Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …

Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …

Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …

The COVID-19 pandemic has posed great challenges to public health services, government
agencies, and policymakers, raising huge social conflicts between public health and …

This study presents a deep-learning-based forecast model for spatial and temporal
prediction of pluvial flooding. The developed model can produce the flooding situation for …

Material molecular representation (MMR) plays an important role in material property or
chemical reaction prediction. However, traditional expert-designed MMR methods face …