Bowen Zhao
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Electrical and Electronic Engineering
- Signal Processing top 10%
- Topics
- Time Series Analysis and Forecasting (9 papers)Anomaly Detection Techniques and Applications (5 papers)Wireless Signal Modulation Classification (4 papers)
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Bowen Zhao
19 papers receiving 782 citations
Hit Papers
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 318
- Computer Vision and Pattern Recognition 243
- Computer Networks and Communications 153
- Electrical and Electronic Engineering 77
- Signal Processing 75
Countries citing papers authored by Bowen Zhao
This map shows the geographic impact of Bowen Zhao'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 Bowen Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bowen Zhao more than expected).
Fields of papers citing papers by Bowen Zhao
This network shows the impact of papers produced by Bowen Zhao. 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 Bowen Zhao. The network helps show where Bowen Zhao may publish in the future.
Co-authorship network of co-authors of Bowen Zhao
This figure shows the co-authorship network connecting the top 25 collaborators of Bowen Zhao. A scholar is included among the top collaborators of Bowen Zhao 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 Bowen Zhao. Bowen Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Rational multienzyme architecture design with iMARSbreakdown → | 15 |
| 2 | 2 | |
| 3 | 4 | |
| 4 | Densely Knowledge-Aware Network for Multivariate Time Series Classificationbreakdown → | 105 |
| 5 | DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classificationbreakdown → | 67 |
| 6 | 26 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | Deep Contrastive Representation Learning With Self-Distillationbreakdown → | 105 |
| 11 | 8 | |
| 12 | 14 | |
| 13 | 1 | |
| 14 | 12 | |
| 15 | 2 | |
| 16 | 7 | |
| 17 | 103 | |
| 18 | An Efficient Federated Distillation Learning System for Multitask Time Series Classificationbreakdown → | 124 |
| 19 | A federated learning system with enhanced feature extraction for human activity recognitionbreakdown → | 184 |
| 20 | 12 |
About Bowen Zhao
Bowen Zhao is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 20 papers that have together received 795 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (9 papers), Anomaly Detection Techniques and Applications (5 papers) and Wireless Signal Modulation Classification (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (243 citations), Artificial Intelligence (318 citations) and Signal Processing (75 citations). Bowen Zhao has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Huanlai Xing, Zhiwen Xiao, Rong Qu, Xinhan Wang, Fuhong Song, Shouxi Luo, Penglin Dai, Zonghai Zhu, Xin Xu and Li Feng. Their work appears in journals such as Cell, Knowledge-Based Systems and IEEE Transactions on Instrumentation and Measurement.
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.