Mark Campbell

612 citations
26 papers · 353 · 1 hit paper · h-index 8

Impact in

Papers in

Partner nations
United StatesSerbia

In The Last Decade

Mark Campbell

22 papers receiving 336 citations

Mark Campbell's Hit Papers

Generative Artificial Intelligence: Trends and Prospects 2022 · 189 citations
1890+1+2Years since publication50100150

Peers

Mark Campbell
Comparison fields: 5 of 78
  • Health Informatics 44
  • Computer Science Applications 33
  • Artificial Intelligence 129
  • Information Systems 89
  • Safety Research 30
Replace Ajay Bandi with:
Ajay Bandi United States
Ammar Elhassan Jordan
Oğuzhan Topsakal United States
Philipp Schmidt Germany
Jiakai Tang China
James Schaffer United States
Lopamudra Praharaj United States
James Wexler United States
Bryan Wilie Hong Kong
Holy Lovenia Hong Kong
Mark Campbell relative to Ajay Bandi United States Ajay Bandi's profile →
Citations per field
00.5×1.5×2.1×
Ajay Bandi · 1×
Citations per year

Countries citing papers authored by Mark Campbell

Since Specialization
Citations

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

Fields of papers citing papers by Mark Campbell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 1 scholars most cited alongside Mark Campbell, 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 Mark Campbell Line = papers co-authored together Mark Campbell links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Generative Artificial Intelligence: Trends and Prospects
Hit paper breakdown →
2022189
2 202062
3 201920
4 202313
5 20199
6 20208
7 20208
8 20228
9 20255
10 20245
11 20244
12 20244
13 20233
14 20213
15 20232
16 20212
17 20222
18 20202
19 20251
20 20201

About Mark Campbell

Mark Campbell is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Management Information Systems and Computer Vision and Pattern Recognition, having authored 26 papers that have together received 353 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Ethics and Social Impacts of AI (3 papers), Big Data and Business Intelligence (3 papers), AI in Service Interactions (2 papers), Topic Modeling (2 papers), Privacy-Preserving Technologies in Data (2 papers), Advanced Malware Detection Techniques (2 papers) and Software System Performance and Reliability (1 paper). The work is most often cited by research in Health Informatics (44 citations), Computer Science Applications (33 citations), Artificial Intelligence (129 citations), Information Systems (89 citations) and Safety Research (30 citations). Mark Campbell has collaborated with scholars based in United States and Serbia. Frequent co-authors include Mladjan Jovanovic. Their work appears in journals such as Computer.

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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