Repositorio Institucional Universidad EAFIT ::Examinando por Materia "Machine learning"

Examinando por Materia "Machine learning"

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A Novel Ornstein Uhlenbeck Levy Model Conditioned on an Unknown Mean : Frecasting of the VIX
(Universidad EAFIT, 2024) Aguirre Posada, Mario; Almonacid Hurtado, Paula María; Pérez Monsalve, Juan Pablo
A retail demand forecasting system of product groups characterized by time series based on “ensemble machine learning” techniques with feature enginnering
(Universidad EAFIT, 2022) Mejía Chitiva, Santiago; Aguilar Castro, José Lisandro
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 Lisandro
Big 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.
Análisis predictivo de la deserción laboral en BPO : aplicaciones avanzadas de Machine Learning
(Universidad EAFIT, 2023) Castelblanco Benítez, Julián; Almonacid Hurtado, Paula Maria
Análisis y predicción de recontactos en un contact center
(Universidad EAFIT, 2021) Velásquez Gaviria, Diana Catalina; Quintero Montoya, Olga Lucía
The main purpose of this work is to solve in a methodical and formal way, making use of ma- chine learning models, a real problem of the productive sector that allows in addition to adding value for decision making, to provide a methodology and a compact, simple and reliable model that can be deployed and put into production in the technological platform that supports the call handling of a contact center so that benefits can be generated in the provision of the service for different sectors, generating efficiencies in the use of the channel and maximizing the customer experience in the attention of their requirements. To achieve this purpose, an airline dataset was taken containing the detail of all historical calls made by customers to a contact center during a period of 7 months (February to August 2019) and information associated with the performance of the agents handling those calls in order to predict whether users will generate at least one recontact to the contact center for the attention of their requirements before three days with respect to their initial contact. The methodology used was focused on making an ap- propriate selection of features and choosing a machine learning model that generates optimal results and enables an easy implementation allowing to identify in real time those customers with high probabilities of recontacting so that a strategy can be developed with them to impro- ve their experience. It was found that with 7 variables associated with the historical behavior of customers in the use of the channel such as frequency of calls (amount), average duration, number of agents who have served the customer (agents), time elapsed between first and last call (validity), number of days per month in which calls are made (AverageDaysInMonth), ave- rage number of calls per day (Average-Day) and time elapsed since the customer’s last call to the cut-off date of the analysis (Recency), it is possible to predict with a simple model and with very good results (AUC=88.9 %) whether a customer will call the contact center again.
Analítica de datos aplicada a la cobranza de cartera
(Universidad EAFIT, 2019) Montoya Yepes, Juan David; Parra Giraldo, Diana Carolina
In order to improve the portfolio collection work performed daily at Cobroactivo LLC, it was decided to use the data stored in this company to optimize its processes. To do this, it was necessary to implement data analytics models, creating a Big Data environment that would allow efficient access to information through a Data Warehouse, as well as some tools to make an exploratory analysis of the existing data and evaluate the weak pointsthat must be improved when managing users. Warehouse, as well as some tools to make an exploratory analysis of the existing data and evaluate the weak points that must be improved when managing users. Likewise, three Machine Learning models were developed in charge of debugging the debtors, predicting the payment probabilities and optimally recommending which advisor should be assigned to each debtor and which is the appropriate contact channel for him / her. Finally, two web applications were developed. The first allows the monitoring of the company's internal processes by automating repetitive processes and decreasing their execution time from weeks o seconds; the second allows the monitoring of collection work by customers and banks, thereby offering added value.
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 Ocaris
Clasificación de inventarios multicriterio mediante el uso de Modelos de Aprendizaje Automático (ML) en la industria automotriz
(Universidad EAFIT, 2024) Vesga Vesga, Luis Rodrigo; Castro Zualuaga, Carlos Alberto
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding
(Universidad EAFIT, 2021-12-01) Calderón-Vargas, Carlos; Muñoz Castaño, José; Vargas Rincón, María; Rincón Acosta, Víctor Manuel; Mendieta Hernández, Miguel; Hospital Universitario La Samaritana; Hospital Universitario La Samaritana; Hospital Universitario La Samaritana; Universidad El Bosque; Universidad El Bosque
