Method based on data mining techniques for breast cancer recurrence analysis

Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery

Implementación de una arquitectura tecnológica basada en computación en la nube para el desarrollo de la aplicación web “gestor urbano” para apoyar la oficina legal de la universidad del Sinú

Esta investigación resalta la importancia de Cloud Computing (computación en la Nube) como una de las tecnologías más importantes de la industria 4.0, y un elemento clave para lograr la transformación digital promovida por el gobierno colombiano, ya que permite soportar y desplegar a las otras tecnologías y aplicaciones que gestionan la información de las operaciones de una organización y que están disponibles desde cualquier dispositivo electrónico con conexión a internetThis research highlights the importance of Cloud Computing (cloud computing) as one of the most important technologies in industry 4.0, and a key element to achieve the digital transformation promoted by the Colombian government, since it allows supporting and deploying the other technologies and applications that manage the information of an organization’s operations and that are available from any electronic device with an Internet connectio

Detection in images of skin lesions using computer vision and deep learning

Introducción— La problemática a tratar en este trabajo es la detección de melanoma, el cual es uno de los distintos cánceres de piel que existen, el cual presenta una alta tasa de mortalidad. Objetivo— En este documento se presenta un proyecto de investigación en el área de Inteligencia Artificial cuyo objetivo es la detección de melanoma por medio del análisis de imágenes utilizando Deep Learning. Metodología— Inicialmente se aplican operaciones morfológicas sobre la imagen para dejar solo el objeto de interés. Luego esta imagen se ingresa a una red neuronal convolucional, la cual ha sido entrenada para la detección de melanomas. Resultados— La arquitectura de red convolucional propuesta presenta unos resultados aceptables en la métrica de accuracy para la identificación de melanoma maligno o benigno. Sin embargo, se propone realizar futuros experimentos que puedan mejorar estos resultados. Conclusiones— Gracias a las técnicas de Deep Learning con esta clase de herramientas se está ofreciendo un sistema muy poderoso y útil a la hora de determinar el diagnóstico de este tipo de enfermedades.Introduction— The problem to be addressed in this work is the detection of melanoma, which is one of the different skin cancers that exist, which has a high mortality rate. Objective— This document presents a research project in Artificial Intelligence whose objective is the detection of melanoma through image analysis using Deep Learning. Methodology— Initially, morphological operations are applied to the image to leave only the object of interest. This image is then fed into a convolutional neural network, which has been trained for melanoma detection. Results— The proposed convolutional network architecture presents acceptable results in the accuracy metric for the identification of malignant or bening melanoma. However, it is proposed to carry out future experiments that can improve these results. Conclusions— Thanks to Deep Learning techniques with this class of tools, a very powerful and useful system is being offered when it comes to determining the diagnosis of this type of disease