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Titlebook: Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference; Kenji Matsui,Sigeru Omatu,Sara Rodríguez Gonzá

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發(fā)表于 2025-3-21 19:16:38 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference
編輯Kenji Matsui,Sigeru Omatu,Sara Rodríguez González
視頻videohttp://file.papertrans.cn/282/281821/281821.mp4
概述Highlights the latest research on distributed computing and artificial intelligence.Presents the outcomes of the 18th International Conference on Distributed Computing and Artificial Intelligence 2021
叢書(shū)名稱(chēng)Lecture Notes in Networks and Systems
圖書(shū)封面Titlebook: Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference;  Kenji Matsui,Sigeru Omatu,Sara Rodríguez Gonzá
描述.This book offers the exchange of ideas between scientists and technicians from both the academic and industrial sector which is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The 18th International Symposium on Distributed Computing and Artificial Intelligence 2021 (DCAI 2021) is a forum to present the applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. The present edition 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. ..This year’s technical program presents both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 55 papers were submitted to main track and special sessions, by authors from 24 different countries, representing a truly “wide area network” of research activity. The DCAI’21 technical program has selected 21 papers, and, as in past editions, it will be special issues in ranked journals such as Electr
出版日期Conference proceedings 2022
關(guān)鍵詞Intelligent Computing; Distributed Computing; Computational Intelligence; Artificial Intelligence; DCAI2
版次1
doihttps://doi.org/10.1007/978-3-030-86261-9
isbn_softcover978-3-030-86260-2
isbn_ebook978-3-030-86261-9Series 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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https://doi.org/10.1007/978-981-97-4453-4uspicious. This kind of tool should be very useful for data analysts, it compensates for the lack of interpretability of the common detection models, and it can help to understand why some malicious files are undetected.
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Legitimacy and EU Foreign Policygoal, the study was conducted by the CRISP-DM methodology and using the RapidMiner software. The best model was obtained using the Decision Tree algorithm and with Cross-Validation as the sampling method, obtaining an accuracy of 0.884, an AUC value of 0.942 and an F1-Score of 0.881.
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Parallelization of the Poisson-Binomial Radius Distance for Comparing Histograms of ,-grams,h other classic alternatives. We present a GPU-based parallelization of the PBR distance for alleviating the cost of comparing large histograms of .-grams. Our experiments were performed with publicly available datasets of .-grams and showed that speed-ups between 12 and 17 times can be achieved with respect to the sequential implementation.
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,Malware Analysis with Artificial Intelligence and a Particular Attention on?Results Interpretabilituspicious. This kind of tool should be very useful for data analysts, it compensates for the lack of interpretability of the common detection models, and it can help to understand why some malicious files are undetected.
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A Search Engine for Scientific Publications: A Cybersecurity Case Study,-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.
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,A Tree-Based Approach to Forecast the?Total Nitrogen in Wastewater Treatment?Plants,ased with an approximate error of 1.6?mg/L. Considering the best candidate model identified, our objective was to extract the rules generated by the model to understand the factors that lead to high values at the level of total nitrogen.
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