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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Doctor's Office in New Orleans.jpg|120px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
A '''[[rural health clinic]]''' ('''RHC''') is a special facility designation of the U.S. [[Centers for Medicare and Medicaid Services]] (CMS), defined as a clinic in a non-urbanized area designated by the Health Resources and Services Administration as being in a health professional shortage or medically underserved area.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


In September 1999, nearly 3,500 RHCs were operating across 45 states. By January 2013, that number rose to nearly 3,800. RHCs were established by the Rural Health Clinic Services Act of 1977, otherwise known as Public Law 95-210. The program was established to address an inadequate supply of physicians serving Medicare beneficiaries and Medicaid recipients in rural areas and to increase the utilization of non-physician practitioners. To qualify as an RHC the facility must be located in a non-urban area, as described by the United States Census Bureau, and must be defined as being in a medically underserved area by one of several possibilities. ('''[[Rural health clinic|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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