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Classification Improvements in Automated Gunshot Residue ( GSR ) Scans
Author(s) -
Mandel Micha,
Israelsohn Azulay Osnat,
Zidon Yigal,
Tsach Tsadok,
Cohen Yaron
Publication year - 2018
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.13711
Subject(s) - binary classification , computer science , sample (material) , binary number , artificial intelligence , pattern recognition (psychology) , mathematics , chromatography , chemistry , support vector machine , arithmetic
Classification of particles as gunshot residues ( GSR s) is conducted using a semiautomatic approach in which the system first classifies particles based on an automatic elemental analysis, and then, examiners manually analyze particles having compositions which are characteristic of or consistent with GSR s. Analyzing all the particles in the second stage is time consuming with many particles classified by the initial automated system as being potentially GSR s excluded as such by the forensic examiner. In this paper, a new algorithm is developed to improve the initial classification step. The algorithm is based on a binary tree that was trained on almost 16,000 particles from 43 stubs used to sample hands of suspects. The classification algorithm was tested on 5,900 particles from 23 independent stubs and performed very well in terms of false positive and false negative rates. A routine use of the new algorithm can reduce significantly the analysis time of GSR s.

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