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

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'''"[[Journal:Laboratory information system – Where are we today?|Laboratory information system – Where are we today?]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Wider implementation of [[laboratory information system]]s (LIS) in [[Clinical laboratory|clinical laboratories]] in Serbia was initiated 10 years ago. The first LIS in the Railway Health Care Institute was implemented nine years ago. Before the LIS was initiated, manual admission procedures limited daily output of patients. Moreover, manual entering of patient data and ordering tests on analyzers was problematic and time-consuming. After completing tests, [[laboratory]] personnel had to write results in a patient register (with potential errors) and provide invoices for health insurance organizations. The first LIS brought forward some advantages with regards to these obstacles, but it also showed various weaknesses. These can be summarized as rigidity of the system and inability to fulfill user expectation. After four years of use, we replaced this system with another LIS. Hence, the main aim of this paper is to evaluate the advantages of using LIS in the Railway Health Care Institute's laboratory and also to discuss further possibilities for its application. ('''[[Journal:Laboratory information system – Where are we today?|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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