• Dr.R.Ablin
• Er.G.Prabin

Counterfeit currency used to deceive people is one of the biggest problems facing every country. One solution is to use machine learning techniques to detect false prices. This information is generated by collecting high-quality images of real and counterfeit coins using commercial cameras. Apply a wavelet transform to an image to extract features from an image. The variance, skewness, kurtosis and entropy attributes obtained from these images using the wavelet transform. Tremendous technological advances in the printing and scanning industry have exacerbated the problem of counterfeiting. As a result, counterfeit currency affects the economy and lowers the value of the original currency. Therefore, the detection of counterfeit money is the most desirable. Most of these previous methods are based on hardware and image processing methods. Finding fake results using this method is ineffective and time consuming. To overcome the problems, we propose to detect counterfeit money using deep neural networks. This document identifies counterfeit money by analyzing currency images.

convolutional neural network, wavelet transform, Counterfeit currency, image processing.

"Examining Financial Markets Using Convolutional Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.l676-l682, May-2023, Available :http://www.jetir.org/papers/JETIR2305B92.pdf

"Examining Financial Markets Using Convolutional Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppl676-l682, May-2023, Available at : http://www.jetir.org/papers/JETIR2305B92.pdf