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Armin Mehri, P. B., Dario Carpio, and Angel D. Sappa. (2023). SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution. IEEE access, Vol. 11, 121457–121469.
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Gisel Bastidas-Guacho, P. M. - V., Boris Vintimilla, Angel D. Sappa. (2023). Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches. In IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7.
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Pabelco Zambrano, F. C., Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe. (2023). UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review. In IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7.
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Boris Vintimilla, J. V., Henry Velesaca. (2023). Deep Learning-based Human Height Estimation from a Stereo Vision System. In IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7.
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Henry O. Velesaca, P. L. S., Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. (2022). Video Analytics in Urban Environments: Challenges and Approaches. In ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series (Vol. 224, pp. 101–122).
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Jorge L. Charco, A. D. S., Boris X. Vintimilla, Henry O. Velesaca. (2022). Human Body Pose Estimation in Multi-view Environments. In ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series (Vol. 224, pp. 79–99).
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Ulises Gildardo Quiroz Antúnez, A. I. M. R., María Fernanda Calderón Vega, Adán Guillermo Ramírez García. (2022). APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE. Granja, Vol. 36(Issue 2).
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Patricia L. Suárez, A. D. S. and B. X. V. (2021). Deep learning-based vegetation index estimation. In Generative Adversarial Networks for Image-to-Image Translation Book. (Vol. Chapter 9, pp. 205–232).
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Nayeth I. Solorzano Alcivar, R. L., Stalyn Gonzabay Yagual, & Boris X. Vintimilla. (2020). Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language. In ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 (Vol. 77).
Abstract: This paper explains how Natural Language (NL) processing by computers, through smart
programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written
statements. The study recognized the need to improve the implemented web platform using a
dashboard in which we collected a set of extensive data to measure assessment factors of using
children´s educational games. In this case, applying NL is a strategy to give assessments, build, and
display more precise written statements to enhance the understanding of children´s gaming behavior.
We propose the development of a new tool to assess the use of written explanations rather than a
statistical representation of feedback information for the comprehension of parents and teachers with
a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim
explanations of children´s behavior playing educational videogames as NL interpretation instead of
statistical representations. An educational series of digital game applications for mobile devices,
identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a
dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary
schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing
the degrees of playability and usability factors obtained from the data produced when children play a
MIDI game. The results obtained are presented in a comprehensive guiding evaluation report
applying NL for parents and teachers. These guiding evaluations are useful to enhance children's
learning understanding related to the school curricula applied to ludic digital games.
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Abel Rubio, W. A., Leandro González & Jonathan Aviles-Cedeno. (2023). Distributed Intelligence in Autonomous PEM Fuel Cell Control. Energies 2023, Vol. 16(Issue 12).
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