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'''[[Environmental informatics]]''' ('''EI''') is a developing field of science that applies [[information]] processing, management, and sharing strategies to the interdisciplinary field of environmental science. Applications include the integration of information and knowledge, the application of computational intelligence to environmental data, and the identification of the environmental impacts of information technology. EI helps scientists define information processing requirements, analyze real-world problems, and solve those problems using informatics methodologies and tools.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


As EI has continued to evolve, several other definitions have been offered over the years. Some consider it "an emerging field centering around the development of standards and protocols, both technical and institutional, for sharing and integrating environmental data and information." Others consider it the application of "[r]esearch and system development focusing on the environmental sciences relating to the creation, collection, storage, processing, modelling, interpretation, display and dissemination of data and information."
[[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 />
 
''Recently featured'':
Environmental informatics emerged roughly around the late 1980s in Central Europe. For example, in 1986 Germany's ''Gesellschaft für Informatik'' (Society for Computer Science) created the technical committee ''Informatik im Umweltschutz'' (Computer Science in Environmental Protection) dedicated to "the whole spectrum of subjects related to informatics in environmental protection." ('''[[Environmental informatics|Full article...]]''')<br />
{{flowlist |
 
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
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* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
''Recently featured'': [[Application programming interface]], [[Immunoinformatics]], [[Life sciences industry]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
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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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