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.
217 related articles for article (PubMed ID: 37292319)
1. Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning. AbuShanab Y; Al-Ammari WA; Gowid S; Sleiti AK Heliyon; 2023 Jun; 9(6):e16716. PubMed ID: 37292319 [TBL] [Abstract][Full Text] [Related]
2. Dataset for measured viscosity of Polyalpha-Olefin- boron nitride nanofluids. Sleiti AK Data Brief; 2021 Apr; 35():106881. PubMed ID: 33665269 [TBL] [Abstract][Full Text] [Related]
3. Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction. Adun H; Wole-Osho I; Okonkwo EC; Ruwa T; Agwa T; Onochie K; Ukwu H; Bamisile O; Dagbasi M Neural Comput Appl; 2022; 34(13):11233-11254. PubMed ID: 35291505 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology. Li T; Yang J; Jiang P; AlAteah AH; Alsubeai A; Alfares AM; Sufian M Materials (Basel); 2024 Sep; 17(18):. PubMed ID: 39336274 [TBL] [Abstract][Full Text] [Related]
6. Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al Alarifi IM; Nguyen HM; Naderi Bakhtiyari A; Asadi A Materials (Basel); 2019 Nov; 12(21):. PubMed ID: 31690020 [TBL] [Abstract][Full Text] [Related]
7. Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest. Uzun Ozsahin D; Duwa BB; Ozsahin I; Uzun B Diagnostics (Basel); 2024 Feb; 14(4):. PubMed ID: 38396424 [TBL] [Abstract][Full Text] [Related]
8. Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning. Muneer R; Hashmet MR; Pourafshary P; Shakeel M Nanomaterials (Basel); 2023 Mar; 13(7):. PubMed ID: 37049303 [TBL] [Abstract][Full Text] [Related]
9. Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine. Li Y; Jiang P; She Q; Lin G Environ Pollut; 2018 Oct; 241():1115-1127. PubMed ID: 30029320 [TBL] [Abstract][Full Text] [Related]
10. Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric. Sarkar J; Prottoy ZH; Bari MT; Al Faruque MA Heliyon; 2021 Sep; 7(9):e08000. PubMed ID: 34585015 [TBL] [Abstract][Full Text] [Related]
11. Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Tabbussum R; Dar AQ Environ Sci Pollut Res Int; 2021 May; 28(20):25265-25282. PubMed ID: 33453033 [TBL] [Abstract][Full Text] [Related]
12. A comparative experimental investigation of dynamic viscosity of ZrO Ajeena AM; Farkas I; Víg P Heliyon; 2023 Oct; 9(10):e21113. PubMed ID: 37886762 [TBL] [Abstract][Full Text] [Related]
13. Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir. Mustafa A; Tariq Z; Mahmoud M; Abdulraheem A Sci Rep; 2023 Mar; 13(1):3956. PubMed ID: 36894553 [TBL] [Abstract][Full Text] [Related]
14. Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids. Zhao N; Li Z Materials (Basel); 2017 May; 10(5):. PubMed ID: 28772913 [TBL] [Abstract][Full Text] [Related]
15. Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment. Shateri M; Sobhanigavgani Z; Alinasab A; Varamesh A; Hemmati-Sarapardeh A; Mosavi A; S S Nanomaterials (Basel); 2020 Sep; 10(9):. PubMed ID: 32906742 [TBL] [Abstract][Full Text] [Related]
16. Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance. Sada SO; Ikpeseni SC Heliyon; 2021 Feb; 7(2):e06136. PubMed ID: 33553780 [TBL] [Abstract][Full Text] [Related]
17. Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing. Javadi F; Qaderi K; Ahmadi MM; Rahimpour M; Madadi MR; Mahdavi-Meymand A Sci Rep; 2022 Nov; 12(1):19390. PubMed ID: 36371476 [TBL] [Abstract][Full Text] [Related]
18. Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel. Wong YJ; Arumugasamy SK; Chung CH; Selvarajoo A; Sethu V Environ Monit Assess; 2020 Jun; 192(7):439. PubMed ID: 32556670 [TBL] [Abstract][Full Text] [Related]
19. Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift. Moges G; McDonnell K; Delele MA; Ali AN; Fanta SW Environ Sci Pollut Res Int; 2023 Feb; 30(8):21927-21944. PubMed ID: 36280637 [TBL] [Abstract][Full Text] [Related]
20. Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production. Taheri E; Amin MM; Fatehizadeh A; Rezakazemi M; Aminabhavi TM J Environ Manage; 2021 Aug; 292():112759. PubMed ID: 33984638 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]