Purpose Road accidents have come to be considered a major public health problem worldwide. The aim of many studies is therefore to identify the main factors contributing to the severity of crashes. Methods This paper examines a large-scale data mining technique known as association rule mining, which can predict future accidents in advance and allow drivers to avoid the dangers. However, this technique produces a very large number of decision rules, preventing decision makers from making their own selection of the most relevant rules. In this context, the integration of a multi-criteria decision analysis approach would be particularly useful for decision makers affected by the redundancy of the extracted rules. Conclusion An analysis of road accidents in the province of Marrakech (Morocco) between 2004 and 2014 shows that the proposed approach serves this purpose; it may provide meaningful information that could help in developing suitable prevention policies to improve road safety.

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Recently, association rule mining plays a vital role in knowledge discovery in database. In fact, in most cases, the real datasets lead to a very large number of rules, which do not allow users to make their own selection of the most relevant. The difficult task is mining useful and nonredundant rules. Several approaches have been proposed, such as rule clustering, informative cover method and quality measurements. Another way to selecting relevant association rules, we believe that it is necessary to integrate a decisional approach within the knowledge discovery process. Therefore, in this paper, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis. In other side, the general process of association rules extraction becomes more and more complex, to solve such problem, we also proposed a multi-agent system for modeling the different process of our proposed approach. Therefore, we conclude our work by an empirical study applied to a set of banking data to illustrate the performance of our approach.

Renewable energy (RE) systems play a key role in producing electricity worldwide. The integration of RE systems is carried out in a distributed aspect via an autonomous hybrid microgrid (A-HMG) system. The A-HMG concept provides a series of technological solutions that must be managed optimally. As a solution, this paper focuses on the application of a recent nature-inspired metaheuristic optimization algorithm named a multimodal delayed particle swarm optimization (MDPSO). The proposed algorithm is applied to an A-HMG to find the minimum levelized cost of energy (LCOE), the lowest loss of power supply probability (LPSP), and the maximum renewable factor (REF). Firstly, a smart energy management scheme (SEMS) is proposed to coordinate the power flow among the various system components that formed the A-HMG. Then, the MDPSO is integrated with the SEMS to perform the optimal sizing for the A-HMG of a fishing village that is located in the coastal city of Essaouira, Morocco. The proposed A-HMG comprises photovoltaic panels (PV), wind turbines (WTs), battery storage systems, and diesel generators (DGs). The results of the optimization in this location show that A-HMG system can be applied for this location with a high renewable factor that is equal to 90%. Moreover, the solution is very promising in terms of the LCOE and the LPSP indexes that are equal to 0.17$/kWh and 0.12%, respectively. Therefore, using renewable energy can be considered as a good alternative to enhance energy access in remote areas as the fishing village in the city of Essaouira, Morocco. Furthermore, a sensitivity analysis is applied to highlight the impact of varying each energy source in terms of the LCOE index.