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The increase in analysis of real life social networks has led to a better understanding of the ways humans socialize in a group. Since trust is an important part of any social interaction, researchers use such networks to understand the nuances of trust relationships. One of the major requirements in trust applications is identifying the trustworthy actors in these networks. This paper proposes a...
This paper presents an analysis of sparse and incomplete Electronic Health Record (EHR) data for the prediction of patients with the risk of Potentially Preventable Events (PPEs). PPEs are admissions, readmissions, complications and emergency department visits that could have been avoided if the patient had been given the appropriate interventions. Machine learning techniques have made the task of...
The problem of gold farmer detection is the problem of detecting players with illicit behaviors in massively multiplayer online games (MMOs) and has been studied extensively. Detecting gold farmers or other deviant actors in social systems is traditionally understood as a binary classification problem, but the issue of false negatives is significant for administrators as residual actors can serve...
This paper presents a methodology for developing an improved feature selection technique that will help in accurate prediction of outcomes after hematopoietic stem cell transplantation (HSCT) for patients with acute myelogenous leukaemia (AML). Allogeneic HSCT using related or unrelated donors is the standard treatment for many patients with blood related malignancies who are unlikely to be cured...
In this paper, we put forward our approach for answering aggregated queries over imprecise data using domain specific taxonomies. A new concept we call the weighted hierarchical hyper graph has been introduced, which helps in answering aggregated queries when dealing with imprecise databases. We assume that the existence of a knowledge base is permanent and independent of the imprecision in the database...
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