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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- Spectroscopy and Chemometric Analyses 12
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- Meat and Animal Product Quality 10
- Co-authors
- Manuel Fernández-Delgado (46 shared papers)Senén Barro (12 shared papers)Manisha Sanjay Sirsat (6 shared papers)Sadi A. Alawadi (1 shared paper)Manuel Febrero–Bande (1 shared paper)Teresa Antequera (7 shared papers)Arno Formella (9 shared papers)Rehanullah Khan (1 shared paper)
In The Last Decade
Eva Cernadas
62 papers receiving 2.9k citations
Eva Cernadas's Hit Papers
Peers
Comparison fields: 5 of 198
- Artificial Intelligence 892
- Analytical Chemistry 223
- Computer Vision and Pattern Recognition 385
- Environmental Engineering 182
- Media Technology 110
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 67 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 | 1810 |
| 2 | 2018 | 274 | |
| 3 | 2017 | 82 | |
| 4 | 2016 | 75 | |
| 5 | 2018 | 72 | |
| 6 | 2006 | 63 | |
| 7 | 2013 | 54 | |
| 8 | 2004 | 53 | |
| 9 | 2013 | 51 | |
| 10 | 2021 | 40 | |
| 11 | 2002 | 28 | |
| 12 | 2004 | 24 | |
| 13 | 2003 | 21 | |
| 14 | 2020 | 20 | |
| 15 | 2019 | 19 | |
| 16 | 2022 | 17 | |
| 17 | 2004 | 16 | |
| 18 | 2011 | 16 | |
| 19 | 2020 | 15 | |
| 20 | 2004 | 15 |
About Eva Cernadas
Eva Cernadas is a scholar working on Analytical Chemistry, Animal Science and Zoology, Computer Vision and Pattern Recognition, Artificial Intelligence and Water Science and Technology, having authored 67 papers that have together received 2.9k indexed citations. Recurring topics across this work include Spectroscopy and Chemometric Analyses (12 papers), Identification and Quantification in Food (11 papers), Meat and Animal Product Quality (10 papers), Water Quality Monitoring Technologies (8 papers), Smart Agriculture and AI (7 papers), Face and Expression Recognition (6 papers), Neural Networks and Applications (5 papers) and Digital Imaging for Blood Diseases (4 papers). The work is most often cited by research in Artificial Intelligence (892 citations), Analytical Chemistry (223 citations), Computer Vision and Pattern Recognition (385 citations), Environmental Engineering (182 citations) and Media Technology (110 citations). Eva Cernadas has collaborated with scholars based in Spain, Portugal and Jordan. Frequent co-authors include Manuel Fernández-Delgado, Senén Barro, Manisha Sanjay Sirsat, Sadi A. Alawadi, Manuel Febrero–Bande, Teresa Antequera, Arno Formella, Rehanullah Khan, Pablo Garcia Rodriguez and Jorge Ribeiro. Their work appears in journals such as Computers and Electronics in Agriculture, Electronics, Neural Processing Letters, Neural Networks and Neural Computing and Applications.
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.