Shukai Li

460 citations
30 papers · 236 indexed · h-index 7
Topics
Imbalanced Data Classification Techniques (7 papers)Anomaly Detection Techniques and Applications (4 papers)Transportation and Mobility Innovations (3 papers)

In The Last Decade

Shukai Li

24 papers receiving 228 citations

Peers

Shukai Li
Comparison fields: 5 of 81
  • Pulmonary and Respiratory Medicine 57
  • Finance 35
  • Public Health, Environmental and Occupational Health 29
  • Molecular Biology 28
  • General Health Professions 27
Replace Isaac Dialsingh with:
Isaac Dialsingh Trinidad and Tobago
Liuquan Sun China
Henry Laniado Colombia
Hui-Ching Wu Taiwan
Jane Xu United Kingdom
David Ohlssen Switzerland
Jason C. Hsu Taiwan
Raymond Zhang United States
Elı́as Moreno Spain
Hani M. Samawi United States
Shukai Li relative to Isaac Dialsingh Trinidad and Tobago Isaac Dialsingh's profile →
Citations per field
00.5×3.4×
Isaac Dialsingh · 1×
Citations per year

Countries citing papers authored by Shukai Li

Since Specialization
Citations

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

Fields of papers citing papers by Shukai Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shukai Li. 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 Shukai Li. The network helps show where Shukai Li may publish in the future.

Co-authorship network of co-authors of Shukai Li

This figure shows the co-authorship network connecting the top 25 collaborators of Shukai Li. A scholar is included among the top collaborators of Shukai Li 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 Shukai Li. Shukai Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 1
4 2
5 1
6 2
7 6
8 1
9 24
10 67
11 1
12 16
13 0
14 2
15
Learning to Locate Relative Outliers
4
16 5
17 7
18
Web data integration based on Web services
1
19 2
20 1

About Shukai Li

Shukai Li is a scholar working on Transportation, Conservation and Artificial Intelligence, having authored 30 papers that have together received 236 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (7 papers), Anomaly Detection Techniques and Applications (4 papers) and Transportation and Mobility Innovations (3 papers). The work is most often cited by research in Finance (35 citations), Transportation (17 citations) and Pulmonary and Respiratory Medicine (57 citations). Shukai Li has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Robert M. Reed, M. Bellanger, Michelle N. Eakin, Zafar Zafarí, Ivor W. Tsang, Narendra S. Chaudhari, Robert C. Hampshire, Qi Luo, Yan Li and Di Liang. Their work appears in journals such as European Journal of Operational Research, CHEST Journal and Expert Systems with Applications.

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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