Muhyeeddin Kamel Alqaraleh

@zu.edu.jo

Software Engineering / Faculty of Information Technology
Zarqa University

Muhyeeddin Kamel Alqaraleh


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https://researchid.co/malqaraleh

EDUCATION

PhD Computer Engineering / Systems analysis and Data Processing

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Artificial Intelligence, Software
53

Scopus Publications

1763

Scholar Citations

25

Scholar h-index

44

Scholar i10-index

Scopus Publications

  • Brain tumor detection with real-world predictions in Jordan hospitals
    Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Abdullah Alourani
    Scientific Reports, 2026
    The rising incidence of brain tumors and their diverse characteristics make early and accurate diagnosis increasingly challenging. Traditional diagnostic techniques, while effective, often rely on subjective assessment, highlighting the potential of machine learning (ML) to enhance diagnostic accuracy and efficiency. This study evaluates the performance of seven ML algorithms—Decision Tree, AdaBoost, k-Nearest Neighbors (k-NN), Neural Network, Logistic Regression, Random Forest, and Support Vector Machine (SVM)—for brain tumor classification. A comprehensive dataset of 7,023 instances, encompassing glioma, meningioma, pituitary tumors, and healthy samples, was used in a three-way balanced design, with models validated through stratified 10-fold cross-validation. With AUC values near 1.00, Specifically, the Neural Network achieved the highest performance with AUC = 0.996, accuracy = 0.958, F1 = 0.958, precision = 0.958, and recall = 0.958, followed closely by SVM (AUC = 0.993, accuracy = 0.940). the results show that sophisticated models like SVM and neural networks perform better in terms of prediction than more straightforward models like AdaBoost and Decision Trees. The work investigates data augmentation strategies like SMOTE to alleviate class imbalances and further improve model resilience. It also talks about how interpretable AI techniques like SHAP and LIME can be included to increase clinical acceptance and trust. In order to solve ethical issues with algorithmic bias and data protection, federated learning is also taken into consideration for safe multi-institutional collaboration. Notably, our models showed excellent dependability in correctly categorizing tumors when evaluated on actual clinical cases from Jordanian hospitals, highlighting their potential for practical implementation in rural healthcare settings. This research establishes benchmarks for ML-based tumor classification, paving the way for improved diagnostic capabilities in diverse and resource-constrained clinical environments, Validation on retrospective, anonymized cases from Jordanian hospitals confirmed clinical applicability, with models maintaining > 92% accuracy on real-world data.
  • Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease
    Raed Alazaidah, Hamza Abuassi, Mo’ath Alluwaici, Mowafaq Alzboon, Mohammad Subhi Al-Batah, Muhyeeddin Alqaralleh
    International Journal of Online and Biomedical Engineering, 2025
    Dementia is term related to many symptoms regarding brain abilities for old people. These symptoms include losing memory and thinking abilities. There are many causes leading to dementia, such as vascular dementia, Parkinson’s disease, and also severe head injury. But one of the biggest reasons is Alzheimer’s disease. Diagnostic of Alzheimer’s is challenging for the psychiatrists. There are many ways to diagnostic Alzheimer’s from conducting tests for memory to thinking skills to being evaluated by a healthcare professional. Brain-imaging as MRI, can be used to diagnose Alzheimer’s dementia earlier. This paper proposes a hybrid model to predict Alzheimer’s early by combining different machine learning (ML) models with deep learning models. Many models in this hybrid are used to get the powerful from each model and increasing the accuracy and to overcome the shortage of other models if it exist. We use two datasets of MRI for the brain from Kaggle. The result shows some hybrid models achieved outstanding results, as MobileNet with KNN scores the highest accuracy of 0.96, precision of 0.96, recall of 0.96, and F1-score of 0.96. This suggests that KNN is highly effective in leveraging the MobileNet. These top classifiers from the hybrid models indicate that combining robust feature extractors such as MobileNet, InceptionV3, and VGG16 with effective ML algorithms such as KNN, MLP, and random forest (RF) provides the best results for Alzheimer’s disease prediction.
