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 *

188 related articles for article (PubMed ID: 36548370)

  • 1. Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete.
    Qian Y; Sufian M; Accouche O; Azab M
    PLoS One; 2022; 17(12):e0278161. PubMed ID: 36548370
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.
    Khan K; Ahmad W; Amin MN; Ahmad A; Nazar S; Alabdullah AA
    Polymers (Basel); 2022 Jul; 14(15):. PubMed ID: 35956580
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.
    Dai L; Wu X; Zhou M; Ahmad W; Ali M; Sabri MMS; Salmi A; Ewais DYZ
    Materials (Basel); 2022 Jun; 15(13):. PubMed ID: 35806575
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature.
    Ahmad A; Ostrowski KA; Maślak M; Farooq F; Mehmood I; Nafees A
    Materials (Basel); 2021 Jul; 14(15):. PubMed ID: 34361416
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.
    Shen Z; Deifalla AF; Kamiński P; Dyczko A
    Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629548
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions.
    Amin MN; Ahmad W; Khan K; Ahmad A; Nazar S; Alabdullah AA
    Materials (Basel); 2022 Jul; 15(15):. PubMed ID: 35955144
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.
    Al-Hashem MN; Amin MN; Ahmad W; Khan K; Ahmad A; Ehsan S; Al-Ahmad QMS; Qadir MG
    Materials (Basel); 2022 Oct; 15(19):. PubMed ID: 36234267
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Research on prediction of compressive strength of fly ash and slag mixed concrete based on machine learning.
    Wang M; Kang J; Liu W; Su J; Li M
    PLoS One; 2022; 17(12):e0279293. PubMed ID: 36574382
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.
    Ho LS; Tran VQ
    PLoS One; 2024; 19(3):e0297364. PubMed ID: 38442109
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete.
    Khan K; Ahmad W; Amin MN; Aslam F; Ahmad A; Al-Faiad MA
    Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629456
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The Role of Supplementary Cementitious Materials (SCMs) in Ultra High Performance Concrete (UHPC): A Review.
    Park S; Wu S; Liu Z; Pyo S
    Materials (Basel); 2021 Mar; 14(6):. PubMed ID: 33802943
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches.
    Ullah HS; Khushnood RA; Farooq F; Ahmad J; Vatin NI; Ewais DYZ
    Materials (Basel); 2022 Apr; 15(9):. PubMed ID: 35591498
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients.
    Pan X; Xiao Y; Suhail SA; Ahmad W; Murali G; Salmi A; Mohamed A
    Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744254
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms.
    Khan K; Ahmad W; Amin MN; Ahmad A; Nazar S; Alabdullah AA; Arab AMA
    Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744167
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters.
    Khan K; Ahmad W; Amin MN; Ahmad A; Nazar S; Al-Faiad MA
    Polymers (Basel); 2022 Jun; 14(12):. PubMed ID: 35746085
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development and characterization of non-proprietary ultra high performance concrete.
    Saleem MA; Liaquat F; Saleem MM; Aziz M; Aslam F; Mohamed A
    Heliyon; 2024 Jan; 10(2):e24260. PubMed ID: 38298661
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers.
    Zou Y; Zheng C; Alzahrani AM; Ahmad W; Ahmad A; Mohamed AM; Khallaf R; Elattar S
    Gels; 2022 Apr; 8(5):. PubMed ID: 35621569
    [TBL] [Abstract][Full Text] [Related]  

  • 18. New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete.
    Anjum M; Khan K; Ahmad W; Ahmad A; Amin MN; Nafees A
    Materials (Basel); 2022 Sep; 15(18):. PubMed ID: 36143573
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Autogenous Shrinkage, Microstructure, and Strength of Ultra-High Performance Concrete Incorporating Carbon Nanofibers.
    Lim JLG; Raman SN; Safiuddin M; Zain MFM; Hamid R
    Materials (Basel); 2019 Jan; 12(2):. PubMed ID: 30669570
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.