بدائل البحث:
design optimization » bayesian optimization (توسيع البحث)
robust optimization » process optimization (توسيع البحث), robust estimation (توسيع البحث), joint optimization (توسيع البحث)
class robust » less robust (توسيع البحث)
binary mask » binary image (توسيع البحث)
mask design » task design (توسيع البحث), based design (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
robust optimization » process optimization (توسيع البحث), robust estimation (توسيع البحث), joint optimization (توسيع البحث)
class robust » less robust (توسيع البحث)
binary mask » binary image (توسيع البحث)
mask design » task design (توسيع البحث), based design (توسيع البحث)
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Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model
منشور في 2025"…This framework integrates a novel biologically inspired optimization algorithm, the Indian Millipede Optimization Algorithm (IMOA), for effective feature selection. …"
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Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
منشور في 2025"…The consistent accuracy of 70.28% does not represent a flaw in the SVM. algorithm, but rather the predictive performance ceiling of the feature itself, whose simplicity and class overlap limit the model's discriminatory ability. …"
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Supplementary Material 8
منشور في 2025"…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…"
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DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
منشور في 2024"…Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. …"
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Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
منشور في 2024"…Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. …"