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Titlebook: Computational Science and Its Applications – ICCSA 2022 Workshops; Malaga, Spain, July Osvaldo Gervasi,Beniamino Murgante,Chiara Garau Con

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发表于 2025-3-21 16:15:31 | 显示全部楼层 |阅读模式
书目名称Computational Science and Its Applications – ICCSA 2022 Workshops
副标题Malaga, Spain, July
编辑Osvaldo Gervasi,Beniamino Murgante,Chiara Garau
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Science and Its Applications – ICCSA 2022 Workshops; Malaga, Spain, July  Osvaldo Gervasi,Beniamino Murgante,Chiara Garau Con
描述.The eight-volume set LNCS 13375 – 13382 constitutes the proceedings of the 22nd International Conference on Computational Science and Its Applications, ICCSA 2022, which was held in Malaga, Spain during July 4 – 7, 2022. ..The first two volumes contain the proceedings from ICCSA 2022, which are the 57 full and 24 short papers presented in these books were carefully reviewed and selected from 279 submissions...The other six volumes present the workshop proceedings, containing 285 papers out of 815 submissions. These six volumes includes the proceedings of the following workshops:.​.Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2022); Workshop on Advancements in Applied Machine-learning and Data Analytics (AAMDA 2022); Advances in information Systems and Technologies for Emergency management, risk assessment and mitigation based on the Resilience (ASTER 2022); Advances in Web Based Learning (AWBL 2022); Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2022); Bio and Neuro inspired Computing and Applications (BIONCA 2022); Configurational Analysis For Cities (CA Cities 2022); Computa
出版日期Conference proceedings 2022
关键词artificial intelligence; computer networks; computer systems; correlation analysis; data mining; distribu
版次1
doihttps://doi.org/10.1007/978-3-031-10536-4
isbn_softcover978-3-031-10535-7
isbn_ebook978-3-031-10536-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-031-58449-7proved this model’s performance by using Natural Language Processing (NLP) and Deep Learning (DL) techniques. This was done by evaluating the same dataset used by the previous researcher to train the proposed model. The overall results show that LSTM outperform both GRU and CNN models in terms of F1
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Sarfraz Nazir,Alessandro Capocchialgorithms. Customer-website interaction is considered a key input variable in creating personalized recommendations. Customers are supposed to use the website and leave interaction data regarding product configurations they’re interested in. To prove the efficiency of this methodology, we developed
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Michael P. Weinstein,R. Eugene Turnerufacturing and transmission line applications. In this pipeline, the Bayesian optimization solution is used to suggest the best combination of hyperparameters for model variables. We analyze four multi-output models, such as Adaptive Boosting, Gradient Boosting, Random Forest and MultiLayer Perceptr
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https://doi.org/10.1007/978-1-4614-3188-6t more effectively. Another gap that we aim to fill is the temporal representation of the facts stored in the knowledge graph. At the best of our knowledge, this is the first attempt to associate the temporal validity to the KG relations. This additional information can be used to further enhance th
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Miguel Esteban,Tomohiro Akiyama,Takashi MinoTherefore, it is considered a necessity to determine appropriate strategies for the use of Metaverse in the educational field and to start determining its widespread effect until the infrastructure of Metaverse matures.
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Natural Language Processing and Deep Learning Based Techniques for Evaluation of Companies’ Privacy proved this model’s performance by using Natural Language Processing (NLP) and Deep Learning (DL) techniques. This was done by evaluating the same dataset used by the previous researcher to train the proposed model. The overall results show that LSTM outperform both GRU and CNN models in terms of F1
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