PV systems are prone to external environmental conditions that affect PV system operations.
Visual inspection of the impacts of faults on PV system is considered a better practice rather …

Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …

By exploiting the inherent hardware characteristics of wireless devices, radio frequency
fingerprint identification (RFFI) has been widely applied in device authentication and …

Current data stream clustering algorithms face low efficiency in both the online and offline
phases, and struggle to address the problem of cluster boundary overlap caused by concept …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Data stream clustering (DSC) is one of the most significant and widely studied research
directions in the field of data mining. However, for the processing of large-scale data …

At present, there exist some problems in granular clustering methods, such as lack of
nonlinear membership description and global optimization of granular data boundaries. To …

The proliferation of high-dimensional complex data in various fields such as multimedia,
social media, and sensor networks has led to an increasing demand for real-time clustering …

The synthetic minority oversampling technique (SMOTE) has been praised by researchers in
class-imbalanced classification. Although SMOTE eliminates imbalances between classes …

We propose polar encoding, a representation of categorical and numerical [0, 1]-valued
attributes with missing values to be used in a classification context. We argue that this is a …