With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …

Many real-world applications adopt multi-label data streams as the need for algorithms to
deal with rapidly changing data increases. Changes in data distribution, also known as …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …

Last decade demonstrate the massive growth in organizational data which keeps on
increasing multi‐fold as millions of records get updated every second. Handling such vast …

The detection of local outliers over high-volume data streams is critical for diverse real-time
applications in the real world, where the distributions in different subsets of the data tend to …

Abstract Concept drift detection is a crucial task in data stream evolving environments. Most
of state of the art approaches designed to tackle this problem monitor the loss of predictive …

The embedded, computational and cloud elements of industrial cyber physical systems
(CPS) generate large volumes of data at high velocity to support the operations and …

The accuracy and performance of Machine Learning (ML) models can gradually or even
suddenly degrade when the underlying statistical distribution of data streams changes over …

Time series data with missing values are ubiquitous in real applications due to various
unforeseen faults during data generation, storage, and transmission. Time-Series Data …

The notion of concept drift refers to the phenomenon that the distribution generating the
observed data changes over time. If drift is present, machine learning models can become …