  • Exploring the impact of artificial intelligence integration on medication error reduction: A nursing perspective
    Muhyeeddin Alqaraleh, Wesam Taher Almagharbeh, Muhammad Waleed Ahmad
    Nurse Education in Practice, 2025
  • A Comparative Analysis of Machine Learning Models for Robust UAV-Bird Classification in Aerial Surveillance
    Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon
    International Journal of Robotics and Control Systems, 2025
    The proliferation of Unmanned Aerial Vehicles (UAVs) necessitates advanced surveillance systems to distinguish them from birds, a critical challenge for airspace security. This study addresses the problem of high false alarm rates in traditional systems by evaluating the efficacy of various machine learning models for accurate, real-time classification. The research contribution is a comprehensive benchmarking of six machine learning algorithms—Logistic Regression, Neural Networks, Stochastic Gradient Descent, CN2 Rule Induction, Naive Bayes, and Support Vector Machines—trained on a curated dataset of bird and drone images. The methodology involved rigorous preprocessing, including resizing, normalization, and augmentation, followed by stratified 10-fold cross-validation. Results demonstrated that Neural Networks, Support Vector Machines, and Logistic Regression were the top performers. The Neural Network model achieved the highest accuracy (98.6%) and AUC (0.998), with the lowest LogLoss (0.056), significantly minimizing false positives and negatives. In contrast, Naive Bayes underperformed substantially (accuracy 82.2%, LogLoss 5.528). The discussion contextualizes these findings within existing literature, highlighting the superiority of complex models capable of capturing nonlinear patterns in image data. This study concludes that advanced machine learning models, particularly deep learning architectures, are highly effective for UAV-bird discrimination, thereby enhancing real-time surveillance capabilities. Future work will focus on integrating these models with radar data and testing them in dynamic operational environments.
  • Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Subhi Al-Batah Mohammad
    Latia, 2025
    Parallel programs that require sizeable computational electricity increasingly depend on grid computing structures. Efficient, helpful resource discovery algorithms are critical for optimizing resource allocation and minimizing execution time in these environments. This look presents a unique hierarchical and weighted resource discovery algorithm designed to decorate resource distribution while decreasing communique costs among grid nodes. A behavioural modelling technique systematically establishes the weighted resource discovery algorithm's accuracy and effectiveness. The behavioural model is carried out using StarUML. At the same time, the NuSMV version checker is hired to verify essential residences along with reachability, equity, and impasse-free operation of the resource discovery procedure. Critical overall performance metrics, including the quantity of inspected nodes consistent with request and the frequency of re-discovery operations, are used to evaluate the rules' efficiency and flexibility. The weighted resource discovery algorithm also evaluates the efficiency of finding loose resources with high-bandwidth connections, optimizing overall grid resource allocation. In addition to enhancing resource localization, the observation introduces resource facts tables, which store information crucial for powerful, proper resource allocation. This study aims to develop grid computing competencies by addressing resource discovery challenges. The hierarchical shape and weighted valid resource selection decorate valid resource inspection, adaptability, and high-bandwidth utilization. Behavioural modelling and formal verification verify the algorithm's accuracy and applicability in grid environments. By using weighted resource discovery and resource information tables, this study drastically improves resource discovery's performance and effectiveness in grid computing, optimizing overall performance and proper resource allocation.
