Yerlan Idelbayev
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Molecular Biology
- Pharmacology
- Computational Mechanics
- Co-authors
- Miguel Á. Carreira-PerpiñánYiwen TaoChen ZhangErik GerwickBrendan M. DugganJie MinWilliam H. GerwickGarrison W. Cottrell
- Topics
- Advanced Neural Network Applications (4 papers)Sparse and Compressive Sensing Techniques (3 papers)Neural Networks and Applications (2 papers)
- Journals
- Scientific ReportsICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Partner nations
- United StatesMexicoGermany
In The Last Decade
Yerlan Idelbayev
9 papers receiving 285 citations
Peers
Comparison fields: 5 of 74
- Computer Vision and Pattern Recognition 142
- Artificial Intelligence 124
- Molecular Biology 63
- Pharmacology 32
- Computational Mechanics 31
Countries citing papers authored by Yerlan Idelbayev
This map shows the geographic impact of Yerlan Idelbayev'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 Yerlan Idelbayev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yerlan Idelbayev more than expected).
Fields of papers citing papers by Yerlan Idelbayev
This network shows the impact of papers produced by Yerlan Idelbayev. 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 Yerlan Idelbayev. The network helps show where Yerlan Idelbayev may publish in the future.
Co-authorship network of co-authors of Yerlan Idelbayev
This figure shows the co-authorship network connecting the top 25 collaborators of Yerlan Idelbayev. A scholar is included among the top collaborators of Yerlan Idelbayev 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 Yerlan Idelbayev. Yerlan Idelbayev 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 | 3 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 66 | |
| 9 | 119 | |
| 10 | 87 |
About Yerlan Idelbayev
Yerlan Idelbayev is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 10 papers that have together received 290 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Sparse and Compressive Sensing Techniques (3 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Computational Mathematics (19 citations), Computer Vision and Pattern Recognition (142 citations) and Artificial Intelligence (124 citations). Yerlan Idelbayev has collaborated with scholars based in United States, Mexico and Germany. Frequent co-authors include Miguel Á. Carreira-Perpiñán, Yiwen Tao, Chen Zhang, Erik Gerwick, Brendan M. Duggan, Jie Min, William H. Gerwick, Garrison W. Cottrell, Eugene C. Lin and Nicholas Roberts. Their work appears in journals such as Scientific Reports and ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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.