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About

About

Rita P. Ribeiro is an Assistant Professor at the Department of Computer Science at the Faculty of Sciences of the University of Porto (FCUP) and a Senior Researcher at the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the Institute of Systems Engineering and Computing, Technology and Science (INESCTEC). Her main research interests focus on learning problems in imbalanced domains, anomaly detection, evaluation issues in learning tasks and application problems related to social good and environmental impact. She has been involved in several research projects concerning ecological problems, fraud detection and predictive maintenance applications. She is a member of the program committee of several international conferences, also serves as an editor and reviewer for several international journals and has been involved in the organization of various scientific events.

Interest
Topics
Details

Details

  • Name

    Rita Paula Ribeiro
  • Role

    Senior Researcher
  • Since

    01st January 2008
009
Publications

2025

Anomaly Detection in Pet Behavioural Data

Authors
Silva, I; Ribeiro, RP; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT II

Abstract
Pet owners are increasingly becoming conscious of their pet's necessities and are paying more attention to their overall wellness. The well-being of their pets is intricately linked to their own emotional and physical well-being. Some veterinary system solutions are emerging to provide proactive healthcare options for pets. One such solution offers the continuous monitoring of a pet's activity through accelerometer tracking devices. Based on data collected by this application, in this paper, we study different time aggregation and three unsupervised machine learning techniques to identify anomalies in pet behaviour data. Specifically, three algorithms, Isolation Forest, Local Outlier Factor, and K-Nearest Neighbour, with various thresholds to differentiate between normal and abnormal events. Results conducted on ten pets (five cats and five dogs) show that the most effective approach is to use daily data divided into periods. Moreover, the Local Outlier Factor is the best algorithm for detecting anomalies when prioritizing the identification of true positives. However, it also produces a high false positive ratio.

2025

Data Science for Fighting Environmental Crime

Authors
Barbosa, M; Ribeiro, C; Gomes, F; Ribeiro, RP; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT II

Abstract
The rise of environmental crimes has become a major concern globally as they cause significant damage to ecosystems, public health and result in economic losses. The availability of vast sensor data provides an opportunity to analyze environmental data proactively. This helps to detect irregularities and uncover potential criminal activities. This paper highlights the critical role played by machine learning (ML) and remote sensing technologies in the continuously evolving scenarios of environmental crime. By examining some case studies on detecting illegal fishing, illegal oil spills, illegal landfills, and illegal logging, we delve into the practical implementation of data-driven approaches for environmental crime detection. Our goal with this study is to provide an overview of the existing research in this area and foster the use of ML and data science techniques to enhance environmental crime detection.

2024

Detecting and Explaining Anomalies in the Air Production Unit of a Train

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

Publication
39TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2024

Abstract
Predictive maintenance methods play a crucial role in the early detection of failures and errors in machinery, preventing them from reaching critical stages. This paper presents a comprehensive study on a real-world dataset called MetroPT3, with data from a Metro do Porto train's air production unit (APU) system. The dataset comprises data collected from various analogue and digital sensors installed on the APU system, enabling the analysis of behavioural changes and deviations from normal patterns. We propose a data-driven predictive maintenance framework based on a Long Short-Term Memory Autoencoder (LSTM-AE) network. The LSTM-AE efficiently identifies abnormal data instances, leading to a reduction in false alarm rates. We also implement a Sparse Autoencoder (SAE) approach for comparative analysis. The experimental results demonstrate that the LSTM-AE outperforms the SAE regarding F1 Score, Recall, and Precision. Furthermore, to gain insights into the reasons for anomaly detection, we apply the Shap method to determine the importance of features in the predictive maintenance model. This approach enhances the interpretability of the model to support the decision-making process better.

2024

Super-Resolution Analysis for Landfill Waste Classification

Authors
Molina, M; Ribeiro, RP; Veloso, B; Carna, J;

Publication
ADVANCES IN INTELLIGENT DATA ANALYSIS XXII, PT I, IDA 2024

Abstract
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning.

2024

From fault detection to anomaly explanation: A case study on predictive maintenance

Authors
Gama, J; Ribeiro, RP; Mastelini, S; Davari, N; Veloso, B;

Publication
JOURNAL OF WEB SEMANTICS

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black -box models are popular approaches based on deep -learning techniques due to their predictive accuracy. This paper proposes a neural -symbolic architecture that uses an online rule -learning algorithm to explain when the black -box model predicts failures. The proposed system solves two problems in parallel: (i) anomaly detection and (ii) explanation of the anomaly. For the first problem, we use an unsupervised state-of-the-art autoencoder. For the second problem, we train a rule learning system that learns a mapping from the input features to the autoencoder's reconstruction error. Both systems run online and in parallel. The autoencoder signals an alarm for the examples with a reconstruction error that exceeds a threshold. The causes of the signal alarm are hard for humans to understand because they result from a non-linear combination of sensor data. The rule that triggers that example describes the relationship between the input features and the autoencoder's reconstruction error. The rule explains the failure signal by indicating which sensors contribute to the alarm and allowing the identification of the component involved in the failure. The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure. We evaluate the proposed system in a real -world case study of Metro do Porto and provide explanations that illustrate its benefits.

Supervised
thesis

2023

Evaluating Fairness, Explainability and Robustness of AI Systems

Author
Sérgio Gabriel Pontes de Jesus

Institution
UP-FCUP

2023

Clustering Massive, Noisy, and Unstructured Textual Streams

Author
Cesar Henrique Goersch Andrade

Institution
UP-FCUP

2023

Anomaly Detection in Pet Behavioural Data

Author
Inês Pinto e Silva

Institution
UP-FCUP

2023

Learning from imbalanced data streams

Author
Ehsan Aminian

Institution
UP-FCUP

2023

A Machine Learning Approach for Predicting Claims Reserving

Author
Amanda Custódio Tavares

Institution
UP-FCUP