Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Aikaterini S. Karampasi"'
Autor:
Aikaterini S. Karampasi, Antonis D. Savva, Vasileios Ch. Korfiatis, Ioannis Kakkos, George K. Matsopoulos
Publikováno v:
Applied Sciences, Vol 11, Iss 13, p 6216 (2021)
Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis,
Externí odkaz:
https://doaj.org/article/8efdfc153a6b437c813e49adfe3d9d0a
Autor:
George K. Matsopoulos, Kyriakos Garganis, Aikaterini S. Karampasi, Kostakis Gkiatis, Ioannis Kakkos
Publikováno v:
Handbook of Artificial Intelligence in Healthcare ISBN: 9783030791605
Epilepsy is one of the most common neurological disorders, with millions affected worldwide, disturbing the normal brain activity and causing abnormal dynamics to be initiated in various regions of the brain. In order, to define the different ictal s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e2fbcd5c23e0f25ce3e0bce01547b54
https://doi.org/10.1007/978-3-030-79161-2_1
https://doi.org/10.1007/978-3-030-79161-2_1
Autor:
Kostakis Gkiatis, Aikaterini S. Karampasi, Panteleimon Asvestas, Ioannis Zorzos, Ioannis Kakkos, Georgios N. Dimitrakopoulos, George K. Matsopoulos, Stavros-Theofanis Miloulis
Publikováno v:
IEEE BigData
Diagnosis of Autism Spectrum Disorder (ASD) is a complex task that typically relies on the expertise of the clinician due to the lack of specific quantitative biomarkers. As a consequence, automatic categorization of an individual within the ASD taxo
The purpose of the current study was to classify people with autism spectrum disorder (ASD) using resting state functional magnetic resonance imaging data. Toward this direction, data were retrieved from the Autism Brain Imaging Data Exchange initiat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25e81973d54720a5aec9a8e01e776a58
https://doi.org/10.1016/b978-0-12-818466-0.00006-x
https://doi.org/10.1016/b978-0-12-818466-0.00006-x
Autor:
Hanna A. Alonim, Ramesa Shafi Bhat, Hillel D. Braude, Undurti N. Das, Jonathan Delafield-Butt, Barbara A. Demeneix, Afaf El-Ansary, Tatyana El-Kour, Jean-Baptiste Fini, Gilbert M. Foley, Stephanny Freeman, Ira Glovinsky, Aikaterini S. Karampasi, Marinos Kyriakopoulos, Michelle Leemans, Ido Lieberman, George K. Matsopoulos, Magda Mostafa, Isabelle Mueller, Majia H. Nadesan, Natalia Neophytou, Nina Newman, Melissa Olive, M.A. Ovchinnikova, Neophytos Papaneophytou, Tanya Paparella, Ivanka Pejić, Sandra Pretorius, O.G. Safonicheva, Ruby Moye Salazar, Antonis D. Savva, Giora Scheingesicht, Danny Tayar, Gil Tippy, Colwyn Trevarthen, Ed Tronick, Alisa Vig, Serena Wieder
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20ba50079eeb402df9c1f456efa9e5c8
https://doi.org/10.1016/b978-0-12-818466-0.01002-9
https://doi.org/10.1016/b978-0-12-818466-0.01002-9
Autor:
George K. Matsopoulos, Ioannis Kakkos, Aikaterini S. Karampasi, Antonis D. Savva, Vasileios Ch. Korfiatis
Publikováno v:
Applied Sciences, Vol 11, Iss 6216, p 6216 (2021)
Applied Sciences
Volume 11
Issue 13
Applied Sciences
Volume 11
Issue 13
Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis,