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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Talia JOfCloudComp2019 8.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:A view of programming scalable data analysis: From clouds to exascale|A view of programming scalable data analysis: From clouds to exascale]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and [[cloud computing]] systems, whereas in the near future exascale systems will be used to implement extreme-scale [[data analysis]]. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. ('''[[Journal:A view of programming scalable data analysis: From clouds to exascale|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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