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Grant support

The Universidad de Valladolid supported this study with the predoctoral contracts of 2020, co-funded by Santander Bank. This work has been financed also by the Spanish Ministry of Science and Innovation, under project PID2020-113533RB-C33. The Universidad de Valladolid also supported this study with ERASMUS+ KA-107. Finally, we have to thank the MOVILIDAD DE DOCTORANDOS Y DOCTORANDAS UVa 2023 from the University of Valladolid. We also appreciate the help of other members of our departments.

Analysis of institutional authors

Mateo-Romero, Hector FelipeCorresponding Author

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March 11, 2025
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Proceedings Paper
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Enhancing Solar Cell Classification Using Mamdani Fuzzy Logic Over Electroluminescence Images: A Comparative Analysis with Machine Learning Methods

Publicated to:Communications in Computer and Information Science. 1938 159-173 - 2024-01-01 1938(), DOI: 10.1007/978-3-031-52517-9_11

Authors: Mateo-Romero, Hector Felipe; Carbono dela Rosa, Mario Eduardo; Hernandez-Callejo, Luis; Gonzalez-Rebollo, Miguel Angel; Cardenoso-Payo, Valentin; Alonso-Gomez, Victor; Gallardo-Saavedra, Sara

Affiliations

Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico - Author
Univ Valladolid, Valladolid, Spain - Author

Abstract

This work presents a Mamdani Fuzzy Logic model capable of classifying solar cells according to their energetic performance. The model has 3 different inputs: The proportion of black pixels, gray pixels, and white pixels. One additional output for informing of possible bad inputs is also provided. The three values are obtained from an Electroluminescence image of the cell. The model has been developed using cells whose performance has been obtained by measuring the Intensity-Voltage Curves of the cells. The performance of the model has been shown by testing it with a validation set, obtaining a 99.0% of accuracy, when other methods such as Ensemble Classifiers and Decision Trees obtain a 97.7%. This shows that the presented model is capable of solving the problem better than traditional Machine Learning methods.

Keywords

ElectroluminescenceFuzzy logicMachine learninMachine learningPhotovoltaic

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Communications in Computer and Information Science, Q4 Agency Scopus (SJR), its regional focus and specialization in Computer Science (Miscellaneous), give it significant recognition in a specific niche of scientific knowledge at an international level.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-09:

  • WoS: 1
  • Scopus: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Mexico.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MATEO ROMERO, HECTOR FELIPE) .

the author responsible for correspondence tasks has been MATEO ROMERO, HECTOR FELIPE.