AIMC Topic: Neural Networks, Computer

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Individual muscle strengths in rehabilitation outcomes of distal radius fracture.

Journal of neuroengineering and rehabilitation
BACKGROUND: Distal radius fractures (DRFs) are common fracture types and elderly patients often struggle to achieve functional recovery, which could be overcome by precise rehabilitation. This study aims to develop an innovative approach for acquirin...

Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.

Biomedical engineering online
BACKGROUND: Wearable sensors combined with deep-learning models are increasingly being used to predict biomechanical variables. Researchers have focused on either simple neural networks or complex pretrained models with multiple layers. In addition, ...

generation of peptide binders with desired properties by deep generative models reinforced through enrichment of focused sets for iterative fine-tuning.

Chemical communications (Cambridge, England)
Recurrent neural networks underwent reinforcement procedures for generation of peptide binders with desired properties. Docking and scoring of peptides from these models allowed enrichment of focused sets with validated sequences for iterative fine-...

Shear wave velocity prediction using Long Short-Term Memory Network with generative adversarial mechanism.

PloS one
Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by ...

An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China.

PloS one
Debris flows represent a persistent challenge for disaster prediction in mountainous regions due to their highly nonlinear and multivariate triggering mechanisms. This study proposes an explainable deep learning framework, the Improved Kepler Optimiz...

A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention mechanism.

PloS one
Addressing the issue of insufficient key feature extraction leading to low recognition rates in existing deep learning-based flow pattern identification methods, this paper proposes a novel flow pattern image recognition model, Enhanced DenseNet with...

Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.

PloS one
Cell microscopy is the main tool that allows researchers to study microorganisms and plays a key role in observing and understanding the morphology, interactions, and development of microorganisms. However, there exist limitations in both the techniq...

Rockburst grade evaluation and parameter sensitivity analysis based on SSA-PNN framework: Considering rock mass strength.

PloS one
The occurrence of rockburst is closely related to the strength and stress conditions of rock mass. The Lalin Railway tunnel in China was taken as an example, the strength and stress parameters of rock mass at 22 rockburst locations were obtained by u...

Uncertainty-based cardiac image registration using variational autoencoder with nonuniformly spaced control points.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheless, several challenges have plague...

LG-TriCapsNet: A lightweight graph capsule framework with nearest neighbor graphs for multi-disease EEG classification.

Computers in biology and medicine
EEG signal classification for neurological disorders is a very critical task in the healthcare field, demanding accuracy and efficiency. Due to the diversity of these disorders and the complexity of the EEG signals, the task of diagnosing these disor...