Nidhi Goel
- Artificial Intelligence top 5%
- Oncology
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- M. RizwanUdai P. SinghKaushik GhoshNidhi TyagiPramod KumarDeepak GunjanSatyajit MahapatraMegha Agarwal
- Topics
- COVID-19 diagnosis using AI (12 papers)Radiomics and Machine Learning in Medical Imaging (9 papers)Solar Radiation and Photovoltaics (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaMolecular and Cellular BiologyInorganic Chemistry
- Partner nations
- IndiaUnited StatesChina
In The Last Decade
Nidhi Goel
81 papers receiving 828 citations
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 210
- Oncology 193
- Electrical and Electronic Engineering 164
- Computer Vision and Pattern Recognition 147
- Radiology, Nuclear Medicine and Imaging 108
Countries citing papers authored by Nidhi Goel
This map shows the geographic impact of Nidhi Goel'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 Nidhi Goel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nidhi Goel more than expected).
Fields of papers citing papers by Nidhi Goel
This network shows the impact of papers produced by Nidhi Goel. 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 Nidhi Goel. The network helps show where Nidhi Goel may publish in the future.
Co-authorship network of co-authors of Nidhi Goel
This figure shows the co-authorship network connecting the top 25 collaborators of Nidhi Goel. A scholar is included among the top collaborators of Nidhi Goel 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 Nidhi Goel. Nidhi Goel 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 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 3 | |
| 11 | 13 | |
| 12 | 10 | |
| 13 | 3 | |
| 14 | 3 | |
| 15 | 1 | |
| 16 | 1 | |
| 17 | 2 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | Improving Network Reliability and QOS in EOIPThrough Application Layer Signaling Protocol(RTP) | 1 |
About Nidhi Goel
Nidhi Goel is a scholar working on Health Informatics, Gastroenterology and Hardware and Architecture, having authored 89 papers that have together received 874 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (12 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Solar Radiation and Photovoltaics (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (147 citations), Oncology (193 citations) and Gastroenterology (37 citations). Nidhi Goel has collaborated with scholars based in India, United States and China. Frequent co-authors include M. Rizwan, Udai P. Singh, Kaushik Ghosh, Nidhi Tyagi, Pramod Kumar, Deepak Gunjan, Satyajit Mahapatra, Megha Agarwal, Priyanka Anand and Uma Chaudhary. Their work appears in journals such as SHILAP Revista de lepidopterología, Molecular and Cellular Biology and Inorganic Chemistry.
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.