Browsing by Subject "Deep learning"
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Item A Deep Learning Approach for Intrusion Detection using Recurrent Neural Network(Department of computer Science & information Technology, 2018) Rai DipendraIntrusion detection discover a critical part in guaranteeing data security and the key innovation is to precisely recognize different assaults in the system. In this dissertation, the intrusion detection model based on deep learning is investigated, and a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS) is used. The performance of the model is based on binary and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the model has been studied. The performance of the model is compared with Naïve Bayes, Multilayer Perceptron and Support Vector Machine that has been analyzed by previous researchers on the benchmark data set. The test results demonstrate that RNN-IDS is remarkably appropriate for displaying high precision and its execution is better than that of machine learning techniques in both binary and multiclass classification. The RNN-IDS demonstrate enhances the precision of the intrusion identification and gives other examination strategy to intrusion detection discovery. Keywords: Recurrent neural networks, RNN-IDS, intrusion detection, deep learning, machine learning.Item Stock Market Forecasting with LSTM and Sentiment Analysis(Department of Computer Science and Information Technology, 2020) Shrestha, AshishStock market is an industry where lots of data is generated daily and benefits are reaped on the basis of accurate prediction. Many people invest in stock market having some prediction and more luck. A decision in stock market plays an important role in the investor’s life. Also, stock market is a very complex system and non-linear in nature. So, then it is very difficult to analyse all the impacting factors before making a decision. Making decision with traditional techniques may be time consuming and may not ensure the reliability of the prediction. Data from stock market is a time series data and different variations of neural networks are widely being used for stock forecasting and prediction problems. Among various architectures of deep neural network, LSTM is one of the design that supports time steps of arbitrary sizes and is free of vanishing gradient problem. Furthermore, sentiment analysis is becoming popular in predicting the stock market behavior based on investors reactions. This research work studies the usage of LSTM networks on stock market prediction. Also, assuming that news articles have impact on stock market, this is an attempt to study the relationship between news and stock trend. From the result of the experiment carried out during this research work showed that the financial news do affect the stock market. Sentiment scores from financial news in addition to the stock indices do make the prediction or forecasting process more predictable. Keywords: Deep neural network, Deep learning, LSTM, Neural Network ,Stock Market, Sentiment Analysis, Time Series.