Classification and detection of Counterfeit Indian Currency using novel deep learning architecture and prediction accuracy comparison with VGG 19

The objective of the paper is to use the Neural Network Support Vector Machine (NNSVM) algorithm to compare the prediction accuracy, so as to predict and classify Indian Currency using the parameters taken from the currency data set with VGG19. For each of the group 130 samples, thus for two groups...

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Bibliographic Details
Main Authors: Kondareddy, D., Kumar, G. R., Chandrasekharan, N.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithms
Counterfeit
Counterfeiting
Datasets
Machine learning
Neural networks
Support vector machines
Workflow
Online Access:Get full text
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Summary:The objective of the paper is to use the Neural Network Support Vector Machine (NNSVM) algorithm to compare the prediction accuracy, so as to predict and classify Indian Currency using the parameters taken from the currency data set with VGG19. For each of the group 130 samples, thus for two groups a total of 260 samples were collected for this investigation. Group 1 uses Neural Network Support Vector Machine while group 2 uses VGG 19 and according to the workflow that was followed, Neural Network Support Vector Machine code has been implemented based on the imported data set using anaconda software and Jupyter notebook is launched. The simulation results shows that Neural Network Support Vector Machine algorithm has 95.4% prediction accuracy whereas VGG 19 has 80.1% prediction accuracy. This gives a significance value of 0.0026 that is (p
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0229401