The performance of any biometric recognition system heavily dependents on finding a good
and suitable feature representation space where observations from different classes are well …

Abstract Recently, The Internet of Vehicles (IoV) concept is becoming very popular due to
sharing of the data between vehicles and the infrastructure. The sharing of data is very …

Convolutional neural networks (CNNs) have been successfully applied to the single target
tracking task in recent years. Generally, training a deep CNN model requires numerous …

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …

Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …

Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …

Action recognition is a challenging research area in which several convolutional neural
networks (CNN) based action recognition methods are recently presented. However, such …

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent
manufacturing systems. Deep learning-based methods have been recently developed for …

In order to exploit the abundant potential information of the unlabeled data and contribute to
analyzing the correlation among heterogeneous data, we propose the semi-supervised …

Designing suitable convolutional neural networks (CNNs) for different image data requires
much human effort and expertise, in recent years, this process has been greatly accelerated …