[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119254-en":3,"doc-seo-119254-105":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},119254,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Integrating Handwriting Analysis and Machine Learning for Enhanced Personality Trait Prediction","This thesis presents an in-depth exploration of graphology and its integration with machine learning to analyze personality traits through handwriting. The work connects historical graphological ideas from the 19th century with modern predictive modeling grounded in neuromuscular expression. A dataset of 1,108 handwriting samples was used, labeled by a business-oriented graphology expert and enriched with CENPARMI contributions. Models including KNN, Random Forest, Logistic Regression, and transfer learning via VGG16 were evaluated with SMOTE balancing and ensemble strategies such as Majority Voting and Stacking.","Integrating Handwriting Analysis and Machine Learning for Enhanced Personality Trait Prediction  \nMaedeh Safar  \nA Thesis  \nin  \nThe Department  \nof  \nComputer Science and Software Engineering  \nPresented in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science (Software Engineering) at  \nConcordia University  \nMontreal, Quebec, Canada  \nMay 2024  \n© Maedeh Safar, 2024  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Maedeh Safar  \nEntitled: Integrating Handwriting Analysis and Machine Learning for Enhanced  \nPersonality Trait Prediction  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Applied Science (Software Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the final examining committee:  \nDr. Shin Hwei Tan  Chair Dr. Charalambos Poullis  Examiner Dr. Shin Hwei Tan  Examiner Dr. Ching Y. Suen  Thesis Supervisor  \nApproved by   Dr. Hovhannes Harutyunyan   \nChair of Department or Graduate Program Director  \n Dr._Mourad Debbabi   \nDean of Faculty Gina Cody of ENCS  \nAbstract  \nIntegrating Handwriting Analysis and Machine Learning for Enhanced Personality Trait  \nPrediction  \nMaedeh Safar  \nThis thesis presents an in-depth exploration of graphology and its integration with machine learning to analyze personality traits through handwriting. The motivation for this research stems from the brain's ability to express personality traits through neuromuscular movements, particularly in handwriting. This study bridges the historical graphological methods, tracing back to the 19th century, with contemporary machine learning techniques.  \nThis research utilized a dataset of 1,108 handwriting examples. CENPARMI contributed 234 of these, while the remaining 874 were procured through a business-oriented graphology expert. The data used comprises a diverse set of handwriting samples, analyzed using machine learning algorithms such as KNN, Random Forest, Logistic Regression, and specifically the VGG16 model for transfer learning. The research employs techniques like SMOTE for data balancing and ensemble methods for classification, including Majority Voting and Stacking Method.  \nExperimental results demonstrate a significant improvement in the accuracy of personality trait predictions after using SMOTE, with the highest accuracy exceeding 90% for traits like \"Agreeableness\" and \"Open to Experience\" using the Ensemble method (Stacking Method). Thus, this integrated approach produces better results.  \nThe main contributions of this research lie in its innovative integration of graphology and machine learning for personality assessment, methodological advancements in handling imbalanced datasets, and the application of transfer learning in handwriting analysis. The improved accuracy in personality trait prediction illustrates the potential of this interdisciplinary approach in fields such as psychology and personalized services, offering new insights into personality psychology and opening avenues for future research in this domain.  \nAcknowledgments  \nI would like to express my heartfelt gratitude to those who have been instrumental in the successful completion of this thesis. My deepest thanks go to Dr. Ching Y. Suen for his patient guidance and invaluable support, which have been vital in shaping this work. I am particularly grateful to my husband, Mahdi Tajeddin, for his support and encouragement during this demanding journey. Special appreciation is also extended to Graziella Pettinati for her dedicated assistance in graphology and dataset labeling. Thanks to Mr. Nicola Nobile for his assistance and the resources he provided for my study. I must acknowledge the joy and happiness my daughter Ala brought me throughout this challenging period, and I am thankful for my friends who offered both stimulating discussions and w","cbCaiqgvshNtqGmC","https://ap.wps.com/l/cbCaiqgvshNtqGmC","pdf",2957482,1,91,"English","en",105,"# Introduction\n## Literature Review\n## Features\n## Experimental Results\n## Conclusions","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"The thesis aims to integrate graphology with machine learning to predict personality traits from handwriting with improved accuracy.\"},{\"question\":\"What dataset and labeling sources were used?\",\"answer\":\"The research uses 1,108 handwriting examples, with 234 provided by CENPARMI and the remaining 874 collected through a business-oriented graphology expert for dataset labeling.\"},{\"question\":\"Which machine learning methods and techniques are applied?\",\"answer\":\"The study evaluates KNN, Random Forest, Logistic Regression, and VGG16 for transfer learning, and it uses SMOTE for data balancing and ensemble methods such as Majority Voting and Stacking.\"}]","Integrating Handwriting Analysis and Machine Learning for Enhanced Personality Trait Prediction | 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