A comparative study of word embedding techniques for SMS spam detection-Reference-Cited by-同舟云学术

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A comparative study of word embedding techniques for SMS spam detection

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)
language:
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Author:

Joseph Prashob¹,Yerima Suleiman Y.²

Affiliation:

1. School Computer Science and Informatics, De Montfort University,Faculty of Computing, Engineering and Media,Leicester,United Kingdom

2. Cyber Technology Institute, De Montfort University,Faculty of Computing, Engineering, and Media,Leicester,United Kingdom

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10008224/10008233/10008245.pdf?arnumber=10008245

Reference27 articles.

1. Email Spam Detection using Bidirectional Long Short Term Memory with Convolutional Neural Network

2. Word sense disambiguation for spam filtering

3. LSTM Based Short Message Service (SMS) Modeling for Spam Classification

4. Contributions to the study of SMS spam filtering

5. Semi-supervised novelty detection with one class SVM for SMS spam detection

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Impact of Preprocessing Techniques Towards Word Embedding;Advances in Visual Informatics;2023-10-20

2. SMS Spam Detection Using Deep Learning Techniques: A Comparative Analysis of DNN Vs LSTM Vs Bi-LSTM;2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES);2023-04-28

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