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The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
24 Research products, page 1 of 3

  • COVID-19
  • Other research products
  • 2018-2022
  • Closed Access
  • Repository of University of Nova Gorica
  • COVID-19

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  • Closed Access English
    Authors: 
    Gupta, Rajan; Pandey, Gaurav; Pal, Saibal K.;
    Country: Slovenia

    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.

  • Other research product . Other ORP type . 2022
    Closed Access Slovenian
    Authors: 
    Melinc Mlekuž, Maja;
    Country: Slovenia

    Prispevek predstavlja izhodišča za razpravo o jezikovnih smernicah in učnih ciljih v vrtcih in šolah s slovenskim učnim jezikom v Tržaški in Goriški pokrajini ter v večstopenjski šoli s slovensko-italijanskim dvojezičnim poukom v Špetru. Izhaja iz rezultatov kvantitativne raziskave o poteku pouka na daljavo med epidemijo covid-19, izvedene med pedagoškim kadrom, dijaki in starši otrok, ki obiskujejo vrtce in šole s slovenskim učnim jezikom in dvojezičnim slovensko-italijanskim poukom v Italiji. Izsledke dopolnjuje analiza petnajstih polstrukturiranih globinskih intevjujev z učitelji o didaktično-metodičnih izzivih pri poučevanju učencev, katerih prvi ali primarni jezik ni slovenski, slovenščina pa tudi ni jezik okolja, v katerem živijo.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna; VIMAL, RAJ; S, Sankararaman;
    Country: Slovenia

    The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

  • Closed Access English
    Authors: 
    de Marco, Ario; Barile, Lucio;
    Country: Slovenia
  • Other research product . Other ORP type . 2021
    Closed Access Slovenian
    Authors: 
    Zajc, Ivana;
    Country: Slovenia

    Fakultete bodo predavanja izvajale deloma v živo, deloma prek spleta, v skupnih prostorih študentskih domov pa bodo obvezne maske. Študij na daljavo je po mnenju študentov manj kakovosten in napornejši, univerzam pa prinaša visoke stroške.

  • Other research product . Other ORP type . 2020
    Closed Access Slovenian
    Authors: 
    Škorjanc, Tina;
    Country: Slovenia
  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Swapna, Mohanachandran Nair Sindhu; Sreejyothi, S.; Raj, Vimal; Sankararaman, Sankaranarayana Iyer;
    Country: Slovenia

    A first report of unveiling the fractality and fractal nature of severe acute respiratory syndrome coronavirus (SARS CoV-2) responsible for the pandemic disease widely known as coronavirus disease 2019 (COVID 19) is presented. The fractal analysis of the electron microscopic and atomic force microscopic images of 40 coronaviruses (CoV), by the normal and differential box-counting method, reveals its fractal structure. The generalised dimension indicates the multifractal nature of the CoV. The higher value of fractal dimension and lower value of Hurst exponent (H) suggest higher complexity and greater roughness. The statistical analysis of generalised dimension and H is understood through the notched box plot. The study on CoV clusters also confirms its fractal nature. The scale-invariant value of the box-counting fractal dimension of CoV yields a value of 1.820. The study opens the possibility of exploring the potential of fractal analysis in the medical diagnosis of SARS CoV-2.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.

  • Other research product . Other ORP type . 2020
    Closed Access Slovenian
    Authors: 
    Papler, Drago;
    Country: Slovenia
  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    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.

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
24 Research products, page 1 of 3
  • Closed Access English
    Authors: 
    Gupta, Rajan; Pandey, Gaurav; Pal, Saibal K.;
    Country: Slovenia

    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.

  • Other research product . Other ORP type . 2022
    Closed Access Slovenian
    Authors: 
    Melinc Mlekuž, Maja;
    Country: Slovenia

    Prispevek predstavlja izhodišča za razpravo o jezikovnih smernicah in učnih ciljih v vrtcih in šolah s slovenskim učnim jezikom v Tržaški in Goriški pokrajini ter v večstopenjski šoli s slovensko-italijanskim dvojezičnim poukom v Špetru. Izhaja iz rezultatov kvantitativne raziskave o poteku pouka na daljavo med epidemijo covid-19, izvedene med pedagoškim kadrom, dijaki in starši otrok, ki obiskujejo vrtce in šole s slovenskim učnim jezikom in dvojezičnim slovensko-italijanskim poukom v Italiji. Izsledke dopolnjuje analiza petnajstih polstrukturiranih globinskih intevjujev z učitelji o didaktično-metodičnih izzivih pri poučevanju učencev, katerih prvi ali primarni jezik ni slovenski, slovenščina pa tudi ni jezik okolja, v katerem živijo.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna; VIMAL, RAJ; S, Sankararaman;
    Country: Slovenia

    The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

  • Closed Access English
    Authors: 
    de Marco, Ario; Barile, Lucio;
    Country: Slovenia
  • Other research product . Other ORP type . 2021
    Closed Access Slovenian
    Authors: 
    Zajc, Ivana;
    Country: Slovenia

    Fakultete bodo predavanja izvajale deloma v živo, deloma prek spleta, v skupnih prostorih študentskih domov pa bodo obvezne maske. Študij na daljavo je po mnenju študentov manj kakovosten in napornejši, univerzam pa prinaša visoke stroške.

  • Other research product . Other ORP type . 2020
    Closed Access Slovenian
    Authors: 
    Škorjanc, Tina;
    Country: Slovenia
  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Swapna, Mohanachandran Nair Sindhu; Sreejyothi, S.; Raj, Vimal; Sankararaman, Sankaranarayana Iyer;
    Country: Slovenia

    A first report of unveiling the fractality and fractal nature of severe acute respiratory syndrome coronavirus (SARS CoV-2) responsible for the pandemic disease widely known as coronavirus disease 2019 (COVID 19) is presented. The fractal analysis of the electron microscopic and atomic force microscopic images of 40 coronaviruses (CoV), by the normal and differential box-counting method, reveals its fractal structure. The generalised dimension indicates the multifractal nature of the CoV. The higher value of fractal dimension and lower value of Hurst exponent (H) suggest higher complexity and greater roughness. The statistical analysis of generalised dimension and H is understood through the notched box plot. The study on CoV clusters also confirms its fractal nature. The scale-invariant value of the box-counting fractal dimension of CoV yields a value of 1.820. The study opens the possibility of exploring the potential of fractal analysis in the medical diagnosis of SARS CoV-2.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.

  • Other research product . Other ORP type . 2020
    Closed Access Slovenian
    Authors: 
    Papler, Drago;
    Country: Slovenia
  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    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.