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Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people's quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%). [ABSTRACT FROM AUTHOR]

The purpose of this article is to share our impressions and experiences from the anthropological research focusing on the Ontario Ojibwa First Nation, which was our first expedition outside of Europe. We focused on the identity of this indigenous people, their culture preservation practices and processes of assimilation and Americanization taking place among the indigenous groups. Even though it is natural that changes occur, the question is: are those changes within the culture or against the culture? As it was merely our first approach to the field and the topic, the answers to those questions will be explored in detail during our next expeditions to the field. [ABSTRACT FROM AUTHOR]