Results 81 to 90 of about 2,584 (186)

Enhanced Modelling Performance with Boosting Ensemble Meta-Learning and Optuna Optimization

SN Computer Science
AbstractImproving modeling performance on imbalanced multi-class classification problems has continued to attract attention from researchers considering the critical and significant role such models should play in mitigating the prevalent problem. Ensemble Learning (EL) techniques are among the key methods utilized by researchers as they are known for ...
Tertsegha J. Anande, Sami Alsaadi, Mark S. Leeson   +2 more
openaire   +1 more source

Beyond Traditional Covariates: An Interpretable Machine Learning Workflow for Improved Hybrid Pharmacometric Modeling

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 8, August 2026.
ABSTRACT Model‐informed precision dosing is often constrained by the limited generalizability of traditional population pharmacokinetic models, especially in critically ill patients. A hybrid machine learning‐population pharmacokinetic framework is proposed to improve a priori pharmacokinetic predictions by integrating real‐world clinical data.
Freek J. A. Relouw, Jort J. M. T. Lokers, Tim Preijers, Sebastiaan D. T. Sassen, Natal A. W. van Riel, Birgit C. P. Koch   +5 more
wiley   +1 more source

Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation

Underground Space
This study aims to develop and evaluate a natural gradient boosting (NGBoost) model optimized with Optuna for estimating ground settlement during tunnel excavation, incorporating Shapley additive explanations (SHAP) to perform interpretability analysis ...
Yuxin Chen, Mohammad Hossein Kadkhodaei, Jian Zhou   +2 more
doaj   +1 more source

Based on the WSP-Optuna-LightGBM model for wind power prediction

Journal of Physics: Conference Series
Abstract In order to optimize energy dispatch and enhance the predictive performance of wind power forecast, this study proposes a WSP-Optuna-LightGBM mixed regression prediction model based on wind speed-power curve (WSP), Optuna parameter optimization, and Light Gradient Boosting Machine (LightGBM).
Bo Xiang, Zhenqing Liu, Linhong Huang, Ming Qin   +3 more
openaire   +1 more source

Overall Prediction of Breakthrough Curves in Fracture Networks Using Graph‐Based Simulation and Machine Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Discrete fracture network (DFN) models are widely used to simulate fluid and solute transport through fracture networks that serve as their preferential pathways. In DFN models, fractures are generated stochastically based on fracture properties.
S. Okamoto, K. Nakata, K. Mori, T. Saito
wiley   +1 more source

Optimization of diabetes prediction methods based on combinatorial balancing algorithm

Nutrition & Diabetes
Background Diabetes, as a significant disease affecting public health, requires early detection for effective management and intervention. However, imbalanced datasets pose a challenge to accurate diabetes prediction.
HuiZhi Shao, Xiang Liu, DaShuai Zong, QingJun Song   +3 more
doaj   +1 more source

Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization

IEEE Access
Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide, making early and accurate detection crucial for effective treatment.
Prabhat Kumar Sahu, Taiyaba Fatma
doaj   +1 more source

A Kolmogorov–Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract The computational cost of geochemical solvers is a challenging matter. For reactive transport simulations, where chemical calculations are performed up to billions of times, it is crucial to reduce the total computational time. Existing publications have explored various machine learning approaches to determine the most effective data‐driven ...
Leonardo Boledi, Dirk Bosbach, Jenna Poonoosamy   +2 more
wiley   +1 more source

Replacing Tunable Parameters in Weather and Climate Models With State‐Dependent Functions Using Reinforcement Learning

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 8, August 2026.
Abstract Weather and climate models rely on parameterizations to represent unresolved sub‐grid processes. Traditional schemes rely on fixed coefficients that are weakly constrained and tuned offline, contributing to persistent biases that limit their ability to adapt to underlying physics.
Pritthijit Nath, Sebastian Schemm, Henry Moss, Peter Haynes, Emily Shuckburgh, Mark J. Webb   +5 more
wiley   +1 more source

Otimização Multiobjetiva de um Integrador Numérico Adaptativo Utilizando o Optuna

Vetor
A integração numérica de equações diferenciais ordinárias é um dos problemas mais comuns em análise numérica. Diversos métodos e técnicas estão disponíveis na literatura, assim como diferentes combinações de heurísticas e parâmetros.
Daniel Augusto de Sousa Mendes, Rafael Pereira Bianchin, Ângelo de Carvalho Paulino, Angelo Passaro   +3 more
doaj   +1 more source