Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Gabriela Vozáriková"'
Autor:
'Ľubomír Antoni, Erik Bruoth, Alexander Szabari, Gabriela Vozáriková, Peter Bugata, Peter Bugata Jr, Dávid Gajdoš, Dávid Hudák, Vladimíra Kmečová, Monika Staňková\\'
Publikováno v:
Computing in Cardiology Conference (CinC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bf147c627390c76514437fc628b0fbb
https://doi.org/10.22489/cinc.2022.067
https://doi.org/10.22489/cinc.2022.067
Publikováno v:
Statistical Papers. 62:1899-1938
We revisit and update estimating variances, fundamental quantities in a time series forecasting approach called kriging, in time series models known as FDSLRMs, whose observations can be described by a linear mixed model (LMM). As a result of applyin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4dd179a8aae536bddb5eeb1514e1f1c5
https://doi.org/10.1007/s00362-020-01165-5
https://doi.org/10.1007/s00362-020-01165-5
Autor:
Lubomir Antoni, Erik Bruoth, Peter Bugata, Dávid Gajdoš, Šimon Horvát, Dávid Hudák, Vladimíra Kmečová, Richard Staňa, Monika Staňková, Alexander Szabari, Gabriela Vozáriková
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9783031093418
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aaf0c83255063788e39969bae63fc967
https://doi.org/10.1007/978-3-031-09342-5_42
https://doi.org/10.1007/978-3-031-09342-5_42
Publikováno v:
VISIGRAPP (5: VISAPP)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8be3bce11b419065f55c7c46e019248e
https://doi.org/10.5220/0010177700150024
https://doi.org/10.5220/0010177700150024
Autor:
Ľubomír Antoni, Erik Bruoth, Peter Bugata, Peter Bugata Jr, Dávid Gajdoš, Šimon Horvát, Dávid Hudák, Vladimíra Kmečová, Richard Staňa, Monika Staňková, Alexander Szabari, Gabriela Vozáriková
Publikováno v:
Physiological Measurement. 43:064008
Objective. Within the PhysioNet/Computing in Cardiology Challenge 2021, we focused on the design of a machine learning algorithm to identify cardiac abnormalities from electrocardiogram recordings (ECGs) with a various number of leads and to assess t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58a5caa21625a7bcf89fa9ca9c096b07
https://doi.org/10.1088/1361-6579/ac69a8
https://doi.org/10.1088/1361-6579/ac69a8