Showing 1,921 - 1,940 results of 4,482 for search 'significantly ((less decrease) OR (((greatest decrease) OR (((nn decrease) OR (mean decrease))))))', query time: 0.45s Refine Results
  1. 1921

    BP neural network structure diagram. by Hongqi Wang (2208238)

    Published 2024
    “…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
  2. 1922

    Structure diagram of GBDT model. by Hongqi Wang (2208238)

    Published 2024
    “…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
  3. 1923

    Model prediction error analysis index. by Hongqi Wang (2208238)

    Published 2024
    “…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
  4. 1924

    Fitting curve parameter table. by Hongqi Wang (2208238)

    Published 2024
    “…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
  5. 1925

    Model prediction error analysis. by Hongqi Wang (2208238)

    Published 2024
    “…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
  6. 1926
  7. 1927

    Frontier Analysis Based on ASDR and SDI. by Guanghui Yu (423945)

    Published 2025
    “…The frontier analysis identified 15 countries with the greatest potential for improvement. According to the BAPC model, the ASDR for females is projected to rise across the 20–80 age group, while for males, the increase is particularly pronounced in the 55–75 age group. …”
  8. 1928

    Complexity comparison of different models. by Li Yuan (102305)

    Published 2025
    “…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”
  9. 1929

    Dynamic window based median filtering algorithm. by Li Yuan (102305)

    Published 2025
    “…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”
  10. 1930

    Flow of operation of improved KMA. by Li Yuan (102305)

    Published 2025
    “…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”
  11. 1931

    Improved DAE based on LSTM. by Li Yuan (102305)

    Published 2025
    “…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”
  12. 1932

    Autoencoder structure. by Li Yuan (102305)

    Published 2025
    “…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”
  13. 1933
  14. 1934
  15. 1935
  16. 1936
  17. 1937
  18. 1938
  19. 1939
  20. 1940