  • Diabetes Prediction and Management Using Machine Learning Approaches
    Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah
    Data and Metadata, 2025
    Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78.57%, and then the Random Forest algorithm had the second position with 76.30% accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems.
  • Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging
    Mohammad Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh
    Data and Metadata, 2025
    Brain cancer remains one of the most challenging medical conditions due to its intricate nature and the critical functions of the brain. Effective diagnostic and treatment strategies are essential, particularly given the high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for the identification and monitoring of brain tumors, offering detailed insights into tumor morphology and behavior. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the analysis of medical imaging, significantly enhancing diagnostic precision and efficiency. This study classifies three primary brain tumor types—glioma, meningioma, and general brain tumors—utilizing a comprehensive dataset comprising 15,000 MR images obtained from Kaggle. We evaluated the performance of six distinct machine learning models: K-Nearest Neighbors (KNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forests. Each model's effectiveness was assessed through multiple metrics, including classification accuracy (CA), Area Under the Curve (AUC), F1 score, precision, and recall. Our findings reveal that KNN and Neural Networks achieved remarkable classification accuracies of 98.5% and 98.4%, respectively, significantly surpassing the performance of other evaluated models. These results underscore the promise of ML algorithms, particularly KNN and Neural Networks, in improving the diagnostic process for brain cancer through MR imaging. Future research will focus on validating these models with real-world clinical data, aiming to refine and enhance diagnostic methodologies, thus contributing to the development of more accurate, efficient, and accessible tools for brain cancer diagnosis and management.
  • Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends
    Mutaz Abdel Wahed, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah
    Latia, 2025
    Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews of AI applications in this field often lack cohesion, with each study adopting a unique approach. The aim of this study is to provide a detailed examination of AI's role in breast cancer diagnosis through citation analysis, helping to categorize the key areas that attract academic attention. It also includes a thematic analysis to identify the specific research topics within each category. A total of 30,200 studies related to breast cancer and AI, published between 2015 and 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, and Google Scholar. After applying inclusion and exclusion criteria, 32 relevant studies were identified. Most of these studies utilized classification models for breast cancer prediction, with high accuracy being the most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged as the preferred model in many studies. The findings indicate that both the quantity and quality of AI-based algorithms in breast cancer diagnosis are increases in the given years. AI is increasingly seen as a complement to healthcare sector and clinical expertise, with the target of enhancing the accessibility and affordability of quality healthcare worldwide.
  • Predicting Blood Type: Assessing Model Performance with ROC Analysis
    Malik A. Altayar, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Wesam T. Almagharbeh
    Data and Metadata, 2025
