Forthcoming Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are also listed here. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Artificial Intelligence and Soft Computing (One paper in press)

Regular Issues

  • BD Currency Detection: A CNN-Based Approach with Mobile App Integration   Order a copy of this article
    by M.D. Zahurul Haque, Syed Jubayer Jaman, Md Robiul Islam, Usama Abdun Noor 
    Abstract: Automated currency recognition systems are critical for enhancing financial security, facilitating commerce, and providing assistive solutions for visually impaired populations. Conventional approaches, including manual verification and optical scanning techniques, exhibit inherent limitations in accuracy, processing speed, and adaptability. This paper presents a robust currency recognition framework leveraging Convolutional Neural Networks (CNNs) for the precise classification of Bangladeshi banknotes. A comprehensive dataset of 50,000 annotated images was curated, preprocessed, and augmented to train a deep CNN architecture optimized for multi-class currency denomination classification. The proposed model demonstrated exceptional performance, achieving a training accuracy of 99.77% and validation accuracy of 100%, substantially outperforming traditional image processing-based methods. To facilitate practical deployment, the trained model was optimized and converted to TensorFlow Lite format, enabling efficient inference on resource-constrained mobile devices. An Android-based mobile application was developed to provide real-time, offline currency recognition capabilities. Experimental results demonstrate the efficacy of deep learning methodologies in addressing currency recognition challenges, offering a scalable, accurate, and accessible solution with significant implications for financial inclusion and assistive technology applications.
    Keywords: Convolutional Neural Networks; Currency Recognition; Bangladeshi Banknotes; Deep Learning; TensorFlow Lite; Mobile Application; Assistive Technology.
    DOI: 10.1504/IJAISC.2026.10081214
     

 

Return to top