Prinkle Sharma
Impact in
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
- Signal Processing top 10%
- Advanced Malware Detection Techniques
Papers in ⓘ
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- Vehicular Ad Hoc Networks (VANETs) 10
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- Anomaly Detection Techniques and Applications 4
- Privacy-Preserving Technologies in Data 3
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Hong Liu (4 shared papers)Jonathan Petit (2 shared papers)David Austin (1 shared paper)J. H. Gillanders (1 shared paper)Hong Liu (1 shared paper)Jyoti Grover (2 shared papers)Meenakshi Tripathi (1 shared paper)
- Journals
- IEEE Access (2 papers)IEEE Open Journal of Vehicular Technology (1 paper)Scientific Reports (1 paper)IEEE Internet of Things Journal (1 paper)IEEE Conference Proceedings (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Prinkle Sharma
10 papers receiving 349 citations
Peers
Comparison fields: 5 of 40
- Automotive Engineering 108
- Signal Processing 65
- Computer Networks and Communications 135
- Artificial Intelligence 144
- Electrical and Electronic Engineering 257
Countries citing papers authored by Prinkle Sharma
This map shows the geographic impact of Prinkle Sharma'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 Prinkle Sharma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prinkle Sharma more than expected).
Fields of papers citing papers by Prinkle Sharma
This network shows the impact of papers produced by Prinkle Sharma. 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 Prinkle Sharma. The network helps show where Prinkle Sharma may publish in the future.
Co-authors
The 7 scholars most cited alongside Prinkle Sharma, 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 | 2020 | 119 | |
| 2 | 2018 | 115 | |
| 3 | 2017 | 46 | |
| 4 | 2019 | 41 | |
| 5 | 2022 | 25 | |
| 6 | 2018 | 9 | |
| 7 | 2020 | 4 | |
| 8 | 2025 | 2 | |
| 9 | Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET | 2018 | 1 |
| 10 | 2024 | 1 | |
| 11 | 2024 | 0 |
About Prinkle Sharma
Prinkle Sharma is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Computer Networks and Communications, Automotive Engineering and Control and Systems Engineering, having authored 11 papers that have together received 363 indexed citations. Recurring topics across this work include Vehicular Ad Hoc Networks (VANETs) (10 papers), Autonomous Vehicle Technology and Safety (5 papers), Network Security and Intrusion Detection (5 papers), Anomaly Detection Techniques and Applications (4 papers), Privacy-Preserving Technologies in Data (3 papers), Advanced Malware Detection Techniques (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Smart Grid Security and Resilience (1 paper). The work is most often cited by research in Automotive Engineering (108 citations), Signal Processing (65 citations), Computer Networks and Communications (135 citations), Artificial Intelligence (144 citations) and Electrical and Electronic Engineering (257 citations). Prinkle Sharma has collaborated with scholars based in United States and India. Frequent co-authors include Hong Liu, Jonathan Petit, David Austin, J. H. Gillanders, Hong Liu, Jyoti Grover and Meenakshi Tripathi. Their work appears in journals such as IEEE Access, IEEE Open Journal of Vehicular Technology, Scientific Reports, IEEE Internet of Things Journal and IEEE Conference Proceedings.
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.