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Wilton Agila, G. R., Raul M. del Toro, Livington Miranda. (2023). Qualitative model for an oxygen therapy system based on Renewable Energy. In 12th International Conference on Renewable Energy Research and Applications (ICRERA 2023) Oshawa 29 August – 1 September 2023 (365–371).
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Patricia Súarez, H. V., Dario Carpio & Angel Sappa. (2023). Corn Kernel Classification From Few Training Samples. In journal Artificial Intelligence in Agriculture, Vol. 9, pp. 89–99.
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Rafael E. Rivadeneira, H. O. V., Angel D. Sappa. (2023). Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach. In 17th International Conference On Signal Image Technology & Internet Based System, Bangkok, 8-10 November 2023 (pp. 311–318).
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Patricia L. Suarez, D. C., Angel Sappa. (2023). Boosting Guided Super-Resolution Performance with Synthesized Images. In 17th International Conference On Signal Image Technology & Internet Based Systems, Bangkok, 8-10 November 2023 (pp. 189–195).
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Patricia L. Suarez, D. C., Angel Sappa. (2023). Depth Map Estimation from a Single 2D Image. In 17th International Conference On Signal Image Technology & Internet Based Systems, Bangkok, 8-10 November 2023 (pp. 347–353).
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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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Xavier Soria, Y. L., Mohammad Rouhani & Angel D. Sappa. (2023). Tiny and Efficient Model for the Edge Detection Generalization. In Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023) Paris 2-6 October 2023 (pp. 1356–1365).
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Rubio Abel, Agila Wilton, González Leandro, & Aviles Jonathan. (2023). A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells. In 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 (pp. 273–278).
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Suarez Patricia, Carpio Dario, & Sappa Angel D. (2023). A Deep Learning Based Approach for Synthesizing Realistic Depth Maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 (Vol. 14234 LNCS, pp. 369–380).
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Jacome-Galarza L.-R., R. R. M. - A., Paillacho Corredores J., Benavides Maldonado J.-L. (2022). Time series in sensor data using state of the art deep learning approaches: A systematic literature review. In VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. (Vol. Vol. 252, pp. 503–514).
Abstract: IoT (Internet of Things) and AI (Artificial Intelligence) are becoming
support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity
recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results.
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