[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119398-en":3,"doc-seo-119398-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119398,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","DIAGNOSIS OF DISEASES THROUGH GENES BY USING MACHINE LEARNING","The thesis develops an automated approach for diagnosing diseases from genetic information using machine learning methods. It focuses on building effective datasets and modeling strategies to support accurate identification of disease-related patterns. The work considers multiple machine learning classifiers and feature representations, and compares their performance to determine which configuration best improves predictive accuracy. Results indicate that hybrid designs combining different representations and learning components can outperform standard machine learning baselines, supporting more reliable disease diagnosis decisions.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY Institute of Graduate Studies  \nInformation Technologies  \nDIAGNOSIS OF DISEASES THROUGH GENESBY  \nUSING MACHINE LEARNING  \nIsraa Jihad Abed ABED  \nMaster`s Thesis  \nSupervisor  \nAsst. Prof. Dr. Oguz KARAN  \nIstanbul, 2023  \nDIAGNOSIS OF DISEASES THROUGH GENES BY USING  \nMACHINE LEARNING  \nIsraa Jihad Abed ABED  \nInformation Technologies  \nMaster`s Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled Development of DIAGNOSIS OF DISEASES THROUGH GENES BY USING MACHINE LEARNING prepared by ISRAA JİHAD ABED ABED and submitted on 11/04/2023 has been accepted unanimously for the degree of Master of Science in Information Technologies.  \nAsst. Prof. Dr. Oguz KARAN  \nSupervisor  \nThesis Defense Committee Members:  \nAsst. Prof. Dr. . Oguz KARAN  \nAsst. Prof. Dr. Abdullahi abdu  \nIBRAHIM  \nDepartment of Electrical and  \nElectronics Engineering,  \nAltınbaş University  \nDepartment of Computer  \nEngineering,  \nAltınbaş University  \n__________________  \n__________________  \nAssoc. Prof. Dr. Izzet paruğ DURU Department of Medical Imaging  \nTechniques,  \nIstanbul Gedik University    \nI hereby declare that this thesis meets all format and submission requirements ofa Master’s thesis.  \nSubmission date of the thesis to the Institute of Graduate Studies:  / /   \nI hereby declare that all information presented in this graduation project has been obtained in full accordance with academic rules and ethical conduct. I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and vice versa as required by the abovementioned rules and conduct.  \nIsraa Jihad Abed ABED  \nSignature  \nABSTRACT  \nDIAGNOSIS OF DISEASES THROUGH GENES BY USING  \nMACHINE LEARNING  \nABED, Israa Jihad Abed  \nM.Sc., Information Technologies, Altınbaş University,  \nSupervisor: Asst. Prof. Dr. Oguz KARAN  \nDate: April / 2023  \nPages: 72  \nThe Internet is a vast and ever-expanding source of texts, from casual posts to in-depth talks, with a wide range of perspectives and ends in mind. Attempts at polarity recognition and target extraction are time-consuming because it's important to figure out how the object is oriented and where it's going. The implications for national defence and the study of politics are profound. Some research has been done, however, on a subject that is peripheral to the present discussion. Therefore, the primary goal of this research is to identify the optimal solution, particularly with regards to Arabic political articles, for which there is currently no dataset. The need to reevaluate traditional approaches to extracting sentiment from texts has arisen in response to the growing complexity of both text sources and subjects. This dissertation's overarching goal is to help the government make better choices through the use of an opinion-mining-based support system. In particular, the research focuses on political Arabic articles and the classification method used to develop a new decisionmaking technique. This dissertation also suggested a new framework for detecting polarity and targets in published political articles using a combination of lexicon-based techniques, machine learning, and a hybrid approach. As part of the research, four distinct corpora were constructed (V1, V2, V3, and V4) . The Mandalay archive houses these compiled corpora from various online sources. In order to determine the polarity of papers, this research utilised machine learning and a hybrid approach. Support Vector Machine (SVM), Naive  \nBayes (NB), KNearest Neighbor (KNN), and Decision Tree (DT) are included in the firstgroup, as are the two feature extractions, Bundle of Words (BOW), and n-grams. The latter strategy combined a lexicon-based vector with a classification based on Rough Set Theory (RST) . In this research, the human-based seed word was used in conjunction with lower approximation, a sentence target extraction technique. The res","cbCaidJoEURg0W0d","https://ap.wps.com/l/cbCaidJoEURg0W0d","pdf",2582424,1,76,"English","en",105,"# 1. 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