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@article{179054,
        author = {Praveena R and Mohamed Faizul R and Muniasamy A and Venkadesh K and Saran C M},
        title = {Fraud call detection using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8365-8368},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179054},
        abstract = {This project proposes a machine learning
based approach for detecting fraudulent calls, a 
significant threat to individuals and organizations 
worldwide. Leveraging audio signal processing and 
conversation pattern analysis, our model achieves high 
accuracy in distinguishing between legitimate and 
fraudulent calls. By integrating machine learning 
algorithms, we can improve the efficiency and 
effectiveness of fraud detection, reducing financial 
losses and security risks. Our system can be applied in 
various 
domains, 
including 
telecommunication 
companies and customer service, to detect and prevent 
fraudulent calls. This project demonstrates the 
potential of machine learning in enhancing fraud call 
detection and highlights the importance of continued 
research in this area. 
The proposed system uses a combination of audio 
features and machine learning algorithms to detect 
fraudulent calls. Our approach involves extracting 
relevant audio features from call recordings, training a 
machine learning model on a labeled dataset, and 
evaluating its performance on a test dataset. The results 
show that our model achieves high accuracy in detecting 
fraudulent calls, outperforming traditional rule-based 
systems. This project contributes to the development of 
more effective fraud call detection systems, Enabling 
individuals and organizations to better protect 
themselves against financial losses and security 
risks.The significance of this project lies in its potential 
to reduce financial losses and security risks associated 
with fraudulent calls. By developing a robust and 
accurate fraud call detection system, we can help 
individuals and organizations protect themselves 
against these threats. Furthermore, this project 
demonstrates the effectiveness of machine learning in 
detecting fraudulent calls, highlighting it’s potential for 
application in other areas of telecommunication 
security.},
        keywords = {Fraud Call Detection, Machine Learning,  Audio Signal Processing, Conversation Pattern  Analysis, Telecommunication Security},
        month = {May},
        }