John Keane

6.3k total citations · 2 hit papers
166 papers, 4.1k citations indexed

About

John Keane is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, John Keane has authored 166 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Artificial Intelligence, 31 papers in Information Systems and 28 papers in Management Science and Operations Research. Recurrent topics in John Keane's work include Fuzzy Logic and Control Systems (22 papers), Biomedical Text Mining and Ontologies (22 papers) and Neural Networks and Applications (21 papers). John Keane is often cited by papers focused on Fuzzy Logic and Control Systems (22 papers), Biomedical Text Mining and Ontologies (22 papers) and Neural Networks and Applications (21 papers). John Keane collaborates with scholars based in United Kingdom, United States and Ireland. John Keane's co-authors include Claus Offe, Joshua Cohen, Xiao‐Jun Zeng, Goran Nenadić, Adrian Stetco, L. Mikhailov, Sajid Siraj, Fateme Dinmohammadi, David Flynn and Xingyu Zhao and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Journal of Operational Research and IEEE Transactions on Power Systems.

In The Last Decade

John Keane

157 papers receiving 3.7k citations

Hit Papers

Contradictions of the Welfare State. 1988 2026 2000 2013 1988 2018 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Keane United Kingdom 28 1.2k 605 601 506 496 166 4.1k
Raymond Chiong Australia 39 1.6k 1.3× 405 0.7× 297 0.5× 589 1.2× 453 0.9× 197 5.3k
Jatinder N.D. Gupta United States 51 1.4k 1.2× 326 0.5× 645 1.1× 222 0.4× 638 1.3× 241 9.6k
Fosca Giannotti Italy 44 3.7k 3.1× 380 0.6× 175 0.3× 666 1.3× 371 0.7× 190 10.3k
Natalia Díaz-Rodríguez Spain 19 4.2k 3.6× 312 0.5× 399 0.7× 225 0.4× 369 0.7× 49 7.3k
Yukun Bao China 46 1.1k 0.9× 950 1.6× 232 0.4× 839 1.7× 1.0k 2.1× 119 5.7k
Alireza Abbasi Australia 35 312 0.3× 454 0.8× 301 0.5× 582 1.2× 498 1.0× 150 3.7k
Richard Benjamins Spain 10 3.1k 2.7× 224 0.4× 248 0.4× 143 0.3× 274 0.6× 28 5.3k
John G. Breslin Ireland 35 1.3k 1.1× 1.2k 2.1× 429 0.7× 506 1.0× 225 0.5× 238 4.9k
Ibrahim H. Osman Lebanon 31 952 0.8× 202 0.3× 236 0.4× 108 0.2× 433 0.9× 71 4.5k
Dimitris Askounis Greece 27 520 0.4× 487 0.8× 195 0.3× 185 0.4× 619 1.2× 173 2.6k

Countries citing papers authored by John Keane

Since Specialization
Citations

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

Fields of papers citing papers by John Keane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Keane

This figure shows the co-authorship network connecting the top 25 collaborators of John Keane. A scholar is included among the top collaborators of John Keane 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 John Keane. John Keane 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.
Couth, Samuel, Hein Heuvelman, Christopher Bull, et al.. (2022). Assessment of non-directed computer-use behaviours in the home can indicate early cognitive impairment: A proof of principle longitudinal study. Aging & Mental Health. 27(1). 193–202. 2 indexed citations
2.
Allmendinger, Richard, et al.. (2021). HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis. IEEE Transactions on Evolutionary Computation. 26(6). 1206–1220. 5 indexed citations
3.
Stetco, Adrian, Fateme Dinmohammadi, Xingyu Zhao, et al.. (2018). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy. 133. 620–635. 588 indexed citations breakdown →
4.
Mellor, Joseph, Michael A. Stone, & John Keane. (2018). Application of Data Mining to “Big Data” Acquired in Audiology: Principles and Potential. Trends in Hearing. 22. 2759788529–2759788529. 10 indexed citations
5.
Mellor, Joseph, Michael A. Stone, & John Keane. (2018). Application of Data Mining to a Large Hearing-Aid Manufacturer’s Dataset to Identify Possible Benefits for Clinicians, Manufacturers, and Users. Trends in Hearing. 22. 2759785344–2759785344. 9 indexed citations
6.
Karystianis, George, et al.. (2017). Automatic mining of symptom severity from psychiatric evaluation notes. International Journal of Methods in Psychiatric Research. 27(1). 24 indexed citations
7.
Martínez-Costa, Catalina, Chris Wroe, George Demetriou, et al.. (2017). Experiments to Create Ontology-based Disease Models for Diabetic Retinopathy from Different Biomedical Resources.. 2 indexed citations
8.
Bull, Christopher, Joseph Mellor, Samuel Couth, et al.. (2016). Combining data mining and text mining for detection of early stage dementia:the SAMS framework. Language Resources and Evaluation. 3 indexed citations
9.
Kovačević, Aleksandar, et al.. (2015). Combining knowledge- and data-driven methods for de-identification of clinical narratives. Journal of Biomedical Informatics. 58. S53–S59. 42 indexed citations
10.
Karystianis, George, et al.. (2015). Using local lexicalized rules to identify heart disease risk factors in clinical notes. Journal of Biomedical Informatics. 58. S183–S188. 22 indexed citations
11.
Zeng, Xiao‐Jun, et al.. (2011). Core-generating approximate minimum entropy discretization for rough set feature selection in pattern classification. International Journal of Approximate Reasoning. 52(6). 863–880. 31 indexed citations
12.
Wang, Di, Xiao‐Jun Zeng, & John Keane. (2010). An Evolving-Construction Scheme for Fuzzy Systems. IEEE Transactions on Fuzzy Systems. 18(4). 755–770. 26 indexed citations
13.
Spasić, ‪Irena, et al.. (2010). Medication information extraction with linguistic pattern matching and semantic rules. Journal of the American Medical Informatics Association. 17(5). 532–535. 51 indexed citations
14.
Yang, Hui, John Keane, Casey Bergman, & Goran Nenadić. (2009). Assigning roles to protein mentions: The case of transcription factors. Journal of Biomedical Informatics. 42(5). 887–894. 4 indexed citations
15.
Yang, Hui, Goran Nenadić, & John Keane. (2008). Identification of transcription factor contexts in literature using machine learning approaches. BMC Bioinformatics. 9(S3). S11–S11. 15 indexed citations
16.
Budgen, David, Mark Turner, Michelle Russell, et al.. (2005). Managing healthcare information: the role of the broker.. PubMed. 112. 3–16. 11 indexed citations
17.
Turner, Mark, Michelle Russell, David Budgen, et al.. (2004). Using Web service technologies to create an information broker: an experience report. International Conference on Software Engineering. 552–561. 25 indexed citations
18.
Sakellariou, Rizos, et al.. (2001). Euro-Par 2001 parallel processing : 7th International Euro-Par Conference, Manchester, UK, August 28-31, 2001 : proceedings. 3 indexed citations
19.
Keane, John. (1998). The Virtual Factory Approach to Quality Education. Quality progress. 31(10). 62–64. 5 indexed citations
20.
Keane, John. (1997). Transformaciones estructurales de la esfera pública. Estudios Sociológicos de El Colegio de México. 15(43). 47–77. 10 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026