The Impact of Deep Learning Techniques on SMS Spam Filtering

Wael Hassan Gomaa
<span title="">2020</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
Over the past decade, phone calls and bulk SMS have been fashionable. Although many advertisers assume that SMS has died, it is still alive. It is one of the simplest and most cost-effective marketing tools for companies to communicate on a personal level to their customers. The spread of SMS has led to the risk of spam. Most of the previous studies that attempted to detect spam were based on manually extracted features using classical machine learning classifiers. This paper explores the
more &raquo; ... of applying various deep learning techniques on SMS spam filtering; by comparing the results of seven different deep neural network architectures and six classifiers for classical machine learning. Proposed methodologies are based on the automatic extraction of the required features. On a benchmark data set consisting of 5574 records, a fabulous accuracy of 99.26% has been resulted using Random Multimodel Deep Learning (RMDL) architecture.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.14569/ijacsa.2020.0110167</a> <a target="_blank" rel="external noopener" href="">fatcat:3eiy544pgzgdpcm5evdtxbqbsi</a> </span>
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