  • Central Fault Tolerance for Dual Database Server Real Time System
    AAS Rana Dhia'a Abdu-aljabar
    Eng. & Tech. Journal 28 (12) , 2010
    2010

MOST CITED SCHOLAR PUBLICATIONS

  • A comparative analysis study of lung cancer detection and relapse prediction using XGBoost classifier
    RD Abdu-Aljabar, OA Awad
    IOP conference series: materials science and engineering 1076 (1), 012048 , 2021
    2021
    Citations: 49
  • Design and Implementation of Neural Network in FPG
    RD Abdu-aljabar
    2012
    Citations: 28
  • Multiclass lung cancer diagnosis by gene expression programming and microarray datasets
    H Azzawi, J Hou, R Alanni, Y Xiang, R Abdu-Aljabar, A Azzawi
    International Conference on Advanced Data Mining and Applications, 541-553 , 2017
    2017
    Citations: 20
  • Prediction of NSCLC recurrence from microarray data with GEP
    YX Russul Al-Anni , Jingyu Hou, Rana Dhia'a Abdu-aljabar
    IET Systems Biology 11 (3), 77-85 , 2017
    2017
    Citations: 15
  • Improving Lung Cancer Relapse Prediction Using the Developed Optuna_XGB Classification Model.
    RD Abdu-Aljabar, OA Awad
    International Journal of Intelligent Engineering & Systems 16 (1) , 2023
    2023
    Citations: 8
  • Lung Cancer Relapse Prediction Using Parallel XGBOOST: Bioinformation
    RD Abdu-Aljabar, OA Awad
    Iraqi Journal of Information and Communication Technology 5 (2), 10-20 , 2022
    2022
    Citations: 4
  • Parallel extreme gradient boosting classifier for lung cancer detection
    OAA Rana Dhia’a Abdualjabar
    Indonesian Journal of Electrical Engineering and Computer Science 24, 1610~1617 , 2021
    2021
    Citations: 4
  • A comparative study of breast cancer detection and recurrence prediction using CatBoost classifier
    RD Abdu-aljabar, KD Aljafaar, ZJM Ameen, HA Naman
    Acta Polytechnica 65 (2), 136-142 , 2025
    2025
    Citations: 3
  • Design a Security Network System against Internet Worms
    RD Abdu-Aljabar
    2012
    Citations: 2
  • YOLOv8-Edge: High-Accuracy Real-Time Social Distancing Monitoring on Resource-Constrained Embedded Systems
    K Aljafaar, Z Ameen, R Abdu-aljabar, R Al-anni
    Al-Khwarizmi Engineering Journal 22 (1), 44-54 , 2026
    2026
  • An Electrocardiogram Classification Based on The Walrus Optimization Algorithm Improved The BP Neural Network Intelligence Model.
    ZJ Mohammed Ameen, R Abdu-aljabar, K Aljafaar
    International Journal of Intelligent Engineering & Systems 18 (4) , 2025
    2025
  • LUNG CANCER RELAPSE PREDICTION USING PARALLEL XGBOOST
    RD Abdu-Aljabar, OA Awad
    Iraqi Journal of Information and Communication Technology (IJICT) 5 (2) , 2022
    2022
  • student absence registration and reporting
    RD Alanni, AM Al-Saraaf, MM Noori
    i-Manager's Journal on Information Technology 4 (4), 24-32 , 2015
    2015
  • Central Fault Tolerance for Dual Database Server Real Time System
    AAS Rana Dhia'a Abdu-aljabar
    Eng. & Tech. Journal 28 (12) , 2010
    2010