Forthcoming Articles

International Journal of Modelling, Identification and Control

International Journal of Modelling, Identification and Control (IJMIC)

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 Articles will not be published until the final proofs are validated by their authors.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 Modelling, Identification and Control (2 papers in press)

Regular Issues

  •   Free full-text access Open AccessImproved BP algorithm for measurement of urban business environment based on machine learning
    ( Free Full-text Access ) CC-BY-NC-ND
    by Huihui Deng, Weitao Liu, Zhuolin Xiao, Yaxi Zhang 
    Abstract: To overcome these limitations, this study proposes a machine learning-driven urban business environment measurement model based on an improved Back Propagation Neural Network (BPNN). First, the core constituent dimensions of the business environment are systematically reviewed. The proposed model is compared against benchmark models including the standard BP, Support Vector Machine (SVM), and Random Forest (RF). Performance evaluation results demonstrate that the proposed improved BP model achieves significantly superior performance across key metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R-squared, R2), enabling more accurate fitting and prediction of urban business environment levels. This study provides an efficient and objective new paradigm for the quantitative assessment of business environments and offers valuable references for intelligent measurement studies of other complex socio-economic systems.
    Keywords: business environment; BP neural network; machine learning; improved algorithm; double optimisation mechanism; support vector machine; SVM.
    DOI: 10.1504/IJMIC.2026.10080695
     
  • Outlier detection algorithm based on deviation characteristic   Order a copy of this article
    by Yong Wang, Hongbin Wang, Pengcheng Sun, Xinliang Yin 
    Abstract: Outlier mining focuses on researching rare events through detection and analysis to dig out the valuable knowledge from them. In the static data set environment, the traditional LOF algorithm calculates the local outlier factor through the whole data set and requires a lot of computing time. To solve this problem, the algorithm divides the data space into grids, and calculates the local outlier factor based on the centroids of the grids. Since the grid number is less than data point number, the time complexity is obviously reduced under acceptable error. When the new data points are added, it can rapidly detect outliers. The contrast experiment results show that the new algorithm can reduce the computation time and improve the efficiency, while achieving comparable accuracy.
    Keywords: outlier detection; local outlier factor; deviation characteristic; fast LOF detection algorithm.

 

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