Classifying Dental Care Providers Through Machine Learning with Features Ranking
DOI:
https://doi.org/10.56294/dm2025755Keywords:
Machine learning, dental provider classification, feature ranking, ensemble models, healthcare analyticsAbstract
This study investigates the application of machine learning (ML) models for classifying dental providers into two categories—standard rendering providers and safety net clinic (SNC) providers—using a 2018 dataset of 24,300 instances with 20 features. The dataset, characterized by high missing values (38.1%), includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), and beneficiary demographics. Feature ranking methods such as information gain, Gini index, and ANOVA were employed to identify critical predictors, revealing treatment-related metrics (TXMT_USER_CNT, TXMT_SVC_CNT) as top-ranked features. Twelve ML models, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting, were evaluated using 10-fold cross-validation. Classification accuracy was tested across incremental feature subsets derived from rankings. The Neural Network achieved the highest accuracy (94.1%) using all 20 features, followed by Gradient Boosting (93.2%) and Random Forest (93.0%). Models showed improved performance as more features were incorporated, with SGD and ensemble methods demonstrating robustness to missing data. Feature ranking highlighted the dominance of treatment service counts and annotation codes in distinguishing provider types, while demographic variables (AGE_GROUP, CALENDAR_YEAR) had minimal impact. The study underscores the importance of feature selection in enhancing model efficiency and accuracy, particularly in imbalanced healthcare datasets. These findings advocate for integrating feature-ranking techniques with advanced ML algorithms to optimize dental provider classification, enabling targeted resource allocation for underserved populations.
References
1. Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip. 2025;3:54.
2. Alqaraleh M, Al-Batah M, Salem Alzboon M, Alzaghoul E. Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision. Data Metadata. 2025;4:460. DOI: https://doi.org/10.56294/dm2025460
3. Salem Alzboon M, Subhi Al-Batah M, Alqaraleh M, Alzboon F, Alzboon L. Guardians of the Web: Harnessing Machine Learning to Combat Phishing Attacks. Gamification Augment Real [Internet]. 2025 Jan;3:91. Available from: http://dx.doi.org/10.56294/gr202591 DOI: https://doi.org/10.56294/gr202591
4. Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M, Solayman Migdadi H. From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection. LatIA [Internet]. 2025 Jan 1;3:84. Available from: https://latia.ageditor.uy/index.php/latia/article/view/84 DOI: https://doi.org/10.62486/latia202584
5. Alzboon MS, Subhi Al-Batah M, Alqaraleh M, Alzboon F, Alzboon L. Phishing Website Detection Using Machine Learning. Gamification Augment Real [Internet]. 2025 Jan 16;3:81. Available from: http://dx.doi.org/10.56294/gr202581 DOI: https://doi.org/10.56294/gr202581
6. Alqaraleh M, Salem Alzboon M, Mohammad SA-B. Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling. LatIA [Internet]. 2025 Jan 1;3:97. Available from: https://latia.ageditor.uy/index.php/latia/article/view/97 DOI: https://doi.org/10.62486/latia202597
7. Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance. Gamification Augment Real [Internet]. 2025 Jan 1;3:63. Available from: https://gr.ageditor.ar/index.php/gr/article/view/63 DOI: https://doi.org/10.56294/gr202563
8. Wahed MA, Alqaraleh M, Alzboon MS, Subhi Al-Batah M. AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics. Semin Med Writ Educ [Internet]. 2025 Jan 1;4:35. Available from: https://mw.ageditor.ar/index.php/mw/article/view/35 DOI: https://doi.org/10.56294/mw202535
9. Abdel Wahed M, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip [Internet]. 2025 Jan 1;3:54. Available from: https://multidisciplinar.ageditor.uy/index.php/multidisciplinar/article/view/54 DOI: https://doi.org/10.62486/agmu202554
