Analyzing Diabetic Dynamics with MRK4, and LSTM Techniques with Multiplicative Calculus

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.