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This paper aims to propose a machine learning-based automatic labeling methodology for chemical tanker activities that can be applied to any port with any number of active tankers and the identification of important predictors. The methodology can be applied to any type of activity tracking that is based on automatically generated geospatial data.

The proposed methodology uses three machine learning algorithms (artificial neural networks, support vector machines (SVMs) and random forest) along with information fusion (IF)-based sensitivity analysis to classify chemical tanker activities. The data set is split into training and test data based on vessels, with two vessels in the training data and one in the test data set. Important predictors were identified using a receiver operating characteristic comparative approach, and overall variable importance was calculated using IF from the top models.

Results show that an SVM model has the best balance between sensitivity and specificity, at 93.5% and 91.4%, respectively. Speed, acceleration and change in the course on the ground for the vessels are identified as the most important predictors for classifying vessel activity.

The study evaluates the vessel movements waiting between different terminals in the same port, but not their movements between different ports for their tank-cleaning activities.

The findings in this study can be used by port authorities, shipping companies, vessel operators and other stakeholders for decision support, performance tracking, as well as for automated alerts.

This analysis makes original contributions to the existing literature by defining and demonstrating a methodology that can automatically label vehicle activity based on location data and identify certain characteristics of the activity by finding important location-based predictors that effectively classify the activity status.

This paper aims to answer how effective the loan-to-value (LTV) regulation has been since 2011 for conventional and Islamic (participation) banks in Turkey in terms of curbing mortgage loan growth and delinquency[1].

The authors first use unit root tests and tests of difference in loan and property price data in pre-LTV and post-LTV period. Second, the authors follow Chow test and ordinary least squares regression analyses to test for a structural break when sensitivity of mortgage loan and delinquency growth changes to property price changes considered.

The authors find that two periods are statistically different, while the significance level is lower for Islamic banks. Moreover, loan growth has become less responsive to property price increases; delinquency sensitivity to property price changes has significantly increased in the post-LTV period for conventional banks, while this is not the case for Islamic (participation) banks.

This paper not only increases empirical evidence regarding the effectiveness of LTV ratio policy but also fills the gap in the literature by providing a comparison between conventional banks and Islamic (participation) banks.