Open Access. Powered by Scholars. Published by Universities.®
Programming Languages and Compilers Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- LSTM (2)
- Machine learning (2)
- ARIMA (1)
- Asteroid detection (1)
- Astronomy (1)
-
- Autoencoder (1)
- Cyber security (1)
- Data Science (1)
- Deep Learning (1)
- Deep learning (1)
- Dimensionality reduction (1)
- Ensemble (1)
- LASSO (1)
- Long short term memory (1)
- MLR (1)
- Machine Learning (1)
- NIDS (1)
- Near-earth object (1)
- Network intrusion detection (1)
- Neural network (1)
- PCA (1)
- RNN (1)
- Random Forest (1)
- SVR (1)
- Supervised Regression (1)
- VIX (1)
- VIX Futures (1)
- Publication
Articles 1 - 3 of 3
Full-Text Articles in Programming Languages and Compilers
Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection With Machine Learning, Rachel Meyer
Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection With Machine Learning, Rachel Meyer
ELAIA
Asteroid detection is a common field in astronomy for planetary defense, requiring observations from survey telescopes to detect and classify different objects. The amount of data collected each night is continually increasing as new and better-designed telescopes begin collecting information each year. This amount of data is quickly becoming unmanageable, and researchers are looking for ways to better process this data. The most feasible current solution is to implement computer algorithms to automatically detect these sources and then use machine learning to create a more efficient and accurate method of classification. Implementation of such methods has previously focused on larger …
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …