Graph data have become increasingly important, and graph node clustering has emerged
as a fundamental task in data analysis. In recent years, graph node clustering has gradually …

Recently deep learning methods have shown significant progress in data clustering tasks.
Deep clustering methods (including distance-based methods and subspace-based …

The Internet of Things (IoT) is an emerging technology that has earned a lot of research
attention and technical revolution in recent years. Significantly, IoT connects and integrates …

For accurately identifying the crack severity of turbine blades, a novel intelligent diagnosis
framework is proposed in our paper, which uses multiscale sparse filtering (MSF)-based …

Clustering large and high-dimensional document data has got a great interest. However,
current clustering algorithms lack efficient representation learning. Implementing deep …

In order to generate a probability density function (PDF) for fitting the probability distributions
of practical data, this study proposes a deep learning method which consists of two …

Supervised learning-based methods have been widely used for fault diagnosis of rotating
machinery. The performance of these methods usually relies on the labeled fault samples …

Deep learning, a derived from machine learning, has grown into widespread usage with
applications as diverse as cancer detection, elephant spotting, and game development. The …

Accurate identification of plant diseases is a critical task in agricultural production. The
existing deep learning crop disease recognition methods require a large number of labeled …

Recently, graph neural networks (GNNs) have become a hot topic in machine learning
community. This paper presents a Scopus-based bibliometric overview of the GNNs' …