James Montgomery

1.4k total citations
70 papers, 815 citations indexed

About

James Montgomery is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, James Montgomery has authored 70 papers receiving a total of 815 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 22 papers in Computational Theory and Mathematics and 8 papers in Information Systems. Recurrent topics in James Montgomery's work include Metaheuristic Optimization Algorithms Research (30 papers), Evolutionary Algorithms and Applications (25 papers) and Advanced Multi-Objective Optimization Algorithms (21 papers). James Montgomery is often cited by papers focused on Metaheuristic Optimization Algorithms Research (30 papers), Evolutionary Algorithms and Applications (25 papers) and Advanced Multi-Objective Optimization Algorithms (21 papers). James Montgomery collaborates with scholars based in Australia, Canada and Cuba. James Montgomery's co-authors include Stephen Chen, Antonio Bolufé-Röhler, Saurabh Garg, Marcus Randall, Tim Hendtlass, Erfan Aghasian, Joel Scanlan, Longxiang Gao, Andrew Lewis and Kenneth C. Kirkby and has published in prestigious journals such as PLoS ONE, IEEE Access and Remote Sensing.

In The Last Decade

James Montgomery

67 papers receiving 785 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Montgomery Australia 16 415 236 77 71 65 70 815
Stefan C. Kremer Canada 18 485 1.2× 136 0.6× 166 2.2× 32 0.5× 22 0.3× 66 1.3k
Bettina Speckmann Netherlands 23 166 0.4× 216 0.9× 118 1.5× 78 1.1× 65 1.0× 139 1.7k
Thiemo Krink Denmark 20 955 2.3× 398 1.7× 80 1.0× 31 0.4× 20 0.3× 35 1.7k
Harish Doraiswamy United States 18 99 0.2× 109 0.5× 77 1.0× 35 0.5× 47 0.7× 37 790
M. Ashraful Amin Bangladesh 19 223 0.5× 51 0.2× 40 0.5× 68 1.0× 20 0.3× 108 1.1k
Bob McKay South Korea 20 1.1k 2.5× 236 1.0× 77 1.0× 154 2.2× 42 0.6× 92 1.5k
Joonwhoan Lee South Korea 23 292 0.7× 29 0.1× 35 0.5× 32 0.5× 63 1.0× 98 1.9k
Kevin Buchin Netherlands 19 143 0.3× 113 0.5× 81 1.1× 38 0.5× 41 0.6× 91 1.1k
Gonzalo Martínez-Muñoz Spain 20 756 1.8× 65 0.3× 33 0.4× 115 1.6× 32 0.5× 41 1.2k
Carlos N. Silla Brazil 20 935 2.3× 65 0.3× 48 0.6× 174 2.5× 11 0.2× 71 2.1k

Countries citing papers authored by James Montgomery

Since Specialization
Citations

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

Fields of papers citing papers by James Montgomery

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Montgomery

This figure shows the co-authorship network connecting the top 25 collaborators of James Montgomery. A scholar is included among the top collaborators of James Montgomery 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 James Montgomery. James Montgomery 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.
O’Reilly, Małgorzata M., et al.. (2025). Markov decision process and approximate dynamic programming for a patient assignment scheduling problem. Annals of Operations Research. 347(3). 1493–1531.
2.
Randall, Marcus, et al.. (2024). A Systematic Review of Crop Planning Optimisation Under Climate Change. Water Resources Management. 38(6). 1867–1881. 3 indexed citations
3.
Randall, Marcus & James Montgomery. (2024). The Accumulated Experience Ant Colony for the travelling salesman problem. eCite Digital Repository (University of Tasmania). 79–87.
4.
Lewis, Andrew, et al.. (2023). Business As Usual Versus Climate-responsive, Optimised Crop Plans – A Predictive Model for Irrigated Agriculture in Australia in 2060. Water Resources Management. 37(6-7). 2721–2735. 2 indexed citations
5.
Taskhiri, Mohammad Sadegh, et al.. (2022). Design and Testing of a Novel Unoccupied Aircraft System for the Collection of Forest Canopy Samples. Forests. 13(2). 153–153. 10 indexed citations
6.
Yeom, Soonja, et al.. (2022). Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review. Education Sciences. 12(11). 781–781. 15 indexed citations
7.
Garg, Saurabh, et al.. (2022). Resource scheduling and provisioning for processing of dynamic stream workflows under latency constraints. Future Generation Computer Systems. 131. 166–182. 3 indexed citations
8.
Montgomery, James, et al.. (2021). Automatic construction of accurate bioacoustics workflows under time constraints using a surrogate model. Applied Soft Computing. 113. 107944–107944. 1 indexed citations
9.
Taskhiri, Mohammad Sadegh, et al.. (2021). Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds. Remote Sensing. 13(22). 4677–4677. 48 indexed citations
10.
Battula, Sudheer Kumar, et al.. (2020). A Generic Stochastic Model for Resource Availability in Fog Computing Environments. IEEE Transactions on Parallel and Distributed Systems. 32(4). 960–974. 13 indexed citations
11.
Aghasian, Erfan, Saurabh Garg, & James Montgomery. (2020). An automated model to score the privacy of unstructured information—Social media case. Computers & Security. 92. 101778–101778. 11 indexed citations
12.
Montgomery, James, et al.. (2020). Bioacoustics Data Analysis – A Taxonomy, Survey and Open Challenges. IEEE Access. 8. 57684–57708. 29 indexed citations
13.
Battula, Sudheer Kumar, Saurabh Garg, James Montgomery, & Byeong Ho Kang. (2019). An Efficient Resource Monitoring Service for Fog Computing Environments. IEEE Transactions on Services Computing. 13(4). 709–722. 32 indexed citations
14.
Aghasian, Erfan, Saurabh Garg, & James Montgomery. (2018). A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks. Computers. 7(3). 42–42. 5 indexed citations
15.
Garg, Saurabh, et al.. (2018). Scalable preprocessing of high volume environmental acoustic data for bioacoustic monitoring. PLoS ONE. 13(8). e0201542–e0201542. 11 indexed citations
16.
Montgomery, James, et al.. (2018). Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting. Journal of Biomedical Informatics. 86. 49–58. 46 indexed citations
17.
Garg, Saurabh, et al.. (2017). Automatic and Efficient Denoising of Bioacoustics Recordings Using MMSE STSA. IEEE Access. 6. 5010–5022. 23 indexed citations
18.
Chen, Stephen, et al.. (2015). A Review of Thresheld Convergence. SSRN Electronic Journal. 3(1). 1–13. 9 indexed citations
19.
Wasinger, Rainer, et al.. (2014). Towards the effective use of multiple displays in teaching and learning environments. eCite Digital Repository (University of Tasmania). 1 indexed citations
20.
Chen, Stephen, James Montgomery, & Antonio Bolufé-Röhler. (2014). Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution. Applied Intelligence. 42(3). 514–526. 123 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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