Data Sheet 1_AI-enabled workforce integration: blended human resource contribution rate in Chinese companies.pdf by Kexin Zhang (301752)

Parametric study of accuracy of the Random forest algorithm for different proportions of test samples and different choice of random states. by Abeer Aljohani (18497914)

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Supplementary file 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.docx by Jing-Hong Xiao (22780781)

“…After standard quality control, 295,589 SNPs were retained. Fourteen machine-learning algorithms were evaluated using SNPs selected through traditional GWAS filtering and refined via wrapper-based feature selection with a best-first search algorithm. …”

Data Sheet 2_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip by Yonghua Pang (20998022)

“…To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).…”

Data Sheet 1_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip by Yonghua Pang (20998022)

“…To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).…”

Cognitive test and self-perceived health and social functioning presented for the low-moderate and high PA levels group. by Xin Zhang (35492)

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Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches by Lisa Lombardo (7301969)

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Python code for a rule-based NLP model for mapping circular economy indicators to SDGs by Zahir Barahmand (18008947)

“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”

Quantum Chemistry-Guided Machine Learning for Accelerated Design of CO₂‑Solubilizing Deep Eutectic Solvents by Dingkai Hu (16277547)

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Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Levy function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rastrigin function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Levy function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rastrigin function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Levy function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Levy function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rastrigin function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rastrigin function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”