Major hyperparameters of RF-MLPR. by 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. by 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. by 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. …”

Effect of different doses of the defatted extract (DE) of the leaves of <i>S. malaccense</i> and <i>S. samarangense</i> on urea and creatinine levels. Data represented as mean±SEM... by Fatma A. Moharram (9276104)

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The results of linear mixed model. by Soheila Qanbari (20455173)

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The flow of the participants from enrollment to analysis. by Soheila Qanbari (20455173)

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Correlation between treatment-induced changes in lumbar motor control and N150 amplitude. by Soheila Qanbari (20455173)

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Sensory-evoked potentials of N80 and N150. by Soheila Qanbari (20455173)

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The mean and standard deviation of outcome measures. by Soheila Qanbari (20455173)

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Linear relationships (Spearman’s r (p-value)) the treatment-induced changes in clinical and neurophysiological data. by Soheila Qanbari (20455173)

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The demographical and clinical characteristics of subjects at baseline. by Soheila Qanbari (20455173)

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Electrode montage and electric field distribution simulated with SimNIBS software. by Soheila Qanbari (20455173)

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MRI grading systems’ diagnostic accuracy for MD. by Neda Azarpey (20308334)

“…<div><p>Background</p><p>The diagnosis of Meniere’s Disease (MD) presents significant challenges due to its complex symptomatology and the absence of definitive biomarkers. …”

MRI-based cochlear hydrops grading and PLE in MD. by Neda Azarpey (20308334)

“…<div><p>Background</p><p>The diagnosis of Meniere’s Disease (MD) presents significant challenges due to its complex symptomatology and the absence of definitive biomarkers. …”

Cochlear hydrops classification in MRI systems. by Neda Azarpey (20308334)

“…<div><p>Background</p><p>The diagnosis of Meniere’s Disease (MD) presents significant challenges due to its complex symptomatology and the absence of definitive biomarkers. …”

Particle size distribution of GO-GA NPs. by Huda H. Elbehery (22492996)

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Fourier-transform infrared spectroscopy (FTIR) spectra of GA (black), GO (blue), and GO–GA NPs (red). by Huda H. Elbehery (22492996)

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Toxicity of GO and GO-GA NPs on <i>C. maculatus</i> adults after 24h exposure. by Huda H. Elbehery (22492996)

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Mean persistence (±SE) of GO and GO-GA NPs on <i>Callosobruchus maculatus.</i> by Huda H. Elbehery (22492996)

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Post-effect of GO and GO-GA NPs on progeny production of <i>Callosobruchus maculatus.</i> by Huda H. Elbehery (22492996)

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