Alexei Sharpanskykh

82 papers receiving 688 citations

Peers

Alexei Sharpanskykh
Comparison fields: 5 of 100
  • Radiological and Ultrasound Technology 96
  • Management Information Systems 167
  • Statistics, Probability and Uncertainty 98
  • Management Science and Operations Research 105
  • Medical Laboratory Technology 10
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Antonio De Nicola Italy
Andreas Gregoriades Cyprus
Donna H. Rhodes United States
Ren‐Jye Dzeng Taiwan
Hsi-Mei Hsu Taiwan
Zhenyong Wu China
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Citations per year

Countries citing papers authored by Alexei Sharpanskykh

Since Specialization
Citations

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

Fields of papers citing papers by Alexei Sharpanskykh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009106
2 200983
3 200640
4 201129
5 201129
6 201123
7 201820
8 201919
9 202118
10 201916
11
Verifying Interlevel Relations within Multi-Agent Systems
200616
12 201915
13 201914
14 202013
15 202012
16 200812
17
Automated support for adaptive incident management
200412
18
Formal modeling and analysis of organizations
200611
19 201411
20 202010

About Alexei Sharpanskykh

Alexei Sharpanskykh is a scholar working on Artificial Intelligence, Management Information Systems, Radiological and Ultrasound Technology, Management Science and Operations Research and Social Psychology, having authored 90 papers that have together received 757 indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (19 papers), Occupational Health and Safety Research (18 papers), Business Process Modeling and Analysis (17 papers), Human-Automation Interaction and Safety (13 papers), Risk and Safety Analysis (12 papers), Service-Oriented Architecture and Web Services (12 papers), Evacuation and Crowd Dynamics (11 papers) and Complex Systems and Decision Making (11 papers). The work is most often cited by research in Radiological and Ultrasound Technology (96 citations), Management Information Systems (167 citations), Statistics, Probability and Uncertainty (98 citations), Management Science and Operations Research (105 citations) and Medical Laboratory Technology (10 citations). Alexei Sharpanskykh has collaborated with scholars based in Netherlands, United Kingdom and Canada. Frequent co-authors include Viara Popova, Jan Treur, Catholijn M. Jonker, Sybert Stroeve, Tibor Bosse, Lourens van der Meij, Richard Curran, H.A.P. Blom, Barry Kirwan and Christophe De Wagter. Their work appears in journals such as Applied Intelligence, Simulation Modelling Practice and Theory, International Journal of Crashworthiness, Reliability Engineering & System Safety and Journal of Air Transport Management.

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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