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Impact of Artificial Intelligence Research on Politics of the European Union Member States: The Case Study of Portugal

Publication . Reis, João Carlos Gonçalves dos; Santo, Paula; Melão, Nuno

Currently, artificial intelligence (AI) is at the center of academic and public debate. However, its implications on politics remain little understood. To understand the impact of the AI phenomenon on politics of the European Union (EU), we have carried out qualitative multimethod research by performing a systematic literature review and a case study. The first method was performed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA), in order to report the state-of-the-art in the existing literature and explore the most relevant research areas. The second method contained contributions from experts in data science and AI of the Portuguese scientific community. The results showed that solutions such as intelligent decision support systems are improving the political decision-making process and impacting the Portuguese society at local, regional, and national levels. We also found that practitioners and scientists are currently shifting their interests from environmental and biological sciences to healthcare services, which is bringing new challenges in terms of protecting patient/citizen data and growing concerns about handling of critical information. Future research may focus on comparative studies with other EU States to obtain a comprehensive and holistic understanding of the AI phenomenon.

2020Journal article

Open access

Impact of Artificial Intelligence Research on Politics of the European Union Member States: The Case Study of Portugal

Publication . Reis, João; Santo, Paula; Melão, Nuno

2020Journal article

Open access

Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks

Publication . Antunes, André; Ferreira, Bruno; Marques, Nuno; Carriço, Nelson

The current paper presents a hyper parameterization optimization process for a convolutional neural network (CNN) applied to pipe burst locations in water distribution networks (WDN). The hyper parameterization process of the CNN includes the early stopping termination criteria, dataset size, dataset normalization, training set batch size, optimizer learning rate regularization, and model structure. The study was applied using a case study of a real WDN. Obtained results indicate that the ideal model parameters consist of a CNN with a convolutional 1D layer (using 32 filters, a kernel size of 3 and strides equal to 1) for a maximum of 5000 epochs using a total of 250 datasets (using data normalization between 0 and 1 and tolerance equal to max noise) and a batch size of 500 samples per epoch step, optimized with Adam using learning rate regularization. This model was evaluated for distinct measurement noise levels and pipe burst locations. Results indicate that the parameterized model can provide a pipe burst search area with more or less dispersion depending on both the proximity of pressure sensors to the burst or the noise measurement level.

2023Journal article

Open access