利用机器学习和深度学习技术对垃圾邮件进行分类

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Bandar Alshawi, Amr Munshi, Majid Alotaibi, Ryan Alturki, Nasser Allheeib
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引用次数: 0

摘要

摘要:如今,互联网和社交媒体网络的使用率越来越高,已成为一种重要的沟通媒介。电子邮件是专业可靠的通信方式之一。对垃圾邮件进行自动分类已成为一个备受关注的领域。为了检测垃圾邮件,本研究使用了一个数据集,其中包括垃圾邮件和非垃圾邮件。为了获得更高的准确率,我们使用了机器学习技术来应用各种技术。为了提高准确率,还使用了嵌入式 NLP。为此,这项工作采用了 BERT 模型,以达到令人满意的垃圾邮件检测效果。此外,还将结果与 KNN、LSTM 和 Bi-LSTM 等最先进的方法进行了比较。Bi-LSTM 和 LSTM 的检测结果分别为 97.94% 和 86.02%。由于所达到的准确率较高,因此所介绍的方法在检测垃圾邮件方面前景广阔。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of SPAM mail utilizing machine learning and deep learning techniques
Abstract: The Internet and social media networks usage has increased nowadays and become a prominent medium of communicating. Email is one of the professional reliable methods of communication. Automatic classifications of spam emails have become an area of interest. In order to detect spam emails, this study utilizes a dataset, including spam and non-spam emails. Various techniques are applied to obtain higher accuracy using machine learning techniques. NLP is also utilized for improvising accuracy using embeddings. For that, this work utilizes the BERT model, to achieve satisfactory detection of spam emails. Further, the results are compared with state-of-the-art methods, including, KNN, LSTM and Bi-LSTM. The results obtained by Bi-LSTM and LSTM were 97.94% and 86.02%, respectively. The presented methodology is promising in detecting spam emails due to the higher accuracy achieved.
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