Reasoning, the most important human brain operation, is characterized by a degree of fuzziness. In the present paper we construct a fuzzy model for the reasoning process giving through the calculation of probabilities and possibilities of all possible individuals’ profiles a quantitative/qualitative view of their behaviour during the above process. In this model the main stages of human reasoni...

This paper proposes a general discussion of the handling of imprecise and uncertain information in temporal reasoning in the framework of fuzzy sets and possibility theory. The introduction of fuzzy features in temporal reasoning can be related to different issues. First, it can be motivated by the need of a gradual, linguistic-like description of temporal relations even in the face of complete...

In order to safeguard the safety of passengers and reduce maintenance costs, it is necessary to analyze and evaluate the security risk of the Railway Signal System. However, the conventional Fuzzy Analytical Hierarchy Process (FAHP) can not describe the fuzziness and randomness of the judgment, accurately, and once the fuzzy sets are described using subjection degree function, the concept of fu...

Since the appearance of the first paper on fuzzy sets proposed by Zadeh in 1965, the relationship between probability and fuzziness in the representation of uncertainty has been discussed among many people. The question is whether probability theory itself is sufficient to deal with uncertainty. In this paper the relationship between probability and fuzziness is analyzed by the process of perce...

Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater...

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the...

Although uncertainties exist in spatial data mining, they have not been paid much attention to. Uncertainty may have an influence on the confidential level, supportable level, and interesting level of spatial data mining. This paper proposes uncertainty-based spatial data mining. First, the concept is given in the integrated contexts of both uncertainty and spatial data mining. The inherent unc...

A pharmacophore-based approach for compiling focused screening libraries is presented. It integrates information from three-dimensional molecular alignments into correlation vector-based database screening. The pharmacophore model is represented by a number of spheres of Gaussian-distributed feature densities. Different degrees of "fuzziness" can be introduced to influence the model's resolutio...

In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a general Bayesian netw...

In this paper, we introduce the notions of inconsistency p-measure, consistency p-measure and fuzziness p-measure. We discuss various properties of them. We investigate the degree of inconsistency and roughness p-measure in a decision table.