Ehsan Zare Borzeshi
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition
- Statistical and Nonlinear Physics
- Signal Processing
- Co-authors
- Massimo PiccardiKaspar RiesenHorst BunkeRaghavendra ChalapathyRichard Yi Da XuOscar Perez‐ConchaAdil BagirovSattar Seifollahi
- Topics
- Topic Modeling (7 papers)Natural Language Processing Techniques (7 papers)Text and Document Classification Technologies (4 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Neural Networks and Learning SystemsIEEE Signal Processing Letters
- Partner nations
- AustraliaSwitzerlandUnited Kingdom
In The Last Decade
Ehsan Zare Borzeshi
14 papers receiving 221 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 180
- Molecular Biology 78
- Computer Vision and Pattern Recognition 53
- Statistical and Nonlinear Physics 13
- Signal Processing 11
Countries citing papers authored by Ehsan Zare Borzeshi
This map shows the geographic impact of Ehsan Zare Borzeshi'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 Ehsan Zare Borzeshi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ehsan Zare Borzeshi more than expected).
Fields of papers citing papers by Ehsan Zare Borzeshi
This network shows the impact of papers produced by Ehsan Zare Borzeshi. 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 Ehsan Zare Borzeshi. The network helps show where Ehsan Zare Borzeshi may publish in the future.
Co-authorship network of co-authors of Ehsan Zare Borzeshi
This figure shows the co-authorship network connecting the top 25 collaborators of Ehsan Zare Borzeshi. A scholar is included among the top collaborators of Ehsan Zare Borzeshi 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 Ehsan Zare Borzeshi. Ehsan Zare Borzeshi 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 | 4 | |
| 3 | English-Basque Statistical and Neural Machine Translation | 4 |
| 4 | BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset. | 15 |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 93 | |
| 8 | 8 | |
| 9 | PersoNER: Persian named-entity recognition | 10 |
| 10 | 23 | |
| 11 | 3 | |
| 12 | 16 | |
| 13 | 31 | |
| 14 | 9 |
About Ehsan Zare Borzeshi
Ehsan Zare Borzeshi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology, having authored 14 papers that have together received 231 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (7 papers) and Text and Document Classification Technologies (4 papers). The work is most often cited by research in Artificial Intelligence (180 citations), Health Informatics (5 citations) and Computer Vision and Pattern Recognition (53 citations). Ehsan Zare Borzeshi has collaborated with scholars based in Australia, Switzerland and United Kingdom. Frequent co-authors include Massimo Piccardi, Kaspar Riesen, Horst Bunke, Raghavendra Chalapathy, Richard Yi Da Xu, Oscar Perez‐Concha, Adil Bagirov, Sattar Seifollahi, Nazanin Esmaili and Mubarak Shah. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and IEEE Signal Processing Letters.
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.