This thesis aims to estimate pore fluid saturation values in an oil sands reservoir using ensemble tree based machine learning models. Oil sands reservoirs provide an interesting opportunity to explore a relatively new ...

This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable ...

Inventory management, as an important business issue, plays a significant role in promoting business development. This study aims to apply data mining techniques, such as time series clustering and time series prediction ...

This research proposes a new network-based method for the assessment of earthquake relationships in space-time-magnitude patterns. The method is applied to the study of volcanic seismicity in Hawaii, over a time period ...

Identifying pattern groups from large temporal data sets, preserving clustering schemes obtained from different heuristic algorithms and presenting temporal pattern profiles for a specific day and previous days are significant ...

Improvement in software development practices to predict and reduce software defects can lead to major cost savings. The goal of this thesis is to demonstrate the value of static analysis metrics and rules in predicting ...

Access to spoken information presents challenges in educational, work, and public settings for many individuals. Speech Recognition technology offers a potential strategy for increasing access in various settings, including ...