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Identification of Noise Pollution Prone Regions in Mumbai Using Expectation-Maximization Clustering Technique

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Emerging Technology in Modelling and Graphics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 937))

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Abstract

Noise pollution is escalating at an alarming rate as a one of the critical outcomes of urbanization. This led to harmful effect on the health of human being as it can cause annoyance, hypertension, heart disease, and sleep disturbances. Despite all measures to control noise pollution that have been taken in Mumbai so far, those are prone to vulnerabilities. The differences in these vulnerability-inducing causes arise a need for an effective analysis. The motive of this paper is to have data mining to come to aid to create a model that provides the heterogeneity of the data by grou** similar objects together to find the noise pollution regions in the Mumbai state with respect to different factors.

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Author information

Authors and Affiliations

  1. Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India

    Arushi Agarwal, Pratibha Chaudhary, Rana Majumdar, Sunil Kumar Chowdhary & Abhishek Srivastava

Authors
  1. Arushi Agarwal
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  2. Pratibha Chaudhary
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  3. Rana Majumdar
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  4. Sunil Kumar Chowdhary
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  5. Abhishek Srivastava
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Corresponding author

Correspondence to Rana Majumdar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Debika Bhattacharya

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Agarwal, A., Chaudhary, P., Majumdar, R., Chowdhary, S.K., Srivastava, A. (2020). Identification of Noise Pollution Prone Regions in Mumbai Using Expectation-Maximization Clustering Technique. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_1

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  • DOI: https://doi.org/10.1007/978-981-13-7403-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7402-9

  • Online ISBN: 978-981-13-7403-6

  • eBook Packages: EngineeringEngineering (R0)

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