Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives.

The aim of this study is to review the available knowledge concerning the use of artificial Intelligence (AI) in general in different areas of Forensic Sciences from human identification to postmortem interval estimation and the estimation of different causes of death. This paper aims to emphasize the different uses of AI, especially in Forensic Medicine, and elucidate its technical part. This will be achieved through an explanation of different technologies that have been so far employed and through new ideas that may contribute as a first step to the adoption of new practices and to the development of new technologies.

The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis.

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.

Visualising key information and communication technologies (ICT) indicators for children and young individuals in Europe.

DGmap is an online interactive tool that visualises indicators drawn from large-scale European and international databases reflecting the use of information and communication technologies (ICT) amongst children and young individuals in Europe. A large number of indicators are estimated and visualised on an interactive map revealing convergences and divergences amongst European countries. Apart from its main feature, that of facilitating users to observe discrepancies between countries, the map offers the potentiality of downloading or customising country reports, information concerning the estimation of the indices and their values as spreadsheets, while covering a period from 2015 and onwards.

Multi-Modal Song Mood Detection with Deep Learning.

The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing.

A semantically-aided architecture for a web-based monitoring system for carotid atherosclerosis.

Carotid atherosclerosis is a multifactorial disease and its clinical diagnosis depends on the evaluation of heterogeneous clinical data, such as imaging exams, biochemical tests and the patient's clinical history. The lack of interoperability between Health Information Systems (HIS) does not allow the physicians to acquire all the necessary data for the diagnostic process. In this paper, a semantically-aided architecture is proposed for a web-based monitoring system for carotid atherosclerosis that is able to gather and unify heterogeneous data with the use of an ontology and to create a common interface for data access enhancing the interoperability of HIS.