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Several solution for assessing Particulate Matter concentrations

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dc.contributor.author POHOATA, Alin
dc.contributor.author DUNEA, Daniel
dc.contributor.author LUNGU, Emil
dc.contributor.author SALISTEANU, Corneliu
dc.contributor.author NEDELCU, Otilia
dc.date.accessioned 2020-11-03T16:04:27Z
dc.date.available 2020-11-03T16:04:27Z
dc.date.issued 2018
dc.identifier.citation POHOATA, Alin, DUNEA, Daniel, LUNGU, Emil et al. Several solution for assessing Particulate Matter concentrations. In: CAIM 2018: The 26th Conference on Applied and Industrial Mathematics: Book of Abstracts, Technical University of Moldova, September 20-23, 2018. Chişinău: Bons Offices, 2018, p. 55. en_US
dc.identifier.uri http://repository.utm.md/handle/5014/11069
dc.description Only Abstract en_US
dc.description.abstract Forecasting and analysis of the Particulate Matter (PM) concentrations is a subject of high interest for the public health. PM contains the inhalable particles that penetrate the thoracic region of the respiratory system determining numerous negative health effects particularly for younger children (0-10 years). We present in this article several methods of assessing the trends of PM concentrations, based on feedforward neural networks (FANN) combined with a wavelet decomposition of the time series values using smoothing filters to adjust the PM model outputs. en_US
dc.language.iso en en_US
dc.publisher Bons Offices en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject Particulate Matter en_US
dc.subject methods en_US
dc.subject feedforward neural networks en_US
dc.subject PM concentrations en_US
dc.title Several solution for assessing Particulate Matter concentrations en_US
dc.type Article en_US


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