Incremental learning is one of the most important abilities of human beings. In the age of
artificial intelligence, it is the key task to make neural network models as powerful as human …

Learning from data streams (incremental learning) is increasingly attracting research focus
due to many real-world streaming problems and due to many open challenges, among …

Due to the immense growth of e-commerce and increased online based payment
possibilities, credit card fraud has become deeply relevant global issue. Recently, there has …

Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the
data modeling of many of the real-world processes that are being digitized. The …

Abstract In Unsupervised Domain Adaptation (UDA), a network is trained on a source
domain and adapted on a target domain where no labeled data is available. Existing UDA …

In recent years, machine learning (ML) modeling (often referred to as artificial intelligence)
has become increasingly popular for personnel selection purposes. Numerous …

One popular approach employed to tackle classification problems in a static environment
consists in using a Dynamic Classifier Selection (DCS)-based method to select a custom …

The increasing demand for smart vehicles with many sensing capabilities will escalate data
traffic in vehicular networks. Meanwhile, available network resources are limited. The …

One of the key challenges in the maritime industry refers to minimizing the time a vessel
cannot be utilized, which has multiple effects. The latter is addressed through maintenance …

Real-time fraud detection in credit card transactions is challenging due to the intrinsic
properties of transaction data, namely data imbalance, noise, borderline entities and …