Amit Agarwal
Impact in
- Artificial Intelligence top 5%
- Machine Learning and ELM
- Sentiment Analysis and Opinion Mining
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
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- Advanced Neural Network Applications
Papers in
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- Sentiment Analysis and Opinion Mining 7
- Advanced Text Analysis Techniques 2
- Machine Learning and ELM 2
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- Complex Network Analysis Techniques 7
- Opinion Dynamics and Social Influence 2
- Co-authors
- Frank Seide (1 shared paper)Durga Toshniwal (8 shared papers)Yew-Soon Ong (1 shared paper)Guang-Bin Huang (1 shared paper)M.H. Lim (1 shared paper)Rajendra Kumar Roul (2 shared papers)Ankush Mittal (1 shared paper)Bhumika Gupta (1 shared paper)
- Journals
- IEEE Geoscience and Remote Sensing Letters (1 paper)Computer Networks (1 paper)IEEE Access (1 paper)IEEE Transactions on Emerging Topics in Computational Intelligence (1 paper)Scientific Reports (1 paper)
- Partner nations
- IndiaSingaporeUnited States
In The Last Decade
Amit Agarwal
16 papers receiving 436 citations
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 291
- Computer Vision and Pattern Recognition 100
- Signal Processing 45
- Health Informatics 5
- Statistical and Nonlinear Physics 41
Countries citing papers authored by Amit Agarwal
This map shows the geographic impact of Amit Agarwal'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 Amit Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amit Agarwal more than expected).
Fields of papers citing papers by Amit Agarwal
This network shows the impact of papers produced by Amit Agarwal. 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 Amit Agarwal. The network helps show where Amit Agarwal may publish in the future.
Co-authors
The 13 scholars most cited alongside Amit Agarwal, 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 | 2016 | 255 | |
| 2 | 2006 | 70 | |
| 3 | 2017 | 31 | |
| 4 | 2019 | 20 | |
| 5 | 2018 | 19 | |
| 6 | 2019 | 13 | |
| 7 | 2015 | 10 | |
| 8 | 2020 | 9 | |
| 9 | 2021 | 7 | |
| 10 | 2020 | 7 | |
| 11 | 2017 | 6 | |
| 12 | 2021 | 5 | |
| 13 | 2018 | 4 | |
| 14 | 2021 | 4 | |
| 15 | 2021 | 2 | |
| 16 | 2020 | 2 | |
| 17 | 2024 | 0 | |
| 18 | 2024 | 0 |
About Amit Agarwal
Amit Agarwal is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Sociology and Political Science, Information Systems and Communication, having authored 18 papers that have together received 464 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (7 papers), Complex Network Analysis Techniques (7 papers), Misinformation and Its Impacts (5 papers), Spam and Phishing Detection (2 papers), Advanced Text Analysis Techniques (2 papers), Social Media and Politics (2 papers), Machine Learning and ELM (2 papers) and Opinion Dynamics and Social Influence (2 papers). The work is most often cited by research in Artificial Intelligence (291 citations), Computer Vision and Pattern Recognition (100 citations), Signal Processing (45 citations), Health Informatics (5 citations) and Statistical and Nonlinear Physics (41 citations). Amit Agarwal has collaborated with scholars based in India, Singapore and United States. Frequent co-authors include Frank Seide, Durga Toshniwal, Yew-Soon Ong, Guang-Bin Huang, M.H. Lim, Rajendra Kumar Roul, Ankush Mittal, Bhumika Gupta, Jatin Bedi and Poonam Rani. Their work appears in journals such as IEEE Geoscience and Remote Sensing Letters, Computer Networks, IEEE Access, IEEE Transactions on Emerging Topics in Computational Intelligence and Scientific Reports.
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.