An abdominal Aortic Aneurysm (AAA) is a form of vascular disease causing focal enlargement of the abdominal aorta. As part of the present study, we use series of computer tomography scans (CT-scans) of small AAAs taken at different times to model and predict the spatio-temporal evolution of AAAs. Using the proposed methodology and available CT scan data, the prediction of an AAA can be made for any time using truncated Gaussian process regression. The results of our case study show excellent outcomes of our algorithms when they are compared to the true CT scan images. Second part of the thesis concerns the temporal modeling of sentiments expressed through textual information in Social networks. As part of this study, we explore the issues related to the temporal models and provide an efficient method which overcomes the inefficiencies associated with traditional schemes. A nonparametric, computationally efficient temporal model is provided using truncated Gaussian process regression. The model is built so that a noise parameter is estimated using the sentiment classification error metrics and inserted in the regression setting. This makes the method generic and any form of quantification of sentiments (through manual labeling or by some other classification scheme) can be used with improvement on final results. Baseline sentiment analysis schemes are used in conjunction with the proposed temporal model on data crawled from Twitter to express the utility of the scheme.
Read
