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The theory of T-spherical fuzzy (T-SF) sets possesses remarkable capability to manage intricate uncertain information. The REGIME method is a well-established technique concerning discrete choice analysis. This paper comes up with a multiple-criteria choice analysis approach supported by the REGIME structure for manipulating T-SF uncertainties. This paper constructs new-created measurements such as superiority identifiers and guide indices for relative attractiveness and fittingness, respectively, between T-SF characteristics. This study evolves the T-SF REGIME I and II prioritization procedures for decision support. The application and comparative studies exhibit the effectiveness and favorable features of the propounded T-SF REGIME methodology in real decisions. [ABSTRACT FROM AUTHOR]

The theory of T-spherical fuzzy (T-SF) sets possesses remarkable capability to manage intricate uncertain information. The REGIME method is a well-established technique concerning discrete choice analysis. This paper comes up with a multiple-criteria choice analysis approach supported by the REGIME structure for manipulating T-SF uncertainties. This paper constructs new-created measurements such as superiority identifiers and guide indices for relative attractiveness and fittingness, respectively, between T-SF characteristics. This study evolves the T-SF REGIME I and II prioritization procedures for decision support. The application and comparative studies exhibit the effectiveness and favorable features of the propounded T-SF REGIME methodology in real decisions. [ABSTRACT FROM AUTHOR]

The paper contains some remarks on building automated counterpart of a comparison of some generalized rough approximations of sets, where the classical indiscernibility relation is generalized to arbitrary binary relation. Our focus was on translating rationality postulates for such operators by means of the Mizar system – the software and the database which allows for expressing and checking mathematical knowledge for the logical correctness. The main objective was the formal (and machine-checked) proof of Theorem 4.1 from A. Gomolińska's paper "A Comparative Study of Some Generalized Rough Approximations", hence the present title. We provide also the discussion on how to make the presentation more efficient to reuse the reasoning techniques of the Mizar verifier. [ABSTRACT FROM AUTHOR]

Ontology Driven Software Development (ODSD) combines traditional Model Based Software Development (MBSD) techniques with ontology technology in order to provide extensions to and advantages over MBSD. The goal of the paper is to identify current ODSD approaches and to provide qualitative and comparative analysis of the collection of identified approaches. Main research questions of the paper concern the ways of how ontologies are integrated toMBSD process and how their usage advances MBSD. Benefits and challenges of each of the discussed approaches are presented. The analysis is based on literature and projects reviews in the fields of ontology engineering, MBSD and ODSD. The result of the analysis provides understanding of what is the role of ontologies in ODSD and shows whether application of ontology technologies to the MBSD process gives rise to a new paradigm called consistency preserving software development or not. [ABSTRACT FROM AUTHOR]

Construction site selection is a complex probleminvolving many alternatives and conflicting criteria with vague and imprecise evaluations. Fuzzymulti-criteria decision-making methods are the most effective tools to obtain optimum solutions under possibilistic uncertainty. In this paper, a novel interval hesitant fuzzy CODAS method is proposed and applied to a residential construction site selection problem. A comparative analysis with ordinary fuzzy CODAS method is applied for validating the proposed method. Also, a sensitivity analysis is conducted for the stability of the ranking results of the interval hesitant fuzzy CODAS method. The results of the analyses demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The aim of this paper is twofold. Firstly, to discuss a clustering of a given set of the European banks into groups based on their performance during 1999-2013. Secondly, to compare different dissimilarity measures and to determine which of them suits best for clustering banking ratios. Six ratios that reveal profitability, efficiency, stability and loan portfolio quality of the banks were used. The similarity/dissimilarity between banks was estimated using measures that are based on time series or functional data properties. Two dissimilarity measures that are not commonly used in the literature are proposed and two measures are extended from univariate into multivariate case. The results of our study show that there is no dissimilarity measure which would provide the best clustering results for all ratios. However, dissimilarity measures based on functional data properties in many cases outperfomed measures based on time series properties. The choice of the number of clusters is not that clear. According to different banking ratios, it is found that banks could be grouped into 6-12 clusters. [ABSTRACT FROM AUTHOR]

This paper reviews the existing definitions and formulas of entropy for interval-valued intuitionistic fuzzy sets (IVIFSs) and demonstrates that they cannot fully capture the uncertainty of IVIFSs. Then considering both fuzziness and intuitionism of IVIFSs, we introduce a novel axiomatic definition of entropy for IVIFSs and develop several entropy formulas. Example analyses show that the developed entropy formulas can fully reflect both fuzziness and intuitionism of IVIFSs. Furthermore, based on the entropy formulas of IVIFSs, a method is proposed to solve multi-attribute decision making problems with IVIFSs. Additionally, an investment alternative selection example is provided to validate the practicality and effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results. [ABSTRACT FROM AUTHOR]