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Volume & Issue no: Volume 3, Issue 5, September - October 2014

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Title:
Using Hybrid Model of Turing Band Method and Feed Forward Neural Network Improving Performance Accuracy of Distributed Web System
Author Name:
Miss. Jyoti J. Dhayagude, Dr. Pradeep K. Deshmukh
Abstract:
Abstarct Recently the prediction method was introduced for forecasting the web performance by using the innovative application of Turing Band (TB) geostatistical experiment. However this method is having major limitation which is required to be rectified further. The forecasting accuracy of this proposed method is poor. In addition to this, this method was only assuming the input data in linear form to forecast the web performance, however by considering the real time environment, the input data may be vary and non-linear as well. Therefore this approach fails for different measurement data as well as prediction horizon lengths. The later limitation is out of scope of this paper, we are presenting the extended TB method to improve the accuracy of forecasting. We have studied many methods those are used to improve the forecasting accuracies. The artificial neural network (ANN) is one of the best methods to improve the forecasting accuracy. In this paper we are presenting the hybrid model of TB and Feed Forward Neural Network (FFNN) together for improving the overall accuracy performance. The experimental study is performed using the real time web log dataset and comparative analysis of forecasting accuracy is done in between TB and Hybrid TB method. KEYWORDS:- Forecasting accuracy, feed forward neural network, geostatistics, spatio-temporal prediction, Web of Things (WoT), Web performance forecasting.
Cite this article:
Miss. Jyoti J. Dhayagude, Dr. Pradeep K. Deshmukh , " Using Hybrid Model of Turing Band Method and Feed Forward Neural Network Improving Performance Accuracy of Distributed Web System" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 3, Issue 5, September - October 2014 , pp. 038-043 , ISSN 2278-6856.
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
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