    Introduction: Personal identification is a critical aspect of forensic sciences, security, and healthcare. While conventional biometrics systems such as DNA profiling and iris scanning offer high accuracy, they are time-consuming and costly. Objectives: This study investigates the relationship between fingerprint patterns and ABO blood group classification to explore potential correlations between these two traits.Methods: The study analyzed 200 individuals, categorizing their fingerprints into three types: loops, whorls, and arches. Blood group classification was also recorded. Statistical analysis, including chi-square and Pearson correlation tests, was used to assess associations between fingerprint patterns and blood groups.Results: Loops were the most common fingerprint pattern, while blood group O+ was the most prevalent among the participants. Statistical analysis revealed no significant correlation between fingerprint patterns and blood groups (p > 0.05), suggesting that these traits are independent.Conclusions: Although the study showed limited correlation between fingerprint patterns and ABO blood groups, it highlights the importance of future research using larger and more diverse populations, incorporating machine learning approaches, and integrating multiple biometric signals. This study contributes to forensic science by emphasizing the need for rigorous protocols and comprehensive investigations in personal identification.
  • Revolutionizing Blood Banks: AI-Driven Fingerprint-Blood Group Correlation for Enhanced Safety
    Malik A. Altayar, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Wesam T. Almagharbeh
    Data and Metadata, 2025
    Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning and genomic profiling are more accurate but expensive, time-consuming, and more difficult to implement. This study focuses on the relationship between the fingerprint patterns and the ABO blood group as a biometric identification tool. A total of 200 subjects were included in the study, and fingerprint types (loops, whorls, and arches) and blood groups were compared. Associations were evaluated with statistical tests, including chi-square and Pearson correlation.The study found that the loops were the most common fingerprint pattern and the O+ blood group was the most prevalent. Discussion: Even though there was some associative pattern, there was no statistically significant difference in the fingerprint patterns of different blood groups. Overall, the results indicate that blood group data do not significantly improve personal identification when used in conjunction with fingerprinting.Although the study shows weak correlation, it may emphasize the efforts of multi-modal based biometric systems in enhancing the current biometric systems. Future studies may focus on larger and more diverse samples, and possibly machine learning and additional biometrics to improve identification methods. This study addresses an element of the ever-changing nature of the fields of forensic science and biometric identification, highlighting the importance of resilient analytical methods for personal identification.
  • Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision
    Mohammad Al-batah, Mohammad Al-Batah, Mowafaq Salem Alzboon, Esra Alzaghoul
    Data and Metadata, 2025
  • Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data
    Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Abdullah Alourani
    Data and Metadata, 2025
  • From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Hatim Solayman Migdadi
    Latia, 2025
  • AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics
    Mutaz Abdel Wahed, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah
    Seminars in Medical Writing and Education, 2025
  • Classifying Dental Care Providers Through Machine Learning with Features Ranking
    Mohammad Subhi Al-Batah Al-batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammed Hasan Abu-Arqoub, Rashiq Rafiq Marie
    Data and Metadata, 2025
  • Comparative Analysis of Classifier Performance on Reduced Feature Sets
    Raed Alazaidah, Wael Hadi, Suhaila Abu Owaida, Alaa Bani-bakr, Mo'ath Alluwaici, et al.
    2025 1st International Conference on Computational Intelligence Approaches and Applications Icciaa 2025 Proceedings, 2025
  • Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Lana Yasin Al Aesa, Mohammed Hasan Abu-Arqoub, Rashiq Rafiq Marie, Firas Hussein Alsmadi
    Data and Metadata, 2025
  • Enhancing Autism Spectrum Disorder Detection: A Novel Ensemble Learning Framework with Multi-Model Integration
    Mo'men Malkawi, Anas Khaleel, Mohammad Bataineh, Rabee Alkhatib, Osama Nayfeh, et al.
    2025 1st International Conference on Computational Intelligence Approaches and Applications Icciaa 2025 Proceedings, 2025
