As an extension of probability theory, evidence theory is able to better handle unknown and
imprecise information. Owing to its advantages, evidence theory has more flexibility and …

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 …

How to measure the complexity of physiological signals in biological system is an open
problem. Various entropy algorithms have been presented, but most of them fail to account …

As a generation of fuzzy sets, intuitionistic fuzzy sets (IFSs) have a more powerful ability to
represent and address the uncertainty of information. Therefore, IFSs have been used in …

Abstract Dempster–Shafer (D–S) evidence theory is useful for handling uncertainty
problems in multisensor data fusion. However, the question of how to handle highly …

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

Interval-valued intuitionistic fuzzy sets are widely used in multi-attribute decision-making
problems to select the optimal alternative, but how to measure uncertainty is an open and …

The evaluation of green supply chain management (GSCM) practices is a typical multi-
criteria decision making (MCDM) problem. Considering widely existed uncertainty …

Dempster–Shafer theory (DST) has extensive and important applications in information
fusion. However, when the evidences are highly conflicting with each other, the Dempster's …

In real applications of artificial and intelligent decision-making systems, how to represent the
knowledge involved with uncertain information is still an open issue. The negation method …