Laura Morán‐Fernández
- Artificial Intelligence top 10%
- Machine Learning and Data Classification 12
- Neural Networks and Applications 4
- Imbalanced Data Classification Techniques 4
- Text and Document Classification Technologies 3
-
- Face and Expression Recognition 8
-
- Gene expression and cancer classification 7
- Bioinformatics and Genomic Networks 3
- Machine Learning in Bioinformatics 2
- Co-authors
- Verónica Bolón‐CanedoAmparo Alonso‐BetanzosBrais CancelaBeatriz RemeseiroNoelia Sánchez‐MaroñoKonstantinos SechidisGavin BrownBorja Seijo-Pardo
- Partner nations
- SpainSwitzerlandUnited Kingdom
In The Last Decade
Laura Morán‐Fernández
25 papers receiving 293 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Health Informatics 9
- Artificial Intelligence 132
- Computer Vision and Pattern Recognition 58
- Health Information Management 9
- Information Systems 33
Countries citing papers authored by Laura Morán‐Fernández
This map shows the geographic impact of Laura Morán‐Fernández'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 Laura Morán‐Fernández with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laura Morán‐Fernández more than expected).
Fields of papers citing papers by Laura Morán‐Fernández
This network shows the impact of papers produced by Laura Morán‐Fernández. 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 Laura Morán‐Fernández. The network helps show where Laura Morán‐Fernández may publish in the future.
Co-authorship network
The 13 scholars most cited alongside Laura Morán‐Fernández, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 5 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 9 | |
| 4 | 2023 | 5 | |
| 5 | 2023 | 3 | |
| 6 | 2023 | 3 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 15 | |
| 9 | 2022 | 2 | |
| 10 | 2022 | 2 | |
| 11 | 2022 | 1 | |
| 12 | 2022 | 6 | |
| 13 | 2019 | 15 | |
| 14 | 2019 | 3 | |
| 15 | Análisis de algoritmos de cuantificación basados en ajuste de distribuciones | 2018 | 0 |
| 16 | 2018 | 2 | |
| 17 | 2017 | 3 | |
| 18 | Data complexity measures for analyzing the effect of SMOTE over microarrays. | 2016 | 5 |
| 19 | 2016 | 30 | |
| 20 | 2015 | 9 |
About Laura Morán‐Fernández
Laura Morán‐Fernández is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Transportation, having authored 26 papers that have together received 308 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (12 papers), Face and Expression Recognition (8 papers), Gene expression and cancer classification (7 papers), Neural Networks and Applications (4 papers), Imbalanced Data Classification Techniques (4 papers), Bioinformatics and Genomic Networks (3 papers), Text and Document Classification Technologies (3 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Health Informatics (9 citations), Artificial Intelligence (132 citations) and Computer Vision and Pattern Recognition (58 citations). Laura Morán‐Fernández has collaborated with scholars based in Spain, Switzerland and United Kingdom. Frequent co-authors include Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos, Brais Cancela, Beatriz Remeseiro, Noelia Sánchez‐Maroño, Konstantinos Sechidis, Gavin Brown, Borja Seijo-Pardo, Jaime Alonso and Juan Touriño.
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.