In this paper we propose schemes for using learning in video analysis tasks like content based filtering and shot summarization. Shot segmentation is performed by our neuro-fuzzy framework, which extracts fuzzy rules for video segmentation from the trained neuro-fuzzy network. We explore Independent Component Analysis and extract Independent Components that act as features for describing the co...

One of the anticipated challenges of today’s e-learning is to solve the problem of recommending from a large number of learning materials. In this study, we introduce a novel architecture for an e-learning recommender system. More specifically, this paper comprises the following phases i) to propose an e-learning recommender system based on content-based filtering and good learners’ ratings, an...

Recommender systems are software applications that belong to a class of personalized information filtering technologies that aim to support decision making in large information space. There are various techniques being used to achieve this goal in traditional and mobile recommender systems. The recommender systems techniques are usually classified in four main categories: Collaborative Filterin...

Along with high development of multimedia information technique, the provider of badness information embeds some badness information to image or directly saves as a image file, avoiding the filter of image, which brings extreme effect of security hidden trouble in society. An information audit system based on image content filtering is provided in this paper. At first, we discuss some basic met...

Spam detection or filtering process is required to cope with the harmfull effect of spam e-mails affecting directly or indirectly to the users. SPAM e-mails have a direct cost in terms of time, server storage space, network bandwidth consumptions and indirect costs to protect privacy and security breaches [6]. For providing solution to solve this problem various techniques has been implemented....

In this poster, we will illustrate an integrated approach to Web filtering, whose main features are flexible filtering policies taking into account both users’ characteristics and resource content, the specification of an ontology for the filtering domain, and the support for the main filtering strategies currently available. Our approach has been implemented in two prototypes, which address th...

Recommender Systems are now popular both commercially and in the research community, where various approaches have been adopted and validated on large scale. In content-based filtering, the systems examine items previously chosen by the actual user, whereas in collaborative filtering, recommendations are based on the information of similar users or items. We also analyzed that recommendations a...

Online Social Networks (OSNs) have become an important part of daily life. Users build networks to represent their social relationships. Users can upload and exchange the information related to their personal lives. Online Social Networks is to give users the ability to control the messages posted on their own private space to avoid unwanted content is displayed. The project proposes a new syst...

Recommender systems have become significant tools in electronic commerce, proposing effectively those items that best meet the preferences of users. A variety of techniques have been proposed for the recommender systems such as, collaborative filtering and content-based filtering. This study proposes a new hybrid recommender system that focuses on improving the performance under the “new user c...