Brian Mac Namee

3.0k total citations · 1 hit paper
118 papers, 1.6k citations indexed

About

Brian Mac Namee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Sociology and Political Science. According to data from OpenAlex, Brian Mac Namee has authored 118 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 14 papers in Sociology and Political Science. Recurrent topics in Brian Mac Namee's work include Anomaly Detection Techniques and Applications (14 papers), Machine Learning and Data Classification (12 papers) and Data Stream Mining Techniques (12 papers). Brian Mac Namee is often cited by papers focused on Anomaly Detection Techniques and Applications (14 papers), Machine Learning and Data Classification (12 papers) and Data Stream Mining Techniques (12 papers). Brian Mac Namee collaborates with scholars based in Ireland, United Kingdom and China. Brian Mac Namee's co-authors include John D. Kelleher, Elizabeth Hunter, Sarah Jane Delany, Pádraig Cunningham, Oisín Boydell, Quan Le, Mark Scanlon, Derek Greene, Arjun Pakrashi and Owen I. Corrigan and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Brian Mac Namee

96 papers receiving 1.5k citations

Hit Papers

align trajectories

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.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

2015 384 citations John D. Kelleher, Brian Mac Namee et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian Mac Namee Ireland	19	691	208	190	180	171	118	1.6k
John D. Kelleher Ireland	22	684 1.0×	87 0.4×	107 0.6×	195 1.1×	102 0.6×	166	2.1k
Zhiyuan Chen United States	20	932 1.3×	304 1.5×	340 1.8×	102 0.6×	290 1.7×	86	1.6k
Chao Gao China	28	604 0.9×	110 0.5×	379 2.0×	66 0.4×	149 0.9×	165	2.5k
Benjamin Letham United States	11	713 1.0×	253 1.2×	108 0.6×	81 0.5×	157 0.9×	24	2.1k
Saleem Ullah Pakistan	28	1.3k 1.8×	219 1.1×	217 1.1×	173 1.0×	553 3.2×	71	2.9k
Siddique Latif Australia	19	718 1.0×	391 1.9×	262 1.4×	51 0.3×	141 0.8×	39	1.7k
Fengli Xu China	22	448 0.6×	143 0.7×	306 1.6×	42 0.2×	191 1.1×	71	1.6k
Sean J. Taylor United States	9	388 0.6×	218 1.0×	103 0.5×	85 0.5×	159 0.9×	18	2.0k
Firuz Kamalov United Arab Emirates	21	826 1.2×	105 0.5×	201 1.1×	38 0.2×	275 1.6×	89	2.2k
Xiuju Fu Singapore	23	615 0.9×	134 0.6×	141 0.7×	128 0.7×	143 0.8×	101	2.5k

Countries citing papers authored by Brian Mac Namee

Since Specialization

Citations

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

Fields of papers citing papers by Brian Mac Namee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Mac Namee. 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 Brian Mac Namee. The network helps show where Brian Mac Namee may publish in the future.

Co-authorship network of co-authors of Brian Mac Namee

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Mac Namee. A scholar is included among the top collaborators of Brian Mac Namee 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 Brian Mac Namee. Brian Mac Namee 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

Curran, Kathleen M., et al.. (2025). Reducing inference cost of Alzheimer’s disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners. Scientific Reports. 15(1). 5521–5521. 1 indexed citations

Namee, Brian Mac, et al.. (2024). The Effects of Media Bias on News Recommendations. IEEE Access. 12. 83391–83404.

Namee, Brian Mac, et al.. (2024). Guidelines for the Evolving Role of Generative AI in Introductory Programming Based on Emerging Practice. 10–16. 10 indexed citations

Upton, John, et al.. (2024). Using milk flow profiles for subclinical mastitis detection. SHILAP Revista de lepidopterología. 9. 100537–100537. 2 indexed citations

Becker, Brett A., et al.. (2024). Post Primary Teachers' Perspectives on Machine Learning and Artificial Intelligence in the Leaving Certificate Computer Science Curriculum. 1–2.

Namee, Brian Mac, et al.. (2023). No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations. Zenodo (CERN European Organization for Nuclear Research). 1–7. 8 indexed citations

Zimmermann, Jesko, et al.. (2022). Using deep learning to classify grassland management intensity in ground-level photographs for more automated production of satellite land use maps. Remote Sensing Applications Society and Environment. 26. 100741–100741. 14 indexed citations

Yang, Linyi, et al.. (2022). A Rationale-Centric Framework for Human-in-the-loop Machine Learning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 6986–6996. 17 indexed citations

Pakrashi, Arjun, et al.. (2022). The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection. IEEE Access. 10. 70645–70661. 6 indexed citations

10.

Shahid, Arsalan, et al.. (2022). A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis. Healthcare. 10(5). 755–755. 9 indexed citations

11.

Zhao, Liang, Kun Chen, Jie Song, et al.. (2020). Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data. IEEE Access. 9. 5453–5465. 37 indexed citations

12.

Henchion, Maeve, et al.. (2020). Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks. Language Resources and Evaluation. 6740–6744. 5 indexed citations

13.

Keogh, Alison, Seamas C. Donnelly, Ronan Mullan, et al.. (2020). A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data. SHILAP Revista de lepidopterología. 4(3). 78–88. 7 indexed citations

14.

Henchion, Maeve, et al.. (2019). A Topic-Based Approach to Multiple Corpus Comparison.. Research Repository UCD (University College Dublin). 64–75. 2 indexed citations

15.

Namee, Brian Mac, et al.. (2018). Identifying Urban Canopy Coverage from Satellite Imagery Using Convolutional Neural Networks.. Research Repository UCD (University College Dublin). 315–326. 1 indexed citations

16.

Pakrashi, Arjun & Brian Mac Namee. (2017). Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours. Arrow@dit (Dublin Institute of Technology). 51–63. 7 indexed citations

17.

Hunter, Elizabeth, Brian Mac Namee, & John D. Kelleher. (2016). An Open Data Driven Epidemiological Agent-Based Model for Irish Towns.. 92–103. 2 indexed citations

18.

Zubiaga, Arkaitz & Brian Mac Namee. (2015). Knowing What You Don?t Know: Choosing the Right Chart to Show Data Distributions to Non-Expert Users. Arrow@dit (Dublin Institute of Technology). 2 indexed citations

19.

Namee, Brian Mac, et al.. (2011). Drift Detection Using Uncertainty Distribution Divergence. Arrow - TU Dublin (Technological University Dublin). 604–608. 16 indexed citations

20.

Peters, Christopher, Simon Dobbyn, Brian Mac Namee, & Carol O’Sullivan. (2003). Smart Objects for Attentive Agents. Arrow@dit (Dublin Institute of Technology). 18 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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