Shemim Begum
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics
- Health Information Management top 10%
- Co-authors
- Ram SarkarManosij GhoshKushal Kanti GhoshUjjwal MaulikMunish KumarMinakshi DharTulika SahaSriparna Saha
- Topics
- Gene expression and cancer classification (7 papers)Machine Learning in Bioinformatics (3 papers)Face and Expression Recognition (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionHealth Information Management
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsBMC Bioinformatics
- Partner nations
- IndiaUnited Kingdom
In The Last Decade
Shemim Begum
10 papers receiving 293 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 188
- Molecular Biology 136
- Computer Vision and Pattern Recognition 96
- Computational Theory and Mathematics 24
- Health Information Management 17
Countries citing papers authored by Shemim Begum
This map shows the geographic impact of Shemim Begum'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 Shemim Begum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shemim Begum more than expected).
Fields of papers citing papers by Shemim Begum
This network shows the impact of papers produced by Shemim Begum. 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 Shemim Begum. The network helps show where Shemim Begum may publish in the future.
Co-authorship network of co-authors of Shemim Begum
This figure shows the co-authorship network connecting the top 25 collaborators of Shemim Begum. A scholar is included among the top collaborators of Shemim Begum 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 Shemim Begum. Shemim Begum 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 | 0 | |
| 3 | 5 | |
| 4 | 11 | |
| 5 | 12 | |
| 6 | 39 | |
| 7 | 93 | |
| 8 | 103 | |
| 9 | 4 | |
| 10 | 9 | |
| 11 | 29 |
About Shemim Begum
Shemim Begum is a scholar working on Health Information Management, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 11 papers that have together received 306 indexed citations. Recurring topics across this work include Gene expression and cancer classification (7 papers), Machine Learning in Bioinformatics (3 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Artificial Intelligence (188 citations), Computer Vision and Pattern Recognition (96 citations) and Health Information Management (17 citations). Shemim Begum has collaborated with scholars based in India and United Kingdom. Frequent co-authors include Ram Sarkar, Manosij Ghosh, Kushal Kanti Ghosh, Ujjwal Maulik, Munish Kumar, Ujjwal Maulik, Minakshi Dhar, Tulika Saha, Sriparna Saha and Pushpak Bhattacharyya. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and BMC Bioinformatics.
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.