Perfecting Counterfeit Banknote Detection-A Classification Strategy
Malladi Tejasvi1, A. Nayeemulla Khan2, A. Shahina3
1Malladi Tejasvi, Department of Computing Sciences and Engineering, VIT Chennai (Tamil Nadu), India.
2A.Nayeemulla Khan, Department of Computing Sciences and Engineering, VIT Chennai (Tamil Nadu), India.
3A.Shahina, Department of Information Technology, SSN College of Engineering, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 434-440 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3597048619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine Learning Algorithms for Classification use the Data Provided to Learn a Function that would discriminate between the classes. It is this learning that dictates how well a classifier is able to approximate the function, when presented with unseen data. Counterfeit banknotes are a scourge to every nation. Automated processes to quickly detect counterfeit notes with high accuracy are the essential need. We employ various classification and dimensionality reduction techniques to achieve perfect classification of the Banknote authentication dataset [1] using Artificial Neural Network, Support Vector Machine and K Nearest Neighbours classifiers.
Keyword: Machine Learning, Neural Networks, Counterfeit Banknote Detection, Supervised Learning.
Scope of the Article: Classification