可怖 发表于 2025-3-21 17:30:39

书目名称Rough Sets影响因子(影响力)<br>        http://impactfactor.cn/if/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets影响因子(影响力)学科排名<br>        http://impactfactor.cn/ifr/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets网络公开度<br>        http://impactfactor.cn/at/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets网络公开度学科排名<br>        http://impactfactor.cn/atr/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets被引频次<br>        http://impactfactor.cn/tc/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets被引频次学科排名<br>        http://impactfactor.cn/tcr/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets年度引用<br>        http://impactfactor.cn/ii/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets年度引用学科排名<br>        http://impactfactor.cn/iir/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets读者反馈<br>        http://impactfactor.cn/5y/?ISSN=BK0831898<br><br>        <br><br>书目名称Rough Sets读者反馈学科排名<br>        http://impactfactor.cn/5yr/?ISSN=BK0831898<br><br>        <br><br>

Stable-Angina 发表于 2025-3-21 21:03:49

General Rough Modeling of Cluster Analysislusterings is invented, and this opens the subject to clearer conceptions and contamination-free theoretical proofs. Numeric ideas of validation are also proposed to be replaced by those based on general rough approximation. The essential approach is explained in brief and supported by an example.

水土 发表于 2025-3-22 02:20:34

Possible Coverings in Incomplete Information Tables with Similarity of Valuesaximum possible ones which are derived from the minimum and the maximum possible indiscernibility relations that are equal to the intersection and the union of those from possible tables. The approximations are equal to those derived using the minimum and the maximum possibly indiscernible classes.

Cpr951 发表于 2025-3-22 06:08:55

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Fresco 发表于 2025-3-22 10:57:10

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obeisance 发表于 2025-3-22 16:25:16

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熔岩 发表于 2025-3-22 17:06:09

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PANT 发表于 2025-3-22 21:52:38

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Emmenagogue 发表于 2025-3-23 03:47:58

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prostatitis 发表于 2025-3-23 08:53:14

DDAE-GAN: Seismic Data Denoising by Integrating Autoencoder and Generative Adversarial Networkdge of complex unknown noise. In this paper, for seismic denoising, we propose a new method with three techniques to handle them effectively. First, a Generative Adversarial Network (GAN) is employed to generate a large number of paired clean-noisy data using real noise. Second, a deep denoising aut
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查看完整版本: Titlebook: Rough Sets; International Joint Sheela Ramanna,Chris Cornelis,Davide Ciucci Conference proceedings 2021 Springer Nature Switzerland AG 202