Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PF999L |
Repository | sid.inpe.br/sibgrapi/2017/08.18.05.36 |
Last Update | 2017:08.18.05.36.24 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.18.05.36.24 |
Metadata Last Update | 2020:02.19.02.01.23 administrator |
Citation Key | AfonsoPerWebHooPap:2017:PaDiId |
Title | Parkinson's Disease Identification Through Deep Optimum-Path Forest Clustering  |
Format | On-line |
Year | 2017 |
Date | Oct. 17-20, 2017 |
Access Date | 2021, Jan. 21 |
Number of Files | 1 |
Size | 1057 KiB |
Context area | |
Author | 1 Afonso, Luis Claudio Sugi 2 Pereira, Clayton Reginaldo 3 Weber, Silke Anna Theresa 4 Hook, Christian 5 Papa, João Paulo |
Affiliation | 1 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 |
Editor | Torchelsen, 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 Address | sugi.luis@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-18 05:36:24 :: sugi.luis@gmail.com -> administrator :: 2020-02-19 02:01:23 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Parkinson's disease, Optimum-Path Forest, Handwriting Dynamics. |
Abstract | Approximately 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PF999L |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PF999L |
Language | en |
Target File | PID4953679.pdf |
User Group | sugi.luis@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
| |