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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:CarrierCloud.png|180px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''[[Carrier cloud]]''' is class of [[cloud computing]] service that merges the high-performance capabilities and reliability of a communications service provider's network with the lower costs and flexibility provided by traditional public cloud services. The carrier cloud attempts to remove the data bottleneck and security issues that often occur in and to the virtualized data center due to lack of control of data flow over the public Internet.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Carrier cloud service is similar to public cloud service in that infrastructures are converged into a single, optimized computing package, and services are shared across a group or organization. Carrier cloud service, however, utilizes the existing and upgraded network structures of the communication service provider (CSP) to provide end-to-end services over their own network. Since the CSP more readily controls the data flow through its content delivery networks and/or dedicated virtual private networks, it can better manage issues with bandwidth, latency, and jitter. Additional "last mile" carrier-grade services already provided by CSPs in cities also "offset the latencies associated with cross-country or inter-continental backhaul." ('''[[Carrier cloud|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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''Recently featured'':
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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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