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Titlebook: Computer Applications; 38th CCF Conference Min Zhang,Bin Xu,Zeguang Lu Conference proceedings 2024 The Editor(s) (if applicable) and The A

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發(fā)表于 2025-3-21 17:45:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Applications
副標(biāo)題38th CCF Conference
編輯Min Zhang,Bin Xu,Zeguang Lu
視頻videohttp://file.papertrans.cn/234/233474/233474.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Computer Applications; 38th CCF Conference  Min Zhang,Bin Xu,Zeguang Lu Conference proceedings 2024 The Editor(s) (if applicable) and The A
描述The two-volume set CCIS 1959 and 1960 constitutes the refereed post-conference proceedings of the 38th CCF National Conference on Computer Applications, CCF NCCA 2023, held in Suzhou, China, during July 16–20, 2023..The 39 revised full papers presented in these proceedings were carefully reviewed and selected from 197 submissions. The papers are organized in the following topical sections:.Volume I:.Artificial intelligence and application..Volume II:.Data science and technology; pattern recognition and machine learning; network communication and security; frontier and comprehensive applications..
出版日期Conference proceedings 2024
關(guān)鍵詞Artificial Intelligence and Applications; Pattern Recognition and Machine Learning; Data Science and T
版次1
doihttps://doi.org/10.1007/978-981-99-8761-0
isbn_softcover978-981-99-8760-3
isbn_ebook978-981-99-8761-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

書目名稱Computer Applications影響因子(影響力)




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書目名稱Computer Applications網(wǎng)絡(luò)公開度學(xué)科排名




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Flue-Cured Tobacco Grading Method Based on a Convolutional Neural Network carried out in the tobacco leaf image dataset with 10 grades and more than 4000 images to realize the grade of tobacco leaf images. Compared with the classical neural network, this method has higher accuracy and can classify tobacco images of different grades more accurately.
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S. I. Sukhoruchkin,Z. N. Sorokod four groups of data are selected to calculate the transformation model; Finally, the transformation model is applied to the image to be registered. Experiments show that the algorithm increases matching accuracy by almost 8% while simultaneously saving roughly 25% of the time cost.
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An Iterative Selection Matching Algorithm Based on Fast Sample Consistencyd four groups of data are selected to calculate the transformation model; Finally, the transformation model is applied to the image to be registered. Experiments show that the algorithm increases matching accuracy by almost 8% while simultaneously saving roughly 25% of the time cost.
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1865-0929 on..Volume II:.Data science and technology; pattern recognition and machine learning; network communication and security; frontier and comprehensive applications..978-981-99-8760-3978-981-99-8761-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Computer Applications978-981-99-8761-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
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