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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Duncan FrontBioengBiotech2019 7.jpg|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:Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system|Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Our national data and infrastructure security issues affecting the “bioeconomy” are evolving rapidly. Simultaneously, the conversation about cybersecurity of the U.S. [[Agriculture industry|food and agricultural system]] (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse, and they require advanced technologies and efficiencies that rely on computer technologies, big data, [[Cloud computing|cloud-based]] data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bioeconomy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safety management Hazard Analysis Critical Control Point (HACCP) system concept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). ('''[[Journal:Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system|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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