Mateen M. Rizki

550 total citations
49 papers, 367 citations indexed

About

Mateen M. Rizki is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Mateen M. Rizki has authored 49 papers receiving a total of 367 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 6 papers in Biomedical Engineering. Recurrent topics in Mateen M. Rizki's work include Neural Networks and Applications (15 papers), Evolutionary Algorithms and Applications (15 papers) and Metaheuristic Optimization Algorithms Research (9 papers). Mateen M. Rizki is often cited by papers focused on Neural Networks and Applications (15 papers), Evolutionary Algorithms and Applications (15 papers) and Metaheuristic Optimization Algorithms Research (9 papers). Mateen M. Rizki collaborates with scholars based in United States. Mateen M. Rizki's co-authors include Louis A. Tamburino, Michael Conrad, Michael A. Zmuda, Claude C. Grigsby, George Preti, Jae Kwak, Gary K. Beauchamp, Kunio Yamazaki, M. Koksal and David B. Fogel and has published in prestigious journals such as Analytical Chemistry, IEEE Transactions on Evolutionary Computation and Physiology & Behavior.

In The Last Decade

Mateen M. Rizki

43 papers receiving 349 citations

Peers

Mateen M. Rizki

CVPR

Thiago Mosqueiro United States

Chen Yi China

Thomas Miconi United States

Pietro Di Lena Italy

Emmanouil Skoufos United States

Ulrike Meyer Germany

Martin Martin Indonesia

Christopher Town United Kingdom

Maciej Komosiński Poland

Thiago Mosqueiro United States

Mateen M. Rizki

34 ×0.2

49 ×0.7

12 ×0.2

CVPR

46 ×0.9

13 ×0.3

Citations per year, relative to Mateen M. Rizki Mateen M. Rizki (= 1×) peers Thiago Mosqueiro

Countries citing papers authored by Mateen M. Rizki

Since Specialization

Citations

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

Fields of papers citing papers by Mateen M. Rizki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mateen M. Rizki. 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 Mateen M. Rizki. The network helps show where Mateen M. Rizki may publish in the future.

Co-authorship network of co-authors of Mateen M. Rizki

This figure shows the co-authorship network connecting the top 25 collaborators of Mateen M. Rizki. A scholar is included among the top collaborators of Mateen M. Rizki 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 Mateen M. Rizki. Mateen M. Rizki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Rizki, Mateen M., et al.. (2019). Conditional Dilated Convolution Attention Tracking Model. abs 1510 8660. 453–458.

Rizki, Mateen M., et al.. (2015). Manifold and transfer subspace learning for cross-domain vehicle recognition in dynamic systems. International Conference on Information Fusion. 1954–1961. 3 indexed citations

Rizki, Mateen M., et al.. (2014). Gender classification under extended operating conditions. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9079. 90790R–90790R. 1 indexed citations

Rizki, Mateen M., et al.. (2014). Deep learning for image classification. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9079. 90790T–90790T. 10 indexed citations

Kwak, Jae, et al.. (2013). Changes in volatile compounds of human urine as it ages: Their interaction with water. Journal of Chromatography B. 941. 50–53. 16 indexed citations

Kwak, Jae, Claude C. Grigsby, George Preti, et al.. (2013). Changes in volatile compounds of mouse urine as it ages: Their interactions with water and urinary proteins. Physiology & Behavior. 120. 211–219. 23 indexed citations

Kwak, Jae, Claude C. Grigsby, Mateen M. Rizki, et al.. (2012). Differential binding between volatile ligands and major urinary proteins due to genetic variation in mice. Physiology & Behavior. 107(1). 112–120. 50 indexed citations

Grigsby, Claude C., et al.. (2012). Differential profiling of volatile organic compound biomarker signatures utilizing a logical statistical filter-set and novel hybrid evolutionary classifiers. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8402. 84020K–84020K. 3 indexed citations

Rizki, Mateen M., et al.. (2012). The effects of clothing on gender classification using LIDAR data. 134–139. 2 indexed citations

10.

Tamburino, Louis A., Mateen M. Rizki, & Michael A. Zmuda. (2003). Computational resource management in supervised learning systems. Zenodo (CERN European Organization for Nuclear Research). 1074–1079. 1 indexed citations

11.

Rizki, Mateen M., et al.. (2003). EVOLUTIONARY OPTIMIZATION OF GAUSSIAN WINDOWING FUNCTIONS FOR DATA PREPROCESSING. International Journal of Artificial Intelligence Tools. 12(1). 17–35. 6 indexed citations

12.

Tamburino, Louis A., et al.. (2003). Automated feature detection using evolutionary learning processes. Zenodo (CERN European Organization for Nuclear Research). 1080–1087.

13.

Zmuda, Michael A., Mateen M. Rizki, & Louis A. Tamburino. (2003). Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems. Applied Soft Computing. 2(4). 269–282. 8 indexed citations

14.

Rizki, Mateen M., et al.. (2002). The Multi-Tiered Tournament Selection for evolutionary neural network synthesis. 207–215. 3 indexed citations

15.

Tamburino, Louis A. & Mateen M. Rizki. (1996). Resource Allocation for a Hybrid Evolutionary Learning System Used for Pattern Recognition.. 207–216. 2 indexed citations

16.

Zmuda, Michael A., Louis A. Tamburino, & Mateen M. Rizki. (1996). An evolutionary learning system for synthesizing complex morphological filters. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 26(4). 645–653. 6 indexed citations

17.

Rizki, Mateen M., Louis A. Tamburino, & Michael A. Zmuda. (1990). <title>Adaptive search for morphological feature detectors</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 1350. 150–159. 6 indexed citations

18.

Conrad, Michael & Mateen M. Rizki. (1989). The artificial worlds approach to emergent evolution. Biosystems. 23(2-3). 247–258. 17 indexed citations

19.

Rizki, Mateen M. & Michael Conrad. (1985). Evolve III: A discrete events model of an evolutionary ecosystem. Biosystems. 18(1). 121–133. 25 indexed citations

20.

Conrad, Michael & Mateen M. Rizki. (1980). Computational illustration of the bootstrap effect. Biosystems. 13(1-2). 57–64. 11 indexed citations

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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