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A qualitative study of question-posing anxiety in Chinese postgraduates in UK TESOL programs

  • Release time:2026-04-02
  • Hits:

  • DOI number: 

    10.1186/s40537-026-01421-9

  • Journal: 

    Journal of Big Data

  • Key Words: 

    Deep neural network；Generative adversarial network；Recurrent neural network；Classification improvement algorithm；Credit usage prediction；Precision credit marketing

  • Abstract: 

    Optimizing credit marketing strategies not only improves banks’economic performance but also meets the evolving needs of the market economy. Toachieve this goal, this paper puts forward an integrated optimization strategy from two perspectives: credit usage prediction and credit interval prediction.For credit usage prediction, the paper applies decision trees to extract business rules and filters them accordingly. Meanwhile, a credit usage predictionmodel is built using Deep Neural Networks, where Recurrent Neural Networks are used to capture time-series features and Categorical Boosting modelare used to process non-time-series feaures. Experimental results show that the credit usage prediction model achieves an accuracy of 95.2% to 97.%and an Area Under the Receiver Operating Characteristic Curve ranging from 0.93 to 0.95 on two datasets, significantly outperforming baseline modelsThe credit interval prediction model reaches a maximum Mean Absolute Error of 3.651, a minimum Mean Squared Eror of 23.250, and a coefficient of-determination greater than 0.896. In addition, the integrated strategy based on both models shows strong adaptability across diferent scenarios. Thesefindings suggest that the proposed models demonstrate reliable predictive performance and can provide analytical support for optimizing credit marketing strategies.

  • Translation or Not: 

    no

  • Date of Publication: 

    2026-04-02

  • Included Journals: 

    SCI、EI
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