Beatriz Pérez‐Sánchez

27 papers receiving 246 citations

Peers

Beatriz Pérez‐Sánchez
Comparison fields: 5 of 86
  • Artificial Intelligence 153
  • Control and Systems Engineering 36
  • Electrical and Electronic Engineering 36
  • Computer Vision and Pattern Recognition 26
  • Computer Networks and Communications 26
Replace Amanpreet Singh with:
Amanpreet Singh India
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Citations per field
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Citations per year

Countries citing papers authored by Beatriz Pérez‐Sánchez

Since Specialization
Citations

This map shows the geographic impact of Beatriz Pérez‐Sánchez'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 Beatriz Pérez‐Sánchez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beatriz Pérez‐Sánchez more than expected).

Fields of papers citing papers by Beatriz Pérez‐Sánchez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Beatriz Pérez‐Sánchez. 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 Beatriz Pérez‐Sánchez. The network helps show where Beatriz Pérez‐Sánchez may publish in the future.

Co-authorship network of co-authors of Beatriz Pérez‐Sánchez

This figure shows the co-authorship network connecting the top 25 collaborators of Beatriz Pérez‐Sánchez. A scholar is included among the top collaborators of Beatriz Pérez‐Sánchez 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 Beatriz Pérez‐Sánchez. Beatriz Pérez‐Sánchez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 8
3 4
4 5
5
LANN-DSVD: A privacy-preserving distributed algorithm for machine learning.
1
6
Validación del Cuestionario de Violencia entre Novios-Revisado (DVQ-R)
1
7 4
8
A fast learning algorithm for high dimensional problems: an application to microarrays.
1
9 61
10 6
11 1
12
Relationship between psychoactive substance use and family maltreatment: a prison population analysis
3
13 12
14
Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms
1
15
A distributed learning algorithm based on two-layer artificial neural networks and genetic algorithms.
1
16 7
17 9
18 28
19 1
20
Classification of computer intrusions using functional networks. A comparative study.
13

About Beatriz Pérez‐Sánchez

Beatriz Pérez‐Sánchez is a scholar working on Artificial Intelligence, Signal Processing and Computer Science Applications, having authored 29 papers that have together received 255 indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Machine Learning and ELM (5 papers) and Data Stream Mining Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (153 citations), Signal Processing (23 citations) and Control and Systems Engineering (36 citations). Beatriz Pérez‐Sánchez has collaborated with scholars based in Spain, Mexico and Canada. Frequent co-authors include Óscar Fontenla-Romero, Bertha Guijarro‐Berdiñas, Amparo Alonso‐Betanzos, David Martínez‐Rego, Noelia Sánchez‐Maroño, José Luis Calvo‐Rolle, Carolina Bringas Molleda, Elena Hernández-Pereira, Francisco Javier Rodríguez‐Díaz and Diego Peteiro-Barral. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Pattern Recognition.

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.

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