  • The Influence of Relative Advantage on the Acceptance of Artificial Intelligence in Jordanian SMEs
    Mutaz Khaled Yousef Abdel Wahed, Najah Al-shanableh, Seyed Ghasem Saatchi, Basema Mohammad Salameh Abu thwaib, Hussam Mohd Al-Shorman, et al.
    Studies in Computational Intelligence, 2025
  • The Influence of Compatibility on the Acceptance of Artificial Intelligence in Kuwaiti Universities
    Seyed Ghasem Saatchi, Mutaz Khaled Yousef Abdel Wahed, Muhyeeddin Kamel Salman Alqaraleh, Hussam Mohd Al-Shorman, Tawfeeq Alanazi, et al.
    Studies in Computational Intelligence, 2025
  • Impact of User Interface Attractiveness on the Willingness to Use Artificial Intelligence Among SMEs
    Najah Al-shanableh, Muhyeeddin Kamel Salman Alqaraleh, Mowafaq Salem Alzboon, Sabha Maria Nawaf Alka’awneh, Mutaz Khaled Yousef Abdel Wahed, et al.
    Studies in Computational Intelligence, 2025
  • The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from SMEs
    Mowafaq Salem Alzboon, Hussam Mohd Al-Shorman, Sabha Maria” Nawaf Alka’awneh, Seyed Ghasem Saatchi, Muhyeeddin Kamel Salman Alqaraleh, Enas Ismail Mohammad Samara, Mutaz Khaled Yousef Abdel Wahed, Sulieman Ibraheem Mohammad, Ala’a M. Al-Momani, Ayman Ahmad Abu Haija
    Studies in Computational Intelligence, 2025
  • The Impact of Artificial Intelligence on Corporate Performance: A Study of Financial and Non-financial Aspects in the Service Sector
    Ayman Ahmad Abu Haija, Enas Ismail Mohammad Samara, Hussam Mohd Al-Shorman, Mazen Alzyoud, Basema Mohammad Salameh Abu thwaib, et al.
    Studies in Computational Intelligence, 2025
  • Emerging Technologies in the Middle East: Artificial Intelligence Adoption and Performance Expectancy in SMEs
    Muhyeeddin Kamel Salman Alqaraleh, Mowafaq Salem Alzboon, Hussam Mohd Al-Shorman, Mutaz Khaled Yousef Abdel Wahed, Seyed Ghasem Saatchi, et al.
    Studies in Computational Intelligence, 2025
  • Perceived Security and Privacy in Artificial Intelligence Adoption: Extending TAM in the Context of Jordanian SMEs
    Sabha Maria Nawaf Alka’awneh, Hasliza Abdul Halim, Mutaz Khaled Yousef Abdel Wahed, Muhyeeddin Kamel Salman Alqaraleh, Mowafaq Salem Alzboon, et al.
    Studies in Computational Intelligence, 2025
  • Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Mutaz Abdel Wahed, Ahmad Abuashour, Firas Hussein Alsmadi
    International Journal of Online and Biomedical Engineering, 2024
  • Enhancing Internet-based Resource Discovery: The Efficacy of Distributed Quadtree Overlay
    Muhyeeddin Alqaraleh
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
  • Enhanced Resource Discovery Algorithm for Efficient Grid Computing
    Muhyeeddin Alqaraleh
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
  • Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
    Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon
    Data and Metadata, 2024
  • Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
    Data and Metadata, 2024
  • AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security
    Salem Alzboon Mowafaq, Alqaraleh Muhyeeddin, Mohammad Subhi Al-Batah
    Data and Metadata, 2024
  • Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah
    International Journal of Advanced Computer Science and Applications, 2024
  • Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis
    Mohammad Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Fawaz Ahmad Alzaghoul
    Data and Metadata, 2024
  • Advanced Landslide Detection Using Machine Learning and Remote Sensing Data
    Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hatim Solayman Migdadi, Mutasem Alkhasawneh, Muhyeeddin Alqaraleh
    Data and Metadata, 2024
  • From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health
    Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Faisal Alzboon, Lujin Alzboon, Mohammad Nayef Alamoush
    Seminars in Medical Writing and Education, 2024
  • Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception
    Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Azmi Halasa, Mohammad Al-Batah, Ahmad Fuad Bader
    2024 7th International Conference on Internet Applications Protocols and Services Netapps 2024, 2024
  • Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats
    Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Azmi Halasa, Mohammad Al-Batah, Ahmad Fuad Bader
    2024 7th International Conference on Internet Applications Protocols and Services Netapps 2024, 2024
  • Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux
    Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammad Al-Batah, Ahmad Fuad Bader, Salma Abdel Wahed
    2024 7th International Conference on Internet Applications Protocols and Services Netapps 2024, 2024
  • Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease
    Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Faisal Alzboon, Lujin Alzboon, Mohammad Nayef Alamoush
    Seminars in Medical Writing and Education, 2024
  • AI-Driven UAV Distinction: Leveraging Advanced Machine Learning
    Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mutaz Abdel Wahed, Abdullah Alourani, Ahmad Fuad Bader, Mohammad Al-Batah
    2024 7th International Conference on Internet Applications Protocols and Services Netapps 2024, 2024
  • Automating Web Data Collection: Challenges, Solutions, and Python-Based Strategies for Effective Web Scraping
    Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Jaradat Ayman, Mohammad Al-Batah, Ahmad Fuad Bader
    2024 7th International Conference on Internet Applications Protocols and Services Netapps 2024, 2024
  • Advanced Machine Learning Models for Real-Time Drone and Bird Differentiation in Aerial Surveillance Systems
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Al-Batah, Oqeili Saleh, Hatim Solayman Migdadi, Ali Elrashidi, Raed Alazaidah
    2024 25th International Arab Conference on Information Technology Acit 2024, 2024
  • Innovative Machine Learning Solutions for Automated Kidney Tumor Detection in CT Imaging Through Comparative Analysis
    Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Al-Batah, Hatim Solayman Migdadi, Oqeili Saleh, Raed Alazaidah, Ali Elrashidi
    2024 25th International Arab Conference on Information Technology Acit 2024, 2024
  • Classifying Psychiatric Patients Using Machine Learning
    Hmmam M. Alrjoob, Raed Alazaidah, Radwan Batyha, Hayel Khafajeh, Esraa Abu Elsoud, Ala'a Saeb Al-Sherideh, Ali Elrashidi, Muhyeeddin Alqaraleh, Waleed Amer
    2024 25th International Arab Conference on Information Technology Acit 2024, 2024
  • Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan
    Raed Alazaidah, Ghassan Samara, Anwar Katrawi, Wael Hadi, Maram Y. Al-Safarini, Farah Al-Mamoori, Ali Elrashidi, Muhyeeddin Alqaraleh, Haneen Alzoubi
    2024 25th International Arab Conference on Information Technology Acit 2024, 2024
  • Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods
    Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Hamadah Bader
    International Journal of Online and Biomedical Engineering, 2023
  • A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer
    Mowafaq Salem Alzboon, Mohammad Al-Batah, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Bader
    2023 IEEE 10th International Conference on Communications and Networking Comnet 2023 Proceedings, 2023
  • A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes
    Mowafaq Salem Alzboon, Mohammad Al-Batah, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Bader
    2023 IEEE 10th International Conference on Communications and Networking Comnet 2023 Proceedings, 2023
  • Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis
    Mowafaq Salem Alzboon, Sami Qawasmeh, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Bader, Mohammad Al-Batah
    2023 3rd International Conference on Emerging Smart Technologies and Applications Esmarta 2023, 2023
  • Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research
    Mowafaq Salem Alzboon, Sami Qawasmeh, Muhyeeddin Alqaraleh, Ahmad Abuashour, Ahmad Fuad Bader, Mohammad Al-Batah
    2023 3rd International Conference on Emerging Smart Technologies and Applications Esmarta 2023, 2023
  • The Two Sides of AI in Cybersecurity: Opportunities and Challenges
    Mowafaq Salem Alzboon, Ahmad Fuad Bader, Ahmad Abuashour, Muhyeeddin Kamel Alqaraleh, Belal Zaqaibeh, Mohammad Al-Batah
    Proceedings of 2023 2nd International Conference on Intelligent Computing and Next Generation Networks Icngn 2023, 2023
  • Toward achieving self-resource discovery in distributed systems based on distributed quadtree
    Journal of Theoretical and Applied Information Technology, 2020
  • The modern hosting computing systems for small and medium businesses
    Academy of Entrepreneurship Journal, 2019

