Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...

State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evalua...

Social network services (SNS) are used more often today, which results in SNS data being generated. Furthermore, greater emphasis is placed on extracting various sorts of information through the collection, processing, and analysis massive volumes data. Although big processing can extract a lot from data, it takes long time resources. As result, gaining insights necessitates significant investm...

Collaborative Filtering (CF) is the most successful approach to Recommender Systems (RS). In this paper, we suggest methods for global and personalized visualization of CF data. Users and items are first embedded into a high-dimensional latent feature space according to a predictor function particularly designated to conform with visualization requirements. The data is then projected into 2-dim...

This paper deals with the optimal solution to the sampled-data minimum variance filtering problem for linear systems with noise in the states and in the measurements. The solution is derived in the time-domain by using a fast sampling zeroorder hold input discretization of the continuous time systems together with a lifting technique. The original sampled-data system is transformed into an equi...

RFID holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data has to be collected, filtered, and transformed into semantic application data. RFID data, however, contains false readings and duplicates. Such data cannot be used direct...

An intention of MapReduce Sets for Filtering expressions analysis has to suggest criteria how Filtering expressions in Filtering data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for Filtering Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific Filtering process that are ex...