Z
Zhihan Lv
Researcher at Qingdao University
Publications - 460
Citations - 14707
Zhihan Lv is an academic researcher from Qingdao University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 54, co-authored 313 publications receiving 8997 citations. Previous affiliations of Zhihan Lv include Chinese Academy of Sciences & Warsaw University of Technology.
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FLDS: An Intelligent Feature Learning Detection System for Visualizing Medical Images Supporting Fetal Four-chamber Views
TL;DR: Wang et al. as discussed by the authors proposed an intelligent feature learning detection system (FLDS) for fetal four-chamber (FC) views to detect the four chambers, which is an extremely challenging task due to several key factors, such as numerous speckles in US images, the fetal four chambers with small size and unfixed positions, and category confusion caused by the similarity of cardiac chambers.
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Multi-Modal Description of Public Safety Events Using Surveillance and Social Media
TL;DR: Case studies on the real public safety event show the proposed model has good performance and high effectiveness and the superiority of the proposed framework is demonstrated.
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Big data analytics for sustainability
TL;DR: Big data analytics is clearly on a penetrative path across all arenas that rely on technology and sustainability is a paradigm for thinking about the future in which environmental, societal and economic considerations are equitable in the pursuit of an improved lifestyle.
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Non-uniformity quantification of temperature and concentration fields by statistical measure and image analysis
TL;DR: In this article, a non-uniformity coefficient (NUC) based on uniform design theory is illustrated by using numerical modeling of microchannel heat sinks with complex structure and computer experiments combined with image analysis.
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Dynamic systems based on preference graph and distance
TL;DR: A group decision-making approach fusing preference conflicts and compatibility measure is proposed, focused on dynamic group decision making with preference information of policymakers at each time describing with dynamic preference.