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A Preliminary Study of MLSE/ACE-III stages for Primary Progressive Aphasia Automatic Identification Using Speech Features

dc.contributor.authorValdés Cuervo, Amable José
dc.contributor.authorHerrera Gómez, Elena 
dc.contributor.authorCal Marín, Enrique Antonio de la 
dc.date.accessioned2024-02-26T07:38:14Z
dc.date.available2024-02-26T07:38:14Z
dc.date.issued2023
dc.identifier.isbn978-303142535-6
dc.identifier.issn2367-3370
dc.identifier.urihttps://hdl.handle.net/10651/71523
dc.descriptionInternational Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO (18th. 2023. Salamanca, Spain)
dc.description.sponsorshipThe research has been funded by the Spanish Ministry of Economics and Industry, grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under grant agreement RED2018-102312-T (IA-Biomed), and by the Ministry of Science and Innovation under CERVERA Excellence Network project CER-20211003 (IBERUS) and Missions Science and Innovation project MIG-20211008 (INMERBOT). Also, by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994. By European Union’s Horizon 2020 research and innovation programme (project DIH4CPS) under Grant Agreement no 872548. And by CDTI (Centro para el Desarrollo Tecnológico Industrial) under projects CER-20211003 and CER-20211022 and by ICE (Junta de Castilla y León) under project CCTT3/20/BU/0002.
dc.format.extentp. 323-333
dc.language.isoeng
dc.relation.ispartof8th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023)
dc.relation.ispartofseriesLecture Notes in Networks and Systems; 750
dc.rights© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85172724370&doi=10.1007%2f978-3-031-42536-3_31&partnerID=40&md5=667e22cfb3fa767cd70c0f01af0fe004
dc.titleA Preliminary Study of MLSE/ACE-III stages for Primary Progressive Aphasia Automatic Identification Using Speech Features
dc.typeconference output
dc.identifier.doi10.1007/978-3-031-42536-3_31
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112726RB-I00/ES/INTELIGENCIA COMPUTACIONAL PARA LA MITIGACION DE EMISIONES: NUEVAS METODOLOGIAS DE APRENDIZAJE CON DATOS INCOMPLETOS/
dc.relation.projectIDRED2018-102312-T
dc.relation.projectIDCER-20211003
dc.relation.projectIDMIG-20211008
dc.relation.projectIDSV-PA-21-AYUD/2021/50994
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/872548/EU
dc.relation.projectIDCER-20211003
dc.relation.projectIDCER-20211022
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-031-42536-3_31


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