Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain
information in real applications. Recently, a new perspective of modeling uncertain …

Single-electron transfer (SET) is one of the most common reaction mechanisms for
degrading organic micropollutants (OMPs) in advanced oxidation processes. We collected …

Uncertain information processing is a key problem in classification. Dempster-Shafer
evidence theory (DS evidence theory) is widely used in uncertain information modelling and …

Dempster–Shafer evidence theory is an effective method to deal with information fusion.
However, how to deal with the fusion paradoxes while using the Dempster's combination …

As an important part of machine learning, clustering methods have been continuously paid
attention to. Current clustering methods divide data objects usually based on Euclidean …

As a cornerstone of the world, uncertainty embodies the nature of data and knowledge.
Existing uncertainty theory-based clustering algorithms learn fuzziness, ie, the uncertainty of …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

This paper focuses on the real-time dynamic clustering analysis of power load data based
on the dynamic conditional score (DCS) model, and an autocorrelation increment fuzzy C …

The failure mode and effects analysis (FMEA) is a commonly adopted approach in
engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure …

For situation assessment in air combat, there may be incomplete information because of
new technologies and unknown or uncertain targets and threats. In this paper, an improved …