A machine learning based sentient multimedia framework to increase safety at work.

In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems.

Leveraging network analysis to support experts in their analyses of subjects with MCI and AD.

In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes.

Building Topic-Driven Virtual IoTs in a Multiple IoTs Scenario.

In the last years, several attempts to combine the Internet of Things (IoT) and social networking have been made. In the meantime, things involved in IoT are becoming increasingly sophisticated and intelligent, showing a behavior that tends to look like the one of users in social networks. Therefore, it is not out of place to talk about profiles of things and about information and topics exchanged among them.

A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD.

Creutzfeldt-Jacob disease (CJD) is a rapidly progressive, uniformly fatal transmissible spongiform encephalopathy. Sporadic CJD (sCJD) is the most common form of CJD. Electroencephalography (EEG) is one of the main methods to perform clinical diagnosis of CJD, mainly because of periodic sharp wave complexes (PSWCs).