A hierarchical XGBoost framework incorporating lithology-family information improved classification accuracy and consistency across three independent test wells from the Yanchang ...
A deep learning framework integrating an enhanced restricted Boltzmann machine with bitterling fish optimization achieves over 98 percent accuracy in ...
A data mining study of 232 university students in Peru found that support vector machines predicted satisfaction levels with 84.13 ...
A random forest model for classifying Asian elephant hospitalization outcomes showed moderate overall discrimination across three Thai hospitals but exhibited ...
A secondary analysis shows machine-learning models using routine clinical and cognitive variables achieved moderate-to-good internal performance in classifying Alzheimer's disease ...
A hybrid ProXGBoost model utilizing gradient synchronization and generative adversarial network data augmentation achieved 99.98 percent accuracy in detecting multiclass ...
A simulation study found that only the scaled Brier score consistently met proposed prevalence-related criteria for evaluating hard classifications in ...
Researchers have developed MLIDSC, a self-adaptive online active learning framework that maintains high accuracy on multiclass imbalanced data streams while ...
A new study shows that fine-tuning large language models substantially outperforms prompting and classical baselines for detecting which group online ...
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