Ian Nabney

1.2k total citations · 1 hit paper
20 papers, 860 citations indexed

About

Ian Nabney is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Ian Nabney has authored 20 papers receiving a total of 860 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Cognitive Neuroscience. Recurrent topics in Ian Nabney's work include Neural Networks and Applications (5 papers), Sleep and related disorders (3 papers) and Handwritten Text Recognition Techniques (3 papers). Ian Nabney is often cited by papers focused on Neural Networks and Applications (5 papers), Sleep and related disorders (3 papers) and Handwritten Text Recognition Techniques (3 papers). Ian Nabney collaborates with scholars based in United Kingdom, United States and Australia. Ian Nabney's co-authors include Peter Tiňo, George Vogiatzis, Esther Crawley, Wajahat Kazmi, Yi Sun, Tom Wilkinson, James Dodd, Richard C. Burgess, Caroline Van Holsbeke and Yulan He and has published in prestigious journals such as Sensors, Frontiers in Psychology and Journal of Medical Internet Research.

In The Last Decade

Ian Nabney

18 papers receiving 788 citations

Hit Papers

Netlab: Algorithms for Pattern Recognition 2002 2026 2010 2018 2002 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Netlab: Algorithms for Pattern Recognition
    2002 698 citations Ian Nabney Bristol Research (University of Bristol) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ian Nabney United Kingdom 7 285 162 97 88 78 20 860
Thomas Burr United States 4 300 1.1× 150 0.9× 113 1.2× 93 1.1× 45 0.6× 8 1.0k
Dan Yamins United States 2 393 1.4× 123 0.8× 58 0.6× 75 0.9× 98 1.3× 3 1.1k
Jeff Heaton United States 9 290 1.0× 123 0.8× 93 1.0× 47 0.5× 34 0.4× 16 849
Brian C. Van Essen United States 6 340 1.2× 283 1.7× 79 0.8× 62 0.7× 50 0.6× 10 1.2k
Stefan Westberg United States 3 334 1.2× 282 1.7× 79 0.8× 62 0.7× 47 0.6× 6 1.2k
Mark Ebden United Kingdom 9 280 1.0× 63 0.4× 90 0.9× 70 0.8× 52 0.7× 17 951
Fei Ma China 12 240 0.8× 216 1.3× 39 0.4× 97 1.1× 52 0.7× 90 820
Siamak Mehrkanoon Belgium 20 289 1.0× 213 1.3× 49 0.5× 132 1.5× 28 0.4× 66 956
C.M. Bishop United Kingdom 11 379 1.3× 358 2.2× 114 1.2× 68 0.8× 67 0.9× 13 1.1k

Countries citing papers authored by Ian Nabney

Since Specialization
Citations

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

Fields of papers citing papers by Ian Nabney

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ian Nabney

This figure shows the co-authorship network connecting the top 25 collaborators of Ian Nabney. A scholar is included among the top collaborators of Ian Nabney 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 Ian Nabney. Ian Nabney 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.
Chen, Weitong, et al.. (2025). Beyond EHRs: External Clinical knowledge and cohort Features for medication recommendation. Knowledge-Based Systems. 324. 113763–113763.
2.
Bennett, Sarah, M Johnston, Anthony Duffy, et al.. (2024). Using the Person-Based Approach to Co-Create and Optimize an App-Based Intervention to Support Better Sleep for Adolescents in the United Kingdom: Mixed Methods Study. JMIR Human Factors. 11. e63341–e63341. 1 indexed citations
3.
Nabney, Ian, et al.. (2024). Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances. Sensors. 24(3). 722–722. 3 indexed citations
4.
Morgan, Caitlin, et al.. (2024). Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis. Journal of Medical Internet Research. 26. e52143–e52143. 6 indexed citations
5.
Wilkinson, Tom, et al.. (2024). Exacerbation predictive modelling using real-world data from the myCOPD app. Heliyon. 10(10). e31201–e31201. 3 indexed citations
6.
Bornstein, Marc H., Amy Campbell, Miguel Cordero, et al.. (2023). A Quantitative Evaluation of Thin Slice Sampling for Parent–Infant Interactions. Journal of Nonverbal Behavior. 47(2). 117–210. 3 indexed citations
7.
Nabney, Ian, et al.. (2023). Smartphone Sensors to Measure Individual Sleeping Pattern: Experimental Study. Bristol Research (University of Bristol). 206–210. 1 indexed citations
8.
Burgess, Richard C., Iryna Culpin, Irene Costantini, et al.. (2023). Quantifying the efficacy of an automated facial coding software using videos of parents. Frontiers in Psychology. 14. 1223806–1223806. 6 indexed citations
9.
Crawley, Esther, et al.. (2023). The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review. JMIR mhealth and uhealth. 11. e44123–e44123. 13 indexed citations
10.
Kazmi, Wajahat, et al.. (2020). An Efficient Industrial System for Vehicle Tyre (Tire) Detection and Text Recognition Using Deep Learning. IEEE Transactions on Intelligent Transportation Systems. 22(2). 1264–1275. 21 indexed citations
11.
Kazmi, Wajahat, et al.. (2019). Vehicle tire (tyre) detection and text recognition using deep learning. 1074–1079. 6 indexed citations
12.
He, Yulan, et al.. (2016). Handwritten and Machine-Printed Text Discrimination Using a Template Matching Approach. Bristol Research (University of Bristol). 2. 399–404. 2 indexed citations
13.
Calster, Ben Van, D. Timmerman, Ian Nabney, et al.. (2007). Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery. Neural Computing and Applications. 17(5-6). 489–500. 8 indexed citations
14.
Tiňo, Peter, et al.. (2004). Nonlinear Prediction of Quantitative Structure−Activity Relationships. Journal of Chemical Information and Computer Sciences. 44(5). 1647–1653. 20 indexed citations
15.
Stepney, Susan & Ian Nabney. (2003). The DeCCo project papers II Z Specification of Asp. OpenGrey (Institut de l'Information Scientifique et Technique).
16.
Nabney, Ian. (2002). Netlab: Algorithms for Pattern Recognition. Bristol Research (University of Bristol). 698 indexed citations breakdown →
17.
Nabney, Ian, et al.. (2002). Benchmarking beat classification algorithms. 6. 529–532. 2 indexed citations
18.
Tiňo, Peter, et al.. (2001). GTM-based data visualisation with incomplete data. Aston Publications Explorer (Aston University). 2 indexed citations
19.
Nabney, Ian. (1999). Efficient training of RBF networks for classification. Bristol Research (University of Bristol). 1999. 210–215. 64 indexed citations
20.
Saad, David, et al.. (1996). The Learning Dynamcis of a Universal Approximator. Neural Information Processing Systems. 9. 288–294. 1 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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