Peyman Bayat
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Neurology top 10%
- Radiology, Nuclear Medicine and Imaging
- Computer Networks and Communications
- Co-authors
- Asadollah ShahbahramiGholamhossein EkbatanifardShahram JamaliAli AlmasiradMona SalimiSepideh KhaleghiHoma AzizianKowsar Bagherzadeh
- Topics
- Brain Tumor Detection and Classification (5 papers)IoT and Edge/Fog Computing (4 papers)Cardiac pacing and defibrillation studies (3 papers)
In The Last Decade
Peyman Bayat
27 papers receiving 291 citations
Peers
Comparison fields: 5 of 82
- Computer Vision and Pattern Recognition 86
- Artificial Intelligence 74
- Neurology 70
- Radiology, Nuclear Medicine and Imaging 52
- Computer Networks and Communications 41
Countries citing papers authored by Peyman Bayat
This map shows the geographic impact of Peyman Bayat'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 Peyman Bayat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peyman Bayat more than expected).
Fields of papers citing papers by Peyman Bayat
This network shows the impact of papers produced by Peyman Bayat. 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 Peyman Bayat. The network helps show where Peyman Bayat may publish in the future.
Co-authorship network of co-authors of Peyman Bayat
This figure shows the co-authorship network connecting the top 25 collaborators of Peyman Bayat. A scholar is included among the top collaborators of Peyman Bayat 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 Peyman Bayat. Peyman Bayat is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | 2 | |
| 3 | 5 | |
| 4 | 19 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | 14 | |
| 8 | 4 | |
| 9 | 2 | |
| 10 | 29 | |
| 11 | 1 | |
| 12 | 21 | |
| 13 | 9 | |
| 14 | A Hybrid Optimization Algorithm for Learning Deep Models | 1 |
| 15 | 37 | |
| 16 | 45 | |
| 17 | A Parallel Implementation of Modified Fuzzy Logic for Breast Cancer Detection | 1 |
| 18 | 20 | |
| 19 | 12 | |
| 20 | A new robust centralized DMX algorithm | 2 |
About Peyman Bayat
Peyman Bayat is a scholar working on Neurology, Computer Networks and Communications and Information Systems, having authored 28 papers that have together received 303 indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (5 papers), IoT and Edge/Fog Computing (4 papers) and Cardiac pacing and defibrillation studies (3 papers). The work is most often cited by research in Neurology (70 citations), Computer Vision and Pattern Recognition (86 citations) and Artificial Intelligence (74 citations). Peyman Bayat has collaborated with scholars based in Iran and Malaysia. Frequent co-authors include Asadollah Shahbahrami, Gholamhossein Ekbatanifard, Shahram Jamali, Ali Almasirad, Mona Salimi, Sepideh Khaleghi, Homa Azizian, Kowsar Bagherzadeh, Zahra Mousavi and Abbas Shafiee. Their work appears in journals such as IEEE Access, Applied Soft Computing and Computer Networks.
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.