Optimizing CatBoost Model: AI-based Analysis on Rail Transit Figma Platform Practice

The research introduces a novel approach that utilizes the Frilled Lizard Optimization (FLO) algorithm to enhance the hyperparameters of the CatBoost model. First, the Figma platform is analyzed in terms of its innovative design applications in rail transit. Then, the FLO algorithm is applied to optimize the CatBoost model, improving its accuracy in detecting foreign objects on rail tracks. Experiments were conducted using a dataset of 6,000 images from rail transit scenarios, divided into seven categories such as left-turning track, straight track, train, pedestrians, and others. The result showed that the FLO-CatBoost model demonstrated superior performance in accuracy, achieving a Root Mean Square Error (RMSE) of 0.274, significantly outperforming other algorithms like TSA, MPA, and RSA. Furthermore, FLO-CatBoost reduced the Mean Absolute Percentage Error (MAPE) and showed better efficiency in evaluation time. Finally, the FLO-CatBoost model significantly enhances the design and evaluation processes for intelligent rail transit systems on the Figma platform, providing higher accuracy and efficiency in detecting foreign objects and improving system design performance.

@article{Li2024,
  title     = {Optimizing CatBoost Model: AI-based Analysis on Rail Transit Figma Platform Practice},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ruobing Li and Hong Qian},
  doi       = {10.14569/IJACSA.2024.0151149},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151149}
}