Gang Luo

127 papers receiving 1.9k citations

Peers

Gang Luo
Comparison fields: 5 of 157
  • Health Informatics 46
  • Health Information Management 136
  • Artificial Intelligence 691
  • Signal Processing 203
  • Computer Networks and Communications 335
Replace Ahmet Arslan with:
Ahmet Arslan Türkiye
Deepali Gupta India
Sijia Liu United States
Stefania Montani Italy
Maha Driss Tunisia
Yugal Kumar India
Kalpna Guleria India
Balamurugan Balusamy India
Salem A. Alyami Saudi Arabia
Rachna Jain India
Gang Luo relative to Ahmet Arslan Türkiye Ahmet Arslan's profile →
Citations per field
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Citations per year

Countries citing papers authored by Gang Luo

Since Specialization
Citations

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

Fields of papers citing papers by Gang Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 141 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016257
2 2015171
3 200472
4 200262
5
Challenges and experience in prototyping a multi-modal stream analytic and monitoring application on System S
200761
6 201557
7 201051
8
Proceedings of the 1st ACM International Health Informatics Symposium
201044
9 200944
10
Joint Named Entity Recognition and Disambiguation
201539
11 201537
12 202235
13 201735
14 200534
15 202032
16 201332
17 200331
18 201430
19 201530
20 200830

About Gang Luo

Gang Luo is a scholar working on Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Control and Systems Engineering and Health Information Management, having authored 141 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (16 papers), Data Management and Algorithms (10 papers), Biomedical Text Mining and Ontologies (10 papers), Electronic Health Records Systems (9 papers), Machine Learning in Healthcare (9 papers), Data Quality and Management (7 papers), Semantic Web and Ontologies (7 papers) and Power Systems and Renewable Energy (7 papers). The work is most often cited by research in Health Informatics (46 citations), Health Information Management (136 citations), Artificial Intelligence (691 citations), Signal Processing (203 citations) and Computer Networks and Communications (335 citations). Gang Luo has collaborated with scholars based in United States, China and India. Frequent co-authors include Jeffrey F. Naughton, Curt J. Ellmann, Chin-Yew Lin, Xiaojiang Huang, Zaiqing Nie, Chunqiang Tang, Bryan L. Stone, Flory L. Nkoy, Michael D. Johnson and Philip S. Yu. Their work appears in journals such as Journal of Medical Systems, IEEE Access, Journal of Medical Internet Research, IET Generation Transmission & Distribution and The Journal of Engineering.

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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