G. Madhu
- Artificial Intelligence top 10%
- Information Systems top 10%
- Statistics, Probability and Uncertainty top 5%
- Computer Vision and Pattern Recognition top 10%
- Radiological and Ultrasound Technology top 10%
- Co-authors
- A. GovardhanV. Lakshmana Gomathi NayagamV. R. RenjithSandeep KautishAli Wagdy MohamedIrfan AliMohd Asif ShahB. Srinivas
- Topics
- Digital Imaging for Blood Diseases (8 papers)Privacy-Preserving Technologies in Data (4 papers)Smart Agriculture and AI (4 papers)
- Cited by
- Statistics, Probability and UncertaintyRadiological and Ultrasound TechnologyHealth Information Management
- Partner nations
- IndiaSaudi ArabiaEgypt
In The Last Decade
G. Madhu
24 papers receiving 333 citations
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 154
- Information Systems 82
- Statistics, Probability and Uncertainty 64
- Computer Vision and Pattern Recognition 62
- Radiological and Ultrasound Technology 37
Countries citing papers authored by G. Madhu
This map shows the geographic impact of G. Madhu'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 G. Madhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Madhu more than expected).
Fields of papers citing papers by G. Madhu
This network shows the impact of papers produced by G. Madhu. 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 G. Madhu. The network helps show where G. Madhu may publish in the future.
Co-authorship network of co-authors of G. Madhu
This figure shows the co-authorship network connecting the top 25 collaborators of G. Madhu. A scholar is included among the top collaborators of G. Madhu 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 G. Madhu. G. Madhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 17 | |
| 5 | 24 | |
| 6 | 13 | |
| 7 | 14 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 15 | |
| 11 | 1 | |
| 12 | 4 | |
| 13 | 0 | |
| 14 | 4 | |
| 15 | 17 | |
| 16 | 7 | |
| 17 | 5 | |
| 18 | 21 | |
| 19 | 47 | |
| 20 | 83 |
About G. Madhu
G. Madhu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Health Information Management, having authored 27 papers that have together received 361 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (8 papers), Privacy-Preserving Technologies in Data (4 papers) and Smart Agriculture and AI (4 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (64 citations), Radiological and Ultrasound Technology (37 citations) and Health Information Management (29 citations). G. Madhu has collaborated with scholars based in India, Saudi Arabia and Egypt. Frequent co-authors include A. Govardhan, V. Lakshmana Gomathi Nayagam, V. R. Renjith, Sandeep Kautish, Ali Wagdy Mohamed, Irfan Ali, Mohd Asif Shah, B. Srinivas, Manoj Kumar and Kshira Sagar Sahoo. Their work appears in journals such as Journal of Hazardous Materials, Scientific Reports and Sensors.
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.