Applied Nonlinear Analysis of Deep Learning Models in MBA Streams: A Mathematical and Statistical Perspective
Year: 2024
Type: article
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.
Author: Somasekhar Donthu
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4402449057