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The following topics are dealt with: fuzzy information processing; applications in biology, health and medicine; applications in business and social science; fuzzy classification, clustering and segmentation; fuzzy control theory and applications; database and ontology; decision making; forecasting and prediction; fuzzy inference rules; fuzzy sets; fuzzy logic; fuzzy risk management; intelligent e-services...
The category theory based model (8th GrC Model) has been proposed as the ldquofinalrdquo GrC Model. This paper is to discuss its specialization into the category of qualitative fuzzy sets. Here are the main points: 1) Let Col be the collection of real world fuzzy sets on U, where U is a crisp set. 2) Let V be the type I fuzzy sets of col. Namely, V, denoted by MF(U), is the set of all membership functions...
We develop a possibility based model with the aim of helping firms to deal with difficult-to- predict catastrophic failures. Our study is motivated by the recent economic turmoil and recent large financial firm failures. Our results suggest that specialization is an alternative to portfolio diversification in predicting catastrophic events.
We propose a visualization scheme for the representation of the uncertainty and relations in reasoning using extended logic programming. This scheme has graphical layout and color utilization components borrowed from data mining, cartography, and general principles of graphic design. Even in simple logic programming examples the node count in reasoning grows quickly. Said growth contributes to a ldquolost...
Terrorist acts are intentional and therefore differ significantly from "dumb" random acts that are the subject of most risk analyses. There is significant epistemic (state of knowledge) uncertainty associated with such intentional acts, especially for the likelihood of specific attack scenarios. Also, many of the variables of concern are not numeric and should be treated as purely linguistic...
The concept of an L-fuzzy set generalizes not only the concept of a fuzzy set but also the concepts of interval-valued fuzzy sets and intuitionistic fuzzy sets (as will become clear in this paper). In addition, the class of L-fuzzy sets forms a complete lattice whenever the underlying set L constitutes a complete lattice. Based on these observations, we develop a general approach towards L-fuzzy mathematical...
This paper investigates the utility of using aggregate (average, max etc.) information in a multi-sample classification problem in terms of the accuracy of the overall classification, and the prediction of estimated error. Bayesian networks are presented here in order to allow comparison of these results with those of a previously presented fuzzy inference system. Different structures of Bayesian...
Various approaches have been proposed to perform concept approximation using a concept lattice developed from a formal context. This paper reviews several of these approaches and examines the relationships among them. Since concept approximation is also strongly related to determining the similarity between concepts within a concept lattice, a variety of methods for measuring concept similarity are...
Most of us recognize that when society's collective trends expose us to disasters which may threaten our survival, some rather radical, i.e., fundamental, actions may be necessary to forestall such untoward events. Yet, what signals might trigger such action? Pronouncements that the ldquoend is nearrdquo have not proven to be very effective, and rightfully so. We suggest rather that the impetus for...
Biological data classification is an important data mining research area in biomedical applications. The current challenge problem is that there is a large number of condition attributes (features) in biological data, with which it is difficult for classification methods to deal. In this paper, a new approach based on rough sets and support vector machines is proposed for biological data classification...
Humans frequently use a ldquodivide and conquerrdquo strategy to understand large volumes of data, by grouping similar items into progressively finer categories which form a conceptual hierarchy. Typically, such categories do not have crisp definitions but can be modelled by fuzzy set theory, allowing computers to represent and reason about sets of objects in a way that reflects the human interpretation...
A system is presented which is oriented at aiding medical professionals in the diagnosis of neuromuscular disease using a fuzzy rule-based classification system. Visualization of the fuzzy rules, which are contributors to the overall classification, allows the user to determine their level of confidence with the classification of the system. During the development of this system, the choice between...
In this paper an objective fuzzy approach for fast and accurate porosity vision based inspection is presented. An automated methodology of detection of pores, which are formed in aluminum alloys during production of water-pumps for car engines with the die casting method, is described. The proposed method is based on the correlation of the core of pore candidates with twelve developed matrices resulted...
The objective of this paper is to show the strength of a modified version of particle swarm optimization (PSO) in definition of suitable partitions of fuzzy time series forecasting and increasing its accuracy. Although a lot of contributions have been made to increase the quality of forecasts using fuzzy time series , there are only a few papers considering tuning the length of intervals in forecasting...
A framework for modeling, analyzing and synthesizing nuclear safeguards information with various uncertainties is proposed by using a newly developed belief rule-base inference methodology (RIMER). After a hierarchical analysis of States' nuclear activities on the basis of the International Atomic Energy Agency (IAEA) physical model, the multi-layer structure of the evaluation model for States' nuclear...
In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order to learn a fuzzy classifier system. The two objectives...
Design of neuro-controller for complex dynamic systems is a big challenge faced by the researchers. In this paper we present a design of a robust neuro-controller for a dynamic system to make the system response fast with no overshoot. Here the control action decided by the controller completely depends on the value of the error at that point of time. The position feedback which controls the bandwidth...
While many techniques exist to classify data possessing straightforward characteristics, they tend to fail when dealing with the ldquocurse of dimensionalityrdquo. This condition, in which the ratio of features to samples is very large, is prevalent in many complex, voluminous biomedical datasets acquired using current spectroscopic modalities. We present a novel classification method using an adaptive...
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