Moshe Gabel

566 citations
31 papers · 391 · h-index 13

Impact in

Papers in

Moshe Gabel

30 papers receiving 381 citations

Peers

Moshe Gabel
Comparison fields: 5 of 67
  • Computer Networks and Communications 164
  • Hardware and Architecture 27
  • Signal Processing 36
  • Computer Vision and Pattern Recognition 58
  • Artificial Intelligence 84
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David Arney United States
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Citations per field
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Citations per year

Countries citing papers authored by Moshe Gabel

Since Specialization
Citations

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

Fields of papers citing papers by Moshe Gabel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202157
2 201946
3 199145
4 202126
5 201624
6 202122
7 202118
8 201916
9 201516
10 201215
11 202212
12 202112
13 201712
14 202010
15
Avoiding the streetlight effect: I/O workload analysis with SSDs in mind
201610
16 20229
17 20216
18
Latent Fault Detection With Unbalanced Workloads
20155
19
Communication-efficient Outlier Detection for Scale-out Systems.
20135
20 20225

About Moshe Gabel

Moshe Gabel is a scholar working on Computer Networks and Communications, Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 31 papers that have together received 391 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (6 papers), Data Stream Mining Techniques (6 papers), Non-Invasive Vital Sign Monitoring (5 papers), Cloud Computing and Resource Management (5 papers), Software System Performance and Reliability (4 papers), Anomaly Detection Techniques and Applications (4 papers), Network Security and Intrusion Detection (4 papers) and Context-Aware Activity Recognition Systems (3 papers). The work is most often cited by research in Computer Networks and Communications (164 citations), Hardware and Architecture (27 citations), Signal Processing (36 citations), Computer Vision and Pattern Recognition (58 citations) and Artificial Intelligence (84 citations). Moshe Gabel has collaborated with scholars based in Canada, Israel and United States. Frequent co-authors include Eyal de Lara, Gala Yadgar, Assaf Schuster, Daniel Keren, Bianca Schroeder, Ray E. Eberts, Moshe M. Barash, Frank Rudzicz, Ehud Rivlin and Igor Kviatkovsky. Their work appears in journals such as ACM Transactions on Storage, Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, IEEE Transactions on Engineering Management, Proceedings of the VLDB Endowment and IEEE Transactions on Pattern Analysis and Machine Intelligence.

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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