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Detalhes

Detalhes

  • Nome

    Rita Paula Ribeiro
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
007
Publicações

2022

A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set

Autores
Davari, N; Pashami, S; Veloso, B; Fan, YT; Pereira, PM; Ribeiro, RP; Gama, J; Nowaczyk, S;

Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022

Abstract

2022

Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph

Autores
Shaji, N; Gama, J; Ribeiro, RP; Gomes, P;

Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022

Abstract
Non-traditional data like the applicant’s bank statement is a significant source for decision-making when granting loans. We find that we can use methods from network science on the applicant’s bank statements to convert inherent cash flow characteristics to predictors for default prediction in a credit scoring or credit risk assessment model. First, the credit cash flow is extracted from a bank statement and later converted into a visibility graph or network. Afterwards, we use this visibility network to find features that predict the borrowers’ repayment behaviour. We see that feature selection methods select all the five extracted features. Finally, SMOTE is used to balance the training data. The model using the features from the network and the standard features together is shown having superior performance compared to the model that uses only the standard features, indicating the network features’ predictive power. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Combining Multiple Data Sources to Predict IUCN Conservation Status of Reptiles

Autores
Soares, N; Goncalves, JF; Vasconcelos, R; Ribeiro, RP;

Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022

Abstract
Biodiversity loss is a hot topic. We are losing species at a high rate, even before their extinction risk is assessed. The International Union for Conservation of Nature (IUCN) Red List is the most complete assessment of all species conservation status, yet it only covers a small part of the species identified so far. Additionally, many of the existing assessments are outdated, either due to the ever-evolving nature of taxonomy, or to the lack of reassessments. The assessment of the conservation status of a species is a long, mostly manual process that needs to be carefully done by experts. The conservation field would gain by having ways of automating this process, for instance, by prioritising the species where experts and financing should focus on. In this paper, we present a pipeline used to derive a conservation dataset out of openly available data and obtain predictions, through machine learning techniques, on which species are most likely to be threatened. We applied this pipeline to the different groups within the Reptilia class as a model of one of the most under-assessed taxonomic groups. Additionally, we compared the performance of models using datasets that include different sets of predictors describing species ecological requirements and geographical distributions such as IUCN’s area and extent of occurrence. Our results show that most groups benefit from using ecological variables together with IUCN predictors. Random Forest appeared as the best method for most species groups, and feature selection was shown to improve results.

2022

Data-Driven Predictive Maintenance

Autores
Gama, J; Ribeiro, RP; Veloso, B;

Publicação
IEEE INTELLIGENT SYSTEMS

Abstract

2022

The MetroPT dataset for predictive maintenance

Autores
Veloso, B; Ribeiro, RP; Gama, J; Pereira, PM;

Publicação
SCIENTIFIC DATA

Abstract
AbstractThe paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

Teses
supervisionadas

2021

Economic and Regulatory Schemes to Maximize the Social Benefit of Energy Communities

Autor
Rogério Rui Dias da Rocha

Instituição
UP-FEUP

2020

Data Mining study on data collected in Arctic Oceanographic Campaigns

Autor
Tânia Isabel Alexandre Mestre Ferreira

Instituição
UP-FCUP

2019

Payment Default Prediction in Telco Services

Autor
Ricardo Dias Azevedo

Instituição
UP-FCUP

2019

Exploratory Analysis of Meteorological Data

Autor
Joel Agostinho Nunes Pinto de Sousa

Instituição
UP-FCUP

2019

Anticipation of Perturbances in Telco Services

Autor
Tânia Margarida Marques Carvalho

Instituição
UP-FCUP