10. Wahed MA, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA [Internet]. 2025 Jan 1;3:117. Available from: https://latia.ageditor.uy/index.php/latia/article/view/117 DOI: https://doi.org/10.62486/latia2025117
11. Alzboon MS, Alqaraleh M, Al-Batah MS. Diabetes Prediction and Management Using Machine Learning Approaches. Data Metadata [Internet]. 2025; Available from: https://doi.org/10.56294/dm2025545 DOI: https://doi.org/10.56294/dm2025545
12. Alqaraleh M, Al-Batah MS, Alzboon MS, Alzboon F, Alzboon L, Alamoush MN. Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease. Semin Med Writ Educ [Internet]. 2025; Available from: https://doi.org/10.56294/mw2024.585 DOI: https://doi.org/10.56294/mw2024.585
13. Alqaraleh M, Al-Batah MS, Alzboon MS, Alzboon F, Alzboon L, Alamoush MN. From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health. Semin Med Writ Educ [Internet]. 2025; Available from: https://doi.org/10.56294/mw2024.670 DOI: https://doi.org/10.56294/mw2024.670
14. Abuashour A, Salem Alzboon M, Kamel Alqaraleh M, Abuashour A. Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits. Am J Biomed Sci Res 2024 [Internet]. 2024;22(1):1. Available from: www.biomedgrid.com DOI: https://doi.org/10.34297/AJBSR.2024.22.002913
15. Mowafaq SA, Alqaraleh M, Al-Batah MS. AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security. Data Metadata. 2024;3:417. DOI: https://doi.org/10.56294/dm2024.417
16. Alqaraleh M, Alzboon MS, Al-Batah MS. Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security. Int J Adv Comput Sci Appl [Internet]. 2024;15(11). Available from: http://dx.doi.org/10.14569/IJACSA.2024.01511104 DOI: https://doi.org/10.14569/IJACSA.2024.01511104
17. Wahed MA, Alzboon MS, Alqaraleh M, Al-Batah M, Bader AF, Wahed SA. Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE; 2024. p. 1–7. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823509 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823509
18. Abdel Wahed M, Al-Batah M, Salem Alzboon M, Fuad Bader A, Alqaraleh M. Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception [Internet]. 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS). IEEE; 2024 Nov. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823624 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823624
19. Alzboon MS, Alqaraleh M, Wahed MA, Alourani A, Bader AF, Al-Batah M. AI-Driven UAV Distinction: Leveraging Advanced Machine Learning. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE; 2024. p. 1–7. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823488 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823488
20. Wahed MA, Alzboon MS, Alqaraleh M, Halasa A, Al-Batah M, Bader AF. Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE; 2024. p. 1–8. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823603 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823603
21. Alzboon MS, Al-Shorman HM, Alka’awneh SMN, Saatchi SG, Alqaraleh MKS, Samara EIM, et al. The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector. In: Studies in Computational Intelligence [Internet]. Springer Nature Switzerland; 2024. p. 1–15. Available from: http://dx.doi.org/10.1007/978-3-031-74220-0_1 DOI: https://doi.org/10.1007/978-3-031-74220-0_1
22. Wahed MA, Alzboon MS, Alqaraleh M, Ayman J, Al-Batah M, Bader AF. Automating Web Data Collection: Challenges, Solutions, and Python-Based Strategies for Effective Web Scraping. In: 2024 7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024 [Internet]. IEEE; 2024. p. 1–6. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823528 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823528
23. Al-Batah M, Salem Alzboon M, Alqaraleh M, Ahmad Alzaghoul F. Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis. Data Metadata. 2024;3:465. DOI: https://doi.org/10.56294/dm2024.465
