Apurva Narayan
Impact in
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- Hybrid Renewable Energy Systems
Papers in
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- Anomaly Detection Techniques and Applications 12
- Adversarial Robustness in Machine Learning 7
- Co-authors
- K. Ponnambalam (2 shared papers)Keith W. Hipel (2 shared papers)Sebastian Fischmeister (8 shared papers)Abbas S. Milani (4 shared papers)Rudolf Seethaler (2 shared papers)Bryn Crawford (1 shared paper)Heinz Voggenreiter (1 shared paper)Ushnik Mukherjee (1 shared paper)
- Journals
- Scientific Reports (3 papers)IEEE Access (2 papers)PeerJ Computer Science (2 papers)Computers in Industry (1 paper)Energies (1 paper)
- Partner nations
- CanadaIndiaUnited States
In The Last Decade
Apurva Narayan
47 papers receiving 467 citations
Peers
Comparison fields: 5 of 86
- Energy Engineering and Power Technology 37
- Software 19
- Control and Systems Engineering 86
- Biophysics 21
- Industrial and Manufacturing Engineering 35
Countries citing papers authored by Apurva Narayan
This map shows the geographic impact of Apurva Narayan'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 Apurva Narayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Apurva Narayan more than expected).
Fields of papers citing papers by Apurva Narayan
This network shows the impact of papers produced by Apurva Narayan. 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 Apurva Narayan. The network helps show where Apurva Narayan may publish in the future.
Co-authors
The 25 scholars most cited alongside Apurva Narayan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 112 | |
| 2 | 2021 | 51 | |
| 3 | 2017 | 42 | |
| 4 | 2020 | 40 | |
| 5 | 2017 | 28 | |
| 6 | 2018 | 27 | |
| 7 | 2023 | 23 | |
| 8 | 2023 | 16 | |
| 9 | 2018 | 16 | |
| 10 | 2024 | 14 | |
| 11 | 2009 | 10 | |
| 12 | 2016 | 9 | |
| 13 | 2024 | 7 | |
| 14 | 2021 | 7 | |
| 15 | 2017 | 6 | |
| 16 | 2020 | 6 | |
| 17 | 2022 | 6 | |
| 18 | 2020 | 6 | |
| 19 | 2019 | 5 | |
| 20 | 2019 | 4 |
About Apurva Narayan
Apurva Narayan is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Software, having authored 59 papers that have together received 482 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (12 papers), Adversarial Robustness in Machine Learning (7 papers), Software Reliability and Analysis Research (7 papers), Software Testing and Debugging Techniques (6 papers), Software Engineering Research (4 papers), Formal Methods in Verification (4 papers), Network Security and Intrusion Detection (4 papers) and Software System Performance and Reliability (4 papers). The work is most often cited by research in Energy Engineering and Power Technology (37 citations), Software (19 citations), Control and Systems Engineering (86 citations), Biophysics (21 citations) and Industrial and Manufacturing Engineering (35 citations). Apurva Narayan has collaborated with scholars based in Canada, India and United States. Frequent co-authors include K. Ponnambalam, Keith W. Hipel, Sebastian Fischmeister, Abbas S. Milani, Rudolf Seethaler, Bryn Crawford, Heinz Voggenreiter, Ushnik Mukherjee, Ali Elkamel and Azadeh Maroufmashat. Their work appears in journals such as Scientific Reports, IEEE Access, PeerJ Computer Science, Computers in Industry and Energies.
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.