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 *

121 related articles for article (PubMed ID: 38688970)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. An empirical classification method for South Pars marls by Schmidt hammer rebound index.
    Azarafza M; Ghazifard A; Asasi F; Rahnamarad J
    MethodsX; 2021; 8():101366. PubMed ID: 34430263
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. Coupled effects of fly ash and calcium formate on strength development of cemented tailings backfill.
    Miao X; Wu J; Wang Y; Ma D; Pu H
    Environ Sci Pollut Res Int; 2022 Aug; 29(40):59949-59964. PubMed ID: 35411521
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone.
    Azarafza M; Hajialilue Bonab M; Derakhshani R
    Materials (Basel); 2022 Oct; 15(19):. PubMed ID: 36234239
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Effect of Physical Properties on Mechanical Behaviors of Sandstone under Uniaxial and Triaxial Compressions.
    Alomari EM; Ng KW; Khatri L; Wulff SS
    Materials (Basel); 2023 Jul; 16(13):. PubMed ID: 37445181
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Strength prediction and application of cemented paste backfill based on machine learning and strength correction.
    Zhang B; Li K; Zhang S; Hu Y; Han B
    Heliyon; 2022 Aug; 8(8):e10338. PubMed ID: 36061035
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. Marble Powder as a Soil Stabilizer: An Experimental Investigation of the Geotechnical Properties and Unconfined Compressive Strength Analysis.
    Umar IH; Lin H
    Materials (Basel); 2024 Mar; 17(5):. PubMed ID: 38473679
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques.
    Wudil YS; Al-Najjar OA; Al-Osta MA; Baghabra Al-Amoudi OS; Gondal MA
    ACS Omega; 2023 Jul; 8(29):26391-26404. PubMed ID: 37521636
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.