24. Alqaraleh M. Enhancing Internet-based Resource Discovery: The Efficacy of Distributed Quadtree Overlay. In: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024. 2024. p. 1619–28. DOI: https://doi.org/10.1109/ICAAIC60222.2024.10575078
25. Suryawanshi A, Behera N. Prediction of wear of dental composite materials using machine learning algorithms. Comput Methods Biomech Biomed Engin. 2024;27(3):400–10. DOI: https://doi.org/10.1080/10255842.2023.2187671
26. Suryawanshi A, Behera N. Prediction of mechanical properties of dental composite materials using machine learning algorithms. Materwiss Werksttech. 2023;54(11):1350–61. DOI: https://doi.org/10.1002/mawe.202200294
27. Song IS, Choi ES, Kim ES, Hwang Y, Lee KS, Ahn KH. Associations of Preterm Birth with Dental and Gastrointestinal Diseases: Machine Learning Analysis Using National Health Insurance Data. Int J Environ Res Public Health. 2023;20(3). DOI: https://doi.org/10.3390/ijerph20031732
28. Oguzhan A, Peskersoy C, Devrimci EE, Kemaloglu H, Onder TK. Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education. J Dent Educ. 2024; DOI: https://doi.org/10.1002/jdd.13722
29. Sadegh-Zadeh SA, Bagheri M, Saadat M. Decoding children dental health risks: a machine learning approach to identifying key influencing factors. Front Artif Intell. 2024;7. DOI: https://doi.org/10.3389/frai.2024.1392597
30. Zanini LG, Rubira-Bullen IR, Nunes F de L. Segmentation and Classification of Dental Caries in Cone Beam Tomography Images Using Machine Learning and Image Processing. In: International Joint Conference on Biomedical Engineering Systems and Technologies. 2024. p. 428–35. DOI: https://doi.org/10.5220/0012365300003657
31. Leth Rasmussen E, Have Musaeus M, Dahl MR, Løvschall H, Musaeus P. Enhancing dental and medical students’ self-regulated learning through multiple choice questions: An evaluative study using machine learning. Tidsskr Læring og Medier. 2024;17(29). DOI: https://doi.org/10.7146/lom.v17i29.140337
32. More PP. Automated Dental Cavity Detection Using Machine Learning. Int J Res Appl Sci Eng Technol. 2024;12(1):849–53. DOI: https://doi.org/10.22214/ijraset.2024.58064
33. Farook TH, Ahmed S, Giri J, Rashid F, Hughes T, Dudley J. Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An in Vitro Comparative Study. Int J Dent. 2023;2023. DOI: https://doi.org/10.1155/2023/7542813
34. Kang IA, Njimbouom SN, Kim JD. Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System. Bioengineering. 2023;10(2). DOI: https://doi.org/10.3390/bioengineering10020245
35. Toledo Reyes L, Knorst JK, Ortiz FR, Brondani B, Emmanuelli B, Saraiva Guedes R, et al. Early Childhood Predictors for Dental Caries: A Machine Learning Approach. J Dent Res. 2023;102(9):999–1006. DOI: https://doi.org/10.1177/00220345231170535
36. Chen CC, Mondal K, Vervliet P, Covaci A, O’Brien EP, Rockne KJ, et al. Logistic Regression Analysis of LC-MS/MS Data of Monomers Eluted from Aged Dental Composites: A Supervised Machine-Learning Approach. Anal Chem. 2023;95(12):5205–13. DOI: https://doi.org/10.1021/acs.analchem.2c04362
37. Kwack DW, Park SM. Prediction of medication-related osteonecrosis of the jaw (MRONJ) using automated machine learning in patients with osteoporosis associated with dental extraction and implantation: a retrospective study. J Korean Assoc Oral Maxillofac Surg. 2023;49(3):135–41. DOI: https://doi.org/10.5125/jkaoms.2023.49.3.135
38. Ytzhaik N, Zur D, Goldstein C, Almoznino G. Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study. Metabolites. 2023;13(5). DOI: https://doi.org/10.3390/metabo13050595
39. Schuch HS, Furtado M, Silva GFDS, Kawachi I, Chiavegatto Filho ADP, Elani HW. Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults. JAMA Netw Open. 2023;6(11):E2341625. DOI: https://doi.org/10.1001/jamanetworkopen.2023.41625
40. Wang R, Hass V, Wang Y. Machine Learning Analysis of Microtensile Bond Strength of Dental Adhesives. J Dent Res. 2023;102(9):1022–30. DOI: https://doi.org/10.1177/00220345231175868
