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Titlebook: Distributed Computing and Artificial Intelligence, 17th International Conference; Yucheng Dong,Enrique Herrera-Viedma,Sara Rodríguez Confe

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書目名稱Distributed Computing and Artificial Intelligence, 17th International Conference
編輯Yucheng Dong,Enrique Herrera-Viedma,Sara Rodríguez
視頻videohttp://file.papertrans.cn/282/281812/281812.mp4
概述Highlights the latest research on distributed computing and artificial intelligence.Presents the outcomes of the 17th International Conference on Distributed Computing and Artificial Intelligence 2020
叢書名稱Advances in Intelligent Systems and Computing
圖書封面Titlebook: Distributed Computing and Artificial Intelligence, 17th International Conference;  Yucheng Dong,Enrique Herrera-Viedma,Sara Rodríguez Confe
描述This book brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. DCAI 2020 is a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. This year’s technical program will present both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 83 papers were submitted to main track and special sessions, by authors from 26 different countries representing a truly “wide area network” of research activity. The DCAI’20 technical program has selected 35 papers and, as in past editions, it will be special issues in ranked journals. This symposium is organized by the University of L‘Aquila (Italy). We would like to thank all the contributing authors, the members of the Program Committee and thesponsors (IBM, Armundia Group, EurAI, AEPIA, APPIA, CINI, OIT, UGR, HU, SCU, USAL, AIR Institute and UNIVAQ).. .?.
出版日期Conference proceedings 2021
關(guān)鍵詞Intelligent Computing; Distributed Computing; Computational Intelligence; Artificial Intelligence; DCAI2
版次1
doihttps://doi.org/10.1007/978-3-030-53036-5
isbn_softcover978-3-030-53035-8
isbn_ebook978-3-030-53036-5Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Filtering Distributed Information to Build a Plausible Scene for Autonomous and Connected Vehicles,cy) in order to assess the confidence in the value of a feature, and to select the values that are most plausible. We show that it enables to handle various difficult situations (attacks, failures, etc.), by maintaining a coherent scene at any time despite possibly major defects.
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Geoffrey Lee Williams,Alan Lee Williamsith his development described. The model predicts a high number of false positives, therefore low precision and F-score, but a high 88.4% accuracy and 0.81 AUROC (Area under the Receiver Operating Characteristic).
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Geoffrey Lee Williams,Alan Lee Williams We improved the performance of the model using the focal loss as the loss function of the classifier. As a result, the effectiveness of the proposed model was confirmed on multiple datasets with different ratios of data in each class.
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The European Economy in 100 Quoteslemented multi-agent systems. In order to devise a quantitative comparison, the two considered languages are used to solve the same coordination problem, and obtained implementations are compared to discuss advantages and drawbacks of the two approaches.
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