يعرض 2,341 - 2,360 نتائج من 18,479 نتيجة بحث عن 'significant ((((gap decrease) OR (((greater decrease) OR (a decrease))))) OR (mean decrease))', وقت الاستعلام: 0.67s تنقيح النتائج
  1. 2341
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  3. 2343

    Bluetooth beacons with colour coded lanyards. حسب J Mark Ansermino (13958512)

    منشور في 2025
    "…At both intervention sites, there was a significant reduction in antimicrobial utilization of 47% (Kenya) and 33% (Uganda) compared to baseline. …"
  4. 2344

    A Comparison of Pediatric Prehospital Opioid Encounters and Social Vulnerability حسب Stephen Sandelich (19991783)

    منشور في 2024
    "…The analysis demonstrated that as socioeconomic status (SES) improves, the likelihood of opioid-related activations increases significantly supported by a significant negative linear trend (Estimate = −0.2971, SE = 0.1172, z = −2.54, <i>p</i> = 0.0112. …"
  5. 2345
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  7. 2347
  8. 2348

    Structure diagram of ensemble model. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  9. 2349

    Fitting formula parameter table. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  10. 2350

    Test plan. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  11. 2351

    Fitting surface parameters. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  12. 2352

    Model generalisation validation error analysis. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  13. 2353

    Empirical model prediction error analysis. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  14. 2354

    Fitting curve parameters. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  15. 2355

    Test instrument. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  16. 2356

    Empirical model establishment process. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  17. 2357

    Model prediction error trend chart. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  18. 2358

    Basic physical parameters of red clay. حسب Hongqi Wang (2208238)

    منشور في 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. …"
  19. 2359

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

    منشور في 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. …"
  20. 2360

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

    منشور في 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. …"