M. Julia Flores

782 total citations
36 papers, 485 citations indexed

About

M. Julia Flores is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, M. Julia Flores has authored 36 papers receiving a total of 485 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 10 papers in Signal Processing. Recurrent topics in M. Julia Flores's work include Bayesian Modeling and Causal Inference (17 papers), Data Management and Algorithms (6 papers) and Hydrology and Watershed Management Studies (4 papers). M. Julia Flores is often cited by papers focused on Bayesian Modeling and Causal Inference (17 papers), Data Management and Algorithms (6 papers) and Hydrology and Watershed Management Studies (4 papers). M. Julia Flores collaborates with scholars based in Spain, Australia and Denmark. M. Julia Flores's co-authors include José A. Gámez, Ann E. Nicholson, José M. Puerta, Ana María Martínez, Steven Mascaro, Kevin B. Korb, Luis de la Ossa, Rafael Rumí, Teresa Olivares and Luis Orozco–Barbosa and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Energy and Buildings.

In The Last Decade

M. Julia Flores

35 papers receiving 460 citations

Peers

M. Julia Flores

CVPR

Gaurang Panchal India

Mohssen Mohammed South Africa

Bruno Baruque Spain

Amir Pourabdollah United Kingdom

Masud Moshtaghi Australia

Amit Thakkar India

Lena Pietruczuk Poland

Marc Stöttinger Germany

Shuang Han China

Chuanyang Gong China

Gaurang Panchal India

M. Julia Flores

88 ×0.4

76 ×0.9

57 ×0.8

CVPR

30 ×0.6

63 ×1.3

Citations per year, relative to M. Julia Flores M. Julia Flores (= 1×) peers Gaurang Panchal

Countries citing papers authored by M. Julia Flores

Since Specialization

Citations

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

Fields of papers citing papers by M. Julia Flores

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Julia Flores. 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 M. Julia Flores. The network helps show where M. Julia Flores may publish in the future.

Co-authorship network of co-authors of M. Julia Flores

This figure shows the co-authorship network connecting the top 25 collaborators of M. Julia Flores. A scholar is included among the top collaborators of M. Julia Flores 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 M. Julia Flores. M. Julia Flores 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

Moya, Daniel, et al.. (2025). SEVERIA: A conceptual online tool to assess potential burn severity of wildfires in Mediterranean forests. Ecological Informatics. 92. 103496–103496.

Flores, M. Julia, et al.. (2024). Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks. Environmental and Ecological Statistics. 31(1). 27–56. 3 indexed citations

Flores, M. Julia, et al.. (2024). Evaluation of data augmentation techniques on subjective tasks. Machine Vision and Applications. 35(4). 1 indexed citations

Flores, M. Julia, et al.. (2023). Predicting spotify audio features from Last.fm tags. Multimedia Tools and Applications. 83(16). 48311–48330. 1 indexed citations

Flores, M. Julia, et al.. (2022). Bayesian Networks for Preprocessing Water Management Data. Mathematics. 10(10). 1777–1777. 2 indexed citations

Flores, M. Julia, et al.. (2019). Assessment of flood risk in Mediterranean catchments: an approach based on Bayesian networks. Stochastic Environmental Research and Risk Assessment. 33(11-12). 1991–2005. 8 indexed citations

Flores, M. Julia, et al.. (2019). Machine learning for music genre: multifaceted review and experimentation with audioset. Journal of Intelligent Information Systems. 55(3). 469–499. 37 indexed citations

Martínez-Gómez, Jesús, et al.. (2017). Integration of contextual information into the scene classification problem. Robotics and Autonomous Systems. 97. 171–181. 1 indexed citations

Flores, M. Julia & José A. Gámez. (2015). Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets. 382–389. 1 indexed citations

10.

Nicholson, Ann E., et al.. (2012). Prediction of coffee rust disease using Bayesian networks. 259–266. 25 indexed citations

11.

Flores, M. Julia, et al.. (2011). Incorporating expert knowledge when learning Bayesian network structure: A medical case study. Artificial Intelligence in Medicine. 53(3). 181–204. 97 indexed citations

12.

Flores, M. Julia, José A. Gámez, Ana María Martínez, & Antonio Salmerón. (2011). Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. 43. 593–598. 7 indexed citations

13.

Flores, M. Julia, José A. Gámez, Ana María Martínez, & José M. Puerta. (2011). Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter?. Applied Intelligence. 34(3). 372–385. 28 indexed citations

14.

Flores, M. Julia, José A. Gámez, Ana María Martínez, & José M. Puerta. (2010). Analyzing the impact of the discretization method when comparing Bayesian classifiers. 570–579. 4 indexed citations

15.

Flores, M. Julia, José A. Gámez, Ana María Martínez, & José M. Puerta. (2009). GAODE and HAODE. 313–320. 18 indexed citations

16.

Flores, M. Julia & José A. Gámez. (2007). A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks. Studies in fuzziness and soft computing. 127–152. 5 indexed citations

17.

Flores, M. Julia, José A. Gámez, & Juan L. Mateo. (2007). Mining the ESROM: A study of breeding value classification in Manchego sheep by means of attribute selection and construction. Computers and Electronics in Agriculture. 60(2). 167–177. 8 indexed citations

18.

Flores, M. Julia, José A. Gámez, & Serafı́n Moral. (2006). The Independency tree model: a new approach for clustering and factorisation.. 83–90. 1 indexed citations

19.

Flores, M. Julia, José A. Gámez, & Kristian G. Olesen. (2004). Incremental Compilation of Bayesian Networks in Practice. VBN Forskningsportal (Aalborg Universitet). 1 indexed citations

20.

Flores, M. Julia, José A. Gámez, & Kristian G. Olesen. (2002). Incremental compilation of bayesian networks. arXiv (Cornell University). 233–240. 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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