G
Giuseppe De Pietro
Researcher at Indian Council of Agricultural Research
Publications - 263
Citations - 3638
Giuseppe De Pietro is an academic researcher from Indian Council of Agricultural Research. The author has contributed to research in topics: Decision support system & Computer science. The author has an hindex of 28, co-authored 241 publications receiving 2441 citations. Previous affiliations of Giuseppe De Pietro include National Research Council & Institute for High Performance Computing and Networking, CNR.
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Journal ArticleDOI
Exploring the Use of Artificial Intelligence Techniques to Detect the Presence of Coronavirus Covid-19 Through Speech and Voice Analysis
Laura Verde,Giuseppe De Pietro,Ahmed Ghoneim,Mubarak Alrashoud,Khaled N. Al-Mutib,Giovanna Sannino +5 more
TL;DR: In this paper, the authors explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis, using the Coswara database, an available crowd-sourced database.
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Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.
Rosario Catelli,Rosario Catelli,Francesco Gargiulo,Valentina Casola,Giuseppe De Pietro,Hamido Fujita,Hamido Fujita,Hamido Fujita,Massimo Esposito +8 more
TL;DR: Two multi-lingual deep learning systems have been developed and demonstrated the effectiveness of cross-lingUAL transfer learning to de-identify medical records written in a low resource language such as Italian, using one with high resources such as English.
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Formal specification of wireless and pervasive healthcare applications
TL;DR: It will be shown how it is possible to formalize and constrict mobility characteristics by combining, and in some cases extending, several formal methods to achieve a higher degree of dependability.
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A smart mobile, self-configuring, context-aware architecture for personal health monitoring
Massimo Esposito,Aniello Minutolo,Rosario Megna,Manolo Forastiere,Mario Magliulo,Giuseppe De Pietro +5 more
TL;DR: A smart mobile, self-configuring, context-aware architecture devised to enable the rapid prototyping of personal health monitoring applications for different scenarios, by exploiting commercial wearable sensors and mobile devices as well as knowledge-based technologies is presented.
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Multilingual POS tagging by a composite deep architecture based on character-level features and on-the-fly enriched Word Embeddings
TL;DR: A POS tagging system based on a deep neural network made of a static and task-independent pre-trained model for representing words semantics enriched by morphological information, by approximating the Word Embedding representation learned from an unlabelled corpus by the fastText model is proposed.