Neurocomputing approach for firearm identification

Nor Azura Md Ghani, Choong Yeun Liong, Abdul Aziz Jemain

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)341-352
Number of pages12
JournalPertanika Journal of Science and Technology
Volume26
Issue number1
Publication statusPublished - 1 Jan 2018

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Firearms
pins
Backpropagation
learning
Learning
Testing
testing
calculation

Keywords

  • Backpropagation neural network
  • Combined images
  • Firearm classification
  • Geometric moments

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemical Engineering(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

Neurocomputing approach for firearm identification. / Ghani, Nor Azura Md; Liong, Choong Yeun; Jemain, Abdul Aziz.

In: Pertanika Journal of Science and Technology, Vol. 26, No. 1, 01.01.2018, p. 341-352.

Research output: Contribution to journalArticle

Ghani, Nor Azura Md ; Liong, Choong Yeun ; Jemain, Abdul Aziz. / Neurocomputing approach for firearm identification. In: Pertanika Journal of Science and Technology. 2018 ; Vol. 26, No. 1. pp. 341-352.
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