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Detalhes

Detalhes

  • Nome

    Paula Raissa Silva
  • Cluster

    Informática
  • Cargo

    Assistente de Investigação
  • Desde

    13 setembro 2017
001
Publicações

2023

A DTW Approach for Complex Data A Case Study with Network Data Streams

Autores
Silva, PR; Vinagre, J; Gama, J;

Publicação
Proceedings of the ACM Symposium on Applied Computing

Abstract

2023

Towards federated learning: An overview of methods and applications

Autores
Silva, PR; Vinagre, J; Gama, J;

Publicação
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an in-depth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.This article is categorized under:Technologies > Machine LearningTechnologies > Artificial Intelligence

2022

Federated Anomaly Detection over Distributed Data Streams

Autores
Silva, PR; Viangre, J; Gama, J;

Publicação
CoRR

Abstract

2020

Student Research Abstract: Multimodal Deep Learning Based Approach for Cells State Classification

Autores
Silva, PR;

Publicação
PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20)

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.

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