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Minimum Cost Flow Problem on Dynamic Multi Generative Networks
Ahmad Hosseini, Hassan Salehi Fathabadi, 2010, izvirni znanstveni članek

Ključne besede: Network/graphs, LP problems, Decomposition methods
Objavljeno v RUNG: 14.02.2023; Ogledov: 904; Prenosov: 0
Gradivo ima več datotek! Več...

24.
A hybrid greedy randomized heuristic for designing uncertain transport network layout
Ahmad Hosseini, Eddie Wadbro, 2022, izvirni znanstveni članek

Ključne besede: Operations research, Heuristics, Uncertain Programming, Network Design, Transportation
Objavljeno v RUNG: 14.02.2023; Ogledov: 943; Prenosov: 0
Gradivo ima več datotek! Več...

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Markov chain : a novel tool for electronic ripple analysis
Vijayan Vijesh, K. Satheesh Kumar, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman, 2022, izvirni znanstveni članek

Ključne besede: complex network, Markov chain, rectifier, time series, ripple
Objavljeno v RUNG: 29.11.2022; Ogledov: 1157; Prenosov: 0
Gradivo ima več datotek! Več...

27.
On the fate of stars after a tidal disruption event
Taj Jankovič, druga izvedena dela

Ključne besede: First meeting of the CEEPUS network, predavanje
Objavljeno v RUNG: 24.11.2022; Ogledov: 1113; Prenosov: 0
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28.
Unwrapping Aortic valve dysfunction through complex network analysis: A biophysics approach
Mohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek

Ključne besede: Aortic valve dysfunction, complex network analysis
Objavljeno v RUNG: 14.09.2022; Ogledov: 1161; Prenosov: 0
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Graph based feature extraction and classification of wet and dry cough signals: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek

Opis: 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.
Ključne besede: wet cough, dry cough, complex network, quadratic SVM, neural net
Objavljeno v RUNG: 30.06.2022; Ogledov: 1154; Prenosov: 0
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