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

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
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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 CMLP 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.
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All the code used in this research is publicly available on the first author’s GitHub repository: https://github.com/seper-sw/SMS-Spam-Detection.git