Andreas Grammenos
- Signal Processing top 10%
- Artificial Intelligence
- Radiology, Nuclear Medicine and Imaging
- Pulmonary and Respiratory Medicine
- Computer Networks and Communications
- Co-authors
- Cecilia MascoloPietro CicutaApinan HasthanasombatXia TongJing HanDimitris SpathisJagmohan ChauhanChloë Brown
- Topics
- COVID-19 diagnosis using AI (6 papers)Distributed Control Multi-Agent Systems (5 papers)Music and Audio Processing (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceJournal of Medical Internet Research
- Partner nations
- United KingdomFinlandCyprus
In The Last Decade
Andreas Grammenos
18 papers receiving 275 citations
Peers
Comparison fields: 5 of 64
- Signal Processing 99
- Artificial Intelligence 84
- Radiology, Nuclear Medicine and Imaging 79
- Pulmonary and Respiratory Medicine 65
- Computer Networks and Communications 50
Countries citing papers authored by Andreas Grammenos
This map shows the geographic impact of Andreas Grammenos'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 Andreas Grammenos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Grammenos more than expected).
Fields of papers citing papers by Andreas Grammenos
This network shows the impact of papers produced by Andreas Grammenos. 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 Andreas Grammenos. The network helps show where Andreas Grammenos may publish in the future.
Co-authorship network of co-authors of Andreas Grammenos
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Grammenos. A scholar is included among the top collaborators of Andreas Grammenos 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 Andreas Grammenos. Andreas Grammenos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 4 | |
| 5 | 7 | |
| 6 | 22 | |
| 7 | 23 | |
| 8 | 59 | |
| 9 | 5 | |
| 10 | COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening | 28 |
| 11 | 63 | |
| 12 | 15 | |
| 13 | 2 | |
| 14 | 9 | |
| 15 | 1 | |
| 16 | Federated PCA with Adaptive Rank Estimation. | 5 |
| 17 | 7 | |
| 18 | 22 |
About Andreas Grammenos
Andreas Grammenos is a scholar working on Signal Processing, Computer Networks and Communications and Radiology, Nuclear Medicine and Imaging, having authored 18 papers that have together received 277 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (6 papers), Distributed Control Multi-Agent Systems (5 papers) and Music and Audio Processing (5 papers). The work is most often cited by research in Signal Processing (99 citations), Radiology, Nuclear Medicine and Imaging (79 citations) and Artificial Intelligence (84 citations). Andreas Grammenos has collaborated with scholars based in United Kingdom, Finland and Cyprus. Frequent co-authors include Cecilia Mascolo, Pietro Cicuta, Apinan Hasthanasombat, Xia Tong, Jing Han, Dimitris Spathis, Jagmohan Chauhan, Chloë Brown, Ting Dang and Jon Crowcroft. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Medical Internet Research.
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.