Browsing by Author "Arumawadu, H.I."
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Item Mobile Telecommunication Customers Churn Prediction Model(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Chathuranga, L. L. G.; Rathnayaka, R.M.T.B.; Arumawadu, H.I.The present Sri Lankan mobile industry is extremely dynamic, with new services, technologies, and carriers constantly altering the landscape. Then customers have more choices. So, Predict customer churn is one of the most challengeable target in the telecommunication industry today. The major aim of the study is develop a customer churn prediction model by considering some soft factors like monthly bill, billing complaints, promotions, hotline call time, arcade visit time, negative ratings sent, positive ratings sent, complaint resolve duration, total complaints, and coverage related complaints. This study introduces a Mobile Telecommunication customer churn prediction model using data mining techniques. In this study, three machine learning algorithms namely logistic regression, naive bayes and decision tree are used. Indeed, twenty attributes are mainly carried out to train these three algorithms. Furthermore, the back propagation neural network was trained to predict customer churn. Data set used in this study contains 3,334 subscribers, including 1,289 churners and 2,045 non-churners. According to the results, the trained neural network has two hidden layers with 25 total neurons. The proposed Artificial Neural Network result gives 96% accuracy for mobile telecommunication customer churn prediction. The estimated results suggested that the proposed algorithm gives high performances than traditional machine learning algorithm.Item A New Financial Time Series Approach for Volatility Forecasting(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Rathnayaka, R.M.K.T.; Seneiratna, D.M.K.N.; Arumawadu, H.I.The investment in capital market is easiest, fastest and securable way for building healthy financial foundation today. Because of the economic outlooks causing directly on these market fluctuations, the making decisions in the equity market has been regarding as one of the biggest challenges in the modern economy. The main purpose of this study is to take an attempt to understand the behavioral patterns and seek to develop a new hybrid forecasting approach under the volatility. The results are successfully implemented on Colombo stock exchange (CSE), Sri Lanka over the three year period from January 2013 to December 2015. The empirical results indicated that the new proposed hybrid approach is more suitable for forecasting price indices than traditional time series forecasting methodologies under the high volatility.