Evaluating the Robustness of RNN Models on Diverse Sequential Datasets

Abstract

Recurrent Neural Networks are deep neutral networks that handle sequence data due to their ability to capture and reserve temporal dependencies in the data. This characteristic makes them ideal for applications such as natural language processing, speech recognition, and time series prediction. In this research paper RNN methods are applied and analyzed using the UCI Student Performance dataset and the UCI Human Activity Recognition (HAR) dataset. It studies the impact of hyperparameter optimization and data set balancing on critical performance system of measurement such as model accuracy, memory usage, and time taken for prediction. By altering critical features, the number of RNNs, the dropout rate, and the batch size, the effect of these parameters on model performance is analyzed. The results show that varying the number of RNN units and batch size optimization improves model performance, as well as the effect of computational efficiency. The RNN test produces 99.8% accuracy in the UCI Student Performance data set and 95.11% in the HAR data set. In adding, balanced datasets help to improve generalization by reducing bias and overfitting. A train-test split of 70%-30% is testified to provide the best overall precision compared to computational resource costs. This research tells us how hyperparameter optimization and data handling modify and improve the performance of RNN models.