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About
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About

I received my PhD degree in Computer Science from the University of Porto, Portugal in 2011.
Currently, I'm an assistant professor at the Department of Computer Science of the Faculty of Sciences of the University of Porto and member of LIAAD-INESC TEC, the Artificial Intelligence and Decision Support Lab of University of Porto.
My main research interests include Data Mining and Machine Learning, in particular outlier detection, novelty detection, utility-based learning and evaluation issues on learning tasks.
As a member of LIAAD-INESC TEC, I have been involved in several research projects concerning environmental applications, fraud detection and fault diagnosis. I have also been member of the program committee for several conferences, serving as reviewer of several journals and involved in the organization of some scientific events.

Interest
Topics
Details

Details

  • Name

    Rita Paula Ribeiro
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2008
007
Publications

2022

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

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

Publication
Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

Abstract

2022

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

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

Publication
Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

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

Authors
Soares, N; Gonçalves, JF; Vasconcelos, R; Ribeiro, RP;

Publication
Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

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

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

Publication
IEEE Intelligent Systems

Abstract

2021

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

Authors
Davari, N; Veloso, B; Costa, GD; Pereira, PM; Ribeiro, RP; Gama, J;

Publication
SENSORS

Abstract
In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

Supervised
thesis

2021

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

Author
Rogério Rui Dias da Rocha

Institution
UP-FEUP

2020

Data Mining study on data collected in Arctic Oceanographic Campaigns

Author
Tânia Isabel Alexandre Mestre Ferreira

Institution
UP-FCUP

2019

Payment Default Prediction in Telco Services

Author
Ricardo Dias Azevedo

Institution
UP-FCUP

2019

Exploratory Analysis of Meteorological Data

Author
Joel Agostinho Nunes Pinto de Sousa

Institution
UP-FCUP

2019

Anticipation of Perturbances in Telco Services

Author
Tânia Margarida Marques Carvalho

Institution
UP-FCUP