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In the information age, teachers are no longer the only source of information for students, and the problems of the traditional lecture mode are becoming more and more obvious. Especially in the process of teaching English in colleges and universities, students' personalized and diversified needs for English learning are becoming more and more obvious, and if traditional theoretical lectures or theoretical indoctrination are continued, students' needs and goals will be affected. As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple-attribute group decision-making (MAGDM) problem involving multiple qualitative and quantitative attributes. In this paper, on basis of EDAS technique, a novel spherical fuzzy number EDAS (SFN-EDAS) technique based on SFN Hamming distance (SFNHD) and SFN Jaccard similarity measure (SFNJSM) is built for dealing with MAGDM. Moreover, the MEREC technique with SFNHD and SFNJSM is extended to SFSs to acquire the attribute weights. Finally, SFN-EDAS technique is used for English blended teaching quality evaluation to prove practicability of the developed SFN-EDAS technique and SFN-EDAS technique is compared with existing techniques to further demonstrate its superiority. Hence, the research main achievements for this paper are outlined: (1) the novel EDAS technique is extended to the SFSs environment based on the SFNHD and SFNJSM; (2) the MEREC technique is extended to SFSs to acquire the attribute weights based on the SFNHD and SFNJSM; (3) a novel spherical fuzzy number EDAS (SFN-EDAS) technique based on the SFNHD and SFNJSM is built for dealing with MAGDM; (4) an empirical example and several comparative analysis for English blended teaching quality evaluation is offered to demonstrate the SFN-EDAS technique. [ABSTRACT FROM AUTHOR]

As the rapidly progressing applications of uncertainty theories, the need for modifications to some of their existing mathematical tools or creating new tools to deal correctly with them in various environments is also exposed. Hesitant fuzzy numbers (HFNs), as a particular case of fuzzy numbers, are not an exception to this rule. Considering the necessity of determining the distance between given HFNs in many of their practical applications, this article shows that the existing methods either do not provide correct results or are not able to meet the needs of users. This paper aims to present new methods for distance measures of hesitant fuzzy numbers. To do them, three prevalent distance measures, i.e., the generalized distance measure, the Hamming distance measure, and the Euclidean distance measure, will be optimized into three distinct trinal categories. With the approach of reducing error propagation via reducing some unnecessary mathematical computations, new distance measures on HFNs will be introduced, first. The middle is the modification of the first category, which is more suitable when the given HFNs are equal-distance by the previous formula. Also, as the third category, the weighted form of these distance measures has been proposed, to be used where the real and membership parts of HFNs are not of equal importance. As an application of these, a TOPSIS-based technique for solving multi-attribute group decision-making problems with HFNs has been proposed. A numerical example will be implemented to describe the presented method. Finally, along with the validation of the proposed method, its numerical comparison with some other existing methods will be discussed in detail. [ABSTRACT FROM AUTHOR]

With the proposal of the "Double First Class" construction concept in Chinese universities, cultivating high-quality talents has become the main direction of university education. At present, local universities in China are actively adapting to the changes of the times, taking the construction of the Double First Class as an opportunity, continuously adjusting and deepening the reform of the double innovation and innovation talent cultivation mode, further improving the curriculum system, increasing investment in professional practical teaching, stimulating students' enthusiasm for innovation and entrepreneurship (IAE), and focusing on improving students' comprehensive abilities, achieving significant results in talent cultivation. The "Entrepreneurship and Entrepreneurship" career ability evaluation of local college students could be considered as multiple attribute decision-making (MADM). Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method was employed to deal with MADM. The triangular fuzzy neutrosophic sets (TFNSs) are employed as a better tool for expressing uncertain information during the "Entrepreneurship and Entrepreneurship" career ability evaluation of local college students. In this paper, the triangular fuzzy neutrosophic number CoCoSo (TFNN-CoCoSo) based on the Hamming distance and Euclid distance is constructed to cope with the MADM under TFNSs. The information entropy method is employed to compute the weight values based on the Hamming distance and Euclid distance under TFNSs. Finally, a numerical example of "Entrepreneurship and Entrepreneurship" career ability evaluation of local college students is constructed and some decision comparisons are constructed to verify the TFNN-CoCoSo method. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is an useful tool to solve Multi-criteria decision making, which can translate customer requirements (CRs) into the technical attributes (TAs) of a product and helps maintain a correct focus on true requirements and minimizes misinterpreting customer needs. In applying quality function deployment, rating technical attributes from input variables is a crucial step in fuzzy environments. In this paper, a new approach is developed, which rates technical attributes by objective penalty function and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) based on weighted Hamming distance under the case of uncertain preference characteristics of decision makers in fuzzy quality function deployment. A pair of nonlinear programming models with constraints and a relevant pair of nonlinear programming models with unconstraints called objective penalty function models are proposed to gain the fuzzy important numbers of technical attributes. Then, this paper compares the fuzzy numbers by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method based on weighted Hamming distance in consideration of the uncertain preference characteristics of decision makers. To end with, the developed method is examined with the numerical examples. [ABSTRACT FROM AUTHOR]

