In Silico Created Fire Debris Data for Machine Learning
Analyst and machine learning opinions in fire debris analysis
Convolutional Neural Network Applications in Fire Debris Classification
Validation of ground truth fire debris classification by supervised machine learning
Classification of ground-truth fire debris samples using artificial neural networks
Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples
Model-effects on Likelihood Ratios for Fire Debris Analysis
Model Distribution Effects on Likelihood Ratios in Fire Debris Analysis
Major Chemical Compounds in the Ignitable Liquids Reference Collection and Substrate Databases
Biodegradation of Representative Ignitable Liquid Components on Soil
Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis
Progress Toward the Determination of Correct Classification Rates in Fire Debris Analysis II: Utilizing Soft Independent Modeling of Class Analogy (SIMCA)
Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions
Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: Forensic Fire Debris Analysis
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