K. P. Soman

TL;DR: A highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet is proposed which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

TL;DR: This work uses three different deep learning architectures for the price prediction of NSE listed companies and compares their performance and applies a sliding window approach for predicting future values on a short term basis.

TL;DR: This paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised learning methods such as multi-layer perceptron (MLP), CNN, CNN-recurrent neural network (CNN-RNN), CNN-long short-term memory ( CNN-LSTM) and CNN-gated recurrent unit (GRU), using millions of known good and bad network connections.

TL;DR: This paper demonstrates that the proposed preprocessor with a Shannon energy envelope (SEE) estimator is better able to detect R-peaks than other well-known methods in case of noisy or pathological signals.

TL;DR: Four types of deep learning architectures are used i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available for day-wise closing price of two different stock markets.