R. S. Singh
- Statistics and Probability top 2%
- Artificial Intelligence top 10%
- Infectious Diseases
- Epidemiology
- Surgery
- Co-authors
- Marianna PenskyRamesh ChandraVittorio ColizziPrakash S. BisenMaurizio FrazianoSanjay K. GargG. B. K. S. PrasadRitu Tiwari
- Topics
- Statistical Methods and Inference (11 papers)Statistical Distribution Estimation and Applications (5 papers)Statistical Methods and Bayesian Inference (4 papers)
In The Last Decade
R. S. Singh
17 papers receiving 287 citations
Peers
Comparison fields: 5 of 69
- Statistics and Probability 205
- Artificial Intelligence 97
- Infectious Diseases 50
- Epidemiology 38
- Surgery 37
Countries citing papers authored by R. S. Singh
This map shows the geographic impact of R. S. Singh'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 R. S. Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. S. Singh more than expected).
Fields of papers citing papers by R. S. Singh
This network shows the impact of papers produced by R. S. Singh. 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 R. S. Singh. The network helps show where R. S. Singh may publish in the future.
Co-authorship network of co-authors of R. S. Singh
This figure shows the co-authorship network connecting the top 25 collaborators of R. S. Singh. A scholar is included among the top collaborators of R. S. Singh 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 R. S. Singh. R. S. Singh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Use of Nature-inspired Computing Techniques in Real World Applications (2010-2019) A Brief Review | 0 |
| 2 | 15 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | Status and Challenges of Public Libraries' Automation in Punjab | 4 |
| 6 | 10 | |
| 7 | 69 | |
| 8 | 10 | |
| 9 | 17 | |
| 10 | 8 | |
| 11 | PEAKFIT - a PC based code for analysis of gamma ray spectra | 4 |
| 12 | 21 | |
| 13 | 23 | |
| 14 | 8 | |
| 15 | 56 | |
| 16 | 3 | |
| 17 | 57 | |
| 18 | 17 |
About R. S. Singh
R. S. Singh is a scholar working on Statistics and Probability, Library and Information Sciences and Software, having authored 18 papers that have together received 326 indexed citations. Recurring topics across this work include Statistical Methods and Inference (11 papers), Statistical Distribution Estimation and Applications (5 papers) and Statistical Methods and Bayesian Inference (4 papers). The work is most often cited by research in Statistics and Probability (205 citations), Statistics, Probability and Uncertainty (27 citations) and Artificial Intelligence (97 citations). R. S. Singh has collaborated with scholars based in Canada, India and China. Frequent co-authors include Marianna Pensky, Ramesh Chandra, Vittorio Colizzi, Prakash S. Bisen, Maurizio Fraziano, Sanjay K. Garg, G. B. K. S. Prasad, Ritu Tiwari, Ömer Öztürk and D. Mehta. Their work appears in journals such as Journal of the Royal Statistical Society Series B (Statistical Methodology), The Annals of Statistics and Journal of Statistical Planning and Inference.
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.