Titlebook: Progress in Artificial Intelligence; 23rd EPIA Conference Manuel Filipe Santos,José Machado,Pedro Miguel Mor Conference proceedings 2025 Th

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Improving Fraud Detection with 1D-Convolutional Spiking Neural Networks Through Bayesian Optimization solutions for fraud detection is particularly challenging since the industry needs to respect a 5% threshold of false detection of fraud, avoiding monetary losses. The usage of traditional machine learning algorithms faces other challenges, such as classification discrimination and high energy cons

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A Benchmark of Automated Multivariate Time Series Forecasting Tools for Smart Citiesuable for providing predictive analytics for citizens and city rulers. In this paper, we benchmark seven multivariate open-source AutoTSF tools (AutoARIMAX, AutoGluon, FlaML, AutoTS, MFEDOT and HyperTS) and one univariate AutoTSF tool (FEDOT), measuring both their predictive performances, as well as

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A Data Drift Approach to Update Deployed Energy Prediction Machine Learning ModelsL models. In this work, we address this research gap by proposing a new data drift ML update strategy that only considers changes in the input features. Using the realistic Growing Window (GW) and Rolling Window (RW) ML deployment simulation schemes, we propose two Drift variants (DGW and DRW), whic

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