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Titlebook: Artificial Intelligence and Machine Learning; First International Hai Jin,Yi Pan,Jianfeng Lu Conference proceedings 2024 The Editor(s) (if

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發(fā)表于 2025-3-21 20:05:16 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Intelligence and Machine Learning
期刊簡稱First International
影響因子2023Hai Jin,Yi Pan,Jianfeng Lu
視頻videohttp://file.papertrans.cn/163/162232/162232.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Artificial Intelligence and Machine Learning; First International  Hai Jin,Yi Pan,Jianfeng Lu Conference proceedings 2024 The Editor(s) (if
影響因子.This 3-volume set, CCIS 2058-2060 constitutes the First International Conference, on Artificial Intelligence, IAIC 2023, held in Nanjing, China, in November 2023.. The 85 full papers presented were carefully reviewed and selected from 428 submissions.?The papers are clustered in parts on: Artificial Intelligence and Machine Learning; Data Security and information Security; Computer Networks and IoT. The papers present recent research and developments in artificial intelligence and its applications in machine learning, natural language processing, computer vision, robotics, and ethical considerations..
Pindex Conference proceedings 2024
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書目名稱Artificial Intelligence and Machine Learning影響因子(影響力)




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Artificial Intelligence and Machine Learning978-981-97-1277-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
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978-981-97-1276-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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https://doi.org/10.1007/978-1-349-18048-6phenomena during the printing process. Effective detection of machine printed page numbers is of great practical significance for improving the efficiency of archive organization work; A machine printed page anomaly detection method based on multi-scale self attention encoding decoding is proposed t
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Self System: First Order Knowledge Domain train a global model through coordination by a central server while ensuring the privacy of data on individual edge devices. However, the data on edge devices that participate in FL training are not independently and identically distributed (IID), resulting in challenges related to heterogeneity da
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Self System: First Order Knowledge Domainction tasks. This paper systematically reviews relation extraction techniques based on deep learning, including the application of convolutional neural networks, recurrent neural networks and Transformer models. Firstly, it introduces the representative applications of these three models. Then, rela
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