Showing 41 - 60 results of 241 for search '(( significant decrease decrease ) OR ( significance ((set decrease) OR (mean decrease)) ))~', query time: 0.55s Refine Results
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    Characteristics of HACE respondents by year. by David Henderson (4612354)

    Published 2025
    “…</p><p>Results</p><p>Overall patient satisfaction with general practice declined significantly over the 10 years, with mean positive scores dropping from 90.1% in 2011/12 to 70.5% in 2021/22. …”
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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. …”
  17. 57

    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. …”
  18. 58

    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. …”
  19. 59

    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. …”
  20. 60

    Model generalisation validation 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. …”