Selecting an appropriate robot among the alternative robots is a difficult problem for decision makers, since it is complicated to express attributes as crisp numbers in multiple attribute decision making problems. Generalized trapezoidal hesitant fuzzy numbers(GTHF-numbers) can be efficiently used to estimate information in the decision making process. This paper proposes an advanced type of TOPSIS (Technique for order preference by similarity to ideal solution) method, called TOPSIS method of GTHF-numbers. To do this, we introduce some novel distance measures including Hamming distance measure, Euclidean distance measure, λ-generalized distance measure, λ-generalized Hausdorff distance measure, γ-hybrid Hamming distance, hybrid Euclidean distance and λ-generalized hybrid distance measure on GTHF-numbers. Then, we develop a novel TOPSIS method of GTHF-numbers based on introduced distance measures. Finally, we provide a real example, for an auto company which desires to select a suitable robot for its production process, based on the proposed TOPSIS method of GTHF-numbers to prove the efficiency and the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the picture fuzzy multiple attribute decision making problems where the information about attribute weights is partly known or completely unknown. We introduce some notions, such as picture fuzzy ideal point, the normalized Hamming distance of picture fuzzy numbers. We also introduce the grey relational coefficient between the attribute value vectors of each alternative and the picture fuzzy ideal point. Then we establish the grey relational analysis models to measure the grey relational degree between each alternative and the picture fuzzy ideal point. Based on the grey relational analysis models, we can rank the given alternatives and then select the most desirable one. Finally, we illustrate the developed grey relational analysis models with a numerical example for potential evaluation of emerging technology commercialization. [ABSTRACT FROM AUTHOR]

In the study of intuitionistic fuzzy clustering, the construction of an intuitionistic fuzzy similarity matrix (IFSM) is a fundamental and important issue in the direct clustering analysis, since it determines clustering results and computational efforts. Many methods based on the axioms of intuitionistic fuzzy similarity relations are applicable to IFSM construction. However, most of existing methods may yield a "counterintuitive result" in some cases and consume much computational time. In this paper, we propose a novel intuitionistic fuzzy clustering method to deal with such problems. First, based on the normalized Hamming distance, we define a similarity measure between intuitionistic fuzzy numbers (IFNs), by which a similarity measure between intuitionistic fuzzy sets (IFSs) is induced. Second, a divergence measure between IFSs is obtained by extending the dissimilarity of IFNs. Third, we construct an IFSM by using together the similarity and divergence measures so as to cluster the intuitionistic fuzzy information. Finally, two examples are presented to show the effectiveness and advantages of our method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of triangular cubic fuzzy numbers. We discuss some basic operational laws of triangular cubic fuzzy numbers and then develop triangular cubic fuzzy weighted average (TCFWA) operator. We also define crisp weighted possibility means of TCFNs and hamming distance between TCFNs. Furthermore, we extend the classical VIKOR method to solve the MCDM method based on triangular cubic fuzzy numbers. The new ranking method for TCFNs is used to rank the alternatives. Finally, an illustrative example is given to verify and demonstrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper applies the interval neutrosophic set theory to express fuzzy formal concept. A new interval neutrosophic fuzzy concept lattice is proposed. Furthermore, interval-distance and interval-similarity measure are discussed. We can find some interval neutrosophic fuzzy concepts to meet particular similarity range given by users. The result of this paper may enhance the flexibility of extracting knowledge and expand the capacity of knowledge. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new fuzzy decision making technique called the fuzzy induced heavy ordered weighted averaging distance (FIHOWAD) operator. It is an extension of the ordered weighted averaging (OWA) operator that uses the main characteristics of the induced OWA (IOWA), the Hamming distance, the heavy OWA (HOWA) and uncertain information represented as fuzzy numbers. A key feature of the FIHOWAD operator is that it generalizes a wide range of aggregation operators such as the fuzzy minimum distance, the fuzzy weighted Hamming distance (FWHD), the fuzzy OWA distance (FOWAD), the fuzzy IOWA distance (FIOWAD) and the fuzzy heavy OWA distance (FHOWAD) operators. We studied some of its main properties and different particular cases. Finally, we present a numerical example to illustrate the new approach in a financial decision making problem. [ABSTRACT FROM AUTHOR]