Núria Macià

474 total citations
12 papers, 264 citations indexed

About

Núria Macià is a scholar working on Artificial Intelligence, Transportation and Pharmacology. According to data from OpenAlex, Núria Macià has authored 12 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Transportation and 1 paper in Pharmacology. Recurrent topics in Núria Macià's work include Machine Learning and Data Classification (6 papers), Evolutionary Algorithms and Applications (5 papers) and Machine Learning and Algorithms (4 papers). Núria Macià is often cited by papers focused on Machine Learning and Data Classification (6 papers), Evolutionary Algorithms and Applications (5 papers) and Machine Learning and Algorithms (4 papers). Núria Macià collaborates with scholars based in Spain, United States and Andorra. Núria Macià's co-authors include Ester Bernadó-Mansilla, Albert Orriols-Puig, Tin Kam Ho, Sérgio Escalera, Hugo Jair Escalante, Alexander Statnikov, Evelyne Viegas, Isabelle Guyon, Bisakha Ray and Gavin C. Cawley and has published in prestigious journals such as BMJ, Pattern Recognition and Information Sciences.

In The Last Decade

Núria Macià

12 papers receiving 255 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Núria Macià Spain 9 163 22 18 16 15 12 264
Jerónimo Hernández-González Spain 8 127 0.8× 32 1.5× 8 0.4× 10 0.6× 32 2.1× 24 257
Miroslav Marinov Bulgaria 6 201 1.2× 37 1.7× 2 0.1× 14 0.9× 81 5.4× 19 487
Xi Hang Cao United States 6 84 0.5× 16 0.7× 24 1.5× 17 1.1× 15 272
Arun Kumar Dubey India 9 46 0.3× 59 2.7× 4 0.3× 15 1.0× 43 202
Mengyue Liu China 7 140 0.9× 21 1.0× 11 0.7× 66 4.4× 25 264
Júlio César Nievola Brazil 11 161 1.0× 61 2.8× 20 1.3× 41 2.7× 51 342
Untari Novia Wisesty Indonesia 11 195 1.2× 39 1.8× 77 4.3× 3 0.2× 77 5.1× 54 433
Avdhesh Gupta India 10 76 0.5× 13 0.6× 10 0.6× 39 2.6× 27 328
Krzysztof Dyczkowski Poland 8 88 0.5× 11 0.5× 39 2.2× 32 2.0× 32 2.1× 31 194
Amin Golzari Oskouei Iran 10 187 1.1× 155 7.0× 7 0.4× 11 0.7× 33 2.2× 23 304

Countries citing papers authored by Núria Macià

Since Specialization
Citations

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

Fields of papers citing papers by Núria Macià

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Núria Macià

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

All Works

12 of 12 papers shown
1.
2.
Grignard, Arnaud, et al.. (2018). CityScope Andorra: A Multi-level Interactive and Tangible Agent-based Visualization. Adaptive Agents and Multi-Agents Systems. 1939–1940. 9 indexed citations
3.
Guyon, Isabelle, Hugo Jair Escalante, Sérgio Escalera, et al.. (2016). A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. SPIRE - Sciences Po Institutional REpository. 28 indexed citations
4.
Guyon, Isabelle, Kristin P. Bennett, Gavin C. Cawley, et al.. (2015). Design of the 2015 ChaLearn AutoML challenge. UEA Digital Repository (University of East Anglia). 1–8. 59 indexed citations
5.
Macià, Núria & Ester Bernadó-Mansilla. (2013). Towards UCI+: A mindful repository design. Information Sciences. 261. 237–262. 51 indexed citations
6.
Montesinos, J., Jordi Cortés, Anna Arnau, et al.. (2013). Barcelona baby boom: does sporting success affect birth rate?. BMJ. 347(dec17 9). f7387–f7387. 5 indexed citations
7.
Macià, Núria, Ester Bernadó-Mansilla, Albert Orriols-Puig, & Tin Kam Ho. (2012). Learner excellence biased by data set selection: A case for data characterisation and artificial data sets. Pattern Recognition. 46(3). 1054–1066. 29 indexed citations
8.
Macià, Núria, Albert Orriols-Puig, & Ester Bernadó-Mansilla. (2010). In search of targeted-complexity problems. 1055–1062. 14 indexed citations
9.
Macià, Núria, Albert Orriols-Puig, & Ester Bernadó-Mansilla. (2009). EMO shines a light on the holes of complexity space. 1907–1908. 1 indexed citations
10.
Barenys, Marta, Núria Macià, Joaquín de Lapuente, et al.. (2009). Chronic exposure to MDMA (ecstasy) increases DNA damage in sperm and alters testes histopathology in male rats. Toxicology Letters. 191(1). 40–46. 38 indexed citations
11.
Macià, Núria, Albert Orriols-Puig, & Ester Bernadó-Mansilla. (2008). Genetic-Based Synthetic Data Sets for the Analysis of Classifiers Behavior. 507–512. 14 indexed citations
12.
Macià, Núria, Ester Bernadó-Mansilla, & Albert Orriols-Puig. (2008). Preliminary approach on synthetic data sets generation based on class separability measure. Proceedings - International Conference on Pattern Recognition. 24. 1–4. 14 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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