The purpose of this work is to introduce Deng–Fisher information (DFI), Deng–Fisher
information distance (DFID) and Jensen–Deng–Fisher (JDF) information distance measures …

Multi-source information fusion is a sophisticated estimation process that generates a unified
profile to assess complex situations. Dempster–Shafer evidence theory (DSET) is a practical …

Belief functions theory (BFT) plays a critical role in addressing uncertainty and imprecision in
multi-source data fusion. Unfortunately, the application of Dempster's rule in BFT often …

Divergence could be used to quantify the difference between two probability distributions.
However, how to accurately quantify the disparity between evidences remains a pending …

Uncertainty modeling and reasoning in intelligent systems are crucial for effective decision-
making, such as complex evidence theory (CET) being particularly promising in dynamic …

Dempster–Shafer evidence theory (DSET) is extensively employed in multi-source data
fusion applications. Nonetheless, when belief probability assignments (BPAs) exhibit …

Effectively measuring the information content of Basic Probability Assignments (BPA) is a
crucial prerequisite for addressing uncertain information within the framework of Dempster …

Recently, a theory called random permutation set (RPS) is proposed, which is the extension
of Dempster–Shafer evidence theory. It defines a permutation event space (PES) to …

Time series data contains the amount of information to reflect the development process and
state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the …

Multidimensional monitoring time-series of complex electromechanical systems (CESs)
plays a foundational role in data-based state management, maintenance, and performance …