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Titlebook: Brain and Health Informatics; International Confer Kazayuki Imamura,Shiro Usui,Ning Zhong Conference proceedings 2013 Springer Internationa

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發(fā)表于 2025-3-21 16:42:33 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Brain and Health Informatics
期刊簡(jiǎn)稱International Confer
影響因子2023Kazayuki Imamura,Shiro Usui,Ning Zhong
視頻videohttp://file.papertrans.cn/191/190256/190256.mp4
發(fā)行地址Up-to-date results.State-of-the-art research.Fast-track conference proceedings
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Brain and Health Informatics; International Confer Kazayuki Imamura,Shiro Usui,Ning Zhong Conference proceedings 2013 Springer Internationa
影響因子This book constitutes the refereed proceedings of the International Conference on Brain and Health Informatics, BHI 2013, held in Maebashi, Japan, in October 2013. The 33 revised full papers presented together with 8 workshop papers and 12 special session papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on thinking and perception-centric Investigations of human Information processing system; information technologies for curating, mining, managing and using big brain/health data; information technologies for healthcare; data analytics, data mining, and machine learning; and applications. The topics of the workshop papers are: mental health with ICT; and granular knowledge discovery in biomedical and active-media environments; and the topics of the special sessions are: human centered computing; neuro-robotics; and intelligent healthcare data analytics.
Pindex Conference proceedings 2013
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書目名稱Brain and Health Informatics影響因子(影響力)




書目名稱Brain and Health Informatics影響因子(影響力)學(xué)科排名




書目名稱Brain and Health Informatics網(wǎng)絡(luò)公開度




書目名稱Brain and Health Informatics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Brain and Health Informatics被引頻次




書目名稱Brain and Health Informatics被引頻次學(xué)科排名




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https://doi.org/10.1007/b105752n in the graph. The results showed that the graph and textual tasks consistently activated the anterior and posterior portions of ventrolateral prefrontal cortex (VLPFC), the dorsal and ventral MPFC. The results suggest that the VLPFC and the MPFC commonly contribute to the social statistical information processing.
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Machine Learning for Networkingment design problem is intractable. We therefore propose an approximation approach to resolve this issue. The experimental results demonstrate that the proposed approach successes in reconstructing functional MR images with greater accuracy than by random sampling.
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