IEEE transactions on neural networks and learning systems
Oct 5, 2023
Data production has followed an increased growth in the last years, to the point that traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of generated data. Stream or online ML presents itself as a viable solution ...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients' health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predict...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions. Many efforts have bee...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
Artificial intelligence and machine learning techniques have progressed dramatically and become powerful tools required to solve complicated tasks, such as computer vision, speech recognition, and natural language processing. Since these techniques h...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
The nonuniform sampling (NUS) is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partially sampled exponentials is highly expected in general signal processing and many...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
In recent years, there has been an enormous interest in using deep learning to classify underwater images to identify various objects, such as fishes, plankton, coral reefs, seagrass, submarines, and gestures of sea divers. This classification is ess...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
We develop an approach to estimate a blood alcohol signal from a transdermal alcohol signal using physics-informed neural networks (PINNs). Specifically, we use a generative adversarial network (GAN) with a residual-augmented loss function to estimat...
IEEE transactions on neural networks and learning systems
Sep 1, 2023
Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as c...
IEEE transactions on neural networks and learning systems
Sep 1, 2023
The existing works on human-object interaction (HOI) detection usually rely on expensive large-scale labeled image datasets. However, in real scenes, labeled data may be insufficient, and some rare HOI categories have few samples. This poses great ch...