Pin Ni
Impact in
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- Stock Market Forecasting Methods
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
Papers in
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- Topic Modeling 10
- Natural Language Processing Techniques 9
- Advanced Text Analysis Techniques 5
- Advanced Graph Neural Networks 3
- Machine Learning in Healthcare 2
- Sentiment Analysis and Opinion Mining 2
-
- Blockchain Technology Applications and Security 2
- Co-authors
- Yuming Li (15 shared papers)Victor Chang (12 shared papers)Gangmin Li (13 shared papers)Xuming Bai (5 shared papers)Xutao Wang (2 shared papers)Sheng-Uei Guan (1 shared paper)Patrick C. K. Hung (1 shared paper)Jiayi Zhu (3 shared papers)
- Journals
- Information Systems Frontiers (2 papers)ACM Transactions on Internet Technology (1 paper)Computing (1 paper)Neural Computing and Applications (1 paper)IT Professional (1 paper)
- Partner nations
- ChinaUnited KingdomNew Zealand
In The Last Decade
Pin Ni
22 papers receiving 407 citations
Peers
Comparison fields: 5 of 79
- Management Science and Operations Research 98
- Artificial Intelligence 246
- Health Informatics 6
- Finance 41
- Information Systems 68
Countries citing papers authored by Pin Ni
This map shows the geographic impact of Pin Ni'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 Pin Ni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pin Ni more than expected).
Fields of papers citing papers by Pin Ni
This network shows the impact of papers produced by Pin Ni. 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 Pin Ni. The network helps show where Pin Ni may publish in the future.
Co-authors
The 15 scholars most cited alongside Pin Ni, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 117 | |
| 2 | 2019 | 93 | |
| 3 | 2020 | 43 | |
| 4 | 2022 | 32 | |
| 5 | 2021 | 27 | |
| 6 | 2019 | 18 | |
| 7 | 2021 | 15 | |
| 8 | 2019 | 10 | |
| 9 | 2020 | 8 | |
| 10 | 2019 | 8 | |
| 11 | 2019 | 8 | |
| 12 | 2020 | 6 | |
| 13 | 2020 | 6 | |
| 14 | 2019 | 5 | |
| 15 | 2022 | 5 | |
| 16 | 2019 | 4 | |
| 17 | 2020 | 4 | |
| 18 | 2021 | 2 | |
| 19 | 2021 | 2 | |
| 20 | 2023 | 1 |
About Pin Ni
Pin Ni is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Molecular Biology and Sociology and Political Science, having authored 23 papers that have together received 416 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers), Advanced Text Analysis Techniques (5 papers), Advanced Graph Neural Networks (3 papers), Brain Tumor Detection and Classification (2 papers), Blockchain Technology Applications and Security (2 papers), Machine Learning in Healthcare (2 papers) and Sentiment Analysis and Opinion Mining (2 papers). The work is most often cited by research in Management Science and Operations Research (98 citations), Artificial Intelligence (246 citations), Health Informatics (6 citations), Finance (41 citations) and Information Systems (68 citations). Pin Ni has collaborated with scholars based in China, United Kingdom and New Zealand. Frequent co-authors include Yuming Li, Victor Chang, Gangmin Li, Xuming Bai, Xutao Wang, Sheng-Uei Guan, Patrick C. K. Hung, Jiayi Zhu, Aldo Lipani and Francesca Medda. Their work appears in journals such as Information Systems Frontiers, ACM Transactions on Internet Technology, Computing, Neural Computing and Applications and IT Professional.
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.