Hai Van Pham

35 papers receiving 237 citations

Peers

Hai Van Pham
Comparison fields: 5 of 72
  • Management Science and Operations Research 56
  • Signal Processing 31
  • Computer Vision and Pattern Recognition 52
  • Artificial Intelligence 69
  • Information Systems 39
Replace Hsin-Yu Ha with:
Hsin-Yu Ha United States
Ed Waltz United States
Dongjie Wang United States
Hatem Abdul-Kader Egypt
Jainendra K. Navlakha United States
Constantinos S. Hilas Greece
Micah J. Smith United States
Weize Chen China
Iván López-Arévalo Mexico
Mieczysław A. Kłopotek Poland
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Citations per field
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Citations per year

Countries citing papers authored by Hai Van Pham

Since Specialization
Citations

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

Fields of papers citing papers by Hai Van Pham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai Van Pham. 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 Hai Van Pham. The network helps show where Hai Van Pham may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202048
2 201318
3 201316
4 201514
5 201114
6 201214
7 201513
8 201411
9 201211
10 20228
11 20246
12 20236
13 20196
14 20235
15 20115
16 20205
17 20204
18 20144
19 20174
20 20224

About Hai Van Pham

Hai Van Pham is a scholar working on Computer Vision and Pattern Recognition, Management Science and Operations Research, Information Systems, Signal Processing and Computer Networks and Communications, having authored 39 papers that have together received 248 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (9 papers), Multi-Criteria Decision Making (8 papers), Data Management and Algorithms (7 papers), IoT and Edge/Fog Computing (4 papers), Optimization and Mathematical Programming (3 papers), Rough Sets and Fuzzy Logic (3 papers), Mobile Learning in Education (3 papers) and Personal Information Management and User Behavior (2 papers). The work is most often cited by research in Management Science and Operations Research (56 citations), Signal Processing (31 citations), Computer Vision and Pattern Recognition (52 citations), Artificial Intelligence (69 citations) and Information Systems (39 citations). Hai Van Pham has collaborated with scholars based in Vietnam, Japan and United Kingdom. Frequent co-authors include Philip Moore, Philip Bonello, Trần Thị Ngân, Lê Hoàng Sơn, Võ Trương Như Ngọc, Nguyễn Long Giang, Trần Mạnh Tuấn, Katsuari Kamei, Eric W. Cooper and Hanifa Shah. Their work appears in journals such as IEEE Access, Journal of Intelligent & Fuzzy Systems, Information Sciences, The Knowledge Engineering Review and Scientific Reports.

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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