Nasseh Tabrizi
- Information Systems top 5%
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition
- Sociology and Political Science
- Topics
- Data Quality and Management (4 papers)Sentiment Analysis and Opinion Mining (3 papers)IoT and Edge/Fog Computing (3 papers)
- Journals
- IEEE AccessLecture notes in computer scienceInternational Journal of Advanced Computer Science and Applications
- Partner nations
- United States
In The Last Decade
Nasseh Tabrizi
34 papers receiving 234 citations
Peers
Comparison fields: 5 of 76
- Information Systems 111
- Artificial Intelligence 98
- Computer Networks and Communications 62
- Computer Vision and Pattern Recognition 41
- Sociology and Political Science 29
Countries citing papers authored by Nasseh Tabrizi
This map shows the geographic impact of Nasseh Tabrizi'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 Nasseh Tabrizi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nasseh Tabrizi more than expected).
Fields of papers citing papers by Nasseh Tabrizi
This network shows the impact of papers produced by Nasseh Tabrizi. 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 Nasseh Tabrizi. The network helps show where Nasseh Tabrizi may publish in the future.
Co-authorship network of co-authors of Nasseh Tabrizi
This figure shows the co-authorship network connecting the top 25 collaborators of Nasseh Tabrizi. A scholar is included among the top collaborators of Nasseh Tabrizi 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 Nasseh Tabrizi. Nasseh Tabrizi 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 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 8 | |
| 7 | 8 | |
| 8 | 3 | |
| 9 | 3 | |
| 10 | 20 | |
| 11 | 7 | |
| 12 | 9 | |
| 13 | 1 | |
| 14 | Performance Analysis of Sparks Machine Learning Library. | 3 |
| 15 | 26 | |
| 16 | 1 | |
| 17 | 2 | |
| 18 | Exploring HADOOP as a Platform for Distributed Association Rule Mining | 13 |
| 19 | 1 | |
| 20 | Sigma Feature for Offline Cursive Handwriting Recognition. | 1 |
About Nasseh Tabrizi
Nasseh Tabrizi is a scholar working on Computational Mathematics, Human Factors and Ergonomics and Health Information Management, having authored 37 papers that have together received 250 indexed citations. Recurring topics across this work include Data Quality and Management (4 papers), Sentiment Analysis and Opinion Mining (3 papers) and IoT and Edge/Fog Computing (3 papers). The work is most often cited by research in Human Factors and Ergonomics (26 citations), Information Systems (111 citations) and Artificial Intelligence (98 citations). Nasseh Tabrizi has collaborated with scholars based in United States. Frequent co-authors include Julian Brinkley, Samuel W. Thomas, Qin Ding, David H. Hoffman, Hooman Hedayati and Paul J. Gemperline. Their work appears in journals such as IEEE Access, Lecture notes in computer science and International Journal of Advanced Computer Science and Applications.
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.