Subject Coordination Improvement through Automatic Generation of Meeting Minutes based on Email Communication

El proyecto consiste en la realización de una plataforma digital que permite la realización automática de actas de coordinación de asignaturas a partir de los hilos de las conversaciones establecidas en los envíos de correos electrónicos por parte de los diferentes equipos docentes participantes en dichas asignaturas. Las actas pueden generarse a partir de las conversaciones de los docentes, así como de la comunicación entre alumnos y docentes. Este trabajo se motiva en que las reuniones realizadas de forma virtual raramente van acompañadas de un acta donde se deja constancia de los participantes, de sus declaraciones, de las fechas en las que se realizaron las mismas y de las conclusiones a las que se llegaron. El principal objetivo es de dejar constancia de todos los trabajos de coordinación realizados de una forma sencilla, sin que para ello los intervinientes tengan que realizar ninguna tarea adicional, a partir de los correos enviados entre profesores alumnos de una asignatura. Estos correos se analizan y recopilan de forma automática. Después, se genera el acta en formato PDF, que puede ser leído por prácticamente cualquier dispositivo. La aplicación fue probada en dos asignaturas y se aportan capturas y la información relativa a su uso. La aplicación consiguió recopilar toda la información de los correos de forma automática y crear las actas.This project consists in the development of a digital platform to create meeting minutes for a subject in an automatic way. The data come from the email conversations of the professor of one subject, and the emails between professor and student. The motivation of this work is due to the fact that the virtual meetings normally has not a minute which leaves proof of the participants, participants’ statements, their dates, and conclusions. The main object is to leave proof of all the work of the professors in an easy and simple way. In this way, the participants will not have to make any additional task or work. These emails are analysed and gathered in a automate way. Then, the minutes are created in PDF, which can be read in almost any device. This application has been tested in two subjects and we provide information about its use. The application achieved gathering all the information of the emails in an automate way and created the meeting minutes

Security Guidelines for the Development of Accessible Web Applications through the implementation of intelligent systems

Due to the significant increase in threats, attacks and vulnerabilities that affect the Web in recent years has resulted the development and implementation of tools and methods to ensure security measures in the privacy, confidentiality and data integrity of users and businesses. Under certain circumstances, despite the implementation of these tools do not always get the flow of information which is passed in a secure manner. Many of these security tools and methods cannot be accessed by people who have disabilities or assistive technologies which enable people to access the Web efficiently. Among these security tools that are not accessible are the virtual keyboard, the CAPTCHA and other technologies that help to some extent to ensure safety on the Internet and are used in certain measures to combat malicious code and attacks that have been increased in recent times on the Web. Through the implementation of intelligent systems can detect, recover and receive information on the characteristics and properties of the different tools and hardware devices or software with which the user is accessing a web application and through analysis and interpretation of these intelligent systems can infer and automatically adjust the characteristics necessary to have these tools to be accessible by anyone regardless of disability or navigation context. This paper defines a set of guidelines and specific features that should have the security tools and methods to ensure the Web accessibility through the implementation of intelligent systems

Introducción breve a la ingeniería dirigida por modelos

The software crisis is a concept that has started to be used in 1968, at the first conference organized by the North Atlantic Treaty Organization (NATO) on software development. There, Edsger Dijkstra criticized that projects were not completed in compliance with the classic triple constraint of project management (scope, time and cost), since most of them do not reach the expected requirements, are delivered out of time or exceeds the expected cost. Unfortunately, the current reality is that while there have been proposed new methodologies aimed at solving the usual problems related to software development, there is still no reliable method to estimate the development of computer systems. This work introduces the Model-Driven Engineering approach that, according to the experts, will help to solve many of the problems that thousands of software development teams have daily worldwide.La crisis del software es un concepto que comenzó a utilizarse en 1968, en la primera conferencia organizada por la Organización del Tratado del Atlántico Norte (OTAN) en el desarrollo de software. Allí, Edsger Dijkstra criticó que los proyectos no se completaban debido a la clásica triple restricción de la gestión de proyectos —alcance, tiempo y costo—, ya que la mayoría de ellos no alcanzaban los requisitos previstos, se entregaban fuera de plazo o superaban el costo esperado. Por desgracia, la realidad actual es que; si bien se han propuesto nuevas metodologías destinadas a la solución de los problemas habituales relacionados con el desarrollo de software, todavía no existe un método fiable para estimar el desarrollo de los sistemas informáticos. En este artículo se presenta el enfoque de Ingeniería Dirigida por Modelos que, según los expertos, ayudará a resolver muchos de los problemas que miles de equipos de desarrollo de software tienen a diario en todo el mundo

