AIMC Topic: Computer Simulation

Clear Filters Showing 361 to 370 of 3963 articles

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.

Artificial intelligence in medicine
Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural networ...

Modelling multivariate spatio-temporal data with identifiable variational autoencoders.

Neural networks : the official journal of the International Neural Network Society
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they c...

Neuroscientific insights about computer vision models: a concise review.

Biological cybernetics
The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the hi...

Computational Fuzzy Modelling Approach to Analyze Neuronal Calcium Dynamics With Intracellular Fluxes.

Cell biochemistry and biophysics
Mathematical neuroscience investigates how calcium distribution in nerve cells affects the neurological system. The interaction of numerous systems is necessary for the operation of several cellular processes in neuron cells, such as calcium, buffer,...

From Simulation to Reality: Predicting Torque With Fatigue Onset via Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Muscle fatigue impacts upper extremity function but is often overlooked in biomechanical models. The present work leveraged a transfer learning approach to improve torque predictions during fatiguing upper extremity movements. We developed two artifi...

ExGAT: Context extended graph attention neural network.

Neural networks : the official journal of the International Neural Network Society
As an essential concept in attention, context defines the overall scope under consideration. In attention-based GNNs, context becomes the set of representation nodes of graph embedding. Current approaches choose immediate neighbors of the target or i...

Dynamic dissipative control for fuzzy distributed parameter cyber physical system under input quantization and DoS attack.

PloS one
This article explores the dissipative control for a class of nonlinear DP-CPS (distributed parameter cyber physical system) within a finite-time interval. By utilizing a Takagi-Sugeno (T-S) fuzzy model to represent the system's nonlinear aspects, the...

Applying support vector machines to a diagnostic classification model for polytomous attributes in small-sample contexts.

The British journal of mathematical and statistical psychology
Over several years, the evaluation of polytomous attributes in small-sample settings has posed a challenge to the application of cognitive diagnosis models. To enhance classification precision, the support vector machine (SVM) was introduced for esti...

Weighted Expectile Regression Neural Networks for Right Censored Data.

Statistics in medicine
As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression ...

Combining the probabilistic finite element model and artificial neural network to study nutrient levels in the human intervertebral discs.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Diffusion distance and diffusivity are known to affect nutrient transport rates, but the probabilistic analysis of these two factors remains vacant. There is a lack of effective tools to evaluate disc nutrient levels.