A high-performance nuisance SMS recognition model

Chengyi Li; Mengmeng Tian; Yixin Hong; Zhixuan Xiao; ShuHan Wang; Runjiu Hu

doi:10.1117/12.2637181

15 June 2022 A high-performance nuisance SMS recognition model

Chengyi Li, Mengmeng Tian, Yixin Hong, Zhixuan Xiao, ShuHan Wang, Runjiu Hu

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122851A (2022) https://doi.org/10.1117/12.2637181
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

At present, nuisance SMS is getting more and more intense, and its forms are diversified, hidden and malicious. Not only does it waste cell phone resources, but it also affects people's normal use of cell phones. If the situation is bad, it will also affect the normal social order. In this paper, we use Word2Vec algorithm and LightGBM algorithm to select feature words and build a nuisance SMS identification model. By using the classical nuisance SMS training set for training, the results show that our model has an accuracy rate of over 99%. And our model has higher performance compared with the popular classification models.

Citation Download Citation

Chengyi Li, Mengmeng Tian, Yixin Hong, Zhixuan Xiao, ShuHan Wang, and Runjiu Hu "A high-performance nuisance SMS recognition model", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122851A (15 June 2022); https://doi.org/10.1117/12.2637181

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