Ming Yang

1.3k citations
108 papers · 861 indexed · h-index 15

Impact in

Papers in

Ming Yang

101 papers receiving 822 citations

Peers

Ming Yang
Comparison fields: 5 of 78
  • Signal Processing 206
  • Computer Networks and Communications 379
  • Artificial Intelligence 406
  • Computer Vision and Pattern Recognition 227
  • Information Systems 172
Replace Ling Gao with:
Ling Gao China
Len Hamey Australia
Qiang Huang China
Yang‐Sae Moon South Korea
Michel Barbeau Canada
Seung-Hyun Seo South Korea
G. Padmavathi India
Jialong Zhang China
Ming Yang relative to Ling Gao China Ling Gao's profile →
Citations per field
00.5×1.5×2.2×
Ling Gao · 1×
Citations per year

Countries citing papers authored by Ming Yang

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 108 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200478
2 202159
3 201237
4 201932
5 201629
6 201726
7 201526
8 201424
9 202023
10 201521
11 201819
12 201218
13 200918
14 201416
15 201716
16 201814
17 201014
18 201014
19 202214
20 201414

About Ming Yang

Ming Yang is a scholar working on Signal Processing, Computer Networks and Communications, Artificial Intelligence, General Engineering and Information Systems, having authored 108 papers that have together received 861 indexed citations. Recurring topics across this work include Internet Traffic Analysis and Secure E-voting (35 papers), Network Security and Intrusion Detection (28 papers), Advanced Malware Detection Techniques (21 papers), Wireless Networks and Protocols (14 papers), Mobile Ad Hoc Networks (13 papers), Cooperative Communication and Network Coding (10 papers), User Authentication and Security Systems (8 papers) and Wireless Signal Modulation Classification (7 papers). The work is most often cited by research in Signal Processing (206 citations), Computer Networks and Communications (379 citations), Artificial Intelligence (406 citations), Computer Vision and Pattern Recognition (227 citations) and Information Systems (172 citations). Ming Yang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Junzhou Luo, Wenjia Wu, Zhen Ling, Nikolaos Bourbakis, Shujun Li, Kai Dong, Xiaogang Wang, Xinwen Fu, Tongxin Zhu and Fan Hu. Their work appears in journals such as Computer Networks, Information Sciences, Computer Communications, Sensors and Tsinghua Science & Technology.

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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