David Gil

2.6k total citations
64 papers, 1.5k citations indexed

About

David Gil is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, David Gil has authored 64 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 16 papers in Computer Networks and Communications and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in David Gil's work include IoT and Edge/Fog Computing (10 papers), Context-Aware Activity Recognition Systems (6 papers) and Time Series Analysis and Forecasting (5 papers). David Gil is often cited by papers focused on IoT and Edge/Fog Computing (10 papers), Context-Aware Activity Recognition Systems (6 papers) and Time Series Analysis and Forecasting (5 papers). David Gil collaborates with scholars based in Spain, Sweden and Poland. David Gil's co-authors include Higinio Mora, Magnus Johnsson, Jesús Peral, Antonio Ferrández, Sergio Luján‐Mora, Diego Buenaño-Fernández, Julian Szymański, Ricardo Sellers Rubio, Joaquı́n De Juan and María José Gómez‐Torres and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

David Gil

61 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Gil Spain 20 466 340 319 155 153 64 1.5k
Utku Köse Türkiye 21 411 0.9× 215 0.6× 299 0.9× 199 1.3× 107 0.7× 148 1.6k
Giner Alor‐Hernández Mexico 22 483 1.0× 233 0.7× 509 1.6× 180 1.2× 154 1.0× 179 1.9k
Mutasem K. Alsmadi Saudi Arabia 27 359 0.8× 388 1.1× 455 1.4× 306 2.0× 108 0.7× 109 1.9k
Asadullah Shaikh Saudi Arabia 22 537 1.2× 336 1.0× 449 1.4× 210 1.4× 67 0.4× 169 1.9k
Walayat Hussain Australia 22 536 1.2× 416 1.2× 601 1.9× 136 0.9× 145 0.9× 95 1.8k
Vicente García‐Díaz Spain 24 580 1.2× 398 1.2× 488 1.5× 257 1.7× 57 0.4× 120 2.0k
Paulo Nováis Portugal 24 720 1.5× 246 0.7× 269 0.8× 468 3.0× 163 1.1× 268 2.4k
Fahima Hajjej Saudi Arabia 19 357 0.8× 312 0.9× 382 1.2× 149 1.0× 100 0.7× 58 1.3k
Rachid Benlamri Canada 20 345 0.7× 349 1.0× 632 2.0× 208 1.3× 70 0.5× 78 1.3k
Asadullah Shah Malaysia 22 372 0.8× 231 0.7× 457 1.4× 214 1.4× 127 0.8× 214 1.8k

Countries citing papers authored by David Gil

Since Specialization
Citations

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

Fields of papers citing papers by David Gil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Gil

This figure shows the co-authorship network connecting the top 25 collaborators of David Gil. A scholar is included among the top collaborators of David Gil 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 David Gil. David Gil 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.
Baños, Oresti, et al.. (2024). Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review. International Journal of Medical Informatics. 187. 105469–105469. 7 indexed citations
2.
Ferrández, Antonio, Rocío Lavigne-Cerván, Jesús Peral, et al.. (2024). CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?. PeerJ Computer Science. 10. e1866–e1866. 3 indexed citations
3.
Villalonga, Claudia, et al.. (2023). Deep Learning Approaches Applied to MRI and PET Image Classification of Kidney Tumours: A Systematic Review. Lecture notes in computer science. 254–265.
4.
Beuschlein, Felix, Judith Favier, David Gil, et al.. (2023). Deep Learning Approaches Applied to Image Classification of Renal Tumors: A Systematic Review. Archives of Computational Methods in Engineering. 31(2). 615–622. 6 indexed citations
5.
Buenaño-Fernández, Diego, Mario González, David Gil, & Sergio Luján‐Mora. (2020). Text Mining of Open-Ended Questions in Self-Assessment of University Teachers: An LDA Topic Modeling Approach. IEEE Access. 8. 35318–35330. 78 indexed citations
6.
Szymański, Julian, et al.. (2020). Framework for Integration Decentralized and Untrusted Multi-Vendor IoMT Environments. IEEE Access. 8. 108102–108112. 8 indexed citations
7.
Szymański, Julian, et al.. (2020). Practical I-Voting on Stellar Blockchain. Applied Sciences. 10(21). 7606–7606. 12 indexed citations
8.
Peral, Jesús, et al.. (2020). Using Visualization to Build Transparency in a Healthcare Blockchain Application. Sustainability. 12(17). 6768–6768. 11 indexed citations
9.
Peral, Jesús, et al.. (2020). A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening. Electronics. 9(3). 516–516. 16 indexed citations
10.
Buenaño-Fernández, Diego, David Gil, & Sergio Luján‐Mora. (2019). Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study. Sustainability. 11(10). 2833–2833. 101 indexed citations
11.
Gil, David, Magnus Johnsson, Higinio Mora, & Julian Szymański. (2019). Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems. Complexity. 2019(1). 4 indexed citations
12.
Gil, David, José Luis Fernández‐Alemán, Juan Trujillo, et al.. (2018). The Effect of Green Software: A Study of Impact Factors on the Correctness of Software. Sustainability. 10(10). 3471–3471. 14 indexed citations
13.
Gil, David & Il‐Yeol Song. (2015). Modeling and Management of Big Data: Challenges and opportunities. Future Generation Computer Systems. 63. 96–99. 39 indexed citations
14.
Gil, David, et al.. (2013). Semen Parameters Can Be Predicted from Environmental Factors and Lifestyle Using Artificial Intelligence Methods1. Biology of Reproduction. 88(4). 99–99. 43 indexed citations
15.
Payà, Antonio, Daniel Ruíz Fernández, David Gil, Juan Manuel García‐Chamizo, & Francisco Maciá Pérez. (2012). Mathematical modelling of the lower urinary tract. Computer Methods and Programs in Biomedicine. 109(3). 323–338. 13 indexed citations
16.
García‐Rodríguez, José, et al.. (2012). Using GNG to improve 3D feature extraction—Application to 6DoF egomotion. Neural Networks. 32. 138–146. 15 indexed citations
17.
Johnsson, Magnus & David Gil. (2011). Internal Simulation of Perceptions and Actions. Advances in experimental medicine and biology. 718. 87–100. 1 indexed citations
18.
Johnsson, Magnus, David Gil, Christian Balkenius, & Germund Hesslow. (2010). Supervised Architectures for Internal Simulation of Perceptions and Actions. Lund University Publications (Lund University). 5 indexed citations
19.
Jimeno-Morenilla, Antonio, et al.. (2010). Development of multidisciplinary practical lessons through research-action methodology in the faculties of computer science and educational psychology. RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante). 5487–5493. 1 indexed citations
20.
McNamara, Danielle S., Max M. Louwerse, Xiangen Hu, et al.. (2004). NLS: A Non-Latent Similarity Algorithm. eScholarship (California Digital Library). 26(26). 180–185. 7 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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