Browsing by Author "Perez-Godoy, Maria Dolores"

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    A preliminary study on crop classification with unsupervised algorithms for time series on images with olive trees and cereal crops
    (Springer, 2020-08-29) Rivera, Antonio Jesus; Perez-Godoy, Maria Dolores; Elizondo, David; Deka, Lipika; del Jesus, Maria Jose
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    Analysis of clustering methods for crop type mapping using satellite imagery
    (Elsevier, 2022-04-06) Rivera, Antonio J.; Perez-Godoy, Maria Dolores; Deka, Lipika; del Jesus, Maria J.; Elizondo, David
    With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in the use of these technologies has to do with the ongoing increment on the spatial–temporal resolution together with the free availability of some of these services. Typically time series are used as a pre-processing technique and combined with supervised learning techniques in order to build models for crop type identification in remote images. However, these models suffer from the lack of labelled data sets needed to train them. Unsupervised classification can overcome this limitation but has been less frequently used in this research field. This paper proposes to test and analyse the performance of several unsupervised clustering algorithms towards crop type identification on remote images. In this manner combinations of clustering algorithms and distance measures, a key element in the behaviour of these algorithms, are studied using an experimental design with more than twenty datasets built from the combinations of five crops and more than 45000 parcels. Results highlight better clustering methods and distance measures to create accurate and novel crop mapping models for remote sensing images.
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    Analysis of Transformer Model Applications
    (Springer, 2023-08-29) Cabrera-Bermejo, M. I.; Del Jesus, M. J.; Rivera, A. J.; Elizondo, David; Charte, F.; Perez-Godoy, Maria Dolores
    Since the emergence of the Transformer, many variations of the original architecture have been created. Revisions and taxonomies have appeared that group these models from different points of view. However, no review studies the tasks faced according to the type of data used. In this paper, the modifications applied to Transformers to work with different input data (text, image, video, etc.) and to solve disparate problems are analysed. Building on the foundations of existing taxonomies, this work proposes a new one that relates input data types to applications. The study shows open challenges and can serve as a guideline for the development of Transformer networks for specific applications with different types of data by observing development trends.
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    XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department
    (Elsevier, 2023-01-20) Munoz, J.; Perez-Godoy, Maria Dolores; San Pedro, B.; Charte, F.; Elizondo, David; Rodriguez, C.; Abolafia, M. L.; Perea, A.; Del Jesus, M. J.; Riveria, A. J.
    Nowadays it is increasingly important in many applications to understand how different factors influence a variable of interest in a predictive modeling process. This task becomes particularly important in the context of Explainable Artificial Intelligence. Knowing the relative impact of each variable on the output allows us to acquire more information about the problem and about the output provided by a model. This paper proposes a new methodology, XAIRE, that determines the relative importance of input variables in a prediction environment, considering multiple prediction models in order to increase generality and avoid bias inherent in a particular learning algorithm. Concretely, we present an ensemble-based methodology that promotes the aggregation of results from several prediction methods to obtain a relative importance ranking. Also, statistical tests are considered in the methodology in order to reveal significant differences between the relative importance of the predictor variables. As a case study, XAIRE is applied to the arrival of patients in a Hospital Emergency Department, which has resulted in one of the largest sets of different predictor variables in the literature. Results show the extracted knowledge related to the relative importance of the predictors involved in the case study.