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Titlebook: Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applic; Francisco Zayas-Gato,Antonio Díaz-Lo

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31#
發(fā)表于 2025-3-26 22:08:52 | 只看該作者
32#
發(fā)表于 2025-3-27 03:08:28 | 只看該作者
ssignment, considering a range of clusters to be evaluated..The results highlight how different combinations of algorithms and evaluation criteria handle the clustering complex data, with specific examples showing both effective and ineffective clustering. Comparative tables are presented for each d
33#
發(fā)表于 2025-3-27 07:17:56 | 只看該作者
34#
發(fā)表于 2025-3-27 10:43:25 | 只看該作者
,A Hybrid Intelligence Model Forecasts the?Temperature of?a?Battery Used in?Electric Vehicles,models. This model predict the temperature development with very good results in terms of Mean Squared Error. From this research it is deduced that hybrid models are appropriate tools for prognostic battery performance.
35#
發(fā)表于 2025-3-27 13:46:58 | 只看該作者
,A Comparative Analysis of?Algorithms and?Metrics to?Perform Clustering,ssignment, considering a range of clusters to be evaluated..The results highlight how different combinations of algorithms and evaluation criteria handle the clustering complex data, with specific examples showing both effective and ineffective clustering. Comparative tables are presented for each d
36#
發(fā)表于 2025-3-27 19:57:59 | 只看該作者
Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applic
37#
發(fā)表于 2025-3-27 23:14:05 | 只看該作者
38#
發(fā)表于 2025-3-28 05:53:11 | 只看該作者
39#
發(fā)表于 2025-3-28 07:20:12 | 只看該作者
Energy Forecasting Using Intelligent Models,crease the efficiency of the management systems responsible for the energy resources planning. The immediate benefits will be related to reduction of energy consumption, decrease in associated greenhouse gases emissions and improvement of the sustainability indexes of hotel activities. The predictio
40#
發(fā)表于 2025-3-28 12:56:26 | 只看該作者
,A Deep Learning-Based OCR System Implementation for?Traceability Ensurement in?a?Metal Manufacturin In this context, the labeling of these elements, coupled with optical character recognition (OCR), plays a significant role. Therefore, in this paper, it is accomplished this challenge by applying three pre-trained deep learning OCR models such as Pytesseract, EasyOCR and KerasOCR to a dataset of i
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