Juan José Vargas Morales
Labor:
Unidad académica:
Es estudiante:
No
Programa en que estudia:
Proyectos
Riesgos de privacidad en dispositivos residenciales de Internet de las Cosas usados en Costa Rica
Número de proyecto:
834-B7-268
De 07/Ago/2017 hasta 28/Feb/2019
Clasificación de vehículos y peatones en vídeos de tráfico vial
Número de proyecto:
834-C0-006
De 09/Mar/2021 hasta 08/Mar/2023
Publicaciones
Emotions Classifier based on Facial Expressions
Oct
2021
Descripción:
Emotion recognition is important in the context of smart buildings and IoT, because it allows the environment to have a better notion of the mood of the humans who are present. With a view to developing such projects, in this article we analyze the performance of an emotion classifier that uses a convolutional neural network. Specifically, we focus on analyzing the impact of the epochs and batch size hyperparameters. To do this, we propose an experimental design with the following hypothesis: "The number of epochs that the model trains and the size of the batch given by iteration in each epoch influence the accuracy of an emotion classifier built from networks. convolutional neurons using the VGG16 architecture".
Tipo de publicación: Conference Paper
Publicado en: 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)
Asynchronous Detection of Slowloris Attacks Via Random Forests
Oct
2021
Descripción:
An asynchronous classifier of network flows was developed to detect Slowloris attacks. This classifier was implemented using random forests and its effectiveness was measured by the area under the ROC curve. These random forests were trained from a public dataset. We sought to minimize the number of necessary features that are required to analyze the flows satisfactorily. Finally, it was shown that the chosen features can be used individually to obtain reliable detections in the classifier, with two of the three individual features having an area under the curve greater than 0.95.
Tipo de publicación: Conference Paper
Publicado en: 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)
Recognizing daily-life activities using sensor-collected data in a kitchen
Oct
2021
Descripción:
This paper focuses on the recognition and classification of Activities of Daily Living (ADLs) that are carried out in a kitchen. To do this, a Recurrent Neural Network architecture of the Long-Short Term Memory (LSTM) type is implemented as a classifier. The ARAS dataset is used for training and evaluation. A classifier is obtained with an average value in the F1 metric of 95.33% for the chosen data set.
Tipo de publicación: Conference Paper
Publicado en: 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)