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

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'''[[Health information technology]] (HIT)''' is the application of "hardware and software in an effort to manage and manipulate health data and information." HIT acts as a framework for the comprehensive management of health information originating from consumers, providers, governments, and insurers in order to improve the overall state of health care. Among those improvements, the Congressional Budget Office (CBO) of the United States believes HIT can reduce or eliminate errors from medical transcription, reduce the number of diagnostic tests that get duplicated, and improve patient outcomes and service efficiency among other things.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The "technology" of "health information technology" represents computers, software, and communications infrastructure that can be networked to create systems for manipulating health information. As such, the science of [[Informatics (academic field)|informatics]] and its focus on information processing and systems engineering is also integral to the development, application, and evaluation of HIT. In particular the subdivision of [[health informatics]], which focuses on the resources, devices, and methods required for optimizing the acquisition, storage, retrieval, and use of information in health and biomedicine, is most relevant. However, other subdivisions of informatics such as [[medical informatics]], [[public health informatics]], [[pharmacoinformatics]], and [[translational research informatics]] are able to inform health informatics from different disciplinary perspectives. ('''[[Health information technology|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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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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