Examinando por Materia "Deep Learning"
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Publicación Deep Learning como alternativa en la predicción del precio de las acciones del mercado de valores colombiano(Universidad EAFIT, 2021) Uribe Ramírez, Sebastián; Almonacid Hurtado, Paula MaríaPublicación Development of a machine learning-based methodology for an automatic control model in a Kaolin washing process(Universidad EAFIT, 2023) Contreras Buitrago, Oscar Javier; Martínez Vargas, Juan David; Organización CoronaPublicación Diagnosis evaluation of the coffee leaf rust development stage in the colombian Caturra variety integrating remote sensing, wireless sensor networks and deep learning(Universidad EAFIT, 2019) Sánchez Aristizábal, Alejandro; Sarmiento Garavito, Sebastián; Velásquez Rendón, DavidPublicación FocusNET : an autofocusing learning‐based model for digital lensless holographic microscopy(Universidad EAFIT, 2023) Montoya Zuluaga, Manuel; Trujillo Anaya, Carlos Alejandro; Lopera Acosta, María JosefThis paper reports on a convolutional neural network (CNN) – based regression model, called FocusNET, to predict the accurate reconstruction distance of raw holograms in Digital Lensless Holographic Microscopy (DLHM). This proposal provides a physical-mathematical formulation to extend its use to different DLHM setups than the optical and geometrical conditions utilized for recording the training dataset; this unique feature is tested by applying the proposal to holograms of diverse samples recorded with different DLHM setups. Additionally, a comparison between FocusNET and conventional autofocusing methods in terms of processing times and accuracy is provided. Although the proposed method predicts reconstruction distances with approximately 54 µm standard deviation, accurate information about the samples in the validation dataset is still retrieved. When compared to a method that utilizes a stack of reconstructions to find the best focal plane, FocusNET performs 600 times faster, as no hologram reconstruction is needed. When implemented in batches, the network can achieve up to a 1200-fold reduction in processing time, depending on the number of holograms to be processed. The training and validation datasets, and the code implementations, are hosted on a public GitHub repository that can be freely accessed.Publicación Integración multiómica e Inteligencia Artificial en NSCLC : desde los biomarcadores predictivos hasta los modelos predictivos integrales(Universidad EAFIT, 2025-10-10) Restrepo López, Juan Carlos; Fernández, Geysson JavierPublicación Predicción de direcciones de activos financieros basados en la volatilidad en series temporales utilizando machine learning(Universidad EAFIT, 2026-02-24) Holguín Carvalho, Mateo; Velasco Vera, Henry GiovannyIdentifying effective trading signals in financial assets is a challenge that draws attention across multiple disciplines due to the volatile and dynamic nature of financial markets. The complexity investors face stems from the wide range of factors that influence asset prices, including macroeconomic variables, corporate decisions, and unexpected events, making it difficult to obtain precise estimates of future movements. This is particularly relevant for investors seeking to build portfolios that maximize returns. In this context, some variables exhibit stronger relationships with market-driven factors, making them useful indicators for anticipating price direction. Nevertheless, recent advances in computing and in Machine Learning and Deep Learning techniques have enabled the development of more sophisticated models that facilitate this task. This study compares time-series-based machine learning methodologies, specifically LSTM neural networks and LightGBM decision-tree models, while incorporating Conditional Heteroskedasticity models (GARCH) to improve the classification of buy and sell signals in financial instruments, accounting for both historical patterns and external variables affecting asset behavior. The results show that LightGBM achieved the best predictive performance, with notable metrics such as an F1 Score of 0.823 and an AUC-ROC of 0.923 in validation, whereas LSTM delivered the best financial performance, reaching a cumulative return of 28.05% and a Sharpe Ratio of 0.70, clearly outperforming a Buy-and-Hold strategy. These findings suggest that although daily directional prediction is inherently complex, advanced Machine Learning models can transform weak signals into profitable trading strategies.Publicación Problemas jurídicos ocasionados por los errores en la inteligencia artificial(Universidad EAFIT, 2025) Arroyave Hincapié, Brahian; Rozo Acevedo, Tomás; Toro Valencia, José AlbertoThe main objective of this research is to analyze the complications generated by artificial intelligence in the field of private law, as well as what are the most common mistakes in the field of implementation and creation of these systems. It will explore different types of development, the learning system of these tools, historical background, comparative law and provide solutions to Colombian legislation to fill existing normative gaps.