In this paper we propose an extension to the Fuzzy Cognitive Maps (FCMs) that aims at aggregating a number of reasoning tasks into a one parallel run. The described approach consists in replacing real-valued activation levels of concepts (and further influence weights) by random variables. Such extension, followed by the implemented software tool, allows for determining ranges reached by concep...

Two-dimensional (2D) polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at development of tools for expanding the range of proteins accessible with 2D gels. Proteomics was built around the 2D gel. The idea that multiple proteins can be analyzed in parallel grew...

nowadays we know effective supply chain management a key to business success. therefore, supply chain managers use many practices for scm effectiveness and many tools as their enablers. nevertheless, literature about causal relations between sc enablers and scm practices and performance is scarce. this study reports a cognitive mapping of causal relationships between scm practices, sc enablers ...

This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy CMeans(FCM) algorithm, Kernel Fuzzy CMeans(KFCM), Intuitionistic Kernelized Fuzzy CMeans(KIFCM), Kernelized Type-II Fuzzy CMeans(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qual...

Fuzzy Conceptual Maps have become an important means for describing a particular domain showing the concepts (variables) and the relationship between them. They have been used for several tasks like simulation processes, forecasting or decision support. In general, the task of creating Fuzzy Conceptual Maps is made by experts in a certain domain but it is very promising the automatic creation o...

This paper considers the problem of partitioning noisy images into different regions by fuzzy clustering approach. Based on two fuzzy c-means (FCM) algorithms (FCM S1 and FCM S2), we propose four adaptive algorithms (FCM S11, FCM S12, FCM S21 and FCM S22) which utilize the high correlation of image pixels to increase the algorithms’ robustness to noise. Unlike existing algorithms, our algorithm...

-Clustering algorithms are an integral part of both computational intelligence and pattern recognition. It is unsupervised methods for classifying data into subgroups with similarity based inter cluster and intra cluster. In fuzzy clustering algorithms, mainly used algorithm is Fuzzy c-means (FCM) algorithm. This FCM algorithm is efficient only for spherical data when the input of the data stru...

In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-à-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of cluster...

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages quantifying gradational changes like those pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency perform laboratory analyses on fewer samples, yet still produce an adequate distribution map, would reduce initial cost ne...