By Sundarapandian Vaidyanathan (auth.), Natarajan Meghanathan, Dhinaharan Nagamalai, Nabendu Chaki (eds.)
The foreign convention on Advances in Computing and data know-how (ACITY 2012) presents an exceptional foreign discussion board for either teachers and pros for sharing wisdom and leads to idea, method and purposes of computing device technology and data know-how. the second one overseas convention on Advances in Computing and knowledge expertise (ACITY 2012), held in Chennai, India, in the course of July 13-15, 2012, lined a few issues in all significant fields of computing device technological know-how and data expertise together with: networking and communications, community defense and purposes, net and web computing, ubiquitous computing, algorithms, bioinformatics, electronic snapshot processing and development popularity, synthetic intelligence, gentle computing and functions. Upon a power evaluation strategy, a couple of top of the range, featuring not just cutting edge principles but additionally a based assessment and a powerful argumentation of a similar, have been chosen and picked up within the current court cases, that's composed of 3 various volumes.
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Extra info for Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13-15, 2012, Chennai, India - Volume 2
To improve the performance of this classifier, many solutions are introduced. One of the approaches to solve those mentioned problems is searching in feature space to find optimal subset of features that can improve the classification accuracy of k-NN. This goal can be achieved by assigning the weight to all features in order to eliminate irrelevant ones from noisy data sets. In this paper, we attack this problem and introduce a novel algorithm to deal with. At first, proposed approach assigns different weight to feature using Harmony Search and AUC measure as the fitness function.
In: Proc. : The Prompt Suite: Interactive tools for ontology mapping and merging. : Similarity flooding a versatile graph matching algorithm and its application to schema matching. In: Proc. : Learning to map be tween ontologies on the semantic web. In: Proc. : Similarity flooding a versatile graph matching algorithm. In: Proc. : A survey on ontology mapping. : Ten Challenges for ontology matching. In: Proc. : Ontology mapping:The State of the Art. : QOM: Quick Ontology mapping. GI Jahrestagung (1) (2004) 18      K.
6a. Before inpainting Fig. 6b. After inpainting Fig. 6. Inpainting smaller area Table 1. Performance of Restoration algorithm for images in Fig 5 and 6 In Table 1 NO and O represents non overlapping patches and Overlapping patches respectively. L and S represents larger and smaller target area respectively. It could be observed that the overlapping patches gives better accuracy and lesser error than NonOverlapping patches which implies that reconstruction is better in the former case. The time consumed for reconstruction is more for Larger and Overlapping patches when compared to Smaller and Non-Overlapping Patches.