In a technologically advanced world, artificial intelligence has impacted all fields of activity. The augmentation of online learning by means of emotion recognition systems raises new challenges in terms of obtaining high-performance systems and in interpreting the results. The paper aims to investigate the usage of automated emotion recognition in learning and to develop a deep learning model based on physiological data to recognize emotions often encountered in classrooms. So, an 1D-CNN model based on physiological data is used to recognize seven emotions: boredom, confusion, frustration, curiosity, excitement, concentration, and anxiety. These emotions are described according to the PAD model and the 5 EEG signals, FP1, AF3, F7, T7, FP2, are taken from the DEAP dataset to train and to test the convolutional neural network model. The high accuracy we obtained (i.e. boredom—99.64%, confusion—99.70%, frustration—99.66%, curiosity—99.80%, excitement—99.91%, concentration—99.70%, anxiety—99.21%) proves that the use of signals obtained via only five channels is sufficient to recognize the presence of emotions. Furthermore, an improved method of analysis based on LIME is proposed and used to obtain reliable explanations for the predictions of our model.

The paper presents details on the development of an intelligent system for particulate matter (PM) air pollution monitoring, analysis and forecasting in two pilot cities, Ploiesti and Targoviste, in selected areas near schools, kindergartens and pediatric hospitals. The main purpose of the system is to provide expert early warnings to protect children with health problems. An in-situ PM₁₀, PM_2.5 monitoring network was designed and an online PM_2.5 monitoring network is under development for each city. Two case studies of PM air pollution analysis and forecast for the Ploiesti city are described. The PM air pollution analysis, forecasting, and children health impact analysis were made by using techniques from statistics and artificial intelligence.