41. Erkartal HŞ, Tatlı M, Secgin Y, Toy S, Duman BS. Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks. Eur J Ther. 2023;29(3):352–8. DOI: https://doi.org/10.58600/eurjther1606
42. Dogan OB, Boyacioglu H, Goksuluk D. Machine learning assessment of dental age classification based on cone-beam CT images: a different approach. Dentomaxillofac Radiol. 2024;53(1):67–73. DOI: https://doi.org/10.1093/dmfr/twad009
43. Alqaraleh M. Enhanced Resource Discovery Algorithm for Efficient Grid Computing. In: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024. 2024. p. 925–31. DOI: https://doi.org/10.1109/ICAAIC60222.2024.10575479
44. Al-Batah MS, Salem Alzboon M, Solayman Migdadi H, Alkhasawneh M, Alqaraleh M. Advanced Landslide Detection Using Machine Learning and Remote Sensing Data. Data Metadata [Internet]. 2024 Oct 7;1. Available from: https://dm.ageditor.ar/index.php/dm/article/view/419/782 DOI: https://doi.org/10.56294/dm2024.419
45. Al-Shanableh N, Alzyoud M, Al-Husban RY, Alshanableh NM, Al-Oun A, Al-Batah MS, et al. Advanced ensemble machine learning techniques for optimizing diabetes mellitus prognostication: A detailed examination of hospital data. Data Metadata. 2024;3:363. DOI: https://doi.org/10.56294/dm2024.363
46. Al-Batah MS, Alzboon MS, Alzyoud M, Al-Shanableh N. Enhancing Image Cryptography Performance with Block Left Rotation Operations. Appl Comput Intell Soft Comput. 2024;2024(1):3641927. DOI: https://doi.org/10.1155/2024/3641927
47. Alqaraleh M, Alzboon MS, Al-Batah MS, Wahed MA, Abuashour A, Alsmadi FH. Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment. Int J online Biomed Eng. 2024;20(11):123–45. DOI: https://doi.org/10.3991/ijoe.v20i11.49673
48. Muhyeeddin A, Mowafaq SA, Al-Batah MS, Mutaz AW. Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation. LatIA [Internet]. 2024 Sep 29;2(74):74. Available from: https://latia.ageditor.uy/index.php/latia/article/view/74 DOI: https://doi.org/10.62486/latia202474
49. Putri AK, Alzboon MS. Doctor Adam Talib’s Public Relations Strategy in Improving the Quality of Patient Service. Sinergi Int J Commun Sci. 2023;1(1):42–54. DOI: https://doi.org/10.61194/ijcs.v1i1.19
50. Al-Batah MS, Alzboon MS, Alazaidah R. Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals. Int J Adv Comput Sci Appl. 2023;14(9):508–17. DOI: https://doi.org/10.14569/IJACSA.2023.0140954
51. Alzboon MS, Al-Batah MS. Prostate Cancer Detection and Analysis using Advanced Machine Learning. Int J Adv Comput Sci Appl. 2023;14(8):388–96. DOI: https://doi.org/10.14569/IJACSA.2023.0140843
52. Alzboon MS, Bader AF, Abuashour A, Alqaraleh MK, Zaqaibeh B, Al-Batah M. The Two Sides of AI in Cybersecurity: Opportunities and Challenges. In: Proceedings of 2023 2nd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2023. 2023. DOI: https://doi.org/10.1109/ICNGN59831.2023.10396670
53. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12. DOI: https://doi.org/10.1109/ComNet60156.2023.10366688
54. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12. DOI: https://doi.org/10.1109/ComNet60156.2023.10366703
55. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023. DOI: https://doi.org/10.1109/eSmarTA59349.2023.10293415
56. Alzboon MS, Al-Batah MS, Alqaraleh M, Abuashour A, Bader AFH. Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. Int J online Biomed Eng. 2023;19(15):144–65. DOI: https://doi.org/10.3991/ijoe.v19i15.42417
57. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023. DOI: https://doi.org/10.1109/eSmarTA59349.2023.10293294
58. Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett. 2022;11(4):1183–90. DOI: https://doi.org/10.18576/isl/110418
59. Alzboon M. Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions. Inf Sci Lett. 2022;11(5):1371–84. DOI: https://doi.org/10.18576/isl/110506
60. Alzboon MS, Aljarrah E, Alqaraleh M, Alomari SA. Nodexl Tool for Social Network Analysis. Turkish J Comput Math Educ. 2021;12(14):202–16.
