{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:02:40Z","timestamp":1778083360708,"version":"3.51.4"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,12,17]]},"abstract":"<jats:p>Social communication has evolved, with e-mail still being one of the most common communication means, used for both formal and informal ways. With many languages being digitized for the electronic world, the use of English is still abundant. However, various native languages of different regions are emerging gradually. The Urdu language, coming from South Asia, mostly Pakistan, is also getting its pace as a medium for communications used in social media platforms, websites, and emails. With the increased usage of emails, Urdu\u2019s number and variety of spam content also increase. Spam emails are inappropriate and unwanted messages usually sent to breach security. These spam emails include phishing URLs, advertisements, commercial segments, and a large number of indiscriminate recipients. Thus, such content is always a hazard for the user, and many studies have taken place to detect such spam content. However, there is a dire need to detect spam emails, which have content written in Urdu language. The proposed study utilizes the existing machine learning algorithms including Naive Bayes, CNN, SVM, and LSTM to detect and categorize e-mail content. According to our findings, the LSTM model outperforms other models with a highest score of 98.4% accuracy.<\/jats:p>","DOI":"10.1155\/2021\/6508784","type":"journal-article","created":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T02:50:11Z","timestamp":1639795811000},"page":"1-11","source":"Crossref","is-referenced-by-count":33,"title":["Machine Learning-Based Detection of Spam Emails"],"prefix":"10.1155","volume":"2021","author":[{"given":"Zeeshan Bin","family":"Siddique","sequence":"first","affiliation":[{"name":"Department of Information Technology, The University of Haripur, Haripur, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1615-4605","authenticated-orcid":true,"given":"Mudassar Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, The University of Haripur, Haripur, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8896-547X","authenticated-orcid":true,"given":"Ikram Ud","family":"Din","sequence":"additional","affiliation":[{"name":"Department of Information Technology, The University of Haripur, Haripur, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8253-9709","authenticated-orcid":true,"given":"Ahmad","family":"Almogren","sequence":"additional","affiliation":[{"name":"Chair of Cyber Security, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia"}]},{"given":"Irfan","family":"Mohiuddin","sequence":"additional","affiliation":[{"name":"Chair of Cyber Security, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0126-9944","authenticated-orcid":true,"given":"Shah","family":"Nazir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Swabi, Swabi, Pakistan"}]}],"member":"311","reference":[{"key":"1","first-page":"108","article-title":"Email spam detection using machine learning algorithms","author":"N. Kumar"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-018-0157-5"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2918196"},{"key":"4","first-page":"168","article-title":"A mechanism to detect Urdu spam emails","author":"A. Akhtar"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/72.788645"},{"key":"6","first-page":"710","article-title":"Spam filtering of bi-lingual tweets using machine learning","author":"H. Afzal"},{"key":"7","first-page":"915","article-title":"Email spam filtering using bpnn classification algorithm","author":"S. K. Tuteja"},{"key":"8","first-page":"227","article-title":"An evaluation on the efficiency of hybrid feature selection in spam email classification","author":"M. Mohamad"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.22266\/ijies2018.0630.01"},{"key":"10","first-page":"69","article-title":"Email spam detection: an empirical comparative study of different ml and ensemble classifiers","author":"S. 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