Pasquale De Rosa

Université de Neuchâtel | UniNE · Institut d'informatique (IIUN)

Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e.,, blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger....

Federated learning (FL) is a distributed machine learning paradigm that enables data owners to collaborate on training models while preserving data privacy. As FL effectively leverages decentralized and sensitive data sources, it is increasingly used in ubiquitous computing including remote healthcare, activity recognition, and mobile applications....

In machine learning (ML), the inference phase is the process of applying pre-trained models to new, unseen data with the objective of making predictions. During the inference phase, end-users interact with ML services to gain insights, recommendations, or actions based on the input data. For this reason, serving strategies are nowadays crucial for...

This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulti...

Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. T...

Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger....

Background: The present paper aims to investigate the adoption of Neural Networks for recommendation systems and to propose Deep Learning architectures as advanced frameworks for designing Collaborative Filtering engines. Recommendation systems are data-driven infrastructures which are widely adopted to create effective and cutting-edge smart servi...

Automatic health monitoring and activity recognition systems provide specific information for caregivers and health professionals to prevent injury or disease. With the improvement of sensor technologies, wireless communication and machine learning, systems can now be aware of changes in the user’s state and its environment in order to provide acti...

The aim of this article is to discuss an advanced approach to recommendation systems, based on the adoption of Deep Feed-Forward Neural Networks. Recommendation engines are data-driven infrastructures designed to help customers in their decision-making process, and nowadays represent the “state of the art” in designing smart and personalized servic...