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

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發(fā)表于 2025-3-25 06:20:42 | 只看該作者
A Benchmark of?Automated Multivariate Time Series Forecasting Tools for?Smart Citiesty under a realistic rolling window scheme. Overall, the AutoGluon and AutoTS tools presented the best predictive performances, with AutoGluon requiring a substantially reduced training computational effort when compared with AutoTS.
22#
發(fā)表于 2025-3-25 10:49:38 | 只看該作者
23#
發(fā)表于 2025-3-25 13:33:44 | 只看該作者
Large Language Model for?Querying Databases in?PortugueseA specific domain is explored, focusing on a subscrip- tion-based sports facility, which serves as?an open-source version of a real case study. Utilizing the star schema, the available data in the database is structured to provide accessibility through Portuguese Natural Language queries. Using?a La
24#
發(fā)表于 2025-3-25 17:28:55 | 只看該作者
A Novel Integration of?Federated Learning and?LSTM for?Synthetic Time Series Generation. Particularly in the field of medical diagnosis, where comprehending these patterns is crucial because they now pertain to people’s health. Owing to this paradigm shift?in the use of time series data in all fields, machine and deep learning models specifically created for these tasks must be traine
25#
發(fā)表于 2025-3-25 21:18:35 | 只看該作者
26#
發(fā)表于 2025-3-26 02:27:25 | 只看該作者
27#
發(fā)表于 2025-3-26 07:19:33 | 只看該作者
AIMSM - A Mechanism to?Optimize Systems with?Multiple AI Models: A Case Study in?Computer Vision for?Autonomous Mobile Robotswing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t?have to operate all the time. The AIMSM strategically allows the sys
28#
發(fā)表于 2025-3-26 09:20:05 | 只看該作者
29#
發(fā)表于 2025-3-26 16:13:13 | 只看該作者
30#
發(fā)表于 2025-3-26 17:32:44 | 只看該作者
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