Darsh Shah
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
- Advanced Text Analysis Techniques
- Machine Learning and ELM
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- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 3
- Advanced Text Analysis Techniques 2
- Domain Adaptation and Few-Shot Learning 1
- Surgery 1
- Co-authors
- Regina Barzilay (5 shared papers)Jiang Guo (1 shared paper)Tao Leí (4 shared papers)Preslav Nakov (1 shared paper)Alessandro Moschitti (1 shared paper)Salvatore Romeo (1 shared paper)Manan Shah (1 shared paper)Michael T. Koltz (1 shared paper)
- Journals
- World Neurosurgery (1 paper)JCO Precision Oncology (1 paper)Healthcare (1 paper)SHILAP Revista de lepidopterología (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesIndiaQatar
In The Last Decade
Darsh Shah
12 papers receiving 204 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 145
- Computer Vision and Pattern Recognition 50
- Health Informatics 2
- Information Systems 21
- Water Science and Technology 13
Countries citing papers authored by Darsh Shah
This map shows the geographic impact of Darsh Shah'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 Darsh Shah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Darsh Shah more than expected).
Fields of papers citing papers by Darsh Shah
This network shows the impact of papers produced by Darsh Shah. 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 Darsh Shah. The network helps show where Darsh Shah may publish in the future.
Co-authors
The 25 scholars most cited alongside Darsh Shah, 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 | 95 | |
| 2 | 2018 | 38 | |
| 3 | 2020 | 26 | |
| 4 | 2018 | 16 | |
| 5 | 2020 | 11 | |
| 6 | 2021 | 7 | |
| 7 | Are We Safe Yet? The Limitations of Distributional Features for Fake News Detection. | 2019 | 6 |
| 8 | 2021 | 4 | |
| 9 | 2022 | 4 | |
| 10 | 2022 | 3 | |
| 11 | 2021 | 2 | |
| 12 | 2023 | 1 |
About Darsh Shah
Darsh Shah is a scholar working on Artificial Intelligence, Surgery, Molecular Biology, Genetics and Critical Care and Intensive Care Medicine, having authored 12 papers that have together received 213 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (3 papers), Advanced Text Analysis Techniques (2 papers), Spine and Intervertebral Disc Pathology (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Expert finding and Q&A systems (1 paper), Artificial Intelligence in Healthcare (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Artificial Intelligence (145 citations), Computer Vision and Pattern Recognition (50 citations), Health Informatics (2 citations), Information Systems (21 citations) and Water Science and Technology (13 citations). Darsh Shah has collaborated with scholars based in United States, India and Qatar. Frequent co-authors include Regina Barzilay, Jiang Guo, Tao Leí, Preslav Nakov, Alessandro Moschitti, Salvatore Romeo, Manan Shah, Michael T. Koltz, Lili Yu and Darshan M. Rudakiya. Their work appears in journals such as World Neurosurgery, JCO Precision Oncology, Healthcare, SHILAP Revista de lepidopterología 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.