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dc.contributor.authorAhmad Kadri, Junoh-
dc.contributor.authorMuhammad Naufal, Mansor-
dc.date.accessioned2014-04-21T03:43:23Z-
dc.date.available2014-04-21T03:43:23Z-
dc.date.issued2013-
dc.identifier.citationAdvances in Intelligent Systems and Computing, vol. 206 AISC, 2013, pages 611-618en_US
dc.identifier.isbn978-364236980-3-
dc.identifier.issn2194-5357-
dc.identifier.urihttp://link.springer.com/chapter/10.1007%2F978-3-642-36981-0_56-
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33872-
dc.descriptionLink to publisher's homepage at http://link.springer.com/en_US
dc.description.abstractCrime 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.en_US
dc.language.isoenen_US
dc.publisherSpringer-Verlagen_US
dc.subjectCrime rateen_US
dc.subjectElman Neural Networken_US
dc.subjectFeed Forward Neural Network and Probabilistic Neural Networken_US
dc.subjectPrincipal Component analysisen_US
dc.titleA comprehensive study of crime detection with PCA and different neural network approachen_US
dc.typeArticleen_US
dc.contributor.urlkadri@unimap.edu.myen_US
dc.contributor.urlapairia@yahoo.comen_US
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