Recognition of gasoline in fire debris using machine learning: Part I, application of random forest, gradient boosting, support vector machine, and naïve bayes

Bogdal, C.; Schellenberg, R.; Höpli, O.; Bovens, Michael; Lory, M.

doi:10.1016/j.forsciint.2021.111146

Cited by 22 publications

(8 citation statements)

References 12 publications

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“…Bogdal et al presented a two-part study on recognizing gasoline in fire debris using machine learning [ 53 , 54 ]. Part 1 focused on the development of a machine learning tool using various algorithms.…”

Section: Fire Debris Analysismentioning

confidence: 99%

Interpol review of fire debris analysis and fire investigation 2019–2022

Evans¹

2023

Forensic Science International: Synergy

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Section: Fire Debris Analysismentioning

confidence: 99%

Interpol review of fire debris analysis and fire investigation 2019–2022

Evans¹

2023

Forensic Science International: Synergy

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“…Aliaño-González et al [65] applied PCA and LDA to GC data of firefighters' coats to discriminate those exposed to fire from the non-exposed ones, in order to identify toxic substances to which firefighters can be exposed. Two interesting works of Bogdal et al [66,67] focused on tracing gasoline residues in fire debris, with the aim of identifying arsons. These works tested the use of several machine learning methods to discriminate debris with the presence or absence of ignitable liquid trace.…”

Section: Gas Chromatography (Gc) and Chemometricsmentioning

confidence: 99%

“…Similar studies, but ones which only took into account one honey class (Quercus ilex honeydew and pine honeys) and focused on geographical origin, with samples coming from several regions of Greece and Turkey, respectively, were carried out by Karabagias et al [36] and Duru et al [37] by PCA and stepwise Machine learning is a "new" frontier of chemometrics in which models are computed in an iterative way, with the computation that "learns" from data: once a model has been computed, the calculation starts again using the previous results as starting points, instead of the original data, and this procedure is carried out iteratively until a satisfactory result, or a convergence, is reached. Bogdal et al tested random forest, gradient boosting, support vector machine, naïve bayes, logistic regression [66] and convolutional neural networks on GC-MS spectra converted to images [67]. Satisfactory results were obtained for most of these methods, except for logistic regression and naïve bayes, for which, probably, there were not enough samples.…”

Section: Gas Chromatography (Gc) and Chemometricsmentioning

confidence: 99%

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

et al. 2023

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Instrument automation, technological advancements and improved computational power made separation science an extremely data-rich approach, requiring the use of statistical and data analysis tools that are able to optimize processes and combine multiple outputs. The use of chemometrics is growing, greatly improving the ability to extract meaningful information. Separation–multidetection generates multidimensional data, whose elaboration should not be left to the discretion of the operator. However, some applications or techniques still suffer from the lack of method optimization through DoE and downstream multivariate analysis, limiting their potential. This review aims at summarizing how chemometrics can assist analytical chemists in terms of data elaboration and method design, focusing on what can be achieved by applying chemometric approaches to separation science. Recent applications of chemometrics in separation analyses, in particular in gas, liquid and size-exclusion chromatography, together with field flow fractionation, will be detailed to visualize the state of the art of separation chemometrics, encompassing volatile, soluble and solid (colloidal) analytes. The samples considered will range from food chemistry and environmental chemistry to bio/pharmaceutical science.

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“…The structure of this model focuses on a set of independently trained decision models, combining their predictions to generate the overall prediction, thus reducing the probability of errors in decision-making. Some examples of this type of algorithms are gradient boosting [80] and AdaBoosting [81].…”

Section: Decision Matrixmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Recognition of gasoline in fire debris using machine learning: Part I, application of random forest, gradient boosting, support vector machine, and naïve bayes

Cited by 22 publications

References 12 publications

Interpol review of fire debris analysis and fire investigation 2019–2022

Interpol review of fire debris analysis and fire investigation 2019–2022

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

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