Jordi Planes

2.5k total citations · 1 hit paper
34 papers, 1.2k citations indexed

About

Jordi Planes is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Jordi Planes has authored 34 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Networks and Communications, 18 papers in Artificial Intelligence and 12 papers in Computational Theory and Mathematics. Recurrent topics in Jordi Planes's work include Formal Methods in Verification (11 papers), Constraint Satisfaction and Optimization (10 papers) and Advanced Malware Detection Techniques (10 papers). Jordi Planes is often cited by papers focused on Formal Methods in Verification (11 papers), Constraint Satisfaction and Optimization (10 papers) and Advanced Malware Detection Techniques (10 papers). Jordi Planes collaborates with scholars based in Spain, Ireland and France. Jordi Planes's co-authors include Daniel Gibert, Carles Mateu, Felip Manyà, João Marques‐Silva, Che‐Ming Li, Chu-Min Li, António Morgado, Federico Heras, Mark H. Liffiton and Quan Le and has published in prestigious journals such as Expert Systems with Applications, Pattern Recognition Letters and Journal of Network and Computer Applications.

In The Last Decade

Jordi Planes

34 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

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.

The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

2020 352 citations Daniel Gibert, Carles Mateu et al. profile →

Peers

Jordi Planes

CNC

SP

AI

IS

CTM

Samik Basu United States

Peter Jönsson Sweden

Oded Shmueli Israel

Karthikeyan Bhargavan United Kingdom

Silvio Ranise Italy

Jan Paredaens Belgium

Riccardo Focardi Italy

Éric Filiol France

Chris Jermaine United States

N. Roussopoulos United States

Samik Basu United States

Jordi Planes

598 ×0.7

CNC

288 ×0.4

SP

553 ×0.9

AI

560 ×1.7

IS

152 ×0.7

CTM

Citations per year, relative to Jordi Planes Jordi Planes (= 1×) peers Samik Basu

Countries citing papers authored by Jordi Planes

Since Specialization

Citations

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

Fields of papers citing papers by Jordi Planes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jordi Planes

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

All Works

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20 of 20 papers shown

1.

Gibert, Daniel, et al.. (2023). A Wolf in Sheep’s Clothing: Query-Free Evasion Attacks Against Machine Learning-Based Malware Detectors with Generative Adversarial Networks. 415–426. 10 indexed citations

2.

Gibert, Daniel, Jordi Planes, Carles Mateu, & Quan Le. (2022). Fusing feature engineering and deep learning: A case study for malware classification. Expert Systems with Applications. 207. 117957–117957. 46 indexed citations

3.

Gibert, Daniel, Matt Fredrikson, Carles Mateu, Jordi Planes, & Quan Le. (2021). Enhancing the insertion of NOP instructions to obfuscate malware via deep reinforcement learning. Computers & Security. 113. 102543–102543. 12 indexed citations

4.

Gibert, Daniel, Carles Mateu, & Jordi Planes. (2020). Orthrus: A Bimodal Learning Architecture for Malware Classification. 1–8. 11 indexed citations

5.

Gibert, Daniel, Carles Mateu, & Jordi Planes. (2019). A Hierarchical Convolutional Neural Network for Malware Classification. 1–8. 28 indexed citations

6.

Gibert, Daniel, et al.. (2018). Classification of Malware by Using Structural Entropy on Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 56 indexed citations

7.

Alsinet, Teresa, et al.. (2017). A Distributed Approach for the Analysis of Discussions in Twitter.. International Joint Conference on Artificial Intelligence. 45–56. 1 indexed citations

8.

Alsinet, Teresa, Josep Argelich, Ramón Béjar, et al.. (2017). Weighted argumentation for analysis of discussions in Twitter. International Journal of Approximate Reasoning. 85. 21–35. 15 indexed citations

9.

Alsinet, Teresa, Josep Argelich, Ramón Béjar, et al.. (2017). An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships. Pattern Recognition Letters. 105. 191–199. 5 indexed citations

10.

Guirado, Fernando, et al.. (2016). Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments. The Journal of Supercomputing. 73(1). 354–369. 18 indexed citations

11.

Guirado, Fernando, et al.. (2015). Multi-criteria genetic algorithm applied to scheduling in multi-cluster environments. Journal of Simulation. 9(4). 287–295. 7 indexed citations

12.

Morgado, António, Federico Heras, Mark H. Liffiton, Jordi Planes, & João Marques‐Silva. (2013). Iterative and core-guided MaxSAT solving: A survey and assessment. Constraints. 18(4). 478–534. 84 indexed citations

13.

Morgado, António, et al.. (2012). Iterative SAT Solving for Minimum Satisfiability. 30. 922–927. 6 indexed citations

14.

Li, Chu Min, et al.. (2010). Resolution-based lower bounds in MaxSAT. Constraints. 15(4). 456–484. 18 indexed citations

15.

Béjar, Ramón, et al.. (2009). A ROBUST COMPETENCE EVALUATION FORMAL MODEL. 5528–5536. 1 indexed citations

16.

Marques‐Silva, João & Jordi Planes. (2008). Algorithms for Maximum Satisfiability using Unsatisfiable Cores. 2008 Design, Automation and Test in Europe. 408–413. 12 indexed citations

17.

Argelich, Josep, Chu-Min Li, Felip Manyà, & Jordi Planes. (2008). The First and Second Max-SAT Evaluations. 4(2-4). 251–278. 27 indexed citations

18.

Li, Che‐Ming, Felip Manyà, & Jordi Planes. (2007). New Inference Rules for Max-SAT. Journal of Artificial Intelligence Research. 30. 321–359. 83 indexed citations

19.

Alsinet, Teresa, Felip Manyà, & Jordi Planes. (2007). An efficient solver for weighted Max-SAT. Journal of Global Optimization. 41(1). 61–73. 6 indexed citations

20.

Li, Chu-Min, Felip Manyà, & Jordi Planes. (2006). Detecting disjoint inconsistent subformulas for computing lower bounds for Max-SAT. National Conference on Artificial Intelligence. 86–91. 39 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.

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