Land covers mix and high input dimension are two important issues that affect the classification accuracy of remote sensing images. Fuzzy classification has been developed to represent the mixture of land covers. Two fuzzy classifiers of fuzzy rules-based (FRB) and fuzzy neural network (FNN) were studied to illustrate the interpretability of fuzzy classification. Based on the FNN classifier, a ...

The ability to analyze large amounts of data for the extraction of valuable information presents a competitive advantage for any organization. The technologies of data warehousing, OLAP, and data classification support that ability. The data warehouse is a central data pool which integrates heterogeneous data sources and provides strategic information for analysis and decision support. OnLine A...

In this paper, the local fuzzy fractal dimension (LFFD) is proposed to represent the feature of local image complexity. The definition of LFFD is an extension of the box-counting dimension of discrete sets by incorporating the fuzzy set. The relationship between LFFD and local intensity changing is investigated by experiments, which proves that LFFD is an important feature of edges and is insen...

Although fuzzy operators have deserved a large attention in the Euclidean case, almost nothing exists concerning the geodesic case. In this paper, we address this question, by de"ning fuzzy geodesic distances between points in a fuzzy set, and geodesic balls in a fuzzy set (based on the comparison of fuzzy numbers), from which we derive fuzzy geodesic mathematical morphology operators. The prop...

In this paper, we present coreset constructions for the fuzzy Kmeans problem. First, we show that one can construct a weak coresets for fuzzy K-means. Second, we show that there are coresets for fuzzy K-means with respect to balanced fuzzy K-means solutions. Third, we use these coresets to develop a randomized approximation algorithm whose runtime is polynomial in the number of the given points...

In this paper, we introduce a design of fuzzy neural networks based on scatter space for nonlinear modeling. To design the networks, we partition the input space in the scatter form using fuzzy c-means (FCM) clustering algorithm which generates the fuzzy rules in the premise part of the proposed networks. The partitioned spaces express the fuzzy rules of the networks. Through this method, we ar...

This paper proposes fuzzy multi-criteria decision-making approach integrated with fuzzy real option value theory. The applicability of the proposed method was shown on a real-world supermarket location selection problem. Based on the interviews with the experts, the evaluation criteria for retail location selection were identi ̄ed. Then the network for fuzzy analytic network process (ANP) method...

The purpose of this study was to collect key success factors (KSFs) that determine information security management in cloud computing. Through contemporary literature reviews, we emphasized four major aspects: the external dimension, the internal dimension, the technology dimension, and the execution dimension. We used these dimensions to develop a questionnaire for collecting the responses fro...

Considering the increasing interest in fuzzy theory and possible applications, the concept of fuzzy metric space concept has been introduced by several authors from different perspectives. This paper interprets the theory in terms of metrics evaluated on fuzzy numbers and defines a strong Hausdorff topology. We study interrelationships between this theory and other fuzzy theories such as intuit...

In this paper, following the ideas of (continuous) t-norm and interval numbers, a concept of (continuous) interval-valued t-norm is proposed. Based on the interval-valued fuzzy set and the continuous interval-valued t-norm, we propose a notion of interval-valued fuzzy metric space, which is a generalization of fuzzy metric space in the sense of George and Veeramani [Fuzzy Sets and Systems 64 (1...