scholarly journals Preprocessing of data from air-gas monitoring sensors during underground mining of gas-bearing coal seams for neural network analysis

2021 ◽  
Vol 330 ◽  
pp. 03005
Author(s):  
Mark Dvoryanchikov ◽  
Larisa Pavlova ◽  
Inna Weiss
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Analysis ◽  
Underground Mining ◽  
Primary Study ◽  
Neural Network Analysis ◽  
Coal Seams ◽  
Gas Monitoring ◽  
Machine Learning Methods ◽  
Gas Bearing

The paper describes the stages of primary study and preprocessing of data obtained from air-gas monitoring sensors for their further analysis using machine learning methods.

scholarly journals Neural network analysis of mortality risk predictors in patients after acute coronary syndrome

2020 ◽  
Vol 25 (3) ◽  
pp. 3645 ◽  
Author(s):  
D. A. Shvets ◽  
A. Yu. Karasev ◽  
M. V. Smolyakov ◽  
S. V. Povetkin ◽  
V. I. Vishnevsky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Analysis ◽  
Mortality Risk ◽  
Clinical Laboratory ◽  
Neural Network Analysis ◽  
Instrumental Data ◽  
Coronary Syndrome ◽  
History Of ◽  
Complicated Course

Aim. To study the possibilities of neural network analysis of clinical and instrumental data to predict the mortality risk in patients after acute coronary syndrome (ACS).Material and methods. The study involved 400 patients after ACS which who observed for 62 months. The criterion for the complicated course of coronary artery disease (CAD) is the cardiovascular death. Group 1 consisted of 310 patients with uncomplicated course of CAD; group 2 — 90 patients with complicated course of CAD. To predict mortality risk, the machine learning method and neural network analysis was used. Machine learning was carried out with the inclusion of clinical, laboratory and instrumental (electrocardiography, echocardiography) parameters (49 in total). To solve the classification problems, two types of neural network architectures were used: Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN). The ratio in the examples for learning and validation was 340/60. The method of learning with a teacher was used on the available data in which the outcomes were known, and the neural network parameters were adjusted so as to minimize the error.Results. The following factors made the highest contribution to the mortality risk after ACS: age; history of MI and acute cerebrovascular accident; atrial fibrillation, class 2-3 heart failure; no history of percutaneous coronary intervention; stage 3 chronic kidney disease; reduced left ventricle ejection fraction. Most of the deaths occurred in the 2nd and 4th years of follow-up, which may be due to the low effectiveness of secondary prevention of CAD. CNN architecture had higher accuracy (sensitivity — 68%; specificity — 84%; area under curve=0,74). An advantage of CNN is its ability to analyze patterns over time using recurrent neural networks.Conclusion. Neural network analysis of clinical, laboratory and instrumental data allows configuring network parameters for mortality risk prediction. CNN predicts 5-year mortality risk after ACS with a sensitivity of 68% and a specificity of 84%.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

NEURAL NETWORK ANALYSIS OF INTERACTION OF POSTURAL CONTROL MACHANISMS UNDER ARTUFICIAL FEEDBACK

2016 ◽  
Vol 34 (2) ◽  
pp. 025-036
Author(s):  
Oleg G. Gorshkov ◽  
◽  
Irina B. Starchenko ◽  
Andrey S. Sliva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Postural Control ◽  
Neural Network Analysis

scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

scholarly journals Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

2021 ◽  
Author(s):  
Daniil A. Boiko ◽  
Evgeniy O. Pentsak ◽  
Vera A. Cherepanova ◽  
Evgeniy G. Gordeev ◽  
Valentine P. Ananikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Carbon Material ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Material Surface ◽  
Neural Network Analysis ◽  
Pd Nanoparticle ◽  
Sem Imaging

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.

scholarly journals Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110326
Author(s):  
Koffi Dumor ◽  
Li Yao ◽  
Jean-Paul Ainam ◽  
Edem Koffi Amouzou ◽  
Williams Ayivi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Bilateral Trade ◽  
Belt And Road Initiative ◽  
Neural Network Analysis ◽  
Analysis Techniques ◽  
Belt And Road ◽  
The Impact ◽  
Trade Partners

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.

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