Narrow Results

The novel concept of Spherical Fuzzy Sets provides a larger preference domain for decision makers to assign membership degrees since the squared sum of the spherical parameters is allowed to be at most 1.0. Spherical fuzzy sets are a generalization of Pythagorean Fuzzy Sets, picture fuzzy sets and neutrosophic sets. Spherical Fuzzy Sets are newly developed one of the extensions of ordinary fuzzy sets. In this paper, we proposed a MCDM method based on spherical fuzzy information. The method uses entropy theory to calculate the criteria weights, and calculates the similarity ratio of alternatives by using cosine similarity theory. Then alternatives are ranked according to their similarity ratio in descending order. To show the applicability of the proposed method, an illustrative example is given. We conclude that the proposed method is a useful tool for handling multi-period decision making problems in spherical fuzzy environment.

A patient-oriented model of assessment of cardiovascular (CV) health of men obtained as a result of medical observation and the observation itself is considered. The specificity of proposed methodology is determined by orientation toward men, by focus on selfobservation of CV health, by composition of indicators of men’s CV health as well as forms and methods of their estimation.A technique TAMECH of assessment of men’s cardiovascularhealthbased on a patient-oriented model, the theory of fuzzy sets, a formal conceptual analysis and linguistic summary is proposed and its application is considered.

One of the most relevant concepts in statistics is data variability or dispersion. We can find a wide number of studies related to the measurement of dispersion for quantitative data, however, the measurement of dispersion for qualitative data is being poorly developed despite the increasing weight in the Science of linguistic terms to manage information. In fact, only a few measures can be found within a qualitative framework, and their properties have not received much attention. In this chapter, we stress this terrible theoretical lack, exploring a desirable set of properties to be accomplished by an ordinal dispersion measure that allows a structured view of most existing ordinal dispersion measures and a better understanding of ordinal dispersion.

We show here some of our results on intuitionistic fuzzy topological spaces. In 1983, K.T. Atanassov proposed a generalization of the notion of fuzzy set: the concept of intuitionistic fuzzy set. D. Çoker constructed the fundamental theory on intuitionistic fuzzy topological spaces, and D. Çoker and other mathematicians studied compactness, connectedness, continuity, separation, convergence and paracompactness in intuitionistic fuzzy topological spaces. Finally, G.-J Wang and Y.Y. He showed that every intuitionistic fuzzy set may be regarded as an L-fuzzy set for some appropriate lattice L. Nevertheless, the results obtained by above authors are not redundant with other for ordinary fuzzy sense. Recently, Smarandache defined and studied neutrosophic sets (NSs) which generalize IFSs. This author defined also the notion of neutrosophic topology. We proved that neutrosophic topology does not generalize the concept of intuitionistic fuzzy topology.

We propose a new method for ranking alternatives represented by Atanassov's intuitionistic fuzzy sets (A-IFSs) which takes into account the amount of information related to an alternative (expressed by a distance from the ideal positive alternative) and the reliability of information (how sure the information is).

Type-1 OWA operator provides us with a new technique for directly aggregating linguistic variables expressing human experts' opinions or preferences by fuzzy sets via OWA mechanism in soft decision making. However, the existing Direct Approach to performing type-1 OWA operation involves high computational overhead. In this paper, a fast approach, called α-level Approach, is suggested to implement the type-1 OWA operator. Experimental results have shown that the α-level Approach can achieve much higher computing efficiency in performing type-1 OWA operation than the Direct Approach.

Bilevel decision techniques are developed for decentralized decision problems, which may be defined by fuzzy coefficients. Based on a fuzzy linear bilevel (FLBL) model and two FLBL algorithms, this research develops a FLBL decision support system (FLBLDSS). It first introduces a satisfactory-degree-adjustable FLBL model. Then, the system structure and function modules of this FLBLDSS are presented. Finally the key algorithms are illustrated.

Fabric-hand evaluation is one of the key features and measures in textile material selection for fashion design. Fabric-hand evaluation requires considering multiple criteria with in a group of evaluators. The evaluation process often involves fuzziness in the weights of criteria and the judgments of evaluators. This study first develops a multi-level textile material fabric-hand evaluation model. It then proposes a fuzzy multi-criteria group decision-making (FMCGDM) method for the evaluation. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method and applied in textile material fabric-hand evaluation.

This extended abstract of the thesis “Computational Intelligence in Image Segmentation”, explains the formulation of the objectives and expected contributions, a short outline of the problem domain's current knowledge, some proposed solutions to the problem, preliminary results obtained so far, and conclusions.

Association rules are used for describing association among attribute values in the field of data mining. Inspired by the Apriori algorithm, the paper presents a new approach of mining fuzzy association rules. There exists similarity between the new algorithm(NAL) and the Apriori algorithm, however, the NAL method can handle several data types (categories, list, number and linguistic term) at the same time. We adopt fuzzy techniques, so that all data types could be represented and operated from fuzzy points of view. The novel method can be used to find many useful association rules.

Recently, Yong-Ming Li proposed fuzzy systems based upon genuine many-valued implications for SISO cases. From the opinion of application, it is expected that the fuzzy systems have monotonicity if both the antecedent and consequent parts of fuzzy rules are monotone. Hence we discuss the monotonicity of this fuzzy systems in this paper. However, some instances show that the fuzzy systems do not necessarily have monotonicity even if both antecedent and consequent parts of fuzzy rules have monotonicity. And then we give the sufficient conditions that the fuzzy systems based upon R-implications, some S-implications and some QL-implications have monotonicity. It is pointed out that our proofs are different with, strictly speaking, more complex than H. Seki's proofs because there does not exist the uniform formula of output in Li's system while H. Seki's proofs have them.

In this paper, we first propose the notions of fuzzy soft set relations and discuss some basic properties. Then, we introduce the notions of the anti-reflexive kernel, the symmetric kernel, the reflexive closure, and the symmetric closure of a fuzzy soft set relation and obtain some interesting results.

A crisp image segmentation can be characterized in terms of the set of edges that separates the adjacent regions of the segmentation. Based on these edges, an alternative way to define a fuzzy image segmentation is introduced in this paper. In this sense, the notion of fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which could in this way be understood as the fuzzy boundary of the image. Also, an algorithm to construct this fuzzy boundary is provided based on the relations that exist between the fuzzy boundary set problem and the (crisp) hierarchical image segmentation problem. Finally, some computational experiences have been included in order to show the fuzzy boundaries of some digital images.