M. A. Ganaie
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Neurology top 5%
- Electrical and Electronic Engineering
- Radiology, Nuclear Medicine and Imaging top 10%
- Co-authors
- Ponnuthurai Nagaratnam SuganthanM. TanveerA. K. MalikMinghui HuIman BeheshtiReshma RastogiYuan‐Hai ShaoChin‐Teng Lin
- Topics
- Face and Expression Recognition (27 papers)Machine Learning and ELM (19 papers)Neural Networks and Applications (9 papers)
In The Last Decade
M. A. Ganaie
38 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 185
- Artificial Intelligence 988
- Computer Vision and Pattern Recognition 610
- Neurology 268
- Electrical and Electronic Engineering 216
- Radiology, Nuclear Medicine and Imaging 212
Countries citing papers authored by M. A. Ganaie
This map shows the geographic impact of M. A. Ganaie'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 M. A. Ganaie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. A. Ganaie more than expected).
Fields of papers citing papers by M. A. Ganaie
This network shows the impact of papers produced by M. A. Ganaie. 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 M. A. Ganaie. The network helps show where M. A. Ganaie may publish in the future.
Co-authorship network of co-authors of M. A. Ganaie
This figure shows the co-authorship network connecting the top 25 collaborators of M. A. Ganaie. A scholar is included among the top collaborators of M. A. Ganaie 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 M. A. Ganaie. M. A. Ganaie 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 | 10 | |
| 4 | 4 | |
| 5 | 22 | |
| 6 | Deep learning for brain age estimation: A systematic reviewbreakdown → | 84 |
| 7 | 15 | |
| 8 | Ensemble deep learning: A reviewbreakdown → | 1196 |
| 9 | 35 | |
| 10 | 11 | |
| 11 | 51 | |
| 12 | 44 | |
| 13 | 19 | |
| 14 | 4 | |
| 15 | 119 | |
| 16 | 29 | |
| 17 | 9 | |
| 18 | 6 | |
| 19 | 14 | |
| 20 | 40 |
About M. A. Ganaie
M. A. Ganaie is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology, having authored 40 papers that have together received 2.4k indexed citations. Recurring topics across this work include Face and Expression Recognition (27 papers), Machine Learning and ELM (19 papers) and Neural Networks and Applications (9 papers). The work is most often cited by research in Neurology (268 citations), Artificial Intelligence (988 citations) and Computer Vision and Pattern Recognition (610 citations). M. A. Ganaie has collaborated with scholars based in India, Singapore and Australia. Frequent co-authors include Ponnuthurai Nagaratnam Suganthan, M. Tanveer, A. K. Malik, M. Tanveer, Minghui Hu, Iman Beheshti, Reshma Rastogi, Yuan‐Hai Shao, Chin‐Teng Lin and Imran Razzak. Their work appears in journals such as Expert Systems with Applications, Pattern Recognition and IEEE Transactions on Fuzzy Systems.
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.