Qianlong Wang
Impact in
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data
- Anomaly Detection Techniques and Applications
-
- IoT and Edge/Fog Computing
Papers in
-
- Topic Modeling 9
- Privacy-Preserving Technologies in Data 8
- Natural Language Processing Techniques 7
- Sentiment Analysis and Opinion Mining 6
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- Electric Motor Design and Analysis 11
- Journals
- IEEE Internet of Things Journal (4 papers)IET Electric Power Applications (4 papers)Advanced Functional Materials (4 papers)IEEE Transactions on Network Science and Engineering (3 papers)Journal of Colloid and Interface Science (3 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Qianlong Wang
87 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 536
- Computer Networks and Communications 340
- Signal Processing 123
- Electrical and Electronic Engineering 630
- Materials Chemistry 351
Countries citing papers authored by Qianlong Wang
This map shows the geographic impact of Qianlong Wang'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 Qianlong Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qianlong Wang more than expected).
Fields of papers citing papers by Qianlong Wang
This network shows the impact of papers produced by Qianlong Wang. 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 Qianlong Wang. The network helps show where Qianlong Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Qianlong Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 11 | |
| 7 | 2024 | 21 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 1 | |
| 10 | 2023 | 2 | |
| 11 | 2023 | 18 | |
| 12 | 2023 | 6 | |
| 13 | 2023 | 33 | |
| 14 | 2021 | 13 | |
| 15 | 2021 | 16 | |
| 16 | 2021 | 9 | |
| 17 | 2021 | 4 | |
| 18 | 2020 | 57 | |
| 19 | 2019 | 11 | |
| 20 | 2019 | 222 |
About Qianlong Wang
Qianlong Wang is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Analytical Chemistry, Renewable Energy, Sustainability and the Environment and Computer Networks and Communications, having authored 98 papers that have together received 1.8k indexed citations. Recurring topics across this work include Electric Motor Design and Analysis (11 papers), Graphene research and applications (10 papers), Topic Modeling (9 papers), Privacy-Preserving Technologies in Data (8 papers), Natural Language Processing Techniques (7 papers), Thermal properties of materials (7 papers), Sentiment Analysis and Opinion Mining (6 papers) and Blockchain Technology Applications and Security (6 papers). The work is most often cited by research in Artificial Intelligence (536 citations), Computer Networks and Communications (340 citations), Signal Processing (123 citations), Electrical and Electronic Engineering (630 citations) and Materials Chemistry (351 citations). Qianlong Wang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Pan Li, Lixing Yu, Yifan Guo, Changqing Luo, Jinlong Ji, Xuhui Chen, Tianxi Ji, Hao Chen, Shujing Chen and Weixian Liao. Their work appears in journals such as IEEE Internet of Things Journal, IET Electric Power Applications, Advanced Functional Materials, IEEE Transactions on Network Science and Engineering and Journal of Colloid and Interface Science.
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.