Applied Nonlinear Analysis of Deep Learning Models in MBA Streams: A Mathematical and Statistical Perspective

Abstract

As educational institutions strive to enhance student performance and outcomes, integration of advanced computing techniques becomes even more critical. To address this, we simulated student performance in MBA streams using a Nonlinear Deep Radial Basis Function (RBF) network. This method uses the capacity of the RBF network to control nonlinearities via its radial basis functions together with a deep learning technique to increase the capability of the model. Following hybrid optimization with gradient descent and evolutionary algorithms, we assessed the performance of the network using cross-valuation techniques. Over typical linear models, the nonlinear deep RBF model exhibited better performance. With a mean absolute error (MAE) of 0.25 and a root mean square error (RMSE) of 0.35 it attained a prediction accuracy of 87.5%. These results suggest that while nonlinear modeling shows a clear increase in the accuracy of the model to estimate student performance, it may effectively represent the complex interactions driving academic success.