Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

The manuscript represents a comeprehensive and systematic literature review on the
machine learning methods in the emerging applications of the smart cities. Application …

In machine learning, a combination of classifiers, known as an ensemble classifier, often
outperforms individual ones. While many ensemble approaches exist, it remains, however, a …

Precise reference evapotranspiration (ETo) estimation and prediction are the first steps to
realize efficient agricultural water resources management. As machine learning methods are …

Due to the frequency of malicious network activities and network policy violations, intrusion
detection systems (IDSs) have emerged as a group of methods that combats the …

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion
detection mechanisms to monitor computer resources and generate reports on anomalous …

The emergence of ground-breaking technologies such as artificial intelligence, cloud
computing, big data powered by the Internet, and its highly valued real-world applications …

Distributed Denial of Service (DDoS) attacks have been the prominent attacks over the last
decade. A Network Intrusion Detection System (NIDS) should seamlessly configure to fight …

Stacking and bagging are widely used ensemble learning approaches that make use of
multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous …

In order to address the issues that arise in modern power systems, such as system
dynamics, stability, control, efficiency, reliability, economy, planning and policy, and so on …