Retraction Notice: An Intelligent Model of Email Spam Classification
Ajay Chakravarty, V. Manikandan · 2022
One of the biggest dangers to the modern Internet is email spam. Numerous anti-spam filters have been developed to counter this danger. Predicting the labels of emails in a personalised mailbox is one of these filters' biggest challenges. Private data loss is another risk posed by these spam mailings. Modern studies have classified text messages as spam by using some stylistic characteristics of the texts. The detection of email spam can be significantly impacted by the use of well-known terms, phrases, abbreviations, and idioms. Nowadays, spam emails are successfully filtered automatically using machine learning algorithms. We evaluated the absolute most famous machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Counterfeit safe framework, and Unpleasant sets) and of their appropriateness to the issue of spam in this paper, where we introduced the NLP component that can channel spam and non-spam emails and furthermore classify into various spam sends. categorization of email. The algorithms' descriptions and a comparison of how well they performed on the SpamAssassin spam corpus are offered.