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 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Development and national scale implementation of an open-source electronic laboratory information system (OpenELIS) in Côte d’Ivoire: Sustainability lessons from the first 13 years|Development and national scale implementation of an open-source electronic laboratory information system (OpenELIS) in Côte d’Ivoire: Sustainability lessons from the first 13 years]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Côte d'Ivoire has a tiered [[public health laboratory]] system of nine [[Reference laboratory|reference laboratories]], 77 [[Laboratory|laboratories]] at regional and general [[hospital]]s, and 100 laboratories among 1,486 district health centers. Prior to 2009, nearly all of these laboratories used paper registers and reports to collect and report laboratory data to clinicians and national disease monitoring programs. Since 2009 the Ministry of Health (MOH) in Côte d'Ivoire has sought to implement a comprehensive set of activities aimed at strengthening the laboratory system. One of these activities is the sustainable development, expansion, and technical support of an open-source electronic [[laboratory information system]] (LIS) called [[OpenELIS]], with the long-term goal of Ivorian technical support and managerial sustainment of the system ... ('''[[Journal:Development and national scale implementation of an open-source electronic laboratory information system (OpenELIS) in Côte d’Ivoire: Sustainability lessons from the first 13 years|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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