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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

The entire research flowchart. by Lang Gao (12963876)

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

Variable importance metric as measured by mean decrease in gini index (standardized between 0 and 100) for externalizing problem behavior, based on random forest with the number of... by Samrachana Adhikari (7818143)

Variable importance metric as measured by mean decrease in gini index (standardized between 0 and 100) for internalizing problem behavior, based on random forest with the number of... by Samrachana Adhikari (7818143)

“phylum_name” peptides in the MLI samples. by Matthys G. Potgieter (16384775)

“…Here we describe a novel approach, <b>MetaNovo</b>, that combines existing open-source software tools to perform scalable <i>de novo</i> sequence tag matching with a novel algorithm for probabilistic optimization of the entire <b>UniProt</b> knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines.…”

“kingdom_name” peptides in the MLI samples. by Matthys G. Potgieter (16384775)

“…Here we describe a novel approach, <b>MetaNovo</b>, that combines existing open-source software tools to perform scalable <i>de novo</i> sequence tag matching with a novel algorithm for probabilistic optimization of the entire <b>UniProt</b> knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines.…”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

UniPept pept2lca analysis of MLI samples. by Matthys G. Potgieter (16384775)

“…Here we describe a novel approach, <b>MetaNovo</b>, that combines existing open-source software tools to perform scalable <i>de novo</i> sequence tag matching with a novel algorithm for probabilistic optimization of the entire <b>UniProt</b> knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines.…”

UniPept <i>pept2lca</i> analysis of 9MM samples. by Matthys G. Potgieter (16384775)

“…Here we describe a novel approach, <b>MetaNovo</b>, that combines existing open-source software tools to perform scalable <i>de novo</i> sequence tag matching with a novel algorithm for probabilistic optimization of the entire <b>UniProt</b> knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines.…”

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)

“…This approach preserves environmental variability while retaining accuracy compared to an exhaustive predictive model (EPM) based on extensive sampling of the entire area of interest. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

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

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Flow diagram of the reachable workspace. by Zesheng Wang (15244172)

“…To address this challenge, this paper proposes a novel self-calibration methodology based on a global optimization strategy. Taking the 5PUS-RPUR parallel robot as an example, its inverse kinematics is established based on screw theory. …”