Examinando por Materia "CIENCIA DE LA INFORMACIÓN"
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Ítem A Dynamic Approach to Modeling Count Data Based on Intensity Functions of Non-Homogeneous Poisson Processes and Functional Data Techniques(Universidad EAFIT, 2024) Chavarría Serna, Juan Esteban; Ortiz Arias, Santiago; Velasco, HenryÍtem A Multivariate Outlier Detection Methodology Based on S-Orthogonal DOBIN Projections(Universidad EAFIT, 2024) Cano Campiño, Andrés Mauricio; Ortiz Arias, SantiagoÍtem A predictive approach based on fuzzy cognitive maps with federated learning(Universidad EAFIT, 2023) Garatejo Vargas, Edison Camilo; Aguilar Castro, José Lizandro; Hoyos, WilliamPublicación Análisis comparativo de modelos predictivos para la estimación de PM2.5 : un enfoque basado en aprendizaje automático y predicción conformal(Universidad EAFIT, 2024) Camelo Valera, Matías; Martínez Vargas, Juan David; Sepúlveda Cano, Lina MariaFine particulate matter (𝑃𝑀2.5pollution poses a significant environmental and public health challenge, requiring accurate predictive models for its monitoring and control. This study compares different machine learning approaches, including Linear Regression, Random Forest, and XGBoost, with and without the inclusion of mobility variables, to estimate 𝑃𝑀2.5 levels. Additionally, inductive conformal prediction is implemented to quantify uncertainty in the estimates and provide confidence intervals with 𝛼=0.05. The results show that while XGBoost experiences performance deterioration during training when mobility variables are included, it achieves the best validation performance with the lowest mean absolute error and the highest coefficient of determination. Conformal prediction enabled the establishment of confidence intervals with 89.26% coverage, close to the expected 95%, ensuring model reliability across different spatial and temporal scenarios. In conclusion, the use of machine learning models combined with advanced validation and calibration techniques, such as conformal prediction, enhances the accuracy and reliability of 𝑃𝑀2.5 estimation. However, the quality of input variables, particularly mobility-related data, remains a challenge, highlighting the need to incorporate meteorological information and improve data resolution. These findings contribute to the development of more reliable predictive tools for environmental management and air quality policy decision-making.Ítem Análisis de discurso basado en modelos grandes de lenguaje(Universidad EAFIT, 2024) Jiménez Jaimes, Edgar Leandro; Montoya Múnera, Edwin NelsonThis thesis explores the implementation of natural language processing techniques and large language models (LLMs) to support discourse analysis tasks in the context of the "Tenemos que hablar Colombia" program. Techniques such as topic modeling, sentiment analysis, clustering, visualization, and the creation of a conversational assistant based on Retrieval Augmented Generation (RAG) have been addressed using advanced text modeling, vector embeddings, and prompt engineering approaches. A text classification model focused on predicting the label of the verbal indicator variable, assigned manually by the interviewer, is also presented, although this model is not directly applied to discourse analysis. This work adds to the studies of the " Tenemos que hablar Colombia " program, where other authors have contributed through computational linguistics analysis and machine learning techniques. Using advanced NLP techniques, we have sought to improve the interpretation of text data and its application in discourse analysis. The results have shown improvements in the accuracy of data classification and analysis through the techniques explored, providing a better understanding of citizen perceptions.Ítem Análisis de discurso de los máximos responsables de las empresas participantes en el COLCAP(Universidad EAFIT, 2024) Cuervo Garcia, Dairo Alberto; Pantoja Robayo, Javier Orlando; Ceballos Cañón, Johan ArmandoÍtem Análisis de explicabilidad en modelos predictivos basados en técnicas de aprendizaje automático sobre el riesgo de re-ingresos hospitalarios(Universidad EAFIT, 2023) Lopera Bedoya, Juan Camilo; Aguilar Castro, José LisandroBig Data and medical care are essential to analyze the risk of re-hospitalization of patients with chronic diseases and can even help prevent their deterioration. By leveraging the information, healthcare institutions can deliver accurate preventive care, and thus, reduce hospital admissions. The level of risk calculation will allow planning the spending on in-patient care, in order to ensure that medical spaces and resources are available to those who need it most. This article presents several supervised models to predict when a patient can be hospitalized again, after its discharge. In addition, an explainability analysis will be carried out with the predictive models to extract information associated with the predictions they make, in order to determine, for example, the degree of importance of the predictors/descriptors. In this way, it seeks to make the results obtained more understandable for health personnel.