EN
Analyzing Diabetic Dynamics with MRK4, and LSTM Techniques with Multiplicative Calculus
Abstract
This study compares the use of Long Short-Term Memory (LSTM) networks for predictive modeling with multiplicative calculus. We evaluate and quantitatively analyze both methodologies to determine their prediction performance. While LSTM networks are investigated for them power to learn and generalize patterns, the multiplicative calculus technique is analyzed for its ability to grasp complex connections within the data. This study attempts to shed light on the efficacy of each approach by carefully analyzing error measures including mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results aid in the comprehending of the subtleties related to LSTM networks and multiplicative calculus, assisting practitioners and researchers in choosing the best method for tasks involving predictive modeling.
Keywords
References
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Details
Primary Language
English
Subjects
Experimental Mathematics , Biological Mathematics , Applied Mathematics (Other)
Journal Section
Research Article
Authors
Publication Date
December 30, 2024
Submission Date
February 22, 2024
Acceptance Date
October 24, 2024
Published in Issue
Year 1970 Volume: 45 Number: 4