8,141 research outputs found

    Parameter reduction analysis under interval-valued m-polar fuzzy soft information

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    [EN] This paper formalizes a novel model that is able to use both interval representations, parameterizations, partial memberships and multi-polarity. These are differing modalities of uncertain knowledge that are supported by many models in the literature. The new structure that embraces all these features simultaneously is called interval-valued multi-polar fuzzy soft set (IVmFSS, for short). An enhanced combination of interval-valued m-polar fuzzy (IVmF) sets and soft sets produces this model. As such, the theory of IVmFSSs constitutes both an interval-valued multipolar-fuzzy generalization of soft set theory; a multipolar generalization of interval-valued fuzzy soft set theory; and an interval-valued generalization of multi-polar fuzzy set theory. Some fundamental operations for IVmFSSs, including intersection, union, complement, “OR”, “AND”, are explored and investigated through examples. An algorithm is developed to solve decision-making problems having data in interval-valued m-polar fuzzy soft form. It is applied to two numerical examples. In addition, three parameter reduction approaches and their algorithmic formulation are proposed for IVmFSSs. They are respectively called parameter reduction based on optimal choice, rank based parameter reduction, and normal parameter reduction. Moreover, these outcomes are compared with existing interval-valued fuzzy methods; relatedly, a comparative analysis among reduction approaches is investigated. Two real case studies for the selection of best site for an airport construction and best rotavator are studied.J. C. R. Alcantud is grateful to the Junta de Castilla y León and the European Regional Development Fund (Grant CLU-2019-03) for the financial support to the research unit of excellence “Economics Management for Sustainability” (GECOS).Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

    Parameter Reduction of Neutrosophic Soft Sets and Their Applications

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    Related Study of Soft Set and Its Application A Review

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    Abstract In the present paper some literature related to soft sets are collected. The literature is motivated by Molodsov

    Soft Set Theory for Data Reduction

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    The recent changes in utility structureso development in renewable technologies and increased. There are many data exist all stored data stored in the computer using intemet, everyday data was stored. This data poses a problem when we need to use data" but the data are too numerous and scattered on the internet blur of data. Therefore, there are techniques required and are introduced to overcome this problem. Discussion discussed is Knowledge Discovery in Databases and techniques used are multi-soft set of techniques. Dataset is a set of multi-value data. By using Multi soft sets irq can reduce the data based on the theory of soft sets

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    Achieving Efficient Decision Making Through Hybrid Reduction in Soft Set Theory

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    The main intention of proposing an alternative technique is to ensure consistency is been upheld besides successfully reducing the file. Of all the reduction techniques available currently, only normal parameter reduction has managed to address the issue of consistency at optimal and suboptimal level. In this paper, we initiated another form of reduction known as hybrid reduction by complementing the normal parameter reduction with object reduction. It has already demonstrated that the proposed hybrid reduction has successfully reduced data by 55% with the sample used, thus proving that it as a good alternative for the process of decision making using less amount of data

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