Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

With the growth of data and intelligent technologies, the healthcare sector opened numerous
technology that enabled services for patients, clinicians, and researchers. One major hurdle …

Recently, considerable literature has grown up around the theme of few-shot named entity
recognition (NER), but little published benchmark data specifically focused on the practical …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

The complexity and high dimensions of big data sonar, as well as the unavoidable presence
of unwanted signals such as noise, clutter, and reverberation in the environment of sonar …

Some businesses on product development prefer to use a chatbot for judging the customer's
view. Today, the ability of a chatbot to consider the context is challenging due to its technical …

Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …

In the field of medical image analysis within deep learning (DL), the importance of
employing advanced DL techniques cannot be overstated. DL has achieved impressive …

This research proposes a hybrid improved marine predator algorithm (IMPA) and deep
gated recurrent unit (DGRU) model for profit prediction in financial accounting information …

Recognizing relations between entities is a pivotal task of relational learning. Learning
relation representations from distantly-labeled datasets is difficult because of the abundant …