Ankesh Anand
Impact in
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- Advanced Malware Detection Techniques
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- Adversarial Robustness in Machine Learning
- Internet Traffic Analysis and Secure E-voting
- Text and Document Classification Technologies
Papers in
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- Digital Media Forensic Detection 2
- Generative Adversarial Networks and Image Synthesis 2
- Advanced Image Processing Techniques 2
- Multimodal Machine Learning Applications 1
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- Imbalanced Data Classification Techniques 1
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Noseong Park (3 shared papers)Tanmoy Chakraborty (3 shared papers)Bei-Tseng Chu (1 shared paper)David K. Park (1 shared paper)Youngmin Kim (1 shared paper)Hong‐Kyu Park (2 shared papers)Jaegul Choo (2 shared papers)Kookjin Lee (2 shared papers)
- 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 48 citations
Peers
Comparison fields: 5 of 19
- Signal Processing 18
- Artificial Intelligence 32
- Information Systems 21
- Computer Vision and Pattern Recognition 16
- 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 | MMGAN: Manifold Matching Generative Adversarial Network for Generating Images. | 2017 | 3 |
| 5 | HoME: a Household Multimodal Environment. | 2018 | 3 |
| 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 52 indexed citations. Recurring topics across this work include Digital Media Forensic Detection (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Image Processing Techniques (2 papers), Spam and Phishing Detection (1 paper), Multimodal Machine Learning Applications (1 paper), Imbalanced Data Classification Techniques (1 paper), Advanced Malware Detection Techniques (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Signal Processing (18 citations), Artificial Intelligence (32 citations), Information Systems (21 citations), Computer Vision and Pattern Recognition (16 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 Noseong Park, Tanmoy Chakraborty, Bei-Tseng Chu, David K. Park, Youngmin Kim, Hong‐Kyu Park, Jaegul Choo, Kookjin Lee, Hugo Larochelle 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.