Daniel Keren

61 papers receiving 980 citations

Peers

Daniel Keren
Comparison fields: 5 of 102
  • Signal Processing 249
  • Computer Networks and Communications 449
  • Computer Graphics and Computer-Aided Design 43
  • Computer Vision and Pattern Recognition 243
  • Artificial Intelligence 363
Replace Jizhong Han with:
Jizhong Han China
L.M. Patnaik India
Yingxue Zhang China
Toshinori Munakata United States
Yuan Qi China
Jan Korst Netherlands
Bo Wu United States
Lixin Han China
Ji Ming United Kingdom
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Citations per year

Countries citing papers authored by Daniel Keren

Since Specialization
Citations

This map shows the geographic impact of Daniel Keren'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 Daniel Keren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Keren more than expected).

Fields of papers citing papers by Daniel Keren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Keren. 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 Daniel Keren. The network helps show where Daniel Keren may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Keren, 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 Daniel Keren Line = papers co-authored together Daniel Keren links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200696
2 200776
3 200649
4 201146
5 201344
6 200642
7 201337
8 199936
9 200835
10 201431
11 201028
12 201427
13 201426
14 201325
15 200725
16 201523
17 201423
18 201020
19 200019
20 200319

About Daniel Keren

Daniel Keren is a scholar working on Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing and Aerospace Engineering, having authored 68 papers that have together received 1.0k indexed citations. Recurring topics across this work include Data Stream Mining Techniques (17 papers), Data Management and Algorithms (15 papers), Advanced Database Systems and Queries (13 papers), Robotics and Sensor-Based Localization (8 papers), Advanced Vision and Imaging (8 papers), Advanced Image and Video Retrieval Techniques (7 papers), Image and Object Detection Techniques (6 papers) and Transportation and Mobility Innovations (5 papers). The work is most often cited by research in Signal Processing (249 citations), Computer Networks and Communications (449 citations), Computer Graphics and Computer-Aided Design (43 citations), Computer Vision and Pattern Recognition (243 citations) and Artificial Intelligence (363 citations). Daniel Keren has collaborated with scholars based in Israel, United States and Belgium. Frequent co-authors include Assaf Schuster, Izchak Sharfman, Margarita Osadchy, Minos Garofalakis, Vasilis Samoladas, Ehud Rivlin, Moshe Gabel, Irad Yavneh, Evgeni Magid and Hagit Hel‐Or. Their work appears in journals such as Journal of Mathematical Imaging and Vision, Proceedings of the VLDB Endowment, ACM Transactions on Database Systems, IEEE Transactions on Knowledge and Data Engineering and Journal of Parallel and Distributed Computing.

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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