Results 131 to 140 of about 17,602 (258)

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Trends in marine species distribution models: a review of methodological advances and future challenges

Ecography, EarlyView.
Correlative species distribution models (SDMs) are quantitative tools in biogeography and macroecology. Building upon the ecological niche concept, they correlate environmental covariates to species presence to model habitat suitability and predict species distributions.
Moritz Klaassen, Tiago A. Marques, Filipe Alves, Marc Fernandez   +3 more
wiley   +1 more source

Sparse Bayesian Learning for Label Efficiency in Cardiac Real-Time MRI [PDF]

Felix Terhag, Philipp Knechtges, Achim Basermann, Anja Bach, Darius Gerlach, Jens Tank, Raúl Tempone   +6 more
openalex   +1 more source

‘ukbioprepr': an R package to support reproducible preparation of environmental data for biodiversity modelling in the UK

Ecography, EarlyView.
Biodiversity modelling is essential for explaining and predicting ecological responses to environmental change and assessing progress towards targets in the Kunming‐Montreal Global Biodiversity Framework (CBD 2022). The UK benefits from rich biodiversity time‐series data and numerous open‐source environmental datasets.
Charlotte Rose Rush, Joseph Cooper, Cecilia Larrosa, Martin Wilkes   +3 more
wiley   +1 more source

Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding [PDF]

Yuanhao Li, Badong Chen, Wenjun Bai, Yasuharu Koike, Okito Yamashita   +4 more
openalex   +1 more source

Continuous outcome estimation in N‐of‐1 trials for accelerated decision‐making

Epilepsia, EarlyView.
Abstract Objective N‐of‐1 trials aim to determine the therapeutic effect for a single individual. This individualized approach necessitates collecting multiple data points over time through repeated alternating periods of active treatment and a comparator or control condition.
Victoria Defelippe, František Bartoš, Eric‐Jan Wagenmakers, Kees P. J. Braun, Floor E. Jansen, Willem M. Otte   +5 more
wiley   +1 more source

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

Earthquake Engineering &Structural Dynamics, EarlyView.
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 Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model [PDF]

Jinchao Feng, Sui Tang
openalex   +1 more source

Ensemble Deep Learning–Based Wind Power Forecasting With Self‐Adaptive Osprey Optimization Algorithm

Energy Science &Engineering, EarlyView.
Design of Self‐Adaptive Osprey (SAO) algorithm: The novel SAO algorithm is designed by integrating the exploration capability of the conventional Osprey algorithm by including the self‐adaptiveness for enhancing the convergence rate. Ensemble Deep Learning for wind power forecasting: The wind forecasting is employed using the proposed Ensemble learning
Johncy Bai Johnson, Sivarani Thankamony Saradha Devi, Kumar Chandrasekaran, Sourav Barua   +3 more
wiley   +1 more source

Interpretable Tree‐Based Models for Predicting Short‐Term Rockburst Risk Considering Multiple Factors

Energy Science &Engineering, EarlyView.
Interpretable tree‐based models integrate microseismic, geological, and mining indicators to predict short‐term rockburst risk. SHAP analysis reveals the dominant role of energy‐related features and clarifies nonlinear factor interactions, enabling transparent and reliable early‐warning in deep coal mines.
Shuai Chen, Linming Dou, Atao Li, Wu Cai, Lei Zhang   +4 more
wiley   +1 more source