  Recurrent neural network (RNN) is an effective neural network in solving very
complex supervised and unsupervised tasks. There has been a significant
improvement in RNN field such as natural language processing, speech
processing, computer vision and other multiple domains. This paper deals with
RNN application on different use cases like Incident Detection, Fraud
Detection, and Android Malware Classification. The best performing neural
network architecture is chosen by conducting different chain of experiments for
different network parameters and structures. The network is run up to 1000
epochs with learning rate set in the range of 0.01 to 0.5.Obviously, RNN
performed very well when compared to classical machine learning algorithms.
This is mainly possible because RNNs implicitly extracts the underlying
features and also identifies the characteristics of the data. This helps to
achieve better accuracy.
