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Crime Prediction System

  • Somula Ramasubbareddy
  • T. Aditya Sai Srinivas
  • K. Govinda
  • S. S. Manivannan
Chapter
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Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)

Abstract

The work implemented here is crime prediction system (CPS). We first created hypothetical datasets samples of major city areas and different crimes taking place and then we used the algorithms to analyze it. We used HTML and CSS along with PHP, while wamp as a Web server to this application. The objective of the proposed work is to analyze and predict the chance of a crime happening using apriori algorithm. In addition, we used decision tree as a searching algorithm and naïve Bayesian classifier to predict about the crime in particular geographical location at a particular point of time. The result of this can be used to raise people’s awareness regarding the dangerous locations and to help agencies to predict future crime in a specific location within a particular time.

Keywords

Machine learning Apriori algorithm Decision tree Naïve Bayes classifier Prediction 

References

  1. 1.
    Balaid A, Rozan MZA, Hikmi SN, Memon J (2016) Knowledge maps: a systematic literature review and directions for future research. Int J Inf Manag 36(3):451–475CrossRefGoogle Scholar
  2. 2.
    Lu B, Tsou BK (2010) Combining a large sentiment lexicon and machine learning for subjectivity classification. In 2010 international conference on machine learning and cybernetics, vol 6. IEEE, pp 3311–3316Google Scholar
  3. 3.
    Alvari H, Hajibagheri A, Sukthankar G (2014) Community detection in dynamic social networks: a game-theoretic approach. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining. IEEE Press, pp 101–107Google Scholar
  4. 4.
    Farid H (2006) Digital doctoring: how to tell the real from the fake. Significance 3(4):162–166MathSciNetCrossRefGoogle Scholar
  5. 5.
    Stough R, McBride D (2014) Big data and US public policy. Rev Policy Res 31(4):339–342Google Scholar
  6. 6.
    Buchta C, Kober M, Feinerer I, Hornik K (2012) Spherical k-means clustering. J Stat Softw 50(10):1–22Google Scholar
  7. 7.
    Kulis B, Jordan MI (2011) Revisiting k-means: new algorithms via Bayesian nonparametrics. arXiv preprint arXiv:1111.0352
  8. 8.
    Kaur N, Sahiwal JK, Kaur N (2012) Efficient k-means clustering algorithm using ranking method in data mining. Int J Adv Res Comput Eng Technol 1(3):85–91Google Scholar
  9. 9.
    Li X, Juhola M (2014) Country crime analysis using the self-organizing map, with special regard to demographic factors. AI & Soc 29(1):53–68CrossRefGoogle Scholar
  10. 10.
    Malathi A, Baboo DSS (2011) Algorithmic crime prediction model based on the analysis of crime clusters. Global J Comput Sci Technol 11(11):47–51Google Scholar
  11. 11.
    Okonkwo RO, Enem FO (2011) Combating crime and terrorism using data mining techniques. In 10th international conference IT people centred development, Nigeria Computer Society, NigeriaGoogle Scholar
  12. 12.
    Mande U, Srinivas Y, Murthy JVR, Kakinada VV (2012) Feature specific criminal mapping using data mining techniques and generalized Gaussian mixture model. Int J Comput Sci Commun Netw 2(3):375–379Google Scholar
  13. 13.
    Baboo SS (2011) An enhanced algorithm to predict a future crime using data mining. Int J Comput Appl 975:8887Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Somula Ramasubbareddy
    • 1
  • T. Aditya Sai Srinivas
    • 2
  • K. Govinda
    • 2
  • S. S. Manivannan
    • 2
  1. 1.Department of Information TechnologyVNRVJIETHyderabadIndia
  2. 2.SCOPEVIT UniversityVelloreIndia

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