| Records |
| Author |
Patricia Suarez, Henry Velesaca, Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos |
| Title |
Deep Learning based Shrimp Classification |
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Conference Article |
| Year |
2022 |
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17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) |
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13598 LNCS |
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36-45 |
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cidis @ cidis @ |
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194 |
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| Author |
Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca |
| Title |
Transformer based Image Dehazing. |
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Conference Article |
| Year |
2022 |
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16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. |
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148-154 |
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195 |
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| Author |
Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. |
| Title |
Video Analytics in Urban Environments: Challenges and Approaches. |
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Book Chapter |
| Year |
2022 |
Publication |
ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series |
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224 |
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101-122 |
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196 |
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Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca. |
| Title |
Human Body Pose Estimation in Multi-view Environments. |
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Book Chapter |
| Year |
2022 |
Publication |
ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series |
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224 |
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79-99 |
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cidis @ cidis @ |
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197 |
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| Author |
Angel D. Sappa. |
| Title |
ICT Applications for Smart Cities |
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2022 |
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Intelligent Systems Reference Library |
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224 |
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cidis @ cidis @ |
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198 |
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| Author |
Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David |
| Title |
TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION |
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Conference Article |
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2022 |
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IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, |
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2022-August |
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707-714 |
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cidis @ cidis @ |
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199 |
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Ulises Gildardo Quiroz Antúnez, Alejandro Ismael Monterroso Rivas, María Fernanda Calderón Vega, Adán Guillermo Ramírez García |
| Title |
APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE |
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Journal Article |
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2022 |
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Granja |
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Vol. 36 |
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Issue 2 |
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cidis @ cidis @ |
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200 |
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Benítez-Quintero J., Quevedo-Pinos O., Calderon, Fernanda |
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Notes on Sulfur Fluxes in Urban Areas with Industrial Activity |
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Conference Article |
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2022 |
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20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022, |
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2022-July |
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cidis @ cidis @ |
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201 |
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| Author |
Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho |
| Title |
Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework |
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Conference Article |
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2022 |
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Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 |
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256 |
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119-132 |
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In gamification, the development of “visual metrics for educational
video games linked to social assistance robots in the framework of inclusive education” seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child's experiences represents a limitation, especially for those with atypical behaviors. 'LOLY,' a social assistance robot, works together with mobile applications associated with the family of educational video game series called 'MIDI-AM,' forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching-learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games
through established metrics, which allow monitoring the user's facial expressions
during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly
adaptable for inclusive early childhood education. |
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no |
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cidis @ cidis @ |
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180 |
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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. |
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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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252 |
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503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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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cidis @ cidis @ |
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152 |
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