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

Examinando por Materia "Machine learning"

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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
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 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
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
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. 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.
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.
Evaluación de una red neuronal para la solución de ecuaciones diferenciales
(Universidad EAFIT, 2023) Machado-Loaiza, José Manuel; Guarín-Zapata, Nicolás
Genomic Prediction and Genome-Wide Association Analysis in Common Bean (Phaseolus vulgaris l.) × Tepary bean (P. acutifolius a. gray) Inter-specific Advanced Lines at the Caribbean Coast of Colombia
(Universidad EAFIT, 2023) López Hernández, Luis Felipe; Villanueva Mejía, Diego Fernando; Cortés Vera, Andrés Javier
The negative effects of the climate change are risking global food security with 828 million people facing hunger, which is almost 16 times the population of Colombia. Given this scenario, legumes as common bean has offered a nature-based solution to source nutrients for rural communities in Latin America thanks to their high content of nutrients. For this reason, it is imperative to speed up the molecular genetic breading of common beans so that they can be cultivated in regions affected by extreme climate change, one of which is coastal Colombian. Therefore, in order to bridge this gap, this study aimed coupling an advanced panel of common bean (Phaseolus vulgaris L.) × tepary bean (P. acutifolius A. Gray) inter-specific lines with Bayesian regression algorithms to identify novel sources of adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with inter-specific ancestries were genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components and two biomass variables were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities in coastal Colombia. We explored the comparative analysis of several regression approaches where the model with the best performance in all traits and environments was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori GWAS models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per environment and trait were determined to the top 500 most explicative markers according to their β regression effects. These 500 SNPs on average overlapped in 5.24 % across localities, which reinforced the environmentally dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs), and selected the top 10 genotypes for each environment and trait as part of a recommendation scheme targeting narrow adaption. The genotypes and SNP markers identified in this study as candidates for abiotic stress have the potential to be used in the following cycles as part of the long-term bean breeding program for coastal tropical regions.
Hacia un método de predicción de resultados de evaluación en un contexto de micro aprendizaje
(Universidad EAFIT, 2020) Sánchez Castrillón, Jose David; Vallejo, Paola; Tabares Betancur, Marta Silvia; Tabares Betancur, Marta Silvia
This paper presents a method for predicting the evaluation results of learners interacting with a context-aware microlearning system. We use ASUM-DM to guide di erent data analytics tasks, including applying a genetic algorithm that selects the prediction's highest weight features. Then, we apply machine learning models like Random Forest, Gradient Boosting Tree, Decision Tree, SVM, and Neural Networks to train data and evaluate the context's e ects, either success or failure of the learner's evaluation. We are interested in nding the model of signi cant context-in uence to the learner's evaluation results. The Random Forest model provided an accuracy of 94%, which was calculated with the cross-validation technique. Thus, it is possible to conclude that the model can accurately predict the evaluation result and relate it with the learner context. The model result is a useful insight for sending noti cations to the learners to improve the learning process. We want to provide recommendations about learner behavior and context and adapt the microlearning content in the future.
Implementación de herramientas de apoyo en el proceso de decisión del Pricing y distribución comercial para los productos del activo bancario del segmento personas dentro de la banca minorista
(Universidad EAFIT, 2023) Franco Amaya, Jair Fabian; Ardila Rodríguez, Jhon Sebastian; Rojas Ormaza, Brayan Ricardo
Imputation method based on recurrent neural networks for the internet of things
(Universidad EAFIT, 2018) Rodríguez Colina, Sebastián; Mejía Gutiérrez, Ricardo
The Internet of Things (IoT) refers to the new technological paradigm in which sensors and common objects, like household appliances, connect to and interact through the Internet -- This new paradigm, and the use of Artificial Intelligence (AI) and modern data analysis techniques, powers the development of smart products and services; which promise to revolutionize the industry and humans way of living -- Nonetheless, there are plenty of issues that need to be solved in order to have reliable products and services based on the IoT -- Among others, the problem of missing data posses great threats to the applicability of AI and data analysis to IoT applications -- This manuscript shows an analysis of the missing data problem in the context of the IoT, as well as the current imputation methods proposed to solve the problem -- This analysis leads to the conclusion that current solutions are very limited when considering how broad the context of IoT applications may be -- Additionally, this manuscript exposes that there is not a common experimental set up in which the authors have tested their proposed imputation methods; moreover, the experiments found in the literature, lack reproducibility and do not carefully consider how the missing data problem may present in the IoT -- Consequently, the reader will find two proposals in this manuscript: i) an experimental set up to properly test imputation methods in the context of the IoT; and ii) an imputation method that is general enough as to be applied to several IoT scenarios -- The latter is based on Recurrent Neural Networks, a family of supervised learning methods which have excel at exploiting patterns in sequential data and intrinsic association between the variables of data
Inteligencia artificial para optimizar la producción de carne
(Universidad EAFIT, 2020-12-01) Martinez Guerrero, Christian Alexander; Martinez-Guerrero, Christian Alexander; García, Rodrigo; Aguilar, Jose; Toro, Mauricio; Pinto, Angel; Rodríguez, Paul; GIDITIC
Intelligent model for monitoring, evaluating, and recommending strategies to improve the innovation processes of MSMEs
(Universidad EAFIT, 2024) Gutiérrez Buitrago, Ana Gissel; Aguilar Castro, José Lisandro; Montoya Múnera, Edwin Nelson; Ortega Álvarez, Ana María
The research focuses on how to improve the innovation process in micro, small and medium-sized enterprises (MSMEs). The study is framed within the Smart Innovation paradigm. In this context, innovation is considered a relevant factor for organizational performance that allows the creation and improvement of competitive advantages through the implementation of new ideas, products, concepts, and services to increase market positioning. For organizations aiming to enhance innovation performance, using intelligent systems and artificial intelligence to guide the innovation process poses a challenge. To address this problem, the goal was to develop methodologies, models and approaches to support decision-making related to the intelligent management of the innovation process. To achieve this, specific objectives were defined. The first one is to design an intelligent model to support innovation processes in MSMEs. The second objective is to apply Artificial Intelligence (AI) techniques to customer data sources in social networks and organizational data of MSMEs, aiming to enhance the innovation process; The third objective is to develop an intelligent system to evaluate the innovation levels in MSMEs. The fourth objective is to instantiate a case study in the fashion cluster of the department of Norte de Santander and in the national context, as part of the applied methodology. To fulfill these objectives, research articles were developed. The process began with a literature review article on the current challenges in applying AI techniques to improve innovation processes in MSMEs. A proposed innovation model was made based on the different innovation models that exist in the literature, and the four research articles were written in compliance with the scientific standards that accredit them, to meet the specific objectives outlined in this doctoral thesis. Each article evaluated the strategies/models using various data sets. The results demonstrated the capacity of the proposed methodologies and models for managing of innovation processes. For instance, the proposals enable the prediction of the level of innovation, and the definition of innovation problems, among other aspects, with positive results in performance metrics.

Examinando por Materia "Machine learning"

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