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.
Papers
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Proceedings Article
A Mobile Reasoning System for Supporting the Monitoring of Chronic Diseases
TL;DR: A mobile reasoning system which can be used to build knowledge-based Decision Support Systems for monitoring and managing ubiquitously and seamlessly chronic patients, specifically designed and developed as a light-weight solution suitable for resource-limited mobile devices.
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An agent based platform for task distribution in virtual environments
TL;DR: The goal is the implementation of Utility Computing services that enable users to submit their source code and to have their applications executed without concerning about resource allocation, task distribution, and load-balancing.
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Introduction to special section on formal methods in pervasive computing
TL;DR: This article presents emerging formal methods for the description of both entities and their behavior in pervasive computing environments and introduces this special issue, which covers some of the topics aforementioned.
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Evaluation of artificial intelligence techniques for the classification of different activities of daily living and falls
TL;DR: The numerical results show that the algorithm with the highest classification accuracy is the ensemble based on subspace as the ensemble method and on KNN as learner type, which is better than those in the other papers in the literature that face this specific 17-class problem.
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A neural network approach to classify carotid disorders from Heart Rate Variability analysis
Laura Verde,Giuseppe De Pietro +1 more
TL;DR: This study proposes an automated methodology capable of identifying the presence of a carotid disease from the Heart Rate Variability analysis of electrocardiographic signals that is more effective than any of the main algorithm existing in literature.