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An Intelligent Public Grievance Reporting System-iReport

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ICCCE 2020

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 698))

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Abstract

Public grievance is important in making governance very effective. Generally, the citizens are expected to report their grievances through a grievance reporting system. In the literature, efforts have been made through an interactive web portal, IVR system, mobile applications, etc. However, none of these approaches are providing a feedback mechanism and the status of grievance instantly. In this paper, we address the issues of public grievance reporting using cloud vision. We built an intelligent public grievance reporting system that tracks the grievance of the citizens instantly. The system captures the images and segregates them, and directly drops the information about the issue or a problem to the nearest responsible authority. The system resolves the issue effectively without much human effort and even it can be used by illiterate people. We have conducted the experiments on the real-time system and the results improve the grievance reporting effectively.

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References

  1. Xiao HB, Poovendran (2018) Google’s cloud vision API is Not Robust to Noise. In: 16th IEEE international conference on machine learning and applications (ICMLA), 18 January 2018

    Google Scholar 

  2. Wu F, Wang X (2013) A geocoding algorithm for natural language address with neighborhood properties. In: Proceedings of 2nd international conference on computer science and network technology, 10 June 2013

    Google Scholar 

  3. Khedkar S, Thube S (2017) Real-time databases for applications. Int. Res. J. Eng. Tech. (IRJET) 4(06):2078–2082

    Google Scholar 

  4. https://www.ghmc.gov.in/KeyContacts.aspx

  5. ICRF (Innovative Citizen Redressal Forum). http://www.icrf.org.in

  6. Marci DK, Avdan U (2018) Address standardization using the natural language process for improving geocoding results. Comput Environ Urban Syst 70:1–8

    Article  Google Scholar 

  7. Geocoding concepts, techniques & secrets from SGSI (2012). http://www.sgsi.com

  8. Agatha W, Scott C (2008) Geocoding in ArcGIS tutorial. ESRI Press

    Google Scholar 

  9. Wu F, Wang X, Wang N (2012) Neighborhood modeling algorithm of the geographic entity in natural language geocoding. In: 2nd international conference on computer science and network technology

    Google Scholar 

  10. Sarnobat A, Rachamadugu R, Talker P (2017) An Interactive Method for Tracking & Geocoding. Int J Recent Innov Trends Comput Commun 4(1):36–39

    Google Scholar 

  11. https://darpg.gov.in/sites/default/files/UMCPGRAMS.pdf

  12. Chandra S, Kush A (2012) Assessing grievances redressing mechanism in India. Int. J. Comput. Appl. 42(5):12–19

    Google Scholar 

  13. Abrha FW (2016) Assessment of responsiveness and transparency: the case of Mekelle municipality. J Civil Legal Sci 5:191

    Google Scholar 

  14. Suhardi NBK, Prayitno D, Sembiring, J, Yustianto P (2017) Public complaint service engineering based on good governance principles. IEEE

    Google Scholar 

  15. Dailiati1 S, Hernimawati, Sudaryanto (2018) Principles of good governance in the department of population and civil records Pekanbaru. In: International conference on earth and environmental science

    Google Scholar 

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Correspondence to M. Laxmaiah .

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Laxmaiah, M., Mahesh, K. (2021). An Intelligent Public Grievance Reporting System-iReport. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_19

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  • DOI: https://doi.org/10.1007/978-981-15-7961-5_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7960-8

  • Online ISBN: 978-981-15-7961-5

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