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 Articles will not be published until the final proofs are validated by their authors.

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International Journal of Signal and Imaging Systems Engineering (3 papers in press)

Regular Issues

  • Robust Zero-Watermarking Technique Based on Do-Resnet and Adaptive Pelicon Optimization   Order a copy of this article
    by Sambhaji Marutirao Shedole, Santhi Vaithiyanathan 
    Abstract: This research presents a revolutionary deep learning-based zero watermarking technology that appears to increase image security After that, zero-watermarking was utilised to secure the copyright data of highly invisible photographs The pretrained DO-ResNet model was originally developed to extract high-dimensional deep features from images The deep features are then selected using the low-frequency coefficients of the Discrete Fourier transform (DFT) Furthermore, by employing an adaptive Pelican optimisation algorithm, the loss of result in the optimal area can be reduced The experimental results demonstrate that the approach is robust, secure, and invisible, and that it can acquire watermark information reliably The proposed method handles geometric and common attacks more efficiently by automatically extracting rich, high-dimensional, complex information from images The obtained simulation results demonstrate that, for the USC-SIPI dataset, the proposed method performs better than other existing methods in terms of accuracy (99.32%), PSNR (51.62), and SSIM (97.35). For the MS-COCO dataset, the proposed method attains an accuracy (98.45%), PSNR (37.58%), and SSIM (93.25%).
    Keywords: mean perceptual hash function (MPHF); watermarking Extraction algorithm; watermarking Embedding algorithm; Hermite chaotic neural network (HCNN); adaptive Pelican optimization (APO); common attacks an.
    DOI: 10.1504/IJSISE.2024.10067249
     
  • Offline Writer Identification using Latin and Arabic Scripts: a Comprehensive Literature Review and Perspectives   Order a copy of this article
    by Yaâcoub Hannad, Abdelillah Semma 
    Abstract: Handwriting-based writer identification has been recognized as a reliable aspect of behavioral biometrics. This paper provides a comprehensive review of established offline writer identification systems in the independent text mode. It aims to present the current state of writer identification methods and identify potential avenues for advancing this research field. The paper elucidates the typical architectural framework employed in offline writer identification systems and provides an overview of the most prevalent handwritten databases containing Latin and Arabic samples. A key contribution of this work is presenting state-of-the-art approaches in chronological order, contrasting with existing publications, to stimulate interest among new researchers and facilitate their exploration of this field. This work is intended to serve as a valuable resource for aspiring researchers seeking to enter the field of writer identification, while actively enhancing our understanding of current writer identification methods and propelling advancements within this research domain.
    Keywords: Writer identification survey; Handwriting analysis; Latin and Arabic script; Offline systems.
    DOI: 10.1504/IJSISE.2024.10067301
     
  • YOLO Evolution: A Comprehensive Review and Bibliometric Analysis of Object Detection Advancements   Order a copy of this article
    by Annu Dabas, Ekta Narwal 
    Abstract: In recent years, much progress has been made in real-time object detection algorithms. This paper reviewed various versions of one such algorithm, you only look once (YOLO), ranging from YOLOv1 to YOLOv8. We have briefly mentioned the significant improvements made in these versions and compared their performance using the average precision (AP) metric. We then carried out a bibliometric analysis of the research publications mentioning you only look once or yolo in their title, abstract or keywords in Scopus and Web of Science databases and title or abstract in Dimensions database from 2016 to mid-2024, which involved number of publications in each year, publication count from each country, distribution of publication in various disciplines, co-authorship analysis, and cooccurrence analysis of the keywords mentioned in these publications. We concluded the areas where this algorithm is widely used and where more research is still required.
    Keywords: You Only Look Once (YOLO); Object detection; Computer vision; Machine Learning (ML); Deep learning; Convolutional Neural Network (CNN); Bibliometric research.
    DOI: 10.1504/IJSISE.2024.10067588
     

 

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