بدائل البحث:
samples optimization » kepler optimization (توسيع البحث), compared optimization (توسيع البحث), based optimization (توسيع البحث)
whale optimization » swarm optimization (توسيع البحث)
all samples » soil samples (توسيع البحث), small sample (توسيع البحث)
binary base » binary mask (توسيع البحث), ciliary base (توسيع البحث), binary image (توسيع البحث)
base whale » based whole (توسيع البحث), baleen whale (توسيع البحث)
samples optimization » kepler optimization (توسيع البحث), compared optimization (توسيع البحث), based optimization (توسيع البحث)
whale optimization » swarm optimization (توسيع البحث)
all samples » soil samples (توسيع البحث), small sample (توسيع البحث)
binary base » binary mask (توسيع البحث), ciliary base (توسيع البحث), binary image (توسيع البحث)
base whale » based whole (توسيع البحث), baleen whale (توسيع البحث)
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Flow diagram of the proposed model.
منشور في 2025"…Local Interpretable Model-agnostic Explanations (LIME) were applied to improve interpretability. Across all algorithm models, LR–ABC hybrids outperformed their baseline models (e.g., Random Forest: 85.2% → 91.36% accuracy). …"
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Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity
منشور في 2019"…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. …"
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Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx
منشور في 2025"…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …"
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DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
منشور في 2024"…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …"
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Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
منشور في 2024"…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …"
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Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
منشور في 2025"…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …"
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Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease
منشور في 2025"…<i>Z</i> score standardization and independent sample <i>t</i> test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. …"
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Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx
منشور في 2024"…Utilizing two methods for handling missing values—imputation or deletion—we created various cohorts: DM-all, DM-slim, OS-all, OS-slim, CSS-all, and CSS-slim. …"