Research on the use of social networks for health-related purposes is limited. This study aims to characterize the purpose and use of Facebook and Twitter groups concerning colorectal cancer, breast cancer, and diabetes. We searched in Facebook ( www.facebook.com ) and Twitter ( www.twitter.com ) using the terms "colorectal cancer," "breast cancer," and "diabetes." Each important group has been analyzed by extracting its network name, number of members, interests, and Web site URL. We found 216 breast cancer groups, 171 colorectal cancer groups, and 527 diabetes groups on Facebook and Twitter. The largest percentage of the colorectal cancer groups (25.58%) addresses prevention, similarly to breast cancer, whereas diabetes groups are mainly focused on research issues (25.09%). There are more social groups about breast cancer and diabetes on Facebook (around 82%) than on Twitter (around 18%). Regarding colorectal cancer, the difference is less: Facebook had 62.23%, and Twitter 31.76%. Social networks are a useful tool for supporting patients suffering from these three diseases. Regarding the use of these social networks for disease support purposes, Facebook shows a higher usage rate than Twitter, perhaps because Twitter is newer than Facebook, and its use is not so generalized.

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.

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In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.

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In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving performance. The app can configure, select, receive, process, represent graphically, and store the signals from electrocardiogram (ECG), electromyogram (EMG) and galvanic skin response (GSR) modules and accelerometers, a magnetometer and a gyroscope. The Android app is synchronized in two steps with a driving simulator that we previously developed using the Unity game engine to analyze driving security and efficiency. The Android app was tested with different sensors working simultaneously at various sampling rates and in different Android devices. We also tested the synchronized working of the driving simulator and the Android app with 25 people and analyzed the relation between data from the ECG, EMG, GSR, and gyroscope sensors and from the simulator. Among others, some significant correlations between a gyroscope-based feature calculated by the Android app and vehicle data and particular traffic offences were found. The Android app can be applied with minor adaptations to other different users such as patients with chronic diseases or athletes.

The Cloud Computing paradigm means a radical change over the IT technologies. This transform offers us many benefits in terms of e-services.Cloud Computing offers us a new solution for the implementation of electronic management system in a huge variety of fields. So the e-health is included on these solutions.Despite the fact that Cloud Computing is under development there are a lot of opportunities of implementation of Cloud Computing over e-health services. So in this paper we are going to discuss the viability of the implementation of this new model over an Electronic Health Records (EHRs) system.To find an answer of this issue we are going to analyze the benefits and constraints that can be given in this kind of systems. RESUMENEl paradigma de Cloud Computing supone un cambio radical en las tecnologías IT.Lo cual puede ofrecer muchos beneficios a los servicios electrónicos. Cloud nos ofrece una nueva solución para la implantación de sistemas de gestión electrónicos en muchos campos, entre ellos el de e-health.A pesar de que este tipo de soluciones están aún en pleno desarrollo existen muchas oportunidades para el campo de ehealth. En este artículo vamos a exponer la viabilidad de la implementación de este nuevo modelo sobre un sistema de Historiales Clínicos Electrónicos (HCEs) a nivel teórico. Para encontrar respuesta a todo esto analizaremos los beneficios y obstáculos de esta solución sobre un sistema de HCEs.