A comprehensive study of crime detection with PCA and different neural network approach

Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/33872

Title:	A comprehensive study of crime detection with PCA and different neural network approach
Authors:	Ahmad Kadri, Junoh Muhammad Naufal, Mansor kadri@unimap.edu.my apairia@yahoo.com
Keywords:	Crime rate Elman Neural Network Feed Forward Neural Network and Probabilistic Neural Network Principal Component analysis
Issue Date:	2013
Publisher:	Springer-Verlag
Citation:	Advances in Intelligent Systems and Computing, vol. 206 AISC, 2013, pages 611-618
Abstract:	Crime rate in Malaysia is almost in awareness stage. The centre for Public Policy Studies Malaysia reports that the ratio of police to population is 3.6 officers to 1,000 citizens in Malaysia. This lack of manpower sources ratios alone are not a comprehensive afford of crime fighting capabilities. Thus, dealing with these circumstances, we present a comprehensive study to determine bandit behavior with PCA and different neural network algorithm such as Elman Neural Network (ELMNN), Feed Forward Neural Network (FFNN) and Cascade-Forward Neural Network (CFNN). This system provided a good justification as a monitoring supplementary tool for the Malaysian police arm forced.
Description:	Link to publisher's homepage at http://link.springer.com/
URI:	http://link.springer.com/chapter/10.1007%2F978-3-642-36981-0_56 http://dspace.unimap.edu.my:80/dspace/handle/123456789/33872
ISBN:	978-364236980-3
ISSN:	2194-5357
Appears in Collections:	Institute of Engineering Mathematics (Articles)

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