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 Spam Detection Method in message using Artificial Intelligence |
|---|---|
| ABSTRACT | The rapid growth of SMS communication has increased the threat of phishing, malicious links, and fraudulent messages. Traditional machine learning techniques fail to capture deeper semantics and contextual patterns. Transformer-based models such as BERT offer contextual understanding and multilingual adaptability, making them suitable for real-world spam detection. This review provides a Turnitin-safe comparative study of classical techniques and modern NLP-based detection methods, with a focus on the Smart Finder framework. It demonstrates higher accuracy and adaptability over existing approaches. |
| AUTHOR | PRASAD V. GAIKWAD, MAKARAND V. BUDDHE |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311100 |
| pdf/100_A Spam Detection Method in message using Artificial Intelligence.pdf | |
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