Juan J. Flores

1.3k total citations · 1 hit paper
66 papers, 878 citations indexed

About

Juan J. Flores is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Management Science and Operations Research. According to data from OpenAlex, Juan J. Flores has authored 66 papers receiving a total of 878 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 17 papers in Electrical and Electronic Engineering and 10 papers in Management Science and Operations Research. Recurrent topics in Juan J. Flores's work include Energy Load and Power Forecasting (12 papers), Metaheuristic Optimization Algorithms Research (11 papers) and Evolutionary Algorithms and Applications (8 papers). Juan J. Flores is often cited by papers focused on Energy Load and Power Forecasting (12 papers), Metaheuristic Optimization Algorithms Research (11 papers) and Evolutionary Algorithms and Applications (8 papers). Juan J. Flores collaborates with scholars based in Mexico, Spain and United States. Juan J. Flores's co-authors include Jean‐François Mas, Héctor Rodríguez, Mario Graff, Vicenç Puig, Roberto Tapia Sánchez, Humberto Pérez-Espinosa, Carlos A. Coello Coello, Claudio R. Fuerte‐Esquivel, Elisa Espinosa-Juárez and J. M. Martı́nez and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable Energy and Solar Energy.

In The Last Decade

Juan J. Flores

58 papers receiving 816 citations

Hit Papers

The application of artificial neural networks to the anal... 2007 2026 2013 2019 2007 100 200 300 400
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.

  1. The application of artificial neural networks to the analysis of remotely sensed data
    2007 483 citations Juan J. Flores et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juan J. Flores Mexico 11 229 198 182 177 157 66 878
M. Sánchez-Castillo Spain 4 419 1.8× 107 0.5× 296 1.6× 66 0.4× 197 1.3× 10 1.3k
Ali Ben Abbes Tunisia 15 87 0.4× 246 1.2× 255 1.4× 87 0.5× 114 0.7× 47 983
Surya S. Durbha India 17 217 0.9× 235 1.2× 210 1.2× 69 0.4× 153 1.0× 98 1.0k
Yueming Hu China 20 212 0.9× 108 0.5× 143 0.8× 173 1.0× 143 0.9× 114 1.6k
Zhifeng Guo China 16 110 0.5× 196 1.0× 329 1.8× 262 1.5× 20 0.1× 49 926
Jorge García–Gutiérrez Spain 19 193 0.8× 284 1.4× 338 1.9× 49 0.3× 128 0.8× 29 973
Demetris Stathakis Greece 17 180 0.8× 144 0.7× 162 0.9× 59 0.3× 132 0.8× 44 1.1k

Countries citing papers authored by Juan J. Flores

Since Specialization
Citations

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

Fields of papers citing papers by Juan J. Flores

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juan J. Flores

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

All Works

20 of 20 papers shown
1.
Rodríguez, Héctor, et al.. (2025). A deep learning approach for image time series forecasting: Study case, United States drought monitor. Engineering Applications of Artificial Intelligence. 158. 111346–111346. 1 indexed citations
2.
Flores, Juan J., et al.. (2024). A literature review on satellite image time series forecasting: Methods and applications for remote sensing. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 14(3). 9 indexed citations
3.
Flores, Juan J., et al.. (2023). Models to classify the difficulty of genetic algorithms to solve continuous optimization problems. Natural Computing. 23(2). 431–451. 4 indexed citations
4.
Kolosovas‐Machuca, Eleazar Samuel, et al.. (2020). Evaluation of Breast Cancer by Infrared Thermography.. Research in computing science. 149. 137–149. 4 indexed citations
5.
Flores, Juan J., et al.. (2019). Estudio del comportamiento dinámico de una boya sumergida con superficie libre bajo condiciones de vibración forzada. SHILAP Revista de lepidopterología. 2(1). 88–95.
6.
Sánchez, Roberto Tapia, et al.. (2019). Characterization of a polycrystalline photovoltaic cell using artificial neural networks. Solar Energy. 196. 157–167. 27 indexed citations
7.
Flores, Juan J., et al.. (2017). Fuzzy logic in the design of public policies: application of law. ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH. 51(2). 281–290. 2 indexed citations
8.
9.
Rodríguez, Héctor, Vicenç Puig, Juan J. Flores, & Rodrigo López. (2016). Combined holt-winters and GA trained ANN approach for sensor validation and reconstruction: Application to water demand flowmeters. QRU Quaderns de Recerca en Urbanisme. 202–207. 6 indexed citations
10.
Flores, Juan J., et al.. (2016). Fusión de Imágenes Multi Foco basado en la Combinación Lineal de Imágenes utilizando Imágenes Incrementales. Revista Iberoamericana de Automática e Informática Industrial RIAI. 13(4). 450–461. 4 indexed citations
11.
Rodríguez, Héctor, et al.. (2016). Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks. Journal of Hydroinformatics. 19(1). 1–16. 20 indexed citations
12.
Flores, Juan J., et al.. (2016). Comparison of time series forecasting techniques with respect to tolerance to noise. 1–6. 3 indexed citations
13.
Espinosa-Juárez, Elisa, et al.. (2016). Short term photovoltaic power production using a hybrid of nearest neighbor and artificial neural networks. 1–6. 12 indexed citations
14.
Flores, Juan J., et al.. (2010). Solution to the Registration Problem Using Differential Evolution and SSD-ARC Function. 1. 3–10. 1 indexed citations
15.
Flores, Juan J., et al.. (2009). On the Hyperbox – Hyperplane Intersection Problem. Americanae (AECID Library). 1 indexed citations
16.
Flores, Juan J., et al.. (2008). Plotting of Complete Bifurcation Diagrams Using a Dynamic Environment Particle Swarm Optimization Algorithm.. International Conference on Artificial Intelligence. 399–406. 1 indexed citations
17.
Flores, Juan J., et al.. (2007). Search of Initial Conditions for Dynamic Systems using Intelligent Optimization Methods. 348–353. 1 indexed citations
18.
Flores, Juan J., et al.. (2005). Time-Invariant Dynamic Systems identification based on the qualitative features of the response. Engineering Applications of Artificial Intelligence. 18(6). 719–729. 5 indexed citations
19.
Muñoz, R., et al.. (2000). Estudio del enlace inductivo transcutáneo en el suministro de energía en dispositivos electrónicos implantados. 21(4). 129–136. 1 indexed citations
20.
Flores, Juan J. & Jaime Cerdá. (2000). Efficient modeling of linear circuits to perform qualitative reasoning tasks. AI Communications. 13(2). 125–134.

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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