Moises Sudit

25 papers receiving 419 citations

Peers

Moises Sudit
Comparison fields: 5 of 46
  • Signal Processing 113
  • Industrial and Manufacturing Engineering 85
  • Computer Networks and Communications 194
  • Information Systems 150
  • Management Science and Operations Research 60
Replace C. Raghavendra Rao with:
C. Raghavendra Rao India
David J. Musliner United States
Dana Nau United States
Mohammadreza Ramezanpour Iran
Yezid Donoso Colombia
Akshat Kumar Singapore
Marin Golub Croatia
Rosario G. Garroppo Italy
Zhaopin Su China
Mohammad Ahsan Chishti India
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Citations per year

Countries citing papers authored by Moises Sudit

Since Specialization
Citations

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

Fields of papers citing papers by Moises Sudit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200699
2 200758
3 200738
4 200733
5 200530
6 200624
7 200921
8 200621
9 201216
10 201016
11 199414
12
Incremental graph matching for Situation Awareness
200914
13 201312
14 201811
15 200710
16 20068
17 20108
18
INFERD and Entropy for Situational Awareness.
20076
19
Hierarchical Higher Level Data Fusion using Fuzzy Hamming and Hypercube Clustering.
20085
20 20164

About Moises Sudit

Moises Sudit is a scholar working on Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 28 papers that have together received 459 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (5 papers), Graph Theory and Algorithms (5 papers), Data Quality and Management (5 papers), Network Security and Intrusion Detection (4 papers), Vehicle Routing Optimization Methods (3 papers), Optimization and Packing Problems (3 papers), Military Defense Systems Analysis (3 papers) and Advanced Graph Theory Research (3 papers). The work is most often cited by research in Signal Processing (113 citations), Industrial and Manufacturing Engineering (85 citations), Computer Networks and Communications (194 citations), Information Systems (150 citations) and Management Science and Operations Research (60 citations). Moises Sudit has collaborated with scholars based in United States and Canada. Frequent co-authors include Rakesh Nagi, Shanchieh Jay Yang, Michael E. Kuhl, Michael Kühl, Sanjay Joshi, Katie McConky, William Hughes, Richard R. Brooks, J. Salerno and Rajan Batta. Their work appears in journals such as Information Fusion, Discrete Applied Mathematics, Computers & Operations Research, Naval Research Logistics (NRL) and Journal of the Operational Research Society.

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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