Bank Note Authentication: A Genetic Algorithm Supported Neural based Approach

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Spandan Sen Sarma


Recent research works have focused on detection of authenticity of Bank Notes using several machine learning techniques. Accurate separation of original notes from the forged one is a challenging job. In the present work Neural Network (NN) has been trained using Genetic algorithm (GA employed to detect authenticity of bank notes by classifying them into two separate classes. The initial weight vector to the input layer of the NN has been optimized gradually using the optimization techniques to enhance the performance of NN to a greater extent. The experimental results of the proposed method have been compared with a well-known Multilayer Perceptron Feed-Forward Network (MLP-FFN) and also with the NN. Performance measures like accuracy, precision, recall and F-measure have been used to compare the performances of the algorithms. The experimental results have revealed significant improvement over the existing performances to detect forgery of bank notes using GA.


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Spandan Sen Sarma

Department of Information Technology Academy of Technology Aedconagar, Hooghly – 712121, India