In artificial intelligence systems, a question on how to express the uncertainty in knowledge
remains an open issue. The negation scheme provides a new perspective to solve this …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

Condition monitoring of rail transport systems has become a phenomenon of global interest
over the past half a century. The approaches to condition monitoring of various rail transport …

Complex evidence theory (CET) is an effective method for uncertainty reasoning in
knowledge-based systems with good interpretability that has recently attracted much …

Uncertainty is inevitable in the decision-making process of real applications. Quantum
mechanics has become an interesting and popular topic in predicting and explaining human …

Abstract Information quality has attracted increasing attention in recent years. In this paper,
we propose a measure to quantify the information quality of intuitionistic fuzzy information …

Few-shot learning in image classification is developed to learn a model that aims to identify
unseen classes with only few training samples for each class. Fewer training samples and …

With the development of quantum decision making, how to bridge classical theory with
quantum framework has gained much attention in past few years. Recently, a complex …

Dempster–Shafer evidence theory has been widely used in various fields of applications,
because of the flexibility and effectiveness in modeling uncertainties without prior …

Fuzzy complex event processing-based decision-making systems have received
considerable research interests recently. In particular, a well-managed operator distribution …