[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121218-en":3,"doc-seo-121218-105":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},121218,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Integrating Three Machine Learning Algorithms in Ensemble Learning Model for Improving Content-based Spam Email Recognition","Email spam consists of unwanted messages that may include links to phishing sites and can be harmful to recipients. This study builds an email spam recognition approach using machine learning classification, separating spam and non-spam emails with CRISP-DM. Experiments use ESC datasets containing 5172 rows and 3002 feature columns. Three algorithms—Naive Bayes, Logistic Regression, and Random Forest—are evaluated using F1-score, accuracy, precision, and recall. Random Forest reaches 97.3% accuracy, while the combined ensemble model achieves 98.9% accuracy, 97.6% precision, 97.4% recall, and 96.7% F1-score.","Integrating Three Machine Learning Algorithms in Ensemble Learning Model for Improving Content-based Spam Email Recognition  \nAli Q. Saeed 1*, Mohammed Hasan Aldulaimi2, Ismail Abdulwahhab Ismail3, Ibrahim M. Ahmed4, Yahya Ahmed Yahya1, Qasem M. Kharma5, Taher M. Ghazal6  \n1 Technical Engineering College for Computer and AI,  \nNorthern Technical University, Mosul, 41000, Nineveh, IRAQ  \n2 Department of Computer Techniques Engineering, College of Engineering,  \nAl-Mustaqbal University, Hillah, 51001, Babylon, IRAQ  \n3 epartment of Translation, College of Arts, Alnoor University, Mosul, 41012 Nineveh, IRAQ  \n4 ollege of Computer Sciences and Mathematics, University of Mosu , 41000, Nineveh, IRAQ  \n5 Software Engineering Department,  \nHourani Center for Applied Scientific Research, A-Ahliyya Amman University, Amman, JORDAN  \n6 esearch Innovation and Entrepreneurship Unit, University of Buraimi, Buraimi 51 OMAN  \n*Corresponding Author: [ali.qasim@ntu.edu.iq](ali.qasim@ntu.edu.iq)  \nDOI: [https://doi.org/10.30880/jscdm.2024.05.02.014](https://doi.org/10.30880/jscdm.2024.05.02.014)  \nArticle Info  \nReceived: 29 June 2024  \nAccepted: 3 December 2024  \nAvailable online: 18 December 2024  \nKeywords  \nEmail spam, machine learning, classification, ensemble, random forest, naive Bayes, linear regression.  \nAbstract  \nEmail spam refers to junk files, images, or data sent through email that might contain links leading to phishing websites. This email is often sent repeatedly to random users, and sometimes it may be dangerous. The objective of this study is to predict and recognize whether the emails sent to users are spam or not by using machine learning classification algorithms. Email Spam Classification (ESC) datasets are used in this study for spam detection tests. The ESC datasets contain 5172 rows and 3002 columns of spam and non-spam features. The methodology used in this study is the CRISP-DM to guide the process of evaluating the performance of three machine learning algorithms: Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF) . Subsequently, an ensemble model that integrates the three machine learning algorithms is proposed to improve the performance of spam email recognition. The selected evaluation metrics are F1-Score, accuracy, precision, and recall. Based on the results, the RF algorithm has the highest accuracy of 97.3% in classifying spam emails, with an F1 score of 96.8%, precision of 96.2%, and recall of 96.0%. The NB achieves the best second results, which are slightly different from the RF, and the LR achieves considerably lower results than the other two algorithms. The ensemble model that integrates the three algorithms performs best in classifying spam emails with 98.9% accuracy, 97.6% precision, 97.4% recall, and 96.7% F1-score.  \n1. Introduction  \nThe email application is a popular method of communicating on the Internet. In this metropolitan era, people are communicating with each other through various platforms and online networks. Emails can be accessed by  \nindividuals who have access to the Internet using their email accounts. Correspondingly, emails can be constructed by the existence of recipients' and senders' email addresses [1], [2] . The email address is the destination where the email message will be sent and from where it is sent. Email is one of the easiest ways to spread the message from one person to another in different places. Besides, it is an organized step because it is kept inside a proper database so that it can be retrieved easily at any time with the help of the Internet [3], [4] . Multiple platforms like [Google.com](Google.com), [yahoo.com](yahoo.com), and many more can connect emails. The platform acts as the intermediary between sender and receiver to send and receive email messages. Meanwhile, users commonly use many types of emails, such as newsletters, promotions, surveys, and lead nurturing emails.  \nThe cost-effectiveness and speed of email communication are among","cbCaiq4QtzYCWlGo","https://ap.wps.com/l/cbCaiq4QtzYCWlGo","pdf",624227,1,9,"English","en",105,"# Introduction\n## Email as a communication platform\n## Spam risks and challenges\n## Prior research and evaluation rationale","[{\"question\":\"What problem does the study address in email security?\",\"answer\":\"The study targets spam email recognition by predicting whether incoming emails are spam or non-spam, aiming to reduce harmful phishing and unwanted messages.\"},{\"question\":\"Which machine learning algorithms are compared in the experiments?\",\"answer\":\"The study evaluates Naive Bayes, Logistic Regression, and Random Forest using CRISP-DM to guide model evaluation.\"},{\"question\":\"How does the proposed ensemble model perform compared with individual algorithms?\",\"answer\":\"The ensemble model combining the three algorithms performs best, reaching 98.9% accuracy, 97.6% precision, 97.4% recall, and 96.7% F1-score, outperforming Random Forest alone.\"}]","Integrating Three Machine Learning Algorithms in Ensemble Learning Model for Improving Content-based Spam Email Recognition | 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problem does the study address in email security?","Question",{"text":76,"@type":77},"The study targets spam email recognition by predicting whether incoming emails are spam or non-spam, aiming to reduce harmful phishing and unwanted messages.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are compared in the experiments?",{"text":81,"@type":77},"The study evaluates Naive Bayes, Logistic Regression, and Random Forest using CRISP-DM to guide model evaluation.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed ensemble model perform compared with individual algorithms?",{"text":85,"@type":77},"The ensemble model combining the three algorithms performs best, reaching 98.9% accuracy, 97.6% precision, 97.4% recall, and 96.7% F1-score, outperforming Random Forest 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