Factors influencing the discovery of new genetic signatures. by Wei Liu (20030)

A. Scatter plot of the training set data before batch correction; B: Scatter plot of the training set data after batch correction. by Min-xiao Wang (20868922)

Range of point clouds. by Xinpeng Yao (18882573)

“…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”

Results of ablation experiment. by Xinpeng Yao (18882573)

“…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”

Transformer Encoder network structure. by Xinpeng Yao (18882573)

“…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”

Line chart of frame rate. by Xinpeng Yao (18882573)

“…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”

The total loss and three-component loss. by Xinpeng Yao (18882573)

“…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”

Improved upsampling module based on Transformer. by Xinpeng Yao (18882573)

“…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”

Principal component analysis (PCA) was applied to reduce the dimensionality of the dataset, and hierarchical clustering was then used to identify kinematic subpopulations. by Nancy A. Juárez-Contreras (22794057)

Riemann Hypothesis Solution: A Structured Hilbert-Polya Operator Realization via Entropy Geometry by Jennifer Bulyaki (22283308)

Spatial patterns of the CAPs estimated from both splits are reproducible and strongly correlated with at least one of the basis CAPs. by Kangjoo Lee (11648345)

Notation guide. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

Decision tree evaluation. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

CNN model evaluation. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

ROC curve CNN. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

RCNN model evaluation. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

Accuracy of ML classifiers. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

Random forest evaluation. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

ROC curve RCNN. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

Correlation matrix. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”