Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers ...

data envelopment analysis (dea) is a method for measuring the relative efficiencies of a set of decision-making units (dmus) that use multiple inputs to produce multiple outputs. in this paper, we study the measurement of dmu performances in dea in situations where input and/or output values are given as imprecise data. by imprecise data we mean situations where we only know that the actual val...

The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, i...

Detecting outliers is an important task for many applications including fraud detection or consistency validation in real world data. Particularly in the presence of uncertain data or imprecise data, similar objects regularly deviate in their attribute values. The notion of outliers has thus to be defined carefully. When considering outlier detection as a task which is complementary to clusteri...

In this paper we present a method of decomposing a neutrosophic database relation with Neutrosophic attributes into basic relational form. Our objective is capable of manipulating incomplete as well as inconsistent information. Fuzzy relation or vague relation can only handle incomplete information. Authors are taking the Neutrosophic Relational database [8], [2] to show how imprecise data can ...

The stochastic ordering of random variables is extended to the cases where the available data are imprecise quantities, rather than crisp. To do this, using some elements of fuzzy set theory, we suggest the fuzzy reversed hazard rate and fuzzy mean inactivity time functions and apply them to construct some new fuzzy stochastic orders for ranking fuzzy random variables. In addition, we study the...

The modeling and solving the optimization problem is one of the most important daily problem.By notation the nature of data in practice which are imprecise, fully fuzzy linear programming problem (FFLP) is a power full tool to modeling the practical optimization problem. In This paper after introducing FFLP, a new method to solve it is proposed. a linear ranking function for defuzzifying the FF...

Abstract 3D graphene foam is the main aim of this research work. Graphene synthesized on Ni-foam by CVD technique. The has been characterized XRD, FESEM, Raman spectroscopy and BET techniques. resistance with a variance temperature measured through an LCR meter analyzed classical neutrosophic analysis. As result, it seen that expressing both conductor semiconductor electric properties also obse...

The selection of students is a complex decision making process, in which multiple selection criteria often need to be considered and where subjectiveness and imprecision are usually present, resulting that fuzzy and imprecise data should be used. This paper formulates the student selection process as a multicriteria decision analysis problem, concretely as a ranking problem, by using the ELECTR...