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'''[[Public health informatics]]''' has been defined as "the systematic application of [[information]] and computer science and technology to public health practice, research, and learning." Like other types of informatics, public health informatics is a multidisciplinary field, involving the studies of [[Informatics (academic field)|informatics]], computer science, psychology, law, statistics, epidemiology, and microbiology.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


In 2000, researcher William A. Yasnoff and his colleagues identified four key aspects that differentiate public health informatics from [[Health informatics|medical informatics]] and other informatics specialty areas. Public health informatics focuses on "applications of information science and technology that promote the health of populations as opposed to the health of specific individuals" and that "prevent disease and injury by altering the conditions or the environment that put populations of individuals at risk." It also "explore[s] the potential for prevention at all vulnerable points in the causal chains leading to disease, injury, or disability" and "reflect[s] the governmental context in which public health is practiced." ('''[[Public health informatics|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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{{flowlist |
''Recently featured'': [[Health information technology]], [[Clinical decision support system]], [[Medical practice management system]]
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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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