Earl E. Gose
Impact in
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- Face and Expression Recognition
- Image Retrieval and Classification Techniques
- Digital Imaging for Blood Diseases
- Artificial Intelligence top 10%
- AI in cancer detection
- Neural Networks and Applications
Papers in
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- Neural Networks and Applications 3
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- Digital Imaging for Blood Diseases 2
- Co-authors
- Anthony N. Mucciardi (5 shared papers)L V Ackerman (2 shared papers)Eric Baer (1 shared paper)A. Harry Klopf (1 shared paper)W. Earl Barnes (4 shared papers)Andreas Acrivos (1 shared paper)Ervin Kaplan (3 shared papers)Federico C. Viñas (1 shared paper)
- Journals
- Neurological Research (2 papers)IEEE Transactions on Computers (2 papers)Cancer (2 papers)AIChE Journal (1 paper)The Journal of Chemical Physics (1 paper)
- Partner nations
- United States
In The Last Decade
Earl E. Gose
23 papers receiving 396 citations
Peers
Comparison fields: 5 of 94
- Computer Vision and Pattern Recognition 127
- Artificial Intelligence 179
- Biophysics 26
- Health Informatics 4
- Radiology, Nuclear Medicine and Imaging 64
Countries citing papers authored by Earl E. Gose
This map shows the geographic impact of Earl E. Gose'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 Earl E. Gose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Earl E. Gose more than expected).
Fields of papers citing papers by Earl E. Gose
This network shows the impact of papers produced by Earl E. Gose. 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 Earl E. Gose. The network helps show where Earl E. Gose may publish in the future.
Co-authors
The 17 scholars most cited alongside Earl E. Gose, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1971 | 146 | |
| 2 | 1972 | 52 | |
| 3 | 1972 | 48 | |
| 4 | 1967 | 45 | |
| 5 | 1998 | 35 | |
| 6 | 1973 | 26 | |
| 7 | 1972 | 22 | |
| 8 | 2001 | 19 | |
| 9 | 1969 | 12 | |
| 10 | 1985 | 7 | |
| 11 | 1999 | 6 | |
| 12 | 1957 | 5 | |
| 13 | 1965 | 5 | |
| 14 | 1990 | 4 | |
| 15 | 1966 | 4 | |
| 16 | 1982 | 4 | |
| 17 | 1972 | 3 | |
| 18 | 1965 | 3 | |
| 19 | 1987 | 2 | |
| 20 | 1963 | 1 |
About Earl E. Gose
Earl E. Gose is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine, having authored 23 papers that have together received 452 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Cardiac Imaging and Diagnostics (2 papers), Digital Imaging for Blood Diseases (2 papers), Gas Dynamics and Kinetic Theory (2 papers), Medical Imaging Techniques and Applications (2 papers), Cell Image Analysis Techniques (2 papers), Combustion and flame dynamics (2 papers) and Spine and Intervertebral Disc Pathology (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (127 citations), Artificial Intelligence (179 citations), Biophysics (26 citations), Health Informatics (4 citations) and Radiology, Nuclear Medicine and Imaging (64 citations). Earl E. Gose has collaborated with scholars based in United States. Frequent co-authors include Anthony N. Mucciardi, L V Ackerman, Eric Baer, A. Harry Klopf, W. Earl Barnes, Andreas Acrivos, Ervin Kaplan, Federico C. Viñas, Eric Chern-Pin Chua and David L. Roseman. Their work appears in journals such as Neurological Research, IEEE Transactions on Computers, Cancer, AIChE Journal and The Journal of Chemical Physics.
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.