Dan Smith

30 papers receiving 435 citations

Peers

Dan Smith
Comparison fields: 5 of 114
  • Signal Processing 146
  • Developmental Biology 16
  • Computer Vision and Pattern Recognition 110
  • Ecology 97
  • Pollution 39
Replace Xiao Xie with:
Xiao Xie China
Yves Bédard Canada
Timo Kaukoranta Finland
Ali Zeeshan Ijaz Pakistan
Xiaojie Wang China
Florence Le Ber France
P. Nye United States
Zhangliang Chen China
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Citations per field
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Citations per year

Countries citing papers authored by Dan Smith

Since Specialization
Citations

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

Fields of papers citing papers by Dan Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dan Smith, 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 Dan Smith Line = papers co-authored together Dan Smith 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 2003166
2 2006104
3 201442
4 200340
5 199925
6 200519
7
Conservative stemming for search and indexing
200515
8 201410
9 19997
10
An efficient implementation of max tree with linked list and hash table
20036
11 20006
12 20015
13
Database Systems Engineering
19955
14 20064
15
Web searching behaviour for academic resources
20133
16 19993
17 20073
18
An efficient strategy for implementing iterative area openings using the max tree
20032
19 20242
20 20202

About Dan Smith

Dan Smith is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Information Systems and Management Science and Operations Research, having authored 37 papers that have together received 486 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (5 papers), Image Retrieval and Classification Techniques (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Advanced Database Systems and Queries (4 papers), Data Quality and Management (4 papers), Music and Audio Processing (3 papers), Information Retrieval and Search Behavior (2 papers) and Spam and Phishing Detection (2 papers). The work is most often cited by research in Signal Processing (146 citations), Developmental Biology (16 citations), Computer Vision and Pattern Recognition (110 citations), Ecology (97 citations) and Pollution (39 citations). Dan Smith has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Ling Ma, Ben Milner, Eric W. Triplett, Barbara J. Benson, Angela D. Kent, Janpeter Schilling, Janani Vivekananda, Richard Harvey, Nick Ryan and Ze’ev Gedalof. Their work appears in journals such as Soil Research, Canadian Journal of Forest Research, Information and Software Technology, Monthly Review and Journal of Safety Research.

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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