A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …
between different communicating devices. The data should be communicated securely …
A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions
The stock market has been an attractive field for a large number of organizers and investors
to derive useful predictions. Fundamental knowledge of stock market can be utilised with …
to derive useful predictions. Fundamental knowledge of stock market can be utilised with …
Multi-source aggregated classification for stock price movement prediction
Predicting stock price movements is a challenging task. Previous studies mostly used
numerical features and news sentiments of target stocks to predict stock price movements …
numerical features and news sentiments of target stocks to predict stock price movements …
[HTML][HTML] The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction
The future is unknown and uncertain, but there are ways to predict future events and reap
the rewards safely. One such opportunity is the application of machine learning and artificial …
the rewards safely. One such opportunity is the application of machine learning and artificial …
Bayesian optimization based dynamic ensemble for time series forecasting
Among various time series (TS) forecasting methods, ensemble forecast is extensively
acknowledged as a promising ensemble approach achieving great success in research and …
acknowledged as a promising ensemble approach achieving great success in research and …
Survey of feature selection and extraction techniques for stock market prediction
In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …
Pearson correlation coefficient-based performance enhancement of broad learning system for stock price prediction
G Li, A Zhang, Q Zhang, D Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Accurate prediction of a stock price is a challenging task due to the complexity, chaos, and
non-linearity nature of financial systems. In this brief, we proposed a multi-indicator feature …
non-linearity nature of financial systems. In this brief, we proposed a multi-indicator feature …
Quantitative stock portfolio optimization by multi-task learning risk and return
Selecting profitable stocks for investments is a challenging task. Recent research has made
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction
Stock market forecasting has been a subject of interest for many researchers; the essential
market analyses can be integrated with historical stock market data to derive a set of …
market analyses can be integrated with historical stock market data to derive a set of …
Predicting stock prices with finbert-lstm: Integrating news sentiment analysis
W jun Gu, Y hao Zhong, S zun Li, C song Wei… - Proceedings of the …, 2024 - dl.acm.org
The stock market's ascent typically mirrors the flourishing state of the economy, whereas its
decline is often an indicator of an economic downturn. Therefore, for a long time, significant …
decline is often an indicator of an economic downturn. Therefore, for a long time, significant …