Multiple linear regression analysis of trust change predictors. by Alain Abi-Rizk (8549358)

Subjects:

Obstacle uniform linear motion scenarios. 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. …”

The linearly decreasing convergence factor a. by Junhao Wei (6816803)

Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller. by Hua Huo (1818133)

“…<p>Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller.…”

STL Linear Combination Forecast Graph. 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. …”

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

Subjects:

Linear regression. by Myriam Aïche (22290591)

Heteroscedastic linear mixed model main results, estimating the volume (ml) of beer sold per day on the transformed (cube root) scale, on the original scale and on the relative sca... by Eleni Mantzari (6991172)

Subjects:

The most common machine learning methods to predict ν_s. by Fahd Saeed Alakbari (10701871)

Subjects:

Methodological flowchart for this study, generated in Microsoft Office Word 2023 (https://www.microsof.com). by Abdolhossein Boali (19996390)

Subjects:

Investigating the trend of land use class changes. by Abdolhossein Boali (19996390)

Subjects:

Wind erosion monitoring during the 15 years (2008–2023) using the ensemble model, generated in the ArcGIS 10.7.1 software (www.esri.com) and R 4.3.2 software (www.r-project.org). by Abdolhossein Boali (19996390)

Subjects:

Fig 5 - by Abdolhossein Boali (19996390)

Subjects:

Importance of variables in the study area in 2023, generated in the R 4.3.2 software (www.r-project.org) and Microsoft Office Excel 2023 (https://www.microsof.com). by Abdolhossein Boali (19996390)

Subjects:

The maps illustrating wind erosion in northeastern Iran for 2008 and 2023, generated using various models from the SDM package. by Abdolhossein Boali (19996390)

Subjects:

Fig 8 - by Abdolhossein Boali (19996390)

Subjects:

Field-based data for spatio-temporal wind erosion risk analysis. by Abdolhossein Boali (19996390)

Subjects:

The selection of indicators based on their correlation with ground surface data. by Abdolhossein Boali (19996390)

Subjects:

Fig 4 - by Abdolhossein Boali (19996390)

Subjects:

Spatio-temporal data mining modeling for wind erosion risk assessment. by Abdolhossein Boali (19996390)

Subjects: