These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

132 related articles for article (PubMed ID: 26381787)

  • 1. Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.
    Abderrahim H; Chellali MR; Hamou A
    Environ Sci Pollut Res Int; 2016 Jan; 23(2):1634-41. PubMed ID: 26381787
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.
    Chellali MR; Abderrahim H; Hamou A; Nebatti A; Janovec J
    Environ Sci Pollut Res Int; 2016 Jul; 23(14):14008-17. PubMed ID: 27040548
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluating the predictability of PM
    Hur SK; Oh HR; Ho CH; Kim J; Song CK; Chang LS; Lee JB
    Environ Pollut; 2016 Nov; 218():1324-1333. PubMed ID: 27613320
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: a case study in Taiyuan City, China.
    Zhang H; Liu Y; Shi R; Yao Q
    J Air Waste Manag Assoc; 2013 Jul; 63(7):755-63. PubMed ID: 23926845
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of ambient PM10 and toxic metals using artificial neural networks.
    Chelani AB; Gajghate DG; Hasan MZ
    J Air Waste Manag Assoc; 2002 Jul; 52(7):805-10. PubMed ID: 12139345
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.
    Voukantsis D; Karatzas K; Kukkonen J; Räsänen T; Karppinen A; Kolehmainen M
    Sci Total Environ; 2011 Mar; 409(7):1266-76. PubMed ID: 21276603
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong.
    Lu WZ; Wang WJ; Wang XK; Xu ZB; Leung AY
    Environ Monit Assess; 2003 Sep; 87(3):235-54. PubMed ID: 12952354
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting.
    McKendry IG
    J Air Waste Manag Assoc; 2002 Sep; 52(9):1096-101. PubMed ID: 12269670
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Application of artificial neural networks on the prediction of surface ozone concentrations].
    Shen LL; Wang YX; Duan L
    Huan Jing Ke Xue; 2011 Aug; 32(8):2231-5. PubMed ID: 22619942
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Improving artificial neural network model predictions of daily average PM10 concentrations by applying principle component analysis and implementing seasonal models.
    Taşpınar F
    J Air Waste Manag Assoc; 2015 Jul; 65(7):800-9. PubMed ID: 26079553
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.
    Maca P; Pech P
    Comput Intell Neurosci; 2016; 2016():3868519. PubMed ID: 26880875
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki.
    Vlachogianni A; Kassomenos P; Karppinen A; Karakitsios S; Kukkonen J
    Sci Total Environ; 2011 Mar; 409(8):1559-71. PubMed ID: 21277004
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Analysis of traffic and meteorology on airborne particulate matter in Münster, northwest Germany.
    Gietl JK; Klemm O
    J Air Waste Manag Assoc; 2009 Jul; 59(7):809-18. PubMed ID: 19645265
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Forecasting PM10 in metropolitan areas: Efficacy of neural networks.
    Fernando HJ; Mammarella MC; Grandoni G; Fedele P; Di Marco R; Dimitrova R; Hyde P
    Environ Pollut; 2012 Apr; 163():62-7. PubMed ID: 22325432
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Input strategy analysis for an air quality data modelling procedure at a local scale based on neural network.
    Ragosta M; D'Emilio M; Giorgio GA
    Environ Monit Assess; 2015 May; 187(5):307. PubMed ID: 25925158
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improving of local ozone forecasting by integrated models.
    Gradišar D; Grašič B; Božnar MZ; Mlakar P; Kocijan J
    Environ Sci Pollut Res Int; 2016 Sep; 23(18):18439-50. PubMed ID: 27287489
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Forecasting of daily total atmospheric ozone in Isfahan.
    Yazdanpanah H; Karimi M; Hejazizadeh Z
    Environ Monit Assess; 2009 Oct; 157(1-4):235-41. PubMed ID: 18843548
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.
    Lu WZ; Wang WJ; Wang XK; Yan SH; Lam JC
    Environ Res; 2004 Sep; 96(1):79-87. PubMed ID: 15261787
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.
    Alam MS; McNabola A
    J Air Waste Manag Assoc; 2015 May; 65(5):628-40. PubMed ID: 25947321
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The impact of the congestion charging scheme on air quality in London. Part 1. Emissions modeling and analysis of air pollution measurements.
    Kelly F; Anderson HR; Armstrong B; Atkinson R; Barratt B; Beevers S; Derwent D; Green D; Mudway I; Wilkinson P;
    Res Rep Health Eff Inst; 2011 Apr; (155):5-71. PubMed ID: 21830496
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.