Optimization process of BO algorithm. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

BGCN optimization framework. by Shuhui Luo (19173211)

Hyperparameters for the XGBoost model. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 3. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Distribution of cross-section stypes. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Example of data used in Table 1. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 7. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 8. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 4. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Features of shear strength database for RC walls. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Overview of our approach. by Carlos A. Gomez-Uribe (21492788)

Implementation of the low-discrepancy spherical trajectory in C++ from Selection and evaluation of spherical acquisition trajectories for industrial computed tomography by F. Bauer (10836998)

Implementation of the low-discrepancy spherical trajectory and visualization in Matlab from Selection and evaluation of spherical acquisition trajectories for industrial computed t... by F. Bauer (10836998)

Effective region sampling strategy. by Yu Gu (162342)

“…Divide the entire path planning process into two stages: quickly finding the initial path and optimizing the path. …”

Dynamic Principal Component Analysis in High Dimensions by Xiaoyu Hu (4159)

“…Specifically, we formulate an optimization problem by combining the local linear smoothing and regularization penalty together with the orthogonality constraint, which can be effectively solved by manifold optimization algorithms. …”

Data Sheet 2_Early stroke detection through machine learning in the prehospital setting.pdf by María Ríos Delgado (22004942)

Data Sheet 1_Early stroke detection through machine learning in the prehospital setting.pdf by María Ríos Delgado (22004942)

Table 1_Early stroke detection through machine learning in the prehospital setting.xlsx by María Ríos Delgado (22004942)

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

UR5 robotic arm. by Yu Gu (162342)

“…Divide the entire path planning process into two stages: quickly finding the initial path and optimizing the path. …”