Recent advancements in deep learning (DL) have posed a significant challenge for
automatic speech recognition (ASR). ASR relies on extensive training datasets, including …

In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

In the coming 6G communications, network densification, high throughput, positioning
accuracy, energy efficiency, and many other key performance indicator requirements are …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL)
proved to be effective in optimizing the management of integrated energy systems in …

The issue of electroencephalogram (EEG)-based emotion recognition has great academic
and practical significance. Currently, there are numerous research trying to address this …

Spatially consistent and up-to-date maps of human settlements are crucial for addressing
policies related to urbanization and sustainability, especially in the era of an increasingly …

Deep learning has greatly increased the capabilities of" intelligent" technical systems over
the last years [1]. This includes the industrial automation sector [1]-[4], where new data …