Abstract In Industrial Internet-of-Things, data streams across heterogeneous networks which
results in several cyber–physical attacks. Moreover, the security of unlabeled data is a …

Abstract Cyber–Physical Production Systems (CPPSs) are hybrid systems composed of a
discrete and continuous part. However, most of the applied machine learning algorithms …

Starting from the general observation that a measurement delivered by a sensor is subject to
an uncertainty, its use in a decision chain must take into account this imprecise character. It …

Abstract Multivariate Time Series (MTS) data obtained from large scale systems carry
resourceful information about the internal system status. Multivariate Time Series Clustering …

Background Alignment-free methods are a popular approach for comparing biological
sequences, including complete genomes. The methods range from probability distributions …

In the field of Cyber-Physical Systems (CPS), there is a large number of machine learning
methods, and their intrinsic hyper-parameters are hugely varied. Since no agreed-on …

Pathogens such as bacteria and viruses are leading causes of disease worldwide, which
makes it essential to identify them in DNA samples. Instead of analysing raw DNA …

Abstract Siber-Fiziksel Sistemler'den elde edilen Çok-değişkenli Zaman Serileri (CZS) verisi,
sistemin karakteristik özellikleri hakkında değerli bilgiler içermektedir. Bir Makine Öğrenmesi …

Reinforcement Learning (RL) is a learning paradigm used for sequential decision-making
problems. It includes the interaction of an agent with an environment via taking actions. As a …