Performance on GradEva. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

The considered test problems. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Performance on FunEva. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Performance on Iter. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Continuation of Table 2. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

SHAP bar plot. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Sample screening flowchart. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Descriptive statistics for variables. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

SHAP summary plot. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

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

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Display of the web prediction interface. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

The data processing and analysis pipeline. by Xin Zhang (35492)

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach by Sanaa Badr (20628838)

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

“…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …”

Table_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Table_2_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)