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 *

476 related articles for article (PubMed ID: 37867169)

  • 1. A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China.
    Li X; Zhang X
    Environ Sci Pollut Res Int; 2023 Nov; 30(55):117485-117502. PubMed ID: 37867169
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning-based time series models for effective CO
    Kumari S; Singh SK
    Environ Sci Pollut Res Int; 2023 Nov; 30(55):116601-116616. PubMed ID: 35780266
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of hepatitis E using machine learning models.
    Guo Y; Feng Y; Qu F; Zhang L; Yan B; Lv J
    PLoS One; 2020; 15(9):e0237750. PubMed ID: 32941452
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.
    Zhao Z; Zhai M; Li G; Gao X; Song W; Wang X; Ren H; Cui Y; Qiao Y; Ren J; Chen L; Qiu L
    BMC Infect Dis; 2023 Feb; 23(1):71. PubMed ID: 36747126
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.
    Adeyinka DA; Muhajarine N
    BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.
    Wang YW; Shen ZZ; Jiang Y
    BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
    Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
    Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.
    Chukwunonso BP; Al-Wesabi I; Shixiang L; AlSharabi K; Al-Shamma'a AA; Farh HMH; Saeed F; Kandil T; Al-Shaalan AM
    Environ Sci Pollut Res Int; 2024 May; 31(23):33685-33707. PubMed ID: 38691282
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.
    Yu CS; Chang SS; Chang TH; Wu JL; Lin YJ; Chien HF; Chen RJ
    J Med Internet Res; 2021 May; 23(5):e27806. PubMed ID: 33900932
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development and performance comparison of optimized machine learning-based regression models for predicting energy-related carbon dioxide emissions.
    Koca Akkaya E; Akkaya AV
    Environ Sci Pollut Res Int; 2023 Dec; 30(58):122381-122392. PubMed ID: 37966648
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.
    Ye GH; Alim M; Guan P; Huang DS; Zhou BS; Wu W
    PLoS One; 2021; 16(3):e0248597. PubMed ID: 33725011
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.
    Lou HR; Wang X; Gao Y; Zeng Q
    BMC Public Health; 2022 Nov; 22(1):2167. PubMed ID: 36434563
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.
    Yang P; Cheng P; Zhang N; Luo D; Xu B; Zhang H
    Front Public Health; 2024; 12():1401161. PubMed ID: 39022407
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.
    Zhang R; Song H; Chen Q; Wang Y; Wang S; Li Y
    PLoS One; 2022; 17(1):e0262009. PubMed ID: 35030203
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.
    Sudarshan VK; Brabrand M; Range TM; Wiil UK
    Comput Biol Med; 2021 Aug; 135():104541. PubMed ID: 34166880
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.
    Khullar S; Singh N
    Environ Sci Pollut Res Int; 2022 Feb; 29(9):12875-12889. PubMed ID: 33988840
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India.
    Singh PK; Pandey AK; Ahuja S; Kiran R
    Environ Sci Pollut Res Int; 2022 Apr; 29(17):25461-25472. PubMed ID: 34841483
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks.
    Ma M; Liu C; Wei R; Liang B; Dai J
    J Appl Clin Med Phys; 2022 Mar; 23(3):e13558. PubMed ID: 35170838
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.
    Zhao D; Zhang H; Cao Q; Wang Z; He S; Zhou M; Zhang R
    PLoS One; 2022; 17(2):e0262734. PubMed ID: 35196309
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 24.