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

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'''"[[Journal:Emerging cybersecurity threats in radiation oncology|Emerging cybersecurity threats in radiation oncology]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Modern image-guided radiation therapy is dependent on information technology and data storage applications that, like any other digital technology, are at risk from cyberattacks. Owing to a recent escalation in cyberattacks affecting radiation therapy treatments, the American Society for Radiation Oncology's ''Advances in Radiation Oncology'' is inaugurating a new special manuscript category devoted to [[cybersecurity]] issues. We conducted a review of emerging cybersecurity threats and a literature review of cyberattacks that affected [[Radiation oncologist|radiation oncology]] practices. In the last 10 years, numerous attacks have led to an interruption of radiation therapy for thousands of patients, and some of these catastrophic incidents have been described as being worse than [[coronavirus disease 2019]]'s impact on healthcare centers in New Zealand ... ('''[[Journal:Emerging cybersecurity threats in radiation oncology|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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