Now showing items 1-5 of 5

    • Aplicação de redes neurais artificiais em transplantes renais: classificação de nefrotoxicidade e rejeição celular aguda 

      Maciel, Rafael Fabio [UNIFESP] (Universidade Federal de São Paulo (UNIFESP), 2010-10-27)
      BACKGROUND: Complications associated with kidney transplantation and immunosuppression can be prevented or treated effectively if diagnosed in early stages with monitoring post-transplant. OBJECTIVE: To present the results ...

    • Comparison of Brazilian and American norms for the International Affective Picture System (IAPS) 

      Ribeiro, Rafaela Larsen [UNIFESP]; Pompéia, Sabine [UNIFESP]; Bueno, Orlando Francisco Amodeo [UNIFESP] (Associação Brasileira de Psiquiatria - ABP, 2005-09-01)
      OBJECTIVE: The present article compares Brazilian and American norms for the International Affective Picture System (IAPS), a set of normative emotional photographic slides for experimental investigations. METHODS: Subjects ...

    • Feature selection before EEG classification supports the diagnosis of Alzheimer's disease 

      Trambaiolli, L. R.; Spolaor, N.; Lorena, A. C. [UNIFESP]; Anghinah, R.; Sato, J. R. (Elsevier Ireland Ltd, 2017)
      Objective: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered ...

    • Pattern-reversal visual evoked potentials as a diagnostic tool for ocular malingering 

      Soares, Tarciana de Souza [UNIFESP]; Sacai, Paula Yuri [UNIFESP]; Berezovsky, Adriana [UNIFESP]; Rocha, Daniel Martins [UNIFESP]; Watanabe, Sung Eun Song [UNIFESP]; Salomao, Solange Rios [UNIFESP] (Consel Brasil Oftalmologia, 2016)
      Purpose: To investigate the contributions of transient pattern-reversal visual evoked potentials in the diagnosis of ocular malingering at a Brazilian university hospital. Methods: Adult patients with suspected malingering ...

    • Stacking machine learning classifiers to identify Higgs bosons at the LHC 

      Alves, A. [UNIFESP] (Iop Publishing Ltd, 2017)
      Machine learning (ML) algorithms have been employed in the problem of classifying signal and background events with high accuracy in particle physics. In this paper, we compare the performance of a widespread ML technique, ...