Hwee-Boon Low
Impact in
- Artificial Intelligence top 5%
- Text and Document Classification Technologies
- Advanced Text Analysis Techniques
- Advanced Clustering Algorithms Research
- Topic Modeling
- Information Systems top 5%
- Web Data Mining and Analysis
- Spam and Phishing Detection
Papers in
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- Text and Document Classification Technologies 4
- Advanced Text Analysis Techniques 2
- Algorithms and Data Compression 2
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- Web Data Mining and Analysis 2
- Service-Oriented Architecture and Web Services 1
- Co-authors
- Man Lan (4 shared papers)Chew-Lim Tan (2 shared papers)Chew‐Lim Tan (2 shared papers)Ji He (1 shared paper)Lionel Wee (1 shared paper)Ying Lü (1 shared paper)
- Journals
- Computer Networks and ISDN Systems (1 paper)National University of Singapore (3 papers)PubMed (1 paper)Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (1 paper)
- Partner nations
- Singapore
In The Last Decade
Hwee-Boon Low
5 papers receiving 301 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 275
- Information Systems 140
- Computer Vision and Pattern Recognition 75
- Signal Processing 39
- Communication 8
Countries citing papers authored by Hwee-Boon Low
This map shows the geographic impact of Hwee-Boon Low'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 Hwee-Boon Low with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hwee-Boon Low more than expected).
Fields of papers citing papers by Hwee-Boon Low
This network shows the impact of papers produced by Hwee-Boon Low. 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 Hwee-Boon Low. The network helps show where Hwee-Boon Low may publish in the future.
Co-authors
The 6 scholars most cited alongside Hwee-Boon Low, 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 | 2005 | 105 | |
| 2 | 2005 | 83 | |
| 3 | Proposing a new term weighting scheme for text categorization | 2006 | 77 |
| 4 | 2006 | 55 | |
| 5 | 2012 | 3 | |
| 6 | Some Lessons Learnt by a New Comer | 1993 | 1 |
| 7 | 1998 | 0 |
About Hwee-Boon Low
Hwee-Boon Low is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Cell Biology and Molecular Biology, having authored 7 papers that have together received 324 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (4 papers), Advanced Text Analysis Techniques (2 papers), Algorithms and Data Compression (2 papers), Web Data Mining and Analysis (2 papers), Face and Expression Recognition (1 paper), Image Retrieval and Classification Techniques (1 paper), Machine Learning in Bioinformatics (1 paper) and Service-Oriented Architecture and Web Services (1 paper). The work is most often cited by research in Artificial Intelligence (275 citations), Information Systems (140 citations), Computer Vision and Pattern Recognition (75 citations), Signal Processing (39 citations) and Communication (8 citations). Hwee-Boon Low has collaborated with scholars based in Singapore. Frequent co-authors include Man Lan, Chew-Lim Tan, Chew‐Lim Tan, Ji He, Lionel Wee and Ying Lü. Their work appears in journals such as Computer Networks and ISDN Systems, National University of Singapore, PubMed and Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
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.