Physical Sciences and Mathematics Commons^™

Neural Network Variations For Time Series Forecasting, David Ason

CCE Theses and Dissertations

Time series forecasting is an area of research within the discipline of machine learning. The ARIMA model is a well-known approach to this challenge. However, simple models such as ARIMA do not take into consideration complex relationships within the data and quite often fail to produce a satisfactory forecast. Neural networks have been presented in previous works as an alternative. Neural networks are able to capture non-linear relationships within the data and can deliver an improved forecast when compared to ARIMA models.

This dissertation takes neural network variations and applies them to a group of time series datasets found in …

Feature Selection On Permissions, Intents And Apis For Android Malware Detection, Fred Guyton

CCE Theses and Dissertations

Malicious applications pose an enormous security threat to mobile computing devices. Currently 85% of all smartphones run Android, Google’s open-source operating system, making that platform the primary threat vector for malware attacks. Android is a platform that hosts roughly 99% of known malware to date, and is the focus of most research efforts in mobile malware detection due to its open source nature. One of the main tools used in this effort is supervised machine learning. While a decade of work has made a lot of progress in detection accuracy, there is an obstacle that each stream of research is …

Increasing Software Reliability Using Mutation Testing And Machine Learning, Michael Allen Stewart

CCE Theses and Dissertations

Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. The goal of mutation testing was to reduce complex program errors by preventing the related simple errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs.

This …