Eva Cernadas
Impact in
- Artificial Intelligence top 2%
- Machine Learning and Data Classification
- Neural Networks and Applications
- Machine Learning and ELM
- Imbalanced Data Classification Techniques
- Analytical Chemistry top 2%
- Spectroscopy and Chemometric Analyses
Papers in
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- Neural Networks and Applications 5
- Machine Learning and ELM 4
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- Identification and Quantification in Food 9
- Co-authors
- Manuel Fernández-Delgado (43 shared papers)Senén Barro (12 shared papers)Manisha Sirsat (6 shared papers)Sadi Alawadi (1 shared paper)Manuel Febrero–Bande (1 shared paper)Teresa Antequera (5 shared papers)Rehanullah Khan (1 shared paper)Arno Formella (6 shared papers)
In The Last Decade
Eva Cernadas
53 papers receiving 2.6k citations
Eva Cernadas's Hit Papers
Peers
Comparison fields: 5 of 198
- Artificial Intelligence 827
- Analytical Chemistry 187
- Computer Vision and Pattern Recognition 339
- Environmental Engineering 179
- Ecological Modeling 50
Countries citing papers authored by Eva Cernadas
This map shows the geographic impact of Eva Cernadas'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 Eva Cernadas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eva Cernadas more than expected).
Fields of papers citing papers by Eva Cernadas
This network shows the impact of papers produced by Eva Cernadas. 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 Eva Cernadas. The network helps show where Eva Cernadas may publish in the future.
Co-authors
The 25 scholars most cited alongside Eva Cernadas, 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 59 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Do we need hundreds of classifiers to solve real world classification problems Hit paper breakdown → | 2014 | 1731 |
| 2 | 2018 | 252 | |
| 3 | 2017 | 70 | |
| 4 | 2016 | 65 | |
| 5 | 2018 | 64 | |
| 6 | 2006 | 52 | |
| 7 | 2013 | 48 | |
| 8 | 2004 | 45 | |
| 9 | 2013 | 43 | |
| 10 | 2021 | 39 | |
| 11 | 2002 | 24 | |
| 12 | 2003 | 19 | |
| 13 | 2020 | 19 | |
| 14 | 2019 | 18 | |
| 15 | 2004 | 17 | |
| 16 | 2020 | 15 | |
| 17 | 2004 | 15 | |
| 18 | 2022 | 15 | |
| 19 | 2011 | 14 | |
| 20 | 2004 | 14 |
About Eva Cernadas
Eva Cernadas is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Analytical Chemistry and Animal Science and Zoology, having authored 59 papers that have together received 2.7k indexed citations. Recurring topics across this work include Spectroscopy and Chemometric Analyses (11 papers), Identification and Quantification in Food (9 papers), Meat and Animal Product Quality (8 papers), Face and Expression Recognition (6 papers), Water Quality Monitoring Technologies (6 papers), Smart Agriculture and AI (6 papers), Neural Networks and Applications (5 papers) and Machine Learning and ELM (4 papers). The work is most often cited by research in Artificial Intelligence (827 citations), Analytical Chemistry (187 citations), Computer Vision and Pattern Recognition (339 citations), Environmental Engineering (179 citations) and Ecological Modeling (50 citations). Eva Cernadas has collaborated with scholars based in Spain, Portugal and Jordan. Frequent co-authors include Manuel Fernández-Delgado, Senén Barro, Manisha Sirsat, Sadi Alawadi, Manuel Febrero–Bande, Teresa Antequera, Rehanullah Khan, Arno Formella, Jorge Ribeiro and Pablo G. Rodríguez. Their work appears in journals such as Electronics, Computers and Electronics in Agriculture, Applied Sciences, Neural Networks and Neural Processing Letters.
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.