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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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發(fā)表于 2025-3-21 16:41:54 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applic
編輯Francisco Zayas-Gato,Antonio Díaz-Longueira,Esteba
視頻videohttp://file.papertrans.cn/285/284804/284804.mp4
概述Highlights the latest research on distributed computing and artificial intelligence.Presents the special sessions of the 21st International Conference on Distributed Computing and Artificial Intellige
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applic;  Francisco Zayas-Gato,Antonio Díaz-Lo
描述.This volume of LNNS contains accepted papers presented at DCAI conference in the Special Session on Intelligent Systems Applications (ISA), Salamanca (Spain), June 2024. ISA is a major forum for presentation of development and applications of innovative techniques in the fields of building management, assistive technologies, the applied engineering, and industry. The scientific committee of the ISA has selected 9 full papers for its publication. This year ISA Special Session has teamed up with “Logic Journal of the IGPL” (Oxford University Press) for one special issue including selected papers from ISA Special Session 2024. We would like to thank all the contributing authors, National Associations (AEPIA, APPIA), and sponsors (AIR Institute). Finally, we are grateful and value the hard work of the Program Committee members and the Organizing Committee of the University of Salamanca which has been essential for the success of the ISA Special Session 2024..
出版日期Conference proceedings 2024
關(guān)鍵詞Intelligent Computing; Distributed Computing; Computational Intelligence; Artificial Intelligence; DCAI2
版次1
doihttps://doi.org/10.1007/978-3-031-73910-1
isbn_softcover978-3-031-73909-5
isbn_ebook978-3-031-73910-1Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 20:54:18 | 只看該作者
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
板凳
發(fā)表于 2025-3-22 00:24:06 | 只看該作者
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
地板
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regression models, pre-processing methods, and data analyses are compared in the study to forecast the input collector temperature of the heat pump. Specifically, three different techniques have been considered in this research work: Multilayer Perceptrons, Long Short Term Memory networks and Convol
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ssessing battery conditions and increasing the autonomy of electric vehicles requires to improve the behavior models. Prognosticating battery performance, nevertheless, is complex due to multiple variables, such as temperature, wear, and usage guidelines. Understanding battery performance requires a
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發(fā)表于 2025-3-22 20:04:50 | 只看該作者
r datasets. Using six complex datasets and MatLab R2023a software, especially the . function, various clustering algorithms such as k-means, agglomerative clustering and Gaussian mixture distribution are evaluated, along with evaluation criteria such as Calinski-Harabasz index, Davies-Bouldin index,
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emissions. However, the main disadvantage of this type of system is the dependence on weather conditions. This research proposes using a virtual sensor to measure the wind speed 10?m above ground through variables measured at ground level. The modeling process followed a feature selection step befor
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