Machine learning analysis of electroosmotic multi-hybrid immiscible urine flow in a bioactive channel: clinical perspectives.

This research offers an in-depth analysis of electro-osmotically induced, immiscible bio-convective flow of urine incorporated with five different nanoparticles (NPs) and actively motile microorganisms within a bio-reactive microchannel, subjected to electromagnetic field effects. The model accounts for critical multiphysical effects including Joule heating, electromagnetic radiation, Hall and ion-slip currents, and interfacial nanolayer (NL) interactions. The results reveal that an increase in interfacial NL thickness amplifies the urine velocity. An artificial neural network (ANN)-based model is further implemented to predict SFC with remarkable precision, achieving a minimal error of 0.01% and demonstrating excellent agreement with analytical outcomes.