Fernando Sánchez Lasheras

4.0k total citations · 1 hit paper
139 papers, 2.8k citations indexed

About

Fernando Sánchez Lasheras is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Fernando Sánchez Lasheras has authored 139 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Biomedical Engineering, 16 papers in Electrical and Electronic Engineering and 14 papers in Artificial Intelligence. Recurrent topics in Fernando Sánchez Lasheras's work include Dental Implant Techniques and Outcomes (11 papers), Bone Tissue Engineering Materials (10 papers) and Fault Detection and Control Systems (9 papers). Fernando Sánchez Lasheras is often cited by papers focused on Dental Implant Techniques and Outcomes (11 papers), Bone Tissue Engineering Materials (10 papers) and Fault Detection and Control Systems (9 papers). Fernando Sánchez Lasheras collaborates with scholars based in Spain, Poland and Chile. Fernando Sánchez Lasheras's co-authors include Francisco Javier de Cos Juez, P.J. Garcı́a Nieto, Esperanza García–Gonzalo, Celestino Ordóñez, Ana Suárez Sánchez, Javier Roca‐Pardiñas, Javier de Andrés Suárez, Pedro Tedde de Lorca, Nieves Roqueñí and Alicja Krzemień and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Journal of Hazardous Materials.

In The Last Decade

Fernando Sánchez Lasheras

130 papers receiving 2.7k citations

Hit Papers

align trajectories

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.

Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability

2015 256 citations P.J. Garcı́a Nieto, Esperanza García–Gonzalo et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fernando Sánchez Lasheras Spain	29	535	428	381	368	367	139	2.8k
Francisco Javier de Cos Juez Spain	27	548 1.0×	387 0.9×	377 1.0×	322 0.9×	314 0.9×	71	2.2k
Xuan Song China	38	562 1.1×	263 0.6×	478 1.3×	364 1.0×	800 2.2×	199	4.6k
Eric Wai Ming Lee Hong Kong	37	789 1.5×	476 1.1×	428 1.1×	596 1.6×	288 0.8×	170	4.5k
Yanyan Chen China	35	563 1.1×	597 1.4×	715 1.9×	169 0.5×	290 0.8×	352	5.8k
P.J. Garcı́a Nieto Spain	37	862 1.6×	561 1.3×	881 2.3×	980 2.7×	352 1.0×	162	4.4k
Melvin Alexander United States	18	476 0.9×	314 0.7×	121 0.3×	198 0.5×	279 0.8×	31	5.0k
Jean‐Michel Poggi France	17	319 0.6×	166 0.4×	150 0.4×	552 1.5×	491 1.3×	47	3.5k
Matthijs J. Warrens Netherlands	26	444 0.8×	147 0.3×	133 0.3×	410 1.1×	700 1.9×	82	4.8k
Fouzi Harrou Saudi Arabia	43	1.1k 2.1×	1.4k 3.2×	311 0.8×	462 1.3×	1.7k 4.6×	204	5.3k
Jane W. Z. Lu China	38	449 0.8×	619 1.4×	505 1.3×	1.8k 4.9×	326 0.9×	293	5.6k

Countries citing papers authored by Fernando Sánchez Lasheras

Since Specialization

Citations

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

Fields of papers citing papers by Fernando Sánchez Lasheras

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando Sánchez Lasheras. 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 Fernando Sánchez Lasheras. The network helps show where Fernando Sánchez Lasheras may publish in the future.

Co-authorship network of co-authors of Fernando Sánchez Lasheras

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Sánchez Lasheras. A scholar is included among the top collaborators of Fernando Sánchez Lasheras 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 Fernando Sánchez Lasheras. Fernando Sánchez Lasheras 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

Herrero‐Climent, Mariano, Fernando Sánchez Lasheras, J. Israel Martínez-López, Javier Gil, & Aritza Brizuela‐Velasco. (2025). Mechanical Behavior of PEEK and PMMA Graphene and Ti6Al4V Implant-Supported Frameworks: In Silico Study. Materials. 18(2). 441–441. 1 indexed citations

Nieto, P.J. Garcı́a, et al.. (2024). Time series analysis for COMEX platinum spot price forecasting using SVM, MARS, MLP, VARMA and ARIMA models: A case study. Resources Policy. 95. 105148–105148. 7 indexed citations

