Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Pineda-Pampliega EFSAJournal2023 20-S2.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:Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood|Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


[[Risk assessment|Risk and risk–benefit assessments]] of food are complex exercises, in which access to and use of several disconnected individual stand-alone [[database]]s is required to obtain hazard and exposure information. Data obtained from such databases ideally should be in line with the [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR principles]], i.e. the data must be findable, accessible, interoperable, and reusable. However, often cases are encountered when one or more of these principles are not followed. In this project, we set out to assess if existing commonly used databases in risk assessment are in line with the FAIR principles ... ('''[[Journal:Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood|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 />
''Recently featured'':
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{{flowlist |
{{flowlist |
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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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