Fire hazards occurring recently in the world lead to the need of designing accurate fire detection systems in order to save human lives. The newest innovations continue to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications like the adaboost classifier which is well known for its strength in rigid objects detection from images. This paper presents a Fire Detection System (FDS) with an algorithm that works side by side with the adaboost classifier to determine the presence of fire in an image taken by a normal web camera (webcam), in order to decrease the false alarms in an indoor scene. The images are first preprocessed and their selected discrete cosine coefficients are kept for analysis to get better coefficients that will be fed to a neural network for classification and results are compared to a statistical approach used in combination with binary background mask (BBM) and a wavelet-based model of fire's frequency signature(WMF) to test its accuracy.
Social recommender systems exploit two sources of information for making recommendations, the historical rating behavior of users, and the social connections among them. The basic assumption is that if two users are friends, they are likely to share similar preferences. Many recommendation approaches are based on such correlations between the rating and the social behavior of users. However, there is little work in studying whether there actually exist such correlations and how strong they are. In our work, we look at the two views of user behavior, their social connections, and their history of ratings, and investigate two research questions. The first examines if strong activity in one view, e.g., having many friends, implies strong activity in the other view, e.g., having rated many items. The second investigates whether high similarity in one view, e.g., network similarity, implies high similarity in the other view, e.g., rating similarity. We employ various notions of activity and similarity, and identify those that appear to have the stronger impact. Specifically, to some degree, we find that rating behavior determines social behavior, and that the opposite relationship is weaker.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.