Examinando por Materia "Aprendizaje profundo"
Mostrando 1 - 8 de 8
Resultados por página
Opciones de ordenación
Ítem Aplicación de técnicas de aprendizaje automático para la proyección de la tasa de cambio entre COP y USD(Universidad EAFIT, 2022) Granada Carvajal, Lorena; Pérez Ramírez, Fredy OcarisÍtem Descubriendo la distribución espacial de comercio en ciudades con economías informales(Universidad EAFIT, 2020) Saldarriaga Marín, Juan Camilo; Duque Cardona, Juan CarlosAn economic census aims to record economic activity in a city by collecting geo-referenced survey data. Although its benefits are significant, the costs are very high and, for this reason, it is very rare that the economic census information is up to date and complete. In this work we propose a new methodology to detect and georeference the visible commercial activity in a city or region in an efficient way, generating automated reports of visible commercial activity in a region of interest. This methodology tries to estimate the spatial distribution that allows having an economic census but only for visible commerce. We contrast the results of our methodology with official information from the Chamber of Commerce to estimate the spatial distribution of informal visible commerce or unregistered commerce in the municipality of Envigado.Ítem 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 DavidÍtem 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.Ítem Modelo de aprendizaje profundo reforzado aplicado al trading de Bitcoin(Universidad EAFIT, 2022) Obando Morales, Sebastián; Jaramillo Posada, Juan RodrigoThe stock market is affected by many types of factors, such as market sentiment, going upwards (bulls) or downwards (bears), the behavior of the economy, or unexpected political events. By For this reason, it is not possible to predict its behavior, which means that it is not possible to decide when to enter or when to exit with certainty. An approach such as deep reinforcement learning, which can emulate the experience of a negotiator (trader) who does not necessarily predict prices, but, market entry and exit times, would be a viable option. The present work sought to implement a reinforced deep learning approach to stock trading (bitcoins, stocks, and commodities), which has shown positive results in the literature with returns positive on investment. The bot, the result of this work, obtained a return of 5%. These positive results open the door to trying new approaches that include new combinations in the way of interpreting indicators to find winning strategies that increase profitability.Ítem Predicción de incumplimiento de pagos de crédito en una entidad financiera utilizando chats de servicio al cliente(Universidad EAFIT, 2023) Patiño Serna, Javier; Martínez Vargas, Juan David; Vallejo Correa, Paola AndreaÍtem Predicting Stock prices in Latin America using Associative Deep Neural Networks(Universidad EAFIT, 2023) Gallego Rojas, Juan Fernando; Almonacid Hurtado, Paula MaríaThe stock market is a critical sector of the global economy, and predicting stock prices is of great interest to investors and companies. However, the movements of the market are volatile, non-linear, and complicated. This topic has attracted the attention of researchers, who have proposed formal models that demonstrate accurate predictions can be made with appropriate variables and techniques. Deep learning algorithms are often used for this purpose due to their superior accuracy in time series-based and complex pattern analysis. This paper proposes to predict the opening, closing, highest, and lowest stock prices of select Latin American market indexes using associative deep neural networks that can simultaneously predict related values based on the Long Short Term Memory (LSTM) technique, known for its high accuracy in this area. As well as using classic econometric methods for the analysis of time series such as ARIMA models. The proposed model achieved a good performance in terms of prediction, which in turn allows finding interesting trading opportunities for investors. The results of the models were measured using the average RMSE of the predicted prices metric and compared with those obtained using a naive model.Ítem VaR para asignación de capital y modelo de predicción de pérdidas para una entidad financiera de Colombia(Universidad EAFIT, 2022) Henao Cortés, Camilo; González Almendra, Johnnatan; Almonacid Hurtado, Paula María; Mosquera López, Stephanía