• K Sudheer Kumar
• CH B M Swaroop
• K Satya Sudheer
• B Sai Deepthi

SMS spam has become a significant concern for mobile users, causing frustration and inconvenience. Machine learning has proven to be an effective solution for filtering out spam messages. However, implementing these methods in real-time scenarios comes with unique challenges. A recent study aims to address these challenges by developing a real-time SMS spam filtering system that leverages machine learning. The primary focus of this research is to optimize the system's performance in real-time classification by concentrating on data preparation, feature engineering, algorithm selection, and model deployment. By tailoring these aspects to the requirements of real-time classification, the system can efficiently combat SMS spam while maintaining a high level of accuracy and low latency. Another promising area of investigation is the integration of natural language processing (NLP) techniques to analyze the content of SMS messages more comprehensively. By identifying subtle spam characteristics, such as deceptive language or manipulative tactics, the system can improve its overall accuracy in filtering out spam messages. Expanding the system's applicability to other messaging platforms and languages can also broaden its impact in combating spam across various communication channels. This will not only benefit mobile users but also contribute to a safer and more secure digital environment.

SMS spam filtering, Real-time classification, Machine Learning

"SMS SPAM FILTERING USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.a207-a214, May-2024, Available :http://www.jetir.org/papers/JETIR2405027.pdf

"SMS SPAM FILTERING USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppa207-a214, May-2024, Available at : http://www.jetir.org/papers/JETIR2405027.pdf