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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PF999L
Repositorysid.inpe.br/sibgrapi/2017/08.18.05.36
Last Update2017:08.18.05.36.24 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.18.05.36.24
Metadata Last Update2022:06.14.00.08.45 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.28
Citation KeyAfonsoPerWebHooPap:2017:PaDiId
TitleParkinson's Disease Identification Through Deep Optimum-Path Forest Clustering
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size1057 KiB
2. Context
Author1 Afonso, Luis Claudio Sugi
2 Pereira, Clayton Reginaldo
3 Weber, Silke Anna Theresa
4 Hook, Christian
5 Papa, João Paulo
Affiliation1 UFSCar - Federal University of São Carlos, Department of Computing, São Carlos, Brazil
2 UFSCar - Federal University of São Carlos, Department of Computing, São Carlos, Brazil
3 UNESP - São Paulo State University, Medical School, Botucatu, Brazil
4 Ostbayerische Tech. Hochschule, Fakultat Informatik/Mathematik, Regensburg, Germany
5 UNESP - São Paulo State University, School of Sciences, Bauru, Brazil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresssugi.luis@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-18 05:36:24 :: sugi.luis@gmail.com -> administrator ::
2022-06-14 00:08:45 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsParkinson's disease
Optimum-Path Forest
Handwriting Dynamics
AbstractApproximately 50,000 to 60,000 new cases of Parkinson's disease (PD) are diagnosed yearly. Despite being non-lethal, PD shortens life expectancy of the ones affected with such disease. As such, researchers from different fields of study have put great effort in order to develop methods aiming the identification of PD in its early stages. This work uses handwriting dynamics data acquired by a series of tasks and proposes the application of a deep-driven graph-based clustering algorithm known as Optimum-Path Forest to learn a dictionary-like representation of each individual in order to automatic identify Parkinson's disease. Experimental results have shown promising results, with results comparable to some state-of-the-art approaches in the literature.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Parkinson's Disease Identification...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Parkinson's Disease Identification...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 18/08/2017 02:36 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PF999L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF999L
Languageen
Target FilePID4953679.pdf
User Groupsugi.luis@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 9
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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