The approximate nearest neighbor (ANN) search in high-dimensional spaces is a
fundamental but computationally very expensive problem. Many methods have been …

Feature selection algorithms, such as ReliefF, are very important for processing high‐
dimensionality data sets. However, widespread use of popular and effective such algorithms …

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine
learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing …

Cross-modal hashing (CMH) has recently received increasing attention with the merit of
speed and storage in performing large-scale cross-media similarity search. However, most …

Many multivariate time series anomaly detection frameworks have been proposed and
widely applied. However, most of these frameworks do not consider intrinsic relationships …

The rise of data-intensive scalable computing systems, such as Apache Spark, has
transformed data processing by enabling the efficient manipulation of large datasets across …

Understanding solar radiation variability is essential for efficiently planning and managing
solar energy systems. The transmission of solar radiation to the ground is generally affected …

Approximate nearest neighbor search plays a fundamental role in many areas, and the k-
nearest neighbor graph (KNNG) becomes a promising solution, especially in high …

Locality‐sensitive hashing (LSH) is widely used in the context of nearest neighbor search of
large‐scale high‐dimensions. However, there are serious imbalance problems between the …