IEEE transactions on neural networks and learning systems
Oct 29, 2024
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of tradit...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large mem...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mut...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning al...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
The brain's mystery for efficient and intelligent computation hides in the neuronal encoding, functional circuits, and plasticity principles in natural neural networks. However, many plasticity principles have not been fully incorporated into artific...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at...
Acute respiratory infections (ARIs) represent a significant global health burden, contributing to high morbidity and mortality rates, particularly in vulnerable populations. Traditional methods for diagnosing and tracking ARIs often face limitations ...
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples t...
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