ABSTRAT:
Evaluation of Machine Learning Algorithms for the Detection of Fake Bank Currency Anju Yadav* SCIT, Manipal University Jaipur anju.anju.yadav@gmail.com Vivek Kumar Verma SCIT, Manipal University Jaipur vermavivek123@gmail.com Tarun Jain SCIT, Manipal University Jaipur tarunjainjain02@gmail.com Vipin Pal NIT, Meghalaya vipinrwr@gmail.com Abstract— The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in the financial market. During demonetization time it is seen that so much of fake currency is floating in market. In general by a human being it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this paper six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication. To implement this we have applied Support Vector machine, Random Forest, Logistic Regression, Naïve Bayes, Decision Tree, K- Nearest Neighbour by considering three train test ratio 80:20, 70:30 and 60:40 and measured their performance on the basis various quantitative analysis parameter like Precision, Accuracy, Recall, MCC, F1-Score and others. And some of SML algorithm are giving 100 % accuracy for particular train test ratio.
EXISTING SYSTEM :
It is a SML model that may be used for classification as well as regression problems of prediction but mainly in industry it is used for classification problems. KNN is lazy algorithm means it learns very slowly as its training is very slow due to the consideration of whole dataset for classification. And it is also known as parametric learning algorithm as it will not consider any information form the underlying data. Basically, KNN uses the concept of feature similarity to find out the new data point values [9] i.e., the value assigned to the new data point is based on the matching of its value to the points of training set [9]. KNN is applied on dataset by considering three different train test ratio (80:20, 60:40, and 70:30) to predict whether the bank currency is forge or genuine. For train test ratio 80:20 ROC curve and learning curves are drawn see Fig. 5(a). Accuracy of SVM is observed around
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
Probabilistic NN method is used for classification of bank currency [9] and in [10] LVQ classifier is used for detecting note authentication. Both the papers authors applied above approaches on US Dollars data set. Recognition of euro banknotes has been proposed by using perceptron of three layer and to classify bank currency into a particular class by considering input as an image of bank currency. The back propagation method is used to train the model. Further for validation radial basis function is used to discard the invalid data
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
