Paul Fergus

3.5k total citations
123 papers, 1.8k citations indexed

About

Paul Fergus is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Paul Fergus has authored 123 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Networks and Communications, 25 papers in Computer Vision and Pattern Recognition and 17 papers in Artificial Intelligence. Recurrent topics in Paul Fergus's work include Context-Aware Activity Recognition Systems (22 papers), Neonatal and fetal brain pathology (8 papers) and Wildlife Ecology and Conservation (8 papers). Paul Fergus is often cited by papers focused on Context-Aware Activity Recognition Systems (22 papers), Neonatal and fetal brain pathology (8 papers) and Wildlife Ecology and Conservation (8 papers). Paul Fergus collaborates with scholars based in United Kingdom, Netherlands and France. Paul Fergus's co-authors include Dhiya Al‐Jumeily, Abir Hussain, Carl Chalmers, Chelsea Dobbins, Khaled Abdel‐Aziz, Madjid Merabti, Abir Hussain, Shamaila Iram, Serge A. Wich and Mohammed Khalaf and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Proceedings of the IEEE.

In The Last Decade

Paul Fergus

117 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul Fergus United Kingdom 24 287 281 256 232 226 123 1.8k
Abir Hussain United Kingdom 31 874 3.0× 122 0.4× 197 0.8× 599 2.6× 320 1.4× 229 3.0k
Zafer Cömert Türkiye 31 1.5k 5.3× 339 1.2× 126 0.5× 858 3.7× 397 1.8× 78 3.5k
Mohammed Ghazal United Arab Emirates 28 570 2.0× 69 0.2× 343 1.3× 685 3.0× 327 1.4× 318 3.3k
Chuan‐Yu Chang Taiwan 31 834 2.9× 46 0.2× 114 0.4× 984 4.2× 294 1.3× 228 3.6k
Prabal Datta Barua Australia 33 1.1k 3.7× 45 0.2× 1.1k 4.2× 457 2.0× 407 1.8× 167 3.9k
V́ıctor González-Castro Spain 20 311 1.1× 35 0.1× 43 0.2× 245 1.1× 102 0.5× 59 1.2k
Yuan Zhang China 25 358 1.2× 28 0.1× 822 3.2× 265 1.1× 726 3.2× 180 2.8k
Marcin Grzegorzek Germany 31 1.3k 4.6× 64 0.2× 168 0.7× 1.7k 7.1× 631 2.8× 233 3.8k
Md Ekrim Hossin Malaysia 5 681 2.4× 22 0.1× 82 0.3× 238 1.0× 134 0.6× 18 1.9k
Nooritawati Md Tahir Malaysia 18 271 0.9× 28 0.1× 198 0.8× 392 1.7× 343 1.5× 235 1.5k

Countries citing papers authored by Paul Fergus

Since Specialization
Citations

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

Fields of papers citing papers by Paul Fergus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Fergus

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Fergus. A scholar is included among the top collaborators of Paul Fergus 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 Paul Fergus. Paul Fergus 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.
Hill, Russell A., Hilary Chappell, K. Holden, et al.. (2025). Increasing citizen scientist accuracy with artificial intelligence on UK camera‐trap data. Remote Sensing in Ecology and Conservation. 11(6). 641–655.
2.
Fergus, Paul, Carl Chalmers, Stuart Nixon, et al.. (2024). Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data. Sensors. 24(24). 8122–8122. 1 indexed citations
3.
Fergus, Paul, Carl Chalmers, Steven N. Longmore, et al.. (2023). Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps. Remote Sensing. 15(11). 2730–2730. 11 indexed citations
4.
Chalmers, Carl, Paul Fergus, Serge A. Wich, et al.. (2023). Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning. Remote Sensing. 15(10). 2638–2638. 15 indexed citations
5.
McShea, William J., Hila Shamon, Michael A. Tabak, et al.. (2022). An evaluation of platforms for processing camera‐trap data using artificial intelligence. Methods in Ecology and Evolution. 14(2). 459–477. 67 indexed citations
6.
Chalmers, Carl, et al.. (2022). Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning. Sensors. 22(14). 5386–5386. 10 indexed citations
7.
Reilly, Denis, et al.. (2022). The Categorical Data Conundrum: Heuristics for Classification Problems—A Case Study on Domestic Fire Injuries. IEEE Access. 10. 70113–70125. 13 indexed citations
8.
Chalmers, Carl, Paul Fergus, Serge A. Wich, & Steven N. Longmore. (2021). Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning. Liverpool John Moores University. 1–7. 14 indexed citations
9.
Chalmers, Carl, et al.. (2021). An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection. Sensors. 21(12). 4074–4074. 18 indexed citations
10.
Reilly, Denis, et al.. (2021). Misper-Bayes: A Bayesian Network Model for Missing Person Investigations. IEEE Access. 9. 49990–50000. 1 indexed citations
11.
Piel, A., et al.. (2021). Noninvasive Technologies for Primate Conservation in the 21st Century. International Journal of Primatology. 43(1). 133–167. 28 indexed citations
12.
Fergus, Paul, et al.. (2020). SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity. Liverpool John Moores University. 6 indexed citations
13.
Fergus, Paul, Abir Hussain, Dhiya Al‐Jumeily, De-Shuang Huang, & Nizar Bouguila. (2017). Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms. BioMedical Engineering OnLine. 16(1). 89–89. 57 indexed citations
14.
Aljaaf, Ahmed J., Dhiya Al‐Jumeily, Abir Hussain, et al.. (2015). Toward an optimal use of artificial intelligence techniques within a clinical decision support system. 548–554. 25 indexed citations
15.
Fergus, Paul, Abid Hussain, & Dhiya Al‐Jumeily. (2014). A Smart Framework for Predicting the Onset of Nocturnal Enuresis (PrONE) in Children and Young People. Liverpool John Moores University. 1 indexed citations
16.
Dobbins, Chelsea, Paul Fergus, Madjid Merabti, & David Llewellyn‐Jones. (2012). Monitoring and measuring sedentary behaviour with the aid of human digital memories. 395–398. 10 indexed citations
17.
Merabti, Madjid, et al.. (2011). SCCIR: Smart Cities Critical Infrastructure Response Framework. 460–464. 12 indexed citations
18.
Merabti, M., et al.. (2009). A hierarchically structured global data collection network. 605–606. 1 indexed citations
19.
Fergus, Paul, et al.. (2004). Controlling Networked Devices In Ubiquitous Computing Environments using Biofeedback. 1 indexed citations
20.
Fergus, Paul, et al.. (2003). Distributed emergent semantics in P2P networks. 4 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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