In the era of grid computing, resource allocation plays a vital role for assigning the available resources. This paper describes how to reduce the search time for the best available resources and assure instant provisioning of the lately added resources to the grid thereby using clustering and artificial neural networks. The efficacy is achieved through K-Means clustering algorithm which is used to cluster the similar type of resources on the basis of their configuration as high, medium or low thereby decreasing the search time by searching only into the cluster of high availability instead of searching for the best from all of the available resources. Thereafter artificial neural network trained with feed forward propagation is deployed to automatically assign the newly added resources to appropriate cluster. This approach significantly reduces the computational time of resource allocation.
In areas such as computer vision and image processing, image segmentation has been and still is a relevant research area due to its wide spread usage and application. The traditional segmentation technique which is used in gray-scale mathematical morphology is watershed transform. Region Growing is an approach to image segmentation in which neighbouring pixels are examined and added to a region class if no edges are detected. This process is iterated for each boundary pixel in the region. In this paper, we made enhancements in watershed algorithm and region growing algorithm for image and color segmentation. The new enhanced algorithm is implemented in MATLAB and results are compared with the existing technique in the form of visualization and on the basis of Liu's Ffactor values.
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