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- W4310257733 abstract "With the era of digital communication, email has become critical and nearly vital in today's world. Emails and various social networking sites have grown considerably over the past decade, and their use has increased because of this growth. To take advantage of the widespread usage of spam, cybercriminals send it via email and online social networking sites and through a variety of other distribution channels. Phishing and email fraud are two issues that affect most people who use the Internet. Spam has become a big concern in recent years, with email spam being the most well-known. Despite the high level of performance provided by antivirus software, computers can be infected by various sources, including Trojans, bots and, most typically, spam messages. A reliable method of detecting spam communications must be implemented to avoid this problem. This work develops deep learning-based classifiers using Gated Recurrent Unit (GRU) to see ham and spam messages. The proposed GRU with TF-IDF vectorizer has reported the highest accuracy for detecting spam emails." @default.
- W4310257733 created "2022-11-30" @default.
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- W4310257733 date "2022-11-25" @default.
- W4310257733 modified "2023-09-29" @default.
- W4310257733 title "Intelligent System for Detecting Email Spam Messages Using GRU" @default.
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- W4310257733 doi "https://doi.org/10.1007/978-981-19-5292-0_7" @default.
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