بدائل البحث:
larger decrease » marked decrease (توسيع البحث)
teer decrease » mean decrease (توسيع البحث), greater decrease (توسيع البحث)
we decrease » _ decrease (توسيع البحث), nn decrease (توسيع البحث), mean decrease (توسيع البحث)
a decrease » _ decrease (توسيع البحث), _ decreased (توسيع البحث), _ decreases (توسيع البحث)
larger decrease » marked decrease (توسيع البحث)
teer decrease » mean decrease (توسيع البحث), greater decrease (توسيع البحث)
we decrease » _ decrease (توسيع البحث), nn decrease (توسيع البحث), mean decrease (توسيع البحث)
a decrease » _ decrease (توسيع البحث), _ decreased (توسيع البحث), _ decreases (توسيع البحث)
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5627
RICTOR silencing inhibits cell proliferation via UGCG regulation.
منشور في 2025"…(<b>C</b>) Cell proliferation studies show a decrease in the proliferation of MCF-7_RICTOR<sup>SH</sup> cells (mean ± SEM, <i>n</i> = 4) compared to MCF-7_SCRAM<sup>SH</sup> cells. …"
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5629
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Major hyperparameters of RF-SVR.
منشور في 2024"…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"
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5637
Pseudo code for coupling model execution process.
منشور في 2024"…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"
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5638
Major hyperparameters of RF-MLPR.
منشور في 2024"…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"
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5639
Results of RF algorithm screening factors.
منشور في 2024"…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"
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5640
Schematic diagram of the basic principles of SVR.
منشور في 2024"…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"