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:Fig2 Matielo Publications2018 6-4.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:A bibliometric analysis of Cannabis publications: Six decades of research and a gap on studies with the plant|A bibliometric analysis of ''Cannabis'' publications: Six decades of research and a gap on studies with the plant]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


In this study we performed a bibliometric analysis focusing on the general patterns of scientific publications about ''[[wikipedia:Cannabis|Cannabis]]'', revealing their trends and limitations. Publications related to ''Cannabis'', released from 1960 to 2017, were retrieved from the Scopus database using six search terms. The search term “[[wikipedia:Genetics|Genetics]]” returned 53.4% of publications, while “forensic genetics” and “[[wikipedia:Traceability|traceability]]” represented 2.3% and 0.1% of the publications, respectively. However, 43.1% of the studies were not directly related to ''Cannabis'' and, in some cases, ''Cannabis'' was just used as an example in the text. A significant increase in publications was observed after 2001, with most of the publications coming from Europe, followed by North America. Although the term "''Cannabis''" was found in the title, abstract, or keywords of 1284 publications, we detected a historical gap in studies on the plant. ('''[[Journal:A bibliometric analysis of Cannabis publications: Six decades of research and a gap on studies with the plant|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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