Vanika Singhal
Impact in
-
- Digital Imaging for Blood Diseases
- Face and Expression Recognition
- Media Technology top 10%
- Remote-Sensing Image Classification
Papers in
-
- Image and Signal Denoising Methods 5
- Face recognition and analysis 3
- Face and Expression Recognition 2
- Digital Imaging for Blood Diseases 2
-
- Sparse and Compressive Sensing Techniques 7
- Co-authors
- Angshul Majumdar (14 shared papers)Hemant Kumar Aggarwal (1 shared paper)Snigdha Tariyal (1 shared paper)Rabab Ward (1 shared paper)Amita Chudgar (1 shared paper)Deepa Anand (1 shared paper)Sandeep Dutta (2 shared papers)
- Journals
- IEEE Access (2 papers)Prospects (1 paper)IEEE Transactions on Smart Grid (1 paper)IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (1 paper)Neural Processing Letters (1 paper)
- Partner nations
- IndiaCanadaUnited States
In The Last Decade
Vanika Singhal
18 papers receiving 293 citations
Peers
Comparison fields: 5 of 60
- Computer Vision and Pattern Recognition 146
- Media Technology 49
- Artificial Intelligence 108
- Computational Mechanics 55
- Building and Construction 30
Countries citing papers authored by Vanika Singhal
This map shows the geographic impact of Vanika Singhal'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 Vanika Singhal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vanika Singhal more than expected).
Fields of papers citing papers by Vanika Singhal
This network shows the impact of papers produced by Vanika Singhal. 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 Vanika Singhal. The network helps show where Vanika Singhal may publish in the future.
Co-authors
The 7 scholars most cited alongside Vanika Singhal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 87 | |
| 2 | 2014 | 45 | |
| 3 | 2017 | 43 | |
| 4 | 2017 | 18 | |
| 5 | 2017 | 17 | |
| 6 | 2019 | 16 | |
| 7 | 2018 | 16 | |
| 8 | 2017 | 15 | |
| 9 | 2015 | 9 | |
| 10 | 2018 | 7 | |
| 11 | 2018 | 6 | |
| 12 | 2022 | 5 | |
| 13 | 2017 | 5 | |
| 14 | 2017 | 4 | |
| 15 | 2017 | 4 | |
| 16 | 2019 | 4 | |
| 17 | 2017 | 3 | |
| 18 | 2021 | 1 | |
| 19 | 2024 | 0 |
About Vanika Singhal
Vanika Singhal is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing, Artificial Intelligence and Biomedical Engineering, having authored 19 papers that have together received 305 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (7 papers), Image and Signal Denoising Methods (5 papers), Music and Audio Processing (3 papers), Face recognition and analysis (3 papers), Cell Image Analysis Techniques (2 papers), Face and Expression Recognition (2 papers), Water Systems and Optimization (2 papers) and Digital Imaging for Blood Diseases (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (146 citations), Media Technology (49 citations), Artificial Intelligence (108 citations), Computational Mechanics (55 citations) and Building and Construction (30 citations). Vanika Singhal has collaborated with scholars based in India, Canada and United States. Frequent co-authors include Angshul Majumdar, Hemant Kumar Aggarwal, Snigdha Tariyal, Rabab Ward, Amita Chudgar, Deepa Anand and Sandeep Dutta. Their work appears in journals such as IEEE Access, Prospects, IEEE Transactions on Smart Grid, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and Neural Processing Letters.
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.