Radzi, N. H. M. and Gwari, I. S.B and Mustaffa, N. H. and Sallehuddin, R. (2019) Support vector machine with principle component analysis for road traffic crash severity classification. In: International Conference on Green Engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019, 4-5 Feb 2019, Eastin Hotel Makkasan Bangkok, Thailand.
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Official URL: http://www.dx.doi.org/10.1088/1757-899X/551/1/0120...
Abstract
Road traffic crash (RTC) is one among the leading causes of death in the world, including Nigeria. It also turns many victims completely disabled and generally affected the socio-economic development in the society. In this paper, we proposed to predict the road crash severity injuries in Nigeria by identifying the most significant contributory factors using Principal Component Analysis with Support Vector Machine (SVM) used for classification algorithm. Road crash data from year 2013-2015 obtained from Federal Road Safety Corps Nigeria is used in this study. The result shows that and increased to 87% compared to 82% without feature selection.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | economic and social effects, economics, green computing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computing |
| ID Code: | 90982 |
| Deposited By: | Narimah Nawil |
| Deposited On: | 31 May 2021 13:21 |
| Last Modified: | 31 May 2021 13:21 |
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