The work explores the integration of behavioral analytics and artificial intelligence (AI) techniques, specifically Graph Convolutional Networks (GCNs) and deep learning, to predict student academic ...

Dealing with the extensive length, feature sparsity, and substantial ambiguity inherent in text poses significant challenges for classification tasks. Prompt-learning offers a promising approach by ...

Intelligent Vehicular Ad-hoc Networks (INVANETs) optimize road travel in Intelligent Transportation Systems (ITS), exploiting smart technologies to enhance safety, efficiency, and reduce congestion.

Graph analysis, crucial in fraud detection and social networks, often execute in a non-optimized fashion on Non-Uniform Memory Access (NUMA) machines. To enhance performance while providing ...

Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-the-art for point ...

Unmanned Aerial Vehicles (UAVs) are increasingly used to augment ground networks, creating an integrated air-ground heterogeneous network architecture. In such networks, each link has unique ...

With the increasing application numbers of radio frequency identification (RFID) multitags in recent years, the position distribution of RFID multitag has a significant impact on the reading ...

And this is where a longer-term performance graph comes into play. Data by YCharts. Markel has actually outperformed Berkshire Hathaway over the long term.