The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better accuracy. The six algorithms are implemented to classify aircraft type and its country of origin using a Waikato Environment for Knowledge Analysis (WEKA) workbench. Additionally, we report our parameter optimisation results for SMO by varying the cost parameters to obtain the optimum result. It is observed that in both classifications, SMO with linear kernel obtained the best performance as compared to the other classifiers in terms of classification accuracy, which is 100%.

Performance comparison of machine learning classifiers on aircraft databases

Modelling of maintenance and inspection policies for marine systems using Monte Carlo simulation and Delay-Time Analysis

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Modelling of maintenance and inspection policies for marine systems using Monte Carlo simulation and Delay-Time Analysis