A neural network trained on synthetic micromechanical simulation data reliably predicts composite failure modes across general three-dimensional multiaxial stress states ...
Spatiotemporal universal kriging incorporating topographic and river recharge covariates accurately reproduces groundwater dynamics while demonstrating that hyperparameter uncertainty is practically ...
A multi-strategy enhanced dung beetle optimizer improves hyperparameter selection and calibration reliability for multimodal emotion recognition under controlled feature conditions.
New AutoRL methods using hyperparameter optimization and metalearning achieve best solutions in 23 of 28 Sequential Ordering Problem instances while ...
Researchers developed a Newtonian physics-inspired particle swarm optimization framework that tuned lightweight transfer learning models to classify five diabetic retinopathy ...
A comparative study in Isfahan Province demonstrates that CatBoost delivers superior predictive accuracy for seasonal water consumption by integrating environmental ...
Computer scientists have introduced an adaptive, hyperparameter-free online regression framework that dynamically detects concept drift and maintains predictive accuracy across ...
A hybrid stacking ensemble of XGBoost, LightGBM and CatBoost with domain-informed features predicts concrete compressive strength with an R² of ...
A multi-scale 3D-CNN tuned by a quantum-inspired metaheuristic optimizer classified eight basketball tactical patterns from player tracking data with 94.3% ...
Researchers combined a strengthened whale optimization algorithm with a kernel extreme learning machine to predict ultra-high-performance concrete strength, achieving test ...
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