Fake Currency Detection using Ensemble Learning
Ashok Kumar, Savya Sachi, Anshoo Bhatia, Pravesh Belwal, Santosh Kumar, Vivek Bhatnagar · 2023
Fake currency is one of the most serious issues that may arise during financial transactions. For a developing nation like India., this is becoming a significant obstacle. Because of advancements in printing and scanning technology., it is now feasible for anybody to create counterfeit banknotes with the use of the most up-to-date hardware equipment. Identifying false notes manually is a procedure that is both time-consuming and messy; hence., there is a need for automated approaches that make fake money detection a more effective and efficient process. For this machine learning model i.e. ensemble learning is proposed in this research for detecting the forgery currency. A data set consisting of forgery and original currency images have been chosen for the analysis. The proposed model is compared in accordance with performance parameters like accuracy., precision., recall., and f1-score. With other existing models like naive bayes., random forest., and decision tree to prove efficiency of the proposed algorithm. The evaluation of these algorithms prove that the proposed algorithm ensemble learning is highly efficient with 99% accuracy in detecting forgery currency. This can be used in some of the automated machines.