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@InProceedings{AfonsoPerWebHooPap:2017:PaDiId,
               author = "Afonso, Luis Claudio Sugi and Pereira, Clayton Reginaldo and 
                         Weber, Silke Anna Theresa and Hook, Christian and Papa, Jo{\~a}o 
                         Paulo",
          affiliation = "UFSCar - Federal University of S{\~a}o Carlos, Department of 
                         Computing, S{\~a}o Carlos, Brazil and UFSCar - Federal University 
                         of S{\~a}o Carlos, Department of Computing, S{\~a}o Carlos, 
                         Brazil and UNESP - S{\~a}o Paulo State University, Medical 
                         School, Botucatu, Brazil and Ostbayerische Tech. Hochschule, 
                         Fakultat Informatik/Mathematik, Regensburg, Germany and UNESP - 
                         S{\~a}o Paulo State University, School of Sciences, Bauru, 
                         Brazil",
                title = "Parkinson's Disease Identification Through Deep Optimum-Path 
                         Forest Clustering",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4953679.pdf",
        urlaccessdate = "2021, Jan. 25"
}


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