Danielle Dean
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
- Advanced Image Processing Techniques
Papers in
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- Artificial Intelligence Applications 7
- Neural Networks and Applications 3
- Topic Modeling 1
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- Advanced Neural Network Applications 3
- Generative Adversarial Networks and Image Synthesis 1
- Journals
- Apress eBooks (10 papers)CERN Document Server (European Organization for Nuclear Research) (1 paper)
In The Last Decade
Danielle Dean
11 papers receiving 328 citations
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 131
- Health Informatics 6
- Artificial Intelligence 83
- Computer Graphics and Computer-Aided Design 7
- Information Systems 33
Countries citing papers authored by Danielle Dean
This map shows the geographic impact of Danielle Dean'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 Danielle Dean with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle Dean more than expected).
Fields of papers citing papers by Danielle Dean
This network shows the impact of papers produced by Danielle Dean. 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 Danielle Dean. The network helps show where Danielle Dean may publish in the future.
Co-authors
The 2 scholars most cited alongside Danielle Dean, 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 | 197 | |
| 2 | 2018 | 65 | |
| 3 | 2018 | 22 | |
| 4 | 2018 | 16 | |
| 5 | 2018 | 13 | |
| 6 | Deep Learning with Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform | 2018 | 9 |
| 7 | 2018 | 8 | |
| 8 | 2018 | 7 | |
| 9 | 2018 | 7 | |
| 10 | 2018 | 1 | |
| 11 | 2018 | 1 |
About Danielle Dean
Danielle Dean is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Environmental Engineering and Nuclear Energy and Engineering, having authored 11 papers that have together received 346 indexed citations. Recurring topics across this work include Artificial Intelligence Applications (7 papers), Advanced Neural Network Applications (3 papers), Neural Networks and Applications (3 papers), Cloud Computing and Resource Management (2 papers), Topic Modeling (1 paper), Hydrological Forecasting Using AI (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (131 citations), Health Informatics (6 citations), Artificial Intelligence (83 citations), Computer Graphics and Computer-Aided Design (7 citations) and Information Systems (33 citations). Frequent co-authors include Wee Hyong Tok and Mathew Salvaris. Their work appears in journals such as Apress eBooks and CERN Document Server (European Organization for Nuclear Research).
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.