A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. Some researches often involve continuous random variables. In order to apply these continuous variables to BN models, these variables should convert into discrete variables with limited states, often two. During the discretization process, one pr...

BACKGROUND New direct-acting antiviral agents for hepatitis C genotype 1 infection, boceprevir and telaprevir, offer enhanced sustained virologic response (SVR) among both treatment-naïve and treatment-experienced patients. AIM To determine the relative efficacy of the new direct-acting antiviral agents by applying a multiple treatment comparison meta-analysis. DESIGN We included published ...

This paper describes the technology developed in the Defense Advanced Research Projects Agency’s (DARPA) High-Performance Knowledge-Base (HPKB) Program that was transferred to follow-on applications. These applications include the following: 1. A data-mining project using naïve Bayesian networks for the U.S. Marine Corps; 2. A Theatre Ballistic Missile Reasoner project, using Bayesian networks ...

The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classification is the process of assigning a document to one or more target categories, based on its contents. In the proposed method, machine learning methods for text classification is ...

In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance f...

Theory of mind research has looked at how learners infer an agent’s unobservable mental states from observable actions. However, such research has tended to neglect another observable source of data: the agent’s reactions to events. In particular, the agent’s facial reactions might provide important information about her mental states that are otherwise ambiguous given her actions. Here we pres...

The goal is to develop a novel approach for cardiac disease prediction and diagnosis using intelligent agents. Initially the symptoms are preprocessed using filter and wrapper based agents. The filter removes the missing or irrelevant symptoms. Wrapper is used to extract the data in the data set according to the threshold limits. Dependency of each symptom is identified using dependency checker...