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Bhopal, India

Pandey G.S.,RGPV | Jain R.C.,SATI
Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014 | Year: 2014

The protein structure has always been under significant exploration, as this is vitally responsible for the basic functionality. Understanding the formation of these structures has gradually been called as 'the protein folding problem'. The solution for this problem is basically concerned with the ultimate aim to attain the native state. Broadly the methods to predict the ultimate goal, are categorized as- the template -based (homology modeling, threading/fold recognition) and template -free (ab initio) methods, which are discussed later in detail. In this paper, we have followed the ab initio methodology, to develop an ARTNN based approach to cluster the stable folds, out of the search space. The approach implemented through either of the two models (SF-ART or FC-ART model) could help enhance the performance of obtaining the three dimensional native state by providing more productive pathway, using the crude ab initio parameters, emphasizing quality performance with reduced execution time. This could thus be considered as a productive ab initio- clustering approach. © 2014 IEEE. Source


Gour B.,The Saints | Bandopadhyaya T.K.,Bansal Institute of Science and Technology | Patel R.,RGPV
3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 | Year: 2010

ART1 based clustering approach is used for classification, which groups fingerprints into more compact classes. ART1 is a efficient technique for grouping fingerprints in to N number of classes, which speedup the process of fingerprint recognition. After classification of fingerprints the key-fingerprint class is used for the purpose of fingerprint identification. The key-fingerprint is recognized by using Monolithic and Modular Neural Network and their performance has been compared on the bases of time and accuracy. Due to modularity, Modular Neural Network gives better performance on the classified databases as compared to Monolithic Neural Network even with poor quality fingerprints. Monolithic Neural Network takes average of 44.7 seconds with an accuracy of 98%, correct recognition where as Modular Neural Network takes average time 1.84 seconds with an accuracy of 100% correct recognition. © 2010 IEEE. Source


Khan A.U.,The Saints | Bandopadhyaya T.K.,Bansal Institute of Science and Technology | Sharma S.,RGPV
3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 | Year: 2010

Selection of stocks that are suitable for investment is a complex task. The main aim of every investor is to earn maximum possible returns on investments. There are many conventional techniques being used and these include technical and fundamental analysis. The main issue with any approach is the proper weighting of criteria to obtain a list of stocks that are suitable for investments. This paper proposes an improved method for stock picking using self-organizing maps and technical analysis. The stock selected using this method outperformed the BSE-30 Index by about 28.41% based on one month of stock data. © 2010 IEEE. Source


Hussain J.K.,RGPV | Kumar V.,RRCAT | Chouksey S.,RRCAT
Optics Communications | Year: 2011

A pulsed wire method is described for undulator field measurement. The results of the second field integral are compared with Hall probe results. The pulsed wire data are taken at different locations of the sensor along the length of the wire and with different gap of the undulator. A planar undulator with six periods is used in the pulsed wire set up. The total length of the undulator is 30 cm. The pulsed wire result shows good agreement with the Hall probe data. © 2010 Elsevier B.V. All rights reserved. Source


Shrivastava A.K.,Academics | Vidwans A.,RGPV | Saxena A.,TIET
Proceedings - 5th International Conference on Computational Intelligence and Communication Networks, CICN 2013 | Year: 2013

A Mobile Ad-hoc Network (MANET) is a dynamic wireless network that can be formed without the need for any pre-existing infrastructure in which each node can act as a router. One of the major challenges of MANET is the design of vigorous routing algorithms that adapt to the frequent and randomly varying network topology. Numbers of routing protocols have been proposed and several of them have been widely simulated or implemented as well. In proposed paper, we evaluate the performance of On-demand Multipath Distance Vector (AOMDV) routing protocol with two Mac layer protocol IEEE802.11 and TDMA for four parameters namely, Packet delivery fraction, Average End-to-End delay, Normalized Routing Load and Throughput. We conclude that working of 802.11 is more efficient than TDMA. © 2013 IEEE. Source

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