ISSUE
发表于 2025-3-21 18:17:12
书目名称Artificial Neural Networks and Machine Learning – ICANN 2023影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162665<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2023读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162665<br><br> <br><br>
Geyser
发表于 2025-3-22 00:08:01
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duplicate
发表于 2025-3-22 04:20:49
Zug-, Druck- und Scherfestigkeit,erformed person identification tasks using ten sub-datasets sampled from a large-scale CBP dataset. Our proposed method achieved higher recognition accuracy than other relevant methods in spite of its relatively low computational cost on segment-by-segment and aggregated recognition tasks, respectively.
无目标
发表于 2025-3-22 08:38:14
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积习已深
发表于 2025-3-22 09:39:48
,An Echo State Network-Based Method for Identity Recognition with Continuous Blood Pressure Data,erformed person identification tasks using ten sub-datasets sampled from a large-scale CBP dataset. Our proposed method achieved higher recognition accuracy than other relevant methods in spite of its relatively low computational cost on segment-by-segment and aggregated recognition tasks, respectively.
CRAMP
发表于 2025-3-22 14:58:26
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MORT
发表于 2025-3-22 20:16:33
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悬挂
发表于 2025-3-22 23:34:32
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舔食
发表于 2025-3-23 02:20:46
https://doi.org/10.1007/978-3-662-11735-4olecular Structure-based Double-Central Drug-Drug Interaction prediction(MSDC-DDI). MSDC-DDI utilizes a double-central encoder and a cross-dependent schema to generate the representations of the drugs. MSDC-DDI made effective and accurate predictions, which achieved up to more than 99% in DDI prediction.
debunk
发表于 2025-3-23 07:02:27
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