One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

3D modeling qualities of the AS single-template models built with optimal HDDRs and alignments. by Giacomo Janson (8138517)

Overall rankings of single-objective optimization algorithms. by Máté Mohácsi (20469514)

Experimental comparisons of single category indexes under different algorithms. by Zhiqiang Chen (218692)

Comparison of annotation precision for single category labels using different algorithms. by Zhiqiang Chen (218692)

Average target value and evaluation metrics of the optimized explanations with different optimization strategies and machine learning models. by Bettina Soós (10860933)

Filtering parameter optimization for Imputation Models. by Genelle F. Harrison (12119661)

Greedy optimization algorithm. by Alexis Bénichou (19832361)

Overall rankings of optimization algorithms. by Máté Mohácsi (20469514)

ROC curves of single feature models. by Amirmohammad Shahbandegan (14265549)

Two exemplar data sets and model output using optimal parameters derived from each algorithm. by Dominic M. Dunstan (14838835)

WERs for different SE algorithms. by Fahad Khalil Peracha (15453204)

Calibration plot of the apparent and optimism-corrected models. by Dominika Szabo (14129304)

Dawnn model selection and hyperparmeter optimisation by George Hall (9670952)

Adjusted R² accuracy of EUR-to-AFR trans-ancestral PRS model up to the first 200 iterations of the TITR optimization algorithm for TURF functional model. by Bradley Crone (19346873)

I-NSGA-II-RF algorithm. by Xiaohua Zeng (793632)

“…Finally, the selected feature subset and parameters are input to the RF for training, prediction, and iterative optimization. Experimental results show that the I-NSGA-II-RF algorithm has the highest average accuracy, the smallest optimal solution set, and the shortest running time compared to the unmodified multi-objective feature selection algorithm and the single target feature selection algorithm. …”

The pareto front obtained by each algorithm. by Xiaohua Zeng (793632)

“…Finally, the selected feature subset and parameters are input to the RF for training, prediction, and iterative optimization. Experimental results show that the I-NSGA-II-RF algorithm has the highest average accuracy, the smallest optimal solution set, and the shortest running time compared to the unmodified multi-objective feature selection algorithm and the single target feature selection algorithm. …”

Optimization of <i>KIR3DL1/S1</i> allele imputation using data from Europeans. by Genelle F. Harrison (12119661)

Model comparison experimental results. by Chunhua Yang (346871)

“…Intersection over Union (IoU) is used to optimize the loss function calculation. The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. …”

Different model detection results comparison. by Chunhua Yang (346871)

“…Intersection over Union (IoU) is used to optimize the loss function calculation. The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. …”