Maya Kabkab

About

Maya Kabkab is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Computational Mechanics. According to data from OpenAlex, Maya Kabkab has authored 2 papers receiving a total of 34 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Computer Vision and Pattern Recognition, 1 paper in Computer Networks and Communications and 1 paper in Computational Mechanics. Recurrent topics in Maya Kabkab’s work include Sparse and Compressive Sensing Techniques (1 paper), Advanced Image Processing Techniques (1 paper) and Mobile Ad Hoc Networks (1 paper). Maya Kabkab is often cited by papers focused on Sparse and Compressive Sensing Techniques (1 paper), Advanced Image Processing Techniques (1 paper) and Mobile Ad Hoc Networks (1 paper). Maya Kabkab collaborates with scholars based in United States. Maya Kabkab's co-authors include Pouya Samangouei, Rama Chellappa and Richard J. La and has published in prestigious journals such as Internet Mathematics and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Co-authorship network of co-authors of Maya Kabkab i

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Fields of papers citing papers by Maya Kabkab

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Maya Kabkab

Since Specialization

Citations

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

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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