The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Nowadays, neural networks have been widely applied in many fields such as pattern
recognition, signal and image processing and control theory. Over the past two decades or …

In this paper, a magnetic resonance imaging (MRI) oriented novel attention-based glioma
grading network (AGGN) is proposed. By applying the dual-domain attention mechanism …

Common spatial pattern (CSP) technique has been very popular in terms of
electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain …

In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …

In this paper, a novel attention augmented Wasserstein generative adversarial network (AA-
WGAN) is proposed for fundus retinal vessel segmentation, where a U-shaped network with …

Generative adversarial network (GAN) is an overwhelming yet promising method to address
the data imbalance problem. However, most existing GANs that are usually inspired by …

This article is concerned with the partial-node-based (PNB) state estimation problem for
delayed complex networks (DCNs) subject to intermittent measurement outliers (IMOs). In …

In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is
proposed for cancer detection from histopathology images. To build a highly generalized …

This article addresses the state estimation problem for a class of nonlinear complex
networks (CNs) under attack. First, a novel important‐data‐based (IDB) attack strategy is …