Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

The increasing number of security attacks have inspired researchers to employ various
classifiers, such as support vector machines (SVMs), to deal with them in Intrusion detection …

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to
predict the NOx emission concentration based on the combination of mutual information …

Due to the widespread diffusion of network connectivity, the demand for network security
and protection against cyber-attacks is ever increasing. Intrusion detection systems (IDS) …

Feature selection is an essential step in various tasks, where filter feature selection
algorithms are increasingly attractive due to their simplicity and fast speed. A common filter …

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the
shortage of data monitoring devices and the difficulty of comprehensive coverage of …

To help individuals or companies make a systematic and more accurate decisions,
sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection …

Rapid data growth presents many challenges for Machine Learning (ML) tasks as it can
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …

Nowadays, it has become a necessity for operational and reliable operation of networks due
to our increased dependency over the network services. However, intruders are …

Intrusion detection is a challenging technology in the area of cyberspace security for
protecting a system from malicious attacks. A novel accurate and effective misuse intrusion …