Moises Sudit
- Computer Networks and Communications top 5%
- Artificial Intelligence top 10%
- Information Systems top 5%
- Signal Processing top 5%
- Industrial and Manufacturing Engineering top 5%
- Co-authors
- Rakesh NagiShanchieh Jay YangMichael E. KuhlMichael KühlSanjay JoshiWilliam HughesKatie McConkyGeorge Tadda
- Topics
- Graph Theory and Algorithms (5 papers)Data Quality and Management (5 papers)Advanced Graph Neural Networks (5 papers)
- Cited by
- Signal ProcessingIndustrial and Manufacturing EngineeringComputer Networks and Communications
- Journals
- Journal of the Operational Research SocietyComputers & Operations ResearchInformation Fusion
- Partner nations
- United StatesCanada
In The Last Decade
Moises Sudit
25 papers receiving 419 citations
Peers
Comparison fields: 5 of 46
- Computer Networks and Communications 194
- Artificial Intelligence 151
- Information Systems 150
- Signal Processing 113
- Industrial and Manufacturing Engineering 85
Countries citing papers authored by Moises Sudit
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
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-authorship network of co-authors of Moises Sudit
This figure shows the co-authorship network connecting the top 25 collaborators of Moises Sudit. A scholar is included among the top collaborators of Moises Sudit based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Moises Sudit. Moises Sudit is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 12 | |
| 5 | 16 | |
| 6 | Significant information encapsulation and valence exploitation (SIEVE) for discovery | 1 |
| 7 | 16 | |
| 8 | Process refinement using Biosensor location problem | 1 |
| 9 | Incremental graph matching for Situation Awareness | 14 |
| 10 | Hierarchical Higher Level Data Fusion using Fuzzy Hamming and Hypercube Clustering. | 5 |
| 11 | 58 | |
| 12 | INFERD and Entropy for Situational Awareness. | 6 |
| 13 | 33 | |
| 14 | 10 | |
| 15 | 99 | |
| 16 | 2 | |
| 17 | 24 | |
| 18 | 30 | |
| 19 | 14 | |
| 20 | 1 |
About Moises Sudit
Moises Sudit is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computer Networks and Communications, having authored 28 papers that have together received 459 indexed citations. Recurring topics across this work include Graph Theory and Algorithms (5 papers), Data Quality and Management (5 papers) and Advanced Graph Neural Networks (5 papers). The work is most often cited by research in Signal Processing (113 citations), Industrial and Manufacturing Engineering (85 citations) and Computer Networks and Communications (194 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, William Hughes, Katie McConky, George Tadda, Genshe Chen and Subrata Das. Their work appears in journals such as Journal of the Operational Research Society, Computers & Operations Research and Information Fusion.
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.