AIMC Topic: Neural Networks, Computer

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GeneWorker: An end-to-end robotic reinforcement learning approach with collaborative generator and worker networks.

Neural networks : the official journal of the International Neural Network Society
Reinforcement learning aided by the skill conception exhibits potent capabilities in guiding autonomous agents toward acquiring meaningful behaviors. However, in the current landscape of reinforcement learning, a skill is often merely a rudimentary a...

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained ...

deepAFT: A nonlinear accelerated failure time model with artificial neural network.

Statistics in medicine
The Cox regression model or accelerated failure time regression models are often used for describing the relationship between survival outcomes and potential explanatory variables. These models assume the studied covariates are connected to the survi...

AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.

Artificial intelligence in medicine
Early detection of pneumoconiosis by routine health screening of workers in the mining industry is critical for preventing the progression of this incurable disease. Automated pneumoconiosis classification in chest X-ray images is challenging due to ...

EHR coding with hybrid attention and features propagation on disease knowledge graph.

Artificial intelligence in medicine
And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first cons...

Bio-inspired deep learning-personalized ensemble Alzheimer's diagnosis model for mental well-being.

SLAS technology
Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynam...

Predicting thermodynamic adhesion energies of membrane fouling in planktonic anammox MBR via backpropagation neural network model.

Bioresource technology
Predicting thermodynamic adhesion energies was a critical strategy for mitigating membrane fouling. This study utilized a backpropagation (BP) neural network model to predict the thermodynamic adhesion energies associated with membrane fouling in a p...

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.

Journal of biomedical optics
SIGNIFICANCE: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the ...