Ling Jing

63 papers receiving 609 citations

Peers

Ling Jing
Comparison fields: 5 of 97
  • Computer Vision and Pattern Recognition 221
  • Computational Mathematics 6
  • Media Technology 86
  • Artificial Intelligence 209
  • Water Science and Technology 83
Replace Md. Monirul Kabir with:
Md. Monirul Kabir Bangladesh
Xuchun Li Singapore
Kuan-Ming Lin Taiwan
Arto Kaarna Finland
Barenya Bikash Hazarika India
Wenbin Li China
Songfeng Zheng United States
Xiekai Zhang China
Xiaohui Yang China
Ling Jing relative to Md. Monirul Kabir Bangladesh Md. Monirul Kabir's profile →
Citations per field
00.5×5.5×
Md. Monirul Kabir · 1×
Citations per year

Countries citing papers authored by Ling Jing

Since Specialization
Citations

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

Fields of papers citing papers by Ling Jing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ling Jing, 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 Ling Jing Line = papers co-authored together Ling Jing 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 2012105
2 200961
3 200534
4 200625
5 200724
6 201223
7 202321
8 201720
9 201720
10 201516
11 202016
12 201914
13 201914
14 201813
15 201811
16 201911
17 202011
18 201911
19 202410
20 202310

About Ling Jing

Ling Jing is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Atmospheric Science and Molecular Biology, having authored 67 papers that have together received 627 indexed citations. Recurring topics across this work include Face and Expression Recognition (33 papers), Remote-Sensing Image Classification (19 papers), Machine Learning and ELM (10 papers), Remote Sensing and Land Use (9 papers), Advanced Algorithms and Applications (8 papers), Text and Document Classification Technologies (6 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Fluoride Effects and Removal (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (221 citations), Computational Mathematics (6 citations), Media Technology (86 citations), Artificial Intelligence (209 citations) and Water Science and Technology (83 citations). Ling Jing has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Nai-Yang Deng, Yuan‐Hai Shao, Zhimin Yang, Chunhua Zhang, Jinxin Zhang, Zhen Ling, Yingyi Chen, Li Sun, Wenwen Qiang and Yong Wang. Their work appears in journals such as Engineering Applications of Artificial Intelligence, Knowledge-Based Systems, International Journal of Remote Sensing, Neural Networks and IEEE Geoscience and Remote Sensing Letters.

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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