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@article{168355,
        author = {Kalpesh Koli and Isha uge and Rahul Kolpe and Sayaji Jadhav and Dnyanda Shinde},
        title = {An Analytical Approach to UPI Fraud Detection Using Machine Learning and Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1228-1232},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168355},
        abstract = {The fast development of Bound together Installments Interface (UPI) exchanges has driven to an increment in false exercises, posturing noteworthy dangers to clients and budgetary educate. This venture presents a machine learning-based approach for recognizing false UPI exchanges. We assess and compare the execution of different calculations, counting Calculated Relapse, K-Nearest Neighbors (KNN), Back Vector Machine (SVM), Gullible Bayes, Choice Tree, Irregular Timberland, and a Convolutional Neural Organize (CNN). The dataset utilized for preparing incorporates numerous highlights related to UPI exchanges, with information preprocessing strategies such as scaling connected to guarantee demonstrate productivity. The models were prepared and tried on the dataset, and their exactness scores were compared to distinguish the most compelling calculation for extortion discovery.},
        keywords = {UPI Fraud Detection, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, Decision Tree, Random Forest.},
        month = {October},
        }