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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | AI BASED SPAM SPOILER FOR E-MAIL SERVICES |
| Authors Name: | RAJAHA MUTHIAHA C , Ms ARUNA T N , PRIYANKA R , REESHMA R , THANUSU C |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_194964
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| Published Paper Id: | IJNRD2305302 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | In recent times, cyber cover incidents have come down constantly. In the utmost of these incidents, the bushwhacker used colorful types of spam emails as a driving force and successfully compromised the websites of government systems, well-known companies, politicians, and social associations in numerous countries. increase. The discovery of spam emails from a large quantum of dispatch data is attracting attention. still, spam dispatch disguise ways are getting decreasingly sophisticated, and being discovery styles are ill-equipped to keep pace with decreasingly sophisticated fraud ways and the volume of emails. This design develops a new and effective approach called Spam Spoiler, which uses LSTM-grounded GRUs to classify large quantities of dispatch data into four distinct classes generally fraudulent, draining, and suspicious emails. suggested to do The new process includes two crucial phases. A sample expansion phase and a test phase with enough samples. An LSTM-grounded GRU, this design efficiently retrieves meaningful information from emails that can be used as substantiation for forensic analysis. Experimental results show that Spam Spoiler outperforms ML algorithms, achieving 98 bracket delicacy using his LSTM novel fashion with an iterative grade unit. Dispatch content analysis covers different types of motifs. Spam Spoiler effectively outperforms being styles, leaving the bracket process robust and dependable. |
| Keywords: | AI,E-Mail, LSTM, GRU, Spam Detection |
| Cite Article: | "AI BASED SPAM SPOILER FOR E-MAIL SERVICES", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.d16-d22, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305302.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305302 Registration ID: 194964 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: d16-d22 Country: Virudhunagar, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305302 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305302 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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