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Titlebook: Big Data; 9th CCF Conference, Xiangke Liao,Wei Zhao,Dan Huang Conference proceedings 2022 Springer Nature Singapore Pte Ltd. 2022 artifici

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发表于 2025-3-21 18:45:35 | 显示全部楼层 |阅读模式
期刊全称Big Data
期刊简称9th CCF Conference,
影响因子2023Xiangke Liao,Wei Zhao,Dan Huang
视频video
学科分类Communications in Computer and Information Science
图书封面Titlebook: Big Data; 9th CCF Conference,  Xiangke Liao,Wei Zhao,Dan Huang Conference proceedings 2022 Springer Nature Singapore Pte Ltd. 2022 artifici
影响因子This book constitutes the proceedings of the 9th CCF Conference on Big Data, BigData 2021, held in Guangzhou, China, in January 2022. Due to the COVID-19 pandemic BigData 2021 was postponed to 2022. .The 21 full papers presented in this volume were carefully reviewed and selected from 66 submissions. They present recent research on theoretical and technical aspects on big data, as well as on digital economy demands in big data applications. .
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书目名称Big Data影响因子(影响力)




书目名称Big Data影响因子(影响力)学科排名




书目名称Big Data网络公开度




书目名称Big Data网络公开度学科排名




书目名称Big Data被引频次




书目名称Big Data被引频次学科排名




书目名称Big Data年度引用




书目名称Big Data年度引用学科排名




书目名称Big Data读者反馈




书目名称Big Data读者反馈学科排名




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Comparative Analysis of Hadoop MapReduce and Spark Based on People’s Livelihood Appeal Dataisk, and reveals much higher efficiency in data processing. In this paper, we will conduct comparative analysis of Hadoop MapReduce and Spark based on people’s Livelihood appeal data which is obtained from Pingshan district, Shenzhen, China.
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Automatic Classification of Medicinal Plants of Leaf Images Based on Convolutional Neural Networkifferent feature extraction methods and classification algorithms. In this paper, a novel dataset, which was based on Ethiopian medicinal plants, that use the leaf part of the plant, as a medicine was used to automatically classify the plants accordingly using their leaf image. An attempt has been m
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Attention-Based Iterated Dilated Convolutional Neural Networks for Joint Intent Classification and Sworks model, there are less linear structures, and the parallel efficiency of program execution is higher. Experimental results demonstrate that our joint training model outperforms the accuracy compared to the baseline models on benchmark ATIS and Snips datasets respectively.
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Quality-Sensitive Feature Extraction for End Product Quality Prediction in Injection Molding Processer and sensitive phase extraction layer. The feature extraction layer is to overcome the non-linear problem in the injection molding process, the three-way data and the unequal length issue are addressed by the long short-term memory network. The key time node extraction layer extracts crucial point
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https://doi.org/10.1007/978-3-030-20202-6ns from TCM data and explored the latent patterns and knowledge of it. The results of this paper is helpful to verify the TCM theory and provide assistance and reference to the clinical process. In order to increase the practicability of our discoveries, we developed a system to provide data explora
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