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

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig14 Baker BiodiversityDataJournal2014 2.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:Open source data logger for low-cost environmental monitoring|Open source data logger for low-cost environmental monitoring]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The increasing transformation of biodiversity into a data-intensive science has seen numerous independent systems linked and aggregated into the current landscape of [[biodiversity informatics]]. This paper outlines how we can move forward with this program, incorporating real-time environmental monitoring into our methodology using low-power and low-cost computing platforms.
[[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'':
Low power and cheap computational projects such as Arduino and Raspberry Pi have brought the use of small computers and micro-controllers to the masses, and their use in fields related to biodiversity science is increasing (e.g. Hirafuji shows the use of Arduino in agriculture. There is a large amount of potential in using automated tools for monitoring environments and identifying species based on these emerging hardware platforms, but to be truly useful we must integrate the data they generate with our existing systems. This paper describes the construction of an open-source environmental data logger based on the Arduino platform and its integration with the web content management system [[Drupal]] which is used as the basis for Scratchpads among other biodiversity tools. ('''[[Journal:Open source data logger for low-cost environmental monitoring|Full article...]]''')<br />
{{flowlist |
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* [[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]]
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* [[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]]
: ▪ [[Journal:From the desktop to the grid: Scalable bioinformatics via workflow conversion|From the desktop to the grid: Scalable bioinformatics via workflow conversion]]
}}
: ▪ [[Journal:Terminology spectrum analysis of natural-language chemical documents: Term-like phrases retrieval routine|Terminology spectrum analysis of natural-language chemical documents: Term-like phrases retrieval routine]]

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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