This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …

As vehicle complexity and road congestion increase, combined with the emergence of
electric vehicles, the need for intelligent transportation systems to improve on-road safety …

Improving and enhancing the effectiveness of software vulnerability detection methods is
urgently needed today. In this study, we propose a new source code vulnerability detection …

In the evolving landscape of vehicular networks, it is crucial to ensure robust security and
efficient data handling. In this work, We introduce a novel federated learning (FL) algorithm …

Modern cities require a tighter integration with Information and Communication
Technologies (ICT) for bringing new services to the citizens. The Smart City is the …

Auxetic metamaterials have been applied in many domains due to their unique auxetic
behavior, tunable local kinematics, and morphological intelligence. However, the classic …

Graph embedding learning is a fundamental task when dealing with diverse datasets. While
encoder–decoder architectures, such as U-Nets, have shown great success in image pixel …

To achieve new applications for 5G communications, physical layer security has recently
drawn significant attention. In a wiretap channel system, our goal is to minimize information …

We aim to address the near-duplicate image (NDI) detection problem with a deep learning
network. With the advancement of digital acquisition devices and easy-to-use image editing …

The integration of orthogonal time frequency space signals into integrated sensing and
communication systems has emerged as a highly promising approach for constructing …