Publication:
Filtering Turkish Spam Using LSTM from Deep Learning Techniques

dc.authorscopusid57217830257
dc.authorscopusid56589621700
dc.authorscopusid22953804000
dc.contributor.authorEryilmaz, E.E.
dc.contributor.authorŞahin, D.Ö.
dc.contributor.authorKilic, E.
dc.date.accessioned2025-12-11T00:22:40Z
dc.date.issued2020
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Eryilmaz] Ersin Enes, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Şahin] Durmuş Ozkan, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Kilic] Erdal, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkeyen_US
dc.descriptionIEEE Societyen_US
dc.description.abstractE-mails are used effectively by people or communities who want to do propaganda, advertisement, and phishing because of their ease of use and low cost. People or communities who want to achieve their goals send unnecessary and spam to the e-mail accounts they never knew. These mails cause serious financial and moral damages to internet users and also engage in internet traffic. Unsolicited e-mails (spam) are a method sent to the recipient without their consent and generally for malicious or promotional purposes. In this study, spam was detected with Keras deep learning library on the Turkish dataset. Turkish email dataset contains 800 e-mails, half of which are spam e-mails. With the deep learning algorithm long short term memory (LSTM), a 100% accuracy rate has been achieved in the Turkish e-mail dataset. © 2020 IEEE.en_US
dc.identifier.doi10.1109/ISDFS49300.2020.9116440
dc.identifier.isbn9781728169392
dc.identifier.scopus2-s2.0-85087632638
dc.identifier.urihttps://doi.org/10.1109/ISDFS49300.2020.9116440
dc.identifier.urihttps://hdl.handle.net/20.500.12712/36267
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof-- 8th International Symposium on Digital Forensics and Security, ISDFS 2020 -- 2020-06-01 through 2020-06-02 -- Beirut -- 161222en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDeep Learningen_US
dc.subjectKeras Libraryen_US
dc.subjectLSTMen_US
dc.subjectMachine Learningen_US
dc.subjectSpam Detectionen_US
dc.subjectTurkish Spam Filteringen_US
dc.titleFiltering Turkish Spam Using LSTM from Deep Learning Techniquesen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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