We investigate the viability of statistical relational machine learning algorithms for the task of
identifying malignancy of renal masses using radiomics-based imaging features. Features …

With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most
studies have focused on either identifying the subject's status through classification …

Although count data are increasingly ubiquitous, surprisingly little work has employed
probabilistic graphical models for modeling count data. Indeed the univariate case has been …

Big data is information management system through the integration of various traditional
data techniques. Big data usually contains high volume of personal and authenticated …

In the ever evolving Internet threat landscape, Distributed Denial-of-Service (DDoS) attacks
remain a popular means to invoke service disruption. DDoS attacks, however, have evolved …

Current atlas-based methods for MRI analysis assume brain images map to a “normal”
template. This assumption, however, does not hold when analyzing abnormal brain shapes …

A major challenge in medical studies, especially those that are longitudinal, is the problem
of missing measurements which hinders the effective application of many machine learning …

We present our BoostSRL system, a Java-based learning system that inductively learns
probabilistic logic clauses from data. Our system is capable of learning different types of …

The objective of this paper is to review the state-of-the-art of statistical relational learning
(SRL) models developed to deal with machine learning and data mining in relational …

Throughout the Internet age, computer network-based threats have been commonplace,
with distributed denial-of-service (DDoS) attacks as a centerpiece. These attacks can knock …