Bayesian optimized neural network modeling for radiative flow of MHD hybrid nanoliquid with oxytactic microbes for biomedical applications.
7 affiliations
- 1Department of Computer Engineering, Biruni University, Istanbul, 34010, Turkey. [email protected].
- 2Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia.
- 3Department of Basic Health Sciences in the Foundation Year for Health Colleges Program, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
- 4Computer Science department, College of Sciences and Humanities, Prince Sattam Bin Abdulaziz University, Aflaj, Kingdom of Saudi Arabia.
- 5Faculty of Applied Sciences and Humanities, Department of mathematics, Amran University, Amran, Yemen. [email protected].
- 6School of Exact Sciences, National Pedagogical University of Uzbekistan named after Nizami, Tashkent, Uzbekistan.
- 7Department of Mathematics, Firat University, 23119 Elazig, Türkiye.
Abstract
This work examines the impacts of Joule heating and viscous dissipation on the bioconvection flow of a CNTs-water hybrid nanofluid over a sheet while optimising for the Soret and Dufour effects. The complex relationships governing the system are simulated and comprehended using a neural network technique based on Bayesian Regularized Optimizers. In commercial and ecological uses, oxytactic and gyrotactic microbes are significant. While oxytactic microbes improve wastewater treatment by migrating into oxygen-rich areas and speeding up pollutant destruction, gyrotactic microorganisms aid mineral transport by lining up with fluid motion. While microbial activity improves mixing and transport, the introduction of CNTs-based hybrid nanofluids increases thermal conductivity, allowing sophisticated bioreactors and diagnostic systems. All things considered, the model enables bioengineering and biomedical applications that call for exact thermal and solutal control. To reduce error and get approximation results for various CNTs-water hybrid nanofluid's system scenarios, the generated synthetic data is subjected to the Bayesian-Regularized approach. The effectiveness of the Bayesian Regularized technique, developed for computational fluid dynamics of hybrid nanofluid, is demonstrated by learning curves on mean square error, regression analysis, transition state index, error distribution histogram, and fitness plots.
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BibTeX
@article{abbas2026bayesian,
title = {{Bayesian optimized neural network modeling for radiative flow of MHD hybrid nanoliquid with oxytactic microbes for biomedical applications.}},
author = {Ansar Abbas and Shouki A Ebad and Dhouha Choukaier and Abed Saif Ahmed Alghawli and Fuad Alsarari and Farkhod Alisherov and Gulnar Hamidova Abdulhamid},
journal = {Discover nano},
year = {2026},
month = oct,
doi = {10.1186/s11671-026-05065-9},
url = {https://europepmc.org/article/MED/42963509},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Bayesian optimized neural network modeling for radiative flow of MHD hybrid nanoliquid with oxytactic microbes for biomedical applications.
AU - Ansar Abbas
AU - Shouki A Ebad
AU - Dhouha Choukaier
AU - Abed Saif Ahmed Alghawli
AU - Fuad Alsarari
AU - Farkhod Alisherov
AU - Gulnar Hamidova Abdulhamid
PY - 2026
DA - 2026/10/30
JO - Discover nano
DO - 10.1186/s11671-026-05065-9
UR - https://europepmc.org/article/MED/42963509
AB - This work examines the impacts of Joule heating and viscous dissipation on the bioconvection flow of a CNTs-water hybrid nanofluid over a sheet while optimising for the Soret and Dufour effects. The complex relationships governing the system are simulated and comprehended using a neural network technique based on Bayesian Regularized Optimizers. In commercial and ecological uses, oxytactic and gyrotactic microbes are significant. While oxytactic microbes improve wastewater treatment by migrating into oxygen-rich areas and speeding up pollutant destruction, gyrotactic microorganisms aid mineral transport by lining up with fluid motion. While microbial activity improves mixing and transport, the introduction of CNTs-based hybrid nanofluids increases thermal conductivity, allowing sophisticated bioreactors and diagnostic systems. All things considered, the model enables bioengineering and biomedical applications that call for exact thermal and solutal control. To reduce error and get approximation results for various CNTs-water hybrid nanofluid's system scenarios, the generated synthetic data is subjected to the Bayesian-Regularized approach. The effectiveness of the Bayesian Regularized technique, developed for computational fluid dynamics of hybrid nanofluid, is demonstrated by learning curves on mean square error, regression analysis, transition state index, error distribution histogram, and fitness plots.
SN - 2731-9229
ER -