this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

In computer science, uncertain data is the notion of data that contains specific uncertainty. Uncertain data is typically found in the area of sensor networks. When representing such data in a database, some indication of the probability of the various values. There is a growing awareness of the need for database systems to be able to handle and correctly process data with uncertainty. The unce...

Clustering analysis of the gene expression profiles has been used for identifying the functions of unknown genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple clusters as their degrees of membership. It is more appropriate for analyzing gene expression profiles because genes usually belong to multiple functional families. However, general clusteri...

in most industrial zones, electric energy is one of the most important energy sources. since electrical motors are the main energy consumers of industrial factories, consumption optimization in these motors can be considered as a main option related to energy saving. one very effective way to reduce the consumption of these equipment is to use a motor speed controllers or drives. since the loss...

Image segmentation and classification are two important steps in multicolor fluorescence in-situ hybridization (MFISH) images analysis. In this paper we first developed an improved adaptive fuzzy c-means (IAFCM) algorithm for the segmentation of the DAPI channel of M-FISH images to extract interested regions for the following classification task. Then we employed a sparse representation-based c...

Methods for generating fuzzy singleton models from input-output data have been proposed by many authors. This paper introduces a genetic algorithm (GA) based method to generate a fuzzy singleton model taking into account all the necessary constraints to guarantee an analytically inverted representation of the process dynamics which may be used as a fuzzy controller in Internal Model Control (IM...

Corresponding Author: Hamedoun Lamiae Laboratory LIROSA, Department of Computer Science, Faculty of Sciences, Abdelmalek Essaadi University, Mhannech II, BP: 2121, Tetouan, Morocco Tel: +212 662 12 32 33 Email: [email protected] Abstract: The present paper proposes a new Hybrid clustering Process based on Fuzzy Genetic System. The proposed Approach consists of two steps: (1) Using a method ...

Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends an...

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative...