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 *

142 related articles for article (PubMed ID: 38215928)

  • 1. Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study.
    Gholizadeh M; Saeedi R; Bagheri A; Paeezi M
    Environ Res; 2024 Apr; 246():118146. PubMed ID: 38215928
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration.
    Wang R; Yu Y; Chen Y; Pan Z; Li X; Tan Z; Zhang J
    J Environ Manage; 2022 Jan; 302(Pt A):114020. PubMed ID: 34731713
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant.
    Elmaadawy K; Elaziz MA; Elsheikh AH; Moawad A; Liu B; Lu S
    J Environ Manage; 2021 Nov; 298():113520. PubMed ID: 34391109
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant.
    Ekinci E; Özbay B; Omurca Sİ; Sayın FE; Özbay İ
    J Environ Manage; 2023 Dec; 348():119448. PubMed ID: 37931437
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of effluent arsenic concentration of wastewater treatment plants using machine learning and kriging-based models.
    Zounemat-Kermani M; Alizamir M; Keshtegar B; Batelaan O; Hinkelmann R
    Environ Sci Pollut Res Int; 2022 Mar; 29(14):20556-20570. PubMed ID: 34739667
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm.
    Mekaoussi H; Heddam S; Bouslimanni N; Kim S; Zounemat-Kermani M
    Heliyon; 2023 Nov; 9(11):e21351. PubMed ID: 37954260
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of COD in industrial wastewater treatment plant using an artificial neural network.
    Çimen Mesutoğlu Ö; Gök O
    Sci Rep; 2024 Jun; 14(1):13750. PubMed ID: 38877150
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning.
    Liu T; Zhang H; Wu J; Liu W; Fang Y
    J Environ Manage; 2024 Jul; 364():121430. PubMed ID: 38875983
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Artificial neural network based models for predicting the effluent quality of a combined upflow anaerobic sludge blanket and facultative pond: Performance evaluation and comparison of different algorithms.
    Khatri N; Vyas AK; Abdul-Qawy ASH; Rene ER
    Environ Res; 2023 Jan; 217():114843. PubMed ID: 36400228
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm.
    Liu L; Tian X; Ma Y; Lu W; Luo Y
    PLoS One; 2024; 19(6):e0305216. PubMed ID: 38941339
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study.
    Manav-Demir N; Gelgor HB; Oz E; Ilhan F; Ulucan-Altuntas K; Tiwary A; Debik E
    J Environ Manage; 2024 Feb; 351():119899. PubMed ID: 38159310
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of 5-day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms.
    Nafsin N; Li J
    Water Environ Res; 2022 May; 94(5):e10718. PubMed ID: 35502725
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density.
    Ekundayo TC; Ijabadeniyi OA; Igbinosa EO; Okoh AI
    Environ Pollut; 2023 Jan; 317():120734. PubMed ID: 36455774
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology.
    Rios Fuck JV; Cechinel MAP; Neves J; Campos de Andrade R; Tristão R; Spogis N; Riella HG; Soares C; Padoin N
    Chemosphere; 2024 Mar; 352():141472. PubMed ID: 38382719
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models.
    Carreres-Prieto D; García JT; Cerdán-Cartagena F; Suardiaz-Muro J
    Sensors (Basel); 2020 Oct; 20(19):. PubMed ID: 33019750
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm.
    Ting SC; Ismail AR; Malek MA
    J Environ Manage; 2013 Nov; 129():260-5. PubMed ID: 23968912
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods.
    Dostmohammadi M; Pedram MZ; Hoseinzadeh S; Garcia DA
    J Environ Manage; 2024 Jul; 364():121264. PubMed ID: 38870783
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System.
    Shyu HY; Castro CJ; Bair RA; Lu Q; Yeh DH
    ACS Environ Au; 2023 Sep; 3(5):308-318. PubMed ID: 37743952
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods.
    Wang D; Thunéll S; Lindberg U; Jiang L; Trygg J; Tysklind M
    J Environ Manage; 2022 Jan; 301():113941. PubMed ID: 34731954
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate.
    Kaliappan J; Srinivasan K; Mian Qaisar S; Sundararajan K; Chang CY; C S
    Front Public Health; 2021; 9():729795. PubMed ID: 34595149
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.