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Author |
Rosero Vasquez Shendry |
Title |
Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. |
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2020 |
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615-625 |
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cidis @ cidis @ |
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145 |
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Viñán-Ludeña, M.S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. |
Title |
Smart university: an architecture proposal for information management using open data for research projects. |
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Journal Article |
Year |
2020 |
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Advances in Intelligent Systems and Computing |
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1137 AISC, 2020 |
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172-178 |
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no |
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cidis @ cidis @ |
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188 |
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Author |
Steven Silva, Dennys Paillacho., David Soque, María Guerra & Jonathan Paillacho |
Title |
Autonomous Intelligent Navigation For Mobile Robots In Closed Environments. |
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Conference Article |
Year |
2021 |
Publication |
The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science |
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1388 |
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391-402 |
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no |
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cidis @ cidis @ |
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187 |
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Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. |
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Conference Article |
Year |
2021 |
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In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 |
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2703-2712 |
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no |
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cidis @ cidis @ |
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148 |
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Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
Title |
LiNet: A Lightweight Network for Image Super Resolution |
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Conference Article |
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2021 |
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25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 |
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7196-7202 |
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no |
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cidis @ cidis @ |
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149 |
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Author |
Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. |
Title |
Thermal Image Super-Resolution Challenge – PBVS 2021. |
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Conference Article |
Year |
2021 |
Publication |
In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 |
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4354-4362 |
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no |
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cidis @ cidis @ |
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151 |
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Author |
Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. |
Title |
Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. |
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Conference Article |
Year |
2021 |
Publication |
The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science |
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1388 |
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164-178 |
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no |
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cidis @ cidis @ |
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191 |
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Author |
Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza |
Title |
Open innovation at university: a systematic literature review |
Type |
Journal Article |
Year |
2020 |
Publication |
Advances in Intelligent Systems and Computing |
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1159 AISC, 2020 |
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3-14 |
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no |
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cidis @ cidis @ |
Serial |
189 |
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Author |
Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. |
Title |
Social media influence: a comprehensive review in general and in tourism domain |
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Journal Article |
Year |
2020 |
Publication |
Smart Innovation, Systems and Technologies. |
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171, 2020 |
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25-35 |
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no |
Call Number |
cidis @ cidis @ |
Serial |
190 |
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Author |
Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
Title |
Time series in sensor data using state of the art deep learning approaches: A systematic literature review. |
Type |
Conference Article |
Year |
2022 |
Publication |
VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. |
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Vol. 252 |
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Pages |
503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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no |
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cidis @ cidis @ |
Serial |
152 |
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