Forthcoming Articles

International Journal of Computing Science and Mathematics

International Journal of Computing Science and Mathematics (IJCSM)

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 Articles will not be published until the final proofs are validated by their authors.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 Computing Science and Mathematics (2 papers in press)

Regular Issues

  • Design of personalised knowledge teaching platform based on C4.5 algorithm and association rules   Order a copy of this article
    by Shasha Chen 
    Abstract: To address the limitations of traditional teaching models in accommodating individual student differences, this study developed a personalised knowledge instruction platform integrating C4.5 algorithm and association rules. The model first constructs personalised knowledge profiles using C4.5 algorithm to extract learning behaviours and preferences. By combining an enhanced Apriori algorithm with data snapshot and hash index mechanisms, it significantly improves rule generation efficiency and recommendation accuracy. Experimental results demonstrate that the model achieves 98.4% accuracy in FIC dataset and 98.2% in ASSISTments dataset, with F1 scores reaching 91.14%. Key metrics including support, confidence, and gain reach 90.73%, 84.22%, and 93.86% respectively, outperforming competing models like LightGBM and XGBoost. This study validates the platforms effectiveness and practical value in personalised teaching recommendations, providing a scalable technical framework for intelligent education systems.
    Keywords: C4.5 algorithm; association rules; teaching; personalisation; recommendation.
    DOI: 10.1504/IJCSM.2026.10078105
     
  • Mental health diagnosis model based on multi-task and multi-view learning   Order a copy of this article
    by Xiaoyu Yang 
    Abstract: Mental health diagnosis remains a global challenge due to limitations in existing models, which often rely on single-task learning or unimodal data sources, leading to low accuracy and poor generalisation across diverse user groups. To address these issues, this paper proposes a novel mental health diagnostic model based on multi-task and multi-view (MTMV) learning. A self-constructed dataset combining smartphone behavioural data and psychological scale responses was preprocessed using a Word2Vec-based Skin gram model, with signal-to-noise ratio (SNR) and information loss rate used to evaluate preprocessing performance. The proposed MTMV framework integrates attention-enhanced feature fusion, expert gating, and cross-task representation sharing to jointly diagnose anxiety, stress, and depression. Experimental results show that the model achieves 97.6% accuracy and 89.2% recall for students, and demonstrates superior performance across multiple occupational groups. These findings indicate that the MTMV-MH model offers a robust and scalable solution for intelligent mental health assessment.
    Keywords: multi-task; multi-view; mental health; Word2Vec; MTMV; SNR.
    DOI: 10.1504/IJCSM.2026.10078231
     

 

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