Forthcoming and Online First Articles

International Journal of Signal and Imaging Systems Engineering

International Journal of Signal and Imaging Systems Engineering (IJSISE)

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 Articles will not be published until the final proofs are validated by their authors.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 published online here, before they appear in a journal issue. 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 Signal and Imaging Systems Engineering (One paper in press)

Regular Issues

  • Grape Cluster and Disease Detection with Hybrid Fuzzy Residual Maxout Network   Order a copy of this article
    by Rajkumar Bhimrao Pawar, Madhuri Rao 
    Abstract: This paper proposes a novel deep-learning method for grape cluster detection. Primarily, the grape cluster image is pre-processed using a bilateral filter to remove noise and the image is enhanced by the decorrelation stretching. Then, segmentation is performed using Mobile U-Net to segment the required regions. Later, features from the segmented images are extracted using different feature extractors. Afterwards, features fed into the Hybrid Fuzzy Residual Maxout Network (HFRMN) model for the detection of grape clusters. Here, the HFRMN model is designed by the incorporation of Deep Maxout Network (DMN), Deep Residual Network (DRN), and the Fuzzy concept. Finally, disease detection is accomplished by utilizing the proposed HFRMN. Moreover, HFRMN attained superior values of 90.0% for True Positive Rate (TPR), 91.3% for True Negative Rate (TNR), 91.4% for accuracy, 92.4% for F1-score, 71.3% for computational efficiency, 1.04% for Mean Squared Error (MSE), and 89.6% for Mean Average Precision (MAP).
    Keywords: grape cluster; bilateral filter; decorrelation stretching; Deep Maxout Network; Deep Residual Network.
    DOI: 10.1504/IJSISE.2024.10069173
     

 

Return to top