International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Spam Message Detection Using Machine Learning |
|---|---|
| ABSTRACT | This paper presents a web-accessible intelligent framework designed to automatically differentiate harmful and unwanted digital messages from legitimate ones. The architecture leverages supervised classification methods alongside Natural Language Processing (NLP) pipelines, where TFIDF vectorization transforms unprocessed text into quantifiable feature representations. A probabilistic Multinomial Naïve Bayes model, trained on annotated corpora, performs two-class discrimination between unsolicited and genuine messages. The platform is served via a Flask-based lightweight backend that processes user input and returns instant classification decisions. Experimental evaluations demonstrate strong predictive accuracy and minimal processing delays, confirming the system's readiness for real-world deployment. By automating the detection of phishing attempts, fraudulent promotions, and malicious content, the solution meaningfully enhances the safety of digital communication environments. |
| AUTHOR | DR. H S SARASWATHI, PRAGNA K P, SINDHU B M, TEJASWINI B P, VINUTHA G N Associate Professor, Department of IS&E, JIT, Davangere, India Student, Dept. of IS&E, JIT, Davanagere, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405068 |
| pdf/68_Spam Message Detection Using Machine Learning.pdf | |
| KEYWORDS | |
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