BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

187 related articles for article (PubMed ID: 36949097)

  • 1. Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition.
    Li W; Jiang X
    Sci Rep; 2023 Mar; 13(1):4665. PubMed ID: 36949097
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Electricity price forecast based on the STL-TCN-NBEATS model.
    Zhang B; Song C; Jiang X; Li Y
    Heliyon; 2023 Jan; 9(1):e13029. PubMed ID: 36820190
    [TBL] [Abstract][Full Text] [Related]  

  • 3. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network.
    Singla P; Duhan M; Saroha S
    Earth Sci Inform; 2022; 15(1):291-306. PubMed ID: 34804244
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.
    Ayoobi N; Sharifrazi D; Alizadehsani R; Shoeibi A; Gorriz JM; Moosaei H; Khosravi A; Nahavandi S; Gholamzadeh Chofreh A; Goni FA; Klemeš JJ; Mosavi A
    Results Phys; 2021 Aug; 27():104495. PubMed ID: 34221854
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Effects of short-term exposure to air pollution on hospital admissions of young children for acute lower respiratory infections in Ho Chi Minh City, Vietnam.
    ; Le TG; Ngo L; Mehta S; Do VD; Thach TQ; Vu XD; Nguyen DT; Cohen A
    Res Rep Health Eff Inst; 2012 Jun; (169):5-72; discussion 73-83. PubMed ID: 22849236
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A conditional random field based feature learning framework for battery capacity prediction.
    Wang HK; Zhang Y; Huang M
    Sci Rep; 2022 Aug; 12(1):13221. PubMed ID: 35918374
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs.
    Yu W; Wang X; Jiang X; Zhao R; Zhao S
    Environ Sci Pollut Res Int; 2024 Jan; 31(1):262-279. PubMed ID: 38015396
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of TCN-biGRU neural network in [Formula: see text] concentration prediction.
    Shi T; Li P; Yang W; Qi A; Qiao J
    Environ Sci Pollut Res Int; 2023 Dec; 30(56):119506-119517. PubMed ID: 37930575
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Traffic flow prediction using bi-directional gated recurrent unit method.
    Wang S; Shao C; Zhang J; Zheng Y; Meng M
    Urban Inform; 2022; 1(1):16. PubMed ID: 36471871
    [TBL] [Abstract][Full Text] [Related]  

  • 10. STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production.
    Tebong NK; Simo T; Takougang AN; Ntanguen PH
    Heliyon; 2023 Jun; 9(6):e16456. PubMed ID: 37303512
    [TBL] [Abstract][Full Text] [Related]  

  • 11. SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations.
    Wu F; Li D; Zhao J; Jiang H; Luo X
    Sci Total Environ; 2024 Jul; 933():173116. PubMed ID: 38734080
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level.
    Ehteram M; Ghanbari-Adivi E
    Environ Sci Pollut Res Int; 2023 Aug; 30(40):92903-92921. PubMed ID: 37501025
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Novel Method for Regional NO
    Liu B; Zhang L; Wang Q; Chen J
    Comput Intell Neurosci; 2021; 2021():6631614. PubMed ID: 33927755
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing.
    Liu B; Guo X; Lai M; Wang Q
    Comput Intell Neurosci; 2020; 2020():8834699. PubMed ID: 33061948
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM
    Shen J; Valagolam D; McCalla S
    PeerJ; 2020; 8():e9961. PubMed ID: 32983651
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions.
    Yang H; Liu Z; Li G
    Chemosphere; 2022 Nov; 307(Pt 3):135798. PubMed ID: 35964719
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau.
    Zhao L; Luo T; Jiang X; Zhang B
    PeerJ; 2023; 11():e15851. PubMed ID: 37637158
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A novel combined model for prediction of daily precipitation data using instantaneous frequency feature and bidirectional long short time memory networks.
    Latifoğlu L
    Environ Sci Pollut Res Int; 2022 Jun; 29(28):42899-42912. PubMed ID: 35092586
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.
    Li X; Peng L; Yao X; Cui S; Hu Y; You C; Chi T
    Environ Pollut; 2017 Dec; 231(Pt 1):997-1004. PubMed ID: 28898956
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model.
    Wang M; Yang Z; Tai C; Zhang F; Zhang Q; Shen K; Guo C
    PLoS One; 2023; 18(4):e0284815. PubMed ID: 37099504
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.