Physical Sciences and Mathematics Commons^™

Prediction Of Sudden Cardiac Death Using Ensemble Classifiers, Ayman Momtaz El-Geneidy

CCE Theses and Dissertations

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised …

Development Of Criteria For Mobile Device Cybersecurity Threat Classification And Communication Standards (Ctc&Cs), Emmanuel Jigo

CCE Theses and Dissertations

The increasing use of mobile devices and the unfettered access to cyberspace has introduced new threats to users. Mobile device users are continually being targeted for cybersecurity threats via vectors such as public information sharing on social media, user surveillance (geolocation, camera, etc.), phishing, malware, spyware, trojans, and keyloggers. Users are often uninformed about the cybersecurity threats posed by mobile devices. Users are held responsible for the security of their device that includes taking precautions against cybersecurity threats. In recent years, financial institutions are passing the costs associated with fraud to the users because of the lack of security.

The …

Machine Learning Methods For Septic Shock Prediction, Aiman A. Darwiche

CCE Theses and Dissertations

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model …

The Impact Of Cost On Feature Selection For Classifiers, Richard Clyde Mccrae

CCE Theses and Dissertations

Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic problem is formulated as a binary classification task in which trained classifiers make predictions based on a set of input features. In diagnosis, these features are typically procedures or tests with associated costs. The cost of applying a trained classifier for diagnosis may be estimated as the total cost of obtaining values for the features that serve as inputs for the classifier. Obtaining classifiers based on a low cost set of input features with acceptable classification accuracy is of interest to practitioners and researchers. What makes this …

The Effect Of Code Obfuscation On Authorship Attribution Of Binary Computer Files, Steven Hendrikse

CCE Theses and Dissertations

In many forensic investigations, questions linger regarding the identity of the authors of the software specimen. Research has identified methods for the attribution of binary files that have not been obfuscated, but a significant percentage of malicious software has been obfuscated in an effort to hide both the details of its origin and its true intent. Little research has been done around analyzing obfuscated code for attribution. In part, the reason for this gap in the research is that deobfuscation of an unknown program is a challenging task. Further, the additional transformation of the executable file introduced by the obfuscator …

Immunology Inspired Detection Of Data Theft From Autonomous Network Activity, Theodore O. Cochran

CCE Theses and Dissertations

The threat of data theft posed by self-propagating, remotely controlled bot malware is increasing. Cyber criminals are motivated to steal sensitive data, such as user names, passwords, account numbers, and credit card numbers, because these items can be parlayed into cash. For anonymity and economy of scale, bot networks have become the cyber criminal’s weapon of choice. In 2010 a single botnet included over one million compromised host computers, and one of the largest botnets in 2011 was specifically designed to harvest financial data from its victims. Unfortunately, current intrusion detection methods are unable to effectively detect data extraction techniques …

Feature Selection And Classification Methods For Decision Making: A Comparative Analysis, Osiris Villacampa

CCE Theses and Dissertations

The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional …