Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/1860
Title: Convolutional Neural Network Based SMS Spam Detection
Authors: Popovac M.
Karanović, Mirjana 
Sladojević, Srđan 
Arsenović, Marko 
Anderla, Andraš 
Issue Date: 1-Jan-2018
Journal: 2018 26th Telecommunications Forum, TELFOR 2018 - Proceedings
Abstract: © 2018 IEEE. SMS spam refers to undesired text message. Machine Learning methods for anti-spam filters have been noticeably effective in categorizing spam messages. Dataset used in this research is known as Tiago's dataset. Crucial step in the experiment was data preprocessing, which involved reducing text to lower case, tokenization, removing stopwords. Convolutional Neural Network was the proposed method for classification. Overall model's accuracy was 98.4%. Obtained model can be used as a tool in many applications.
URI: https://open.uns.ac.rs/handle/123456789/1860
ISBN: 9781538671702
DOI: 10.1109/TELFOR.2018.8611916
Appears in Collections:FTN Publikacije/Publications

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