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

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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.

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Author information

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Fahim Niaz, Jian Zhang, Yang Zheng, Muhammad Younas & Naveed Imran

  2. School of Computer Science, University of Hull, Hull, UK

    Muhammad Khalid

  3. Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland

    Kashif Naseer Qureshi

Authors
  1. Fahim Niaz
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  2. Jian Zhang
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  3. Muhammad Khalid
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  4. Kashif Naseer Qureshi
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  5. Yang Zheng
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  6. Muhammad Younas
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  7. Naveed Imran
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Contributions

Fahim Niaz and Jian Zhang emerged as the primary driving forces, spearheading the conceptualization, design, and execution of the study. Fahim Niaz played a pivotal role in data collection and analysis, while Author Jian Zhang was instrumental in developing computational models and interpreting results. Muhammad Khalid and Kashif Naseer Qureshi were actively engaged in the revision process, providing critical feedback and ensuring methodological rigor. They also contributed significantly to refining the manuscript drafts. Yang Zheng, Muhammad Younas, and Naveed Imran played crucial roles in the presentation and clarity of the work. Yang Zheng focused on designing figures, while Muhammad Younas concentrated on the related work, and Naveed Imran contributed to the writing process, including the discussion and conclusion sections. Collectively, the diverse skills and expertise of the team harmonized to produce a comprehensive and well-rounded research paper.

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Correspondence to Jian Zhang.

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Niaz, F., Zhang, J., Khalid, M. et al. AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor. Computing 106, 2851–2873 (2024). https://doi.org/10.1007/s00607-024-01300-2

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  • DOI: https://doi.org/10.1007/s00607-024-01300-2

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