Implementing Neural Network Model for Banknote Classification

The ANN will be trained on a data set of Banknotes, and Binary Classification will be performed on the inputs. The Banknotes Data set will consists of data about the bank notes and classes denoting whether a banknote is genuine or counterfeit.

Features in Banknote Classification Dataset:

These include various characteristics or measurements derived from images or physical properties of banknotes.

• Variance: Measures the spread of pixel values across the image.
• Skewness: Indicates the asymmetry or distortion in pixel intensity values.
• Curtosis: Describes the sharpness of peaks in pixel intensity distribution.
• Entropy: Reflects the disorder or complexity in pixel intensity values.

Labels in Banknote Classification Dataset:

Each banknote in the dataset is typically associated with a label or class indicating its authenticity:

• “0” or “1” could be used to denote genuine (authentic) or counterfeit banknotes, respectively.
• Alternatively, the labels might use descriptive terms like “Genuine” and “Counterfeit”.

The dataset used in this project can be found in the Github repository linked in the Links below.

Banknote classification is a critical task in financial systems, ensuring the validity and integrity of monetary transactions. With advancements in machine learning, particularly deep learning, the accuracy and efficiency of banknote classification systems have significantly improved. In this article, we’ll learn about the basic implementation of a Artificial Neural Networks using TensorFlow and Keras. We’ll add a dense hidden layer to the model. Artificial Neural Networks will be trained on a dataset and binary classification will be performed to predict the output class.