Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …

With the rapid development of technology, social media as a communication platform has
caused a significant increase in the dissemination of false information and fake news. We …

Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

Abstract Visual Question Answering (VQA) systems have achieved great success in general
scenarios. In medical domain, VQA systems are still in their infancy as the datasets are …

Multi-label learning has garnered much research interest due to its wide range of real-world
applications. Many multi-label learning methods have been proposed; however, few have …

Relation extraction is a crucial step in the constructions of knowledge graphs (KGs).
However, relation extraction is performed manually in the manufacturing field due to the …

The Fintech mobile payment platform is expanding rapidly; this expansion, in turn, creates
numerous risks. There is an urgent need to better understand these risks and to spur more …

Multi-label data classification has received much attention due to its wide range of
application domains. Unfortunately, a class imbalance problem often occurs in multi-label …

To practice the evidence-based medicine, clinicians are interested to find the most suitable
research for the clinical decision making. The use of knowledge graphs (KGs) in evidence …

Multi-label active learning (MLAL) reduces the cost of manual annotation for multi-label
problems by selecting high-quality unlabeled data. Existing MLAL methods usually perform …