Showing 2,481 - 2,500 results of 18,343 for search 'significantly ((((((nn decrease) OR (larger decrease))) OR (mean decrease))) OR (a decrease))', query time: 0.60s Refine Results
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    A Comparison of Pediatric Prehospital Opioid Encounters and Social Vulnerability by Stephen Sandelich (19991783)

    Published 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. …”
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    Structure diagram of ensemble 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. …”
  18. 2498

    Fitting formula 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. …”
  19. 2499

    Test plan. 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. …”
  20. 2500

    Fitting surface parameters. 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. …”