Muhyeeddin Kamel Alqaraleh

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
Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease
Raed Alazaidah, Hamza Abuassi, Mo’ath Alluwaici, Mowafaq Alzboon, Mohammad Subhi Al-Batah, et al.
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
International Journal of Robotics and Control Systems, 2025
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
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, et al.
Data and Metadata, 2025
Enhancing Autism Spectrum Disorder Detection: A Novel Ensemble Learning Framework with Multi-Model Integration
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
Studies in Computational Intelligence, 2025
The Influence of Compatibility on the Acceptance of Artificial Intelligence in Kuwaiti Universities
Studies in Computational Intelligence, 2025
Impact of User Interface Attractiveness on the Willingness to Use Artificial Intelligence Among SMEs
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, et al.
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
Studies in Computational Intelligence, 2025
Emerging Technologies in the Middle East: Artificial Intelligence Adoption and Performance Expectancy in SMEs
Studies in Computational Intelligence, 2025
Perceived Security and Privacy in Artificial Intelligence Adoption: Extending TAM in the Context of Jordanian SMEs
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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, et al.
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

Muhyeeddin Kamel Alqaraleh

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Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS