Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image
segmentation processes. Thus, the precise segmentation of organs and their lesions may …

To improve forecasting accuracy and capture intricate interactions within transportation
networks, information fusion approaches are crucial for traffic predictions based on graph …

Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of
applications. However, the graph data used for training may contain sensitive personal …

Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …

Biofuel supply chains (BSCs) face diverse uncertainties that pose serious challenges. This
has led to an expanding body of research focused on studying these challenges. Hence …

In the era of big data, there has been a surge in the availability of data containing rich spatial
and temporal information, offering valuable insights into dynamic systems and processes for …

Graph Neural Networks have proven to be very valuable models for the solution of a wide
variety of problems on molecular graphs, as well as in many other research fields involving …

Accurate estimation of transportation flow is a challenging task in Intelligent Transportation
Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates …

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-
structured data. Their ability to capture complex relationships and dependencies within …