IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK
Abstract & Details
Research Area
Computer Engineering
Keywords
Counterfeits Detection
Convolutional Neural Networks
MobileNet
Support Vector Machines (SVM)
Random Forest
Hybrid Model
Indian Currency
Image Classification
Machine Learning.
Abstract
The proliferation of counterfeit currency poses a significant threat to economic stability, necessitating advanced methods for effective detection. This project, titled "Identification of Fake Indian Currency Using Convolutional Neural Networks," proposes a novel approach to counterfeit detection by leveraging deep learning techniques. It explores several models, including MobileNet, a hybrid of MobileNet with Support Vector Machines (SVM), and a variant integrating MobileNet with both SVM and Random Forest, along with VGG16 and VGG19 architectures. MobileNet, known for its efficiency and accuracy in image classification, is evaluated for its ability to distinguish genuine Indian currency from counterfeit notes. The hybrid models aim to enhance detection capabilities by combining the strengths of MobileNet with traditional classifiers like SVM and Random Forest, using ensemble learning techniques to improve classification performance. These models are assessed based on accuracy, precision, recall, and overall robustness in real-world scenarios, with results highlighting the potential of convolutional neural networks to significantly improve counterfeit currency detection systems and strengthen financial security measures.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Pathuri Bharath | Vasireddy Venkatadri Institute Of Technology |
| 2 | N.Durga Rao | Vasireddy Venkatadri Institute Of Technology |
| 3 | Gopalam Lakshmi Sivannarayana | Vasireddy Venkatadri Institute Of Technology |
| 4 | Bogolu venkata Ramanji Reddy | Vasireddy Venkatadri Institute Of Technology |
| 5 | Ambati chaitanya krishna | Vasireddy Venkatadri Institute Of Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Bharath, Pathuri, Rao, N.Durga, Sivannarayana, Gopalam Lakshmi, Reddy, Bogolu venkata Ramanji, & krishna, Ambati chaitanya (2025). IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 1125-1133.
MLA Style
Bharath, Pathuri, et al. "IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 1125-1133.
IEEE Style
Pathuri Bharath, N.Durga Rao, Gopalam Lakshmi Sivannarayana, Bogolu venkata Ramanji Reddy, and Ambati chaitanya krishna, "IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 1125-1133, 2025.
Vancouver Style
Bharath Pathuri, Rao N.Durga, Sivannarayana Gopalam Lakshmi, Reddy Bogolu venkata Ramanji, krishna Ambati chaitanya. IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):1125-1133.
Harvard Style
Bharath, Pathuri, Rao, N.Durga, Sivannarayana, Gopalam Lakshmi, Reddy, Bogolu venkata Ramanji, & krishna, Ambati chaitanya (2025) 'IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 1125-1133.
Chicago Style
Bharath, Pathuri, et al. "IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 1125-1133.
Turabian Style
Bharath, Pathuri, et al. "IDENTIFICATION OF FAKE INDIAN CURRENCY USING CONVOLUTIONAL NEURAL NETWORK." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 1125-1133.
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