IEEE transactions on neural networks and learning systems
Sep 3, 2024
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classificati...
IEEE transactions on neural networks and learning systems
Sep 3, 2024
Semantic comprehension aims to reasonably reproduce people's real intentions or thoughts, e.g., sentiment, humor, sarcasm, motivation, and offensiveness, from multiple modalities. It can be instantiated as a multimodal-oriented multitask classificati...
IEEE transactions on neural networks and learning systems
Sep 3, 2024
To overcome the energy efficiency bottleneck of the von Neumann architecture and scaling limit of silicon transistors, an emerging but promising solution is neuromorphic computing, a new computing paradigm inspired by how biological neural networks h...
IEEE transactions on neural networks and learning systems
Sep 3, 2024
Gait synchronization has attracted significant attention in research on assistive lower-limb exoskeletons because it can circumvent conflicting movements and improve the assistance performance. This study proposes an adaptive modular neural control (...
IEEE transactions on neural networks and learning systems
Sep 3, 2024
With the adoption of smart systems, artificial neural networks (ANNs) have become ubiquitous. Conventional ANN implementations have high energy consumption, limiting their use in embedded and mobile applications. Spiking neural networks (SNNs) mimic ...
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose ...
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug discovery. Deep neural networks have made impressive progress in accelerating and improving drug dis...
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Deep neural networks often suffer from performance inconsistency for multiorgan segmentation in medical images; some organs are segmented far worse than others. The main reason might be organs with different levels of learning difficulty for segmenta...
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to b...
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI rem...