Kawa Nazemi

821 citations
53 papers · 200 indexed · h-index 9

Kawa Nazemi

45 papers receiving 193 citations

Peers

Kawa Nazemi
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
Replace Enrico Daga with:
Enrico Daga United Kingdom
Dmitry Mouromtsev Russia
Dirk Burkhardt Germany
Frederico Araújo Dur�ão Brazil
Hartmut Ziegler Germany
Marko Luther Germany
Anna Tordai Netherlands
Emanuele Storti Italy
Jeni Tennison United Kingdom
Albert Meroño-Peñuela United Kingdom
Kawa Nazemi relative to Enrico Daga United Kingdom Enrico Daga's profile →
Citations per field
00.5×1.5×2.3×
Enrico Daga · 1×
Citations per year

Countries citing papers authored by Kawa Nazemi

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Kawa Nazemi Line = papers co-authored together Kawa Nazemi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20240
3 20233
4 202319
5 20230
6 20231
7 20232
8 20223
9 20204
10
Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts.
20202
11 20201
12 20150
13
Fupol simulators and advanced visualization framework integration
20141
14
Visual Variables in Adaptive Visualizations.
20133
15 20122
16
Analytical Semantics Visualization for Discovering Latent Signals in Large Text Collections
20120
17 20101
18
Semantic Visualization Cockpit: Adaptable Composition of Semantics-Visualization Techniques for Knowledge-Exploration
20105
19
Intuitive Authoring on Web: a User-Centered Software Design Approach
20082
20
Adaptive Tutoring in Virtual Learning Worlds
20071

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.

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