Clarà, Marc, Fernando Sánchez Lasheras, Leticia González-Blanco, et al.. (2023). An empirical staging model for schizophrenia using machine learning. European Psychiatry. 66(S1). S626–S627. 1 indexed citations

González‐Santander, Juan Luis & Fernando Sánchez Lasheras. (2023). Sums Involving the Digamma Function Connected to the Incomplete Beta Function and the Bessel functions. Mathematics. 11(8). 1937–1937. 2 indexed citations

Lasheras, Fernando Sánchez, et al.. (2022). Computer vision syndrome in healthcare workers using video display terminals: an exploration of the risk factors. Journal of Advanced Nursing. 78(7). 2095–2110. 22 indexed citations

Lasheras, Fernando Sánchez, et al.. (2022). Resultados obtenidos en la prueba MIR de 2021, según nacionalidad y baremo académico. Educación Médica. 25(5). 205–205.

Lasheras, Fernando Sánchez, Vicente Martín, Sergio Luis Suárez Gómez, et al.. (2022). A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines. Mathematics. 10(7). 1024–1024. 3 indexed citations

Lasheras, Fernando Sánchez, et al.. (2022). Work-related overexertion injuries in cleaning occupations: An exploration of the factors to predict the days of absence by means of machine learning methodologies. Applied Ergonomics. 105. 103847–103847. 4 indexed citations

Nieto, P.J. Garcı́a, Esperanza García–Gonzalo, Fernando Sánchez Lasheras, José P. Paredes–Sánchez, & Pedro Riesgo Fernández. (2019). Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques. Journal of Computational and Applied Mathematics. 357. 284–301. 71 indexed citations

10.

Lasheras, Fernando Sánchez, et al.. (2019). The MIR 2018 Exam: Psychometric Study and Comparison with the Previous Nine Years. Medicina. 55(12). 751–751. 2 indexed citations

11.

Curbelo, José, José María Galván‐Román, Elena H. Martínez‐Lapiscina, et al.. (2017). Popularidad de Neurología en España: análisis de la elección de la especialidad. Neurología. 35(8). 543–550. 7 indexed citations

12.

García, Celso Iglesias, Pilar A. Sáiz, Manuel Bousoño, et al.. (2016). Possible delayed effect of unemployment on suicide. European Psychiatry. 2 indexed citations

13.

Ordóñez, Celestino, Fernando Sánchez Lasheras, Francisco Javier de Cos Juez, & Antonio Bernardo-Sánchez. (2016). Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions. Journal of Computational and Applied Mathematics. 311. 704–717. 34 indexed citations

14.

Cuesta, E., et al.. (2015). A Statistical Approach To Prediction Of The CMM Drift Behaviour Using A Calibrated Mechanical Artefact. Metrology and Measurement Systems. 22(3). 417–428. 9 indexed citations

15.

Nieto, P.J. Garcı́a, Fernando Sánchez Lasheras, Francisco Javier de Cos Juez, & J.R. Alonso Fernández. (2011). Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain). Journal of Hazardous Materials. 195. 414–421. 33 indexed citations

16.

Suárez, Javier de Andrés, Fernando Sánchez Lasheras, Pedro Tedde de Lorca, & Francisco Javier de Cos Juez. (2011). A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy. Journal of Accounting and Management Information Systems. 10(3). 351–374. 22 indexed citations

17.

Juez, Francisco Javier de Cos, et al.. (2010). Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Mathematical and Computer Modelling. 54(7-8). 1665–1670. 16 indexed citations

18.

Guzmán, Dani, Francisco Javier de Cos Juez, Fernando Sánchez Lasheras, Richard M. Myers, & Laura K. Young. (2010). Deformable mirror model for open-loop adaptive optics using multivariate adaptive regression splines. Optics Express. 18(7). 6492–6492. 27 indexed citations

19.

Lasheras, Fernando Sánchez, J.A. Vilán, P.J. Garcı́a Nieto, & Juan José del Coz Díaz. (2010). The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process. Mathematical and Computer Modelling. 52(7-8). 1169–1176. 47 indexed citations

20.

Juez, Francisco Javier de Cos, et al.. (2009). Study of posterolateral lumbar arthrodesis by means of a finite element model. Mathematical and Computer Modelling. 50(5-6). 680–694. 2 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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