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An Improved Traffic Crime Predictive System using Multinomial Naive Bayes Text Classification Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 39 |
| Year of Publication: 2019 |
| Authors: H. A. Akpughe, P. O. Asagba, C. Ugwu |
10.5120/ijca2019918483
|
H. A. Akpughe, P. O. Asagba, C. Ugwu . An Improved Traffic Crime Predictive System using Multinomial Naive Bayes Text Classification Algorithm. International Journal of Computer Applications. 182, 39 ( Feb 2019), 24-31. DOI=10.5120/ijca2019918483
Abstract
Traffic law enforcement agencies in Nigeria have faced a huge setback as they do not have records of offenders or criminals that have been persecuted in the past. In this paper, a system was developed that can predict the possible class of traffic crime together with the penalty attached to that class of criminal offence that a known traffic criminal offender is most likely to commit next. The likelihood and frequency table will be constructed from a dataset of traffic crime data, the likelihood of a user falling under a particular class of traffic crime will also be established. Also, proposed to be designed and developed is a predictive system that uses object-oriented analysis and design methodology (OOADM), improved naïve bayes text classification algorithm to solve these problems. This will be achieved by implementing the stated model with python model-view-controller (MVC) framework known as Django Framework. This improved system is implemented using a real-time, cloud-hosted NOSQL database called FireBase which guarantees scalability. From the results, it was found out that the speed and predictability of probability of any user falling under a class 1 crime type was 81.42% and 10.39%, 8.19% for class 2 and class 3 respectively.
References
- Adeyinka A. F., Ndako V. A., & Faith P. A, (2013). Design and Implementation of Crime Investigation System using Biometric Approach. The Pacific Journal of Science and Technology. 14(2)
- Olaniyan O, Mapayi T & Ibikunle F. A. (2012). An ICT-BAsed E-collaborative Application for law enforcement Agencies in Nigeria. Computing, Information Systems, Development Informatics & Allied Research, 13-18.
- Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze, (2009). Introduction to Information Retrieval. Cambridge University Press, Cambridge England.
- Lee H.F. & Schaefer S.K. (1997). Sequencing methods for automated storage and retrieval systems with dedicated storage. Computer and Industrial Engineering, 32(2).
- Hakan Hekim, Serdar K. Gul and Bahadir K, Akcam (2013). Police use of Information Technologies in Criminal Investigations. European Scientific Journal, 9(4).
- Aruleba, K.D., Akomolafe, D.T. and Afeni, B. (2016) A Full Text Retrieval System in a Digital Library Environment. Intelligent Information Management, 8, 1-8.
- Ian Clarke and Chris Mellish, (1999). A Distributed Decentralized Information storage and Retrieval System. Division of Information, University of Edinburgh.
- Enakrire T. R., Olorunfemi Y. D. and Emmanuel O. A. (2013). The use of Databases for Information Storage and Retrieval in Selected Banks in Delta State, Nigeria. International Journal or Scientific and Technology Research 2(3), 11-24.
- Rikard K. (2012). Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality. University of Borås School of Business and Informatics,
- Marlin, B. (2004). Collaborative filtering: a machine learning perspective. Thes is research. Department of computer science, University of Toronto. 1-118. laborative filtering: a machine learning perspective. Thes is research. Department of computer science, University of Toronto. 1-118.
- Leif Azzopard and Vishwa Vinay, (2009). Accessibility in Information Retrieval. Department of Computing Science, University of Glasglow, Glasglow UK.
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