RECENT SCHOLAR PUBLICATIONS

  • Brain tumor detection with real-world predictions in Jordan hospitals
    M Alqaraleh, MS Al-Batah, MS Alzboon, A Alourani
    Scientific Reports , 2025
    2025
  • A Survey on Crowd Scene Anomaly Detection: Trends, Challenges, and Future Directions
    MS Alzboon, MT Al Zawahra, MS Al-Batah, M Alqaraleh
    2025 26th International Arab Conference on Information Technology (ACIT … , 2025
    2025
  • A Survey on Weakly Supervised Anomaly Detection: Techniques, Challenges, and Future Directions
    MT Al Zawahra, MS Alzboon, M Alqaraleh, MS Al-Batah
    2025 26th International Arab Conference on Information Technology (ACIT … , 2025
    2025
  • A Comparative Analysis of Machine Learning Models for Robust UAV-Bird Classification in Aerial Surveillance
    M Alqaraleh, MS Al-Batah, MS Alzboon
    International Journal of Robotics and Control Systems 5 (6), 2938-2956 , 2025
    2025
  • Hybrid Deep and Machine Learning Framework for Predicting Alzheimer's Disease.
    R Alazaidah, H Abuassi, M Alluwaici, MS Alzboon, MS Al-Batah, ...
    International Journal of Online & Biomedical Engineering 21 (10) , 2025
    2025
    Citations: 3
  • Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats
    MA Wahed, MS Alzboon, M Alqaraleh, A Halasa, M Al-Batah, AF Bader
    2024 7th International Conference on Internet Applications, Protocols, and … , 2025
    2025
    Citations: 25
  • Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux
    SAW Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammad ...
    7th International Conference on Internet Applications, Protocols, and … , 2025
    2025
    Citations: 25
  • AI-Driven UAV Distinction: Leveraging Advanced Machine Learning
    MAB Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mutaz Abdel Wahed, Abdullah ...
    7th International Conference on Internet Applications, Protocols, and … , 2025
    2025
    Citations: 19
  • Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception
    AFB Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Azmi ...
    7th International Conference on Internet Applications, Protocols, and … , 2025
    2025
    Citations: 15
  • Automating Web Data Collection: Challenges, Solutions, and Python-Based Strategies for Effective Web Scraping
    AFB Mutaz Abdel Wahed, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Jaradat ...
    7th International Conference on Internet Applications, Protocols, and … , 2025
    2025
    Citations: 28
  • Impact of User Interface Attractiveness on the Willingness to Use Artificial Intelligence Among SMEs
    N Al-shanableh, MKS Alqaraleh, MS Alzboon, SMN Alka’awneh, ...
    Intelligence-Driven Circular Economy: Regeneration Towards Sustainability … , 2025
    2025
    Citations: 2
  • Emerging Technologies in the Middle East: Artificial Intelligence Adoption and Performance Expectancy in SMEs
    MKS Alqaraleh, MS Alzboon, HM Al-Shorman, MKYA Wahed, SG Saatchi, ...
    Intelligence-Driven Circular Economy: Regeneration Towards Sustainability … , 2025
    2025
    Citations: 2
  • The Impact of Artificial Intelligence on Corporate Performance: A Study of Financial and Non-financial Aspects in the Service Sector
    AAA Haija, EIM Samara, HM Al-Shorman, M Alzyoud, BMSA thwaib, ...
    Intelligence-Driven Circular Economy: Regeneration Towards Sustainability … , 2025
    2025
    Citations: 4
  • and Performance Expectancy in SMEs
    SI Shelash, KM Al-hawajreh
    Intelligence-Driven Circular Economy: Regeneration Towards Sustainability … , 2025
    2025
  • Perceived Security and Privacy in Artificial Intelligence Adoption: Extending TAM in the Context
    SMN Alkaʼawneh, HA Halim, MKYA Wahed, MKS Alqaraleh, MS Alzboon, ...
    Intelligence-Driven Circular Economy: Regeneration Towards Sustainability … , 2025
    2025
  • Exploring the impact of artificial intelligence integration on medication error reduction: a nursing perspective
    M Alqaraleh, WT Almagharbeh, MW Ahmad
    Nurse Education in Practice 86, 104438 , 2025
    2025
    Citations: 18
  • Diabetes prediction and management using machine learning approaches
    MS Alzboon, M Alqaraleh, MS Al-Batah
    arXiv preprint arXiv:2506.11501 , 2025
    2025
    Citations: 12
  • Improving oral cancer outcomes through machine learning and dimensionality reduction
    MS Al-Batah, M Alqaraleh, MS Alzboon
    arXiv preprint arXiv:2506.10189 , 2025
    2025
    Citations: 9
  • Optimizing genetic algorithms with multilayer perceptron networks for enhancing tinyface recognition
    MS Al-Batah, MS Alzboon, M Alqaraleh
    arXiv preprint arXiv:2506.10184 , 2025
    2025
    Citations: 6
  • Diabetes Prediction and Management Using Machine Learning Approaches
    M Salem Alzboon, M Alqaraleh, M Subhi Al-Batah
    arXiv e-prints, arXiv: 2506.11501 , 2025
    2025

