بدائل البحث:
processing optimization » process optimization (توسيع البحث), process optimisation (توسيع البحث), routing optimization (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
data processing » image processing (توسيع البحث)
primary data » primary care (توسيع البحث)
binary b » binary _ (توسيع البحث)
b model » _ model (توسيع البحث), a model (توسيع البحث), 2 model (توسيع البحث)
processing optimization » process optimization (توسيع البحث), process optimisation (توسيع البحث), routing optimization (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
data processing » image processing (توسيع البحث)
primary data » primary care (توسيع البحث)
binary b » binary _ (توسيع البحث)
b model » _ model (توسيع البحث), a model (توسيع البحث), 2 model (توسيع البحث)
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161
Big Data Model Building Using Dimension Reduction and Sample Selection
منشور في 2023"…Furthermore, such subdata cannot be useful to build alternative models because it is not an appropriate representative sample of the full data. In this article, we propose a novel algorithm for better model building and prediction via a process of selecting a “good” training sample. …"
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162
Data_Sheet_1_Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier.PDF
منشور في 2021"…Also, we implemented class weighting and an optimal oversampling technique to overcome the class imbalance in the primary data. …"
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163
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164
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165
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166
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
منشور في 2025"…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"
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167
DATASET AI
منشور في 2025"…Performance metrics include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC).</p><p dir="ltr">All data have been de-identified and processed in accordance with institutional ethical standards.…"
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168
Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
منشور في 2025"…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …"
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169
Supplementary file 1_OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial.xlsx
منشور في 2025"…</p>Methods<p>We extracted data from Kaplan-Meier (KM) curves, reconstructed individual patient data (IPD) using an iterative KM algorithm, and fitted parametric survival functions to the IPD data. …"
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170
Dataset: Spatial Variability and Uncertainty of Soil Nitrogen across the Conterminous United States at Different Depths
منشور في 2022"…We used a random forest-regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm) at 5 km spatial grids. …"