Su-Kit Tang

58 papers receiving 358 citations

Peers

Su-Kit Tang
Comparison fields: 5 of 75
  • Automotive Engineering 44
  • Computer Vision and Pattern Recognition 73
  • Environmental Engineering 50
  • Atmospheric Science 46
  • Computer Science Applications 13
Replace Sibghat Ullah Bazai with:
Sibghat Ullah Bazai Pakistan
Muhammad Attique Khan Saudi Arabia
Wengen Li China
S. Abirami India
Mayank Arya Chandra India
Szymon Łukasik Poland
Lhoussaine Masmoudi Morocco
Alejandro H. Ballado Philippines
Su-Kit Tang relative to Sibghat Ullah Bazai Pakistan Sibghat Ullah Bazai's profile →
Citations per field
00.5×3.4×
Sibghat Ullah Bazai · 1×
Citations per year

Countries citing papers authored by Su-Kit Tang

Since Specialization
Citations

This map shows the geographic impact of Su-Kit Tang'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 Su-Kit Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Su-Kit Tang more than expected).

Fields of papers citing papers by Su-Kit Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Su-Kit Tang. 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 Su-Kit Tang. The network helps show where Su-Kit Tang may publish in the future.

Co-authors

The 25 scholars most cited alongside Su-Kit Tang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Su-Kit Tang Line = papers co-authored together Su-Kit Tang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202228
2 202123
3 202221
4 202218
5 202415
6 202313
7 202313
8 202413
9 202312
10 201811
11 202211
12 202011
13 202010
14 201410
15 20239
16 20229
17 20229
18 20237
19 20257
20 20217

About Su-Kit Tang

Su-Kit Tang is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Environmental Engineering, having authored 67 papers that have together received 368 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (9 papers), Smart Agriculture and AI (6 papers), Advanced Neural Network Applications (5 papers), Advanced Battery Technologies Research (5 papers), Flood Risk Assessment and Management (5 papers), Electric Vehicles and Infrastructure (4 papers), Hydrological Forecasting Using AI (4 papers) and Plant Disease Management Techniques (4 papers). The work is most often cited by research in Automotive Engineering (44 citations), Computer Vision and Pattern Recognition (73 citations), Environmental Engineering (50 citations), Atmospheric Science (46 citations) and Computer Science Applications (13 citations). Su-Kit Tang has collaborated with scholars based in Macao, Italy and United States. Frequent co-authors include Rita Tse, Giovanni Pau, Hong Lin, Zhenping Qiang, Silvia Mirri, Paola Salomoni, Yanbing Chen, Wei Ke, Alberto Cardoso and Giovanni Delnevo. Their work appears in journals such as Applied Sciences, Atmosphere, Sensors, Frontiers in Plant Science and Electronics.

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.

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