Browsing by Author "González Toral, Hernán Santiago"

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A ranking-based approach for supporting the initial selection of primary studies in a Systematic Literature Review
(Institute of Electrical and Electronics Engineers Inc., 2019) Freire Zurita, Renan Gonzalo; González Toral, Hernán Santiago; Saquicela Galarza, Víctor Hugo; Gualán Saavedra, Ronald Marcelo
Traditionally most of the steps involved in a Systematic Literature Review (SLR) process are manually executed, causing inconvenience of time and effort, given the massive amount of primary studies available online. This has motivated a lot of research focused on automating the process. Current state-of-the-art methods combine active learning methods and manual selection of primary studies from a smaller set so they can maximize the finding of relevant papers while at the same time minimizing the number of manually reviewed papers. In this work, we propose a novel strategy to further improve these methods whose early success heavily depends on an effective selection of initial papers to be read by researchers using a PCAbased method which combines different document representation and similarity metric approaches to cluster and rank the content within the corpus related to an enriched representation of research questions within the SLR protocol. Validation was carried out over four publicly available data sets corresponding to SLR studies from the Software Engineering domain. The proposed model proved to be more efficient than a BM25 baseline model as a mechanism to select the initial set of relevant primary studies within the top 100 rank, which makes it a promising method to bootstrap an active learning cycle.
Implementación de un sistema para dispositivos móviles. Caso páctico: manejo de autorizaciones remotas en una entidad financiera
(2012) González Toral, Hernán Santiago; Malgiaritta Morales, Oscar Federico; Méndez Rojas, Mabel
Land cover classification of high resolution images from an ecuadorian andean zone using deep convolutional neural networks and transfer learning
(2020) González Toral, Hernán Santiago; Saquicela Galarza, Víctor Hugo; Lupercio Novillo, Rosa Lucía
Different deep learning models have recently emerged as a popular method to apply machine learning in a variety of domains including remote sensing, where several approaches for the classification of land cover and use have been proposed. However, acquiring a suitably large data set with labelled samples for training such models is often a significant challenge to tackle, that leads to suboptimal models not being able to generalize well over different types of land cover. In this paper, we present an approach to perform land cover classification on a small dataset of high-resolution imagery from an area in the Andes of Ecuador using deep convolutional neural networks and techniques such as transfer learning, data augmentation, and some finetuning considerations. Results demonstrated that this method can achieve good classification accuracies if it is backed with good strategies to increase the number of samples in an imbalanced dataset.