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:Fig3 vanGanns eJHI2015 9-1.jpg|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:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''
'''"[[Journal:The development of the Public Health Research Data Management System|The development of the Public Health Research Data Management System]]"'''


The design and development of the Public Health Research Data Management System highlights how it is possible to construct an [[information]] system, which allows greater access to well, preserved public health research data to enable it to be reused and shared. The Public Health Research Data Management System (PHRDMS) manages clinical, health service, community and survey research data within a secure web environment. The conceptual model under pinning the PHRDMS is based on three main entities: participant, community and health service. The PHRDMS was designed to provide data management to allow for data sharing and reuse. The system has been designed to enable rigorous research and ensure that: data that are unmanaged be managed, data that are disconnected be connected, data that are invisible be findable, data that are single use be reusable, within a structured collection. The PHRDMS is currently used by researchers to answer a broad range of policy relevant questions, including monitoring incidence of renal disease, cardiovascular disease, diabetes and mental health problems in different risk groups. ('''[[Journal:The development of the Public Health Research Data Management System|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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Latest 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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