Examinando por Materia "Redes neuronales"
Mostrando 1 - 19 de 19
Resultados por página
Opciones de ordenación
Ítem Aplicación de métodos de visión artificial para la detección de huevos en una planta de producción avícola(Universidad EAFIT, 2024) Cardona Ortiz, César Augusto; Ortiz Arias, SantiagoÍtem Comparación entre el método tradicional y algunos basados en inteligencia artificial para el estudio del riesgo crediticio en instituciones financieras colombianas(Universidad EAFIT, 2018) Arango Correa, Diana Marcela; Colmenares Colmenares, Laura Juliana; Rave Contreras, Isabel Cristina; Martínez Negrete, Milton AlfonsoArtificial intelligence models are an open problem for application in various fields of science and search of variable relationships especially when the distribution of events doesn’t depend on a linear function; through this work we want to compare the traditional method most used for credit behavior monitoring with advanced models of artificial intelligence -- The guides that exist in Colombia for management of credit risk are given by the Financial Superintendence of Colombia, international standards such as Basel II, Basel III and Solvency are based on the logistic regression and the discriminant analysis, models used by financial institutions in Colombia to measure credit behavior, thus we carried out an investigation to explore the utility of new models -- This paper addresses one of the traditional methods used in financial institutions, that is, logistic regression, and compares it with alternative methods such as neural networks and random forests -- From the literature review and using a database provided by a banking entity, the dependent variables and the response variable are selected, the logistic regression models, random forests and neural networks are calibrated in the Microsoft Azure Machine Learning application and they are compared to each other with indicators of precision and accuracy such as ROC (from receiver operating characteristic) curve and confusion matrix, obtaining for the models of artificial intelligence, results as good as the traditional one; so they can be used by the financial sector as alternate and / or complementary methods in the analysis of credit riskÍtem Desarrollo de librería para manejo de redes neuronales en Java para tecnofactor(Universidad EAFIT, 2019) Arboleda Echeverry, Juan Camilo; Rincón Bermúdez, Rafael DavidThe aim of the present project is to design and develop a working library that enables the creation and adaptation of Neural Networks, defined in a way that is simple to use by Java developers. The developed library will be used to design and obtain a neural network capable of recognizing handwritten digits, from the MNIST database.Ítem Desarrollo de un algoritmo de aprendizaje por refuerzo profundo para resolver el despacho hidrotérmico colombiano considerando escenarios hidrológicos y de demanda bajo incertidumbre(Universidad EAFIT, 2022) Ramírez Arango, Alejandro; Aguilar Castro, José LisandroEconomic dispatch is a widely analyzed optimization problem in the electricity sector, which seeks to make the best use of available resources to meet demand at minimum cost. This problem has a great complexity in its solution due to the uncertainty of multiple parameters, being of special interest the hydrological uncertainty for the Colombian case due to its high dependence on hydroelectric plants. In this paper, we view economic dispatch as a multistage decision making problem and propose a Reinforcement Learning to solve the Colombian economic dispatch problem considering hydrological scenarios, due to its ability to handle uncertainty and sequential decisions. The policy performance of our algorithm is compared with classic deterministic method. The main advantage of our method is it can learn from a robust policy to deal the inflow and load demand scenarios.Ítem Estimación de precio de oferta para una planta hidroeléctrica de baja regulación en la bolsa de energía(Universidad EAFIT, 2021) Mosquera Galvis, Liceth Cristina; Quintero Montoya,Olga LuciaÍtem Evaluación de una red neuronal para la solución de ecuaciones diferenciales(Universidad EAFIT, 2023) Machado-Loaiza, José Manuel; Guarín-Zapata, NicolásÍtem Hurto a personas en la ciudad de Medellín : análisis predictivo de la cantidad de casos en diferentes zonas de la ciudad a partir de modelos de machine learning implementando técnicas de MLOps(Universidad EAFIT, 2023) Arboleda Colorado, Jeferson Stiven; Martínez Vargas, Juan DavidRobbery of individuals in Medellín is an issue demanding immediate attention. This prompted the study of the phenomenon within an analytics project, spanning data collection, database construction, modeling, and production deployment. It's worth noting that MLOps methodology was employed utilizing AWS services. Visual tools related to the phenomenon were integrated, facilitating analysis.Ítem Identificación de patrones socioeconómicos en Medellín a partir de imágenes satelitales(Universidad EAFIT, 2024) Ceballos Betancur, Mariana; Martínez Vargas, Juan David; Torres Madronero, María ConstanzaÍtem Modelo de medición del riesgo de liquidez para un fondo de empleados a partir del modelo de la Superintendencia Financiera de Colombia(Universidad EAFIT, 2019) Valencia Sánchez, Edwin; Gómez Salazar, Elkin ArcesioThis research project aims to address the current need for employee funds to implement a methodology for liquidity risk measurement that is optimal, reliable and generates quality information for decision-making. The methodology currently applied does not allow the identification of liquidity problems with sufficient anticipation. The employee funds have similar characteristics to the entities monitored by the Financial Superintendence of Colombia; therefore, a model of liquidity risk measurement will be designed from the model implemented by this regulatory entity. The model will be proposed incorporating qualitative and transactional variables, which through the use of neural networks allow determining the probability of withdrawal of resources from the partners and thus prevent the materialization of risk. In this way, the model complies with the regulations for the entities monitored by the Superintendence of the Solidarity Economy.