Kawa Nazemi
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- Data Visualization and Analytics 33
- Video Analysis and Summarization 4
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- Scientific Computing and Data Management 3
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- Advanced Text Analysis Techniques 11
- Semantic Web and Ontologies 10
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- Web Data Mining and Analysis 4
- Recommender Systems and Techniques 3
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- Innovative Teaching and Learning Methods 3
Kawa Nazemi
45 papers receiving 193 citations
Peers
Comparison fields: 5 of 68
- Computer Vision and Pattern Recognition 84
- Geography, Planning and Development 14
- Management Information Systems 22
- Information Systems and Management 17
- Artificial Intelligence 76
Countries citing papers authored by Kawa Nazemi
This map shows the geographic impact of Kawa Nazemi'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 Kawa Nazemi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kawa Nazemi more than expected).
Fields of papers citing papers by Kawa Nazemi
This network shows the impact of papers produced by Kawa Nazemi. 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 Kawa Nazemi. The network helps show where Kawa Nazemi may publish in the future.
Co-authorship network
The 19 scholars most cited alongside Kawa Nazemi, 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 | 2024 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 3 | |
| 4 | 2023 | 19 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 1 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 3 | |
| 9 | 2020 | 4 | |
| 10 | Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts. | 2020 | 2 |
| 11 | 2020 | 1 | |
| 12 | 2015 | 0 | |
| 13 | Fupol simulators and advanced visualization framework integration | 2014 | 1 |
| 14 | Visual Variables in Adaptive Visualizations. | 2013 | 3 |
| 15 | 2012 | 2 | |
| 16 | Analytical Semantics Visualization for Discovering Latent Signals in Large Text Collections | 2012 | 0 |
| 17 | 2010 | 1 | |
| 18 | Semantic Visualization Cockpit: Adaptable Composition of Semantics-Visualization Techniques for Knowledge-Exploration | 2010 | 5 |
| 19 | Intuitive Authoring on Web: a User-Centered Software Design Approach | 2008 | 2 |
| 20 | Adaptive Tutoring in Virtual Learning Worlds | 2007 | 1 |
About Kawa Nazemi
Kawa Nazemi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems and Management, having authored 53 papers that have together received 200 indexed citations. Recurring topics across this work include Data Visualization and Analytics (33 papers), Advanced Text Analysis Techniques (11 papers), Semantic Web and Ontologies (10 papers), Video Analysis and Summarization (4 papers), Web Data Mining and Analysis (4 papers), Innovative Teaching and Learning Methods (3 papers), Recommender Systems and Techniques (3 papers) and Scientific Computing and Data Management (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (84 citations), Geography, Planning and Development (14 citations) and Management Information Systems (22 citations). Kawa Nazemi has collaborated with scholars based in Germany, Latvia and Spain. Frequent co-authors include Dirk Burkhardt, Jörn Kohlhammer, Dieter W. Fellner, Egīls Ginters, Bernhard G. Humm, Arjan Kuijper, Alexander Kock, David Hoppe, Christian Stab and Maja Ćukušić.
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.