An Efficient Approach towards Crimes against Women using Time Series Algorithm

Print
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Mayank Motwani, Pratha Purwar, Rachit Mathur, Aatif Jamshed
10.5120/ijca2018916730

Mayank Motwani, Pratha Purwar, Rachit Mathur and Aatif Jamshed. An Efficient Approach towards Crimes against Women using Time Series Algorithm. International Journal of Computer Applications 179(34):22-26, April 2018. BibTeX

@article{10.5120/ijca2018916730,
	author = {Mayank Motwani and Pratha Purwar and Rachit Mathur and Aatif Jamshed},
	title = {An Efficient Approach towards Crimes against Women using Time Series Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {179},
	number = {34},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {22-26},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume179/number34/29219-2018916730},
	doi = {10.5120/ijca2018916730},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

One of the major issues in every nation these days is the rise in crime against women. Every day we come across various cases of abuse against women. Study of past crime data can help us in analysing crime patterns and important hidden relations between the crimes. So, crimes predicting model can be simulated which will study verified past crime records and predict future criminal activities. In recent past, there has been an increased interest in time series research. This has been used particularly for finding useful similar trends in multivariate time series in various applied fields such as environmental research, agriculture, sales and finance. This paper elaborates upon the use of time series algorithm in accurately predicting and extracting patterns that occur frequently within a dataset to obtain useful hidden information.

References

  1. Aarti Bansal, Comparative Study of Data Mining Algorithms for Analyzing Crimes against Women. International Journal of Innovations & Advancement in Computer Science, Volume 4, Issue 9 September 2015.
  2. Divya Bansal and Lekha Bhambhu, Execution of APRIORI Algorithm of Data Mining Directed Towards Tumultuous Crimes Concerning Women. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 9, September 2013.
  3. Philippe Esling and Carlos Agon, Time series data mining, ACM Computing Surveys, 2013.
  4. Girish K. Jha and Kanchan Sinha, Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System, Agricultural Economics Research Review, Vol. 26 (No.2) July-December 2013 (pp 229-239).
  5. Wang Peiying, Research on Current Female Crime Control and Prevention Strategies (ISBN: 978-1-61284-109-0/11) 2011.
  6. Shrawan Ram and Amit Doegar, A Comparative Study of Data Mining Techniques for Predicting Disease Using Statlog Heart Disease Database, International Journal of Advanced Research in Computer Science and Software Engineering , Volume 5, Issue 6, June 2015 5(6), June- 2015, pp. 1202-1210.
  7. Michael Schaidnagel, Christian Abele, Fritz Lauxy, Ilia Petrovy, Sales Prediction with Parameterized Time Series Analysis, The Fifth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA), 2013.
  8. Chintan Shah and Anjali g. Jivani, Comparison of Data Mining Classification Algorithms for Breast Cancer Prediction, IEEE International Conference on Computing, Communications and Networking Technologies (ICCCNT), 4-6 July, 2013 (IEEE-31661) 2013.
  9. Veepu Uppal and Gunjan Chindwani, An Empirical Study of Application of Data Mining Techniques in Library System, International Journal of Computer Applications (0975 – 8887) Volume 74– No.11, July 2013
  10. Neal Wagner and Zbigniew Michalewicz, Intelligent techniques for forecasting multiple time series in real-world systems, IJICC, 2011.
  11. Xiangchun Xiong and Yangon Kim , Analysis of Breast Cancer Using Data Mining & Statistical Techniques , IEEE Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN’05) (ISBN: 0-7695-2294-7/05) 2005.
  12. Chong Zhu , Xiangli Zhang , Jingguo Sun , Bin Huang, Algorithm for Mining Sequential Pattern in Time Series Data, International Conference on Communications and MobileComputing,2009.
  13. Aatif Jamshed and Pawan Singh Mehra (2012), “Modified Block Playfair Cipher using Random Shift Key Generation”, International Journal of Computer Applications, Vol. 58, pp. 2012/1/1.
  14. Aatif Jamshed ,Surbhi Chandhok and Romil Anand (2017), “Analysis of Sequential Mining Algorithms”, International Journal of Computer Applications, Vol. 165, pp. 12-2017/5.
  15. Aatif Jamshed ,Surbhi Chandhok and Romil Anand (2017), “An Analysis of Sentimental Data using Machine Learning Techniques”, International Journal of Computer Applications, Vol. 166, pp. 3-2017.
  16. Aatif Jamshed ,Garima Verma(2013), “Mobile Devices integration with Grid by Using Efficient Scheduling for Local Resource”, Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)Volume No. 1 Issue No.2, December 2013.

Keywords

Crime prediction, time series, clustering, multivariate time series