Hierarchical Optimization on large models. by Nisha Ann Viswan (20371285)

Dropout optimization model framework structure diagram. by Peng Yang (296696)

An optimal solution for the HFS instance. by Xiang Tian (4369285)

“…The results demonstrate that the proposed IPMMPO-CP outperforms the compared algorithms and models.</p></div>…”

Flat optimization method. by Nisha Ann Viswan (20371285)

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design by Shu-Sen Chen (1777108)

“…Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. …”

Schematic diagram of the Dropout optimization. by Peng Yang (296696)

ROC curves of different models. by Yan Liu (25061)

Changes in loss curves of different models. by Yan Liu (25061)

Feature matching errors of different models. by Yan Liu (25061)

Schematic diagram of P-Net, R-Net, O-Net models. by Yan Liu (25061)

Comparison based on hard instances from [79]. by Xiang Tian (4369285)

“…The results demonstrate that the proposed IPMMPO-CP outperforms the compared algorithms and models.</p></div>…”

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…Different techniques are available to address the class imbalance in classification models and can be categorized as data-level, algorithm-level, and hybrid methods. …”

Diversity and specificity of lipid patterns in basal soil food web resources by Jakob Kühn (7288466)

“…In marine environments, multivariate optimization models (Quantitative Fatty Acid Signature Analysis) and Bayesian approaches (source-tracking algorithm) were established to predict the proportion of predator diets using lipids as tracers. …”

ROC curves for the test set of four models. by Meng Cao (105914)

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

Schematic diagram of PM model. by Xiang Tian (4369285)

“…The results demonstrate that the proposed IPMMPO-CP outperforms the compared algorithms and models.</p></div>…”

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

Face recognition algorithm flow based on the PCA. by Na Lin (69470)

Data Sheet 7_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 2_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 9_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.xlsx by Xuehui Fan (10762662)