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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578669
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dc.contributor.authorNor Azura Md Ghani (UITM)
dc.contributor.authorChoong-Yeun Liong (UITM)
dc.contributor.authorAbdul Aziz Jemain (UKM)
dc.date.accessioned2023-11-06T03:05:29Z-
dc.date.available2023-11-06T03:05:29Z-
dc.date.issued2018-01
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116115
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578669-
dc.descriptionThis paper is an attempt to perceive and order guns using a two-layer neural system model taking into account a feedforward backpropagation calculation. Numerical properties from the joined pictures were utilised for enhanced gun characterisation execution. Inputs of the system model were 747 pictures blackmailed from the discharging pin impression of five differing guns model, Parabellum Vector SPI 9mm. Components created from the dataset were further grouped into preparation set (523 components), testing set (112 components) and acceptance set (112 components). Under managed learning, exact results exhibited that a two-layer BPNN of 11-11-5 arrangement, with tansig/purelin exchange capacities and a “trainlm” preparing calculation, had productively delivered 87% right aftereffect of grouping. The order result serves to be progressed and contrasted with the previous works. Finally, the joined picture districts can offer some accommodating data on the grouping of gun.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-26-1-1
dc.rightsUKM
dc.subjectFirearm classification
dc.subjectCombined images
dc.subjectGeometric moments
dc.subjectBackpropagation neural network
dc.titleNeurocomputing approach for firearm identification
dc.typeJournal Article
dc.format.volume26
dc.format.pages341-352
dc.format.issue1
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