For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …

Abstract Machine learning (ML)-based urban waterlogging susceptibility studies suffer from
class imbalance, as fewer positive samples are generally available than potential negative …

For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Oversampling is a strategy employed in machine learning to handle imbalanced datasets by
creating copies of the minority class instances to balance the dataset, thus reducing bias …

The imbalanced datasets and their classification has pulled in as a hot research topic over
the years. It is used in different fields, for example, security, finance, health, and many others …

The rapid influx of data-driven models into the industrial sector has been facilitated by the
proliferation of sensor technology, enabling the collection of vast quantities of data …

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

The most important applications of vehicular ad hoc networks (VANET) are dynamic traffic
light control. Sybil attack creates multiple fake identities for the same vehicle. In this paper …

Reverse k nearest neighbor search (RkNNS) plays an important role in various data
processing and analysis tasks, seeking to pinpoint data considering the query data q among …