Résumé: This paper presents a Multi-Agent System (MAS) approach for designing an air pollution simulator. The aim is to simulate the concentration of air pollutants emitted from sources (eg factories) and to investigate the emergence of cooperation between the emission source managers and the impact this has on air quality. The emission sources are controlled by agents. The agents try to achieve their goals (ie increase production, which has the side effect of raising air pollution) and also cooperate with others agents by altering their emission rate according to the air quality. The agents play an adapted version of the evolutionary N-Person Prisoners' Dilemma game in a non-deterministic environment; they have two decisions: decrease or increase the emission. The rewards/penalties are influenced by the pollutant concentration which is, in turn, determined using climatic parameters. In order to give predictions about the Plume Dispersion) model and an ANN (Artificial Neural Network) prediction model. The prediction is calculated using the dispersal information and real data about climatic parameters (wind speed, humidity, temperature and rainfall). Every agent cooperates with its neighbours that emit the same pollutant, and it learns how to adapt its strategy to gain more reward. When the pollution level exceeds the maximum allowed level, agents are penalised according to their participation. The system has been tested using real data from the region of Annaba (North-East Algeria). It helped to investigate how the regulations enhance the cooperation and may help controlling the air quality. The designed system helps the environmental agencies to …

Résumé: This paper describes the development of a global air quality prediction model based on the combination of five different pollutants predicted values; specifically: O3, PM10, SO2, NOx and COx. Each pollutant concentration prediction is obtained from a radial basis function (RBF) neural network developed in order to predict 12 hours ahead the five air pollutant parameters for the region of Annaba, northeastern Algeria. Given the measurement of air pollutant concentration and three chosen metrological parameters (wind speed, temperature and humidity) at time t, the models can predict the air pollutant concentrations at t+12 hours. Once these concentrations are obtained, a second artificial neural network (ANN) given by a multi-layered perceptron (MLP) is used to combine them and forecast the air quality over a scale ranging from 1 for very good to 5 for very bad.

Résumé: Shoulder surfing is a common threat used to steal sensitive information, specifically credential and PIN number either by human or recorder cameras. The proposed solution is designed for both PCs and smartphone-based login interfaces. In this paper, we propose a new authentication scheme based on augmented misinformation existing techniques in addition to the introduction of a novel one for maximizing security level and keeping it simple to use. We reuse the crossing-based technique with keys layout randomisation support in addition to displacement factor consideration. Moreover, we introduce white keys-based technique that is used for increasing the security level when keeping the same difficulty-level of exploitation. The evaluation results show that the proposed scheme outperforms well known methods of the state of the art and gives a trade-off between the usability and the security levels against …