61. Alomari SA, Salaimeh S Al, Jarrah E Al, Alzboon MS. Enhanced logistics information service systems performance: using theoretical model and cybernetics’ principles. WSEAS Trans Bus Econ [Internet]. 2020 Apr;17:278–87. Available from: https://wseas.com/journals/bae/2020/a585107-896.pdf DOI: https://doi.org/10.37394/23207.2020.17.29
62. Alomari SA, Alzboon MS, Al-Batah MS, Zaqaibeh B. A novel adaptive schema to facilitates playback switching technique for video delivery in dense LTE cellular heterogeneous network environments. Int J Electr Comput Eng [Internet]. 2020 Oct;10(5):5347. Available from: http://ijece.iaescore.com/index.php/IJECE/article/view/16563 DOI: https://doi.org/10.11591/ijece.v10i5.pp5347-5367
63. Alomari SA, Alqaraleh M, Aljarrah E, Alzboon MS. Toward achieving self-resource discovery in distributed systems based on distributed quadtree. J Theor Appl Inf Technol. 2020;98(20):3088–99.
64. Alzboon MS, Mahmuddin M, Arif S. Resource discovery mechanisms in shared computing infrastructure: A survey. In: Advances in Intelligent Systems and Computing. 2020. p. 545–56. DOI: https://doi.org/10.1007/978-3-030-33582-3_51
65. Shawawreh S, Alomari SA, Alzboon MS, Al Salaimeh S. Evaluation of knowledge quality in the E -learning system. Int J Eng Res Technol. 2019;12(4):548–53.
66. Alomari, Alzboon, Zaqaibeh, Al-Batah, Saleh Ali, Mowafaq Salem, Belal MS. An Effective Self-Adaptive Policy for Optimal Video Quality over Heterogeneous Mobile Devices and Network Discovery Services. Appl Math Inf Sci [Internet]. 2019 May;13(3):489–505. Available from: http://www.naturalspublishing.com/Article.asp?ArtcID=19739 DOI: https://doi.org/10.18576/amis/130322
67. Banikhalaf M, Alomari SA, Alzboon MS. An advanced emergency warning message scheme based on vehicles speed and traffic densities. Int J Adv Comput Sci Appl. 2019;10(5):201–5. DOI: https://doi.org/10.14569/IJACSA.2019.0100526
68. Alzboon MS. Internet of things between reality or a wishing - list : a survey. Int J Eng & Technol. 2019;7(June):956–61.
69. Al-Batah M, Zaqaibeh B, Alomari SA, Alzboon MS. Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers. Int J online Biomed Eng. 2019;15(8):62–73. DOI: https://doi.org/10.3991/ijoe.v15i08.10617
70. Al Tal S, Al Salaimeh S, Ali Alomari S, Alqaraleh M. The modern hosting computing systems for small and medium businesses. Acad Entrep J. 2019;25(4):1–7.
