A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection-Reference-Cited by-同舟云学术

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A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection

Published:2023-12-02 Issue: Volume: Page:525-536
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Ramanujam E.,Shankar K.,Sharma Arpit

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-6690-5_40

Reference62 articles.

1. Roy PK, Singh JP, Banerjee S (2020) Deep learning to filter SMS spam. Future Gener Comput Syst 102:524–533

2. Almeida TA, Hidalgo JMG, Yamakami A (2011) Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of the 11th ACM symposium on document engineering, pp 259–262

3. Gupta M, Bakliwal A, Agarwal S, Mehndiratta P (2018) A comparative study of spam SMS detection using machine learning classifiers. In: 2018 eleventh international conference on contemporary computing (IC3). IEEE, pp 1–7

4. Abayomi-Alli O, Misra S, Abayomi-Alli A, Odusami M (2019) A review of soft techniques for SMS spam classification: methods, approaches and applications. Eng Appl Artif Intell 86:197–212

5. Shafi’I MA, Abd Latiff MS, Chiroma H, Osho O, Abdul-Salaam G, Abubakar AI, Herawan T (2017) A review on mobile SMS spam filtering techniques. IEEE Access 5:15650–15666

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. Performance Evaluation of Meta-data features for Spam SMS Classification using Sequential Models;2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS);2024-02-24

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