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  Published Paper Details:

  Paper Title

MACHINE LEARNING FOR PREDICTION OF NOISE POLLUTION

  Authors

  Mr. Abhijeet Ganpat Khadke,  Dr. G.R. Bamnote,  Dr. S.W. Ahmad

  Keywords

Noise Pollution Prediction, Noise Pollution Monitoring, Noise Tracking, Noise Pollution Prediction Using Machine Learning, IOT Based Noise Tracking Device

  Abstract


Noise pollution is a developing problem now a days. The quickest effect is an illness of human mental health. Now days sleep disorder is a serious cause. Continuous propagation of noise can create frustration. Noise can make a more contribution to cardiovascular effect in human beings. That is a reason it is very difficult to live a healthy life for all. This system work on the methods of Internet of Things (IOT) which is an evolving technology. It also check the live intensity of noise in a particular area and also maintain a log over the internet, for further analysis and prediction. This system sensing the presence of polluted noise range. A micro controller is use to process the noise data and generate reports for authorities to keep a watch and take some precaution over the excessive noise. It also classify the noise using machine learning algorithm. This system can be used in various areas. It can be use near schools, college campus, hospital and no honking area and traffic areas.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2009175

  Paper ID - 198182

  Page Number(s) - 1324-1332

  Pubished in - Volume 8 | Issue 9 | September 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mr. Abhijeet Ganpat Khadke,  Dr. G.R. Bamnote,  Dr. S.W. Ahmad,   "MACHINE LEARNING FOR PREDICTION OF NOISE POLLUTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 9, pp.1324-1332, September 2020, Available at :http://www.ijcrt.org/papers/IJCRT2009175.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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