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Paper Title

Utilizing Deep learning Techniques For Detecting Counterfeit Bank Currency

Article Identifiers

Registration ID: IJNRD_220045

Published ID: IJNRDTH00143

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Keywords

: CNN,SVM,Counterfeit currency, Technological Advancements,Image processing,Gray scale conversion,Edge detection,Segmentation,Deep learning

Abstract

Counterfeit Currency has always been an issue which has created a lot of problems in the market. The increasing technological advancements have made the possibility for creating more counterfeit currency which are circulated in the market which reduces the overall economy of the country. There are machines present at banks and other commercial areas to check the authenticity of the currencies. But a common man does not have access to such systems and hence a need for a software to detect fake currency arises, which can be used by common people. This proposed system uses Image Processing to detect whether the currency is genuine or counterfeit. The system is designed completely using Python programming language. It consists of the steps such as gray scale conversion, edge detection, segmentation, etc. which are performed using suitable methods. The first order and second order statistical features are extracted initially from the input and undergoes deeplearning algorithm CNN. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8 percentage. The experimental results are compared with state of-the methods and produced reliable results.

How To Cite (APA)

Gudepu Jogesh Babu (May-2024). Utilizing Deep learning Techniques For Detecting Counterfeit Bank Currency. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), 297-339. https://ijnrd.org/papers/IJNRDTH00143.pdf

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Other Publication Details

Paper Reg. ID: IJNRD_220045

Published Paper Id: IJNRDTH00143

Research Area: Computer Engineeringร‚ย 

Author Type: Indian Author

Country: visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRDTH00143.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDTH00143

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