Ítem Análisis de los resultados de la aplicación del instrumento para la evaluación docente de la universidad EAFIT(Universidad EAFIT, 2024) Fernández Carmona, Laura Catalina; Guarín Zapata, Nicolás; Mola Ávila, José AntonioPublicación Análisis de patrones de violencia armada en la frontera de Colombia con Venezuela usando algoritmos de aprendizaje automático(Universidad EAFIT, 2025) Lopera Pai, Daniela; Aguilar Castro, José LisandroÍtem Análisis de registros de mantenimiento de centrales de generación de energía con técnicas de procesamiento de lenguaje natural(Universidad EAFIT, 2024) Ocampo Davila, Andrés Alonso; Salazar Martínez, Carlos AndresÍtem Análisis de riesgo de impago en el sector financiero : enfoque en tarjetas de crédito(Universidad EAFIT, 2024) Herrera Olivares, Maher Stehisy; Moreno Reyes, Nicolás AlbertoÍtem Análisis del efecto que tienen los subsidios a la demanda para la adquisición de vivienda nueva en los ingresos monetarios de los beneficiarios(Universidad EAFIT, 2022) Betancur Londoño, David; Dávalos Álvarez, EleonoraPublicación Análisis del volumen útil diario del embalse de El Peñol de 2010 a 2023 a partir de datos funcionales(Universidad EAFIT, 2025) Giraldo Gómez, Sebastián; Ortiz Arias, SantiagoThis study analyzes the hydroelectric behavior of the El Peñol reservoir, with an emphasis on its historical dynamics. Comparisons were made with four Colombian reservoirs: El Peñol, Playas, Punchiná, and San Lorenzo. To achieve this, functional statistical techniques were applied to historical data from the period 2010-2023 provided by XM, along with information on the El Niño and La Niña phenomena obtained from the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM). The variables analyzed include the turbined volume, daily usable volume, total energy generation, and market prices, with the main objective of identifying temporal patterns, seasonal trends, and functional relationships between these variables. The analysis included the calculation of functional means, the estimation of functional variances, and the application of functional principal component analysis (functional PCA). These techniques made it possible to reduce the dimensionality of the data and understand the main factors influencing hydroelectric behavior. As part of the methodology, Fourier smoothing was used to represent the variables as continuous curves, facilitating noise removal and capturing underlying trends. This approach allowed for functional comparisons between the reservoirs, highlighting both similarities and differences in their operation. The results of this functional analysis provide a solid foundation for interpreting hydrological patterns in the Antioquia region, with special attention to the El Peñol reservoir and its impact on regional hydroelectric efficiency. This reservoir, one of the most important in the country, faces significant challenges arising from fluctuations in water availability and the effects of climate change, emphasizing the need for sustainable management strategies. In this context, functional indicators were developed to evaluate the sustainability of the reservoir’s operation and propose improvements in its management. This study contributes to the advancement of specific analytical tools for hydroelectric management in Colombia, also establishing a precedent for future research aimed at reservoirs with similar characteristics, both regionally and internationally.Ítem Análisis predictivo de la deserción laboral en BPO : aplicaciones avanzadas de Machine Learning(Universidad EAFIT, 2023) Castelblanco Benítez, Julián; Almonacid Hurtado, Paula MariaÍtem Análisis predictivo del riesgo de default en microcrédito un enfoque de machine learning en sector financiero(Universidad EAFIT, 2024) Mendoza Trillos, Laura; Suárez Sierra, Biviana MarcelaÍtem Análisis y predicción de la deserción de empleados : un caso de estudio en la industria de software colombiana(Universidad EAFIT, 2022) Sierra Buriticá, Eliana Marcela; Almonacid Hurtado, Paula MaríaThe objective of this study is to carry out the analysis and prediction of the desertion of employees of a software company in Medellín, based on a private database that contains 19 characteristics of 1497 workers, where 900 are active in the company and the rest have left their job. In the first place, a descriptive and exploratory analysis was carried out, where it was found that there was some variables that did not contribute information to the model, such as: Type of identification, start date of the contract, among others, also in this part the correlation of some variables and proceeded to eliminate them from the set of descriptive characteristics of the problem, since that leaving them would be leaving redundant information in the model. Second, they trained 4 machine learning models (Niave Bayes, Random Forest, Decision Tree, Logistic Regression) and the results obtained by each were compared, in order to find the one that best fits the problem of labor desertion, in this step it was found that the best classifier of machine learning is a decision tree (Decision Tree) with 14 layers, since metrics such as its curve of learning and ROC curve gave better results than the other two trained models.Ítem Análisis y predicción de ventas de motos haciendo uso de la metodología “Customer Value Map” y técnicas de Machine Learning(Universidad EAFIT, 2024) Díaz Cordero, Sandra Marcela; Martínez Vargas, Juan David; Vallejo Correa, Paola AndreaÍtem 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, SantiagoPublicación Aplicación de redes neuronales convolucionales y técnicas de procesamiento de lenguaje natural para el análisis de sentimiento en datos financieros(Universidad EAFIT, 2025) Fernández Ceballos, Juan Manuel; Almonacid Hurtado, Paula MaríaÍtem Aplicación de técnicas no-lineales de reducción de dimensionalidad y clustering para detección de observaciones anómalas multidimensionales(Universidad EAFIT, 2024) Romero Cardona, Daniel; Ortiz Arias, Santiago