Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreasing order by absolute value for the cancer outcomes. by Eric Wang (30587)

“…The features are sorted in decreasing order by absolute value for the cancer outcomes.…”

Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

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Assessment values of machine learning models. by Bin Pan (742525)

“…Additionally, the model’s generalization ability is verified through transfer learning. Although the performance of the StackBoost model decreases when applied to different datasets, it still shows considerable transferability, making it a more generalizable prediction model for aqueous solubility.…”

Table of values for <i>p.</i> by Long Di (9977453)

“…Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

P values of Kruskal–Wallis test at 95% significance level. by Fahd Saeed Alakbari (10701871)

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TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

RMSE versus learning rate. by Zhe Hu (787283)

“…Field experiments demonstrate that the predicted values from the LSTM model closely align with the measured values, maintaining short-term shape error prediction accuracy within 3 mm. …”

Yearly crude rates normalized by their 1999 value. by Tony Wong (779773)

“…<p>Values below 1 indicate crude rate decreases relative to their 1999 value, those above 1 indicate increases. …”

Comparison of environmental perception time results at different learning rates. by Wenhui Wang (34265)

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Comparative evaluation of gray matter segmentation using Multi-Layer U-Net, Vanilla U-Net, Attention U-Net, and state-of-the-art methods. The results of the proposed model are calc... by Muhammad Zubair (728141)

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Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

outcome of HPDL-MIAIHD technique with recent methods. by Naif Almakayeel (16846021)

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CT outcome of HPDL-MIAIHD technique with other methods. by Naif Almakayeel (16846021)

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CT outcome of HPDL-MIAIHD technique with recent methods. by Naif Almakayeel (16846021)

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Oviduct Glycoprotein 1 (OVGP1) Diagnoses Polycystic Ovary Syndrome (PCOS) Based on Machine Learning Algorithms by Fengjuan Wang (768949)

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The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.72 g/cc and DTc = 51.47 us/ft. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTc trends models with experimental measured values for RHOB = 2.27 g/cc and DTs =  248.99 us/ft. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTc trends models with experimental measured values for RHOB = 2.02 g/cc and DTs =  265.03 us/ft. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.33 g/cc and DTc = 118.67 us/ft. by Fahd Saeed Alakbari (10701871)

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The MAE value of the model under raw data. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”