Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Window Size Impact in Human Activity Recognition
2014479 citationsOresti Baños, Juan Manuel Gálvez et al.Sensorsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Ignacio Rojas'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 Ignacio Rojas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ignacio Rojas more than expected).
This network shows the impact of papers produced by Ignacio Rojas. 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 Ignacio Rojas. The network helps show where Ignacio Rojas may publish in the future.
Co-authorship network of co-authors of Ignacio Rojas
This figure shows the co-authorship network connecting the top 25 collaborators of Ignacio Rojas.
A scholar is included among the top collaborators of Ignacio Rojas 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 Ignacio Rojas. Ignacio Rojas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cabestany, Joan, Ignacio Rojas, & Gonzalo Joya. (2011). Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Mlaga, Spain, June ... Computer Science and General Issues). Springer eBooks.1 indexed citations
13.
Pomares, H., et al.. (2009). Prediction of Time Series Using RBF Neural Networks: A New Approach of Clustering. The International Arab Journal of Information Technology. 6. 138–143.35 indexed citations
14.
Guillén, Alberto, et al.. (2009). Applying Mutual Information for Prototype or Instance Selection in Regression Problems. The European Symposium on Artificial Neural Networks.2 indexed citations
15.
Valenzuela, Olga, et al.. (2005). Automatic classification of prostate cancer using pseudo-gaussian radial basis function neural network. The European Symposium on Artificial Neural Networks. 145–150.3 indexed citations
16.
Pomares, H., et al.. (2005). Hierarchical Structure for function approximation using radial basis function. International Conference on Applied Mathematics. 228–233.1 indexed citations
17.
Rojas, Ignacio & H. Pomares. (2004). Soft-computing techniques for time series forecasting.. The European Symposium on Artificial Neural Networks. 93–102.2 indexed citations
18.
Álvarez, M., et al.. (2004). Lattice ICA for the separation of speech signals. University of Regensburg Publication Server (University of Regensburg). 337–342.
19.
Rojas, Ignacio, et al.. (2001). The synergy between multideme genetic algorithms and fuzzy systems. The European Symposium on Artificial Neural Networks. 199–204.1 indexed citations
20.
Rojas, Ignacio, et al.. (1998). What are the main factors involved in the design of a Radial Basis Function Network. The European Symposium on Artificial Neural Networks. 1–6.4 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.