Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre

Sobre

Nascida nos anos 90, completei o meu percurso académico no Instituto de Engenharia da Universidade do Porto(ISEP) com o grau de Mestre no curso de Sistemas Computacionais.

Atualmente, encontro-me a trabalhar numa bolsa de investigação no LIAAD, no projeto NanoSTIMA, onde estou a desenvolver métodos para prever com exatidão mudanças no estado de um paciente com insuficiência renal. 

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ana Rita Nogueira
  • Cargo

    Investigador Auxiliar
  • Desde

    01 outubro 2016
Publicações

2023

Causal Reasoning in Data

Autores
Nogueira, AR;

Publicação

Abstract

2023

Time-Series Pattern Verification in CNC Machining Data

Autores
Silva, JM; Nogueira, AR; Pinto, J; Alves, AC; Sousa, R;

Publicação
Progress in Artificial Intelligence - 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5-8, 2023, Proceedings, Part I

Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series’ target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % ± 1.2 and an F1-score of 95.4 % ± 1.3.

2022

Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis

Autores
Nogueira, AR; Ferreira, CA; Gama, J;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract
In hospital and after ICU discharge deaths are usual, given the severity of the condition under which many of them are admitted to these wings. Because of this, there is an urge to identify and follow these cases closely. Furthermore, as ICU data is usually composed of variables measured in varying time intervals, there is a need for a method that can capture causal relationships in this type of data. To solve this problem, we propose ItsPC, a causal Bayesian network that can model irregular multivariate time-series data. The preliminary results show that ItsPC creates smaller and more concise networks while maintaining the temporal properties. Moreover, its irregular approach to time-series can capture more relationships with the target than the Dynamic Bayesian Networks.

2022

Methods and tools for causal discovery and causal inference

Autores
Nogueira, AR; Pugnana, A; Ruggieri, S; Pedreschi, D; Gama, J;

Publicação
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

2022

Semi-causal decision trees

Autores
Nogueira, AR; Ferreira, CA; Gama, J;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
Typically, classification algorithms use correlation analysis to make decisions. However, these decisions and the models they learn are not easily understandable for the typical user. Causal discovery is the field that studies the means to find causal relationships in observational data. Although highly interpretable, causal discovery algorithms tend to not perform so well in classification problems. This paper aims to propose a hybrid decision tree approach (SC tree) that mixes causal discovery with correlation analysis through the implementation of a custom metric to split the data in the tree's construction (Semi-causal gain ratio). In the results, the proposed methodology obtained a significant performance improvement (11.26% mean error rate) when compared to several causal baselines CDT-PS (23.67% ) and CDT-SPS (25.14%), matching closely the performance of J48 (10.20%), used as a correlation baseline, in ten binary data sets. Besides, when compared with PC in discrete data sets, the proposed approach obtained substantial improvement (16.17% against 28.07% in terms of mean error rate).