4 papers
The Nonverbal Syntax Framework: An Evidence-Based Tiered System for Inferring Learner States from Observable Behavioral Cues
Sherzod Turaev, Mary John, Jaloliddin Rustamov +4
Understanding learners' cognitive and affective states underpins adaptive educational systems and effective teaching. Although research links nonverbal cues to internal states, no…
Scalable Graph Attention-based Instance Selection via Mini-Batch Sampling and Hierarchical Hashing
Zahiriddin Rustamov, Ayham Zaitouny, Nazar Zaki
Instance selection (IS) addresses the critical challenge of reducing dataset size while keeping informative characteristics, becoming increasingly important as datasets grow to mil…
GAIS: A Novel Approach to Instance Selection with Graph Attention Networks
Zahiriddin Rustamov, Ayham Zaitouny, Rafat Damseh +1
Instance selection (IS) is a crucial technique in machine learning that aims to reduce dataset size while maintaining model performance. This paper introduces a novel method called…
GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification
Zahiriddin Rustamov, Abderrahmane Lakas, Nazar Zaki
Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel gra…