Fourier analysis has been an instrumental tool in the development of signal processing. This
leads us to wonder whether this framework could similarly benefit generative modelling. In …

This paper presents classification and analysis of the mathematical models of the spread of
COVID-19 in different groups of population such as family, school, office (3–100 people) …

Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but
current methods lack interpretability. We leverage common properties of existing methods …

This study addresses a significant gap in the field of time series regression modeling by
highlighting the central role of data augmentation in improving model accuracy. The primary …

Machine learning (ML) has the potential to improve various supply chain management
(SCM) tasks, namely demand forecasting, risk management, inventory management …

Despite recent breakthroughs in the domain of implicit generative models, the task of
evaluating these models remains a challenging task. With no single metric to assess overall …

This paper presents SimilarityTS, a toolkit for the evaluation of similarity for multivariate time
series. This software enables the comparison and assessment of the similarity between time …

Deep learning has transformed data generation, particularly in creating synthetic sensor
data. This capability is invaluable in fields like autonomous driving, robotics, and computer …

Focusing on the role of neural networks within Generative Artificial Intelligence, this paper
reviews their operating principles, recent developments, and implications for research in the …

This literature review examines the technologies of Machine Learning (ML) in Predictive
Maintenance (PdM), highlighting the necessity for industries to boost production efficiency …