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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Prahladh EgyptJofForSci22 12.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Introductory evidence on data management and practice systems of forensic autopsies in sudden and unnatural deaths: A scoping review|Introductory evidence on data management and practice systems of forensic autopsies in sudden and unnatural deaths: A scoping review]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The investigation into sudden unexpected and unnatural deaths supports criminal justice, aids in litigation, and provides important information for [[public health]], including surveillance, [[epidemiology]], and prevention programs. The use of mortality data to convey trends can inform policy development and resource allocations. Hence, data practices and [[Information management|data management systems]] in [[Forensic science|forensic medicine]] are critical. This study scoped literature and described the body of knowledge on data management and practice systems in forensic medicine. Five steps of the methodological framework of Arksey and O’Malley guided this scoping review. A combination of keywords, Boolean terms, and medical subject headings was used to search [[PubMed]], EBSCOhost (CINAHL with full text and Health Sources), Cochrane Library, Scopus, Web of Science, Science Direct, WorldCat, and Google Scholar for peer review papers in English from June 18–24 of 2020, with an updated search also occurring in November 2021. This study included ... ('''[[Journal:Introductory evidence on data management and practice systems of forensic autopsies in sudden and unnatural deaths: A scoping review|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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