Apurva Narayan
Impact in
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- Hybrid Renewable Energy Systems
Papers in
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- Anomaly Detection Techniques and Applications 14
- Adversarial Robustness in Machine Learning 11
- Software 7
- Software Reliability and Analysis Research 7
- Software Testing and Debugging Techniques 6
- Co-authors
- Kumaraswamy Ponnambalam (2 shared papers)Keith William Hipel (2 shared papers)Milad Ramezankhani (5 shared papers)Sebastian Fischmeister (8 shared papers)Abbas Sadeghzadeh Milani (6 shared papers)Rudolf Seethaler (4 shared papers)Heinz F. Voggenreiter (2 shared papers)Bryn J. Crawford (1 shared paper)
- Journals
- IEEE Access (2 papers)PeerJ Computer Science (2 papers)Scientific Reports (2 papers)The Analyst (1 paper)Engineering Applications of Artificial Intelligence (1 paper)
- Partner nations
- CanadaIndiaUnited States
In The Last Decade
Apurva Narayan
56 papers receiving 581 citations
Peers
Comparison fields: 5 of 88
- Energy Engineering and Power Technology 37
- Software 21
- Biophysics 26
- Industrial and Manufacturing Engineering 45
- Artificial Intelligence 140
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 69 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 118 | |
| 2 | 2021 | 59 | |
| 3 | 2017 | 46 | |
| 4 | 2020 | 46 | |
| 5 | 2018 | 36 | |
| 6 | 2017 | 31 | |
| 7 | 2023 | 30 | |
| 8 | 2024 | 25 | |
| 9 | 2018 | 20 | |
| 10 | 2023 | 20 | |
| 11 | 2016 | 11 | |
| 12 | 2007 | 11 | |
| 13 | 2024 | 11 | |
| 14 | 2021 | 10 | |
| 15 | 2009 | 10 | |
| 16 | 2022 | 8 | |
| 17 | 2022 | 7 | |
| 18 | 2020 | 6 | |
| 19 | 2020 | 6 | |
| 20 | 2019 | 6 |
About Apurva Narayan
Apurva Narayan is a scholar working on Artificial Intelligence, Software, Computer Vision and Pattern Recognition, Automotive Engineering and Computational Theory and Mathematics, having authored 69 papers that have together received 594 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (14 papers), Adversarial Robustness in Machine Learning (11 papers), Software Reliability and Analysis Research (7 papers), Software Testing and Debugging Techniques (6 papers), Autonomous Vehicle Technology and Safety (4 papers), Formal Methods in Verification (4 papers), Software System Performance and Reliability (4 papers) and Software Engineering Research (4 papers). The work is most often cited by research in Energy Engineering and Power Technology (37 citations), Software (21 citations), Biophysics (26 citations), Industrial and Manufacturing Engineering (45 citations) and Artificial Intelligence (140 citations). Apurva Narayan has collaborated with scholars based in Canada, India and United States. Frequent co-authors include Kumaraswamy Ponnambalam, Keith William Hipel, Milad Ramezankhani, Sebastian Fischmeister, Abbas Sadeghzadeh Milani, Rudolf Seethaler, Heinz F. Voggenreiter, Bryn J. Crawford, C. Patvardhan and Siby Samuel. Their work appears in journals such as IEEE Access, PeerJ Computer Science, Scientific Reports, The Analyst and Engineering Applications of Artificial Intelligence.
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.