Major hyperparameters of RF-MLPR. حسب Jintao Li (448681)

"…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 R² 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. …"

Results of RF algorithm screening factors. حسب Jintao Li (448681)

"…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 R² 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. …"

Schematic diagram of the basic principles of SVR. حسب Jintao Li (448681)

"…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 R² 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. …"

Statins induced flatter neuronal somas. حسب Shuk C. Tsoi (21192243)

Differential expression profiling in the ASFV-ΔI7L-infected primary porcine alveolar macrophages (PAMs) by RNA sequencing analysis. حسب Meilin Li (1832905)

Comparison of the cohesion ranges of different food categories under IDDSI levels. حسب Muxi Chen (9294270)

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Burn out assessment throughout demographic structure of the participant. حسب Nehal Mohamed Eisa (21148787)

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Relationships between cohesiveness and IDDSI levels. حسب Muxi Chen (9294270)

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Demographic structure and Employment categorization. حسب Nehal Mohamed Eisa (21148787)

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Stack column chart represent Emotional Exhaustion through demographic structure. حسب Nehal Mohamed Eisa (21148787)

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Hardness increase across different IDDSI levels and food categories. حسب Muxi Chen (9294270)

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Relationships between adhesiveness and IDDSI levels. حسب Muxi Chen (9294270)

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Comparison of adhesiveness ranges for different food categories under IDDSI levels. حسب Muxi Chen (9294270)

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Cohesiveness changes across IDDSI levels and food categories. حسب Muxi Chen (9294270)

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Donut chart shows Demographic structure and Employment categorization. حسب Nehal Mohamed Eisa (21148787)

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i & ii: PCA represent the (i) 2d and (ii) 3d to identify the underlying dimensions of the lifestyle questions. حسب Nehal Mohamed Eisa (21148787)

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Multiple linear regression analysis results. حسب Muxi Chen (9294270)

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Relationships between hardness and IDDSI levels. حسب Muxi Chen (9294270)

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Stack column chart represent Depersonalization through demographic structure. حسب Nehal Mohamed Eisa (21148787)

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Adhesiveness changes across different IDDSI levels and food categories. حسب Muxi Chen (9294270)

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