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
Virtual Reality as a Distraction Intervention to Relieve Pain and Distress During Medical Procedures: A Comprehensive Literature Review
Paola Indovina,Paola Indovina,Daniela Barone,Luigi Gallo,Andrea Chirico,Andrea Chirico,Giuseppe De Pietro,Antonio Giordano,Antonio Giordano +8 more
TL;DR: VR has proven to be effective in reducing procedural pain, as almost invariably observed even in patients subjected to extremely painful procedures, such as patients with burn injuries undergoing wound care, and physical therapy.
Proceedings ArticleDOI
A comprehensive investigation and comparison of Machine Learning Techniques in the domain of heart disease
Seyedamin Pouriyeh,Sara Vahid,Giovanna Sannino,Giuseppe De Pietro,Hamid R. Arabnia,Juan B. Gutierrez +5 more
TL;DR: Results of the experiments indicate that the SVM method using the boosting technique outperforms the other aforementioned methods for the prediction of heart disease.
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Reinforcement learning for intelligent healthcare applications: A survey.
TL;DR: A review of the role of RL in healthcare by investigating past work, and highlighting any limitations and possible future contributions is presented.
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Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering
TL;DR: This paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems, which is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain.
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Deep neural network for hierarchical extreme multi-label text classification
TL;DR: An analysis of a Deep Learning architecture devoted to text classification, considering the extreme multi-class and multi-label text classification problem, when a hierarchical label set is defined and a methodology named Hierarchical Label Set Expansion (HLSE) is presented.