Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Accompanied with the development of green wireless communication, the green Internet of
Vehicles (GIoV) has been a latent solution for future transportation. Among them, intelligent …

The rise in demand for energy storage solutions and the widespread adoption of electric
vehicles (EVs) have given rise to the creation of vehicle-to-everything (V2X) topologies. V2X …

Trajectory prediction in dynamic and highly interactive scenarios is a critical method for
achieving advanced autonomous driving. Maximizing the guidance and constraints provided …

With the acceleration of urbanization, the problem of urban traffic congestion has become
increasingly serious. This study aims to develop an intelligent traffic flow control system …

With the rapid development of Cooperative Intelligent Transportation System (C-ITS), it
becomes an urgent problem to effectively evaluate the data transmission efficiency of code …

As a common neurological disorder in early childhood and adolescence, an efficient and
accurate diagnosis of Attention-Defect/Hyperactivity Disorder (ADHD) has always been one …

Modeling complex spatial and temporal dependencies in multivariate time series data is
crucial for traffic forecasting. Graph convolutional networks have proved to be effective in …

The popularity of machine learning has motivated the idea of Energy-Based Learning (EBL),
which used Energy-Based Models (EBMs) proposed by Prof. Yann to capture dependencies …

Image super-resolution reconstruction is one of the methods to improve resolution by
learning the inherent features and attributes of images. However, the existing super …