Difference between revisions of "Template:Article of the week"

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'''"[[Journal:ISO 15189 accreditation: Navigation between quality management and patient safety|ISO 15189 accreditation: Navigation between quality management and patient safety]]"'''
<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]]"'''


Accreditation is a valuable resource for [[Clinical laboratory|clinical laboratories]], and the development of an international standard for their accreditation represented a milestone on the path towards improved quality and safety in [[laboratory]] medicine. The recent revision of the international standard, [[ISO 15189]], has further strengthened its value not only for improving the [[Quality management system|quality system]] of a clinical laboratory but also for better answering the request for competence, focus on customers’ needs and ultimate value of laboratory services. Although in some countries more general standards such as [[ISO 9000|ISO 9001]] for quality systems or [[ISO 17025]] for testing laboratories are still used, there is increasing recognition of the value of ISO 15189 as the most appropriate and useful standard for the accreditation of medical laboratories. In fact, only this international standard recognizes the importance of all steps of the total testing process, namely extra-analytical phases, the need to focus on technical competence in addition to quality systems, and the focus on customers’ needs. ('''[[Journal:ISO 15189 accreditation: Navigation between quality management and patient safety|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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