Michael G. Madden
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Management Science and Operations Research top 5%
- Control and Systems Engineering top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Shehroz S. KhanTom HowleyAlan G. RyderNiall O’ConnorMarie-Louise O’ConnellGerard J. LyonsP. NolanDayong Gao
- Topics
- Fault Detection and Control Systems (11 papers)Anomaly Detection Techniques and Applications (8 papers)Spectroscopy and Chemometric Analyses (7 papers)
- Partner nations
- IrelandUnited StatesSaudi Arabia
In The Last Decade
Michael G. Madden
59 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Artificial Intelligence 611
- Computer Vision and Pattern Recognition 207
- Management Science and Operations Research 135
- Control and Systems Engineering 134
- Computer Networks and Communications 110
Countries citing papers authored by Michael G. Madden
This map shows the geographic impact of Michael G. Madden'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 Michael G. Madden with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael G. Madden more than expected).
Fields of papers citing papers by Michael G. Madden
This network shows the impact of papers produced by Michael G. Madden. 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 Michael G. Madden. The network helps show where Michael G. Madden may publish in the future.
Co-authorship network of co-authors of Michael G. Madden
This figure shows the co-authorship network connecting the top 25 collaborators of Michael G. Madden. A scholar is included among the top collaborators of Michael G. Madden 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 Michael G. Madden. Michael G. Madden is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 1 | |
| 8 | 21 | |
| 9 | 1 | |
| 10 | One-class classification: taxonomy of study and review of techniquesbreakdown → | 399 |
| 11 | 7 | |
| 12 | 6 | |
| 13 | 9 | |
| 14 | Probabilistic detection of short events, with application to critical care monitoring | 9 |
| 15 | 0 | |
| 16 | 2 | |
| 17 | Context-sensitive regression analysis for distributed data | 0 |
| 18 | 34 | |
| 19 | 11 | |
| 20 | 2 |
About Michael G. Madden
Michael G. Madden is a scholar working on Biophysics, Artificial Intelligence and Management Science and Operations Research, having authored 65 papers that have together received 1.3k indexed citations. Recurring topics across this work include Fault Detection and Control Systems (11 papers), Anomaly Detection Techniques and Applications (8 papers) and Spectroscopy and Chemometric Analyses (7 papers). The work is most often cited by research in Artificial Intelligence (611 citations), Biophysics (81 citations) and Management Science and Operations Research (135 citations). Michael G. Madden has collaborated with scholars based in Ireland, United States and Saudi Arabia. Frequent co-authors include Shehroz S. Khan, Tom Howley, Alan G. Ryder, Niall O’Connor, Marie-Louise O’Connell, Gerard J. Lyons, P. Nolan, Dayong Gao, Gunnar Lucko and Jennifer Conroy. Their work appears in journals such as Scientific Reports, IEEE Access and BMC Bioinformatics.
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.