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 Articles will not be published until the final proofs are validated by their authors.

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International Journal of Artificial Intelligence and Soft Computing (2 papers in press)

Regular Issues

  • A Comparative Study of Two Deep learning Architectures for Gesture Recognition on ArSL2018 Dataset   Order a copy of this article
    by Houssem Lahiani, Mondher Frikha 
    Abstract: This study presents a comparative evaluation of two convolutional neural network (CNN) architectures for Arabic Sign Language recognition using the ArSL2018 dataset, which contains 54,049 images across 32 alphabet classes. The objective is to identify an efficient model that can support assistive technologies for the Arabic-speaking deaf community. The first approach applies a pre-trained MobileNetV2 as a feature extractor with a fully connected layer, while the second extends MobileNetV2 by adding convolutional and pooling layers. Performance was assessed through accuracy, precision, recall, and F1-score. Results indicate that the pre-trained MobileNetV2 configuration achieved superior accuracy (95%) compared to the extended model (93.85%), with per-class performance ranging from 82.91% to 99.10%. These outcomes highlight the potential of lightweight CNN architectures for real-world deployment, offering both effectiveness and efficiency in enhancing communication accessibility through gesture-based interaction technologies.
    Keywords: CNN; ArASL; MobileNetV2; HMI.

  • Deep Learning-Based Visual Detection Method for Defects in Automotive Flowing Water Turn Signal Lights   Order a copy of this article
    by Guoyang Wan, Hanqi LI, Chengwen Wang, Bingyou Liu, Jincheng Chen, Hong Zhang 
    Abstract: To address the challenge of high-speed detection of vehicle flowing water turn signal lights, a novel detection method based on deep learning is proposed. This approach aims to achieve swift and efficient detection of flowing water lights. The proposed method incorporates an innovative deep learning detector designed to dynamically measure key points of the flowing water light and its operational status. Furthermore, this paper emphasizes the optimisation of the anchor-free object detection network through the utilisation of the heatmap regression method for detecting industrial objects. The incorporation of an attention module and the refinement of the heatmap generation methodology facilitate improvements in both the detector's accuracy and the speed of training process convergence. The outcomes of the experiment demonstrate that the detection system devised in this paper exhibits commendable speed, accuracy, and user-friendly operation.
    Keywords: deep learning; defect detection; object detection; visual inspection; turn signal light.
    DOI: 10.1504/IJAISC.2025.10074297
     

 

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