Enhancing COVID-19 Impact Prediction through the Application of LSTM Networks in Deep Learning.

Abstract

Artificial Neural Networks (ANNs) have been around for a while, and as technology has progressed, more people can have now access to Graphical Processing Units (GPUs), Tensor Processing Units (TPUs), and complex architectures. These days, deep neural networks are of the utmost significance in pattern recognition. One special application of the ANNs is the sequence classification and prediction. A special type of neural network with the capacity to remember patterns along with the temporal aspects have been widely used, they are the recurrent Neural Networks (RNNs). The Long Short Term Memory Networks (LSTMs) are improved versions of RNN with a better dealing of vanishing gradient problems. In this chapter, we discuss an LSTMs with their regular implementation as well as time distributed and bidirectional implementations for the purpose of sequence prediction.  Every day, new information about the COVID-19 pandemic's effects is released, and people all over the world are still dealing with its aftermath. Long Short-Term Memory (LSTM) networks are trained with this data in order to predict              estimates of the global impact of the COVID-19 pandemic. The LSTM architectures are discussed and compared with a vanilla RNN and the results are presented here. The results show the LSTMs outperform RNNs when the mean absolute error is compared for all the models.