Bingyi Kang

Bio: Bingyi Kang is an academic researcher from Northwest A&F University. The author has contributed to research in topics: Fuzzy set & Fuzzy number. The author has an hindex of 19, co-authored 52 publications receiving 1562 citations. Previous affiliations of Bingyi Kang include Southwest University & China University of Mining and Technology.

A Method of Converting Z-number to Classical Fuzzy Number

Abstract: The notion Z-number introduced by Zadeh in 2011 has more capability to describe the uncertain information. Now that the theories about Z-number is not mature, how to convert Z-number to classical fuzzy number is rather signicant for application. In this paper, a method of transforming Z-number to classical fuzzy number is proposed according to the Fuzzy Expectation of fuzzy set. At last, a simple example is used to illustrated the procedure of the proposed approach.

Decision Making Using Z-numbers under Uncertain Environment

Abstract: Multi-criteria decision making (MCDM) under uncertain environment is still an open issue. Recently, Znumber has been developed by Zadeh to model fuzzy numbers with the confidence degree. In this paper, a new MCDM method based on Z-number is proposed to deal with linguistic decision making problems. The decision making process can be easily realized step by step with the arithmetic operations on Znumbers. A numerical example on MCDM is used to illustrate the efficiency of the proposed method.

Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers

Abstract: Environmental assessment and decision making is complex leading to uncertainty due to multiple criteria involved with uncertain information. Uncertainty is an unavoidable and inevitable element of any environmental evaluation process. The published literatures rarely include the studies on uncertain data with variable fuzzy reliabilities. This research has proposed an environmental evaluation framework based on Dempster–Shafer theory and Z-numbers. Of which a new notion of the utility of fuzzy number is proposed to generate the basic probability assignment of Z-numbers. The framework can effectively aggregate uncertain data with different fuzzy reliabilities to obtain a comprehensive evaluation measure. The proposed model has been applied to two case studies to illustrate the proposed framework and show its effectiveness in environmental evaluations. Results show that the proposed framework can improve the previous methods with comparability considering the reliability of information using Z-numbers. The proposed method is more flexible comparing with previous work.

A Method of Measuring Uncertainty for Z-Number

Abstract: Z-number is a new framework of fuzzy sets that combines with constraint and reliability. Managing the uncertainty of Z-number is an important issue in the fuzzy decision making of Z-number based. In this paper, we first develop a new uncertainty measure of fuzzy set considering the influence of fuzziness measure and the range (or cardinality) of the fuzzy set. Some properties of the developed uncertainty measure of the fuzzy set are presented. We then propose and discuss method of measuring the uncertainty of Z-number. Examples are used to illustrate the effectiveness of the proposed methodologies.

Supplier selection using AHP methodology extended by D numbers

Abstract: Supplier selection is an important issue in supply chain management (SCM), and essentially is a multi-criteria decision-making problem. Supplier selection highly depends on experts' assessments. In the process of that, it inevitably involves various types of uncertainty such as imprecision, fuzziness and incompleteness due to the inability of human being's subjective judgment. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method. Within the proposed method, D numbers extended fuzzy preference relation has been involved to represent the decision matrix of pairwise comparisons given by experts. An illustrative example is presented to demonstrate the effectiveness of the proposed method.

A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities

Abstract: The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: the false knowledge it carries, its writing style, its propagation patterns, and the credibility of its source. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.