The evolution of medicine in current times has gone hand in hand with technology where more and more solutions are implemented; those supporting certain medical procedures to serve as base in the field of medical professionals. The process of analyzing data has become an essential resource in the practice of any profession; currently, in hospitals, more specifically in the university hospital La Samaritana. No tool allows the supporting of diagnosis to determine the supply or no, proton pump inhibitors, therefore we have developed an app using a machine learning model, based on decision trees through the weka application, which, after analyzing the data collected, allows the doctor to count with a tool to support this procedure. We hope that with this, doctors can perform an effective analysis before prescribing or not prescribing PPIs.
Desarrollo de un sistema de apoyo a la toma de decisiones estilísticas en lenguaje de marca a través de una herramienta de machine learningDesarrollo de un sistema de apoyo a la toma de decisiones estilísticas en lenguaje de marca a través de una herramienta de machine learning
(Universidad EAFIT, 2023) Córdoba García, Miguel de Germán; Maya Castaño, Jorge Hernán
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 Corona
Diseño de un modelo de decisión para la retención del capital humano en el sector Business Process Outsourcing (Bpo) : el caso de Konecta-Colombia
(Universidad EAFIT, 2021) Muñoz Mora, Cristian David; Muñoz Mora, Juan Carlos
El impacto de las herramientas basadas en inteligencia artificial en la actividad profesional de las firmas legales, resultados de un mapeo sistemático de la literatura
(Universidad EAFIT, 2025) Vásquez Mira, Juan Camilo; Suescún Monsalve, Elizabeth
En busca de un mayor bienestar en la ganadería de ceba y levante
(2021-04-05) Martinez Guerrero, Christian Alexander; Christian Alexander Martinez-Guerrero; Garcia, Rodriguez; Aguilas Jose; Toro Mauricio; Pinto Angel; Rodriguez Paul; Vicerrectoría de Descubrimiento y Creación
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. Escuela de Ciencias; Modelado Matemático
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.
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; Ciencias Biológicas y Bioprocesos (CIBIOP)
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.
Estudio de prefactibilidad para la creación de una empresa de consultoría de analítica de datos en Medellín
(Universidad EAFIT, 2024) Zapata Rivera, Oscar Andrés; Salazar Gómez, Francisco Javier; Uribe de Correa, Beatriz Amparo
This pre-feasibility study examines the creation of a data analytics consulting company in Medellín, Colombia. The primary objective is to assess the technical, economic, and commercial viability of the project, considering the political, economic, social, technological, ecological, and legal environment. Using PESTEL analysis, market research, the conceptual and methodological framework, and financial evaluation, the company aims to offer services in data architecture, data engineering, data analytics, artificial intelligence, and machine learning. The results indicate a significant market opportunity for consulting firms supporting the digital transformation of small and medium-sized enterprises (SMEs) in Colombia. This is driven by the increasing demand for advanced technological solutions. The project is deemed profitable with strong growth prospects and potential for expansion into international markets.
Evaluación de una red neuronal para la solución de ecuaciones diferenciales
(Universidad EAFIT, 2023) Machado-Loaiza, José Manuel; Guarín-Zapata, Nicolás
Evaluation of investment alternatives in the Colombian bonds Market Using Machine Learning Algoritms
(Universidad EAFIT, 2025) Gómez Plaza, Sergio Daniel; Cardona Llano, Juan Felipe
The bonds market in Colombia, according to the Colombian Stock Exchange (BVC), represents the largest segment of the country's capital market, where fixed-income instruments account for 80% of the average daily trading volume on exchange systems. Sovereign and corporate bonds are the most sought-after by investors, as they offer stable returns and low risk. However, valuing these assets is not always straightforward due to volatility and economic fluctuations.This study explores how machine learning algorithms can enhance the valuation of these investment options in the Colombian market. Various approaches, such as neural networks and decision trees, will be tested to determine which best predicts the behavior of these assets. These models are expected to support better investment decision-making, particularly during periods of uncertainty. The goal is to leverage artificial intelligence to develop more effective and well-suited tools for an informed market.
Forecasting Colombian Yield Curves & Rates With LSTM and Nelson-Siegel Models
(Universidad EAFIT, 2024) Uribe Ramírez, Sebastián; Almonacid Hurtado, Paula María

Examinando por Materia "Machine learning"

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