Fuhong Song
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Information Systems top 10%
- Topics
- IoT and Edge/Fog Computing (5 papers)Cooperative Communication and Network Coding (4 papers)Advanced MIMO Systems Optimization (2 papers)
- Cited by
- Computer Networks and CommunicationsComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsIEEE Internet of Things JournalKnowledge-Based Systems
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Fuhong Song
9 papers receiving 546 citations
Hit Papers
Peers
Comparison fields: 5 of 79
- Computer Networks and Communications 254
- Computer Vision and Pattern Recognition 190
- Artificial Intelligence 156
- Electrical and Electronic Engineering 97
- Information Systems 86
Countries citing papers authored by Fuhong Song
This map shows the geographic impact of Fuhong Song'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 Fuhong Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fuhong Song more than expected).
Fields of papers citing papers by Fuhong Song
This network shows the impact of papers produced by Fuhong Song. 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 Fuhong Song. The network helps show where Fuhong Song may publish in the future.
Co-authorship network of co-authors of Fuhong Song
This figure shows the co-authorship network connecting the top 25 collaborators of Fuhong Song. A scholar is included among the top collaborators of Fuhong Song 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 Fuhong Song. Fuhong Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognitionbreakdown → | 99 |
| 2 | 12 | |
| 3 | 103 | |
| 4 | 4 | |
| 5 | A federated learning system with enhanced feature extraction for human activity recognitionbreakdown → | 184 |
| 6 | 65 | |
| 7 | 67 | |
| 8 | On multicast routing with network coding: A multiobjective artificial bee colony algorithm | 6 |
| 9 | 10 | |
| 10 | 0 | |
| 11 | 0 |
About Fuhong Song
Fuhong Song is a scholar working on Computer Networks and Communications, Computer Science Applications and Hardware and Architecture, having authored 11 papers that have together received 550 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (5 papers), Cooperative Communication and Network Coding (4 papers) and Advanced MIMO Systems Optimization (2 papers). The work is most often cited by research in Computer Networks and Communications (254 citations), Computer Vision and Pattern Recognition (190 citations) and Artificial Intelligence (156 citations). Fuhong Song has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Huanlai Xing, Xinhan Wang, Zhiwen Xiao, Shouxi Luo, Bowen Zhao, Penglin Dai, Xin Xu, Rong Qu, Li Feng and Zonghai Zhu. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Internet of Things Journal and Knowledge-Based Systems.
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.