Titlebook: Cognitive Computing – ICCC 2019; Third International Ruifeng Xu,Jianzong Wang,Liang-Jie Zhang Conference proceedings 2019 Springer Nature

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书目名称Cognitive Computing – ICCC 2019影响因子(影响力)




书目名称Cognitive Computing – ICCC 2019影响因子(影响力)学科排名




书目名称Cognitive Computing – ICCC 2019网络公开度




书目名称Cognitive Computing – ICCC 2019网络公开度学科排名




书目名称Cognitive Computing – ICCC 2019被引频次




书目名称Cognitive Computing – ICCC 2019被引频次学科排名




书目名称Cognitive Computing – ICCC 2019年度引用




书目名称Cognitive Computing – ICCC 2019年度引用学科排名




书目名称Cognitive Computing – ICCC 2019读者反馈




书目名称Cognitive Computing – ICCC 2019读者反馈学科排名

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Cognitive Computing – ICCC 2019978-3-030-23407-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Daniel Ranke,Thomas Bauernhansl such powerful approach to study large community of microbial species. For the unknown species in the metagenomic samples, gene assembly and identification without a reference genome is a very challenging problem. To overcome this issue, distributed gene assembly software handling multiple metagenom

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Akshay Avhad,Casper Schou,Ole Madsen them into some structure that machine can handle. The understanding usually consists of two parts, namely intent identification and slot filling. To address this problem, we propose a neural framework, named SI-LSTM, that combines two tasks and integrates CRF into LSTM network, where the slot infor

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A Perspective on S-Layer Researchnt role in driver-assistance systems and driverless operation. Detecting signs, especially small ones, remains challenging under a variety of road traffic conditions. In this manuscript, we propose an end-to-end deep learning model for detecting and recognizing traffic signs in high-resolution image

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Martin Kessel,Shlomo Trachtenbergising solution to build “Five-Distances” of binocular camera detection system based on cloud platform for intelligent safety distance warning. Aiming at the problem of feature extraction in “Five-Distances” detection of stacking in night vision environment, this paper proposes the construction of tw

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Martin Kessel,Shlomo Trachtenbergby large-scale graphs are often sensitive and private. It is a necessity of developing algorithms to publish large-scale graph while not revealing sensitive information. As a standard for data privacy preservation, differential privacy based algorithm are also widely used in publishing graph-based d