Soo-Yeon Ji

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BACKGROUND Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and(More)
BACKGROUND This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods(More)
Understanding mechanisms of protein flexibility is of great importance to structural biology. The ability to detect similarities between proteins and their patterns is vital in discovering new information about unknown protein functions. A Distance Constraint Model (DCM) provides a means to generate a variety of flexibility measures based on a given protein(More)
Many organizations transact in large amounts of data often containing personal identifiable information (PII) and various confidential data. Such organizations are bound by state, federal, and international laws to ensure that the confidentiality of both individuals and sensitive data is not compromised. However, during the privacy preserving process, the(More)
Based on the National Vital Statistics Reports 2001, nearly 115,200 lives are cut short annually because of traumatic injuries and many patients who survive traumatic events have to face the future with life-long disabilities that negatively affect them and their families [29]. Moreover, U.S. trauma center reports suggest that the annual death rate, as well(More)
In recent, numerous useful visual analytics tools have been designed to help domain experts solve analytical problems. However, most of the tools do not reflect the nature of solving real-world analytical tasks collaboratively because they have been designed for single users in desktop environments. In this paper, a complete visual analytics system is(More)
Detection of abnormal internet traffic has become a significant area of research in network security. Due to its importance, many predictive models are designed by utilizing machine learning algorithms. The models are well designed to show high performances in detecting abnormal internet traffic behaviors. However, they may not guarantee reliable detection(More)