A two-way interactive broadband satellite architecture to break the digital divide barrier

September 24-26, 2007, Turin, Ital

TV-Centric technologies to provide remote areas with two-way satellite broadband access

October 1-2, 2007, Rome, Italy TV-Centric Technologies To Provide Remote Areas With Two-Way Satellite Broadband Acces

A Survey on Personalized Multimedia Content Search

Millions of users share opinions on different aspects of life every day. User can give opinions through their comments related to multimedia contents. For analyzing user’s demands we can evaluate user comments as a common online resource. Comments given by user provide clues about them & also their interest area. In this paper, we done survey on different techniques used for personalized search in relevant information retrieval. Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict ‘rating’ or ‘preference’ that are given by users. We propose a personalized search system, called MovieMine, enhance this proposed system to provide more relevant data summarized according to past & present comments left by user. Large movie data center provide movie review which can be utilized for more specific output. Experimental evaluations show that our proposed techniques are efficient and perform better than previously proposed methods. As in research work we can study more types of sentiment analysis with different recommender systems approach. DOI: 10.17762/ijritcc2321-8169.15024

Improving the quality of the personalized electronic program guide

As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms