ABSTRACT In cardiovascular research, electromagnetic fields (EMFs) induced by Riga plates are applied to study and potentially manipulate blood flow dynamics, offering insights for therapies against arterial plaque deposition and for understanding varied blood flow behaviors. This research focuses on predicting the flow patterns of blood infused with gold and maghemite nanoparticles (gold-maghemite/blood) inside an EM microchannel under these electromagnetic influences and abruptly change in pressure gradient. The study models these flows by considering radiation heat emission and Darcy drag forces within porous media. Mathematical representation involves time-variant partial differential equations, resolved through Laplace transform (LT) to yield compact-form expressions for the model variables. The outcomes, including shear stress (SS) and rate of heat transfer (RHT) across the microchannel, are analyzed and displayed graphically, highlighting the effects of modified Hartmann number and electrode width on these parameters. Hybrid nano-blood (HNB) and nano-blood (NB) exhibit distinct thermal characteristics, with HNB transferring more heat within the blood flow. These study implements a cutting-edge AI-powered approach for high-fidelity evaluation of critical flow parameters, achieving unprecedented prediction accuracy. Validation results confirm the algorithm’s excellence, with SS predictions reaching 99.552% (testing) and 97.019% (cross-validation) accuracy, while RHT predictions show 100% testing accuracy and 97.987% cross-validation reliability. This convergence of nanotechnology with advanced machine learning paves the way for transformative clinical applications that could redefine standards of care in surgical oncology, interventional cardiology, and therapeutic radiology. This model underpins potential applications such as controlled drug release and magnetic fluid hyperthermia, enhancing procedures like cardiopulmonary bypass, vascular surgery, and diagnostic imaging.
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