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 *

120 related articles for article (PubMed ID: 39039187)

  • 1. Prediction of HPC compressive strength based on machine learning.
    Jin L; Duan J; Jin Y; Xue P; Zhou P
    Sci Rep; 2024 Jul; 14(1):16776. PubMed ID: 39039187
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Advanced Predictive Modeling of Concrete Compressive Strength and Slump Characteristics: A Comparative Evaluation of BPNN, SVM, and RF Models Optimized via PSO.
    Chen X; Zhang X; Chen WZ
    Materials (Basel); 2024 Sep; 17(19):. PubMed ID: 39410362
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques.
    Wu Y; Zhou Y
    Environ Sci Pollut Res Int; 2022 Dec; 29(59):89198-89209. PubMed ID: 35849229
    [TBL] [Abstract][Full Text] [Related]  

  • 4. High-Performance Concrete Strength Prediction Based on Machine Learning.
    Liu Y
    Comput Intell Neurosci; 2022; 2022():5802217. PubMed ID: 35669631
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.
    Latif SD
    Environ Sci Pollut Res Int; 2021 Jun; 28(23):30294-30302. PubMed ID: 33590396
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag.
    Wu X; Zhu F; Zhou M; Sabri MMS; Huang J
    Materials (Basel); 2022 Jun; 15(13):. PubMed ID: 35806704
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network.
    Du G; Bu L; Hou Q; Zhou J; Lu B
    PLoS One; 2021; 16(5):e0250795. PubMed ID: 33939736
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.
    Latif SD
    Environ Sci Pollut Res Int; 2021 Dec; 28(46):65935-65944. PubMed ID: 34327638
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete.
    Al-Shamiri AK; Yuan TF; Kim AJH
    Materials (Basel); 2020 Feb; 13(5):. PubMed ID: 32106394
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm.
    Li P; Zhang Y; Gu J; Duan S
    Sci Rep; 2024 Mar; 14(1):6414. PubMed ID: 38494524
    [TBL] [Abstract][Full Text] [Related]  

  • 11. On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance.
    Wan Z; Xu Y; Šavija B
    Materials (Basel); 2021 Feb; 14(4):. PubMed ID: 33546376
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack.
    Chen H; Qian C; Liang C; Kang W
    PLoS One; 2018; 13(1):e0191370. PubMed ID: 29346451
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of Compressive Strength of Partially Saturated Concrete Using Machine Learning Methods.
    Candelaria MDE; Kee SH; Lee KS
    Materials (Basel); 2022 Feb; 15(5):. PubMed ID: 35268896
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method.
    Huang J; Sabri MMS; Ulrikh DV; Ahmad M; Alsaffar KAM
    Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744249
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network.
    Wang W; Cui X; Qi Y; Xue K; Liang R; Bai C
    Sensors (Basel); 2024 Apr; 24(9):. PubMed ID: 38732979
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash.
    Farooq F; Czarnecki S; Niewiadomski P; Aslam F; Alabduljabbar H; Ostrowski KA; Śliwa-Wieczorek K; Nowobilski T; Malazdrewicz S
    Materials (Basel); 2021 Aug; 14(17):. PubMed ID: 34501024
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method.
    Huang J; Zhou M; Yuan H; Sabri MMS; Li X
    Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629527
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine Learning-Based Operational State Recognition and Compressive Property Prediction in Fused Filament Fabrication.
    Li Y; Xu G; Zhao W; Wang T; Li H; Liu Y; Wang G
    3D Print Addit Manuf; 2023 Dec; 10(6):1347-1360. PubMed ID: 38116211
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An ensemble learning-based prediction model for the compressive strength degradation of concrete containing superabsorbent polymers (SAP).
    Hosseinzadeh M; Mousavi SS; Dehestani M
    Sci Rep; 2024 Aug; 14(1):18535. PubMed ID: 39122829
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm.
    Al-Sodani KAA; Adewumi AA; Mohd Ariffin MA; Maslehuddin M; Ismail M; Salami HO; Owolabi TO; Mohamed HD
    Materials (Basel); 2021 Jun; 14(11):. PubMed ID: 34205101
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.