AIMC Topic: Neural Networks, Computer

Clear Filters Showing 1641 to 1650 of 31376 articles

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.

IEEE transactions on neural networks and learning systems
The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manua...

Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images ba...

Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement.

IEEE transactions on neural networks and learning systems
Pavlovian associative memory plays an important role in our daily life and work. The realization of Pavlovian associative memory at the deoxyribonucleic acid (DNA) molecular level will promote the development of biological computing and broaden the a...

Video-Based Multiphysiological Disentanglement and Remote Robust Estimation for Respiration.

IEEE transactions on neural networks and learning systems
Remote noncontact respiratory rate estimation by facial visual information has great research significance, providing valuable priors for health monitoring, clinical diagnosis, and anti-fraud. However, existing studies suffer from disturbances in epi...

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction.

IEEE transactions on neural networks and learning systems
Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representation framework for describing protein functions with annotated terms. Each ontology is a specific fu...

Semi-Supervised Multimodal Representation Learning Through a Global Workspace.

IEEE transactions on neural networks and learning systems
Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations or to translate signals from one domain to another (as in image captioning or text-to-image g...

GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data.

IEEE transactions on neural networks and learning systems
Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate development and heterogeneity. While numerous machine-learning methods have been proposed to infer GRNs ...

Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories.

PloS one
With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiote...

Can deepfakes manipulate us? Assessing the evidence via a critical scoping review.

PloS one
Deepfakes are one of the most recent developments in misinformation technology and are capable of superimposing one person's face onto another in video format. The potential of this technology to defame and cause harm is clear. However, despite the g...

Neuronal correlates of sleep in honey bees.

Neural networks : the official journal of the International Neural Network Society
Honey bees Apis mellifera follow the day-night cycle for their foraging activity, entering rest periods during darkness. Despite considerable research on sleep behaviour in bees, its underlying neurophysiological mechanisms are not well understood, p...