A notion of Quadripartitioned Single Valued Neutrosophic Sets (QSVNS) is introduced and a theoretical study on various set-theoretic operations on them has been carried out. The definitions of distance, similarity measure and entropy have been proposed. Finally an application of the proposed similarity measure in a problem pertaining to pattern recognition has been shown.

We introduce the fuzzy measure as a unifying structure for modeling knowledge about an uncertain variable. We show that a large class of well established types of uncertainty representation can be modeled in this framework. A measure of entropy associated with a fuzzy measure is introduced and its manifestation for different fuzzy measures is described. The problem of uncertain decision making ...

In this paper we introduce the conditional entropy without a fuzzy measure for the union of fuzzy partitions. We recall its properties and we solve the system of functional equations which derives from these conditions, taking into account the locality principle and the independence axiom.

The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to...

Rényi entropy of order α is a general measure of entropy. In this paper we derive estimations for the Rényi entropy of the mixture of sources in terms of the entropy of the single sources. These relations allow to compute the Rényi entropy dimension of arbitrary order of a mixture of measures. The key for obtaining these results is our new definition of the weighted Rényi entropy. It is shown t...

Two points are made in this paper: first, energy of random structures is not determined uniquely by any finite set of the characteristics of microstructure. The information lost is characterized by entropy of microstructure; it describes the scattering of the values of energy. Therefore, entropy of microstructure is a key thermodynamic parameter in phenomenological modeling of the behavior of r...

The optimal design of data acquisition is not obvious in Bayesian Network models. The dependency structure may vary dramatically, which makes learning and information evaluation complicated and sometimes non-intuitive. Our application, and the motivation for working on this topic, is prospect selection for petroleum exploration in the North Sea. Here, the data gathering is often carried out dur...

introduction the chemical characteristic of groundwater as a major source of water supply for human life is an important factor in determining the usage of it for agriculture, industry and drinking. useful techniques and different methods of studying the chemical composition of groundwater such as correlation coefficient, descriptive statistics, factor analysis and cluster analysis provide an u...

Many algorithms of machine learning use an entropy measure as optimization criterion. Among the widely used entropy measures, Shannon’s is one of the most popular. In some real world applications, the use of such entropy measures without precautions, could lead to inconsistent results. Indeed, the measures of entropy are built upon some assumptions which are not fulfilled in many real cases. Fo...