Repository of University of Nova Gorica

Search the repository
A+ | A- | Help | SLO | ENG

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


11 - 13 / 13
First pagePrevious page12Next pageLast page
11.
Graph based feature extraction and classification of wet and dry cough signals: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2021, original scientific article

Abstract: This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.
Keywords: wet cough, dry cough, complex network, quadratic SVM, neural net
Published in RUNG: 30.06.2022; Views: 1112; Downloads: 0
This document has many files! More...

12.
Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Rajan Gupta, Gaurav Pandey, Saibal K. Pal, 2021, original scientific article

Abstract: Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005 and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.
Keywords: epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Published in RUNG: 15.07.2021; Views: 2303; Downloads: 33
URL Link to full text
This document has many files! More...

13.
Genetic programming and artificial neural network modeling of PM10 emission close to a steel plant
Uroš Župerl, Miha Kovačič, Sandra Senčič, 2013, original scientific article

Keywords: steel plant, PM10 concentrations, modeling, genetic programming, artificial neural network
Published in RUNG: 25.04.2014; Views: 5515; Downloads: 166
URL Link to full text
Search done in 0.02 sec.
Back to top