Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Alessandro Passaro"'
Autor:
Filippo Randelli, Alessandro Passaro
Publikováno v:
Agroecology and Sustainable Food Systems. 46:1198-1223
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9d739cc208c46a9a277e2812da2c572
https://doi.org/10.1080/21683565.2022.2104421
https://doi.org/10.1080/21683565.2022.2104421
Autor:
Antonina Starita, Alessandro Passaro
Publikováno v:
Journal of Artificial Evolution and Applications. 2008:1-15
The particle swarm optimization (PSO) algorithm is designed to find a single optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd0adb6675a32ee8e63b19866ec7a9dd
https://doi.org/10.1155/2008/482032
https://doi.org/10.1155/2008/482032
Autor:
Alessio Micheli, Antonina Starita, Anna Maria Rossi, Alessandro Passaro, Valentina Maggini, Flavio Baronti
Publikováno v:
Biological and Artificial Intelligence Environments ISBN: 9781402034312
Head and neck squamous cell carcinoma (HNSCC) has already been proved to be linked with smoking and alcohol drinking habits. However the individual risk could be modified by genetic polymorphisms of enzymes involved in the metabolism of tobacco carci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a0797e581a2970d249db314de7c48fe
https://doi.org/10.1007/1-4020-3432-6_2
https://doi.org/10.1007/1-4020-3432-6_2
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540712305
XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result is important. We propose an algorithm which applies clustering in order
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::366dfcf74c1b290c1b10da0313f6b706
https://doi.org/10.1007/978-3-540-71231-2_6
https://doi.org/10.1007/978-3-540-71231-2_6
Exploring biomolecular and medical data is a challenging area where the need for data driven methodologies is constantly increasing. Machine Learning (ML) methods have the ability to learns from data, inferring from examples a general hypothesis that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92e50401acb331d2c01dafd1ec585cf8
https://doi.org/10.1016/b978-044452855-1/50012-x
https://doi.org/10.1016/b978-044452855-1/50012-x
Autor:
Corneliu T.C. Arsene, Flavio Baronti, Pedro Barroso, Elia Biganzoli, Patrizia Boracchi, Lutgarde M.C. Buydens, Adrian Cassidy, Bertil E. Damato, Andy Devos, Tadeusz A. Dyba, Antonio Eleuteri, John K. Field, José Manuel Fonseca, Mithat Gönen, Marinette van der Graaf, Roger Green, Timo R. Hakulinen, Arend Heerschap, Peter Jančovič, Andrew S. Jones, Michael W. Kattan, Münevver Köküer, Xenofon Kotsiakis, Michail G. Kounelakis, Michele de Laurentiis, Paulo J.G. Lisboa, Alberto Mario Marchevsky, Alessio Micheli, Leopoldo Milano, André Damas Mora, Raouf N.G. Naguib, Alessandro Passaro, Peter T. Scardino, Christian Setzkorn, Arjan W. Simonetti, Antonina Starita, Roberto Tagliaferri, Azzam F.G. Taktak, Sabine van Huffel, Julia A. Woolgar, H. Banfield Younghusband, Michalis Zervakis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2592bb00d80ede5473bdaa1f8c2422ab
https://doi.org/10.1016/b978-044452855-1/50001-5
https://doi.org/10.1016/b978-044452855-1/50001-5
Publikováno v:
GECCO Workshops
XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result is important. We propose an algorithm to join the rules produced from m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4da1af67a1d94bffca3fbebcbdf58ae
https://doi.org/10.1145/1102256.1102272
https://doi.org/10.1145/1102256.1102272
Autor:
Zervakis Michalis, Alessandro Passaro, V. Stalbovskaya, Flavio Baronti, Anna Maria Rossi, M. Blazantonakis, D. De Rossi, Valentina Maggini, M. Marcucci, Antonina Starita, R. Gonçalvez, Alessio Micheli
Summarization: Research in medical domains is facing new challenges as the available information increases in quantity and quality. In this context, Machine Learning methodologies can provide the right tools for data analysis, which can cope with rec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4037::83b3506bb06e90f57c267b37c7c5d39d
http://purl.tuc.gr/dl/dias/013C067A-BA28-40AC-9073-7FBC97707FA7
http://purl.tuc.gr/dl/dias/013C067A-BA28-40AC-9073-7FBC97707FA7