Browsing by Author "Oplatková, Z. K.,"
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Item Comparative State-of-the-Art Survey of Classical Fuzzy Set and Intuitionistic Fuzzy Sets in Multi-Criteria Decision Making.(Springer International Publishing AG, 2016-07-18) Afful-Dadzie, E.,; Oplatková, Z. K.,; Prieto, L. A. B.Fuzzy sets extend deterministic multi-criteria decision-making (MCDM) methods to deal with uncertainty and imprecision in decision making. Over the years, many generalizations have been proposed to the classical Fuzzy sets to deal with different kinds of imprecise and subjective data. One such generalization is Atanassov’s Intuitionistic Fuzzy Set (IFS) which is becoming increasingly popular in MCDM research. Together, the two notions of uncertainty modeling: ‘classical fuzzy set’ (Zadeh) and intuitionistic fuzzy set (Atanassov) have been utilized in many real-world MCDM applications spanning diverse disciplines. As IFS grows in popularity by the day, this paper conducts a literature survey to (1) compare the trend of publications of ‘classical fuzzy’ set theory and its generalized form, the intuitionistic fuzzy set (IFS) as used in MCDM methods from 2000 to 2015; (2) classify their contributions into three novel tracks of applications, hybrid, and extended approaches; (3) determine which MCDM method is the most used together with the two forms of fuzzy modeling; and (4) report on other measures such as leading authors and their country affiliations, yearly scholarly contributions, and the subject areas where most of the two fuzzy notions in MCDM approaches are applied. Finally, the study presents trends and directions as far as the applications of classical fuzzy set and intuitionistic fuzzy sets in MCDM are concerned.Item Model for assessing quality of online health information: a fuzzy VIKOR based method.(John Wiley & Sons, Inc., 2015-11-25) Afful-Dadzie, E.,; Nabareseh, S.,; Oplatková, Z. K.,; Klímek, P.Today, tens and thousands of websites provide health-related information on various topics to a growing number of consumers. However, the lay user is often faced with a challenge of determining the quality of information provided by one site from the other. To ensure the protection of users from sites that provide unreliable and unsafe information, there has to be a competent reviewing body that rates and ranks the quality of information provided by each site. This paper (i) proposes a new criteria framework for assessing the quality of online health information and (ii) uses a fuzzy ‘visekriterijumska optimicija i kompromisno resenje’ method to demonstrate how online health information providers could be assessed and ranked based on their quality. The fuzzy modelling uses pre-defined linguistic variables parameterized by triangular fuzzy numbers in the assessment and subsequent ranking of providers under a particular health topic. A numerical example is demonstrated using diabetes online information providers to show how the assessment and ranking is carried out. The proposed framework provides functional basis for evaluating the quality of internet health information providers on any particular health topic.