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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Dialysis machines by irvin calicut.jpg|250px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
An '''[[end-stage renal disease facility]]''' ('''ESRD facility''', '''dialysis facility''', or '''dialysis center''') is a medical facility that operates to assist people with irreversible loss of kidney function (stage five), requiring a regular course of dialysis or a kidney transplant to survive. The facility may operate independently, as part of a [[hospital]]-based unit, or as a self-care unit that furnishes only self-dialysis services.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The U.S. [[Centers for Medicare and Medicaid Services]] (CMS) describes four types of ESRD facilities, including the renal transplantation center, for ESRD transplant patients; the renal dialysis center, a hospital unit for ESRD dialysis patients; a renal dialysis facility, a direct or stand-alone dialysis unit for ESRD patients; and a self-dialysis unit attached to one of the previous three, providing self-dialysis services. Patients undergoing dialysis at these facilities require two important documentation steps: the patient assessment and the patient plan of care. U.S. Federal regulation requires a comprehensive 13-point assessment, including current health status, laboratory profile, and nutritional status. ('''[[End-stage renal disease facility|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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