Metric Learning in Histopathological Image Classification: Opening the Black Box

Autor: Amato, D, Calderaro, S, Lo Bosco, G, Rizzo, R, Vella, Filippo

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physician

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3658::c0dcf6feae17bd9bf28ee7869de7fd6f
https://hdl.handle.net/10447/598073

Immersive Virtual Reality for Cultural Heritage Exploration

Autor: Lo Bosco, G, Farella, M, Di Piazza, G.

Virtual Reality can provide an immersive experience that allows cultural heritage to be experienced in a more realistic and immersive way than traditional showcasing techniques. The objective of this paper is to provide a software pipeline that can b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3658::263e6500620c9fc1f112bc2b266ce058
https://hdl.handle.net/10447/591319

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Towards a deep-learning-based methodology for supporting satire detection

Autor: Cuzzocrea A., Lo Bosco G., Maiorana M., Pilato G., Schicchi D.

Publikováno v: DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages, pp. 92–96, Virtual, Pittsburgh, 29 June 2021-30 June 2021
info:cnr-pdr/source/autori:Cuzzocrea A.; Bosco G.L.; Maiorana M.; Pilato G.; Schicchi D./congresso_nome:DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages/congresso_luogo:Virtual, Pittsburgh/congresso_data:29 June 2021-30 June 2021/anno:2021/pagina_da:92/pagina_a:96/intervallo_pagine:92–96

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system explo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c9b0c0f55d1c1270d95fda7fe4b0b5f0
http://www.cnr.it/prodotto/i/458858

Multi-class Text Complexity Evaluation via Deep Neural Networks

Autor: Cuzzocrea A., Lo Bosco G., Pilato G., Schicchi D.

Publikováno v: Lecture notes in computer science 11872 LNCS (2019): 313–322. doi:10.1007/978-3-030-33617-2_32
info:cnr-pdr/source/autori:Cuzzocrea A.; Lo Bosco G.; Pilato G.; Schicchi D./titolo:Multi-class Text Complexity Evaluation via Deep Neural Networks/doi:10.1007%2F978-3-030-33617-2_32/rivista:Lecture notes in computer science/anno:2019/pagina_da:313/pagina_a:322/intervallo_pagine:313–322/volume:11872 LNCS

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=cnr_________::846e841e1d3c6140cdbaadeed5861a13
http://www.scopus.com/record/display.url?eid=2-s2.0-85076965344&origin=inward