H. D. Navone
Impact in
- Instrumentation top 10%
- Astronomy and Astrophysical Research
- Analytical Chemistry top 5%
- Spectroscopy and Chemometric Analyses
Papers in
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- Neural Networks and Applications 7
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- Stellar, planetary, and galactic studies 7
- Co-authors
- H. A. Ceccatto (10 shared papers)P.F. Verdes (9 shared papers)Pablo M. Granitto (9 shared papers)G. I. Perren (3 shared papers)Guillermo H. Kaufmann (3 shared papers)J. C. Muzzio (4 shared papers)A. Moitinho (1 shared paper)R. Vázquez (1 shared paper)
In The Last Decade
H. D. Navone
25 papers receiving 415 citations
Peers
Comparison fields: 5 of 83
- Instrumentation 51
- Analytical Chemistry 77
- Astronomy and Astrophysics 100
- Environmental Engineering 74
- Statistical and Nonlinear Physics 51
Countries citing papers authored by H. D. Navone
This map shows the geographic impact of H. D. Navone'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 H. D. Navone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites H. D. Navone more than expected).
Fields of papers citing papers by H. D. Navone
This network shows the impact of papers produced by H. D. Navone. 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 H. D. Navone. The network helps show where H. D. Navone may publish in the future.
Co-authors
The 16 scholars most cited alongside H. D. Navone, 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 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2002 | 111 | |
| 2 | 1994 | 97 | |
| 3 | 2021 | 42 | |
| 4 | 2001 | 36 | |
| 5 | 2023 | 22 | |
| 6 | 2003 | 18 | |
| 7 | 1987 | 16 | |
| 8 | 2001 | 16 | |
| 9 | Automatic identification of weed seeds by color image processing | 2000 | 12 |
| 10 | 2000 | 12 | |
| 11 | 2002 | 12 | |
| 12 | 2022 | 11 | |
| 13 | 1995 | 11 | |
| 14 | 1995 | 11 | |
| 15 | Frost prediction with machine learning techniques | 2000 | 10 |
| 16 | 2007 | 8 | |
| 17 | 2009 | 8 | |
| 18 | 2001 | 7 | |
| 19 | 2014 | 7 | |
| 20 | 1989 | 6 |
About H. D. Navone
H. D. Navone is a scholar working on Artificial Intelligence, Astronomy and Astrophysics, Statistical and Nonlinear Physics, Signal Processing and Computer Vision and Pattern Recognition, having authored 29 papers that have together received 481 indexed citations. Recurring topics across this work include Stellar, planetary, and galactic studies (7 papers), Neural Networks and Applications (7 papers), Time Series Analysis and Forecasting (6 papers), Complex Systems and Time Series Analysis (5 papers), Optical measurement and interference techniques (3 papers), Astronomy and Astrophysical Research (3 papers), Advanced Thermodynamics and Statistical Mechanics (2 papers) and Molecular spectroscopy and chirality (2 papers). The work is most often cited by research in Instrumentation (51 citations), Analytical Chemistry (77 citations), Astronomy and Astrophysics (100 citations), Environmental Engineering (74 citations) and Statistical and Nonlinear Physics (51 citations). H. D. Navone has collaborated with scholars based in Argentina, Portugal and Australia. Frequent co-authors include H. A. Ceccatto, P.F. Verdes, Pablo M. Granitto, G. I. Perren, Guillermo H. Kaufmann, J. C. Muzzio, A. Moitinho, R. Vázquez, D. D. Carpintero and Rubén D. Piacentini. Their work appears in journals such as Monthly Notices of the Royal Astronomical Society, Celestial Mechanics and Dynamical Astronomy, Solar Physics, Climate Dynamics and Computers and Electronics in Agriculture.
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.