Results 81 to 90 of about 3,964 (194)

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, Volume 55, Issue 9, Page 1811-1827, 25 July 2026.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

A highly accurate and interpretable Gradient Boosting machine learning model for predicting permeability in strongly heterogeneous reservoirs using the Optuna framework

Journal of Geophysics and Engineering
Permeability, a key parameter for evaluating the capability of fluid flow within reservoir pores, significantly influences the calculation of effective reservoir thickness, reserves assessment, and production evaluation.
Ximei Jiang, Weichao Yan, Huilin Xing, Ping Feng, Jianmeng Sun, Yangchen Fan   +5 more
semanticscholar   +1 more source

Application of the Optuna-NeuralProphet model for predicting step-like landslide displacement

AIP Advances
Displacement prediction is crucial to landslide engineering monitoring and early warning. An Optuna-NeuralProphet model is proposed based on the Optuna framework and the NeuralProphet model to address the challenge of predicting step-like landslide ...
Ming Huang, Hougang Yang, Fan Yang
doaj   +1 more source

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

Energy Science &Engineering, Volume 14, Issue 7, Page 3434-3457, July 2026.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi   +2 more
wiley   +1 more source

Classification of Alzheimer's Disease Using a Hybrid Technique Integration Between CNN and Optuna Optimization

NTU Journal of Engineering and Technology
Alzheimer's Disease (AD) is considered one of the most prevalent neurological disorders, primarily affecting elderly people and adversely impacting their brain functions. This disease is characterized by the gradual deterioration of cognitive functions,
Nawzt Sadiq Jaafar Al-Bayati, Raid Rafi Omar Al-Nima   +1 more
doaj   +1 more source

A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios

Journal of Forecasting, Volume 45, Issue 4, Page 1797-1828, July 2026.
ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley   +1 more source

Supporting Informed Shutter Operations for SMILE's SXI Using Ensemble Classifiers for Soft Proton High Flux Period Predictions

Space Weather, Volume 24, Issue 7, July 2026.
Abstract Satellite X‐ray imagers are highly vulnerable to contamination by soft protons (∼100 keV), which can trigger spurious charge‐coupled device events and force interruptions in observations. For XMM‐Newton, such contamination reduced usable science time by ∼40%.
Simon Mischel, Elena A. Kronberg, C. P. Escoubet   +2 more
wiley   +1 more source

The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification

JOIV: International Journal on Informatics Visualization
This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification
Nadya Alinda Rahmi, Sarjon Defit, - Okfalisa   +2 more
doaj   +1 more source

Beyond Mean Solar Wind Conditions: Turbulence‐Aware Forecasting of the AE Index

Space Weather, Volume 24, Issue 7, July 2026.
Abstract The auroral electrojet (AE) index is a key indicator of high latitude geomagnetic activity and is widely used in operational space weather monitoring, yet forecasting AE from upstream solar wind conditions remains challenging due to nonlinear coupling, internal magnetospheric dynamics, and multiscale variability.
Cara L. Waters, Christopher H. K. Chen, Mathew J. Owens   +2 more
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