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

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'''[[Software as a service]]''' ('''SaaS''') — sometimes referred to as "on-demand software" — is a software delivery model in which software and its associated data are hosted centrally (on the [[Cloud computing|cloud]], for example) and are typically accessed by users using a thin client, normally using a web browser over the Internet. The customer subscribes to this "service" rather than requiring a software license, and the software doesn't require an implementation on customer premises.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


A SaaS solution is typically a "multi-tenant solution," meaning more than one entity is sharing the server and database resource(s) hosted by the vendor, though in the process potentially limiting customer customization. With this model, a single version of the application with a single configuration (hardware, network, operating system, etc.) is used for all customers. To support scalability, the application is installed on multiple machines. In some cases, a second version of the application may be set up to offer a select group of customers a separate instance of the software environment, better enabling customers to customize their configuration. (This could be accomplished with platform as a service (PaaS), for example. This is contrasted with traditional software, where multiple physical copies of the software — each potentially of a different version, with a potentially different configuration, and often customized — are installed across various customer sites. ('''[[Software as a service |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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