BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

145 related articles for article (PubMed ID: 38474927)

  • 1. Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features.
    Chan KY; Yiu KFC; Kim D; Abu-Siada A
    Sensors (Basel); 2024 Feb; 24(5):. PubMed ID: 38474927
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution.
    Ullah I; Muhammad Hasanat S; Aurangzeb K; Alhussein M; Rizwan M; Anwar MS
    PeerJ Comput Sci; 2023; 9():e1487. PubMed ID: 37810340
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network.
    Ozcan A; Catal C; Kasif A
    Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770422
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Fisher Information Based Meteorological Factors Introduction and Features Selection for Short-Term Load Forecasting.
    Cai S; Liu L; Sun H; Yan J
    Entropy (Basel); 2018 Mar; 20(3):. PubMed ID: 33265275
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A clustering-based fuzzy wavelet neural network model for short-term load forecasting.
    Kodogiannis VS; Amina M; Petrounias I
    Int J Neural Syst; 2013 Oct; 23(5):1350024. PubMed ID: 23924415
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An Insight of Deep Learning Based Demand Forecasting in Smart Grids.
    Aguiar-Pérez JM; Pérez-Juárez MÁ
    Sensors (Basel); 2023 Jan; 23(3):. PubMed ID: 36772509
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep learning-driven hybrid model for short-term load forecasting and smart grid information management.
    Wen X; Liao J; Niu Q; Shen N; Bao Y
    Sci Rep; 2024 Jun; 14(1):13720. PubMed ID: 38877081
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting.
    Liu J; Chen J; Yan G; Chen W; Xu B
    iScience; 2023 Aug; 26(8):107456. PubMed ID: 37575195
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands.
    Butt FM; Hussain L; Mahmood A; Lone KJ
    Math Biosci Eng; 2020 Dec; 18(1):400-425. PubMed ID: 33525099
    [TBL] [Abstract][Full Text] [Related]  

  • 10. FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model.
    Joloudari JH; Saadatfar H; GhasemiGol M; Alizadehsani R; Sani ZA; Hasanzadeh F; Hassannataj E; Sharifrazi D; Mansor Z
    Math Biosci Eng; 2022 Feb; 19(4):3609-3635. PubMed ID: 35341267
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Invariant feature based label correction for DNN when Learning with Noisy Labels.
    Deng L; Yang B; Kang Z; Xiang Y
    Neural Netw; 2024 Apr; 172():106137. PubMed ID: 38309136
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.
    Alsharekh MF; Habib S; Dewi DA; Albattah W; Islam M; Albahli S
    Sensors (Basel); 2022 Sep; 22(18):. PubMed ID: 36146256
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building.
    Le T; Vo MT; Kieu T; Hwang E; Rho S; Baik SW
    Sensors (Basel); 2020 May; 20(9):. PubMed ID: 32392858
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning.
    Shi Y; Xu X
    Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590953
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting.
    Moriano J; Rodríguez FJ; Martín P; Jiménez JA; Vuksanovic B
    Sensors (Basel); 2016 Jan; 16(1):. PubMed ID: 26771613
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data.
    Rodríguez-Pérez R; Bajorath J
    ACS Omega; 2018 Sep; 3(9):12033-12040. PubMed ID: 30320286
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures.
    Ai S; Chakravorty A; Rong C
    Sensors (Basel); 2019 Feb; 19(3):. PubMed ID: 30744206
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep learning-based neural networks for day-ahead power load probability density forecasting.
    Zhou Y; Zhu D; Chen H; Guo S; Xu CY; Chang FJ
    Environ Sci Pollut Res Int; 2023 Feb; 30(7):17741-17764. PubMed ID: 36201077
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers.
    Taha K; van de Leur RR; Vessies M; Mast TP; Cramer MJ; Cauwenberghs N; Verstraelen TE; de Brouwer R; Doevendans PA; Wilde A; Asselbergs FW; van den Berg MP; D'hooge J; Kuznetsova T; Teske AJ; van Es R
    Int J Cardiovasc Imaging; 2023 Nov; 39(11):2149-2161. PubMed ID: 37566298
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Feature-based deep neural network approach for predicting mortality risk in patients with COVID-19.
    Chang TY; Huang CK; Weng CH; Chen JY
    Eng Appl Artif Intell; 2023 Sep; 124():106644. PubMed ID: 37366394
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.