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 *

135 related articles for article (PubMed ID: 36431407)

  • 1. Predicting the Engineering Properties of Rocks from Textural Characteristics Using Some Soft Computing Approaches.
    Fereidooni D; Sousa L
    Materials (Basel); 2022 Nov; 15(22):. PubMed ID: 36431407
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comprehensive study on the Python-based regression machine learning models for prediction of uniaxial compressive strength using multiple parameters in Charnockite rocks.
    Kochukrishnan S; Krishnamurthy P; D Y; Kaliappan N
    Sci Rep; 2024 Mar; 14(1):7360. PubMed ID: 38548837
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data.
    Asteris PG; Karoglou M; Skentou AD; Vasconcelos G; He M; Bakolas A; Zhou J; Armaghani DJ
    Ultrasonics; 2024 Jul; 141():107347. PubMed ID: 38781796
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Statistical models for estimating the uniaxial compressive strength and elastic modulus of rocks from different hardness test methods.
    Teymen A
    Heliyon; 2021 May; 7(5):e06891. PubMed ID: 34007924
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models.
    Fereidooni D; Karimi Z; Ghasemi F
    PLoS One; 2024; 19(6):e0302944. PubMed ID: 38857272
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks.
    Hassan MY; Arman H
    Sci Rep; 2022 Dec; 12(1):20969. PubMed ID: 36470991
    [TBL] [Abstract][Full Text] [Related]  

  • 7. HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks.
    Hassan MY; Arman H
    Sci Rep; 2023 Aug; 13(1):14101. PubMed ID: 37644208
    [TBL] [Abstract][Full Text] [Related]  

  • 8. ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength.
    Jalal FE; Iqbal M; Khan WA; Jamal A; Onyelowe K; Lekhraj
    Sci Rep; 2024 Jun; 14(1):14597. PubMed ID: 38918592
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests.
    Wang Y; Hasanipanah M; Rashid ASA; Le BN; Ulrikh DV
    Materials (Basel); 2023 May; 16(10):. PubMed ID: 37241358
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP.
    Jalal FE; Xu Y; Iqbal M; Javed MF; Jamhiri B
    J Environ Manage; 2021 Jul; 289():112420. PubMed ID: 33831756
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning.
    Rahman T; Sarkar K
    Rock Mech Rock Eng; 2021; 54(6):3175-3191. PubMed ID: 33867648
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Correlation of P-wave velocity with mechanical and physical properties of limestone with statistical analysis.
    Arman H
    Sci Rep; 2021 Dec; 11(1):24104. PubMed ID: 34916572
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Determination of Drilling Rate Index Based on Rock Strength Using Regression Analysis.
    Yenice H
    An Acad Bras Cienc; 2019; 91(3):e20181095. PubMed ID: 31618413
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ultrasonic pulse velocity for the evaluation of physical and mechanical properties of a highly porous building limestone.
    Vasanelli E; Colangiuli D; Calia A; Sileo M; Aiello MA
    Ultrasonics; 2015 Jul; 60():33-40. PubMed ID: 25769219
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Assessment of strength properties of cemented paste backfill by ultrasonic pulse velocity test.
    Yılmaz T; Ercikdi B; Karaman K; Külekçi G
    Ultrasonics; 2014 Jul; 54(5):1386-94. PubMed ID: 24602334
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite.
    Wang M; Liu W; Liu H; Xie T; Wang Q; Xu W
    Sci Rep; 2024 Apr; 14(1):9880. PubMed ID: 38688970
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Strength Characteristics, Ultrasonic Wave Velocity, and the Correlation between Them in Clay Bricks under Dry and Saturated Conditions.
    Jamshidi A; Sousa L
    Materials (Basel); 2024 Mar; 17(6):. PubMed ID: 38541507
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Dataset on physical properties and mechanical parameters of limestone rocks from Central Apennines (Italy) by laboratory test on intact rock specimens.
    Martino S; Di Luzio E; Discenza ME; Esposito C; Kundu J; Minnillo M
    Data Brief; 2023 Feb; 46():108886. PubMed ID: 36687157
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Strength and ultrasonic properties of cemented paste backfill.
    Ercikdi B; Yılmaz T; Külekci G
    Ultrasonics; 2014 Jan; 54(1):195-204. PubMed ID: 23706262
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Assessment of Böhme Abrasion Value of Natural Stones through Artificial Neural Networks (ANN).
    Strzałkowski P; Köken E
    Materials (Basel); 2022 Mar; 15(7):. PubMed ID: 35407865
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.