1 code implementation • 14 Mar 2024 • Alexander Stevens, Chun Ouyang, Johannes De Smedt, Catarina Moreira
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics.
1 code implementation • 26 Feb 2023 • Chihcheng Hsieh, Isabel Blanco Nobre, Sandra Costa Sousa, Chun Ouyang, Margot Brereton, Jacinto C. Nascimento, Joaquim Jorge, Catarina Moreira
In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data).
no code implementations • 6 Feb 2023 • André Luís, Chihcheng Hsieh, Isabel Blanco Nobre, Sandra Costa Sousa, Anderson Maciel, Catarina Moreira, Joaquim Jorge
This paper proposes a novel multimodal DL architecture incorporating medical images and eye-tracking data for abnormality detection in chest x-rays.
no code implementations • 6 Feb 2023 • João Serras, Anderson Maciel, Soraia Paulo, Andrew Duchowski, Regis Kopper, Catarina Moreira, Joaquim Jorge
Desktop-based virtual colonoscopy has been proven to be an asset in the identification of colon anomalies.
1 code implementation • 4 Mar 2022 • Catarina Moreira, Yu-Liang Chou, Chihcheng Hsieh, Chun Ouyang, Joaquim Jorge, João Madeiras Pereira
This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model).
no code implementations • 3 Mar 2022 • Catarina Moreira, Isabel Blanco Nobre, Sandra Costa Sousa, João Madeiras Pereira, Joaquim Jorge
There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively.
no code implementations • 15 Feb 2022 • Abdul Karim Obeid, Peter Bruza, Catarina Moreira, Axel Bruns, Daniel Angus
Through this formalisation, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences.
no code implementations • 3 Sep 2021 • Bemali Wickramanayake, Zhipeng He, Chun Ouyang, Catarina Moreira, Yue Xu, Renuka Sindhgatta
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.
no code implementations • 19 Jul 2021 • Chihcheng Hsieh, Catarina Moreira, Chun Ouyang
We design an extension of DiCE, namely DiCE4EL (DiCE for Event Logs), that can generate counterfactual explanations for process prediction, and propose an approach that supports deriving milestone-aware counterfactual explanations at key intermediate stages along process execution to promote interpretability.
no code implementations • 16 Jul 2021 • Chun Ouyang, Renuka Sindhgatta, Catarina Moreira
As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models.
1 code implementation • 16 Jun 2021 • Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira, Renuka Sindhgatta
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.
BIG-bench Machine Learning Explainable Artificial Intelligence (XAI) +2
no code implementations • 16 May 2021 • Catarina Moreira, Jose Acacio de Barros
Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed.
no code implementations • 7 Mar 2021 • Yu-Liang Chou, Catarina Moreira, Peter Bruza, Chun Ouyang, Joaquim Jorge
This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence.
1 code implementation • 8 Dec 2020 • Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira, Renuka Sindhgatta
Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models.
no code implementations • 21 Jul 2020 • Catarina Moreira, Yu-Liang Chou, Mythreyi Velmurugan, Chun Ouyang, Renuka Sindhgatta, Peter Bruza
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
no code implementations • 2 Jun 2020 • Shahram Dehdashti, Catarina Moreira, Abdul Karim Obeid, Peter Bruza
This paper uses deformed coherent states, based on a deformed Weyl-Heisenberg algebra that unifies the well-known SU(2), Weyl-Heisenberg, and SU(1, 1) groups, through a common parameter.
no code implementations • 30 May 2020 • Catarina Moreira, Matheus Hammes, Rasim Serdar Kurdoglu, Peter Bruza
This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory.
no code implementations • 21 Feb 2020 • Catarina Moreira, Renuka Sindhgatta, Chun Ouyang, Peter Bruza, Andreas Wichert
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
Decision Making Interpretability Techniques for Deep Learning
no code implementations • 22 Dec 2019 • Renuka Sindhgatta, Chun Ouyang, Catarina Moreira
The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model.
no code implementations • 11 May 2019 • Catarina Moreira, Lauren Fell, Shahram Dehdashti, Peter Bruza, Andreas Wichert
We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models.
no code implementations • 29 Nov 2018 • Catarina Moreira
In this paper, we make a review on the concepts of rationality across several different fields, namely in economics, psychology and evolutionary biology and behavioural ecology.
no code implementations • 16 Jul 2018 • Catarina Moreira, Andreas Wichert
The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action.
no code implementations • 2 Oct 2017 • Catarina Moreira, Emmanuel Haven, Sandro Sozzo, Andreas Wichert
In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands.
no code implementations • 26 Aug 2015 • Catarina Moreira, Andreas Wichert
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events.
no code implementations • 25 Mar 2015 • Catarina Moreira
Process mining is a technique that performs an automatic analysis of business processes from a log of events with the promise of understanding how processes are executed in an organisation.
no code implementations • 12 Feb 2015 • Andreas Wichert, Catarina Moreira
We investigate exact indexing for high dimensional Lp norms based on the 1-Lipschitz property and projection operators.
no code implementations • 21 Jan 2015 • Catarina Moreira, Bruno Martins, Pável Calado
More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise.
no code implementations • 21 Jan 2015 • Catarina Moreira, Bruno Martins, Pável Calado
The task of expert finding has been getting increasing attention in information retrieval literature.
no code implementations • 30 Sep 2014 • Catarina Moreira, Andreas Wichert
This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making.
no code implementations • 12 Jun 2013 • Catarina Moreira, Andreas Wichert
To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list.
no code implementations • 2 Feb 2013 • Catarina Moreira, Pável Calado, Bruno Martins
The task of expert finding has been getting increasing attention in information retrieval literature.