M.C. Pegalajar

1.8k total citations
75 papers, 1.3k citations indexed

About

M.C. Pegalajar is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Building and Construction. According to data from OpenAlex, M.C. Pegalajar has authored 75 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 29 papers in Electrical and Electronic Engineering and 10 papers in Building and Construction. Recurrent topics in M.C. Pegalajar's work include Neural Networks and Applications (21 papers), Energy Load and Power Forecasting (20 papers) and Analytical Chemistry and Sensors (10 papers). M.C. Pegalajar is often cited by papers focused on Neural Networks and Applications (21 papers), Energy Load and Power Forecasting (20 papers) and Analytical Chemistry and Sensors (10 papers). M.C. Pegalajar collaborates with scholars based in Spain, Saudi Arabia and United Kingdom. M.C. Pegalajar's co-authors include Manuel Pegalájar Cuéllar, Miguel Delgado‐Rodríguez, L. G. B. Ruiz, Armando Blanco, L.F. Capitán‐Vallvey, Ignacio de Orbe-Payá, Manuel I. Capel, Miguel Molina-Solana, Alfonso Salinas‐Castillo and Rossella Arcucci and has published in prestigious journals such as SHILAP Revista de lepidopterología, Analytical Chemistry and The Science of The Total Environment.

In The Last Decade

M.C. Pegalajar

73 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M.C. Pegalajar Spain 19 418 366 167 147 137 75 1.3k
Manuel Pegalájar Cuéllar Spain 18 343 0.8× 356 1.0× 185 1.1× 135 0.9× 89 0.6× 64 1.2k
Xu Yang China 20 294 0.7× 334 0.9× 209 1.3× 64 0.4× 283 2.1× 98 1.4k
Chunjie Li China 14 166 0.4× 568 1.6× 126 0.8× 30 0.2× 85 0.6× 58 886
Yiping Liu China 24 972 2.3× 469 1.3× 229 1.4× 57 0.4× 183 1.3× 84 2.4k
Zhenyu Lu China 24 549 1.3× 272 0.7× 148 0.9× 32 0.2× 183 1.3× 120 1.7k
S. Kannan India 23 223 0.5× 1.8k 4.9× 143 0.9× 50 0.3× 454 3.3× 157 2.6k
Mengshi Li China 22 150 0.4× 1.0k 2.7× 66 0.4× 106 0.7× 616 4.5× 127 1.6k
Chengke Wu China 29 181 0.4× 632 1.7× 147 0.9× 574 3.9× 49 0.4× 168 2.8k
Alessandro Massaro Italy 21 126 0.3× 355 1.0× 370 2.2× 29 0.2× 68 0.5× 198 1.7k
Yuan Chen China 26 159 0.4× 664 1.8× 170 1.0× 32 0.2× 105 0.8× 102 2.3k

Countries citing papers authored by M.C. Pegalajar

Since Specialization
Citations

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

Fields of papers citing papers by M.C. Pegalajar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M.C. Pegalajar

This figure shows the co-authorship network connecting the top 25 collaborators of M.C. Pegalajar. A scholar is included among the top collaborators of M.C. Pegalajar 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 M.C. Pegalajar. M.C. Pegalajar 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.
Ruiz, L. G. B., et al.. (2025). A parallel approach to accelerate neural network hyperparameter selection for energy forecasting. Expert Systems with Applications. 279. 127386–127386.
2.
Balfaqih, Mohammed, et al.. (2025). Deep Feature Selection of Meteorological Variables for LSTM-Based PV Power Forecasting in High-Dimensional Time-Series Data. Algorithms. 18(8). 496–496. 3 indexed citations
3.
Ruiz, L. G. B., et al.. (2024). A GPU-accelerated adaptation of the PSO algorithm for multi-objective optimization applied to artificial neural networks to predict energy consumption. Applied Soft Computing. 160. 111711–111711. 12 indexed citations
4.
Ruiz, L. G. B., et al.. (2024). A Novel Non-Intrusive Load Monitoring Algorithm for Unsupervised Disaggregation of Household Appliances. Information. 15(2). 87–87. 3 indexed citations
5.
Ruiz, L. G. B., et al.. (2024). An Application of Fuzzy Symbolic Time-Series for Energy Demand Forecasting. International Journal of Fuzzy Systems. 26(3). 703–717. 1 indexed citations
6.
Ruiz, L. G. B., et al.. (2023). Application of classical and advanced machine learning models to predict personality on social media. Expert Systems with Applications. 216. 119498–119498. 15 indexed citations
7.
Ruiz, L. G. B., et al.. (2023). Study of violence against women and its characteristics through the application of text mining techniques. International Journal of Data Science and Analytics. 18(1). 35–48. 1 indexed citations
8.
Ruiz, L. G. B., et al.. (2023). An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks. Big Data and Cognitive Computing. 7(2). 92–92. 5 indexed citations
9.
Ruiz, L. G. B., et al.. (2023). Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption. SHILAP Revista de lepidopterología. 5(2). 431–447. 4 indexed citations
10.
Ruiz, L. G. B., et al.. (2023). Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings. International Journal of Intelligent Systems. 2023(1). 1 indexed citations
11.
Pegalajar, M.C., et al.. (2023). Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution. AgriEngineering. 5(1). 355–368. 4 indexed citations
12.
Ruiz, L. G. B., et al.. (2023). Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques. Expert Systems with Applications. 231. 120816–120816. 16 indexed citations
13.
Ruiz, L. G. B., et al.. (2022). Electric demand forecasting with neural networks and symbolic time series representations. Applied Soft Computing. 122. 108871–108871. 14 indexed citations
14.
Ruiz, L. G. B., et al.. (2022). Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study. Energies. 15(22). 8732–8732. 15 indexed citations
15.
Ruiz, L. G. B., et al.. (2020). An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling. International Journal of Approximate Reasoning. 121. 23–38. 17 indexed citations
16.
Pegalajar, M.C.. (2019). Individual Learning Strategies in Novels Students of Education. SHILAP Revista de lepidopterología. 1 indexed citations
17.
Erenas, Miguel M., et al.. (2011). A surface fit approach with a disposable optical tongue for alkaline ion analysis. Analytica Chimica Acta. 694(1-2). 128–135. 10 indexed citations
18.
Delgado‐Rodríguez, Miguel, Manuel Pegalájar Cuéllar, & M.C. Pegalajar. (2008). Multiobjective Hybrid Optimization and Training of Recurrent Neural Networks. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 38(2). 381–403. 41 indexed citations
19.
Blanco, Armando, Miguel Delgado‐Rodríguez, & M.C. Pegalajar. (2001). Fuzzy automaton induction using neural network. International Journal of Approximate Reasoning. 27(1). 1–26. 21 indexed citations
20.
Blanco, Armando, Miguel Delgado‐Rodríguez, & M.C. Pegalajar. (2000). A genetic algorithm to obtain the optimal recurrent neural network. International Journal of Approximate Reasoning. 23(1). 67–83. 60 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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