Pranav Dass
- Modeling and Simulation top 2%
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Economics and Econometrics top 10%
- Computer Networks and Communications
- Co-authors
- Ramesh Chandra PooniaSandeep KumarLinesh RajaVijander SinghVaibhav BhatnagarPankaj AgarwalKendall E. NygardChowdhury
- Topics
- COVID-19 diagnosis using AI (3 papers)COVID-19 epidemiological studies (2 papers)DNA and Biological Computing (2 papers)
- Journals
- Multimedia Tools and ApplicationsArchives of Computational Methods in EngineeringJournal of Discrete Mathematical Sciences and Cryptography
- Partner nations
- IndiaUnited StatesNorway
In The Last Decade
Pranav Dass
15 papers receiving 269 citations
Peers
Comparison fields: 5 of 71
- Modeling and Simulation 107
- Radiology, Nuclear Medicine and Imaging 73
- Artificial Intelligence 72
- Economics and Econometrics 70
- Computer Networks and Communications 31
Countries citing papers authored by Pranav Dass
This map shows the geographic impact of Pranav Dass'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 Pranav Dass with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pranav Dass more than expected).
Fields of papers citing papers by Pranav Dass
This network shows the impact of papers produced by Pranav Dass. 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 Pranav Dass. The network helps show where Pranav Dass may publish in the future.
Co-authorship network of co-authors of Pranav Dass
This figure shows the co-authorship network connecting the top 25 collaborators of Pranav Dass. A scholar is included among the top collaborators of Pranav Dass 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 Pranav Dass. Pranav Dass 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 | 9 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 137 | |
| 9 | 88 | |
| 10 | 0 | |
| 11 | 6 | |
| 12 | 3 | |
| 13 | 1 | |
| 14 | Gender Differences in Perceptions of Genetically Modified Foods | 6 |
| 15 | 2 | |
| 16 | 8 | |
| 17 | Energy Demand Prediction Using Neural Networks | 9 |
| 18 | 5 | |
| 19 | 1 |
About Pranav Dass
Pranav Dass is a scholar working on Modeling and Simulation, Communication and Computer Networks and Communications, having authored 19 papers that have together received 282 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), COVID-19 epidemiological studies (2 papers) and DNA and Biological Computing (2 papers). The work is most often cited by research in Modeling and Simulation (107 citations), Health Informatics (5 citations) and Radiology, Nuclear Medicine and Imaging (73 citations). Pranav Dass has collaborated with scholars based in India, United States and Norway. Frequent co-authors include Ramesh Chandra Poonia, Sandeep Kumar, Linesh Raja, Vijander Singh, Vaibhav Bhatnagar, Pankaj Agarwal, Kendall E. Nygard, Chowdhury, Harish Sharma and Jagdish Chand Bansal. Their work appears in journals such as Multimedia Tools and Applications, Archives of Computational Methods in Engineering and Journal of Discrete Mathematical Sciences and Cryptography.
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.