[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125803-en":3,"doc-seo-125803-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125803,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","CREDIT CARD FRAUD DETECTION USING NEW PREPROCESSING AND HYBRID MACHINE LEARNING TECHNIQUES - Thesis","Credit card fraud detection focuses on recognizing fraudulent transactions within highly imbalanced data, where misclassification has major financial impact. This thesis proposes a workflow that introduces new preprocessing steps and evaluates hybrid machine learning techniques to improve detection accuracy. The work is grounded in a structured study design covering research problem definition, objectives, hypotheses, dataset characteristics, data exploration, and a review of related literature on financial fraud and hybrid modeling approaches.","CREDIT CARD FRAUD DETECTION USING NEW PREPROCESSING AND HYBRID MACHINE LEARNING TECHNIQUES  \nESRAA FAISAL MALIK GASIM  \nUNIVERSITI SAINS MALAYSIA  \nCREDIT CARD FRAUD DETECTION USING NEW PREPROCESSING AND HYBRID MACHINE LEARNING TECHNIQUES  \nby  \nESRAA FAISAL MALIK GASIM  \nThesis submitted in fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nACKNOWLEDGEMENT  \nThe Prophet Muhammad ﷺ said:  \n‘He who is not grateful to people, is not grateful to Allah’  \nThroughout the writing of this dissertation, I have received a great deal of support and assistance. Words cannot express my gratitude to my supervisor Ts. Dr. Khaw Khai Wah, your insightful feedback pushed me to sharpen my thinking and brought my work to a higher level. I would also like to thank my co-supervisor Ts. Dr. Chew XinYing, you provided me with the tools I needed to successfully complete my work and finish my dissertation.  \nI also could not have undertaken this journey without my defense committee members for letting my proposal defense be an enjoyable moment and for your valuable comments and suggestions that helped me sharpen my work and dissertation. Thanks should also go to the librarians, and research participants from the university especially SOM, who impacted and inspired me.  \nThis endeavor would not have been possible without my family, especially my parents, brothers, and husband for their unlimited emotional support. Their belief in me has kept my spirits and motivation high during this process. I am also grateful tomy friends who have supported me along the way. Thanks to all of you !  \nTABLE OF CONTENTS  \nACKNOWLEDGEMENT......................................................................................... ii  \nTABLE OF CONTENTS ......................................................................................... iii  \nLIST OF TABLES ................................................................................................... vii  \nLIST OF FIGURES .................................................................................................. ix  \nLIST OF ABBREVIATIONS .................................................................................. xi  \nLIST OF APPENDICES ......................................................................................... xv  \nABSTRAK ............................................................................................................... xvi  \nABSTRACT............................................................................................................. xix  \nCHAPTER 1 INTRODUCTION.......................................................................... 1  \n1.1 Background of the Research ............................................................................ 1  \n1.2 Problem Statement ........................................................................................... 8  \n1.3 Research Questions ........................................................................................ 10  \n1.4 Research Objectives ....................................................................................... 11  \n1.5 Scope .............................................................................................................. 11  \n1.5.1 Dataset Characteristics ................................................................... 12  \n1.6 Significance of Research ................................................................................ 13  \n1.7 Definition of Key Terms ................................................................................ 16  \n1.8 Organization of The Thesis ............................................................................ 19  \nCHAPTER 2 LITERATURE REVIEW............................................................ 21  \n2.1 Introduction .................................................................................................... 21  \n2.2 Financial Fraud Detection Using ML Algorithms .............................","cbCaidBAm0WPjXWX","https://ap.wps.com/l/cbCaidBAm0WPjXWX","pdf",552208,1,45,"English","en",105,"# ACKNOWLEDGEMENT\n# TABLE OF CONTENTS\n## LIST OF TABLES\n## LIST OF FIGURES\n## LIST OF ABBREVIATIONS\n## LIST OF APPENDICES\n# CHAPTER 1 INTRODUCTION\n## Background of the Research\n## Problem Statement\n## Research Questions\n## Research Objectives\n## Scope\n## Significance of Research\n## Definition of Key Terms\n## Organization of The Thesis\n# CHAPTER 2 LITERATURE REVIEW\n## Introduction\n## Financial Fraud Detection Using ML Algorithms\n## Credit Card Fraud Detection\n## Highly Imbalanced Datasets\n## Summary\n# CHAPTER 3 METHODOLOGY\n## Introduction\n## Hypotheses\n## Research Methodology Organization\n## Data Collection\n## Data Exploration","[{\"question\":\"What is the main research focus of this thesis?\",\"answer\":\"The thesis focuses on credit card fraud detection using new preprocessing steps and hybrid machine learning techniques to better identify fraudulent transactions.\"},{\"question\":\"Why is dataset imbalance important in credit card fraud detection?\",\"answer\":\"Fraud datasets are highly imbalanced, causing standard models to underperform on the minority fraud class, which affects detection reliability.\"},{\"question\":\"What areas are covered before implementing the proposed approach?\",\"answer\":\"The thesis includes a literature review covering financial fraud and credit card fraud, along with discussion of highly imbalanced datasets, then defines objectives, hypotheses, and data collection and exploration steps.\"}]","CREDIT CARD FRAUD DETECTION USING NEW PREPROCESSING AND HYBRID MACHINE LEARNING TECHNIQUES - 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