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https://ptsldigital.ukm.my/jspui/handle/123456789/578205| Title: | Neurocomputing approach for firearm identification |
| Authors: | Choong-Yeun Liong (UKM) Abdul Aziz Jemain (UKM) |
| Keywords: | Firearm classification Combined images Geometric moments Backpropagation neural network |
| Issue Date: | Jan-2018 |
| Description: | This 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. |
| News Source: | Pertanika Journals |
| ISSN: | 0128-7680 |
| Volume: | 26 |
| Pages: | 341-352 |
| Publisher: | Universiti Putra Malaysia Press |
| Appears in Collections: | Journal Content Pages/ Kandungan Halaman Jurnal |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_113386+Source01+Source010.PDF | 532.2 kB | Adobe PDF |  View/Open |
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