Optimized process of the random forest algorithm. by Hongxia Li (493545)

“…Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. …”

C-level road irregularity sample. by Jianwei Liu (163716)

Description of optimal model parameters. by Libo Liu (552605)

Performance evaluation of four samples on the validation data. by Mohd Sakib (20565228)

The ANFIS algorithm details. by Mohammadmahdi Taheri (21722285)

“…Finally, in the FGP stage, optimization and purchase amount of each share was done. …”

The flowchart of Algorithm 2. by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. …”

Optimal Latin square sampling distribution. by Xueyong Pan (20390363)

“…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

MAE, R-squared, RMSE & SMAPE of the final model. by Gayatree Nayak (15426871)

Optimal Sampling for Generalized Linear Models Under Measurement Constraints by Tao Zhang (43681)

“…We propose a response-free sampling procedure optimal sampling under measurement constraints (OSUMC) for generalized linear models. …”

Final MBT3D benchmarks comparison on bumblebee dataset. by Luc Nicolas Stiemer (17027735)

Genetic algorithm flowchart. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”

Example of sample data. by Xiying Wang (4859998)

“…Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. …”

The Search process of the genetic algorithm. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”

Simulation sample. by Xiaogang Li (299205)

Genetic algorithm iteration data chart. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”

Simulated trajectories from real data, optimal schedule, and nearest optimal schedule. by Samuel Christensen (9922554)

Sample classes from the HMDB51 dataset. by Yasir Khan Jadoon (21433231)