Details
Name
Gonçalo Duarte NunesRole
ResearcherSince
03rd March 2023
Nationality
PortugalCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
goncalo.d.nunes@inesctec.pt
2025
Authors
da Silva, JMPP; Duarte Nunes, G; Ferreira, A;
Publication
Abstract
2025
Authors
Silva, S; Nunes, GD; da Silva, JP; Meireles, A; Bidarra, D; Moreira, J; Novais, S; Dias, I; Sousa, R; Frazao, O;
Publication
29TH INTERNATIONAL CONFERENCE ON OPTICAL FIBER SENSORS
Abstract
In this study, we demonstrate the measurement of electric power using an optical ground wire ( OPGW). The tests were conducted on an OPGW cable from a high-voltage transmission line in Sines, Portugal, operating at 400 kV. A buried fiber position, free of 50 Hz and 100 Hz frequency interference, was selected to confirm that the 50 Hz frequency is not due to mechanical perturbation or electronic noise. Additionally, two suspended fiber positions (at 2500 m and 8500 m), where these frequencies were clearly observed, were analyzed. This study also examined the positioning of poles and splice detection between cables.
2025
Authors
Nogueira, AR; Pinto, J; da Silva, JP; Nunes, GD; Curral, M; Sousa, RT;
Publication
Progress in Artificial Intelligence - 24th EPIA Conference on Artificial Intelligence, EPIA 2025, Faro, Portugal, October 1-3, 2025, Proceedings, Part I
Abstract
Manual selection of real estate properties can pose considerable challenges for agents since it needs a careful balance of various factors to satisfy client requirements while also manoeuvring through the complexities of the market. Although automated valuation models are widely used to estimate property market values, they are not designed to support property recommendation tasks. To address this gap, filtering-based recommendation methods have been explored, including collaborative and content-based approaches. However, these methods face several limitations in the real estate domain. This paper proposes a recommendation methodology designed to identify houses that closely resemble a given property, allowing agents to select the best matches based on geographical and physical characteristics. To assess the performance of the proposed methodology, we employ a range of evaluation metrics that measure different aspects of the model’s effectiveness in ranking and recommending relevant items. The findings suggest that, while geographic features may slightly influence ranking behaviour, the model is capable of producing diverse and relevant recommendations consistently. © 2025 Elsevier B.V., All rights reserved.
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