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 | A Survey on SMS Spam Filtering using Support Vector Machines and Naive Bayes |
|---|---|
| ABSTRACT | This paper presents survey of spam detection of short message service (SMS) using two techniques: support vector machines (SVM) and Naive Bayes (NB) through machine learning (ML). Spam SMS messages continue to create significant challenges to mobile users by causing privacy breaches, financial fraud, and communication overload. Machine learning methods have been widely deployed to detect such spam effectively. This survey presents a comprehensive review of SMS spam detection and classification using two widely adopted algorithms: Support Vector Machines (SVM) and Naïve Bayes (NB). The paper concludes by summarizing research gaps and future directions for improving real-world SMS spam detection systems. |
| AUTHOR | RUPALI BANSODE, SUVARNA PINGLE PG Student, Dept. of CSE, PES College of Engineering, Chh. Sambhajinagar, India Assistant Professor, Dept. of CSE., PES College of Engineering, Chh. Sambhajinagar, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312098 |
| pdf/98_A Survey on SMS Spam Filtering using Support Vector Machines and Naive Bayes.pdf | |
| KEYWORDS | |
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