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صفحه اصلی
/
چهاردهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Advanced SMS Spam Detection using Deep Complex Models and Sine-Cosine Algorithm
نویسندگان :
Sepehr Rezaei
1
Mohammadreza Shams
2
Mohsen Alambardar Meybodi
3
1- University of Isfahan
2- University of Isfahan
3- University of Isfahan
کلمات کلیدی :
Spam Detection،Sine-Cosine Algorithm،Complex Model،Deep Learning
چکیده :
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.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.8.0
