Description
Chair Florin Manolache
TFI Virtual Room A
One of the recurring problems of everyday life in big urban areas is the traffic congestion. Nowadays advance in the technologies powering the Internet of Vehicles together with state-of-the-art artificial intelligence algorithms offer the means to improve traffic flow with little changes to the existing infrastructure. This paper proposes a reinforcement learning based solution to traffic...
In this paper we propose a deep learning based method to forecast the energy consumption in public buildings, based on past measurements. The method integrates two neural networks, namely a Feed-Forward Neural Network and a Long-
Short Term Memory Network. Our approach consists of three main steps: data processing, training and validation, and finally the forecasting step. We validated the...
In most Machine Learning models, the data used for training or testing is public, available to anyone who wishes to see it. New research has improved these models, by adding privacy and distributing the processing load on multiple workers in the cloud. The aim of the paper is to perform an analysis between a classical approach (in which we have access to all the data) and one in which the...
This paper comparatively analyzes the performance of two machine learning algorithms (i.e. Random Forests and Gradient Boosting) in the field of forecasting the energy consumption based on historical data. The two algorithms are applied
in order to forecast the energy consumption individually, and then combined together by using a Weighted Average Ensemble Method. The comparison among the...
Swarm communication represents a communication paradigm that offers support in building and running executable choreographies that can be used to model business workflows. In the current paper we present how Self Sovereign Identities (SSIs) can be used to improve the Swarm communication. The usage of the SSIs allows us to have a decentralized way to identify entities involved in Swarm...
