Due to its inferior characteristics, an observed (noisy) image's direct use gives rise poor segmentation results. Intuitively, using noise-free image can favorably impact segmentation. Hence, the accurate estimation of residual between and images is important task. To do so, we elaborate on residual-driven Fuzzy C-Means (FCM) for segmentation, which first approach that realizes leads participat...

Hard c-means can be used for building classifiers in supervised machine learning. For example, in a n-class problem, c clusters are built for each of the classes. This results into n · c centroids. Then, new examples can be classified according to the nearest centroid. In this work we consider the problem of building classifiers using fuzzy clustering techniques. In particular, we consider the ...

The Multiple Prototype Fuzzy Clustering Model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identi...ed. In the model, it is assumed that the membership of each entity to a cluster expresses a part of the cluster prototype re‡ected in the e...

Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...

Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...