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This comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of
16 Ιουν 2021 · Here, we propose a detection model based on the LSTM algorithm for identifying spam and non-spam emails using a dataset from Kaggle comprising a total of 5.572 entries.
3 Ιουν 2016 · The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and...
11 Μαΐ 2022 · PDF | Spam emails have been traditionally seen as just annoying and unsolicited emails containing advertisements, but they increasingly include scams,... | Find, read and cite all the...
Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering.
1 Ιαν 2021 · open access. Abstract. Unsolicited emails such as phishing and spam emails cost businesses and individuals millions of dollars annually. Several models and techniques to automatically detect spam emails have been introduced and developed yet non showed 100% predicative accuracy.
This work examines the definition of spam, the user's information requirements and the role of the spam filter as one component of a large and complex information universe, and outlines several uncertainties and proposes experimental methods to address them.
