Image segmentation generally refers to the process that partitions an image into mutually exclusive regions that cover the image. Among the various image segmentation techniques, traditional image segmentation methods like edge detection, region based, watershed transformation etc. are widely used but have certain drawbacks, which cannot be used for the accurate result. In this paper clustering based techniques is employed on images which results into segmentation of images. The performance of Fuzzy C-means (FCM) integrated with the Particle Swarm optimization (PSO) technique and its variations are analyzed in different application fields. To analyze and grade the performance, computational and time complexity of techniques in different fields several metrics are used namely global consistency error, probabilistic rand index and variation of information are used. This experimental performance analysis shows that FCM along with fractional order Darwinian PSO give better performance in terms of classification accuracy, as compared to other variation of other techniques used. The integrated algorithm tested on images proves to give better results visually as well as objectively. Finally, it is concluded that fractional order Darwinian PSO along with neighborhood Fuzzy C-means and partial differential equation based level set method is an effective image segmentation technique to study the intricate contours provided the time complexity should be as small as possible to make it more real time compatible.

In recent times machine learning algorithms are used for internet traffic classification. The infinite number of websites in the internet world can be classified into different categories in different ways. In educational institutions, these websites can be classified into two categories, educational websites and non-educational websites. Educational websites are used to acquire knowledge, to explore educational topics while the non-educational websites are used for entertainment and to keep in touch with people. In case of blocking these non-educational websites students use proxy websites to unblock them. Therefore, in educational institutes for the optimum use of network resources the use of non-educational and proxy websites should be banned. In this paper, we use five ML classifiers Naïve Bayes, RBF, C4.5, MLP and Bayes Net to classify the educational and non-educational websites. Results show that Bayes Net gives best performance in both full feature and reduced feature data sets for intended classification of internet traffic in terms of classification accuracy, recall and precision values as compared to other classifiers.