Daniel Peralta

1.6k citations
40 papers · 1.2k · h-index 19

Impact in

Papers in

    • Machine Learning and Data Classification 8
    • Imbalanced Data Classification Techniques 7
    • Anomaly Detection Techniques and Applications 5
    • Biometric Identification and Security 11

Daniel Peralta

37 papers receiving 1.2k citations

Peers

Daniel Peralta
Comparison fields: 5 of 128
  • Signal Processing 404
  • Computer Vision and Pattern Recognition 426
  • Information Systems 310
  • Safety Research 103
  • Artificial Intelligence 408
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Peralta

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Peralta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Peralta. 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 Daniel Peralta. The network helps show where Daniel Peralta may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Peralta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Peralta Line = papers co-authored together Daniel Peralta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014192
2 2015122
3 2015121
4 201989
5 201773
6 201263
7 201562
8 201659
9 201359
10 201540
11 201439
12 201729
13 201729
14 201426
15 201826
16 202022
17 202421
18 202020
19 201618
20 201916

About Daniel Peralta

Daniel Peralta is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Information Systems and Computational Theory and Mathematics, having authored 40 papers that have together received 1.2k indexed citations. Recurring topics across this work include Biometric Identification and Security (11 papers), Machine Learning and Data Classification (8 papers), Imbalanced Data Classification Techniques (7 papers), Face and Expression Recognition (6 papers), User Authentication and Security Systems (6 papers), Anomaly Detection Techniques and Applications (5 papers), Rough Sets and Fuzzy Logic (5 papers) and Forensic Fingerprint Detection Methods (5 papers). The work is most often cited by research in Signal Processing (404 citations), Computer Vision and Pattern Recognition (426 citations), Information Systems (310 citations), Safety Research (103 citations) and Artificial Intelligence (408 citations). Daniel Peralta has collaborated with scholars based in Belgium, Spain and United Kingdom. Frequent co-authors include Francisco Herrera, Isaac Triguero, José M. Benítez, Salvador García, Yvan Saeys, Jaume Bacardit, Alberto Fernández, Mikel Galar, Daniel Paternain and Edurne Barrenechea. Their work appears in journals such as Knowledge-Based Systems, Cytometry Part A, IEEE Transactions on Fuzzy Systems, International Journal of Computational Intelligence Systems 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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