Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Paula Raissa Silva
  • Cluster

    Informática
  • Cargo

    Assistente de Investigação
  • Desde

    13 setembro 2017
001
Publicações

2020

Multimodal deep learning based approach for cells state classification: Student research abstract

Autores
Silva, PR;

Publicação
Proceedings of the ACM Symposium on Applied Computing

Abstract
With the advances of the big data era in biology, deep learning have been incorporated in analysis pipelines trying to transform biological information into valuable knowledge. Deep learning demonstrated its power in promoting bioinformatics field including sequence analysis, bio-molecular property and function prediction, automatic medical diagnosis and to analyse cell imaging data. The ambition of this work is to create an approach that can fully explore the relationships across modalities and subjects through mining and fusing features from multi-modality data for cell state classification. The system should be able to classify cell state through multimodal deep learning techniques using heterogeneous data such as biological images, genomics and clinical annotations. Our pilot study addresses the data acquisition process and the framework capable to extract biological parameters from cell images. © 2020 Owner/Author.

2018

An Approach to Extract Proper Implications Set from High-dimension Formal Contexts using Binary Decision Diagram

Autores
Santos, P; Neves, J; Silva, P; Dias, SM; Zárate, L; Song, M;

Publicação
Proceedings of the 20th International Conference on Enterprise Information Systems

Abstract

2018

ImplicPBDD: A New Approach to Extract Proper Implications Set from High-Dimension Formal Contexts Using a Binary Decision Diagram †

Autores
Santos, PG; Ruas, PHB; Neves, JCV; Silva, PR; Dias, SM; Zarate, LE; Song, MAJ;

Publicação
Information

Abstract
Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the PropIm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance—up to 80% faster—than its original algorithm, regardless of the number of attributes, objects and densities.

2018

Minimal Implications Base for Social Network Analyzes

Autores
Raissa, P; Dias, S; Song, M; Zárate, L;

Publicação
International Journal of Web Information Systems

Abstract

2018

Professional Competence Identification Through Formal Concept Analysis

Autores
Silva, PR; Dias, SM; Brandão, WC; Song, MA; Zárate, LE;

Publicação
Enterprise Information Systems - Lecture Notes in Business Information Processing

Abstract