Harshvardhan GM
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Health Information Management top 5%
- Radiology, Nuclear Medicine and Imaging
- Information Systems
- Co-authors
- Mahendra Kumar GourisariaManjusha PandeySiddharth Swarup RautaraySatish ChandraVijander SinghDebanjan KonarXin GaoMin Xu
- Topics
- Generative Adversarial Networks and Image Synthesis (4 papers)AI in cancer detection (4 papers)COVID-19 diagnosis using AI (3 papers)
- Partner nations
- IndiaUnited StatesChile
In The Last Decade
Harshvardhan GM
20 papers receiving 472 citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 162
- Computer Vision and Pattern Recognition 96
- Health Information Management 49
- Radiology, Nuclear Medicine and Imaging 46
- Information Systems 39
Countries citing papers authored by Harshvardhan GM
This map shows the geographic impact of Harshvardhan GM'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 Harshvardhan GM with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harshvardhan GM more than expected).
Fields of papers citing papers by Harshvardhan GM
This network shows the impact of papers produced by Harshvardhan GM. 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 Harshvardhan GM. The network helps show where Harshvardhan GM may publish in the future.
Co-authorship network of co-authors of Harshvardhan GM
This figure shows the co-authorship network connecting the top 25 collaborators of Harshvardhan GM. A scholar is included among the top collaborators of Harshvardhan GM 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 Harshvardhan GM. Harshvardhan GM 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 | 1 | |
| 3 | 18 | |
| 4 | 25 | |
| 5 | 2 | |
| 6 | 14 | |
| 7 | 9 | |
| 8 | 6 | |
| 9 | 1 | |
| 10 | Semantic Analysis of Sentiments through Web-Mined Twitter Corpus. | 10 |
| 11 | 11 | |
| 12 | 27 | |
| 13 | 14 | |
| 14 | 9 | |
| 15 | 2 | |
| 16 | 15 | |
| 17 | 19 | |
| 18 | 13 | |
| 19 | A comprehensive survey and analysis of generative models in machine learningbreakdown → | 265 |
| 20 | 26 |
About Harshvardhan GM
Harshvardhan GM is a scholar working on Health Information Management, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 20 papers that have together received 488 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (4 papers), AI in cancer detection (4 papers) and COVID-19 diagnosis using AI (3 papers). The work is most often cited by research in Health Information Management (49 citations), Health Informatics (12 citations) and Artificial Intelligence (162 citations). Harshvardhan GM has collaborated with scholars based in India, United States and Chile. Frequent co-authors include Mahendra Kumar Gourisaria, Manjusha Pandey, Siddharth Swarup Rautaray, Satish Chandra, Vijander Singh, Debanjan Konar, Xin Gao, Min Xu, Tianyang Wang and Pradeep Kumar Singh. Their work appears in journals such as IEEE Access, Applied Sciences 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.