This map shows the geographic impact of Diego Andina'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 Diego Andina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Andina more than expected).
This network shows the impact of papers produced by Diego Andina. 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 Diego Andina. The network helps show where Diego Andina may publish in the future.
Co-authorship network of co-authors of Diego Andina
This figure shows the co-authorship network connecting the top 25 collaborators of Diego Andina.
A scholar is included among the top collaborators of Diego Andina based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Diego Andina. Diego Andina is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cortina-Januchs, M. G., J. Quintanilla-Domínguez, Diego Andina, & A. Vega-Corona. (2012). ANN and Fuzzy c-Means applied to environmental pollution prediction. UPM Digital Archive (Technical University of Madrid).3 indexed citations
4.
Marcano-Cedeño, Alexis & Diego Andina. (2012). Data mining for the diagnosis of type 2 diabetes. UPM Digital Archive (Technical University of Madrid). 1–6.7 indexed citations
5.
Ibarra‐Manzano, Oscar, et al.. (2012). Air pollution data classification by SOM Neural Network. UPM Digital Archive (Technical University of Madrid). 1–5.2 indexed citations
6.
Quintanilla-Domínguez, J., et al.. (2010). Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms. UPM Digital Archive (Technical University of Madrid).10 indexed citations
7.
Jevtić, Aleksandar, et al.. (2010). Building a swarm of robotic bees. World Automation Congress. 1–6.21 indexed citations
8.
Andina, Diego, et al.. (2010). Modeling logic and neural approaches to bankruptcy prediction models. World Automation Congress. 1–6.1 indexed citations
Ruelas, R., et al.. (2009). A Greater Knowledge Extraction Coded as Fuzzy Rules and Based on the Fuzzy and Typicality Degrees of the GKPFCM Clustering Algorithm. Intelligent Automation & Soft Computing. 15. 555–571.3 indexed citations
Tarquís, Ana M., et al.. (2008). Election of Water Resources Management Entity Using a Multi-Criteria Decision (MCD) Method in Salta Province (Argentine). SHILAP Revista de lepidopterología.5 indexed citations
13.
Ruelas, R., et al.. (2008). Classification of domestic water consumption using an Anfis model. World Automation Congress. 1–9.4 indexed citations
14.
Jevtić, Aleksandar, et al.. (2008). Telecommunications Network Planning and Maintenance. International Conference on Information Systems.2 indexed citations
Andina, Diego, et al.. (2007). Metaplasticity Artificial Neural Networks Model Application to Radar Detection. SHILAP Revista de lepidopterología.3 indexed citations
Jevtić, Aleksandar & Diego Andina. (2007). Swarm intelligence and its applications in swarm robotics. UPM Digital Archive (Technical University of Madrid). 41–46.27 indexed citations
19.
Vega-Corona, A. & Diego Andina. (2004). Cad system for identification of microcalcifications in digitized mammography applying GRNN neural networks. World Automation Congress. 17. 161–168.1 indexed citations
20.
Andina, Diego, et al.. (2004). Importance sampling in neural detector training phase. World Automation Congress. 17. 43–48.4 indexed citations
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.