5 papers
Enhancing Semi-Supervised Multi-View Graph Convolutional Networks via Supervised Contrastive Learning and Self-Training
Huaiyuan Xiao, Fadi Dornaika, Jingjun Bi
The advent of graph convolutional network (GCN)-based multi-view learning provides a powerful framework for integrating structural information from heterogeneous views, enabling ef…
MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network
Chenping Pei, Fadi Dornaika, Jingjun Bi
Existing Multi-view Clustering (MVC) methods based on subspace learning focus on consensus representation learning while neglecting the inherent topological structure of data. Desp…
Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions
Mohamad Abou Ali, Fadi Dornaika
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems wit…
A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Data
Jingjun Bi, Fadi Dornaika
Recently, graph-based semi-supervised learning and pseudo-labeling have gained attention due to their effectiveness in reducing the need for extensive data annotations. Pseudo-labe…
SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging
Salah Eddine Bekhouche, Gaby Maroun, Fadi Dornaika +1
Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vita…