José M. Cecilia

2.5k total citations
118 papers, 1.6k citations indexed

About

José M. Cecilia is a scholar working on Computational Theory and Mathematics, Molecular Biology and Computer Networks and Communications. According to data from OpenAlex, José M. Cecilia has authored 118 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computational Theory and Mathematics, 27 papers in Molecular Biology and 21 papers in Computer Networks and Communications. Recurrent topics in José M. Cecilia's work include Computational Drug Discovery Methods (19 papers), Protein Structure and Dynamics (11 papers) and DNA and Biological Computing (11 papers). José M. Cecilia is often cited by papers focused on Computational Drug Discovery Methods (19 papers), Protein Structure and Dynamics (11 papers) and DNA and Biological Computing (11 papers). José M. Cecilia collaborates with scholars based in Spain, Italy and United Kingdom. José M. Cecilia's co-authors include José M. Garcı́a, Horacio Pérez‐Sánchez, Ginés D. Guerrero, Raquel Martínez‐España, Andrés Bueno-Crespo, Juan‐Carlos Cano, Alberto Inuggi, Stamatios N. Sotiropoulos, Manuel Ujaldón and Carlos T. Calafate and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

José M. Cecilia

109 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
José M. Cecilia Spain 20 286 267 260 207 198 118 1.6k
W.J. Melssen Netherlands 24 289 1.0× 178 0.7× 436 1.7× 144 0.7× 47 0.2× 48 2.7k
Yong Liang China 21 562 2.0× 147 0.6× 355 1.4× 91 0.4× 92 0.5× 109 1.7k
Jiawei Luo China 15 435 1.5× 198 0.7× 720 2.8× 59 0.3× 202 1.0× 71 2.1k
Weikuan Jia China 31 103 0.4× 120 0.4× 684 2.6× 82 0.4× 89 0.4× 128 3.2k
Jiřı́ Pospı́chal Slovakia 13 141 0.5× 300 1.1× 397 1.5× 38 0.2× 115 0.6× 57 1.7k
Birmohan Singh India 17 85 0.3× 81 0.3× 591 2.3× 133 0.6× 134 0.7× 62 1.9k
Juan J. Rodríguez Spain 25 282 1.0× 170 0.6× 1.8k 7.1× 82 0.4× 150 0.8× 76 3.3k
Jair Cervantes Mexico 15 126 0.4× 49 0.2× 688 2.6× 130 0.6× 150 0.8× 48 2.3k
Asdrúbal López‐Chau Mexico 12 113 0.4× 48 0.2× 639 2.5× 108 0.5× 137 0.7× 61 2.1k

Countries citing papers authored by José M. Cecilia

Since Specialization
Citations

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

Fields of papers citing papers by José M. Cecilia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of José M. Cecilia

This figure shows the co-authorship network connecting the top 25 collaborators of José M. Cecilia. A scholar is included among the top collaborators of José M. Cecilia 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 José M. Cecilia. José M. Cecilia 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.
Martín, Ángel, et al.. (2025). Edge-enabled GNSS-IR for efficient water level monitoring in harsh environments. Internet of Things. 34. 101766–101766.
2.
Muñoz, Andrés, et al.. (2024). GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management. Journal of Ambient Intelligence and Smart Environments. 16(3). 389–405.
3.
Pietrosémoli, Ermanno, et al.. (2024). AI*LoRa: Enabling Efficient Long-Range Communication with Machine Learning at the Edge. RiuNet (Universitat Politècnica de València). 458–463. 1 indexed citations
4.
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
5.
Hernández, Daniel, José M. Cecilia, Carlos T. Calafate, et al.. (2023). Assignment and Take-Off Approaches for Large-Scale Autonomous UAV Swarms. IEEE Transactions on Intelligent Transportation Systems. 24(5). 4836–4847. 3 indexed citations
6.
Cecilia, José M., et al.. (2022). Low-cost automated GPS, Electrical Conductivity and Temperature sensing device (EC+T Track) and Android platform for water quality monitoring campaigns. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
7.
8.
Hernández, Daniel, José M. Cecilia, Juan‐Carlos Cano, & Carlos T. Calafate. (2022). Flood Detection Using Real-Time Image Segmentation from Unmanned Aerial Vehicles on Edge-Computing Platform. Remote Sensing. 14(1). 223–223. 48 indexed citations
9.
Hernández, Daniel, Juan‐Carlos Cano, Federico Silla, Carlos T. Calafate, & José M. Cecilia. (2021). AI-Enabled Autonomous Drones for Fast Climate Change Crisis Assessment. IEEE Internet of Things Journal. 9(10). 7286–7297. 21 indexed citations
10.
Periñán-Pascual, Carlos, et al.. (2021). COVIDSensing: Social Sensing Strategy for the Management of the COVID-19 Crisis. Electronics. 10(24). 3157–3157. 8 indexed citations
11.
Terroso-Sáenz, Fernando, et al.. (2021). Human Mobility Prediction With Region-Based Flows and Water Consumption. IEEE Access. 9. 88651–88663. 7 indexed citations
12.
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
13.
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
14.
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
15.
Jimeno‐Sáez, Patricia, Javier Senent‐Aparicio, José M. Cecilia, & Julio Pérez‐Sánchez. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental Research and Public Health. 17(4). 1189–1189. 54 indexed citations
16.
Terroso-Sáenz, Fernando, Andrés Muñoz, & José M. Cecilia. (2019). QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis. Sensors. 19(22). 4882–4882. 8 indexed citations
17.
Cecilia, José M., et al.. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems. 19(7). 2246–2257. 8 indexed citations
18.
Navarro, S., et al.. (2018). ENMX: An elastic network model to predict the FOREX market evolution. Simulation Modelling Practice and Theory. 86. 1–10. 15 indexed citations
19.
Jimeno‐Sáez, Patricia, Javier Senent‐Aparicio, Julio Pérez‐Sánchez, David Pulido‐Velazquez, & José M. Cecilia. (2017). Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water. 9(5). 347–347. 39 indexed citations
20.
Guerrero, Ginés D., et al.. (2014). A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing. BioMed Research International. 2014. 1–8. 9 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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