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Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 74 - Number 15
Year of Publication: 2013
Authors:
Prashant Ruwali
Vikas Tripathi
10.5120/12965-0240

Prashant Ruwali and Vikas Tripathi. Article: Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario. International Journal of Computer Applications 74(15):45-50, July 2013. Full text available. BibTeX

@article{key:article,
	author = {Prashant Ruwali and Vikas Tripathi},
	title = {Article: Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {74},
	number = {15},
	pages = {45-50},
	month = {July},
	note = {Full text available}
}

Abstract

Road traffic noise has remained one of the greatest concerns during the past few decades. it has found to be the major sources of pollution in the metropolitan city areas [7]. With the increase in urbanization and motorization the number of vehicles has increased which further increased this problem by manifolds. [4] Thus, in view of the above stated problem our aim is perform prediction of noise levels using certain available regression based and supervised learning algorithms. Modelling and prediction of traffic noise by using generally used prediction algorithms is a very complicated and non linear process, due to high involvement of several factors over which noise level depends. [3]. However, after analysis we have been able to found appropriate results with a certain levels of accuracy.

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