Bin Ji
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Machine Learning in Healthcare
Papers in
-
- Topic Modeling 13
- Natural Language Processing Techniques 10
- Advanced Computational Techniques and Applications 4
- Adversarial Robustness in Machine Learning 2
- Co-authors
- Shasha Li (12 shared papers)Yusong Tan (3 shared papers)Qingbo Wu (4 shared papers)Jie Yu (8 shared papers)Huijun Liu (10 shared papers)Jun Ma (9 shared papers)Jiaju Wu (5 shared papers)Jie Yu (4 shared papers)
In The Last Decade
Bin Ji
26 papers receiving 240 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 152
- Health Informatics 2
- Media Technology 13
- Otorhinolaryngology 5
- Management Science and Operations Research 14
Countries citing papers authored by Bin Ji
This map shows the geographic impact of Bin Ji'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 Bin Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Ji more than expected).
Fields of papers citing papers by Bin Ji
This network shows the impact of papers produced by Bin Ji. 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 Bin Ji. The network helps show where Bin Ji may publish in the future.
Co-authors
The 25 scholars most cited alongside Bin Ji, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 50 | |
| 2 | 2020 | 43 | |
| 3 | 2020 | 23 | |
| 4 | 2020 | 20 | |
| 5 | 2018 | 17 | |
| 6 | 2020 | 16 | |
| 7 | 2022 | 12 | |
| 8 | 2009 | 11 | |
| 9 | 2009 | 10 | |
| 10 | 2022 | 9 | |
| 11 | 2016 | 9 | |
| 12 | 2022 | 5 | |
| 13 | 2022 | 4 | |
| 14 | Shape feature selection and weed recognition based on image processing and ant colony optimization. | 2010 | 2 |
| 15 | 2012 | 2 | |
| 16 | 2011 | 2 | |
| 17 | 2018 | 2 | |
| 18 | 2020 | 2 | |
| 19 | 2022 | 2 | |
| 20 | 2022 | 2 |
About Bin Ji
Bin Ji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Software and Analytical Chemistry, having authored 39 papers that have together received 250 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (10 papers), Advanced Computational Techniques and Applications (4 papers), Biomedical Text Mining and Ontologies (4 papers), Model-Driven Software Engineering Techniques (3 papers), Spectroscopy and Chemometric Analyses (3 papers), Retinal Imaging and Analysis (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Artificial Intelligence (152 citations), Health Informatics (2 citations), Media Technology (13 citations), Otorhinolaryngology (5 citations) and Management Science and Operations Research (14 citations). Bin Ji has collaborated with scholars based in China and Singapore. Frequent co-authors include Shasha Li, Yusong Tan, Qingbo Wu, Jie Yu, Huijun Liu, Jun Ma, Jiaju Wu, Jie Yu, Rui Liu and Weixing Zhu. Their work appears in journals such as Mathematical Biosciences & Engineering, Science China Information Sciences, Journal of Biomedical Informatics, IEEE Transactions on Knowledge and Data Engineering and BMC Medical Informatics and Decision Making.
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.