A Comprehensive Approach to SMS Spam Filtering Integrating Embedded and Statistical Features
Author:
Affiliation:
1. Alzahra University,Faculty of Engineering,Data Mining Laboratory, Department of Computer Engineering,Tehran,Iran
2. Alzahra University,Faculty of Engineering,Department of Computer Engineering,Tehran,Iran
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10326217/10326219/10326281.pdf?arnumber=10326281
Reference20 articles.
1. A Comparative Analysis of Recurrent Neural Network and Support Vector Machine for Binary Classification of Spam Short Message Service;odera,0
2. SMS spam filtering and thread identification using bi-level text classification and clustering techniques
3. HQEBSKG: Hybrid Query Expansion Based on Semantic Knowledgebase and Grouping;keyvanpour;IETE J Res,2020
4. Spam Filtering of Mobile SMS Using CNN–LSTM Based Deep Learning Model;hossain;Lecture Notes in Networks and Systems,2022
5. Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning can They Capture?
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