Abstract

Spamming of email is a big issue in the recent era. Certain individuals involve them for unlawful lead, phishing, and misrepresentation. Sending suspicious links through spam messages can damage our system and may investigate our framework. Email spam recognition is expected to anticipate spam messages from preventing into clients' inboxes, further developing the client experience. This undertaking will distinguish those spam messages by using an AI approach. AI is one of the uses of Automated intelligence that permits the system to peruse and improve, as a matter of fact, without explicit projects. This paper will examine the AI algorithm, which is Naive Bayes. It is a probabilistic classifier, which predicts the possibility of the likelihood of an article, and it is chosen for email spam identification with the best accuracy and Precision.

References

1. Karim, S. Azam, B. Shanmugam, K. Kannoorpatti and M. Alazab. They describe a focused literature survey of Artificial Intelligence Revised (AI) and Machine learning methods for email spam detection
2. K. Agarwal and T. Kumar Harisinghaney et al. (2014) and Mohamad & Selamat (2015) have used the “image and textual dataset for e-mail spam detection with the utilization of assorted methods”.
3. Harisinghaney et al. (2014) have used methods of KNN algorithm, Reversed DBSCAN algorithm with experiments on dataset. For the text recognition, OCR library is employed but this OCR doesn’t perform well.
4. Mohamad & Selamat (2015) uses the feature selection hybrid approach of TF-IDF (Team Frequency Inverse Document Frequency) and Rough pure math

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