Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Multivariate time series classification is one of the increasingly important issues in machine
learning. Existing methods focus on establishing the global long-range dependencies or …

There is a recent growing interest in applying Deep Learning techniques to tabular data, in
order to replicate the success of other Artificial Intelligence areas in this structured domain …

Machine learning techniques are now integral to the advancement of intelligent urban
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …

The dark web, as an integral part of the multi-layered structure of the internet, provides
anonymity and high levels of concealment absent on the surface web. Users can engage in …

In natural language processing (NLP), transformer based models have surpassed recurrent
neural networks (RNN) as state of the art, being introduced specifically to address the …

In today's automotive industry, modern vehicles are equipped with numerous sensors to
monitor the vehicle's state. Accurate prediction of component wear-and-tear requires the …

Process mining has emerged as a ground-breaking interdisciplinary realm that bridges data
science with traditional process modelling. This innovative approach fosters a unique way of …

A transzformátor modellek jelentős előrelépéseket hoztak az idősor-elemzésben, mivel
képesek hosszú távú függőségeket megragadni. A tanulmány a transzformátor modellek …