Maryam Bukhari
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Biomedical Engineering
- Management Science and Operations Research
- Co-authors
- Sadaf YasminMuazzam MaqsoodIrfan MehmoodSeungmin RhoSaira GillaniAhmed A. Abd El‐LatifMucheol KimMehr Yahya Durrani
- Topics
- Stock Market Forecasting Methods (4 papers)AI in cancer detection (4 papers)Energy Load and Power Forecasting (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingArtificial Intelligence
- Partner nations
- PakistanSouth KoreaSaudi Arabia
In The Last Decade
Maryam Bukhari
21 papers receiving 268 citations
Peers
Comparison fields: 5 of 54
- Computer Vision and Pattern Recognition 124
- Artificial Intelligence 109
- Radiology, Nuclear Medicine and Imaging 93
- Biomedical Engineering 50
- Management Science and Operations Research 33
Countries citing papers authored by Maryam Bukhari
This map shows the geographic impact of Maryam Bukhari'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 Maryam Bukhari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maryam Bukhari more than expected).
Fields of papers citing papers by Maryam Bukhari
This network shows the impact of papers produced by Maryam Bukhari. 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 Maryam Bukhari. The network helps show where Maryam Bukhari may publish in the future.
Co-authorship network of co-authors of Maryam Bukhari
This figure shows the co-authorship network connecting the top 25 collaborators of Maryam Bukhari. A scholar is included among the top collaborators of Maryam Bukhari 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 Maryam Bukhari. Maryam Bukhari 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 | 0 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 1 | |
| 9 | 10 | |
| 10 | 6 | |
| 11 | 2 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | 4 | |
| 15 | 7 | |
| 16 | 14 | |
| 17 | 1 | |
| 18 | 52 | |
| 19 | 27 | |
| 20 | 51 |
About Maryam Bukhari
Maryam Bukhari is a scholar working on Computer Vision and Pattern Recognition, Management Science and Operations Research and Artificial Intelligence, having authored 24 papers that have together received 277 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (4 papers), AI in cancer detection (4 papers) and Energy Load and Power Forecasting (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (124 citations), Radiology, Nuclear Medicine and Imaging (93 citations) and Artificial Intelligence (109 citations). Maryam Bukhari has collaborated with scholars based in Pakistan, South Korea and Saudi Arabia. Frequent co-authors include Sadaf Yasmin, Muazzam Maqsood, Irfan Mehmood, Seungmin Rho, Saira Gillani, Ahmed A. Abd El‐Latif, Mucheol Kim, Mehr Yahya Durrani, Khalid Bashir Bajwa and Hassan Ugail. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Image and Vision Computing.
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.