John G. Hughes

1.2k total citations
55 papers, 568 citations indexed

About

John G. Hughes is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, John G. Hughes has authored 55 papers receiving a total of 568 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 22 papers in Information Systems and 15 papers in Computer Networks and Communications. Recurrent topics in John G. Hughes's work include Data Mining Algorithms and Applications (13 papers), AI-based Problem Solving and Planning (9 papers) and Rough Sets and Fuzzy Logic (9 papers). John G. Hughes is often cited by papers focused on Data Mining Algorithms and Applications (13 papers), AI-based Problem Solving and Planning (9 papers) and Rough Sets and Fuzzy Logic (9 papers). John G. Hughes collaborates with scholars based in United Kingdom, United States and Russia. John G. Hughes's co-authors include David Bell, Sarabjot Singh Anand, Jianhan Zhu, Jun Hong, Maurice Mulvenna, Werner Dubitzky, Matthias Baumgarten, Pearl Pu, Jun Hong and Ming Tan and has published in prestigious journals such as Knowledge-Based Systems, European Polymer Journal and Rapid Communications in Mass Spectrometry.

In The Last Decade

John G. Hughes

47 papers receiving 515 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John G. Hughes United Kingdom	13	260	213	119	94	92	55	568
Willi Klösgen Germany	10	385 1.5×	419 2.0×	104 0.9×	139 1.5×	182 2.0×	27	736
Anthony J.T. Lee Taiwan	16	282 1.1×	200 0.9×	64 0.5×	180 1.9×	150 1.6×	33	622
Flora S. Tsai Singapore	18	315 1.2×	397 1.9×	76 0.6×	79 0.8×	34 0.4×	48	677
Marie-Aude Aufaure France	12	111 0.4×	148 0.7×	76 0.6×	69 0.7×	34 0.4×	39	354
Atsuyoshi Nakamura Japan	8	152 0.6×	129 0.6×	75 0.6×	27 0.3×	37 0.4×	47	384
Szymon Jaroszewicz Poland	14	188 0.7×	223 1.0×	37 0.3×	73 0.8×	103 1.1×	38	601
Bharat Bhasker India	10	240 0.9×	191 0.9×	68 0.6×	39 0.4×	19 0.2×	40	464
Tobun D. Ng United States	14	315 1.2×	407 1.9×	76 0.6×	106 1.1×	23 0.3×	34	734
Brij Masand United States	11	330 1.3×	365 1.7×	43 0.4×	41 0.4×	44 0.5×	22	595
Hassan Haghighi Iran	13	339 1.3×	153 0.7×	217 1.8×	44 0.5×	22 0.2×	70	566

Countries citing papers authored by John G. Hughes

Since Specialization

Citations

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

Fields of papers citing papers by John G. Hughes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John G. Hughes

This figure shows the co-authorship network connecting the top 25 collaborators of John G. Hughes. A scholar is included among the top collaborators of John G. Hughes 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 G. Hughes. John G. Hughes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Romine, William L., et al.. (2020). Development of a Daily Use Caregiver Sleep Survey (DUCSS). GeroPsych. 33(4). 209–222.

Lophatananon, Artitaya, et al.. (2018). Online Decision Support Tool for Personalized Cancer Symptom Checking in the Community (REACT): Acceptability, Feasibility, and Usability Study. JMIR Cancer. 4(2). e10073–e10073. 5 indexed citations

Zheng, Huiru, Piyush Ojha, Stephen McClean, et al.. (2005). A Computational Approach for Peptidomic Analysis in Taxonomic Study of Amphibian Species. IEEE Transactions on NanoBioscience. 4(3). 241–247. 1 indexed citations

Baumgarten, Matthias, Alex G. Büchner, & John G. Hughes. (2003). Tree Growth Based Episode Mining without Candidate Generation.. International Conference on Artificial Intelligence. 108–114. 2 indexed citations

Baumgarten, Matthias, Alex G. Büchner, & John G. Hughes. (2003). Tree-Growth based Sequential and Associative Pattern Discovery.. Software Engineering and Knowledge Engineering. 240–244.

Zheng, Huiru, Piyush Ojha, Stephen McClean, et al.. (2003). Heuristic charge assignment for deconvolution of electrospray ionization mass spectra. Rapid Communications in Mass Spectrometry. 17(5). 429–436. 25 indexed citations

Hughes, John G., et al.. (2002). Knowledge representation and problem-solving using object-oriented paradigm. 275–279.

Hughes, John G., et al.. (2002). FORM: a formal object-oriented requirements model based upon conceptual graphs. 6. 221–228.

Pu, Pearl & John G. Hughes. (2002). Integrating AGV schedules in a scheduling system for a flexible manufacturing environment. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 3149–3154. 15 indexed citations

10.

Zheng, Hui, et al.. (2001). Analysis and interpolation of complex HPLC/MS spectra for drug discovery. Technology and Health Care. 9(1). 95–97. 1 indexed citations

11.

Zhu, Jianhan, Jun Hong, & John G. Hughes. (2001). PageRate. Research Portal (Queen's University Belfast). 131–132. 9 indexed citations

12.

Mulvenna, Maurice, et al.. (1999). An Internet-enabled Knowledge Discovery Process. Ulster University Research Portal (Ulster University). 21 indexed citations

13.

Anand, Sarabjot Singh, David Patterson, & John G. Hughes. (1998). Knowledge intensive exception spaces. National Conference on Artificial Intelligence. 574–579. 9 indexed citations

14.

Schuster, Alfons, Werner Dubitzky, Philippe Lopes, et al.. (1997). Aggregating features and matching cases on vague linguistic expressions. International Joint Conference on Artificial Intelligence. 1. 252–257. 8 indexed citations

15.

Dubitzky, Werner, Alfons Schuster, Philippe Lopes, et al.. (1997). Corporate evidential decision making in performance prediction domains. Uncertainty in Artificial Intelligence. 38–45. 5 indexed citations

16.

Mulvenna, Maurice, et al.. (1996). Re-engineering Business Processes to Facilitate Data Mining. Ulster University Research Portal (Ulster University). 3 indexed citations

17.

Mulvenna, Maurice, Ronan McIvor, Anne Marie Ward, & John G. Hughes. (1996). The Application Of Case Based Reasoning To The Interpretation Of Financial Data For Acquisition Analysis. Ulster University Research Portal (Ulster University). 222–232. 1 indexed citations

18.

Dubitzky, Werner, David Bell, & John G. Hughes. (1996). A generic, object‐oriented case‐knowledge representation scheme, and its integration into a wider information management scenario. Expert Systems. 13(3). 219–233. 2 indexed citations

19.

Ye, Peng, et al.. (1994). Job cost and constraint relaxation for scheduling problem solving in the CLP paradigm. European Conference on Artificial Intelligence. 640–644. 1 indexed citations

20.

Anand, Sarabjot Singh, David Bell, & John G. Hughes. (1994). Database mining in the architecture of a semantic pre-processor for state-aware query optimization. 287–298. 2 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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