Forthcoming Articles

International Journal of Simulation and Process Modelling

International Journal of Simulation and Process Modelling (IJSPM)

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 Articles will not be published until the final proofs are validated by their authors.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 also listed here. 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 Simulation and Process Modelling (2 papers in press)

Regular Issues

  • Simulation-driven deep learning framework for early poultry disease detection using faecal image classification with optimised CNN architectures   Order a copy of this article
    by Nonita Sharma, Monika Mangla, Manik Rakhra, Baljinder Kaur, Raj Kumar Mohanta, Bijay Kumar Paikaray 
    Abstract: The present investigation aims to devise an optimized Convolutional Neural Network (CNN) framework to identify prominent poultry diseases based on faecal images. The proposed research model uses a tri-convolutional layer architecture to enhance the accuracy of poultry disease classification and improve feature extraction. Three major diseases, namely coccidiosis, salmonella, and Newcastle, have been considered. The prime objective of current research is to achieve early detection by employing advanced deep-learning techniques. The proposed model uses fecal images to identify pathological conditions using the TriConvLayer architecture accurately. In this work, custom CNN models, viz. SoloConvLayer, TriConvLayer, and FiveConvLayer models are used to achieve an accuracy of 97%, 98%, and 98%, respectively. The achieved result advocates the efficacy of the proposed approach. It thus has the potential to revolutionize early disease detection in poultry farming, a major step towards improving animal health and farm productivity.
    Keywords: deep learning; poultry disease detection; convolutional neural networks; CNN; faecal image analysis; poultry health monitoring; disease classification; simulation framework.
    DOI: 10.1504/IJSPM.2025.10073525
     
  • Process-oriented simulation and optimisation of 2D trabecular bone via fractal modelling   Order a copy of this article
    by Liu Yuhong, J.I.A. Liyan, Wang Zheng, Jincai Chang 
    Abstract: A process-oriented method for simulating and reconstructing 2D trabecular bone microstructures is proposed, based on Fractional Brownian Motion (FBM) and multi-parameter morphological con-straints. By adjusting the Hurst exponent, the Fractal Dimensions(FD) of trabecular structures can be controlled, enabling the reconstruction of biomimetic porous geometries with tunable porosity and thickness. A complete modelling framework is developed, including image preprocessing, pa-rameter extraction, simulation generation, and post-optimisation, and is validated using real femoral cross-sectional images. Comparative results show that this method outperforms existing Graph Convolutional Networks GCN-based approaches in preserving topological fidelity and geometric fidelity. This study provides a novel, simulation-driven pathway for customizable bone scaffold design and process modelling in bioengineering.
    Keywords: fractal modelling; trabecular bone structure; 2D geometric reconstruction; parameter-driven simulation; morphological optimisation.
    DOI: 10.1504/IJSPM.2025.10076820
     

 

Return to top