Prabhakaran, P (2024) Advanced Statistical and Nonlinear Analysis Techniques for Deep Learning in MBA Education. Advanced Statistical and Nonlinear Analysis Techniques for Deep Learning in MBA Education, 31 (7). pp. 38-52. ISSN 1074-133X

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Abstract

Integration of sophisticated statistical and nonlinear analysis techniques by deep learning
has become a main strategy for enhancing MBA program educational outcomes. Our work
provides the DRAN, which combines residual learning with attention processes, thereby
focusing on the most relevant aspects of educational datasets. Nonlinear regression and
clustering are among advanced statistical techniques that assist the DRAN model to
effectively capture complex relationships in the data, hence improving knowledge of
student behavior and performance. The approach trains the DRAN model to estimate
academic achievements and offer tailored learning interventions after preprocessing of
student data including grades, attendance, and engagement measurements. The DRAN
model outperforms traditional machine learning methods with an accuracy of 92.7% in
assessing student performance and an improvement of 15% in the precision of tailored
learning recommendations. These findings show how deep learning might transform MBA
education by arming educators with useful insights that drive student success. Together,
deep residual learning and nonlinear dynamics increase forecast accuracy and enable
flexible learning environments fit for particular requirements. This research contributes to
the growing corpus of information on the application of artificial intelligence in education
and paves the basis for next breakthroughs in individualized learning systems.

Item Type: Article
Uncontrolled Keywords: Deep Residual Attention Network, MBA education, nonlinear analysis, personalized learning, predictive modeling.
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Dr. B Sivakumar
Date Deposited: 04 Dec 2025 05:45
Last Modified: 04 Dec 2025 05:45
URI: https://ir.psgcas.ac.in/id/eprint/2557

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