FMADM: YAGER MODEL IN FUZZY DECISION MAKING

This paper presents Yager model, i.e. standard form of Fuzzy Multi-Atributte Decision Making (FMADM) in fuzzy decision environment. Simulasion of this model would be performed under scope of fuzzy decisionmaking process to show its existence. As academics, researchers, and practitioners know on it, besides theFMADM, so is there Fuzzy Multi-Objective Decision Making (FMODM) at where the both has their same derivation, e.i. Fuzzy Multi-Criteria Decision Making (FMCDM). Related to the matter, significant value that could be represented then gives contribution to team work-oriented principal of decision makers.Keywords: Yager Model, FMADM, FMODM, FMCDM, fuzzy decision-making

Fitting ST-OWA operators to empirical data

The OWA operators gained interest among researchers as they provide a continuum of aggregation operators able to cover the whole range of compensation between the minimum and the maximum. In some circumstances, it is useful to consider a wider range of values, extending below the minimum and over the maximum. ST-OWA are able to surpass the boundaries of variation of ordinary compensatory operators. Their application requires identification of the weighting vector, the t-norm, and the t-conorm. This task can be accomplished by considering both the desired analytical properties and empirical data.<br /

An explainable prediction method based on Fuzzy Rough Sets, TOPSIS and hexagons of opposition: Applications to the analysis of Information Disorder

This paper presents a novel approach for predicting and explaining instances of Information Disorder. The paper reports two significant findings: i) the use of structures of opposition to describe relationships between instances of Information Disorder, and ii) the development of an explainable prediction method that combines Fuzzy Rough Sets and TOPSIS with these structures. The findings have the potential to assist analysts and decision-makers in gaining a deeper understanding of the phenomenon of Information Disorder. The results are based on real data and demonstrate promising applications for future research