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dc.contributor.authorAhmad Kadri, Junoh-
dc.contributor.authorMuhammad Naufal, Mansor-
dc.contributor.authorAlezar, Mat Ya'acob-
dc.contributor.authorFarah Adibah, Adnan-
dc.contributor.authorSyafawati, Ab. Saad-
dc.contributor.authorNornadia, Mohd Yazid-
dc.date.accessioned2014-04-21T04:14:45Z-
dc.date.available2014-04-21T04:14:45Z-
dc.date.issued2013-
dc.identifier.citationAdvanced Materials Research, vol. 816-817, 2013, pages 610-615en_US
dc.identifier.isbn978-303785867-7-
dc.identifier.issn1022-6680-
dc.identifier.urihttp://www.scientific.net/AMR.816-817.610-
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33874-
dc.descriptionLink to publisher's homepage at http://www.ttp.net/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 Discrete Cosine Transform (DCT), Support vector machine (SVM) and k Nearest Neighbor (k-NN) Classifier. This system provided a good justification as a monitoring supplementary tool for the Malaysian police arm forced.en_US
dc.language.isoenen_US
dc.publisherTrans Tech Publicationsen_US
dc.subjectCrime rateen_US
dc.subjectDiscrete cosine transformen_US
dc.subjectK-Nearest Neighboren_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.titleCrime detection with DCT and artificial intelligent approachen_US
dc.typeArticleen_US
dc.contributor.urlkadri@unimap.edu.myen_US
dc.contributor.urlapairia@yahoo.comen_US
dc.contributor.urlalezar@unimap.edu.myen_US
dc.contributor.urlfarahadiba@unimap.edu.myen_US
dc.contributor.urlsyafawatisaad@unimap.edu.myen_US
dc.contributor.urlnornadia_mohdyazid@yahoo.comen_US
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