Ankesh Anand
Impact in
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- Advanced Malware Detection Techniques
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- Text and Document Classification Technologies
- Adversarial Robustness in Machine Learning
- Internet Traffic Analysis and Secure E-voting
Papers in
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- Advanced Image Processing Techniques 2
- Digital Media Forensic Detection 2
- Generative Adversarial Networks and Image Synthesis 2
- Multimodal Machine Learning Applications 1
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- Domain Adaptation and Few-Shot Learning 1
- Imbalanced Data Classification Techniques 1
- Co-authors
- Tanmoy Chakraborty (3 shared papers)Noseong Park (3 shared papers)Bei-Tseng Chu (1 shared paper)Hong‐Kyu Park (2 shared papers)Youngmin Kim (1 shared paper)Kookjin Lee (2 shared papers)Jaegul Choo (2 shared papers)David K. Park (1 shared paper)
- Journals
- HAL (Le Centre pour la Communication Scientifique Directe) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesIndiaSouth Korea
In The Last Decade
Ankesh Anand
5 papers receiving 47 citations
Peers
Comparison fields: 5 of 19
- Signal Processing 17
- Artificial Intelligence 32
- Information Systems 21
- Computer Vision and Pattern Recognition 15
- Computer Networks and Communications 9
Countries citing papers authored by Ankesh Anand
This map shows the geographic impact of Ankesh Anand'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 Ankesh Anand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankesh Anand more than expected).
Fields of papers citing papers by Ankesh Anand
This network shows the impact of papers produced by Ankesh Anand. 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 Ankesh Anand. The network helps show where Ankesh Anand may publish in the future.
Co-authors
The 25 scholars most cited alongside Ankesh Anand, 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 | 2018 | 32 | |
| 2 | 2018 | 7 | |
| 3 | Contrastive Self-Supervised Learning | 2020 | 7 |
| 4 | HoME: a Household Multimodal Environment. | 2018 | 3 |
| 5 | MMGAN: Manifold Matching Generative Adversarial Network for Generating Images. | 2017 | 2 |
| 6 | 2024 | 0 |
About Ankesh Anand
Ankesh Anand is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Signal Processing and Infectious Diseases, having authored 6 papers that have together received 51 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (2 papers), Digital Media Forensic Detection (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Spam and Phishing Detection (1 paper), Advanced Malware Detection Techniques (1 paper), Multimodal Machine Learning Applications (1 paper) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Signal Processing (17 citations), Artificial Intelligence (32 citations), Information Systems (21 citations), Computer Vision and Pattern Recognition (15 citations) and Computer Networks and Communications (9 citations). Ankesh Anand has collaborated with scholars based in United States, India and South Korea. Frequent co-authors include Tanmoy Chakraborty, Noseong Park, Bei-Tseng Chu, Hong‐Kyu Park, Youngmin Kim, Kookjin Lee, Jaegul Choo, David K. Park, Bernd Bohnet and Youngmin Kim. Their work appears in journals such as HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).
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.