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In this paper we propose and construct Fuzzy Algebraic Additive Model, for the estimation of risk in various fields of human activities or nature’s behavior. Though the proposed model is useful in a wide range of scientific fields, it was designed for to torrential risk evaluation in the area of river Evros. Clearly the model’s performance improves when the number of parameters and the actual data increases. A Fuzzy Decision Support System was designed and implemented to incorporate the model’s risk estimation capacity and the risk estimation output of the system was compared with the output of other existing methods with very interesting results.

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from the superiority of the contourlet method to the state of the art multi-scale techniques. A genetic algorithm is applied for feature weighting with the objective of increasing classification accuracy. Although fuzzy classifiers are interpretable, the majority are order sensitive and suffer from the lack of generalization. In this study, a kernel SVM is integrated with a nerofuzzy rule-based classifier to form a support vector based fuzzy neural network ( SVFNN). This classifier benefits from the superior classification power of SVM in high dimensional data spaces and also from the efficient human-like reasoning of fuzzy and neural networks in handling uncertainty information. We use the Mammographic Image Analysis Society (MIAS) standard data set and the features extracted of the digital mammograms are applied to the fuzzy-SVM classifiers to assess the performance. Our experiments resulted in 95.6%,91.52%,89.02%, 85.31% classification accuracy for the subclass FSVM, SVFNN, fuzzy rule based and kernel SVM classifiers respectively and we conclude that the subclass fuzzy-SVM is superior to the other classifiers.

In this paper, the Urysohn and completely Hausdorff axioms in general topology are generalized to L-topological spaces so as to be compatible with pointwise metrics. Some properties and characterizations are also derived

An L-fuzzifying matroid is a pair (E, I), where I is a map from2E to L satisfying three axioms. In this paper, the notion of closure operatorsin matroid theory is generalized to an L-fuzzy setting and called L-fuzzifyingclosure operators. It is proved that there exists a one-to-one correspondencebetween L-fuzzifying matroids and their L-fuzzifying closure operators.

We provide fuzzy quasi-metric versions of a fixed point theorem ofGregori and Sapena for fuzzy contractive mappings in G-complete fuzzy metricspaces and apply the results to obtain fixed points for contractive mappingsin the domain of words.

In this paper, we present an application of intuitionistic fuzzyprogramming to a two person bi-matrix game (pair of payoffs matrices) for thesolution with mixed strategies using linear membership and non-membershipfunctions. We also introduce the intuitionistic fuzzy(IF) goal for a choiceof a strategy in a payoff matrix in order to incorporate ambiguity of humanjudgements; a player wants to maximize his/her degree of attainment of the IFgoal. It is shown that this solution is the optimal solution of a mathematicalprogramming problem. Finally, we present a numerical example to illustratethe methodology.

The aim of this paper is to study the categorical relations betweenmatroids, Goetschel-Voxman’s fuzzy matroids and Shi’s fuzzifying matroids.It is shown that the category of fuzzifying matroids is isomorphic to that ofclosed fuzzy matroids and the latter is concretely coreflective in the categoryof fuzzy matroids. The category of matroids can be embedded in that offuzzifying matroids as a simultaneously concretely reflective and coreflectivesubcategory.

In the present paper, a partial order on a non- Archimedean fuzzymetric space under the Lukasiewicz t-norm is introduced and fixed point theoremsfor single and multivalued mappings are proved.

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