Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Michael E. Sigman"'
Publikováno v:
Chemosensors, Vol 10, Iss 10, p 377 (2022)
Convolutional neural networks (CNNs) are inspired by the visual cortex of the brain. In this work, CNNs, are applied to classify ground truth samples as positive or negative for ignitable liquid residue (ILR+ and ILR−, respectively). Known ground t
Externí odkaz:
https://doaj.org/article/70a851db795d4e0faa383fbf97702a8e
Autor:
Michael E. Sigman, Mary R. Williams
Publikováno v:
Separations, Vol 6, Iss 1, p 13 (2019)
The practice of forensic fire debris analysis and data interpretation in operational (i [...]
Externí odkaz:
https://doaj.org/article/049ba641b411459d91781726fe1f6fc5
Publikováno v:
Separations, Vol 5, Iss 4, p 52 (2018)
Classification of un-weathered ignitable liquids is a problem that is currently addressed by visual pattern recognition under the guidelines of Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatogra
Externí odkaz:
https://doaj.org/article/667fc550af5a428b9e2eae9c47c699ec
Publikováno v:
Separations, Vol 5, Iss 3, p 44 (2018)
Computational models for determining the strength of fire debris evidence based on likelihood ratios (LR) were developed and validated against data sets derived from different distributions of ASTM E1618-14 designated ignitable liquid class and subst
Externí odkaz:
https://doaj.org/article/252e7db6f8224f28b9fe2e6f1414ff46
Autor:
Michael E. Sigman, Mary R. Williams
Publikováno v:
Frontiers in Analytical Science. 3
Forensic science standards often require the analyst to report in categorical terms. Categorical reporting without reference to the strength of the evidence, or the strength threshold that must be met to sustain or justify the decision, obscures the
Publikováno v:
Forensic Chemistry. 29:100426
Autor:
Michael E. Sigman, Mary R. Williams
Publikováno v:
WIREs Forensic Science. 2
Autor:
Richard Coulson, Michael E. Sigman, Liqiang Ni, Anuradha Akmeemana, Alyssa Allen, Mary R. Williams
Publikováno v:
Forensic Chemistry. 7:38-46
A simple method is introduced for assessing the evidentiary value of fire debris samples. The method relies on models built by random draws from a database of ignitable liquid and substrate pyrolysis samples. A stratified random draw from database re
Publikováno v:
Forensic Chemistry. 6:19-27
A mixture containing 14 hydrocarbons representing six distinct compound types (e.g. normal alkanes, branched alkanes, cyclic alkanes, aromatics, polynuclear aromatics, and oxygenates) was deposited onto ninety grams of potting soil and allowed to rem
Publikováno v:
Forensic Chemistry. 23:100313
Neural networks are a class of biologically inspired machine learning models that are used for classification and regression problems. This work assesses the classification performance of neural networks on ground-truth fire debris samples using ions