Dezhi Hong
- Artificial Intelligence top 5%
- Building and Construction top 2%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Electrical and Electronic Engineering
- Co-authors
- Kamin WhitehouseRajesh K. GuptaArka BhattacharyaDavid CullerHongning WangBharathan BalajiJason KohYuvraj Agarwal
- Topics
- Context-Aware Activity Recognition Systems (12 papers)Anomaly Detection Techniques and Applications (12 papers)Time Series Analysis and Forecasting (11 papers)
- Journals
- Applied EnergyIEEE Transactions on Knowledge and Data EngineeringPervasive and Mobile Computing
- Partner nations
- United StatesChinaGermany
In The Last Decade
Dezhi Hong
57 papers receiving 908 citations
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 311
- Building and Construction 273
- Computer Vision and Pattern Recognition 253
- Signal Processing 197
- Electrical and Electronic Engineering 178
Countries citing papers authored by Dezhi Hong
This map shows the geographic impact of Dezhi Hong'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 Dezhi Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dezhi Hong more than expected).
Fields of papers citing papers by Dezhi Hong
This network shows the impact of papers produced by Dezhi Hong. 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 Dezhi Hong. The network helps show where Dezhi Hong may publish in the future.
Co-authorship network of co-authors of Dezhi Hong
This figure shows the co-authorship network connecting the top 25 collaborators of Dezhi Hong. A scholar is included among the top collaborators of Dezhi Hong 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 Dezhi Hong. Dezhi Hong 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 | 4 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 5 | |
| 7 | 6 | |
| 8 | 8 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 7 | |
| 12 | 2 | |
| 13 | 16 | |
| 14 | 6 | |
| 15 | 2 | |
| 16 | 19 | |
| 17 | 4 | |
| 18 | 164 | |
| 19 | High-dimensional Time Series Clustering via Cross-Predictability. | 14 |
| 20 | 45 |
About Dezhi Hong
Dezhi Hong is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 60 papers that have together received 937 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (12 papers), Anomaly Detection Techniques and Applications (12 papers) and Time Series Analysis and Forecasting (11 papers). The work is most often cited by research in Building and Construction (273 citations), Signal Processing (197 citations) and Computer Vision and Pattern Recognition (253 citations). Dezhi Hong has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Kamin Whitehouse, Rajesh K. Gupta, Arka Bhattacharya, David Culler, Hongning Wang, Bharathan Balaji, Jason Koh, Yuvraj Agarwal, Jorge Ortiz and Jingkun Gao. Their work appears in journals such as Applied Energy, IEEE Transactions on Knowledge and Data Engineering and Pervasive and Mobile Computing.
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.