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Titlebook: Artificial Intelligence and Mobile Services – AIMS 2021; 10th International C Yi Pan,Zhi-Hong Mao,Liang-Jie Zhang Conference proceedings 20

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期刊全稱(chēng)Artificial Intelligence and Mobile Services – AIMS 2021
期刊簡(jiǎn)稱(chēng)10th International C
影響因子2023Yi Pan,Zhi-Hong Mao,Liang-Jie Zhang
視頻videohttp://file.papertrans.cn/163/162251/162251.mp4
學(xué)科分類(lèi)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Artificial Intelligence and Mobile Services – AIMS 2021; 10th International C Yi Pan,Zhi-Hong Mao,Liang-Jie Zhang Conference proceedings 20
影響因子This book constitutes the proceedings of the 10th International Conference on Artificial Intelligence and Mobile Services, AIMS 2021, held as a virtual conference as part of SCF 2021, during December 10-14, 2021..The 9 full presented were carefully reviewed and selected from 20 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, mobile backend as a service (MBaaS), user experience of AI and mobile services..
Pindex Conference proceedings 2022
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書(shū)目名稱(chēng)Artificial Intelligence and Mobile Services – AIMS 2021影響因子(影響力)




書(shū)目名稱(chēng)Artificial Intelligence and Mobile Services – AIMS 2021影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Artificial Intelligence and Mobile Services – AIMS 2021網(wǎng)絡(luò)公開(kāi)度




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書(shū)目名稱(chēng)Artificial Intelligence and Mobile Services – AIMS 2021被引頻次




書(shū)目名稱(chēng)Artificial Intelligence and Mobile Services – AIMS 2021被引頻次學(xué)科排名




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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
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Artificial Intelligence and Mobile Services – AIMS 202110th International C
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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,
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