Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Manuele Bicego"'
Publikováno v:
Image Analysis and Processing – ICIAP 2022 ISBN: 9783031064326
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d02355c5ce8446aff65c3b18007ddd15
http://hdl.handle.net/11562/1074150
http://hdl.handle.net/11562/1074150
Autor:
Simona Schiavi, Alberto Azzari, Antonella Mensi, Nicole Graziano, Alessandro Daducci, Manuele Bicego, Matilde Inglese, Maria Petracca
Although structural disconnection represents the hallmark of multiple sclerosis (MS) pathophysiology, classification attempts based on structural connectivity have achieved low accuracy levels. Here, we set out to fill this gap, exploring the perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4949295d9016708708f2cd1e1666d7b4
http://hdl.handle.net/11573/1623321
http://hdl.handle.net/11573/1623321
Autor:
Antonella Mensi, Simona Schiavi, Maria Petracca, Nicole Graziano, Alessandro Daducci, Matilde Inglese, Manuele Bicego
Publikováno v:
Computational Intelligence Methods for Bioinformatics and Biostatistics ISBN: 9783031208362
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2dda6f562f170cd9619c95022403355
https://doi.org/10.1007/978-3-031-20837-9_13
https://doi.org/10.1007/978-3-031-20837-9_13
Publikováno v:
Image Analysis and Processing – ICIAP 2022 ISBN: 9783031064326
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb1cd3b9788d7ed37aea8384f8c7d700
https://doi.org/10.1007/978-3-031-06433-3_7
https://doi.org/10.1007/978-3-031-06433-3_7
Publikováno v:
Pattern Recognition Letters. 128:231-236
We study the problem of Protein Remote Homology Detection, which assesses the functional similarity of two proteins. We approach this as a problem of binary multiple-instance learning (MIL) that aims to distinguish between homologous and non-homologo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1795e55910650aeddf5d7936336fc733
https://doi.org/10.1016/j.patrec.2019.08.027
https://doi.org/10.1016/j.patrec.2019.08.027
Publikováno v:
Ecological Informatics. 51:177-184
Analysis of seismic data is the most important method for volcano monitoring. Such data typically consists in digital signals acquired with an arrangement of triaxial seismic sensors which are strategically deployed on the volcano and its surrounding
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::983991137bbd20c7e89f3fc5665aa2cc
https://doi.org/10.1016/j.ecoinf.2019.03.004
https://doi.org/10.1016/j.ecoinf.2019.03.004
Publikováno v:
Engineering Applications of Artificial Intelligence. 77:46-58
The use of robotic mobile sensors for environmental monitoring applications has gained increasing attention in recent years. In this context, a common application is to determine the region of space where the analyzed phenomena is above or below a gi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e2696d1742ed9230408dd0c9459dfea
https://doi.org/10.1016/j.engappai.2018.09.015
https://doi.org/10.1016/j.engappai.2018.09.015
Autor:
Manuele Bicego, Mauricio Orozco-Alzate
Publikováno v:
ICPR
The Rectified Nearest Feature Line Segment (RN-FLS) classifier is an improved version of the Nearest Feature Line (NFL) classification rule. RNFLS corrects two drawbacks of NFL, namely the interpolation and extrapolation inaccuracies, by applying two
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::010dde870f82433daae6a7b5cc61696e
https://doi.org/10.1109/icpr48806.2021.9412815
https://doi.org/10.1109/icpr48806.2021.9412815