Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Edoardo Ramalli"'
1
Akademický článek
A Citizen Science Approach for Analyzing Social Media With Crowdsourcing
Autor: Carlo Bono, Mehmet Oguz Mulayim, Cinzia Cappiello, Mark James Carman, Jesus Cerquides, Jose Luis Fernandez-Marquez, Maria Rosa Mondardini, Edoardo Ramalli, Barbara Pernici
Publikováno v: IEEE Access, Vol 11, Pp 15329-15347 (2023)
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing
Externí odkaz: https://doaj.org/article/362294e9c3ae4c9bbf3c996bf47a5570
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2
Akademický článek
Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering
Autor: Edoardo Ramalli, Gabriele Scalia, Barbara Pernici, Alessandro Stagni, Alberto Cuoci, Tiziano Faravelli
Publikováno v: Frontiers in Big Data, Vol 4 (2021)
The development of scientific predictive models has been of great interest over the decades. A scientific model is capable of forecasting domain outcomes without the necessity of performing expensive experiments. In particular, in combustion kinetics
Externí odkaz: https://doaj.org/article/310eb2be797e444da27892ce52c24f65
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3
Pipeline Design for Data Preparation for Social Media Analysis
Autor: Carlo A. Bono, Cinzia Cappiello, Barbara Pernici, Edoardo Ramalli, Monica Vitali
Publikováno v: Journal of Data and Information Quality.
In a data-driven culture, in which analytics applications are the main resources for supporting decision-making, the use of high-quality datasets is mandatory to minimize errors and risks. For this reason, data analysis tasks need to be preceded by a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f18bc1ac199869ea3f1209398bdc4601
https://doi.org/10.1145/3597305
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4
Knowledge graph embedding for experimental uncertainty estimation
Autor: Edoardo Ramalli, Barbara Pernici
Publikováno v: Information Discovery and Delivery.
Purpose Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurement
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4e9fd2dda96f4b0d7a197237cc7525c9
https://doi.org/10.1108/idd-06-2022-0060
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7
Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding
Autor: Edoardo Ramalli, Alberto Parravicini, Guido W. Di Donato, Mirko Salaris, Celine Hudelot, Marco D. Santambrogio
Drug repurposing is more relevant than ever due to drug development's rising costs and the need to respond to emerging diseases quickly. Knowledge graph embedding enables drug repurposing using heterogeneous data sources combined with state-of-the-ar
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84ed18ac8f63fbfe1ec3c9efc08b173c
http://arxiv.org/abs/2108.13051
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9
Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering
Autor: Alberto Cuoci, Gabriele Scalia, Tiziano Faravelli, Edoardo Ramalli, Alessandro Stagni, Barbara Pernici
Publikováno v: Frontiers in Big Data
Frontiers in Big Data, Vol 4 (2021)
The development of scientific predictive models has been of great interest over the decades. A scientific model is capable of forecasting domain outcomes without the necessity of performing expensive experiments. In particular, in combustion kinetics
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1200c7d9dfa9580ece5270ca3320c95
http://hdl.handle.net/11311/1184337
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10
Solving write conflicts in GPU-accelerated graph computation: A PageRank case-study
Autor: Alberto Parravicini, Diego Piccinotti, Edoardo Ramalli, Rolando Brondolin, Marco D. Santambrogio
Publikováno v: RTSI
Graph ranking algorithms, such as PageRank, are widely used in a number of real-world applications like web search. As the size of the graphs on which these algorithms are applied gets bigger and bigger, it becomes necessary to devise powerful and fl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e7c60a3db36744729ed37f65aeeaa72
http://hdl.handle.net/11311/1119037
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