IEEE transactions on neural networks and learning systems
Feb 28, 2025
Human activity recognition (HAR) is a popular research field in computer vision that has already been widely studied. However, it is still an active research field since it plays an important role in many current and emerging real-world intelligent s...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is ...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Spiking neural networks (SNNs) mimic their biological counterparts more closely than their predecessors and are considered the third generation of artificial neural networks. It has been proven that networks of spiking neurons have a higher computati...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust r...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Video motion magnification is the task of making subtle minute motions visible. Many times subtle motion occurs while being invisible to the naked eye, e.g., slight deformations in muscles of an athlete, small vibrations in the objects, microexpressi...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Since digital spiking signals can carry rich information and propagate with low computational consumption, spiking neural networks (SNNs) have received great attention from neuroscientists and are regarded as the future development object of neural n...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called ...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning to classify objects by training on nonrepeating video frames in temp...
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging...