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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书目名称Advances in Data-Driven Computing and Intelligent Systems影响因子(影响力)

书目名称Advances in Data-Driven Computing and Intelligent Systems影响因子(影响力)学科排名

书目名称Advances in Data-Driven Computing and Intelligent Systems网络公开度

书目名称Advances in Data-Driven Computing and Intelligent Systems网络公开度学科排名

书目名称Advances in Data-Driven Computing and Intelligent Systems被引频次

书目名称Advances in Data-Driven Computing and Intelligent Systems被引频次学科排名

书目名称Advances in Data-Driven Computing and Intelligent Systems年度引用

书目名称Advances in Data-Driven Computing and Intelligent Systems年度引用学科排名

书目名称Advances in Data-Driven Computing and Intelligent Systems读者反馈

书目名称Advances in Data-Driven Computing and Intelligent Systems读者反馈学科排名

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

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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

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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

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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 “learning 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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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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