In this paper, a rule extraction method using a modified fuzzy minmax neural network for dynamic hand gesture recognition is introduced. We present a feature relevance measure for the pattern classification based on FMM neural networks. During the learning process, the feature distribution information is utilized to compensate the hyperbox distortion which may be caused by eliminating the overl...

In this paper, we present a rule extraction method using a modified fuzzy min-max neural network for dynamic hand gesture recognition. We introduce a feature relevance measure for the pattern classification based on FMM neural networks. During the learning process, the feature distribution information is utilized to compensate the hyperbox distortion which may be caused by eliminating the overl...

Wireless Sensor Networks (WSN) generates a huge amount of data for efficient application of discovering essential knowledge from it is important. Usually, WSN data is generated in streams and forwarded to a sink. Raw data leads to higher communication overhead adversely affecting WSN performance. Association mining processes data to locate frequent patterns. Thus, when association mining is app...

OBJECTIVE The aim of this paper is to present a novel fuzzy classification framework for the automatic extraction of fuzzy rules from labeled numerical data, for the development of efficient medical diagnosis systems. METHODS AND MATERIALS The proposed methodology focuses on the accuracy and interpretability of the generated knowledge that is produced by an iterative, flexible and meaningful ...

This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed ...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, na...

Fuzzy rule based systems provide a framework for representing & processing information in a way that resembles human communication & reasoning process. Two approaches can be found in the literature which is used for rule based generation; In Knowledge Driven Models the requisite rule base is provide by domain expert & knowledge engineers. In the Data Driven Models the rule base is generated fro...

in this paper, a  fuzzy numerical procedure for solving fuzzy linear volterra integro-differential equations of the second kind under strong  generalized differentiability is designed. unlike the existing numerical methods, we do not replace the original fuzzy equation by a $2times 2$ system ofcrisp equations, that is the main difference between our method  and other numerical methods.error ana...