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wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
were optimization » before optimization (Expand Search), swarm optimization (Expand Search), whale optimization (Expand Search)
binary studies » library studies (Expand Search), primary studies (Expand Search), literary studies (Expand Search)
studies were » studies per (Expand Search), studies met (Expand Search), studies done (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. …”
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Flow diagram of the proposed model.
Published 2025“…<div><p>Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. …”
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Datasets and their properties.
Published 2023“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”
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Parameter settings.
Published 2023“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”
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Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
Published 2022“…In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. …”
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Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures
Published 2021“…The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. …”
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SHAP bar plot.
Published 2025“…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. …”
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Sample screening flowchart.
Published 2025“…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. …”
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Descriptive statistics for variables.
Published 2025“…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. …”
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SHAP summary plot.
Published 2025“…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. …”
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ROC curves for the test set of four models.
Published 2025“…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. …”
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Display of the web prediction interface.
Published 2025“…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. …”
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