MOST CITED SCHOLAR PUBLICATIONS

  • The role of perceived trust in embracing artificial intelligence technologies: Insights from SMEs
    MS Alzboon, HM Al-Shorman, SMN Alka’awneh, SG Saatchi, ...
    Intelligence-Driven Circular Economy: Regeneration Towards Sustainability … , 2025
    2025
    Citations: 88
  • The influence of compatibility on the acceptance of artificial intelligence in Kuwaiti universities
    S Saatchi
    Studies in Computational Intelligence , 2024
    2024
    Citations: 70
  • Emerging technologies in the Middle East: artificial intelligence adoption and performance expectancy in Jordanian SMEs
    M Alqaraleh
    Studies in Computational Intelligence , 2024
    2024
    Citations: 68
  • Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends.
    MA Wahed, M Alqaraleh, SABM Salem Alzboon, M
    LatIA [Internet]. 2025 Jan 1; 3: 117 , 2025
    2025
    Citations: 64
  • Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods.
    MS Alzboon, MS Al-Batah, M Alqaraleh, A Abuashour, ...
    International Journal of Online & Biomedical Engineering 19 (15) , 2023
    2023
    Citations: 62
  • The Two Sides of AI in Cybersecurity: Opportunities and Challenges
    MS Alzboon, AF Bader, A Abuashour, MK Alqaraleh, B Zaqaibeh, ...
    2023 International Conference on Intelligent Computing and Next Generation … , 2023
    2023
    Citations: 61
  • Impact of user interface attractiveness on the willingness to use artificial intelligence among Jordanian SMEs
    N Al-Shanableh
    Studies in Computational Intelligence , 2024
    2024
    Citations: 60
  • A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer
    MS Alzboon, M Al-Batah, M Alqaraleh, A Abuashour, AF Bader
    2023 IEEE Tenth International Conference on Communications and Networking … , 2023
    2023
    Citations: 60
  • The Impact of Artificial Intelligence on Corporate Performance: A Study of Financial and Non-financial aspects in the Jordanian Service Sector
    and others Abu Haija
    Studies in Computational Intelligence , 2024
    2024
    Citations: 59
  • Perceived security and privacy in artificial intelligence adoption: extending TAM in the context of Jordanian SMEs
    S Alka’awneh
    Studies in Computational Intelligence , 2024
    2024
    Citations: 58
  • The Influence of Relative Advantage on the Acceptance of Artificial Intelligence in Jordanian SMEs
    MA Wahed
    Studies in Computational Intelligence , 2024
    2024
    Citations: 51
  • Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment
    Muhyeeddin Alqaraleh1, Mowafaq Salim Alzboon2, Mohammad Subhi Al-Batah3 ...
    International Journal of Online and Biomedical Engineerin 20 (11), 123–145 , 2024
    2024
    Citations: 47
  • Comparative study of classification mechanisms of machine learning on multiple data mining tool kits
    A Abuashour, M Salem Alzboon, M Kamel Alqaraleh, A Abuashour
    Am J Biomed Sci Res 2024 (22), 1 , 2024
    2024
    Citations: 45
  • Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis
    MS Alzboon, S Qawasmeh, M Alqaraleh, A Abuashour, AF Bader, ...
    2023 3rd International Conference on Emerging Smart Technologies and … , 2023
    2023
    Citations: 44
  • Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research
    MS Alzboon, S Qawasmeh, M Alqaraleh, A Abuashour, AF Bader, ...
    2023 3rd International Conference on Emerging Smart Technologies and … , 2023
    2023
    Citations: 44
  • Application of artificial intelligence for diagnosing tumors in the female reproductive system: a systematic review
    MA Wahed, M Alqaraleh, MS Alzboon, MS Al Batah
    Multidisciplinar (Montevideo) 3, 15 , 2025
    2025
    Citations: 42
  • Nodexl Tool for Social Network Analysis
    MS Alzboon, E Aljarrah, M Alqaraleh, SA Alomari
    Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12 (14 … , 2021
    2021
    Citations: 42
  • Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation
    A Muhyeeddin, SA Mowafaq, MS Al-Batah, AW Mutaz
    LatIA 2, 74-74 , 2024
    2024
    Citations: 39
  • The modern hosting computing systems for small and medium businesses
    S Al Tal, S Al Salaimeh, SA Alomari, M Alqaraleh
    Academy of Entrepreneurship Journal 25 (4), 1-7 , 2019
    2019
    Citations: 38
  • AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security
    MS Alzboon, M Alqaraleh, M Al-Batah
    Data and Metadata 3 (417) , 2024
    2024
    Citations: 37