Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and
machine learning. Recent breakthrough results in image analysis and speech recognition …

In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

The standard objective in machine learning is to train a single model for all users. However,
in many learning scenarios, such as cloud computing and federated learning, it is possible …

Recurrent sequence-to-sequence models using encoder-decoder architecture have made
great progress in speech recognition task. However, they suffer from the drawback of slow …

Human activity recognition (HAR) is currently recognized as a key element of a more
general framework designed to perform continuous monitoring of human behaviors in the …

Federated learning (FL) is a heavily promoted approach for training ML models on sensitive
data, eg, text typed by users on their smartphones. FL is expressly designed for training on …

This article proposes recurrent neural networks (RNNs) for the WiFi fingerprinting indoor
localization. Instead of locating a mobile user's position one at a time as in the cases of …

Cybersecurity mainly prevents the hardware, software, and data present in the system that
has an active internet connection from external attacks. Organizations mainly deploy …

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …

Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users'
preferences on items. With users and items exploding, such a matrix is usually high …