BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

310 related articles for article (PubMed ID: 35454516)

  • 1. Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete.
    Yuan X; Tian Y; Ahmad W; Ahmad A; Usanova KI; Mohamed AM; Khallaf R
    Materials (Basel); 2022 Apr; 15(8):. PubMed ID: 35454516
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar.
    Fei Z; Liang S; Cai Y; Shen Y
    Materials (Basel); 2023 Jan; 16(2):. PubMed ID: 36676320
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model.
    Nunez I; Marani A; Nehdi ML
    Materials (Basel); 2020 Sep; 13(19):. PubMed ID: 33003383
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.
    Zheng D; Wu R; Sufian M; Kahla NB; Atig M; Deifalla AF; Accouche O; Azab M
    Materials (Basel); 2022 Jul; 15(15):. PubMed ID: 35897626
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete.
    Anjum M; Khan K; Ahmad W; Ahmad A; Amin MN; Nafees A
    Polymers (Basel); 2022 Sep; 14(18):. PubMed ID: 36146051
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Influence of Pretreatment Methods on Compressive Performance Improvement and Failure Mechanism Analysis of Recycled Aggregate Concrete.
    Lv D; Huang K; Wang W
    Materials (Basel); 2023 May; 16(10):. PubMed ID: 37241433
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques.
    Alhakeem ZM; Jebur YM; Henedy SN; Imran H; Bernardo LFA; Hussein HM
    Materials (Basel); 2022 Oct; 15(21):. PubMed ID: 36363023
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.
    Jagadesh P; Khan AH; Priya BS; Asheeka A; Zoubir Z; Magbool HM; Alam S; Bakather OY
    PLoS One; 2024; 19(5):e0303101. PubMed ID: 38739642
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches.
    Javed MF; Fawad M; Lodhi R; Najeh T; Gamil Y
    Sci Rep; 2024 Apr; 14(1):8381. PubMed ID: 38600161
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network.
    Bu L; Du G; Hou Q
    Materials (Basel); 2021 Jul; 14(14):. PubMed ID: 34300839
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Testing and Prediction of the Strength Development of Recycled-Aggregate Concrete with Large Particle Natural Aggregate.
    Li C; Wang F; Deng X; Li Y; Zhao S
    Materials (Basel); 2019 Jun; 12(12):. PubMed ID: 31212785
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods.
    Amin MN; Ahmad A; Khan K; Ahmad W; Nazar S; Faraz MI; Alabdullah AA
    Materials (Basel); 2022 Jun; 15(12):. PubMed ID: 35744356
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.