Forthcoming Articles

International Journal of Electronic Healthcare

International Journal of Electronic Healthcare (IJEH)

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 Articles will not be published until the final proofs are validated by their authors.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 Electronic Healthcare (2 papers in press)

Regular Issues

  • IoT driven Deepsensestick: the real-time obstacle, crowd, and health risk detection for visually impaired people   Order a copy of this article
    by Blanie Scrimshaw William, Y. Bevish Jinila 
    Abstract: Visually impaired people (VIP) encounter challenges during navigation individually, and it is critical when there is a health crisis, such as a pandemic. This work proposes an integrated technique that is associated with the IoT-based smart electronic walking stick and deep learning techniques. This provides significant support and safety to VIPs while navigating through pandemic-sensitive environments. For the real-time semantic segmentation, fast segmentation convolutional neural network (FSCNN) is utilised to interpret the environments at a pixel level for differentiating the roads, walls, open spaces, and other objects. The captured visual objects are detected and classified using the detection transformer (U3DETR), which handles complex scenes with enhanced accuracy. Meanwhile, a hybrid approach is used for the enhancement of detection accuracy at varying contrast, which uses a fusion approach. The proposed work supports the VIPs for navigation independently, and provides safety from the pandemic and all hard situations.
    Keywords: visually impaired people; FSCNN; detection transformer; P2PNet.
    DOI: 10.1504/IJEH.2026.10080242
     
  • Health recommender systems: a comparative analysis of classical and deep learning approaches, trade-offs, and emerging hybrid models for responsible AI   Order a copy of this article
    by Mounir El Moussaoui, Majid Ben Yakhlef, Soulaiman El Hazzat 
    Abstract: Health recommendation systems have become an essential tool in the healthcare system, playing a crucial role in the personalisation of recommendations and in clinical decision support. In this study, we propose to compare the performance of classical machine learning techniques and latest deep learning techniques based on a five- dimensional evaluation framework. Deep learning techniques have shown remarkable performance on complex and high-dimensional datasets, while classical techniques remain important in the field due to their interpretability and lower computational complexity. Moreover, the study includes trends in the field, such as federated learning, causal inference, as well as hybrid approaches that combine the two paradigms. Based on the analysis, the study emphasises the importance of selecting the appropriate solution for a specific context and calls on the research community to highlight the importance of validation, fairness, explainability, and data privacy in the deployment of health recommendation systems in clinical practice environments.
    Keywords: health recommender systems; HRSs; deep learning; interpretability; federated learning; clinical decision support.
    DOI: 10.1504/IJEH.2026.10080369
     

 

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