Sin-Chun Ng
- Artificial Intelligence top 5%
- Control and Systems Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Electrical and Electronic Engineering
- Co-authors
- Man-Fai LeungS.H. LeungYunfeng WuAndrew Kwok-Fai LuiRay C. C. CheungRangaraj M. RangayyanChi-Chung CheungSheung‐On Choy
- Topics
- Neural Networks and Applications (36 papers)Advanced Adaptive Filtering Techniques (16 papers)Advanced Algorithms and Applications (16 papers)
- Partner nations
- Hong KongChinaUnited Kingdom
In The Last Decade
Sin-Chun Ng
91 papers receiving 944 citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 461
- Control and Systems Engineering 161
- Computer Vision and Pattern Recognition 152
- Signal Processing 138
- Electrical and Electronic Engineering 118
Countries citing papers authored by Sin-Chun Ng
This map shows the geographic impact of Sin-Chun Ng'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 Sin-Chun Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sin-Chun Ng more than expected).
Fields of papers citing papers by Sin-Chun Ng
This network shows the impact of papers produced by Sin-Chun Ng. 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 Sin-Chun Ng. The network helps show where Sin-Chun Ng may publish in the future.
Co-authorship network of co-authors of Sin-Chun Ng
This figure shows the co-authorship network connecting the top 25 collaborators of Sin-Chun Ng. A scholar is included among the top collaborators of Sin-Chun Ng 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 Sin-Chun Ng. Sin-Chun Ng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 19 | |
| 2 | A BERT Framework to Sentiment Analysis of Tweetsbreakdown → | 109 |
| 3 | 0 | |
| 4 | 95 | |
| 5 | 4 | |
| 6 | 25 | |
| 7 | 36 | |
| 8 | A Hybrid Framework for Restaurant Recommender System | 2 |
| 9 | Feature Extraction of the Carapace for marine Turtle Species Categorization | 2 |
| 10 | A Hybrid Model for Pattern Recognition of Marine Turtle Species | 1 |
| 11 | 25 | |
| 12 | Development of a mental stress relaxation tool based on guided imagery and Microsoft Kinect | 2 |
| 13 | 15 | |
| 14 | 1 | |
| 15 | 7 | |
| 16 | Software Agents to Assist in Distance Learning Environments. | 4 |
| 17 | 60 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 5 |
About Sin-Chun Ng
Sin-Chun Ng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 96 papers that have together received 1.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (36 papers), Advanced Adaptive Filtering Techniques (16 papers) and Advanced Algorithms and Applications (16 papers). The work is most often cited by research in Artificial Intelligence (461 citations), Signal Processing (138 citations) and Management Science and Operations Research (108 citations). Sin-Chun Ng has collaborated with scholars based in Hong Kong, China and United Kingdom. Frequent co-authors include Man-Fai Leung, S.H. Leung, Yunfeng Wu, Andrew Kwok-Fai Lui, Ray C. C. Cheung, Rangaraj M. Rangayyan, Chi-Chung Cheung, Sheung‐On Choy, C.F. So and Hangjun Che. Their work appears in journals such as IEEE Access, Sensors and IEEE Signal Processing Magazine.
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.