Ramesh Chandra Poonia
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies 10
- Health Informatics top 5%
- Artificial Intelligence top 5%
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- Mobile Ad Hoc Networks 22
- Opportunistic and Delay-Tolerant Networks 17
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- Vehicular Ad Hoc Networks (VANETs) 18
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- Smart Agriculture and AI 10
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- COVID-19 diagnosis using AI 8
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- Advanced Steganography and Watermarking Techniques 6
- Chaos-based Image/Signal Encryption 6
- Co-authors
- Sandeep KumarLinesh RajaVijander SinghVaibhav BhatnagarAbdul Khader Jilani SaudagarAyman AltameemSoumya Ranjan NayakPranav Dass
- Journals
- SHILAP Revista de lepidopterología (2 papers)Scientific Reports (2 papers)IEEE Access (1 paper)
- Partner nations
- IndiaSaudi ArabiaNorway
In The Last Decade
Ramesh Chandra Poonia
113 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 146
- Modeling and Simulation 185
- Health Information Management 105
- Health Informatics 25
- Artificial Intelligence 449
- Computer Networks and Communications 312
Countries citing papers authored by Ramesh Chandra Poonia
This map shows the geographic impact of Ramesh Chandra Poonia'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 Ramesh Chandra Poonia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ramesh Chandra Poonia more than expected).
Fields of papers citing papers by Ramesh Chandra Poonia
This network shows the impact of papers produced by Ramesh Chandra Poonia. 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 Ramesh Chandra Poonia. The network helps show where Ramesh Chandra Poonia may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ramesh Chandra Poonia, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 13 | |
| 9 | 2023 | 2 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2022 | 11 | |
| 13 | 2022 | 34 | |
| 14 | 2022 | 55 | |
| 15 | 2022 | 18 | |
| 16 | 2022 | 1 | |
| 17 | 2021 | 21 | |
| 18 | 2021 | 47 | |
| 19 | 2021 | 2 | |
| 20 | Design a New Protocol and Compare with BB84 Protocol for Quantum Key Distribution. | 2017 | 1 |
About Ramesh Chandra Poonia
Ramesh Chandra Poonia is a scholar working on Modeling and Simulation, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 129 papers that have together received 1.7k indexed citations. Recurring topics across this work include Mobile Ad Hoc Networks (22 papers), Vehicular Ad Hoc Networks (VANETs) (18 papers), Opportunistic and Delay-Tolerant Networks (17 papers), Smart Agriculture and AI (10 papers), COVID-19 epidemiological studies (10 papers), COVID-19 diagnosis using AI (8 papers), Advanced Steganography and Watermarking Techniques (6 papers) and Chaos-based Image/Signal Encryption (6 papers). The work is most often cited by research in Modeling and Simulation (185 citations), Health Information Management (105 citations) and Health Informatics (25 citations). Ramesh Chandra Poonia has collaborated with scholars based in India, Saudi Arabia and Norway. Frequent co-authors include Sandeep Kumar, Linesh Raja, Vijander Singh, Vaibhav Bhatnagar, Abdul Khader Jilani Saudagar, Ayman Altameem, Soumya Ranjan Nayak, Pranav Dass, Pankaj Agarwal and Ankit Kumar. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.
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.