This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR (Susceptible-Exposed-Infected-Removed), through extensive simulations of the effects of social network on epidemic spread in a Small World (SW) network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread, allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.

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In a group decision support system, the various decision-makers have their own information, constrains, decision strategies, preferences, and objectives which are generally not shared or communicated. This implies that the group decision process is distributed between the different entities implicated and impacted by various group members' characteristics. Solution to this problem is to find a decision that would be acceptable to all the decision-makers, following the necessity of a negotiation process that allows the elaboration of a common agreement for a group that faces a conflict on the decision to take. In the current study, the authors propose to establish a communication platform for a group decision support system (GDSS) based on web services, incorporating a multicriteria analysis methods and a negotiation protocol.

Computer aided systems for detection and diagnosis on mammograms are one of the automatic solutions that help the radiologist in detecting abnormalities in an efficient way as a second reader of digital mammograms. To this end, we propose, in this paper, a methodology for computeraided detection of breast masses on screening mammograms, which joins multidisciplinary axes such as medical domain, image processing and biological pattern recognition. For this, we focus on minimizing false positive findings and increasing true positive cases using all benefits of fuzzy processing and artificial immune recognition system.

In the published paper, the second author name was printed wrongly. The second author name should be read as \Ahmed Bounekkar". The Editorial O±ce apologizes for any inconvenience that it may have caused.

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In the present study, the authors propose a group decision support system (Web-GDSS), which allows multi-agents systems and multicriteria analysis systems to help decision-makers in order to obtain a collective decision, using web services. The proposed system operates on two main stages. First, decision-makers are in a different location away from each other. They must store their location in databases and invoke the appropriate web service. Second, in the case of negotiation between decision-makers, monotonic concession protocol will lead to an agreement using CONDORCET and BORDA voting methods.

Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision‐making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S‐OLAP) technology. Although the S‐OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well‐known multicriteria classification method, the dominance‐based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S‐OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.