[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119440-en":3,"doc-seo-119440-105":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},119440,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","SMS Spam Classification Using Machine Learning - Thesis","Email and text messaging have become essential as mobile use expands rapidly, while Short Message Service (SMS) supports fast global communication for personal and business purposes. Alongside legitimate messages, users frequently receive fraudulent and irrelevant SMS content, creating a persistent SMS spam problem. Identifying spam messages accurately is a difficult but critical task, treated as a serious challenge in text analytics. This thesis focuses on classifying SMS spam using machine learning approaches.","CALIFORNIA STATE UNIVERSITY SAN MARCOS  \nTHESIS SIGNATURE PAGE  \nTHESIS SUBMITTED IN PARTIAL FULFILLMENT  \nOF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nIN  \nCOMPUTER SCIENCE  \nTHESIS TITLE: SMS SPAM CLASSIFICATION USING MACHINE LEARNING  \nAUTHOR: MANDAR SHIVAJI HANCHATE  \nDATE OF SUCCESSFUL DEFENSE: 04/12/2023  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN  \nPARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE.  \nDR. NAHIDEBRAHIMI MAJD  THESIS COMMITTEE CHAIR  \nDR. SREEDEVI GUTTA  THESIS COMMITTEE MEMBER  \nTHESIS COMMITTEE MEMBER  \n\n| SIGNATURE |\n| --- |\n| SIGNATURE |\n\nSIGNATURE  \n\n| DATE |\n| --- |\n| DATE |\n\nDATE  \nACKNOWLEDGMENT  \nI would like to express my gratitude and appreciation to all those who have contributed to the completion of this thesis. First and foremost, I am grateful to my advisor Dr. Nahid Ebrahimi Majd, for her unwavering guidance and support throughout the research process. Her insightful feedback, encouragement, and patience have been invaluable in shaping this thesis.  \nI am also thankful to Dr. Sreedevi Gutta for her input, suggestions, and guidance have greatly enhanced the quality of this work. Lastly, I would like to acknowledge the support and resources provided by California State University San Marcos. Without the infrastructure and facilities offered by the institution, this thesis would not have been possible. Thank you all for your support, encouragement, and guidance throughout this process.  \nTABLE OF CONTENT  \nLIST OF THE FIGURES................................................................................................................. 0  \nLIST OF THE TABLES .................................................................................................................. 0  \nABSTRACT...................................................................................................................................... 1  \nCHAPTER 1: INTRODUCTION .................................................................................................... 1  \n1.1 Problem Statement: .................................................................................................................. 1  \n1.2 Purpose Of The Study And Motivation:...................................................................................... 1  \n1.3 Research Methodology: ............................................................................................................ 1  \n1.4 Research Scope: ........................................................................................................................ 2  \nCHAPTER 2: RELATED WORK................................................................................................... 3  \nCHAPTER 3: METHODOLOGY................................................................................................... 4  \n3.1 Dataset: .................................................................................................................................... 4  \n3.2 Data Preprocessing: .................................................................................................................. 4  \n3.3 Proposed Approach:.................................................................................................................. 5  \n3.4 Machine Learning Techniques: .................................................................................................. 6  \n3.5 Deep Learning Techniques: ....................................................................................................... 8  \nCHAPTER 4: ANALYSIS OF THE RESULTS.............................................................................. 8  \n4.1 Evaluation Metrics: ................................................................................................................... 8  \n4.2 Results: .........................................................................................................................","cbCairkxb9PmyUAQ","https://ap.wps.com/l/cbCairkxb9PmyUAQ","pdf",723111,1,19,"English","en",105,"# Chapter 1: Introduction\n## Problem Statement\n## Purpose Of The Study And Motivation\n## Research Methodology\n## Research Scope\n# Chapter 2: Related Work\n# Chapter 3: Methodology\n## Dataset\n## Data Preprocessing\n## Proposed Approach\n## Machine Learning Techniques\n## Deep Learning Techniques\n# Chapter 4: Analysis of the Results\n## Evaluation Metrics\n## Results\n## Discussion\n# Chapter 5: Conclusion\n# References","[{\"question\":\"Why is SMS spam detection important and difficult?\",\"answer\":\"Fraudulent and irrelevant SMS messages are common and highly inconvenient for users. Accurate detection is challenging and considered a serious issue in text analysis.\"},{\"question\":\"What does the thesis method section cover?\",\"answer\":\"The methodology includes the dataset, data preprocessing, a proposed approach, and both machine learning and deep learning techniques.\"},{\"question\":\"How are model performance results evaluated?\",\"answer\":\"The results analysis uses evaluation metrics and reports findings such as confusion matrices and ROC curves, along with recall and F1 score trends across epochs for the BERT+BiLSTM model.\"}]","SMS Spam Classification Using Machine Learning - Thesis | PDF",1785724294,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"sms-spam-classification-using-machine-learning-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/sms-spam-classification-using-machine-learning-thesis/119440/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is SMS spam detection important and difficult?","Question",{"text":75,"@type":76},"Fraudulent and irrelevant SMS messages are common and highly inconvenient for users. Accurate detection is challenging and considered a serious issue in text analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the thesis method section cover?",{"text":80,"@type":76},"The methodology includes the dataset, data preprocessing, a proposed approach, and both machine learning and deep learning techniques.",{"name":82,"@type":73,"acceptedAnswer":83},"How are model performance results evaluated?",{"text":84,"@type":76},"The results analysis uses evaluation metrics and reports findings such as confusion matrices and ROC curves, along with recall and F1 score trends across epochs for the BERT+BiLSTM model.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]