Suet-Peng Yong

639 citations
33 papers · 390 indexed · h-index 12

Suet-Peng Yong

32 papers receiving 363 citations

Peers

Suet-Peng Yong
Comparison fields: 5 of 85
  • Computer Vision and Pattern Recognition 168
  • Artificial Intelligence 127
  • Signal Processing 36
  • Media Technology 24
  • Automotive Engineering 32
Replace Alhassan Mumuni with:
Alhassan Mumuni Ghana
Fuseini Mumuni Ghana
Kamal Kumar Ghanshala India
Zhiguo Yu China
Fayçal Hamdaoui Tunisia
Xun Gong China
Khaled Bayoudh Tunisia
Chunjiang Ge China
Enrique Domínguez Spain
Mehrez Marzougui Saudi Arabia
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Citations per year

Countries citing papers authored by Suet-Peng Yong

Since Specialization
Citations

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

Fields of papers citing papers by Suet-Peng Yong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Suet-Peng Yong. 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 Suet-Peng Yong. The network helps show where Suet-Peng Yong may publish in the future.

Co-authorship network

The 15 scholars most cited alongside Suet-Peng Yong, 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 Suet-Peng Yong Line = papers co-authored together Suet-Peng Yong links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20244
2 201839
3 201732
4 201717
5 201612
6 20165
7 20168
8 20162
9 20163
10 20158
11 201524
12 20150
13 20156
14 20157
15 20141
16
People Detection Enrichment for Abnormal Human Activity Detection
20132
17 20128
18 201219
19 20121
20 20102

About Suet-Peng Yong

Suet-Peng Yong is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Ocean Engineering and Management Science and Operations Research, having authored 33 papers that have together received 390 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (8 papers), Advanced Image and Video Retrieval Techniques (7 papers), Reservoir Engineering and Simulation Methods (5 papers), Image Retrieval and Classification Techniques (5 papers), Stock Market Forecasting Methods (4 papers), Hydraulic Fracturing and Reservoir Analysis (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Video Analysis and Summarization (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (168 citations), Artificial Intelligence (127 citations), Signal Processing (36 citations), Media Technology (24 citations) and Automotive Engineering (32 citations). Suet-Peng Yong has collaborated with scholars based in Malaysia, New Zealand and Sweden. Frequent co-authors include Sameer Khan, Said Jadid Abdulkadir, Jeremiah D. Deng, Martin Purvis, Nordin Zakaria, William Pao, Denis Kleyko, Baharum Baharudin, Evgeny Osipov and Low Tang Jung. Their work appears in journals such as Soft Computing, Multimedia Tools and Applications, Pattern Recognition, Journal of Computers and Algorithms.

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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