International Conference on Information and Knowledge Technology

فارسی

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Advanced SMS Spam Detection using Deep Complex Models and Sine-Cosine Algorithm

Authors :

Sepehr Rezaei¹ Mohammadreza Shams² Mohsen Alambardar Meybodi³

1- University of Isfahan 2- University of Isfahan 3- University of Isfahan

Keywords :

Spam Detection،Sine-Cosine Algorithm،Complex Model،Deep Learning

Abstract :

With the increasing use of mobile phones and messaging services, SMS spam has become a significant issue for users. In this paper, we propose a novel approach to tackle this problem by using Sine-Cosine Algorithm (SCA) and Complex Multi-Layer Perceptron (C-MLP). Specifically, we apply the SCA method to reduce the dimensionality of the feature space and C-MLP to improve the performance of spam detection. Also, in this paper, we investigate the effectiveness of different classification algorithms, including Support Vector Machines, Random Forests, K-nearest neighbors, Naive Bayes, bagging, and voting approaches. Our experimental results show that the proposed approach achieves high accuracy and outperforms existing methods in terms of both accuracy and F-measure. The proposed approach can be helpful in designing effective SMS spam filters and improving the overall user experience.