Nikhitha Rajashekar

Title: Comparing Graph Analytic approach to analyze Enterprise Architecture Models. The goal of this thesis work is to analyze Enterprise Architecture(EA) models. Graph analytics can provide a deeper understanding of the relationship between the components of the model. Machine learning(supervised and unsupervised learning) methods can be used to provide decision support for enterprise architects in-order to analyze EA models and also avoid polluting model repository by identifying duplicates.

The goal is to build multi-class prediction model for the engine production rework data which predicts failure part of the engine”. Applied different data pre-processing step to remove noise and outliers. Performing feature engineering and parameter tuning to boost model performance. Comparing and selecting best performing machine learning algorithms by ensemble methods. Model is evaluated for better accuracy of predicting multiple target class(R/CARET library).

Analysis of biomedical data using R programming, python, and Galaxy open source platform.