Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …

Dear Editor, This letter proposes a symmetry-preserving dual-stream graph neural network
(SDGNN) for precise representation learning to an undirected weighted graph (UWG) …

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 …

Artificial neural networks are prone to suffer from catastrophic forgetting. Networks trained on
something new tend to rapidly forget what was learned previously, a common phenomenon …

Graph neural networks (GNNs) have garnered significant attention in hyperspectral image
(HSI) change detection (CD). However, existing GNN-based methods extract features by …

Aspect-based sentiment analysis (ABSA) aims to use interactions between aspect terms and
their contexts to predict sentiment polarity for given aspects in sentences. Current …

Pedestrians are often vulnerable users of urban roads and ensuring their safety is a
pressing challenge in the filed of intelligent transportation. Multiple pedestrian tracking is …

In online learning, personalized course recommendations that align with learners'
preferences and future needs are essential. Thus, the development of efficient recommender …