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:Fig6 BuřitaJOfSysInteg2018 9-1.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:Information management in context of scientific disciplines|Information management in context of scientific disciplines]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


This paper aims to analyze publications with the theme of [[information management]] (IM), cited on Web of Science (WoS) or Scopus. The frequency of publishing about IM has approached linear growth, from a few articles in the period 1966–1970 to 100 at the WoS and 600 at Scopus in the period 2011–2015. From this selection of publications, this analysis looked at 21 of the most cited articles on WoS and 21 of the most cited articles on Scopus, published in 31 different journals, oriented to [[informatics]] and computer science; economics, business, and management; medicine and psychology; art and the humanities; and ergonomics. The diversity of interest in IM in various areas of science, technology, and practice was confirmed. The content of the selected articles was analyzed in its area of interest, in relation to IM, and whether the definition of IM was mentioned. One of the goals was to confirm the hypothesis that IM is included in many scientific disciplines, that the concept of IM is used loosely, and it is mostly mentioned as part of data or information processing. ('''[[Journal:Information management in context of scientific disciplines|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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