Optimizing Deep Learning Model Parameters with the Bees Algorithm for Improved Medical Text Classification
This paper introduces a novel mechanism to obtain the optimal parameters of a deep learning model using the Bees Algorithm, which is a recent promising swarm intelligence algorithm. The optimization problem is to maximize the accuracy of classifying ailments based on medical text given the initial hyper-parameters to be adjusted throughout a definite number of iterations. Experiments included two different datasets: English and Arabic. The highest accuracy achieved is 99.63% on the English dataset using Long Short-Term Memory (LSTM) along with the Bees Algorithm, and 88% on the Arabic dataset using AraBERT.