Special Issue on Algorithms in Decision Support Systems Vol.2

Currently, decision support systems (DSSs) are essential tools that provide information and support for decision making on possible problems that, due to their level of complexity, cannot be easily solved by humans [...

Mejora de la coordinación de las asignaturas a través de la generación automática de actas basadas en la comunicación vía email

El proyecto consiste en la realización de una plataforma digital que permite la realización automática de actas de coordinación de asignaturas a partir de los hilos de las conversaciones establecidas en los envíos de correos electrónicos por parte de los diferentes equipos docentes participantes en dichas asignaturas. Las actas pueden generarse a partir de las conversaciones de los docentes, así como de la comunicación entre alumnos y docentes. Este trabajo se motiva en que las reuniones realizadas de forma virtual raramente van acompañadas de un acta donde se deja constancia de los participantes, de sus declaraciones, de las fechas en las que se realizaron las mismas y de las conclusiones a las que se llegaron. El principal objetivo es de dejar constancia de todos los trabajos de coordinación realizados de una forma sencilla, sin que para ello los intervinientes tengan que realizar ninguna tarea adicional, a partir de los correos enviados entre profesores alumnos de una asignatura. Estos correos se analizan y recopilan de forma automática. Después, se genera el acta en formato PDF, que puede ser leído por prácticamente cualquier dispositivo. La aplicación fue probada en dos asignaturas y se aportan capturas y la información relativa a su uso. La aplicación consiguió recopilar toda la información de los correos de forma automática y crear las actas

A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management

Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management

An artificial neural network approach for predicting hypertension using NHANES data

This paper focus on a neural network classification model to estimate the association among gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It also shows that artificial neural network techniques applied to large clinical data sets may provide a meaningful data-driven approach to categorize patients for population health management, and support in the control and detection of hypertensive patients, which is part of the critical factors for diseases of the heart. Data was obtained from the National Health and Nutrition Examination Survey from 2007 to 2016. This paper utilized an imbalanced data set of 24,434 with (69.71%) non-hypertensive patients, and (30.29%) hypertensive patients. The results indicate a sensitivity of 40%, a specificity of 87%, precision of 57.8% and a measured AUC of 0.77 (95% CI [75.01–79.01]). This paper showed results that are to some degree more effectively than a previous study performed by the authors using a statistical model with similar input features that presents a calculated AUC of 0.73. This classification model can be used as an inference agent to assist the professionals in diseases of the heart field, and can be implemented in applications to assist population health management programs in identifying patients with high risk of developing hypertensio

Towards an ontology to describe the taxonomy of common modules in learning management systems

This article have the objective a create ontology for "common modules in a Learning Management Systems", the steps for the build Ontology were: Determine the domain and scope of the ontology, Consider reusing existing ontology, Enumerate important terms in the ontology, Define the classes and the class hierarch, Define the properties of classes�slot and Define the facets of the slot, finally be explained how the ontology is composed

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

A proposal for sentiment analysis on twitter for tourism-based applications

Ponencia de la conferencia "17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018; Granada; Spain; 26 September 2018 through 28 September 2018"People rely on other people’s opinions to make decisions, especially if they belong to their circle of trust. In addition, there are lots of websites of recognized prestige that provide people opinions about different products and services, which are read by millions of people before making a decision. That is why systems for sentiment analysis are becoming increasingly important to automatically process the information and determine feelings of users. They analyze their written words, usually conditioned by the characteristics of microblogging platforms, in which a large number of messages are published every day, providing a great source of information, impossible to be managed manually. In this work, we show a proposal to analyze the feeling that Twitter users have towards different hotels or hotel chains through a platform that could be easily adapted to other contexts. The goal is to create q a structure based on independent and interchangeable components that will make it possible to conduct studies in a more uniform, open and transparent way