AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor

Abstract

In recent years, the significance of millimeter wave sensors has achieved a paramount role, especially in the non-invasive and ubiquitous analysis of various materials and objects. This paper introduces a novel IoT-based fake currency detection using millimeter wave (mmWave) that leverages machine and deep learning algorithms for the detection of fake and genuine currency based on their distinct sensor reflections. To gather these reflections or signatures from different currency notes, we utilize multiple receiving (RX) antennae of the radar sensor module. Our proposed framework encompasses three different approaches for genuine and fake currency detection, Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Transfer Learning Technique (TLT). After extensive experiments, the proposed framework exhibits impressive accuracy and obtained classification accuracy of 96%, 94%, and 98% for CNN, k-NN, and TLT in distinguishing 10 different currency notes using radar signals.