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Titlebook: Advances in Artificial Intelligence; Selected Papers from Yukio Ohsawa,Katsutoshi Yada,Naohiro Matsumura Conference proceedings 2020 The Ed

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期刊全稱Advances in Artificial Intelligence
期刊簡(jiǎn)稱Selected Papers from
影響因子2023Yukio Ohsawa,Katsutoshi Yada,Naohiro Matsumura
視頻videohttp://file.papertrans.cn/147/146742/146742.mp4
發(fā)行地址Highlights recent research in artificial intelligence.Gathers selected papers presented at the Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2018), held in Niigata, Japan,
學(xué)科分類Advances in Intelligent Systems and Computing
圖書封面Titlebook: Advances in Artificial Intelligence; Selected Papers from Yukio Ohsawa,Katsutoshi Yada,Naohiro Matsumura Conference proceedings 2020 The Ed
影響因子.This book presents selected and extended papers from the largest conference on artificial intelligence in Japan, which was expanded into an internationalized event for the first time in 2019: the 33rd Annual Conference of the Japanese Society for Artificial Intelligence (JSAI 2019), held on June 4–June 7, 2019 at TOKI MESSE in Niigata, Japan.?.The book’s content has been divided into six major sections, on (I) knowledge engineering, (II) agents, (III) education and culture, (IV) natural language processing, (V) machine learning and data mining, and (VI) cyber physics.?.Given its scope, the book offers a valuable reference guide for professionals, undergraduate and graduate students engaged in disciplines, fields, technologies, or philosophies relevant to AI, e.g., computer/data science, robotics, linguistics, and physics, introducing them to recent advances in this area and discussing the human society of tomorrow..
Pindex Conference proceedings 2020
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978-3-030-39877-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Pat Langdon,Jonathan Lazar,Hua Dongf capturing their information. Various graph-based techniques exist that report on the characteristics of nodes and edges, e.g., author-citation networks, social interactions, and so on. A significant amount of information can be extracted by summarizing the surrounding network structure of nodes, e
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Pat Langdon,Jonathan Lazar,Hua Dongical health checkup data. In this study, we carefully examine insurance claims data to identify onset of diseases and use the data for supervised learning. We aim to predict whether lifestyle-related diseases, except cancer, will develop within a year. We adopt the undersampling and bagging approach
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https://doi.org/10.1007/978-1-349-27726-1tems and sensors that can recognize and interpret human affects. Affective computing has been applied in various domains, and one of the applied domains is in the marketing area to increase the consumers’ appeal and attraction. In particular, advertisements (ads) can convey amounts of information in
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https://doi.org/10.1007/978-1-349-27726-1 For instance, the time-series deep Markov model has been proposed along with an inference network trained using variational inference. However, the original proposal did not fully leverage the model ability for data assimilation. Therefore, we aim to evaluate the suitability of a deep Markov model
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https://doi.org/10.1007/978-3-540-29828-1esent a negotiating agent that can search for suitable bids that obtain high joint utility values near a Nash bargaining solution by using a novel bid searching strategy. The proposed agent finished in second place in the social welfare category in International Automated Negotiating Agents Competit
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