Comparative Analysis of Performance of Deep Learning Classification Approach based on LSTM-RNN for Textual and Image Datasets

Deep learning approaches can be applied to a large amount of data for the purpose of simplifying and improving the engineering practice of automated decision-making activities rather than relying on human encoded heuristics. The need for generating faster and effective decisions about systems, processes, and applications gave rise to many artificial intelligences motivated approaches such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), fuzzy analytics, etc. Deep learning deploys diverse multiple layers of cascaded processing elements to enable features extraction and transformations. These deep learning approaches conduct multiple levels of depiction corresponding to distinct abstraction levels. There are several applications of deep learning algorithms including weather forecasting, object recognition, stock market performance forecasts, medical diagnosis, and emergency warning systems. This paper investigates the performance of the deep learning approach on the basis of processing components, data representation, and data types. To achieve this, a deep learning algorithm based on a long short-term memory-recurrent neural network (LSTM-RNN) was utilized to learn hidden patterns and features in the textual and image datasets respectively. The outcomes reveal that the performance of the image-based deep learning model was better in terms of speed due to well-defined patterns of data representation against the data with sentiments-based deep learning by 3.49 mins. to 18.25 mins. While the LSTM-RNN with images offered better classification accuracy by 96.50% to 85.69% due to complex network architecture, processing elements, and features of the underlying datasets