Master\u2019s Thesis Defense at the College of Computer Science and Mathematics\/ Department of Mathematics Sciences entitled:<\/p>\n
\u201c<\/strong>Improving fuzzy c-means clustering using the generalized normal distribution optimization algorithm for unsupervised dataset<\/strong>\u201d<\/strong><\/p>\n The Master\u2019s thesis of the student Moatasem Mahmood Ibrahim<\/strong> Mohammed<\/strong> was discussed on Sunday<\/strong>, July 13, 2025<\/strong>, in the discussion hall at the College of Computer Science and Mathematics, University of Mosul, under the supervision of Prof. Dr. Omar Saber Qasim<\/strong>, Dr. <\/strong>Talal Fadhil Hussein<\/strong>.<\/p>\n In this thesis, the performance of the FCM algorithm by integrating it with the Generalized Normal Distribution Optimization Algorithm (GNDOA), ametaheuristic optimization technique that has demonstrated its effectiveness in solving complex and nonlinear optimization problems. The proposed model utilizes GNDO to select the most relevant features from the dataset, thereby reducing dimensionality, improving data quality for clustering, and enhancing the exploration of the search space. we focus on the application of the performance of the hybrid \ud835\udc3a\ud835\udc41\ud835\udc37\ud835\udc42\u2212\ud835\udc39\ud835\udc36\ud835\udc40 algorithm was evaluated using the Silhouette Score to assess clustering accuracy and feature Selection effectiveness. Additionally, the optimal number of clusters was determined using the Calinski-Harabasz (CH) index. The evaluation was conducted on several benchmark datasets obtained from the UCI Machine Learning Repository. Experimental results demonstrated the superiority of the proposed algorithm over traditional methods, showing significant improvements in clustering accuracy, feature selection, and the ability to handle high-dimensional data. This study highlights the importance of leveraging intelligent optimization algorithm such as GNDO to enhance the efficiency of clustering techniques. The findings indicate that the proposed algorithm \ud835\udc3a\ud835\udc41\ud835\udc37\ud835\udc42\u2212\ud835\udc39\ud835\udc36\ud835\udc40\ud835\udc36\ud835\udc3b is the most efficient model, offering higher accuracy and optimal feature selection. As such, it is well suited for advanced applications in healthcare, medical data analysis, bioinformatics classification, pattern recognition, and computer vision.<\/p>\n The Examination Committee included:<\/strong><\/p>\n Prof. Dr. Abdulghafoor Jasim Salim \/ Committee Chair <\/strong><\/p>\n Asst. Prof. Dr. Niam Abdulmunim Abdulmajeed \/ Committee Member<\/strong><\/p>\n Asst. Prof. Dr. Fatima Mahmood Hassan \/ Committee Member<\/strong><\/p>\n Prof. Dr. Omar Saber Qasim \/ Committee Member (Supervisor)<\/strong><\/p>\n Dr<\/strong>. <\/strong>Talal Fadhil Hussein <\/strong>\/ Committee Member (Supervisor)<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" Master\u2019s Thesis Defense at the College of Computer Science and Mathematics\/ Department of Mathematics Sciences entitled: \u201cImproving fuzzy c-means clustering using the generalized normal distribution optimization algorithm for unsupervised dataset\u201d The Master\u2019s thesis of the student Moatasem Mahmood Ibrahim Mohammed was discussed on Sunday, July 13, 2025, in the discussion hall at the College of Computer Science and Mathematics, University of Mosul, under the supervision of Prof. Dr. Omar Saber Qasim, Dr. Talal Fadhil Hussein. In this thesis, the performance of the FCM algorithm by integrating it with the Generalized Normal Distribution Optimization Algorithm (GNDOA), ametaheuristic optimization technique that has demonstrated [Read More]<\/a><\/p>\n","protected":false},"author":24,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-41097","post","type-post","status-publish","format-standard","hentry","category-viva"],"_links":{"self":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/41097","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/users\/24"}],"replies":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/comments?post=41097"}],"version-history":[{"count":1,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/41097\/revisions"}],"predecessor-version":[{"id":41098,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/41097\/revisions\/41098"}],"wp:attachment":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media?parent=41097"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/categories?post=41097"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/tags?post=41097"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}