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 *

165 related articles for article (PubMed ID: 24453872)

  • 1. A hybrid approach for short-term forecasting of wind speed.
    Tatinati S; Veluvolu KC
    ScientificWorldJournal; 2013; 2013():548370. PubMed ID: 24453872
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso.
    Wang T
    PeerJ Comput Sci; 2021; 7():e732. PubMed ID: 34712801
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting.
    Ding M; Zhou H; Xie H; Wu M; Liu KZ; Nakanishi Y; Yokoyama R
    ISA Trans; 2021 Feb; 108():58-68. PubMed ID: 32958296
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Daily air quality index forecasting with hybrid models: A case in China.
    Zhu S; Lian X; Liu H; Hu J; Wang Y; Che J
    Environ Pollut; 2017 Dec; 231(Pt 2):1232-1244. PubMed ID: 28939124
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting.
    Yang S; Yuan A; Yu Z
    Environ Sci Pollut Res Int; 2023 Jan; 30(5):11689-11705. PubMed ID: 36098919
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.
    Ren Y; Suganthan PN; Srikanth N
    IEEE Trans Neural Netw Learn Syst; 2016 Aug; 27(8):1793-8. PubMed ID: 25222957
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A hybrid wavelet transform based short-term wind speed forecasting approach.
    Wang J
    ScientificWorldJournal; 2014; 2014():914127. PubMed ID: 25136699
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer.
    Xinxin W; Xiaopan S; Xueyi A; Shijia L
    PLoS One; 2023; 18(9):e0289161. PubMed ID: 37682883
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction.
    Bommidi BS; Kosana V; Teeparthi K; Madasthu S
    Environ Sci Pollut Res Int; 2023 Mar; 30(14):40018-40030. PubMed ID: 36602735
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Noise model based ν-support vector regression with its application to short-term wind speed forecasting.
    Hu Q; Zhang S; Xie Z; Mi J; Wan J
    Neural Netw; 2014 Sep; 57():1-11. PubMed ID: 24874183
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm.
    Cui Y; Huang C; Cui Y
    Environ Sci Pollut Res Int; 2020 Mar; 27(7):7353-7365. PubMed ID: 31884551
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Performance enhancement of short-term wind speed forecasting model using Realtime data.
    Ashraf M; Raza B; Arshad M; Khan BM; Zaidi SSH
    PLoS One; 2024; 19(5):e0302664. PubMed ID: 38820359
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network.
    Sun W; Wang X; Tan B
    Environ Sci Pollut Res Int; 2022 Jul; 29(33):49684-49699. PubMed ID: 35220530
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms.
    Ibrahim M; Alsheikh A; Al-Hindawi Q; Al-Dahidi S; ElMoaqet H
    Comput Intell Neurosci; 2020; 2020():8439719. PubMed ID: 32377179
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.
    Chen Q; Wen D; Li X; Chen D; Lv H; Zhang J; Gao P
    PLoS One; 2019; 14(9):e0222365. PubMed ID: 31509599
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A hybrid prediction model for forecasting wind energy resources.
    Zhang Y; Pan G
    Environ Sci Pollut Res Int; 2020 Jun; 27(16):19428-19446. PubMed ID: 32215801
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.
    Chai T; Xue H
    PLoS One; 2021; 16(5):e0250948. PubMed ID: 33970943
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction.
    Zhang Y; Chen Y
    Environ Sci Pollut Res Int; 2022 Mar; 29(15):22661-22674. PubMed ID: 34797536
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Optimization scheme of wind energy prediction based on artificial intelligence.
    Zhang Y; Li R; Zhang J
    Environ Sci Pollut Res Int; 2021 Aug; 28(29):39966-39981. PubMed ID: 33763837
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.
    Song C; Yao L; Hua C; Ni Q
    Environ Monit Assess; 2021 May; 193(6):363. PubMed ID: 34041601
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.