Examinando por Materia "Machine"

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    Ítem
    Descriptive and predictive analytics of the cement and concrete production process
    (2022-06) Madrid Peláez, Juan Pablo; Cardeño Luján, Salomón; Universidad EAFIT
    The paper uses both a Machine Learning and a combined Taylor Modeling with Heuristics approach to predict the Compressive Strength, the main characteristic to determine the physicochemical behaviors of concrete and cement. Implementing a data-based approach, can result in a reduction of the variability of the process by improving the overall reliability. The AdaBoost, ANN and SVM algorithms were implemented, where the AdaBoost performed the best in terms of fitness, error, and predictions, which was expected due to its k-folds nature. To counter the black-box nature of Machine Learning, a Taylor Modeling algorithm was implemented to build the mathematical model of the data with the use of an evolutionary algorithm to find the best parameters for the model.
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    Ítem
    An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
    (SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.