Bayesian neural networks - Open Access .click

Results 211 to 220 of about 227,027 (269)

Are we ready for causal discovery in biological systems using deep learning? [PDF]

Brief Bioinform
Yeo HC, Selvarajoo K.
europepmc +1 more source

A review on rockburst prediction and prevention to shape an ontology‐based framework for better decision‐making for underground excavations

Deep Underground Science and Engineering, EarlyView.
A scientometric analysis of 2449 journal articles and a comprehensive review of 336 papers were conducted, discussing and identifying challenges and research gaps in rockburst prediction and prevention and proposing an ontology‐based framework for better decision‐making in underground excavations. Abstract With underground engineering projects becoming
Hongchuan Yan +6 more
wiley +1 more source

A weight-sharing Bayesian neural network for consistent feature selection with applications in cancer gene expression data. [PDF]

BMC Bioinformatics
Mishra A, Xia W, Pazhayidam George C.
europepmc +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu +8 more
wiley +1 more source

Analysis of the anti-scalping mechanism of hospital appointment registration based on Bayesian theory. [PDF]

Front Public Health
Zou X, Wang L, Sun J, Guo S, Huang Y.
europepmc +1 more source

Harnessing the power of machine and deep learning for transferring joint species distribution models considering the structure of biotic interactions

Ecography, EarlyView.
The transferability of single or joint species distribution models ((j)SDMs) depends on their ability to predict beyond the observed environmental range and to remain consistent despite shifts in biotic interactions. Transfer accuracy may be improved by recent advances in the application of deep learning that provide greater flexibility and potentially
Marco Basile +44 more
wiley +1 more source

Short-term forecasting of Indonesia electricity generation using MATLAB based on NARX neural network. [PDF]

PLoS One
Pranata N, Saputri FR.
europepmc +1 more source

Ictal–interictal continuum and status epilepticus: Two sides of the same coin? A prospective magnetic resonance imaging study

Epilepsia, EarlyView.
Abstract Objective Status epilepticus (SE) is the most severe expression of seizures, encompassing both SE with prominent motor symptoms and nonconvulsive SE (NCSE). Ictal–interictal continuum (IIC), an electroencephalographic phenomenon, is characterized by periodic discharges (PD), spike‐and‐waves or sharp‐and‐waves (SW), or lateralized rhythmic ...
Pilar Bosque‐Varela +15 more
wiley +1 more source

AI-driven multimodal imaging fusion using swin transformer and optimized tensor fusion networks for pneumonia detection. [PDF]

Sci Rep
Sikindar S, Raghavendran CV, Madhavi G.
europepmc +1 more source

Rhythms in longitudinal thalamic recordings are linked to seizure risk

Epilepsia, EarlyView.
Abstract Objective Seizure unpredictability remains a major clinical challenge for people with epilepsy. Previous works have shown that seizure risk is associated with circadian and multi‐day cycles in both brain and physiological signals. However, it remains unclear whether neural activity from deep brain structures such as the anterior nucleus of the
Xinbing Zhang +5 more
wiley +1 more source

deep learning
bayesian neural network
machine learning

uncertainty quantification
bayesian inference
bayesian deep learning

neural networks
physics
neural network