Canghong Jin

416 citations
37 papers · 275 · h-index 11

Impact in

Papers in

Canghong Jin

33 papers receiving 261 citations

Peers

Canghong Jin
Comparison fields: 5 of 89
  • Food Science 58
  • Transportation 16
  • Information Systems 51
  • Plant Science 81
  • Artificial Intelligence 55
Replace Shima Khoshraftar with:
Shima Khoshraftar Canada
Gaurav Tewari India
Fuqiang Yu China
Malika Belkadi Algeria
Lizhong Xiao China
Yun-Wei Lin Taiwan
Rachida Aoudjit Algeria
Sudhakar Pandey India
Zeyuan Chen China
Jeong-Hwan Hwang South Korea
Canghong Jin relative to Shima Khoshraftar Canada Shima Khoshraftar's profile →
Citations per field
00.5×1.5×2.3×
Shima Khoshraftar · 1×
Citations per year

Countries citing papers authored by Canghong Jin

Since Specialization
Citations

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

Fields of papers citing papers by Canghong Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Canghong Jin, 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 Canghong Jin Line = papers co-authored together Canghong Jin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202233
2 201431
3 202223
4 202222
5 202120
6 202318
7 201916
8 202314
9 200812
10 201712
11 201111
12 20248
13 20208
14 20205
15 20245
16 20205
17 20244
18 20084
19 20223
20 20232

About Canghong Jin

Canghong Jin is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Transportation and Building and Construction, having authored 37 papers that have together received 275 indexed citations. Recurring topics across this work include Data Management and Algorithms (6 papers), Traffic Prediction and Management Techniques (6 papers), Service-Oriented Architecture and Web Services (5 papers), Human Mobility and Location-Based Analysis (5 papers), Mycotoxins in Agriculture and Food (4 papers), Transportation Planning and Optimization (3 papers), Advanced Software Engineering Methodologies (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Food Science (58 citations), Transportation (16 citations), Information Systems (51 citations), Plant Science (81 citations) and Artificial Intelligence (55 citations). Canghong Jin has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Minghui Wu, Xiaofeng Ji, Wentao Lyu, Yingping Xiao, Wen Wang, Hua Yang, Kevin Esterling, Matthew Wiley, Vagelis Hristidis and Ying Jing. Their work appears in journals such as Food Control, China Communications, IEEE Access, Food Research International and Computer Modeling in Engineering & Sciences.

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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