Hua Huo
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Molecular Biology
- Control and Systems Engineering
- Neurology
- Topics
- Parkinson's Disease Mechanisms and Treatments (5 papers)Machine Learning and ELM (4 papers)Face and Expression Recognition (4 papers)
In The Last Decade
Hua Huo
45 papers receiving 265 citations
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 78
- Computer Vision and Pattern Recognition 55
- Molecular Biology 39
- Control and Systems Engineering 27
- Neurology 22
Countries citing papers authored by Hua Huo
This map shows the geographic impact of Hua Huo'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 Hua Huo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hua Huo more than expected).
Fields of papers citing papers by Hua Huo
This network shows the impact of papers produced by Hua Huo. 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 Hua Huo. The network helps show where Hua Huo may publish in the future.
Co-authorship network of co-authors of Hua Huo
This figure shows the co-authorship network connecting the top 25 collaborators of Hua Huo. A scholar is included among the top collaborators of Hua Huo 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 Hua Huo. Hua Huo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 4 | |
| 7 | 17 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 22 | |
| 12 | 1 | |
| 13 | 2 | |
| 14 | A Method of Fine-grained Text Sentiment Analysis Based on Machine Learning | 3 |
| 15 | Estimation of Explosive Performances for Three Fused Ring Nitramine Compounds and Synthesis of Their Nitrification Parent Ring Compounds | 0 |
| 16 | 2 | |
| 17 | RFID Anti-collision Algorithm Based on Parallel Processing | 1 |
| 18 | Oil monitoring methods based on information theory | 1 |
| 19 | Data envelopment analysis in evaluation of resource allocation efficiency in military institute for drug and instrument control | 2 |
| 20 | Document Similarity Degree Measuring Based on Compressed Sparse Matrix Vector Multiplication Technique | 2 |
About Hua Huo
Hua Huo is a scholar working on Computer Vision and Pattern Recognition, Ecological Modeling and Artificial Intelligence, having authored 53 papers that have together received 273 indexed citations. Recurring topics across this work include Parkinson's Disease Mechanisms and Treatments (5 papers), Machine Learning and ELM (4 papers) and Face and Expression Recognition (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (55 citations), Artificial Intelligence (78 citations) and Neurology (22 citations). Hua Huo has collaborated with scholars based in China, Australia and India. Frequent co-authors include Zhonghua Liu, Kaibing Zhang, Fa Zhu, Zhihui Lai, Zhuguo Li, Zhendong Zheng, Weihua Ou, Zhihui Lai, Xian Jia and Huibin Wang. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Access.
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.