The image of the Aekley function. by Zhengyu Xu (8550660)

“…A typical layered model was established, and the PSO algorithm, FA algorithm, and PSO-IFAH algorithm were applied to inversion calculations. …”

Judgment matrix of criterion layer U₃. by Yixuan Lu (9987980)

Judgment matrix of criterion layer U₄. by Yixuan Lu (9987980)

Judgment matrix of criterion layer U₂. by Yixuan Lu (9987980)

Judgment matrix of criterion layer U₁. by Yixuan Lu (9987980)

Expert scoring values of criterion layer U₁. by Yixuan Lu (9987980)

ICN pooling layer operation. by Jianpeng Zhang (528185)

DACS optimized RBF flow chart. by Bowen Li (200859)

“…The optimal parameters such as the width and weight of RBF are determined, and the optimal RDC-RBF fault diagnosis model is established. …”

A new fast filtering algorithm for a 3D point cloud based on RGB-D information by Chaochuan Jia (7256237)

“…Then, the optimal segmentation threshold of the V image that is calculated by using the Otsu algorithm is applied to segment the color mapping image into a binary image, which is used to extract the valid point cloud from the original point cloud with outliers. …”

Flowchart of stacking model integration. 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. …”

Prediction results of the stacking model. 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. …”

The solid material attributes. by Xiaogang Li (299205)

“…Firstly, an analysis model is derived to calculate effective material and geometric parameters of the layer of solid scatterers. …”

Simulation sample. by Xiaogang Li (299205)

“…Firstly, an analysis model is derived to calculate effective material and geometric parameters of the layer of solid scatterers. …”

Data for Figs 3–9. by Xiaogang Li (299205)

“…Firstly, an analysis model is derived to calculate effective material and geometric parameters of the layer of solid scatterers. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

Algorithm comparison test results. by Hang Ren (1312905)

Technical approach. 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. …”

Data set presentation. 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. …”

Pearson correlation coefficient matrix plot. 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. …”

SHAP of stacking. 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. …”