This paper describes the development of a global air quality prediction model based on the combination of five different pollutants predicted values; specifically: O 3 , PM 10 , SO 2 , NO x and CO x . 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.

This paper describes an agent based approach for simulating the control of an air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD)is combined with an Artificial Neural Network (ANN) to predict the concentration levels of three different air pollutants. The two models (GPM and ANN) are integrated with a MAS (multi-agent system). The MAS models pollutant sources controllers and air pollution monitoring agencies as software agents. The population of agents cooperates with each other in order to reduce their emissions and control the air pollution. Leaks or natural sources of pollution are modelled as uncontrolled sources. A cooperation strategy is simulated and its impact on air pollution evolution is assessed and compared. The simulation scenario is built using data about Annaba (a city in North-East Algeria). The simulation helps to compare and assess the efficiency of policies to control air pollution during crises, and takes in to account uncontrolled sources.

Abstract. Environmental issues, specifically pollution are considered as major concerns in many cities in the world. They have a direct influence on our health and quality of life. The use of computers models can help to forecast the impact of human activities on ecosystem equilibrium. We are interested in the use of MAS (Multi-Agent System) for modelling and simulating the environmental issues related to pollution. In this paper, we present a review of recent studies using a MAS approach for designing environmental pollution simulation models. Interactions between the three components of the environmental problem (Social, Economic and Ecological) are presented. On the light of these interactions, studies published from 2009 to 2013 are reviewed. Models are presented in terms of: model's purpose, studied variables, used data, representation of space and time, decision-making mechanism and implementation.

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In the last few years, the evolution of information technology has resulted in the development of several interesting and sensitive fields such as the dark Web and cyber-criminality, especially using ransomware attacks. This paper aims to bring out only critical features and make their observation, or not, in software behaviour sufficient to decide whether it is ransomware or not. Therefore, we propose a new solution for ransomware detection based on machine learning algorithms and system calls. First, we introduce our produced dataset of collected system calls of both ransomware and Benignware. Then, we push preprocessing steps deeply to reduce efficiently data dimensionality. After that, we introduce a new technique to select pertinent features. Next, we bring out the critical system calls, their importance and their contribution to the distinction between dataset elements. Finally, we present our model that achieves an overall accuracy of 99.81% after K-Fold cross-validation.