Clustering algorithms have been widely used artificial intelligence, data mining and machine learning, etc. It is unsupervised classification and is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, to analysis interval data needs Type II fuzzy log...

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the...

Electricity has become an important concern in today’s society. This is due to the fact that electric grid now a greater number of non-linear components. The AC-powered locomotive one these aim this paper was model and predict reactive power produced by AC locomotive. presents study on modelling prediction locomotives. Reactive flow significant impact network voltage levels efficiency. research...

The  tunnel  boring  machine  (TBM)  penetration  rate  estimation  is  one  of  the  crucial  and complex  tasks  encountered  frequently  to  excavate  the  mechanical  tunnels.  Estimating  the machine  penetration  rate  may  reduce  the  risks  related  to  high  capital  costs  typical  for excavation  operation.  Thus  establishing  a  relationship  between  rock  properties  and  TBM pe...

The aim of this paper is to propose an exploratory study on simple, accurate and 19 computationally efficient movement classification technique for prosthetic hand application. The 20 surface myoelectric signals were acquired from 2 muscles – Flexor Carpi Ulnaris and Extensor Carpi 21 Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a 22 new comb...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

Recently Kernelized Fuzzy C-Means clustering technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performa...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

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...