AIMC Topic: Neural Networks, Computer

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Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

Scientific reports
In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional...

A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints.

Neural networks : the official journal of the International Neural Network Society
Controllable Pareto front learning (CPFL) approximates the Pareto optimal solution set and then locates a non-dominated point with respect to a given reference vector. However, decision-maker objectives were limited to a constraint region in practice...

FPWT: Filter pruning via wavelet transform for CNNs.

Neural networks : the official journal of the International Neural Network Society
The enormous data and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive problem, filter pruning has become one of the practical approaches. At presen...

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Neural networks : the official journal of the International Neural Network Society
Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional conv...

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model.

Magnetic resonance imaging
The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconst...

Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction.

Annals of emergency medicine
STUDY OBJECTIVE: Although the importance of primary percutaneous coronary intervention has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory activation remains subopt...

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks.

International endodontic journal
The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especi...

Model-based federated learning for accurate MR image reconstruction from undersampled k-space data.

Computers in biology and medicine
Deep learning-based methods have achieved encouraging performances in the field of Magnetic Resonance (MR) image reconstruction. Nevertheless, building powerful and robust deep learning models requires collecting large and diverse datasets from multi...

Enhanced Nanoparticle Recognition via Deep Learning-Accelerated Plasmonic Sensing.

Biosensors
Surface plasmon microscopy proves to be a potent tool for capturing interferometric scattering imaging data of individual particles at both micro and nanoscales, offering considerable potential for label-free analysis of bio-particles and bio-molecul...

DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.

International journal of molecular sciences
As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, pla...