BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

247 related articles for article (PubMed ID: 32291639)

  • 1. Forecasting the annual household electricity consumption of Chinese residents using the DPSO-BP prediction model.
    Wen L; Yuan X
    Environ Sci Pollut Res Int; 2020 Jun; 27(17):22014-22032. PubMed ID: 32291639
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Forecasting CO
    Wen L; Yuan X
    Sci Total Environ; 2020 May; 718():137194. PubMed ID: 32088474
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.
    Zhao X; Han M; Ding L; Calin AC
    Environ Sci Pollut Res Int; 2018 Jan; 25(3):2899-2910. PubMed ID: 29143932
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Forecast of carbon emissions in China based on time lag
    Xu Z; Xiong P; Xie L; Huang X; Li C
    Environ Technol; 2024 Jan; 45(2):329-348. PubMed ID: 35929884
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel grey power-Markov model for the prediction of China's electricity consumption.
    Sun L; Yang Y; Ning T; Zhu J
    Environ Sci Pollut Res Int; 2022 Mar; 29(15):21717-21738. PubMed ID: 34767163
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks.
    Pavićević M; Popović T
    Sensors (Basel); 2022 Jan; 22(3):. PubMed ID: 35161797
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process.
    Shi X; Huang G; Hao X; Yang Y; Li Z
    Sensors (Basel); 2021 Jun; 21(13):. PubMed ID: 34201548
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model.
    Peng S; Tan J; Ma H
    Environ Sci Pollut Res Int; 2024 Apr; 31(16):24567-24583. PubMed ID: 38448771
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.
    Jin H
    PLoS One; 2021; 16(5):e0236685. PubMed ID: 33983991
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province.
    Yang Z; Li B; Wu H; Li M; Fan J; Chen M; Long J
    Environ Sci Pollut Res Int; 2023 Mar; 30(12):33504-33515. PubMed ID: 36480138
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Quarterly electricity consumption prediction based on time series decomposition method and gray model.
    Sun Y; Zhang F
    Environ Sci Pollut Res Int; 2023 Sep; 30(42):95410-95424. PubMed ID: 37544948
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Grey optimization Verhulst model and its application in forecasting coal-related CO
    Duan H; Luo X
    Environ Sci Pollut Res Int; 2020 Dec; 27(35):43884-43905. PubMed ID: 32737788
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Electricity forecasting on the individual household level enhanced based on activity patterns.
    Gajowniczek K; Ząbkowski T
    PLoS One; 2017; 12(4):e0174098. PubMed ID: 28423039
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China.
    Sun J; Dong H; Gao Y; Fang Y; Kong Y
    Comput Intell Neurosci; 2021; 2021():1502932. PubMed ID: 34745245
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Forecasting annual natural gas consumption via the application of a novel hybrid model.
    Gao F; Shao X
    Environ Sci Pollut Res Int; 2021 May; 28(17):21411-21424. PubMed ID: 33415637
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China.
    Zhang P; Ma X; She K
    PLoS One; 2019; 14(12):e0225362. PubMed ID: 31805165
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data.
    Chung J; Jang B
    PLoS One; 2022; 17(11):e0278071. PubMed ID: 36417448
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model.
    Lu S
    Comput Intell Neurosci; 2021; 2021():1026978. PubMed ID: 34804136
    [TBL] [Abstract][Full Text] [Related]  

  • 19. How climate change affects electricity consumption in Chinese cities-a differential perspective based on municipal monthly panel data.
    Wang Y; Hou L; Shi J; Li Y; Wang Y; Zheng Y
    Environ Sci Pollut Res Int; 2023 Jun; 30(26):68577-68590. PubMed ID: 37126162
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multi-Step Hourly Power Consumption Forecasting in a Healthcare Building with Recurrent Neural Networks and Empirical Mode Decomposition.
    Fernández-Martínez D; Jaramillo-Morán MA
    Sensors (Basel); 2022 May; 22(10):. PubMed ID: 35632071
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.