Ítem Movement in video classification using structured data : Workout videos application(Universidad EAFIT, 2023) Múnera Muñoz, Jonathan Damián; Tabares Betancur, Marta SilviaNowadays, several video movement classification methodologies are based on reading and processing each frame using image classification algorithms. However, it is rare to find approaches using angle distribution over time. This paper proposes video movement classification based on the exercise states calculated from each frame's angles. Different video classification approaches and their respective variables and models were analyzed to achieve this, using unstructured data: images. Besides, structure data as angles from critical joints Armpits, legs, elbows, hips, and torso inclination were calculated directly from workout videos, allowing the implementation of classification models such as the KNN and Decision Trees. The result shows these techniques can achieve similar accuracy, close to 95\%, concerning Neural Networks algorithms, the primary model used in the previously mentioned approaches. Finally, it was possible to conclude that using structured data for movement classification models allows for lower performance costs and computing resources than using unstructured data without compromising the quality of the model.Publicación Optimización de portafolios de inversión mediante pronósticos de volatilidad de «commodities» y acciones, utilizando modelos GARCH y «deep learning»(2024) Villa Cardona, Jairo Alonso; Cruz Castañeda, VivianÍtem Planteamiento e identificación de las variables para la adaptación del modelo de evaluación de crédito Factoring con redes neuronales (Warren) para Redcapital Colombia(Universidad EAFIT, 2024) Jaramillo Hurtado, Andrés; Sánchez Ribero, Gustavo AlbertoÍtem Predicción de deserción estudiantil mediante técnicas de aprendizaje de máquina(Universidad EAFIT, 2020) Lopera Vargas, Andrés; Ramírez Echeverri, Sergio AugustoÍtem Predicción del cargue de rutas de distribución mediante aprendizaje de máquina(Universidad EAFIT, 2023) Ramírez Aguilar, Santiago; Téllez Falla, Diego Fernando; Marentes Cubillos, Luis AndrésÍtem Predicción del precio de transacción sobre el tipo de cambio XAU-USD (oro) para el mercado de contado del commodittie a corto plazo(Universidad EAFIT, 2023) Cardona Restrepo, Jorge Esteban; Castilla Rueda, Rafael Andrés; Almonacid Hurtado, Paula MaríaÍtem Red asociativa usando LSTM para la predicción diaria de apertura y cierres de índices bursátiles(Universidad EAFIT, 2022) Mejía Uribe, Simón Pedro José; Laniado, Henry; Almonacid Hurtado, Paula MaríaÍtem Stock Market Forecasting : Comparing Machine Learning and Deep Learning with Risk-Return Model Selection and Evaluation in a Walk-forward Approach(Universidad EAFIT, 2024) Castro Marín, Carlos Andrés; Olarte Hernández, Tomás; Olarte Hernández, TomásThis study compares the effectiveness of machine learning and deep learning algorithms in forecasting stock market direction using daily market data of Apple Inc. stock. We aim to determine if these algorithms can identify repeatable patterns across time using price and volume history and assess which are most capable. To ensure robustness, we employ a walk-forward validation approach to maintain the temporal dimension of the data and simulate real trading conditions. This method allows us to test models across different market conditions and measure their predictive power. We prioritize model selection and evaluation based on financial return and risk metrics, focusing on profitability rather than traditional machine learning performance metrics, which often do not correlate with financial outcomes. Our findings show that traditional machine learning algorithms, specifically Random Forest, outperform deep learning models under the selected asset and conditions tested. Machine learning models exceed the stock benchmark regarding Sharpe ratio, while deep learning models struggle to manage risk effectively, leading to poorer performance. This discrepancy is likely due to the complex solution space deep learning algorithms navigate to optimize and the amount of data required by these models. However, we hypothesize that the latter could improve its performance if tested with different architectures and hyperparameters, including newly developed transformer attention-based architectures and models such as TimeGPT and others, shown in the related work section.Ítem Towards a method to predict possible obstructions in a sewage system: a case study applied in the Aburrá Valley, Colombia(Universidad EAFIT, 2022) Valderrama Laverde, Diego Andrés; Tabares Betancur, Marta SilviaÍtem Variability modeling language and tool to represent, configure and evaluate Convolutional Neural Network architectures(Universidad EAFIT, 2024) Murillo Portocarrero, Julián Alexander; Mazo Peña, RaúlThe process of designing Convolutional Neural Network (CNN) architectures currently relies on manual design or the involvement of experts. This process is not only time-consuming but also expensive due to the sheer number of combinations that architects need to do to arrive at the right network hyper-parameters that fit the current problem. In this work, we analyze the current state-of-the-art, in which several approaches have been proposed to automate such processes and explore alternatives for the automated design of Convolutional Neural Networks and Neural Architecture Search (NAS). Additionally, this work proposes a method for hyper-parameter variability generation from a variability model of such convolutional neural networks. The variability model proposed in this master thesis is used to represent, intensively, the valid combinations of parameters corresponding to each convolutional neural network. The language, called CNN variability language, borrows some concepts from Software Product Lines (SPL) and was created on the VariaMos platform to enable architects and engineers not just to create CNN architectures but also to automatically generate configurations, generate executable Jupyter Notebooks for each configuration, and generate comparison reports to speed up the NAS process.