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Resultados de búsqueda para Machine Learning

A Microservices e-Health System for Ecological Frailty Assessment using Wearables

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

Autores: Francisco M. Garcia-Moreno / Maria Bermudez-Edo / José Luis Garrido / Estefanía Rodríguez-García / José Manuel Pérez-Mármol / María José Rodríguez-Fórtiz / 
Palabras Clave: E-Health - elderly frailty assessment - IoT - Machine Learning - microservices architecture - mobile health systems - sensors - wearable devices

Smart Nursing Homes: Self-Management Architecture Based on IoT and Machine Learning for Rural Areas

The rate of world population aging is increasing. This situation directly affects all countries socially and economically and, increasing their compromise and effort to improve the living conditions of this sector of society. In environments with large influxes of elderly people, such as nursing homes, the use of technology has shown promise in improving their quality of life. The use of smart devices allows people to automate everyday tasks and learn from them to predict future actions. Additionally, smartphones capture a wealth of information that allows to adapt to nearby actuators according to people’s preferences and even detect anomalies in their behaviour. Current works are proposing new frameworks to detect these behaviours and act accordingly. However, these works are not focused on managing multi-device environments where sensors and smartphones data are considered to automate environments with elderly people or to learn from them. Also, the most of these works require a permanent Internet connection, so the full benefit of smart devices is not completely achieved. In this work, we present an architecture that takes the data from sensors and smartphones in order to adapt the behaviour of the actuators of the environment. In addition, it uses this data to learn from the environment to predict actions or to extrapolate the actions that should be executed according to similar behaviours. The architecture is implemented through a use-case based on a nursing home located in a rural area. Thanks to this work, the quality of life of the elderly is improved in a simple, affordable and transparent way for them.

Autores: Daniel Flores-Martin / Javier Rojo / Enrique Moguel / Javier Berrocal / Juan Manuel Murillo Rodríguez / 
Palabras Clave: Elderly people - Internet of Things - Machine Learning - Nursing Homes

Integración de algoritmos de Machine Learning en Bases de Datos de Arrays

La integración de algoritmos de aprendizaje automático en bases de datos de arrays ha sido una línea de investigación a la que no se le ha dedicado un gran esfuerzo investigador durante mucho tiempo. Sin embargo, en los últimos años se ha experimentado una explosión en la cantidad de trabajo investigador en este campo. En esta propuesta de trabajo se introducen las bases de datos de arrays y se proponen una serie de objetivos obtenidos a partir de algunos de los principales problemas encontrados en la literatura actual.

Autores: Sebastián Villarroya / Andrea Rey / Adrián Berges / 
Palabras Clave: Bases de Datos de Arrays - Datos Raster - Machine Learning

Integrating Complex Event Processing and Machine Learning: an Intelligent Architecture for Detecting IoT Security Attacks (Abstract)

The Internet of Things (IoT) is growing globally at a fast pace. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity threats that target them. To deal with this problem, we propose an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives.

Autores: José Roldán-Gómez / Juan Boubeta-Puig / José Luis Martínez / Guadalupe Ortiz / 
Palabras Clave: Complex Event Processing - Internet of Things - Machine Learning - Model-Driven Development - Security attack - Software Architecture

System for EIT reconstruction based on Machine Learning techniques

Electrical Impedance Tomography (EIT) is a non-invasive technique that can be used to obtain information from inside bodies. To reconstruct internal body images using EIT, it is necessary to solve a mathematical ill-posed problem called inverse problem. We have developed a software called SageTomo that is able to reconstruct EIT images using Machine Learning techniques to solve the inverse problem. Furthermore, SageTomo allows users both to train and store Machine Learning models for EIT reconstruction, as well as to generate and store datasets for training these models.

Autores: Martín Aller Domínguez / David Mera Pérez / José Manuel Cotos Yáñez / Ledicia Díaz Lago / 
Palabras Clave: eit - Electrical Impedance Tomography - Machine Learning

BIGOWL: Knowledge Centered Big Data Analytics

En las últimas décadas el aumento de fuentes de información en diferentes campos de la sociedad desde la salud hasta las redes sociales ha puesto de manifiesto la necesidad de nuevas técnicas para su análisis, lo que se ha venido a llamar el Big Data. Los problemas clásicos de optimización no son ajenos a este cambio de paradigma, como por ejemplo el problema del viajante de comercio (TSP), ya que se puede beneficiar de los datos que proporciona los diferentes sensores que se encuentran en las ciudades y que podemos acceder a ellos gracias a los portales de Open Data. Cuando estamos realizando análisis, ya sea de optimización o machine learning en Big Data, una de las formas más usada de abordarlo es mediante workflows de análisis. Estos están formados por componentes que hacen cada paso del análisis. El flujo de información en workflows puede ser anotada y almacenada usando herramientas de la Web Semántica para facilitar la reutilización de dichos componentes o incluso el workflow completo en futuros análisis, facilitando as+AO0, su reutilización y a su vez, mejorando el proceso de creación de estos. Para ello se ha creado la ontología BIGOWL, que permite trazar la cadena de valor de los datos de los workflows mediante semántica y además ayuda al analista en la creación de workflow gracias a que va guiando su composición con la información que contiene por la anotación de algoritmos, datos, componentes y workflows. La problemática que ha abordado y resuelto BIGOWL se encuentra en dar estructura a esta información para poder ser integrada en los componentes. Para para validar el modelo semántico, se presentan una serie de consultas SPARQL y reglas de razonamiento para guiar el proceso de creación y validación de dos casos de estudio, que consisten en: primero, el procesamiento en streaming de datos de tráfico real con Spark para la optimización de rutas en el entorno urbano de la ciudad de Nueva York+ADs y segundo, clasificación usando algoritmos de minería de datos de un conjunto de datos académicos como son los de la flor de Iris.

