Computer Engineering Commons^™

A Knowledge-Based Clinical Toxicology Consultant For Diagnosing Multiple Exposures, Joel D. Schipper, Douglas D. Dankel Ii, A. Antonio Arroyo, Jay L. Schauben

Publications

Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances.

Methods: The system is automatically trained using data mining …

Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan

Research Collection School Of Computing and Information Systems

ContextSQL injection (SQLI) and cross site scripting (XSS) are the two most common and serious web application vulnerabilities for the past decade. To mitigate these two security threats, many vulnerability detection approaches based on static and dynamic taint analysis techniques have been proposed. Alternatively, there are also vulnerability prediction approaches based on machine learning techniques, which showed that static code attributes such as code complexity measures are cheap and useful predictors. However, current prediction approaches target general vulnerabilities. And most of these approaches locate vulnerable code only at software component or file levels. Some approaches also involve process attributes that …

A Window Of Opportunity: Assessing Behavioural Scoring, Kenneth Kennedy, Brian Mac Namee, Sarah Jane Delany, Michael O'Sullivan, Neil Watson

Articles

After credit has been granted, lenders use behavioural scoring to assess the likelihood of default occurring during some specific outcome period. This assessment is based on customers’ repayment performance over a given fixed period. Often the outcome period and fixed performance period are arbitrarily selected, causing instability in making predictions. Behavioural scoring has failed to receive the same attention from researchers as application scoring. The bias for application scoring research can be attributed, in part, to the large volume of data required for behavioural scoring studies. Furthermore, the commercial sensitivities associated with such a large pool of customer data often …