Machine learning has become a powerful tool for systems biologists, from diagnosing
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …

With the rapid development of emerging technologies such as self-media, the Internet of
Things, and cloud computing, massive data applications are crossing the threshold of the …

Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …

Predictive process monitoring (PPM) is a specific task under the umbrella of Process Mining
that aims to predict several factors of a business process (eg, next activity prediction) based …

Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

Unequal data distribution among different classes usually cause a class imbalance problem.
Due to the class imbalance, the classification models become biased toward the majority …

The change in data distribution over time (known as concept drift) makes the classification
process complex because of the discrepancy between current and incoming data …

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 …

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 …

Real-time safety assessment (RTSA) of dynamic systems holds substantial implications
across diverse fields, including industrial and electronic applications. However, the …