DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
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doi.org/10.1109/infocom.2018.8485905 →Countries where authors are citing DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
This map shows the geographic impact of DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics more than expected).
Fields of papers citing DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
This network shows the impact of DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics.
About DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
This paper, published in 2018, received 346 indexed citations . Written by Xukan Ran, Xiaodan Zhu, Zhenming Liu and Jiasi Chen covering the research area of Computer Networks and Communications and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (230 citations), Computer Networks and Communications (194 citations), Artificial Intelligence (61 citations), Electrical and Electronic Engineering (58 citations) and Information Systems (25 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/infocom.2018.8485905.