Seda Arslan Tuncer
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Cognitive Neuroscience top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Cardiology and Cardiovascular Medicine
- Co-authors
- Ahmet ÇınarAhmet AlkanSuat ToramanTaner TuncerDuygu KayaMehmet KalaycıMurat FıratCaner Feyzi Demir
- Topics
- COVID-19 diagnosis using AI (8 papers)Retinal Imaging and Analysis (7 papers)EEG and Brain-Computer Interfaces (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsChemometrics and Intelligent Laboratory Systems
- Partner nations
- TürkiyeFinlandUnited States
In The Last Decade
Seda Arslan Tuncer
48 papers receiving 527 citations
Peers
Comparison fields: 5 of 109
- Computer Vision and Pattern Recognition 165
- Artificial Intelligence 148
- Cognitive Neuroscience 118
- Radiology, Nuclear Medicine and Imaging 113
- Cardiology and Cardiovascular Medicine 84
Countries citing papers authored by Seda Arslan Tuncer
This map shows the geographic impact of Seda Arslan Tuncer'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 Seda Arslan Tuncer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seda Arslan Tuncer more than expected).
Fields of papers citing papers by Seda Arslan Tuncer
This network shows the impact of papers produced by Seda Arslan Tuncer. 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 Seda Arslan Tuncer. The network helps show where Seda Arslan Tuncer may publish in the future.
Co-authorship network of co-authors of Seda Arslan Tuncer
This figure shows the co-authorship network connecting the top 25 collaborators of Seda Arslan Tuncer. A scholar is included among the top collaborators of Seda Arslan Tuncer 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 Seda Arslan Tuncer. Seda Arslan Tuncer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 14 | |
| 10 | 4 | |
| 11 | Age Detection by Deep Learning from Dental Panoramic Radiographs | 2 |
| 12 | 1 | |
| 13 | 3 | |
| 14 | 9 | |
| 15 | 13 | |
| 16 | 6 | |
| 17 | 21 | |
| 18 | 49 | |
| 19 | Retinal Görüntülerden Optik Diskin Aktif Kontur Yöntemi ile Bölütlenmesi | 1 |
| 20 | Real-Time Random Number Generation With RO-Based Double PUF | 4 |
About Seda Arslan Tuncer
Seda Arslan Tuncer is a scholar working on Health Information Management, Radiology, Nuclear Medicine and Imaging and Health Informatics, having authored 55 papers that have together received 542 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (8 papers), Retinal Imaging and Analysis (7 papers) and EEG and Brain-Computer Interfaces (7 papers). The work is most often cited by research in Health Informatics (18 citations), Computer Vision and Pattern Recognition (165 citations) and Health Information Management (32 citations). Seda Arslan Tuncer has collaborated with scholars based in Türkiye, Finland and United States. Frequent co-authors include Ahmet Çınar, Ahmet Alkan, Suat Toraman, Taner Tuncer, Duygu Kaya, Mehmet Kalaycı, Murat Fırat, Caner Feyzi Demir, Derya Avcı and Mohammad Mazyad Hazzazi. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Chemometrics and Intelligent Laboratory Systems.
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.