İzzeddin Gür
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Semantic Web and Ontologies
- Speech and dialogue systems
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Topic Modeling 9
- Natural Language Processing Techniques 8
- Speech and dialogue systems 2
- Intelligent Tutoring Systems and Adaptive Learning 1
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- Multimodal Machine Learning Applications 3
- Data Visualization and Analytics 1
- Co-authors
- Xifeng Yan (6 shared papers)Yu Su (6 shared papers)Semih Yavuz (5 shared papers)Mudhakar Srivatsa (2 shared papers)Huan Sun (2 shared papers)Brian M. Sadler (1 shared paper)Aleksandra Faust (3 shared papers)Dilek Hakkani‐Tür (1 shared paper)
- Journals
- The VLDB Journal (1 paper)Fire Technology (1 paper)arXiv (Cornell University) (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesTürkiye
In The Last Decade
İzzeddin Gür
13 papers receiving 265 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 245
- Computer Vision and Pattern Recognition 58
- Information Systems 50
- Management Science and Operations Research 12
- Information Systems and Management 6
Countries citing papers authored by İzzeddin Gür
This map shows the geographic impact of İzzeddin Gür'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 İzzeddin Gür with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites İzzeddin Gür more than expected).
Fields of papers citing papers by İzzeddin Gür
This network shows the impact of papers produced by İzzeddin Gür. 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 İzzeddin Gür. The network helps show where İzzeddin Gür may publish in the future.
Co-authors
The 25 scholars most cited alongside İzzeddin Gür, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 61 | |
| 2 | 2018 | 51 | |
| 3 | 2016 | 37 | |
| 4 | 2022 | 31 | |
| 5 | 2023 | 20 | |
| 6 | 2018 | 19 | |
| 7 | 2018 | 17 | |
| 8 | 2018 | 14 | |
| 9 | 2017 | 14 | |
| 10 | 2018 | 13 | |
| 11 | 2017 | 7 | |
| 12 | 2014 | 4 | |
| 13 | 2022 | 4 |
About İzzeddin Gür
İzzeddin Gür is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics and Ocean Engineering, having authored 13 papers that have together received 292 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (8 papers), Multimodal Machine Learning Applications (3 papers), Speech and dialogue systems (2 papers), Web Data Mining and Analysis (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper), Advanced X-ray and CT Imaging (1 paper) and Data Visualization and Analytics (1 paper). The work is most often cited by research in Artificial Intelligence (245 citations), Computer Vision and Pattern Recognition (58 citations), Information Systems (50 citations), Management Science and Operations Research (12 citations) and Information Systems and Management (6 citations). İzzeddin Gür has collaborated with scholars based in United States and Türkiye. Frequent co-authors include Xifeng Yan, Yu Su, Semih Yavuz, Mudhakar Srivatsa, Huan Sun, Brian M. Sadler, Aleksandra Faust, Dilek Hakkani‐Tür, Pararth Shah and Gökhan Tür. Their work appears in journals such as The VLDB Journal, Fire Technology, arXiv (Cornell University) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.