Preksha Pareek
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Biomedical Engineering
- Human-Computer Interaction top 10%
- Computer Networks and Communications
- Co-authors
- Ankit ThakkarKetan KotechaShruti PatilK. V. PremaAjith AbrahamLubna A. GabrallaTanupriya ChoudhuryAshutosh Sharma
- Topics
- Human Pose and Action Recognition (3 papers)Radiomics and Machine Learning in Medical Imaging (1 paper)Artificial Intelligence and Decision Support Systems (1 paper)
- Journals
- SHILAP Revista de lepidopterologíaInternational Journal of Environmental Research and Public HealthArtificial Intelligence Review
In The Last Decade
Preksha Pareek
9 papers receiving 299 citations
Hit Papers
Peers
Comparison fields: 5 of 67
- Computer Vision and Pattern Recognition 213
- Artificial Intelligence 159
- Biomedical Engineering 68
- Human-Computer Interaction 29
- Computer Networks and Communications 21
Countries citing papers authored by Preksha Pareek
This map shows the geographic impact of Preksha Pareek'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 Preksha Pareek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Preksha Pareek more than expected).
Fields of papers citing papers by Preksha Pareek
This network shows the impact of papers produced by Preksha Pareek. 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 Preksha Pareek. The network helps show where Preksha Pareek may publish in the future.
Co-authorship network of co-authors of Preksha Pareek
This figure shows the co-authorship network connecting the top 25 collaborators of Preksha Pareek. A scholar is included among the top collaborators of Preksha Pareek 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 Preksha Pareek. Preksha Pareek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 17 | |
| 7 | 6 | |
| 8 | 2 | |
| 9 | 12 | |
| 10 | A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applicationsbreakdown → | 262 |
| 11 | Classifying the population as BPL or non-BPL using Multilayer Neural Network | 3 |
About Preksha Pareek
Preksha Pareek is a scholar working on Artificial Intelligence, Health Information Management and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 313 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (3 papers), Radiomics and Machine Learning in Medical Imaging (1 paper) and Artificial Intelligence and Decision Support Systems (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (213 citations), Human-Computer Interaction (29 citations) and Health Informatics (7 citations). Preksha Pareek has collaborated with scholars based in India, Qatar and China. Frequent co-authors include Ankit Thakkar, Ketan Kotecha, Shruti Patil, K. V. Prema, Ajith Abraham, Lubna A. Gabralla, Tanupriya Choudhury, Ashutosh Sharma, Shreya Mahajan and Shivali Amit Wagle. Their work appears in journals such as SHILAP Revista de lepidopterología, International Journal of Environmental Research and Public Health and Artificial Intelligence Review.
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.