Evaluating Machine Learning Algorithms for Fake Currency Detection
Currency is a critical asset in any economy, yet it is vulnerable to counterfeiting, undermining its value and disrupting economic stability. Counterfeit currency is particularly prevalent during economic transition, such as demonetization, as fake notes are circulated to mimic real currency. Due...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2012/ http://eprints.intimal.edu.my/2012/1/jods2024_33.pdf http://eprints.intimal.edu.my/2012/2/552 |
| Summary: | Currency is a critical asset in any economy, yet it is vulnerable to counterfeiting, undermining its
value and disrupting economic stability. Counterfeit currency is particularly prevalent during
economic transition, such as demonetization, as fake notes are circulated to mimic real currency.
Due to the subtle similarities between genuine and fake notes, distinguishing between them can be
challenging. Consequently, financial institutions like banks and ATMs require robust automated
systems to accurately detect counterfeit currency. In this study, we evaluate the effectiveness of
six supervised machine learning algorithms—K-Nearest Neighbor, Decision Trees, Support
Vector Machine, Random Forests, Logistic Regression, and Naive Bayes—in detecting the
authenticity of banknotes. Additionally, we examine the performance of LightGBM, a gradientboosting
algorithm, in comparison to these traditional methods. Our findings contribute to
developing reliable, automated systems for counterfeit detection, and enhancing financial security |
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