Autores: Cristóbal Barba-González / José García-Nieto / Maria Del Mar Roldan-Garcia / Ismael Navas-Delgado / Antonio J. Nebro / Jose F Aldana Montes / 
Palabras Clave: big data - Machine Learning - Optimización - Web Semantic

Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach

Different industrial and medical situations require the non-invasive extraction of information from the inside of bodies. This is usually done through tomographic methods that generate images based oninternal body properties. However, the image reconstruction involves a mathematical inverse problem, which accurate resolution demands large computation time and capacity. In this paper we explore the use of Machine Learning to develop an accurate solver for reconstructing Electrical Impedance Tomography images on real-time. We compare the results with the Iterative Gauss-Newton and the Primal Dual Interior Point Method, which are both largely used and well-validated solvers. The approaches were compared from the qualitative as well as the quantitative viewpoints. The former was focused on correctly detecting the internal body features. The latter was based on accurately predicting internalproperty distributions. Experiments revealed that our approach achieved better accuracy and Cohen’s kappa coefficient (97.57% and 94.60% respectively) from the qualitative viewpoint. Moreover, it also obtained better quantitative metrics with a Mean Absolute Percentage Error of 18.28%. Experiments confirmed that Neural Networks algorithms can reconstruct internal body properties with high accuracy, so they would be able to replace more complex and slower alternatives.

Autores: Xosé Fernández-Fuentes / David Mera / Andrés Gómez / Ignacio Vidal-Franco / 
Palabras Clave: Artificial neural networks - Conductivity - Electrical Impedance Tomography - Inverse Problems - Machine Learning

TAPON: a two-phase machine learning approach for semantic labelling

Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then finding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.

Autores: Daniel Ayala / Inma Hernandez / David Ruiz / Miguel Toro / 
Palabras Clave: Information Integration - Machine Learning - Semantic labelling

Transformaciones de Datos con Machine Learning

Una de las tareas más comunes que los ingenieros tienen que llevar a cabo y que consumen más tiempo es la transformación de datos. Proponemos usar los avances en Inteligencia Artificial (IA), y en particular, en el área de Machine Learning (ML), para abordar este problema. Para ello, definimos una arquitectura que es capaz de inferir las transformaciones de datos a partir de un conjunto de pares de datos entrada-salida. Una vez que nuestro sistema haya aprendido cómo los datos de entrada se relacionan con los de salida, podrá realizar la traducción de nuevos datos de entrada automáticamente.

Autores: Loli Burgueño / Jordi Cabot / Sébastien Gérard / 
Palabras Clave: Machine Learning - MDE - Transformación de datos

Un Recorrido por los Principales Proveedores de Servicios de Machine Learning y Predicción en la Nube

Los medios tecnológicos para el consumo, producción e intercambio de información no hacen más que aumentar cada día que pasa. Nos encontramos envueltos en el fenómeno Big Data, donde ser capaces de analizar esta informa- ción con el objetivo de poder inferir situaciones del futuro basándonos en datos del pasado y del presente, nos puede reportar una ventaja competitiva que nos distinga claramente de otras opciones. Dentro de las múltiples disciplinas exis- tentes para el análisis de grandes cantidades información encontramos el Ma- chine Learning y, a su vez, dentro de este podemos destacar la capacidad predic- tiva que nos proporcionan muchas de las opciones existentes actualmente en el mercado. En este trabajo realizamos un análisis de estas principales opciones de APIs predictivas en la nube, las comparamos entre sí, y finalmente llevamos a cabo una experimentación con datos reales de la Red de Vigilancia y Control de la Calidad del Aire de la Junta de Andalucía. Los resultados demuestran que estas herramientas son una opción muy interesante a considerar a la hora de tratar de predecir valores de contaminantes que pueden afectar a nuestra salud seriamente, pudiéndose llevar a cabo acciones preventivas sobre la población afectada.

Autores: David Corral-Plaza / Juan Boubeta-Puig / Manuel Resinas, / 
Palabras Clave: API - big data - Cloud - Machine Learning - Predicción - Software as a Service

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