A large number of accuracy measures for image classification are actually available in the literature for cris classification. Overall accuracy, producer accuracy, user accuracy, kappa index and tau value are some examples. But in contrast to this effort in measuring the accuracy in a crisp framework, few proposals can be found in order to determine accuracy for soft classifiers. In this paper ...

We consider single-class classification (SCC) as a two-person game between the learner and an adversary. In this game the target distribution is completely known to the learner and the learner’s goal is to construct a classifier capable of guaranteeing a given tolerance for the false-positive error while minimizing the false negative error. We identify both “hard” and “soft” optimal classificat...

Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As the DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images with DCT we used two popular soft computing te...

The notion of soft sets is introduced as a general mathematical tool for dealing with uncertainty. In this work, we consider the concepts of GL-soft perfect sets, GR-soft perfect sets and G -soft perfect sets were introduced in the soft topological space (X, A, τ) with a soft G which are extensions of the concepts soft τG-closed, soft τG-dense in itself and soft τG-perfect, respectively. Also, ...

Classification of texture patterns is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, we used two popular soft computing t...

Automatic digital mammograms reading become highly enviable, as the number of mammograms to be examined by physician increases enormously. It is premised that the computer aided diagnosis system is mandatory to assist physicians/radiologists to achieve high efficiency and productivity. To handle uncertainties of medical images, fuzzy soft set theory has been merely scrutinized, even though the ...

In this paper, the concept of (∈γ ,∈γ ∨qδ)-fuzzy soft hinterior ideals over a hemiring is introduced and investigated. Some characterization theorems of h-semisimple hemirings are derived in terms of (∈γ ,∈γ ∨qδ)-fuzzy soft left (right) h-ideals and (∈γ ,∈γ ∨qδ)-fuzzy soft hinterior ideals. 2010 AMS Classification: 20M12, 08A72

Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images using DCT we used two popular soft computing techn...

By means of soft intersection-union sum and product, we make a new approach to the hemiring theory via soft set theory with the concepts of soft union (h-ideals, h-bi-ideals, h-quasi-ideals and h-interior ideals). Also, we investigate some characteristics of h-semisimple and h-quasihemiregular hemirings using these kinds of soft union h-ideals. 2000 AMS Classification: 16Y60; 13E05.

In this paper, we firstly defined neutrosophic parameterized neutrosophic soft sets(npn−soft sets) which is combination of a neutrosophic sets and a soft sets. Our npn−soft sets generalizes the concept of the other soft sets such as; fuzzy soft sets, intuitionistic fuzzy soft sets, neutrosophic soft sets, fuzzy parameterized soft sets, intuitionistic fuzzy parameterized soft sets, neutrosophic ...