BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

145 related articles for article (PubMed ID: 38442812)

  • 1. High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.
    Man X; Liu R; Zhang Y; Yu W; Kong F; Liu L; Luo Y; Feng T
    Environ Res; 2024 Jun; 251(Pt 1):118609. PubMed ID: 38442812
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model.
    Mu X; Wang S; Jiang P; Wu Y
    J Environ Sci (China); 2023 Oct; 132():122-133. PubMed ID: 37336603
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model.
    Li M; Yang Q; Yuan Q; Zhu L
    Chemosphere; 2022 Aug; 301():134817. PubMed ID: 35523298
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning models accurately predict ozone exposure during wildfire events.
    Watson GL; Telesca D; Reid CE; Pfister GG; Jerrett M
    Environ Pollut; 2019 Nov; 254(Pt A):112792. PubMed ID: 31421571
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013-2019.
    Meng X; Wang W; Shi S; Zhu S; Wang P; Chen R; Xiao Q; Xue T; Geng G; Zhang Q; Kan H; Zhang H
    Environ Pollut; 2022 Apr; 299():118865. PubMed ID: 35063542
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach.
    Liu R; Ma Z; Liu Y; Shao Y; Zhao W; Bi J
    Environ Int; 2020 Sep; 142():105823. PubMed ID: 32521347
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology.
    Wang W; Liu X; Bi J; Liu Y
    Environ Int; 2022 Jan; 158():106917. PubMed ID: 34624589
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Estimation of Surface Ozone Concentration and Health Impact Assessment in China].
    Zhao N; Lu YM
    Huan Jing Ke Xue; 2022 Mar; 43(3):1235-1245. PubMed ID: 35258187
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021.
    Lyu Y; Ju Q; Lv F; Feng J; Pang X; Li X
    Environ Pollut; 2022 Aug; 306():119420. PubMed ID: 35526642
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.
    Zhan Y; Luo Y; Deng X; Grieneisen ML; Zhang M; Di B
    Environ Pollut; 2018 Feb; 233():464-473. PubMed ID: 29101889
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States.
    Ren X; Mi Z; Georgopoulos PG
    Environ Int; 2020 Sep; 142():105827. PubMed ID: 32593834
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.
    Balram D; Lian KY; Sebastian N
    Ecotoxicol Environ Saf; 2020 Dec; 205():111168. PubMed ID: 32846299
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China.
    Cheng Y; He LY; Huang XF
    J Environ Manage; 2021 Dec; 299():113670. PubMed ID: 34479147
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Cooperative simultaneous inversion of satellite-based real-time PM
    Yan X; Zuo C; Li Z; Chen HW; Jiang Y; He B; Liu H; Chen J; Shi W
    Environ Pollut; 2023 Jun; 327():121509. PubMed ID: 36967005
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A machine learning method to estimate PM
    Chen G; Li S; Knibbs LD; Hamm NAS; Cao W; Li T; Guo J; Ren H; Abramson MJ; Guo Y
    Sci Total Environ; 2018 Sep; 636():52-60. PubMed ID: 29702402
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Optimized neural network for daily-scale ozone prediction based on transfer learning.
    Ma W; Yuan Z; Lau AKH; Wang L; Liao C; Zhang Y
    Sci Total Environ; 2022 Jun; 827():154279. PubMed ID: 35248640
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China.
    Yan X; Guo Y; Zhang Y; Chen J; Jiang Y; Zuo C; Zhao W; Shi W
    J Environ Manage; 2024 Feb; 351():119942. PubMed ID: 38150930
    [TBL] [Abstract][Full Text] [Related]  

  • 18. New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.
    Wang S; Mu X; Jiang P; Huo Y; Zhu L; Zhu Z; Wu Y
    Int J Environ Res Public Health; 2022 Jun; 19(12):. PubMed ID: 35742435
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A novel bagging ensemble approach for predicting summertime ground-level ozone concentration.
    Mohan S; Saranya P
    J Air Waste Manag Assoc; 2019 Feb; 69(2):220-233. PubMed ID: 30303768
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Characteristics of Ozone Pollution, Meteorological Impact, and Evaluation of Forecasting Results Based on a Neural Network Model in Beijing-Tianjin-Hebei Region].
    Zhu YY; Liu B; Gui HL; Li JJ; Wang W
    Huan Jing Ke Xue; 2022 Aug; 43(8):3966-3976. PubMed ID: 35971695
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.