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robust optimization » process optimization (Expand Search), robust estimation (Expand Search), joint optimization (Expand Search)
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robust optimization » process optimization (Expand Search), robust estimation (Expand Search), joint optimization (Expand Search)
path optimization » swarm optimization (Expand Search), whale optimization (Expand Search), based optimization (Expand Search)
class robust » less robust (Expand Search)
class path » class data (Expand Search)
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DataSheet1_Quantum-inspired optimization for wavelength assignment.PDF
Published 2023“…Playing a central role in modern telecommunications, this problem belongs to NP-complete class for a general case so that obtaining optimal solutions for industry-relevant cases is exponentially hard. …”
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PathOlOgics_RBCs Python Scripts.zip
Published 2023“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”
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Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model
Published 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.
Published 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
Published 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
Published 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
Published 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. …”