Zobrazeno 1 - 10 of 13 pro vyhledávání: '"Daniele Maccagnola"'
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Akademický článek
Translating Image XAI to Multivariate Time Series
Autor: Lorenzo Tronchin, Ermanno Cordelli, Lorenzo Ricciardi Celsi, Daniele Maccagnola, Massimo Natale, Paolo Soda, Rosa Sicilia
Publikováno v: IEEE Access, Vol 12, Pp 27484-27500 (2024)
As Artificial Intelligence (AI) is becoming part of our daily lives, the need to understand and trust its decisions is becoming a pressing issue. EXplainable AI (XAI) aims at answering this demand, providing tools to get insights into the models’ b
Externí odkaz: https://doaj.org/article/85f104f2c1004b65a0815692ba65591b
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2
Explaining crash predictions on multivariate time series data
Autor: Francesco Spinnato, Riccardo Guidotti, Mirco Nanni, Daniele Maccagnola, Giulia Paciello, Antonio Bencini Farina
Publikováno v: DS 2022-25th International Conference on Discovery Science, pp. 556–566, Montpellier, France, 10-12/10/2022
Discovery Science ISBN: 9783031188398
In Assicurazioni Generali, an automatic decision-making model is used to check real-time multivariate time series and alert if a car crash happened. In such a way, a Generali operator can call the customer to provide first assistance. The high sensit
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17aee4fdda1ffa2d698d91845a683a95
https://openportal.isti.cnr.it/doc?id=people______::a9d228ef15b73c245a3f25356887f48c
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4
Overlapping Kernel-based community detection with node attributes
Autor: Enza Messina, Daniele Maccagnola, Rabah Djennadi, Elisabetta Fersini
Publikováno v: KDIR
Scopus-Elsevier
Community Detection is a fundamental task in the field of Social Network Analysis, extensively studied in literature. Recently, some approaches have been proposed to detect communities distinguishing their members between kernel that represents opini
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c8ad87b62c25f53def07f08156cc0bf
http://hdl.handle.net/10281/135764
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5
An Interactive Method for Inferring Demographic Attributes in Twitter
Autor: Timothy Cribbin, Enza Messina, Daniele Maccagnola, Valentina Beretta
Publikováno v: HT
26th ACM Conference on Hypertext and Social Media (Hypertext 2015)
Twitter data offers an unprecedented opportunity to study demographic differences in public opinion across a virtually unlimited range of subjects. Whilst demographic attributes are often implied within user data, they are not always easily identifie
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c92a359a47f9cac7c998aed6e1e0a7c
https://doi.org/10.1145/2700171.2791031
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6
A latent representation model for sentiment analysis in heterogeneous social networks
Autor: Patrick Gallinari, Daniele Maccagnola, Vincent Guigue, Debora Nozza, Enza Messina
Publikováno v: International Conference on Software Engineering and Formal Methods
International Conference on Software Engineering and Formal Methods, 2014, Grenoble, France. pp.201-213, ⟨10.1007/978-3-319-15201-1_13⟩
Software Engineering and Formal Methods ISBN: 9783319152004
SEFM Workshops
International audience; The growing availability of social media platforms, in particular microblogs such as Twitter, opened new way to people for expressing their opinions. Sentiment Analysis aims at inferring the polarity of these opinions, but mos
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::274385c05c8329dc3c2bd70c983b4e5e
https://hal.science/hal-02503482
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7
Stochastic Simulation of the Coagulation Cascade: A Petri Net Based Approach
Autor: Daniela Mari, Francesco Archetti, Davide Castaldi, Daniele Maccagnola
Publikováno v: Lecture Notes in Computer Science ISBN: 9783642369483
Euro-Par Workshops
In this paper we developed a Stochastic Petri Net (SPN) based model to introduce uncertainty to capture the variability of biological systems. The coagulation cascade, one of the most complex biochemical networks, has been widely analyzed in literatu
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1e8193c65432422cbd141510e50a7845
https://doi.org/10.1007/978-3-642-36949-0_28
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8
An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics
Autor: Mauro Castelli, Daniele Maccagnola, Leonardo Vanneschi, Davide Castaldi, Francesco Archetti, Ilaria Giordani
Publikováno v: GECCO (Companion)
In the last few years researchers have dedicated several efforts to the definition of Genetic Programming (GP) [?] systems based on the semantics of the solutions, where by semantics we generally intend the behavior of a program once it is executed o
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18a169cf353042a34745f70020fdbd40
http://hdl.handle.net/10281/59135
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9
MINING FOR VARIABILITY IN THE COAGULATION PATHWAY: A SYSTEMS BIOLOGY APPROACH
Autor: Francesco Archetti, Daniela Mari, Davide Castaldi, Daniele Maccagnola
Publikováno v: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics ISBN: 9783642371882
EvoBIO
In this paper authors perform a variability analysis of a Stochastic Petri Net (SPN) model of the Tissue Factor induced coagulation cascade, one of the most complex biochemical networks. This pathway has been widely analyzed in literature mostly with
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::575ee49af12807fc84b24dcad5a5efb4
http://hdl.handle.net/10281/42219
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10
Enhance user-level Sentiment Analysis on microblogs with approval relations
Autor: Daniele Maccagnola, Federico Alberto Pozzi, Enza Messina, Elisabetta Fersini
Publikováno v: AI*IA 2013: Advances in Artificial Intelligence ISBN: 9783319035239
AI*IA
Sentiment Analysis for polarity classification on microblogs is generally based on the assumption that texts are independent and identically distributed (i.i.d). Although these methods are aimed at handling the complex characteristics of natural lang
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13c26d6adede3af1696d68f8bf0a3fbb
http://hdl.handle.net/10281/59461
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