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

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'''"[[Journal:Management and disclosure of quality issues in forensic science: A survey of current practice in Australia and New Zealand|Management and disclosure of quality issues in forensic science: A survey of current practice in Australia and New Zealand]]"'''  
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The investigation of [[Quality (business)|quality]] issues detected within the [[Forensic science|forensic]] process is a critical feature in robust [[quality management system]]s (QMSs) to provide assurance of the validity of reported [[laboratory]] results and inform strategies for [[Continual improvement process|continuous improvement]] and innovation. A survey was conducted to gain insight into the current state of practice in the management and handling of quality issues amongst the government service provider agencies of Australia and New Zealand. The results demonstrate the value of standardized quality system structures for the recording and management of quality issues, but also areas where inconsistent reporting increases the risk of overlooking important data to inform continuous improvement ... ('''[[Journal:Management and disclosure of quality issues in forensic science: A survey of current practice in Australia and New Zealand|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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