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Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Aut

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书目名称Intelligent Systems and Applications
副标题Proceedings of the 2
编辑Kohei Arai
视频video
概述Presents recent research in intelligent systems and applications.Gathers the proceedings of the Intelligent Systems Conference 2024 (IntelliSys 2024).Focuses on various areas of intelligent systems an
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Aut
描述.This volume is a collection of meticulously crafted, insightful, and state-of-the-art papers presented at the Intelligent Systems Conference 2024, held in Amsterdam, The Netherlands, on 5-6 September 2024. The conference received an overwhelming response, with a total of 535 submissions. After a rigorous double-blind peer review process, 181 papers were selected for presentation. These papers span a wide range of scientific topics, including Artificial Intelligence, Computer Vision, Robotics, Intelligent Systems, and more...We hope that readers find this volume both interesting and valuable. Furthermore, we expect that the conference and its proceedings will inspire further research and technological advancements in these critical areas of study...Thank you for engaging with this collection of works from the Intelligent Systems Conference 2024. Your interest and support contribute significantly to the ongoing progress and innovation in the field of intelligent systems..
出版日期Conference proceedings 2024
关键词Artificial Intelligence; Deep Learning; Neural Networks; Fuzzy Logic; Expert Systems; Computational Intel
版次1
doihttps://doi.org/10.1007/978-3-031-66428-1
isbn_softcover978-3-031-66427-4
isbn_ebook978-3-031-66428-1Series 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
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,Improving Convolutional End-to-End Memory Networks with BERT for Question Answering,ing overfitting issue. Our experiment shows that the proposed model exhibits remarkable performance, outperforming top results achieved by other memory networks models on the Facebook ‘bAbI 1k’ dataset with an accuracy of 92.86.
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,Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Gemanually labelled data from the 2021 flood in Germany and the 2023 Chile forest fires were considered. The results show that generic fine-tuning combined with 10 rounds of AL outperformed all other approaches. Consequently, a broadly applicable model for the identification of disaster-related Tweets
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,Integrating Deep Reinforcement Learning into Agent-Based Models for Predicting Farmer Adaptation Unrshed of trans-boundary Lake Champlain Basin. The DRL-ABM specifically focuses on predicting the adoption of adaptation actions by simulated farmers that reduce the adverse impact of agricultural production on water quality in the face of global climate change induced shifting frequencies and intens
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