In recent years, interdisciplinary research has become increasingly popular within the
scientific community. The fields of materials science and chemistry have also gradually …

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep
learning (DL) for revolutionizing resource allocation in wireless networks. GNN-based …

Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis
of several brain disorders, including Alzheimer's disease and epilepsy. Until recently …

Graph neural networks (GNNs) have demonstrated exceptional performance in processing
various types of graph data, such as citation networks and social networks, etc. Although …

Identifying anomalies in multi-dimensional sequential data is crucial for ensuring optimal
performance across various domains and in large-scale systems. Traditional contrastive …

Abstract Recently, Transformers and MLPs based models have dominated and made
significant progress in time series analysis. However, these methods struggle to capture the …

This survey article explores graph-based approaches to multimodal human activity
recognition in indoor environments, emphasizing their relevance to advancing multimodal …

Data augmentation has been widely used across various research domains in recent years.
However, data augmentation applied to real-world graph-structured data tends to suffer from …

Time-series classification (TSC) covers the supervised learning problem where input data is
provided in the form of series of values observed through repeated measurements over time …

With the increasing traffic congestion problems in metropolises, traffic prediction plays an
essential role in intelligent traffic systems. Notably, various deep learning models, especially …