Naïve Bayes Filter for Communication & Enhancing Semantic in Email
Mariyan Richard A1, Prasad Naik2, Suhas A3, Drakshaveni G4
1Mariyan Richard A, Assistant Professor, Dept. Of MCA NMIT, Bengaluru, India.
2Dr. Prasad Naik Hamsavath, HOD, Dept. Of MCA. NMIT, Bengaluru, India.
3Suhas A, Dept. Of MCA NMIT, Bengaluru, India.
4Drakshaveni G, Assistant Professor, Dept. of MCA, BMSIT, Bengaluru, India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 282-288 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4904119420 | DOI: 10.35940/ijrte.D4904.119420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due tothe current pandemic of COVID-19, the world has turned into ONLINE modeand an increase in online communication thereby information exchange, sharing useful data through emails and other social Medias. So addressing the security issues places a vital role in computer security and shouldhave thepriorities. We need a security check to enhance the inbox so that the important information or emails should not reach to the spam box. In this paper to improve the filtering techniques, wehave adopted the Naïve Bayes approach in implementation and enhancing the spam filter in the email. Bayes’s approach is efficient, accurate, and simple in implementing the proposed algorithm. Bayes algorithm is used to verify correct semantic information of the email and avoidsthe pass to pass approach if the incoming mail is important. The Python language is used to develop the proposed algorithm.
Keywords: Naïve Bayes, String Sematic, Spam Filtering, Python Language.