Brian Mac Namee
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies 8
- Artificial Intelligence top 2%
- Anomaly Detection Techniques and Applications 14
- Machine Learning and Data Classification 12
- Data Stream Mining Techniques 12
- Topic Modeling 11
- Machine Learning and Algorithms 7
- Signal Processing top 5%
- Health Informatics top 5%
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- Data Visualization and Analytics 8
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- Social Robot Interaction and HRI 7
- Co-authors
- John D. KelleherElizabeth HunterSarah Jane DelanyPádraig CunninghamMark ScanlonQuan LeOisín BoydellDerek Greene
- Journals
- IEEE Access (4 papers)Expert Systems with Applications (4 papers)Journal of Artificial Societies and Social Simulation (3 papers)
- Partner nations
- IrelandUnited KingdomChina
In The Last Decade
Brian Mac Namee
96 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 176
- Modeling and Simulation 180
- Artificial Intelligence 691
- Signal Processing 208
- Health Informatics 24
- Computer Science Applications 93
Countries citing papers authored by Brian Mac Namee
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
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
The 25 scholars most cited alongside Brian Mac Namee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 10 | |
| 4 | 2024 | 2 | |
| 5 | 2023 | 8 | |
| 6 | 2023 | 0 | |
| 7 | 2022 | 14 | |
| 8 | 2022 | 6 | |
| 9 | 2022 | 17 | |
| 10 | 2022 | 9 | |
| 11 | 2020 | 37 | |
| 12 | Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks | 2020 | 5 |
| 13 | 2020 | 7 | |
| 14 | A Topic-Based Approach to Multiple Corpus Comparison. | 2019 | 2 |
| 15 | Identifying Urban Canopy Coverage from Satellite Imagery Using Convolutional Neural Networks. | 2018 | 1 |
| 16 | Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours | 2017 | 7 |
| 17 | An Open Data Driven Epidemiological Agent-Based Model for Irish Towns. | 2016 | 2 |
| 18 | Knowing What You Don?t Know: Choosing the Right Chart to Show Data Distributions to Non-Expert Users | 2015 | 2 |
| 19 | 2011 | 16 | |
| 20 | Smart Objects for Attentive Agents | 2003 | 18 |
About Brian Mac Namee
Brian Mac Namee is a scholar working on Artificial Intelligence, Computer Science Applications and Modeling and Simulation, having authored 118 papers that have together received 1.6k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (14 papers), Machine Learning and Data Classification (12 papers), Data Stream Mining Techniques (12 papers), Topic Modeling (11 papers), Data Visualization and Analytics (8 papers), COVID-19 epidemiological studies (8 papers), Machine Learning and Algorithms (7 papers) and Social Robot Interaction and HRI (7 papers). The work is most often cited by research in Modeling and Simulation (180 citations), Artificial Intelligence (691 citations) and Signal Processing (208 citations). Brian Mac Namee has collaborated with scholars based in Ireland, United Kingdom and China. Frequent co-authors include John D. Kelleher, Elizabeth Hunter, Sarah Jane Delany, Pádraig Cunningham, Mark Scanlon, Quan Le, Oisín Boydell, Derek Greene, Arjun Pakrashi and Owen I. Corrigan. Their work appears in journals such as IEEE Access, Expert Systems with Applications, Journal of Artificial Societies and Social Simulation, Information Sciences and Scientific Reports.
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.