Spam Detection in SMS Based on Feature Selection Techniques-Reference-Cited by-同舟云学术

Spam Detection in SMS Based on Feature Selection Techniques

Published:2018-09-02 Issue: Volume: Page:555-563
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Sharaff Aakanksha

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-1498-8_49

Reference12 articles.

1. Almeida,T.A, Hidalgo, J.M.G., Yamakami, A.: Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of ACM symposium on document engineering (DocEng’11), Mountain View, CA, pp. 1–4 (2011)

2. Uysal, A.K., Gunal, S., Ergin, S., et al.: The impact of feature extraction and selection on SMS spam filtering. Electron. Electr. Eng. 19(5), 67–72 (2013)

3. Jain, G., Sharma, M., Agarwal, B.: Spam detection on social media using semantic convolutional neural network. Int. J. Knowl. Discov. Bioinform. (IJKDB) 8(1), 12–26 (2018)

4. Nagwani, N.K., Sharaff, A.: SMS spam filtering and thread identification using bi-level text classification and clustering techniques. J. Inf. Sci. 43(1), 75–87 (2017)

5. Suleiman, D., Al-Naymat, G.: SMS Spam Detection using H2O Framework. Proc. Comput. Sci. 113, 154–161 (2017)

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