Abstract The Restricted Boltzmann Machine (RBM) has aroused wide interest in machine
learning fields during the past decade. This review aims to report the recent developments in …

A finite mixture model is well-known in statistics due to its versatility and is being actively
researched. This paper reviews finite mixture model literature via bibliometric analysis …

Aiming at the problem that the most existing fault diagnosis methods could not effectively
recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy …

Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the
effective connection between flights and improve the capacity and service efficiency of …

Classifier has been widely applied in machine learning, such as pattern recognition, medical
diagnosis, credit scoring, banking and weather prediction. Because of the limited local …

Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks
that cause bearing fault and machinery breakdown. A vibration signal analysis method is the …

In this paper, we propose a novel hybrid text classification model based on deep belief
network and softmax regression. To solve the sparse high-dimensional matrix computation …

Image recognition has always been a hot research topic in the scientific community and
industry. The emergence of convolutional neural networks (CNN) has made this technology …

Various real-world applications can be formulated as feature selection problems, which
have been known to be NP-hard. In this paper, we propose an effective feature selection …

As a significant application of energy, smart grid is a complicated interconnected power grid
that involves sensors, deployment strategies, smart meters, and real-time data processing. It …