Marco Mora
Impact in
- Analytical Chemistry top 5%
- Spectroscopy and Chemometric Analyses
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- Face and Expression Recognition
Papers in
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- Machine Learning and ELM 18
- Neural Networks and Applications 7
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- Handwritten Text Recognition Techniques 7
- Co-authors
- Claudio Fredes (10 shared papers)Ricardo J. Barrientos (16 shared papers)Ruber Hernández-García (13 shared papers)José Naranjo-Torres (7 shared papers)Marcos Carrasco-Benavides (7 shared papers)Matilde Santos (5 shared papers)Sigfredo Fuentes (5 shared papers)David Zabala‐Blanco (6 shared papers)
In The Last Decade
Marco Mora
61 papers receiving 810 citations
Marco Mora's Hit Papers
Peers
Comparison fields: 5 of 121
- Analytical Chemistry 138
- Computer Vision and Pattern Recognition 174
- Plant Science 281
- Signal Processing 73
- Artificial Intelligence 197
Countries citing papers authored by Marco Mora
This map shows the geographic impact of Marco Mora'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 Marco Mora with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Mora more than expected).
Fields of papers citing papers by Marco Mora
This network shows the impact of papers produced by Marco Mora. 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 Marco Mora. The network helps show where Marco Mora may publish in the future.
Co-authors
The 25 scholars most cited alongside Marco Mora, 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 64 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A Review of Convolutional Neural Network Applied to Fruit Image Processing Hit paper breakdown → | 2020 | 274 |
| 2 | 2016 | 47 | |
| 3 | 2015 | 37 | |
| 4 | 2017 | 34 | |
| 5 | 2013 | 34 | |
| 6 | 2022 | 30 | |
| 7 | 2020 | 22 | |
| 8 | 2012 | 21 | |
| 9 | 2018 | 21 | |
| 10 | 2020 | 18 | |
| 11 | 2005 | 18 | |
| 12 | 2023 | 17 | |
| 13 | 2019 | 16 | |
| 14 | 2019 | 16 | |
| 15 | 2023 | 15 | |
| 16 | 2014 | 15 | |
| 17 | 2016 | 14 | |
| 18 | 2017 | 14 | |
| 19 | 2023 | 13 | |
| 20 | 2017 | 13 |
About Marco Mora
Marco Mora is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Plant Science, Signal Processing and Analytical Chemistry, having authored 64 papers that have together received 853 indexed citations. Recurring topics across this work include Machine Learning and ELM (18 papers), Spectroscopy and Chemometric Analyses (9 papers), Horticultural and Viticultural Research (8 papers), Handwritten Text Recognition Techniques (7 papers), Biometric Identification and Security (7 papers), Neural Networks and Applications (7 papers), Advanced Memory and Neural Computing (6 papers) and Leaf Properties and Growth Measurement (6 papers). The work is most often cited by research in Analytical Chemistry (138 citations), Computer Vision and Pattern Recognition (174 citations), Plant Science (281 citations), Signal Processing (73 citations) and Artificial Intelligence (197 citations). Marco Mora has collaborated with scholars based in Chile, Argentina and Spain. Frequent co-authors include Claudio Fredes, Ricardo J. Barrientos, Ruber Hernández-García, José Naranjo-Torres, Marcos Carrasco-Benavides, Matilde Santos, Sigfredo Fuentes, David Zabala‐Blanco, Boris Lucero and Fernando Córdova‐Lepe. Their work appears in journals such as Applied Sciences, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, Computers and Electronics in Agriculture and Symmetry.
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.