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. …”

Entire procedure of this study. by Wenwei Wu (9352541)

NucB sampling: by Carlos A. Gomez-Uribe (21492788)

Alpha amylase sampling: by Carlos A. Gomez-Uribe (21492788)

Fitness score statistics for BADASS optimization of alpha-amylase (AMY_BACSU). by Carlos A. Gomez-Uribe (21492788)

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)

“…Inspired by pseudorandom sampling methods for Monte–Carlo-algorithms, we also suggest an entirely new trajectory design, the low-discrepancy spherical trajectory, which extends the concept of equiangular planar trajectories into three dimensions and can be used for benchmarking and comparison to other spherical trajectories. …”

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)

“…Inspired by pseudorandom sampling methods for Monte–Carlo-algorithms, we also suggest an entirely new trajectory design, the low-discrepancy spherical trajectory, which extends the concept of equiangular planar trajectories into three dimensions and can be used for benchmarking and comparison to other spherical trajectories. …”

Data Sheet 1_AutoRA: an innovative algorithm for automatic delineation of reference areas in support of smart soil sampling and digital soil twins.pdf by Hugo Rodrigues (5954366)

“…In this study, we introduce the autoRA algorithm, an innovative automated soil sampling design method that utilizes Gower’s Dissimilarity Index to delineate RAs automatically. …”

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. …”

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. …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In the context of facies recovery using simulations, the task of optimal sampling is formalized and addressed using a maximum information extraction criterion. …”

Table_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.DOCX by Carles Rubio Maturana (17448078)

“…Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide.…”