Deep convolutional neural networks on multichannel time series for human activity recognition
Impact in
Classified as
- Authors
- Jian YangMinh Nhut NguyenPhyo Phyo SanXiaoli LiShonali Krishnaswamy
- Journal
- International Conference on Artificial Intelligence
In The Last Decade
doi.org/w3680411 →Countries where authors are citing Deep convolutional neural networks on multichannel time series for human activity recognition
This map shows the geographic impact of Deep convolutional neural networks on multichannel time series for human activity recognition. 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 Deep convolutional neural networks on multichannel time series for human activity recognition with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep convolutional neural networks on multichannel time series for human activity recognition more than expected).
Fields of papers citing Deep convolutional neural networks on multichannel time series for human activity recognition
This network shows the impact of Deep convolutional neural networks on multichannel time series for human activity recognition. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep convolutional neural networks on multichannel time series for human activity recognition.
About Deep convolutional neural networks on multichannel time series for human activity recognition
This paper, published in 2015, received 670 indexed citations . Written by Jian Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiaoli Li and Shonali Krishnaswamy covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (413 citations), Artificial Intelligence (226 citations), Biomedical Engineering (195 citations), Computer Networks and Communications (114 citations) and Signal Processing (99 citations). Published in International Conference on Artificial Intelligence.
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/w3680411.