71. Alzboon MS, Alomari S, Al-Batah MS, Alomari SA, Banikhalaf M. The characteristics of the green internet of things and big data in building safer, smarter, and sustainable cities Vehicle Detection and Tracking for Aerial Surveillance Videos View project Evaluation of Knowledge Quality in the E-Learning System View pr [Internet]. Vol. 6, Article in International Journal of Engineering and Technology. 2017. p. 83–92. Available from: https://www.researchgate.net/publication/333808921
72. Arif S, Alzboon MS, Mahmuddin M. Distributed quadtree overlay for resource discovery in shared computing infrastructure. Adv Sci Lett. 2017;23(6):5397–401. DOI: https://doi.org/10.1166/asl.2017.7384
73. Mahmuddin M, Alzboon MS, Arif S. Dynamic network topology for resource discovery in shared computing infrastructure. Adv Sci Lett. 2017;23(6):5402–5. DOI: https://doi.org/10.1166/asl.2017.7385
74. Mowafaq Salem Alzboon M. Mahmuddin ASCA. Challenges and Mitigation Techniques of Grid Resource Management System. In: National Workshop on FUTURE INTERNET RESEARCH (FIRES2016). 2016. p. 1–6.
75. Al-Batah MS. Ranked features selection with MSBRG algorithm and rules classifiers for cervical cancer. Int J Online Biomed Eng. 2019;15(12):4. DOI: https://doi.org/10.3991/ijoe.v15i12.10803
76. Al-Batah MS. Integrating the principal component analysis with partial decision tree in microarray gene data. IJCSNS Int J Comput Sci Netw Secur. 2019;19(3):24-29.
77. Alzboon MS, Arif AS, Mahmuddin M. Towards self-resource discovery and selection models in grid computing. ARPN J Eng Appl Sci. 2016;11(10):6269–74.
78. Al-Batah MS, Al-Eiadeh MR. An improved binary crow-JAYA optimisation system with various evolution operators, such as mutation for finding the max clique in the dense graph. Int J Comput Sci Math. 2024;19(4):327-38. DOI: https://doi.org/10.1504/IJCSM.2024.139088
79. Alzboon MS, Sintok UUM, Sintok UUM, Arif S. Towards Self-Organizing Infrastructure : A New Architecture for Autonomic Green Cloud Data Centers. ARPN J Eng Appl Sci. 2015;1–7.
80. Kapoor S, Sharma A, Verma A, Dhull V, Goyal C. A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos. Int Arab J Inf Technol [Internet]. 2023;20(4). DOI: https://doi.org/10.34028/iajit/20/4/2
81. Al-Batah MS. Testing the probability of heart disease using classification and regression tree model. Annu Res Rev Biol. 2014;4(11):1713-25. DOI: https://doi.org/10.9734/ARRB/2014/7786
82. SalemAlzboon, Mowafaq and Arif, Suki and Mahmuddin, M and Dakkak O. Peer to Peer Resource Discovery Mechanisms in Grid Computing: A Critical Review. In: The 4th International Conference on Internet Applications, Protocols and Services (NETAPPS2015). 2015. p. 48–54.
83. Al-Batah MS. Modified recursive least squares algorithm to train the hybrid multilayered perceptron (HMLP) network. Appl Soft Comput. 2010;10(1):236-44. DOI: https://doi.org/10.1016/j.asoc.2009.06.018
84. Al-Batah MS, Al-Eiadeh MR. An improved discreet Jaya optimisation algorithm with mutation operator and opposition-based learning to solve the 0-1 knapsack problem. Int J Math Oper Res. 2023;26(2):143-69. DOI: https://doi.org/10.1504/IJMOR.2023.134491
85. Al-Oqily I, Alzboon M, Al-Shemery H, Alsarhan A. Towards autonomic overlay self-load balancing. In: 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013. Ieee; 2013. p. 1–6. DOI: https://doi.org/10.1109/SSD.2013.6564018
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