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 *

118 related articles for article (PubMed ID: 38918592)

  • 1. 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]  

  • 2. 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]  

  • 3. Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms.
    Azam A; Bardhan A; Kaloop MR; Samui P; Alanazi F; Alzara M; Yosri AM
    Sci Rep; 2022 Aug; 12(1):14454. PubMed ID: 36002470
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer.
    Ngo AQ; Nguyen LQ; Tran VQ
    PLoS One; 2023; 18(6):e0286950. PubMed ID: 37289821
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression.
    Ahmad M; Al-Zubi MA; Kubińska-Jabcoń E; Majdi A; Al-Mansob RA; Sabri MMS; Ali E; Naji JA; Elnaggar AY; Zamin B
    Sci Rep; 2023 Aug; 13(1):13593. PubMed ID: 37604957
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.
    Banadkooki FB; Ehteram M; Ahmed AN; Teo FY; Ebrahimi M; Fai CM; Huang YF; El-Shafie A
    Environ Sci Pollut Res Int; 2020 Oct; 27(30):38094-38116. PubMed ID: 32621196
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Decision tree models for the estimation of geo-polymer concrete compressive strength.
    Zhou J; Su Z; Hosseini S; Tian Q; Lu Y; Luo H; Xu X; Chen C; Huang J
    Math Biosci Eng; 2024 Jan; 21(1):1413-1444. PubMed ID: 38303471
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms.
    Dang MP; Le HG; Nguyen NP; Le Chau N; Dao TP
    Comput Intell Neurosci; 2022; 2022():9151146. PubMed ID: 36507229
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.
    Tikhamarine Y; Malik A; Souag-Gamane D; Kisi O
    Environ Sci Pollut Res Int; 2020 Aug; 27(24):30001-30019. PubMed ID: 32445152
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Flood discharge prediction using improved ANFIS model combined with hybrid particle swarm optimisation and slime mould algorithm.
    Samantaray S; Sahoo P; Sahoo A; Satapathy DP
    Environ Sci Pollut Res Int; 2023 Jul; 30(35):83845-83872. PubMed ID: 37351742
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network.
    Wang P; Qiao H; Xue C; Feng Q
    Materials (Basel); 2023 Apr; 16(9):. PubMed ID: 37176254
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. A novel neural-evolutionary framework for predicting weight on the bit in drilling operations.
    Dowlatabadi M; Azizi S; Dehbashi M; Sadeqi H
    Sci Rep; 2023 Oct; 13(1):18539. PubMed ID: 37898632
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India.
    Kushwaha NL; Kudnar NS; Vishwakarma DK; Subeesh A; Jatav MS; Gaddikeri V; Ahmed AA; Abdelaty I
    Heliyon; 2024 May; 10(10):e31085. PubMed ID: 38784559
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization.
    Negi BB; Aliveli M; Behera SK; Das R; Sinharoy A; Rene ER; Pakshirajan K
    Environ Res; 2023 Feb; 219():115073. PubMed ID: 36535392
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Spatial mapping of land susceptibility to dust emissions using optimization of attentive Interpretable Tabular Learning (TabNet) model.
    Razavi-Termeh SV; Sadeghi-Niaraki A; Sorooshian A; Abuhmed T; Choi SM
    J Environ Manage; 2024 May; 358():120682. PubMed ID: 38670008
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization.
    Teferra DM; Ngoo LMH; Nyakoe GN
    Heliyon; 2023 Jan; 9(1):e12802. PubMed ID: 36704286
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models.
    Ghasemi M; Hasani Zonoozi M; Rezania N; Saadatpour M
    Environ Sci Pollut Res Int; 2022 Oct; 29(48):72839-72852. PubMed ID: 35616836
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis.
    Al-Qaness MAA; Helmi AM; Dahou A; Elaziz MA
    Biosensors (Basel); 2022 Oct; 12(10):. PubMed ID: 36290958
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.