Xue model exploration for oxytactic microbes in radiative MHD hybrid nanoliquid using machine learning technique.

The goal of this investigation is to thoroughly analyze and validate the proposed model by using the back-propagation Levenberg-Marquardt (BLM) methodology as an effective training strategy. The efficiency and precision of the suggested methodology have been verified using the generated mean square error (MSE), error histograms (EH), suggested solutions, and regression plots. The impact of temperature and solutal gradients on oxytactic microbes in the bioconvection flow of hybrid nanofluid is briefly analyzed in this paper using the Xue model. This model is useful in biomedical engineering, environmental research, and advanced thermal systems, where microorganism-nanofluid interactions are significant. It aids in the design of microfluidic devices and bioreactors by anticipating the impacts of oxytactic microbes on mass and heat transport under thermal radiation and cross-diffusion (Soret-Dufour) conditions. In nanomedicine, the approach enables regulated medication delivery and tailored therapy via hybrid nanofluid. It is also useful in renewable energy systems, such as bio-inspired cooling devices and solar thermal collectors, where better heat transfer is needed. Furthermore, the machine-learning framework enables more rapid and accurate predictions of complicated bioconvection events in industrial and biological systems.