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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Martin Memorial Medical Center 001.JPG|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
An '''[[ambulatory surgery center]]''' ('''ASC''') is a health care facility where surgical procedures not requiring [[Hospital|hospitalization]] are performed, with an expected duration of services less than 24 hours following admission. Such surgery is commonly less complicated than that requiring hospitalization. Avoiding hospitalization can result in cost savings to the party responsible for paying for the patient's health care. The ASC may also be known as an outpatient surgery center, same day surgery center, or surgicenter.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


An ASC specializes in providing surgical procedures, including certain pain management and diagnostic (e.g., colonoscopy) services in an outpatient setting. In simple terms, ASC-qualified procedures can be considered procedures that are more intensive than those done in the average doctor's office but not so intensive as to require a hospital stay. ('''[[Ambulatory surgery center|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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''Recently featured'': [[Data center]], [[Carrier cloud]], [[Forest informatics]]
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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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