Song Qi
- Artificial Intelligence top 2%
- Computer Networks and Communications top 5%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 10%
- Co-authors
- Dae-Ki ChoWei ChengBo ZongMartin Renqiang MinHaifeng ChenCristian LumezanuKaihong GuoYongming Li
- Topics
- AI in cancer detection (3 papers)Machine Learning and ELM (2 papers)Smart Grid and Power Systems (2 papers)
- Partner nations
- ChinaUnited States
In The Last Decade
Song Qi
12 papers receiving 877 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 715
- Computer Networks and Communications 314
- Signal Processing 190
- Computer Vision and Pattern Recognition 161
- Control and Systems Engineering 128
Countries citing papers authored by Song Qi
This map shows the geographic impact of Song Qi's research. 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 Song Qi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Qi more than expected).
Fields of papers citing papers by Song Qi
This network shows the impact of papers produced by Song Qi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Song Qi. The network helps show where Song Qi may publish in the future.
Co-authorship network of co-authors of Song Qi
This figure shows the co-authorship network connecting the top 25 collaborators of Song Qi. A scholar is included among the top collaborators of Song Qi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Song Qi. Song Qi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 47 | |
| 6 | 16 | |
| 7 | 32 | |
| 8 | 93 | |
| 9 | Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detectionbreakdown → | 628 |
| 10 | 7 | |
| 11 | 7 | |
| 12 | 8 | |
| 13 | 71 |
About Song Qi
Song Qi is a scholar working on Energy Engineering and Power Technology, Artificial Intelligence and Media Technology, having authored 13 papers that have together received 912 indexed citations. Recurring topics across this work include AI in cancer detection (3 papers), Machine Learning and ELM (2 papers) and Smart Grid and Power Systems (2 papers). The work is most often cited by research in Artificial Intelligence (715 citations), Signal Processing (190 citations) and Computer Networks and Communications (314 citations). Song Qi has collaborated with scholars based in China and United States. Frequent co-authors include Dae-Ki Cho, Wei Cheng, Bo Zong, Martin Renqiang Min, Haifeng Chen, Cristian Lumezanu, Kaihong Guo, Yongming Li, Pin Wang and Shanshan Lv. Their work appears in journals such as Applied Soft Computing, Biomedical Signal Processing and Control and Digital Signal Processing.
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.