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 *

132 related articles for article (PubMed ID: 35629470)

  • 1. Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites.
    Kuppusamy Y; Jayaseelan R; Pandulu G; Sathish Kumar V; Murali G; Dixit S; Vatin NI
    Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629470
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete.
    Gunasekara C; Atzarakis P; Lokuge W; Law DW; Setunge S
    Polymers (Basel); 2021 Mar; 13(6):. PubMed ID: 33804194
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete.
    Aneja S; Sharma A; Gupta R; Yoo DY
    Materials (Basel); 2021 Apr; 14(7):. PubMed ID: 33915938
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica.
    Ahmed HU; Mohammed AS; Mohammed AA
    Environ Sci Pollut Res Int; 2022 Oct; 29(47):71232-71256. PubMed ID: 35595907
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes.
    Ahmed HU; Mohammed AS; Mohammed AA; Faraj RH
    PLoS One; 2021; 16(6):e0253006. PubMed ID: 34125869
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction Model for Mechanical Properties of Lightweight Aggregate Concrete Using Artificial Neural Network.
    Yoon JY; Kim H; Lee YJ; Sim SH
    Materials (Basel); 2019 Aug; 12(17):. PubMed ID: 31443400
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model.
    Khalaf AA; Kopecskó K; Merta I
    Polymers (Basel); 2022 Mar; 14(7):. PubMed ID: 35406295
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Advancing Concrete Mix Proportion through Hybrid Intelligence: A Multi-Objective Optimization Approach.
    Chen F; Xu W; Wen Q; Zhang G; Xu L; Fan D; Yu R
    Materials (Basel); 2023 Sep; 16(19):. PubMed ID: 37834585
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete.
    Kekez S; Kubica J
    Materials (Basel); 2021 Sep; 14(19):. PubMed ID: 34640033
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms.
    Ahmad A; Ahmad W; Chaiyasarn K; Ostrowski KA; Aslam F; Zajdel P; Joyklad P
    Polymers (Basel); 2021 Oct; 13(19):. PubMed ID: 34641204
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.
    Safiuddin M; Raman SN; Abdus Salam M; Jumaat MZ
    Materials (Basel); 2016 May; 9(5):. PubMed ID: 28773520
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Compressive Strength Prediction of Rubber Concrete Based on Artificial Neural Network Model with Hybrid Particle Swarm Optimization Algorithm.
    Huang XY; Wu KY; Wang S; Lu T; Lu YF; Deng WC; Li HM
    Materials (Basel); 2022 May; 15(11):. PubMed ID: 35683231
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete.
    Dao DV; Ly HB; Trinh SH; Le TT; Pham BT
    Materials (Basel); 2019 Mar; 12(6):. PubMed ID: 30934566
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm.
    Tosee SVR; Faridmehr I; Bedon C; Sadowski Ł; Aalimahmoody N; Nikoo M; Nowobilski T
    Materials (Basel); 2021 Oct; 14(20):. PubMed ID: 34683782
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.
    Amin MN; Khan K; Ahmad W; Javed MF; Qureshi HJ; Saleem MU; Qadir MG; Faraz MI
    Polymers (Basel); 2022 May; 14(10):. PubMed ID: 35632011
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete.
    Ahmed HU; Mohammed AA; Mohammed A
    PLoS One; 2022; 17(5):e0265846. PubMed ID: 35613110
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.
    Amin MN; Khan K; Javed MF; Aslam F; Qadir MG; Faraz MI
    Materials (Basel); 2022 May; 15(10):. PubMed ID: 35629515
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach.
    Song H; Ahmad A; Ostrowski KA; Dudek M
    Materials (Basel); 2021 Aug; 14(16):. PubMed ID: 34443041
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An artificial neural network to model response of a radiotherapy beam monitoring system.
    Cho YB; Farrokhkish M; Norrlinger B; Heaton R; Jaffray D; Islam M
    Med Phys; 2020 Apr; 47(4):1983-1994. PubMed ID: 31955428
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.