Jesús M. Pérez
- Artificial Intelligence top 2%
- Imbalanced Data Classification Techniques 8
- Machine Learning and Data Classification 6
- Advanced Clustering Algorithms Research 5
- Anomaly Detection Techniques and Applications 3
- Signal Processing top 5%
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- Complex Network Analysis Techniques 3
- Information Systems top 5%
- Data Mining Algorithms and Applications 5
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- Network Security and Intrusion Detection 3
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- Forest ecology and management 2
Jesús M. Pérez
30 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 151
- Artificial Intelligence 618
- Signal Processing 199
- Statistical and Nonlinear Physics 144
- Computer Vision and Pattern Recognition 174
- Information Systems 163
Countries citing papers authored by Jesús M. Pérez
This map shows the geographic impact of Jesús M. Pérez'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 Jesús M. Pérez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jesús M. Pérez more than expected).
Fields of papers citing papers by Jesús M. Pérez
This network shows the impact of papers produced by Jesús M. Pérez. 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 Jesús M. Pérez. The network helps show where Jesús M. Pérez may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Jesús M. Pérez, 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 | 0 | |
| 2 | 2018 | 1 | |
| 3 | 2017 | 1 | |
| 4 | Generation of the database gurekddcup | 2017 | 7 |
| 5 | 2016 | 12 | |
| 6 | 2016 | 5 | |
| 7 | An update of the J48Consolidated WEKA’s class: CTC algorithm enhanced with the notion of coverage | 2016 | 0 |
| 8 | J48Consolidated: an implementation of CTC algorithm for WEKA | 2016 | 2 |
| 9 | 2015 | 1 | |
| 10 | 2015 | 23 | |
| 11 | 2012 | 1 | |
| 12 | 2012 | 1 | |
| 13 | Luchas campesinas y reforma agraria : Memorias de un dirigente de la ANUC en la costa caribe | 2010 | 9 |
| 14 | Unsupervised anomaly detection system for Nidis-s based on payload and probabilistic suffix trees. | 2009 | 1 |
| 15 | 2009 | 40 | |
| 16 | 2009 | 3 | |
| 17 | Evaluation of malware clustering based on its dynamic behaviour | 2008 | 13 |
| 18 | 2006 | 33 | |
| 19 | 2004 | 2 | |
| 20 | Homozygous form of the Pelger-Huët anomaly. | 1999 | 10 |
About Jesús M. Pérez
Jesús M. Pérez is a scholar working on Artificial Intelligence, Signal Processing and Information Systems, having authored 33 papers that have together received 1.3k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (8 papers), Machine Learning and Data Classification (6 papers), Data Mining Algorithms and Applications (5 papers), Advanced Clustering Algorithms Research (5 papers), Complex Network Analysis Techniques (3 papers), Network Security and Intrusion Detection (3 papers), Anomaly Detection Techniques and Applications (3 papers) and Forest ecology and management (2 papers). The work is most often cited by research in Artificial Intelligence (618 citations), Signal Processing (199 citations) and Statistical and Nonlinear Physics (144 citations). Jesús M. Pérez has collaborated with scholars based in Spain, Australia and United Kingdom. Frequent co-authors include Javier Muguerza, Ibai Gurrutxaga, Olatz Arbelaitz, Iñigo Perona, J. Martín, Sixto Arnaiz, Federico Mijangos, L. M. León, José Manuel Laza and Miguel Á. Rodríguez. Their work appears in journals such as Expert Systems with Applications, Pattern Recognition and Information Sciences.
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.