MOOC Dropout Prediction Using Explainable Relational Graph Convolution
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3637528
Abstract: This study addresses the issues of high rates of student attrition and the paucity of explainability of predictive models in Massive Open Online Courses (MOOCs) by proposing a framework for predicting student attrition based on… read more here.
Keywords: relational graph; dropout; mooc; prediction ... See more keywords
Connecting Embeddings Based on Multiplex Relational Graph Attention Networks for Knowledge Graph Entity Typing
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3142056
Abstract: Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, we observe that it still faces two non-trivial… read more here.
Keywords: connecting embeddings; entity; knowledge graph; entity typing ... See more keywords
Application of Directed Relational Graph to Air Plasma Chemistry During Plasma Relaxation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Plasma Science"
DOI: 10.1109/tps.2021.3068641
Abstract: Computations involving air plasma chemistry are often confronted with the necessity to deal with a large number of chemical species and reactions. In this article, an algorithm is demonstrated, which efficiently identifies and eliminates unimportant… read more here.
Keywords: plasma chemistry; relational graph; air plasma; directed relational ... See more keywords
Relational Graph Convolution Network with Multi Features for AntiCOVID-19 Drugs Discovery using 3CLpro Potential Target
Sign Up to like & getrecommendations! Published in 2024 at "Current Bioinformatics"
DOI: 10.2174/0115748936280392240219054047
Abstract: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or moleculargraphs and feature representations might… read more here.
Keywords: relational graph; drug; prediction; graph ... See more keywords