ABS
发表于 2025-3-21 18:46:54
书目名称Artificial Intelligence and Mobile Services – AIMS 2021影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0162251<br><br> <br><br>书目名称Artificial Intelligence and Mobile Services – AIMS 2021读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0162251<br><br> <br><br>
体贴
发表于 2025-3-21 23:55:23
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MURKY
发表于 2025-3-22 01:46:14
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苦涩
发表于 2025-3-22 06:36:58
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陪审团
发表于 2025-3-22 12:46:31
Multimodal Social Media Sentiment Analysis Based on Cross-Modal Hierarchical Attention Fusiont outstanding pre-training models are used to obtain emotional features of various modalities. And then, we propose cross-modal global fusion and cross-modal high-level semantic fusion methods to combine the features of different levels. At last, we conduct extensive experiments on a real Chinese mu
无畏
发表于 2025-3-22 14:04:42
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Console
发表于 2025-3-22 17:36:48
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最低点
发表于 2025-3-23 00:44:02
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异端邪说下
发表于 2025-3-23 04:51:37
Artificial Intelligence and Mobile Services – AIMS 202110th International C
Rodent
发表于 2025-3-23 06:16:32
A Combination of Resampling Method and Machine Learning for Text Classification on Imbalanced Dataset. Results show that, 5 different oversampling method and SmoteTomek method can rebalance the dataset effectively, which can improve the recognition rate of models on the minority class obviously, while undersampling methods decrease the overall accuracy of models on imbalanced dataset. Meanwhile,