Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Graph contrastive learning (GCL) shows excellent potential in unsupervised graph representation learning. Data augmentation (DA), responsible for generating diverse views, plays a vital role in GCL, and its optimal choice heavily depends on the downs...
Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Mixed-precision quantization plays a pivotal role in deploying deep neural networks in resource-constrained environments. However, the task of finding the optimal bit-width configurations for different layers under deployable mixed-precision quantiza...
Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solu...
Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electrom...
Computer methods and programs in biomedicine
Oct 18, 2024
BACKGROUND AND OBJECTIVE: Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical ima...
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...
Addressing the challenges of current scraper conveyor health assessments being influenced by expert knowledge and the relative difficulty in establishing degradation models for equipment, this study proposed a method for assessing the health status o...
The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedas...
Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the ute...
BACKGROUND AND AIMS: EUS is sensitive in detecting pancreatic neuroendocrine neoplasm (pNEN). However, the endoscopic diagnosis of pNEN is operator-dependent and time-consuming because pNEN mimics normal pancreas and other pancreatic lesions. We inte...
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