Adam Zagorecki

953 total citations
26 papers, 612 citations indexed

About

Adam Zagorecki is a scholar working on Artificial Intelligence, Sociology and Political Science and Signal Processing. According to data from OpenAlex, Adam Zagorecki has authored 26 papers receiving a total of 612 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 6 papers in Sociology and Political Science and 6 papers in Signal Processing. Recurrent topics in Adam Zagorecki's work include Bayesian Modeling and Causal Inference (9 papers), Disaster Management and Resilience (5 papers) and Data Management and Algorithms (4 papers). Adam Zagorecki is often cited by papers focused on Bayesian Modeling and Causal Inference (9 papers), Disaster Management and Resilience (5 papers) and Data Management and Algorithms (4 papers). Adam Zagorecki collaborates with scholars based in United Kingdom, United States and Slovakia. Adam Zagorecki's co-authors include Louise K. Comfort, Kilkon Ko, David E. Johnson, Jozef Ristvej, Marek J. Drużdżel, Mark G. Dunn, Ken R. McNaught, Tomáš Loveček and David Johnson and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Systems Man and Cybernetics Systems and American Behavioral Scientist.

In The Last Decade

Adam Zagorecki

25 papers receiving 570 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Adam Zagorecki United Kingdom 10 260 103 86 78 63 26 612
Jinkyu Lee South Korea 12 267 1.0× 237 2.3× 52 0.6× 35 0.4× 114 1.8× 25 969
Giampiero E. G. Beroggi Netherlands 14 160 0.6× 93 0.9× 186 2.2× 64 0.8× 61 1.0× 49 674
Raj Prasanna New Zealand 16 253 1.0× 165 1.6× 31 0.4× 65 0.8× 31 0.5× 52 674
B.A. van de Walle Netherlands 11 192 0.7× 35 0.3× 52 0.6× 73 0.9× 30 0.5× 32 402
Robin L. Dillon United States 18 319 1.2× 31 0.3× 154 1.8× 126 1.6× 44 0.7× 54 985
Kirsi Virrantaus Finland 15 152 0.6× 74 0.7× 48 0.6× 87 1.1× 29 0.5× 47 955
Flávio Horita Brazil 11 179 0.7× 70 0.7× 45 0.5× 38 0.5× 29 0.5× 51 606
John R. Harrald United States 16 395 1.5× 55 0.5× 146 1.7× 111 1.4× 78 1.2× 49 1.1k
Frank Fiedrich Germany 11 241 0.9× 99 1.0× 61 0.7× 135 1.7× 389 6.2× 36 866
Jozef Ristvej Slovakia 14 125 0.5× 54 0.5× 27 0.3× 89 1.1× 14 0.2× 53 590

Countries citing papers authored by Adam Zagorecki

Since Specialization
Citations

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

Fields of papers citing papers by Adam Zagorecki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adam Zagorecki

This figure shows the co-authorship network connecting the top 25 collaborators of Adam Zagorecki. A scholar is included among the top collaborators of Adam Zagorecki based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Adam Zagorecki. Adam Zagorecki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zagorecki, Adam, et al.. (2023). A picture paints a thousand words: supporting organizational learning in the emergency services with data visualization. The Learning Organization. 30(2). 231–250. 2 indexed citations
2.
Zagorecki, Adam, et al.. (2021). The SERIES model: Can a standardized approach benefit practitioner evaluation of emergency response systems?. Journal of Emergency Management. 19(3). 273–291. 1 indexed citations
3.
Zagorecki, Adam, et al.. (2020). The SERIES model: development of a practitioner focused emergency response evaluation system. International Journal of Emergency Services. 9(3). 313–337. 4 indexed citations
4.
Zagorecki, Adam, et al.. (2015). An approximation of surprise index as a measure of confidence. D-Scholarship@Pitt (University of Pittsburgh). 39. 3 indexed citations
5.
Zagorecki, Adam, et al.. (2015). Modeling women's menstrual cycles using PICI gates in Bayesian network. International Journal of Approximate Reasoning. 70. 123–136.
7.
Zagorecki, Adam. (2015). A Versatile Approach to Classification of Multivariate Time Series Data. SHILAP Revista de lepidopterología. 5. 407–410. 17 indexed citations
8.
Zagorecki, Adam. (2014). Feature Selection for Naive Bayesian Network Ensemble using Evolutionary Algorithms. SHILAP Revista de lepidopterología. 2. 381–385. 8 indexed citations
9.
Loveček, Tomáš, et al.. (2013). Portal of Security and Safety Engineering as a Tool to Increase the Sustainable Development of Higher Education in Security in the European Union and Beyond. Communications - Scientific letters of the University of Zilina. 15(2). 56–62. 1 indexed citations
10.
Zagorecki, Adam, et al.. (2013). A System for Automated General Medical Diagnosis using Bayesian Networks. Studies in health technology and informatics. 192. 461–5. 5 indexed citations
11.
Zagorecki, Adam, et al.. (2012). Executive Dashboard Systems for Emergency Management. Communications - Scientific letters of the University of Zilina. 14(2). 82–89. 7 indexed citations
12.
Johnson, David, et al.. (2011). Improved Situational Awareness in Emergency Management through Automated Data Analysis and Modeling. Journal of Homeland Security and Emergency Management. 8(1). 9 indexed citations
13.
Drużdżel, Marek J. & Adam Zagorecki. (2010). Local probability distributions in bayesian networks: knowledge elicitation and inference. 7 indexed citations
14.
Zagorecki, Adam, Kilkon Ko, & Louise K. Comfort. (2010). Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments. Journal of Artificial Societies and Social Simulation. 13(3). 8 indexed citations
15.
McNaught, Ken R. & Adam Zagorecki. (2009). Using dynamic Bayesian networks for prognostic modelling to inform maintenance decision making. 1155–1159. 28 indexed citations
16.
Zagorecki, Adam, et al.. (2006). Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning. The Florida AI Research Society. 860–865. 9 indexed citations
17.
Zagorecki, Adam & Marek J. Drużdżel. (2006). Probabilistic Independence of Causal Influences.. 325–332. 1 indexed citations
18.
Zagorecki, Adam & Marek J. Drużdżel. (2004). An Empirical Study of Probability Elicitation Under Noisy-OR Assumption.. The Florida AI Research Society. 880–886. 37 indexed citations
19.
Comfort, Louise K., Kilkon Ko, & Adam Zagorecki. (2004). Coordination in Rapidly Evolving Disaster Response Systems. American Behavioral Scientist. 48(3). 295–313. 248 indexed citations
20.
Comfort, Louise K., Kilkon Ko, & Adam Zagorecki. (2003). Modeling Fragility in Rapidly Evolving Disaster Response Systems. eScholarship (California Digital Library). 7 indexed citations

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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