Sandeep Saini
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 10%
- Plant Science
- Co-authors
- Vineet SahulaAbhishek SharmaM.B. SrinivasSreehari VeeramachaneniShubhajit Roy ChowdhuryAnkita JhaB. N. JohriAnil Prakash
- Topics
- Low-power high-performance VLSI design (10 papers)Human Pose and Action Recognition (8 papers)Hand Gesture Recognition Systems (8 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessSAE technical papers on CD-ROM/SAE technical paper series
In The Last Decade
Sandeep Saini
54 papers receiving 404 citations
Peers
Comparison fields: 5 of 91
- Artificial Intelligence 144
- Electrical and Electronic Engineering 105
- Biomedical Engineering 97
- Computer Vision and Pattern Recognition 89
- Plant Science 44
Countries citing papers authored by Sandeep Saini
This map shows the geographic impact of Sandeep Saini'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 Sandeep Saini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sandeep Saini more than expected).
Fields of papers citing papers by Sandeep Saini
This network shows the impact of papers produced by Sandeep Saini. 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 Sandeep Saini. The network helps show where Sandeep Saini may publish in the future.
Co-authorship network of co-authors of Sandeep Saini
This figure shows the co-authorship network connecting the top 25 collaborators of Sandeep Saini. A scholar is included among the top collaborators of Sandeep Saini 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 Sandeep Saini. Sandeep Saini 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 | 3 | |
| 3 | 2 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | 0 | |
| 8 | 4 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 7 | |
| 13 | 9 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 4 | |
| 17 | 3 | |
| 18 | 11 | |
| 19 | 36 | |
| 20 | 4 |
About Sandeep Saini
Sandeep Saini is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 58 papers that have together received 446 indexed citations. Recurring topics across this work include Low-power high-performance VLSI design (10 papers), Human Pose and Action Recognition (8 papers) and Hand Gesture Recognition Systems (8 papers). The work is most often cited by research in Human-Computer Interaction (26 citations), Artificial Intelligence (144 citations) and Computer Vision and Pattern Recognition (89 citations). Sandeep Saini has collaborated with scholars based in India, Taiwan and Germany. Frequent co-authors include Vineet Sahula, Abhishek Sharma, M.B. Srinivas, Sreehari Veeramachaneni, Shubhajit Roy Chowdhury, Ankita Jha, B. N. Johri, Anil Prakash, Ankit Kumar and Victor Wray. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and SAE technical papers on CD-ROM/SAE technical paper series.
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.