Research Article
A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning
@ARTICLE{10.4108/eetiot.7134, author={Prabakaran Raghavendran and Tharmalingam Gunasekar and Saikat Gochhait}, title={A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning}, journal={EAI Endorsed Transactions on Internet of Things}, volume={11}, number={1}, publisher={EAI}, journal_a={IOT}, year={2025}, month={3}, keywords={fractional-order differential equation, Mittag-Leffler function, gamma function, Ramadan Group transform of the fractional derivative}, doi={10.4108/eetiot.7134} }- Prabakaran Raghavendran
Tharmalingam Gunasekar
Saikat Gochhait
Year: 2025
A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning
IOT
EAI
DOI: 10.4108/eetiot.7134
Abstract
This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, specifically neural networks, to solve these equations. The paper demonstrates that machine learning can enhance the solution process through data generation, model design, and optimization. Examples provided illustrate how combining traditional methods with machine learning can effectively solve fractional differential equations.
Copyright © 2025 P. Prabakaran et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
