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codon optimization » wolf optimization (Expand Search)
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data codon » data code (Expand Search), data codes (Expand Search), data codings (Expand Search)
codon optimization » wolf optimization (Expand Search)
lead optimization » global optimization (Expand Search), swarm optimization (Expand Search), whale optimization (Expand Search)
binary model » final model (Expand Search), injury model (Expand Search), tiny model (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
model lead » modes lead (Expand Search), model left (Expand Search)
data codon » data code (Expand Search), data codes (Expand Search), data codings (Expand Search)
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21
Friedman average rank sum test results.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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22
IRBMO vs. variant comparison adaptation data.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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23
Supplementary Material 8
Published 2025“…In AMR studies, datasets often contain more susceptible isolates than resistant ones, leading to biased model performance. SMOTE overcomes this issue by generating synthetic samples of the minority class (resistant isolates) through interpolation rather than simple duplication, thereby improving model generalization.…”
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24
Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…<br>The consistency of the results across different kernels demonstrates that the information contained in the habitat, by itself, leads to a very simple optimal decision rule (mostly the prediction of the most frequent class per habitat), which cannot be improved solely by model adjustments. …”
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25
Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
Published 2025“…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”
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26
Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx
Published 2024“…The analysis discerns three distinct clusters denoting varying degrees of urban integration within these familial units, namely high-level, medium-level, and low-level urban integration. We applied binary logit regression models to analyze the influence of family urban integration on the mental health among migrant workers. …”