Ankit Jha
Impact in
- Oceanography top 5%
- Ocean Waves and Remote Sensing
- Oceanographic and Atmospheric Processes
- Environmental Engineering top 10%
- Hydrological Forecasting Using AI
Papers in
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- Domain Adaptation and Few-Shot Learning 9
- Natural Language Processing Techniques 2
- Topic Modeling 2
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- Multimodal Machine Learning Applications 7
- Advanced Neural Network Applications 5
- Advanced Image and Video Retrieval Techniques 4
- Human Pose and Action Recognition 2
- Co-authors
- M. C. Deo (1 shared paper)Biplab Banerjee (20 shared papers)Subhasis Chaudhuri (3 shared papers)Enrico Fini (1 shared paper)Elisa Ricci (1 shared paper)Shivam Pande (1 shared paper)Jocelyn Chanussot (1 shared paper)A. Boccalatte (1 shared paper)
In The Last Decade
Ankit Jha
21 papers receiving 415 citations
Peers
Comparison fields: 5 of 57
- Oceanography 188
- Environmental Engineering 152
- Earth-Surface Processes 53
- Computer Vision and Pattern Recognition 90
- Media Technology 37
Countries citing papers authored by Ankit Jha
This map shows the geographic impact of Ankit Jha'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 Ankit Jha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankit Jha more than expected).
Fields of papers citing papers by Ankit Jha
This network shows the impact of papers produced by Ankit Jha. 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 Ankit Jha. The network helps show where Ankit Jha may publish in the future.
Co-authors
The 19 scholars most cited alongside Ankit Jha, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2001 | 266 | |
| 2 | 2023 | 24 | |
| 3 | 2023 | 23 | |
| 4 | 2023 | 23 | |
| 5 | 2024 | 17 | |
| 6 | 2020 | 16 | |
| 7 | 2024 | 8 | |
| 8 | 2023 | 8 | |
| 9 | 2025 | 7 | |
| 10 | 2023 | 6 | |
| 11 | 2019 | 5 | |
| 12 | 2023 | 5 | |
| 13 | 2024 | 5 | |
| 14 | 2021 | 3 | |
| 15 | 2024 | 2 | |
| 16 | 2024 | 2 | |
| 17 | 2021 | 2 | |
| 18 | 2021 | 2 | |
| 19 | 2024 | 1 | |
| 20 | 2016 | 1 |
About Ankit Jha
Ankit Jha is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Computer Networks and Communications and Environmental Engineering, having authored 27 papers that have together received 427 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (9 papers), Multimodal Machine Learning Applications (7 papers), Advanced Neural Network Applications (5 papers), Remote-Sensing Image Classification (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Human Pose and Action Recognition (2 papers), Natural Language Processing Techniques (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Oceanography (188 citations), Environmental Engineering (152 citations), Earth-Surface Processes (53 citations), Computer Vision and Pattern Recognition (90 citations) and Media Technology (37 citations). Ankit Jha has collaborated with scholars based in India, Germany and France. Frequent co-authors include M. C. Deo, Biplab Banerjee, Subhasis Chaudhuri, Enrico Fini, Elisa Ricci, Shivam Pande, Jocelyn Chanussot, A. Boccalatte, Gemma Roig and Debabrata Pal. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, Solar Energy, Ocean Engineering, Pattern Recognition Letters and Briefings in Bioinformatics.
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.