Accurate SMS Spam Detection Using Support Vector Machine in Comparison with Logistic Regression-Reference-Cited by-同舟云学术

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Accurate SMS Spam Detection Using Support Vector Machine in Comparison with Logistic Regression

Published:2023-04-06 Issue: Volume: Page:
ISSN:
Container-title:2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
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Author:

K Ram Bheemesh.¹,N Deepa.¹

Affiliation:

1. Saveetha University,Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Department of Computer Science and Engineering,Chennai,Tamilnadu

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10142230/10142223/10142266.pdf?arnumber=10142266

Reference16 articles.

1. An Effective Model for SMS Spam Detection Using Content-Based Features and Averaged Neural Network;sheikhi;Int Journal of Engineering,2020

2. Mobile SMS Spam Filtering for Nepali Text Using Naïve Bayesian and Support Vector Machine

3. Support Vector Machines and Random Forests Modeling for Spam Senders Behavior Analysis

4. Strong and stable Data communication Using Artificial Intelligence method in Mobile Ad-Hoc Networks

5. Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks

Cited by 1 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. Safeguarding SMS: A Dynamic Duo Approach to Tackle Spam Using LDA and QDA;2023 Innovations in Power and Advanced Computing Technologies (i-PACT);2023-12-08

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