  This paper presents a novel methodology for modeling memristive biosensing
within COMSOL Multiphysics, focusing on critical performance metrics such as
antigen-antibody binding concentration and output resistive states. By studying
the impact of increasing inlet concentrations, insights into binding
concentration curve and output resistance variations are uncovered. The
resultant simulation data effectively trains a support vector machine
classifier (SVMC), achieving a remarkable accuracy rate of 97%. The
incorporation of artificial intelligence (AI) through SVM demonstrates
promising strides in advancing AI-based memristive biosensing modeling,
potentially elevating their performance standards and applicability across
diverse domains.
