This paper presents a method for forward management of buildings fire risks. This method combines artificial intelligence and probability through the use of Bayesian Networks.

Since the stimulation of both feelings of need and temptation have become excessive with the spread of internet advertising, the e-consumer have begun to feel increasingly lost and overwhelmed by offers in a purchasing cycle whose process is mostly unstructured, unguided, and unassisted or-in other words-non user-friendly. As a result, he displays a confused and suspicious attitude and desperately turns to the comparison shopping engines (CSEs) to save time and identify the best matching offer for his search request. Thus, the article in question serves as an investigation of the comparison shopping engines to know if they are up to the task of satisfying the needs of the e-consumer. This study adopts an exploratory approach about the history of online shopping engines, their operating modes, categories, and business plans as well as how they are perceived, used and evaluated. Then, a detailed identification of the various shortcomings that CSEs manifest on the side of both e-consumers and e-merchants was presented in order to eventually discuss the numerous innovations and scientific research which have been developed on the subject.

In this paper, a new method is proposed to analyze the vulnerability of fire spreading in building. This study is based on the application of the artificial intelligence using fuzzy logic. After an overview of the phenomenon of spreading [1] [2], an identification of the various parameters that may influence this phenomenon has been achieved and by applying fuzzy logic technique, the vulnerability analysis has been established. This study represents an important step for modeling a decision support system in order to optimize the fire risk in a building.