Andrés Bueno-Crespo

947 total citations
42 papers, 637 citations indexed

About

Andrés Bueno-Crespo is a scholar working on Artificial Intelligence, Plant Science and Environmental Engineering. According to data from OpenAlex, Andrés Bueno-Crespo has authored 42 papers receiving a total of 637 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 8 papers in Plant Science and 5 papers in Environmental Engineering. Recurrent topics in Andrés Bueno-Crespo's work include Machine Learning and ELM (10 papers), Smart Agriculture and AI (7 papers) and Greenhouse Technology and Climate Control (5 papers). Andrés Bueno-Crespo is often cited by papers focused on Machine Learning and ELM (10 papers), Smart Agriculture and AI (7 papers) and Greenhouse Technology and Climate Control (5 papers). Andrés Bueno-Crespo collaborates with scholars based in Spain, United Kingdom and Belgium. Andrés Bueno-Crespo's co-authors include Raquel Martínez‐España, José M. Cecilia, José‐Luis Sancho‐Gómez, J. Soto, Andrés Muñoz, David Pulido‐Velazquez, Julio Pérez‐Sánchez, Patricia Jimeno‐Sáez, Javier Senent‐Aparicio and Rosa-María Menchón-Lara and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Andrés Bueno-Crespo

41 papers receiving 622 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrés Bueno-Crespo Spain 15 143 136 95 90 86 42 637
Ahmed M. Elshewey Egypt 21 170 1.2× 308 2.3× 62 0.7× 105 1.2× 97 1.1× 56 976
Zahraa Tarek Egypt 14 123 0.9× 177 1.3× 32 0.3× 99 1.1× 47 0.5× 36 625
Ken Cai China 13 93 0.7× 127 0.9× 75 0.8× 100 1.1× 20 0.2× 32 783
Syed Mohammad Hassan Zaidi Pakistan 17 121 0.8× 56 0.4× 246 2.6× 66 0.7× 33 0.4× 58 937
Mahtabin Rodela Rozbu Bangladesh 3 44 0.3× 106 0.8× 55 0.6× 80 0.9× 33 0.4× 3 598
Mahady Hasan Bangladesh 11 81 0.6× 75 0.6× 85 0.9× 76 0.8× 39 0.5× 104 597
Yao Tong China 19 115 0.8× 111 0.8× 40 0.4× 59 0.7× 43 0.5× 63 1.1k
Min Wan China 7 102 0.7× 68 0.5× 70 0.7× 139 1.5× 13 0.2× 30 920
Chunwei Liu China 17 79 0.6× 238 1.8× 149 1.6× 149 1.7× 48 0.6× 70 964
Md Abdus Samad South Korea 16 61 0.4× 65 0.5× 58 0.6× 32 0.4× 55 0.6× 72 681

Countries citing papers authored by Andrés Bueno-Crespo

Since Specialization
Citations

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

Fields of papers citing papers by Andrés Bueno-Crespo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrés Bueno-Crespo. 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 Andrés Bueno-Crespo. The network helps show where Andrés Bueno-Crespo may publish in the future.

Co-authorship network of co-authors of Andrés Bueno-Crespo

This figure shows the co-authorship network connecting the top 25 collaborators of Andrés Bueno-Crespo. A scholar is included among the top collaborators of Andrés Bueno-Crespo 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 Andrés Bueno-Crespo. Andrés Bueno-Crespo 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.
Fernández-López, Carmen, et al.. (2024). Biodegradation behaviour of pharmaceutical compounds and selected metabolites in activated sludge. A forecasting decision system approach. Journal of Environmental Health Science and Engineering. 22(1). 229–243. 4 indexed citations
2.
Muñoz, Andrés, et al.. (2024). A multi-DL fuzzy approach to image recognition for a real-time traffic alert system. Journal of Ambient Intelligence and Smart Environments. 17(1). 101–116. 1 indexed citations
3.
Bueno-Crespo, Andrés, et al.. (2023). SEPARATE: A tightly coupled, seamless IoT infrastructure for deploying AI algorithms in smart agriculture environments. Internet of Things. 22. 100734–100734. 10 indexed citations
4.
Bueno-Crespo, Andrés, et al.. (2023). Evaluation of synthetic data generation for intelligent climate control in greenhouses. Applied Intelligence. 53(21). 24765–24781. 11 indexed citations
5.
Bueno-Crespo, Andrés, et al.. (2023). Data-driven evaluation of machine learning models for climate control in operational smart greenhouses. Journal of Ambient Intelligence and Smart Environments. 15(1). 3–17. 7 indexed citations
6.
Muñoz, Andrés, et al.. (2023). A real-time traffic alert system based on image recognition: A case of study in Spain. RODIN (Universidad de Cádiz). 1–7. 2 indexed citations
7.
Garrido, M. Carmen, et al.. (2022). Evaporation Forecasting through Interpretable Data Analysis Techniques. Electronics. 11(4). 536–536. 7 indexed citations
8.
Bueno-Crespo, Andrés, et al.. (2022). SEPARATE: A Tightly Coupled, Seamless IoT Infrastructure for Deploying AI Algorithms in Smart Agriculture Environments. SSRN Electronic Journal. 1 indexed citations
9.
Martínez‐España, Raquel, et al.. (2021). Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data. Scientific Reports. 11(1). 15173–15173. 27 indexed citations
10.
Martínez‐España, Raquel, et al.. (2020). A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers. Sensors. 20(24). 7129–7129. 17 indexed citations
11.
Pérez‐Sánchez, Horacio, et al.. (2020). QN-Docking: An innovative molecular docking methodology based on Q-Networks. Applied Soft Computing. 96. 106678–106678. 8 indexed citations
12.
Navarro, Juan M., et al.. (2020). Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network. Sensors. 20(3). 903–903. 14 indexed citations
13.
Guillén, Miguel Arias, et al.. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77(1). 818–840. 76 indexed citations
14.
Bueno-Crespo, Andrés, et al.. (2020). Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain. TUGraz OPEN Library (Graz University of Technology). 29 indexed citations
15.
Bueno-Crespo, Andrés, et al.. (2019). Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images. PLoS ONE. 14(7). e0219388–e0219388. 51 indexed citations
16.
Martínez‐España, Raquel, et al.. (2018). Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain.. JUCS - Journal of Universal Computer Science. 24. 261–276. 41 indexed citations
17.
Bueno-Crespo, Andrés, et al.. (2018). Multi-objective optimal design of submerged arches using extreme learning machine and evolutionary algorithms. Applied Soft Computing. 71. 826–834. 8 indexed citations
18.
Menchón-Lara, Rosa-María, José‐Luis Sancho‐Gómez, & Andrés Bueno-Crespo. (2016). Early-stage atherosclerosis detection using deep learning over carotid ultrasound images. Applied Soft Computing. 49. 616–628. 38 indexed citations
19.
Bueno-Crespo, Andrés, et al.. (2016). Fetal MRI as Complementary Study of Congenital Cystic Adenomatoid Malformation During Pregnancy: A Single Case Report. Cureus. 8(4). e570–e570. 1 indexed citations
20.
Muñoz, Andrés, et al.. (2014). Heart Health Risk Assessment System: A Nonintrusive Proposal Using Ontologies and Expert Rules. BioMed Research International. 2014. 1–12. 15 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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