Moshe Kam

3.6k citations
187 papers · 2.1k · h-index 22

Impact in

Papers in

Moshe Kam

177 papers receiving 2.0k citations

Peers

Moshe Kam
Comparison fields: 5 of 132
  • Computer Networks and Communications 686
  • Signal Processing 259
  • Artificial Intelligence 669
  • Computer Vision and Pattern Recognition 389
  • Control and Systems Engineering 388
Replace Wei‐Min Shen with:
Wei‐Min Shen United States
Huanlai Xing China
Bo Li China
Chun Tung Chou Australia
Yuanyan Tang China
Anthony R. Cassandra United States
Wee Sun Lee Singapore
Alan Fern United States
Shimon Whiteson Netherlands
Zhiwen Xiao China
Moshe Kam relative to Wei‐Min Shen United States Wei‐Min Shen's profile →
Citations per field
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Wei‐Min Shen · 1×
Citations per year

Countries citing papers authored by Moshe Kam

Since Specialization
Citations

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

Fields of papers citing papers by Moshe Kam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 187 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997165
2 1992142
3 2016115
4 198890
5 202087
6 201668
7 200261
8 199745
9 199445
10 200144
11 199744
12 201541
13 201440
14 199837
15 199137
16 200930
17 200429
18 201327
19 201227
20 199426

About Moshe Kam

Moshe Kam is a scholar working on Computer Networks and Communications, Artificial Intelligence, Electrical and Electronic Engineering, Aerospace Engineering and Computer Vision and Pattern Recognition, having authored 187 papers that have together received 2.1k indexed citations. Recurring topics across this work include Distributed Sensor Networks and Detection Algorithms (30 papers), Target Tracking and Data Fusion in Sensor Networks (28 papers), Mobile Ad Hoc Networks (15 papers), Neural Networks and Applications (13 papers), Fault Detection and Control Systems (12 papers), Robotic Path Planning Algorithms (11 papers), Advanced Statistical Process Monitoring (10 papers) and Antenna Design and Analysis (9 papers). The work is most often cited by research in Computer Networks and Communications (686 citations), Signal Processing (259 citations), Artificial Intelligence (669 citations), Computer Vision and Pattern Recognition (389 citations) and Control and Systems Engineering (388 citations). Moshe Kam has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include P. Kalata, Pramod Abichandani, W. Steven Gray, A. Guez, Krishna Chintalapudi, Rachel Greenstadt, Richard Conn, Steven Weber, Lex Fridman and Spiros Mancoridis. Their work appears in journals such as Journal of Forensic Sciences, IEEE Intelligent Systems, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), IEEE Transactions on Aerospace and Electronic Systems and Cytometry Part A.

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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