Titlebook: Advances in Data-Driven Computing and Intelligent Systems; Selected Papers from Swagatam Das,Snehanshu Saha,Jagdish C. Bansal Conference pr

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書目名稱Advances in Data-Driven Computing and Intelligent Systems影響因子(影響力)




書目名稱Advances in Data-Driven Computing and Intelligent Systems影響因子(影響力)學(xué)科排名




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書目名稱Advances in Data-Driven Computing and Intelligent Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Data-Driven Computing and Intelligent Systems被引頻次




書目名稱Advances in Data-Driven Computing and Intelligent Systems被引頻次學(xué)科排名




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書目名稱Advances in Data-Driven Computing and Intelligent Systems年度引用學(xué)科排名




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書目名稱Advances in Data-Driven Computing and Intelligent Systems讀者反饋學(xué)科排名

LUCY

Lecture Notes in Networks and Systemshttp://image.papertrans.cn/a/image/147695.jpg

SHRIK

存心

https://doi.org/10.1007/978-3-211-72138-4arious existing solutions in the domain of interest. Such solutions increase safety and comfort of the patients, enhance quality of their treatment in hospitals, reduce the costs and the influence of human errors. However, the existing solutions are aimed at solving particular tasks. The use of diff

被告

The middle cranial base and cavernous sinus to develop a POS tagger for the Assamese language. Due to the scarcity of digital linguistic resources, Assamese lacks high-performing POS taggers. To fill this gap, long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM) are explored in the proposed research to develop a P

LAST

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The middle cranial base and cavernous sinusers, industry stakeholders and software developers globally. Inconsistent evidence for gameplay learning effects compared with traditional learning delivery tends to bear the “l(fā)earning gaming puzzle” continuing. Sparse findings grapple with open innovation-related aspects and a less clear impact is

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Cave and Karst Systems of the Worlditerature some of the existing approaches such as statistical-based, density-based are applied for pattern detection whereas traditional approaches may not suitable for all scenarios, since they are limited with their properties. In this study, we propose deep learning-based behavior recognition to

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