Decision-making is a critical problem in various circumstances where some vagueness and ambiguity is found in information. To handle these types of problems, entropy is an important measure of information theory which is exploited to evaluate the uncertain degree of any data. There are two methodologies to determine the entropy, one is probabilistic in nature and other is non-probabilistic. It is shown that for every probabilistic measure there is a corresponding non-probabilistic measure. In this article, some logarithmic non-probabilistic entropy measures have been proposed for the fuzzy rough set corresponding to existing probabilistic entropy measures. The proposed measures are employed in a decision-making problem, which is related to the agriculture. Finally, these proposed measures are compared with the existing trigonometric entropy measures for fuzzy rough sets.

This article describes how information technology and internet together infused organizations with huge amount of data. Consequently, accumulating, storing, understanding and analyzing data at a large scale is equally important and complex. Out of this data not all is information data, in order to extract information, one needs to discard redundant, irrelevant and unnecessary data. This article aims to introduce a data reduction technique which will be useful to discard irrelevant data. Here in data-reduction, the authors have used fuzzy-soft set techniques, namely fuzzy-soft information matrixes. Further, they have introduced a new fuzzy-soft information measure of fuzzy-soft matrixes.

This paper introduces a new divergence measure for a fuzzy matrix with proof of its validity. In addition, the properties are proved for the new fuzzy divergence measure. A method to solve decision making problem is developed by using the proposed fuzzy divergence measure. Finally, the application of this fuzzy divergence measure to decision making is shown using real-life example

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