23,973 research outputs found

    Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

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    Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

    Interval type-2 fuzzy multi-attribute decision-making approaches for evaluating the service quality of Chinese commercial banks

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In today’s world, with increased competition, the service quality of Chinese commercial banks is recognized as a major factor that is responsible for enhancing competitiveness. Therefore, it is necessary to evaluate and analyse the service quality of Chinese commercial banks to realize their stable development. The service quality evaluation could be recognized as a multi-attribute decision-making (MADM) problem with multiple assessment attributes, both being of a qualitative and quantitative nature. Owing to the growing complexity and high uncertainty of the financial environment, the assessments of attributes cannot always possibly express using a real and/or type-1 fuzzy number. Additionally, a heterogeneous relationship often exists among the attributes under many real decision cases. In this study, we create two MADM approaches to handle decision-making problems with interval type-2 fuzzy numbers (IT2FNs) and offer their application to service quality evaluations of commercial banks problems. Specifically, we first define some operations on IT2FNs based on Archimedean T-norms (ATs) and develop a bi-directional projection measure of IT2FNs. Next, by combining the generalized Banzhaf index, the Choquet integral and IT2FNs, we propose the interval type-2 fuzzy Archimedean Choquet (IT2FAC) operator, the Banzhaf IT2FAC (BIT2FAC) operator and the 2-additive BIT2FAC (2ABIT2FAC) operator. Then, we establish two optimal models for deriving the weights of attributes based on a bi-directional projection measure of IT2FNs and Banzhaf function. Finally, we create two novel MADM methods under interval type-2 fuzzy contexts, where an illustrative case concerning the service quality evaluation of Chinese commercial banks is used to explain the created MADM approaches

    Intertemporal Choice of Fuzzy Soft Sets

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    This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie

    Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.

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    open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the experts’ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of ‘Technology’, ‘Quality’, and ‘Operation’ have respectively the highest importance. Furthermore, the strategies for “new business models development’, ‘Improving information systems’ and ‘Human resource management’ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information

    Dominance Measuring Method Performance under Incomplete Information about Weights.

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    In multi-attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative?s intensity of dominance, known as dominance measuring methods. Different dominancemeasuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we useMonte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions orweights represented by fuzzy numbers.Moreover, dominance measuringmethod performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one
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