The week's pick
Reseach Article
Use of Predictive Modeling for Prediction of Future Terrorist Attacks in Pakistan
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 15 |
| Year of Publication: 2018 |
| Authors: Hina Muhammad Ismail, Hameedullah Kazi |
10.5120/ijca2018915765
|
Hina Muhammad Ismail, Hameedullah Kazi . Use of Predictive Modeling for Prediction of Future Terrorist Attacks in Pakistan. International Journal of Computer Applications. 179, 15 ( Jan 2018), 8-16. DOI=10.5120/ijca2018915765
Abstract
Recent years have seen an increased interest in Data mining related to terrorism. Large volumes of terrorism records can be analyzed efficiently using data mining techniques to get solutions for crime investigation by law enforcement agencies. On 2014 edition of Global Terrorism Index (GTI, 2015), which systematically rank and compares 162 countries to the impact of terrorism, Pakistan was ranked as the third most affected country. The area of predicting terrorist incidents in the perspective of Pakistan is not adequately explored by the data mining research community which assert a serious concern. This study is focused on analyzing Incident data set from Global Terrorism Database (GTD) specific to Pakistan from year 1970 to 2014 by using predictive modeling. Prediction of future terrorist attacks according to City, Attack type, Target type, Claim mode, Weapon type and Motive of attack through classification techniques will facilitates the decision making process by security organizations as to learn from the previous stored attack information and then rate the targeted sectors/ areas of Pakistan accordingly for security measures.
References
- AGARWAL, J., NAGPAL, R., & SEHGAL, R. (2013). Crime Analysis using K-Means Clustering. International Journal of Computer Applications, Vol. 83, No. 4, pp.1-4.
- BOBBITT, P. (2010) The New Rules of Engagement: Nine Imperatives for Our Post-9/11 World. Newsweek , Available: http://www.newsweek.com/how-fight-war-terror-better-70899, Date accessed: 2015, 30 April.
- Chicago Project on Security and Terrorism: (CPOST), University of Chicago. Available: http://cpostdata.uchicago.edu/search_new.php, Date accessed: 2015, 26 April.
- DESHMUKH, S. R., DALVI, A. S., BHALERAO, T. J., DAHALE, A. A., BHARATI, R. S., & KADAM, C. R. (2015) Crime Investigation using Data Mining. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 3, pp. 22-25.
- Global Terrorism Index (GTI). Retrieved from: http://economicsandpeace.org/research/iep-indices-data/global-terrorism-index, Date accessed: 2015, 20 March.
- HAN, J., & KAMBER, M. & PEI. J. (2012) Data Mining Concepts and Techniques, 3rd ed., The Morgan Kaufmann series in data management systems, USA, 740 pp.
- HUSSAIN, S. E. (2010) Terrorism in Pakistan: Incident Patterns, Terrorists' characteristics, And the Impact of Terrorist Arrests on Terrorism. Ph.D. Dissertation, University of Pennsylvania, Pennsylvania.
- KHAN, M., & DIN, F. U. (2014) Data Classification and Text Mining in CRIMS in the Perspective of Pakistan. Proceedings of the International Conference on Engineering and Emerging Technologie.
- KHELIFA, B., AMINA, B., MADJID, M. (2013) Predictive Surveillance for the Detection of Suspicious Objects, Proceedings of the 14th Annual Postgraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting.
- MALATHI, A., & BABOO, S. (2011) Algorithmic Crime Prediction Model on the Analysis of Crime Clusters, Global Journal of Computer Science and Technology, Vol. 11, No. 11, pp. 47- 51.
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); the WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
- National Consortium for the Study of Terrorism and Responses to Terrorism (START), Global Terrorism Database. Available: http://www.start.umd.edu/gtd, Date accessed: 2015, 14 January.
- NIEVES, S., & CRUZ, A. (2011) Finding Patterns of Terrorist Groups in Iraq: A Knowledge Discovery Analysis. Proceedings of the 9th Latin American and Caribbean Conference for Engineering and Technology, pp. 3-5.
- RAND Database of Worldwide Terrorism Incidents (RDWTI), Available: http://www.rand.org/nsrd/projects/terrorism-incidents.html, Date accessed: 2015, 30 January.
- SHARMA, A., & SHARMA, S. (2012). An Intelligent Analysis of Web Crime Data Using Data Mining. International Journal of Engineering and Innovative Technology. Vol. 2, No. 3, pp. 203-206.
- TAPE, T.G. Interpreting Diagnostic Tests, University of Nebraska Medical Center, Available: http://gim.unmc.edu/dxtests/, Date accessed: 2015, Nov 15.
- UTHRA, R. G. (2014) Data Mining Techniques to Analyze Crime Data , International Journal for Technological Research in Engineering , Vol.1, No. 9, pp. 882-884.
- WITTEN, I. H., & FRANK, E. (2011) Data Mining Practical Machine Learning Tools and Techniques, 3rd ed., The Morgan Kaufmann series in data management systems, USA, 665 pp.
- ZHAO, Y. (2012) R and Data Mining: Examples and Case Studies, Academic Press, Elsevier, Australia, 164 pp.
Index Terms
Keywords