The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems (IDS) are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (IDS) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results.

No abstract

No abstract

Socioeconomic status (SES) influences all the determinants of health, conditioning health throughout life. The aim of the present study was to explore the relationship between socioeconomic status and obesity in adolescence through an analysis of the patterns of contact between peers as a function of this parameter. A cross-sectional study was performed, analyzing a sample of 235 students aged 14 to 18 and 11 class networks. Social network analysis was used to analyze structural variables of centrality from a sociocentric perspective. We found that adolescents with a medium-low SES presented a two-fold higher probability of being overweight, but we did not detect any differences in the configuration of their social networks when compared with those of normal-weight adolescents. However, we did find significant differences in the formation of networks according to SES in the overall sample and disaggregated by gender, whereby adolescents with a high SES in general presented a higher capacity to form wider social networks. Elucidating the relationship between SES and overweight and its influence on social network formation can contribute to the design of preventative strategies against overweight and obesity in adolescents, since their social environment can provide them with several resources to combat excess weight.